  We define an action of the degenerate two boundary braid algebra
$\mathcal{G}_d$ on the $\mathbb{C}$-vector space $M\otimes N\otimes V^{\otimes
d}$, where $M$ and $N$ are arbitrary modules for the general linear Lie
superalgebra $\mathfrak{gl}(n|m)$, and $V$ is the natural representation. When
$M$ and $N$ are parametrized by rectangular hook Young diagrams, this action
factors through a quotient $\mathcal{H}^{\operatorname{ext}}_d$. The
irreducible summands of $M\otimes N\otimes V^{\otimes d}$ for the centralizer
of $\mathfrak{gl}(n|m)$, remain irreducible once regarded as modules for this
quotient $\mathcal{H}^{\operatorname{ext}}_d$.


  Though the observation of the quantum anomalous Hall effect and nonlocal
transport response reveals nontrivial band topology governed by the Berry
curvature in twisted bilayer graphene, some recent works reported nonlinear
Hall signals in graphene superlattices that are caused by the extrinsic
disorder scattering rather than the intrinsic Berry curvature dipole moment. In
this work, we report a Berry curvature dipole induced intrinsic nonlinear Hall
effect in high-quality twisted bilayer graphene devices. We also find that the
application of the displacement field substantially changes the direction and
amplitude of the nonlinear Hall voltages, as a result of a field-induced
sliding of the Berry curvature hotspots. Our work not only proves that the
Berry curvature dipole could play a dominant role in generating the intrinsic
nonlinear Hall signal in graphene superlattices with low disorder densities,
but also demonstrates twisted bilayer graphene to be a sensitive and
fine-tunable platform for second harmonic generation and rectification.


  Using the classical S.Lie method we obtain a complete description of
infinitesimal symmetries of a holomorphic PDE system defining the Segre family
of a real analytic hypersurface. This gives a new proof of some well known
results of CR geometry.


  We propose a novel probe technique capable of performing local
low-temperature spectroscopy on a 2D electron system (2DES) in a semiconductor
heterostructure. Motivated by predicted spatially-structured electron phases,
the probe uses a charged metal tip to induce electrons to tunnel locally,
directly below the tip, from a "probe" 2DES to a "subject" 2DES of interest. We
test this concept with large-area (non-scanning) tunneling measurements, and
predict a high spatial resolution and spectroscopic capability, with minimal
influence on the physics in the subject 2DES.


  The probability that a particle, crossing the shock along a given direction,
be reflected backwards along another direction, was shown to be the key element
in determining the spectrum of non--thermal particles accelerated via Fermi
mechanism around a shock. Here an explicit equation for this probability
distribution is given, for both the upstream and downstream sections. Though
analytically intractable, this equation can be easily solved numerically.


  Fast and accurate waveform models are necessary for measuring the properties
of inspiraling binary neutron star systems such as GW170817. We present a
frequency-domain surrogate version of the aligned-spin binary neutron star
waveform model using the effective one body formalism known as SEOBNRv4T. This
model includes the quadrupolar and octopolar adiabatic and dynamical tides. The
version presented here is improved by the inclusion of the spin-induced
quadrupole moment effect, and completed by a prescription for tapering the end
of the waveform to qualitatively reproduce numerical relativity simulations.
The resulting model has 14 intrinsic parameters. We reduce its dimensionality
by using universal relations that approximate all matter effects in terms of
the leading quadrupolar tidal parameters. The implementation of the time-domain
model can take up to an hour to evaluate using a starting frequency of 20Hz,
and this is too slow for many parameter estimation codes that require $O(10^7)$
sequential waveform evaluations. We therefore construct a fast and faithful
frequency-domain surrogate of this model using Gaussian process regression. The
resulting surrogate has a maximum mismatch of $4.5\times 10^{-4}$ for the
Advanced LIGO detector, and requires 0.13s to evaluate for a waveform with a
starting frequency of 20Hz. Finally, we perform an end-to-end test of the
surrogate with a set of parameter estimation runs, and find that the surrogate
accurately recovers the parameters of injected waveforms.


  Recent advancements in Text-to-Image (T2I) models have raised significant
safety concerns about their potential misuse for generating inappropriate or
Not-Safe-For-Work (NSFW) contents, despite existing countermeasures such as
NSFW classifiers or model fine-tuning for inappropriate concept removal.
Addressing this challenge, our study unveils GuardT2I, a novel moderation
framework that adopts a generative approach to enhance T2I models' robustness
against adversarial prompts. Instead of making a binary classification,
GuardT2I utilizes a Large Language Model (LLM) to conditionally transform text
guidance embeddings within the T2I models into natural language for effective
adversarial prompt detection, without compromising the models' inherent
performance. Our extensive experiments reveal that GuardT2I outperforms leading
commercial solutions like OpenAI-Moderation and Microsoft Azure Moderator by a
significant margin across diverse adversarial scenarios.


  A recent study in the 2017 Living to 100 Monograph published by the Society
of Actuaries suggests, in contrast to previous research, that the risk of death
after 110 increases with age. By fitting a Gompertz model to estimated central
death rates for the oldest old, the authors challenge existing theory and
empirical research indicating a deceleration of mortality at older ages and the
emergence of a plateau. We argue that their results are inconclusive for three
reasons: (1) the data selection was arbitrary; (2) the statistical analysis was
inappropriate; and (3) the presentation of the results is misleading and
inadequate. We therefore claim that the hypothesis that the human force of
mortality increases after age 110 has not been proved.


  Let rho and sigma be two central relations on a finite set A. It is known
that the clones Pol(rho) and Pol(sigma) which consists of all operations on A
that preserve rho respectively sigma are among the maximal clones on A. In this
paper, we find all central relations sigma such that the join clone of Pol(rho)
and Pol(sigma) is a maximal subclone of Pol(rho) where rho is a fixed central
relation.


  At zero temperature the sublattice magnetization of the quantum spin-1/2
Heisenberg antiferromagnet on a body-centered cubic lattice with competing
first and second neighbor exchange (J1 and J2) is investigated using the
non-linear spin wave theory. The zero temperature phases of the model consist
of a two sublattice N\'{e}el phase for small J_2 (AF_1) and a collinear phase
at large J_2 (AF_2). We show that quartic corrections due to spin-wave
interactions enhance the sublattice magnetization in both the AF_1 and the AF_2
phase. The magnetization corrections are prominent near the classical
transition point of the model and in the J_2> J_1 regime. The ground state
energy with quartic interactions is also calculated. It is found that up to
quartic corrections the first order phase transition (previously observed in
this model) between the AF_1 and the AF_2 phase survives.


  In this paper, we use the optimization formulation of nonlinear Kalman
filtering and smoothing problems to develop second-order variants of iterated
Kalman smoother (IKS) methods. We show that Newton's method corresponds to a
recursion over affine smoothing problems on a modified state-space model
augmented by a pseudo measurement. The first and second derivatives required in
this approach can be efficiently computed with widely available automatic
differentiation tools. Furthermore, we show how to incorporate line-search and
trust-region strategies into the proposed second-order IKS algorithm in order
to regularize updates between iterations. Finally, we provide numerical
examples to demonstrate the method's efficiency in terms of runtime compared to
its batch counterpart.


  Robots that solve complex tasks in environments too dangerous for humans to
enter are desperately needed, e.g. for search and rescue applications. We
describe our mobile manipulation robot Momaro, with which we participated
successfully in the DARPA Robotics Challenge. It features a unique locomotion
design with four legs ending in steerable wheels, which allows it both to drive
omnidirectionally and to step over obstacles or climb. Furthermore, we present
advanced communication and teleoperation approaches, which include immersive 3D
visualization, and 6D tracking of operator head and arm motions. The proposed
system is evaluated in the DARPA Robotics Challenge, the DLR SpaceBot Cup
Qualification and lab experiments. We also discuss the lessons learned from the
competitions.


  We show that given $m$ proteins (or protein backbones, which are modeled as
3D polygonal chains each of length O(n)) the problem of protein local structure
alignment under the discrete Fr\'{e}chet distance is as hard as Independent
Set. So the problem does not admit any approximation of factor
$n^{1-\epsilon}$. This is the strongest negative result regarding the protein
local structure alignment problem. On the other hand, if $m$ is a constant,
then the problem can be solved in polygnomial time.


  Eigen's quasispecies system with explicit space and global regulation is
considered. Limit behavior and stability of the system in a functional space
under perturbations of a diffusion matrix with nonnegative spectrum are
investigated. It is proven that if the diffusion matrix has only positive
eigenvalues then the solutions of the distributed system converge to the
equilibrium solution of the corresponding local dynamical system. These results
imply that the error threshold does not change if the spatial interactions
under the principle of global regulation are taken into account.


  We present the X-ray analysis of the most luminous obscured active galactic
nuclei (AGN) inside local ultraluminous infrared galaxies (ULIRGs). Our sample
consists of ten sources, harbouring AGN components with estimated luminosity in
excess of ~10^12 L_sun and yet unidentified at optical wavelengths because of
their large obscuration. According to the Chandra and XMM-Newton spectra, only
in two cases out of ten clear AGN signatures are detected at 2-10 keV in the
shape of reflected emission. The X-ray flux from the starburst (SB) components,
instead, is always broadly consistent with the expectations based on their IR
emission. The most convincing explanation for the missing AGN detections is
therefore the Compton-thickness of the X-ray absorber. In general, the
combination of our mid-IR and X-ray spectral analysis suggests that the
environment surrounding the AGN component in ULIRGs is much richer in gas and
dust than in ordinary active galaxies, and the degree of AGN absorption can be
tentatively related to the SB intensity, indicating a strong interaction
between the two processes and supporting the ULIRG/quasar evolutionary scheme.


  Discrete transforms such as the discrete Fourier transform (DFT) and the
discrete Hartley transform (DHT) are important tools in numerical analysis. The
successful application of transform techniques relies on the existence of
efficient fast transforms. In this paper some fast algorithms are derived. The
theoretical lower bound on the multiplicative complexity for the DFT/DHT are
achieved. The approach is based on the factorization of DHT matrices.
Algorithms for short blocklengths such as $N \in \{3, 5, 6, 12, 24 \}$ are
presented.


  We develop K$\omega$, an open-source linear algebra library for the shifted
Krylov subspace methods. The methods solve a set of shifted linear equations
$(z_k I-H)x^{(k)}=b\, (k=0,1,2,...)$ for a given matrix $H$ and a vector $b$,
simultaneously. The leading order of the operational cost is the same as that
for a single equation. The shift invariance of the Krylov subspace is the
mathematical foundation of the shifted Krylov subspace methods. Applications in
materials science are presented to demonstrate the advantages of the algorithm
over the standard Krylov subspace methods such as the Lanczos method. We
introduce benchmark calculations of (i) an excited (optical) spectrum and (ii)
intermediate eigenvalues by the contour integral on the complex plane. In
combination with the quantum lattice solver $\mathcal{H} \Phi$, K$\omega$ can
realize parallel computation of excitation spectra and intermediate eigenvalues
for various quantum lattice models.


  Data poisoning is an attack on machine learning models wherein the attacker
adds examples to the training set to manipulate the behavior of the model at
test time. This paper explores poisoning attacks on neural nets. The proposed
attacks use "clean-labels"; they don't require the attacker to have any control
over the labeling of training data. They are also targeted; they control the
behavior of the classifier on a $\textit{specific}$ test instance without
degrading overall classifier performance. For example, an attacker could add a
seemingly innocuous image (that is properly labeled) to a training set for a
face recognition engine, and control the identity of a chosen person at test
time. Because the attacker does not need to control the labeling function,
poisons could be entered into the training set simply by leaving them on the
web and waiting for them to be scraped by a data collection bot.
  We present an optimization-based method for crafting poisons, and show that
just one single poison image can control classifier behavior when transfer
learning is used. For full end-to-end training, we present a "watermarking"
strategy that makes poisoning reliable using multiple ($\approx$50) poisoned
training instances. We demonstrate our method by generating poisoned frog
images from the CIFAR dataset and using them to manipulate image classifiers.


  We review the "critical point" concept for large earthquakes and enlarge it
in the framework of so-called "finite-time singularities". The singular
behavior associated with accelerated seismic release is shown to result from a
positive feedback of the seismic activity on its release rate. The most
important mechanisms for such positive feedback are presented. We introduce and
solve analytically a novel simple model of geometrical positive feedback in
which the stress shadow cast by the last large earthquake is progressively
fragmented by the increasing tectonic stress. Finally, we present a somewhat
speculative figure that tends to support a mechanism based on the decay of
stress shadows. This figure suggests that a large earthquake in Southern
California of size similar to the 1812 great event is maturing.


  Best subset of groups selection (BSGS) is the process of selecting a small
part of non-overlapping groups to achieve the best interpretability on the
response variable. It has attracted increasing attention and has far-reaching
applications in practice. However, due to the computational intractability of
BSGS in high-dimensional settings, developing efficient algorithms for solving
BSGS remains a research hotspot. In this paper,we propose a group-splicing
algorithm that iteratively detects the relevant groups and excludes the
irrelevant ones. Moreover, coupled with a novel group information criterion, we
develop an adaptive algorithm to determine the optimal model size. Under mild
conditions, it is certifiable that our algorithm can identify the optimal
subset of groups in polynomial time with high probability. Finally, we
demonstrate the efficiency and accuracy of our methods by comparing them with
several state-of-the-art algorithms on both synthetic and real-world datasets.


  In recent years there has been substantial development in algorithms for
quantum phase estimation. In this work we provide a new approach to online
Bayesian phase estimation that achieves Heisenberg limited scaling that
requires exponentially less classical processing time with the desired error
tolerance than existing Bayesian methods.
  This practically means that we can perform an update in microseconds on a CPU
as opposed to milliseconds for existing particle filter methods. Our approach
assumes that the prior distribution is Gaussian and exploits the fact, when
optimal experiments are chosen, the mean of the prior distribution is given by
the position of a random walker whose moves are dictated by the measurement
outcomes. We then argue from arguments based on the Fisher information that our
algorithm provides a near-optimal analysis of the data. This work shows that
online Bayesian inference is practical, efficient and ready for deployment in
modern FPGA driven adaptive experiments.


  We discuss some mistakes and curiosities concerned with the celebrated First
International Topological Conference in Moscow, 1935.


  We report a new reversal mechanism of magnetic vortex cores in nanodot
elements driven by out-of-plane currents, occurring through two coupled
edge-solitons via dynamic transformations between magnetic solitons of
different topological charges. This mechanism differs completely from the well
known switching process mediated by the creation and annihilation of
vortex-antivortex pairs in terms of the associated topological solitons,
energies, and spin-wave emissions. Strongly localized out-of-plane gyrotropic
fields induced by the fast motion of the two coupled edge-solitons enable a
magnetization dip that plays a crucial role in the formation of the reversed
core magnetization. This work provides a new physical insight into the dynamic
transformations of magnetic solitons in nanoelements.


  We report on the redshift of the BL Lac object 3FGL J0909.0+2310 based on
observations obtained with the OSIRIS Multi Object Spectrograph (MOS) mounted
on the 10.4-m Gran Telescopio Canarias. A redshift of 0.432+/-0.002 was
obtained by the identification of three absorption features (CaII K&H and
G-band) detected in the spectrum of the BL Lac host galaxy. The closest object
to the BL Lac at an angular separation of 3.8" (~21 kpc at this distance) has a
similar redshift of 0.431+/-0.002. This companion galaxy could be the most
likely cause of the nuclear activity as postulated by studies based on more
extended data sets and cosmological models. MOS allows us to study the object's
neighbourhood within a field of view of approximately 7'x2' and we find two
small groups of galaxies at redshifts 0.28 and 0.39 which are probably not
related to the activity of 3FGL~J0909.0+2310.


  We introduce a natively distributed mini-application benchmark representative
of plastic spiking neural network simulators. It can be used to measure
performances of existing computing platforms and to drive the development of
future parallel/distributed computing systems dedicated to the simulation of
plastic spiking networks. The mini-application is designed to generate spiking
behaviors and synaptic connectivity that do not change when the number of
hardware processing nodes is varied, simplifying the quantitative study of
scalability on commodity and custom architectures. Here, we present the strong
and weak scaling and the profiling of the computational/communication
components of the DPSNN-STDP benchmark (Distributed Simulation of Polychronous
Spiking Neural Network with synaptic Spike-Timing Dependent Plasticity). In
this first test, we used the benchmark to exercise a small-scale cluster of
commodity processors (varying the number of used physical cores from 1 to 128).
The cluster was interconnected through a commodity network. Bidimensional grids
of columns composed of Izhikevich neurons projected synapses locally and toward
first, second and third neighboring columns. The size of the simulated network
varied from 6.6 Giga synapses down to 200 K synapses. The code demonstrated to
be fast and scalable: 10 wall clock seconds were required to simulate one
second of activity and plasticity (per Hertz of average firing rate) of a
network composed by 3.2 G synapses running on 128 hardware cores clocked @ 2.4
GHz. The mini-application has been designed to be easily interfaced with
standard and custom software and hardware communication interfaces. It has been
designed from its foundation to be natively distributed and parallel, and
should not pose major obstacles against distribution and parallelization on
several platforms.


  The problem of creating a three-sided dice with the probability of it landing
on each of its sides being equal to 1/3 has been around for many years. Various
approaches have been attempted, but as different authors achieved at different
results, no uniform answer has been found. In this paper, the probability of a
cylinder-shaped coin landing on its side is investigated, aiming to predict the
final position of the coin, based on the starting physical and geometrical
parameters of the coin. Pure physical methods and computer modeling have been
used to find the parameters of a "fair" coin and to determine which parameters
of the system affect the probability of the coin landing on an edge, and in
what way. Statistical analysis of the situation has been made, to ensure that
the probabilities we are trying to find, correspond to the appropriate physical
events. Overall, two main possibilities for modeling the situation are
considered: single-axis rotation (flat rotation) and the superposition of
rotation against multiple axes (volumetric rotation). For each, a model was
built and it was compared with computer modeling and real-life experiments.
Dynamical and energetic approaches have been considered, and a model of the
coin during its impact with the ground has been created. A robot that is
capable of launching cylindrical coins, picking them up, and recording the
result of the experiment, controlling starting parameters precisely, and
distributing them to our needs, and automatically record the results of the
experiment, was created. In conclusion, the parameters of a "fair" coin are
provided, which have even probabilities landing on all its sides, as well as
the dependencies of the probability of a coin landing on its edge, on different
parameters.


  We examine the superfluid-Mott insulator (SF-MI) transition of
antiferromagnetically interacting spin-1 bosons trapped in a square or
triangular optical lattice. We perform a strong-coupling expansion up to the
third order in the transfer integral $t$ between the nearest-neighbor lattices.
As expected from previous studies, an MI phase with an even number of bosons is
considerably more stable against the SF phase than it is with an odd number of
bosons. Results for the triangular lattice are similar to those for the square
lattice, which suggests that the lattice geometry does not strongly affect the
stability of the MI phase against the SF phase.


  We consider the conformal Einstein equations for massless collisionless gas
cosmologies which admit an isotropic singularity. It is shown that the Cauchy
problem for these equations is well-posed with data consisting of the limiting
particle distribution function at the singularity.


  This study examines the Raman scattering and charge transfer properties of
molecules adsorbed on the surface of a tetrahedral Au$_{120}$ nanoparticle
based on the time-dependent density functional tight-binding (TD-DFTB) method.
We study Raman scattering (SERS) enhancements for pyridine where the molecule
is adsorbed either on the tip (V complex) or surface (S complex) of the
nanoparticle. The scattering intensity is enhanced by a factor of 3-15 due to
chemical effects while significantly larger enhancements (in the order of
10$^2$-10$^4$) are observed for plasmon resonance excitation at an energy of
2.5 eV depending on the adsorption site. Furthermore, we demonstrate charge
transfer between the nanoparticle and a fullerene-based molecule after pulsed
excitation of the plasmon resonance which shows how plasmon excitation can lead
to negative molecular ion formation. All of these results are consistent with
earlier studies using either TD-DFT theory or experimental measurements.


  For asymptotically flat black holes, Reall-Santos method is a convenient tool
to compute leading higher derivative corrections to the thermodynamic
quantities without actually solving the modified field equations. However,
there are subtleties in its generalization to asymptotically AdS black holes
with general higher derivative corrections. First of all, it is necessary to
know all the higher derivative holographic counterterms and the surface terms
implementing the variational principle and subtracting the divergence. One then
needs to solve for the modified AdS radius and rescale the time coordinate in
an appropriate way such that the induced metric on the conformal boundary of
AdS black hole is not modified. We observe that Reall-Santos method can be
directly applied to a particular 4-derivative gravity model, known as the
Einstein-Weyl gravity, which does not modify the AdS radius and requires only
the Gibbons-Hawking-York term and holographic counterterms for the 2-derivative
theory. We thus suggest that to compute the thermodynamic quantities of AdS
black holes in general 4-derivative theories of gravity, one simply needs to
transform it to a Einstein-Weyl gravity with identical thermodynamic variables
by appropriate field redefinitions. We explicitly verify this proposal with
spherically-symmetric and static charged black holes in Einstein-Maxwell theory
extended with generic 4-derivative interactions.


  3D Slicer is a powerful platform for 3D data visualization and analysis, but
has a significant learning curve for new users. Generative AI applications,
such as ChatGPT, have emerged as a potential method of bridging the gap between
various sources of documentation using natural language. The limited exposure
of LLM services to 3D Slicer documentation, however, means that ChatGPT and
related services tend to suffer from significant hallucination. The objective
of this project is to build a chatbot architecture, called SlicerChat, that is
optimized to answer 3D Slicer related questions and able to run locally using
an open-source model. The core research questions explored in this work revolve
around the answer quality and speed differences due to fine-tuning, model size,
and the type of domain knowledge included in the prompt. A prototype SlicerChat
system was built as a custom extension in 3D Slicer based on the Code-Llama
Instruct architecture. Models of size 1.1B, 7B and 13B were fine-tuned using
Low rank Adaptation, and various sources of 3D Slicer documentation were
compiled for use in a Retrieval Augmented Generation paradigm. Testing
combinations of fine-tuning and model sizes on a benchmark dataset of five 3D
Slicer questions revealed that fine-tuning had no impact on model performance
or speed compared to the base architecture, and that larger models performed
better with a significant speed decrease. Experiments with adding 3D Slicer
documentation to the prompt showed that Python sample code and Markdown
documentation were the most useful information to include, but that adding 3D
Slicer scene data and questions taken from Discourse also improved model
performance. In conclusion, this project shows the potential for integrating a
high quality, local chatbot directly into 3D Slicer to help new users and
experienced developers alike to more efficiently use the software.


  There is strong evidence that the mass in the Universe is dominated by dark
matter, which exerts gravitational attraction but whose exact nature is
unknown. In particular, all galaxies are believed to be embedded in massive
haloes of dark matter. This view has recently been challenged by surprisingly
low random stellar velocities in the outskirts of ordinary elliptical galaxies,
which were interpreted as indicating a lack of dark matter (Mendez et al. 2001;
Romanowsky et al. 2003). Here we show that the low velocities are in fact
compatible with galaxy formation in dark-matter haloes. Using numerical
simulations of disc-galaxy mergers, we find that the stellar orbits in the
outer regions of the resulting ellipticals are very elongated. These stars were
torn by tidal forces from their original galaxies during the first close
passage and put on outgoing trajectories. The elongated orbits, combined with
the steeply falling density profile of the observed tracers, explain the
observed low velocities even in the presence of large amounts of dark matter.
Projection effects when viewing a triaxial elliptical can lead to even lower
observed velocities along certain lines of sight.


  Correct inference of genetic regulations inside a cell is one of the greatest
challenges in post genomic era for the biologist and researchers. Several
intelligent techniques and models were already proposed to identify the
regulatory relations among genes from the biological database like time series
microarray data. Recurrent Neural Network (RNN) is one of the most popular and
simple approach to model the dynamics as well as to infer correct dependencies
among genes. In this paper, Bat Algorithm (BA) is applied to optimize the model
parameters of RNN model of Gene Regulatory Network (GRN). Initially the
proposed method is tested against small artificial network without any noise
and the efficiency is observed in term of number of iteration, number of
population and BA optimization parameters. The model is also validated in
presence of different level of random noise for the small artificial network
and that proved its ability to infer the correct inferences in presence of
noise like real world dataset. In the next phase of this research, BA based RNN
is applied to real world benchmark time series microarray dataset of E. coli.
The results prove that it can able to identify the maximum number of true
positive regulation but also include some false positive regulations.
Therefore, BA is very suitable for identifying biological plausible GRN with
the help RNN model.


  Pedestrian Detection is the most critical module of an Autonomous Driving
system. Although a camera is commonly used for this purpose, its quality
degrades severely in low-light night time driving scenarios. On the other hand,
the quality of a thermal camera image remains unaffected in similar conditions.
This paper proposes an end-to-end multimodal fusion model for pedestrian
detection using RGB and thermal images. Its novel spatio-contextual deep
network architecture is capable of exploiting the multimodal input efficiently.
It consists of two distinct deformable ResNeXt-50 encoders for feature
extraction from the two modalities. Fusion of these two encoded features takes
place inside a multimodal feature embedding module (MuFEm) consisting of
several groups of a pair of Graph Attention Network and a feature fusion unit.
The output of the last feature fusion unit of MuFEm is subsequently passed to
two CRFs for their spatial refinement. Further enhancement of the features is
achieved by applying channel-wise attention and extraction of contextual
information with the help of four RNNs traversing in four different directions.
Finally, these feature maps are used by a single-stage decoder to generate the
bounding box of each pedestrian and the score map. We have performed extensive
experiments of the proposed framework on three publicly available multimodal
pedestrian detection benchmark datasets, namely KAIST, CVC-14, and UTokyo. The
results on each of them improved the respective state-of-the-art performance. A
short video giving an overview of this work along with its qualitative results
can be seen at https://youtu.be/FDJdSifuuCs. Our source code will be released
upon publication of the paper.


  We show that there is no $(95,40,12,20)$ strongly regular graph and,
consequently, there is no $(96,45,24,18)$ strongly regular graph, no two-graph
on $96$ vertices, and no partial geometry $\rm{pg}(5,9,3)$. The main idea of
the result is based on the star complement technique and requires a small
amount of computation.


  We establish the following results on higher order
$\mathcal{S}^p$-differentiability, $1<p<\infty$, of the operator function
arising from a continuous scalar function $f$ and self-adjoint operators
defined on a fixed separable Hilbert space: (i) $f$ is $n$ times continuously
Fr\'{e}chet $\mathcal{S}^p$-differentiable at every bounded self-adjoint
operator if and only if $f\in C^n(\mathbb{R})$; (ii) if $f',\ldots,f^{(n-1)}\in
C_b(\mathbb{R})$ and $f^{(n)}\in C_0(\mathbb{R})$, then $f$ is $n$ times
continuously Fr\'{e}chet $\mathcal{S}^p$-differentiable at every self-adjoint
operator; (iii) if $f',\ldots,f^{(n)}\in C_b(\mathbb{R})$, then $f$ is $n-1$
times continuously Fr\'{e}chet $\mathcal{S}^p$-differentiable and $n$ times
G\^{a}teaux $\mathcal{S}^p$-differentiable at every self-adjoint operator. We
also prove that if $f\in B_{\infty1}^n(\mathbb{R})\cap
B_{\infty1}^1(\mathbb{R})$, then $f$ is $n$ times continuously Fr\'{e}chet
$\mathcal{S}^q$-differentiable, $1\le q<\infty$, at every self-adjoint
operator. These results generalize and extend analogous results of [10] to
arbitrary $n$ and unbounded operators as well as substantially extend the
results of [2,4,19] on higher order $\mathcal{S}^p$-differentiability of $f$ in
a certain Wiener class, G\^{a}teaux $\mathcal{S}^2$-differentiability of $f\in
C^n(\mathbb{R})$ with $f',\ldots,f^{(n)}\in C_b(\mathbb{R})$, and G\^{a}teaux
$\mathcal{S}^q$-differentiability of $f$ in the intersection of the Besov
classes $B_{\infty1}^n(\mathbb{R})\cap B_{\infty1}^1(\mathbb{R})$. As an
application, we extend $\mathcal{S}^p$-estimates for operator Taylor remainders
to a broad set of symbols. Finally, we establish explicit formulas for
Fr\'{e}chet differentials and G\^{a}teaux derivatives.


  We propose a class of graphene-based moir\'e systems hosting flat bands on
kagome and honeycomb moir\'e superlattices. These systems are formed by
stacking a graphene layer on a 2D substrate with lattice constant approximately
$\sqrt{3}$ times that of graphene. When the moir\'e potentials are induced by a
2D irreducible corepresentation in the substrate, the model shows a rich phase
diagram of low energy bands including eigenvalue fragile phases as well as
kagome and honeycomb flat bands. Spin-orbit coupling in the substrate can lift
symmetry protected degeneracies and create spin Chern bands, and we observe
spin Chern numbers up to three. We additionally propose a moir\'e system formed
by stacking two graphene-like layers with similar lattice constants and Fermi
energies but with Dirac Fermi velocities of opposite sign. This system exhibits
multiple kagome and honeycomb flat bands simultaneously. Both models we propose
resemble the hypermagic model of [Scheer $\textit{et al.}$, Phys. Rev. B
$\textbf{106}$, 115418 (2022)] and may provide ideal platforms for the
realization of strongly correlated topological phases.


  Simplicity of universal minimal quantum affine W-algebras is studied. As an
application, we find the values of the center, for which the vacuum module over
a superconformal algebra is irreducible.


  G183$-$35 is an unusual white dwarf that shows an H$\alpha$ line split into
five components, instead of the usual three components seen in strongly
magnetic white dwarfs. Potential explanations for the unusual set of lines
includes a double degenerate system containing two magnetic white dwarfs and/or
rotational modulation of a complex magnetic field structure. Here we present
time-resolved spectroscopy of G183$-$35 obtained at the Gemini Observatory.
These data reveal two sets of absorption lines that appear and disappear over a
period of about 4 hours. We also detect low-level (0.2%) variability in optical
photometry at the same period. We demonstrate that the spectroscopic and
photometric variability can be explained by the presence of spots on the
surface of the white dwarf and a change in the average field strength from
about 4.6 MG to 6.2 MG. The observed variability is clearly due to G183$-$35's
relatively short spin period. However, rotational modulation of a complex
magnetic field by itself cannot explain the changes seen in the central
H$\alpha$ component. An additional source of variability in the line profiles,
most likely due to a chemically inhomogeneous surface composition, is also
needed. We propose further observations of similar objects to test this
scenario.


  Given the fantastic experimental effort, it is important to thoroughly
explore the signature space of inflationary models. The fact that higher
derivative operators do not renormalize lower derivative ones allows us to find
a large class of technically natural single-clock inflationary models where, in
the context of the Effective Field Theory of Inflation, the leading
interactions have many derivatives. We systematically explore the 3-point
function induced by these models and their overlap with the standard
equilateral and orthogonal templates. We find that in order to satisfactorily
cover the signature space of these models, two new additional templates need to
be included. We then perform the optimal analysis of the WMAP 9-year data for
the resulting four templates, finding that the overall significance of a
non-zero signal is between 2--2.5$\sigma$, depending on the choice of parameter
space, partially driven by the preference for nonzero $f_{NL}^{\rm orth}$ in
WMAP9.


  We propose a way to study one-dimensional statistical mechanics models with
complex-valued action using transfer operators. The argument consists of two
steps. First, the contour of integration is deformed so that the associated
transfer operator is a perturbation of a normal one. Then the transfer operator
is studied using methods of semi-classical analysis.
  In this paper we concentrate on the second step, the main technical result
being a semi-classical estimate for powers of an integral operator which is
approximately normal.


  For the simulation of equilibrium states and finite-temperature response
functions of strongly-correlated quantum many-body systems, we compare the
efficiencies of two different approaches in the framework of the density matrix
renormalization group (DMRG). The first is based on matrix product
purifications. The second, more recent one, is based on so-called minimally
entangled typical thermal states (METTS). For the latter, we highlight the
interplay of statistical and DMRG truncation errors, discuss the use of
self-averaging effects, and describe schemes for the computation of response
functions. For critical as well as gapped phases of the spin-1/2 XXZ chain and
the one-dimensional Bose-Hubbard model, we assess the computation costs and
accuracies of the two methods at different temperatures. For almost all
considered cases, we find that, for the same computation cost, purifications
yield more accurate results than METTS -- often by orders of magnitude. The
METTS algorithm becomes more efficient only for temperatures well below the
system's energy gap. The exponential growth of the computation cost in the
evaluation of response functions limits the attainable timescales in both
methods and we find that in this regard, METTS do not outperform purifications.


  Very recently a compact object with a mass in the range $2.50\div 2.67\,
M_{\odot}$ has been discovered via gravitational waves detection of a compact
binary coalescence. The mass of this object makes it among the heaviest neutron
star never detected or the lightest black hole ever observed. Here we show that
a neutron star with this observed mass, can be consistently explained with the
mass-radius relation obtained by Extended Theories of Gravity. Furthermore,
equations of state, consistent with LIGO observational constraints, are
adopted. We consider also the influence of rotation and show that masses of
rotating neutron stars can exceed $2.6 M_\odot$ for some equations of state
compatible with LIGO data.


  Surprisingly, until recently CP violation in pion decay was not ruled out
experimentally even at the percent level. I have derived the first experimental
limit, $-0.01<A_{CP}<0.02$, from old data on the anomalous magnetic moment
$g-2$ of the muon. New data from the Brookhaven $g-2$ experiment might extend
the search by a few orders of magnitude, but probably will not probe the
theoretically-interesting sub-$10^{-7}$ regime.


  Large classes of AdS$_p$ supergravity backgrounds describing the IR dynamics
of $p$-branes wrapped on a Riemann surface are determined by a solution to the
Liouville equation. The regular solutions of this equation lead to the
well-known wrapped brane supergravity solutions associated with the constant
curvature metric on a compact Riemann surface. We show that some singular
solutions of the Liouville equation have a physical interpretation as explicit
point-like brane sources on the Riemann surface. We uncover the details of this
picture by focusing on $\mathcal{N}=1$ theories of class $\mathcal{S}$ arising
from M5-branes on a punctured Riemann surface. We present explicit AdS$_5$
solutions dual to these SCFTs and check the holographic duality by showing the
non-trivial agreement of 't Hooft anomalies.


  We consider the problem of learning $N$ identical copies of an unknown
$n$-qubit quantum graph state with product measurements. These graph states
have corresponding graphs where every vertex has exactly $d$ neighboring
vertices. Here, we detail an explicit algorithm that uses product measurements
on multiple identical copies of such graph states to learn them. When $n \gg d$
and $N = O(d \log(1/\epsilon) + d^2 \log n ),$ this algorithm correctly learns
the graph state with probability at least $1- \epsilon$. From channel coding
theory, we find that for arbitrary joint measurements on graph states, any
learning algorithm achieving this accuracy requires at least $\Omega(\log
(1/\epsilon) + d \log n)$ copies when $d=o(\sqrt n)$. We also supply bounds on
$N$ when every graph state encounters identical and independent depolarizing
errors on each qubit.


  Measuring the losses arising from different materials and interfaces is
crucial to improving the coherence of superconducting quantum circuits.
Although this has been of interest for a long time, current studies can either
only provide bounds to those losses, or require several devices for a complete
characterization. In this work, we introduce a method to measure the microwave
losses of materials and interfaces with a single multi-mode superconducting
resonator. We demonstrate a formalism for analyzing the loss sensitivity of
multi-mode systems and discuss the design strategies of multi-mode resonators
for material loss studies. We present two types of multi-mode superconducting
resonators for the study of bulk superconductors: the forky
whispering-gallery-mode resonator (FWGMR) and the ellipsoidal cavity. We use
these resonators to measure the surface dielectric, conductor, and seam losses
of high-purity (5N5) aluminum and aluminum alloy (6061), as well as how they
are affected by chemical etching, diamond turning, and thin-film coating. We
find that chemical etching and diamond turning reduce both the surface
dielectric and conductive losses of high-purity aluminum, but provide no
appreciable improvement to the seam. Coating the surfaces of diamond-turned
aluminum alloys with e-beam evaporated or sputtered aluminum thin-films
significantly reduces all three losses under study. In addition, we study the
effect of chemical etching on the surface of high-purity aluminum using
transmission electron microscopy (TEM) and find that the chemical etching
process creates a thinner and more uniform oxide layer, consistent with the
observed improvement in the surface dielectric loss.


  Blockchain front-running involves multiple agents, other than the legitimate
agent, claiming a payment from performing a contract. It arises because of the
public nature of blockchain transactions and potential network congestion. This
paper notes that disputes over payments are similar to classic ownership
disputes (such as King Solomon's dilemma). We propose a simultaneous report
mechanism that resolves Solomon's dilemma (using only ordinal preference
information) and also eliminates blockchain front-running. In each case, the
mechanism relies on threats to remove ownership from all claimants and
preferences from the legitimate claimant over allocations to other agents.


  Neural machine translation (NMT) models generally adopt an encoder-decoder
architecture for modeling the entire translation process. The encoder
summarizes the representation of input sentence from scratch, which is
potentially a problem if the sentence is ambiguous. When translating a text,
humans often create an initial understanding of the source sentence and then
incrementally refine it along the translation on the target side. Starting from
this intuition, we propose a novel encoder-refiner-decoder framework, which
dynamically refines the source representations based on the generated
target-side information at each decoding step. Since the refining operations
are time-consuming, we propose a strategy, leveraging the power of
reinforcement learning models, to decide when to refine at specific decoding
steps. Experimental results on both Chinese-English and English-German
translation tasks show that the proposed approach significantly and
consistently improves translation performance over the standard encoder-decoder
framework. Furthermore, when refining strategy is applied, results still show
reasonable improvement over the baseline without much decrease in decoding
speed.


  PredNet, a deep predictive coding network developed by Lotter et al.,
combines a biologically inspired architecture based on the propagation of
prediction error with self-supervised representation learning in video. While
the architecture has drawn a lot of attention and various extensions of the
model exist, there is a lack of a critical analysis. We fill in the gap by
evaluating PredNet both as an implementation of the predictive coding theory
and as a self-supervised video prediction model using a challenging video
action classification dataset. We design an extended model to test if
conditioning future frame predictions on the action class of the video improves
the model performance. We show that PredNet does not yet completely follow the
principles of predictive coding. The proposed top-down conditioning leads to a
performance gain on synthetic data, but does not scale up to the more complex
real-world action classification dataset. Our analysis is aimed at guiding
future research on similar architectures based on the predictive coding theory.


  This paper presents a data-efficient approach to learning transferable
forward models for robotic push manipulation. Our approach extends our previous
work on contact-based predictors by leveraging information on the pushed
object's local surface features. We test the hypothesis that, by conditioning
predictions on local surface features, we can achieve generalisation across
objects of different shapes. In doing so, we do not require a CAD model of the
object but rather rely on a point cloud object model (PCOM). Our approach
involves learning motion models that are specific to contact models. Contact
models encode the contacts seen during training time and allow generating
similar contacts at prediction time. Predicting on familiar ground reduces the
motion models' sample complexity while using local contact information for
prediction increases their transferability. In extensive experiments in
simulation, our approach is capable of transfer learning for various test
objects, outperforming a baseline predictor. We support those results with a
proof of concept on a real robot.


  We present medium- and broad-band Hubble Space Telescope (HST) photometry of
a sample of 35 central stars (CSs) of Planetary Nebulae (PNs) in the Large
Magellanic Cloud (LMC). The observations were made with the WFPC2 and STIS
instruments on board the HST. By observing LMC objects, our sample is free of
the distance uncertainty that is the dominate source of error in the
determination of CS luminosities in Galactic PNs. By observing with the HST, we
resolve the nebula and therefore we often detect the CSs unambiguously. We
obtain core masses of 16 of the objects by comparing their positions on the HR
diagram to theoretical evolutionary tracks, once we determine the stellar
effective temperature through Zanstra analysis. This sample of CS masses is the
largest and most reliable set obtained in an extra-Galactic environment. We
find an average mass of 0.65 Msun, though a few of the objects have very high
mass. This average value is consistent with the average mass of the white dwarf
population in the Galaxy. As the immediate precursors of white dwarfs, the
study of the mass distribution of PN CSs should help to constrain the
initial-to-final mass relation within environments of differing metallicity.
Finally, by exploring the physical connections between the star and the nebula,
we establish the importance of the study of PNs in the LMC to constrain the
energy input from the wind during the post-AGB phase.


  Aims. We attempt to determine the optical depth of spiral galaxy disks by a
statistical study of new Tully-Fisher data from the ongoing KLUN+ survey, and
to clarify the difference between the true and apparent behavior of optical
depth. Methods. By utilizing so-called normalized distances, a subsample of the
data is identified to be as free from selection effects as possible. For these
galaxies, a set of apparent quantities are calculated for face-on positions
using the Tully-Fisher diameter and magnitude relations. These values are
compared with direct observations to determine the mean value of the parameter
C describing the optical depth. Results. The present study suggests that spiral
galaxy disks are relatively optically thin tauB = 0.1, at least in the
outermost regions, while they appear in general to be optically thick tauB > 1
when the apparent magnitude and average surface brightness are studied
statistically.


  We study the problem of {\sl certification}: given queries to a function $f :
\{0,1\}^n \to \{0,1\}$ with certificate complexity $\le k$ and an input
$x^\star$, output a size-$k$ certificate for $f$'s value on $x^\star$. This
abstractly models a central problem in explainable machine learning, where we
think of $f$ as a blackbox model that we seek to explain the predictions of.
  For monotone functions, a classic local search algorithm of Angluin
accomplishes this task with $n$ queries, which we show is optimal for local
search algorithms. Our main result is a new algorithm for certifying monotone
functions with $O(k^8 \log n)$ queries, which comes close to matching the
information-theoretic lower bound of $\Omega(k \log n)$. The design and
analysis of our algorithm are based on a new connection to threshold phenomena
in monotone functions.
  We further prove exponential-in-$k$ lower bounds when $f$ is non-monotone,
and when $f$ is monotone but the algorithm is only given random examples of
$f$. These lower bounds show that assumptions on the structure of $f$ and query
access to it are both necessary for the polynomial dependence on $k$ that we
achieve.


  The Semantic Web drives towards the use of the Web for interacting with
logically interconnected data. Through knowledge models such as Resource
Description Framework (RDF), the Semantic Web provides a unifying
representation of richly structured data. Adding logic to the Web implies the
use of rules to make inferences, choose courses of action, and answer
questions. This logic must be powerful enough to describe complex properties of
objects but not so powerful that agents can be tricked by being asked to
consider a paradox. The Web has several characteristics that can lead to
problems when existing logics are used, in particular, the inconsistencies that
inevitably arise due to the openness of the Web, where anyone can assert
anything. N3Logic is a logic that allows rules to be expressed in a Web
environment. It extends RDF with syntax for nested graphs and quantified
variables and with predicates for implication and accessing resources on the
Web, and functions including cryptographic, string, math. The main goal of
N3Logic is to be a minimal extension to the RDF data model such that the same
language can be used for logic and data. In this paper, we describe N3Logic and
illustrate through examples why it is an appropriate logic for the Web.


  Automatic dialogue coherence evaluation has attracted increasing attention
and is crucial for developing promising dialogue systems. However, existing
metrics have two major limitations: (a) they are mostly trained in a simplified
two-level setting (coherent vs. incoherent), while humans give Likert-type
multi-level coherence scores, dubbed as "quantifiable"; (b) their predicted
coherence scores cannot align with the actual human rating standards due to the
absence of human guidance during training. To address these limitations, we
propose Quantifiable Dialogue Coherence Evaluation (QuantiDCE), a novel
framework aiming to train a quantifiable dialogue coherence metric that can
reflect the actual human rating standards. Specifically, QuantiDCE includes two
training stages, Multi-Level Ranking (MLR) pre-training and Knowledge
Distillation (KD) fine-tuning. During MLR pre-training, a new MLR loss is
proposed for enabling the model to learn the coarse judgement of coherence
degrees. Then, during KD fine-tuning, the pretrained model is further finetuned
to learn the actual human rating standards with only very few human-annotated
data. To advocate the generalizability even with limited fine-tuning data, a
novel KD regularization is introduced to retain the knowledge learned at the
pre-training stage. Experimental results show that the model trained by
QuantiDCE presents stronger correlations with human judgements than the other
state-of-the-art metrics.


  Besides maintaining health precautions, vaccination has been the only
prevention from SARS-CoV-2, though no clinically proved 100% effective vaccine
has been developed till date. At this stage, to withhold the debris of this
pandemic, experts need to know the impact of the vaccine efficacy rate's
threshold and how long this pandemic may extent with vaccines that have
different efficacy rates. In this article, a mathematical model study has been
done on the importance of vaccination and vaccine efficiency rate during an
ongoing pandemic. We simulated a five compartment mathematical model to analyze
the pandemic scenario in both California, and whole U.S. We considered four
vaccines, Pfizer, Moderna, AstraZeneca, and Johnson and Johnson, which are
being used rigorously to control the COVID-19 pandemic, in addition with two
special cases: a vaccine with 100% efficacy rate and no vaccine under use. Both
the infection and death rates are very high in California. Our model suggests
that the pandemic situation in California will be under control in the last
quartile of the year 2023 if frequent vaccination is continued with the Pfizer
vaccine. During this time, six waves will happen from the beginning of the
immunization where the case fatality and recovery rates will be 1.697% and
98.30%, respectively. However, according to the considered model, this period
might be extended to the mid of 2024 when vaccines with lower efficacy rates
are used. The more effective a vaccine, the less people suffer from this malign
infection. Although specific groups of people get prioritized initially, mass
vaccination is needed to control the spread of the disease.


  We report a measurement of the differential cross section for the $\gamma n
\to \pi^- p$ process from the CLAS detector at Jefferson Lab in Hall B for
photon energies between 1.0 and 3.5 GeV and pion center-of-mass (c.m.) angles
($\theta_{c.m.}$) between 50$^\circ$ and 115$^\circ$. We confirm a previous
indication of a broad enhancement around a c.m. energy ($\sqrt{s}$) of 2.2 GeV
at $\theta_{c.m.}=90^\circ$ in the scaled differential cross section, $s^7
{\frac{d\sigma}{dt}}$. Our data show the angular dependence of this enhancement
as the scaling region is approached in the kinematic region from 70$^\circ$ to
105$^\circ$.


  For a non-compact simple Lie algebra $\mathfrak{g}$ over $\mathbb{R}$, we
denote by $\mathcal{O}^{\mathbb{C}}_{\min,\mathfrak{g}}$ the unique complex
nilpotent orbit in $\mathfrak{g} \otimes_\mathbb{R} \mathbb{C}$ containing all
minimal real nilpotent orbits in $\mathfrak{g}$. In this paper, we give a
complete classification of symmetric pairs $(\mathfrak{g},\mathfrak{h})$ such
that $\mathcal{O}^{\mathbb{C}}_{\min,\mathfrak{g}} \cap \mathfrak{g}^d =
\emptyset$, where $\mathfrak{g}^d$ denotes the dual Lie algebra of
$(\mathfrak{g},\mathfrak{h})$. Furthermore, for symmetric pairs $(G,H)$ with
real simple Lie group $G$, we apply our classification to theorems given by T.
Kobayashi [J. Lie Theory (2023)], and study bounded multiplicity properties of
restrictions on $H$ of infinite-dimensional irreducible $G$-representations
with minimum Gelfand--Kirillov dimension.


  We construct $\bf genRBF$ kernel, which generalizes the classical Gaussian
RBF kernel to the case of incomplete data. We model the uncertainty contained
in missing attributes making use of data distribution and associate every point
with a conditional probability density function. This allows to embed
incomplete data into the function space and to define a kernel between two
missing data points based on scalar product in $L_2$. Experiments show that
introduced kernel applied to SVM classifier gives better results than other
state-of-the-art methods, especially in the case when large number of features
is missing. Moreover, it is easy to implement and can be used together with any
kernel approaches with no additional modifications.


  We investigate properties of order continuous operators on pre-Riesz spaces
with respect to the embedding of the range space into a vector lattice cover
or, in particular, into its Dedekind completion. We show that order continuity
is preserved under this embedding for positive operators, but not in general.
  For the vector lattice $\ell_0^\infty$ of eventually constant sequences, we
consider the pre-Riesz space of regular operators on $\ell_0^\infty$ and show
that making the range space Dedekind complete does not provide a vector lattice
cover of the pre-Riesz space. A similar counterexample is obtained for the
directed part of the space of order continuous operators on $\ell_0^\infty$.


  The quark's behaviour while influenced by a strong stochastic gluon field is
analyzed. An approximate procedure for calculating the effective Hamiltonian is
developed and the corresponding ground state within the Hartree-Fock-Bogolyubov
approach is found. The comparative analysis of various Hamiltonian models is
given and transition to the chiral limit in the Keldysh model is discussed in
detail.


  Carbon offsets from voluntarily avoided deforestation projects are generated
based on performance vis-\`a-vis ex-ante deforestation baselines. We examined
the impacts of 27 forest conservation projects in six countries on three
continents using synthetic control methods for causal inference. We compare the
project baselines with ex-post counterfactuals based on observed deforestation
in control sites. Our findings show that most projects have not reduced
deforestation. For projects that did, reductions were substantially lower than
claimed. Methodologies for constructing deforestation baselines for
carbon-offset interventions thus need urgent revisions in order to correctly
attribute reduced deforestation to the conservation interventions, thus
maintaining both incentives for forest conservation and the integrity of global
carbon accounting.


  In this paper we discuss a technique for selectively loading a particle into
a magneto-gravitational trap using the sublimation of camphor to release
particles from a tungsten probe tip directly into the trapping region. This
sublimation-activated release (SAR) loading technique makes use of
micropositioners with tungsten probe tips, as well as the relatively fast rate
of sublimation of camphor at room temperature, to selectively load particles
having diameters ranging from $\SI{8}{\micro \meter}$ to $\SI{100}{\micro
\meter}$ or more. The advantages of this method include its ability to
selectively load unique particles or particles in limited supply, its low loss
compared to alternative techniques, the low speed of the particle when
released, and the versatility of its design which allows for loading into traps
with complex geometries. SAR is demonstrated here by loading a particle into a
magneto-gravitational trap, but the technique could also be applicable to other
levitated optomechanical systems.


  Recent developments in communication technologies, such as 5G, together with
innovative computing paradigms, such as edge computing, provide further
possibilities for the implementation of real-time networked control systems.
However, privacy and cyber-security concerns arise when sharing private data
between sensors, agents and a third-party computing facility. In this paper, a
secure version of the distributed formation control is presented, analyzed and
simulated, where gradient-based formation control law is implemented in the
edge, with sensor and actuator information being secured by fully homomorphic
encryption method based on learning with error (FHE-LWE) combined with a
proposed mixed uniform-logarithmic quantizer (MULQ). The novel quantizer is
shown to be suitable for realizing secure control systems with FHE-LWE where
the critical real-time information can be quantized into a prescribed bounded
space of plaintext while satisfying a sector bound condition whose lower and
upper-bound can be made sufficiently close to an identity. An absolute
stability analysis is presented, that shows the asymptotic stability of the
closed-loop secure control system.


  We study properties of Markov chain Monte Carlo simulations of classical spin
models with local updates. We derive analytic expressions for the mean value of
the acceptance rate of single-spin-flip algorithms for the one-dimensional
Ising model. We find that for the Metropolis algorithm the average acceptance
rate is a linear function of energy. We further provide numerical results for
the energy dependence of the average acceptance rate for the 3- and 4-state
Potts model, and the XY model in one and two spatial dimensions. In all cases,
the acceptance rate is an almost linear function of the energy in the critical
region. The variance of the acceptance rate is studied as a function of the
specific heat. While the specific heat develops a singularity in the vicinity
of a phase transition, the variance of the acceptance rate stays finite.


  In phase-contrast magnetic resonance imaging (PC-MRI), spin velocity
contributes to the phase measured at each voxel. Therefore, estimating velocity
from potentially wrapped phase measurements is the task of solving a system of
noisy congruence equations. We propose Phase Recovery from Multiple Wrapped
Measurements (PRoM) as a fast, approximate maximum likelihood estimator of
velocity from multi-coil data with possible amplitude attenuation due to
dephasing. The estimator can recover the fullest possible extent of unambiguous
velocities, which can greatly exceed twice the highest venc. The estimator uses
all pairwise phase differences and the inherent correlations among them to
minimize the estimation error. Correlations are directly estimated from
multi-coil data without requiring knowledge of coil sensitivity maps, dephasing
factors, or the actual per-voxel signal-to-noise ratio. Derivation of the
estimator yields explicit probabilities of unwrapping errors and the
probability distribution for the velocity estimate; this, in turn, allows for
optimized design of the phase-encoded acquisition. These probabilities are also
incorporated into spatial post-processing to further mitigate wrapping errors.
Simulation, phantom, and in vivo results for three-point PC-MRI acquisitions
validate the benefits of reduced estimation error, increased recovered velocity
range, optimized acquisition, and fast computation. A phantom study at 1.5T
demonstrates 48.5% decrease in root mean squared error using PRoM with
post-processing versus a conventional "dual-venc" technique. Simulation and 3T
in vivo results likewise demonstrate the proposed benefits.


  Semantic segmentation of 3D meshes is an important problem for 3D scene
understanding. In this paper we revisit the classic multiview representation of
3D meshes and study several techniques that make them effective for 3D semantic
segmentation of meshes. Given a 3D mesh reconstructed from RGBD sensors, our
method effectively chooses different virtual views of the 3D mesh and renders
multiple 2D channels for training an effective 2D semantic segmentation model.
Features from multiple per view predictions are finally fused on 3D mesh
vertices to predict mesh semantic segmentation labels. Using the large scale
indoor 3D semantic segmentation benchmark of ScanNet, we show that our virtual
views enable more effective training of 2D semantic segmentation networks than
previous multiview approaches. When the 2D per pixel predictions are aggregated
on 3D surfaces, our virtual multiview fusion method is able to achieve
significantly better 3D semantic segmentation results compared to all prior
multiview approaches and competitive with recent 3D convolution approaches.


  A persistent current qubit has two quantum states with opposite currents
flowing in a superconducting loop. Their magnetic field couple to nuclear
spins. The qubit state is not only perturbed by the spins but it also gets
entangled with the spins' state on a very short timescale. However, when the
same spins are exposed to a strong but less than critical external magnetic
field, then the qubit field is just a small perturbation on top of the external
field and the entanglement with each spin is negligible. For a qubit which is
more microscopic than certain threshold this partial entanglement results in
negligible partial decoherence.


  This paper analyzes the benefits of sampling intraday returns in intrinsic
time for the standard and pre-averaging realized variance (RV) estimators. We
theoretically show in finite samples and asymptotically that the RV estimator
is most efficient under the new concept of realized business time, which
samples according to a combination of observed trades and estimated tick
variance. Our asymptotic results carry over to the pre-averaging RV estimator
under market microstructure noise. The analysis builds on the assumption that
asset prices follow a diffusion that is time-changed with a jump process that
separately models the transaction times. This provides a flexible model that
separately captures the empirically varying trading intensity and tick variance
processes, which are particularly relevant for disentangling the driving forces
of the sampling schemes. Extensive simulations confirm our theoretical results
and show that realized business time remains superior also under more general
noise and process specifications. An application to stock data provides
empirical evidence for the benefits of using realized business time sampling to
construct more efficient RV estimators as well as for an improved forecasting
performance.


  The electronic structure of the recently synthesised (3x3) reconstructed
silicene on (4x4) Ag(111) is investigated by first-principles calculations. New
states emerge due to the strong hybridization between silicene and Ag.
Analyzing the nature and composition of these hybridized states, we show that
i) it is possible to clearly distinguish them from states coming from the Dirac
cone of free-standing silicene or from the sp-bands of bulk Ag and ii) assign
their contribution to the description of the linearly dispersing band observed
in photoemission. Furthermore, we show that silicene atoms contribute to the
Fermi level, which leads to similar STM patterns as observed below or above the
Fermi level. Our findings are crucial for the proper interpretation of
experimental observations.


  The behavior of the gradient descent (GD) algorithm is analyzed for a deep
neural network model with skip-connections. It is proved that in the
over-parametrized regime, for a suitable initialization, with high probability
GD can find a global minimum exponentially fast. Generalization error estimates
along the GD path are also established. As a consequence, it is shown that when
the target function is in the reproducing kernel Hilbert space (RKHS) with a
kernel defined by the initialization, there exist generalizable early-stopping
solutions along the GD path. In addition, it is also shown that the GD path is
uniformly close to the functions given by the related random feature model.
Consequently, in this "implicit regularization" setting, the deep neural
network model deteriorates to a random feature model. Our results hold for
neural networks of any width larger than the input dimension.


  We numerically investigate the effect of in-plane anisotropic Fermi surface
(FS) on the flux-flow resistivity $\rho_{\rm f}$ under rotating magnetic field
on the basis of the quasiclassical Green's function method. We demonstrate that
one can detect the phase in pairing potential of Cooper pair through the
field-angular dependence of $\rho_{\rm f}$ even if the FS has in-plane
anisotropy. In addition, we point out one can detect the gap-node directions
irrespective of the FS anisotropy by measuring $\rho_{\rm f}$ under rotating
field.


  Du, Kakade, Wang, and Yang recently established intriguing lower bounds on
sample complexity, which suggest that reinforcement learning with a
misspecified representation is intractable. Another line of work, which centers
around a statistic called the eluder dimension, establishes tractability of
problems similar to those considered in the Du-Kakade-Wang-Yang paper. We
compare these results and reconcile interpretations.


  We investigate the optimal model reduction problem for large-scale
quadratic-bilinear (QB) control systems. Our contributions are threefold.
First, we discuss the variational analysis and the Volterra series formulation
for QB systems. We then define the $\mathcal H_2$-norm for a QB system based on
the kernels of the underlying Volterra series and also propose a truncated
$\mathcal H_2$-norm. Next, we derive first-order necessary conditions for an
optimal approximation, where optimality is measured in term of the truncated
$\mathcal H_2$-norm of the error system. We then propose an iterative model
reduction algorithm, which upon convergence yields a reduced-order system that
approximately satisfies the newly derived optimality conditions. We also
discuss an efficient computation of the reduced Hessian, using the special
Kronecker structure of the Hessian of the system. We illustrate the efficiency
of the proposed method by means of several numerical examples resulting from
semi-discretized nonlinear partial differential equations and show its
competitiveness with the existing model reduction schemes for QB systems such
as moment-matching methods and balanced truncation.


  Accretion discs are present around both stellar-mass black holes in X-ray
binaries and supermassive black holes in active galactic nuclei. A wide variety
of circumstantial evidence implies that many of these discs are warped. The
standard Bardeen--Petterson model attributes the shape of the warp to the
competition between Lense--Thirring torque from the central black hole and
viscous angular-momentum transport within the disc. We show that this
description is incomplete, and that torques from the companion star (for X-ray
binaries) or the self-gravity of the disc (for active galactic nuclei) can play
a major role in determining the properties of the warped disc. Including these
effects leads to a rich set of new phenomena. For example, (i) when a companion
star is present and the warp arises from a misalignment between the companion's
orbital axis and the black hole's spin axis, there is no steady-state solution
of the Pringle--Ogilvie equations for a thin warped disc when the viscosity
falls below a critical value; (ii) in AGN accretion discs, the warp can excite
short-wavelength bending waves that propagate inward with growing amplitude
until they are damped by the disc viscosity. We show that both phenomena can
occur for plausible values of the black hole and disc parameters, and briefly
discuss their observational implications.


  In a recent Letter we have shown that in shift-symmetric Horndeski theory the
scalar field is forced to obtain a nontrivial configuration in black hole
spacetimes, unless a linear coupling with the Gauss-Bonnet invariant is tuned
away. As a result, black holes generically have hair in this theory. In this
companion paper, we first review our argument and discuss it in more detail. We
then present actual black hole solutions in the simplest case of a theory with
the linear scalar-Gauss-Bonnet coupling. We generate exact solutions
numerically for a wide range of values of the coupling and also construct
analytic solutions perturbatively in the small coupling limit. Comparison of
the two types of solutions indicates that non-linear effects that are not
captured by the perturbative solution lead to a finite area, as opposed to a
central, singularity. Remarkably, black holes have a minimum size, controlled
by the length scale associated with the scalar-Gauss-Bonnet coupling. We also
compute some phenomenological observables for the numerical solution for a wide
range of values of the scalar-Gauss-Bonnet coupling. Deviations from the
Schwarzschild geometry are generically very small.


  The images of the silicon test object has been carried out. An image for
in-line hard X-ray hologram is presented. The transmission of a hologram image
for hard X-ray radiation using Fresnel phase zone plate has been investigated.


  DUSTY solves the problem of radiation transport in a dusty environment. The
code can handle both spherical and planar geometries. The user specifies the
properties of the radiation source and dusty region, and the code calculates
the dust temperature distribution and the radiation field in it. The solution
method is based on a self-consistent equation for the radiative energy density,
including dust scattering, absorption and emission, and does not introduce any
approximations. The solution is exact to within the specified numerical
accuracy.
  DUSTY has built in optical properties for the most common types of
astronomical dust and comes with a library for many other grains. It supports
various analytical forms for the density distribution, and can perform a full
dynamical calculation for radiatively driven winds around AGB stars. The
spectral energy distribution of the source can be specified analytically as
either Planckian or broken power-law. In addition, arbitrary dust optical
properties, density distributions and external radiation can be entered in user
supplied files. Furthermore, the wavelength grid can be modified to accommodate
spectral features. A single DUSTY run can process an unlimited number of
models, with each input set producing a run of optical depths, as specified.
The user controls the detail level of the output, which can include both
spectral and imaging properties as well as other quantities of interest.


  The Kepler mission has discovered over 2500 exoplanet candidates in the first
two years of spacecraft data, with approximately 40% of them in candidate
multi-planet systems. The high rate of multiplicity combined with the low rate
of identified false-positives indicates that the multiplanet systems contain
very few false-positive signals due to other systems not gravitationally bound
to the target star (Lissauer, J. J., et al., 2012, ApJ 750, 131). False
positives in the multi- planet systems are identified and removed, leaving
behind a residual population of candidate multi-planet transiting systems
expected to have a false-positive rate less than 1%. We present a sample of 340
planetary systems that contain 851 planets that are validated to substantially
better than the 99% confidence level; the vast majority of these have not been
previously verified as planets. We expect ~2 unidentified false-positives
making our sample of planet very reliable. We present fundamental planetary
properties of our sample based on a comprehensive analysis of Kepler light
curves and ground-based spectroscopy and high-resolution imaging. Since we do
not require spectroscopy or high-resolution imaging for validation, some of our
derived parameters for a planetary system may be systematically incorrect due
to dilution from light due to additional stars in the photometric aperture.
None the less, our result nearly doubles the number of verified exoplanets.


  Spatio-temporal data is intrinsically high dimensional, so unsupervised
modeling is only feasible if we can exploit structure in the process. When the
dynamics are local in both space and time, this structure can be exploited by
splitting the global field into many lower-dimensional "light cones". We review
light cone decompositions for predictive state reconstruction, introducing
three simple light cone algorithms. These methods allow for tractable inference
of spatio-temporal data, such as full-frame video. The algorithms make few
assumptions on the underlying process yet have good predictive performance and
can provide distributions over spatio-temporal data, enabling sophisticated
probabilistic inference.


  A more general scale-setting procedure for General Relativity with
Renormalization Group corrections is proposed. Theoretical aspects of the
scale-setting procedure and the interpretation of the renormalization group
running scale are discussed. The procedure is elaborated for several highly
symmetric systems with matter in the form of an ideal fluid and for two models
of running of the Newton coupling and the cosmological term. For a static
spherically symmetric system with the matter obeying the polytropic equation of
state the running scale-setting is performed analytically. The obtained result
for the running scale matches the Ansatz introduced in a recent paper by
Rodrigues, Letelier and Shapiro which provides an excellent explanation of
rotation curves for a number of galaxies. A systematic explanation of the
galaxy rotation curves using the scale-setting procedure introduced in this
paper is identified as an important future goal.


  Waves in excitable media can be treated by a simple geometric theory. The
propagation velocity is assumed known and evolution of wave fronts is
determined by elementary physical principles (Fermat's principle, Huygens'
principle). Based on this geometric theory a fast computational method is
developed. By this method the distorting effect of the spatial grid is avoided.
The method is applied to the cases when a circular obstacle is surrounded by a
homogeneous and heterogeneous medium, respectively. The numerical simulations
show that the method is convenient, fast and reliable.


  Spectral singularities at non-zero frequencies play an important role in
investigating cyclic or seasonal time series. The publication [2] introduced
the generalized filtered method-of-moments approach to simultaneously estimate
singularity location and long-memory parameters. This paper continues studies
of these simultaneous estimators. A wide class of Gegenbauer-type
semi-parametric models is considered. Asymptotic normality of several
statistics of the cyclic and long-memory parameters is proved. New adjusted
estimates are proposed and investigated. The theoretical findings are
illustrated by numerical results. The methodology includes wavelet
transformations as a particular case.


  In this paper, we consider the problem of reconstructing a dense 3D model
using images captured from different views. Recent methods based on
convolutional neural networks (CNN) allow learning the entire task from data.
However, they do not incorporate the physics of image formation such as
perspective geometry and occlusion. Instead, classical approaches based on
Markov Random Fields (MRF) with ray-potentials explicitly model these physical
processes, but they cannot cope with large surface appearance variations across
different viewpoints. In this paper, we propose RayNet, which combines the
strengths of both frameworks. RayNet integrates a CNN that learns
view-invariant feature representations with an MRF that explicitly encodes the
physics of perspective projection and occlusion. We train RayNet end-to-end
using empirical risk minimization. We thoroughly evaluate our approach on
challenging real-world datasets and demonstrate its benefits over a piece-wise
trained baseline, hand-crafted models as well as other learning-based
approaches.


  Accurately capturing dynamic scenes with wide-ranging motion and light
intensity is crucial for many vision applications. However, acquiring
high-speed high dynamic range (HDR) video is challenging because the camera's
frame rate restricts its dynamic range. Existing methods sacrifice speed to
acquire multi-exposure frames. Yet, misaligned motion in these frames can still
pose complications for HDR fusion algorithms, resulting in artifacts. Instead
of frame-based exposures, we sample the videos using individual pixels at
varying exposures and phase offsets. Implemented on a monochrome pixel-wise
programmable image sensor, our sampling pattern simultaneously captures fast
motion at a high dynamic range. We then transform pixel-wise outputs into an
HDR video using end-to-end learned weights from deep neural networks, achieving
high spatiotemporal resolution with minimized motion blurring. We demonstrate
aliasing-free HDR video acquisition at 1000 FPS, resolving fast motion under
low-light conditions and against bright backgrounds - both challenging
conditions for conventional cameras. By combining the versatility of pixel-wise
sampling patterns with the strength of deep neural networks at decoding complex
scenes, our method greatly enhances the vision system's adaptability and
performance in dynamic conditions.


  We propose a generalized formula for calculating the dipole polarizability of
spherical multilayer nanoshells (MNSs) within the long-wavelength approximation
(LWA). Given a MNS with a finite number of concentric layers, radii, and
dielectric properties, embedded in a dielectric medium, in the presence of a
uniform electric field, we show that its frequency-dependent and complex dipole
polarizability can be expressed in terms of the dipole polarizability of the
preceding MNS. This approach is different from previous more involved methods
where the LWA polarizability of a MNS is usually derived from scattering
coefficients. Using both finite-element method- and Mie theory-based
simulations, we show that our proposed formula reproduces the usual LWA
results, when it is used to predict absorption spectra, by comparing the
results to simulated spectra obtained from MNSs with $n$ number of layers up to
$n = 6$ layers. An iterative algorithm for calculating the dipole
polarizability of a MNS based on the generalized formula is presented. A
Fr\"{o}hlich function whose zeroes correspond to the dipolar localized surface
plasmon resonances (LSPRs) supported by the MNS is proposed. We identify a
pairing behaviour by some LSPRs in the Fr\"{o}hlich function that might also be
useful for mode characterization.


  Goodness-of-fit tests based on the empirical Wasserstein distance are
proposed for simple and composite null hypotheses involving general
multivariate distributions. For group families, the procedure is to be
implemented after preliminary reduction of the data via invariance.This
property allows for calculation of exact critical values and p-values at finite
sample sizes. Applications include testing for location--scale families and
testing for families arising from affine transformations, such as elliptical
distributions with given standard radial density and unspecified location
vector and scatter matrix. A novel test for multivariate normality with
unspecified mean vector and covariance matrix arises as a special case. For
more general parametric families, we propose a parametric bootstrap procedure
to calculate critical values. The lack of asymptotic distribution theory for
the empirical Wasserstein distance means that the validity of the parametric
bootstrap under the null hypothesis remains a conjecture. Nevertheless, we show
that the test is consistent against fixed alternatives. To this end, we prove a
uniform law of large numbers for the empirical distribution in Wasserstein
distance, where the uniformity is over any class of underlying distributions
satisfying a uniform integrability condition but no additional moment
assumptions. The calculation of test statistics boils down to solving the
well-studied semi-discrete optimal transport problem. Extensive numerical
experiments demonstrate the practical feasibility and the excellent performance
of the proposed tests for the Wasserstein distance of order p = 1 and p = 2 and
for dimensions at least up to d = 5. The simulations also lend support to the
conjecture of the asymptotic validity of the parametric bootstrap.


  Using real-time charge sensing and gate pulsing techniques we measure the
ratio of the rates for tunneling into the excited and ground spin states of a
single-electron AlGaAs/GaAs quantum dot in a parallel magnetic field. We find
that the ratio decreases with increasing magnetic field until tunneling into
the excited spin state is completely suppressed. However, we find that by
adjusting the voltages on the surface gates to change the orbital configuration
of the dot we can restore tunneling into the excited spin state and that the
ratio reaches a maximum when the dot is symmetric.


  Let $g:\mathbb{N}\to\{-1,1\}$ be a completely multiplicative function, $\mu$
be the M\"obius function and $\mu_2^2(n)$ be the indicator that $n$ is
cubefree. We prove that $f=\mu^2g$ and $f=\mu_2^2g$ have unbounded partial
sums. Our proofs are built upon Klurman and Mangerel's proof of Chudakov's
conjecture, Klurman's work on correlations of multiplicative functions and
Tao's resolution of the Erd\H{o}s discrepancy problem.


  Two new expansions for partial sums of Gauss' triangular and square numbers
series are given. As a consequence, we derive a family of inequalities for the
overpartition function $\bar{p}(n)$ and for the partition function $p_1(n)$
counting the partitions of $n$ with distinct odd parts. Some further
inequalities for variations of partition function are proposed as conjectures.


  Let $W_i=\{W_i(t), t\in \mathbb{R}_+\}, i=1,2$ be two Wiener processes and
$W_3=\{W_3(\mathbf{t}), \mathbf{t}\in \mathbb{R}_+^2\}$ be a two-parameter
Brownian sheet, all three processes being mutually independent. We derive upper
and lower bounds for the boundary non-crossing probability
$$P_f=P\{W_1(t_1)+W_2(t_2)+W_3(\mathbf{t})+h(\mathbf{t})\leq u(\mathbf{t}),
\mathbf{t}\in\mathbb{R}_+^2\},$$ where $h, u: \mathbb{R}_+^2\rightarrow
\mathbb{R}_+$ are two measurable functions. We show further that for large
trend functions $\gamma f>0$ asymptotically when $\gamma \to \infty$ we have
that $\ln P_{\gamma f}$ is the same as $\ln P_{\gamma \underline{f}}$ where
$\underline{f}$ is the projection of $f$ on some closed convex set of the
reproducing kernel Hilbert Space of $W$. It turns out that our approach is
applicable also for the additive Brownian pillow.


  The protons in water ice are subject to so called \emph{ice rules} resulting
in an extensive ground state degeneracy. We study how an external electric
field reduces this ground state degeneracy in hexagonal water ice I$_h$ within
a minimal model. We observe polarization plateaus when the field is aligned
along the $[001]$ and $[010]$ directions. In each case, one plateau occurs at
intermediate polarization with reduced but still extensive degeneracy. The
remaining ground states can be mapped to dimer models on the honeycomb and the
square lattice, respectively. Upon tilting the external field, we observe an
order-disorder transition of Kasteleyn type into a plateau at saturated
polarization and vanishing entropy. This transition is investigated
analytically using the Kasteleyn matrix and numerically using a modified
directed-loop Monte Carlo simulation. The protons in both cases exhibit
algebraically decaying correlations. Moreover, the features of the static
structure factor are discussed.


  Adaptive regularization methods pre-multiply a descent direction by a
preconditioning matrix. Due to the large number of parameters of machine
learning problems, full-matrix preconditioning methods are prohibitively
expensive. We show how to modify full-matrix adaptive regularization in order
to make it practical and effective. We also provide a novel theoretical
analysis for adaptive regularization in non-convex optimization settings. The
core of our algorithm, termed GGT, consists of the efficient computation of the
inverse square root of a low-rank matrix. Our preliminary experiments show
improved iteration-wise convergence rates across synthetic tasks and standard
deep learning benchmarks, and that the more carefully-preconditioned steps
sometimes lead to a better solution.


  Poulalhon and Schaeffer introduced an elegant method to linearly encode a
planar triangulation optimally. The method is based on performing a special
depth-first search algorithm on a particular orientation of the triangulation:
the minimal Schnyder wood. Recent progress toward generalizing Schnyder woods
to higher genus enables us to generalize this method to the toroidal case. In
the plane, the method leads to a bijection between planar triangulations and
some particular trees. For the torus we obtain a similar bijection but with
particular unicellular maps (maps with only one face).


  A method of construction of decomposition of correlation functions of
developed turbulence in a compressible fluid on Mach number {\em Ma} is
generalized now for a model of stochastic magnetic hydrodynamics. With the help
of the field theory renormalization group method the composite operators of
transversal fields are studied. It is shown, that in the case of hydrodynamical
interaction of fluid flows is prevalent the compressible effects are essential
in inertial range. On the contrary the corrections to developed turbulent
spectra due to compressibility are insignificant if the magnetic interaction of
flows is more important one.


  Semi-supervised node classification on graph-structured data has many
applications such as fraud detection, fake account and review detection, user's
private attribute inference in social networks, and community detection.
Various methods such as pairwise Markov Random Fields (pMRF) and graph neural
networks were developed for semi-supervised node classification. pMRF is more
efficient than graph neural networks. However, existing pMRF-based methods are
less accurate than graph neural networks, due to a key limitation that they
assume a heuristics-based constant edge potential for all edges. In this work,
we aim to address the key limitation of existing pMRF-based methods. In
particular, we propose to learn edge potentials for pMRF. Our evaluation
results on various types of graph datasets show that our optimized pMRF-based
method consistently outperforms existing graph neural networks in terms of both
accuracy and efficiency. Our results highlight that previous work may have
underestimated the power of pMRF for semi-supervised node classification.


  We study the problem of sorting under incomplete information, when queries
are used to resolve uncertainties. Each of $n$ data items has an unknown value,
which is known to lie in a given interval. We can pay a query cost to learn the
actual value, and we may allow an error threshold in the sorting. The goal is
to find a nearly-sorted permutation by performing a minimum-cost set of
queries.
  We show that an offline optimum query set can be found in poly time, and that
both oblivious and adaptive problems have simple competitive algorithms. The
competitive ratio for the oblivious problem is $n$ for uniform query costs, and
unbounded for arbitrary costs; for the adaptive problem, the ratio is 2.
  We present a unified adaptive strategy for uniform costs that yields the
following improved results: (1) a 3/2-competitive randomized algorithm; (2) a
5/3-competitive deterministic algorithm if the dependency graph has no
2-components after some preprocessing, which has competitive ratio
$3/2+\mathrm{O}(1/k)$ if the components obtained have size at least $k$; and
(3) an exact algorithm for laminar families of intervals. The first two results
have matching lower bounds, and we have a lower bound of 7/5 for large
components.
  We also give a randomized adaptive algorithm with competitive ratio
$1+\frac{4}{3\sqrt{3}}\approx 1.7698$ for arbitrary query costs, and we show
that the 2-competitive deterministic adaptive algorithm can be generalized for
queries returning intervals and for a more general vertex cover problem, by
using the local ratio technique. Moreover, we prove that the advice complexity
of the adaptive problem is $\lfloor n/2\rfloor$ if no error threshold is
allowed, and $\lceil n/3\cdot\lg 3\rceil$ for the general case.
  Finally, we present some graph-theoretical results on co-threshold tolerance
graphs, and we discuss uncertainty variants of some classical interval
problems.


  We study the dynamics of identical leaky integrate-and-fire neurons with
symmetric non-local coupling. Upon varying control parameters (coupling
strength, coupling range, refractory period) we investigate the system's
behaviour and highlight the formation of chimera states. We show that the
introduction of a refractory period enlarges the parameter region where chimera
states appear and affects the chimera multiplicity.


  One of the most important checks for systematic errors in CMB studies is the
cross correlation of maps made by independent experiments. In this paper we
report on the cross correlation between maps from the Atacama B-mode Search
(ABS) and Atacama Cosmology Telescope (ACT) experiments in both temperature and
polarization. These completely different measurements have a clear correlation
with each other and with the Planck satellite in both the EE and TE spectra at
$\ell<400$ over the roughly $1100$ deg$^2$ common to all three. The TB, EB, and
BB cross spectra are consistent with noise. Exploiting such cross-correlations
will be important for future experiments operating in Chile that aim to probe
the $30<\ell<8,000$ range.


  Studying atmospheric neutrino oscillations in the few-GeV range with a
multimegaton detector promises to determine the neutrino mass hierarchy. This
is the main science goal pursued by the future KM3NeT/ORCA water Cherenkov
detector in the Mediterranean Sea. In this paper, the processes that limit the
obtainable resolution in both energy and direction in charged-current neutrino
events in the ORCA detector are investigated. These processes include the
composition of the hadronic fragmentation products, the subsequent particle
propagation and the photon-sampling fraction of the detector. GEANT simulations
of neutrino interactions in seawater produced by GENIE are used to study the
effects in the 1 - 20 GeV range. It is found that fluctuations in the hadronic
cascade in conjunction with the variation of the inelasticity y are most
detrimental to the resolutions. The effect of limited photon sampling in the
detector is of significantly less importance. These results will therefore also
be applicable to similar detectors/media, such as those in ice.


  While the Industry 4.0 is idolizing the potential of an artificial
intelligence embedded into "things", it is neglecting the role of the human
component, which is still indispensable in different manufacturing activities,
such as a machine setup or maintenance operations. The present research study
first proposes an Industrial Internet pyramid as emergent human-centric
manufacturing paradigm within Industry 4.0 in which central is the role of a
Ubiquitous Knowledge about the manufacturing system intuitively accessed and
used by the manufacturing employees. Second, the prototype of a
Service-oriented Digital Twin, which leverage on a flexible ontology-oriented
knowledge structure and on augmented reality combined to a vocal interaction
system for an intuitive knowledge retrieval and fruition, has been designed and
developed to deliver this manufacturing knowledge. Two test-beds, complimentary
for the problems in practice (the former on the maintenance-production
interface in a large enterprise, the latter majorly focused in production and
setups in a small and medium enterprise), show the significant benefits in
terms of time, costs and process quality, thus validating the approach
proposed. This research shows that a human-centric and knowledge-driven
approach can drive the performance of Industry 4.0 initiatives and lead a Smart
Factory towards its full potential.


  Condensed Matter Physics (CMP) seeks to understand the microscopic
interactions of matter at the quantum and atomistic levels, and describes how
these interactions result in both mesoscopic and macroscopic properties. CMP
overlaps with many other important branches of science, such as Chemistry,
Materials Science, Statistical Physics, and High-Performance Computing. With
the advancements in modern Machine Learning (ML) technology, a keen interest in
applying these algorithms to further CMP research has created a compelling new
area of research at the intersection of both fields. In this review, we aim to
explore the main areas within CMP, which have successfully applied ML
techniques to further research, such as the description and use of ML schemes
for potential energy surfaces, the characterization of topological phases of
matter in lattice systems, the prediction of phase transitions in off-lattice
and atomistic simulations, the interpretation of ML theories with
physics-inspired frameworks and the enhancement of simulation methods with ML
algorithms. We also discuss in detail the main challenges and drawbacks of
using ML methods on CMP problems, as well as some perspectives for future
developments.