  A signed graph is a simple graph with two types of edges: positive and
negative edges. Switching a vertex $v$ of a signed graph corresponds to
changing the type of each edge incident to $v$.
  A homomorphism from a signed graph $G$ to another signed graph $H$ is a
mapping $\varphi: V(G) \rightarrow V(H)$ such that, after switching some of the
vertices of $G$, $\varphi$ maps every edge of $G$ to an edge of $H$ of the same
type. The chromatic number $\chi_s(G)$ of a signed graph $G$ is the order of a
smallest signed graph $H$ such that there is a homomorphism from $G$ to $H$.
  The maximum average degree $mad(G)$ of a graph $G$ is the maximum of the
average degrees of all the subgraphs of $G$. We denote $\mathcal{M}_k$ the
class of signed graphs with maximum average degree less than $k$ and
$\mathcal{P}_g$ the class of planar signed graphs of girth at least $g$.
  We prove:
  $\chi_s(\mathcal{P}_{7}) \le 5$,
  $\chi_s(\mathcal{M}_{\frac{17}{5}}) \le 10$ which implies
$\chi_s(\mathcal{P}_{5}) \le 10$,
  $\chi_s(\mathcal{M}_{4-\frac{8}{q+3}}) \le q+1$ with $q$ a prime power
congruent to 1 modulo 4.


  An experimental demonstration of a parametric oscillation of a magnetization
in a ferromagnet was performed recently by applying a microwave voltage,
indicating the potential to be applied in a switching method in non-volatile
memories. In the previous works, the modulation of a perpendicular magnetic
anisotropy field produced by the microwave voltage was small compared with an
external magnetic field pointing in an in-plane direction. A recent trend is,
however, opposite, where an efficiency of the voltage controlled magnetic
anisotropy (VCMA) effect is increased significantly by material research and
thus, the modulated magnetic anisotropy field can be larger than the external
magnetic field. Here, we solved the Landau-Lifshitz-Gilbert equation
numerically and investigated the magnetization dynamics driven under a wide
range of the microwave VCMA effect. We evaluated bifurcation diagrams, which
summarize local maxima of the magnetization dynamics. For low modulation
amplitudes, the local maximum is a single point because the dynamics is the
periodic parametric oscillation. The bifurcation diagrams show distributions of
the local maxima when the microwave magnetic anisotropy field becomes larger
than the external magnetic field. The appearance of this broadened distribution
indicates complex dynamics such as chaotic and transient-chaotic behaviors,
which were confirmed from an analysis of temporal dynamics.


  Fragmentation functions for eta mesons are extracted at next-to-leading order
accuracy of QCD in a global analysis of data taken in electron-positron
annihilation and proton-proton scattering experiments. The obtained
parametrization is in good agreement with all data sets analyzed and can be
utilized, for instance, in future studies of double-spin asymmetries for
single-inclusive eta production. The Lagrange multiplier technique is used to
estimate the uncertainties of the fragmentation functions and to assess the
role of the different data sets in constraining them.


  In this paper we propose a new model for volatility fluctuations in financial
time series. This model relies on a non-stationary gaussian process that
exhibits aging behavior. It turns out that its properties, over any finite time
interval, are very close to continuous cascade models. These latter models are
indeed well known to reproduce faithfully the main stylized facts of financial
time series. However, it involve a large scale parameter (the so-called
"integral scale" where the cascade is initiated) that is hard to interpret in
finance. Moreover the empirical value of the integral scale is in general
deeply correlated to the overall length of the sample. This feature is
precisely predicted by our model that turns out, as illustrated on various
examples from daily stock index data, to quantitatively reproduce the empirical
observations.


  We perform factor analysis on the raw data of the four major neighborhood and
shortest paths-based centrality metrics (Degree, Eigenvector, Betweeenness and
Closeness) and propose a novel quantitative measure called the
Core-Intermediate-Peripheral (CIP) Index to capture the extent with which a
node could play the role of a core node (nodes at the center of a network with
larger values for any centrality metric) vis-a-vis a peripheral node (nodes
that exist at the periphery of a network with lower values for any centrality
metric). We conduct factor analysis (varimax-based rotation of the
Eigenvectors) on the transpose matrix of the raw centrality metrics dataset,
with the node ids as features, under the hypothesis that there are two factors
(core and peripheral) that drive the values incurred by the nodes with respect
to the centrality metrics. We test our approach on a diverse suite of 12
complex real-world networks.


  The Precision Reactor Oscillation and Spectrum Experiment, PROSPECT, is
designed to make a precise measurement of the antineutrino spectrum from a
highly-enriched uranium reactor and probe eV-scale sterile neutrinos by
searching for neutrino oscillations over meter-long distances. PROSPECT is
conceived as a 2-phase experiment utilizing segmented $^6$Li-doped liquid
scintillator detectors for both efficient detection of reactor antineutrinos
through the inverse beta decay reaction and excellent background
discrimination. PROSPECT Phase I consists of a movable 3-ton antineutrino
detector at distances of 7 - 12 m from the reactor core. It will probe the
best-fit point of the $\nu_e$ disappearance experiments at 4$\sigma$ in 1 year
and the favored region of the sterile neutrino parameter space at $>$3$\sigma$
in 3 years. With a second antineutrino detector at 15 - 19 m from the reactor,
Phase II of PROSPECT can probe the entire allowed parameter space below 10
eV$^{2}$ at 5$\sigma$ in 3 additional years. The measurement of the reactor
antineutrino spectrum and the search for short-baseline oscillations with
PROSPECT will test the origin of the spectral deviations observed in recent
$\theta_{13}$ experiments, search for sterile neutrinos, and conclusively
address the hypothesis of sterile neutrinos as an explanation of the reactor
anomaly.


  Virtual reality (VR) games are gradually becoming more elaborated and
feature-rich, but fail to reach the complexity of traditional digital games.
One common feature that is used to extend and organize complex gameplay is the
in-game inventory, which allows players to obtain and carry new tools and items
throughout their journey. However, VR imposes additional requirements and
challenges that impede the implementation of this important feature and hinder
games to unleash their full potential. Our current work focuses on the design
space of inventories in VR games. We introduce this sparsely researched topic
by constructing a first taxonomy of the underlying design considerations and
building blocks. Furthermore, we present three different inventories that were
designed using our taxonomy and evaluate them in an early qualitative study.
The results underline the importance of our research and reveal promising
insights that show the huge potential for VR games.


  Sperm whales (Physeter macrocephalus) navigate underwater with a series of
impulsive, click-like sounds known as echolocation clicks. These clicks are
characterized by a multipulse structure (MPS) that serves as a distinctive
pattern. In this work, we use the stability of the MPS as a detection metric
for recognizing and classifying the presence of clicks in noisy environments.
To distinguish between noise transients and to handle simultaneous emissions
from multiple sperm whales, our approach clusters a time series of MPS measures
while removing potential clicks that do not fulfil the limits of inter-click
interval, duration and spectrum. As a result, our approach can handle high
noise transients and low signal-to-noise ratio. The performance of our
detection approach is examined using three datasets: seven months of recordings
from the Mediterranean Sea containing manually verified ambient noise; several
days of manually labelled data collected from the Dominica Island containing
approximately 40,000 clicks from multiple sperm whales; and a dataset from the
Bahamas containing 1,203 labelled clicks from a single sperm whale. Comparing
with the results of two benchmark detectors, a better trade-off between
precision and recall is observed as well as a significant reduction in false
detection rates, especially in noisy environments. To ensure reproducibility,
we provide our database of labelled clicks along with our implementation code.


  Floating car data of car-following behavior in cities were compared to
existing microsimulation models, after their parameters had been calibrated to
the experimental data. With these parameter values, additional simulations have
been carried out, e.g. of a moving car which approaches a stopped car. It
turned out that, in order to manage such kinds of situations without producing
accidents, improved traffic models are needed. Good results have been obtained
with the proposed generalized force model.


  The high cost of communicating gradients is a major bottleneck for federated
learning, as the bandwidth of the participating user devices is limited.
Existing gradient compression algorithms are mainly designed for data centers
with high-speed network and achieve $O(\sqrt{d} \log d)$ per-iteration
communication cost at best, where $d$ is the size of the model. We propose
hyper-sphere quantization (HSQ), a general framework that can be configured to
achieve a continuum of trade-offs between communication efficiency and gradient
accuracy. In particular, at the high compression ratio end, HSQ provides a low
per-iteration communication cost of $O(\log d)$, which is favorable for
federated learning. We prove the convergence of HSQ theoretically and show by
experiments that HSQ significantly reduces the communication cost of model
training without hurting convergence accuracy.


  Many graphics rendering algorithms used in both real-time games and virtual
reality applications can get performance boosts by temporally reusing previous
computations. However, algorithms based on temporal reuse are typically
measured using trivial benchmarks with very limited dynamic features. To this
end, in [1] we presented TauBench 1.0, a benchmark designed to stress temporal
reuse algorithms. Now, we release TauBench version 1.1, which improves the
usability of the original benchmark. In particular, these improvements reduce
the size of the dataset significantly, resulting in faster loading and
rendering times, and in better compatibility with 3D software that impose
strict size limits for the scenes.


  Large-scale distributed training of deep neural networks suffer from the
generalization gap caused by the increase in the effective mini-batch size.
Previous approaches try to solve this problem by varying the learning rate and
batch size over epochs and layers, or some ad hoc modification of the batch
normalization. We propose an alternative approach using a second-order
optimization method that shows similar generalization capability to first-order
methods, but converges faster and can handle larger mini-batches. To test our
method on a benchmark where highly optimized first-order methods are available
as references, we train ResNet-50 on ImageNet. We converged to 75% Top-1
validation accuracy in 35 epochs for mini-batch sizes under 16,384, and
achieved 75% even with a mini-batch size of 131,072, which took only 978
iterations.


  We analyze dark pulse Kerr frequency combs in optical resonators with normal
group-velocity dispersion using the Lugiato-Lefever model. We show that in the
time domain these correspond to interlocked switching waves between the upper
and lower homogeneous states, and explain how this fact accounts for many of
their experimentally observed properties. Modulational instability does not
play any role in their existence. Furthermore, we provide a detailed map
indicating where stable dark pulse Kerr combs can be found in parameter space,
and how they are destabilized for increasing values of frequency detuning.


  Molecular complexity builds up at each step of the Sun-like star formation
process, starting from simple molecules and ending up in large polyatomic
species. Complex organic molecules (COMs; such as methyl formate, HCOOCH$_3$,
dymethyl ether, CH$_3$OCH$_3$, formamide, NH$_2$CHO, or glycoaldehyde,
HCOCH$_2$OH) are formed in all the components of the star formation recipe
(e.g. pre-stellar cores, hot-corinos, circumstellar disks, shocks induced by
fast jets), due to ice grain mantle sublimation or sputtering as well as
gas-phase reactions. Understanding in great detail the involved processes is
likely the only way to predict the ultimate molecular complexity reached in the
ISM, as the detection of large molecules is increasingly more difficult with
the increase of the number of atoms constituting them.
  Thanks to the recent spectacular progress of astronomical observations, due
to the Herschel (sub-mm and IR), IRAM and SMA (mm and sub-mm), and NRAO (cm)
telescopes, an enormous activity is being developed in the field of
Astrochemistry, extending from astronomical observatories to chemical
laboratories. We are involved in several observational projects providing
unbiased spectral surveys (in the 80-300 and 500-2000 GHz ranges) with
unprecedented sensitivity of templates of dense cores and protostars. Forests
of COM lines have been detected. In this chapter we will focus on the chemistry
of both cold prestellar cores and hot shocked regions, (i) reviewing results
and open questions provided by mm-FIR observations, and (ii) showing the need
of carrying on the observations of COMs at lower frequencies, where SKA will
operate. We will also emphasize the importance of analysing the spectra by the
light of the experimental studies performed by our team, who is investigating
the chemical effects induced by ionising radiation bombarding astrophysically
relevant ices.


  Tur\'an observed that logarithmic partial sums $\sum_{n\le x}\frac{f(n)}{n}$
of completely multiplicative functions (in the particular case of the Liouville
function $f(n)=\lambda(n)$) tend to be positive. We develop a general approach
to prove two results aiming to explain this phenomena. Firstly, we show that
for every $\varepsilon>0$ there exists some $x_0\ge 1,$ such that for any
completely multiplicative function $f$ satisfying $-1\le f(n)\le 1$, we have
$$\sum_{n\le x}\frac{f(n)}{n}\ge -\frac{1}{(\log\log{x})^{1-\varepsilon}},
\quad x\ge x_0.$$ This improves a previous bound due to Granville and
Soundararajan. Secondly, we show that if $f$ is a typical (random) completely
multiplicative function $f:\mathbb{N}\to \{-1,1\}$, the probability that
$\sum_{n\le x}\frac{f(n)}{n}$ is negative for a given large $x,$ is
$O(\exp(-\exp(\frac{\log x\cdot \log\log\log x}{C\log \log x}))).$ This
improves on recent work of Angelo and Xu.


  We investigate the impact of s-wave spin-singlet pairing on antiferromagnetic
semimetals with Dirac points or nodal loops at the Fermi level. The electron
pairing is generally shown to convert the semimetal into a tunable nodal
superconductor. The changeover from fully gapped to gapless phases is dictated
by symmetry properties of the antiferromagnetic-superconducting state that set
the occurrence of a large variety of electronic topological transitions. We
provide a general criterion for predicting a series of transitions between
nodal and fully gapped superconducting phases. Different types of
antiferromagnetic patterns are then employed to explicitly demonstrate the
microscopic mechanisms that control the character of the quasiparticle
spectrum. These findings unveil a novel type of nodal superconductivity
emerging from the interplay of Dirac fermions and conventional forms of
ordering.


  Stochastic volatility models describe asset prices $S_t$ as driven by an
unobserved process capturing the random dynamics of volatility $\sigma_t$.
Here, we quantify how much information about $\sigma_t$ can be inferred from
asset prices $S_t$ in terms of Shannon's mutual information $I(S_t :
\sigma_t)$. This motivates a careful numerical and analytical study of
information theoretic properties of the Heston model. In addition, we study a
general class of discrete time models motivated from a machine learning
perspective. In all cases, we find a large uncertainty in volatility estimates
for quite fundamental information theoretic reasons.


  This paper is concerned with the existence of periodic orbits on energy
hypersurfaces in cotangent bundles of Riemannian manifolds defined by
mechanical Hamiltonians. In \cite{bpv} it was proved that, provided certain
geometric assumptions are satisfied, regular mechanical hypersurfaces in
$\mathbb{R}^{2n}$, in particular non-compact ones, contain periodic orbits if
one homology group among the top half does not vanish. In the present paper we
extend the above mentioned existence result to a class of hypersurfaces in
cotangent bundles of Riemannian manifolds with flat ends.


  Graph few-shot learning is of great importance among various graph learning
tasks. Under the few-shot scenario, models are often required to conduct
classification given limited labeled samples. Existing graph few-shot learning
methods typically leverage Graph Neural Networks (GNNs) and perform
classification across a series of meta-tasks. Nevertheless, these methods
generally rely on the original graph (i.e., the graph that the meta-task is
sampled from) to learn node representations. Consequently, the graph structure
used in each meta-task is identical. Since the class sets are different across
meta-tasks, node representations should be learned in a task-specific manner to
promote classification performance. Therefore, to adaptively learn node
representations across meta-tasks, we propose a novel framework that learns a
task-specific structure for each meta-task. To handle the variety of nodes
across meta-tasks, we extract relevant nodes and learn task-specific structures
based on node influence and mutual information. In this way, we can learn node
representations with the task-specific structure tailored for each meta-task.
We further conduct extensive experiments on five node classification datasets
under both single- and multiple-graph settings to validate the superiority of
our framework over the state-of-the-art baselines. Our code is provided at
https://github.com/SongW-SW/GLITTER.


  Air quality is a critical component of environmental health. Monitoring and
analysis of particulate matter with a diameter of 2.5 micrometers or smaller
(PM2.5) plays a pivotal role in understanding air quality changes. This study
focuses on the application of a new bandpass bootstrap approach, termed the
Variable Bandpass Periodic Block Bootstrap (VBPBB), for analyzing time series
data which provides modeled predictions of daily mean PM2.5 concentrations over
16 years in Manhattan, New York, the United States. The VBPBB can be used to
explore periodically correlated (PC) principal components for this daily mean
PM2.5 dataset. This method uses bandpass filters to isolate distinct PC
components from datasets, removing unwanted interference including noise, and
bootstraps the PC components. This preserves the PC structure and permits a
better understanding of the periodic characteristics of time series data. The
results of the VBPBB are compared against outcomes from alternative block
bootstrapping techniques. The findings of this research indicate potential
trends of elevated PM2.5 levels, providing evidence of significant semi-annual
and weekly patterns missed by other methods.


  The analysis of photometrical observations of comet 29P/Schwassmann-Wachmann
1 is presented. The broadband observations were carried out for 15 nights from
2012 to 2019 at the Lisnyky observational station of Taras Shevchenko National
University of Kyiv. Apparent magnitudes and dust productivity level $Af\rho$ in
filter R were calculated. Middle and height dust activity of the comet is
characterized by parameter $Af\rho$ which varied from 1246 to 17563 cm during
all periods of observation. Based on the morphological analysis, four jet-like
structures were detected in the coma on almost all dates. Using the geometrical
model for the jet structure interpretation during all observation sets, we
obtained following results: the nucleus rotation periodof 57\pm2 days, the
rotational axis orientation, the locations of active regions for four jet-like
structures within a narrow belt near the equator.


  We briefly review the lepton flavor violating Z-decays at GigaZ as a probe of
supersymmetry by focusing on $Z \to \ell_i \overline{\ell}_j$ in two
representative supersymmetric models: the minimal supersymmetric model without
R-parity and the supersymmetric seesaw model. We conclude that under the
current experimental constraints from LEP and $\ell_i\to \ell_j \gamma$, these
rare decays can still be enhanced to reach the sensitivity of the GigaZ.
Therefore, supersymmetry can be probed via these decays at GigaZ.


  We calculate the effects of induced interactions on the transition
temperature to the BCS state in dilute Fermi gases. For a pure Fermi system
with 2 species having equal densities, the transition temperature is suppressed
by a factor $(4e)^{1/3}\approx 2.2$, and for $\nu$ fermion species, the
transition temperature is increased by a factor $(4e)^{\nu /3-1} \approx
2.2^{\nu-3}$. For mixtures of fermions and bosons the exchange of boson density
fluctuations gives rise to an effective interaction, and we estimate the
increase of the transition temperature due to this effect.


  Let $M(\alpha)$ denote the Mahler measure of the algebraic number $\alpha$.
In a recent paper, Dubickas and Smyth constructed a metric version of the
Mahler measure on the multiplicative group of algebraic numbers. Later, Fili
and the author used similar techniques to study a non-Archimedean version. We
show how to generalize the above constructions in order to associate, to each
point in $(0,\infty]$, a metric version $M_x$ of the Mahler measure, each
having a triangle inequality of a different strength. We are able to compute
$M_x(\alpha)$ for sufficiently small $x$, identifying, in the process, a
function $\bar M$ with certain minimality properties. Further, we show that the
map $x\mapsto M_x(\alpha)$ defines a continuous function on the positive real
numbers.


  Inspired by frame-based methods, state-of-the-art event-based optical flow
networks rely on the explicit construction of correlation volumes, which are
expensive to compute and store, rendering them unsuitable for robotic
applications with limited compute and energy budget. Moreover, correlation
volumes scale poorly with resolution, prohibiting them from estimating
high-resolution flow. We observe that the spatiotemporally continuous traces of
events provide a natural search direction for seeking pixel correspondences,
obviating the need to rely on gradients of explicit correlation volumes as such
search directions. We introduce IDNet (Iterative Deblurring Network), a
lightweight yet high-performing event-based optical flow network directly
estimating flow from event traces without using correlation volumes. We further
propose two iterative update schemes: "ID" which iterates over the same batch
of events, and "TID" which iterates over time with streaming events in an
online fashion. Our top-performing ID model sets a new state of the art on DSEC
benchmark. Meanwhile, the base ID model is competitive with prior arts while
using 80% fewer parameters, consuming 20x less memory footprint and running 40%
faster on the NVidia Jetson Xavier NX. Furthermore, the TID model is even more
efficient offering an additional 5x faster inference speed and 8 ms ultra-low
latency at the cost of only a 9% performance drop, making it the only model
among current literature capable of real-time operation while maintaining
decent performance.


  This paper is a theoretical study of the following Non-Rigid Structure from
Motion problem. What can be computed from a monocular view of a parametrically
deforming set of points? We treat various variations of this problem for affine
and polynomial deformations with calibrated and uncalibrated cameras. We show
that in general at least three images with quasi-identical two deformations are
needed in order to have a finite set of solutions of the points' structure and
calculate some simple examples.


  We propose a new framework for creating and easily manipulating 3D models of
arbitrary objects using casually captured videos. Our core ingredient is a
novel hierarchy deformation model, which captures motions of objects with a
tree-structured bones. Our hierarchy system decomposes motions based on the
granularity and reveals the correlations between parts without exploiting any
prior structural knowledge. We further propose to regularize the bones to be
positioned at the basis of motions, centers of parts, sufficiently covering
related surfaces of the part. This is achieved by our bone occupancy function,
which identifies whether a given 3D point is placed within the bone. Coupling
the proposed components, our framework offers several clear advantages: (1)
users can obtain animatable 3D models of the arbitrary objects in improved
quality from their casual videos, (2) users can manipulate 3D models in an
intuitive manner with minimal costs, and (3) users can interactively add or
delete control points as necessary. The experimental results demonstrate the
efficacy of our framework on diverse instances, in reconstruction quality,
interpretability and easier manipulation. Our code is available at
https://github.com/subin6/HSNB.


  A flexible fiber model based on the discrete element method (DEM) is
presented and validated for the simulation of uniaxial compression of flexible
fibers in a cylindrical container. It is found that the contact force models in
the DEM simulations have a significant impact on compressive forces exerted on
the fiber bed. Only when the geometry-dependent normal contact force model and
the static friction model are employed, the simulation results are in good
agreement with experimental results. Systematic simulation studies show that
the compressive force initially increases and eventually saturates with an
increase in the fiber-fiber friction coefficient, and the fiber-fiber contact
forces follow a similar trend. The compressive force and lateral
shear-to-normal stress ratio increase linearly with increasing fiber-wall
friction coefficient. In uniaxial compression of frictional fibers, more static
friction contacts occur than dynamic friction contacts with static friction
becoming more predominant as the fiber-fiber friction coefficient increases.


  We analyze the global theory of boundary conditions for a constrained quantum
system with classical configuration space a compact Riemannian manifold $M$
with regular boundary $\Gamma=\partial M$. The space $\CM$ of self-adjoint
extensions of the covariant Laplacian on $M$ is shown to have interesting
geometrical and topological properties which are related to the different
topological closures of $M$. In this sense, the change of topology of $M$ is
connected with the non-trivial structure of $\CM$. The space $\CM$ itself can
be identified with the unitary group $\CU(L^2(\Gamma,\C^N))$ of the Hilbert
space of boundary data $L^2(\Gamma,\C^N)$. A particularly interesting family of
boundary conditions, identified as the set of unitary operators which are
singular under the Cayley transform, $\CC_-\cap \CC_+$ (the Cayley manifold),
turns out to play a relevant role in topology change phenomena. The singularity
of the Cayley transform implies that some energy levels, usually associated
with edge states, acquire an infinity energy when by an adiabatic change the
boundary condition reaches the Cayley submanifold $\CC_-$. In this sense
topological transitions require an infinite amount of quantum energy to occur,
although the description of the topological transition in the space $\CM$ is
smooth. This fact has relevant implications in string theory for possible
scenarios with joint descriptions of open and closed strings. In the particular
case of elliptic self--adjoint boundary conditions, the space $\CC_-$ can be
identified with a Lagrangian submanifold of the infinite dimensional
Grassmannian. The corresponding Cayley manifold $\CC_-$ is dual of the Maslov
class of $\CM$.


  It is common in functional data analysis to look at a set of related
functions: a set of learning curves, a set of brain signals, a set of spatial
maps, etc. One way to express relatedness is through an additive model, whereby
each individual function $g_{i}\left(x\right)$ is assumed to be a variation
around some shared mean $f(x)$. Gaussian processes provide an elegant way of
constructing such additive models, but suffer from computational difficulties
arising from the matrix operations that need to be performed. Recently Heersink
& Furrer have shown that functional additive model give rise to covariance
matrices that have a specific form they called quasi-Kronecker (QK), whose
inverses are relatively tractable. We show that under additional assumptions
the two-level additive model leads to a class of matrices we call restricted
quasi-Kronecker, which enjoy many interesting properties. In particular, we
formulate matrix factorisations whose complexity scales only linearly in the
number of functions in latent field, an enormous improvement over the cubic
scaling of na\"ive approaches. We describe how to leverage the properties of
rQK matrices for inference in Latent Gaussian Models.


  We establish sharp tail asymptotics for component-wise extreme values of
bivariate Gaussian random vectors with arbitrary correlation between the
components. We consider two scaling regimes for the tail event in which we
demonstrate the existence of a restricted large deviations principle, and
identify the unique rate function associated with these asymptotics. Our
results identify when the maxima of both coordinates are typically attained by
two different vs. the same index, and how this depends on the correlation
between the coordinates of the bivariate Gaussian random vectors. Our results
complement a growing body of work on the extremes of Gaussian processes. The
results are also relevant for steady-state performance and simulation analysis
of networks of infinite server queues.


  Fine-tuning pre-trained foundational language models (FLM) for specific tasks
is often impractical, especially for resource-constrained devices. This
necessitates the development of a Lifelong Learning (L3) framework that
continuously adapts to a stream of Natural Language Processing (NLP) tasks
efficiently. We propose an approach that focuses on extracting meaningful
representations from unseen data, constructing a structured knowledge base, and
improving task performance incrementally. We conducted experiments on various
NLP tasks to validate its effectiveness, including benchmarks like GLUE and
SuperGLUE. We measured good performance across the accuracy, training
efficiency, and knowledge transfer metrics. Initial experimental results show
that the proposed L3 ensemble method increases the model accuracy by 4% ~ 36%
compared to the fine-tuned FLM. Furthermore, L3 model outperforms naive
fine-tuning approaches while maintaining competitive or superior performance
(up to 15.4% increase in accuracy) compared to the state-of-the-art language
model (T5) for the given task, STS benchmark.


  It is well-known that the representation theory of the finite group of
unipotent upper-triangular matrices $U_n$ over a finite field is a wild
problem. By instead considering approximately irreducible representations
(supercharacters), one obtains a rich combinatorial theory analogous to that of
the symmetric group, where we replace partition combinatorics with
set-partitions. This paper studies Diaconis--Isaacs' concept of superinduction
in pattern groups. While superinduction shares many desirable properties with
usual induction, it no longer takes characters to characters. We begin by
finding sufficient conditions guaranteeing that super-induction is in fact
induction. It turns out for natural embedding of $U_m$ in $U_n$,
super-induction is induction. We conclude with an explicit combinatorial
algorithm for computing this induction analogous to the Pieri-formulas for the
symmetric group.


  The bisimulation proof method can be enhanced by employing `bisimulations
up-to' techniques. A comprehensive theory of such enhancements has been
developed for first-order (i.e., CCS-like) labelled transition systems (LTSs)
and bisimilarity, based on abstract fixed-point theory and compatible
functions.
  We transport this theory onto languages whose bisimilarity and LTS go beyond
those of first-order models. The approach consists in exhibiting fully abstract
translations of the more sophisticated LTSs and bisimilarities onto the
first-order ones. This allows us to reuse directly the large corpus of up-to
techniques that are available on first-order LTSs. The only ingredient that has
to be manually supplied is the compatibility of basic up-to techniques that are
specific to the new languages. We investigate the method on the pi-calculus,
the lambda-calculus, and a (call-by-value) lambda-calculus with references.


  In this article, we describe the structure of the $R$-algebra of Drinfeld
modular forms $M(\Gamma_0(T))_R$ (resp., $M^0(\Gamma_0(T))_R$) of level
$\Gamma_0(T)$ and the structure of mod-$\p$ reduction of
$M_{\mfp}^0(\Gamma_0(T))$ for $\p \neq (T)$. As a result, we are able to study
the properties of the weight filtration for $M_{k,l}(\Gamma_0(T))$. Finally, we
prove a result on mod-$\p$ congruences for Drinfeld modular forms of level
$\Gamma_0(\p T)$ for $\p \neq (T)$.


  Scrambling in interacting quantum systems out of equilibrium is particularly
effective in the chaotic regime. Under time evolution, initially localized
information is said to be scrambled as it spreads throughout the entire system.
This spreading can be analyzed with the spectral form factor, which is defined
in terms of the analytic continuation of the partition function. The latter is
equivalent to the survival probability of a thermofield double state under
unitary dynamics. Using random matrices from the Gaussian unitary ensemble
(GUE) as Hamiltonians for the time evolution, we obtain exact analytical
expressions at finite $N$ for the survival probability. Numerical simulations
of the survival probability with matrices taken from the Gaussian orthogonal
ensemble (GOE) are also provided. The GOE is more suitable for our comparison
with numerical results obtained with a disordered spin chain with local
interactions. Common features between the random matrix and the realistic
disordered model in the chaotic regime are identified. The differences that
emerge as the spin model approaches a many-body localized phase are also
discussed.


  We develop a non-perturbative analysis of the electro-production of heavy
vector mesons ($\phi$, $J/\Psi$) from threshold to high energy. We use the
holographic construction with bulk confinement enforced through a soft wall.
Using Witten diagrams, we evaluate the pertinent cross sections for heavy
vector mesons ($\phi$, $J/\Psi$) production and study their dependence on both
the incoming virtual photon polarization as well as the outgoing polarization
of the heavy meson. Our results for $J/\Psi$ electro-production compares well
with the available HERA data at low and intermediate $Q^2$, and for a wide
range of momentum transfer. We also predict the quasi-real electro-production
of $J/\Psi$ near threshold.


  The superconducting and normal state characteristics of yttrium hexaboride
(YB$_6$) have been investigated for the single crystals with a transition
temperatures $T_c$ ranging between 6 K and 7.6 K. The extracted set of
microscopic parameters [the coherence length $\xi$(0) $\sim$ 320$\div$340
${\AA}$, the penetration depth $\lambda$(0) $\sim$ 1100$\div$1600 ${\AA}$ and
the mean free path of charge carriers $l$ = 31$\div$58 ${\AA}$, the
Ginzburg-Landau-Maki parameters $\kappa$$_{1,2}$(0) $\sim$ 3.3$\div$4.8 and the
superconducting gap $\Delta$(0) $\sim$ 10.3$\div$14.8 K] confirms the type II
superconductivity in "dirty limit" ($\xi$$\gg$ $l$) with a medium to strong
electron-phonon interaction (the electron-phonon interaction constant
$\lambda_{e-ph}$ = 0.93$\div$0.96) and $s$-type pairing of charge carriers in
this compound [2$\Delta$(0)$/k_BT_c$ $\approx$ 4]. The comparative analysis of
charge transport (resistivity, Hall and Seebeck coefficients) and thermodynamic
(heat capacity, magnetization) properties in the normal state in YB$_6$ allowed
to detect a transition into the cage-glass state at $T^*$ $\sim$ 50 K with a
static disorder in the arrangement of the Y$^{3+}$ ions. We argue that the
significant $T_c$ variations in the YB$_6$ single crystals are determined by
two main factors: (i) the superconductivity enhancement is related with the
increase of the number of isolated vacancies, both at yttrium and boron sites,
which leads to the development of an instability in the hexaboride lattice;
(ii) the $T_c$ depression is additionally stimulated by the spin polarization
of conduction electrons emerged and enhanced by the magnetic field in the
vicinity of defect complexes in the YB$_6$ matrix.


  The resource constraints and accuracy requirements for Internet of Things
(IoT) memory chips need three-dimensional (3D) monolithic integrated circuits,
of which the increasing stack layers (currently more than 176) also cause
excessive energy consumption and increasing wire length. In this paper, a novel
3D wireless network on chips (3DWiNoCs) model transmitting signal directly to
the destination in arbitrary layer is proposed and characterized. However, due
to the the reflection and refraction characteristics in each layer, the complex
and diverse wireless paths in 3DWiNoC add great difficulty to the channel
characterization. To facilitate the modeling in massive layer NoC situation,
both boundary-less model boundary-constrained 3DWiNoC model are proposed, of
which the channel gain can be obtained by a computational efficient approximate
algorithm. These 3DWiNoC models with approximation algorithm can well
characterize the 3DWiNoC channel in aspect of complete reflection and
refraction characteristics, and avoid massive wired connections, high power
consumption of cross-layer communication and high-complexity of 3DWiNoC channel
characterization. Numerical results show that: 1) The difference rate between
the two models is lower than 0.001% (signal transmit through 20 layers); 2) the
channel gain decreases sharply if refract time increases; and 3) the
approximate algorithm can achieve an acceptable accuracy (error rate lower than
0.1%).


  Although stance detection has made great progress in the past few years, it
is still facing the problem of unseen targets. In this study, we investigate
the domain difference between targets and thus incorporate attention-based
conditional encoding with adversarial domain generalization to perform unseen
target stance detection. Experimental results show that our approach achieves
new state-of-the-art performance on the SemEval-2016 dataset, demonstrating the
importance of domain difference between targets in unseen target stance
detection.


  Restricted Boltzmann Machines are simple and powerful generative models that
can encode any complex dataset. Despite all their advantages, in practice the
trainings are often unstable and it is difficult to assess their quality
because the dynamics are affected by extremely slow time dependencies. This
situation becomes critical when dealing with low-dimensional clustered
datasets, where the time required to sample ergodically the trained models
becomes computationally prohibitive. In this work, we show that this divergence
of Monte Carlo mixing times is related to a phenomenon of phase coexistence,
similar to that which occurs in physics near a first-order phase transition. We
show that sampling the equilibrium distribution using the Markov chain Monte
Carlo method can be dramatically accelerated when using biased sampling
techniques, in particular the Tethered Monte Carlo (TMC) method. This sampling
technique efficiently solves the problem of evaluating the quality of a given
trained model and generating new samples in a reasonable amount of time.
Moreover, we show that this sampling technique can also be used to improve the
computation of the log-likelihood gradient during training, leading to dramatic
improvements in training RBMs with artificial clustered datasets. On real
low-dimensional datasets, this new training method fits RBM models with
significantly faster relaxation dynamics than those obtained with standard PCD
recipes. We also show that TMC sampling can be used to recover the free-energy
profile of the RBM. This proves to be extremely useful to compute the
probability distribution of a given model and to improve the generation of new
decorrelated samples in slow PCD-trained models.


  We show that the $B-L$ Supersymmetric Standard Model with Inverse Seesaw
(BLSSMIS) provides new Dark Matter (DM) candidates (lightest right-handed
sneutrino and lightest $B-L$ neutralino) with mass of order few hundreds GeV,
while most of other SUSY spectrum can be quite heavy, consistently with the
current Large Hadron Collider (LHC) constraints. We emphasize that the thermal
relic abundance and direct detection experiments via relic neutralino
scattering with nuclei impose stringent constraints on the $B-L$ neutralinos.
These constraints can be satisfied by few points in the parameter space where
the $B-L$ lightest neutralino is higgsino-like, which cannot explain the
observed Galactic Center (GC) gamma-ray excess measured by Fermi-LAT. The
lightest right-handed sneutrino DM is analysed. We show that for a wide region
of parameter space the lightest right-handed sneutrino, with mass between 80
GeV and 1.2 TeV, can satisfy the limits of the relic abundance and the
scattering cross with nuclei. We also show that the lightest right-handed
sneutrino with mass ${\cal O}(100)$ GeV can account for the observed GC
gamma-ray results.


  Embedding covert streams into a cover channel is a common approach to
circumventing Internet censorship, due to censors' inability to examine
encrypted information in otherwise permitted protocols (Skype, HTTPS, etc.).
However, recent advances in machine learning (ML) enable detecting a range of
anti-censorship systems by learning distinct statistical patterns hidden in
traffic flows. Therefore, designing obfuscation solutions able to generate
traffic that is statistically similar to innocuous network activity, in order
to deceive ML-based classifiers at line speed, is difficult.
  In this paper, we formulate a practical adversarial attack strategy against
flow classifiers as a method for circumventing censorship. Specifically, we
cast the problem of finding adversarial flows that will be misclassified as a
sequence generation task, which we solve with Amoeba, a novel reinforcement
learning algorithm that we design. Amoeba works by interacting with censoring
classifiers without any knowledge of their model structure, but by crafting
packets and observing the classifiers' decisions, in order to guide the
sequence generation process. Our experiments using data collected from two
popular anti-censorship systems demonstrate that Amoeba can effectively shape
adversarial flows that have on average 94% attack success rate against a range
of ML algorithms. In addition, we show that these adversarial flows are robust
in different network environments and possess transferability across various ML
models, meaning that once trained against one, our agent can subvert other
censoring classifiers without retraining.


  We propose and experimentally test a method to fabricate patterns of steep,
sharp features on surfaces, by exploiting the nonlinear dynamics of uniformly
ion bombarded surfaces. We show via theory, simulation, and experiment, that
the steepest parts of the surface evolve as one-dimensional curves which move
in the normal direction at constant velocity. The curves are a special solution
to the nonlinear equations that arises spontaneously whenever the initial
patterning on the surface contains slopes larger than a critical value;
mathematically they are traveling waves (shocks) that have the special property
of being undercompressive. We derive the evolution equation for the curves by
considering long-wavelength perturbations to the one-dimensional traveling
wave, using the unusual boundary conditions required for an undercompressive
shock, and we show this equation accurately describes the evolution of shapes
on surfaces, both in simulations and in experiments. Because evolving a
collection of one-dimensional curves is fast, this equation gives a
computationally efficient and intuitive method for solving the inverse problem
of finding the initial surface so the evolution leads to a desired target
pattern. We illustrate this method by solving for the initial surface that will
produce a lattice of diamonds connected by steep, sharp ridges, and
experimentally demonstrating the evolution of the initial surface into the
target pattern.


  A method is proposed for the calculation of vibrational quantum and thermal
expectation values of physical properties from first principles. Thermal lines
are introduced: these are lines in configuration space parametrized by
temperature, such that the value of any physical property along them is
approximately equal to the vibrational average of that property. The number of
sampling points needed to explore the vibrational phase space is reduced by up
to an order of magnitude when the full vibrational density is replaced by
thermal lines. Calculations of the vibrational averages of several properties
and systems are reported, namely the internal energy and the electronic band
gap of diamond and silicon, and the chemical shielding tensor of L-alanine.
Thermal lines pave the way for complex calculations of vibrational averages,
including large systems and methods beyond semi-local density functional
theory.


  The following critical phenomenon was recently discovered. When a memoryless
source is compressed using a variable-length fixed-distortion code, the fastest
convergence rate of the (pointwise) compression ratio to the optimal $R(D)$
bits/symbol is either $O(\sqrt{n})$ or $O(\log n)$. We show it is always
$O(\sqrt{n})$, except for discrete, uniformly distributed sources.


  Recently, state-of-the-art results have been achieved in semantic
segmentation using fully convolutional networks (FCNs). Most of these networks
employ encoder-decoder style architecture similar to U-Net and are trained with
images and the corresponding segmentation maps as a pixel-wise classification
task. Such frameworks only exploit class information by using the ground truth
segmentation maps. In this paper, we propose a multi-task learning framework
with the main aim of exploiting structural and spatial information along with
the class information. We modify the decoder part of the FCN to exploit class
information and the structural information as well. We intend to do this while
also keeping the parameters of the network as low as possible. We obtain the
structural information using either of the two ways: i) using the contour map
and ii) using the distance map, both of which can be obtained from ground truth
segmentation maps with no additional annotation costs. We also explore
different ways in which distance maps can be computed and study the effects of
different distance maps on the segmentation performance. We also experiment
extensively on two different medical image segmentation applications: i.e i)
using color fundus images for optic disc and cup segmentation and ii) using
endoscopic images for polyp segmentation. Through our experiments, we report
results comparable to, and in some cases performing better than the current
state-of-the-art architectures and with an order of 2x reduction in the number
of parameters.


  Magnetic diffractive scattering of coherent metastable helium atomic beams
from CoO(001) surfaces reveal the presence of an anomalous enhancement in the
intensity of the half-order antiferromagnetic diffraction peak over the
temperature range of 250-320K. Electronic cluster calculations based on
Hartree-Fock states with configuration interactions, together with mean-field
and Monte Carlo studies of this system suggest that this behavior is associated
with low-lying surface spin excited states.


  We have developed a microscopic many-body theory of two-dimensional coherent
spectroscopy (2DCS) for a model of polarons in one-dimensional (1D) materials.
Our theory accounts for contributions from all three processes: excited-state
emission (ESE), ground-state bleaching (GSB), and excited-state absorption
(ESA). While the ESE and GSB contributions can be accurately described using a
Chevy's ansatz with one particle-hole excitation, the ESA process requires
information about the many-body eigenstates involving two impurities. To
calculate these double polaron states, we have extended the Chevy's ansatz with
one particle-hole excitation. The validity of this ansatz was verified by
comparing our results with an exact calculation using Bethe's ansatz. Our
numerical results reveal that in the weak interaction limit, the ESA
contribution cancels out the total ESE and GSB contributions, resulting in less
significant spectral features. However, for strong interactions, the features
of the ESA contribution and the combined ESE and GSB contributions remain
observable in the 2DCS spectra. These features provide valuable information
about the interactions between polarons. Additionally, we have investigated the
mixing time dynamics, which characterize the quantum coherences of the polaron
resonances. Overall, our theory provides a comprehensive framework for
understanding and interpreting the 2DCS spectra of polarons in 1D materials,
shedding light on their interactions and coherent dynamics.


  We use separation of variables as a tool to identify and to analyze exactly
soluble time-dependent quantum mechanical potentials. By considering the most
general possible time-dependent re-definition of the spatial coordinate, as
well as general transformations on the wavefunctions, we show that separation
of variables applies and exact solubility occurs only in a very restricted
class of time-dependent models. We consider the formal structure underlying our
findings, and the relationship between our results and other work on
time-dependent potentials. As an application of our methods, we apply our
results to the calculations of propagators.


  This paper introduces Seeker, a system that allows users to interactively
refine search rankings in real time, through feedback in the form of likes and
dislikes. When searching online, users may not know how to accurately describe
their product of choice in words. An alternative approach is to search an
embedding space, allowing the user to query using a representation of the item
(like a tune for a song, or a picture for an object). However, this approach
requires the user to possess an example representation of their desired item.
Additionally, most current search systems do not allow the user to dynamically
adapt the results with further feedback. On the other hand, users often have a
mental picture of the desired item and are able to answer ordinal questions of
the form: "Is this item similar to what you have in mind?" With this
assumption, our algorithm allows for users to provide sequential feedback on
search results to adapt the search feed. We show that our proposed approach
works well both qualitatively and quantitatively. Unlike most previous
representation-based search systems, we can quantify the quality of our
algorithm by evaluating humans-in-the-loop experiments.


  Recent computational advances in the accurate prediction of protein
three-dimensional (3D) structures from amino acid sequences now present a
unique opportunity to decipher the interrelationships between proteins. This
task entails--but is not equivalent to--a problem of 3D structure comparison
and classification. Historically, protein domain classification has been a
largely manual and subjective activity, relying upon various heuristics.
Databases such as CATH represent significant steps towards a more systematic
(and automatable) approach, yet there still remains much room for the
development of more scalable and quantitative classification methods, grounded
in machine learning. We suspect that re-examining these relationships via a
Deep Learning (DL) approach may entail a large-scale restructuring of
classification schemes, improved with respect to the interpretability of
distant relationships between proteins. Here, we describe our training of DL
models on protein domain structures (and their associated physicochemical
properties) in order to evaluate classification properties at CATH's
"homologous superfamily" (SF) level. To achieve this, we have devised and
applied an extension of image-classification methods and image segmentation
techniques, utilizing a convolutional autoencoder model architecture. Our DL
architecture allows models to learn structural features that, in a sense,
'define' different homologous SFs. We evaluate and quantify pairwise
'distances' between SFs by building one model per SF and comparing the loss
functions of the models. Hierarchical clustering on these distance matrices
provides a new view of protein interrelationships--a view that extends beyond
simple structural/geometric similarity, and towards the realm of
structure/function properties.


  Mathematical oncology provides unique and invaluable insights into tumour
growth on both the microscopic and macroscopic levels. This review presents
state-of-the-art modelling techniques and focuses on their role in
understanding glioblastoma, a malignant form of brain cancer. For each
approach, we summarise the scope, drawbacks, and assets. We highlight the
potential clinical applications of each modelling technique and discuss the
connections between the mathematical models and the molecular and imaging data
used to inform them. By doing so, we aim to prime cancer researchers with
current and emerging computational tools for understanding tumour progression.
Finally, by providing an in-depth picture of the different modelling
techniques, we also aim to assist researchers who seek to build and develop
their own models and the associated inference frameworks.


  Photon-number-resolving (PNR) detectors are a key enabling technology in
photonic quantum information processing. Here, we demonstrate the PNR capacity
of conventional superconducting nanowire single-photon detectors by performing
ultra-high-resolution time-tagging of the detector-generated electrical pulses.
This method provides a viable approach for PNR with high detection efficiency
and a high operational repetition rate. We present the implementation of such a
PNR detector in the telecom C-band and its characterization by measuring the
photon-number statistics of coherent light with tunable intensity.
Additionally, we demonstrate the capabilities of the detection method by
measuring photon-number correlations of non-classical states.


  In intuitionistic mathematics, the Brouwer Continuity Theorem states that all
total real functions are (uniformly) continuous on the unit interval. We study
this theorem and related principles from the point of view of Reverse
Mathematics over a base theory accommodating higher types and Nonstandard
Analysis. With regard to the bigger picture, Reverse Mathematics provides a
classification of theorems of ordinary mathematics based on computability. Our
aim is to provide an alternative classification of theorems based on the
central tenet of Feferman's Explicit Mathematics}, namely that a proof of
existence of an object yields a procedure to compute said object. Our
classification gives rise to the Explicit Mathematics theme (EMT). Intuitively
speaking, the EMT states that a standard object with certain properties can be
computed by a functional if and only if this object exists classically with
these same standard and nonstandard properties. Hence, we establish the EMT for
a series of intuitionistic principles in this paper.


  We show that a special case of the Feferman-Vaught composition theorem gives
rise to a natural notion of automata for finite words over an infinite
alphabet, with good closure and decidability properties, as well as several
logical characterizations. We also consider a slight extension of the
Feferman-Vaught formalism which allows to express more relations between
component values (such as equality), and prove related decidability results.
  From this result we get new classes of decidable logics for words over an
infinite alphabet.


  This paper describes a modular connectionist model of the acquisition of
receptive inflectional morphology. The model takes inputs in the form of phones
one at a time and outputs the associated roots and inflections. Simulations
using artificial language stimuli demonstrate the capacity of the model to
learn suffixation, prefixation, infixation, circumfixation, mutation, template,
and deletion rules. Separate network modules responsible for syllables enable
to the network to learn simple reduplication rules as well. The model also
embodies constraints against association-line crossing.


  In this work the model is constructed to describe the black hole enclosed in
the dust cosmological background in case of zero spatial curvature. This model
is based on our exact solution of the class of LTB inhomogeneous solutions. We
considered the properties of the model and built the R-T-structure of the
resulting space-time. It was shown that central region includes the
Schwarzchild-like black hole. We derived the equations of motion of the test
particle from the point of view of the observer comoving with cosmological
expansion. We found analytical expressions for observable orbital and radial
velocities of the particle and plotted the surface profile of the total
velocity in this case. In comoving coordinate frame it is impossible to study
the questions concerning the black hole horizon but one can observe the local
motion of the particles influenced by the cosmological expansion.


  We consider the classical null p-brane dynamics in D-dimensional curved
backgrounds and apply the Batalin-Fradkin-Vilkovisky approach for BRST
quantization of general gauge theories. Then we develop a method for solving
the tensionless $p$-brane equations of motion and constraints. This is possible
whenever there exists at least one Killing vector for the background metric. It
is shown that the same method can be also applied for the tensile 1-branes.
Finally, we give two examples of explicit exact solutions in four dimensions.


  The paper examines a question of how much more resources do organized
business interests have when compared to resources of civil society groups in
the context of privacy lobbying in the European Union (EU). To answer to the
question, the paper draws from classical literature on power resources and
pluralism. The empirical material comes from a lobbying register maintained by
the EU. According to the results, (a) there is only a small difference in terms
of the average financial and human resources, but a vast difference when
absolute amounts are used. Furthermore, (b) organized business interests are
better affiliated with each other and other organizations. Finally, (c) many
organized business interests maintain their offices in the United States,
whereas the non-governmental organizations observed are mostly European. With
these results and the accompanying discussion, the paper contributes to the
underresearched but inflammatory topic of privacy politics.


  Elucidating the interaction between magnetic moments and itinerant carriers
is an important step to spintronic applications. Here, we investigate magnetic
and transport properties in d0 ferromagnetic SiC single crystals prepared by
postimplantation pulsed laser annealing. Magnetic moments are contributed by
the p states of carbon atoms, but their magnetic circular dichroism is
different from that in semi-insulating SiC samples. The anomalous Hall effect
and negative magnetoresistance indicate the influence of d0 spin order on free
carriers. The ferromagnetism is relatively weak in N-implanted SiC compared
with that in Al-implanted SiC after annealing. The results suggest that d0
magnetic moments and itinerant carriers can interact with each other, which
will facilitate the development of SiC spintronic devices with d0
ferromagnetism.


  Let $G$ be a simply connected, solvable Lie group and $\Gamma$ a lattice in
$G$. The deformation space $\mathcal{D}(\Gamma,G)$ is the orbit space
associated to the action of $\Aut(G)$ on the space $\mathcal{X}(\Gamma,G)$ of
all lattice embeddings of $\Gamma$ into $G$. Our main result generalises the
classical rigidity theorems of Mal'tsev and Sait\^o for lattices in nilpotent
Lie groups and in solvable Lie groups of real type. We prove that the
deformation space of every Zariski-dense lattice $\Gamma$ in $G$ is finite and
Hausdorff, provided that the maximal nilpotent normal subgroup of $G$ is
connected. This implies that every lattice in a solvable Lie group virtually
embeds as a Zariski-dense lattice with finite deformation space. We give
examples of solvable Lie groups $G$ which admit Zariski-dense lattices $\Gamma$
such that $\mathcal{D}(\Gamma,G)$ is countably infinite, and also examples
where the maximal nilpotent normal subgroup of $G$ is connected and
simultaneously $G$ has lattices with uncountable deformation space.


  We have recently derived a gapless theory of the linear response of a
Bose-condensed gas to external perturbations at finite temperature and used it
to explain quantitatively the measurements of condensate excitations and decay
rates made at JILA [D. S. Jin et.al., Phys. Rev. Lett. 78, 764 (1997)]. The
theory describes the dynamic coupling between the condensate and non-condensate
via a full quasiparticle description of the time-dependent normal and anomalous
averages and includes all Beliaev and Landau processes. In this paper we
provide a full discussion of the numerical calculations and a detailed analysis
of the theoretical results in the context of the JILA experiment. We provide
unambiguous proof that the dipole modes are obtained accurately within our
calculations and present quantitative results for the relative phase of the
oscillations of the condensed and uncondensed atom clouds. One of the main
difficulties in the implementation of the theory is obtaining results which are
not sensitive to basis cutoff effects and we have therefore developed a novel
asymmetric summation method which solves this problem and dramatically improves
the numerical convergence. This new technique should make the implementation of
the theory and its possible future extensions feasible for a wide range of
condensate populations and trap geometries.


  We study the Vapnik-Chervonenkis (VC) density of definable families in
certain stable first-order theories. In particular we obtain uniform bounds on
VC density of definable families in finite U-rank theories without the finite
cover property, and we characterize those abelian groups for which there exist
uniform bounds on the VC density of definable families.


  We study constraints on Composite Higgs models with fermion partial
compositeness from electroweak precision measurements, including the 2022
$W$-boson mass result from the CDF collaboration. We focus on models where the
Composite Higgs sector arises from underlying four-dimensional strongly
interacting gauge theories with fermions, and where the SM fermions obtain
their mass via linear mixing terms between the fermions and the composite
sector -- the so-called fermion partial compositeness scenario. In general, the
Composite Higgs sector leads to a small and positive $S$ parameter, and a
negative $T$ parameter, but the fermion partial compositeness sector results in
an overall positive $T$ parameter in a large part of parameter space. We,
therefore, find good agreement between the full composite models and the
current electroweak precision measurement bounds on $S$ and $T$ from LEP and
CDF, including the offset and correlation of $S,T$ with respect to the SM
predictions.


  The paper establishes the property of splittability of billiard boundary
sequences in n dimensional cube into subsequences of fractional parts. This
reveals a new property of integrable and weak perturbated Hamilton systems:
under a simple assumption, the boundary motion of elliptic orbits on stable KAM
tori, if considering in cartesian coordinates, can be splitted into a countable
set of discrete rotations. The rate of the split process, expressed in terms of
some exceptional sets density, in dependence of number-theoretical
characteristics of the orbits frequencies, is also examined.


  For each positive integer $r$, let $S_r$ denote the $r^{th}$ Schemmel totient
function, a multiplicative arithmetic function defined by
\[S_r(p^{\alpha})=\begin{cases} 0, & \mbox{if } p\leq r; \\ p^{\alpha-1}(p-r),
& \mbox{if } p>r \end{cases}\] for all primes $p$ and positive integers
$\alpha$. The function $S_1$ is simply Euler's totient function $\phi$. Masser
and Shiu have established several fascinating results concerning sparsely
totient numbers, positive integers $n$ satisfying $\phi(n)<\phi(m)$ for all
integers $m>n$. We define a sparsely Schemmel totient number of order $r$ to be
a positive integer $n$ such that $S_r(n)>0$ and $S_r(n)<S_r(m)$ for all $m>n$
with $S_r(m)>0$. We then generalize some of the results of Masser and Shiu.


  The study of arithmetic properties of coefficients of modular forms $f(\tau)
= \sum a(n)q^n$ has a rich history, including deep results regarding
congruences in arithmetic progressions. Recently, work of C.-S. Radu, S.
Ahlgren, B. Kim, N. Andersen, and S. L\"{o}brich have employed the
$q$-expansion principle of P. Deligne and M. Rapoport in order to determine
more about where these congruences can occur. Here, we extend the method to
give additional results for a large class of modular forms. We also give
analogous results for generalized Frobenius partitions and the two mock theta
functions $f(q)$ and $\omega(q).$


  Automatic deep learning segmentation models has been shown to improve both
the segmentation efficiency and the accuracy. However, training a robust
segmentation model requires considerably large labeled training samples, which
may be impractical. This study aimed to develop a deep learning framework for
generating synthetic lesions that can be used to enhance network training. The
lesion synthesis network is a modified generative adversarial network (GAN).
Specifically, we innovated a partial convolution strategy to construct an
Unet-like generator. The discriminator is designed using Wasserstein GAN with
gradient penalty and spectral normalization. A mask generation method based on
principal component analysis was developed to model various lesion shapes. The
generated masks are then converted into liver lesions through a lesion
synthesis network. The lesion synthesis framework was evaluated for lesion
textures, and the synthetic lesions were used to train a lesion segmentation
network to further validate the effectiveness of this framework. All the
networks are trained and tested on the public dataset from LITS. The synthetic
lesions generated by the proposed approach have very similar histogram
distributions compared to the real lesions for the two employed texture
parameters, GLCM-energy and GLCM-correlation. The Kullback-Leibler divergence
of GLCM-energy and GLCM-correlation were 0.01 and 0.10, respectively. Including
the synthetic lesions in the tumor segmentation network improved the
segmentation dice performance of U-Net significantly from 67.3% to 71.4%
(p<0.05). Meanwhile, the volume precision and sensitivity improve from 74.6% to
76.0% (p=0.23) and 66.1% to 70.9% (p<0.01), respectively. The synthetic data
significantly improves the segmentation performance.


  Next generation cosmic microwave background spectral distortion and pulsar
timing array experiments have the potential to probe primordial fluctuations at
small scales with remarkable sensitivity. We demonstrate the potential of these
probes to either detect signatures of primordial black holes (PBHs) sourced
from primordial overdensities within the standard thermal history of the
universe over a 13-decade mass range ${\cal O}(0.1-10^{12})M_\odot$, or
constrain their existence to a negligible abundance. Our conclusions are based
only on global cosmological signals, and are robust under changes in i) the
statistical properties of the primordial density fluctuations (whether Gaussian
or non-Gaussian), ii) the merger and accretion history of the PBHs and
assumptions about associated astrophysical processes, and iii) clustering
statistics. Any positive detection of enhanced primordial fluctuations at small
scales would have far-reaching implications from the content of dark matter to
origin of BHs in the centers of galaxies, and to the field content of the
inflation. On the other hand, their non-detection would also have important
corollaries. For example, non-detection up to forecast sensitivities would tell
us that PBHs larger than a fraction of a solar mass can constitute no more than
a negligible fraction of dark matter. Moreover, non-detection will also rule
out the scenario that PBHs generated by primordial overdensities could be the
progenitors of super-massive black holes (SMBHs), of topical interest as there
are only a few widely accepted proposals for the formation of SMBHs, an even
more pressing question after the detection of active galactic nuclei over a
billion solar masses at redshifts $z \geq 7$. Finally, non-detection sets the
strongest bounds on the amplitude of small scale inflationary fluctuations for
over 6 decades.


  Dark Matter (DM) may be a thermal relic that annihilates into heavier states
in the early Universe. This Forbidden DM framework accommodates a wide range of
DM masses from keV to weak scales. An exponential hierarchy between the DM mass
and the weak scale follows from the exponential suppression of the thermally
averaged cross section. Stringent constraints from the cosmic microwave
background are evaded because annihilations turn off at late times. We provide
an example where DM annihilates into dark photons, which is testable through
large DM self-interactions and direct detection.


  The main idea of the interstitial-ice model is that liquid water consists of
an intact hexagonal lattice with both vacant lattice positions and interstitial
water molecules. Narten, Danford and Levy derived the model parameters from the
X-ray diffraction patterns of liquid water. However, their results were
counter-intuitive: their model had almost no vacancies, the interstitial
concentration was temperature independent and the ice lattice was deformed
resulting in extra anisotropy. To overcome these problems, we refined the model
and derived the model parameters from the proton nuclear magnetic resonance
frequency of pure water. Without any extra anisotropy, a significant
concentration of vacancies was found (3.7% of the lattice positions at
0{\deg}C) and the concentration of both the vacancies and interstitials
increased quasi linear with temperature (0.05 M/{\deg}C). This improved model
successfully explains the thermodynamic data of water and is therefore a
promising candidate for a coherent and analytical water model.


  In this work we prove the weighted Gevrey regularity of solutions to the
incompressible Euler equation with initial data decaying polynomially at
infinity. This is motivated by the well-posedness problem of vertical boundary
layer equation for fast rotating fluid. The method presented here is based on
the basic weighted $L^2$- estimate, and the main difficulty arises from the
estimate on the pressure term due to the appearance of weight function.


  In this paper, superconvergence points are located for the approximation of
the Riesz derivative of order $\alpha$ using classical Lobatto-type polynomials
when $\alpha \in (0,1)$ and generalized Jacobi functions (GJF) for arbitrary
$\alpha > 0$, respectively. For the former, superconvergence points are zeros
of the Riesz fractional derivative of the leading term in the truncated
Legendre-Lobatto expansion. It is observed that the convergence rate for
different $\alpha$ at the superconvergence points is at least $O(N^{-2})$
better than the optimal global convergence rate. Furthermore, the interpolation
is generalized to the Riesz derivative of order $\alpha > 1$ with the help of
GJF, which deal well with the singularities. The well-posedness, convergence
and superconvergence properties are theoretically analyzed. The gain of the
convergence rate at the superconvergence points is analyzed to be
$O(N^{-(\alpha+3)/2})$ for $\alpha \in (0,1)$ and $O(N^{-2})$ for $\alpha > 1$.
Finally, we apply our findings in solving model FDEs and observe that the
convergence rates are indeed much better at the predicted superconvergence
points.


  We present the first observation in hadronic collisions of the electroweak
production of vector boson pairs (VV, V=W,Z) where one boson decays to a dijet
final state . The data correspond to 3.5 inverse femtobarns of integrated
luminosity of ppbar collisions at sqrt(s)=1.96 TeV collected by the CDFII
detector at the Fermilab Tevatron. We observe 1516+/-239(stat)+/-144(syst)
diboson candidate events and measure a cross section sigma(ppbar->VV+X) of
18.0+/-2.8(stat)+/-2.4(syst)+/-1.1(lumi) pb, in agreement with the expectations
of the standard model.


  Two graphs $G_1$ and $G_2$ on $n$ vertices are said to pack if there exist
injective mappings of their vertex sets into $[n]$ such that the images of
their edge sets are disjoint. A longstanding conjecture due to Bollob\'as and
Eldridge and, independently, Catlin, asserts that, if $(\Delta_1(G)+1)
(\Delta_2(G)+1) \le n+1$, then $G_1$ and $G_2$ pack. We consider the validity
of this assertion under the additional assumption that $G_1$ or $G_2$ has
bounded codegree. In particular, we prove for all $t \ge 2$ that, if $G_1$
contains no copy of the complete bipartite graph $K_{2,t}$ and $\Delta_1 > 17 t
\cdot \Delta_2$, then $(\Delta_1(G)+1) (\Delta_2(G)+1) \le n+1$ implies that
$G_1$ and $G_2$ pack. We also provide a mild improvement if moreover $G_2$
contains no copy of the complete tripartite graph $K_{1,1,s}$, $s\ge 1$.


  In this paper, we investigate deep learning (DL)-enabled signal demodulation
methods and establish the first open dataset of real modulated signals for
wireless communication systems. Specifically, we propose a flexible
communication prototype platform for measuring real modulation dataset. Then,
based on the measured dataset, two DL-based demodulators, called deep belief
network (DBN)-support vector machine (SVM) demodulator and adaptive boosting
(AdaBoost) based demodulator, are proposed. The proposed DBN-SVM based
demodulator exploits the advantages of both DBN and SVM, i.e., the advantage of
DBN as a feature extractor and SVM as a feature classifier. In DBN-SVM based
demodulator, the received signals are normalized before being fed to the DBN
network. Furthermore, an AdaBoost based demodulator is developed, which employs
the $k$-Nearest Neighbor (KNN) as a weak classifier to form a strong combined
classifier. Finally, experimental results indicate that the proposed DBN-SVM
based demodulator and AdaBoost based demodulator are superior to the single
classification method using DBN, SVM, and maximum likelihood (MLD) based
demodulator.


  Coupled frustrated spin-1/2 chains in high magnetic fields described within
the ferro- antiferromagnetic J1-J2 Heisenberg model are studied by the DMRG,
the hard core boson, and the spin wave theory approaches. Multipolar phases
related to magnon bound states are destroyed (supported) by weak
antiferromagnetic (ferromagnetic) interchain couplings Jic. We show that
quantum spin nematics might be found for LiVCuO4 whereas for Li(Na)Cu2O2 it is
prevented by a sizeable antiferromagnetic Jic. Also for Li2ZrCuO4 with a small
antiferromagnetic Jic expected triatic or quartic phases are unlikely, too. The
saturation field is found to be strongly affected even by a relatively small
Jic.


  We give an effective procedure that produces a natural number in its output
from any natural number in its input, that is, it computes a total function.
The elementary operations of the procedure are Turing-computable. The procedure
has a second input which can contain the Goedel number of any Turing-computable
total function whose range is a subset of the set of the Goedel numbers of all
Turing-computable total functions. We prove that the second input cannot be set
to the Goedel number of any Turing-computable function that computes the output
from any natural number in its first input. In this sense, there is no Turing
program that computes the output from its first input. The procedure is used to
define creative procedures which compute functions that are not
Turing-computable. We argue that creative procedures model an aspect of
reasoning that cannot be modeled by Turing machines.


  A collaborative convex framework for factoring a data matrix $X$ into a
non-negative product $AS$, with a sparse coefficient matrix $S$, is proposed.
We restrict the columns of the dictionary matrix $A$ to coincide with certain
columns of the data matrix $X$, thereby guaranteeing a physically meaningful
dictionary and dimensionality reduction. We use $l_{1,\infty}$ regularization
to select the dictionary from the data and show this leads to an exact convex
relaxation of $l_0$ in the case of distinct noise free data. We also show how
to relax the restriction-to-$X$ constraint by initializing an alternating
minimization approach with the solution of the convex model, obtaining a
dictionary close to but not necessarily in $X$. We focus on applications of the
proposed framework to hyperspectral endmember and abundances identification and
also show an application to blind source separation of NMR data.


  The orientation of individual C60 molecules adsorbed on Cu(100) is reversibly
switched when the tip of a scanning tunneling microscope is approached to
contact the molecule. The probability of switching rises sharply upon
displacing the tip beyond a threshold. A mechanical mechanism is suggested to
induce the rotation of the molecule.


  We report the results from a detailed spectro-imaging and temporal analysis
of an archival XMM-Newton observation of a new intermediate polar XMMU
J185330.7-012815. Its X-ray spectrum can be well-described by a
multi-temperature thermal plasma model with the K-lines of heavy elements
clearly detected. Possible counterparts of XMMU J185330.7-012815 have been
identified in optical and UV bands. The low value of the inferred X-ray-to-UV
and X-ray-to-optical flux ratios help to safely rule out the possibility as an
isolated neutron star. We confirm the X-ray periodicity of ~238 s, but
different from the previous preliminary result, we do not find any convincing
evidence of phase-shift in this observation. We further investigate its
properties through an energy-resolved temporal analysis and find the pulsed
fraction monotonically increases with energy.


  During the recent developments of quantum theory it has been clarified that
the observable quantities (like energy or position) may be represented by
operators (with real spectra) which are manifestly non-Hermitian. The
mathematical consistency of the resulting models of stable quantum systems
requires a reconstruction of an alternative, amended, physical inner product of
states. We point out the less known fact that for more than one observable the
task is not always feasible. The difficulty is re-analyzed and its elementary
linear-algebraic interpretation and treatment are outlined.


  With the rapid growth of financial services, fraud detection has been a very
important problem to guarantee a healthy environment for both users and
providers. Conventional solutions for fraud detection mainly use some
rule-based methods or distract some features manually to perform prediction.
However, in financial services, users have rich interactions and they
themselves always show multifaceted information. These data form a large
multiview network, which is not fully exploited by conventional methods.
Additionally, among the network, only very few of the users are labelled, which
also poses a great challenge for only utilizing labeled data to achieve a
satisfied performance on fraud detection.
  To address the problem, we expand the labeled data through their social
relations to get the unlabeled data and propose a semi-supervised attentive
graph neural network, namedSemiGNN to utilize the multi-view labeled and
unlabeled data for fraud detection. Moreover, we propose a hierarchical
attention mechanism to better correlate different neighbors and different
views. Simultaneously, the attention mechanism can make the model interpretable
and tell what are the important factors for the fraud and why the users are
predicted as fraud. Experimentally, we conduct the prediction task on the users
of Alipay, one of the largest third-party online and offline cashless payment
platform serving more than 4 hundreds of million users in China. By utilizing
the social relations and the user attributes, our method can achieve a better
accuracy compared with the state-of-the-art methods on two tasks. Moreover, the
interpretable results also give interesting intuitions regarding the tasks.


  Let $\pi $ be an irreducible smooth complex representation of a general
linear $p$-adic group and let $\sigma $ be an irreducible complex supercuspidal
representation of a classical $p$-adic group of a given type, so that
$\pi\otimes\sigma $ is a representation of a standard Levi subgroup of a
$p$-adic classical group of higher rank. We show that the reducibility of the
representation of the appropriate $p$-adic classical group obtained by
(normalized) parabolic induction from $\pi\otimes\sigma $ does not depend on
$\sigma $, if $\sigma $ is "separated" from the supercuspidal support of $\pi
$. (Here, "separated" means that, for each factor $\rho $ of a representation
in the supercuspidal support of $\pi $, the representation parabolically
induced from $\rho\otimes\sigma $ is irreducible.) This was conjectured by E.
Lapid and M. Tadi\'c. (In addition, they proved, using results of C. Jantzen,
that this induced representation is always reducible if the supercuspidal
support is not separated.)
  More generally, we study, for a given set $I$ of inertial orbits of
supercuspidal representations of $p$-adic general linear groups, the category
$\CC _{I,\sigma}$ of smooth complex finitely generated representations of
classical $p$-adic groups of fixed type, but arbitrary rank, and supercuspidal
support given by $\sigma $ and $I$, show that this category is equivalent to a
category of finitely generated right modules over a direct sum of tensor
products of extended affine Hecke algebras of type $A$, $B$ and $D$ and
establish functoriality properties, relating categories with disjoint $I$'s. In
this way, we extend results of C. Jantzen who proved a bijection between
irreducible representations corresponding to these categories. The proof of the
above reducibility result is then based on Hecke algebra arguments, using
Kato's exotic geometry.


  Automated deception detection systems can enhance health, justice, and
security in society by helping humans detect deceivers in high-stakes
situations across medical and legal domains, among others. This paper presents
a novel analysis of the discriminative power of dimensional representations of
facial affect for automated deception detection, along with interpretable
features from visual, vocal, and verbal modalities. We used a video dataset of
people communicating truthfully or deceptively in real-world, high-stakes
courtroom situations. We leveraged recent advances in automated emotion
recognition in-the-wild by implementing a state-of-the-art deep neural network
trained on the Aff-Wild database to extract continuous representations of
facial valence and facial arousal from speakers. We experimented with unimodal
Support Vector Machines (SVM) and SVM-based multimodal fusion methods to
identify effective features, modalities, and modeling approaches for detecting
deception. Unimodal models trained on facial affect achieved an AUC of 80%, and
facial affect contributed towards the highest-performing multimodal approach
(adaptive boosting) that achieved an AUC of 91% when tested on speakers who
were not part of training sets. This approach achieved a higher AUC than
existing automated machine learning approaches that used interpretable visual,
vocal, and verbal features to detect deception in this dataset, but did not use
facial affect. Across all videos, deceptive and truthful speakers exhibited
significant differences in facial valence and facial arousal, contributing
computational support to existing psychological theories on affect and
deception. The demonstrated importance of facial affect in our models informs
and motivates the future development of automated, affect-aware machine
learning approaches for modeling and detecting deception and other social
behaviors in-the-wild.


  Let $\Omega$ be a proper open cone in a real Banach space $V$. We show that
the tube domain $V \oplus i\Omega$ over $\Omega$ is biholomorphic to a bounded
symmetric domain if and only if $\Omega$ is a normal linearly homogeneous
Finsler symmetric cone, which is equivalent to the condition that $V$ is a
unital JB-algebra in an equivalent norm and $\Omega$ is the interior of $\{v^2:
v\in V\}$.


  The cell cycle is one of the most fundamental biological processes important
for understanding normal physiology and various pathologies such as cancer.
Single cell RNA sequencing technologies give an opportunity to analyse the cell
cycle transcriptome dynamics in an unprecedented range of conditions (cell
types and perturbations), with thousands of publicly available datasets. Here
we review the main computational tasks in such analysis: 1) identification of
cell cycle phases, 2) pseudotime inference, 3) identification and profiling of
cell cycle-related genes, 4) removing cell cycle effect, 5) identification and
analysis of the G0 (quiescent) cells. We review seventeen software packages
that are available today for the cell cycle analysis using scRNA-seq data.
Despite huge progress achieved, none of the packages can produce complete and
reliable results with respect to all aforementioned tasks. One of the major
difficulties for existing packages is distinguishing between two patterns of
cell cycle transcriptomic dynamics: normal and characteristic for embryonic
stem cells (ESC), with the latter one shared by many cancer cell lines.
Moreover, some cell lines are characterized by a mixture of two subpopulations,
one following the standard and one ESC-like cell cycle, which makes the
analysis even more challenging. In conclusion, we discuss the difficulties of
the analysis of cell cycle-related single cell transcriptome and provide
certain guidelines for the use of the existing methods.


  The penetrating power of X rays underpins important applications such as
medical radiography. However, this same attribute makes it challenging to
achieve flexible on-demand patterning of X-ray beams. One possible path to this
goal is ``ghost projection'', a method which may be viewed as a reversed form
of classical ghost imaging. This technique employs multiple exposures, of a
single illuminated non-configurable mask that is transversely displaced to a
number of specified positions, to create any desired pattern. An experimental
proof-of-concept is given for this idea, using hard X rays. The written pattern
is arbitrary, up to a tunable constant offset, and its spatial resolution is
limited by both (i) the finest features present in the illuminated mask and
(ii) inaccuracies in mask positioning and mask exposure time. In principle, the
method could be used to make a universal lithographic mask in the hard-X-ray
regime. Ghost projection might also be used as a dynamically-configurable
beam-shaping element, namely the hard-X-ray equivalent of a spatial light
modulator. The underpinning principle can be applied to gamma rays, neutrons,
electrons, muons, and atomic beams. Our flexible approach to beam shaping gives
a potentially useful means to manipulate such fields.


  As an interesting surface plasmon phenomenon discovered several years ago,
electromagnetic field redistribution in nanoparticle dimer on film system
provides a novel thought to enhance the light power on a plain film which could
been widely used in surface enhanced Raman scattering (SERS), solar cells,
photo-catalysis, etc. Homodimers on film are mainly investigated in past years,
while the properties of heterodimers on film are still unclear. In this work,
size difference induced electromagnetic field redistribution in Ag nanoparticle
dimer on Au film system is investigated first. The results obtained from finite
element method indicate that the smaller nanoparticle has much greater ability
to focus light energy on Au film, which even reached more than 5 time compared
to the larger one. Further researches indicate that this energy focusing
ability has a strong relationship to the wavelength and diameter ration in
dimer. Similar focusing phenomenon is found in the system of thick wire-smaller
particle on film. Later, the SERS spectra collected in the small
nanoparticle-large nanowire system provide an experimental evidence for this
theoretic predication. Our results strengthen the understanding of surface
plasmon on plane film and have potential application prospects in the surface
plasmon related fields.


  Parameter quantization in convolutional neural networks (CNNs) can help
generate efficient models with lower memory footprint and computational
complexity. But, homogeneous quantization can result in significant degradation
of CNN model accuracy. In contrast, heterogeneous quantization represents a
promising approach to realize compact, quantized models with higher inference
accuracies. In this paper, we propose HQNNA, a CNN accelerator based on
non-coherent silicon photonics that can accelerate both homogeneously quantized
and heterogeneously quantized CNN models. Our analyses show that HQNNA achieves
up to 73.8x better energy-per-bit and 159.5x better throughput-energy
efficiency than state-of-the-art photonic CNN accelerators.


  We present updated results for $\varepsilon_K$ determined directly from the
standard model (SM) with lattice QCD inputs such as $\hat{B}_K$, $|V_{cb}|$,
$|V_{us}|$, $\xi_0$, $\xi_2$, $\xi_\text{LD}$, $f_K$, and $m_c$. We find that
the standard model with exclusive $|V_{cb}|$ and other lattice QCD inputs
describes only 65\% of the experimental value of $|\varepsilon_K|$ and does not
explain its remaining 35\%, which leads to a strong tension in
$|\varepsilon_K|$ at the $4.6\sigma \sim 4.2\sigma$ level between the SM theory
and experiment. We also find that this tension disappears when we use the
inclusive value of $|V_{cb}|$ obtained using the heavy quark expansion based on
QCD sum rules.


  This overview of what we can hope to learn from high-statistics experiments
in B physics in the next few years includes: (a) a review of parameters of the
Cabibbo-Kobayashi-Maskawa (CKM) Matrix; (b) direct determination of magnitudes
of CKM elements; (c) forthcoming information from studies of kaons; (d) CP
violation in B decays; (e) aspects of rate measurements; (f) the role of
charm-anticharm annihilation; (g) remarks on tagging; and (h) effects beyond
the standard model.


  The current wealth of charmless B decay data may suggest the presence of
final state rescattering. In a factorized amplitude approach, better fits are
found by incorporating two SU(3) rescattering phase differences, giving delta ~
65 degree and sigma ~ 90 - 100 degree. Fitting with unitarity phase phi_3 as a
fit parameter gives phi_3 ~ 96 degree, the CP asymmetries A_{pi pi}, S_{pi pi}
agree better with BaBar, and the sigma phase is slightly lower. Keeping phi_3 =
60 degree fixed in fit gives S_{pi pi} ~-0.9, which agrees better with Belle.
With the sizable delta, sigma rescattering phases as fitted, many direct CP
asymmetries flip sign, and B0 --> pi0 pi0, K- K+ rates are of order 10^{-6},
which can be tested soon.


  We introduce and analyze an extended Hubbard model, in which intersite
Coulomb interaction as well as a staggered local potential (SLP) are
considered, on the square lattice at half band filling, in the thermodynamic
limit. Using both Hartree-Fock approximation and Kotliar and Ruckenstein slave
boson formalism, we show that the model harbors charge order (CO) as well as
joint spin and charge modulations (SCO) at finite values of the SLP, while the
spin density wave (SDW) is stabilized for vanishing SLP, only. We determine
their phase boundaries and the variations of the order parameters in dependence
on the SLP, as well as on the on-site and nearest-neighbor interactions.
Domains of coexistence of CO and SCO phases, suitable for resistive switching
experiments, are unraveled. We show that the novel SCO systematically turns
into the more conventional SDW phase when the zero-SLP limit is taken. We also
discuss the nature of the different phase transitions, both at zero and finite
temperature. In the former case, no continuous CO to SDW (or SCO) phase
transition occurs. In contrast, a paramagnetic phase (PM), which is accompanied
with continuous phase transitions towards both spin or charge ordered phases,
sets in at finite temperature. A good quantitative agreement with numerical
simulations is demonstrated, and a comparison between the two used approaches
is performed.


  It has become widely accepted that particles with long-range hopping do not
undergo Anderson localization. However, several recent studies demonstrated
localization of particles with long-range hopping. In particular, it was
recently shown that the effect of long-range hopping in 1D lattices can be
mitigated by cooperative shielding, which makes the system behave effectively
as one with short-range hopping. Here, we show that cooperative shielding,
demonstrated previously for 1D lattices, extends to 3D lattices with isotropic
long-range $r^{-\alpha}$ hopping, but not to 3D lattices with dipolar-like
anisotropic long-range hopping. We demonstrate the presence of localization in
3D lattices with uniform ($\alpha=0$) isotropic long-range hopping and the
absence of localization with uniform anisotropic long-range hopping by using
the scaling behaviour of eigenstate participation ratios. We use the scaling
behaviour of participation ratios and energy level statistics to show that the
existence of delocalized, non-ergodic extended, or localized states in the
presence of disorder depends on both the exponents $\alpha$ and the isotropy of
the long-range hopping amplitudes.


  This paper studies a multi-UAV wireless network, in which multiple UAV users
share the same spectrum to send individual messages to their respectively
associated ground base stations (GBSs). The UAV users aim to optimize their
locations to maximize the weighted sum rate. While most existing work considers
simplified line-of-sight (LoS) or statistic air-to-ground (A2G) channel models,
we exploit the location-specific channel knowledge map (CKM) to enhance the
placement performance in practice. However, as the CKMs normally contain
discrete site- and location-specific channel data without analytic model
functions, the corresponding weighted sum rate function becomes
non-differentiable in general. In this case, conventional optimization
techniques relying on function derivatives are inapplicable to solve the
resultant placement optimization problem. To address this issue, we propose a
novel iterative algorithm based on the derivative-free optimization. In each
iteration, we first construct a quadratic function to approximate the
non-differentiable weighted sum rate under a set of interpolation conditions,
and then update the UAVs' placement locations by maximizing the approximate
quadratic function subject to a trust region constraint. Numerical results show
the convergence of the proposed algorithm. It is also shown that the proposed
algorithm achieves a weighted sum rate close to the optimal design based on
exhaustive search with much lower implementation complexity, and it
significantly outperforms the conventional optimization method based on
simplified LoS channel models and the heuristic design with each UAV hovering
above its associated GBS.


  In data analysis, latent variables play a central role because they help
provide powerful insights into a wide variety of phenomena, ranging from
biological to human sciences. The latent tree model, a particular type of
probabilistic graphical models, deserves attention. Its simple structure - a
tree - allows simple and efficient inference, while its latent variables
capture complex relationships. In the past decade, the latent tree model has
been subject to significant theoretical and methodological developments. In
this review, we propose a comprehensive study of this model. First we summarize
key ideas underlying the model. Second we explain how it can be efficiently
learned from data. Third we illustrate its use within three types of
applications: latent structure discovery, multidimensional clustering, and
probabilistic inference. Finally, we conclude and give promising directions for
future researches in this field.


  In this paper, we shed new light on the spectrum of the relation algebra we
call $A_{n}$, which is obtained by splitting the non-flexible diversity atom of
$6_{7}$ into $n$ symmetric atoms. Precisely, we show that the minimum value in
$\text{Spec}(A_{n})$ is at most $2n^{6 + o(1)}$, which is the first polynomial
bound and improves upon the previous bound due to Dodd \& Hirsch (\textit{J.
Relational Methods in Computer Science} 2013). We also improve the lower bound
to $2n^{2} + 4n + 1$, which is asymptotically double the trivial bound of
$n^{2} + 2n + 3$.
  In the process, we obtain stronger results regarding $\text{Spec}(A_{2})
=\text{Spec}(32_{65})$. Namely, we show that $1024$ is in the spectrum, and no
number smaller than 26 is in the spectrum. Our improved lower bounds were
obtained by employing a SAT solver, which suggests that such tools may be more
generally useful in obtaining representation results.