  Neural differential equations may be trained by backpropagating gradients via
the adjoint method, which is another differential equation typically solved
using an adaptive-step-size numerical differential equation solver. A proposed
step is accepted if its error, \emph{relative to some norm}, is sufficiently
small; else it is rejected, the step is shrunk, and the process is repeated.
Here, we demonstrate that the particular structure of the adjoint equations
makes the usual choices of norm (such as $L^2$) unnecessarily stringent. By
replacing it with a more appropriate (semi)norm, fewer steps are unnecessarily
rejected and the backpropagation is made faster. This requires only minor code
modifications. Experiments on a wide range of tasks -- including time series,
generative modeling, and physical control -- demonstrate a median improvement
of 40% fewer function evaluations. On some problems we see as much as 62% fewer
function evaluations, so that the overall training time is roughly halved.


  We calculate the partial electron shear viscosity $\eta_{ee}$ limited by
electron-electron collisions in a strongly degenerate electron gas taking into
account the Landau damping of transverse plasmons. The Landau damping strongly
suppresses $\eta_{ee}$ in the domain of ultrarelativistic degenerate electrons
and modifies its %asymptotic temperature behavior. The efficiency of the
electron shear viscosity in the cores of white dwarfs and envelopes of neutron
stars is analyzed.


  When plaintiffs prevail in a discrimination case, a major component of the
calculation of economic loss is the length of time they would have been in the
higher position had they been treated fairly during the period in which the
employer practiced discrimination. This problem is complicated by the fact that
one's eligibility for promotion is subject to termination by retirement and
both the promotion and retirement processes may be affected by discriminatory
practices. This semi-competing risk setup is decomposed into a retirement
process and a promotion process among the employees. Predictions for the
purpose of compensation are made by utilizing the expected promotion and
retirement probabilities of similarly qualified members of the nondiscriminated
group. The restricted mean durations of three periods are estimated - the time
an employee would be at the lower position, at the higher level and in
retirement. The asymptotic properties of the estimators are presented and
examined through simulation studies. The proposed restricted mean job duration
estimators are shown to be robust in the presence of an independent frailty
term. Data from the reverse discrimination case, Alexander v. Milwaukee, where
White-male lieutenants were discriminated in promotion to captain are
reanalyzed. While the appellate court upheld liability, it reversed the
original damage calculations, which heavily depended on the time a plaintiff
would have been in each position. The results obtained by the proposed method
are compared to those made at the first trial. Substantial differences in both
directions are observed.


  The idea that spacetime geometry is built from quantum entanglement has been
widely accepted in the last years. But how exactly the geometry is related with
quantum states is still unclear. In this note, based on the idea of deep
learning, we propose a mechanism for Susskind's QM=GR hypothesis, spacetime
geometry as the optimal generative network of quantum states. We speculate that
the space geometry stems as a geodesic tensor network which defines the quantum
state complexity of a fundamental quantum state under a given metric. Spacetime
corresponds to an evolving tensor network that generates an evolutional
fundamental quantum system. This mechanism provides (a) a constructive
correspondence between quantum states and spacetime geometry; (b)a spacetime
structure emerging from a highly constrained geodesic so that the QEC-like
structure shown in AdS/CFT can be naturally realized and (c) a mechanism to
derive the gravity equation from the concept of quantum state complexity. With
this mechanism, spacetime can have a quantum mechanical description. We hope
this may lead to another view direction to understand the basic rules of our
world.


  Optimal power flow (OPF) is the central optimization problem in electric
power grids. Although solved routinely in the course of power grid operations,
it is known to be strongly NP-hard in general, and weakly NP-hard over tree
networks. In this paper, we formulate the optimal power flow problem over tree
networks as an inference problem over a tree-structured graphical model where
the nodal variables are low-dimensional vectors. We adapt the standard dynamic
programming algorithm for inference over a tree-structured graphical model to
the OPF problem. Combining this with an interval discretization of the nodal
variables, we develop an approximation algorithm for the OPF problem. Further,
we use techniques from constraint programming (CP) to perform interval
computations and adaptive bound propagation to obtain practically efficient
algorithms. Compared to previous algorithms that solve OPF with optimality
guarantees using convex relaxations, our approach is able to work for arbitrary
distribution networks and handle mixed-integer optimization problems. Further,
it can be implemented in a distributed message-passing fashion that is scalable
and is suitable for "smart grid" applications like control of distributed
energy resources. We evaluate our technique numerically on several benchmark
networks and show that practical OPF problems can be solved effectively using
this approach.


  By combining analytic and numerical methods, edge states on a finite width
graphene ribbon in a magnetic field are studied in the framework of low-energy
effective theory that takes into account the possibility of quantum Hall
ferromagnetism (QHF) gaps and dynamically generated Dirac-like masses. The
analysis is done for graphene ribbons with both zigzag and armchair edges. The
characteristic features of the spectrum of the edge states in both these cases
are described. In particular, the conditions for the existence of the gapless
edge states are established. Implications of these results for the
interpretation of recent experiments are discussed.


  We study inverse problems F(f) = g with perturbed right hand side g^{obs}
corrupted by so-called impulsive noise, i.e. noise which is concentrated on a
small subset of the domain of definition of g. It is well known that
Tikhonov-type regularization with an L^1 data fidelity term yields
significantly more accurate results than Tikhonov regularization with classical
L^2 data fidelity terms for this type of noise. The purpose of this paper is to
provide a convergence analysis explaining this remarkable difference in
accuracy. Our error estimates significantly improve previous error estimates
for Tikhonov regularization with L^1-fidelity term in the case of impulsive
noise. We present numerical results which are in good agreement with the
predictions of our analysis.


  We investigate the features of charge transport in a novel holographic
quantum phase transition (QPT) model with two metallic phases: normal metallic
and novel metallic. The scaling behaviors of direct current (DC) resistivity
and thermal conductivity at low temperatures in both metallic phases are
numerically computed. The numerical results and the analytical ones governed by
the near horizon geometry agree perfectly. Then, the features of low-frequency
alternating current (AC) electric conductivity are systematically investigated.
A remarkable characteristic is that the normal metallic phase is a coherent
system, whereas the novel metallic phase is an incoherent system with
non-vanishing intrinsic conductivity. Especially, in the novel metallic phase,
the incoherent behavior becomes stronger when the strength of the momentum
dissipation enhances.


  The new exact weak solution of the equations for the ideal incompressible
fluid dynamics in the finite layer inside two plates with solid boundaries is
obtained. The solution meets a devised non-linear finite dimension Hamiltonian
dynamic system for the coordinates and the Lamb impulses of N point vortex
dipoles (PVD), i.e. extremely small solid spherical particles, moving with
respect to the fluid. For N=2 the necessary condition for the collapse (or the
converging in one point during the finite time) of two PVD is stated. On the
base of the proposed theory, the new hydrodynamic mechanism of converging for
two small spherical particles (of the same radius) is introduced and used for
interpreting the observed paradoxical effects of attraction for micro particles
with the same sign of electrical charge in the colloid and dusty plasma
systems. The correspondence of the condition for collapse of two PVD with the
experimentally observed data where convergence of two like-charged
micro-spheres moving in the fluid layer is observed only when definite
necessary restrictions on the fluid layer depth are imposed from above and
below (J. C. Crocker, D. G. Grier, 1996) is stated. We also state the
possibility of correspondence between the obtained condition of collapse for
two PVD in the limit of unbounded fluid and known numerical results on the
threshold of the impact parameter for the realization of resonance bond state
(and corresponding stochastic dynamic regime) for two finite size vortex
dipoles during their scattering in unbounded fluid (S. V. Manakov, L. N.
Shchur, 1983; L. Tophoj, H. Aref, 2008). Here we show that in the latter
process the size of dipoles is not so important as it was thought before.


  We investigate the optimal selection of weight windows for the problem of
weighted least squares. We show that weight windows should be symmetric around
its center, which is also its peak. We consider the class of tapered rectangle
window weights, which are nonincreasing away from the center. We show that the
best rectangle window is optimal for such window definitions. We also extend
our results to the least absolutes and more general case of arbitrary loss
functions to find similar results.


  We present a differentially private mechanism to display statistics (e.g.,
the moving average) of a stream of real valued observations where the bound on
each observation is either too conservative or unknown in advance. This is
particularly relevant to scenarios of real-time data monitoring and reporting,
e.g., energy data through smart meters. Our focus is on real-world data streams
whose distribution is light-tailed, meaning that the tail approaches zero at
least as fast as the exponential distribution. For such data streams,
individual observations are expected to be concentrated below an unknown
threshold. Estimating this threshold from the data can potentially violate
privacy as it would reveal particular events tied to individuals [1]. On the
other hand an overly conservative threshold may impact accuracy by adding more
noise than necessary. We construct a utility optimizing differentially private
mechanism to release this threshold based on the input stream. Our main
advantage over the state-of-the-art algorithms is that the resulting noise
added to each observation of the stream is scaled to the threshold instead of a
possibly much larger bound; resulting in considerable gain in utility when the
difference is significant. Using two real-world datasets, we demonstrate that
our mechanism, on average, improves the utility by a factor of 3.5 on the first
dataset, and 9 on the other. While our main focus is on continual release of
statistics, our mechanism for releasing the threshold can be used in various
other applications where a (privacy-preserving) measure of the scale of the
input distribution is required.


  Fish tracking plays a vital role in understanding fish behavior and ecology.
However, existing tracking methods face challenges in accuracy and robustness
dues to morphological change of fish, occlusion and complex environment. This
paper proposes FishMOT(Multiple Object Tracking for Fish), a novel fish
tracking approach combining object detection and IoU matching, including basic
module, interaction module and refind module. Wherein, a basic module performs
target association based on IoU of detection boxes between successive frames to
deal with morphological change of fish; an interaction module combines IoU of
detection boxes and IoU of fish entity to handle occlusions; a refind module
use spatio-temporal information uses spatio-temporal information to overcome
the tracking failure resulting from the missed detection by the detector under
complex environment. FishMOT reduces the computational complexity and memory
consumption since it does not require complex feature extraction or identity
assignment per fish, and does not need Kalman filter to predict the detection
boxes of successive frame. Experimental results demonstrate FishMOT outperforms
state-of-the-art multi-object trackers and specialized fish tracking tools in
terms of MOTA, accuracy, computation time, memory consumption, etc..
Furthermore, the method exhibits excellent robustness and generalizability for
varying environments and fish numbers. The simplified workflow and strong
performance make FishMOT as a highly effective fish tracking approach. The
source codes and pre-trained models are available at:
https://github.com/gakkistar/FishMOT


  This Letter presents a search for new light resonances decaying to pairs of
quarks and produced in association with a high-$p_{\textrm{T}}$ photon or jet.
The dataset consists of proton-proton collisions with an integrated luminosity
of 36.1 fb$^{-1}$ at a centre-of-mass energy of $\sqrt{s}=13$ TeV recorded by
the ATLAS detector at the Large Hadron Collider. Resonance candidates are
identified as massive large-radius jets with substructure consistent with a
particle decaying into a quark pair. The mass spectrum of the candidates is
examined for local excesses above background. No evidence of a new resonance is
observed in the data, which are used to exclude the production of a
lepto-phobic axial-vector $Z^\prime$ boson.


  We consider a model of interacting neurons where the membrane potentials of
the neurons are described by a multidimensional piecewise deterministic Markov
process (PDMP) with values in ${\mathbb R}^N, $ where $ N$ is the number of
neurons in the network. A deterministic drift attracts each neuron's membrane
potential to an equilibrium potential $m.$ When a neuron jumps, its membrane
potential is reset to $0,$ while the other neurons receive an additional amount
of potential $\frac{1}{N}.$ We are interested in the estimation of the jump (or
spiking) rate of a single neuron based on an observation of the membrane
potentials of the $N$ neurons up to time $t.$ We study a Nadaraya-Watson type
kernel estimator for the jump rate and establish its rate of convergence in
$L^2 .$ This rate of convergence is shown to be optimal for a given H\"older
class of jump rate functions. We also obtain a central limit theorem for the
error of estimation. The main probabilistic tools are the uniform ergodicity of
the process and a fine study of the invariant measure of a single neuron.


  We investigate a gradient flow structure of the Ginzburg--Landau--Devonshire
(GLD) model for anisotropic ferroelectric materials by reconstructing its
energy form. We show that the modified energy form admits at least one
minimizer. Under some regularity assumptions for the electric charge
distribution and the initial polarization field, we prove that the $L^2$
gradient flow structure has a unique solution. To simulate the GLD model
numerically, we propose an energy-stable semi-implicit time-stepping scheme and
a hybridizable discontinuous Galerkin method for space discretization. Some
numerical tests are provided to verify the stability and convergence of the
proposed numerical scheme as well as some properties of ferroelectric
materials.


  In this paper we consider the nature of the machine intelligences we have
created in the context of our human intelligence. We suggest that the
fundamental difference between human and machine intelligence comes down to
\emph{embodiment factors}. We define embodiment factors as the ratio between an
entity's ability to communicate information vs compute information. We
speculate on the role of embodiment factors in driving our own intelligence and
consciousness. We briefly review dual process models of cognition and cast
machine intelligence within that framework, characterising it as a dominant
System Zero, which can drive behaviour through interfacing with us
subconsciously. Driven by concerns about the consequence of such a system we
suggest prophylactic courses of action that could be considered. Our main
conclusion is that it is \emph{not} sentient intelligence we should fear but
\emph{non-sentient} intelligence.


  In this paper, we study inference for chains of variable order under two
distinct contamination regimes. Consider we have a chain of variable memory on
a finite alphabet containing zero. At each instant of time an independent coin
is flipped and if it turns head a contamination occurs. In the first regime a
zero is read independent of the value of the chain. In the second regime, the
value of another chain of variable memory is observed instead of the original
one. Our results state that the difference between the transition probabilities
of the original process and the corresponding ones of the contaminated process
may be bounded above uniformly. Moreover, if the contamination probability is
small enough, using a version of the Context algorithm we are able to recover
the context tree of the original process through a contaminated sample.


  Let $X$ be a K\"ahler manifold with semi-ample canonical bundle $K_X$. It is
proved by Jian-Shi-Song that for any K\"ahler class $\gamma$, there exists
$\delta>0$ such that for all $t\in (0, \delta)$ there exists a unique cscK
metric $g_t$ in $K_X+ t \gamma $. In this paper, we prove that $\{ (X, g_t)
\}_{ t\in (0, \delta)} $ have uniformly bounded K\"ahler potentials, volume
forms and diameters. As a consequence, these metric spaces are pre-compact in
the Gromov-Hausdorff sense.


  Many metallic transition-metal oxides turn insulating when grown as films
that are only a few unit-cells thick. The microscopic origins of these
thickness induced metal-to-insulator transitions however remain under dispute.
Here, we simulate the extreme case of a monolayer of an inconspicuous
correlated metal -- the strontium vanadate SrVO$_3$ -- deposited on a SrTiO$_3$
substrate. Crucially, our system can have a termination to vacuum consisting of
either a SrO or a VO$_2$ top layer. While we find that both lead to Mott
insulating behavior at nominal stoichiometry, the phase diagram emerging upon
doping -- chemically or through an applied gate voltage -- is qualitatively
different. Indeed, our many-body calculations reveal a cornucopia of nonlocal
fluctuations associated with (in)commensurate antiferromagnetic, ferromagnetic,
as well as stripe and checkerboard orbital ordering instabilities. Identifying
that the two geometries yield crystal-field splittings of opposite signs, we
elucidate the ensuing phases through the lens of the orbital degrees of
freedom. Quite generally, our work highlights that interface and surface
reconstruction and the deformation or severing of coordination polyhedra in
ultra-thin films drive rich properties that are radically different from the
material's bulk physics.


  Wilson loops have been measured at strong coupling, $\beta=0.5$, on a $12^4$
lattice in noncompact simulations of pure SU(2) without gauge fixing. There is
no sign of quark confinement.


  In this paper we analyze a space-time unfitted finite element method for the
discretization of scalar surface partial differential equations on evolving
surfaces. For higher order approximations of the evolving surface we use the
technique of (iso)parametric mappings for which a level set representation of
the evolving surface is essential. We derive basic results in which certain
geometric characteristics of the exact space-time surface are related to
corresponding ones of the numerical surface approximation. These results are
used in a complete error analysis of a higher order space-time TraceFEM.


  Digital pathology has advanced substantially over the last decade however
tumor localization continues to be a challenging problem due to highly complex
patterns and textures in the underlying tissue bed. The use of convolutional
neural networks (CNNs) to analyze such complex images has been well adopted in
digital pathology. However in recent years, the architecture of CNNs have
altered with the introduction of inception modules which have shown great
promise for classification tasks. In this paper, we propose a modified
"transition" module which learns global average pooling layers from filters of
varying sizes to encourage class-specific filters at multiple spatial
resolutions. We demonstrate the performance of the transition module in AlexNet
and ZFNet, for classifying breast tumors in two independent datasets of scanned
histology sections, of which the transition module was superior.


  Recent successes in Generative Artificial Intelligence (GenAI) have led to
new technologies capable of generating high-quality code, natural language, and
images. The next step is to integrate GenAI technology into products, a task
typically conducted by software developers. Such product development always
comes with a certain risk of liability. Within this article, we want to shed
light on the current state of two such risks: data protection and copyright.
Both aspects are crucial for GenAI. This technology deals with data for both
model training and generated output. We summarize key aspects regarding our
current knowledge that every software developer involved in product development
using GenAI should be aware of to avoid critical mistakes that may expose them
to liability claims.


  A general rule determining how extremal branes can intersect in a
configuration with zero binding energy is presented. It is derived in a model
independent way and without explicit use of supersymmetry, solving a set of
classical equations of motion. When specializing to M and type II theories, it
is shown that some intersection rules can be consistently interpreted as
boundary rules for open branes ending on other branes.


  A theoretical study of the resonance optical response of assemblies of
oriented short (as compared to an optical wavelength) linear Frenkel chains is
carried out using a two-level model. We show that both transmittivity and
reflectivity of the film may behave in a bistable fashion and analyze how the
effects found depend on the film thickness and on the inhomogeneous width of
the exciton optical transition.


  The difference of \tau_{_B} and \tau_{_{\Lambda_b}} indicates the role of the
light flavors. We calculate the lifetimes of B-meson and \Lambda_b based on the
weak effective Hamiltonian while assuming the heavy baryon is constructed by a
heavy b-quark and a diquark containing two light quarks. In this scenario, we
use the information of the measured ratio \tau_{_{\Lambda_b}}/\tau_{_B} as
input to predict rates of the inclusive weak decays of \Sigma_{b}^{(*)} and
\Xi_{b}^{(*)} into non-bottom final states. We find that these rates of
\Sigma_{b}^{(*)} and \Xi_{b}^{(*)} are much larger than that of B-mesons and
\Lambda_b. We also give the predictions for the lifetimes of \Omega_b and
\Omega_{b}^{*}. Phenomenological implication of our result is discussed.


  Starting from a sequence regarded as a walk through some set of values, we
consider the associated loop-erased walk as a sequence of directed edges, with
an edge from $i$ to $j$ if the loop erased walk makes a step from $i$ to $j$.
We introduce a coloring of these edges by painting edges with a fixed color as
long as the walk does not loop back on itself, then switching to a new color
whenever a loop is erased, with each new color distinct from all previous
colors. The pattern of colors along the edges of the loop-erased walk then
displays stretches of consecutive steps of the walk left untouched by the
loop-erasure process. Assuming that the underlying sequence generating the
loop-erased walk is a sequence of independent random variables, each uniform on
$[N]:=\{1, 2, ..., N\}$, we condition the walk to start at $N$ and stop the
walk when it first reaches the subset $[k]$, for some $1 \leq k \leq N-1$. We
relate the distribution of the random length of this loop-erased walk to the
distribution of the length of the first loop of the walk, via Cayley's
enumerations of trees, and via Wilson's algorithm. For fixed $N$ and $k$, and
$i = 1,2, ...$, let $B_i$ denote the event that the loop-erased walk from $N$
to $[k]$ has $i +1$ or more edges, and the $i^{th}$ and $(i+1)^{th}$ of these
edges are colored differently. We show that given that the loop-erased random
walk has $j$ edges for some $1\leq j \leq N-k$, the events $B_i$ for $1 \leq i
\leq j-1$ are independent, with the probability of $B_i$ equal to $1/(k+i+1)$.
This determines the distribution of the sequence of random lengths of
differently colored segments of the loop-erased walk, and yields asymptotic
descriptions of these random lengths as $N \to \infty$.


  We study the semiclassical holographic correspondence between 2d CFT n-point
conformal blocks and massive particle configurations in the asymptotically AdS3
space. On the boundary we use the heavy-light approximation in which case two
of primary operators are the background for the other (n-2) operators
considered as fluctuations. In the bulk the particle dynamics can be reduced to
the hyperbolic time slice. Although lacking exact solutions we nevertheless
show that for any n the classical n-point conformal block is equal to the
length of the dual geodesic network connecting n-3 cubic vertices of worldline
segments. To this end, both the bulk and boundary systems are reformulated as
potential vector fields. Gradients of the conformal block and geodesic length
are given respectively by accessory parameters of the monodromy problem and
particle momenta of the on-shell worldline action represented as a function of
insertion points. We show that the accessory parameters and particle momenta
are constrained by two different algebraic equation systems which nevertheless
have the same roots thereby guaranteeing the correspondence.


  While not all researchers prioritize social impact, it is undeniably a
crucial aspect that adds significance to their work. The objective of this
paper is to explore potential gender differences in the social attention paid
to researchers and to examine their association with specific fields of study.
To achieve this goal, the paper analyzes four dimensions of social influence
and examines three measures of social attention to researchers. The dimensions
are media influence (mentions in mainstream news), political influence
(mentions in public policy reports), social media influence (mentions in
Twitter), and educational influence (mentions in Wikipedia). The measures of
social attention to researchers are: proportion of publications with social
mentions (social attention orientation), mentions per publication (level of
social attention), and mentions per mentioned publication (intensity of social
attention). By analyzing the rankings of authors -- for the four dimensions
with the three measures in the 22 research fields of the Web of Science
database -- and by using Spearman correlation coefficients, we conclude that:
1) significant differences are observed between fields; 2) the dimensions
capture different and independent aspects of the social impact. Finally, we use
non-parametric means comparison tests to detect gender bias in social
attention. We conclude that for most fields and dimensions with enough non-zero
altmetrics data, gender differences in social attention are not predominant,
but are still present and vary across fields.


  Given an $n$-gon, the poset of all collections of pairwise non-crossing
diagonals is isomorphic to the face poset of some convex polytope called
\textit{associahedron}. We replace in this setting the $n$-gon (viewed as a
disc with $n$ marked points on the boundary) with an arbitrary oriented surface
with a number of labeled marked points ("vertices"). With appropriate
definitions we arrive at cell complexes $\mathcal{D}$ (generalization of) and
its barycentric subdivision $\mathcal{BD}$. The complex $\mathcal{D}$
generalizes the associahedron. If the surface is closed, the complex
$\mathcal{D}$ (as well as $\mathcal{BD}$) is homotopy equivalent to the space
of metric ribbon graphs $RG_{g,n}^{met}$, or, equivalently, to the decorated
moduli space $\widetilde{\mathcal{M}}_{g,n}$. For bordered surfaces, we prove
the following: (1) Contraction of a boundary edge does not change the homotopy
type of the support of the complex. (2) Contraction of a boundary component to
a new marked point yields a forgetful map between two diagonal complexes which
is homotopy equivalent to the Kontsevich's tautological circle bundle. Thus,
contraction of a boundary component gives a natural simplicial model for the
tautological bundle. As an application, we compute the psi-class, that is, the
first Chern class in combinatorial terms. The latter result is an application
of the local combinatorial formula. (3) In the same way, contraction of several
boundary components corresponds to Whitney sum of the tautological bundles.


  The in-plane phonon-drag thermopower $S^g$, diffusion thermopower $S^d$ and
the power factor $PF$ are theoretically investigated in twisted bilayer
graphene (tBLG) as a function of twist angle $\theta$, temperature $T$ and
electron density $n_s$ in the region of low $T$ (1-20 K). As $\theta$
approaches magic angle $\theta_m$, the $S^g$ and $S^d$ are found to be strongly
enhanced, which is manifestation of great suppression of Fermi velocity
${\nu_F}^*$ of electrons in moire flat band near $\theta_m$. This enhancement
decreases with increasing $\theta$ and $T$. In the Bloch- Gruneisen (BG)
regime, it is found that $S^g \sim {\nu_F}^{* -2}$, $T^3$ and ${n_s}^{-1/2}$.
As $T$ increases, the exponent $\delta$ in $S^g \sim T^\delta$, changes from 3
to nearly zero and a maximum $S^g$ value of $\sim$ 10 mV/K at $\sim$ 20 K is
estimated. $S^g$ is larger (smaller) for smaller $n_s$ in low (high)
temperature region. On the other hand, $S^d$, taken to be governed by Mott
formula, $\sim {\nu_F}^{* -1}$, $T$ and ${n_s}^{-1/2}$ and $S^d<<S^g$ for $T >
\sim$ 2 K. The power factor $PF$ is also found to be strongly $\theta$
dependent and very much enhanced. Consequently, possibility of a giant figure
of merit is discussed.In tBLG, $\theta$ acts as a strong tuning parameter of
both $S^g$ and $S^d$ and $PF$ in addition to $T$ and $n_s$. Our results are
qualitatively compared with the measured out-of-plane thermopower in tBLG.


  A study of turbulent impurity transport by means of quasilinear and nonlinear
gyrokinetic simulations is presented for Wendelstein 7-X (W7-X). The
calculations have been carried out with the recently developed gyrokinetic code
stella. Different impurity species are considered in the presence of various
types of background instabilities: ITG, TEM and ETG modes for the quasilinear
part of the work; ITG and TEM for the nonlinear results. While the quasilinear
approach allows one to draw qualitative conclusions about the sign or relative
importance of the various contributions to the flux, the nonlinear simulations
quantitatively determine the size of the turbulent flux and check the extent to
which the quasilinear conclusions hold. Although the bulk of the nonlinear
simulations are performed at trace impurity concentration, nonlinear
simulations are also carried out at realistic effective charge values, in order
to know to what degree the conclusions based on the simulations performed for
trace impurities can be extrapolated to realistic impurity concentrations. The
presented results conclude that the turbulent radial impurity transport in W7-X
is mainly dominated by ordinary diffusion, which is close to that measured
during the recent W7-X experimental campaigns. It is also confirmed that
thermo-diffusion adds a weak inward flux contribution and that, in the absence
of impurity temperature and density gradients, ITG- and TEM-driven turbulence
push the impurities inwards and outwards, respectively.


  The task of Compositional Zero-Shot Learning (CZSL) is to recognize images of
novel state-object compositions that are absent during the training stage.
Previous methods of learning compositional embedding have shown effectiveness
in closed-world CZSL. However, in Open-World CZSL (OW-CZSL), their performance
tends to degrade significantly due to the large cardinality of possible
compositions. Some recent works separately predict simple primitives (i.e.,
states and objects) to reduce cardinality. However, they consider simple
primitives as independent probability distributions, ignoring the heavy
dependence between states, objects, and compositions. In this paper, we model
the dependence of compositions via feasibility and contextuality.
Feasibility-dependence refers to the unequal feasibility relations between
simple primitives, e.g., \textit{hairy} is more feasible with \textit{cat} than
with \textit{building} in the real world. Contextuality-dependence represents
the contextual variance in images, e.g., \textit{cat} shows diverse appearances
under the state of \textit{dry} and \textit{wet}. We design Semantic Attention
(SA) and generative Knowledge Disentanglement (KD) to learn the dependence of
feasibility and contextuality, respectively. SA captures semantics in
compositions to alleviate impossible predictions, driven by the visual
similarity between simple primitives. KD disentangles images into unbiased
feature representations, easing contextual bias in predictions. Moreover, we
complement the current compositional probability model with feasibility and
contextuality in a compatible format. Finally, we conduct comprehensive
experiments to analyze and validate the superior or competitive performance of
our model, Semantic Attention and knowledge Disentanglement guided Simple
Primitives (SAD-SP), on three widely-used benchmark OW-CZSL datasets.


  We summarize our results for the impact of anisotropic fermionic velocities
in (2+1)-dimensional QED on the critical number of fermion flavors, N^c_f, and
dynamical mass generation. We apply different approximation schemes for the
gauge boson vacuum polarization and the fermion-boson vertex to analyze the
according Dyson-Schwinger equations in a finite volume. Our results point
towards large variations of N^c_f away from the isotropic point in agreement
with other approaches.


  We reinvestigate the UV spectrum of NGC1535 by means of recently developed
fully line-blanketed non-LTE models. These new models account for the wind in
spherical geometry while handling the atomic data in a very similar way to the
TLUSTY code. This approach ensures at the same time realistic predictions of
the photospheric absorption lines and of the emission lines formed in the wind.
Our analysis confirms the results of previous studies. We derive T_* = 70kK,
log M_dot = -7.8M_sun/year and v_infty = 2000km/s.


  For decades, much software engineering research has been dedicated to
devising automated solutions aimed at enhancing developer productivity and
elevating software quality. The past two decades have witnessed an unparalleled
surge in the development of intelligent solutions tailored for software
engineering tasks. This momentum established the Artificial Intelligence for
Software Engineering (AI4SE) area, which has swiftly become one of the most
active and popular areas within the software engineering field.
  This Future of Software Engineering (FoSE) paper navigates through several
focal points. It commences with a succinct introduction and history of AI4SE.
Thereafter, it underscores the core challenges inherent to AI4SE, particularly
highlighting the need to realize trustworthy and synergistic AI4SE.
Progressing, the paper paints a vision for the potential leaps achievable if
AI4SE's key challenges are surmounted, suggesting a transition towards Software
Engineering 2.0. Two strategic roadmaps are then laid out: one centered on
realizing trustworthy AI4SE, and the other on fostering synergistic AI4SE.
While this paper may not serve as a conclusive guide, its intent is to catalyze
further progress. The ultimate aspiration is to position AI4SE as a linchpin in
redefining the horizons of software engineering, propelling us toward Software
Engineering 2.0.


  The L^2 -critical defocusing nonlinear Schrodinger initial value problem on
R^d is known to be locally well-posed for initial data in L^2. Hamiltonian
conservation and the pseudoconformal transformation show that global
well-posedness holds for initial data u_0 in Sobolev H^1 and for data in the
weighted space (1+|x|) u_0 in L^2. For the d=2 problem, it is known that global
existence holds for data in H^s and also for data in the weighted space
(1+|x|)^{\sigma} u_0 in L^2 for certain s, \sigma < 1. We prove: If global
well-posedness holds in H^s then global existence and scattering holds for
initial data in the weighted space with \sigma = s.


  Sensor systems typically operate under resource constraints that prevent the
simultaneous use of all resources all of the time. Sensor management becomes
relevant when the sensing system has the capability of actively managing these
resources; i.e., changing its operating configuration during deployment in
reaction to previous measurements. Examples of systems in which sensor
management is currently used or is likely to be used in the near future include
autonomous robots, surveillance and reconnaissance networks, and waveform-agile
radars. This paper provides an overview of the theory, algorithms, and
applications of sensor management as it has developed over the past decades and
as it stands today.


  We discuss the implications of the recent constraints on the Higgs sector of
the Minimal Supersymmetric extension of the Standard Model obtained by the
ATLAS and CMS collaborations at the lHC with $\sqrt s=7$ TeV and 36 pb$^{-1}$
of data. The main production and detection channel that is relevant in these
analyses is the gluon--gluon and bottom quark fusion mechanisms leading to
neutral Higgs bosons which subsequently decay into tau lepton pairs, $gg, b\bar
b \to Higgs \to \tau^+\tau^-$. In this note, we show that: i) the exclusion
limits are in fact more general than indicated by the ATLAS and CMS analyses
and are essentially independent of the scenario for the supersymmetric particle
spectrum; ii) when the exclusion limits are applied to the lowest theory
prediction for the Higgs production cross section times branching ratio, when
all theoretical uncertainties are taken into account, the bounds are somewhat
less stringent; iii) the exclusion limits from the $pp \to Higgs \to \tau^+
\tau^-$ process are so strong that only a modest improvement would be possible
when other MSSM Higgs detection channels are considered, even with femtobarn
level accumulated data. Finally and most important, we point out that the
prospects for the search for the Standard Model Higgs boson in the inclusive
$gg \to H \to \tau^+\tau^-$ channel, that is not currently considered by the
ATLAS and CMS collaborations, turn out to be very promising and with a few
inverse femtobarn data it might provide a convincing discovery signal in the
difficult 115--135 GeV mass range for the standard Higgs boson.


  The widespread adoption of Large Language Models (LLMs), exemplified by
OpenAI's ChatGPT, brings to the forefront the imperative to defend against
adversarial threats on these models. These attacks, which manipulate an LLM's
output by introducing malicious inputs, undermine the model's integrity and the
trust users place in its outputs. In response to this challenge, our paper
presents an innovative defensive strategy, given white box access to an LLM,
that harnesses residual activation analysis between transformer layers of the
LLM. We apply a novel methodology for analyzing distinctive activation patterns
in the residual streams for attack prompt classification. We curate multiple
datasets to demonstrate how this method of classification has high accuracy
across multiple types of attack scenarios, including our newly-created attack
dataset. Furthermore, we enhance the model's resilience by integrating safety
fine-tuning techniques for LLMs in order to measure its effect on our
capability to detect attacks. The results underscore the effectiveness of our
approach in enhancing the detection and mitigation of adversarial inputs,
advancing the security framework within which LLMs operate.


  Let N be a nest on a Hilbert space H and AlgN the corresponding nest algebra.
We determine the hypocompact radical of AlgN . Other topological radicals are
also characterized.


  This is the text of a summary of the workshop on asymmetric explosions held
in Austin in June, 2003. A brief review is given of the author's own interests
in dynamo theory as it may apply in the core collapse ambience. Of particular
interest are saturation fields for the cases with central neutron stars and
black holes and the possibility of driving MHD jets with the resulting fields.
Interesting physics that may arise with large fields such as effects on the
equation of state to produce anisotropic pressure and effects on neutrino cross
sections and transport are briefly outlined. A brief summary of the
contributions to the workshop is then given with special credit to Scratchy
Serapkin. Of special note were the summaries of the advances due to
spectropolarimetry in revealing the asymmetric nature of supernovae. Major
progress in understanding the binary progenitors and explosion physics of Type
Ia was presented. Other talks entwined the nature of asymmetric core collapse,
gamma-ray bursts and "hypernovae." My final charge to the attendees was "Go
thee forth and think about rotation and magnetic fields!"


  This paper is devoted to initial-boundary value problem of an extensible beam
equation with degenerate nonlocal energy damping in
$\Omega\subset\mathbb{R}^n$: $u_{tt}-\kappa\Delta u+\Delta^2u-\gamma(\Vert
\Delta u\Vert^2+\Vert u_t\Vert^2)^q\Delta u_t+f(u)=0$. We prove the global
existence and uniqueness of weak solutions, which gives a positive answer to an
open question in [24]. Moreover, we establish the existence of a strong
attractor for the corresponding weak solution semigroup, where the ``strong"
means that the compactness and attractiveness of the attractor are in the
topology of a stronger space $\mathcal{H}_{\frac{1}{q}}$.


  We report magnetic and magnetocaloric properties of Sm0.6-xLaxSr0.4MnO3 (x =
0-0.6). A rapid increase around TC and an anomalous peak at a temperature
T*<<TC occur in magnetization which lead to normal and inverse magnetocaloric
effects (MCE), respectively. While TC increases with increasing x (TC=118 K for
x=0 and TC=363 K for x=0.6), T* increases from 30 K (x=0) to 120 K (x=0.4) and
then decreases to 105 K (x=0.5). The \DeltaSm reaches +1.07 Jkg-1K-1 at 10 K
and -4 Jkg-1K-1 around TC in x=0.4 for \Delta H=5T. The inverse MCE is
attributed to antiferromagnetic coupling between Sm-4f and Mn-3d magnetic
moments.


  Classification of damages in honeycomb composite sandwich structure (HCSS) is
important to decide remedial actions. However, previous studies have only
detected damages using deviations of monitoring signal from healthy (baseline)
using a guided wave (GW) based structural health monitoring system.
Classification between various types of damages has not been reported for
challenging cases. We show that using careful feature engineering and machine
learning it is possible to classify between various types of damages such as
core crush (CC), high density core (HDC), lost film adhesive (LFA) and teflon
release film (TRF). We believe that we are the first to report numerical models
for four types of damages in HCSS, which is followed up with experimental
validation. We found that two out of four damages affect the GW signal in a
particularly similar manner. We extracted and evaluated multiple features from
time as well as frequency domains, and also experimented with features relative
to as baseline as well as those that were baseline-free. Using Pearson's
correlation coefficient based filtering, redundant features were eliminated.
Finally, using an optimal feature set determined using feature elimination,
high accuracy was achieved with a random forest classifier on held-out signals.
For evaluating performance of the proposed method for different damage sizes,
we used simulated data obtained from extensive parametric studies and got an
accuracy of 77.89%. Interpretability studies to determine importance of various
features showed that features computed using the baseline signal prove more
effective as compared to baseline-free features.


  For modern high-resolution imaging sensors, pixel binning is performed in
low-lighting conditions and in case high frame rates are required. To recover
the original spatial resolution, single-image super-resolution techniques can
be applied for upscaling. To achieve a higher image quality after upscaling, we
propose a novel binning concept using tetromino-shaped pixels. It is embedded
into the field of compressed sensing and the coherence is calculated to
motivate the sensor layouts used. Next, we investigate the reconstruction
quality using tetromino pixels for the first time in literature. Instead of
using different types of tetrominoes as proposed elsewhere, we show that using
a small repeating cell consisting of only four T-tetrominoes is sufficient. For
reconstruction, we use a locally fully connected reconstruction (LFCR) network
as well as two classical reconstruction methods from the field of compressed
sensing. Using the LFCR network in combination with the proposed tetromino
layout, we achieve superior image quality in terms of PSNR, SSIM, and visually
compared to conventional single-image super-resolution using the very deep
super-resolution (VDSR) network. For PSNR, a gain of up to
\SI[retain-explicit-plus]{+1.92}{dB} is achieved.


  Facial expressions play an important role in conveying the emotional states
of human beings. Recently, deep learning approaches have been applied to image
recognition field due to the discriminative power of Convolutional Neural
Network (CNN). In this paper, we first propose a novel Multi-Region Ensemble
CNN (MRE-CNN) framework for facial expression recognition, which aims to
enhance the learning power of CNN models by capturing both the global and the
local features from multiple human face sub-regions. Second, the weighted
prediction scores from each sub-network are aggregated to produce the final
prediction of high accuracy. Third, we investigate the effects of different
sub-regions of the whole face on facial expression recognition. Our proposed
method is evaluated based on two well-known publicly available facial
expression databases: AFEW 7.0 and RAF-DB, and has been shown to achieve the
state-of-the-art recognition accuracy.


  A method previously proposed for calculating the radius of gyration and the
intrinsic viscosity of dendrimers is modified to give a more accurate
description of existing experimental data. The new method includes some
features that were not previously considered, namely: a) a correction term to
take into account the contribution of individual friction beads, whose volumes
are not negligible in comparison with the molecule size, b) a realistic
distribution of internal angles between successive beads that define branching
points in the molecule, c) a distribution of distances between branching points
computed from Molecular Dynamics simulations of a small dendrimer with explicit
solvent. Modification a) alone is able to give a good description of the
experimental results obtained for polypropylene-imide with a diaminobutane core
in water, while the simultaneous use of the three modifications is needed to
adequately describe the experimental data of monodendrons and tridendrons of
polybenzylether in THF.


  Exact Bayesian structure discovery in Bayesian networks requires exponential
time and space. Using dynamic programming (DP), the fastest known sequential
algorithm computes the exact posterior probabilities of structural features in
$O(2(d+1)n2^n)$ time and space, if the number of nodes (variables) in the
Bayesian network is $n$ and the in-degree (the number of parents) per node is
bounded by a constant $d$. Here we present a parallel algorithm capable of
computing the exact posterior probabilities for all $n(n-1)$ edges with optimal
parallel space efficiency and nearly optimal parallel time efficiency. That is,
if $p=2^k$ processors are used, the run-time reduces to
$O(5(d+1)n2^{n-k}+k(n-k)^d)$ and the space usage becomes $O(n2^{n-k})$ per
processor. Our algorithm is based the observation that the subproblems in the
sequential DP algorithm constitute a $n$-$D$ hypercube. We take a delicate way
to coordinate the computation of correlated DP procedures such that large
amount of data exchange is suppressed. Further, we develop parallel techniques
for two variants of the well-known \emph{zeta transform}, which have
applications outside the context of Bayesian networks. We demonstrate the
capability of our algorithm on datasets with up to 33 variables and its
scalability on up to 2048 processors. We apply our algorithm to a biological
data set for discovering the yeast pheromone response pathways.


  Many service systems use technology to notify customers about their expected
waiting times or queue lengths via delay announcements. However, in many cases,
either the information might be delayed or customers might require time to
travel to the queue of their choice, thus causing a lag in information. In this
paper, we construct a neutral delay differential equation (NDDE) model for the
queue length process and explore the use of velocity information in our delay
announcement. Our results illustrate that using velocity information can have
either a beneficial or detrimental impact on the system. Thus, it is important
to understand how much velocity information a manager should use. In some
parameter settings, we show that velocity information can eliminate
oscillations created by delays in information. We derive a fixed point equation
for determining the optimal amount of velocity information that should be used
and find closed form upper and lower bounds on its value. When the oscillations
cannot be eliminated altogether, we identify the amount of velocity information
that minimizes the amplitude of the oscillations. However, we also find that
using too much velocity information can create oscillations in the queue
lengths that would otherwise be stable.


  We investigate theoretically the possibility of a wetting transition induced
by geometric roughness of a solid substrate for the case where the flat
substrate does not show a wetting layer. Our approach makes use of a novel
closed-form expression which relates the interaction between two sinusoidally
modulated interfaces to the interaction between two flat interfaces. Within the
harmonic approximation, we find that roughness-induced wetting is indeed
possible if the substrate roughness, quantified by the substrate surface area,
exceeds a certain threshold. In addition, the molecular interactions between
the substrate and the wetting substance have to satisfy several conditions.
These results are expressed in terms of a lower bound on the wetting potential
for a flat substrate in order for roughness-induced wetting to occur. This
lower bound has the following properties: A minimum is present at zero or very
small separation between the two interfaces, as characteristic for the
non-wetting situation in the flat case. Most importantly, the wetting potential
needs to have a pronounced maximum at a separation comparable to the amplitude
of the substrate roughness. These findings are in agreement with the
experimental observation of roughness-induced surface premelting at a glass-ice
interface as well as the calculation of the dispersion interaction for the
corresponding glass-water-ice system.


  Increased adoption and deployment of phasor measurement units (PMU) has
provided valuable fine-grained data over the grid. Analysis over these data can
provide insight into the health of the grid, thereby improving control over
operations. Realizing this data-driven control, however, requires validating,
processing and storing massive amounts of PMU data. This paper describes a PMU
data management system that supports input from multiple PMU data streams,
features an event-detection algorithm, and provides an efficient method for
retrieving archival data. The event-detection algorithm rapidly correlates
multiple PMU data streams, providing details on events occurring within the
power system. The event-detection algorithm feeds into a visualization
component, allowing operators to recognize events as they occur. The indexing
and data retrieval mechanism facilitates fast access to archived PMU data.
Using this method, we achieved over 30x speedup for queries with high
selectivity. With the development of these two components, we have developed a
system that allows efficient analysis of multiple time-aligned PMU data
streams.


  The Chow-Liu algorithm (IEEE Trans.~Inform.~Theory, 1968) has been a mainstay
for the learning of tree-structured graphical models from i.i.d.\ sampled data
vectors. Its theoretical properties have been well-studied and are
well-understood. In this paper, we focus on the class of trees that are
arguably even more fundamental, namely {\em homogeneous} trees in which each
pair of nodes that forms an edge has the same correlation $\rho$. We ask
whether we are able to further reduce the error probability of learning the
structure of the homogeneous tree model when {\em active learning} or {\em
active sampling of nodes or variables} is allowed. Our figure of merit is the
{\em error exponent}, which quantifies the exponential rate of decay of the
error probability with an increasing number of data samples. At first sight, an
improvement in the error exponent seems impossible, as all the edges are
statistically identical. We design and analyze an algorithm Active Learning
Algorithm for Trees with Homogeneous Edge (Active-LATHE), which surprisingly
boosts the error exponent by at least 40\% when $\rho$ is at least $0.8$. For
all other values of $\rho$, we also observe commensurate, but more modest,
improvements in the error exponent. Our analysis hinges on judiciously
exploiting the minute but detectable statistical variation of the samples to
allocate more data to parts of the graph in which we are less confident of
being correct.


  While iron emission lines are well studied in black hole systems, both in
X-ray binaries and Active Galactic Nuclei, there has been less of a focus on
these lines in neutron star low-mass X-ray binaries (LMXBs). However, recent
observations with Suzaku and XMM-Newton have revealed broad asymmetric iron
line profiles in 4 neutron star LMXBs, confirming an inner disk origin for
these lines in neutron star systems. Here, we present a search for iron lines
in 6 neutron star LMXBs. For each object we have simultaneous Chandra and RXTE
observations at 2 separate epochs, allowing for both a high resolution
spectrum, as well as broadband spectral coverage. Out of the six objects in the
survey, we only find significant iron lines in two of the objects, GX 17+2 and
GX 349+2. However, we cannot rule out that there are weak, broad lines present
in the other sources. The equivalent width of the line in GX 17+2 is consistent
between the 2 epochs, while in GX 349+2 the line equivalent width increases by
a factor of ~3 between epochs as the source flux decreases by a factor of 1.3.
This suggests that the disk is highly ionized, and the line is dominated by
recombination emission. We find that there appears to be no specific locations
in the long-term hardness-intensity diagrams where iron emission lines are
formed, though more sources and further observations are required.


  We show that the number of integer solutions for a pair of bilinear equations
in at least 2*6 variables has (up to logarithms) the expected upper bound
unless there is a structural reason why it is not the case.


  In this paper, a novel Gaussian bare-bones bat algorithm (GBBBA) and its
modified version named as dynamic exploitation Gaussian bare-bones bat
algorithm (DeGBBBA) are proposed for solving optimal reactive power dispatch
(ORPD) problem. The optimal reactive power dispatch (ORPD) plays a fundamental
role in ensuring stable, secure, reliable as well as economical operation of
the power system. The ORPD problem is formulated as a complex and nonlinear
optimization problem of mixed integers including both discrete and continuous
control variables. Bat algorithm (BA) is one of the most popular metaheuristic
algorithms which mimics the echolocation of the microbats and which has also
outperformed some other metaheuristic algorithms in solving various
optimization problems. Nevertheless, the standard BA may fail to balance
exploration and exploitation for some optimization problems and hence it may
often fall into local optima. The proposed GBBBA employs the Gaussian
distribution in updating the bat positions in an effort to mitigate the
premature convergence problem associated with the standard BA. The GBBBA takes
advantages of Gaussian sampling which begins from exploration and continues to
exploitation. DeGBBBA is an advanced variant of GBBBA in which a modified
Gaussian distribution is introduced so as to allow the dynamic adaptation of
exploitation and exploitation in the proposed algorithm. Both GBBBA and DeGBBBA
are used to determine the optimal settings of generator bus voltages, tap
setting transformers and shunt reactive sources in order to minimize the active
power loss, total voltage deviations and voltage stability index. Simulation
results show that GBBBA and DeGBBBA are robust and effective in solving the
ORPD problem.


  Autonomous Vehicles (AVs) redefine transportation with sophisticated
technology, integrating sensors, cameras, and intricate algorithms.
Implementing machine learning in AV perception demands robust hardware
accelerators to achieve real-time performance at reasonable power consumption
and footprint. Lot of research and development efforts using different
technologies are still being conducted to achieve the goal of getting a fully
AV and some cars manufactures offer commercially available systems.
Unfortunately, they still lack reliability because of the repeated accidents
they have encountered such as the recent one which happened in California and
for which the Cruise company had its license suspended by the state of
California for an undetermined period [1]. This paper critically reviews the
most recent findings of machine vision systems used in AVs from both hardware
and algorithmic points of view. It discusses the technologies used in
commercial cars with their pros and cons and suggests possible ways forward.
Thus, the paper can be a tangible reference for researchers who have the
opportunity to get involved in designing machine vision systems targeting AV


  We present a linear-response formalism for a system of correlated electrons
out of equilibrium, as relevant for the probe optical absorption in pump-probe
experiments. We consider the time dependent optical conductivity
$\sigma(\omega,t)$ and its nonequilibrium properties. As an application we
numerically study a single highly excited charged particle in the spin
background, as described within the two-dimensional $t$-$J$ model. Our results
show that the optical sum rule approaches the equilibrium-like one very fast,
however, the time evolution and the final asymptotic behavior of the absorption
spectra in the finite systems considered still reveal dependence on the type of
initial pump perturbation. This is observed in the evolution of its main
features: the mid-infrared peak and the Drude weight.


  We study the high-power asymptotic behavior of the sum-rate capacity of
multi-user interference networks with an equal number of transmitters and
receivers. We assume that each transmitter is cognizant of the message it
wishes to convey to its corresponding receiver and also of the messages that a
subset of the other transmitters wish to send. The receivers are assumed not to
be able to cooperate in any way so that they must base their decision on the
signal they receive only. We focus on the network's pre-log, which is defined
as the limiting ratio of the sum-rate capacity to half the logarithm of the
transmitted power. We present both upper and lower bounds on the network's
pre-log. The lower bounds are based on a linear partial-cancellation scheme
which entails linearly transforming Gaussian codebooks so as to eliminate the
interference in a subset of the receivers. Inter alias, the bounds give a
complete characterization of the networks and side-information settings that
result in a full pre-log, i.e., in a pre-log that is equal to the number of
transmitters (and receivers) as well as a complete characterization of networks
whose pre-log is equal to the full pre-log minus one. They also fully
characterize networks where the full pre-log can only be achieved if each
transmitter knows the messages of all users, i.e., when the side-information is
"full".


  As a new joint source-channel coding scheme, the double polar (D-Polar) codes
have been proposed recently. In this letter, a novel joint source-channel
decoder, namely the joint successive cancellation list (J-SCL) decoder, is
proposed to improve the decoding performance of the D-Polar codes. We merge the
trellis of the source polar code and that of the channel polar code to
construct a compound trellis. In this compound trellis, the joint
source-channel nodes represent both of the information bits and the
high-entropy bits. Based on the compound trellis, the J-SCL decoder is designed
to recover the source messages by combining the source SCL decoding and channel
SCL decoding. The J-SCL decoder doubles the number of the decoding paths at
each decoding level and then reserves the L paths with the smallest joint
path-metric (JPM). For the JSC node, the JPM is updated considering both the
channel decision log-likelihood ratios (LLRs) and the source decision LLRs.
Simulation results show that the J-SCL decoder outperforms the turbo-like BP
(TL-BP) decoder with lower complexity.


  According to the von Neumann-Halperin and Lapidus theorems, in a Hilbert
space the iterates of products or, respectively, of convex combinations of
orthoprojections are strongly convergent. We extend these results to the
iterates of convex combinations of products of some projections in a complex
Banach space. The latter is assumed uniformly convex or uniformly smooth for
the orthoprojections, or reflexive for more special projections, in particular,
for the hermitian ones. In all cases the proof of convergence is based on a
known criterion in terms of the boundary spectrum.


  Over the past decade, wind energy has gained more attention in the world.
However, owing to its indirectness and volatility properties, wind power
penetration has increased the difficulty and complexity in dispatching and
planning of electric power systems. Therefore, it is needed to make the
high-precision wind power prediction in order to balance the electrical power.
For this purpose, in this study, the prediction performance of linear
regression, k-nearest neighbor regression and decision tree regression
algorithms is compared in detail. k-nearest neighbor regression algorithm
provides lower coefficient of determination values, while decision tree
regression algorithm produces lower mean absolute error values. In addition,
the meteorological parameters of wind speed, wind direction, barometric
pressure and air temperature are evaluated in terms of their importance on the
wind power parameter. The biggest importance factor is achieved by wind speed
parameter. In consequence, many useful assessments are made for wind power
predictions.


  The recent evidence for coherent elastic neutrino-nucleus scattering
(CE$\nu$NS) in the NCC-1701 germanium detector using antineutrinos from the
Dresden-II nuclear reactor is in good agreement with standard model
expectations. However, we show that a $2\sigma$ improvement in the fit to the
data can be achieved if the quenching factor is described by a modified
Lindhard model. We also place constraints on the parameter space of a light
vector or scalar mediator that couples to neutrinos and quarks, and on a
neutrino magnetic moment. We demonstrate that the constraints are quite
sensitive to the quenching factor at low recoil energies by comparing
constraints for the standard Lindhard model with those by marginalizing over
the two parameters of the modified Lindhard model.


  We study plasma effects in a micromachined high-electron mobility transistor
(HEMT) with the microcantilever serving as the gate using the developed a
model. The model accounts for mechanical motion of the microcantilever and
spatio-temporal variations (plasma effects) of the two-dimensional electron
gas(2DEG) system in the transistor channel. The microcantilever mechanical
motion is described in the point-mass approximation. The hydrodynamic electron
transport model is used to describe distributed electron plasma phenomena in
the 2DEG system. Using the developed model, we calculated the response function
characterizing the amplitude microcantilever oscillations and the output
electric signal as functions of the signal frequency and the bias voltage for
the devices with different parameters. We find the voltage dependences of the
frequency of the mechanical resonance and its damping. In particular, it is
demonstrated that the amplitudes of the mechanical oscillations and output
electric signal exhibit pronounced maxima at the bias voltages close to the
voltage of the 2DEG channel depletion followed by a steep drop with further
increase in the bias voltage.


  Quantum signal processing (QSP) represents a real scalar polynomial of degree
$d$ using a product of unitary matrices of size $2\times 2$, parameterized by
$(d+1)$ real numbers called the phase factors. This innovative representation
of polynomials has a wide range of applications in quantum computation. When
the polynomial of interest is obtained by truncating an infinite polynomial
series, a natural question is whether the phase factors have a well defined
limit as the degree $d\to \infty$. While the phase factors are generally not
unique, we find that there exists a consistent choice of parameterization so
that the limit is well defined in the $\ell^1$ space. This generalization of
QSP, called the infinite quantum signal processing, can be used to represent a
large class of non-polynomial functions. Our analysis reveals a surprising
connection between the regularity of the target function and the decay
properties of the phase factors. Our analysis also inspires a very simple and
efficient algorithm to approximately compute the phase factors in the $\ell^1$
space. The algorithm uses only double precision arithmetic operations, and
provably converges when the $\ell^1$ norm of the Chebyshev coefficients of the
target function is upper bounded by a constant that is independent of $d$. This
is also the first numerically stable algorithm for finding phase factors with
provable performance guarantees in the limit $d\to \infty$.


  The notion of nonpositive curvature in Alexandrov's sense is extended to
include p-uniformly convex Banach spaces. Infinite dimensional manifolds of
semi-negative curvature with a p-uniformly convex tangent norm fall in this
class on nonpositively curved spaces, and several well-known results, such as
existence and uniqueness of best approximations from convex closed sets, or the
Bruhat-Tits fixed point theorem, are shown to hold in this setting, without
dimension restrictions. Homogeneous spaces G/K of Banach-Lie groups of
semi-negative curvature are also studied, explicit estimates on the geodesic
distance and sectional curvature are obtained. A characterization of convex
homogeneous submanifolds is given in terms of the Banach-Lie algebras. A
splitting theorem via convex expansive submanifolds is proven, inducing the
corresponding splitting of the Banach-Lie group G. Finally, these notions are
used to study the structure of the classical Banach-Lie groups of bounded
linear operators acting on a Hilbert space, and the splittings induced by
conditional expectations in such setting.


  We study visually grounded VideoQA in response to the emerging trends of
utilizing pretraining techniques for video-language understanding.
Specifically, by forcing vision-language models (VLMs) to answer questions and
simultaneously provide visual evidence, we seek to ascertain the extent to
which the predictions of such techniques are genuinely anchored in relevant
video content, versus spurious correlations from language or irrelevant visual
context. Towards this, we construct NExT-GQA -- an extension of NExT-QA with
10.5$K$ temporal grounding (or location) labels tied to the original QA pairs.
With NExT-GQA, we scrutinize a series of state-of-the-art VLMs. Through
post-hoc attention analysis, we find that these models are extremely weak in
substantiating the answers despite their strong QA performance. This exposes
the limitation of current VLMs in making reliable predictions. As a remedy, we
further explore and propose a grounded-QA method via Gaussian mask optimization
and cross-modal learning. Experiments with different backbones demonstrate that
this grounding mechanism improves both grounding and QA. With these efforts, we
aim to push towards trustworthy VLMs in VQA systems. Our dataset and code are
available at https://github.com/doc-doc/NExT-GQA.


  We measure velocity offsets in the NaI $\lambda\lambda5890, 5896$ (Na D)
interstellar medium absorption lines to track how neutral galactic winds change
as their host galaxies evolve. Our sample of $\sim$80,000 SDSS spectra at
$0.010 < z < 0.325$ includes starburst, post-starburst, and quiescent galaxies,
forming an evolutionary sequence of declining star formation rate (SFR). We
detect bulk flows across this sequence, mostly at higher host stellar
masses($log(M_{\star}/M_{\odot})>10$). Along this sequence, the fraction of
outflows decreases ($76\pm2\%$ to $65\pm4\%$ to a 3$\sigma$ upper limit of
$34\%$), and the mean velocity offset changes from outflowing to inflowing
($-84.6\pm5.9$ to $-71.6\pm11.4$ to $76.6\pm2.3\,km\,s^{-1}$). Even within the
post-starburst sample, wind speed decreases with time elapsed since the
starburst ended. These results reveal that outflows diminish as galaxies age.
For post-starbursts, there is evidence for an AGN contribution, especially to
the speediest outflows: 1) SFR declines faster in time than outflow velocity, a
decoupling arguing against massive stellar feedback; 2) of the few outflows
strong enough to escape the interstellar medium (9/105), three of the four
hosts with measured emission lines are Seyfert galaxies. For disky starburst
galaxies, however, the trends suggest flows out of the stellar disk plane (with
outflow 1/2-opening angle $> 45$ degree) instead of from the nucleus: the wind
velocity decreases as the disk becomes more edge-on, and the outflow fraction,
constant at $\sim$90$\%$ for disk inclinations $i<45$ degree, steadily
decreases from $\sim$90$%$ to 20$\%$ for $i>45$ degree.


  Having examined the application of quasi-equilibrium to hydrostatic silicon
burning in Paper I of this series, Hix & Thielemann (1996), we now turn our
attention to explosive silicon burning. Previous authors have shown that for
material which is heated to high temperature by a passing shock and then cooled
by adiabatic expansion, the results can be divided into three broad categories;
\emph{incomplete burning}, \emph{normal freezeout} and \emph{$\alpha$-rich
freezeout}, with the outcome depending on the temperature, density and cooling
timescale. In all three cases, we find that the important abundances obey
quasi-equilibrium for temperatures greater than approximately 3 GK, with
relatively little nucleosynthesis occurring following the breakdown of
quasi-equilibrium. We will show that quasi-equilibrium provides better
abundance estimates than global nuclear statistical equilibrium, even for
normal freezeout and particularly for $\alpha$-rich freezeout. We will also
examine the accuracy with which the final nuclear abundances can be estimated
from quasi-equilibrium.


  We study a distributed stochastic multi-armed bandit where a client supplies
the learner with communication-constrained feedback based on the rewards for
the corresponding arm pulls. In our setup, the client must encode the rewards
such that the second moment of the encoded rewards is no more than $P$, and
this encoded reward is further corrupted by additive Gaussian noise of variance
$\sigma^2$; the learner only has access to this corrupted reward. For this
setting, we derive an information-theoretic lower bound of
$\Omega\left(\sqrt{\frac{KT}{\mathtt{SNR} \wedge1}} \right)$ on the minimax
regret of any scheme, where $ \mathtt{SNR} := \frac{P}{\sigma^2}$, and $K$ and
$T$ are the number of arms and time horizon, respectively. Furthermore, we
propose a multi-phase bandit algorithm, $\mathtt{UE\text{-}UCB++}$, which
matches this lower bound to a minor additive factor. $\mathtt{UE\text{-}UCB++}$
performs uniform exploration in its initial phases and then utilizes the {\em
upper confidence bound }(UCB) bandit algorithm in its final phase. An
interesting feature of $\mathtt{UE\text{-}UCB++}$ is that the coarser estimates
of the mean rewards formed during a uniform exploration phase help to refine
the encoding protocol in the next phase, leading to more accurate mean
estimates of the rewards in the subsequent phase. This positive reinforcement
cycle is critical to reducing the number of uniform exploration rounds and
closely matching our lower bound.


  In the context of the Paris Agreement, Chile has pledged to reduce Greenhouse
Gases (GHG) intensity by at least 30% below 2007 levels by 2030, and to phase
out coal as a energy source by 2040, among other strategies. In pursue of these
goals, Chile has implemented a $5 per tonne of CO2 emission tax, first of its
kind in Latin America. However, such a low price has proven to be insufficient.
In our work, we study an alternative approach for capping and pricing carbon
emissions in the Chilean electric sector; the cap and trade paradigm. We model
the Chilean electric market (generators and emissions auctioneer) as a two
stage capacity expansion equilibrium problem, where we allow future investment
and trading of emission permits among generator agents. The model studies
generation and future investments in the Chilean electric sector in two regimes
of demand: deterministic and stochastic. We show that the current Chilean
Greenhouse Gases (GHG) intensity pledge does not drive an important shift in
the future Chilean electric matrix. To encourage a shift to greener
technologies, a more stringent carbon budget must be considered, resulting in a
carbon price approximately ten times higher than the present one. We also show
that achieving the emissions reduction goal does not necessarily results in
further reductions of carbon generation, or phasing out coal in the longer
term. Finally, we demonstrate that under technology change costs reductions,
higher demand scenarios will relax the need for stringent carbon budgets to
achieve new renewable energy investments and hence meet the Chilean pledges.
These results suggest that some aspects of the Chilean pledge require further
analysis, of the economic impact, particularly with the recent announcement of
achieving carbon neutrality towards 2050.


  We construct a microscopic model based on superexchange theory for a moir\'e
bilayer in chromium trihalides (Cr$X_3$, $X=$Br, I). In particular, we derive
analytically the interlayer Heisenberg exchange and the interlayer
Dzyaloshinskii-Moriya interaction with arbitrary distances (x) between spins.
Importantly, our model takes into account sliding and twisting geometries in
the interlayer $X$-$X$ hopping processes. Our approach can directly access the
$x$-dependent interlayer exchange without large unit-cell calculations. We
argue that deducing interlayer exchange by various sliding bilayers may lead to
an incomplete result in a moir\'e bilayer. Using the \textit{ab initio}
tight-binding Hamiltonian, we numerically evaluate the exchange interactions in
CrI$_3$. We find that our analytical model agrees with previous comprehensive
density functional theory studies. Furthermore, our findings reveal the
important role of the correlation effects in the $X$'s $p$ orbitals, which
gives rise to a rich interlayer magnetic interaction with remarkable
tunability.


  Data privacy protection studies how to query a dataset while preserving the
privacy of individuals whose sensitive information is contained in the dataset.
The information privacy model protects the privacy of an individual by using a
noisy channel, called privacy channel, to filter out most information of the
individual from the query's output.
  This paper studies how to construct privacy channels, which is challenging
since it needs to evaluate the maximal amount of disclosed information of each
individual contained in the query's output, called individual channel capacity.
Our main contribution is an interesting result which can transform the problem
of evaluating a privacy channel's individual channel capacity, which equals the
problem of evaluating the capacities of an infinite number of channels, into
the problem of evaluating the capacities of a finite number of channels. This
result gives us a way to utilize the results in the information theory to
construct privacy channels. As some examples, it is used to construct several
basic privacy channels, such as the random response privacy channel, the
exponential privacy channel and the Gaussian privacy channel, which are
respective counterparts of the random response mechanism, the exponential
mechanism and the Gaussian mechanism of differential privacy.


  We determine the solar neutrino fluxes from the global analysis of the most
up-to-date terrestrial and solar neutrino data including the final results of
the three phases of Borexino. The analysis are performed in the framework of
three-neutrino mixing with and without accounting for the solar luminosity
constraint. We discuss the independence of the results on the input from the
Gallium experiments. The determined fluxes are then compared with the
predictions provided by the latest Standard Solar Models. We quantify the
dependence of the model comparison with the assumptions about the normalization
of the solar neutrino fluxes produced in the CNO-cycle as well as on the
particular set of fluxes employed for the model testing.


  In this paper, a new shift strategy for the implicitly restarted generalized
second-order Arnoldi (GSOAR) method is proposed. In implicitly restarted
processes, we can get a $k$-step GSOAR decomposition from a $m$-step GSOAR
decomposition by performing $p = m-k$ implicit shifted QR iterations. The
problem of the implicitly restarted GSOAR is the mismatch between the number of
shifts and the dimension of the subspace. There are $2p$ shifts for $p$ QR
iterations. We use the shifts to filter out the unwanted information in the
current subspace; when more shifts are used, one obtains a better updated
subspace. But, if we use more than $p$ shifts, the structure of the GSOAR
decomposition will be destroyed. We propose a novel method which can use all
$2p$ candidates and preserve the special structure. The new method vastly
enhances the overall efficiency of the algorithm. Numerical experiments
illustrate the efficiency of every restart process.


  The mussel byssus system, comprising of the adhesive plaque, distal thread,
and proximal thread, plays a crucial role in the survival of marine mussels
amongst ocean waves. Whilst recent research has explored the stress-strain
behaviour of the distal thread and proximal thread through experimental
approaches, little attention has been paid to the potential analytical or
modelling methods within the current literature. In this work, analytical and
finite element (FE) inverse methods were employed for the first time to
identify the hyperelastic mechanical properties of both the plaque portion and
the proximal thread. The results have demonstrated the feasibility of applied
inverse methods in determining the mechanical properties of the constituents of
the mussel byssus system, with the residual sum of squares of 0.0004 ($N^2$)
and 0.01 ($mm^2$) for the proximal thread and the plaque portion, respectively.
By leveraging mechanical and optical tests, this inverse methodology offers a
simple and powerful means to anticipate the material properties for different
portions of the mussel byssus system, thus providing insights for mimetic
applications in engineering and materials design.


  Premier cloud service providers (CSPs) offer two types of purchase options,
namely on-demand and spot instances, with time-varying features in availability
and price. Users like startups have to operate on a limited budget and
similarly others hope to reduce their costs. While interacting with a CSP,
central to their concerns is the process of cost-effectively utilizing
different purchase options possibly in addition to self-owned instances. A job
in data-intensive applications is typically represented by a directed acyclic
graph which can further be transformed into a chain of tasks. The key to
achieving cost efficiency is determining the allocation of a specific deadline
to each task, as well as the allocation of different types of instances to the
task. In this paper, we propose a framework that determines the optimal
allocation of deadlines to tasks. The framework also features an optimal policy
to determine the allocation of spot and on-demand instances in a predefined
time window, and a near-optimal policy for allocating self-owned instances. The
policies are designed to be parametric to support the usage of online learning
to infer the optimal values against the dynamics of cloud markets. Finally,
several intuitive heuristics are used as baselines to validate the cost
improvement brought by the proposed solutions. We show that the cost
improvement over the state-of-the-art is up to 24.87% when spot and on-demand
instances are considered and up to 59.05% when self-owned instances are
considered.


  The possibilities for new or unusual kinds of topological, locally linear
periodic maps of non-prime order on closed, simply connected 4-manifolds with
positive definite intersection pairings are explored. On the one hand, certain
permutation representations on homology are ruled out under appropriate
hypotheses. On the other hand, an interesting homologically nontrivial,
pseudofree, action of the cyclic group of order 25 on a connected sum of ten
copies of the complex projective plane is constructed.


  We report point-contact measurements of anisotropic magnetoresistance (AMR)
in a single crystal of antiferromagnetic (AFM) Mott insulator Sr2IrO4. The
point-contact technique is used here as a local probe of magnetotransport
properties on the nanoscale. The measurements at liquid nitrogen temperature
revealed negative magnetoresistances (MRs) (up to 28%) for modest magnetic
fields (250 mT) applied within the IrO2 a-b plane and electric currents flowing
perpendicular to the plane. The angular dependence of MR shows a crossover from
four-fold to two-fold symmetry in response to an increasing magnetic field with
angular variations in resistance from 1-14%. We tentatively attribute the
four-fold symmetry to the crystalline component of AMR and the field-induced
transition to the effects of applied field on the canting of AFM-coupled
moments in Sr2IrO4. The observed AMR is very large compared to the crystalline
AMRs in 3d transition metal alloys/oxides (0.1-0.5%) and can be associated with
the large spin-orbit interactions in this 5d oxide while the transition
provides evidence of correlations between electronic transport, magnetic order
and orbital states. The finding of this work opens an entirely new avenue to
not only gain a new insight into physics associated with spin-orbit coupling
but also better harness the power of spintronics in a more technically
favorable fashion.


  In this paper we propose the decision of energy, environmental and climate
problems of the society with the help of arctic electric power stations. Such
stations can provide the electricity to the whole of Europe and Asian, this can
save from dependence on oil and gas. This method of producing electricity is
completely ecologically safe. Keywords: electrical engineering, power station,
design, generator, renewable, energy system


  Ischemic stroke lesion segmentation from Computed Tomography Perfusion (CTP)
images is important for accurate diagnosis of stroke in acute care units.
However, it is challenged by low image contrast and resolution of the perfusion
parameter maps, in addition to the complex appearance of the lesion. To deal
with this problem, we propose a novel framework based on synthesized pseudo
Diffusion-Weighted Imaging (DWI) from perfusion parameter maps to obtain better
image quality for more accurate segmentation. Our framework consists of three
components based on Convolutional Neural Networks (CNNs) and is trained
end-to-end. First, a feature extractor is used to obtain both a low-level and
high-level compact representation of the raw spatiotemporal Computed Tomography
Angiography (CTA) images. Second, a pseudo DWI generator takes as input the
concatenation of CTP perfusion parameter maps and our extracted features to
obtain the synthesized pseudo DWI. To achieve better synthesis quality, we
propose a hybrid loss function that pays more attention to lesion regions and
encourages high-level contextual consistency. Finally, we segment the lesion
region from the synthesized pseudo DWI, where the segmentation network is based
on switchable normalization and channel calibration for better performance.
Experimental results showed that our framework achieved the top performance on
ISLES 2018 challenge and: 1) our method using synthesized pseudo DWI
outperformed methods segmenting the lesion from perfusion parameter maps
directly; 2) the feature extractor exploiting additional spatiotemporal CTA
images led to better synthesized pseudo DWI quality and higher segmentation
accuracy; and 3) the proposed loss functions and network structure improved the
pseudo DWI synthesis and lesion segmentation performance.


  We relate the the distinguishability of quantum states with their robustness
of the entanglement, where the robustness of any resource quantifies how
tolerant it is to noise. In particular, we identify upper and lower bounds on
the probability of discriminating the states, appearing in an arbitrary
multiparty ensemble, in terms of their robustness of entanglement and the
probability of discriminating states of the closest separable ensemble. These
bounds hold true, irrespective of the dimension of the constituent systems the
number of parties involved, the size of the ensemble, and whether the
measurement strategies are local or global. Additional lower bounds on the same
quantity is determined by considering two special cases of two-state multiparty
ensembles, either having equal entanglement or at least one of them being
separable. The case of equal entanglement reveals that it is always easier to
discriminate the entangled states than the ones in the corresponding closest
separable ensemble, a phenomenon which we refer as "More indistinguishability
with less entanglement". Furthermore, we numerically explore how tight the
bounds are by examining the global discrimination probability of states
selected from Haar-uniformly generated ensembles of two two-qubit states. We
find that for two-element ensembles of unequal entanglements, the minimum of
the two entanglements must possess a threshold value for the ensemble to
exhibit "More indistinguishability with less entanglement".


  We present a hybrid numerical approach to simulate quantum many body problems
on two spatial dimensional quantum lattice models via the non-Abelian ab initio
version of the density matrix renormalization group method on state-of-the-art
high performance computing infrastructures. We demonstrate for the two
dimensional spinless fermion model and for the Hubbard model on torus geometry
that altogether several orders of magnitude in computational time can be saved
by performing calculations on an optimized basis and by utilizing hybrid
CPU-multiGPU parallelization. At least an order of magnitude reduction in
computational complexity results from mode optimization, while a further order
of reduction in wall time is achieved by massive parallelization. Our results
are measured directly in FLOP and seconds. A detailed scaling analysis of the
obtained performance as a function of matrix ranks and as a function of system
size up to $12\times 12$ lattice topology is discussed. Our CPU-multiGPU model
also tremendously accelerates the calculation of the one- and two-particle
reduced density matrices, which can be used to construct various order
parameters and trace quantum phase transitions with high fidelity.


  For recursive circular filtering based on circular statistics, we introduce a
general framework for estimation of a circular state based on different
circular distributions, specifically the wrapped normal distribution and the
von Mises distribution. We propose an estimation method for circular systems
with nonlinear system and measurement functions. This is achieved by relying on
efficient deterministic sampling techniques. Furthermore, we show how the
calculations can be simplified in a variety of important special cases, such as
systems with additive noise as well as identity system or measurement
functions. We introduce several novel key components, particularly a
distribution-free prediction algorithm, a new and superior formula for the
multiplication of wrapped normal densities, and the ability to deal with
non-additive system noise. All proposed methods are thoroughly evaluated and
compared to several state-of-the-art solutions.