title:
The dynamics of dust particles during the laser-induced plasma process:
  Self-similar expansion in a liquid

abstract:
  The role of liquid ions in the dusty plasma produced by laser ablation in a
liquid environment is still unclear. For that purpose, we utilized the
self-similar approach to investigate the effect of liquid ions on plasma
expansion front. The fluid equations have been used for dust particles and ion
species in addition to superthermal electron distribution. We treat the
expansion process in the absence and existence of liquid ions. The impact of
the dust-to-ion initial number density ratio, superthermality factor kappa,
dust-to-electrons initial temperature ratio, and dust-ions atomic number on the
expansion front has been addressed. Our findings show that in the single ion
case, changing the superthermality of energetic electrons has the most
significant influence on the dust ion expansion. Moreover, we observed that the
mass of the liquid ions has the greatest effect on the dynamics of the dust
ions in the double ion case.


title:
Improved tetrahedron method for the Brillouin-zone integration
  applicable to response functions

abstract:
  We improve the linear tetrahedron method to overcome systematic errors due to
overestimations (underestimations) in integrals for convex (concave) functions,
respectively. Our method is applicable to various types of calculations such as
the total energy, the harge (spin) density, response functions, and the phonon
frequency, in contrast with the Bl\"ochl correction, which is applicable to
only the first two. We demonstrate the ability of our method by calculating
phonons in MgB$_2$ and fcc lithium.


title:
The Low-Redshift Lyman Continuum Survey II: New Insights into LyC
  Diagnostics

abstract:
  The Lyman continuum (LyC) cannot be observed at the epoch of reionization (z
{\gtrsim} 6) due to intergalactic H I absorption. To identify Lyman continuum
emitters (LCEs) and infer the fraction of escaping LyC, astronomers have
developed various indirect diagnostics of LyC escape. Using measurements of the
LyC from the Low-redshift Lyman Continuum Survey (LzLCS), we present the first
statistical test of these diagnostics. While optical depth indicators based on
Ly{\alpha}, such as peak velocity separation and equivalent width, perform
well, we also find that other diagnostics, such as the [O III]/[O II] flux
ratio and star formation rate surface density, predict whether a galaxy is a
LCE. The relationship between these galaxy properties and the fraction of
escaping LyC flux suggests that LyC escape depends strongly on H I column
density, ionization parameter, and stellar feedback. We find LCEs occupy a
range of stellar masses, metallicities, star formation histories, and
ionization parameters, which may indicate episodic and/or different physical
causes of LyC escape.


title:
The stable cohomology of self-equivalences of connected sums of products
  of spheres

abstract:
  We identify the cohomology of the stable classifying space of homotopy
automorphisms (relative to an embedded disk) of connected sums of $S^k \times
S^l$, where $3 \le k < l \le 2k - 2$. The result is expressed in terms of Lie
graph complex homology.


title: Gaze-based Object Detection in the Wild

abstract:
  In human-robot collaboration, one challenging task is to teach a robot new
yet unknown objects. Thereby, gaze can contain valuable information. We
investigate if it is possible to detect objects (object or no object) from gaze
data and determine their bounding box parameters. For this purpose, we explore
different sizes of temporal windows, which serve as a basis for the computation
of heatmaps, i.e., the spatial distribution of the gaze data. Additionally, we
analyze different grid sizes of these heatmaps, and various machine learning
techniques are applied. To generate the data, we conducted a small study with
five subjects who could move freely and thus, turn towards arbitrary objects.
This way, we chose a scenario for our data collection that is as realistic as
possible. Since the subjects move while facing objects, the heatmaps also
contain gaze data trajectories, complicating the detection and parameter
regression.


title: CycleGAN-Based Unpaired Speech Dereverberation

abstract:
  Typically, neural network-based speech dereverberation models are trained on
paired data, composed of a dry utterance and its corresponding reverberant
utterance. The main limitation of this approach is that such models can only be
trained on large amounts of data and a variety of room impulse responses when
the data is synthetically reverberated, since acquiring real paired data is
costly. In this paper we propose a CycleGAN-based approach that enables
dereverberation models to be trained on unpaired data. We quantify the impact
of using unpaired data by comparing the proposed unpaired model to a paired
model with the same architecture and trained on the paired version of the same
dataset. We show that the performance of the unpaired model is comparable to
the performance of the paired model on two different datasets, according to
objective evaluation metrics. Furthermore, we run two subjective evaluations
and show that both models achieve comparable subjective quality on the AMI
dataset, which was not seen during training.


title: Towards the Future: Bring Program Correctness back to the focus

abstract:
  Program correctness used to be the main concern of computer software in the
early days when formal semantics was a hot topic. But, the word "correct" was
afterwards replaced by reliable, robust and trustworthy etc., a tradeoff
situation then. This is not because correctness is no longer important, but
because people found no way to get through in this direction. The tradeoff has
led software engineers to focus on techniques and testing tools. Rapid
development of software engineering has now reached a peak and programmers are
now working freely without worrying too much about bugs, since bugs are not
avoidable anyway.
  Is it meaningful to talk about program correctness today? Our answer is yes.
It is the time to seriously consider correctness again, before it is too late,
to prepare for the future. Future generation computer systems should be
correct, both syntactically (statically) and semantically (dynamically).
  The book "OESPA: Semantic Oriented Theory of Programming" (2019) by the first
author has opened a new direction for semantic study. Theoretically speaking,
it is possible now, based on OESPA, to compute program semantics from program
text so that program correctness could be proved.
  But, semantic computations and correctness proving cannot be done by hand
when the size of a program is big. Automatic tools are necessary. This paper
tries to lay a foundation for developing needed auto tools, so that OESPA is
enriched to serve future need. To this end, a new concept named conditional
semantic predicate is proposed. Concepts in OESPA, including semantic
functions, semantic predicates, semantic formulas and semantic calculus, are
re-represented in accordance. Such re-introduction is necessary since the book
is the only publication on semantic calculus so far. The new version of
semantic calculus illustrates how semantics auto-computation would be carried
out.


title: Variance of fluctuations from Noether invariance

abstract:
  The strength of fluctuations, as measured by their variance, is paramount in
the quantitative description of a large class of physical systems, ranging from
simple and complex liquids to active fluids and solids. Fluctuations originate
from the irregular motion of thermal degrees of freedom and statistical
mechanics facilitates their description. Here we demonstrate that fluctuations
are constrained by the inherent symmetries of the given system. For
particle-based classical many-body systems, Noether invariance at second order
in the symmetry parameter leads to exact sum rules. These identities
interrelate the global force variance with the mean potential curvature.
Noether invariance is restored by an exact balance between these distinct
mechanisms. The sum rules provide an important guide for assessing and
constructing theories, for ensuring self-consistency in simulation work, and
for providing a systematic pathway to the theoretical quantification of
fluctuations.


title:
Consistency regularization-based Deep Polynomial Chaos Neural Network
  Method for Reliability Analysis

abstract:
  Polynomial chaos expansion (PCE) is a powerful surrogate model-based
reliability analysis method. Generally, a PCE model with a higher expansion
order is usually required to obtain an accurate surrogate model for some
complex non-linear stochastic systems. However, the high-order PCE increases
the number of labeled data required for solving the expansion coefficients. To
alleviate this problem, this paper proposes a consistency regularization-based
deep polynomial chaos neural network (Deep PCNN) method, including the
low-order adaptive PCE model (the auxiliary model) and the high-order
polynomial chaos neural network (the main model). The expansion coefficients of
the main model are parameterized into the learnable weights of the polynomial
chaos neural network, realizing iterative learning of expansion coefficients to
obtain more accurate high-order PCE models. The auxiliary model uses a proposed
consistency regularization loss function to assist in training the main model.
The consistency regularization-based Deep PCNN method can significantly reduce
the number of labeled data in constructing a high-order PCE model without
losing accuracy by using few labeled data and abundant unlabeled data. A
numerical example validates the effectiveness of the consistency
regularization-based Deep PCNN method, and then this method is applied to
analyze the reliability of two aerospace engineering systems.


title:
Understanding the magnetic response of the Quantum spin liquid compound
  1T-TaS2

abstract:
  1T-tantalum disulphide (1T-TaS2) is a promising material to realize quantum
spin liquid state and fractional excitations. Here, we take a closer look on
the temperature dependent magnetic susceptibility of 1T-TaS2 at low magnetic
fields to understand the formation of local magnetic moment in this system and
its response as a function of temperature and field. We found that the
susceptibility increases with reduction in the temperature even in the nearly
commensurate charge density wave (CDW) phase, which exhibits metallic
conductivity and hence is expected to exhibit temperature independent Pauli
paramagnetic susceptibility. Therefore, it indicates that local moment starts
forming in the nearly commensurate CDW phase itself, which is well above the
temperature where the evidence of Mott insulating state is found in resistivity
measurement. In the commensurate CDW phase, temperature dependence of
susceptibility significantly deviates from the Curie-Weiss law and does not
show the magnetic properties expected from S = 1/2 spin on a two dimensional
triangular lattice. Below T = 10 K, susceptibility follows a power law
dependence on the temperature, which indicates towards the formation of random
singlet state in 1T-TaS2.


title:
Construction and Linearity of Z_pZ_{p^2}-Linear Generalized Hadamard
  Codes

abstract:
  The $\Z_p\Z_{p^2}$-additive codes are subgroups of $\Z_p^{\alpha_1} \times
\Z_{p^2}^{\alpha_2}$, and can be seen as linear codes over $\Z_p$ when
$\alpha_2=0$, $\Z_{p^2}$-additive codes when $\alpha_1=0$, or
$\Z_2\Z_4$-additive codes when $p=2$. A $\Z_p\Z_{p^2}$-linear generalized
Hadamard (GH) code is a GH code over $\Z_p$ which is the Gray map image of a
$\Z_p\Z_{p^2}$-additive code. In this paper, we generalize some known results
for $\Z_p\Z_{p^2}$-linear GH codes with $p=2$ to any $p\geq 3$ prime when
$\alpha_1 \neq 0$. First, we give a recursive construction of
$\Z_p\Z_{p^2}$-additive GH codes of type $(\alpha_1,\alpha_2;t_1,t_2)$ with
$t_1,t_2\geq 1$. Then, we show for which types the corresponding
$\Z_p\Z_{p^2}$-linear GH codes are non-linear over $\Z_p$. Finally, according
to some computational results, we see that, unlike $\Z_4$-linear GH codes, when
$p\geq 3$ prime, the $\Z_{p^2}$-linear GH codes are not included in the family
of $\Z_p\Z_{p^2}$-linear GH codes with $\alpha_1\not =0$.


title: The Aluthge and the mean transforms of $m$-isometries

abstract:
  Let $T\in B(H)$ be a bounded linear operator on a Hilbert space $H$, let $T =
V|T|$ be its polar decomposition of $T$ and let $\lambda\in [0,1]$. The
$\lambda$-Aluthge transform $\Delta_{\lambda}(T)$ and the mean transforms
$M(T)$ are defined respectively by:
\[\Delta_{\lambda}(T):=|T|^{\lambda}V|T|^{1-\lambda} \;\; \text{and} \;\;
M(T):=\frac12(|T|V+V|T|).\] In this paper, we use several examples of weighted
shift operators to prove that the Aluthge and mean transforms do not preserve
the class of $m-$isometries in any directions.


title:
Study of the production and decay properties of neutron-deficient
  nobelium isotopes

abstract:
  The new neutron-deficient isotope $^{249}$No was synthesized for the first
time in the fusion-evaporation reaction $^{204}$Pb($^{48}$Ca,3n)$^{249}$No.
After separation, using the kinematic separator SHELS, the new isotope was
identified with the GABRIELA detection system through genetic correlations with
the known daughter and granddaughter nuclei $^{245}$Fm and $^{241}$Cf. The
alpha-decay activity of $^{249}$No has an energy of 9129(22)$~$keV and
half-life 38.3(2.8) ms. An upper limit of 0.2\% was measured for the fission
branch of $^{249}$No. Based on the present data and recent information on the
decay properties of $^{253}$Rf and aided by Geant4 simulations, the ground
state of $^{249}$No is assigned the 5/2$^+$[622] neutron configuration and a
partial decay scheme from $^{253}$Rf to $^{245}$Fm could be established. The
production cross-section was found to be $\sigma$(3n)=0.47(4) nb at a
mid-target beam energy of 225.4 MeV, which corresponds to the maximum of the
calculated excitation function. Correlations of the $^{249}$No alpha activity
with subsequent alpha decays of energy 7728(20) keV and half-life
$1.2_{-0.4}^{+1.0}$ min provided a firm measurement of the electron-capture or
$\beta^{+}$ branch of $^{245}$Fm to $^{245}$Es. The excitation function for the
1n, 2n and 3n evaporation channels was measured. In the case of the
2n-evaporation channel $^{250}$No, a strong variation of the ground state and
isomeric state populations as a function of bombarding energy could be
evidenced.


title:
Decayless longitudinal oscillations of a solar filament maintained by
  quasi-periodic jets

abstract:
  Context: As a ubiquitous phenomenon, large-amplitude longitudinal filament
oscillations usually decay in 1--4 periods. Recently, we observed a decayless
case of such oscillations in the corona. Aims: We try to understand the
physical process that maintains the decayless oscillation of the filament.
Methods: Multi-wavelength imaging observations and magnetograms are collected
to study the dynamics of the filament oscillation and its associated phenomena.
To explain the decayless oscillations, we also perform one-dimensional
hydrodynamic numerical simulations using the MPI-AMRVAC code. Results: In
observations, the filament oscillates decaylessly with a period of $36.4 \pm
0.3$ min for almost 4 hours before eruption. During oscillations, four
quasi-periodic jets emanate from a magnetic cancellation site near the
filament. The time interval between neighboring jets is $\sim 68.9 \pm 1.0$
min. Numerical simulations constrained by the observations reproduced the
decayless longitudinal oscillations. However, it is surprising to find that the
period of the decayless oscillations is not consistent with the pendulum model.
Conclusions: We propose that the decayless longitudinal oscillations of the
filament are maintained by quasi-periodic jets, which is verified by the
hydrodynamic simulations. More importantly, it is found that, when driven by
quasi-periodic jets, the period of the filament longitudinal oscillations
depends also on the driving period of the jets, not simply the pendulum period.
With a parameter survey in simulations, we derived a formula, by which one can
derive the pendulum oscillation period using the observed period of decayless
filament oscillations and the driving periods of jets.


title:
Temporal Robustness of Temporal Logic Specifications: Analysis and
  Control Design

abstract:
  We study the temporal robustness of temporal logic specifications and show
how to design temporally robust control laws for time-critical control systems.
This topic is of particular interest in connected systems and interleaving
processes such as multi-robot and human-robot systems where uncertainty in the
behavior of individual agents and humans can induce timing uncertainty. Despite
the importance of time-critical systems, temporal robustness of temporal logic
specifications has not been studied, especially from a control design point of
view. We define synchronous and asynchronous temporal robustness and show that
these notions quantify the robustness with respect to synchronous and
asynchronous time shifts in the predicates of the temporal logic specification.
It is further shown that the synchronous temporal robustness upper bounds the
asynchronous temporal robustness. We then study the control design problem in
which we aim to design a control law that maximizes the temporal robustness of
a dynamical system. Our solution consists of a Mixed-Integer Linear Programming
(MILP) encoding that can be used to obtain a sequence of optimal control
inputs. While asynchronous temporal robustness is arguably more nuanced than
synchronous temporal robustness, we show that control design using synchronous
temporal robustness is computationally more efficient. This trade-off can be
exploited by the designer depending on the particular application at hand. We
conclude the paper with a variety of case studies.


title: MatteFormer: Transformer-Based Image Matting via Prior-Tokens

abstract:
  In this paper, we propose a transformer-based image matting model called
MatteFormer, which takes full advantage of trimap information in the
transformer block. Our method first introduces a prior-token which is a global
representation of each trimap region (e.g. foreground, background and unknown).
These prior-tokens are used as global priors and participate in the
self-attention mechanism of each block. Each stage of the encoder is composed
of PAST (Prior-Attentive Swin Transformer) block, which is based on the Swin
Transformer block, but differs in a couple of aspects: 1) It has PA-WSA
(Prior-Attentive Window Self-Attention) layer, performing self-attention not
only with spatial-tokens but also with prior-tokens. 2) It has prior-memory
which saves prior-tokens accumulatively from the previous blocks and transfers
them to the next block. We evaluate our MatteFormer on the commonly used image
matting datasets: Composition-1k and Distinctions-646. Experiment results show
that our proposed method achieves state-of-the-art performance with a large
margin. Our codes are available at https://github.com/webtoon/matteformer.


title:
Characterization of an aerosolized nanoparticle beam beyond the
  diffraction limit through strong field ionization

abstract:
  The study of nanomaterials is an active area of research for technological
applications as well as fundamental science. A common method for studying
properties of isolated nanoparticles is by an in-vacuum particle beam produced
via an aerodynamic lens. Despite being common practice, characterization of
such beams has proven difficult as light scattering detection techniques fail
for particles with sizes beyond the diffraction limit. Here we present a new
technique for characterizing such nanoparticle beams using strong field
ionization. By focusing an ultrafast, mJ-level laser into the particle beam, a
nanoparticle within the laser focus is ionized and easily detected by its
ejected electrons. This method gives direct access to the nanoparticle density
at the location of the focus and, by scanning the focus through the transverse
and longitudinal profiles of the particle beam, the 3-dimensional particle
density distribution can be attained. Further, we show that strong field
ionization is effective in detecting spherical nanoparticles as small as 10 nm
in diameter. Additionally, this technique is an effective tool in optimizing
the particle beam for specific applications. As an example we show that the
particle beam density and width can be manipulated by restricting the gas flow
into the aerodynamic lens.


title: Nearly Minimax Algorithms for Linear Bandits with Shared Representation

abstract:
  We give novel algorithms for multi-task and lifelong linear bandits with
shared representation. Specifically, we consider the setting where we play $M$
linear bandits with dimension $d$, each for $T$ rounds, and these $M$ bandit
tasks share a common $k(\ll d)$ dimensional linear representation. For both the
multi-task setting where we play the tasks concurrently, and the lifelong
setting where we play tasks sequentially, we come up with novel algorithms that
achieve $\widetilde{O}\left(d\sqrt{kMT} + kM\sqrt{T}\right)$ regret bounds,
which matches the known minimax regret lower bound up to logarithmic factors
and closes the gap in existing results [Yang et al., 2021]. Our main technique
include a more efficient estimator for the low-rank linear feature extractor
and an accompanied novel analysis for this estimator.


title:
Generating the Group of Non Zero Elements of a Quadratic Extension of
  $\mathbb{F}_{p}$

abstract:
  It is well known that if $\mathbb{F}$ is a finite field then
$\mathbb{F^{*}}$, the set of non zero elements of $\mathbb{F}$, is a cyclic
group. In this paper we will assume $\mathbb{F}=\mathbb{F}_{p}$ (the finite
field with p elements, p a prime) and $\mathbb{\mathbb{F}}_{p^{2}}$ is a
quadratic extension of $\mathbb{F}_{p}$. In this case, the groups
$\mathbb{F}_{p}^{*}$ and $\mathbb{F}_{p^{2}}^{*}$ have orders $p-1$ and
$p^{2}-1$ respectively. We will provide necessary and sufficient conditions for
an element $u\in\mathbb{F}_{p^{2}}^{*}$ to be a generator. Specifically, we
will prove $u$ is a generator of $\mathbb{F}_{p^{2}}^{*}$ if and only if $N(u)$
generates $\mathbb{F}_{p}^{*}$ and $\frac{u^{2}}{N(u)}$ generates Ker$\,N$,
where $N:\mathbb{F}_{p^{2}}^{*}\rightarrow\mathbb{F}_{p}^{*}$ denotes the norm
map. We will also provide a method for determining if $u$ is not a generator of
Ker$\,N$.


title: Absorption studies of the most diffuse gas in the Large Scale Structure

abstract:
  As the Universe evolves, it develops a web of filamentary structure of
matter. This cosmic web is filled with gas, with the most diffuse gas lying in
the intergalactic regions. At low redshift, the gas is predominantly warm-hot,
and one of its best tracers is X-ray absorption in sightlines to background
quasars. In this Chapter, we present the theoretical background for the
formation of the warm-hot intergalactic medium (WHIM) and present the physical
properties of the WHIM from cosmological hydro-dynamical simulations. We
discuss the feasibility of detecting the WHIM with X-ray absorption lines, with
high-resolution and high signal-to-noise spectra. We present detailed
discussion of observing techniques, including the WHIM ionization balance,
observable lines, the curve of growth, and the diagnostics using the X-ray
lines. We present the current efforts of detecting the WHIM with gratings
on-board Chandra and XMM-Newton observatories. We discuss the criticality of
WHIM detections reported in literature, where robust detections are likely from
the circumgalactic medium of intervening galaxies, or intra-group medium,
rather than truly diffuse gas in the intergalactic medium. Secure detections of
the most diffuse gas in the low redshift large scale structure may have to
await next generation of X-ray telescopes. We end our Chapter with the
discussion of future missions carrying dispersive and non-dispersive
spectrometers. We present figure-of-merit parameters for line detectibility as
well as for the number of WHIM systems that can be detected with future
missions. These will define our ability to account for the missing low-redshift
baryons and to understand the evolution of the Universe over half of its life.


title: Algorithms and Barriers in the Symmetric Binary Perceptron Model

abstract:
  The symmetric binary perceptron ($\texttt{SBP}$) exhibits a dramatic
statistical-to-computational gap: the densities at which known efficient
algorithms find solutions are far below the threshold for the existence of
solutions. Furthermore, the $\texttt{SBP}$ exhibits a striking structural
property: at all positive constraint densities almost all of its solutions are
'totally frozen' singletons separated by large Hamming distance
\cite{perkins2021frozen,abbe2021proof}. This suggests that finding a solution
to the $\texttt{SBP}$ may be computationally intractable. At the same time, the
$\texttt{SBP}$ does admit polynomial-time search algorithms at low enough
densities. A conjectural explanation for this conundrum was put forth in
\cite{baldassi2020clustering}: efficient algorithms succeed in the face of
freezing by finding exponentially rare clusters of large size. However, it was
discovered recently that such rare large clusters exist at all subcritical
densities, even at those well above the limits of known efficient algorithms
\cite{abbe2021binary}. Thus the driver of the statistical-to-computational gap
exhibited by this model remains a mystery.
  In this paper, we conduct a different landscape analysis to explain the
algorithmic tractability of this problem. We show that at high enough densities
the $\texttt{SBP}$ exhibits the multi Overlap Gap Property ($m-$OGP), an
intricate geometrical property known to be a rigorous barrier for large classes
of algorithms. Our analysis shows that the $m-$OGP threshold (a) is well below
the satisfiability threshold; and (b) matches the best known algorithmic
threshold up to logarithmic factors as $m\to\infty$. We then prove that the
$m-$OGP rules out the class of stable algorithms for the $\texttt{SBP}$ above
this threshold. We conjecture that the $m \to \infty$ limit of the $m$-OGP
threshold marks the algorithmic threshold for the problem.


title:
Electromagnetic Antennas for the Resonant Detection of the Stochastic
  Gravitational Wave Background

abstract:
  Stochastic gravitational wave background from the early Universe has a
cut-off frequency close to 100 MHz, due to the horizon of the inflationary
phase. To detect gravitational waves at such frequencies, resonant
electromagnetic cavities are very suitable. In this work, we study the
frequency sensitivity of such detectors, and show how we could use them to
probe this cut-off frequency and also the energy density per frequency of this
stochastic background. This paper paves the way for further experimental
studies to probe the most ancient relic of the Universe.


title:
Superconductivity in monolayer Ba$_2$N electride: a first-principles
  study

abstract:
  The exploration of superconductivity in low-dimensional materials has
attracted intensive attention for decades. Based on first-principles electronic
structure calculations, we have systematically investigated the electronic and
superconducting properties of the two-dimensional electride Ba$_2$N in the
monolayer limit. Our results show that monolayer Ba$_2$N has a low work
function of 3.0 eV and a predicted superconducting transition temperature
($T_c$) of 3.4 K. The superconductivity can be further improved with the
tensile strain, which results from the increase of density of states at the
Fermi level as well as the enhanced coupling between inner-layer electrons and
phonons. Remarkably, at the 4$\%$ tensile strain, the acoustic branches have
noticeable softening at the K point of Brillouin zone and the superconducting
$T_c$ can reach 10.8 K. The effect of lattice strain on the electron transfer
from the superficial region to the inner-layer region of monolayer Ba$_2$N may
also apply to other electride materials and influence their physical
properties.


title:
Quantum engineering of a synthetic thermal bath via Markovian feedback
  control

abstract:
  We propose and investigate a scheme for engineering a synthetic thermal bath
for a bosonic quantum gas in a one-dimensional optical lattice based on
Markovian feedback control. The performance of our scheme is quantified by the
fidelity between the steady state of the system and the effective thermal
state. For double-well and triple-well systems with non-interacting particles,
the steady state is found to be an exact thermal state, which is attributed to
the fact that the transfer rates between all pairs of coupled eigenstates
satisfy detailed balance condition. The senario changes when there are more
lattice sites, where the detailed balance condition does not hold any more, but
remains an accurate approximation. Remarkably, our scheme performs very well at
low and high temperature regimes, with the fidelity close to one. The
performance at the intermediate temperature regime is slightly worse, and the
fidelity shows a gentle decrease with increasing system size. We also discuss
the interacting cases. In contrast to the non-interacting cases, the scheme is
found to perform better at a higher temperature. Another difference is that the
minimal temperature that can be engineered is nonzero and increases with the
interaction strength.


title:
100 Gb/s High Throughput Serial Protocol (HTSP) for Data Acquisition
  Systems

abstract:
  Demands on Field-Programmable Gate Array (FPGA) data transport have been
increasing over the years as frame sizes and refresh rates increase. As the
bandwidths requirements increase the ability to implement data transport
protocol layers using "soft" programmable logic becomes harder and start to
requires harden IP blocks implementation. This paper presents a way to leverage
existing FPGA harden IP blocks to achieve a robust, low latency 100 Gb/s
point-to-point link with minimal programmable logic overhead geared towards the
needs of Data Acquisition Systems.


title: SurvCaus : Representation Balancing for Survival Causal Inference

abstract:
  Individual Treatment Effects (ITE) estimation methods have risen in
popularity in the last years. Most of the time, individual effects are better
presented as Conditional Average Treatment Effects (CATE). Recently,
representation balancing techniques have gained considerable momentum in causal
inference from observational data, still limited to continuous (and binary)
outcomes. However, in numerous pathologies, the outcome of interest is a
(possibly censored) survival time. Our paper proposes theoretical guarantees
for a representation balancing framework applied to counterfactual inference in
a survival setting using a neural network capable of predicting the factual and
counterfactual survival functions (and then the CATE), in the presence of
censorship, at the individual level. We also present extensive experiments on
synthetic and semisynthetic datasets that show that the proposed extensions
outperform baseline methods.


title: Hybrid magnetized stars in the context of multi-messenger astronomy

abstract:
  The most recent detections of LIGO/Virgo and NICER have placed strong
constraints on neutron stars' properties. In this work, we study neutron stars
modeling them as hybrid stars, compact objects with a quark matter core
surrounded by layers of hadronic matter. In addition, we consider the presence
of strong magnetic fields, motivated by magnetars detection, like the recently
discovered Swift J1818.0-1607. We incorporate the effects of the anomalous
magnetic moment of the constituent particles into the equation of state of
dense matter and analyze the implications of different hadron-quark phase
transitions on the dynamic stability of these compact objects. This study shows
that the constraints on the mass, radius, and tidal deformability, imposed by
observations of massive pulsars and gravitational waves can be satisfied within
the framework of our model.


title: Exploring Frequency Adversarial Attacks for Face Forgery Detection

abstract:
  Various facial manipulation techniques have drawn serious public concerns in
morality, security, and privacy. Although existing face forgery classifiers
achieve promising performance on detecting fake images, these methods are
vulnerable to adversarial examples with injected imperceptible perturbations on
the pixels. Meanwhile, many face forgery detectors always utilize the frequency
diversity between real and fake faces as a crucial clue. In this paper, instead
of injecting adversarial perturbations into the spatial domain, we propose a
frequency adversarial attack method against face forgery detectors. Concretely,
we apply discrete cosine transform (DCT) on the input images and introduce a
fusion module to capture the salient region of adversary in the frequency
domain. Compared with existing adversarial attacks (e.g. FGSM, PGD) in the
spatial domain, our method is more imperceptible to human observers and does
not degrade the visual quality of the original images. Moreover, inspired by
the idea of meta-learning, we also propose a hybrid adversarial attack that
performs attacks in both the spatial and frequency domains. Extensive
experiments indicate that the proposed method fools not only the spatial-based
detectors but also the state-of-the-art frequency-based detectors effectively.
In addition, the proposed frequency attack enhances the transferability across
face forgery detectors as black-box attacks.


title:
Superintegrability in $\beta$-deformed Gaussian Hermitian matrix model
  from $W$-operators

abstract:
  This paper is devoted to the phenomenon of superintegrability. This
phenomenon is manifested in the existence of a formula for character averages,
expressed through the same characters at special points and of its various
generalization. In this paper we develop a method of proving such formulas from
first principle from Virasoro constraints and $W$-representation. We apply it
to prove the formula for the Jack functions averages - appropriate analogue of
characters for the $\beta$-deformed Hermitian Gaussian matrix model. We also
sketch the construction of $W$-operators from Calogero-Ruijsenaars
Hamiltonians.


title:
Linear mixed models to handle missing at random data in trial-based
  economic evaluations

abstract:
  Trial-based cost-effectiveness analyses (CEAs) are an important source of
evidence in the assessment of health interventions. In these studies, cost and
effectiveness outcomes are commonly measured at multiple time points, but some
observations may be missing. Restricting the analysis to the participants with
complete data can lead to biased and inefficient estimates. Methods, such as
multiple imputation, have been recommended as they make better use of the data
available and are valid under less restrictive Missing At Random (MAR)
assumption. Linear mixed effects models (LMMs) offer a simple alternative to
handle missing data under MAR without requiring imputations, and have not been
very well explored in the CEA context. In this manuscript, we aim to
familiarise readers with LMMs and demonstrate their implementation in CEA. We
illustrate the approach on a randomised trial of antidepressant, and provide
the implementation code in R and Stata. We hope that the more familiar
statistical framework associated with LMMs, compared to other missing data
approaches, will encourage their implementation and move practitioners away
from inadequate methods.


title: Orbital Angular Momentum of Magnons in Collinear Magnets

abstract:
  We study the orbital angular momentum of magnons for several collinear
ferromagnet (FM) and antiferromagnetic (AF) systems with nontrivial networks of
exchange interactions. Specifically, we consider AF and FM zig-zag and
honeycomb lattices. Our work demonstrates that the arrangement of exchange
interactions may play a more important role at producing the orbital angular
momentum of magnons than the spin-orbit coupling energy and the resulting
non-collinear spin arrangements.


title: Majorana propagator on de Sitter space

abstract:
  We study the dynamics of Majorana fermions in an expanding de Sitter space
and to that aim we construct the vacuum Feynman propagator for Majorana
fermions in de Sitter space. Surprisingly, the Majorana propagator is identical
to that of the Dirac fermions. We then use this propagator and calculate the
one-loop effective action for Majorana fermions, and show that it differs from
that of Dirac fermions by a factor of 1/2, which accounts for the reduced
number of degrees of freedom of the Majorana particle. Finally, we derive the
Majorana and Dirac propagators for mixing fermionic flavors, which are suitable
for studying CP-violating effects in quantum loops.


title:
Systematic Parametrization of the Leading $B$-meson Light-Cone
  Distribution Amplitude

abstract:
  We propose a parametrization of the leading $B$-meson light-cone distribution
amplitude (LCDA) in heavy-quark effective theory (HQET). In position space, it
uses a conformal transformation that yields a systematic Taylor expansion and
an integral bound, which enables control of the truncation error. Our
parametrization further produces compact analytical expressions for a variety
of derived quantities. At a given reference scale, our momentum-space
parametrization corresponds to an expansion in associated Laguerre polynomials,
which turn into confluent hypergeometric functions ${}_1F_1$ under
renormalization-group evolution at one-loop accuracy. Our approach thus allows
a straightforward and transparent implementation of a variety of
phenomenological constraints, regardless of their origin. Moreover, we can
include theoretical information on the Taylor coefficients by using the local
operator production expansion. We showcase the versatility of the
parametrization in a series of phenomenological pseudo-fits.


title:
Forecast of observing time delay of the strongly lensed quasars with
  Muztagh-Ata 1.93m telescope

abstract:
  As a completely independent method, the measurement of time delay of strongly
lensed quasars (TDSL) are crucial to resolve the Hubble tension. Extensive
monitoring is required but so far limited to a small sample of strongly lensed
quasars. Together with several partner institutes, Beijing Normal University is
constructing a 1.93m reflector telescope at the Muztagh-Ata site in west China,
which has the world class observing conditions. The telescope will be equipped
with both a three-channel imager/photometer which covers $3500-11000$ Angstrom
wavelength band, and a low-medium resolution
($\lambda/\delta\lambda=500/2000/7500$) spectrograph. In this paper, we
investigate the capability of Muztagh-Ata 1.93m telescope in measuring time
delays of strongly lensed quasars. We generate mock strongly lensed quasar
systems and light curves with microlensing effects based on five known strongly
lensed quasars, i.e., RX J1131-1231, HE 0435-1223, PG 1115+080, WFI 2033-4723
and SDSS 1206+4332. In particular, RX J1131-1231 is generated with lens
modeling in this work. Due to lack of enough information, we simulate the other
4 systems with the public data without lens modeling. According to simulations,
for RX J1131-like systems (wide variation in time delay between images) the
TDSL measurement can be achieved with the precision about $\Delta t=0.5$ day
with 4 seasons campaign length and 1 day cadence. This accuracy is comparable
to the up-coming TDCOSMO project. And it would be better when the campaign
length keeps longer and with high cadence. As a result, the capability of
Muztagh-Ata 1.93m telescope allows it to join the network of TDSL
observatories. It will enrich the database for strongly lensed quasar
observations and make more precise measurements of time delays, especially
considering the unique coordinate of the site.


title:
Arbitrarily small spectral gaps for random hyperbolic surfaces with many
  cusps

abstract:
  Let $\mathcal{M}_{g,n(g)}$ be the moduli space of hyperbolic surfaces of
genus $g$ with $n(g)$ punctures endowed with the Weil-Petersson metric. In this
paper we show that if $n(g)$ grows significantly faster than $g^{\frac{1}{2}}$,
then for any $\epsilon>0$, a generic hyperbolic surface in
$\mathcal{M}_{g,n(g)}$ has first eigenvalue less than $\epsilon$ as $g\to
\infty$.


title:
A zero-curvature representation of electromagnetism and the conservation
  of electric charge

abstract:
  We show that the laws of electromagnetism in $(D+1)$-dimensional Minkowski
space-time $\mathcal{M}$, explicitly for $D=1$, $2$ and $3$, can be obtained
from an integral representation of the zero-curvature equation in the
corresponding loop space $\mathcal{L}^{(D-1)}(\mathcal{M})$. The conservation
of the electric charge can be seen as the result of a hidden symmetry in this
representation of the dynamical equations.


title:
DRSpeech: Degradation-Robust Text-to-Speech Synthesis with Frame-Level
  and Utterance-Level Acoustic Representation Learning

abstract:
  Most text-to-speech (TTS) methods use high-quality speech corpora recorded in
a well-designed environment, incurring a high cost for data collection. To
solve this problem, existing noise-robust TTS methods are intended to use noisy
speech corpora as training data. However, they only address either
time-invariant or time-variant noises. We propose a degradation-robust TTS
method, which can be trained on speech corpora that contain both additive
noises and environmental distortions. It jointly represents the time-variant
additive noises with a frame-level encoder and the time-invariant environmental
distortions with an utterance-level encoder. We also propose a regularization
method to attain clean environmental embedding that is disentangled from the
utterance-dependent information such as linguistic contents and speaker
characteristics. Evaluation results show that our method achieved significantly
higher-quality synthetic speech than previous methods in the condition
including both additive noise and reverberation.


title:
HP -- A code for the calculation of Hubbard parameters using
  density-functional perturbation theory

abstract:
  We introduce HP, an implementation of density-functional perturbation theory,
designed to compute Hubbard parameters (on-site U and inter-site V) in the
framework of DFT+U and DFT+U+V. The code does not require the usage of
computationally expensive supercells of the traditional linear-response
approach; instead, unit cells are used with monochromatic perturbations that
significantly reduces the computational cost of determining Hubbard parameters.
HP is open-source software distributed under the terms of the GPL as a
component of Quantum ESPRESSO. As with other components, HP is optimized to run
on a variety of different platforms, from laptops to massively parallel
architectures, using native mathematical libraries (LAPACK and FFTW) and a
hierarchy of custom parallelization layers built on top of MPI. The
effectiveness of the code is showcased by computing Hubbard parameters
self-consistently for the phospho-olivine LixMn0.5Fe0.5PO4 (x = 0, 0.5, 1) and
by highlighting the accuracy of predictions of the geometry and intercalation
voltages.


title: EnvEdit: Environment Editing for Vision-and-Language Navigation

abstract:
  In Vision-and-Language Navigation (VLN), an agent needs to navigate through
the environment based on natural language instructions. Due to limited
available data for agent training and finite diversity in navigation
environments, it is challenging for the agent to generalize to new, unseen
environments. To address this problem, we propose EnvEdit, a data augmentation
method that creates new environments by editing existing environments, which
are used to train a more generalizable agent. Our augmented environments can
differ from the seen environments in three diverse aspects: style, object
appearance, and object classes. Training on these edit-augmented environments
prevents the agent from overfitting to existing environments and helps
generalize better to new, unseen environments. Empirically, on both the
Room-to-Room and the multi-lingual Room-Across-Room datasets, we show that our
proposed EnvEdit method gets significant improvements in all metrics on both
pre-trained and non-pre-trained VLN agents, and achieves the new
state-of-the-art on the test leaderboard. We further ensemble the VLN agents
augmented on different edited environments and show that these edit methods are
complementary. Code and data are available at
https://github.com/jialuli-luka/EnvEdit


title: Forecasting with Economic News

abstract:
  The goal of this paper is to evaluate the informational content of sentiment
extracted from news articles about the state of the economy. We propose a
fine-grained aspect-based sentiment analysis that has two main characteristics:
1) we consider only the text in the article that is semantically dependent on a
term of interest (aspect-based) and, 2) assign a sentiment score to each word
based on a dictionary that we develop for applications in economics and finance
(fine-grained). Our data set includes six large US newspapers, for a total of
over 6.6 million articles and 4.2 billion words. Our findings suggest that
several measures of economic sentiment track closely business cycle
fluctuations and that they are relevant predictors for four major macroeconomic
variables. We find that there are significant improvements in forecasting when
sentiment is considered along with macroeconomic factors. In addition, we also
find that sentiment matters to explains the tails of the probability
distribution across several macroeconomic variables.


title:
Texture based Prototypical Network for Few-Shot Semantic Segmentation of
  Forest Cover: Generalizing for Different Geographical Regions

abstract:
  Forest plays a vital role in reducing greenhouse gas emissions and mitigating
climate change besides maintaining the world's biodiversity. The existing
satellite-based forest monitoring system utilizes supervised learning
approaches that are limited to a particular region and depend on manually
annotated data to identify forest. This work envisages forest identification as
a few-shot semantic segmentation task to achieve generalization across
different geographical regions. The proposed few-shot segmentation approach
incorporates a texture attention module in the prototypical network to
highlight the texture features of the forest. Indeed, the forest exhibits a
characteristic texture different from other classes, such as road, water, etc.
In this work, the proposed approach is trained for identifying tropical forests
of South Asia and adapted to determine the temperate forest of Central Europe
with the help of a few (one image for 1-shot) manually annotated support images
of the temperate forest. An IoU of 0.62 for forest class (1-way 1-shot) was
obtained using the proposed method, which is significantly higher (0.46 for
PANet) than the existing few-shot semantic segmentation approach. This result
demonstrates that the proposed approach can generalize across geographical
regions for forest identification, creating an opportunity to develop a global
forest cover identification tool.


title:
NLO QCD corrections to full off-shell production of $t\bar{t}Z$
  including leptonic decays

abstract:
  Motivated by ongoing new physics searches in the top-quark sector at the
Large Hadron Collider we report on the calculation of NLO QCD corrections to
the Standard Model $pp\to t\bar{t}Z+X$ process in the tetra-lepton decay
channel. This calculation is based on the matrix elements for the $e^+ \nu_e\,
\mu^-\bar{\nu}_\mu\, b\bar{b} \,\tau^+\tau^-$ final state and includes all
resonant and non-resonant Feynman diagrams, interferences and off-shell effects
of the top quark as well as the $W$ and $Z$ gauge bosons. Also incorporated are
photon-induced contributions. As it is customary for such studies, we show
theoretical predictions for both fixed and dynamical factorisation and
renormalisation scale choices and different PDF sets. Furthermore, we study the
main theoretical uncertainties that are associated with neglected higher-order
terms in the perturbative expansion and with the parameterisation of the PDF
sets. In order to investigate the size of off-shell effects and higher-order
corrections in top-quark decays, we perform a second computation for this
process, which is based on the narrow-width-approximation where the top quarks,
$W$ and $Z$ gauge bosons are kept on-shell. Results are given for the
integrated and differential fiducial cross sections for the LHC Run II
center-of-mass energy of $13$ TeV.


title:
How the area of solar coronal holes affects the properties of high-speed
  solar wind streams near Earth -- An analytical model

abstract:
  We derive a simple analytical model for the propagation of HSSs from the Sun
to Earth and thereby show how the area of coronal holes and the size of their
boundary regions affect the HSS velocity, temperature, and density near Earth.
We presume that velocity, temperature, and density profiles form across the HSS
cross section close to the Sun and that these spatial profiles translate into
corresponding temporal profiles in a given radial direction due to the solar
rotation. These temporal distributions drive the stream interface to the
preceding slow solar wind plasma and disperse with distance from the Sun. The
HSS properties at 1 AU are then given by all HSS plasma parcels launched from
the Sun that did not run into the stream interface at Earth distance. We show
that the velocity plateau region of HSSs as seen at 1 AU, if apparent,
originates from the center region of the HSS close to the Sun, whereas the
velocity tail at 1 AU originates from the trailing boundary region. The peak
velocity of HSSs at Earth further depends on the longitudinal width of the HSS
close to the Sun. The temperature and density of HSS plasma parcels at Earth
depend on their radial expansion from the Sun to Earth. The radial expansion is
determined by the velocity gradient across the HSS boundary region close to the
Sun and gives the velocity-temperature and density-temperature relationships at
Earth their specific shape. When considering a large number of HSSs, the
presumed correlation between the HSS velocities and temperatures close to the
Sun degrades only slightly up to 1 AU, but the correlation between the
velocities and densities is strongly disrupted up to 1 AU due to the radial
expansion. Finally, we show how the number of particles of the piled-up slow
solar wind in the stream interaction region depends on the velocities and
densities of the HSS and preceding slow solar wind plasma.


title: Some classes of frontals and its representation formulas

abstract:
  We characterize the extendibility of the normal curvature on frontals and we
give a representation formula of this type of frontals. Also we give
representation formulas for wavefronts on all types of singularities and others
sub classes of these. Some applications to asymptotic curves and lines of
curvature on frontals are made.


title:
Improved Counting and Localization from Density Maps for Object
  Detection in 2D and 3D Microscopy Imaging

abstract:
  Object counting and localization are key steps for quantitative analysis in
large-scale microscopy applications. This procedure becomes challenging when
target objects are overlapping, are densely clustered, and/or present fuzzy
boundaries. Previous methods producing density maps based on deep learning have
reached a high level of accuracy for object counting by assuming that object
counting is equivalent to the integration of the density map. However, this
model fails when objects show significant overlap regarding accurate
localization. We propose an alternative method to count and localize objects
from the density map to overcome this limitation. Our procedure includes the
following three key aspects: 1) Proposing a new counting method based on the
statistical properties of the density map, 2) optimizing the counting results
for those objects which are well-detected based on the proposed counting
method, and 3) improving localization of poorly detected objects using the
proposed counting method as prior information. Validation includes processing
of microscopy data with known ground truth and comparison with other models
that use conventional processing of the density map. Our results show improved
performance in counting and localization of objects in 2D and 3D microscopy
data. Furthermore, the proposed method is generic, considering various
applications that rely on the density map approach. Our code will be released
post-review.


title: Extending structures for Zinbiel bialgebras

abstract:
  We introduce the concept of braided Zinbiel bialgebras and construct cocycle
bicrossproducts Zinbiel bialgebras. As an application, we solve the extending
problem for Zinbiel bialgebras by using non-abelian cohomology theory.


title: Simulated observations of heavy elements with CUBES

abstract:
  We investigate the feasibility of robust abundances for selected
neutron-capture elements (Ge, Bi, Hf, U) from near-UV spectroscopy with the
CUBES instrument now in development for the Very Large Telescope. We use the
CUBES end-to-end simulator to synthesise observations of the Ge I 3039 {\AA}
and Hf II 3400 and 3719 {\AA} lines in a very metal-poor star, using the
well-studied star CS 31082-001 as a template. From simulated 4 hr exposures, we
recover estimated abundances to $\pm$0.1 dex for Ge for U $\sim$ 14.25 mag.,
and for Hf for U = 18 mag. These performances neatly highlight the powerful
gain of CUBES for near-UV observations of targets that are two-to-three
magnitudes fainter than the existing observations of CS 31082-001 (U = 12.5
mag.). We also investigate the weak Bi I 3025 {\AA} and U II 3860 {\AA} lines
(for U $\sim$ 14.25 and 16mag., respectively), finding that simulated 4hr
exposures should provide upper limits to these observationally challenging
lines.


title:
Towards Filling the Gaps around Recurrent Events in High-Dimensional
  Framework: Literature Review and Early Comparison

abstract:
  Background Study individuals may face repeated events overtime. However,
there is no consensus around learning approaches to use in a high-dimensional
framework for survival data (when the number of variables exceeds the number of
individuals, i.e., p>n). This study aimed at identifying learning algorithms
for analyzing/predicting recurrent events and at comparing them to standard
statistical models in various data simulation settings. Methods A literature
review (LR) was conducted to provide state-of-the-art methodology. Data were
then simulated including variations of the number of variables and proportion
of active variables. Learning algorithms from the LR were compared to standard
methods in such simulation scheme. Evaluation measures were Harrell's
concordance index (C-index), Kim's C-index and error rate for active variables.
Results Seven publications were identified, consisting in four methodological
studies, one application paper and two review. The broken adaptive ridge
penalization and the RankDeepSurv deep neural network were used for comparison.
On simulated data, the standard models failed when p>n. Penalized Andersen-Gill
and frailty models outperformed, whereas RankDeepSurv reported lower
performances. Conclusion As no guidelines support a specific approach, this
study helps to better understand mechanisms and limits of investigated methods
in such context.


title: Performance of surface codes in realistic quantum hardware

abstract:
  Surface codes are generally studied based on the assumption that each of the
qubits that make up the surface code lattice suffers noise that is independent
and identically distributed (i.i.d.). However, real benchmarks of the
individual relaxation ($T_1$) and dephasing ($T_2$) times of the constituent
qubits of state-of-the-art quantum processors have recently shown that the
decoherence effects suffered by each particular qubit will actually vary in
intensity. In this article, we propose a decoherence model that takes this
non-uniform behaviour into account, the independent non-identically distributed
(i.ni.d.) noise model, and we analyze how the performance of planar codes is
impacted by this new model. For this purpose we employ data from four
state-of-the-art superconducting processors: ibmq\_brooklyn, ibm\_washington,
Zuchongzhi and Rigetti Aspen-11. Our results show that the i.i.d. noise
assumption overestimates the performance of surface codes, which can suffer up
to $85\%$ performance decrements in terms of the code pseudo-threshold when
they are subjected to the i.ni.d. noise model. Furthermore, in this work we
also consider the relationship between code performance and qubit arrangement
in the surface code lattice. We show how surface code performance varies as a
function of qubit placement within the lattice (because of the different
decoherence parameters) and we introduce an algorithm that re-arranges the
qubits of the surface code lattice in a way that optimizes code performance.
The optimum qubit configuration derived with this algorithm can attain planar
code pseudo-threshold values that are up to $249\%$ higher than for the general
planar code architectures.


title:
Auditing Privacy Defenses in Federated Learning via Generative Gradient
  Leakage

abstract:
  Federated Learning (FL) framework brings privacy benefits to distributed
learning systems by allowing multiple clients to participate in a learning task
under the coordination of a central server without exchanging their private
data. However, recent studies have revealed that private information can still
be leaked through shared gradient information. To further protect user's
privacy, several defense mechanisms have been proposed to prevent privacy
leakage via gradient information degradation methods, such as using additive
noise or gradient compression before sharing it with the server. In this work,
we validate that the private training data can still be leaked under certain
defense settings with a new type of leakage, i.e., Generative Gradient Leakage
(GGL). Unlike existing methods that only rely on gradient information to
reconstruct data, our method leverages the latent space of generative
adversarial networks (GAN) learned from public image datasets as a prior to
compensate for the informational loss during gradient degradation. To address
the nonlinearity caused by the gradient operator and the GAN model, we explore
various gradient-free optimization methods (e.g., evolution strategies and
Bayesian optimization) and empirically show their superiority in reconstructing
high-quality images from gradients compared to gradient-based optimizers. We
hope the proposed method can serve as a tool for empirically measuring the
amount of privacy leakage to facilitate the design of more robust defense
mechanisms.


title: Stability of many-body localization in Kicked Ising model

abstract:
  We study many-body localization (MBL) transition in disordered Kicked Ising
model using a polynomially filtered exact diagonalization (POLFED) algorithm.
We quantitatively demonstrate that finite size effects at the MBL transition in
disordered Kicked Ising model are less severe than in random field XXZ spin
chains widely studied in the context of MBL. This allows us to observe
consistent signatures of the transition to MBL phase for a several indicators
of ergodicity breaking. We show that an assumption a power-law divergence of
the correlation length at the MBL transition yields a critical exponent $\nu
\approx 2$, consistent with the Harris criterion for 1D disordered systems.

categories: cond-mat.dis-nn cond-mat.quant-gas cond-mat.stat-mech cond-mat.str-el quant-ph


title:
Addressing Non-Intervention Challenges via Resilient Robotics utilizing
  a Digital Twin

abstract:
  Multi-robot systems face challenges in reducing human interventions as they
are often deployed in dangerous environments. It is therefore necessary to
include a methodology to assess robot failure rates to reduce the requirement
for costly human intervention. A solution to this problem includes robots with
the ability to work together to ensure mission resilience. To prevent this
intervention, robots should be able to work together to ensure mission
resilience. However, robotic platforms generally lack built-in
interconnectivity with other platforms from different vendors. This work aims
to tackle this issue by enabling the functionality through a bidirectional
digital twin. The twin enables the human operator to transmit and receive
information to and from the multi-robot fleet. This digital twin considers
mission resilience, decision making and a run-time reliability ontology for
failure detection to enable the resilience of a multi-robot fleet. This creates
the cooperation, corroboration, and collaboration of diverse robots to leverage
the capability of robots and support recovery of a failed robot.


title:
Topological properties of minimally doubled fermions in two space-time
  dimensions

abstract:
  The two-dimensional Schwinger model is used to explore how lattice fermion
operators perceive the global topological charge $q \in \mathbb{Z}$ of a given
background gauge field. We focus on Karsten-Wilczek and Borici-Creutz fermions,
which are minimally doubled, and compare them to Wilson, Brillouin, naive,
staggered and Adams fermions. For each operator the eigenvalue spectrum in a
background with $q \neq 0$ is determined along with the chiralities of the
eigenmodes, and the spectral flow of the pertinent hermitean operator is worked
out. We find that Karsten-Wilczek and Borici-Creutz fermions perceive the
global topological charge $q$ in the same way as staggered and naive fermions
do.


title:
MAP-Gen: An Automated 3D-Box Annotation Flow with Multimodal Attention
  Point Generator

abstract:
  Manually annotating 3D point clouds is laborious and costly, limiting the
training data preparation for deep learning in real-world object detection.
While a few previous studies tried to automatically generate 3D bounding boxes
from weak labels such as 2D boxes, the quality is sub-optimal compared to human
annotators. This work proposes a novel autolabeler, called multimodal attention
point generator (MAP-Gen), that generates high-quality 3D labels from weak 2D
boxes. It leverages dense image information to tackle the sparsity issue of 3D
point clouds, thus improving label quality. For each 2D pixel, MAP-Gen predicts
its corresponding 3D coordinates by referencing context points based on their
2D semantic or geometric relationships. The generated 3D points densify the
original sparse point clouds, followed by an encoder to regress 3D bounding
boxes. Using MAP-Gen, object detection networks that are weakly supervised by
2D boxes can achieve 94~99% performance of those fully supervised by 3D
annotations. It is hopeful this newly proposed MAP-Gen autolabeling flow can
shed new light on utilizing multimodal information for enriching sparse point
clouds.


title: On symmetric representations of $\text{SL}_2(\mathbb{Z})$

abstract:
  We introduce the notions of symmetric and symmetrizable representations of
$\text{SL}_2(\mathbb{Z})$. The linear representations of
$\text{SL}_2(\mathbb{Z})$ arising from modular tensor categories are symmetric
and have congruence kernel. Conversely, one may also reconstruct modular data
from finite-dimensional symmetric, congruence representations of
$\text{SL}_2(\mathbb{Z})$. By investigating a $\mathbb{Z}/2\mathbb{Z}$-symmetry
of some Weil representations at prime power levels, we prove that all
finite-dimensional congruence representations of $\text{SL}_2(\mathbb{Z})$ are
symmetrizable. We also provide examples of unsymmetrizable noncongruence
representations of $\text{SL}_2(\mathbb{Z})$ that are subrepresentations of a
symmetric one.


title: Contrasting the landscape of contrastive and non-contrastive learning

abstract:
  A lot of recent advances in unsupervised feature learning are based on
designing features which are invariant under semantic data augmentations. A
common way to do this is contrastive learning, which uses positive and negative
samples. Some recent works however have shown promising results for
non-contrastive learning, which does not require negative samples. However, the
non-contrastive losses have obvious "collapsed" minima, in which the encoders
output a constant feature embedding, independent of the input. A folk
conjecture is that so long as these collapsed solutions are avoided, the
produced feature representations should be good. In our paper, we cast doubt on
this story: we show through theoretical results and controlled experiments that
even on simple data models, non-contrastive losses have a preponderance of
non-collapsed bad minima. Moreover, we show that the training process does not
avoid these minima.


title: Towards Everyday Virtual Reality through Eye Tracking

abstract:
  With developments in computer graphics, hardware technology, perception
engineering, and human-computer interaction, virtual reality and virtual
environments are becoming more integrated into our daily lives. Head-mounted
displays, however, are still not used as frequently as other mobile devices
such as smart phones and watches. With increased usage of this technology and
the acclimation of humans to virtual application scenarios, it is possible that
in the near future an everyday virtual reality paradigm will be realized. When
considering the marriage of everyday virtual reality and head-mounted displays,
eye tracking is an emerging technology that helps to assess human behaviors in
a real time and non-intrusive way. Still, multiple aspects need to be
researched before these technologies become widely available in daily life.
Firstly, attention and cognition models in everyday scenarios should be
thoroughly understood. Secondly, as eyes are related to visual biometrics,
privacy preserving methodologies are necessary. Lastly, instead of studies or
applications utilizing limited human participants with relatively homogeneous
characteristics, protocols and use-cases for making such technology more
accessible should be essential. In this work, taking the aforementioned points
into account, a significant scientific push towards everyday virtual reality
has been completed with three main research contributions.


title: Fine-Grained Visual Entailment

abstract:
  Visual entailment is a recently proposed multimodal reasoning task where the
goal is to predict the logical relationship of a piece of text to an image. In
this paper, we propose an extension of this task, where the goal is to predict
the logical relationship of fine-grained knowledge elements within a piece of
text to an image. Unlike prior work, our method is inherently explainable and
makes logical predictions at different levels of granularity. Because we lack
fine-grained labels to train our method, we propose a novel multi-instance
learning approach which learns a fine-grained labeling using only sample-level
supervision. We also impose novel semantic structural constraints which ensure
that fine-grained predictions are internally semantically consistent. We
evaluate our method on a new dataset of manually annotated knowledge elements
and show that our method achieves 68.18\% accuracy at this challenging task
while significantly outperforming several strong baselines. Finally, we present
extensive qualitative results illustrating our method's predictions and the
visual evidence our method relied on. Our code and annotated dataset can be
found here: https://github.com/SkrighYZ/FGVE.


title: IoT Forensic Frameworks (DFIF, IoTDOTS,FSAIoT): A Comprehensive Study

abstract:
  In the Internet of Things, millions of electronic items, including
automobiles, smoke alarms, watches, eyeglasses, webcams, and other devices, are
now connected to the Internet (IoT). Aside from the luxury and comfort that the
individual obtains in the field of IoT, as well as its ability to communicate
and obtain information easily and quickly, the other concerning aspect is the
achievement of privacy and security in this connection, especially given the
rapid increase in the number of existing and new IoT devices. Concerns,
threats, and assaults related to IoT security have been regarded as a potential
and problematic area of research. This necessitates the quick development or
creation of suitable technologies with the nature of crimes in the IoT
environment. On the other hand, criminal investigation specialists encounter
difficulties and hurdles due to various locations, data types, instruments
used, and device recognition. This paper provides an in-depth explanation of
the criminal content of the Internet of Things. It compares its stages to the
detailed stages of traditional digital forensics in terms of similarities and
differences, the frameworks used in dealing with electronic crimes, and the
techniques used in both types. This paper presents previous discussions of
researchers in the field of digital forensics. For the IoT, which brings us to
the most important parts of this paper, which is a comprehensive study of the
IoT criminal frameworks that are used to protect communication in the field of
IoT, such as Digital Forensic Investigation Framework (DFIF), Digital Forensic
Framework for Smart Environments (IoTDOTS), Forensic State Acquisition from the
Internet of Things (FSAIoT), and discusses the challenges in their general
frameworks and provides solutions and strategies.


title:
Stabilized Neural Ordinary Differential Equations for Long-Time
  Forecasting of Dynamical Systems

abstract:
  In data-driven modeling of spatiotemporal phenomena careful consideration
often needs to be made in capturing the dynamics of the high wavenumbers. This
problem becomes especially challenging when the system of interest exhibits
shocks or chaotic dynamics. We present a data-driven modeling method that
accurately captures shocks and chaotic dynamics by proposing a novel
architecture, stabilized neural ordinary differential equation (ODE). In our
proposed architecture, we learn the right-hand-side (RHS) of an ODE by adding
the outputs of two NN together where one learns a linear term and the other a
nonlinear term. Specifically, we implement this by training a sparse linear
convolutional NN to learn the linear term and a dense fully-connected nonlinear
NN to learn the nonlinear term. This is in contrast with the standard neural
ODE which involves training only a single NN for learning the RHS. We apply
this setup to the viscous Burgers equation, which exhibits shocked behavior,
and show better short-time tracking and prediction of the energy spectrum at
high wavenumbers than a standard neural ODE. We also find that the stabilized
neural ODE models are much more robust to noisy initial conditions than the
standard neural ODE approach. We also apply this method to chaotic trajectories
of the Kuramoto-Sivashinsky equation. In this case, stabilized neural ODEs keep
long-time trajectories on the attractor, and are highly robust to noisy initial
conditions, while standard neural ODEs fail at achieving either of these
results. We conclude by demonstrating how stabilizing neural ODEs provide a
natural extension for use in reduced-order modeling by projecting the dynamics
onto the eigenvectors of the learned linear term.


title:
A review of heath economic evaluation practice in the Netherlands: are
  we moving forward?

abstract:
  In 2016, the Dutch National Health Care Institute issued new guidelines that
aggregated and updated previous recommendations on key elements for conducting
economic evaluation. However, the impact on standard practice after the
introduction of the guidelines in terms of design, methodology and reporting
choices, is still uncertain. To assess this impact, we examine and compare key
analysis components of economic evaluations conducted in the Netherlands before
(2010-2015) and after (2016-2020) the introduction of the guidelines. We
specifically focus on two aspects of the analysis that are crucial in
determining the plausibility of the results: statistical methodology and
missing data handling. Our review shows how many components of economic
evaluations have changed in accordance with the new recommendations towards
more transparent and advanced analytic approaches. However, potential
limitations are identified in terms of the statistical software and information
provided to support the choice of missing data methods.


title:
The Asteroid Routing Problem: A Benchmark for Expensive Black-Box
  Permutation Optimization

abstract:
  Inspired by the recent 11th Global Trajectory Optimisation Competition, this
paper presents the asteroid routing problem (ARP) as a realistic benchmark of
algorithms for expensive bound-constrained black-box optimization in
permutation space. Given a set of asteroids' orbits and a departure epoch, the
goal of the ARP is to find the optimal sequence for visiting the asteroids,
starting from Earth's orbit, in order to minimize both the cost, measured as
the sum of the magnitude of velocity changes required to complete the trip, and
the time, measured as the time elapsed from the departure epoch until visiting
the last asteroid. We provide open-source code for generating instances of
arbitrary sizes and evaluating solutions to the problem. As a preliminary
analysis, we compare the results of two methods for expensive black-box
optimization in permutation spaces, namely, Combinatorial Efficient Global
Optimization (CEGO), a Bayesian optimizer based on Gaussian processes, and
Unbalanced Mallows Model (UMM), an estimation-of-distribution algorithm based
on probabilistic Mallows models. We investigate the best permutation
representation for each algorithm, either rank-based or order-based. Moreover,
we analyze the effect of providing a good initial solution, generated by a
greedy nearest neighbor heuristic, on the performance of the algorithms. The
results suggest directions for improvements in the algorithms being compared.


title:
OakInk: A Large-scale Knowledge Repository for Understanding Hand-Object
  Interaction

abstract:
  Learning how humans manipulate objects requires machines to acquire knowledge
from two perspectives: one for understanding object affordances and the other
for learning human's interactions based on the affordances. Even though these
two knowledge bases are crucial, we find that current databases lack a
comprehensive awareness of them. In this work, we propose a multi-modal and
rich-annotated knowledge repository, OakInk, for visual and cognitive
understanding of hand-object interactions. We start to collect 1,800 common
household objects and annotate their affordances to construct the first
knowledge base: Oak. Given the affordance, we record rich human interactions
with 100 selected objects in Oak. Finally, we transfer the interactions on the
100 recorded objects to their virtual counterparts through a novel method:
Tink. The recorded and transferred hand-object interactions constitute the
second knowledge base: Ink. As a result, OakInk contains 50,000 distinct
affordance-aware and intent-oriented hand-object interactions. We benchmark
OakInk on pose estimation and grasp generation tasks. Moreover, we propose two
practical applications of OakInk: intent-based interaction generation and
handover generation. Our datasets and source code are publicly available at
https://github.com/lixiny/OakInk.


title: Interdependence of the new "MUON G-2" Result and the $W$-Boson Mass

abstract:
  The electroweak (EW) sector of the Minimal Supersymmetric extension of the
Standard Model (MSSM), assuming the lightest neutralino as Dark Matter (DM)
candidate, can account for a variety of experimental results. In particular it
can account for the discrepancy between the experimental result for the
anomalous magnetic moment of the muon, $(g-2)_{\mu}$, and its Standard Model
(SM) prediction. The new "MUON G-2" result, combined with the older BNL result
on $(g-2)_{\mu}$, yields a deviation from the SM prediction of $\Delta a_{\mu}
= (25.1 \pm 5.9) \times 10^{-10}$, corresponding to $4.2~\sigma$. Using this
updated bound, together with the other constraints, we calculate the MSSM
prediction for the mass of the $W$ boson, $M_W$. We assume contributions only
from the EW sector, with the colored sector of the MSSM taken to be heavy. We
investigate five scenarios, distinguished by the mechanisms which yield a relic
DM density in agreement with the latest Planck bounds. We find that with the
new $(g-2)_{\mu}$ result taken into account and depending on the scenario,
values up to $M_W^{\mathrm{MSSM}} \lesssim 80.376~\mathrm{GeV}$ are reached.
The largest values are obtained for wino DM and in the case of slepton
co-annihilation, where points well within the $1\,\sigma$ range of the
experimental world average of $M_W^{\mathrm{exp}} = 80.379
\pm~0.012~\mathrm{GeV}$ are reached, whereas the SM predicts a too small value
of $M_W^{\mathrm{SM}} = 80.353~\mathrm{GeV}$. We analyze the dependence of
$M_W^{\mathrm{MSSM}}$ on the relevant masses of the EW superpartners and
demonstrate that future $M_W$ measurements, e.g. at the ILC, could distinguish
between various MSSM realizations. Sizable contributions to
$M_W^{\mathrm{MSSM}}$ are associated with a relatively light
$\tilde{\chi}^0_1$, accompanied by either a light chargino or a light smuon,
setting interesting targets for future collider searches.


title: Two kinetic models for non-instantaneous binary alignment collisions

abstract:
  A new type of kinetic models with non-instantaneous binary collisions is
considered. Collisions are described by a transport process in the joint state
space of a pair of particles. The interactions are of alignment type, where the
states of the particles approach each other. For two spatially homogeneous
models with deterministic or stochastic collision times existence and
uniqueness of solutions, the long time behavior, and the instantaneous limit
are considered, where the latter leads to standard kinetic models of Boltzmann
type.


title: Integrative Few-Shot Learning for Classification and Segmentation

abstract:
  We introduce the integrative task of few-shot classification and segmentation
(FS-CS) that aims to both classify and segment target objects in a query image
when the target classes are given with a few examples. This task combines two
conventional few-shot learning problems, few-shot classification and
segmentation. FS-CS generalizes them to more realistic episodes with arbitrary
image pairs, where each target class may or may not be present in the query. To
address the task, we propose the integrative few-shot learning (iFSL) framework
for FS-CS, which trains a learner to construct class-wise foreground maps for
multi-label classification and pixel-wise segmentation. We also develop an
effective iFSL model, attentive squeeze network (ASNet), that leverages deep
semantic correlation and global self-attention to produce reliable foreground
maps. In experiments, the proposed method shows promising performance on the
FS-CS task and also achieves the state of the art on standard few-shot
segmentation benchmarks.


title:
On an electrostatic problem and a new class of exceptional subdomains of
  $\mathbb{R}^3$

abstract:
  We study the existence of nontrivial unbounded surfaces $S\subset
\mathbb{R}^3$ with the property that the constant charge distribution on $S$ is
an electrostatic equilibrium, i.e. the resulting electrostatic force is normal
to the surface at each point on $S$. Among bounded regular surfaces $S$, only
the round sphere has this property by a result of Reichel $[23]$ (see also
Mendez and Reichel $[16]$) confirming a conjecture of P. Gruber. In the present
paper, we show the existence of nontrivial exceptional domains $\Omega \subset
\mathbb{R}^3$ whose boundaries $S=\partial \Omega$ enjoy the above property.


title: The cosmological constant as a boundary term

abstract:
  We compare the path integral for transition functions in unimodular gravity
and in general relativity. In unimodular gravity the cosmological constant is a
property of states that are specified at the boundaries whereas in general
relativity the cosmological constant is a parameter of the action. Unimodular
gravity with a nondynamical background spacetime volume element has a time
variable that is canonically conjugate to the cosmological constant. Wave
functions depend on time and satisfy a Schr\"odinger equation. On the contrary,
in the covariant version of unimodular gravity with a 3-form gauge field,
proposed by Henneaux and Teitelboim, wave functions are time independent and
satisfy a Wheeler-DeWitt equation, as in general relativity. The 3-form gauge
field integrated over spacelike hypersurfaces becomes a "cosmic time" only in
the semiclassical approximation. In unimodular gravity the smallness of the
observed cosmological constant has to be explained as a property of the initial
state.


title: Commuting maps with the Mean Transform under Jordan product

abstract:
  In this article, we give a complete characterization of the bijective maps
which commute with the mean transform under Jordan product. The main result is
the following :
  Let $H,K$ be two complex Hilbert spaces and $\Phi :B(H) \to B(K)$ be a
bijective map, then
  $$ \mathcal {M}(\Phi(A)\circ\Phi(B))=\Phi(\mathcal{M}(A\circ B)) \;\;
\text{for all}\;\; A, B \in B(H)$$ if and only if there exists a unitary or
anti-unitary operator $U:H\to K $ such that,
  $$ \Phi(T)= UTU^* \; \text{for all} \;T\in B(H).$$


title: Application of Quantum Computers in Foreign Exchange Reserves Management

abstract:
  The main purpose of this article is to evaluate possible applications of
quantum computers in foreign exchange reserves management. The capabilities of
quantum computers are demonstrated by means of risk measurement using the
quantum Monte Carlo method and portfolio optimization using a linear equations
system solver (the Harrow-Hassidim-Lloyd algorithm) and quadratic unconstrained
binary optimization (the quantum approximate optimization algorithm). All
demonstrations are carried out on the cloud-based IBM Quantum(TM) platform.
Despite the fact that real-world applications are impossible under the current
state of development of quantum computers, it is proven that in principle it
will be possible to apply such computers in FX reserves management in the
future. In addition, the article serves as an introduction to quantum computing
for the staff of central banks and financial market supervisory authorities.


title: Near-critical dimers and massive SLE

abstract:
  We consider the dimer model on the square and hexagonal lattices with doubly
periodic weights. Although in the near-critical regime the Kasteleyn matrix is
related to a massive Laplacian, Chhita proved that on the whole plane square
lattice, the corresponding height function has a scaling limit which
surprisingly is not even Gaussian.
  The purpose of this paper is threefold: (a) we establish a rigourous
connection with the massive SLE$_2$ constructed by Makarov and Smirnov (and
recently revisited by Chelkak and Wan); (b) we show that the convergence takes
place in arbitrary ({bounded}) domains subject to Temperleyan boundary
conditions, and that the scaling limit is universal; and (c) we prove conformal
covariance of the scaling limit. This requires allowing the drift to be a
smoothly varying vector field; along the way we prove that the corresponding
loop-erased random walk has a universal scaling limit. Our techniques rely on
the imaginary geometry approach developed in \cite{BLR_DimersGeometry} and some
exact discrete Girsanov identities which are of independent interest.


title:
$\Lambda$ spin polarization in event-by-event relativistic heavy-ion
  collisions

abstract:
  We present a systematic study of $\Lambda$ hyperon's polarization observables
using event-by-event (3+1)D relativistic hydrodynamics. The effects of initial
hot spot size and QGP's specific shear viscosity on the polarization
observables are quantified. We examine the effects of the two formulations of
the thermal shear tensor on the polarization observables using the same
hydrodynamic background. With event-by-event simulations, we make predictions
for the Fourier coefficients of $\Lambda$'s longitudinal polarization $P^z$
with respect to the event planes of different orders of anisotropic flow. We
propose new correlations among the Fourier coefficients of $P^z$ and charged
hadron anisotropic flow coefficients to further test the mapping from fluid
velocity gradients to hyperon's polarization. Finally, we present a system size
scan with Au+Au, Ru+Ru, and O+O collisions at $\sqrt{s_\mathrm{NN}} = 200$ GeV
to study the system size dependence of polarization observables at the
Relativistic Heavy-ion Collider.


title: Adaptive Quantum State Tomography with Active Learning

abstract:
  Recently, tremendous progress has been made in the field of quantum science
and technologies: different platforms for quantum simulation as well as quantum
computing, ranging from superconducting qubits to neutral atoms, are starting
to reach unprecedentedly large systems. In order to benchmark these systems and
gain physical insights, the need for efficient tools to characterize quantum
states arises. The exponential growth of the Hilbert space with system size
renders a full reconstruction of the quantum state prohibitively demanding in
terms of the number of necessary measurements. Here we propose and implement an
efficient scheme for quantum state tomography using active learning. Based on a
few initial measurements, the active learning protocol proposes the next
measurement basis, designed to yield the maximum information gain. For a fixed
total number of measurements and basis configurations, our algorithm maximizes
the information one can obtain about the quantum state under consideration. We
apply the active learning quantum state tomography scheme to reconstruct
different multi-qubit states with varying degree of entanglement as well as to
ground states of a kinetically constrained spin chain. In all cases, we obtain
a significantly improved reconstruction as compared to a reconstruction based
on the exact same number of measurements, but with randomly chosen basis
configurations. Our scheme is highly relevant to gain physical insights in
quantum many-body systems as well as for the characterization of quantum
devices, and paves the way for benchmarking and probing large quantum systems.


title:
Transformer Inertial Poser: Attention-based Real-time Human Motion
  Reconstruction from Sparse IMUs

abstract:
  Real-time human motion reconstruction from a sparse set of wearable IMUs
provides an non-intrusive and economic approach to motion capture. Without the
ability to acquire absolute position information using IMUs, many prior works
took data-driven approaches that utilize large human motion datasets to tackle
the under-determined nature of the problem. Still, challenges such as temporal
consistency, global translation estimation, and diverse coverage of motion or
terrain types remain. Inspired by recent success of Transformer models in
sequence modeling, we propose an attention-based deep learning method to
reconstruct full-body motion from six IMU sensors in real-time. Together with a
physics-based learning objective to predict "stationary body points", our
method achieves new state-of-the-art results both quantitatively and
qualitatively, while being simple to implement and smaller in size. We evaluate
our method extensively on synthesized and real IMU data, and with real-time
live demos.


title: On Decoding Strategies for Neural Text Generators

abstract:
  When generating text from probabilistic models, the chosen decoding strategy
has a profound effect on the resulting text. Yet the properties elicited by
various decoding strategies do not always transfer across natural language
generation tasks. For example, while mode-seeking methods like beam search
perform remarkably well for machine translation, they have been observed to
lead to incoherent and repetitive text in story generation. Despite such
observations, the effectiveness of decoding strategies is often assessed with
respect to only a single task. This work -- in contrast -- provides a
comprehensive analysis of the interaction between language generation tasks and
decoding strategies. Specifically, we measure changes in attributes of
generated text as a function of both decoding strategy and task using human and
automatic evaluation. Our results reveal both previously-observed and
surprising findings. For example, the nature of the diversity-quality trade-off
in language generation is very task-specific; the length bias often attributed
to beam search is not constant across tasks.


title:
Transformer Network-based Reinforcement Learning Method for Power
  Distribution Network (PDN) Optimization of High Bandwidth Memory (HBM)

abstract:
  In this article, for the first time, we propose a transformer network-based
reinforcement learning (RL) method for power distribution network (PDN)
optimization of high bandwidth memory (HBM). The proposed method can provide an
optimal decoupling capacitor (decap) design to maximize the reduction of PDN
self- and transfer impedance seen at multiple ports. An attention-based
transformer network is implemented to directly parameterize decap optimization
policy. The optimality performance is significantly improved since the
attention mechanism has powerful expression to explore massive combinatorial
space for decap assignments. Moreover, it can capture sequential relationships
between the decap assignments. The computing time for optimization is
dramatically reduced due to the reusable network on positions of probing ports
and decap assignment candidates. This is because the transformer network has a
context embedding process to capture meta-features including probing ports
positions. In addition, the network is trained with randomly generated data
sets. Therefore, without additional training, the trained network can solve new
decap optimization problems. The computing time for training and data cost are
critically decreased due to the scalability of the network. Thanks to its
shared weight property, the network can adapt to a larger scale of problems
without additional training. For verification, we compare the results with
conventional genetic algorithm (GA), random search (RS), and all the previous
RL-based methods. As a result, the proposed method outperforms in all the
following aspects: optimality performance, computing time, and data efficiency.


title:
Few-shot Structured Radiology Report Generation Using Natural Language
  Prompts

abstract:
  Chest radiograph reporting is time-consuming, and numerous solutions to
automate this process have been proposed. Due to the complexity of medical
information, the variety of writing styles, and free text being prone to typos
and inconsistencies, the efficacy of quantifying the clinical accuracy of
free-text reports using natural language processing measures is challenging. On
the other hand, structured reports ensure consistency and can more easily be
used as a quality assurance tool. To accomplish this, we present a strategy for
predicting clinical observations and their anatomical location that is easily
extensible to other structured findings. First, we train a contrastive
language-image model using related chest radiographs and free-text radiological
reports. Then, we create textual prompts for each structured finding and
optimize a classifier for predicting clinical findings and their associations
within the medical image. The results indicate that even when only a few
image-level annotations are used for training, the method can localize
pathologies in chest radiographs and generate structured reports.


title: On $d$-stable locally checkable problems on bounded mim-width graphs

abstract:
  In this paper we continue the study of locally checkable problems under the
framework introduced by Bonomo-Braberman and Gonzalez in 2020, by focusing on
graphs of bounded mim-width. We study which restrictions on a locally checkable
problem are necessary in order to be able to solve it efficiently on graphs of
bounded mim-width. To this end, we introduce the concept of $d$-stability of a
check function. The related locally checkable problems contain large classes of
problems, among which we can mention, for example, LCVP problems. We provide an
algorithm which solves all $d$-stable locally checkable problems on graphs of
bounded mim-width in polynomial time and explain how we can include additional
global properties (size of the color classes and connectivity). We explore the
relation between $d$-stable locally checkable problems with the recently
introduced DN-logic (Bergougnoux, Dreier and Jaffke, 2022). We conclude by
listing concrete examples of problems whose complexity on graphs of bounded
mim-width was open so far.


title: Synergizing Physics/Model-based and Data-driven Methods for Low-Dose CT

abstract:
  Since 2016, deep learning (DL) has advanced tomographic imaging with
remarkable successes, especially in low-dose computed tomography (LDCT)
imaging. Despite being driven by big data, the LDCT denoising and pure
end-to-end reconstruction networks often suffer from the black box nature and
major issues such as instabilities, which is a major barrier to apply deep
learning methods in low-dose CT applications. An emerging trend is to integrate
imaging physics and model into deep networks, enabling a hybridization of
physics/model-based and data-driven elements. In this paper, we systematically
review the physics/model-based data-driven methods for LDCT, summarize the loss
functions and training strategies, evaluate the performance of different
methods, and discuss relevant issues and future directions


title: Scheduling UET-UCT DAGs of Depth Two on Two Processors

abstract:
  Given unit execution time (UET) tasks whose precedence constraints form a
directed acyclic graph (DAG), the arcs are associated with unit communication
time (UCT) delays. The problem is to schedule the tasks on two processors in
order to minimize the makespan. Several polynomial algorithms in the literature
are proposed for special classes of digraphs, but the complexity of solving
this problem in general case stills a challenging open question. We propose in
this paper a linear time algorithm to compute an optimal schedule for the class
of DAGs of depth two.


title:
A Quantum Optimal Control Problem with State Constrained Preserving
  Coherence

abstract:
  In this work, we address the problem of maximizing fidelity in a quantum
state transformation process controlled in such a way as to keep decoherence
within given bounds. We consider a three-level $\Lambda$-type atom subjected to
Markovian decoherence characterized by non-unital decoherence channels. We
introduce fidelity as the performance index for the quantum state
transformation process since the goal is to maximize the similarity of the
final state density operator with the one of the desired target state. We
formulate the quantum optimal control problem with state constraints where the
later reflect the fact that the decoherence level remains within a pre-defined
bound. Optimality conditions in the form of a Maximum Principle of Pontryagin
in Gamkrelidze's form are given. These provide a complete set of relations
enabling the computation of the optimal control strategy. This is a novel
approach in the context of quantum systems.


title: Wasserstein-Fisher-Rao Splines

abstract:
  We study interpolating splines on the Wasserstein-Fisher-Rao (WFR) space of
measures with differing total masses. To achieve this, we derive the covariant
derivative and the curvature of an absolutely continuous curve in the WFR
space. We prove that this geometric notion of curvature is equivalent to a
Lagrangian notion of curvature in terms of particles on the cone. Finally, we
propose a practical algorithm for computing splines extending the work of
arXiv:2010.12101.


title:
Evaluating the ILC SUSY reach in the most challenging scenario:
  $\widetilde{\tau}$ NLSP, low $\Delta M$ , lowest cross-section

abstract:
  Pair-production of the superpartner of the $\tau$-lepton, the $\tilde{\tau}$,
is one of the most interesting channels to search for SUSY in: The
$\tilde{\tau}$ is likely to be the lightest scalar lepton, and the signature of
$\tilde{\tau}$ pair production is one of the experimentally most difficult
ones, thereby constituting the "worst" possible scenario for SUSY searches. The
current model-independent $\tilde{\tau}$ limits comes from analyses performed
at LEP but they suffer from the limited energy of this facility. Limits
obtained at the LHC do extend to higher masses, but they are only valid under
strong assumptions. The International Linear Collider, the ILC, is a future
electron-positron collider, to operate initially at an energy of 250 GeV, then
to be upgraded to 500 GeV, and possibly to 1 TeV at a later stage. ILC will be
a powerful facility for SUSY searches. The capability of the ILC for
determining exclusion/discovery limits for the $\tilde{\tau}$ in a
model-independent way is shown in this paper. A detailed study of the "worst"
scenario for $\tilde{\tau}$ exclusion/discovery, taking into account the effect
of the $\tilde{\tau}$ mixing on $\tilde{\tau}$ production cross-section and
detection efficiency, is presented. The study also includes an analysis of the
effect of the overlay particles in the $\tilde{\tau}$ searches. The conclusion
is that both the exclusion and discovery reaches for this "worst" case would
extend to only a few GeV below the kinematic limit at the ILC. Also scenarios
with the $\tilde{\tau}$ and the Lightest SUSY Particle (the LSP) quite close in
mass can be discovered or excluded at most $\tilde{\tau}$ masses. The studies
were done using detailed detector simulation of the ILD concept at the ILC. For
signal, the fast detector simulation SGV was used, while the full Geant4 based
DDSim was used for the standard model backgrounds.