  Engineering of cooling mechanism is of primary importance for the development
of nanoelectronics. Whereas radiation cooling is rather inefficient in nowadays
electronic devices, the strong anisotropy of 2D materials allows for enhanced
efficiency because their hyperbolic electromagnetic dispersion near phonon
resonances allows them to sustain much larger ($\sim 10^5$) number of radiating
channels. In this review, we address radiation cooling in 2D materials,
specifically graphene hexagonal boron nitride (hBN) heterostructures. We
present the hyperbolic dispersion of electromagnetic waves due to anisotropy,
and describe how the spontaneous fluctuations of current in a 2D electronic
channel can radiate thermal energy in its hyperbolic surrounding medium. We
show that both the regime of (i) thermal current fluctuations and (ii)
out-of-equilibrium current fluctuations can be described within the framework
of transmission line theory leading to (i) superPlanckian thermal emission and
(ii) electroluminescent cooling. We discuss a recent experimental investigation
on graphene-on-hBN transistors using electronic noise thermometry. In a high
mobility semimetal like graphene at large bias, a steady-state
out-of-equilibrium situation is caused by the constant Zener tunneling of
electrons opening a route for electroluminescence of hyperbolic electromagnetic
modes. Experiments reveal that, compared to superPlanckian thermal emission,
electroluminescence cooling is particularly prominent once the Zener tunneling
regime is reached: observed cooling powers are nine orders of magnitude larger
than in conventional LEDs.


  Bayesian inference under a set of priors, called robust Bayesian analysis,
allows for estimation of parameters within a model and quantification of
epistemic uncertainty in quantities of interest by bounded (or imprecise)
probability. Iterative importance sampling can be used to estimate bounds on
the quantity of interest by optimizing over the set of priors. A method for
iterative importance sampling when the robust Bayesian inference rely on Markov
chain Monte Carlo (MCMC) sampling is proposed. To accommodate the MCMC sampling
in iterative importance sampling, a new expression for the effective sample
size of the importance sampling is derived, which accounts for the correlation
in the MCMC samples. To illustrate the proposed method for robust Bayesian
analysis, iterative importance sampling with MCMC sampling is applied to
estimate the lower bound of the overall effect in a previously published
meta-analysis with a random effects model. The performance of the method
compared to a grid search method and under different degrees of prior-data
conflict is also explored.


  Fully convolutional networks (FCNs) have been proven very successful for
semantic segmentation, but the FCN outputs are unaware of object instances. In
this paper, we develop FCNs that are capable of proposing instance-level
segment candidates. In contrast to the previous FCN that generates one score
map, our FCN is designed to compute a small set of instance-sensitive score
maps, each of which is the outcome of a pixel-wise classifier of a relative
position to instances. On top of these instance-sensitive score maps, a simple
assembling module is able to output instance candidate at each position. In
contrast to the recent DeepMask method for segmenting instances, our method
does not have any high-dimensional layer related to the mask resolution, but
instead exploits image local coherence for estimating instances. We present
competitive results of instance segment proposal on both PASCAL VOC and MS
COCO.


  We study an exact model of string theory propagating in a space-time
containing regions with closed time-like curves (CTCs) separated from a finite
cosmological region bounded by a Big Bang and a Big Crunch. The model is an
non-trivial embedding of the Taub-NUT geometry into heterotic string theory
with a full conformal field theory (CFT) definition, discovered over a decade
ago as a heterotic coset model. Having a CFT definition makes this an excellent
laboratory for the study of the stringy fate of CTCs, the Taub cosmology, and
the Milne/Misner-type chronology horizon which separates them. In an effort to
uncover the role of stringy corrections to such geometries, we calculate the
complete set of alpha' corrections to the geometry. We observe that the key
features of Taub-NUT persist in the exact theory, together with the emergence
of a region of space with Euclidean signature bounded by time-like curvature
singularities. Although such remarks are premature, their persistence in the
exact geometry is suggestive that string theory theory is able to make physical
sense of the Milne/Misner singularities and the CTCs, despite their
pathological character in General Relativity. This may also support the
possibility that CTCs may be viable in some physical situations, and may be a
natural ingredient in pre-Big-Bang cosmological scenarios.


  A quantum-mechanical theory is PT-symmetric if it is described by a
Hamiltonian that commutes with PT, where the operator P performs space
reflection and the operator T performs time reversal. A PT-symmetric
Hamiltonian often has a parametric region of unbroken PT symmetry in which the
energy eigenvalues are all real. There may also be a region of broken PT
symmetry in which some of the eigenvalues are complex. These regions are
separated by a phase transition that has been repeatedly observed in laboratory
experiments. This paper focuses on the properties of a PT-symmetric $ig\phi^3$
quantum field theory. This quantum field theory is the analog of the
PT-symmetric quantum-mechanical theory described by the Hamiltonian
$H=p^2+ix^3$, whose eigenvalues have been rigorously shown to be all real. This
paper compares the renormalization-group properties of a conventional Hermitian
$g\phi^3$ quantum field theory with those of the PT-symmetric $ig\phi^3$
quantum field theory. It is shown that while the conventional $g\phi^3$ theory
in $d=6$ dimensions is asymptotically free, the $ig\phi^3$ theory is like a
$g\phi^4$ theory in $d=4$ dimensions; it is energetically stable,
perturbatively renormalizable, and trivial.


  Purpose - This paper presents a first step toward developing a comprehensive
methodology for fully resolved numerical simulations of fusion deposition
modeling. Design/methodology/approach - A front-tracking/finite volume method
previously developed for simulations of multiphase flows is extended to model
the injection of hot polymer and its cooling down. Findings - The accuracy and
convergence properties of the new method are tested by grid refinement and the
method is shown to produce convergent solutions for the shape of the filament,
the temperature distribution, contact area and reheat region when new filaments
are deposited on top of previously laid down filaments. Research
limitations/implications - The present paper focuses on modeling the fluid flow
and the cooling. The modeling of solidification, volume changes and residual
stresses will be described in Part II. Practical implications - The ability to
carry our fully resolved numerical simulations of the fusion deposition process
is expected to help explore new deposition strategies and to provide the
"ground truth" for the development of reduced order models. Originality/value -
The present paper is the first fully resolved simulation of the deposition in
fusion filament modeling.


  Richness estimation of an interesting area is always a challenge statistical
work due to small sample size or species identity error. In the literatures,
most richness estimators were only proposed to tackle the underestimation of
the size-limited sample. However, species identity error almost occurs in each
species survey and seriously reduces the accuracy of observed, singleton, and
doubleton richness in turns to influence the behavior of richness estimator.
Therefore, to estimate the true richness, the biased collected data due to
species identity error should be modified before processing the richness
estimation work. In the manuscript, we propose a new approach to correct the
bias of richness estimation due to species identity error. First, a species
list inventory from a subplot obtained by the investigator was used to estimate
the species identity error rate. Then, we can correct the biased observed,
singleton, and doubleton richness of the raw sampling data from the interesting
area. Finally, the richness estimators proposed in the literature could be
supplied to get the more correct estimates based on adjusted observed data. To
investigate the behavior of the proposed method, we performed simulations by
generating data sets from various species models with different species
identity error rates. For the purpose of illustration, the real data was
supplied to demonstrate our proposed approach. A presence/absence weeds species
was surveyed in the organic farmland located at Soft Bridge County in the North
of Taiwan.


  In developed countries like the USA, Germany, and the UK, the security forces
used highly sophisticated equipment, fast vehicles, drones, and helicopters to
catch offenders' vehicles. Whereas, in developing countries with limited
resources such schemes cannot be utilized due to management cost and other
constraints. In this paper, we proposed a framework called CVEH that enables
developing countries to profile the offender vehicle movements through
crowdsourcing technique and act as an early warning system to the law forcing
agencies. It also engages citizens to play their role in improving security
conditions. The proposed CVEH framework allows Vehicle-to-Infrastructure (V2I)
communication to monitor the movement of the offender's vehicle and shared its
information with the Command and Control (CC) centre. The CC centre projects
the path and engages nearly located law enforcement agencies. CVEH is developed
and evaluated on android smartphones. Simulations conducted for this study
exhibit the effectiveness of our framework.


  Recent work in mechanistic interpretability has shown that behaviors in
language models can be successfully reverse-engineered through circuit
analysis. A common criticism, however, is that each circuit is task-specific,
and thus such analysis cannot contribute to understanding the models at a
higher level. In this work, we present evidence that insights (both low-level
findings about specific heads and higher-level findings about general
algorithms) can indeed generalize across tasks. Specifically, we study the
circuit discovered in Wang et al. (2022) for the Indirect Object Identification
(IOI) task and 1.) show that it reproduces on a larger GPT2 model, and 2.) that
it is mostly reused to solve a seemingly different task: Colored Objects
(Ippolito & Callison-Burch, 2023). We provide evidence that the process
underlying both tasks is functionally very similar, and contains about a 78%
overlap in in-circuit attention heads. We further present a proof-of-concept
intervention experiment, in which we adjust four attention heads in middle
layers in order to 'repair' the Colored Objects circuit and make it behave like
the IOI circuit. In doing so, we boost accuracy from 49.6% to 93.7% on the
Colored Objects task and explain most sources of error. The intervention
affects downstream attention heads in specific ways predicted by their
interactions in the IOI circuit, indicating that this subcircuit behavior is
invariant to the different task inputs. Overall, our results provide evidence
that it may yet be possible to explain large language models' behavior in terms
of a relatively small number of interpretable task-general algorithmic building
blocks and computational components.


  Despite a large number of nonlocal kinetic energy density functionals (KEDFs)
available for large-scale calculations, most of those nonlocal KEDFs designed
for the extended systems cannot be directly applied to isolated systems. In
this manuscript, we proposed a generalized scheme to construct nonlocal KEDFs
via the local density approximation kernels and construct a family of KEDFs for
simulations of isolated systems within orbital-free density functional theory.
The performance of KEDFs has been demonstrated by several clusters encompassing
Mg, Si and GaAs. The results show that our constructed KEDFs can achieve high
numerical accuracy and stability for random clusters, therefore, making
orbital-free density functional theory accessible for practical simulations of
isolated systems.


  We examine the time evolution of a two level ion interacting with a light
field in harmonic oscillator trap and in a trap with anharmonicities. The
anharmonicities of the trap are quantified in terms of the deformation
parameter $\tau $ characterizing the q-analog of the harmonic oscillator trap.
Initially the ion is prepared in a Schr\"{o}dinger cat state. The entanglement
of the center of mass motional states and the internal degrees of freedom of
the ion results in characteristic collapse and revival pattern. We calculate
numerically the population inversion I(t), quasi-probabilities $Q(t),$ and
partial mutual quantum entropy S(P), for the system as a function of time.
Interestingly, small deformations of the trap enhance the contrast between
population inversion collapse and revival peaks as compared to the zero
deformation case. For \beta =3 and $4,(% \beta $ determines the average number
of trap quanta linked to center of mass motion) the best collapse and revival
sequence is obtained for \tau =0.0047 and \tau =0.004 respectively. For large
values of \tau decoherence sets in accompanied by loss of amplitude of
population inversion and for \tau \sim 0.1 the collapse and revival phenomenon
disappear. Each collapse or revival of population inversion is characterized by
a peak in S(P) versus t plot. During the transition from collapse to revival
and vice-versa we have minimum mutual entropy value that is S(P)=0. Successive
revival peaks show a lowering of the local maximum point indicating a
dissipative irreversible change in the ionic state. Improved definition of
collapse and revival pattern as the anharminicity of the trapping potential
increases is also reflected in the Quasi- probability versus t plots.


  The Mandelstam-Tamm quantum speed limit puts a bound on how fast a closed
system in a pure state can evolve. In this paper, we derive several extensions
of this quantum speed limit to closed systems in mixed states. We also compare
the strengths of these extensions and examine their tightness. The most widely
used extension of the Mandelstam-Tamm quantum speed limit originates in
Uhlmann's energy dispersion estimate. We carefully analyze the underlying
geometry of this estimate, an analysis that makes apparent that the Bures
metric, or equivalently the quantum Fisher information, will rarely give rise
to tight extensions. This observation leads us to address whether there is a
tightest general extension of the Mandelstam-Tamm quantum speed limit. Using a
geometric construction similar to that developed by Uhlmann, we prove that this
is indeed the case. In addition, we show that tight evolutions of mixed states
are typically generated by time-varying Hamiltonians, which contrasts with the
case for systems in pure states.


  The click statistics from on-off detector systems is quite different from the
counting statistics of the more traditional detectors. This necessitates
introduction of new parameters to characterize the nonclassicality of fields
from measurements using on-off detectors. To properly replace the Mandel Q_M
parameter, we introduce a parameter Q_B. A negative value represents a
sub-binomial statistics. This is possible only for quantum fields, even for
super-Poisson light. It eliminates the problems encountered in discerning
nonclassicality using Mandel's Q_M for on-off data.


  Utilizing the full CLEO-c data sample of 818 pb$^{-1}$ of $e^+e^-$ data taken
at the $\psi(3770)$ resonance, we update our measurements of absolute hadronic
branching fractions of charged and neutral $D$ mesons. We previously
reportedresults from subsets of these data. Using a double tag technique we
obtain branching fractions for three $D^0$ and six $D^+$ modes, including the
reference branching fractions $\mathcal{B} (D^0\to K^-\pi^+)=(3.934 \pm 0.021
\pm 0.061)\%$ and $\mathcal{B} (D^+ \to K^- \pi^+\pi^+)=(9.224 \pm 0.059 \pm
0.157)\%$. The uncertainties are statistical and systematic, respectively. In
these measurements we include the effects of final-state radiation by allowing
for additional unobserved photons in the final state, and the systematic errors
include our estimates of the uncertainties of these effects. Furthermore, using
an independent measurement of the luminosity, we obtain the cross sections
$\sigma(e^+e^-\to D^0\overline{D}{}^0)=(3.607\pm 0.017 \pm 0.056) \
\mathrm{nb}$ and $\sigma(e^+e^-\to D^+D^-)=(2.882\pm 0.018 \pm 0.042) \
\mathrm{nb}$ at a center of mass energy, $E_\mathrm{cm} = 3774 \pm 1$ MeV.


  We present an example of two infinite families of not connective groups. Both
of them are generalized of the 3-dimensional Hantzsche-Wendt group.


  Experimental evidences and theoretical motivations lead to consider the
curved space-time relativity based on the de Sitter group $SO_0(1,4)$ or
$Sp(2,2)$ as an appealing substitute to the flat space-time Poincare
relativity. Quantum elementary systems are then associated to unitary
irreducible representations of that simple Lie group. At the lowest limit of
the discrete series lies a remarkable family of scalar representations
involving Krein structures and related undecomposable representation cohomology
which deserves to be thoroughly studied in view of quantization of the
corresponding carrier fields. The purpose of this note is to present the
mathematical material needed to examine the problem and to indicate possible
extensions of an exemplary case, namely the so-called de Sitterian massless
minimally coupled field, i.e. a scalar field in de Sitter space-time which does
not couple to the Ricci curvature.


  Disasters are constant threats to humankind, and beyond losses in lives, they
cause many implicit yet profound societal issues such as wealth disparity and
digital divide. Among those recovery measures in the aftermath of disasters,
restoring and improving communication services is of vital importance. Although
existing works have proposed many architectural and protocol designs, none of
them have taken human factors and social equality into consideration. Recent
sociological studies have shown that people from marginalized groups (e.g.,
minority, low income, and poor education) are more vulnerable to communication
outages. In this work, we take pioneering efforts in integrating human factors
into an empirical optimization model to determine strategies for post-disaster
communication restoration. We cast the design into a mix-integer non-linear
programming problem, which is proven too complex to be solved. Through a suite
of convex relaxations, we then develop heuristic algorithms to efficiently
solve the transformed optimization problem. Based on a collected dataset, we
further evaluate and demonstrate how our design will prioritize communication
services for vulnerable people and promote social equality compared with an
existing modeling benchmark.


  We investigate the interaction of trains of femtosecond microjoule laser
pulses with dielectric materials by means of a multi-scale model. Our
theoretical predictions are directly confronted with experimental observations
in soda-lime glass. We show that due to the low heat conductivity, a
significant fraction of the laser energy can be accumulated in the absorption
region. Depending on the pulse repetition rate, the material can be heated to
high temperatures even though the single pulse energy is too low to induce a
significant material modification. Regions heated above the glass transition
temperature in our simulations correspond very well to zones of permanent
material modifications observed in the experiments.


  In this paper, we introduce a black-box prompt optimization method that uses
an attacker LLM agent to uncover higher levels of memorization in a victim
agent, compared to what is revealed by prompting the target model with the
training data directly, which is the dominant approach of quantifying
memorization in LLMs. We use an iterative rejection-sampling optimization
process to find instruction-based prompts with two main characteristics: (1)
minimal overlap with the training data to avoid presenting the solution
directly to the model, and (2) maximal overlap between the victim model's
output and the training data, aiming to induce the victim to spit out training
data. We observe that our instruction-based prompts generate outputs with 23.7%
higher overlap with training data compared to the baseline prefix-suffix
measurements. Our findings show that (1) instruction-tuned models can expose
pre-training data as much as their base-models, if not more so, (2) contexts
other than the original training data can lead to leakage, and (3) using
instructions proposed by other LLMs can open a new avenue of automated attacks
that we should further study and explore. The code can be found at
https://github.com/Alymostafa/Instruction_based_attack .


  We provide some new conditions under which the graded Betti numbers of a
monomial ideal can be computed in terms of the graded Betti numbers of smaller
ideals, thus complementing Eliahou and Kervaire's splitting approach. As
applications, we show that edge ideals of graphs are splittable, and we provide
an iterative method for computing the Betti numbers of the cover ideals of
Cohen-Macaulay bipartite graphs. Finally, we consider the frequency with which
one can find particular splittings of monomial ideals and raise questions about
ideals whose resolutions are characteristic-dependent.


  We study analytically and by means of an off-lattice bead-spring dynamic
Monte Carlo simulation model the adsorption kinetics of a single macromolecule
on a structureless flat substrate in the regime of strong physisorption. The
underlying notion of a ``stem-flower'' polymer conformation, and the related
mechanism of ``zipping'' during the adsorption process are shown to lead to a
Fokker-Planck equation with reflecting boundary conditions for the
time-dependent probability distribution function (PDF) of the number of
adsorbed monomers. The theoretical treatment predicts that the mean fraction of
adsorbed segments grows with time as a power law with a power of $(1+\nu)^{-1}$
where $\nu\approx 3/5$ is the Flory exponent. The instantaneous distribution of
train lengths is predicted to follow an exponential relationship. The
corresponding PDFs for loops and tails are also derived. The complete solution
for the time-dependent PDF of the number of adsorbed monomers is obtained
numerically from the set of discrete coupled differential equations and shown
to be in perfect agreement with the Monte Carlo simulation results. In addition
to homopolymer adsorption, we study also regular multiblock copolymers and
random copolymers, and demonstrate that their adsorption kinetics may be
considered within the same theoretical model.


  Large language models have demonstrated their capabilities in storyline
creation and human-like character role-playing. Current language model agents
mainly focus on reasonable behaviors from the level of individuals, and their
behaviors might be hard to constraint on the level of the whole storyline. In
this paper we introduce IBSEN, a director-actor coordinate agent framework that
generates drama scripts and makes the plot played by agents more controllable.
The director agent writes plot outlines that the user desires to see, instructs
the actor agents to role-play their characters, and reschedules the plot when
human players participate in the scenario to ensure the plot is progressing
towards the objective. To evaluate the framework, we create a novel drama plot
that involves several actor agents and check the interactions between them
under the instruction of the director agent. Evaluation results show that our
framework could generate complete, diverse drama scripts from only a rough
outline of plot objectives, meanwhile maintaining the characteristics of
characters in the drama. Our codes and prompts are available at
https://github.com/OpenDFM/ibsen.


  We investigate multi-terminal quantum transport through single monocyclic
aromatic annulene molecules, and their derivatives, using the nonequilibrium
Green function approach in the self-consistent Hartree-Fock approximation. A
new device concept, the Quantum Interference Effect Transistor (QuIET) is
proposed, exploiting perfect destructive interference stemming from molecular
symmetry, and controlling current flow by introducing decoherence and/or
elastic scattering that break the symmetry. This approach overcomes the
fundamental problems of power dissipation and environmental sensitivity that
beset many nanoscale device proposals.


  Prediction has recently been considered as a promising approach to meet
low-latency and high-reliability requirements in long-distance haptic
communications. However, most of the existing methods did not take features of
tasks and the relationship between prediction and communication into account.
In this paper, we propose a task-oriented prediction and communication
co-design framework, where the reliability of the system depends on prediction
errors and packet losses in communications. The goal is to minimize the
required radio resources subject to the low-latency and high-reliability
requirements of various tasks. Specifically, we consider the just noticeable
difference (JND) as a performance metric for the haptic communication system.
We collect experiment data from a real-world teleoperation testbed and use
time-series generative adversarial networks (TimeGAN) to generate a large
amount of synthetic data. This allows us to obtain the relationship between the
JND threshold, prediction horizon, and the overall reliability including
communication reliability and prediction reliability. We take 5G New Radio as
an example to demonstrate the proposed framework and optimize bandwidth
allocation and data rates of devices. Our numerical and experimental results
show that the proposed framework can reduce wireless resource consumption up to
77.80% compared with a task-agnostic benchmark.


  We demonstrate that conditional as well as unconditional basic operations
which are prerequisite for universal quantum gates can be performed with almost
100% fidelity within a strongly interacting two-electron quantum ring. Both
sets of operations are based on a quantum control algorithm that optimizes a
driving electromagnetic pulse for a given quantum gate. The demonstrated
transitions occur on a time scale much shorter than typical decoherence times
of the system.


  We show that a dynamically convex Reeb flow on the standard tight lens space
$(L(p, 1),\xi_{\mathrm{std}})$, $p>1,$ admits a $p$-unknotted closed Reeb orbit
$P$ which is the binding of a rational open book decomposition with disk-like
pages. Each page is a rational global surface of section for the Reeb flow and
the Conley-Zehnder index of the $p$-th iterate of $P$ is $3$. We also check
dynamical convexity in the H\'enon-Heiles system for low positive energies. In
this case the rational open book decomposition follows from the fact that the
sphere-like component of the energy surface admits a $\mathbb{Z}_{3}$-symmetric
periodic orbit and the flow descends to a Reeb flow on the standard tight
$(L(3,2),\xi_{\mathrm{std}})$.


  In this paper, we give a new representation of the Fibonacci numbers. This is
achieved using Fibonacci trees. With the help of this representation, the nth
Fibonacci number can be calculated without having any knowledge about the
previous Fibonacci numbers.


  In previous work, the Lorentzian proper vertex amplitude for a spin-foam
model of quantum gravity was derived. In the present work, the asymptotics of
this amplitude are studied in the semi-classical limit. The starting point of
the analysis is an expression for the amplitude as an action integral with
action differing from that in the EPRL case by an extra `projector' term which
scales linearly with spins only in the asymptotic limit, and is discontinuous
on a submanifold of the integration domain. New tools are introduced to
generalize stationary phase methods to this case. For the case of boundary data
which can be glued to a non-degenerate Lorentzian 4-simplex, the asymptotic
limit of the amplitude is shown to equal the single Feynman term, showing that
the extra term in the asymptotics of the EPRL amplitude has been eliminated.


  Gamma-ray astronomy will play a crucial role in the investigation of
nonthermal processes in the large scale structure of the universe.
Particularly, galaxy clusters (GC) observations at this photon energy will help
us understand the origin of radio emitting high energy particles, the possible
level of cosmic-ray (CR) pressure in intracluster environment, and the strength
of intracluster magnetic fields. In addition here we point out the importance
of these observations for a possible detection of cluster accretion shocks and
for constraining their CR acceleration efficiency. We model spatial and
spectral properties of gamma-ray emission due to shock accelerated CRs in GC
and emphasize the importance of imaging capability of upcoming gamma-ray
facilities for a correct interpretation of observational results.


  We describe the structure of connected graphs with the minimum and maximum
average distance, radius, diameter, betweenness centrality, efficiency and
resistance distance, given their order and size. We find tight bounds on these
graph qualities for any arbitrary number of nodes and edges and analytically
derive the form and properties of such networks.


  Janus single-layer transition metal dichalcogenides, in which the two
chalcogen layers have a different chemical nature, push chemical composition
control beyond what is usually achievable with van der Waals heterostructures.
Here we report such a novel Janus compound, SPtSe, which is predicted to
exhibit strong Rashba spin-orbit coupling. We synthetized it by conversion of a
single-layer of PtSe$_2$ on Pt(111) via sulphurization under H$_2$S atmosphere.
Our in situ and operando structural analysis with grazing incidence synchrotron
X-ray diffraction reveals the process by which the Janus alloy forms. The
crystalline long-range order of the as-grown PtSe$_2$ monolayer is first lost
due to thermal annealing. A subsequent recrystallization in presence of a
source of sulphur yields a highly ordered SPtSe alloy, which is iso-structural
to the pristine PtSe$_2$. The chemical composition is resolved, layer-by-layer,
using angle-resolved X-ray photoelectron spectroscopy, demonstrating that
Se-by-S substitution occurs selectively in the topmost chalcogen layer.


  It was recently established that self-healing slip pulses under uniform
prestress $\tau_b$ are unstable frictional rupture modes, i.e., they either
slowly expand/decay with time t. Furthermore, their dynamics were shown to
follow a reduced-dimensionality description corresponding to a special $L(c)$
line in a plane defined by the pulse propagation velocity $c(t)$ and size
$L(t)$. Yet, uniform prestress is rather the exception than the rule in natural
faults. We study the effects of a spatially-varying prestress $\tau_b(x)$ on 2D
slip pulses, initially generated under a uniform $\tau_b$ along a
rate-and-state friction fault. We consider periodic and constant-gradient
prestress $\tau_b(x)$ around the reference uniform $\tau_b$. For a periodic
$\tau_b(x)$, pulses either sustain and form quasi-limit cycles in the $L-c$
plane or decay predominantly monotonically along the $L(c)$ line, depending on
the instability index of the initial pulse and the properties of the periodic
$\tau_b(x)$. For a constant-gradient $\tau_b(x)$, expanding/decaying pulses
closely follow the $L(c)$ line, with systematic shifts determined by the sign
and magnitude of the gradient. We also find that a spatially-varying
$\tau_b(x)$ can revert the expanding/decaying nature of the initial reference
pulse. Finally, we show that a constant-gradient $\tau_b(x)$, of sufficient
magnitude and specific sign, can lead to the nucleation of a back-propagating
rupture at the healing tail of the initial pulse, generating a bilateral
crack-like rupture. This pulse-to-crack transition, along with the
above-described effects, demonstrate that rich rupture dynamics merge from a
simple, nonuniform prestress. Furthermore, we show that as long as pulses
exist, their dynamics are related to the special $L(c)$ line, providing an
effective, reduced-dimensionality description of unsteady slip pulses under
spatially-varying prestress.


  A study of heavy flavor production in different collision systems in various
kinematic regions presents an opportunity to probe cold nuclear medium and hot
dense matter effects. Results from the PHENIX experiment on $J/\psi$ and open
charm production in Au+Au and Cu+Cu collisions at $\sqrt{s_{NN}}$ =200 GeV are
presented. The data show strong $J/\psi$ suppression in central AA collisions,
similar to NA50 results, and strong suppression in high $p_T$ open charm
production. The $J/\psi$ production in Au+Au and d+Au collisions is compared to
understand the cold nuclear medium effects. The data show significant cold
nuclear effects in charm production in d+Au collisions at forward and backward
rapidity ranges.


  In this paper, we present a large-scale hurricane Michael dataset for visual
perception in disaster scenarios, and analyze state-of-the-art deep neural
network models for semantic segmentation. The dataset consists of around 2000
high-resolution aerial images, with annotated ground-truth data for semantic
segmentation. We discuss the challenges of the dataset and train the
state-of-the-art methods on this dataset to evaluate how well these methods can
recognize the disaster situations. Finally, we discuss challenges for future
research.


  As transformer-based object detection models progress, their impact in
critical sectors like autonomous vehicles and aviation is expected to grow.
Soft errors causing bit flips during inference have significantly impacted DNN
performance, altering predictions. Traditional range restriction solutions for
CNNs fall short for transformers. This study introduces the Global Clipper and
Global Hybrid Clipper, effective mitigation strategies specifically designed
for transformer-based models. It significantly enhances their resilience to
soft errors and reduces faulty inferences to ~ 0\%. We also detail extensive
testing across over 64 scenarios involving two transformer models (DINO-DETR
and Lite-DETR) and two CNN models (YOLOv3 and SSD) using three datasets,
totalling approximately 3.3 million inferences, to assess model robustness
comprehensively. Moreover, the paper explores unique aspects of attention
blocks in transformers and their operational differences from CNNs.


  We study non-analytic terms, which cannot be written in the form of any
positive integer power of field-dependent mass squared, in effective potential
at finite temperature in one-loop approximation for a real scalar field on the
$D$-dimensional space-time, $S_{\tau}^1\times
R^{D-(p+1)}\times\prod_{i=1}^pS_i^1$. The effective potential can be recast
into the integral form in the complex plane by using the integral
representation for the modified Bessel function of the second kind and the
analytical extension for multiple mode summations. The pole structure of the
mode summations is clarified and all the non-analytic terms are obtained by the
residue theorem. We find that the effective potential has a non-analytic term
when the dimension of the flat Euclidean space, $D-(p+1)$ is odd. There appears
only one non-analytic term for the given values of $D$ and $p$, for which the
non-analytic term exists.


  We performed the first systematic study of a new attack on Ethereum that
steals cryptocurrencies. The attack is due to the unprotected JSON-RPC
endpoints existed in Ethereum nodes that could be exploited by attackers to
transfer the Ether and ERC20 tokens to attackers-controlled accounts. This
study aims to shed light on the attack, including malicious behaviors and
profits of attackers. Specifically, we first designed and implemented a
honeypot that could capture real attacks in the wild. We then deployed the
honeypot and reported results of the collected data in a period of six months.
In total, our system captured more than 308 million requests from 1,072
distinct IP addresses. We further grouped attackers into 36 groups with 59
distinct Ethereum accounts. Among them, attackers of 34 groups were stealing
the Ether, while other 2 groups were targeting ERC20 tokens. The further
behavior analysis showed that attackers were following a three-steps pattern to
steal the Ether. Moreover, we observed an interesting type of transaction
called zero gas transaction, which has been leveraged by attackers to steal
ERC20 tokens. At last, we estimated the overall profits of attackers. To engage
the whole community, the dataset of captured attacks is released on
https://github.com/zjuicsr/eth-honey.


  This paper focuses on devising graph signal processing tools for the
treatment of data defined on the edges of a graph. We first show that
conventional tools from graph signal processing may not be suitable for the
analysis of such signals. More specifically, we discuss how the underlying
notion of a `smooth signal' inherited from (the typically considered variants
of) the graph Laplacian are not suitable when dealing with edge signals that
encode a notion of flow. To overcome this limitation we introduce a class of
filters based on the Edge-Laplacian, a special case of the Hodge-Laplacian for
simplicial complexes of order one. We demonstrate how this Edge-Laplacian leads
to low-pass filters that enforce (approximate) flow-conservation in the
processed signals. Moreover, we show how these new filters can be combined with
more classical Laplacian-based processing methods on the line-graph. Finally,
we illustrate the developed tools by denoising synthetic traffic flows on the
London street network.


  In this paper, we investigate the $P-V$ criticality and phase transition of
charged accelerating AdS black holes in the extended thermodynamic phase space
in analogy between black hole system and Van der Waals liquid-gas system, where
the cosmological constant $\Lambda$ is treated as a thermodynamical variable
interpreted as dynamic pressure and its conjugate quantity is the thermodynamic
volume of the black holes. When the electric charge vanishes, we find that no
$P-V$ criticality will appear but the Hawking-Page like phase transition will
be present, just as what Schwarzschild-AdS black holes behave like. For the
charged case, the $P-V$ criticality appears and the accelerating black holes
will undergo a small black hole/large phase transition under the condition that
the acceleration parameter $A$ and the horizon radius $r_h$ meet a certain
simple relation $A r_h=a$, where $a$ is a constant in our discussion. To make
$P-V$ criticality appear, there exists an upper bounds for constant $a$. When
$P-V$ criticality appears, we calculate the critical pressure $P_{c}$, critical
temperature $T_{c}$ and critical specific volume $r_{c}$, and we find that
$\frac{P_{c}r_{c}}{T_{c}}$ is an universal number.


  We prove the $C^{\alpha}$ regularity for weak solutions to a class of
ultraparabolic equation, with measurable coefficients. The results generalized
our recent $C^{\alpha}$ regularity results of Prandtl's system to high
dimensional cases.


  Most state-of-the-art neural machine translation systems, despite being
different in architectural skeletons (e.g. recurrence, convolutional), share an
indispensable feature: the Attention. However, most existing attention methods
are token-based and ignore the importance of phrasal alignments, the key
ingredient for the success of phrase-based statistical machine translation. In
this paper, we propose novel phrase-based attention methods to model n-grams of
tokens as attention entities. We incorporate our phrase-based attentions into
the recently proposed Transformer network, and demonstrate that our approach
yields improvements of 1.3 BLEU for English-to-German and 0.5 BLEU for
German-to-English translation tasks on WMT newstest2014 using WMT'16 training
data.


  We propose a scheme for efficient long-range energy transfer between two
distant light emitters separated by more than one wavelength of light, i.e.
much beyond the classical Forster radius. A hybrid nanoantenna-waveguide system
mediates the transmission of energy, showing enhancements up to 10^8 as
compared to vacuum. Our model shows how energy transfer in nanostructured media
can be boosted, beyond the simple donor Purcell enhancement, and in particular
for large donor-acceptor separations. The scheme we propose connects realistic
emitters and could lead to practical on-chip implementations.


  We present the barycentric Lagrange dual tree traversal (BLDTT) fast
summation method for particle interactions. The scheme replaces well-separated
particle-particle interactions by adaptively chosen particle-cluster,
cluster-particle, and cluster-cluster approximations given by barycentric
Lagrange interpolation at proxy particles on a Chebyshev grid in each cluster.
The BLDTT is kernel-independent and the approximations can be efficiently
mapped onto GPUs, where target particles provide an outer level of parallelism
and source particles provide an inner level of parallelism. We present an
OpenACC GPU implementation of the BLDTT with MPI remote memory access for
distributed memory parallelization. The performance of the GPU-accelerated
BLDTT is demonstrated for calculations with different problem sizes, particle
distributions, geometric domains, and interaction kernels, as well as for
unequal target and source particles. Comparison with our earlier
particle-cluster barycentric Lagrange treecode (BLTC) demonstrates the superior
performance of the BLDTT. In particular, on a single GPU for problem sizes
ranging from $N$=1E5 to 1E8, the BLTC has $O(N\log N)$ scaling, while the BLDTT
has $O(N)$ scaling. In addition, MPI strong scaling results are presented for
the BLTC and BLDTT using $N$=64E6 particles on up to 32 GPUs.


  The purpose of this manuscript is to review my recent activity on three main
research topics. The first concerns the nature of low temperature amorphous
solids and their relation with the spin glass transition in a magnetic field.
This is the subject of the first chapter where I discuss a new model, the KHGPS
model, which allows to make some progress. In the second chapter I review a
second research line that concerns the study of the rigidity/jamming
transitions in particle system models and their relation to constraint
satisfaction and optimization problems in high dimension. Finally in the last
chapter I review my activity on the problem of the dynamics of learning
algorithms in high-dimensional inference and supervised learning problems.


  We investigate the impact of the observed correlation between a galaxies
shape and its surrounding density field on the measurement of third order weak
lensing shear statistics. Using numerical simulations, we estimate the
systematic error contribution to a measurement of the third order moment of the
aperture mass statistic (GGG) from three-point intrinsic ellipticity
correlations (III), and the three-point coupling between the weak lensing shear
experienced by distant galaxies and the shape of foreground galaxies (GGI and
GII). We find that third-order weak lensing statistics are typically more
strongly contaminated by these physical systematics compared to second-order
shear measurements, contaminating the measured three-point signal for
moderately deep surveys with a median redshift z_m ~ 0.7 by ~ 15%. It has been
shown that accurate photometric redshifts will be crucial to correct for this
effect, once a model and the redshift dependence of the effect can be
accurately constrained. To this end we provide redshift-dependent fitting
functions to our results and propose a new tool for the observational study of
intrinsic galaxy alignments. For a shallow survey with z_m ~ 0.4 we find III to
be an order of magnitude larger than the expected cosmological GGG shear
signal. Compared to the two-point intrinsic ellipticity correlation which is
similar in amplitude to the two-point shear signal at these survey depths,
third order statistics therefore offer a promising new way to constrain models
of intrinsic galaxy alignments. Early shallow data from the next generation of
very wide weak lensing surveys will be optimal for this type of study.


  Recursion relations for integrals of amplitudes over the phase space, i.e.
for partial wave amplitudes, are introduced. In their simplest form these
integrals are proportional to the s-wave amplitudes and represent rigorous
lower bounds on the total cross sections. The connection with classical field
equations in $D$ dimensions is established. Previous results on multiparticle
amplitudes are easily reproduced.


  We present a detailed V-band photometric light curve modeling of 30 eclipsing
binaries using the data from Pietrukowicz et al. (2009) collected with the
European Southern Observatory Very Large Telescope (ESO VLT) of diameter 8-m.
The light curve of these 30 eclipsing binaries were selected out of 148 of them
available in the database on the basis of complete phase coverage, regular and
smooth phased light curve shapes. Eclipsing binaries play pivotal role in the
direct measurement of astronomical distances more accurately simply from their
geometry of light curves. The accurate value of Hubble constant (H0) which
measures the rate of expansion of the Universe heavily relies on extragalactic
distance scale measurements. Classification of the selected binary stars in the
sample were done, preliminarily on the basis of Fourier parameters in the a2-a4
plane and final classification was obtained from the Roche lobe geometry. Out
of these 30 eclipsing binaries, only one was found to be detached binary system
while the rest 29 of them belong to the contact binary systems. These contact
binaries were further classified into the A-type and W-type based on their mass
ratios. Since spectroscopic mass ratio measurements were not available for any
of these binary stars, we determined the mass ratios through photometric light
curve modeling with the aid of Wilson-Devinney code as implemented in PHOEBE.
Various geometrical parameters and physical parameters of astrophysical
importance viz., mass, radius and luminosity were obtained from the light
curves of the selected stars.


  The Atacama B-Mode Search (ABS) instrument is a cryogenic ($\sim$10 K)
crossed-Dragone telescope located at an elevation of 5190 m in the Atacama
Desert in Chile that observed for three seasons between February 2012 and
October 2014. ABS observed the Cosmic Microwave Background (CMB) at large
angular scales ($40<\ell<500$) to limit the B-mode polarization spectrum around
the primordial B-mode peak from inflationary gravity waves at $\ell \sim100$.
The ABS focal plane consists of 480 transition-edge sensor (TES) bolometers.
They are coupled to orthogonal polarizations from a planar ortho-mode
transducer (OMT) and observe at 145 GHz. ABS employs an ambient-temperature,
rapidly rotating half-wave plate (HWP) to mitigate systematic effects and move
the signal band away from atmospheric $1/f$ noise, allowing for the recovery of
large angular scales. We discuss how the signal at the second harmonic of the
HWP rotation frequency can be used for data selection and for monitoring the
detector responsivities.


  Inferring latent attributes of people online is an important social computing
task, but requires integrating the many heterogeneous sources of information
available on the web. We propose learning individual representations of people
using neural nets to integrate rich linguistic and network evidence gathered
from social media. The algorithm is able to combine diverse cues, such as the
text a person writes, their attributes (e.g. gender, employer, education,
location) and social relations to other people. We show that by integrating
both textual and network evidence, these representations offer improved
performance at four important tasks in social media inference on Twitter:
predicting (1) gender, (2) occupation, (3) location, and (4) friendships for
users. Our approach scales to large datasets and the learned representations
can be used as general features in and have the potential to benefit a large
number of downstream tasks including link prediction, community detection, or
probabilistic reasoning over social networks.


  Recent studies have shown that the aggregated dynamic flexibility of an
ensemble of thermostatic loads can be modeled in the form of a virtual battery.
The existing methods for computing the virtual battery parameters require the
knowledge of the first-principle models and parameter values of the loads in
the ensemble. In real-world applications, however, it is likely that the only
available information are end-use measurements such as power consumption, room
temperature, device on/off status, etc., while very little about the individual
load models and parameters are known. We propose a transfer learning based deep
network framework for calculating virtual battery state of a given ensemble of
flexible thermostatic loads, from the available end-use measurements. This
proposed framework extracts first order virtual battery model parameters for
the given ensemble. We illustrate the effectiveness of this novel framework on
different ensembles of ACs and WHs.


  We consider the wave equation $\varepsilon^2(-\partial_t^2 + \Delta)u + f(u)
= 0$ for $0<\varepsilon\ll 1$, where $f$ is the derivative of a balanced,
double-well potential, the model case being $f(u) = u-u^3$. For equations of
this form, we construct solutions that exhibit an interface of thickness
$O(\varepsilon )$ that separates regions where the solution is
$O(\varepsilon^k)$ close to $\pm 1$, and that is close to a timelike
hypersurface of vanishing {\em Minkowskian} mean curvature. This provides a
Minkowskian analog of the numerous results that connect the Euclidean
Allen-Cahn equation and minimal surfaces or the parabolic Allen-Cahn equation
and motion by mean curvature. Compared to earlier results of the same
character, we develop a new constructive approach that applies to a larger
class of nonlinearities and yields much more precise information about the
solutions under consideration.


  A detailed study of energy dependence of positive kaon to pion, negative kaon
to pion and total kaon to pion multiplicity ratio have been carried out in pp
collisions at 6.3, 17.3, 62.4, 200 and 900 GeV and also at root s =2.76 TeV and
7 TeV in the framework of UrQMD and DPMJET III model. Energy dependence of
positive kaon to pion and negative kaon to pion show different behavior in case
of UrQMD and DPMJET III model. The presence of the horn like structure in the
variation of positive kaon to pion and negative kaon to pion ratio with energy
for the experimental data is supported by the DPMJET III model. Experimentally
it has been observed that as energy increases, the total kaon to pion
multiplicity ratio increases systematically for pp collisions at lower energies
and becomes independent of energy in LHC energy regime. Our analysis on total
kaon to pion multiplicity ratio with UrQMD data is well supported by the
experimental results obtained by different collaborations in different times.
In case of DPMJET III data, the saturation of kaon to pion ratio at LHC region
has not been observed.


  Accurate and economical modeling of near-surface transport processes is a
standing challenge for various engineering and atmospheric boundary-layer
flows. In this paper, we address this challenge by utilizing a stochastic
one-dimensional turbulence (ODT) model. ODT aims to resolve all relevant scales
of a turbulent flow for a one-dimensional domain. Here ODT is applied to
turbulent channel flow as stand-alone tool. The ODT domain is a wall-normal
line that is aligned with the mean shear. The free model parameters are
calibrated once for the turbulent velocity boundary layer at a fixed Reynolds
number. After that, we use ODT to investigate the Schmidt ($Sc$), Reynolds
($Re$), and Peclet ($Pe$) number dependence of the scalar boundary-layer
structure, turbulent fluctuations, transient surface fluxes, mixing, and
transfer to a wall. We demonstrate that the model is able to resolve relevant
wall-normal transport processes across the turbulent boundary layer and that it
captures state-space statistics of the surface scalar-flux fluctuations. In
addition, we show that the predicted mean scalar transfer, which is quantified
by the Sherwood ($Sh$) number, self-consistently reproduces established scaling
regimes and asymptotic relations. For high asymptotic $Sc$ and $Re$, ODT
results fall between the Dittus--Boelter, $Sh\sim Re^{4/5}\,Sc^{2/5}$, and
Colburn, $Sh\sim Re^{4/5}\,Sc^{1/3}$, scalings but they are closer to the
former. For finite $Sc$ and $Re$, the model prediction reproduces the relation
proposed by Schwertfirm and Manhart (Int. J. Heat Fluid Flow, vol. 28, pp.
1204-1214, 2007) that yields locally steeper effective scalings than any of the
established asymptotic relations. The model extrapolates the scalar transfer to
small asymptotic $Sc\ll Re_\tau^{-1}$ (diffusive limit) with a functional form
that has not been previously described.


  The composite CBS-QB3 method is widely used to obtain accurate energies of
molecules and radicals although its use in the case of singlet diradicals gives
rise to some difficulties. The problem is related to the parameterized
correction this method introduces to account for spin-contamination. We report
a new term specifically designed to describe singlet diradicals separated by at
least one CH2 unit. As a test case, we have computed the formation enthalpy of
a series of diradicals that includes hydrocarbons as well as systems involving
heteroatoms (nitrogen, oxygen). The resulting CBS-QB3 energies are very close
to experiment.


  An algorithm for finding the eigenvalue of a nonnegative irreducible tensor
was recently proposed by Michael Ng, Liqun Qi, and Guanglu Zhou in {\it Finding
the largest eigenvalue of a nonnegative tensor}. However, the authors did not
prove the proposed algorithm converges. In this paper, primitive tensors are
defined and the algorithm is shown to converge for primitive tensors.


  We study the critical behavior in the black p-branes and four dimensional
charged dilaton black holes. We calculate the thermodynamic fluctuations in the
various (microcanonical, canonical, and grandcanonical) ensembles. It is found
that the extremal limit of some black configurations has a critical point and a
phase transition takes place from the extremal to nonextremal black
configurations. Some critical exponents are obtained, which satisfy the scaling
laws. This is related to the fact that the entropy of these black
configurations is a homogeneous function.


  This paper studies noisy low-rank matrix completion: given partial and noisy
entries of a large low-rank matrix, the goal is to estimate the underlying
matrix faithfully and efficiently. Arguably one of the most popular paradigms
to tackle this problem is convex relaxation, which achieves remarkable efficacy
in practice. However, the theoretical support of this approach is still far
from optimal in the noisy setting, falling short of explaining its empirical
success.
  We make progress towards demystifying the practical efficacy of convex
relaxation vis-\`a-vis random noise. When the rank and the condition number of
the unknown matrix are bounded by a constant, we demonstrate that the convex
programming approach achieves near-optimal estimation errors --- in terms of
the Euclidean loss, the entrywise loss, and the spectral norm loss --- for a
wide range of noise levels. All of this is enabled by bridging convex
relaxation with the nonconvex Burer-Monteiro approach, a seemingly distinct
algorithmic paradigm that is provably robust against noise. More specifically,
we show that an approximate critical point of the nonconvex formulation serves
as an extremely tight approximation of the convex solution, thus allowing us to
transfer the desired statistical guarantees of the nonconvex approach to its
convex counterpart.


  The longest recognized stellar stream in the Milky Way Galaxy has an expanse
of over more than half the north sky. There was a physical disturbance within
the stream, 500 million years ago, which could have been the scar of a dark
matter collision. Due to its proximity to the galactic center, the GD-1 stellar
stream can act as an antenna for gravitational perturbations. In 2018, a
significant gap in GD-1 was discovered due to perturbation. A stream gap occurs
when a massive object collides with the stellar stream. Based on the chasm
location and width, we can guess when and where the impact occurred. Using
globular cluster sky coordinates and simulated galactocentric distributions, we
calculated how close each globular cluster came to the GD-1 stream. This would
be the first time a globular cluster has come close enough to the GD-1 stream
to impact another object. Clusters, on the other hand, rarely approach GD-1,
indicating that it was struck by something more exotic, like a clump of dark
matter, when discovered. A simulation and theoretical model were created to
better understand the GD-1 stellar stream behavior. These details can be used
to map the large-scale distribution of dark matter in our galaxy as well as the
small-scale structure of dark matter in the host galaxies of the streams.
Examination of stellar streams and detection of subhalos will not only confirm
the presence of dark matter but also reveal information about its particle
nature.


  We calculate the gravitational-wave spectra produced by the electroweak phase
transition with TeV-scale Beyond-Standard-Model physics in the early universe.
Our study captures the effect of quantum and thermal fluctuations within a
non-perturbative framework. We discover a universal relation between the mean
bubble separation and the strength parameter of the phase transition, which
holds for a wide range of new-physics contributions.
  The ramifications of this result are three-fold: First, they constrain the
gravitational-wave spectra resulting from heavy (TeV-scale) new physics.
Second, they contribute to distinguishing heavy from light new physics directly
from the gravitational-wave signature. Third, they suggest that a concerted
effort of gravitational-wave observations together with collider experiments
could be required to distinguish between different models of heavy new physics.


  Traditional survival analysis methods often struggle with complex
time-dependent data,failing to capture and interpret dynamic characteristics
adequately.This study aims to evaluate the performance of three long-sequence
models,LSTM,Transformer,and Mamba,in analyzing recurrence event data and
integrating them with the Cox proportional hazards model.This study integrates
the advantages of deep learning models for handling long-sequence data with the
Cox proportional hazards model to enhance the performance in analyzing
recurrent events with dynamic time information.Additionally,this study compares
the ability of different models to extract and utilize features from
time-dependent clinical recurrence data.The LSTM-Cox model outperformed both
the Transformer-Cox and Mamba-Cox models in prediction accuracy and model
fit,achieving a Concordance index of up to 0.90 on the test set.Significant
predictors of bladder cancer recurrence,such as treatment stop time,maximum
tumor size at recurrence and recurrence frequency,were identified.The LSTM-Cox
model aligned well with clinical outcomes,effectively distinguishing between
high-risk and low-risk patient groups.This study demonstrates that the LSTM-Cox
model is a robust and efficient method for recurrent data analysis and feature
extraction,surpassing newer models like Transformer and Mamba.It offers a
practical approach for integrating deep learning technologies into clinical
risk prediction systems,thereby improving patient management and treatment
outcomes.


  Conditional independence reasoning has been shown to be helpful in the
context of Bayesian nets to optimize probabilistic inference, and related
techniques have been applied to speed up a number of logical reasoning tasks in
boolean logic by eliminating irrelevant parts of the formulas. This paper shows
that conditional independence reasoning can also be applied to optimize
epistemic model checking, in which one verifies that a model for a number of
agents operating with imperfect information satisfies a formula expressed in a
modal multi-agent logic of knowledge. An optimization technique is developed
that precedes the use of a model checking algorithm with an analysis that
applies conditional independence reasoning to reduce the size of the model. The
optimization has been implemented in the epistemic model checker MCK. The paper
reports experimental results demonstrating that it can yield multiple orders of
magnitude performance improvements.


  Given the coordinates of four terminals in the Euclidean plane we present
explicit formulas for Steiner point coordinates for Steiner minimal tree
problem. We utilize the obtained formulas for evaluation of the influence of
terminal coordinates on the loci of Steiner points.


  We introduce an experimentally accessible network representation for
many-body quantum states based on entanglement between all pairs of its
constituents. We illustrate the power of this representation by applying it to
a paradigmatic spin chain model, the XX model, and showing that it brings to
light new phenomena. The analysis of these entanglement networks reveals that
the gradual establishment of quasi-long range order is accompanied by a
symmetry regarding single-spin concurrence distributions, as well as by
instabilities in the network topology. Moreover, we identify the existence of
emergent entanglement structures, spatially localised communities enforced by
the global symmetry of the system that can be revealed by model-agnostic
community detection algorithms. The network representation further unveils the
existence of structural classes and a cyclic self-similarity in the state,
which we conjecture to be intimately linked to the community structure. Our
results demonstrate that the use of tools and concepts from complex network
theory enables the discovery, understanding, and description of new physical
phenomena even in models studied for decades.


  We study the influence of boundary conditions transverse to the transport
direction for disordered mesoscopic conductors both at the Anderson
metal-insulator transition and in the metallic regime. We show that the
boundary conditions strongly influence the conductance distribution exactly at
the metal-insulator transition and we discuss implications for the standard
picture of one-parameter scaling. We show in particular that the scaling
function that describes the change of conductance with system size depends on
the boundary conditions from the metallic regime up to the metal-insulator
transition. An experiment is proposed that might test the correctness of the
one-parameter scaling theory.


  We report dielectric, thermodynamical, acoustic, and optical properties of a
chiral molecular crystal, 1,1'-bi-2-naphthol 2-dimethylsulfoxide (BINOL2DMSO).
We find two successive phase transitions at Tc1=190 K and Tc2=125 K. The first
transition at Tc1 is characterized by an order-disorder transition of the guest
molecules DMSO along with ferroelectricity. At the second transition of Tc2,
the crystal structure deforms from tetragonal to monoclinic, leading to domain
formation. Low-temperature x-ray diffraction suggests that the space group
changes from P4_12_12 (P4_32_12) to P4_1 (P4_3) at Tc1, and down to P112_1 at
Tc2.


  The differential heating of electrons and ions by turbulence in weakly
collisional magnetized plasmas and the scales at which such energy dissipation
is most effective are still debated. Using a large data sample measured in the
Earth's magnetosheath by the Magnetospheric Multiscale mission and the
coarse-grained energy equations derived from the Vlasov-Maxwell system we find
evidence of a balance over two decades in scales between the energy cascade and
dissipation rates. The decline of cascade rate at kinetic scales (in contrasts
with a constant one in the inertial range), is balanced by an increasing ion
and electron heating rates, estimated via the pressure-strain. Ion scales are
found to contribute most effectively to ion heating, while electron heating
originates equally from ion and electron scales. These results can potentially
impact current understanding of particle heating in turbulent magnetized
plasmas as well as their theoretical and numerical modeling.


  This paper considers a problem of testing, from a finite sample, a
topological conjugacy of two dynamical systems $(X,f)$ and $(Y,g)$. More
precisely, given $x_1,\ldots, x_n \subset X$ and $y_1,\ldots,y_n \subset Y$
such that $x_{i+1} = f(x_i)$ and $y_{i+1} = g(y_i)$ as well as $h: X
\rightarrow Y$, we deliver a number of tests to check if $f$ and $g$ are
topologically conjugated via $h$. The values of the tests are close to zero for
conjugated systems and large for systems that are not conjugated. Convergence
of the test values, in case when sample size goes to infinity, is established.
A number of numerical examples indicating scalability and robustness of the
methods are given. In addition, we show how the presented method specialize to
a test of sufficient embedding dimension in Takens' embedding theorem. Our
methods also apply to the situation when we are given two observables of
deterministic processes, of a form of one or higher dimensional time-series. In
this case, their similarity can be accessed by comparing the dynamics of their
Takens' reconstructions.


  We investigate the effects on extrasolar giant planets [EGPs] of intense
irradiation by their parent stars, describing the issues involved in treating
the model atmosphere problem correctly. We treat the radiative transfer in
detail, allowing the flux from the parent star to interact with all relevant
depths of the planetary atmosphere, with no need for a pre-assumed albedo. We
present a low-resolution optical and near-IR spectrum of a close-in EGP,
focusing on the differences from an isolated planet.
  In our dust-free planetary atmospheres we find that Rayleigh scattering
increases the EGP's flux by orders of magnitude shortward of the CaII H&K
doublet (393 nm), and the spectral features of the parent star are exactly
reflected. In the optical and near-IR the thermal absorption of the planet
takes over, but the absorption features are changed by the irradiation. The
inclusion of dust increases the reflected flux in the blue; the stellar
spectral lines can be seen blueward of H-beta (486 nm).


  This paper presents the power network reconfiguration algorithm HATSGA with a
"R" modeling approach and evaluates its behavior in computing new
reconfiguration topologies for the power network in the Smart Grid context. The
modeling of the power distribution network with the language "R" is used to
represent the network and support the computation of distinct algorithm
configurations towards the evaluation of new reconfiguration topologies. The
HATSGA algorithm adopts a hybrid Tabu Search and Genetic Algorithm strategy and
can be configured in different ways to compute network reconfiguration
solutions. The evaluation of power loss with HATSGA uses the IEEE 14-Bus
topology as the power test scenario. The evaluation of reconfiguration
topologies with minimum power loss with HATSGA indicates that an efficient
solution can be reached with a feasible computational time. This suggests that
HATSGA can be potentially used for computing reconfiguration network topologies
and, beyond that, it can be used for autonomic self-healing management
approaches where a feasible computational time is required.


  The paper studies hereditarily complete superintuitionistic deductive
systems, that is, the deductive system which logic is an extension of the
intuitionistic propositional logic. It is proven that for deductive systems a
criterion of hereditary structurality - similar to one that exists for logics -
does not exists. Nevertheless, it is proven that many standard
superintuitionistic logics (including Int) can be defined by a hereditarily
structurally complete deductive system.


  There has been a strong push recently to examine biological scale simulations
of neuromorphic algorithms to achieve stronger inference capabilities. This
paper presents a set of piecewise linear spiking neuron models, which can
reproduce different behaviors, similar to the biological neuron, both for a
single neuron as well as a network of neurons. The proposed models are
investigated, in terms of digital implementation feasibility and costs,
targeting large scale hardware implementation. Hardware synthesis and physical
implementations on FPGA show that the proposed models can produce precise
neural behaviors with higher performance and considerably lower implementation
costs compared with the original model. Accordingly, a compact structure of the
models which can be trained with supervised and unsupervised learning
algorithms has been developed. Using this structure and based on a spike rate
coding, a character recognition case study has been implemented and tested.


  Since several years ago, the search for multiple populations (MPs) in
Galactic open clusters (OCs) has become a field of increasing interest,
particularly to the light of the general knowledge that MPs are observed in
relatively massive clusters (>= 5 10^4 Mo). I report here measurements of
stellar parameters and abundances of 17 different chemical species, including
light and iron-peak elements of the old open cluster NGC188, from the APOGEE
DR14 database. I selected 15 bonafide cluster red giants that reveal an spread
in [Na/Fe] of +-0.16 dex, caused by two different groups of measurements whose
mean [Na/Fe] values differ by ~ 0.30 dex. A hint for a subtle anti-correlation
with Al and TiII, which also show relative large [X/Fe] dispersions, is also
seen in the data. However, [Na/Fe] abundances have been found to be typically
affected by uncertainties of 0.16 dex. Furthermore, I directly compared the
available spectra for stars with similar atmospheric parameters and it would
seem that they look similar. Therefore, I warn users of large spectroscopic
surveys to be extra careful when finding peculiar abundance results. Analysis
pipelines that run in an unsupervised fashion may produce bad results which may
not be noticed before publication.


  We propose a detailed experimental procedure for preparing relativistic
vortices, governed by the nonlinear Dirac equation, in a two-dimensional
Bose-Einstein condensate (BEC) in a honeycomb optical lattice. Our setup
contains Dirac points, in direct analogy to graphene. We determine a range of
practical values for all relevant physical parameters needed to realize
relativistic vortices in a BEC of $^{87}\mathrm{Rb}$ atoms. Seven distinct
vortex types, including Anderson-Toulouse and Mermin-Ho skyrmion textures and
half-quantum vortices, are obtained, and their discrete spectra and stability
properties are calculated in a weak harmonic trap. We predict that most
vortices are stable with a lifetime between $1$ and $10$ seconds.


  Fourier ptychographic microscopy (FPM) is a computational imaging technique
that overcomes the physical space-bandwidth product (SBP) limit of a
conventional microscope by applying angular diversity illuminations. In the
usual model of FPM, the microscopic system is approximated by being taken as
space-invariant with transfer function determined by a complex pupil function
of the objective. However, in real experimental conditions, several unexpected
"semi-bright and semi-dark" images with strong vignetting effect can be easily
observed when the sample is illuminated by the LED within the "transition zone"
between bright field and dark field. These imperfect images, apparently, are
not coincident with the space-invariant model and could deteriorate the
reconstruction quality severely. In this Letter, we examine the impact of this
space-invariant approximation on FPM image formation based on ray-based and
rigorous wave optics-based analysis. Our analysis shows that for a practical
FPM microscope with a low power objective and a large field of view, the space
invariance is destroyed by diffraction at other stops associated with different
lens elements to a large extent. A modified version of the space-variant model
is derived and discussed. Two simple countermeasures are also presented and
experimentally verified to bypass or partially alleviate the vignetting-induced
reconstruction artifacts.


  In this paper, we propose a new interpretation of local limit theorems for
univariate and multivariate distributions on lattices. We show that - given a
local limit theorem in the standard sense - the distributions are approximated
well by the limit distribution, uniformly on intervals of possibly decaying
length. We identify the maximally allowable decay speed of the interval
lengths. Further, we show that for continuous distributions, the interval type
local law holds without any decay speed restrictions on the interval lengths.
We show that various examples fit within this framework, such as standardized
sums of i.i.d. random vectors or correlated random vectors induced by
multidimensional spin models from statistical mechanics.


  We present magnetotransport measurements performed on two-dimensional hole
gases embedded in carbon doped p-type GaAs/AlGaAs heterostructures grown on
[001] oriented substrates. A pronounced beating pattern in the Shubnikov-de
Haas oscillations proves the presence of strong spin-orbit interaction in the
device under study. We estimate the effective masses of spin-orbit split
subbands by measuring the temperature dependence of the Shubnikov-de Haas
oscillations at different hole densities. While the lighter heavy-hole
effective mass is not energy dependent, the heavier heavy-hole effective mass
has a prominent energy dependence, indicating a strong spin-orbit induced non
parabolicity of the valence band. The measured effective masses show
qualitative agreement with self-consistent numerical calculations.


  We consider Joule heating caused by dissipation of the magnetic field in the
neutron star crust. This mechanism may be efficient in maintaining a relatively
high surface temperature in very old neutron stars. Calculations of the thermal
evolution show that, at the late evolutionary stage ($t \geq 10$ Myr), the
luminosity of the neutron star is approximately equal to the energy released
due to the field dissipation and is practically independent of the atmosphere
models. At this stage, the surface temperature can be of the order of $3 \times
10^{4} - 10^{5}$K. Joule heating can maintain this high temperature during
extremely long time ($\geq 100$ Myr), comparable with the decay time of the
magnetic field.


  We discuss the interplay between standard canonical analysis and canonical
discretization in three-dimensional gravity with cosmological constant. By
using the Hamiltonian analysis, we find that the continuum local symmetries of
the theory are given by the on-shell space-time diffeomorphisms, which at the
action level, corresponds to the Kalb-Ramond transformations. At the time of
discretization, although this symmetry is explicitly broken, we prove that the
theory still preserves certain gauge freedom generated by a constant curvature
relation in terms of holonomies and the Gauss's law in the lattice approach.


  We show the absence of the usual parametrically large overshoot problem of
small-field inflation if initiated by a Coleman-De Luccia (CDL) tunneling
transition from an earlier vacuum in the limit of small inflationary scale
compared to the tunneling scale. For low-power monomial exit potentials
$V(\phi)\sim\phi^n, n<4$, we derive an expression for the amount of overshoot.
This is bounded from above by the width of the steep barrier traversed after
emerging from tunneling and before reaching a slow-roll region of the
potential. For $n\geq 4$ we show that overshooting is entirely absent. We
extend this result through binomials to a general potential written as a series
expansion, and to the case of arbitrary finite initial speed of the inflaton.
This places the phase space of initial conditions for small-field and
large-field inflation on the same footing in a landscape of string theory vacua
populated via CDL tunneling.


  We show that the LHC will be able to differentiate between a four-dimensional
model with quantum gravity at ~1 TeV where the (massless) graviton becomes
strongly coupled to standard model particles at 1 TeV and brane world type
models with a large extra-dimensional volume and massive Kaluza-Klein
gravitons. We estimate that the 14 TeV LHC could put a limit of the order of ~5
TeV on the four dimensional Planck mass in a model independent way.


  In early 80's M.Gromov showed that there exists a constant $\epsilon$ such
that any compact Riemannian manifold $M^n$ with $|K|_{M^n} \cdot diam^2(M^n)
\leq \epsilon$ can be finitely covered by a nilmanifold. The present paper
illustrates by an explicit example that the pinching constant $\epsilon$
depends on the dimension $n$ of the manifold, in particular, it decreases with
the dimension at least as $\frac{12}{n^2}.$


  We develop a gauge invariant canonical perturbation scheme for perturbations
around symmetry reduced sectors in generally covariant theories, such as
general relativity. The central objects of investigation are gauge invariant
observables which encode the dynamics of the system. We apply this scheme to
perturbations around a homogeneous and isotropic sector (cosmology) of general
relativity. The background variables of this homogeneous and isotropic sector
are treated fully dynamically which allows us to approximate the observables to
arbitrary high order in a self--consistent and fully gauge invariant manner.
Methods to compute these observables are given. The question of backreaction
effects of inhomogeneities onto a homogeneous and isotropic background can be
addressed in this framework. We illustrate the latter by considering
homogeneous but anisotropic Bianchi--I cosmologies as perturbations around a
homogeneous and isotropic sector.


  Multifractal properties of a tracer density passively advected by a
compressible random velocity field are characterized. A relationship is
established between the statistical properties of mass on the dynamical fractal
attractor towards which the trajectories converge and large deviations of the
stretching rates of the flow. In the framework of the compressible Kraichnan
model, this result is illustrated by analytical calculations and confirmed by
numerical simulations.


  We count the algebraic numbers of fixed degree by their $\mathbf{w}$-weighted
$l_p$-norm which generalizes the na\"ive height, the length, the Euclidean and
the Bombieri norms. For non-negative integers $k,l$ such that $k+2l\leq n$ and
a Borel subset $B\subset \mathbb{R}\times\mathbb{C}_+^l$ denote by
$\Phi_{p,\mathbf{w},k,l}(Q,B)$ the number of ordered $(k+l)$-tuples in $B$ of
conjugate algebraic numbers of degree $n$ and $\mathbf{w}$-weighted $l_p$-norm
at most $ Q$. We show that $$ \lim_{ Q\to\infty}\frac{\Phi_{p,\mathbf{w},k,l}(
Q,B)}{
Q^{n+1}}=\frac{{\mathrm{Vol}}_{n+1}(\mathbb{B}_{p,\mathbf{w}}^{n+1})}{2\zeta(n+1)}\int_B
\rho_{p,\mathbf{w},k,l}(\mathbf{x},\mathbf{z}){\rm d}\mathbf{x}{\rm
d}\mathbf{z}, $$ where ${\mathrm{Vol}}_{n+1}(\mathbb{B}_{p,\mathbf{w}}^{n+1})$
is the volume of the unit $\mathbf{w}$-weighted $l_p$-ball and
$\rho_{p,\mathbf{w},k,l}$ will denote the correlation function of $k$ real and
$l$ complex zeros of the random polynomial $\sum_{j=1}^n \frac{\eta_j}{w_j}
z^j$, where $\eta_j $ are i.i.d. random variables with density $c_p e^{-|t|^p}$
for $0<p<\infty$ and with constant density on $[-1,1]$ for $p=\infty$. If the
boundary of $B$ is of Lipschitz type, we also estimate the rate of convergence.
We give an explicit formula for $\rho_{p,\mathbf{w},k,l}$, which in the case
$k+2l=n$ has a very simple form. To this end, we obtain a general formula for
the correlations between real and complex zeros of a random polynomial with
arbitrary independent absolutely continuous coefficients.


  Heterogeneous processors with architecturally different cores (CPU and GPU)
integrated on the same die lead to new challenges and opportunities for thermal
and power management techniques because of shared thermal/power budgets between
these cores. In this paper, we show that new parallel programming paradigms
(e.g., OpenCL) for CPU-GPU processors create a tighter coupling between the
workload, the thermal/power management unit and the operating system. Using
detailed thermal and power maps of the die from infrared imaging, we
demonstrate that in contrast to traditional multi-core CPUs, heterogeneous
processors exhibit higher coupled behavior for dynamic voltage and frequency
scaling and workload scheduling, in terms of their effect on performance,
power, and temperature. Further, we show that by taking the differences in core
architectures and relative proximity of different computing cores on the die
into consideration, better scheduling schemes could be implemented to reduce
both the power density and peak temperature of the die. The findings presented
in the paper can be used to improve thermal and power efficiency of
heterogeneous CPU-GPU processors.


  The process of neutrino-pair radiation due to formation and breaking of
Cooper pairs of protons in superconducting cores of neutron stars is considered
with taking into account of the electromagnetic coupling of protons to ambient
electrons. It is shown that plasma polarization strongly modifies the effective
vector weak current of protons. Collective response of ambient electrons to the
proton quantum transition contributes coherently to the complete interaction
with the neutrino field and enhances the rate of neutrino-pair production by
two orders of magnitude.


  We present an expression for the generating function of correlation functions
of the sine-Gordon integrable field theory on a cylinder, with compact space.
This is derived from the Destri-De Vega integrable lattice regularization of
the theory, formulated as an inhomogeneous Heisenberg XXZ spin chain, and from
more recent advances in the computations of spin form factors in the
thermodynamic limit.


  Optimizing statistical measures for community structure is one of the most
popular strategies for community detection, but many of them lack the
flexibility of resolution and thus are incompatible with multi-scale
communities of networks. Here, we further studied a statistical measure of
interest for community detection, asymptotic surprise, an asymptotic
approximation of surprise. We discussed the critical behaviors of asymptotic
surprise in phase transition of community partition theoretically. Then,
according to the theoretical analysis, a multi-resolution method based on
asymptotic surprise was introduced, which provides an alternative approach to
study multi-scale networks, and an improved Louvain algorithm was proposed to
optimize the asymptotic surprise more effectively. By a series of experimental
tests in various networks, we validated the critical behaviors of the
asymptotic surprise further and the effectiveness of the improved Louvain
algorithm, displayed its ability to solve the first-type resolution limit and
stronger tolerance against the second-type resolution limit, and confirmed its
effectiveness of revealing multi-scale community structures in multi-scale
networks.


  Scoring matching (SM), and its related counterpart, Stein discrepancy (SD)
have achieved great success in model training and evaluations. However, recent
research shows their limitations when dealing with certain types of
distributions. One possible fix is incorporating the original score matching
(or Stein discrepancy) with a diffusion matrix, which is called diffusion score
matching (DSM) (or diffusion Stein discrepancy (DSD)). However, the lack of
interpretation of the diffusion limits its usage within simple distributions
and manually chosen matrix. In this work, we plan to fill this gap by
interpreting the diffusion matrix using normalizing flows. Specifically, we
theoretically prove that DSM (or DSD) is equivalent to the original score
matching (or Stein discrepancy) evaluated in the transformed space defined by
the normalizing flow, where the diffusion matrix is the inverse of the flow's
Jacobian matrix. In addition, we also build its connection to Riemannian
manifolds and further extend it to continuous flows, where the change of DSM is
characterized by an ODE.


  Communication is highly overloaded. Despite this, even young children are
good at leveraging context to understand ambiguous signals. We propose a
computational account of overloaded signaling from a shared agency perspective
which we call the Imagined We for Communication. Under this framework,
communication helps cooperators coordinate their perspectives, allowing them to
act together to achieve shared goals. We assume agents are rational
cooperators, which puts constraints on how signals can be sent and interpreted.
We implement this model in a set of simulations demonstrating this model's
success under increasing ambiguity as well as increasing layers of reasoning.
Our model is capable of improving performance with deeper recursive reasoning;
however, it outperforms comparison baselines at even the shallowest level,
highlighting how shared knowledge and cooperative logic can do much of the
heavy-lifting in language.


  Online travel platforms (OTPs), e.g., Ctrip.com or Fliggy.com, can
effectively provide travel-related products or services to users. In this
paper, we focus on the multi-scenario click-through rate (CTR) prediction,
i.e., training a unified model to serve all scenarios. Existing multi-scenario
based CTR methods struggle in the context of OTP setting due to the ignorance
of the cold-start users who have very limited data. To fill this gap, we
propose a novel method named Cold-Start based Multi-scenario Network (CSMN).
Specifically, it consists of two basic components including: 1) User Interest
Projection Network (UIPN), which firstly purifies users' behaviors by
eliminating the scenario-irrelevant information in behaviors with respect to
the visiting scenario, followed by obtaining users' scenario-specific interests
by summarizing the purified behaviors with respect to the target item via an
attention mechanism; and 2) User Representation Memory Network (URMN), which
benefits cold-start users from users with rich behaviors through a memory read
and write mechanism. CSMN seamlessly integrates both components in an
end-to-end learning framework. Extensive experiments on real-world offline
dataset and online A/B test demonstrate the superiority of CSMN over
state-of-the-art methods.


  We give a full description of the Poisson structures on the finitary
incidence algebra $FI(P,R)$ of an arbitrary poset $P$ over a commutative unital
ring $R$.


  Deterministic lateral displacement (DLD) devices separate micrometer-scale
particles in solution based on their size using a laminar microfluidic flow in
an array of obstacles. We investigate array geometries with rational row-shift
fractions in DLD devices by use of a simple model including both advection and
diffusion. Our model predicts novel multi-directional sorting modes that could
be experimentally tested in high-throughput DLD devices containing obstacles
that are much smaller than the separation between obstacles.


  The suppression and collective flow of open and hidden heavy flavor in
ultra-relativistic heavy ion collisions is studied with the partonic transport
model Boltzmann Approach to MultiParton Scatterings (BAMPS). Charm and bottom
quarks interact with the rest of the medium via elastic scatterings with an
improved Debye screening and a running coupling. Heavy flavor electron data
from RHIC can only be described if the cross section is multiplied with K=3.5
to mimic the contribution from radiative processes. With the same value for K
we compare to elliptic flow data of D mesons at the LHC and find a good
agreement. However, the nuclear modification factor of D mesons, non-prompt
J/psi, muons, and electrons at the LHC is underestimated in our calculations.
In addition, we present first BAMPS results on the suppression and elliptic
flow of J/psi mesons, which are treated in the same framework.


  So far, only the energy thresholds of single discrete breathers in nonlinear
Hamiltonian systems have been analytically obtained. In this work, the energy
thresholds of discrete breathers in thermal equilibrium and the energy
thresholds of long-lived discrete breathers which can remain after a long time
relaxation are analytically estimated for nonlinear chains. These energy
thresholds are size dependent. The energy thresholds of discrete breathers in
thermal equilibrium are same as the previous analytical results for single
discrete breathers. The energy thresholds of long-lived discrete breathers in
relaxation processes are different from the previous results for single
discrete breathers but agree well with the published numerical results known to
us. Because real systems are either in thermal equilibrium or in relaxation
processes, the obtained results could be important for experimental detection
of discrete breathers.


  We improve the Peres-Schlag result on pinned distances in sets of a given
Hausdorff dimension. In particular, for Euclidean distances, with $$\Delta^y(E)
= \{|x-y|:x\in E\},$$ we prove that for any $E, F\subset{\Bbb R}^d$, there
exists a probability measure $\mu_F$ on $F$ such that for $\mu_F$-a.e. $y\in
F$,
  (1) $\dim_{{\mathcal H}}(\Delta^y(E))\geq\beta$ if $\dim_{{\mathcal H}}(E) +
\frac{d-1}{d+1}\dim_{{\mathcal H}}(F) > d - 1 + \beta$;
  (2) $\Delta^y(E)$ has positive Lebesgue measure if $\dim_{{\mathcal
H}}(E)+\frac{d-1}{d+1}\dim_{{\mathcal H}}(F) > d$;
  (3) $\Delta^y(E)$ has non-empty interior if $\dim_{{\mathcal
H}}(E)+\frac{d-1}{d+1}\dim_{{\mathcal H}}(F) > d+1$.
  We also show that in the case when $\dim_{{\mathcal
H}}(E)+\frac{d-1}{d+1}\dim_{{\mathcal H}}(F)>d$, for $\mu_F$-a.e. $y\in F$, $$
\left\{t\in{\Bbb R} : \dim_{{\mathcal H}}(\{x\in E:|x-y|=t\}) \geq
\dim_{{\mathcal H}}(E)+\frac{d+1}{d-1}\dim_{{\mathcal H}}(F)-d \right\} $$ has
positive Lebesgue measure. This describes dimensions of slicing subsets of $E$,
sliced by spheres centered at $y$.
  In our proof, local smoothing estimates of Fourier integral operators (FIO)
plays a crucial role. In turn, we obtain results on sharpness of local
smoothing estimates by constructing geometric counterexamples.


  Every calculus student learns Cavalieri's quadrature formula for the
antiderivative of x^n (integer n). We observe here that the logarithmic (n=-1)
case implies all the others.


  Denoising of clinical CT images is an active area for deep learning research.
Current clinically approved methods use iterative reconstruction methods to
reduce the noise in CT images. Iterative reconstruction techniques require
multiple forward and backward projections, which are time-consuming and
computationally expensive. Recently, deep learning methods have been
successfully used to denoise CT images. However, conventional deep learning
methods suffer from the 'black box' problem. They have low accountability,
which is necessary for use in clinical imaging situations. In this paper, we
use a Joint Bilateral Filter (JBF) to denoise our CT images. The guidance image
of the JBF is estimated using a deep residual convolutional neural network
(CNN). The range smoothing and spatial smoothing parameters of the JBF are
tuned by a deep reinforcement learning task. Our actor first chooses a
parameter, and subsequently chooses an action to tune the value of the
parameter. A reward network is designed to direct the reinforcement learning
task. Our denoising method demonstrates good denoising performance, while
retaining structural information. Our method significantly outperforms state of
the art deep neural networks. Moreover, our method has only two parameters,
which makes it significantly more interpretable and reduces the 'black box'
problem. We experimentally measure the impact of our intelligent parameter
optimization and our reward network. Our studies show that our current setup
yields the best results in terms of structural preservation.


  Using the data from loop detector sensors for near-real-time detection of
traffic incidents in highways is crucial to averting major traffic congestion.
While recent supervised machine learning methods offer solutions to incident
detection by leveraging human-labeled incident data, the false alarm rate is
often too high to be used in practice. Specifically, the inconsistency in the
human labeling of the incidents significantly affects the performance of
supervised learning models. To that end, we focus on a data-centric approach to
improve the accuracy and reduce the false alarm rate of traffic incident
detection on highways. We develop a weak supervised learning workflow to
generate high-quality training labels for the incident data without the ground
truth labels, and we use those generated labels in the supervised learning
setup for final detection. This approach comprises three stages. First, we
introduce a data preprocessing and curation pipeline that processes traffic
sensor data to generate high-quality training data through leveraging labeling
functions, which can be domain knowledge-related or simple heuristic rules.
Second, we evaluate the training data generated by weak supervision using three
supervised learning models -- random forest, k-nearest neighbors, and a support
vector machine ensemble -- and long short-term memory classifiers. The results
show that the accuracy of all of the models improves significantly after using
the training data generated by weak supervision. Third, we develop an online
real-time incident detection approach that leverages the model ensemble and the
uncertainty quantification while detecting incidents. Overall, we show that our
proposed weak supervised learning workflow achieves a high incident detection
rate (0.90) and low false alarm rate (0.08).


  We prove a sharp Meyniel-type criterion for hamiltonicity of a balanced
bipartite digraph: For k greater than or equal to 2, a bipartite digraph D with
colour classes of cardinalities k is hamiltonian if the sum of degrees of
vertices u and v is at least 3k+1 for every pair of vertices u, v such that D
does not contain the arc uv nor vu. As a consequence, we obtain a sharp
sufficient condition for hamiltonicity in terms of the minimal degree: a
balanced bipartite digraph D on 2k vertices is hamiltonian if its minimal
degree is at least (3k + 1)/2.


  We have studied the effects of various initial mass functions (IMFs) on the
chemical evolution of the Sagittarius dwarf galaxy (Sgr). In particular, we
tested the effects of the integrated galactic initial mass function (IGIMF) on
various predicted abundance patterns. The IGIMF depends on the star formation
rate and metallicity and predicts less massive stars in a regime of low star
formation, as it is the case in dwarf spheroidals. We adopted a detailed
chemical evolution model following the evolution of $\alpha$-elements, Fe and
Eu, and assuming the currently best set of stellar yields. We also explored
different yield prescriptions for the Eu, including production from neutron
star mergers. Although the uncertainties still present in the stellar yields
and data prevent us from drawing firm conclusions, our results suggest that the
IGIMF applied to Sgr predicts lower [$\alpha$/Fe] ratios than classical IMFs
and lower [hydrostatic/explosive] $\alpha$-element ratios, in qualitative
agreement with observations. In our model, the observed high [Eu/O] ratios in
Sgr is due to reduced O production, resulting from the IGIMF mass cutoff of the
massive oxygen-producing stars, as well as to the Eu yield produced in neutron
star mergers, a more promising site than core-collapse supernovae, although
many uncertainties are still present in the Eu nucleosynthesis. We find that a
model, similar to our previous calculations, based on the late addition of iron
from the Type Ia supernova time-delay (necessary to reproduce the shape of
[X/Fe] versus [Fe/H] relations) but also including the reduction of massive
stars due to the IGIMF, better reproduces the observed abundance ratios in Sgr
than models without the IGIMF.


  We establish the nonexistence of extremizers for a local Fourier restriction
inequality on a certain class of planar convex curves whose curvature satisfies
a natural assumption. We accomplish this by studying the local behavior of the
triple convolution of the arclength measure on the curve with itself, and show
in particular that every extremizing sequence concentrates at a point on the
curve.


  Encoding a qubit in logical quantum states with wavefunctions characterized
by disjoint support and robust energies can offer simultaneous protection
against relaxation and pure dephasing. Using a circuit-quantum-electrodynamics
architecture, we experimentally realize a superconducting $0-\pi$ qubit, which
hosts protected states suitable for quantum-information processing. Multi-tone
spectroscopy measurements reveal the energy level structure of the system,
which can be precisely described by a simple two-mode Hamiltonian. We find that
the parity symmetry of the qubit results in charge-insensitive levels
connecting the protected states, allowing for logical operations. The measured
relaxation (1.6 ms) and dephasing times (25 $\mu$s) demonstrate that our
implementation of the $0-\pi$ circuit not only broadens the family of
superconducting qubits, but also represents a promising candidate for the
building block of a fault-tolerant quantum processor.