  Zero-Shot Learning (ZSL) is an extreme form of transfer learning, where no
labelled examples of the data to be classified are provided during the training
stage. Instead, ZSL uses additional information learned about the domain, and
relies upon transfer learning algorithms to infer knowledge about the missing
instances. ZSL approaches are an attractive solution for sparse datasets. Here
we outline a model to identify the materials with which a work of art was
created, by learning the relationship between English descriptions of the
subject of a piece and its composite materials. After experimenting with a
range of hyper-parameters, we produce a model which is capable of correctly
identifying the materials used on pieces from an entirely distinct museum
dataset. This model returned a classification accuracy of 48.42% on 5,000
artworks taken from the Tate collection, which is distinct from the Rijksmuseum
network used to create and train our model.


  We present a simple model for low-luminosity active galactic nucleus (LLAGN)
feedback through winds produced by a hot accretion flow. The wind carries
considerable energy and deposits it on the host galaxy at kiloparsec scales and
beyond, heating the galactic gas thereby quenching star formation. Our model
predicts that the typical LLAGN can quench more than $10\%$ of star formation
in its host galaxy. We find that long-lived LLAGN winds from supermassive black
holes (SMBH) with masses $\geq 10^8 M_{\odot}$ and mass accretion rates
$\dot{M} > 10^{-3} \medd \ (0.002 \msun / yr)$ can prevent gas collapse and
significantly quench galactic star formation compared to a scenario without
AGN, if the wind persists over 1 Myr. For sustained wind production over
timescales of 10 Myr or longer, SMBHs with $10^8 M_{\odot}$ or larger masses
have important feedback effects with $\dot{M} > 10^{-4} \medd \ (0.0002 \msun /
yr)$.


  Visualisation is often presented as a means of simplifying information and
helping people understand complex data. In this paper we describe the design,
development and evaluation of an interactive visualisation for spreadsheet
formulae (EQUS). The work is justified on the grounds that these are widely
used tools for significant numerical processing and modeling, yet the formula
developed can be easily misunderstood. The development process was one of
iterative refinement engaging an initial target audience of mid-teen learners,
involving re-design and formative evaluation. The resulting visualisation
techniques have been found to be broadly relevant to spreadsheet users beyond
the initial target audience. EQUS has since been developed as fully integrated
plug-in for MS Excel.


  Radio bursts from nearby active M-dwarfs have been frequently reported and
extensively studied in solar or planetary paradigms. Whereas, their
sub-structures or fine structures remain rarely explored despite their
potential significance in diagnosing the plasma and magnetic field properties
of the star. Such studies in the past have been limited by the sensitivity of
radio telescopes. Here we report the inspiring results from the high
time-resolution observations of a known flare star AD Leo with the
Five-hundred-meter Aperture Spherical radio Telescope (FAST). We detected many
radio bursts in the two days of observations with fine structures in the form
of numerous millisecond-scale sub-bursts. Sub-bursts on the first day display
stripe-like shapes with nearly uniform frequency drift rates, which are
possibly stellar analogs to Jovian S-bursts. Sub-bursts on the second day,
however, reveal a different blob-like shape with random occurrence patterns and
are akin to solar radio spikes. The new observational results suggest that the
intense emission from AD Leo is driven by electron cyclotron maser instability
which may be related to stellar flares or interactions with a planetary
companion.


  The cosmic accretion of both dark matter and baryons into halos typically is
measured using some evolving virial relation, but recent work suggests that
most halo growth at late cosmic time (z < 2) is not physical but is rather the
by-product of an evolving virial radius ("pseudo-evolution"). Using Omega25, a
suite of cosmological simulations that incorporate both dark matter and gas
dynamics with differing treatments of gas cooling, star formation, and thermal
feedback, we systematically explore the physics that governs cosmic accretion
into halos and their galaxies. Physically meaningful cosmic accretion of both
dark matter and baryons occurs at z > 1 across our halo mass range: M_200m =
10^{11-14} M_sun. However, dark matter, because it is dissipationless, is
deposited (in a time-average sense) at > R_200m(z) in a shell-like manner, such
that dark-matter mass and density experience little-to-no physical growth at
any radius within a halo at z < 1. In contrast, gas, because it is able to cool
radiatively, experiences significant accretion at all radii, at a rate that
roughly tracks the accretion rate at R_200m, at all redshifts. Infalling gas
starts to decouple from dark matter at ~2 R_200m and continues to accrete to
smaller radii until the onset of strong angular-momentum support at ~ 0.1
R_200m. Thus, while the growth of dark matter is subject to pseudo-evolution,
the growth of baryons is not. The fact that the accretion rate of gas on
galactic scales tracks the accretion rate near R_200m provides insight into the
tight relations between the masses/sizes of galaxies and those of their host
halos across cosmic time.


  In 2019, The Centers for Medicare and Medicaid Services (CMS) launched an
Artificial Intelligence (AI) Health Outcomes Challenge seeking solutions to
predict risk in value-based care for incorporation into CMS Innovation Center
payment and service delivery models. Recently, modern language models have
played key roles in a number of health related tasks. This paper presents, to
the best of our knowledge, the first application of these models to patient
readmission prediction. To facilitate this, we create a dataset of 1.2 million
medical history samples derived from the Limited Dataset (LDS) issued by CMS.
Moreover, we propose a comprehensive modeling solution centered on a deep
learning framework for this data. To demonstrate the framework, we train an
attention-based Transformer to learn Medicare semantics in support of
performing downstream prediction tasks thereby achieving 0.91 AUC and 0.91
recall on readmission classification. We also introduce a novel data
pre-processing pipeline and discuss pertinent deployment considerations
surrounding model explainability and bias.


  A new local discontinuous Galerkin (LDG) method for convection-diffusion
equations on overlapping meshes with periodic boundary conditions was
introduced in \cite{Overlap1}. With the new method, the primary variable $u$
and the auxiliary variable $p=u_x$ are solved on different meshes. In this
paper, we will extend the idea to convection-diffusion equations with
non-periodic boundary conditions, i.e. Neumann and Dirichlet boundary
conditions. The main difference is to adjust the boundary cells. Moreover, we
study the stability and suboptimal error estimates. Finally, numerical
experiments are given to verify the theoretical findings.


  A low energy particle confined by a horizontal reflective surface and gravity
settles in gravitationally bound quantum states. These gravitational quantum
states (GQS) were so far only observed with neutrons. However, the existence of
GQS is predicted also for atoms. The GRASIAN collaboration pursues the first
observation of GQS of atoms, using a cryogenic hydrogen beam. This endeavor is
motivated by the higher densities, which can be expected from hydrogen compared
to neutrons, the easier access, the fact, that GQS were never observed with
atoms and the accessibility to hypothetical short range interactions. In
addition to enabling gravitational quantum spectroscopy, such a cryogenic
hydrogen beam with very low vertical velocity components - a few cm s$^{-1}$,
can be used for precision optical and microwave spectroscopy. In this article,
we report on our methods developed to reduce background and to detect atoms
with a low horizontal velocity, which are needed for such an experiment. Our
recent measurement results on the collimation of the hydrogen beam to 2 mm, the
reduction of background and improvement of signal-to-noise and finally our
first detection of atoms with velocities < 72 m s$^{-1}$ are presented.
Furthermore, we show calculations, estimating the feasibility of the planned
experiment and simulations which confirm that we can select vertical velocity
components in the order of cm s$^{-1}$.


  We compare algorithms for global optimization of polynomial functions in many
variables. It is demonstrated that existing algebraic methods (Gr\"obner bases,
resultants, homotopy methods) are dramatically outperformed by a relaxation
technique, due to N.Z. Shor and the first author, which involves sums of
squares and semidefinite programming. This opens up the possibility of using
semidefinite programming relaxations arising from the Positivstellensatz for a
wide range of computational problems in real algebraic geometry.
  This paper was presented at the Workshop on Algorithmic and Quantitative
Aspects of Real Algebraic Geometry in Mathematics and Computer Science, held at
DIMACS, Rutgers University, March 12-16, 2001.


  We give a self-contained presentation of a novel approach to a construction
of spherical harmonic expansions on the unit sphere in $\NC^n$. We derive a new
formula for coefficients of the expansion of a smooth zonal function defined on
the unit sphere and apply it in some special cases. The expansion for the
Poisson--Szeg\"o kernel for the unit ball in $\NC^n$ obtained by our method
coincides with the result obtained originally by G. Folland, and on the other
hand disproves results recently presented in a paper of V.A. Menegatto et al..


  Regularization plays an important role in generalization of deep neural
networks, which are often prone to overfitting with their numerous parameters.
L1 and L2 regularizers are common regularization tools in machine learning with
their simplicity and effectiveness. However, we observe that imposing strong L1
or L2 regularization with stochastic gradient descent on deep neural networks
easily fails, which limits the generalization ability of the underlying neural
networks. To understand this phenomenon, we first investigate how and why
learning fails when strong regularization is imposed on deep neural networks.
We then propose a novel method, gradient-coherent strong regularization, which
imposes regularization only when the gradients are kept coherent in the
presence of strong regularization. Experiments are performed with multiple deep
architectures on three benchmark data sets for image recognition. Experimental
results show that our proposed approach indeed endures strong regularization
and significantly improves both accuracy and compression (up to 9.9x), which
could not be achieved otherwise.


  We established the associativity of the quantum cohomologies of homogeneous
varieties by using degeneration method in algebraic geometry.


  We show that given three hermitian matrices, what one could call a fuzzy
representation of a membrane, there is a well defined procedure to define a set
of oriented Riemann surfaces embedded in $R^3$ using an index function defined
for points in $R^3$ that is constructed from the three matrices and the point.
The set of surfaces is covariant under rotations, dilatations and translation
operations on $R^3$, it is additive on direct sums and the orientation of the
surfaces is reversed by complex conjugation of the matrices. The index we build
is closely related to the Hanany-Witten effect. We also show that the surfaces
carry information of a line bundle with connection on them.
  We discuss applications of these ideas to the study of holographic matrix
models and black hole dynamics.


  We propose a Dual-Stream Pyramid Registration Network (referred as
Dual-PRNet) for unsupervised 3D medical image registration. Unlike recent
CNN-based registration approaches, such as VoxelMorph, which explores a
single-stream encoder-decoder network to compute a registration fields from a
pair of 3D volumes, we design a two-stream architecture able to compute
multi-scale registration fields from convolutional feature pyramids. Our
contributions are two-fold: (i) we design a two-stream 3D encoder-decoder
network which computes two convolutional feature pyramids separately for a pair
of input volumes, resulting in strong deep representations that are meaningful
for deformation estimation; (ii) we propose a pyramid registration module able
to predict multi-scale registration fields directly from the decoding feature
pyramids. This allows it to refine the registration fields gradually in a
coarse-to-fine manner via sequential warping, and enable the model with the
capability for handling significant deformations between two volumes, such as
large displacements in spatial domain or slice space. The proposed Dual-PRNet
is evaluated on two standard benchmarks for brain MRI registration, where it
outperforms the state-of-the-art approaches by a large margin, e.g., having
improvements over recent VoxelMorph [2] with 0.683->0.778 on the LPBA40, and
0.511->0.631 on the Mindboggle101, in term of average Dice score. Code is
available at: https://github.com/kangmiao15/Dual-Stream-PRNet-Plus.


  We propose a Block Majorization Minimization method with Extrapolation (BMMe)
for solving a class of multi-convex optimization problems. The extrapolation
parameters of BMMe are updated using a novel adaptive update rule. By showing
that block majorization minimization can be reformulated as a block mirror
descent method, with the Bregman divergence adaptively updated at each
iteration, we establish subsequential convergence for BMMe. We use this method
to design efficient algorithms to tackle nonnegative matrix factorization
problems with the $\beta$-divergences ($\beta$-NMF) for $\beta\in [1,2]$. These
algorithms, which are multiplicative updates with extrapolation, benefit from
our novel results that offer convergence guarantees. We also empirically
illustrate the significant acceleration of BMMe for $\beta$-NMF through
extensive experiments.


  The rich phenomena of deformations in neutron-deficient krypton isotopes such
as the shape evolution with neutron number and the shape coexistence attract
the interests of nuclear physicists for decades. It will be interesting to
study such shape phenomena using a novel way, i.e., by thermally exciting the
nucleus. So in this work, we develop the finite temperature covariant density
functional theory for axially deformed nuclei with the treatment of pairing
correlations by BCS approach, and apply this approach for the study of shape
evolutions in $^{72,74}$Kr with increasing temperatures. For $^{72}$Kr, with
temperature increasing, the nucleus firstly experiences a relatively quick
weakening in oblate deformation at temperature $T \sim0.9$ MeV, and then
changes from oblate to spherical at $T \sim2.1$ MeV. For $^{74}$Kr, its global
minimum locates at quadroupole deformation $\beta_2 \sim -0.14$ and abruptly
changes to spherical at $T\sim 1.7$ MeV. The proton pairing transition occurs
at critical temperature 0.6 MeV following the rule $T_c =0.6 \Delta_p (0)$
where $\Delta_p(0)$ is the proton pairing gap at zero temperature. The
signatures of the above pairing transition and shape changes can be found in
the curve of the specific heat. The single-particle level evolutions with the
temperature are presented.


  We calculate the strange quark and antiquark distributions of the nucleon by
using the effective chiral quark model, and find that the strange-antistrange
asymmetry can bring a contribution of about 60--100% to the NuTeV deviation of
$\sin^{2}\theta_{w}$ from the standard value measured in other electroweak
processes. The results are insensitive to different inputs. The light-flavor
quark asymmetry of $\bar{d}-\bar{u}$ is also investigated and found to be
consistent with the experimental measurements. Therefore the chiral quark model
provides a successful picture to understand the NuTeV anomaly, as well as the
light-flavor quark asymmetry and the proton spin problem in previous studies.


  Many methods exist to model and track deformable one-dimensional objects
(e.g., cables, ropes, and threads) across a stream of video frames. However,
these methods depend on the existence of some initial conditions. To the best
of our knowledge, the topic of detection methods that can extract those initial
conditions in non-trivial situations has hardly been addressed. The lack of
detection methods limits the use of the tracking methods in real-world
applications and is a bottleneck for fully autonomous applications that work
with these objects.
  This paper proposes an approach for detecting deformable one-dimensional
objects which can handle crossings and occlusions. It can be used for tasks
such as routing and manipulation and automatically provides the initialization
required by the tracking methods. Our algorithm takes an image containing a
deformable object and outputs a chain of fixed-length cylindrical segments
connected with passive spherical joints. The chain follows the natural behavior
of the deformable object and fills the gaps and occlusions in the original
image. Our tests and experiments have shown that the method can correctly
detect deformable one-dimensional objects in various complex conditions.


  Rapid advances in artificial intelligence (AI) technology have led to
significant accuracy improvements in a myriad of application domains at the
cost of larger and more compute-intensive models. Training such models on
massive amounts of data typically requires scaling to many compute nodes and
relies heavily on collective communication algorithms, such as all-reduce, to
exchange the weight gradients between different nodes. The overhead of these
collective communication operations in a distributed AI training system can
bottleneck its performance, with more pronounced effects as the number of nodes
increases. In this paper, we first characterize the all-reduce operation
overhead by profiling distributed AI training. Then, we propose a new smart
network interface card (NIC) for distributed AI training systems using
field-programmable gate arrays (FPGAs) to accelerate all-reduce operations and
optimize network bandwidth utilization via data compression. The AI smart NIC
frees up the system's compute resources to perform the more compute-intensive
tensor operations and increases the overall node-to-node communication
efficiency. We perform real measurements on a prototype distributed AI training
system comprised of 6 compute nodes to evaluate the performance gains of our
proposed FPGA-based AI smart NIC compared to a baseline system with regular
NICs. We also use these measurements to validate an analytical model that we
formulate to predict performance when scaling to larger systems. Our proposed
FPGA-based AI smart NIC enhances overall training performance by 1.6x at 6
nodes, with an estimated 2.5x performance improvement at 32 nodes, compared to
the baseline system using conventional NICs.


  Machine learning interatomic potentials (MLIPs) enables molecular dynamics
(MD) simulations with ab initio accuracy and has been applied to various fields
of physical science. However, the performance and transferability of MLIPs are
limited by insufficient labeled training data, which require expensive ab
initio calculations to obtain the labels, especially for complex molecular
systems. To address this challenge, we design a novel geometric structure
learning paradigm that consists of two stages. We first generate a large
quantity of 3D configurations of target molecular system with classical
molecular dynamics simulations. Then, we propose geometry-enhanced
self-supervised learning consisting of masking, denoising, and contrastive
learning to better capture the topology and 3D geometric information from the
unlabeled 3D configurations. We evaluate our method on various benchmarks
ranging from small molecule datasets to complex periodic molecular systems with
more types of elements. The experimental results show that the proposed
pre-training method can greatly enhance the accuracy of MLIPs with few extra
computational costs and works well with different invariant or equivariant
graph neural network architectures. Our method improves the generalization
capability of MLIPs and helps to realize accurate MD simulations for complex
molecular systems.


  Neural Radiance Fields (NeRF) have shown promise in generating realistic
novel views from sparse scene images. However, existing NeRF approaches often
encounter challenges due to the lack of explicit 3D supervision and imprecise
camera poses, resulting in suboptimal outcomes. To tackle these issues, we
propose AltNeRF -- a novel framework designed to create resilient NeRF
representations using self-supervised monocular depth estimation (SMDE) from
monocular videos, without relying on known camera poses. SMDE in AltNeRF
masterfully learns depth and pose priors to regulate NeRF training. The depth
prior enriches NeRF's capacity for precise scene geometry depiction, while the
pose prior provides a robust starting point for subsequent pose refinement.
Moreover, we introduce an alternating algorithm that harmoniously melds NeRF
outputs into SMDE through a consistence-driven mechanism, thus enhancing the
integrity of depth priors. This alternation empowers AltNeRF to progressively
refine NeRF representations, yielding the synthesis of realistic novel views.
Extensive experiments showcase the compelling capabilities of AltNeRF in
generating high-fidelity and robust novel views that closely resemble reality.


  The SOL2014-09-01 far-side solar eruptive event produced hard electromagnetic
and radio emissions observed with detectors at near-Earth vantage points.
Especially challenging was a long-duration >100 MeV $\gamma$-ray burst probably
produced by accelerated protons exceeding 300 MeV. This observation raised a
question of how high-energy protons could reach the Earth-facing solar surface.
Some preceding studies discussed a scenario in which protons accelerated by a
CME-driven shock high in the corona return to the solar surface. We continue
with the analysis of this challenging event, involving radio images from the
Nan\c{c}ay Radioheliograph and hard X-ray data from the High Energy Neutron
Detector (HEND) of the Gamma-Ray Spectrometer onboard the Mars Odyssey space
observatory located near Mars. HEND recorded unocculted flare emission. The
results indicate that the emissions observed from the Earth's direction were
generated by flare-accelerated electrons and protons trapped in static long
coronal loops. Their reacceleration is possible in these loops by a shock wave,
which was excited by the eruption, being initially not CME-driven. The results
highlight the ways to address remaining questions.


  A model of a ferromagnet is considered, in which there arise mesoscopic
fluctuations of paramagnetic phase. The presence of these fluctuations
diminishes the magnetization of the ferromagnet, softens the spin-wave
spectrum, increases the spin-wave attenuation, shortens the magnon free path,
lowers the critical point, and can change the order of phase transition. A
special attention is paid to the interplay between these mesoscopic
paramagnetic fluctuations and microscopic fluctuations due to magnons. One of
the main results of this interplay is an essential extension of the region of
parameters where the ferromagnet-paramagnet phase transition is of first order.


  In this paper, we are concerned about smoothing of Filippov systems around
homoclinic-like connections to regular-tangential singularities. We provide
conditions to guarantee the existence of limit cycles bifurcating from such
connections. Additional conditions are also provided to ensured the stability
and uniqueness of such limit cycles. All the proofs are based on the
construction of the first return map of the regularized Filippov system around
homoclinic-like connections. Such a map is obtained by using a recent
characterization of the local behaviour of the regularized Filippov system
around regular-tangential singularities. Fixed point theorems and
Poincar\'{e}-Bendixson arguments are also employed.


  This Thesis presents a 2-dimensional generalization of Houghtons' groups H_n.
H_n is defined to be the group of all permutations p of a disjoint union of
copies of the natural numbers N, with the property that each copy of N contains
a cofinite subset on which p restricts to a translation. Our group G(n) is
defined to be the group of all permutations p of a disjoint union of quadrants
(i.e., copies of NxN) with the property that each quadrant contains a
subquadrant on which p is a translation, while p restricted to the remaining
set is piecewise isometric on a cofinite disjoint union of rays (each isometric
to N). Based on K.S. Brown's treatment of the Houghton group case it is shown
that G(n) is of type FP(n-1); in fact, that G(n) contains a normal subgroup N
with G(n)/N = Z^(n-1) which is of type FP(n-1) and not FPn.


  In topological semimetals and insulators, negative longitudinal
magnetoresistance and angle-dependent planar Hall effect have been reported
arising from the Berry curvature. Using the Boltzmann transport theory, we
present a closed-form expression for the nonequilibrium distribution function
which includes both the effects of the Berry curvature and Lorentz force. Using
this formulation, we obtain analytical expressions for conductivity and
resistivity tensors in Weyl semimetals demonstrating a non-monotonic field
dependence arising from the competition between the two effects.


  Vesicles are becoming a quite popular model for the study of red blood cells
(RBCs). This is a free boundary problem which is rather difficult to handle
theoretically. Quantitative computational approaches constitute also a
challenge. In addition, with numerical studies, it is not easy to scan within a
reasonable time the whole parameter space. Therefore, having quantitative
analytical results is an essential advance that provides deeper understanding
of observed features and can be used to accompany and possibly guide further
numerical development. In this paper shape evolution equations for a vesicle in
a shear flow are derived analytically with precision being cubic (which is
quadratic in previous theories) with regard to the deformation of the vesicle
relative to a spherical shape. The phase diagram distinguishing regions of
parameters where different types of motion (tank-treading, tumbling and
vacillating-breathing) are manifested is presented. This theory reveals
unsuspected features: including higher order terms and harmonics (even if they
are not directly excited by the shear flow) is necessary, whatever the shape is
close to a sphere. Not only does this theory cure a quite large quantitative
discrepancy between previous theories and recent experiments and numerical
studies, but also it reveals a new phenomenon: the VB mode band in parameter
space, which is believed to saturate after a moderate shear rate, exhibits a
striking widening beyond a critical shear rate. The widening results from
excitation of fourth order harmonic. The obtained phase diagram is in a
remarkably good agreement with recent three dimensional numerical simulations
based on the boundary integral formulation. Comparison of our results with
experiments is systematically made.


  The face ring of a simplicial complex modulo m generic linear forms is shown
to have finite local cohomology if and only if the link of every face of
dimension m or more is `nonsingular', i.e., has the homology of a wedge of
spheres of the expected dimension. This is derived from an enumerative result
for local cohomology of face rings modulo generic linear forms, as compared
with local cohomology of the face ring itself. The enumerative result is
generalized in slightly weaker form to squarefree modules. A concept of
`Cohen-Macaulay in codimension c' is defined and characterized for arbitrary
finitely generated modules and coherent sheaves. For the face ring of an
r-dimensional complex Delta, it is equivalent to nonsingularity of Delta in
dimension r-c; for a coherent sheaf on projective space, this condition is
shown to be equivalent to the same condition on any single generic hyperplane
section. The characterization of nonsingularity in dimension m via finite local
cohomology thus generalizes from face rings to arbitrary graded modules.


  In this review, we count and classify certain sublattices of a given lattice,
as motivated by crystallography. We use methods from algebra and algebraic
number theory to find and enumerate the sublattices according to their index.
In addition, we use tools from analytic number theory to determine the
asymptotic behaviour of the corresponding counting functions. Our main focus
lies on similar sublattices and coincidence site lattices, the latter playing
an important role in crystallography. As many results are algebraic in nature,
we also generalise them to $\mathbb{Z}$-modules embedded in $\mathbb{R}^d$.


  Federated learning enables many local devices to train a deep learning model
jointly without sharing the local data. Currently, most of federated training
schemes learns a global model by averaging the parameters of local models.
However, most of these training schemes suffer from high communication cost
resulted from transmitting full local model parameters. Moreover, directly
averaging model parameters leads to a significant performance degradation, due
to the class-imbalanced non-iid data on different devices. Especially for the
real life federated learning tasks involving extreme classification, (1)
communication becomes the main bottleneck since the model size increases
proportionally to the number of output classes; (2) extreme classification
(such as user recommendation) normally have extremely imbalanced classes and
heterogeneous data on different devices. To overcome this problem, we propose
federated multiple label hashing (FedMLH), which leverages label hashing to
simultaneously reduce the model size (up to 3.40X decrease) with communication
cost (up to 18.75X decrease) and achieves significant better accuracy (up to
35.5%} relative accuracy improvement) and faster convergence rate (up to 5.5X
increase) for free on the federated extreme classification tasks compared to
federated average algorithm.


  In recent years, numerous researchers have begun investigating how virtual
reality (VR) tracking and interaction data can be used for a variety of machine
learning purposes, including user identification, predicting cybersickness, and
estimating learning gains. One constraint for this research area is the dearth
of open datasets. In this paper, we present a new open dataset captured with
our VR-based Full-scale Assembly Simulation Testbed (FAST). This dataset
consists of data collected from 108 participants (50 females, 56 males, 2
non-binary) learning how to assemble two distinct full-scale structures in VR.
In addition to explaining how the dataset was collected and describing the data
included, we discuss how the dataset may be used by future researchers.


  We solve numerically the Dokshitzer-Gribov-Lipatov-Altarelli-Parisi (DGLAP)
equations for the evolution of fragmentation functions using the Laguerre
method. We extend this method to include supersymmetric evolution. The solution
to the DGLAP equations is particularly interesting to calculate the expected
spectra of Ultra High Energy Cosmic Rays in models where they are produced by
the decay of a massive particle X, M_X > 10^{12} GeV.


  This note introduces a new range of modified gamma and beta $k$ functions.
The authors present new modified gamma and beta $k$-functions, first and second
summation relations, various functionals, Mellin transforms, and integral
representations. Furthermore, mean, variance and the moment generating function
of a generalized beta distribution are obtained.


  A space X is finite dimensional, locally compact and separable metrizable if
and only if X has a finite basic family: continuous functions Phi_1,...,Phi_n
of X to the reals, R, such that for all continuous f from X to R there are
g_1,..., g_n in C(R) satisfying
f(x)=g_1(Phi_1(x))+g_2(Phi_2(x))+...+g_n(Phi_n(x)) for all x in X. This give
the complete solution to four problems on basic functions posed by Sternfeld,
as well as questions posed by Hattori and others.


  Federated learning (FL) involves several clients that share with a fusion
center (FC), the model each client has trained with its own data. Conventional
FL, which can be interpreted as an estimation or distortion-based approach,
ignores the final use of model information (MI) by the FC and the other
clients. In this paper, we introduce a novel FL framework in which the FC uses
an aggregate version of the MI to make decisions that affect the client's
utility functions. Clients cannot choose the decisions and can only use the MI
reported to the FC to maximize their utility. Depending on the alignment
between the client and FC utilities, the client may have an individual interest
in adding strategic noise to the model. This general framework is stated and
specialized to the case of clustering, in which noisy cluster representative
information is reported. This is applied to the problem of power consumption
scheduling. In this context, utility non-alignment occurs, for instance, when
the client wants to consume when the price of electricity is low, whereas the
FC wants the consumption to occur when the total power is the lowest. This is
illustrated with aggregated real data from Ausgrid \cite{ausgrid}. Our
numerical analysis clearly shows that the client can increase his utility by
adding noise to the model reported to the FC. Corresponding results and source
codes can be downloaded from \cite{source-code}.


  Continuous surveillance of a spatial region using distributed robots and
sensors is a well-studied application in the area of multi-agent systems. This
paper investigates a practically-relevant scenario where robotic sensors are
introduced asynchronously and inter-robot communication is discrete,
event-driven, local and asynchronous. Furthermore, we work with lazy robots;
i.e., the robots seek to minimize their area of responsibility by
equipartitioning the domain to be covered. We adapt a well-known algorithm
which is practicable and known to generally work well for coverage problems.
For a specially chosen geometry of the spatial domain, we show that there
exists a non-trivial sequence of inter-robot communication events which leads
to an instantaneous loss of coverage when the number of robots exceeds a
certain threshold. The same sequence of events preserves coverage and, further,
leads to an equipartition of the domain when the number of robots is smaller
than the threshold. This result demonstrates that coverage guarantees for a
given algorithm might be sensitive to the number of robots and, therefore, may
not scale in obvious ways. It also suggests that when such algorithms are to be
verified and validated prior to field deployment, the number of robots or
sensors used in test scenarios should match that deployed on the field.


  We study the one-dimensional deformed $t-t^{\prime}-J$ model in terms of the
continuum field theories. We found that at low doping concentration and far
away from the phase separation regime, there are two phases: the Luttinger
liquid and the Luther-Emery liquid, depending on t'/t<(t'/t)_c or
t'/t>(t'/t)_c, where (t'/t)_c>0. Moreover, the singlet superconducting
correlations are dominant in the Luther-Emery liquid.


  We study the symmetries enjoyed by the Newtonian equations of motion of the
non-relativistic dark matter fluid coupled to gravity which give rise to the
phenomenon of gravitational instability. We also discuss some consistency
relations involving the soft limit of the (n + 1)-correlator functions of
matter and galaxy overdensities.


  Starting from a general effective Lagrangian for lepton flavor violation
(LFV) in quark-lepton transitions, we derive constraints on the effective
coefficients from the high-mass tails of the dilepton processes $pp \to \ell_k
\ell_l$ (with $k\neq l$). The current (projected) limits derived in this paper
from LHC data with $36~\mathrm{fb}^{-1}$ ($3~\mathrm{ab}^{-1}$) can be applied
to generic new physics scenarios, including the ones with scalar, vector and
tensor effective operators. For purely left-handed operators, we explicitly
compare these LHC constraints with the ones derived from flavor-physics
observables, illustrating the complementarity of these different probes. While
flavor physics is typically more constraining for quark-flavor violating
operators, we find that LHC provides the most stringent limits on several
flavor-conserving ones. Furthermore, we show that dilepton tails offer the best
probes for charm-quark transitions at current luminosities and that they
provide competitive limits for tauonic $b\to d$ transitions at the
high-luminosity LHC phase. As a by-product, we also provide general numerical
expressions for several low-energy LFV processes, such as the semi-leptonic
decays $K\to \pi \ell^{\pm}_k \ell^{\mp}_l$, $B\to \pi \ell^{\pm}_k
\ell^{\mp}_l$ and $B\to K^{(\ast)} \ell^{\pm}_k \ell^{\mp}_l$.


  This paper presents new formulary solutions for quantic polynomial equations
in general forms, where we present five solutions for any fifth degree
polynomial equation with real coefficients, and thereby having the possibility
to calculate the five roots of any quantic equation nearly simultaneously. The
proposed roots for fifth degree polynomials in this paper are structured basing
on new proposed solutions for fourth degree polynomial equations, which we
developed in order to reduce the expression of any quantic polynomial to an
expression of quartic polynomial.


  The magnetic phase diagram of the Mn-based semi-Heusler alloys is determined
at T=0 using first-principles calculations in conjunction with the
frozen-magnon approximation. We show that the magnetism in these systems
strongly depends on the number of conduction electrons, their spin polarization
and the position of the unoccupied Mn 3d states with respect to Fermi energy.
Various magnetic phases are obtained depending on these characteristics. The
conditions leading to diverse magnetic behavior are identified. We find that in
the case of a large conduction electron spin polarization and the unoccupied Mn
3d states lying far above the Fermi level, an RKKY-like ferromagnetic
interaction is dominating. On the other hand, the antiferromagnetic
superexchange becomes important in the presence of large peaks of the
unoccupied Mn 3d states lying close to the Fermi energy. The overall magnetic
behavior depends on the competition of these two exchange mechanisms. The
obtained results are in very good agreement with the available experimental
data.


  Conical inclusions in a lipid bilayer generate an overall spontaneous
curvature of the membrane that depends on concentration and geometry of the
inclusions. Examples are integral and attached membrane proteins, viruses, and
lipid domains. We propose an analytical model to study budding and vesiculation
of the lipid bilayer membrane, which is based on the membrane bending energy
and the translational entropy of the inclusions. If the inclusions are placed
on a membrane with similar curvature radius, their repulsive membrane-mediated
interaction is screened. Therefore, for high inclusion density the inclusions
aggregate, induce bud formation, and finally vesiculation. Already with the
bending energy alone our model allows the prediction of bud radii. However, in
case the inclusions induce a single large vesicle to split into two smaller
vesicles, bending energy alone predicts that the smaller vesicles have
different sizes whereas the translational entropy favors the formation of
equal-sized vesicles. Our results agree well with those of recent computer
simulations.


  The controlled and reversible interconversion between the free-base and the
doubly dehydrogenated form of a 5,10,15,20-tetra(p-hydroxyphenyl)porphyrin
molecule in an ordered array is demonstrated. This is achieved through voltage
pulses by hydrogen transfer between the center of the porphyrin and the tip of
a scanning tunneling microscope (STM). The local dehydrogenation leads to
significant shifts in the energetic positions of the molecular orbitals.
Density functional theory (DFT) calculations corroborate our conclusions and
allow to gain more insight into the different energy level alignment before and
after dehydrogenation. Due to the different conductance at a given voltage a
clear distinction of both molecular species is possible, which also enables the
application as a single-molecular switch.


  The famous Banach-Tarski paradox claims that the three dimensional rotation
group acts on the two dimensional sphere paradoxically. In this paper, we
generalize their result to show that the classical group acts on the flag
manifold paradoxically.


  Our purpose in this article is to study anti-invariant statistical
submersions from holomorphic statistical manifolds. Firstly we introduce
holomorphic statistical submersions satisfying the certain condition, after we
give anti-invariant statistical submersions satisfying the certain condition.
And we supported our results with examples.


  This contribution investigates the connection between Isogeometric Analysis
(IgA) and the Partial Element Equivalent Circuit (PEEC) method for
electrostatic problems. We demonstrate that using the spline-based geometry
concepts from IgA allows for extracting circuit elements without a meshing
step. Moreover, the proposed IgA-PEEC method converges for complex geometries
up to three times faster than the conventional PEEC approach and, in turn, it
requires a significantly lower number of degrees of freedom to solve a problem
with comparable accuracy. The resulting method is closely related to the
isogeometric boundary element method. However, it uses lowest-order basis
functions to allow for straightforward physical and circuit interpretations.
The findings are validated by an analytical example with complex geometry,
i.e., significant curvature, and by a realistic model of a surge arrester.


  Frequency-selective or even frequency-tunable Terahertz (THz) photodevices
are critical components for many technological applications that require
nanoscale manipulation, control and confinement of light. Within this context,
gate-tunable phototransistors based on plasmonic resonances are often regarded
as the most promising devices for frequency-selective detection of THz fields.
The exploitation of constructive interference of plasma waves in such detectors
not only promises frequency selectivity, but also a pronounced sensitivity
enhancement at the target frequencies. However, clear signatures of
plasmon-assisted resonances in THz detectors have been only revealed at
cryogenic temperatures so far, and remain unobserved at application-relevant
room-temperature conditions. In this work, we demonstrate the sought-after
room-temperature resonant detection of THz radiation in short-channel gated
photodetectors made from high-quality single-layer graphene. The survival of
this intriguing resonant regime at room-temperature ultimately relies on the
weak intrinsic electron-phonon scattering in graphene, which avoids the damping
of the plasma oscillations.


  The present level of proliferation of fake, biased, and propagandistic
content online has made it impossible to fact-check every single suspicious
claim or article, either manually or automatically. Thus, many researchers are
shifting their attention to higher granularity, aiming to profile entire news
outlets, which makes it possible to detect likely "fake news" the moment it is
published, by simply checking the reliability of its source. Source factuality
is also an important element of systems for automatic fact-checking and "fake
news" detection, as they need to assess the reliability of the evidence they
retrieve online. Political bias detection, which in the Western political
landscape is about predicting left-center-right bias, is an equally important
topic, which has experienced a similar shift towards profiling entire news
outlets. Moreover, there is a clear connection between the two, as highly
biased media are less likely to be factual; yet, the two problems have been
addressed separately. In this survey, we review the state of the art on media
profiling for factuality and bias, arguing for the need to model them jointly.
We further discuss interesting recent advances in using different information
sources and modalities, which go beyond the text of the articles the target
news outlet has published. Finally, we discuss current challenges and outline
future research directions.


  In this paper we show that the ionic conduction through surface chemisorbed
ambient moisture leads to the remarkably high room temperature selective
response towards hydrogen gas. The surface adsorbed moisture acts as surface
states and shows ionic conduction, as a result of smaller size of ZnO
nanoparticles of 20 +/- 5 nm. This response is enhanced remarkably i.e. from
10% to 190% for 1200 ppm H2 gas when p-type NiO quasi-nanowires (width ~50 nm)
are mixed with these n-type ZnO nanoparticles to form a homogenous NiO/ZnO
nano-bulk p-n heterostructure. The maximum response is obtained for about 50-50
% composition of NiO/ZnO although it is of still n-type character. The dominant
carrier type reversal from n to p type takes place at rather high NiO content
of about 60-80% in ZnO, depicting dominating contribution of ZnO into the
response. The parallel surface ionic current through chemisorbed moisture
(surface states) has been identified as a primary factor for high sensitivity
at room temperature. Thus, the presence of heterojunction barrier at the
NiO-ZnO interface assisted with the surface ionic conductivity due to adsorbed
moisture results in large, selective response to hydrogen at room temperature.


  LiDAR odometry and localization has attracted increasing research interest in
recent years. In the existing works, iterative closest point (ICP) is widely
used since it is precise and efficient. Due to its non-convexity and its local
iterative strategy, however, ICP-based method easily falls into local optima,
which in turn calls for a precise initialization. In this paper, we propose
CoFi, a Coarse-to-Fine ICP algorithm for LiDAR localization. Specifically, the
proposed algorithm down-samples the input point sets under multiple voxel
resolution, and gradually refines the transformation from the coarse point sets
to the fine-grained point sets. In addition, we propose a map based LiDAR
localization algorithm that extracts semantic feature points from the LiDAR
frames and apply CoFi to estimate the pose on an efficient point cloud map.
With the help of the Cylinder3D algorithm for LiDAR scan semantic segmentation,
the proposed CoFi localization algorithm demonstrates the state-of-the-art
performance on the KITTI odometry benchmark, with significant improvement over
the literature.


  We propose an analytic model to study intrinsic spin polarization effect in a
ballistic Rashba bar with two semi-infinite leads. The wave functions expanded
with plane waves in Rashba bar are required to satisfy boundary conditions at
both longitudinal and transverse interfaces. We find out-of-plane spin Hall
accumulation effect can be induced in the Rashba bar even with large dimensions
by injecting unpolarized current from the lead. The longitudinal in-plane spin
Hall effect, however, becomes obscure in large-size sample. An interesting
direction-flipping of the out-of-plane spin accumulation is predicted by
altering the Rashba coupling strength.


  In previous work we proposed a field theory model for multiple M2-branes
based on an algebra with a totally antisymmetric triple product. In this paper
we gauge a symmetry that arises from the algebra's triple product. We then
construct a supersymmetric theory that is consistent with all the symmetries
expected of a multiple M2-brane theory: 16 supersymmetries, conformal
invariance, and an SO(8) R-symmetry that acts on the eight transverse scalars.
The gauge field is not dynamical. The result is a new type of maximally
supersymmetric gauge theory in three dimensions.


  Designing an incentive-compatible auction mechanism that maximizes the
auctioneer's revenue while minimizes the bidders' ex-post regret is an
important yet intricate problem in economics. Remarkable progress has been
achieved through learning the optimal auction mechanism by neural networks. In
this paper, we consider the popular additive valuation and symmetric valuation
setting; i.e., the valuation for a set of items is defined as the sum of all
items' valuations in the set, and the valuation distribution is invariant when
the bidders and/or the items are permutated. We prove that
permutation-equivariant neural networks have significant advantages: the
permutation-equivariance decreases the expected ex-post regret, improves the
model generalizability, while maintains the expected revenue invariant. This
implies that the permutation-equivariance helps approach the theoretically
optimal dominant strategy incentive compatible condition, and reduces the
required sample complexity for desired generalization. Extensive experiments
fully support our theory. To our best knowledge, this is the first work towards
understanding the benefits of permutation-equivariance in auction mechanisms.


  Electromagnetic theory is central to physics. An undergraduate major in
physics typically takes a semester or a year of electromagnetic theory as a
junior or senior, and a graduate student in physics typically takes an
additional semester or year at a more advanced level. In fall 2023, the author
taught his undergraduate electricity and magnetism class using numerical
methods in Haskell in parallel with traditional analytical methods. This
article describes what functional programming has to offer to physics in
general, and electromagnetic theory in particular. We give examples from vector
calculus, the mathematical language in which electromagnetic theory is
expressed, and electromagnetic theory itself.


  The supermassive black holes (SMBHs) that power active galactic nuclei found
at $z\geq 6$ were formed during the epoch of reionization. Because reionization
is a spatially inhomogeneous process, where different regions of the Universe
become reionized at different times, the physical properties of SMBH host
galaxy environments will vary spatially during reionization. We construct a
semi-analytic model to estimate the impact of reionization on SMBH growth due
to reduced gas accretion onto dark matter halos. Using a series of merger
trees, reionization models, and black hole growth models, we find that early
reionization can reduce an SMBH's mass by up to [50, 70, 90] % within dark
matter halos of mass [$10^{12}$, $10^{11}$, $10^{10}$] M$_{\odot}$ by $z$ = 6.
Our findings also suggest that the redshift range in which black hole growth is
impacted by reionization is strongly dependent on whether the Eddington
accretion rate can be exceeded. If so, we find that black hole masses are
significantly suppressed principally during the early phases of reionization
($z$ $\gtrsim$ 10), while they are more readily suppressed across the full
redshift range if super-Eddington growth is not allowed. We find that the
global average impact of reionization may be to reduce the masses of black
holes residing in $\lesssim$ 10$^{11}$ M$_{\odot}$ halos by a factor of
$\gtrsim$ 2. The census of SMBHs that the James Webb Space Telescope is
uncovering provides a promising means by which to test these predictions.


  A cosmological first order electroweak phase transition could explain the
origin of the cosmic matter-antimatter asymmetry. While it does not occur in
the Standard Model, it becomes possible in the presence of a second Higgs
doublet. In this context, we obtain the properties of the new scalars $H_0$,
$A_0$ and $H^{\pm}$ leading to such a phase transition, showing that its key
LHC signature would be the decay $A_0 \rightarrow H_0 Z$, and we analyze the
promising LHC search prospects for this decay in the $\ell \ell b\bar{b}$ and
$\ell \ell W^{+} W^{-}$ final states. Finally, we comment on the impact of the
$A_0 \rightarrow H_0 Z$ decay on current LHC searches for $A_0$ decaying into
SM particles.


  Metasurfaces are paving the way to improve traditional optical components by
integrating multiple functionalities into one optically flat metasurface
design. We demonstrate the implementation of a multifunctional gap surface
plasmon-based metasurface which, in reflection mode, splits orthogonal linear
light polarizations and focuses into different focal spots. The fabricated
configuration consists of 50 nm thick gold nanobricks with different lateral
dimensions, organized in an array of 240 nm x 240 nm unit cells on the top of a
50 nm thick silicon dioxide layer, which is deposited on an optically thick
reflecting gold substrate. Our device features high efficiency (up to ~65%) and
polarization extinction ratio (up to ~30 dB), exhibiting broadband response in
the near-infrared band (750-950 nm wavelength) with the focal length dependent
on the wavelength of incident light. The proposed optical component can be
forthrightly integrated into photonic circuits or fiber optic devices.


  Quantification of intra-retinal boundaries in optical coherence tomography
(OCT) is a crucial task for studying and diagnosing neurological and ocular
diseases. Since manual segmentation of layers is usually a time consuming task
and relay on user, a lot of attempts done to do it automatically and without
interference of user. Although for extracting all layers usually same procedure
is applied but finding the first layer is usually more difficult due to
vanishing it in some region specially close to Fobia. To have a general
software, beside using common methods like applying shortest path algorithm on
global gradient of image, some extra steps are used here to confine search area
for Dijstra algorithm especially for the second layer. Results demonstrates
high accuracy in segmenting all present layers, especially the first one that
is important for diagnosing issue.


  This paper proposes a safety analysis method that facilitates a tunable
balance between the worst-case and risk-neutral perspectives. First, we define
a risk-sensitive safe set to specify the degree of safety attained by a
stochastic system. This set is defined as a sublevel set of the solution to an
optimal control problem that is expressed using the Conditional Value-at-Risk
(CVaR) measure. This problem does not satisfy Bellman's Principle, thus our
next contribution is to show how risk-sensitive safe sets can be
under-approximated by the solution to a CVaR-Markov Decision Process. We adopt
an existing value iteration algorithm to find an approximate solution to the
reduced problem for a class of linear systems. Then, we develop a realistic
numerical example of a stormwater system to show that this approach can be
applied to non-linear systems. Finally, we compare the CVaR criterion to the
exponential disutility criterion. The latter allocates control effort evenly
across the cost distribution to reduce variance, while the CVaR criterion
focuses control effort on a given worst-case quantile--where it matters most
for safety.


  We consider smoothness properties of the generator of a principal Gabor space
on the real line which is invariant under some additional
translation-modulation pair. We prove that if a Gabor system on a lattice with
rational density is a Riesz basis for its closed linear span, and if the closed
linear span, a Gabor space, has any additional translation-modulation
invariance, then its generator cannot decay well in time and in frequency
simultaneously.


  We present an integrated approach by combining analog computing and deep
learning for electrocardiogram (ECG) arrhythmia classification. We propose
EKGNet, a hardware-efficient and fully analog arrhythmia classification
architecture that archives high accuracy with low power consumption. The
proposed architecture leverages the energy efficiency of transistors operating
in the subthreshold region, eliminating the need for analog-to-digital
converters (ADC) and static random access memory (SRAM). The system design
includes a novel analog sequential Multiply-Accumulate (MAC) circuit that
mitigates process, supply voltage, and temperature variations. Experimental
evaluations on PhysioNet's MIT-BIH and PTB Diagnostics datasets demonstrate the
effectiveness of the proposed method, achieving average balanced accuracy of
95% and 94.25% for intra-patient arrhythmia classification and myocardial
infarction (MI) classification, respectively. This innovative approach presents
a promising avenue for developing low-power arrhythmia classification systems
with enhanced accuracy and transferability in biomedical applications.


  Isomorphs are curves in the thermodynamic phase diagram of invariant excess
entropy, structure, and dynamics, while pseudoisomorphs are curves of invariant
structure and dynamics, but not of the excess entropy. The latter curves have
been shown to exist in molecular models with flexible bonds [A. E. Olsen et
al., J. Chem. Phys. 145, 241103 (2016)]. We here present three force-based
methods to trace out pseudoisomorphs based on a single configuration and test
them on the asymmetric dumbbell and 10-bead Lennard-Jones chain models with
bonds modeled as harmonic springs. The three methods are based on requiring
that particle forces, center-of-mass forces, and torques, respectively, are
invariant in reduced units. For each of the two investigated models we identify
a method that works well for tracing out pseudoisomorphs, but these methods are
not the same. Overall, it appears that the more internal degrees of freedom
there are in the molecule studied, the less they affect the gross dynamical
behavior. Moreover, the "internal" degrees of freedom (including rotation) do
not appear to significantly affect the scaling behavior of the
dynamical/transport coefficients provided some "quenching" is performed.


  Programmable quantum devices are now able to probe wave functions at
unprecedented levels. This is based on the ability to project the many-body
state of atom and qubit arrays onto a measurement basis which produces
snapshots of the system wave function. Extracting and processing information
from such observations remains, however, an open quest. One often resorts to
analyzing low-order correlation functions - i.e., discarding most of the
available information content. Here, we introduce wave function networks - a
mathematical framework to describe wave function snapshots based on network
theory. For many-body systems, these networks can become scale free - a
mathematical structure that has found tremendous success in a broad set of
fields, ranging from biology to epidemics to internet science. We demonstrate
the potential of applying these techniques to quantum science by introducing
protocols to extract the Kolmogorov complexity corresponding to the output of a
quantum simulator, and implementing tools for fully scalable cross-platform
certification based on similarity tests between networks. We demonstrate the
emergence of scale-free networks analyzing data from Rydberg quantum simulators
manipulating up to 100 atoms. We illustrate how, upon crossing a phase
transition, the system complexity decreases while correlation length increases
- a direct signature of build up of universal behavior in data space. Comparing
experiments with numerical simulations, we achieve cross-certification at the
wave-function level up to timescales of 4 $\mu$ s with a confidence level of
90%, and determine experimental calibration intervals with unprecedented
accuracy. Our framework is generically applicable to the output of quantum
computers and simulators with in situ access to the system wave function, and
requires probing accuracy and repetition rates accessible to most currently
available platforms.


  We propose protocols to prepare highly excited energy eigenstates of a
trapped ion in a harmonic trap which do not require laser pulses to induce
transitions among internal levels. Instead the protocols rely on smoothly
deforming the trapping potential between single and double well configurations.
The speed of the changes is set to minimize non-adiabatic transitions by
keeping the adiabaticity parameter constant. High fidelities are found for
times more than two orders of magnitude smaller than with linear ramps of the
control parameter. Deformation protocols are also devised to prepare
superpositions to optimize interferometric sensitivity, combining the ground
state and a highly excited state.


  The use of chatbots has spread, generating great interest in the industry for
the possibility of automating tasks within the execution of their processes.
The implementation of chatbots, however simple, is a complex endeavor that
involves many low-level details, which makes it a time-consuming and
error-prone task. In this paper we aim at facilitating the development of
decision-support chatbots that guide users or help knowledge workers to make
decisions based on interactions between different process participants, aiming
at decreasing the workload of human workers, for example, in healthcare to
identify the first symptoms of a disease. Our work concerns a methodology to
systematically build decision-support chatbots, semi-automatically, from
existing DMN models. Chatbots are designed to leverage natural language
understanding platforms, such as Dialogflow or LUIS. We implemented Dialogflow
chatbot prototypes based on our methodology and performed a pilot test that
revealed insights into the usability and appeal of the chatbots developed.


  We present an alternative formalism for calculating the maximally localized
Wannier functions in crystalline solids, obtaining an expression which is
extremely simple and general. In particular, our scheme is exactly invariant
under Brillouin zone folding, and therefore it extends trivially to the
Gamma-point case. We study the convergence properties of the Wannier functions,
their quadratic spread and centers as obtained by our simplified technique. We
show how this convergence can be drastically improved by a simple and
inexpensive ``refinement'' step, which allows for very efficient and accurate
calculations of the polarization in zero external field.


  Model compression techniques on Deep Neural Network (DNN) have been widely
acknowledged as an effective way to achieve acceleration on a variety of
platforms, and DNN weight pruning is a straightforward and effective method.
There are currently two mainstreams of pruning methods representing two
extremes of pruning regularity: non-structured, fine-grained pruning can
achieve high sparsity and accuracy, but is not hardware friendly; structured,
coarse-grained pruning exploits hardware-efficient structures in pruning, but
suffers from accuracy drop when the pruning rate is high. In this paper, we
introduce PCONV, comprising a new sparsity dimension, -- fine-grained pruning
patterns inside the coarse-grained structures. PCONV comprises two types of
sparsities, Sparse Convolution Patterns (SCP) which is generated from
intra-convolution kernel pruning and connectivity sparsity generated from
inter-convolution kernel pruning. Essentially, SCP enhances accuracy due to its
special vision properties, and connectivity sparsity increases pruning rate
while maintaining balanced workload on filter computation. To deploy PCONV, we
develop a novel compiler-assisted DNN inference framework and execute PCONV
models in real-time without accuracy compromise, which cannot be achieved in
prior work. Our experimental results show that, PCONV outperforms three
state-of-art end-to-end DNN frameworks, TensorFlow-Lite, TVM, and Alibaba
Mobile Neural Network with speedup up to 39.2x, 11.4x, and 6.3x, respectively,
with no accuracy loss. Mobile devices can achieve real-time inference on
large-scale DNNs.


  The magmatic and metamorphic zircons were investigated with Raman spectrum
microprobe analysis. We found notable differences between these two kinds of
zircons exhibited by the variation trend of Raman peak intensity from core to
rim of a crystal. In magmatic zircons, the intensity and the ratio H/W of Raman
spectrum peaks gradually decrease from core to rim of a crystal, which is
produced by an increase in metamictization degree and suggests an increase in U
and Th concentrations from core to rim. In metamorphic zircons, there are two
kinds of crystals according to their Raman spectra: the first group of zircons
exhibits a variation trend opposite to those of magmatic zircons, tending to
increase in the Raman peak intensity and H/W value from core to rim of a
crystal, which is produced by a decrease in metamictization degree and
indicates a decrease of U and Th concentrations from core to rim of a crystal.
The second group of zircons exhibits no change in Raman peak intensity and H/W
value through a crystal. The data of infrared and Raman spectra of these
crystals show that they are well crystallized and have no lattice destruction
induced by metamictization, and are thought to crystallize in high temperature
stages of metamorphism. During these stages, the U and Th ions have been
removed by metamorphic fluids from the parent rocks of these zircons. The other
difference between magmatic and metamorphic zircons is the background level of
their Raman spectra, which is high and sloped in magmatic zircons, but low and
horizontal in metamorphic zircons. The differences between magmatic and
metamorphic zircons can be used to identify the genesis of zircons and
understand the origin and evolution history of their parent rocks.


  We visualize the Fermi-Pasta-Ulam-Tsingou (FPUT) recurrence in a classical
Heisenberg ferromagnetic (HF) spin chain by exploiting its gauge eq uivalence
to the nonlinear Schr\"{o}dinger equation (NLSE). We discuss two types of
spatially periodic breather excitations in the spin chain, that are associated
with: (I) Akhmediev breather, and (II) Galilean transformed Akhmediev breather.
The recurrence in the former is exact in the sense that the initial and final
states are identical. In the later, the spin chain undergoes an additional
global rotation during the rec urrence process, which makes the initial and
final states distinguishable. Both the complex solutions (I) and (II)
nevertheless show a definit e phase shift during the recurrence process. A
one-to-one correspondence between HF spin chain and the NLSE seems missing by
virtue of the clo seness of the FPUT recurrence.


  The family of all $k$-independent sets of a graph containing a fixed vertex
$v$ is called a {star} and $v$ is called its center. Stars are interesting for
their relation to Erd\"{o}s-Ko-Rado graphs. Hurlbert and Kamat conjectured that
in trees the largest stars are centered in leafs. This conjecture was disproven
independently by Baber, Borg, and Feghali, Johnson, and Thomas. In this paper
we introduce a tool to bound the size of stars centered at certain vertices by
stars centered at leafs. We use this tool to show that caterpillars and sunlet
graphs satisfy Hurlbert and Kamat's conjecture, and to show that the centers of
the largest stars in lobsters are either leafs or spinal vertices of degree 2.


  Given a definable function $f: S \to \mathbb{R}$ on a definable set $S$, we
study sublevel sets of the form $S^f_t = \{x \in S: f(x) \leq t\}$ for all $t
\in \mathbb{R}$. Using o-minimal structures, we prove that the Euler
characteristic of $S^f_t$ is right continuous with respect to $t$. Furthermore,
when $S$ is compact, we show that $S^f_{t+\delta}$ deformation retracts to
$S^f_t$ for all sufficiently small $\delta > 0$. Applying these results, we
also characterize the connections between the following concepts in topological
data analysis: the Euler characteristic transform (ECT), smooth ECT,
Euler-Radon transform (ERT), and smooth ERT.


  When applying machine learning to problems in NLP, there are many choices to
make about how to represent input texts. These choices can have a big effect on
performance, but they are often uninteresting to researchers or practitioners
who simply need a module that performs well. We propose an approach to
optimizing over this space of choices, formulating the problem as global
optimization. We apply a sequential model-based optimization technique and show
that our method makes standard linear models competitive with more
sophisticated, expensive state-of-the-art methods based on latent variable
models or neural networks on various topic classification and sentiment
analysis problems. Our approach is a first step towards black-box NLP systems
that work with raw text and do not require manual tuning.


  In autonomous driving scenarios, current object detection models show strong
performance when tested in clear weather. However, their performance
deteriorates significantly when tested in degrading weather conditions. In
addition, even when adapted to perform robustly in a sequence of different
weather conditions, they are often unable to perform well in all of them and
suffer from catastrophic forgetting. To efficiently mitigate forgetting, we
propose Domain-Incremental Learning through Activation Matching (DILAM), which
employs unsupervised feature alignment to adapt only the affine parameters of a
clear weather pre-trained network to different weather conditions. We propose
to store these affine parameters as a memory bank for each weather condition
and plug-in their weather-specific parameters during driving (i.e. test time)
when the respective weather conditions are encountered. Our memory bank is
extremely lightweight, since affine parameters account for less than 2% of a
typical object detector. Furthermore, contrary to previous domain-incremental
learning approaches, we do not require the weather label when testing and
propose to automatically infer the weather condition by a majority voting
linear classifier.


  It is well known that individual beliefs cannot be identified using
traditional choice data, unless we impose the practically restrictive and
conceptually awkward assumption that utilities are state-independent. In this
paper, we propose a novel methodology that solves this long-standing
identification problem in a simple way, using a variant of the strategy method.
Our method relies on the concept of a suitable proxy. The crucial property is
that the agent does not have any stakes in the proxy conditional on the
realization of the original state space. Then, instead of trying to identify
directly the agent's beliefs about the state space, we elicit her conditional
beliefs about the proxy given each state realization. The latter can be easily
done with existing elicitation tools and without worrying about the
identification problem. It turns out that this is enough to uniquely identify
the agent's beliefs. We present different classes of proxies that one can
reasonably use, and we show that it is almost always possible to find one which
is easy to implement. Such flexibility makes our method, not only
theoretically-sound, but also empirically appealing. We also show how our new
method allows us to provide a novel well-founded definition of a utility
function over states. Last but not least, it also allows us to cleanly identify
motivated beliefs, freed from confounding distortions caused by the
identification problem.


  The lack of generalizability of deep learning approaches for the automated
diagnosis of pathologies in Wireless Capsule Endoscopy (WCE) has prevented any
significant advantages from trickling down to real clinical practices. As a
result, disease management using WCE continues to depend on exhaustive manual
investigations by medical experts. This explains its limited use despite
several advantages. Prior works have considered using higher quality and
quantity of labels as a way of tackling the lack of generalization, however
this is hardly scalable considering pathology diversity not to mention that
labeling large datasets encumbers the medical staff additionally. We propose
using freely available domain knowledge as priors to learn more robust and
generalizable representations. We experimentally show that domain priors can
benefit representations by acting in proxy of labels, thereby significantly
reducing the labeling requirement while still enabling fully unsupervised yet
pathology-aware learning. We use the contrastive objective along with
prior-guided views during pretraining, where the view choices inspire
sensitivity to pathological information. Extensive experiments on three
datasets show that our method performs better than (or closes gap with) the
state-of-the-art in the domain, establishing a new benchmark in pathology
classification and cross-dataset generalization, as well as scaling to unseen
pathology categories.


  It's well known that in conformal theories the two- and three-point functions
of a subset of the local operators-the conformal primaries-suffice, via the
operator product expansion (OPE), to determine all local correlation functions
of operators. It's less well known that, in superconformal theories, the OPE of
superdescendants is generally undetermined from those of the superprimaries,
and there is no universal notion of superconformal blocks. We recall these and
related aspects of 4d (S)CFTs, and then we focus on the super operator product
expansion (sOPE) of conserved currents in 4d N=1 SCFTs. The current-current OPE
J(x)J(0) has applications to general gauge mediation. We show how
superconformal symmetry, when combined with current conservation, determines
the OPE coefficients of superconformal descendants in terms of those of the
superconformal primaries. We show that only integer-spin real superconformal
primary operators of vanishing R-charge, and their descendants, appear in the
sOPE. We also discuss superconformal blocks for four-point functions of the
conserved currents.


  Magnetic monopoles are hypothetical particles that may exist as quantized
sources and sinks of the magnetic field. In materials, they may appear in an
emergent quantum electrodynamics described by a U(1) lattice gauge theory.
Particularly, quantum spin ice hosts monopoles as bosonic spinons coupled to
emergent gauge fields in a U(1) quantum spin liquid, namely, a deconfined
Coulomb phase. When monopoles are condensed to form a long-range order,
monopoles and gauge fields are screened and confined. Here we show, however,
that monopole supercurrent flows across a junction of two ferromagnets that are
weakly linked through and placed on top of the U(1) QSL, when a gauge-invariant
phase difference of spinons across the junction is generated by quenching or an
applied electric voltage parallel to the junction. This novel phenomenon paves
the way to a new paradigm of spinonics for a dissipationless control of
magnetism.


  We take interest in the early assessment of risk for depression in social
media users. We focus on the eRisk 2018 dataset, which represents users as a
sequence of their written online contributions. We implement four RNN-based
systems to classify the users. We explore several aggregations methods to
combine predictions on individual posts. Our best model reads through all
writings of a user in parallel but uses an attention mechanism to prioritize
the most important ones at each timestep.


  We analyze a simple model of quantum dynamics, which is a discrete-time
deterministic version of the Frederickson-Andersen model. We argue that this
model is integrable, with a quasiparticle description related to the classical
hard-rod gas. Despite the integrability of the model, commutators of physical
operators grow as in generic chaotic models, with a diffusively broadening
front, and local operators obey the eigenstate thermalization hypothesis (ETH).
However, large subsystems violate ETH; as a function of subsystem size,
eigenstate entanglement first increases linearly and then saturates at a scale
that is parametrically smaller than half the system size.


  Quasiparticle interference induced by cobalt adatoms on the surface of the
topological insulator Bi$_{2}$Se$_{3}$ is studied by scanning tunneling
microscopy, angle-resolved photoemission spectroscopy and X-ray magnetic
circular dichroism. It is found that Co atoms are selectively adsorbed on top
of Se sites and act as strong scatterers at the surface, generating anisotropic
standing waves. A long-range magnetic order is found to be absent, and the
surface state Dirac cone remains gapless. The anisotropy of the standing wave
is ascribed to the heavily warped iso-energy contour of unoccupied states,
where the scattering is allowed due to a non-zero out-of-plane spin.


  Macrophages function as sentinel, cell-regulatory hubs capable of initiating,
perpetuating and contributing to the resolution of an inflammatory response,
following their activation from a resting state. Highly complex and varied gene
expression programs within the macrophage enable such functional diversity. To
investigate how programs of gene expression relate to the phenotypic attributes
of the macrophage, the development of in silico modeling methods is needed.
Such models need to cover multiple scales, from molecular pathways in
cell-autonomous immunity and intercellular communication pathways in tissue
inflammation to whole organism response pathways in systemic disease. Here, we
highlight the potential of in silico macrophage modeling as an amenable and
important yet under-exploited tool in aiding in our understanding of the immune
inflammatory response. We also discuss how in silico macrophage modeling can
help in future therapeutic strategies for modulating both the acute protective
effects of inflammation (such as host defense and tissue repair) and the
harmful chronic effects (such as autoimmune diseases).


  We study lepton mixing patterns which can be derived from the $A_5$ family
symmetry and generalized CP. We find five phenomenologically interesting mixing
patterns for which one column of the PMNS matrix is
$(\sqrt{\frac{5+\sqrt{5}}{10}},\frac{1}{\sqrt{5+\sqrt{5}}},\frac{1}{\sqrt{5+\sqrt{5}}})^{T}$
(the first column of the golden ratio mixing),
$(\sqrt{\frac{5-\sqrt{5}}{10}},\frac{1}{\sqrt{5-\sqrt{5}}},\frac{1}{\sqrt{5-\sqrt{5}}})^{T}$
(the second column of the golden ratio mixing), $(1,1,1)^{T}/\sqrt{3}$ or
$(\sqrt{5}+1,-2,\sqrt{5}-1)^{T}/4$. The three lepton mixing angles are
determined in terms of a single real parameter $\theta$, and agreement with
experimental data can be achieved for certain values of $\theta$. The Dirac CP
violating phase is predicted to be trivial or maximal while Majorana phases are
trivial. We construct a supersymmetric model based on $A_5$ family symmetry and
generalized CP. The lepton mixing is exactly the golden ratio pattern at
leading order, and the mixing patterns of case III and case IV are reproduced
after higher order corrections are considered.


  A review of recent lattice QCD hadron structure calculations is presented.
Important hadronic properties such as the axial charge and spin content of the
nucleon, as well as, the mass and axial charge of hyperons and charmed baryons
are discussed.


  Warrants with stock price dependent threshold conditions give the right to
buy specially issued stocks, if the performance of the stock price satisfies
some requirements. Existence of these derivatives changes the price process of
the underlying. We show that in the presence of such warrants one cannot assume
that the stock market is arbitrage free and that the stock is tradeable at
every time moment with the same price for buying and selling. This means that
the usual methods for deriving fair prices for such warrants cannot be used. We
start from a simple model for the firm's value process and discuss some ways to
specify a related model for the stock price process in the presence of warrants
with threshold conditions. We also discuss how indifference pricing approach
can be used for pricing such warrants.


  When creating policies, plans, or designs for people, it is challenging for
designers to foresee all of the ways in which people may reason and behave.
Recently, Large Language Models (LLMs) have been shown to be able to simulate
human reasoning. We extend this work by measuring LLMs ability to simulate
strategic reasoning in the ultimatum game, a classic economics bargaining
experiment. Experimental evidence shows human strategic reasoning is complex;
people will often choose to punish other players to enforce social norms even
at personal expense. We test if LLMs can replicate this behavior in simulation,
comparing two structures: single LLMs and multi-agent systems. We compare their
abilities to (1) simulate human-like reasoning in the ultimatum game, (2)
simulate two player personalities, greedy and fair, and (3) create robust
strategies that are logically complete and consistent with personality. Our
evaluation shows that multi-agent systems are more accurate than single LLMs
(88 percent vs. 50 percent) in simulating human reasoning and actions for
personality pairs. Thus, there is potential to use LLMs to simulate human
strategic reasoning to help decision and policy-makers perform preliminary
explorations of how people behave in systems.


  Lurie's representability theorem gives necessary and sufficient conditions
for a functor to be an almost finitely presented derived geometric stack. We
establish several variants of Lurie's theorem, making the hypotheses easier to
verify for many applications. Provided a derived analogue of Schlessinger's
condition holds, the theorem reduces to verifying conditions on the underived
part and on cohomology groups. Another simplification is that functors need
only be defined on nilpotent extensions of discrete rings. Finally, there is a
pre-representability theorem, which can be applied to associate explicit
geometric stacks to dg-manifolds and related objects.


  A notion of gcd chain has been introduced by the author at ISSAC 2017 for two
univariate monic polynomials with coefficients in a ring R = k[x_1, ..., x_n
]/(T) where T is a primary triangular set of dimension zero. A complete
algorithm to compute such a gcd chain remains challenging. This work treats
completely the case of a triangular set T = (T_1 (x)) in one variable, namely a
power of an irreducible polynomial. This seemingly "easy" case reveals the main
steps necessary for treating the general case, and it allows to isolate the
particular one step that does not directly extend and requires more care.


  In this work we discuss the task of search, localization and recognition of
price zone within a photograph of the price tag. The task is being addressed
for the case when image is acquired by small-scale digital camera and
calculation device has significant resource constraints. The proposed approach
is based on Niblack binarization algorithm, analysis and clasterization of
connected components in conditions of known price tag geometrical model. The
algorithm was tested on a private dataset and has shown high quality.


  We show that any strictly mean convex translator of dimension $n\geq 3$ which
admits a cylindrical estimate and a corresponding gradient estimate is
rotationally symmetric. As a consequence, we deduce that any translating
solution of the mean curvature flow which arises as a blow-up limit of a
two-convex mean curvature flow of compact immersed hypersurfaces of dimension
$n\geq 3$ is rotationally symmetric. The proof is rather robust, and applies to
a more general class of translator equations. As a particular application, we
prove an analogous result for a class of flows of embedded hypersurfaces which
includes the flow of two-convex hypersurfaces by the two-harmonic mean
curvature.


  In scalar QED we study the Schwinger pair production from an initial ensemble
of charged bosons when an electric field is turned on for a finite period
together with or without a constant magnetic field. The scalar QED Hamiltonian
depends on time through the electric field, which causes the initial ensemble
of bosons to evolve out of equilibrium. Using the Liouville-von Neumann method
for the density operator and quantum states for each momentum mode, we
calculate the Schwinger pair-production rate at finite temperature, which is
the pair-production rate from the vacuum times a thermal factor of the
Bose-Einstein distribution.


  The tracks left by charged particles in a gaseous time projection
chamber~(TPC) incorporate important information about the interaction process
and drift of electrons in gas. The electron diffusion information carried by
the tracks is an effective signature to reconstruct $z_0$, the vertex position
in drift direction at which the event takes place. In this paper, we propose to
reconstruct $z_0$ with convolution neural network~(CNN) in the PandaX-III
experiment. A CNN model VGGZ0net is built and validated with Monte Carlo
simulation data. It gives $z_0$ with a 11~cm precision for the events above
2~MeV uniformly distributed along a drift distance of 120~cm, and then the
electron lifetime can be deduced. The energy resolution of detector is
significantly improved after the electron lifetime correction, i.e., from
10.1\% to 4.0\% FWHM at the Q-value of double beta decay of $^{136}$Xe for the
scenario with an electron lifetime of 6.5~ms. The CNN model is also
successfully applied to the experimental data of the PandaX-III prototype
detector for $z_0$ reconstruction.


  We investigated the detection rates and early warning parameters of binary
neutron star (BNS) populations with decihertz gravitational-wave observatories
in a realistic detecting strategy. Assuming 4 years' operation of B-DECIGO, we
based on parameter precision to classify the detectable BNSs into three
categories: (a) sources that merge within 1 year, which could be localized with
an uncertainty of $\Delta\Omega \sim 10^{0}$ deg$^2$; (b) sources that merge in
1-4 years, which take up three quarters of the total events and yield the most
precise angular resolution with $\Delta \Omega\sim 10^{-2}$ deg$^2$ and
time-of-merger accuracy with $\Delta t_c\sim 10^{-1}$ s; and (c) sources that
do not merge during the 4-yr mission window, which enable possible early
warnings, with $\Delta \Omega\sim 10^{-1}$ deg$^2$ and $\Delta t_c\sim 10^{0}$
s. Furthermore, we compared the pros and cons of B-DECIGO with the
third-generation ground-based detectors, and explored the prospects of
detections using 3 other decihertz observatories and 4 BNS population models.
In realistic observing scenarios, we found that decihertz detectors could even
provide early-warning alerts to a source decades before its merger while their
localizations are still as accurate as ground-based facilities. Finally we
found a decrease of events when considering the confusion noise, but this could
be partially solved by a proper noise subtraction.


  Young massive stars are usually found embedded in dense and massive molecular
clumps which are known for being highly obscured and distant. During their
formation process, the degree of deuteration can be used as a potential
indicator of the very early formation stages. This is particularly effective
when employing the abundance of H$_2$D$^+$. However, its low abundances and
large distances make detections in massive sources hard to achieve. We present
an application of the radiative transfer code POLARIS, with the goal to test
the observability of the ortho-H$_2$D$^+$ transition $1_{10}$-$1_{11}$ (372.42
GHz) using simulations of high-mass collapsing cores that include deuteration
chemistry. We analyzed an early and a late stage of the collapse of a 60
M$_{\odot}$ core, testing different source distances. For all cases, we
generated synthetic single-dish and interferometric observations and studied
the differences in both techniques. The column densities we derive are
comparable to values reported for similar sources. These estimates depend on
the extent over which they are averaged, and sources with compact emission they
can be highly affected by beam dilution. Combined ALMA-ACA observations improve
in signal-to-noise ratio and lead to better column density estimates as
compared to ALMA alone. We confirm the feasibility to study ortho-H$_2$D$^+$
emission up to distances of 7 kpc. We provide a proof-of-concept of our
framework for synthetic observations and highlight its importance when
comparing numerical simulations with real observations. This work also proves
how relevant it is to combine single-dish and interferometric measurements to
derive appropriate source column densities.


  It is known that smooth bump functions are absent in the majority of
infinite-dimensional Banach spaces. This is an obstacle in the development of
local analysis, in particular in the questions of extending local maps onto the
whole space. We suggest an approach that substitutes bump functions with
special maps, which we call K-maps. It allows us to extend smooth local maps
from non-smooth spaces, such as $C^q[0,1], q=0,1,...$. We also prove the Borel
lemma for spaces possessing K-maps.


  Most capacitors do not satisfy the conventional assumption of a constant
capacitance. They exhibit memory which is often described by a time-varying
capacitance. It is shown that the classical relation,
$Q\left(t\right)=CV\left(t\right)$, that relates the charge, $Q$, with the
capacitance, $C$, and the voltage, $V$, is not applicable for capacitors with a
time-varying capacitance. The expression for the current, $dQ/dt$, that is
subsequently obtained following the substitution of $C$ by $C\left(t\right)$ in
the classical relation corresponds to an inconsistent circuit. In order to
address the inconsistency, I propose a charge-voltage relation according to
which the charge on a capacitor is expressed by the convolution of its
time-varying capacitance with the first-order time-derivative of the applied
voltage, i.e., $Q\left(t\right)=C\left(t\right)\ast dV/dt$. This relation
corresponds to the universal capacitor which is also known as the fractional
capacitor among the fractional calculus community. Since the fractional
capacitor has an inherent connection with the universal dielectric response
that is expressed by the century old Curie-von Schweidler law, the finding
extends to the study of dielectrics as well.


  Cellular solids are known to exhibit size effects, i.e., differences in the
apparent effective elastic moduli, when the specimen size becomes comparable to
the cell size. The present contribution employs direct numerical simulations
(DNS) of the mesostructure to investigate the influences of porosity, shape of
pores, and thus material distribution along the struts, and orientation of
loading on the size effects and effective moduli of regular honeycomb
structures. Beam models are compared to continuum models for simple shear,
uniaxial loading and pure bending of strips of finite width. It is found that
the honeycomb structure exhibits a considerable anisotropy of the size effects
and that honeycomb structures with circular pores exhibit considerably stronger
size effects than those with hexagonal pores (and thus straight struts).
Positive (stiffening) size effects are observed under simple shear and negative
(softening) size effects under bending and uniaxial loading. The negative size
effects are interpreted in terms of the stress-gradient theory.


  We prove a number of results concerning the Hausdorff and packing dimension
of sets of points which escape (at least in average) to infinity at a given
rate under non-autonomous iteration of exponential maps. In particular, we
generalize the results proved by Sixsmith in 2016 and answer his question on
annular itineraries for exponential maps.


  The Z^0 resonant amplitude is discussed in general R_\xi gauges. When the
original on-shell definition of the Z^0 mass M is employed, a gauge dependence
of M emerges in the next-to-leading approximation which, although small, is of
the same magnitude as the current experimental error. In the following order of
expansion, these unphysical effects are unbounded. The gauge dependence of M
disappears when modified, previously proposed definitions of mass or
self-energies, are used. The relevance of these considerations to the concept
of the mass of unstable particles is pointed out.


  We quantise the new connection formulation of D+1 dimensional General
Relativity developed in our companion papers by Loop Quantum Gravity (LQG)
methods. It turns out that all the tools prepared for LQG straightforwardly
generalise to the new connection formulation in higher dimensions. The only new
challenge is the simplicity constraint. While its "diagonal" components acting
at edges of spin network functions are easily solved, its "off-diagonal"
components acting at vertices are non trivial and require a more elaborate
treatment.


  Ensuring the safety of Vulnerable Road Users (VRUs) is a critical concern in
transportation, demanding significant attention from researchers and engineers.
Recent advancements in Vehicle-to-Everything (V2X) technology offer promising
solutions to enhance VRU safety. Notably, VRUs often travel in groups,
exhibiting similar movement patterns that facilitate the formation of clusters.
The standardized Collective Perception Message (CPM) and VRU Awareness Message
in ETSI's Release 2 consider this clustering behavior, allowing for the
description of VRU clusters. Given the constraints of narrow channel bandwidth,
the selection of an appropriate geometric shape for representing a VRU cluster
becomes crucial for efficient data transmission. In our study we conduct a
comprehensive evaluation of different geometric shapes used to describe VRU
clusters. We introduce two metrics: Cluster Accuracy (CA) and Comprehensive
Area Density Information (CADI), to assess the precision and efficiency of each
shape. Beyond comparing predefined shapes, we propose an adaptive algorithm
that selects the preferred shape for cluster description, prioritizing accuracy
while maintaining a high level of efficiency. The study culminates by
demonstrating the benefits of clustering on data transmission rates. We
simulate VRU movement using real-world data and the transmission of CPMs by a
roadside unit. The results reveal that broadcasting cluster information, as
opposed to individual object data, can reduce the data transmission volume by
two-thirds on average. This finding underscores the potential of clustering in
V2X communications to enhance VRU safety while optimizing network resources.


  The thermal stability of monolithic optical microresonators is essential for
many mesoscopic photonic applications such as ultrastable laser oscillators,
photonic microwave clocks, and precision navigation and sensing. Their
fundamental performance is largely bounded by thermal instability. Sensitive
thermal monitoring can be achieved by utilizing cross-polarized dual-mode beat
frequency metrology, determined by the polarization-dependent
thermorefractivity of a single-crystal microresonator, wherein the heterodyne
radio-frequency beat pins down the optical mode volume temperature for
precision stabilization. Here, we investigate the correlation between the
dual-mode beat frequency and the resonator temperature with time and the
associated spectral noise of the dual-mode beat frequency in a single-crystal
ultrahigh-Q MgF2 resonator to illustrate that dual-mode frequency metrology can
potentially be utilized for resonator temperature stabilization reaching the
fundamental thermal noise limit in a realistic system. We show a resonator
long-term temperature stability of 8.53 {\mu}K after stabilization and unveil
various sources that hinder the stability from reaching sub-{\mu}K in the
current system, an important step towards compact precision navigation, sensing
and frequency reference architectures.


  We study the problem of model extraction in natural language processing, in
which an adversary with only query access to a victim model attempts to
reconstruct a local copy of that model. Assuming that both the adversary and
victim model fine-tune a large pretrained language model such as BERT (Devlin
et al. 2019), we show that the adversary does not need any real training data
to successfully mount the attack. In fact, the attacker need not even use
grammatical or semantically meaningful queries: we show that random sequences
of words coupled with task-specific heuristics form effective queries for model
extraction on a diverse set of NLP tasks, including natural language inference
and question answering. Our work thus highlights an exploit only made feasible
by the shift towards transfer learning methods within the NLP community: for a
query budget of a few hundred dollars, an attacker can extract a model that
performs only slightly worse than the victim model. Finally, we study two
defense strategies against model extraction---membership classification and API
watermarking---which while successful against naive adversaries, are
ineffective against more sophisticated ones.


  We investigate the dust and gas distribution in the disk around HD 142527
based on ALMA observations of dust continuum, 13CO(3-2), and C18O(3-2)
emission. The disk shows strong azimuthal asymmetry in the dust continuum
emission, while gas emission is more symmetric. In this paper, we investigate
how gas and dust are distributed in the dust-bright northern part of the disk
and in the dust-faint southern part. We construct two axisymmetric disk models.
One reproduces the radial profiles of the continuum and the velocity moments 0
and 1 of CO lines in the north and the other reproduces those in the south. We
have found that the dust is concentrated in a narrow ring having ~50AU width
(in FWHM; w_d=30AU in our parameter definition) located at ~170-200AU from the
central star. The dust particles are strongly concentrated in the north. We
have found that the dust surface density contrast between the north and south
amounts to ~70. Compared to the dust, the gas distribution is more extended in
the radial direction. We find that the gas component extends at least from
~100AU to ~250AU from the central star, and there should also be tenuous gas
remaining inside and outside of these radii. The azimuthal asymmetry of gas
distribution is much smaller than dust. The gas surface density differs only by
a factor of ~3-10 between the north and south. Hence, gas-to-dust ratio
strongly depends on the location of the disk: ~30 at the location of the peak
of dust distribution in the south and ~3 at the location of the peak of dust
distribution in the north. Despite large uncertainties, the overall gas-to-dust
ratio is inferred to be ~10-30, indicating that the gas depletion may have
already been under way.