  We present simulations of the 1-dimensional Oslo rice pile model in which the
critical height at each site is randomly reset after each toppling. We use the
fact that the stationary state of this sandpile model is hyperuniform to reach
system of sizes $> 10^7$. Most previous simulations were seriously flawed by
important finite size corrections. We find that all critical exponents have
values consistent with simple rationals: $\nu=4/3$ for the correlation length
exponent, $D =9/4$ for the fractal dimension of avalanche clusters, and $z=10/7
$ for the dynamical exponent. In addition we relate the hyperuniformity
exponent to the correlation length exponent $\nu$. Finally we discuss the
relationship with the quenched Edwards-Wilkinson (qEW) model, where we find in
particular that the local roughness exponent is $\alpha_{\rm loc} = 1$.


  This paper targets to a novel but practical recommendation problem named
exact-K recommendation. It is different from traditional top-K recommendation,
as it focuses more on (constrained) combinatorial optimization which will
optimize to recommend a whole set of K items called card, rather than ranking
optimization which assumes that "better" items should be put into top
positions. Thus we take the first step to give a formal problem definition, and
innovatively reduce it to Maximum Clique Optimization based on graph. To tackle
this specific combinatorial optimization problem which is NP-hard, we propose
Graph Attention Networks (GAttN) with a Multi-head Self-attention encoder and a
decoder with attention mechanism. It can end-to-end learn the joint
distribution of the K items and generate an optimal card rather than rank
individual items by prediction scores. Then we propose Reinforcement Learning
from Demonstrations (RLfD) which combines the advantages in behavior cloning
and reinforcement learning, making it sufficient- and-efficient to train the
model. Extensive experiments on three datasets demonstrate the effectiveness of
our proposed GAttN with RLfD method, it outperforms several strong baselines
with a relative improvement of 7.7% and 4.7% on average in Precision and Hit
Ratio respectively, and achieves state-of-the-art (SOTA) performance for the
exact-K recommendation problem.


  We report that the second and third harmonic signal reductions with insulator
film surface coverage over a gold substrate gives a measure of the electron
density in the spill out volume of the insulator, which is dubbed metal
insulator gap states. For metal-insulator-metal (MIM) structures we observe
enhancement saturation and quenching of the third harmonic efficiencies well
above the efficiencies for metal-insulator (MI) samples. The measured optical
harmonics of scattered light from MI and MIM systems are compared with detailed
simulations of the nonlinear interactions including free electron spill out
into the insulator, nonlocal and electron quantum tunneling effects. Gold
coated substrates are covered with variable thin insulator film thicknesses
using atomic layer deposition. Optical harmonics of light scattered from two
insulator materials (ZnO and Al2O3) are measured in our experiments. Based on
our simulations we conclude that the observed MIM signal enhancement effects
are primarily due to nonlocal phenomena in an electron gas.


  Let $\ell,k$ be fixed positive integers. In an earlier work, the first and
third authors established a bijection between $\ell$-cores with first part
equal to $k$ and $(\ell-1)$-cores with first part less than or equal to $k$.
This paper gives several new interpretations of that bijection. The
$\ell$-cores index minimal length coset representatives for
$\widetilde{S_{\ell}} / S_{\ell}$ where $\widetilde{S_{\ell}}$ denotes the
affine symmetric group and $S_{\ell}$ denotes the finite symmetric group. In
this setting, the bijection has a beautiful geometric interpretation in terms
of the root lattice of type $A_{\ell-1}$. We also show that the bijection has a
natural description in terms of another correspondence due to Lapointe and
Morse.


  Solid-state quantum emitters embedded in circular Bragg resonators are
attractive due to their ability to emit quantum states of light with high
brightness and low multi-photon probability. As for any emitter-microcavity
system, fabrication imperfections limit the spatial and spectral overlap of the
emitter with the cavity mode, thus limiting their coupling strength. Here, we
show that an initial spectral mismatch can be corrected after device
fabrication by repeated wet chemical etching steps. We demonstrate ~16 nm
wavelength tuning for optical modes in AlGaAs resonators on oxide, leading to a
4-fold Purcell enhancement of the emission of single embedded GaAs quantum
dots. Numerical calculations reproduce the observations and suggest that the
achievable performance of the resonator is only marginally affected in the
explored tuning range. We expect the method to be applicable also to circular
Bragg resonators based on other material platforms, thus increasing the device
yield of cavity-enhanced solid-state quantum emitters.


  We raise a question on the existence of continuous roots of families of monic
polynomials (by the root of a family of polynomials we mean a function of the
coefficients of polynomials of a given family that maps each tuple of
coefficients to a root of the polynomial with these coefficients). We prove
that the family of monic second-degree polynomials with complex coefficients
and the families of monic fourth-degree and fifth-degree polynomials with real
coefficients have no continuous root. We also prove that the family of monic
second-degree polynomials with real coefficients has continuous roots and we
describe the set of all such roots.


  Recent advancements in keypoint detection and descriptor extraction have
shown impressive performance in local feature learning tasks. However, existing
methods generally exhibit suboptimal performance under extreme conditions such
as significant appearance changes and domain shifts. In this study, we
introduce a multi-level feature aggregation network that incorporates two
pivotal components to facilitate the learning of robust and accurate features
with domain adaptation. First, we employ domain adaptation supervision to align
high-level feature distributions across different domains to achieve invariant
domain representations. Second, we propose a Transformer-based booster that
enhances descriptor robustness by integrating visual and geometric information
through wave position encoding concepts, effectively handling complex
conditions. To ensure the accuracy and robustness of features, we adopt a
hierarchical architecture to capture comprehensive information and apply
meticulous targeted supervision to keypoint detection, descriptor extraction,
and their coupled processing. Extensive experiments demonstrate that our
method, RADA, achieves excellent results in image matching, camera pose
estimation, and visual localization tasks.


  Dynamic spectrum access is a new paradigm of secondary spectrum utilization
and sharing. It allows unlicensed secondary users (SUs) to exploit
opportunistically the under-utilized licensed spectrum. Market mechanism is a
widely-used promising means to regulate the consuming behaviours of users and,
hence, achieves the efficient allocation and consumption of limited resources.
In this paper, we propose and study a hybrid secondary spectrum market
consisting of both the futures market and the spot market, in which SUs
(buyers) purchase under-utilized licensed spectrum from a spectrum regulator,
either through predefined contracts via the futures market, or through spot
transactions via the spot market. We focus on the optimal spectrum allocation
among SUs in an exogenous hybrid market that maximizes the secondary spectrum
utilization efficiency. The problem is challenging due to the stochasticity and
asymmetry of network information. To solve this problem, we first derive an
off-line optimal allocation policy that maximizes the ex-ante expected spectrum
utilization efficiency based on the stochastic distribution of network
information. We then propose an on-line VickreyCClarkeCGroves (VCG) auction
that determines the real-time allocation and pricing of every spectrum based on
the realized network information and the pre-derived off-line policy. We
further show that with the spatial frequency reuse, the proposed VCG auction is
NP-hard; hence, it is not suitable for on-line implementation, especially in a
large-scale market. To this end, we propose a heuristics approach based on an
on-line VCG-like mechanism with polynomial-time complexity, and further
characterize the corresponding performance loss bound analytically. We finally
provide extensive numerical results to evaluate the performance of the proposed
solutions.


  We give partial boundary regularity for co-dimension one absolutely
area-minimizing currents at points where the boundary consists of a sum of
$C^{1,\alpha}$ submanifolds, possibly with multiplicity, meeting tangentially,
given that the current has a tangent cone supported in a hyperplane with
constant orientation vector; this partial regularity is such that we can
conclude the tangent cone is unique. The proof follows closely the boundary
regularity result given by Hardt and Simon in [9].


  Detecting patterns in real time streaming data has been an interesting and
challenging data analytics problem. With the proliferation of a variety of
sensor devices, real-time analytics of data from the Internet of Things (IoT)
to learn regular and irregular patterns has become an important machine
learning problem to enable predictive analytics for automated notification and
decision support. In this work, we address the problem of learning an irregular
human activity pattern, fall, from streaming IoT data from wearable sensors. We
present a deep neural network model for detecting fall based on accelerometer
data giving 98.75 percent accuracy using an online physical activity monitoring
dataset called "MobiAct", which was published by Vavoulas et al. The initial
model was developed using IBM Watson studio and then later transferred and
deployed on IBM Cloud with the streaming analytics service supported by IBM
Streams for monitoring real-time IoT data. We also present the systems
architecture of the real-time fall detection framework that we intend to use
with mbientlabs wearable health monitoring sensors for real time patient
monitoring at retirement homes or rehabilitation clinics.


  Because of the finiteness of the life span and boundedness of the physical
space, the more reasonable or physical choice is the tempered power-law instead
of pure power-law for the CTRW model in characterizing the waiting time and
jump length of the motion of particles. This paper focuses on providing the
variational formulation and efficient implementation for solving the
corresponding deterministic/macroscopic models, including the space tempered
fractional equation and time tempered fractional equation. The convergence,
numerical stability, and a series of variational equalities are theoretically
proved. And the theoretical results are confirmed by numerical experiments.


  With the increasing demand for deep learning models on mobile devices,
splitting neural network computation between the device and a more powerful
edge server has become an attractive solution. However, existing split
computing approaches often underperform compared to a naive baseline of remote
computation on compressed data. Recent studies propose learning compressed
representations that contain more relevant information for supervised
downstream tasks, showing improved tradeoffs between compressed data size and
supervised performance. However, existing evaluation metrics only provide an
incomplete picture of split computing. This study introduces supervised
compression for split computing (SC2) and proposes new evaluation criteria:
minimizing computation on the mobile device, minimizing transmitted data size,
and maximizing model accuracy. We conduct a comprehensive benchmark study using
10 baseline methods, three computer vision tasks, and over 180 trained models,
and discuss various aspects of SC2. We also release sc2bench, a Python package
for future research on SC2. Our proposed metrics and package will help
researchers better understand the tradeoffs of supervised compression in split
computing.


  For a closed Riemannian manifold of dimension $n\geq 3$ and a subgroup $G$ of
the isometry group, we define and study the $G-$equivariant second Yamabe
constant and we obtain some results on the existence of $G-$invariant nodal
solutions of the Yamabe equation.


  With the aid of Ruppeiner thermodynamic metric defined on a two-dimensional
phase space of dipolar ($m$) and quadrupolar ($q$) order parameters, we derive
an expression for the Ricci scalar ($R$) in the isotropic Blume-Emery-Griffiths
model. Temperature dependence of $R$ is investigated for various values of
bilinear to biquadratic ratio ($r$). Its behavior near the
continuous/discontinuous phase transition temperatures and a tricritical point
is presented. It is found that in addition to the divergence singularity and
finite jumps connected with the phase transitions there are field-dependent
broad extrema in the Ricci scalar.


  Context. The study of non-principal axis (NPA) rotators can provide important
clues to the evolution of the spin state of asteroids. However, so far, very
few studies have focused on NPA-rotating main-belt asteroids (MBAs). One of
MBAs that are known to be in an excited rotation state is asteroid (5247)
Krylov. Aims. By using disk-integrated photometric data, we construct a
physical model of (5247) Krylov including shape and spin state. Methods. We
apply the light curve convex inversion method employing optical light curves
obtained by using ground-based telescopes in three apparitions during 2006,
2016, and 2017, along with infrared light curves obtained by the Wide-field
Infrared Survey Explorer (WISE) satellite in 2010. Results. Asteroid
(5247)~Krylov is spinning in a short axis mode (SAM) characterized by rotation
and precession periods of 368.7 hr and 67.27 hr, respectively. The angular
momentum vector orientation of Krylov is found to be $\lambda_{L} = 298^\circ$
and $\beta_{L} = -58^\circ$. The ratio of the rotational kinetic energy to the
basic spin state energy $E/E_{0} \simeq 1.02$ shows that the (5247) Krylov is
about 2% excited state compared to the Principal Axis (PA) rotation state. The
shape of (5247) Krylov can be approximated by an elongated prolate ellipsoid
with a ratio of moments of inertia of $I_{a}:I_{b}:I_{c}=0.36:0.96:1$. This is
the first physical model of NPA rotator among MBAs. The physical processes that
led to the current NPA rotation cannot be unambiguously reconstructed.


  A datatype defining rewrite system (DDRS) is an algebraic (equational)
specification intended to specify a datatype. When interpreting the equations
from left-to-right, a DDRS defines a term rewriting system that must be
ground-complete. First we define two DDRSs for the ring of integers, each
comprising twelve rewrite rules, and prove their ground-completeness. Then we
introduce natural number and integer arithmetic specified according to unary
view, that is, arithmetic based on a postfix unary append constructor (a form
of tallying). Next we specify arithmetic based on two other views: binary and
decimal notation. The binary and decimal view have as their characteristic that
each normal form resembles common number notation, that is, either a digit, or
a string of digits without leading zero, or the negated versions of the latter.
Integer arithmetic in binary and decimal notation is based on (postfix) digit
append functions. For each view we define a DDRS, and in each case the
resulting datatype is a canonical term algebra that extends a corresponding
canonical term algebra for natural numbers. Then, for each view, we consider an
alternative DDRS based on tree constructors that yields comparable normal
forms, which for that view admits expressions that are algorithmically more
involved. For all DDRSs considered, ground-completeness is proven.


  Hall resistivity and magneto-thermopower have been measured for colossal
magnetoresistive Tl2Mn2O7 over wide temperature and magnetic-field ranges.
These measurements revealed that a small number of free electron-like carriers
is responsible for the magneto-transport properties. In contrast to perovskite
CMR materials, the anomalous Hall coefficient is negligible even in the
ferromagnetic state due to negligibly small skew scattering. The characteristic
feature in Tl2Mn2O7 is that the carrier density changes with temperature and
the magnetic field. The carrier density increases around TC as the temperature
is lowered or as the magnetic field is increased, which explains the CMR of
this material. The conduction-band-edge shift, which is caused by the strong
s-d interaction between localized Mn moments and s-like conduction electrons,
is a possible mechanism for the carrier density change.


  In this paper, we give a procedure for derivation of higher dimensional
periodic recurrence equations(PREs) by nested structure of complex numbers.


  The building of a stellar structure requires knowing the Rosseland mean
opacity at each layer of the model. This mean opacity is very often
interpolated in pre-computed tables due to the overwhelming time to compute it
from monochromatic cross sections. The main drawback to using tables is that
the opacities can be inconsistent with the actual local chemical composition,
for instance in the regions of the star where nucleosynthesis occurs. We
developed a strategy that allows very fast calculations of Rosseland opacities
from monochromatic cross sections. This method has been implemented in the
Toulouse-Geneva evolution code, which we used to compute evolutionary tracks
with models whose Rosseland opacities are fully consistent with the chemical
mix everywhere in the star. Our self-consistent models show very small
structural differences compared to models where the Rosseland opacity is
computed with a fixed metal mixture. The main-sequence evolutionary tracks are
almost the same for models of mass ranging from 2 to 8 solar masses. At a given
surface gravity the relative difference in age is lower than 2% and generally
below 1% between the two kinds of calculations, the self-consistent model being
younger most of the time. Unless such a precision in age is sought out, the use
of tabulated Rosseland opacities with a metal content defined globally is still
acceptable, at least in main-sequence stars where the chemical mix changes only
through nucleosynthesis.


  One of the most ancient forms of life dating to ~3.5 billion years ago,
cyanobacteria are highly abundant organisms that convert light into energy and
motion, often within conjoined filaments and larger colonies. We study how
gradients of light intensity trigger orderly phototactic motions and dense
bacterial communities, which remained quantitatively unexplored despite being
among the oldest forms of active living matter on Earth. The phototaxis drives
a transition from initially polar motions of semiflexible long filaments along
complex curved spatiotemporal trajectories confined within illuminated areas to
their bipolar motility in the ensuing crowded environment. We demonstrate how
simply shining light causes a spontaneous self-assembly of two- and
three-dimensional active nematic states of cyanobacterial filaments, with a
plethora of motile and static topological defects. We quantify light-controlled
evolutions of orientational and velocity order parameters during the transition
between disordered and orientationally ordered states of our photosynthetic
active matter, as well as the subsequent active nematic's fluid-gel
transformation. Patterned illumination and foreign inclusions with different
shapes interact with cyanobacterial active nematics in nontrivial ways, while
inducing soft interfacial boundary conditions and quasi-boojum-like defects.
Commanding this cyanobacterial collective behavior could aid inhibiting
generation of toxins or enhancing production of oxygen and biomaterials.


  We argue that the event horizon of a binary black hole merger, in the
extreme-mass-ratio limit where one of the black holes is much smaller than the
other, can be described in an exact analytic way. This is done by tracing in
the Schwarzschild geometry a congruence of null geodesics that approaches a
null plane at infinity. Its form can be given explicitly in terms of elliptic
functions, and we use it to analyze and illustrate the time-evolution of the
horizon along the merger. We identify features such as the line of caustics at
which light rays enter the horizon, and the critical point at which the
horizons touch. We also compute several quantities that characterize these
aspects of the merger.


  We discuss the phase--space distribution of $\mu$ neutrinos if $\tau$
neutrinos are unstable and decay into $\nu_\mu + scalar$. If this scalar is a
familon or a Majoron, in the generic case the $\nu_\mu$ background is NOT the
straightforward overlap of neutrinos of thermal and decay origins. A delay in
$\nu_\tau$ decay, due to the Pauli exclusion principle, can modify it in a
significant way. We provide the equations to calculate the $\nu_\mu$
distribution and show that, in some cases, there exists a good approximate
solution to them. However, even when such solution is not admitted, the
equations can be numerically solved following a precise pattern. We give such a
solution for a number of typical cases. If $\nu_\mu$ has a mass $\sim 2$ eV and
the see--saw argument holds, $\nu_\tau$ must be unstable and the decay into
$\nu_\mu + scalar$ is a reasonable possibility. The picture leads to a delayed
equivalence redshift, which could allow to reconcile COBE data with a bias
parameter $b\ge 1$.


  Molybdenum disulfide nanoribbons and nanotubes are quasi-1D semiconductors
with strong spin-orbit interaction, a nanomaterial highly promising for quantum
electronic applications. Here, it is demonstrated that a bismuth semimetal
layer between the contact metal and this nanomaterial strongly improves the
properties of the contacts. Two-point resistances on the order of 100k$\Omega$
are observed at room temperature. At cryogenic temperature, Coulomb blockade is
visible. The resulting stability diagrams indicate a marked absence of trap
states at the contacts and the corresponding disorder, compared to previous
devices that use low-work-function metals as contacts. Single-level quantum
transport is observed at temperatures below 100mK.


  Hyperspectral image is a substitution of more than a hundred images, called
bands, of the same region. They are taken at juxtaposed frequencies. The
reference image of the region is called Ground Truth map (GT). the problematic
is how to find the good bands to classify the pixels of regions; because the
bands can be not only redundant, but a source of confusion, and decreasing so
the accuracy of classification. Some methods use Mutual Information (MI) and
threshold, to select relevant bands. Recently there's an algorithm selection
based on mutual information, using bandwidth rejection and a threshold to
control and eliminate redundancy. The band top ranking the MI is selected, and
if its neighbors have sensibly the same MI with the GT, they will be considered
redundant and so discarded. This is the most inconvenient of this method,
because this avoids the advantage of hyperspectral images: some precious
information can be discarded. In this paper we'll make difference between
useful and useless redundancy. A band contains useful redundancy if it
contributes to decreasing error probability. According to this scheme, we
introduce new algorithm using also mutual information, but it retains only the
bands minimizing the error probability of classification. To control
redundancy, we introduce a complementary threshold. So the good band candidate
must contribute to decrease the last error probability augmented by the
threshold. This process is a wrapper strategy; it gets high performance of
classification accuracy but it is expensive than filter strategy.


  Background: A universal unanswered question in neuroscience and machine
learning is whether computers can decode the patterns of the human brain.
Multi-Voxels Pattern Analysis (MVPA) is a critical tool for addressing this
question. However, there are two challenges in the previous MVPA methods, which
include decreasing sparsity and noise in the extracted features and increasing
the performance of prediction.
  Methods: In overcoming mentioned challenges, this paper proposes Anatomical
Pattern Analysis (APA) for decoding visual stimuli in the human brain. This
framework develops a novel anatomical feature extraction method and a new
imbalance AdaBoost algorithm for binary classification. Further, it utilizes an
Error-Correcting Output Codes (ECOC) method for multiclass prediction. APA can
automatically detect active regions for each category of the visual stimuli.
Moreover, it enables us to combine homogeneous datasets for applying advanced
classification.
  Results and Conclusions: Experimental studies on 4 visual categories (words,
consonants, objects and scrambled photos) demonstrate that the proposed
approach achieves superior performance to state-of-the-art methods.


  We determine the precise asymptotic behaviour (in space) of the Green kernel
of simple random walk with drift on the Diestel-Leader graph $DL(q,r)$, where
$q,r \ge 2$. The latter is the horocyclic product of two homogeneous trees with
respective degrees $q+1$ and $r+1$. When $q=r$, it is the Cayley graph of the
wreath product (lamplighter group) ${\mathbb Z}_q \wr {\mathbb Z}$ with respect
to a natural set of generators. We describe the full Martin compactification of
these random walks on $DL$-graphs and, in particular, lamplighter groups. This
completes and provides a better approach to previous results of Woess, who has
determined all minimal positive harmonic functions.


  We consider a high-dimensional sparse normal means model where the goal is to
estimate the mean vector assuming the proportion of non-zero means is unknown.
We model the mean vector by a one-group global-local shrinkage prior belonging
to a broad class of such priors that includes the horseshoe prior. We address
some questions related to asymptotic properties of the resulting posterior
distribution of the mean vector for the said class priors. We consider two ways
to model the global parameter in this paper. Firstly by considering this as an
unknown fixed parameter and then by an empirical Bayes estimate of it. In the
second approach, we do a hierarchical Bayes treatment by assigning a suitable
non-degenerate prior distribution to it. We first show that for the class of
priors under study, the posterior distribution of the mean vector contracts
around the true parameter at a near minimax rate when the empirical Bayes
approach is used. Next, we prove that in the hierarchical Bayes approach, the
corresponding Bayes estimate attains the minimax risk asymptotically under the
squared error loss function. We also show that the posterior contracts around
the true parameter at a near minimax rate. These results generalize those of
van der Pas et al. (2014) \cite{van2014horseshoe}, (2017)
\cite{van2017adaptive}, proved for the horseshoe prior. We have also studied in
this work the asymptotic Bayes optimality of global-local shrinkage priors
where the number of non-null hypotheses is unknown. Here our target is to
propose some conditions on the prior density of the global parameter such that
the Bayes risk induced by the decision rule attains Optimal Bayes risk, up to
some multiplicative constant. Using our proposed condition, under the
asymptotic framework of Bogdan et al. (2011) \cite{bogdan2011asymptotic}, we
are able to provide an affirmative answer to satisfy our hunch.


  We perform simulations for one dimensional continuous-time random walks in
two dynamic random environments with fast (independent spin-flips) and slow
(simple symmetric exclusion) decay of space-time correlations, respectively. We
focus on the asymptotic speeds and the scaling limits of such random walks. We
observe different behaviors depending on the dynamics of the underlying random
environment and the ratio between the jump rate of the random walk and the one
of the environment. We compare our data with well known results for static
random environment. We observe that the non-diffusive regime known so far only
for the static case can occur in the dynamic setup too. Such anomalous
fluctuations give rise to a new phase diagram. Further we discuss possible
consequences for more general static and dynamic random environments.


  Efficient anomaly detection and diagnosis in multivariate time-series data is
of great importance for modern industrial applications. However, building a
system that is able to quickly and accurately pinpoint anomalous observations
is a challenging problem. This is due to the lack of anomaly labels, high data
volatility and the demands of ultra-low inference times in modern applications.
Despite the recent developments of deep learning approaches for anomaly
detection, only a few of them can address all of these challenges. In this
paper, we propose TranAD, a deep transformer network based anomaly detection
and diagnosis model which uses attention-based sequence encoders to swiftly
perform inference with the knowledge of the broader temporal trends in the
data. TranAD uses focus score-based self-conditioning to enable robust
multi-modal feature extraction and adversarial training to gain stability.
Additionally, model-agnostic meta learning (MAML) allows us to train the model
using limited data. Extensive empirical studies on six publicly available
datasets demonstrate that TranAD can outperform state-of-the-art baseline
methods in detection and diagnosis performance with data and time-efficient
training. Specifically, TranAD increases F1 scores by up to 17%, reducing
training times by up to 99% compared to the baselines.


  This paper addresses the domain shift problem for segmentation. As a
solution, we propose OLVA, a novel and lightweight unsupervised domain
adaptation method based on a Variational Auto-Encoder (VAE) and Optimal
Transport (OT) theory. Thanks to the VAE, our model learns a shared
cross-domain latent space that follows a normal distribution, which reduces the
domain shift. To guarantee valid segmentations, our shared latent space is
designed to model the shape rather than the intensity variations. We further
rely on an OT loss to match and align the remaining discrepancy between the two
domains in the latent space. We demonstrate OLVA's effectiveness for the
segmentation of multiple cardiac structures on the public Multi-Modality Whole
Heart Segmentation (MM-WHS) dataset, where the source domain consists of
annotated 3D MR images and the unlabelled target domain of 3D CTs. Our results
show remarkable improvements with an additional margin of 12.5\% dice score
over concurrent generative training approaches.


  In this paper we discuss the relation between the standard covariant quantum
field theory and light-front field theory. We define covariant theory by its
Feynman diagrams, whereas light-front field theory is defined in terms of
light-cone time-ordered diagrams. A general algorithm is proposed that produces
the latter from any Feynman diagram. The procedure is illustrated in several
cases. Technical problems that occur in the light-front formulation and have no
counterpart in the covariant formulation are identified and solved.


  We discuss the interaction between perturbations in the inflaton and the
background during the preheating phase in simple inflationary models. By
numerically solving the Einstein field equations we are able to assess the
impact of non-linear gravitational effects on preheating, and to assess the
accuracy of perturbative discussions of the preheating epoch.


  Open systems possess unique potentials in high-precision sensing, yet the
majority of previous studies rely on the spectral singularities known as
exceptional points. Here we theoretically propose and experimentally
demonstrate universal non-Hermitian sensing in the absence of exceptional
points. The scheme makes use of the intrinsic sensitivity of a non-Hermitian
probe to weak external fields, which can be understood as the direct
consequence of non-Hermiticity. We confirm the basic mechanism by simulating
the sensor-field dynamics using photon interferometry, and, as a concrete
example, demonstrate the enhanced sensing of signals encoded in the setting
angle of a wave plate. While the sensitivity of the probe is ultimately limited
by the measurement noise, we find the non-Hermitian sensor showing superior
performance under background noises that cannot be suppressed through
repetitive measurements. Our experiment opens the avenue of enhanced sensing
without exceptional points, complementing existing efforts aimed at harnessing
the unique features of open systems.


  We propose a CP-odd asymmetry in the supersymmetric process e^+e^- \to
\chi^0_i \chi^0_j \to \chi^0_j \tau stau_k by means of the transverse \tau
polarization. We calculate the asymmetry and cross sections at a future linear
collider in the 500 GeV c.m.s. energy range with longitudinal polarized beams
and high luminosity. We work in the Minimal Supersymmetric Standard Model with
complex parameters \mu, M_1 and A_\tau. The asymmetry can reach values up to 60
%. We also estimate the sensitivity for measuring the \tau polarization
necessary to probe the CP asymmetry.


  The dependence of $\mathrm{f}_{0}$(980) production on the final-state
charged-particle multiplicity in p$-$Pb collisions at $\sqrt{s_{\mathrm{NN}}} =
5.02$ TeV is reported. The production of $\mathrm{f}_{0}$(980) is measured with
the ALICE detector via the $\mathrm{f}_0 (980) \rightarrow \pi^{+}\pi^{-}$
decay channel in a midrapidity region of $-0.5<y<0$. Particle yield ratios of
$\mathrm{f}_{0}$(980) to $\pi$ and $\mathrm{K}^{*}$(892)$^{0}$ are found to be
decreasing with increasing charged-particle multiplicity. The magnitude of the
suppression of the $\mathrm{f}_{0}$(980)/$\pi$ and
$\mathrm{f}_{0}$(980)/$\mathrm{K}^{*}$(892)$^{0}$ yield ratios is found to be
dependent on the transverse momentum $p_{\mathrm{T}}$, suggesting different
mechanisms responsible for the measured effects. Furthermore, the nuclear
modification factor $Q_{\mathrm{pPb}}$ of $\mathrm{f}_{0}$(980) is measured in
various multiplicity ranges. The $Q_{\mathrm{pPb}}$ shows a strong suppression
of the $\mathrm{f}_{0}$(980) production in the $p_{\mathrm{T}}$ region up to
about 4 GeV/$c$. The results on the particle yield ratios and
$Q_{\mathrm{pPb}}$ for $\mathrm{f}_{0}$(980) may help to understand the late
hadronic phase in p$-$Pb collisions and the nature of the internal structure of
$\mathrm{f}_{0}$(980) particle.


  The outcome of an epidemic is closely related to the network of interactions
between the individuals. Likewise, protein functions depend on the 3D
arrangement of their residues and on the underlying energetic interaction
network. Borrowing ideas from the theoretical framework that has been developed
to address the spreading of real diseases, we study the diffusion of a
fictitious epidemic inside the protein non-bonded interaction network. Our
approach allowed to probe the overall stability and the capability to propagate
information in the complex 3D-structures and proved to be very efficient in
addressing different problems, from the assessment of thermal stability to the
identification of allosteric sites.


  The Simons Observatory (SO), due to start full science operations in early
2025, aims to set tight constraints on inflationary physics by inferring the
tensor-to-scalar ratio $r$ from measurements of CMB polarization $B$-modes. Its
nominal design targets a precision $\sigma(r=0) \leq 0.003$ without delensing.
Achieving this goal and further reducing uncertainties requires the mitigation
of other sources of large-scale $B$-modes such as Galactic foregrounds and weak
gravitational lensing. We present an analysis pipeline aiming to estimate $r$
by including delensing within a cross-spectral likelihood, and demonstrate it
on SO-like simulations. Lensing $B$-modes are synthesised using internal CMB
lensing reconstructions as well as Planck-like CIB maps and LSST-like galaxy
density maps. This $B$-mode template is then introduced into SO's
power-spectrum-based foreground-cleaning algorithm by extending the likelihood
function to include all auto- and cross-spectra between the lensing template
and the SAT $B$-modes. Within this framework, we demonstrate the equivalence of
map-based and cross-spectral delensing and use it to motivate an optimized
pixel-weighting scheme for power spectrum estimation. We start by validating
our pipeline in the simplistic case of uniform foreground spectral energy
distributions (SEDs). In the absence of primordial $B$-modes, $\sigma(r)$
decreases by 37% as a result of delensing. Tensor modes at the level of
$r=0.01$ are successfully detected by our pipeline. Even with more realistic
foreground models including spatial variations in the dust and synchrotron
spectral properties, we obtain unbiased estimates of $r$ by employing the
moment-expansion method. In this case, delensing-related improvements range
between 27% and 31%. These results constitute the first realistic assessment of
the delensing performance at SO's nominal sensitivity level. (Abridged)


  Markov Chain Monte Carlo (MCMC) algorithms are often used for approximate
inference inside learning, but their slow mixing can be difficult to diagnose
and the approximations can seriously degrade learning. To alleviate these
issues, we define a new model family using strong Doeblin Markov chains, whose
mixing times can be precisely controlled by a parameter. We also develop an
algorithm to learn such models, which involves maximizing the data likelihood
under the induced stationary distribution of these chains. We show empirical
improvements on two challenging inference tasks.


  Micromagnetic modelling provides the ability to simulate large magnetic
systems accurately without the computational cost limitation imposed by
atomistic modelling. Through micromagnetic modelling it is possible to simulate
systems consisting of thousands of grains over a time range of nanoseconds to
years, depending upon the solver used. Here we present the creation and release
of an open-source multi-timescale micromagnetic code combining three key
solvers: Landau-Lifshitz-Gilbert; Landau-Lifshitz-Bloch; Kinetic Monte Carlo.
This code, called MARS (Models of Advanced Recording Systems), is capable of
accurately simulating the magnetisation dynamics in large and structurally
complex single- and multi-layered granular systems. The short timescale
simulations are achieved for systems far from and close to the Curie point via
the implemented Landau-Lifshitz-Gilbert and Landau-Lifshitz-Bloch solvers
respectively. This enables read/write simulations for general perpendicular
magnetic recording and also state of the art heat assisted magnetic recording
(HAMR). The long timescale behaviour is simulated via the Kinetic Monte Carlo
solver, enabling investigations into signal-to-noise ratio and data longevity.
The combination of these solvers opens up the possibility of multi-timescale
simulations within a single software package. For example the entire HAMR
process from initial data writing and data read back to long term data storage
is possible via a single simulation using MARS. The use of atomistic
parameterisation for the material input of MARS enables highly accurate
material descriptions which provide a bridge between atomistic simulation and
real world experimentation. Thus MARS is capable of performing simulations for
all aspects of recording media research and development. This ranges from
material characterisation and optimisation to system design and implementation.


  A theoretical description of quantum mechanical steady states is developed.
Applications for simple quantum mechanical systems described in terms of
coupled level structures yield a formulation equivalent to time independent
scattering theory. Applications to steady states of thermally relaxing systems
leads to time independent scattering theory in Liouville space that is
equivalent to the tetradic Green's function formalism. It provides however a
direct route to derive particular forms of the Liouville equation applicable in
steady state situations. The theory is applied to study the conduction
properties in the super-exchange model of a metal-molecule-metal contact weakly
coupled to the thermal environment. The energy resolved temperature dependent
transmission probability, as well as its coherent (tunneling) and incoherent
(activated) parts, are calculated using the Redfield approximation. These
components depend differently on the energy gap (or barrier), on the
temperature and on the bridge length. The coherent component is most important
at low temperatures, large energy gaps and small chain lengths. The incoherent
component dominates in the opposite limits. The integrated transmission
provides a generalization of the Landauer conduction formula for small
junctions in the presence of thermal relaxation.


  Following successful proof-of-concept experiments, we constructed compact
varying-gap undulator with Hydraulic-Assist Driver. The 1.5m long undulator
features a pure permanent magnet structure, period of 28.4 mm and a minimum gap
of 6.75mm. The bench test of the device indicated excellent mechanical and
magnetic performances. It demonstrated less than 3 deg RMS optical phase errors
in a wide range of gap variation; ~1.0e-4 (or better) repeatability in
normalized K_und (dK/K) from one gap variation to another; ~1.0e-4 RMS of dK/K
long term (~24hrs) stability. In special mode, when the gap was varied in
correlation with undulator temperature, undulator demonstrated dK/K stability
at the level 5.3e-5 RMS while the undulator temperature change was ~0.4 degC.
In this paper, we discuss the critical design components and criteria required
for undulator mechanical stability and magnetic performance. We also describe
our Lab-View-based control system governing the Hydraulic-Assist Driver.
Finally, we present the test setup and results.


  The initial states which minimize the predictability loss for a damped
harmonic oscillator are identified as quasi-free states with a symmetry
dictated by the environment's diffusion coefficients. For an isotropic
diffusion in phase space, coherent states (or mixtures of coherent states) are
selected as the most stable ones.


  We develop a projected least squares estimator for the change point parameter
in a high dimensional time series model with a potential change point.
Importantly we work under the setup where the jump size may be near the
boundary of the region of detectability. The proposed methodology yields an
optimal rate of convergence despite high dimensionality of the assumed model
and a potentially diminishing jump size. The limiting distribution of this
estimate is derived, thereby allowing construction of a confidence interval for
the location of the change point. A secondary near optimal estimate is proposed
which is required for the implementation of the optimal projected least squares
estimate. The prestep estimation procedure is designed to also agnostically
detect the case where no change point exists, thereby removing the need to
pretest for the existence of a change point for the implementation of the
inference methodology. Our results are presented under a general positive
definite spatial dependence setup, assuming no special structure on this
dependence. The proposed methodology is designed to be highly scalable, and
applicable to very large data. Theoretical results regarding detection and
estimation consistency and the limiting distribution are numerically supported
via monte carlo simulations.


  Dipolar-octupolar (DO) pyrochlore systems Ce$_2$(Zr,Sn,Hf)$_2$O$_7$ have
garnered much attention as recent investigations suggest that they may
stabilize a novel quantum spin ice (QSI), a quantum spin liquid (QSL) with an
emergent $U(1)$ gauge field. In particular, the experimentally estimated
microscopic exchange parameters place Ce$_2$Zr$_2$O$_7$ in the $\pi$-flux QSI
regime, and recent neutron scattering experiments have corroborated some key
theoretical predictions. On the other hand, to make a definitive conclusion,
more multifaceted experimental signatures are desirable. In this regard, recent
neutron scattering investigation of the magnetic field dependence of the spin
correlations in Ce$_2$Zr$_2$O$_7$ may provide valuable information. However,
there have not been any comprehensive theoretical studies for comparison. In
this work, we provide such information using gauge mean-field theory (GMFT),
allowing for theoretical investigation beyond the perturbative regime. In
particular, we construct the phase diagrams for the [110], [111], and [001]
field directions. Furthermore, we demonstrate the distinctive evolution of the
equal-time and dynamical spin structure factors as a function of the magnetic
field for each field direction. These predictions will help future experiments
confirm the true nature of the DO-QSI.


  We present observations in the 0.1-100 keV spectral band of 8 Seyfert 2
galaxies, obtained by means of BeppoSAX. These sources were selected according
to their [OIII] optical emission line flux, to avoid biases against obscuration
on the pc scale. All sources were detected. All of them are weak X-ray
emitters, and most of them are characterized by prominent iron lines at 6.4-7
keV (EW > 500 eV) and by a flat continuum, indicative of heavy obscuration
along our line of sight (N_H > 10^25 cm^-2 in most cases). These results 1)
provide further evidence in favor of the unified scenario, and 2) indicate that
the average obscuration of type 2 AGNs is very likely much higher than deduced
by former X-ray surveys. These findings have important implications for the
synthesis of the X-ray background.


  A new method, the Dynamical Systems Method (DSM), justified recently, is
applied to solving ill-conditioned linear algebraic system (ICLAS). The DSM
gives a new approach to solving a wide class of ill-posed problems. In this
paper a new iterative scheme for solving ICLAS is proposed. This iterative
scheme is based on the DSM solution. An a posteriori stopping rules for the
proposed method is justified. This paper also gives an a posteriori stopping
rule for a modified iterative scheme developed in A.G.Ramm, JMAA,330
(2007),1338-1346, and proves convergence of the solution obtained by the
iterative scheme.


  We determine how the elementary excitations of iridium-oxide materials, which
are dominated by a strong relativistic spin-orbit coupling, appear in Resonant
Inelastic X-ray Scattering (RIXS). Whereas the RIXS spectral weight at the L2
x-ray edge vanishes, we find it to be strong at the L3-edge. Applying this to
Sr2IrO4, we observe that RIXS, besides being sensitive to local doublet to
quartet transitions, meticulously maps out the strongly dispersive delocalized
excitations of the low-lying spin-orbit doublets.


  In this preliminary study, we provide two methods for estimating the
gradients of functions of real value. Both methods are built on derivative
estimations that are calculated using the standard method or the Squire-Trapp
method for any given direction. Gradients are computed as the average of
derivatives in uniformly sampled directions. The first method uses a uniformly
distributed set of axes that consists of orthogonal unit vectors that span the
space. The second method only uses a uniformly distributed set of unit vectors.
Both methods essentially minimize the error through an average of estimations
to cancel error terms. Both methods are essentially a conceptual generalization
of the method used to estimate normal fractal surfaces.


  We study a theory of Dirac fermions on a disk in presence of an
electromagnetic field. Using the heat-kernel technique we compute the
functional determinant which results after decoupling the zero-flux gauge
degrees of freedom from the fermions. We also compute the Green functions of
the remaining fermionic theory with the appropriate boundary conditions.
Finally we analyze the coset model associated to this gauge theory and compute
all its correlations functions.


  We solve the general-relativistic steady-state eigenvalue problem of
neutrino-driven protoneutron star winds, which immediately follow core-collapse
supernova explosions. We provide velocity, density, temperature, and
composition profiles and explore the systematics and structures generic to such
a wind for a variety of protoneutron star characteristics. Furthermore, we
derive the entropy, dynamical timescale, and neutron-to-seed ratio in the
general relativistic framework essential in assessing this site as a candidate
for $r$-process nucleosynthesis. Generally, we find that for a given mass
outflow rate ($\dot{M}$), the dynamical timescale of the wind is significantly
shorter than previously thought. We argue against the existence or viability of
a high entropy ($\gtrsim300$ per k$_{B}$ per baryon), long dynamical timescale
$r$-process epoch. In support of this conclusion, we model the protoneutron
star cooling phase, calculate nucleosynthetic yields in our steady-state
profiles, and estimate the integrated mass loss. We find that transonic winds
enter a high entropy phase only with very low $\dot{M}$
($\lesssim1\times10^{-9}$ M$_\odot$ s$^{-1}$) and extremely long dynamical
timescale ($\tau_\rho\gtrsim0.5$ seconds). Our results support the possible
existence of an early $r$-process epoch at modest entropy ($\sim150$) and very
short dynamical timescale, consistent in our calculations with a very massive
or very compact protoneutron star that contracts rapidly after the preceding
supernova. We explore possible modifications to our models, which might yield
significant $r$-process nucleosynthesis generically. Finally, we speculate on
the effect of fallback and shocks on both the wind physics and nucleosynthesis.


  We prove that a real analytic pseudo-rotation $f$ of the disc or the sphere
is never topologically mixing. When the rotation number of $f$ is of Brjuno
type, the latter follows from a KAM theorem of R\"ussmann on the stability of
real analytic elliptic fixed points. In the non-Brjuno case, we prove that a
pseudo-rotation of class $C^k$, $k\geq 2$, is $C^{k-1}$-rigid using the simple
observation, derived from Franks' Lemma on free discs, that a pseudo-rotation
with small rotation number compared to its $C^1$ (or H\"older) norm must be
close to Identity.
  From our result and a structure theorem by Franks and Handel (on zero entropy
surface diffeomorphisms) it follows that an analytic conservative
diffeomorphism of the disc or the sphere that is topologically mixing must have
positive topological entropy.
  In our proof we need an a priori limit on the growth of the derivatives of
the iterates of a pseudo-rotation that we obtain via an effective finite
information version of the Katok closing lemma for an area preserving surface
diffeomorphism $f$, that provides a controlled gap in the possible growth of
the derivatives of $f$ between exponential and sub-exponential.


  Speech-to-singing voice conversion (STS) task always suffers from data
scarcity, because it requires paired speech and singing data. Compounding this
issue are the challenges of content-pitch alignment and the suboptimal quality
of generated outputs, presenting significant hurdles in STS research. This
paper presents SVPT, an STS approach boosted by a self-supervised singing voice
pre-training model. We leverage spoken language model techniques to tackle the
rhythm alignment problem and the in-context learning capability to achieve
zero-shot conversion. We adopt discrete-unit random resampling and pitch
corruption strategies, enabling training with unpaired singing data and thus
mitigating the issue of data scarcity. SVPT also serves as an effective
backbone for singing voice synthesis (SVS), offering insights into scaling up
SVS models. Experimental results indicate that SVPT delivers notable
improvements in both STS and SVS endeavors. Audio samples are available at
https://speech2sing.github.io.


  We propose a spectroscopic method of identifying broken symmetry states of
bilayer graphene. We demonstrate theoretically that, in contrast to gapped
states, a strained bilayer crystal or nematic phase of the electronic liquid
are distinguishable by the dependence of the lineshape of absorption on the
polarization of the light. This property is characteristic for both the
infrared and far-infrared spectral ranges, which correspond to the absorption
by transitions between low-energy bands and split bands, and transitions
between the low-energy valence and conduction bands, respectively.


  An attack on deep learning systems where intelligent machines collaborate to
solve problems could cause a node in the network to make a mistake on a
critical judgment. At the same time, the security and privacy concerns of AI
have galvanized the attention of experts from multiple disciplines. In this
research, we successfully mounted adversarial attacks on a federated learning
(FL) environment using three different datasets. The attacks leveraged
generative adversarial networks (GANs) to affect the learning process and
strive to reconstruct the private data of users by learning hidden features
from shared local model parameters. The attack was target-oriented drawing data
with distinct class distribution from the CIFAR- 10, MNIST, and Fashion-MNIST
respectively. Moreover, by measuring the Euclidean distance between the real
data and the reconstructed adversarial samples, we evaluated the performance of
the adversary in the learning processes in various scenarios. At last, we
successfully reconstructed the real data of the victim from the shared global
model parameters with all the applied datasets.


  We investigate the effect of orientation-dependent selection effects on
galaxy clustering in redshift space. It is found that if galaxies are aligned
by large-scale tidal fields, then these selection effects give rise to a
dependence of the observed galaxy density on the local tidal field, in addition
to the well-known dependences on the matter density and radial velocity
gradient. This alters the galaxy power spectrum in a way that is different for
Fourier modes parallel to and perpendicular to the line of sight. These tidal
galaxy alignments can thus mimic redshift space distortions, and thus result in
a bias in the measurement of the velocity power spectrum. If galaxy
orientations are affected only by the local tidal field, then the tidal
alignment effect has exactly the same scale and angular dependence as the
redshift space distortions in the linear regime, so it cannot be projected out
or removed by masking small scales in the analysis. We consider several toy
models of tidal alignments and orientation-dependent selection, normalize their
free parameter (an amplitude) to recent observations, and find that they could
bias the velocity amplitude f(z)G(z) by 5--10 per cent in some models, although
most models give much smaller contamination. We conclude that tidal alignments
may be a significant systematic error in redshift space distortion measurements
that aim to test general relativity via the growth of large-scale structure. We
briefly discuss possible mitigation strategies.


  Uncertainty relations play a central role in quantum mechanics. Entropic
uncertainty relations in particular have gained significant importance within
quantum information, providing the foundation for the security of many quantum
cryptographic protocols. Yet, rather little is known about entropic uncertainty
relations with more than two measurement settings. In this note we review known
results and open questions.


  The dissemination of synthetic data can be an effective means of making
information from sensitive data publicly available while reducing the risk of
disclosure associated with releasing the sensitive data directly. While
mechanisms exist for synthesizing data that satisfy formal privacy guarantees,
the utility of the synthetic data is often an afterthought. More recently, the
use of methods from the disease mapping literature has been proposed to
generate spatially-referenced synthetic data with high utility, albeit without
formal privacy guarantees. The objective for this paper is to help bridge the
gap between the disease mapping and the formal privacy literatures. In
particular, we extend an existing approach for generating formally private
synthetic data to the case of Poisson-distributed count data in a way that
allows for the infusion of prior information. To evaluate the utility of the
synthetic data, we conducted a simulation study inspired by publicly available,
county-level heart disease-related death counts. The results of this study
demonstrate that the proposed approach for generating differentially private
synthetic data outperforms a popular technique when the counts correspond to
events arising from subgroups with unequal population sizes or unequal event
rates.


  For each geometrically finite 2-dimensional non-Euclidean crystallographic
group (NEC group), we compute the cohomology groups. In the case where the
group is a Fuchsian group, we also determine the ring structure of the
cohomology.


  We consider a task graph to be executed on a set of processors. We assume
that the mapping is given, say by an ordered list of tasks to execute on each
processor, and we aim at optimizing the energy consumption while enforcing a
prescribed bound on the execution time. While it is not possible to change the
allocation of a task, it is possible to change its speed. Rather than using a
local approach such as backfilling, we consider the problem as a whole and
study the impact of several speed variation models on its complexity. For
continuous speeds, we give a closed-form formula for trees and series-parallel
graphs, and we cast the problem into a geometric programming problem for
general directed acyclic graphs. We show that the classical dynamic voltage and
frequency scaling (DVFS) model with discrete modes leads to a NP-complete
problem, even if the modes are regularly distributed (an important particular
case in practice, which we analyze as the incremental model). On the contrary,
the VDD-hopping model leads to a polynomial solution. Finally, we provide an
approximation algorithm for the incremental model, which we extend for the
general DVFS model.


  We show that the incoherent elastic scattering of partons, as present in a
multi-phase transport model (AMPT), with a modest parton-parton cross-section
of $\sigma=1.5 - 3$ mb, naturally explains the long-range two-particle
azimuthal correlation as observed in proton-proton and proton-nucleus
collisions at the Large Hadron Collider.


  One important factor affecting the critical current density in type-II
superconductors is the formation of artificial pinning centers. Hence, the
engineering of pinning centers in superconducting systems has garnered
considerable attention. In this study, the effect of moir\'e patterned pinning
centers on the critical current density of superconducting tapes is
investigated. The Langevin equation is solved by taking into account the
prominent forces within the superconductor medium, using the appropriate
boundary conditions for vortices. The vortices dynamics are investigated by
performing molecular dynamics simulations, which are used to calculate the
corresponding critical current densities. Results show a significant
enhancement in the critical current density at particular angles of the
relative rotation of the primary lattices. It is also revealed that for
stronger pinning forces, the calculated critical current densities are higher
in the moir\'e lattices compared to the primary lattices of pinning centers.


  We show that the Aharonov-Bohm effect in the nuclear matrix model derives the
statistical nature of nucleons in holographic QCD. For N_c= odd (even), the
nucleon is shown to be a fermion (boson).


  We prove that curves in a non-primitive, base point free, ample linear system
on a K3 surface have maximal variation. The result is deduced from general
restriction theorems applied to the tangent bundle. We also show how to use
specialisation to spectral curves to deduce information about the variation of
curves contained in a K3 surface more directly. The situation for primitive
linear systems is not clear at the moment. However, the maximal variation holds
in genus two and can, in many cases, be deduced from a recent result of van
Geemen and Voisin confirming a conjecture due to Matsushita.


  We propose a new measure of non-classicality of quantum gates which is
particularly suitable for probabilistic devices. This measure enables to
compare, e.g., deterministic devices which prepare entangled states with low
amount of entanglement with probabilistic devices which generate highly
entangled states but which fail sometimes. We provide examples demonstrating
advantages of this new measure over the so far employed entangling power.


  Magnetic anisotropies often originate from the spin-orbit coupling and
determine magnetic ordering patterns. We develop a microscopic theory for DC
electric-field controls of magnetic anisotropies in magnetic Mott insulators
and discuss its applications to Kitaev materials and topological spin textures.
Throughout this paper, we take a microscopic approach based on Hubbard-like
lattice models, tight-binding models with on-site interactions. We derive a
low-energy spin Hamiltonian from a fourth-order perturbation expansion of the
Hubbard-like model. We show in the presence of a strong intra-atomic spin-orbit
coupling that DC electric fields add non-Kitaev interactions such as a
Dzyaloshinskii-Moriya interaction and an off-diagonal $\Gamma'$ interaction to
the Kitaev-Heisenberg model and can induce a topological quantum phase
transition between Majorana Chern insulating phases. We also investigate the
inter-atomic Rashba spin-orbit coupling and its effects on topological spin
textures. DC electric fields turn out to create and annihilate magnetic
skyrmions, hedgehogs, and chiral solitons. We propose several methods of
creating topological spin textures with external electromagnetic fields. Our
theory clarifies that the strong but feasible electric field can control Kitaev
spin liquids and topological spin textures.


  In this paper, we analyze the properties of the recently proposed real-time
equation-of-motion coupled-cluster (RT-EOM-CC) cumulant Green's function
approach [J. Chem. Phys. 2020, 152, 174113]. We specifically focus on
identifying the limitations of the original time-dependent coupled cluster
(TDCC) ansatz and propose an enhanced extended TDCC ansatz ensuring the
exactness in the expansion limit. Additionally, we introduce a practical
cluster-analysis-based approach for characterizing the peaks in the computed
spectral function from the RT-EOM-CC cumulant Green's function approach, which
is particularly useful for the assignments of satellite peaks when many-body
effects dominate the spectra. Our preliminary numerical tests focus on
reproducing, approximating, and characterizing the exact impurity Green's
function of the three-site and four-site single impurity Anderson models using
the RT-EOM-CC cumulant Green's function approach. The numerical tests allow us
to have a direct comparison between the RT-EOM-CC cumulant Green's function
approach and other Green's function approaches in the numerical exact limit.


  In the framework of percolation of color sources, the transverse momentum
distribution in heavy ion and $p+p$ collisions at all centralities and energies
are shown to follow a universal behaviour. The width of the distribution
depends on the fluctuations of the number of color sources per cluster. At low
densities, there are only independent single color sources, no fluctuations
occurs and the distribution is described by a single exponential. At very high
densities, only one cluster of many color sources appears and therefore there
are not fluctuations either and the hardness of the distribution is suppressed.
The Cronin effect in this framework is due to a maximum of the fluctuations
which decreases as the density increases. We obtain a good agreement with
experimental data including the low $p_T$ behaviour and the spectra for
different particles. We show that the transverse momentum and multiplicity
distributions are related to each other in a defined way. This point is
satisfied by the experimental data on $p+p$ collisions at different energies.


  Computer-aided design (CAD) programs are essential to engineering as they
allow for better designs through low-cost iterations. While CAD programs are
typically taught to undergraduate students as a job skill, such software can
also help students learn engineering concepts. A current limitation of CAD
programs (even those that are specifically designed for educational purposes)
is that they are not capable of providing automated real-time help to students.
To encourage CAD programs to build in assistance to students, we used data
generated from students using a free, open source CAD software called Aladdin
to demonstrate how student data combined with machine learning techniques can
predict how well a particular student will perform in a design task. We
challenged students to design a house that consumed zero net energy as part of
an introductory engineering technology undergraduate course. Using data from
128 students, along with the scikit-learn Python machine learning library, we
tested our models using both total counts of design actions and sequences of
design actions as inputs. We found that our models using early design sequence
actions are particularly valuable for prediction. Our logistic regression model
achieved a >60% chance of predicting if a student would succeed in designing a
zero net energy house. Our results suggest that it would be feasible for
Aladdin to provide useful feedback to students when they are approximately
halfway through their design. Further improvements to these models could lead
to earlier predictions and thus provide students feedback sooner to enhance
their learning.


  We study the cubic non linear Schr\"odinger equation (NLS) on compact
surfaces. On the sphere $\mathbb{S}^2$ and more generally on Zoll surfaces, we
prove that, for $s>1/4$, NLS is uniformly well-posed in $H^s$, which is sharp
on the sphere. The main ingredient in our proof is a sharp bilinear estimate
for Laplace spectral projectors on compact surfaces.
  On \'etudie l'\'equation de Schr\"odinger non lin\'eaire (NLS) sur une
surface compacte.Sur la sph\`ere $\mathbb{S}^2$ et plus g\'en\'eralement sur
toute surface de Zoll, on d\'emontre que pour $s>1/4$, NLS est uniform\'ement
bien pos\'ee dans $H^s$, ce qui est optimalsur la sph\`ere. Le principal
ingr\'edient de notre d\'emonstration est une estimation bilin\'eaire pour les
projecteurs spectraux du laplacien sur une surface compacte.


  We investigate the local moment formation in the half-filled SU($N$) Hubbard
model under a staggered ionic potential. As the Hubbard $U$ increases, the
charge fluctuations are suppressed and eventually frozen when $U$ is above a
critical value $U_c$, marking the development of well-defined local moment with
integer $m$ fermions on the A-sublattice and $(N-m)$ fermions on the
B-sublattice, respectively. We obtain an analytical solution for $U_c$ for the
paramagnetic ground state within the variational Gutzwiller approximation and
renormalized mean field theory. For large $N$, $U_c$ is found to depend on $N$
linearly with fixed $m/N$, but sublinearly with fixed $m$. The local moment
formation is accompanied by a peculiar phase transition from the band insulator
to the Mott insulator, where the ionic potential and quasiparticle weight are
renormalized to zero simultaneously. Inside the Mott phase, the low energy
physics is described by the SU($N$) Heisenberg model with conjugate
representations, which is widely studied in the literature.


  This paper discusses the impact of introducing a planet on an eccentric orbit
into a planetesimal disk. That planet's secular perturbations cause the orbits
of the planetesimals to evolve in such a way that at any one time planetesimals
at the same distance from the star have common pericentres and eccentricities.
This causes the surface density distribution of an extended planetesimal disk
to exhibit two spirals, one exterior the other interior to the planet's orbit.
These two spirals unwind in different directions and their structure is
described by two parameters: the time since the planet was introduced and the
planet's eccentricity. At late times the spirals become tightly wound and the
offset centre of symmetry of the pericentre glow approximation is recovered.
Comparison with spiral structure seen in the HD141569 disk shows that its
spiral at 325 AU is similar to that caused by introducing a planet 5 Myr ago
with a mass 0.2-2M_Jup orbiting at 235-250 AU with an eccentricity of 0.05-0.2;
likewise a Saturn mass planet at 150 AU would cause structure like that seen at
200 AU. More definitive statements about any planets orbiting HD141569 from
this model could be made once the effect of the binary companion on the disk is
known, and once the disk's structure has been better characterised down to 100
AU, including the location of the star within the disk. The relatively young
age of this system (~5 Myr) means that if giant planets really do exist at
hundreds of AU from HD141569, this provides a unique opportunity to set
constraints on the mechanism by which those planets came to be at such large
distances.


  For finite Galois extension fields defined by odd degree irreducible
polynomials over algebraic integer ring, we observe "Reciprocity Law" through
Jacobian Variety by embedding all roots of the polynomials into 2-torsion
points of Jacobian Variety. Furthermore, Galois group of the minimal splitting
field of such a polynomial is a subgroup of general linear group with
coefficient $\mathbb{F}_{2}$.


  We study the approximation of a Markov chain on a reduced state space, for
both discrete- and continuous-time Markov chains. In this context, we extend
the existing theory of formal error bounds for the approximated transient
distributions. As a special case, we consider aggregated (or lumped) Markov
chains, where the state space reduction is achieved by partitioning the state
space into macro states. In the discrete-time setting, we bound the stepwise
increment of the error, and in the continuous-time setting, we bound the rate
at which the error grows. In addition, the same error bounds can also be
applied to bound how far an approximated stationary distribution is from
stationarity. Subsequently, we compare these error bounds with relevant
concepts from the literature, such as exact and ordinary lumpability, as well
as deflatability and aggregatability. These concepts define stricter than
necessary conditions to identify settings in which the aggregation error is
zero. We also consider possible algorithms for finding suitable aggregations
for which the formal error bounds are low, and we analyse first experiments
with these algorithms on a range of different models.


  Conformal symmetries appear in many parts of physics and play a unique role
in exploring the Universe. In this work, we consider the possibility of
constructing conformal theories of gravity in the Symmetric Teleparallel
Gravity framework, where gravitation is expressed through non-metricity rather
than curvature or torsion. We demonstrate that it is possible to formulate a
family of conformal theories with second-order field equations and have the
metric tensor as the fundamental variable. Moreover, we consider the addition
of a scalar field to the theory, and compare the results to the Teleparallel
Gravity setting. Finally, we present the scalar-tensor representation of
modified symmetric teleparallel theories, and amongst others, we show that
there is an interesting sub-case that has the possibility of playing the role
of dark energy.


  We present a method for assigning probabilities to the solutions of initial
value problems that have a Lipschitz singularity. To illustrate the method, we
focus on the following toy example: $\frac{d^2r(t)}{dt^2} = r^a$, $r(t=0) =0$,
and $\frac{dr(t)}{dt}\mid_{r(t=0)} =0$, with $a \in ]0,1[$. This example has a
physical interpretation as a mass in a uniform gravitational field on a
frictionless, rigid dome of a particular shape; the case with $a=1/2$ is known
as Norton's dome. Our approach is based on (1) finite difference equations,
which are deterministic; (2) elementary techniques from alpha-theory, a
simplified framework for non-standard analysis that allows us to study
infinitesimal perturbations; and (3) a uniform prior on the canonical phase
space. Our deterministic, hyperfinite grid model allows us to assign
probabilities to the solutions of the initial value problem in the original,
indeterministic model.


  Bluetooth Low Energy (BLE) has become an intrinsic wireless technology for
the Internet of Things (IoT). With the proliferation of BLE-embedded IoT
devices, it is important to study the security and privacy implications of BLE.
The forefront attack to BLE devices is the wireless sniffing attack, which
would lead to more detrimental threats like jamming, encryption cracking or
system penetration. Existing sniffing attacks are based on the correct
detection of BLE connection initiation state, but they become ineffective for
BLE long-lived connections. In this paper, we focus on the adversary setting
with a low-cost single radio and develop a suite of real-time algorithms to
determine the key parameters necessary to follow and sniff a BLE connection in
the connected state. We implement our algorithms in the open source platform
-Ubertooth One and evaluate its performance in terms of sniffing overhead and
accuracy. By comparing with state-of-the-art schemes, experimental results show
that our sniffer achieves much higher sniffing accuracy (over 80\%) and better
stability to BLE operational dynamics.


  These lectures give an introduction to thermal perturbation theory, hard
thermal loops, and their use in a nonperturbative, approximately
self-consistent resummation of the thermodynamical potentials of quantum
chromodynamics.


  Channel and delay coefficient are two essential parameters for the
characterization of a multipath propagation environment. It is crucial to
generate realistic channel and delay coefficient in order to study the channel
characteristics that involves signals propagating through environments with
severe multipath effects. While many deterministic channel models, such as
ray-tracing (RT), face challenges like high computational complexity, data
requirements for geometrical information, and inapplicability for space-ground
links, and nongeometry-based stochastic channel models (NGSCMs) might lack
spatial consistency and offer lower accuracy, we present a scalable tutorial
for the channel modeling of dual mobile space-ground links in urban areas,
utilizing the Quasi Deterministic Radio Channel Generator (QuaDRiGa), which
adopts a geometry-based stochastic channel model (GSCM), in conjunction with an
International Telecommunication Union (ITU) provided state duration model. This
tutorial allows for the generation of realistic channel and delay coefficients
in a multipath environment for dual mobile space-ground links. We validate the
accuracy of the work by analyzing the generated channel and delay coefficient
from several aspects, such as received signal power and amplitude, multipath
delay distribution, delay spread and Doppler spectrum.


  It is shown that a highly phase sensitive polarization squeezed (2n-1)-photon
state can be generated by subtracting a diagonally polarized photon from the 2n
photon component generated in collinear type II downconversion. This
polarization wedge state has the interesting property that its photon number
distribution in the horizontal and vertical polarizations remains sharply
defined for phase shifts of up to 1/n between the circularly polarized
components. Phase shifts at the Heisenberg limit are therefore observed as
nearly deterministic transfers of a single photon between the horizontal and
vertical polarization components.


  Clusters of galaxies allow a direct estimate of the metallicity and metal
production yield on the largest scale so far. It is argued that cluster
metallicity (about 1/3 solar) should be taken as representative of the low-z
universe as a whole. There is now compelling evidence that the bulk of stars
not only in cluster ellipticals but also in field ellipticals and bulges formed
at high redshifts (z>~ 3). Since such stars account for at least 30% of the
baryons now locked into stars, it is argued that at least 30% of stars and
metals formed before z=~3, and correspondingly the metallicity of the universe
at z=3 is predicted to be about 1/10 solar.


  A numerical model is presented for a mechanical oscillator in which the
magnitude of the restoring force is constant. Unlike the harmonic oscillator,
where force is proportional to displacement, this nonlinear system can be
chaotic when externally driven.


  We consider intrinsic linking and knotting in the context of directed graphs.
We construct an example of a directed graph that contains a consistently
oriented knotted cycle in every embedding. We also construct examples of
intrinsically 3-linked and 4-linked directed graphs. We introduce two
operations, consistent edge contraction and H-cyclic subcontraction, as special
cases of minors for digraphs, and show that the property of having a linkless
embedding is closed under these operations. We analyze the relationship between
the number of distinct knots and links in an undirected graph $G$ and its
corresponding symmetric digraph $\overline{DG}$. Finally, we note that the
maximum number of edges for a graph that is not intrinsically linked is $O(n)$
in the undirected case, but $O(n^2)$ for directed graphs.


  We provide and analyze a periodic Anderson model for studying magnetism and
superconductivity in UTe$_2$, a recently-discovered candidate for a topological
spin-triplet superconductor. The 24-band tight-binding model reproduces the
band structure obtained from a DFT$+U$ calculation consistent with an
angle-resolved photoemission spectroscopy. The Coulomb interaction of
$f$-electrons enhances Ising ferromagnetic fluctuation along the $a$-axis and
stabilizes spin-triplet superconductivity of either $B_{3u}$ or $A_{u}$
symmetry. When effects of pressure are taken into account in hopping integrals,
the magnetic fluctuation changes to antiferromagnetic one, and accordingly
spin-singlet superconductivity of $A_{g}$ symmetry is stabilized. Based on the
results, we propose pressure-temperature and magnetic field-temperature phase
diagrams revealing multiple superconducting phases as well as an
antiferromagnetic phase. In particular, a mixed-parity superconducting state
with spontaneous inversion symmetry breaking is predicted.


  Vacuum energy remains the simplest model of dark energy which could drive the
accelerated expansion of the Universe without necessarily introducing any new
degrees of freedom. Inhomogeneous vacuum energy is necessarily interacting in
general relativity. Although the four-velocity of vacuum energy is undefined,
an interacting vacuum has an energy transfer and the vacuum energy defines a
particular foliation of spacetime with spatially homogeneous vacuum energy in
cosmological solutions. It is possible to give a consistent description of
vacuum dynamics and in particular the relativistic equations of motion for
inhomogeneous perturbations given a covariant prescription for the vacuum
energy, or equivalently the energy transfer four-vector, and we construct
gauge-invariant vacuum perturbations. We show that any dark energy cosmology
can be decomposed into an interacting vacuum+matter cosmology whose
inhomogeneous perturbations obey simple first-order equations.


  We report on a measurement of spin-momentum correlations in quasi-elastic
scattering of longitudinally polarized electrons with an energy of 720 MeV from
vector-polarized deuterium. The spin correlation parameter $A^V_{ed}$ was
measured for the $^2 \vec{\rm H}(\vec e,e^\prime p)n$ reaction for missing
momenta up to 350 MeV/$c$ at a four-momentum transfer squared of 0.21
(GeV/c)$^2$. The data give detailed information about the spin structure of the
deuteron, and are in good agreement with the predictions of microscopic
calculations based on realistic nucleon-nucleon potentials and including
various spin-dependent reaction mechanism effects. The experiment demonstrates
in a most direct manner the effects of the D-state in the deuteron ground-state
wave function and shows the importance of isobar configurations for this
reaction.


  Lorentz and Lorentz-Morrey estimates are obtained for gradients of very weak
solutions to quasilinear equations of the form $$\text{div}\,\mathcal{A}(x,
\nabla u)=\text{div}\, |{\bf f}|^{p-2}{\bf f},$$ where
$\text{div}\,\mathcal{A}(x, \nabla u)$ is modelled after the $p$-Laplacian,
$p>1$. The estimates are global over bounded domains that satisfy a mild
exterior uniform thickness condition that involves the $p$-capacity. The vector
field datum ${\bf f}$ is allowed to have low degrees of integrability and thus
solutions may not have finite $L^p$ energy. A higher integrability result at
the boundary of the ground domain is also obtained for infinite energy
solutions to the associated homogeneous equations.


  We investigate the possibility of defining states on timelike hypersurfaces
in quantum field theory. To this end we consider hyperplanes in the real
massive Klein-Gordon theory using the Schroedinger representation. We find a
well defined vacuum wave functional, existing on any hyperplane, with the
remarkable property that it changes smoothly even under Euclidean rotation
through the light-cone. Moreover, particles on timelike hyperplanes exist and
occur in two variants, incoming and outgoing, distinguished by the sign of the
energy. Multi-particle wave functionals take a form similar to those on
spacelike hypersurfaces. The role of unitarity and the inner product is
discussed.


  In this work we present the architecture and results of a fully digital Front
End Electronics (FEE) read out system developed for the GALILEO array. The FEE
system, developed in collaboration with the Advanced Gamma Tracking Array
(AGATA) collaboration, is composed of three main blocks: preamplifiers,
digitizers and preprocessing electronics. The slow control system contains a
custom Linux driver, a dynamic library and a server implementing network
services. The digital processing of the data from the GALILEO germanium
detectors has demonstrated the capability to achieve an energy resolution of
1.53 per mil at an energy of 1.33 MeV.


  ARGONTUBE is a liquid argon time projection chamber (TPC) with an electron
drift length of up to 5 m equipped with cryogenic charge-sensitive
preamplifiers. In this work, we present results on its performance including a
comparison of the new cryogenic charge-sensitive preamplifiers with the
previously used room-temperature-operated charge preamplifiers.


  The primary focus of this thesis is to make Sanskrit manuscripts more
accessible to the end-users through natural language technologies. The
morphological richness, compounding, free word orderliness, and low-resource
nature of Sanskrit pose significant challenges for developing deep learning
solutions. We identify four fundamental tasks, which are crucial for developing
a robust NLP technology for Sanskrit: word segmentation, dependency parsing,
compound type identification, and poetry analysis. The first task, Sanskrit
Word Segmentation (SWS), is a fundamental text processing task for any other
downstream applications. However, it is challenging due to the sandhi
phenomenon that modifies characters at word boundaries. Similarly, the existing
dependency parsing approaches struggle with morphologically rich and
low-resource languages like Sanskrit. Compound type identification is also
challenging for Sanskrit due to the context-sensitive semantic relation between
components. All these challenges result in sub-optimal performance in NLP
applications like question answering and machine translation. Finally, Sanskrit
poetry has not been extensively studied in computational linguistics.
  While addressing these challenges, this thesis makes various contributions:
(1) The thesis proposes linguistically-informed neural architectures for these
tasks. (2) We showcase the interpretability and multilingual extension of the
proposed systems. (3) Our proposed systems report state-of-the-art performance.
(4) Finally, we present a neural toolkit named SanskritShala, a web-based
application that provides real-time analysis of input for various NLP tasks.
Overall, this thesis contributes to making Sanskrit manuscripts more accessible
by developing robust NLP technology and releasing various resources, datasets,
and web-based toolkit.


  One of the most critical challenges in deep reinforcement learning is to
maintain the long-term exploration capability of the agent. To tackle this
problem, it has been recently proposed to provide intrinsic rewards for the
agent to encourage exploration. However, most existing intrinsic reward-based
methods proposed in the literature fail to provide sustainable exploration
incentives, a problem known as vanishing rewards. In addition, these
conventional methods incur complex models and additional memory in their
learning procedures, resulting in high computational complexity and low
robustness. In this work, a novel intrinsic reward module based on the R\'enyi
entropy is proposed to provide high-quality intrinsic rewards. It is shown that
the proposed method actually generalizes the existing state entropy
maximization methods. In particular, a $k$-nearest neighbor estimator is
introduced for entropy estimation while a $k$-value search method is designed
to guarantee the estimation accuracy. Extensive simulation results demonstrate
that the proposed R\'enyi entropy-based method can achieve higher performance
as compared to existing schemes.


  The supercell approach enables us to treat the electronic structure of
defective crystals, but the calculated energy bands are too complicated to
understand or to compare with angle-resolved photoemission spectra because of
inevitable zone folding. We discuss how to visualize supercell band structures
more effectively by incorporating in them unfolded spectral weights and orbital
decompositions. We then apply these ideas to gain a better understanding of the
band structure of graphene containing various types of points defects,
including nitrogen impurity, hydrogen adsorbate, and vacancy defect, and also
the Stone-Wales defect.


  We provide the details of the theory of magnetic symmetry in quasicrystals,
which has previously only been outlined. We develop a practical formalism for
the enumeration of spin point groups and spin space groups, and for the
calculation of selection rules for neutron scattering experiments. We
demonstrate the formalism using the simple, yet non-trivial, example of
magnetically-ordered octagonal quasicrystals in two dimensions. In a companion
paper we shall provide complete results for octagonal quasicrystals in three
dimensions.


  Research in machine learning is making progress in fixing its own
reproducibility crisis. Reinforcement learning (RL), in particular, faces its
own set of unique challenges. Comparison of point estimates, and plots that
show successful convergence to the optimal policy during training, may
obfuscate overfitting or dependence on the experimental setup. Although
researchers in RL have proposed reliability metrics that account for
uncertainty to better understand each algorithm's strengths and weaknesses, the
recommendations of past work do not assume the presence of out-of-distribution
observations. We propose a set of evaluation methods that measure the
robustness of RL algorithms under distribution shifts. The tools presented here
argue for the need to account for performance over time while the agent is
acting in its environment. In particular, we recommend time series analysis as
a method of observational RL evaluation. We also show that the unique
properties of RL and simulated dynamic environments allow us to make stronger
assumptions to justify the measurement of causal impact in our evaluations. We
then apply these tools to single-agent and multi-agent environments to show the
impact of introducing distribution shifts during test time. We present this
methodology as a first step toward rigorous RL evaluation in the presence of
distribution shifts.


  A method for detecting the transition numu->nutau in long-baseline
accelerator experiments, that consists in comparing the far-to-near ratios of
the spectra of quasielastic CC events generated by high- and low-energy beams
of muon neutrinos, is proposed. The test may be accessible to big calorimeters
with muon spectrometry like MINOS, and is limited by statistics rather than
systematics.


  A convenient tool to obtain numerical methods specially tuned on oscillating
functions is exponential fitting. Such methods are needed in various branches
of natural sciences, particularly in physics, since a lot of physical phenomena
exhibit a pronounced oscillatory behavior. Many exponentially-fitted (EF)
symmetric multistep methods for y''=f(x,y) are already developed. To have an
idea of the accuracy we examine their phase properties. The remarkably simple
expression of the phase-lag error obtained in Theorem 2 allows to draw
quantitative conclusions on the merits of each EF version.


  We propose a model addressing the coupling mechanism between two spatially
modulated monolayers. We obtain the mean-field phase diagrams of coupled
bilayers when the two monolayers have the same preferred modulation wavelength.
Various combinations of the monolayer modulated phases are obtained and their
relative stability is calculated. Due to the coupling, a spatial modulation in
one of the monolayers induces a similar periodic structure in the second one.
We have also performed numerical simulations for the case when the two
monolayers have different modulation wavelengths. Complex patterns may arise
from the frustration between the two incommensurate but annealed structures.


  5G networks mainly rely on infrastructure-centric cellular solutions to
address data traffic and service demands. Continuously scaling
infrastructure-centric cellular networks is not exempt of challenges, and
beyond 5G networks should consider the active coexistence and coordination of
infrastructure-centric and device-centric wireless networks. Device-centric
wireless networks will build from D2D and MCNs. Device-centric wireless
networks can push the limits of edge computing and networking to smart devices
(including smartphones, vehicles, machines and robots), and exploit their
mobile computing, storage and connectivity capabilities. These capabilities can
be more efficiently utilized using demand-driven opportunistic networking that
establishes the connections between devices and nodes based on their capacity
to support the requested demand and services. This paper presents results from
experimental field tests that demonstrate the cellular spectral efficiency
gains that can be achieved from the combined use of device-centric wireless
communications and demand-driven opportunistic networking. The field trials
demonstrate that these technologies can improve the cellular spectral
efficiency of conventional cellular communications by up to a factor of 4.7 and
12 in outdoor pedestrian and vehicular scenarios, respectively. These gains
have been obtained using 4G and IEEE 802.11 technologies. However, the
potential of device-centric wireless networks is not constrained to any
particular radio interface. The results presented in this paper provide
empirical evidences that further motivate progressing towards a new paradigm
where edge networking capabilities are moved to smart mobile devices that
become part of the network fabric, and can opportunistically and locally
integrate network management functions to ensure that sufficient resources are
placed where the demand arises.


  This letter gives results on improving protein-ligand binding affinity
predictions based on molecular dynamics simulations using machine learning
potentials with a hybrid neural network potential and molecular mechanics
methodology (NNP/MM). We compute relative binding free energies (RBFE) with the
Alchemical Transfer Method (ATM) and validate its performance against
established benchmarks and find significant enhancements compared to
conventional MM force fields like GAFF2.


  Trust models that rely on recommendation trusts are vulnerable to badmouthing
and ballot-stuffing attacks. To cope with these attacks, existing trust models
use different trust aggregation techniques to process the recommendation trusts
and combine them with the direct trust values to form a combined trust value.
However, these trust models are biased as recommendation trusts that deviate
too much from one's own opinion are discarded. In this paper, we propose a
non-biased trust model that considers every recommendation trusts available
regardless they are good or bad. Our trust model is based on a combination of 2
techniques: the dissimilarity test and the Dempster-Shafer Theory. The
dissimilarity test determines the amount of conflict between 2 trust records,
whereas the Dempster-Shafer Theory assigns belief functions based on the
results of the dissimilarity test. Numerical results show that our trust model
is robust against reputation-based attacks when compared to trust aggregation
techniques such as the linear opinion pooling, subjective logic model,
entropy-based probability model, and regression analysis. In addition, our
model has been extensively tested using network simulator NS-3 in an
Infrastructure-based wireless mesh networks and a Hybrid-based wireless mesh
networks to demonstrate that it can mitigate blackhole and grayhole attacks.


  We introduce biomedical and clinical English model packages for the Stanza
Python NLP library. These packages offer accurate syntactic analysis and named
entity recognition capabilities for biomedical and clinical text, by combining
Stanza's fully neural architecture with a wide variety of open datasets as well
as large-scale unsupervised biomedical and clinical text data. We show via
extensive experiments that our packages achieve syntactic analysis and named
entity recognition performance that is on par with or surpasses
state-of-the-art results. We further show that these models do not compromise
speed compared to existing toolkits when GPU acceleration is available, and are
made easy to download and use with Stanza's Python interface. A demonstration
of our packages is available at: http://stanza.run/bio.


  We describe an experimental platform that generates infrared images of
micrometer-sized samples in the high pressure region of a diamond anvil cell.
Using a 2.3 micron laser as a source of radiation, the system will be
particularly useful in identifying hydride superconductors which exhibit an
anomalous temperature dependence of reflectivity in the 2.3 micron region. Our
system shows an intensity stability within one percent when the sample
temperature is swept from 100 K to 300 K. The spatial stability is of the order
of a few micrometers in the same temperature range.


  Motivated by recent experiments on the rod-like virus bacteriophage fd,
confined to circular and annular domains, we present a theoretical study of
structural transitions in these geometries. Using the continuum theory of
nematic liquid crystals, we examine the competition between bulk elasticity and
surface anchoring, mediated by the formation of topological defects. We show
analytically that bulk defects are unstable with respect to defects sitting at
the boundary. Moreover, in case of an annulus, whose topology does not require
the presence of topological defects, under weak anchoring conditions we find
that nematic textures with boundary defects are stable compared to the defect
free configurations. Thus our simple approach, with no fitting parameters,
suggests a possible symmetry breaking mechanism responsible for the formation
of one-, two- and three-fold textures under annular confinement.


  WebAssembly has become a crucial part of the modern web, offering a faster
alternative to JavaScript in browsers. While boosting rich applications in
browser, this technology is also very efficient to develop cryptojacking
malware. This has triggered the development of several methods to detect
cryptojacking malware. However, these defenses have not considered the
possibility of attackers using evasion techniques. This paper explores how
automatic binary diversification can support the evasion of WebAssembly
cryptojacking detectors. We experiment with a dataset of 33 WebAssembly
cryptojacking binaries and evaluate our evasion technique against two malware
detectors: VirusTotal, a general-purpose detector, and MINOS, a
WebAssembly-specific detector. Our results demonstrate that our technique can
automatically generate variants of WebAssembly cryptojacking that evade the
detectors in 90% of cases for VirusTotal and 100% for MINOS. Our results
emphasize the importance of meta-antiviruses and diverse detection techniques,
and provide new insights into which WebAssembly code transformations are best
suited for malware evasion. We also show that the variants introduce limited
performance overhead, making binary diversification an effective technique for
evasion.


  Diagnosis of breast carcinomas has so far been limited to the morphological
interpretation of epithelial cells and the assessment of epithelial tissue
architecture. Consequently, most of the automated systems have focused on
characterizing the epithelial regions of the breast to detect cancer. In this
paper, we propose a system for classification of hematoxylin and eosin (H&E)
stained breast specimens based on convolutional neural networks that primarily
targets the assessment of tumor-associated stroma to diagnose breast cancer
patients. We evaluate the performance of our proposed system using a large
cohort containing 646 breast tissue biopsies. Our evaluations show that the
proposed system achieves an area under ROC of 0.92, demonstrating the
discriminative power of previously neglected tumor-associated stroma as a
diagnostic biomarker.