title:
SRP-DNN: Learning Direct-Path Phase Difference for Multiple Moving Sound
  Source Localization

abstract:
  Multiple moving sound source localization in real-world scenarios remains a
challenging issue due to interaction between sources, time-varying
trajectories, distorted spatial cues, etc. In this work, we propose to use deep
learning techniques to learn competing and time-varying direct-path phase
differences for localizing multiple moving sound sources. A causal
convolutional recurrent neural network is designed to extract the direct-path
phase difference sequence from signals of each microphone pair. To avoid the
assignment ambiguity and the problem of uncertain output-dimension encountered
when simultaneously predicting multiple targets, the learning target is
designed in a weighted sum format, which encodes source activity in the weight
and direct-path phase differences in the summed value. The learned direct-path
phase differences for all microphone pairs can be directly used to construct
the spatial spectrum according to the formulation of steered response power
(SRP). This deep neural network (DNN) based SRP method is referred to as
SRP-DNN. The locations of sources are estimated by iteratively detecting and
removing the dominant source from the spatial spectrum, in which way the
interaction between sources is reduced. Experimental results on both simulated
and real-world data show the superiority of the proposed method in the presence
of noise and reverberation.


title:
Direct Estimate of the Post-Newtonian Parameter and Cosmic Curvature
  from Galaxy-scale Strong Gravitational Lensing

abstract:
  Einstein's theory of general relativity (GR) has been precisely tested on
solar system scales, but extragalactic tests are still poorly performed. In
this work, we use a newly compiled sample of galaxy-scale strong gravitational
lenses to test the validity of GR on kiloparsec scales. In order to solve the
circularity problem caused by the preassumption of a specific cosmological
model based on GR, we employ the distance sum rule in the
Friedmann-Lema\^{\i}tre-Robertson-Walker metric to directly estimate the
parameterized post-Newtonian (PPN) parameter $\gamma_{\rm PPN}$ and the cosmic
curvature $\Omega_k$ by combining observations of strong lensing and Type Ia
supernovae. This is the first simultaneous measurement of $\gamma_{\rm PPN}$
and $\Omega_k$ without any assumptions about the contents of the universe or
the theory of gravity. Our results show that $\gamma_{\rm
PPN}=1.11^{+0.11}_{-0.09}$ and $\Omega_{k}=0.48^{+1.09}_{-0.71}$, indicating a
strong degeneracy between the two quantities. The measured $\gamma_{\rm PPN}$,
which is consistent with the prediction of 1 from GR, provides a precise
extragalactic test of GR with a fractional accuracy better than 9.0\%. If a
prior of the spatial flatness (i.e., $\Omega_{k}=0$) is adopted, the PPN
parameter constraint can be further improved to $\gamma_{\rm
PPN}=1.07^{+0.07}_{-0.07}$, representing a precision of 6.5\%. On the other
hand, in the framework of GR (i.e., $\gamma_{\rm PPN}=1$), our results are
still marginally compatible with zero curvature
($\Omega_k=-0.12^{+0.48}_{-0.36}$), supporting no significant deviation from a
flat universe.


title: Practical Network Acceleration with Tiny Sets

abstract:
  Network compression is effective in accelerating the inference of deep neural
networks, but often requires finetuning with all the training data to recover
from the accuracy loss. It is impractical in some applications, however, due to
data privacy issues or constraints in compression time budget. To deal with the
above issues, we propose a method named PRACTISE to accelerate the network with
tiny sets of training images. By considering both the pruned part and the
unpruned part of a compressed model, PRACTISE alleviates layer-wise error
accumulation, which is the main drawback of previous methods. Furthermore,
existing methods are confined to few compression schemes, have limited speedup
in terms of latency, and are unstable. In contrast, PRACTISE is stable, fast to
train, versatile to handle various compression schemes, and achieves low
latency. We also propose that dropping entire blocks is a better way than
existing compression schemes when only tiny sets of training data are
available. Extensive experiments demonstrate that PRACTISE achieves much higher
accuracy and more stable models than state-of-the-art methods.


title: See further upon the giants: Quantifying intellectual lineage in science

abstract:
  Newton's centuries-old wisdom of standing on the shoulders of giants raises a
crucial yet underexplored question: Out of all the prior works cited by a
discovery, which one is its giant? Here, we develop a novel,
discipline-independent method to identify the giant for any individual paper,
allowing us to systematically examine the role and characteristics of giants in
science. We find that across disciplines, about 95% of papers stand on the
shoulders of giants, yet the weight of scientific progress rests on relatively
few shoulders. Defining a new measure of giant index, we find that, while
papers with high citations are more likely to be giants, for papers with the
same citations, their giant index sharply predicts a paper's future impact and
prize-winning probabilities. Giants tend to originate from both small and large
teams, being either highly disruptive or highly developmental. And papers that
did not have a giant but later became a giant tend to be home-run papers that
are highly disruptive to science. Given the crucial importance of
citation-based measures in science, the developed concept of giants may offer a
useful new dimension in assessing scientific impact that goes beyond sheer
citation counts.


title:
Agricultural Windfalls and the Seasonality of Political Violence in
  Africa

abstract:
  We study the seasonality of violence against civilians in the cropland of
Africa. We combine 24 years of monthly international cereal prices with
one-degree grid-cell level cropland area and harvest data, and nearly 56
thousand unique incidents, to investigate the relationship between seasonal
agricultural income shocks and political violence staged by armed groups. We
find that a one standard deviation price growth of the major cereal crop
results in a monthly four percent increase in violence against civilians by
political militias in the cropland of Africa during the early post-harvest
season. The effect accrues to more than a ten percent annual increase in
violence, nearly all of which is manifested during the first three months of
the crop year. Other important actors, state forces and rebel groups in
particular, do not appear to be engaging in seasonal conflict related to
agricultural income shocks. In further investigating the mechanism, we show
that the violence by political militias amplifies after plausibly rich harvest
seasons. Because incidents peak during the early post-harvest season when the
value of spoils to be appropriated is highest, we suggest rapacity as the key
mechanism in the nexus of income shocks and political violence in the cropland
of Africa. The study contributes to the growing but ambiguous literature on the
economic causes of conflict in agrarian societies with weak institutions, and
offers an important temporal nuance to this relationship.


title: Fitting Generalized Linear Mixed Models using Adaptive Quadrature

abstract:
  We describe how to approximate the intractable marginal likelihood that
arises when fitting generalized linear mixed models. We prove that non-adaptive
quadrature approximations yield high error asymptotically in every statistical
model satisfying weak regularity conditions. We derive the rate of error
incurred when using adaptive quadrature to approximate the marginal likelihood
in a broad class of generalized linear mixed models, which includes
non-exponential family response and non-Gaussian random effects distributions.
We provide an explicit recommendation for how many quadrature points to use,
and show that this recommendation recovers and explains many empirical results
from published simulation studies and data analyses. Particular attention is
paid to models for dependent binary and survival/time-to-event observations.
Code to reproduce results in the manuscript is found at
https://github.com/awstringer1/glmm-aq-paper-code.


title:
Exploring the Hubble Tension and Spatial Curvature from the Ages of Old
  Astrophysical Objects

abstract:
  We use the age measurements of 114 old astrophysical objects (OAO) in the
redshift range $0\lesssim z\lesssim 8$ to explore the Hubble tension. The age
of the Universe at any $z$ is inversely proportional to the Hubble constant,
$H_0$, so requiring the Universe to be older than the OAO it contains at any
$z$ will lead to an upper limit on $H_0$. Assuming flat $\Lambda$CDM and
setting a Gaussian prior on the matter density parameter $\Omega_{\rm
m}=0.315\pm0.007$ informed by {\it Planck}, we obtain a 95\% confidence-level
upper limit of $H_0<70.6 \rm{~km} \rm{~s}^{-1} \rm{~Mpc}^{-1}$, representing a
$2\sigma$ tension with the measurement using the local distance ladder. We
find, however, that the inferred upper limit on $H_{0}$ depends quite
sensitively on the prior for $\Omega_{\rm m}$, and the Hubble tension between
early-time and local measurements of $H_{0}$ may be due in part to the
inference of both $\Omega_{\rm m}$ and $H_0$ in {\it Planck}, while the local
measurement uses only $H_{0}$. The age-redshift data may also be used for
cosmological model comparisons. We find that the $R_{\rm h}=ct$ universe
accounts well for the data, with a reasonable upper limit on $H_{0}$, while
Einstein-de Sitter fails to pass the cosmic-age test. Finally, we present a
model-independent estimate of the spatial curvature using the ages of 61
galaxies and the luminosity distances of 1,048 Pantheon Type Ia supernovae.
This analysis suggests that the geometry of the Universe is marginally
consistent with spatial flatness at a confidence level of $1.6\sigma$,
characterized as $\Omega_{k}=0.43^{+0.27}_{-0.27}$.


title: Fixed-time Synchronization of Networked Uncertain Euler-Lagrange Systems

abstract:
  This paper considers the fixed-time control problem of a multi-agent system
composed of a class of Euler-Lagrange dynamics with parametric uncertainty and
a dynamic leader under a directed communication network. A distributed
fixed-time observer is first proposed to estimate the desired trajectory and
then a fixed-time controller is constructed by transforming uncertain
Euler-Lagrange systems into second-order systems and utilizing the backstepping
design procedure. The overall design guarantees that the synchronization errors
converge to zero in a prescribed time independent of initial conditions. The
control design conditions can also be relaxed for a weaker finite-time control
requirement.


title:
Quantifying dynamical magic with completely stabilizer preserving
  operations as free

abstract:
  In this paper, we extend the resource theory of magic to the channel case by
considering completely stabilizer preserving operations (CSPOs) as free. We
introduce and characterize the set of CSPO preserving and completely CSPO
preserving superchannels. We quantify the magic of quantum channels by
extending the generalized robustness and the min relative entropy of magic from
the state to the channel domain and show that they bound the single-shot
dynamical magic cost and distillation. We also provide analytical conditions
for qubit interconversion under CSPOs and show that it is a linear programming
feasibility problem and hence can be efficiently solved. Lastly, we give a
classical simulation algorithm whose runtime is related to the generalized
robustness of magic for channels. Our algorithm depends on some pre-defined
precision, and if there is no bound on the desired precision then it achieves a
constant runtime.


title: Data-Driven Minimax Optimization with Expectation Constraints

abstract:
  Attention to data-driven optimization approaches, including the well-known
stochastic gradient descent method, has grown significantly over recent
decades, but data-driven constraints have rarely been studied, because of the
computational challenges of projections onto the feasible set defined by these
hard constraints. In this paper, we focus on the non-smooth convex-concave
stochastic minimax regime and formulate the data-driven constraints as
expectation constraints. The minimax expectation constrained problem subsumes a
broad class of real-world applications, including two-player zero-sum game and
data-driven robust optimization. We propose a class of efficient primal-dual
algorithms to tackle the minimax expectation-constrained problem, and show that
our algorithms converge at the optimal rate of
$\mathcal{O}(\frac{1}{\sqrt{N}})$. We demonstrate the practical efficiency of
our algorithms by conducting numerical experiments on large-scale real-world
applications.


title:
Selective Inverse Kinematics: A Novel Approach to Finding Multiple
  Solutions Fast for High-DoF Robotic

abstract:
  Inverse Kinematics (IK) solves the problem of mapping from the Cartesian
space to the joint configuration space of a robotic arm. It has a wide range of
applications in areas such as computer graphics, protein structure prediction,
and robotics. The problem is commonly solved analytically based on the
structure of the robotic arm or numerically by approximation through recursive
methods, e.g., Jacobian-based methods. Over the past decade, data-driven
methods have also been exploited. Unfortunately, these approaches to IK become
inadequate for high Degree-of-Freedom (DoF) robotic arms. Theoretically, the
redundant DoFs of such robotic arms can provide an infinite number of solutions
to IK for reaching a given target position. The huge solution space could be
exploited for more flexible operations of high-DoF robotic arms. The problem is
that existing approaches are confined and normally produce only one joint
solution for a target position. This paper presents the first work that solves
high-DoF IK by generating multiple distinct joint solutions to reach any given
target position in the working space. The proposed data-driven approach can be
applied to any robotic arms without knowing detailed kinematics information. It
not only obtains multiple distinct joint solutions for a target position, but
also solves the high-DoF IK problem within a millisecond and achieves
subcentimeter distance errors with very sparse training data.


title:
IPD:An Incremental Prototype based DBSCAN for large-scale data with
  cluster representatives

abstract:
  DBSCAN is a fundamental density-based clustering technique that identifies
any arbitrary shape of the clusters. However, it becomes infeasible while
handling big data. On the other hand, centroid-based clustering is important
for detecting patterns in a dataset since unprocessed data points can be
labeled to their nearest centroid. However, it can not detect non-spherical
clusters. For a large data, it is not feasible to store and compute labels of
every samples. These can be done as and when the information is required. The
purpose can be accomplished when clustering act as a tool to identify cluster
representatives and query is served by assigning cluster labels of nearest
representative. In this paper, we propose an Incremental Prototype-based DBSCAN
(IPD) algorithm which is designed to identify arbitrary-shaped clusters for
large-scale data. Additionally, it chooses a set of representatives for each
cluster.


title:
Correlation of velocity and density contributions to spectroscopic
  channel maps: Reality check on Kalberla et.al (2022)

abstract:
  The existence of magnetized turbulence in the interstellar HI is well
accepted. A number of techniques to obtain turbulence spectrum and magnetic
field direction and strength have been developed and successfully applied to HI
spectroscopic data. To better separate the imprints of density and velocity
fluctuations to the channel maps, a new theory-based technique, the Velocity
Decomposition Algorithm (VDA,Yuen et.al 2021), has been created. The technique
demonstrates that the intensity fluctuations are separated into a component pv
that mostly arises from velocity fluctuations and pd that mostly arise from
density fluctuations. The VDA helps to clarify the nature of the filamentary
structure observed in channel maps. A recent publication (Kalberla et.al
2022,K22) claims that the application of VDA to HI4PI data provides negative
correlation of pv and pd,which according to the authors invalidates the
technique since it requires that pv and pd have zero correlation. However, the
quantities pv and pd given by VDA are naturally orthogonal which can be
trivially checked analytically or numerically. That means the correct
application of the VDA to any data must provide zero correlation. This is the
point that we clarify in this paper and search for the cause of the mistake in
the application of the VDA in K22 that resulted in the erroneous conclusion. We
prove analytically that by construction pv and pd are not correlated. We
identify the likely mistake in the VDA expression that K22 used and reproduce
their figures with the incorrect expression. We find that 14 out of 15 figures
in K22 are invalid, and thus their criticism of the VDA is ill-founded and
arises from their use of incorrect expressions. We conclude that the
detrimental mistake that K22 made at their analysis completely invalidate their
scientific claim that Y21 isnt compatible to observation.


title:
An efficient distributed scheduling algorithm for relay-assisted mmWave
  backhaul networks

abstract:
  In this paper, a novel distributed scheduling algorithm is proposed, which
aims to efficiently schedule both the uplink and downlink backhaul traffic in
the relay-assisted mmWave backhaul network with a tree topology. The
handshaking of control messages, calculation of local schedules, and the
determination of final valid schedule are all discussed. Simulation results
show that the performance of the distributed algorithm can reach very close to
the maximum traffic demand of the backhaul network, and it can also adapt to
the dynamic traffic with sharp traffic demand change of small-cell BSs quickly
and accurately.


title:
Direct Light Orbital Angular Momentum Detection in Mid-Infrared based on
  Type-II Weyl Semimetal TaIrTe4

abstract:
  The capability of direct photocurrent detection of orbital angular momentum
(OAM) of light has recently been realized with topological Weyl semimetal, but
limited to near infrared wavelength range. The extension of direct OAM
detection to midinfrared, a wavelength range that plays important role in a
vast range of applications, such as autonomous driving, night vision and motion
detection, is challenging and has not yet been realized. This is because most
studies of photocurrent responses are not sensitive to the phase information
and the photo response is usually very poor in the mid-infrared. In this study,
we designed a photodetector based on Type-II Weyl semimetal tantalum iridium
tellurides with designed electrode geometries for direct detection of the
topological charge of OAM through orbital photogalvanic effect. Our results
indicate helical phase gradient of light can be distinguished by a current
winding around the optical beam axis with a magnitude proportional to its
quantized OAM mode number. The topological enhanced response at mid-infrared of
TaIrTe4 further help overcome the low responsivity issues and finally render
the direct orbital angular momentum detection capability in mid-infrared. Our
study enables on-chip integrated OAM detection, and thus OAM sensitive focal
plane arrays in mid-infrared. Such capability triggers new route to explore
applications of light carrying OAM, especially that it can crucially promote
the performance of many mid-infrared imaging related applications, such as
intricate target recognition and night vision.


title:
Dependence comparisons of order statistics in the proportional hazards
  model

abstract:
  Let $X_1, \ldots , X_n$ be mutually independent exponential random variables
with distinct hazard rates $\lambda_1, \ldots , \lambda_n > 0$ and let $Y_1,
\ldots, Y_n$ be a random sample from the exponential distribution with hazard
rate $\bar \lmd = \sum_{i=1}^n \lmd_i/n$. Also let $X_{1:n} < \cdots < X_{n:n}$
and $Y_{1:n} < \cdots < Y_{n:n}$ be their associated order statistics. It is
shown that for $1\le i <j \le n$, the generalized spacing $X_{j:\, n} - X_{i:\,
n}$ is more dispersed than $Y_{j:\,n} - Y_{i:\, n}$ according to dispersive
ordering. This result is used to solve a long standing open problem that for
$2\le i \le n$ the dependence of $ X_{i:\, n}$ on $X_{1:\, n}$ is less than
that of $Y_{i: \, n}$ on $Y_{1\, :n}$, in the sense of the more stochastically
increasing. This dependence result is also extended to the PHR model. This
extends the earlier work of {\em Genest, Kochar and Xu}[ J.\ Multivariate
Anal.\ {\bf 100} (2009) \ 1587-1592] who proved this result for $i =n$.


title: Knowledge Management for Cloud Computing Field

abstract:
  Migration legacy systems to cloud platforms is a knowledge intensive process.
There is an ever increasing body of knowledge reporting empirical scenarios of
successful and problematic cloud migration. Reusing this body of knowledge,
dispersed and fragmented over the academic/multi-vocal literature, has
practical values to mitigate costly risks and pitfalls in further projects of
legacy to-cloud and cloud-to-cloud migration. In line with this, knowledge
management systems/platforms pertinent to cloud migration are a prime
prerequisite and a strategic imperative for an organization. We have conducted
a qualitative exploratory study to understand the benefits and challenges of
developing Knowledge Management Systems (KMS) for cloud migration in real
trials. Whilst our prototype system demonstration supported the importance and
bene-fits of developing Cloud Migration KMS (CM-KMS), our semi-structured
industry interview study with 11 participants highlighted challenging
impediments against developing this class of KMS. As a result, this study
proposes nine significant challenges that cause the abandon of the design and
maintenance of CM-KMS, including continuous changes and updates, integration of
knowledge, knowledge granularity, preservation of context, automation,
deconstruction of traditional knowledge, dependency on experts, hybrid
knowledge of both vendor-specific and vendor-neutral cloud platforms, and
parsimony. Our results inform cloud architects to pay attention to adopt CM-KMS
for the legacy-to-cloud migration in their organizations.


title:
Vector Bundles associated to Monads on Cartesian Products of Projective
  Spaces

abstract:
  In this paper we construct vector bundles associated to monads on
$X=\mathbb{P}^n\times\mathbb{P}^n\times\mathbb{P}^m\times\mathbb{P}^m$. We
first establish the existence of such monads on $X$. Once the monads exist, the
next natural question is whether the cohomology vector bundle associated to
these monads are simple or not. We study these vector bundles associated to
monads and prove their stability and simplicity.


title: Prime vertex-minors of a prime graph

abstract:
  A split of a graph is a partition $(A,B)$ of its vertex set such that
$\min\{|A|,|B|\} \geq 2$ and for some $A'\subseteq A$ and $B'\subseteq B$, two
vertices $x\in A$ and $y\in B$ are adjacent if and only if $x\in A'$ and $y\in
B'$. A graph is prime if it has no split. A vertex $v$ of a graph is
non-essential if at least two of the three kinds of vertex-minor reductions at
$v$ result in prime graphs. Allys (1994) proved that every prime graph with at
least $5$ vertices has a non-essential vertex unless it is locally equivalent
to a cycle graph. We prove that every prime graph with at least $5$ vertices
has at least $2$ non-essential vertices unless it is locally equivalent to a
cycle graph. Furthermore, we show that every prime graph with at least $5$
vertices has at least $3$ non-essential vertices if and only if it is not
locally equivalent to a graph with two specified vertices $x$ and $y$
consisting of at least two internally-disjoint paths from $x$ to $y$ in which
$x$ and $y$ have no common neighbor.


title:
Background oriented schlieren technique with fast Fourier demodulation
  for measuring large density-gradient fields of fluids

abstract:
  In order to measure the large density-gradient fields of fluids such as
underwater shock waves, we employed a fast Fourier demodulation called Fast
Checkerboard Demodulation (FCD) method in Background Oriented Schlieren (BOS)
technique. BOS is a simple image-based measurement technique that detects the
apparent displacement (local distortion) of a background image caused by the
density gradient of the fluid in front of the background. The cross-correlation
particle image velocimetry (PIV) method, which is commonly employed in the BOS
technique for detecting the apparent displacement, uses a random-dot
background, whereas FCD uses a periodic pattern as the background (e.g.,
checkerboard pattern, lattice grid pattern (grid scale)) to detect the apparent
displacement as the phase change of the pattern in the Fourier space. In this
study, we measured the apparent displacement, which is proportional to density
gradient of fluid, of an underwater shock wave using FCD and PIV. The results
showed that FCD can measure a displacement gradient of up to 2.5 times larger
than that which PIV can measure. Furthermore, we systematically investigated
the measurement limit of FCD-BOS by changing several parameters of the periodic
patterns, such as the grid size. Also, we explored the related parameters in
the Fourier space to understand the limitations. It is worth noting that
FCD-BOS with lattice grid pattern (grid scale) can measure the apparent
displacement as accurately as that with a custom-made pattern, indicating that
large density-gradient fields of fluids can be measured using a simple setup
with a commercially available (inexpensive) pattern.


title:
Nanodiamond grain boundaries and lattice expansion drive Silicon vacancy
  emission heterogeneity

abstract:
  Silicon-vacancy (SiV$^-$) centers in diamond are promising candidates as
sources of single-photons in quantum networks due to their minimal phonon
coupling and narrow optical linewidths. Correlating SiV$^-$ emission with the
defect's atomic-scale structure is important for controlling and optimizing
quantum emission, but remains an outstanding challenge. Here, we use
cathodoluminescence imaging in a scanning transmission electron microscope
(STEM) to elucidate the structural sources of non-ideality in the SiV$^-$
emission from nanodiamonds with sub-nanometer-scale resolution. We show that
different crystalline domains of a nanodiamond exhibit distinct zero-phonon
line (ZPL) energies and differences in brightness, while near-surface SiV$^-$
emitters remain bright. We correlate these changes with local lattice expansion
using 4D STEM and diffraction, and show that associated blue shifts from the
ZPL are due to defect density heterogeneity, while red shifts are due to
lattice distortions.


title: CIS2: A Simplified Commonsense Inference Evaluation for Story Prose

abstract:
  Transformers have been showing near-human performance on a variety of tasks,
but they are not without their limitations. We discuss the issue of conflating
results of transformers that are instructed to do multiple tasks
simultaneously. In particular, we focus on the domain of commonsense reasoning
within story prose, which we call contextual commonsense inference (CCI). We
look at the GLUCOSE (Mostafazadeh et al 2020) dataset and task for predicting
implicit commonsense inferences between story sentences. Since the GLUCOSE task
simultaneously generates sentences and predicts the CCI relation, there is a
conflation in the results. Is the model really measuring CCI or is its ability
to generate grammatical text carrying the results? In this paper, we introduce
the task contextual commonsense inference in sentence selection (CIS$^2$), a
simplified task that avoids conflation by eliminating language generation
altogether. Our findings emphasize the necessity of future work to disentangle
language generation from the desired NLP tasks at hand.


title:
The addition of temporal neighborhood makes the logic of prefixes and
  sub-intervals EXPSPACE-complete

abstract:
  A classic result by Stockmeyer gives a non-elementary lower bound to the
emptiness problem for star-free generalized regular expressions. This result is
intimately connected to the satisfiability problem for interval temporal logic,
notably for formulas that make use of the so-called chop operator. Such an
operator can indeed be interpreted as the inverse of the concatenation
operation on regular languages, and this correspondence enables reductions
between non-emptiness of star-free generalized regular expressions and
satisfiability of formulas of the interval temporal logic of chop under the
homogeneity assumption. In this paper, we study the complexity of the
satisfiability problem for suitable weakenings of the chop interval temporal
logic, that can be equivalently viewed as fragments of Halpern and Shoham
interval logic. We first consider the logic $\mathsf{BD}_{hom}$ featuring
modalities $B$, for \emph{begins}, corresponding to the prefix relation on
pairs of intervals, and $D$, for \emph{during}, corresponding to the infix
relation. The homogeneous models of $\mathsf{BD}_{hom}$ naturally correspond to
languages defined by restricted forms of regular expressions, that use union,
complementation, and the inverses of the prefix and infix relations. Such a
fragment has been recently shown to be PSPACE-complete . In this paper, we
study the extension $\mathsf{BD}_{hom}$ with the temporal neighborhood modality
$A$ (corresponding to the Allen relation \emph{Meets}), and prove that it
increases both its expressiveness and complexity. In particular, we show that
the resulting logic $\mathsf{BDA}_{hom}$ is EXPSPACE-complete.


title:
PhishChain: A Decentralized and Transparent System to Blacklist Phishing
  URLs

abstract:
  Blacklists are a widely-used Internet security mechanism to protect Internet
users from financial scams, malicious web pages and other cyber attacks based
on blacklisted URLs. In this demo, we introduce PhishChain, a transparent and
decentralized system to blacklisting phishing URLs. At present, public/private
domain blacklists, such as PhishTank, CryptoScamDB, and APWG, are maintained by
a centralized authority, but operate in a crowd sourcing fashion to create a
manually verified blacklist periodically. In addition to being a single point
of failure, the blacklisting process utilized by such systems is not
transparent. We utilize the blockchain technology to support transparency and
decentralization, where no single authority is controlling the blacklist and
all operations are recorded in an immutable distributed ledger. Further, we
design a page rank based truth discovery algorithm to assign a phishing score
to each URL based on crowd sourced assessment of URLs. As an incentive for
voluntary participation, we assign skill points to each user based on their
participation in URL verification.


title:
CGraph: Graph Based Extensible Predictive Domain Threat Intelligence
  Platform

abstract:
  Ability to effectively investigate indicators of compromise and associated
network resources involved in cyber attacks is paramount not only to identify
affected network resources but also to detect related malicious resources.
Today, most of the cyber threat intelligence platforms are reactive in that
they can identify attack resources only after the attack is carried out.
Further, these systems have limited functionality to investigate associated
network resources. In this work, we propose an extensible predictive cyber
threat intelligence platform called cGraph that addresses the above
limitations. cGraph is built as a graph-first system where investigators can
explore network resources utilizing a graph based API. Further, cGraph provides
real-time predictive capabilities based on state-of-the-art inference
algorithms to predict malicious domains from network graphs with a few known
malicious and benign seeds. To the best of our knowledge, cGraph is the only
threat intelligence platform to do so. cGraph is extensible in that additional
network resources can be added to the system transparently.


title:
Deep-Learning-Assisted Configuration of Reconfigurable Intelligent
  Surfaces in Dynamic rich-scattering Environments

abstract:
  The integration of Reconfigurable Intelligent Surfaces (RISs) into wireless
environments endows channels with programmability, and is expected to play a
key role in future communication standards. To date, most RIS-related efforts
focus on quasi-free-space, where wireless channels are typically modeled
analytically. Many realistic communication scenarios occur, however, in
rich-scattering environments which, moreover, evolve dynamically. These
conditions present a tremendous challenge in identifying an RIS configuration
that optimizes the achievable communication rate. In this paper, we make a
first step toward tackling this challenge. Based on a simulator that is
faithful to the underlying wave physics, we train a deep neural network as
surrogate forward model to capture the stochastic dependence of wireless
channels on the RIS configuration under dynamic rich-scattering conditions.
Subsequently, we use this model in combination with a genetic algorithm to
identify RIS configurations optimizing the communication rate. We numerically
demonstrate the ability of the proposed approach to tune RISs to improve the
achievable rate in rich-scattering setups.


title: An Optimal-Time RLBWT Construction in BWT-runs Bounded Space

abstract:
  The compression of highly repetitive strings (i.e., strings with many
repetitions) has been a central research topic in string processing, and quite
a few compression methods for these strings have been proposed thus far. Among
them, an efficient compression format gathering increasing attention is the
run-length Burrows--Wheeler transform (RLBWT), which is a run-length encoded
BWT as a reversible permutation of an input string on the lexicographical order
of suffixes. State-of-the-art construction algorithms of RLBWT have a serious
issue with respect to (i) non-optimal computation time or (ii) a working space
that is linearly proportional to the length of an input string. In this paper,
we present \emph{r-comp}, the first optimal-time construction algorithm of
RLBWT in BWT-runs bounded space. That is, the computational complexity of
r-comp is $O(n + r \log{r})$ time and $O(r\log{n})$ bits of working space for
the length $n$ of an input string and the number $r$ of equal-letter runs in
BWT. The computation time is optimal (i.e., $O(n)$) for strings with the
property $r=O(n/\log{n})$, which holds for most highly repetitive strings.
Experiments using a real-world dataset of highly repetitive strings show the
effectiveness of r-comp with respect to computation time and space.


title: Constraining the curvature density parameter in cosmology

abstract:
  The cosmic curvature density parameter has been constrained in the present
work independent of any background cosmological model. The reconstruction is
performed adopting the non-parametric Gaussian Processes (GP). The constraints
on $\Omega_{k0}$ are obtained via a Markov Chain Monte Carlo (MCMC) analysis.
Late-time cosmological probes viz., the Supernova (SN) distance modulus data,
the Cosmic Chronometer (CC) and the radial Baryon Acoustic Oscillations
($r$BAO) measurements of the Hubble data have been utilized for this purpose.
The results are further combined with the data from redshift space distortions
(RSD) which studies the growth of large scale structure in the universe. The
only \textit{a priori} assumption is that the universe is homogeneous and
isotropic, described by the FLRW metric. Results indicate that a spatially flat
universe is well consistent in 2$\sigma$ within the domain of reconstruction
$0<z<2$ for the background data. On combining the RSD data we find that the
results obtained are consistent with spatial flatness mostly within 2$\sigma$
and always within 3$\sigma$ in the domain of reconstruction $0<z<2$.


title: Generalised convexity with respect to families of affine maps

abstract:
  The standard convex closed hull of a set is defined as the intersection of
all images, under the action of a group of rigid motions, of a half-space
containing the given set. In this paper we propose a generalisation of this
classical notion, that we call a $(K,\mathbb{H})$-hull, and which is obtained
from the above construction by replacing a half-space with some other convex
closed subset $K$ of the Euclidean space, and a group of rigid motions by a
subset $\mathbb{H}$ of the group of invertible affine transformations. The main
focus is put on the analysis of $(K,\mathbb{H})$-convex hulls of random samples
from $K$.


title:
Remote Entanglement of Superconducting Qubits via Solid-State Spin
  Quantum Memories

abstract:
  Quantum communication between remote superconducting systems is being studied
intensively to increase the number of integrated superconducting qubits and to
realize a distributed quantum computer. Since optical photons must be used for
communication outside a dilution refrigerator, the direct conversion of
microwave photons to optical photons has been widely investigated. However, the
direct conversion approach suffers from added photon noise, heating due to a
strong optical pump, and the requirement for large cooperativity. Instead, for
quantum communication between superconducting qubits, we propose an
entanglement distribution scheme using a solid-state spin quantum memory that
works as an interface for both microwave and optical photons. The quantum
memory enables quantum communication without significant heating inside the
refrigerator, in contrast to schemes using high-power optical pumps. Moreover,
introducing the quantum memory naturally makes it possible to herald
entanglement and parallelization using multiple memories.


title:
Composite pulses for high fidelity population transfer in three-level
  systems

abstract:
  In this work, we propose a composite pulses scheme by modulating phases to
achieve high fidelity population transfer in three-level systems. To circumvent
the obstacle that not enough variables are exploited to eliminate the
systematic errors in the transition probability, we put forward a cost function
to find the optimal value. The cost function is independently constructed
either in ensuring an accurate population of the target state, or in
suppressing the population of the leakage state, or both of them. The results
demonstrate that population transfer is implemented with high fidelity even
when existing the deviations in the coupling coefficients. Furthermore, our
composite pulses scheme can be extensible to arbitrarily long pulse sequences.
As an example, we employ the composite pulses sequence for achieving the
three-atom singlet state in an atom-cavity system with ultrahigh fidelity. The
final singlet state shows robustness against deviations and is not seriously
affected by waveform distortions. Also, the singlet state maintains a high
fidelity under the decoherence environment.


title: Online Control of Unknown Time-Varying Dynamical Systems

abstract:
  We study online control of time-varying linear systems with unknown dynamics
in the nonstochastic control model. At a high level, we demonstrate that this
setting is \emph{qualitatively harder} than that of either unknown
time-invariant or known time-varying dynamics, and complement our negative
results with algorithmic upper bounds in regimes where sublinear regret is
possible. More specifically, we study regret bounds with respect to common
classes of policies: Disturbance Action (SLS), Disturbance Response (Youla),
and linear feedback policies. While these three classes are essentially
equivalent for LTI systems, we demonstrate that these equivalences break down
for time-varying systems.
  We prove a lower bound that no algorithm can obtain sublinear regret with
respect to the first two classes unless a certain measure of system variability
also scales sublinearly in the horizon. Furthermore, we show that offline
planning over the state linear feedback policies is NP-hard, suggesting
hardness of the online learning problem.
  On the positive side, we give an efficient algorithm that attains a sublinear
regret bound against the class of Disturbance Response policies up to the
aforementioned system variability term. In fact, our algorithm enjoys sublinear
\emph{adaptive} regret bounds, which is a strictly stronger metric than
standard regret and is more appropriate for time-varying systems. We sketch
extensions to Disturbance Action policies and partial observation, and propose
an inefficient algorithm for regret against linear state feedback policies.


title: Electrically driven robust tuning of lattice thermal conductivity

abstract:
  Two-dimensional (2D) materials represented by graphene stand out in future
electrical industry and have been widely studied. As a commonly existing factor
in electronic devices, the electric field has been extensively utilized to
modulate the performance. However, how the electric field regulates thermal
transport is rarely studied. Herein, we investigate the modulation of thermal
transport properties by applying the external electric field ranging from 0 to
0.4 VA-1, with bilayer graphene, monolayer silicene, and germanene as study
cases. The monotonic decreasing trend of thermal conductivity of all the three
materials is revealed. The significant effect on the scattering rate is found
to be responsible for the decreased thermal conductivity by electric field.
Further evidences show that the reconstruction of internal electric field and
the generation of induced charges lead to the increased scattering rate from
strong phonon anharmonicity. Thus, the ultra-low thermal conductivity emerges
with external electric field applied. Applying external electric field to
regulate thermal conductivity enlightens the constructive idea for
high-efficient thermal management.


title:
Universal Behavior of Multipartite Entanglement in Crossing the Quantum
  Critical Point

abstract:
  We investigate multipartite entanglement of the quantum Ising model with
transverse fields for a slow quantum quench crossing a critical point. The
multipartite entanglement is quantified by quantum Fisher information with the
generator defined as the operator of the ferromagnetic order parameter due to
the system symmetry. The quench dynamics begin with the ground state of the
large transverse field in the paramagnetic phase, and then the transverse field
is driven slowly to cross a quantum critical point and ends with a zero
transverse field. Based on methods of matrix product state, we calculate the
quantum Fisher information density of the final state. Numerical results of
both linear and nonlinear quench show that the quantum Fisher information
density of the final state scales as a power law of the quench rate, which has
an opposite exponent of the Kibble-Zurek scaling. Our results reveal that the
multipartite entanglement provides a new viewpoint to understand the dynamics
of quantum phase transition. Based on the observation of entanglement in the
quenched state, we also expect that our result would inspire approaches for
preparing multipartite entangled states in quantum information processing.


title: A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications

abstract:
  Pretrained Language Models (PLMs) such as BERT have revolutionized the
landscape of Natural Language Processing (NLP). Inspired by their
proliferation, tremendous efforts have been devoted to Pretrained Graph Models
(PGMs). Owing to the powerful model architectures of PGMs, abundant knowledge
from massive labeled and unlabeled graph data can be captured. The knowledge
implicitly encoded in model parameters can benefit various downstream tasks and
help to alleviate several fundamental issues of learning on graphs. In this
paper, we provide the first comprehensive survey for PGMs. We firstly present
the limitations of graph representation learning and thus introduce the
motivation for graph pre-training. Then, we systematically categorize existing
PGMs based on a taxonomy from four different perspectives. Next, we present the
applications of PGMs in social recommendation and drug discovery. Finally, we
outline several promising research directions that can serve as a guideline for
future research.


title:
Knowledge Transfer from Large-scale Pretrained Language Models to
  End-to-end Speech Recognizers

abstract:
  End-to-end speech recognition is a promising technology for enabling compact
automatic speech recognition (ASR) systems since it can unify the acoustic and
language model into a single neural network. However, as a drawback, training
of end-to-end speech recognizers always requires transcribed utterances. Since
end-to-end models are also known to be severely data hungry, this constraint is
crucial especially because obtaining transcribed utterances is costly and can
possibly be impractical or impossible. This paper proposes a method for
alleviating this issue by transferring knowledge from a language model neural
network that can be pretrained with text-only data. Specifically, this paper
attempts to transfer semantic knowledge acquired in embedding vectors of
large-scale language models. Since embedding vectors can be assumed as implicit
representations of linguistic information such as part-of-speech, intent, and
so on, those are also expected to be useful modeling cues for ASR decoders.
This paper extends two types of ASR decoders, attention-based decoders and
neural transducers, by modifying training loss functions to include embedding
prediction terms. The proposed systems were shown to be effective for error
rate reduction without incurring extra computational costs in the decoding
phase.


title: Enhancing Causal Estimation through Unlabeled Offline Data

abstract:
  Consider a situation where a new patient arrives in the Intensive Care Unit
(ICU) and is monitored by multiple sensors. We wish to assess relevant
unmeasured physiological variables (e.g., cardiac contractility and output and
vascular resistance) that have a strong effect on the patients diagnosis and
treatment. We do not have any information about this specific patient, but,
extensive offline information is available about previous patients, that may
only be partially related to the present patient (a case of dataset shift).
This information constitutes our prior knowledge, and is both partial and
approximate. The basic question is how to best use this prior knowledge,
combined with online patient data, to assist in diagnosing the current patient
most effectively. Our proposed approach consists of three stages: (i) Use the
abundant offline data in order to create both a non-causal and a causal
estimator for the relevant unmeasured physiological variables. (ii) Based on
the non-causal estimator constructed, and a set of measurements from a new
group of patients, we construct a causal filter that provides higher accuracy
in the prediction of the hidden physiological variables for this new set of
patients. (iii) For any new patient arriving in the ICU, we use the constructed
filter in order to predict relevant internal variables. Overall, this strategy
allows us to make use of the abundantly available offline data in order to
enhance causal estimation for newly arriving patients. We demonstrate the
effectiveness of this methodology on a (non-medical) real-world task, in
situations where the offline data is only partially related to the new
observations. We provide a mathematical analysis of the merits of the approach
in a linear setting of Kalman filtering and smoothing, demonstrating its
utility.


title: Aryl: An Elastic Cluster Scheduler for Deep Learning

abstract:
  Companies build separate training and inference GPU clusters for deep
learning, and use separate schedulers to manage them. This leads to problems
for both training and inference: inference clusters have low GPU utilization
when the traffic load is low; training jobs often experience long queueing time
due to lack of resources. We introduce Aryl, a new cluster scheduler to address
these problems. Aryl introduces capacity loaning to loan idle inference GPU
servers for training jobs. It further exploits elastic scaling that scales a
training job's GPU allocation to better utilize loaned resources. Capacity
loaning and elastic scaling create new challenges to cluster management. When
the loaned servers need to be returned, we need to minimize the number of job
preemptions; when more GPUs become available, we need to allocate them to
elastic jobs and minimize the job completion time (JCT). Aryl addresses these
combinatorial problems using principled heuristics. It introduces the notion of
server preemption cost which it greedily reduces during server reclaiming. It
further relies on the JCT reduction value defined for each additional worker
for an elastic job to solve the scheduling problem as a multiple-choice
knapsack problem. Prototype implementation on a 64-GPU testbed and large-scale
simulation with 15-day traces of over 50,000 production jobs show that Aryl
brings 1.53x and 1.50x reductions in average queuing time and JCT, and improves
cluster usage by up to 26.9% over the cluster scheduler without capacity
loaning or elastic scaling.


title:
Stable fluctuations of iterated perturbed random walks in intermediate
  generations of a general branching process tree

abstract:
  Consider a general branching process, a.k.a. Crump-Mode-Jagers process,
generated by a perturbed random walk $\eta_1$, $\xi_1+\eta_2$,
$\xi_1+\xi_2+\eta_3,\ldots$. Here, $(\xi_1,\eta_1)$, $(\xi_2, \eta_2),\ldots$
are independent identically distributed random vectors with arbitrarily
dependent positive components. Denote by $N_j(t)$ the number of the $j$th
generation individuals with birth times $\leq t$. Assume that $j=j(t)\to\infty$
and $j(t)=o(t^a)$ as $t\to\infty$ for some explicitly given $a>0$ (to be
specified in the paper). The corresponding $j$th generation belongs to the set
of intermediate generations. We provide sufficient conditions under which
finite-dimensional distributions of the process $(N_{\lfloor
j(t)u\rfloor}(t))_{u>0}$, properly normalized and centered, converge weakly to
those of an integral functional of a stable L\'{e}vy process with finite mean.


title:
Weighted estimates for bilinear fractional integral operator on the
  Heisenberg group

abstract:
  In this article, we introduce an analogue of Kenig and Stein's bilinear
fractional integral operator on the Heisenberg group $\mathbb{H}^n$. We
completely characterize exponents $\alpha, \beta$ and $\gamma$ such that the
operator is bounded from $L^{p}(\mathbb{H}^n, |x|^{\alpha p})\times
L^{q}(\mathbb{H}^n, |x|^{\beta q})$ to $L^{r}(\mathbb{H}^n, |x|^{-\gamma r})$.


title: Nested mirror optics for neutron extraction, transport, and focusing

abstract:
  Neutron scattering is a well-established tool for the investigation of the
static and dynamic properties of condensed matter systems over a wide range of
spatial and temporal scales. Many studies of high interest, however, can only
be performed on small samples and typically require elaborate environments for
variation of parameters such as temperature, magnetic field and pressure. To
improve the achievable signal-to-background ratio, focusing devices based on
elliptic or parabolic neutron guides or Montel mirrors have been implemented.
Here we report an experimental demonstration of a nested mirror optics (NMO),
which overcomes some of the disadvantages of such devices. While even simpler
than the original Wolter design, our compact assembly of elliptic mirrors
images neutrons from a source to a target, minimizing geometric aberrations,
gravitational effects and waviness-induced blurring. Experiments performed at
MIRA at FRM-II demonstrate the expected focusing properties and a beam
transport efficiency of 72 % for our first prototype. NMO seem particularly
well-suited to i) extraction of neutrons from compact high-brilliance neutron
moderators, ii) general neutron transport, and iii) focusing and polarizing
neutrons. The phase space of the neutrons hitting a sample can be tailored
on-line to the needed experimental resolution, resulting in small scattering
backgrounds. As additional benefits, NMO situated far away from both the
moderator and the sample are less susceptible to radiation damage and can
easily be replaced. NMO enable a modular and physically transparent realization
of beam lines for neutron physics similar to setups used in visible light
optics.


title: Characterizing categoricity in several classes of modules

abstract:
  We show that the condition of being categorical in a tail of cardinals can be
characterized algebraically for several classes of modules.
  $Theorem.$ Assume $R$ is an associative ring with unity.
  1. The class of locally pure-injective $R$-modules is $\lambda$-categorical
in $all$ $\lambda > |R|+\aleph_0$ if and only if $R \cong M_n(D)$ for $D$ a
division ring and $n \geq 1$.
  2. The class of flat $R$-modules is $\lambda$-categorical in $all$ $\lambda >
|R| + \aleph_0$ if and only if $R \cong M_n(k)$ for $k$ a local ring such that
its maximal ideal is left $T$-nilpotent and $n \geq 1$.
  3. Assume $R$ is a commutative ring. The class of absolutely pure $R$-modules
is $\lambda$-categorical in $all$ $\lambda > |R| + \aleph_0$ if and only if $R$
is a local artinian ring.
  We show that in the above results it is enough to assume
$\lambda$-categoricity in $some$ large cardinal $\lambda$. This shows that
Shelah's Categoricity Conjecture holds for the class of locally pure-injective
modules, flat modules and absolutely pure modules. These classes are not
first-order axiomatizable for arbitrary rings.
  We provide rings such that the class of flat modules is categorical in a tail
of cardinals but it is not first-order axiomatizable.


title: Cross-Modal Common Representation Learning with Triplet Loss Functions

abstract:
  Common representation learning (CRL) learns a shared embedding between two or
more modalities to improve in a given task over using only one of the
modalities. CRL from different data types such as images and time-series data
(e.g., audio or text data) requires a deep metric learning loss that minimizes
the distance between the modality embeddings. In this paper, we propose to use
the triplet loss, which uses positive and negative identities to create sample
pairs with different labels, for CRL between image and time-series modalities.
By adapting the triplet loss for CRL, higher accuracy in the main (time-series
classification) task can be achieved by exploiting additional information of
the auxiliary (image classification) task. Our experiments on synthetic data
and handwriting recognition data from sensor-enhanced pens show an improved
classification accuracy, faster convergence, and a better generalizability.


title: When Does A Spectral Graph Neural Network Fail in Node Classification?

abstract:
  Spectral Graph Neural Networks (GNNs) with various graph filters have
received extensive affirmation due to their promising performance in graph
learning problems. However, it is known that GNNs do not always perform well.
Although graph filters provide theoretical foundations for model explanations,
it is unclear when a spectral GNN will fail. In this paper, focusing on node
classification problems, we conduct a theoretical analysis of spectral GNNs
performance by investigating their prediction error. With the aid of graph
indicators including homophily degree and response efficiency we proposed, we
establish a comprehensive understanding of complex relationships between graph
structure, node labels, and graph filters. We indicate that graph filters with
low response efficiency on label difference are prone to fail. To enhance GNNs
performance, we provide a provably better strategy for filter design from our
theoretical analysis - using data-driven filter banks, and propose simple
models for empirical validation. Experimental results show consistency with our
theoretical results and support our strategy.


title:
Stability analysis of fixed point of fractional-order coupled map
  lattices

abstract:
  We study the stability of synchronized fixed-point state for linear
fractional-order coupled map lattice(CML). We observe that the eigenvalues of
the connectivity matrix determine the stability as for integer-order CML. These
eigenvalues can be determined exactly in certain cases. We find exact bounds in
one-dimensional lattice with translationally invariant coupling using the
theory of circulant matrices. This can be extended to any finite dimension.
Similar analysis can be carried out for the synchronized fixed point of
nonlinear coupled fractional maps where eigenvalues of the Jacobian matrix play
the same role. The analysis is generic and demonstrates that the eigenvalues of
connectivity matrix play a pivotal role in stability analysis of synchronized
fixed point even in coupled fractional maps.


title: Blockchain Security when Messages are Lost

abstract:
  Security analyses for consensus protocols in blockchain research have
primarily focused on the synchronous model, where point-to-point communication
delays are upper bounded by a known finite constant. These models are
unrealistic in noisy settings, where messages may be lost (i.e. incur infinite
delay). In this work, we study the impact of message losses on the security of
the proof-of-work longest-chain protocol. We introduce a new communication
model to capture the impact of message loss called the $0-\infty$ model, and
derive a region of tolerable adversarial power under which the consensus
protocol is secure. The guarantees are derived as a simple bound for the
probability that a transaction violates desired security properties.
Specifically, we show that this violation probability decays almost
exponentially in the security parameter. Our approach involves constructing
combinatorial objects from blocktrees, and identifying random variables
associated with them that are amenable to analysis. This approach improves
existing bounds and extends the known regime for tolerable adversarial
threshold in settings where messages may be lost.


title: SoK: Human-Centered Phishing Susceptibility

abstract:
  Phishing is recognised as a serious threat to organisations and individuals.
While there have been significant technical advances in blocking phishing
attacks, people remain the last line of defence after phishing emails reach
their email client. Most of the existing literature on this subject has focused
on the technical aspects related to phishing. However, the factors that cause
humans to be susceptible to phishing attacks are still not well-understood. To
fill this gap, we reviewed the available literature and we propose a
three-stage Phishing Susceptibility Model (PSM) for explaining how humans are
involved in phishing detection and prevention, and we systematically
investigate the phishing susceptibility variables studied in the literature and
taxonomize them using our model. This model reveals several research gaps that
need to be addressed to improve users' detection performance. We also propose a
practical impact assessment of the value of studying the phishing
susceptibility variables, and quality of evidence criteria. These can serve as
guidelines for future research to improve experiment design, result quality,
and increase the reliability and generalizability of findings.


title:
Quantum interference of resonance fluorescence from Germanium-vacancy
  color centers in diamond

abstract:
  Resonance fluorescence from a quantum emitter is an ideal source to extract
indistinguishable photons. By using the cross polarization to suppress the
laser scattering, we observed resonance fluorescence from GeV color centers in
diamond at cryogenic temperature. The Fourier-transform-limited linewidth
emission with $T_2/2T_1\sim0.86$ allows for two-photon interference based on
single GeV color center. Under pulsed excitation, the 24 ns separated photons
exhibit a Hong-Ou-Mandel visibility of $0.604\pm0.022$, while the
continuous-wave excitation leads to a coalescence time window of 1.05 radiative
lifetime. Together with single-shot readout of spin states, it paves the way
towards building a quantum network with GeV color centers in diamond.


title:
Singing-Tacotron: Global duration control attention and dynamic filter
  for End-to-end singing voice synthesis

abstract:
  End-to-end singing voice synthesis (SVS) is attractive due to the avoidance
of pre-aligned data. However, the auto learned alignment of singing voice with
lyrics is difficult to match the duration information in musical score, which
will lead to the model instability or even failure to synthesize voice. To
learn accurate alignment information automatically, this paper proposes an
end-to-end SVS framework, named Singing-Tacotron. The main difference between
the proposed framework and Tacotron is that the speech can be controlled
significantly by the musical score's duration information. Firstly, we propose
a global duration control attention mechanism for the SVS model. The attention
mechanism can control each phoneme's duration. Secondly, a duration encoder is
proposed to learn a set of global transition tokens from the musical score.
These transition tokens can help the attention mechanism decide whether moving
to the next phoneme or staying at each decoding step. Thirdly, to further
improve the model's stability, a dynamic filter is designed to help the model
overcome noise interference and pay more attention to local context
information. Subjective and objective evaluation verify the effectiveness of
the method. Furthermore, the role of global transition tokens and the effect of
duration control are explored. Examples of experiments can be found at
https://hairuo55.github.io/SingingTacotron.


title: Error Floor Analysis of Irregular Repetition ALOHA

abstract:
  With the rapid expansion of the Internet of Things, the efficient sharing of
the wireless medium by a large amount of simple transmitters is becoming
essential. Scheduling-based solutions are inefficient for this setting, where
small data units are broadcast sporadically by terminals that most of the time
are idle. Modern random access has embraced the challenge and provides suitable
slot-synchronous and asynchronous multiple access solutions based on
replicating the packets and exploiting successive interference cancellation
(SIC) at the receiver. In this work, we focus on asynchronous modern random
access. Specifically, we derive an analytical approximation of the performance
of irregular repetition ALOHA (IRA) in the so-called error floor region.
Numerical results show the tightness of the derived approximation under various
scenarios.


title: Can Deep Learning be Applied to Model-Based Multi-Object Tracking?

abstract:
  Multi-object tracking (MOT) is the problem of tracking the state of an
unknown and time-varying number of objects using noisy measurements, with
important applications such as autonomous driving, tracking animal behavior,
defense systems, and others. In recent years, deep learning (DL) has been
increasingly used in MOT for improving tracking performance, but mostly in
settings where the measurements are high-dimensional and there are no available
models of the measurement likelihood and the object dynamics. The model-based
setting instead has not attracted as much attention, and it is still unclear if
DL methods can outperform traditional model-based Bayesian methods, which are
the state of the art (SOTA) in this context. In this paper, we propose a
Transformer-based DL tracker and evaluate its performance in the model-based
setting, comparing it to SOTA model-based Bayesian methods in a variety of
different tasks. Our results show that the proposed DL method can match the
performance of the model-based methods in simple tasks, while outperforming
them when the task gets more complicated, either due to an increase in the data
association complexity, or to stronger nonlinearities of the models of the
environment.


title:
Spin Seebeck effect in iron oxide thin films: Effects of phase
  transition, phase coexistence, and surface magnetism

abstract:
  Understanding impacts of phase transition, phase coexistence, and surface
magnetism on the longitudinal spin Seebeck effect (LSSE) in a magnetic system
is essential to manipulate the spin to charge current conversion efficiency for
spincaloritronic applications. We aim to elucidate these effects by performing
a comprehensive study of the temperature dependence of LSSE in biphase iron
oxide (BPIO = alpha-Fe2O3 + Fe3O4) thin films grown on Si (100) and Al2O3 (111)
substrates. A combination of temperature-dependent anomalous Nernst effect
(ANE) and electrical resistivity measurements show that the contribution of ANE
from the BPIO layer is negligible compared to the intrinsic LSSE in the
Si/BPIO/Pt heterostructure even at room temperature. Below the Verwey
transition of the Fe3O4 phase, the total signal across BPIO/Pt is dominated by
the LSSE. Noticeable changes in the intrinsic LSSE signal for both Si/BPIO/Pt
and Al2O3/BPIO/Pt heterostructures around the Verwey transition of the Fe3O4
phase and the antiferromagnetic (AFM) Morin transition of the alpha-Fe2O3 phase
are observed. The LSSE signal for Si/BPIO/Pt is found to be almost two times
greater than that for Al2O3/BPIO/Pt, an opposite trend is observed for the
saturation magnetization though. Magnetic force microscopy reveals the higher
density of surface magnetic moments of the Si/BPIO film compared to the
Al2O3/BPIO film, which underscores a dominant role of interfacial magnetism on
the LSSE signal and thereby explains the larger LSSE for Si/BPIO/Pt.


title: Network Robustness Revisited

abstract:
  The robustness of complex networks was one of the first phenomena studied
after the inception of network science. However, many contemporary
presentations of this theory do not go beyond the original papers. Here we
revisit this topic with the aim of providing a deep but didactic introduction.
We pay particular attention to some complications in the computation of giant
component sizes that are commonly ignored. Following an intuitive procedure, we
derive simple formulas that capture the effect of common attack scenarios on
arbitrary (configuration model) networks. We hope that this gentle but
mathematically-grounded introduction will help new researchers discover this
beautiful area of network science.


title: Nonrelativistic strings and the limits of $ \mathcal{N}=2 $ dualities

abstract:
  We carry out the formulation of nonrelativistic (NR) F-string theory for $
\mathcal{N}=2 $ Gaiotto-Maldacena class of geometries in type IIA supergravity.
Considering the NS-NS sector, we reduce the type IIA theory into the NR sigma
models those are formulated over the nonrelativistic target space geometries
known as the torsional String Newton-Cartan (TSNC) backgrounds. Using these
TSNC data, we write down the sigma model action and explore several properties
in the semiclassical limit. In particular, we carry out the \emph{canonical}
analysis of the T-duality transformation rules in TSNC background. Following
the standard canonical prescription of obtaining T-duality transformations, we
map these TSNC sigma models to relativistic sigma models propagating over
general relativity backgrounds. Finally, we conclude with a detailed discussion
on the effects of adding \emph{flavour} branes and thereby taking the
corresponding TSNC limit in the bulk description of $ \mathcal{N}=2 $ quivers.
We compute various field theory observables in the nonrelativistic limit and
outline the structure of the NR Lagrangian following the light cone reduction
of $ \mathcal{N}=2 $ SYM theory.


title: Holomorphic d-scalar curvature on almost Hermitian manifolds

abstract:
  In this paper, we study the existence of constant holomorphic d-scalar
curvature and the prescribing holomorphic d-scalar curvature problem on closed,
connected almost Hermitian manifolds of dimension $n\geq6$. In addition, we
obtain an application and a variation formula for the associated conformal
invariant.


title: Generically supercompact cardinals by forcing with chain conditions

abstract:
  A ccc-generically supercompact cardinal $\kappa$ can be smaller than or equal
to the continuum. On the other hand, such a cardinal $\kappa$ still satisfies
diverse largeness properties, like that it is a stationary limit of
ccc-generically measurable cardinals (Theorem 4.1). This is in a strong
contrast to $\cal P$-generically supercompact cardinals for the class $\cal P$
of all $\sigma$-closed posets, which can be $\aleph_n$ for any n>1.


title:
Emergence of Intergranular Tunneling Dominated Negative
  Magnetoresistance in Helimagnetic Manganese Phosphide Nanorod Thin Films

abstract:
  Helical magnets are emerging as a novel class of materials for spintronics
and sensor applications; however, research on their charge and spin transport
properties in a thin film form is less explored. Herein, we report the
temperature and magnetic field dependent charge transport properties of a
highly crystalline MnP nanorod thin film over a wide temperature range (2-350
K). The MnP nanorod films of 100 nm thickness were grown on Si substrates at
500 oC using molecular beam epitaxy. The temperature dependent resistivity data
exhibits a metallic behavior over the entire measured temperature range.
However, large negative magnetoresistance of up to 12% is observed below 50 K
at which the system enters a stable helical (screw) magnetic state. In this
temperature regime, the MR(H,T) dependence seems to show a magnetic field
manipulated phase coexistence. The observed magnetoresistance is dominantly
governed by the intergranular spin dependent tunneling mechanism. These
findings pinpoint a correlation between the transport and magnetism in this
helimagnetic system.


title:
A spectral boundary integral method for the elastic obstacle scattering
  problem in three dimensions

abstract:
  In this paper, we consider the scattering of a plane wave by a rigid obstacle
embedded in a homogeneous and isotropic elastic medium in three dimensions.
Based on the Helmholtz decomposition, the elastic scattering problem is reduced
to a coupled boundary value problem for the Helmholtz and Maxwell equations. A
novel system of boundary integral equations is formulated and a spectral
boundary integral method is developed for the coupled boundary value problem.
Numerical experiments are presented to demonstrate the superior performance of
the proposed method.


title:
Non-standard linear recurring sequence subgroups and automorphisms of
  irreducible cyclic codes

abstract:
  Let \(\cU\) be the multiplicative group of order~\(n\) in the splitting field
\(\bbF_{q^m}\) of \(x^n-1\) over the finite field \(\bbF_q\). Any map of the
form \(x\rightarrow cx^t\) with \(c\in \cU\) and \(t=q^i\), \(0\leq i<m\), is
\(\bbF_q\)-linear on~\(\bbF_{q^m}\) and fixes \(\cU\) set-wise; maps of this
type will be called {\em standard\/}. Occasionally there are other, {\em
non-standard\/} \(\bbF_q\)-linear maps on~\(\bbF_{q^m}\) fixing \(\cU\)
set-wise, and in that case we say that the pair \((n, q)\) is {\em
non-standard\/}. We show that an irreducible cyclic code of length~\(n\) over
\(\bbF_q\) has ``extra'' permutation automorphisms (others than the {\em
standard\/} permutations generated by the cyclic shift and the Frobenius
mapping that every such code has) precisely when the pair \((n, q)\) is
non-standard; we refer to such irreducible cyclic codes as {\em non-standard\/}
or {\em NSIC-codes\/}. In addition, we relate these concepts to that of a
non-standard linear recurring sequence subgroup as investigated in a sequence
of papers by Brison and Nogueira. We present several families of NSIC-codes,
and two constructions called ``lifting'' and ``extension'' to create new
NSIC-codes from existing ones. We show that all NSIC-codes of dimension two can
be obtained in this way, thus completing the classification for this case
started by Brison and Nogueira.


title: Bloch and Landau type theorems for pluriharmonic mappings

abstract:
  In this paper, we establish two new versions of Landau-type theorems for
pluriharmonic mappings with a bounded distortion. Then using these results, we
derive three Bloch-type theorems of pluriharmonic mappings, which improve the
corresponding results of Chen and Gauthier.


title: HousE: Knowledge Graph Embedding with Householder Parameterization

abstract:
  The effectiveness of knowledge graph embedding (KGE) largely depends on the
ability to model intrinsic relation patterns and mapping properties. However,
existing approaches can only capture some of them with insufficient modeling
capacity. In this work, we propose a more powerful KGE framework named HousE,
which involves a novel parameterization based on two kinds of Householder
transformations: (1) Householder rotations to achieve superior capacity of
modeling relation patterns; (2) Householder projections to handle sophisticated
relation mapping properties. Theoretically, HousE is capable of modeling
crucial relation patterns and mapping properties simultaneously. Besides, HousE
is a generalization of existing rotation-based models while extending the
rotations to high-dimensional spaces. Empirically, HousE achieves new
state-of-the-art performance on five benchmark datasets. Our code is available
at https://github.com/anrep/HousE.


title:
Spectroscopic characterization of singlet-triplet doorway states of
  aluminum monofluoride

abstract:
  Aluminum monofluoride (AlF) possesses highly favorable properties for laser
cooling, both via the A$^1\Pi$ and a$^3\Pi$ states. Determining efficient
pathways between the singlet and the triplet manifold of electronic states will
be advantageous for future experiments at ultralow temperatures. The lowest
rotational levels of the A$^1\Pi, v=6$ and b$^3\Sigma^+, v=5$ states of AlF are
nearly iso-energetic and interact via spin-orbit coupling. These levels thus
have a strongly mixed spin-character and provide a singlet-triplet doorway. We
here present a hyperfine resolved spectroscopic study of the A$^1\Pi, v=6$ //
b$^3\Sigma^+, v=5$ perturbed system in a jet-cooled, pulsed molecular beam.
From a fit to the observed energies of the hyperfine levels, the fine and
hyperfine structure parameters of the coupled states, their relative energies
as well as the spin-orbit interaction parameter are determined. The standard
deviation of the fit is about 15 MHz. We experimentally determine the radiative
lifetimes of selected hyperfine levels by time-delayed ionization, Lamb dip
spectroscopy and accurate measurements of the transition lineshapes. The
measured lifetimes range between 2 ns and 200 ns, determined by the degree of
singlet-triplet mixing for each level.


title:
Comparison on gait characteristics between controlled and free-living
  conditions in old adults

abstract:
  Gait is an important biomarker of functional conditions and gait
characteristics can help us assessing health conditions and managing
progression of diseases. Most of the existing research study the gait in
controlled condition, such as clinical tests. In this paper, we study the gait
characteristics in free-living conditions in old adults and compare them with
that in controlled conditions, i.e., Timed Up and Go (TUG) test. 65 subjects
(12 patients with mobility impairment and 53 healthy controls) are recruited
from elderly nursing institutions. The video data are collected from them in
TUG test and free-living conditions and the 9 gait characteristics, including
gait speed, are extracted from the data. Two-sample tests and independence test
based on copula entropy are conducted on the extracted data to compare the
characteristics in two conditions. Comparison results show that gait
characteristics, such as gait speed, pace, speed variability, etc., in daily
life are different from that of in TUG test. In daily life, people tend to have
slow gait speed, smaller pace and speed variability, more frequent stride, and
smaller acceleration range than in TUG test. We also found that gait speed,
pace, and speed variability have stronger dependence with TUG score in the 3
conditions (TUG, daily life, and both) and that other 5 characteristics have
stronger dependence with TUG score in both condition than in each condition.
The comparison in this study suggests that TUG and daily life conditions are
complementary with each other, and that TUG test can be considered as
intervention on the movement state of human.


title: ZeroGen: Efficient Zero-shot Learning via Dataset Generation

abstract:
  There is a growing interest in dataset generation recently due to the
superior generative capacity of large pre-trained language models (PLMs). In
this paper, we study a flexible and efficient zero-short learning method,
ZeroGen. Given a zero-shot task, we first generate a dataset from scratch using
PLMs in an unsupervised manner. Then, we train a tiny task model (e.g., LSTM)
under the supervision of the synthesized dataset. This approach allows highly
efficient inference as the final task model only has orders of magnitude fewer
parameters comparing to PLMs (e.g., GPT2-XL). Apart from being annotation-free
and efficient, we argue that ZeroGen can also provide useful insights from the
perspective of data-free model-agnostic knowledge distillation, and
unreferenced text generation evaluation. Experiments and analysis on different
NLP tasks, namely, text classification, question answering, and natural
language inference), show the effectiveness of ZeroGen.


title: Bronze Age sundial from Prokletije (Montenegro)

abstract:
  The article presents the results of a study of signs on a Bronze Age slab
"Sun stone" discovered at the foot of Maja e Can in Volusnica massif
(Prokletije National Park, Montenegro). Studies have shown that the slab is an
analemmatic sundial. The "Sun Stone" is more similar in marking to the slabs of
the Srubnaya culture: all the cup marks are small and located along the ellipse
line. Ellipses of bowl-shaped signs of analemmatic sundials from the Northern
Black Sea region are similar in size to the reconstructed ellipse of the "Sun
Stone". In addition, on one sundial from the Northern Black Sea region, the
groove marks the distance that the gnomon must travel on the day of the winter
solstice, similar to the groove on the Sun Stone. In the hour marking of
sundial slabs from the Northern Black Sea region and Western Balkans,
continuity can be traced, confirming the dating of the XV-XII centuries BC and
indicating contacts of representatives of the synchronous local Glasinac
culture of the Western Balkans with representatives of the Srubnaya culture of
the Northern Black Sea region. Keywords: cup marks, hour markers, grooves,
slab, analemmatic sundial, hour line, Bronze Age, Sun Stone.


title:
Pumping and cooling of nanomechanical vibrations generated by Cooper
  pair exchange

abstract:
  We consider a nanoelectromechanical system composed of a carbon nanotube
suspended between two normal leads and coupled to a superconducting scanning
tunneling microscope (STM) tip via vacuum tunnel barrier. Treating the nanotube
as a single-level quantum dot, it is shown that an applied voltage between the
superconducting STM tip and normal leads gives rise to a pumping or a cooling
of the mechanical subsystem depending on the direction of the electronic flow.
It is also demonstrated that the transition between these two regimes is
controlled by the strength of the tunnel coupling between the nanotube and
superconducting STM tip and the relative position of the electronic level. Such
phenomena are realized due to a specific electromechanical coupling that is
fully governed by the quantum dynamics of the Cooper pairs. The amplitude of
the self-sustained oscillations in the pumping regime is analyzed numerically,
and the effective temperature of the mechanical subsystem in the cooling regime
is obtained.


title: ActionFormer: Localizing Moments of Actions with Transformers

abstract:
  Self-attention based Transformer models have demonstrated impressive results
for image classification and object detection, and more recently for video
understanding. Inspired by this success, we investigate the application of
Transformer networks for temporal action localization in videos. To this end,
we present ActionFormer -- a simple yet powerful model to identify actions in
time and recognize their categories in a single shot, without using action
proposals or relying on pre-defined anchor windows. ActionFormer combines a
multiscale feature representation with local self-attention, and uses a
light-weighted decoder to classify every moment in time and estimate the
corresponding action boundaries. We show that this orchestrated design results
in major improvements upon prior works. Without bells and whistles,
ActionFormer achieves 65.6% mAP at tIoU=0.5 on THUMOS14, outperforming the best
prior model by 8.7 absolute percentage points and crossing the 60% mAP for the
first time. Further, ActionFormer demonstrates strong results on ActivityNet
1.3 (36.0% average mAP) and the more recent EPIC-Kitchens 100 (+13.5% average
mAP over prior works). Our code is available at
http://github.com/happyharrycn/actionformer_release


title:
Hybrid Finite Difference with the Physics-informed Neural Network for
  solving PDE in complex geometries

abstract:
  The physics-informed neural network (PINN) is effective in solving the
partial differential equation (PDE) by capturing the physics constraints as a
part of the training loss function through the Automatic Differentiation (AD).
This study proposes the hybrid finite difference with the physics-informed
neural network (HFD-PINN) to fully use the domain knowledge. The main idea is
to use the finite difference method (FDM) locally instead of AD in the
framework of PINN. In particular, we use AD at complex boundaries and the FDM
in other domains. The hybrid learning model shows promising results in
experiments. To use the FDM locally in the complex boundary domain and avoid
the generation of background mesh, we propose the HFD-PINN-sdf method, which
locally uses the finite difference scheme at random points. In addition, the
signed distance function is used to avoid the difference scheme from crossing
the domain boundary. In this paper, we demonstrate the performance of our
proposed methods and compare the results with the different number of
collocation points for the Poisson equation, Burgers equation. We also chose
several different finite difference schemes, including the compact finite
difference method (CDM) and crank-nicolson method (CNM), to verify the
robustness of HFD-PINN. We take the heat conduction problem and the heat
transfer problem on the irregular domain as examples to demonstrate the
efficacy of our framework. In summary, HFD-PINN, especially HFD-PINN-sdf, are
more instructive and efficient, significantly when solving PDEs in complex
geometries.


title: The First Ground Level Enhancement of Solar Cycle 25 on 28 October 2021

abstract:
  Aims. The first relativistic solar proton event of solar cycle 25 (SC25) was
detected on 28 October 2021 by neutron monitors (NMs) on the ground and
particle detectors onboard spacecraft in the near-Earth space. This is the
first ground level enhancement (GLE) of the current cycle. A detailed
reconstruction of the NM response together with the identification of the solar
eruption that generated these particles is investigated based on in-situ and
remote-sensing measurements. Methods. In-situ proton observations from a few
MeV to $\sim$500 MeV were combined with the detection of a solar flare in soft
X-rays (SXRs), a coronal mass ejection (CME), radio bursts and extreme
ultraviolet (EUV) observations to identify the solar origin of the GLE. Timing
analysis was performed and a relation to the solar sources was outlined.
Results. GLE73 reached a maximum particle rigidity of $\sim$2.4 GV and is
associated with type III, type II, type IV radio bursts and an EUV wave. A
diversity of time profiles recorded by NMs was observed. This points to an
anisotropic nature of the event. The peak flux at E$>$10 MeV was only $\sim$30
pfu and remained at this level for several days. The release time of $\geq$1 GV
particles was found to be $\sim$15:40 UT. GLE73 had a moderately hard rigidity
spectrum at very high energies ($\gamma$ $\sim$5.5). Comparison of GLE73 to
previous GLEs with similar solar drivers is performed


title:
A novel general modeling of the viscoelastic properties of fluids:
  application to mechanical relaxation and low frequency oscillation
  measurements of liquid water

abstract:
  The aim of this paper is to calculate the time dependence of the mean
position (and orientation) of a fluid particle when a fluid system at
thermodynamic equilibrium is submitted to a mechanical action. The starting
point of this novel theoretical approach is the introduction of a mechanical
energy functional. Then using the notions of inertial modes and action
temperature, and assuming a mechanical energy equipartition principle per mode,
the model predict the existence of a dynamic phase transition where the
rheological behavior of the medium evolves from a solid-like to a liquid-like
regime when the mechanical action is increased. The well-known Newtonian
behavior is recovered as limiting case. The present modeling is applied to the
analysis of recent liquid water viscoelastic data pointing out a prevalent
elastic behavior in confined geometry. It is demonstrated that the model makes
it possible to understand these data in a coherent and unified way with the
transport properties (viscosity and self-diffusion coefficient). It is
concluded that any finite volume of fluid at rest possesses a static shear
elasticity and should therefore be considered as a solid-like medium.


title: Critical Equimatchable Graphs

abstract:
  A graph G is equimatchable if every maximal matching of G has the same
cardinality. In this paper, we investigate equimatchable graphs such that the
removal of any edge harms the equimatchability, called edge-critical
equimatchable graphs (ECE-graphs). We show that apart from two simple cases,
namely bipartite ECE-graphs and even cliques, all ECE-graphs are 2-connected
factor-critical. Accordingly, we give a characterization of factor-critical
ECE-graphs with connectivity 2. Our result provides a partial answer to an open
question posed by Levit and Mandrescu on the characterization of well-covered
graphs with no shedding vertex. We also introduce equimatchable graphs such
that the removal of any vertex harms the equimatchability, called
vertex-critical equimatchable graphs (VCE-graphs). To conclude, we enlighten
the relationship between various subclasses of equimatchable graphs (including
ECE-graphs and VCE-graphs) and discuss the properties of factor-critical
ECE-graphs with connectivity at least 3.


title:
Willems' fundamental lemma for linear descriptor systems and its use for
  data-driven output-feedback MPC

abstract:
  In this paper we investigate data-driven predictive control of discrete-time
linear descriptor systems. Specifically, we give a tailored variant of Willems'
fundamental lemma, which shows that for descriptor systems the non-parametric
modelling via a Hankel matrix requires less data compared to linear
time-invariant systems without algebraic constraints. Moreover, we use this
description to propose a data-driven framework for optimal control and
predictive control of discrete-time linear descriptor systems. For the latter,
we provide a sufficient stability condition for receding-horizon control before
we illustrate our findings with an example.


title:
DBT-Net: Dual-branch federative magnitude and phase estimation with
  attention-in-attention transformer for monaural speech enhancement

abstract:
  The decoupling-style concept begins to ignite in the speech enhancement area,
which decouples the original complex spectrum estimation task into multiple
easier sub-tasks (i.e., magnitude and phase), resulting in better performance
and easier interpretability. In this paper, we propose a dual-branch federative
magnitude and phase estimation framework, dubbed DBT-Net, for monaural speech
enhancement, which aims at recovering the coarse- and fine-grained regions of
the overall spectrum in parallel. From the complementary perspective, the
magnitude estimation branch is designed to filter out dominant noise components
in the magnitude domain, while the complex spectrum purification branch is
elaborately designed to inpaint the missing spectral details and implicitly
estimate the phase information in the complex domain. To facilitate the
information flow between each branch, interaction modules are introduced to
leverage features learned from one branch, so as to suppress the undesired
parts and recover the missing components of the other branch. Instead of
adopting the conventional RNNs and temporal convolutional networks for sequence
modeling, we propose a novel attention-in-attention transformer-based network
within each branch for better feature learning. More specially, it is composed
of several adaptive spectro-temporal attention transformer-based modules and an
adaptive hierarchical attention module, aiming to capture long-term
time-frequency dependencies and further aggregate intermediate hierarchical
contextual information. Comprehensive evaluations on the WSJ0-SI84 +
DNS-Challenge and VoiceBank + DEMAND dataset demonstrate that the proposed
approach consistently outperforms previous advanced systems and yields
state-of-the-art performance in terms of speech quality and intelligibility.


title:
On the Complexity of Scheduling Problems With a Fixed Number of Parallel
  Identical Machines

abstract:
  In parallel machine scheduling, we are given a set of jobs, together with a
number of machines and our goal is to decide for each job, when and on which
machine(s) it should be scheduled in order to minimize some objective function.
Different machine models, job characteristics and objective functions result in
a multitude of scheduling problems and many of them are NP-hard, even for a
fixed number of identical machines. However, using pseudo-polynomial or
approximation algorithms, we can still hope to solve some of these problems
efficiently.
  In this work, we give conditional running time lower bounds for a large
number of scheduling problems, indicating the optimality of some classical
algorithms. In particular, we show that the dynamic programming algorithm by
Lawler and Moore is probably optimal for $1||\sum w_jU_j$ and $Pm||C_{max}$.
Moreover, the algorithm by Lee and Uzsoy for $P2||\sum w_jC_j$ is probably
optimal as well. There is still small room for improvement for the $1|Rej\leq
Q|\sum w_jU_j$ algorithm by Zhang et al., the algorithm for $1||\sum T_j$ by
Lawler and the FPTAS for $1||\sum w_jU_j$ by Gens and Levner. We also give a
lower bound for $P2|any|C_{max}$ and improve the dynamic program by Du and
Leung from $\mathcal{O}(nP^2)$ to $\mathcal{O}(nP)$ to match this lower bound.
Here, $P$ is the sum of all processing times. The same idea also improves the
algorithm for $P3|any|C_{max}$ by Du and Leung from $\mathcal{O}(nP^5)$ to
$\mathcal{O}(nP^2)$.
  The lower bounds in this work all either rely on the (Strong) Exponential
Time Hypothesis or the $(\min,+)$-conjecture. While our results suggest the
optimality of some classical algorithms, they also motivate future research in
cases where the best known algorithms do not quite match the lower bounds.


title: Sustainable optimal transport in multilayer networks

abstract:
  Traffic congestion is one of the major challenges faced by the transportation
industry. While this problem carries a high economical and environmental cost,
the need for an efficient design of optimal paths for passengers in multilayer
network infrastructures is imperative. We consider an approach based on optimal
transport theory to route passengers preferably along layers that are more
carbon efficient than the road, e.g. rails. By analyzing the impact of this
choice on performance, we find that this approach reduces carbon emissions
considerably, compared to shortest-path minimization. Similarly, we find that
this approach distributes traffic more homogeneously thus alleviating the risk
of traffic congestions. Our results shed light on the impact of distributing
traffic flexibly across layers guided by optimal transport theory.


title:
On structural similarities of combinatorial integral approximation and
  binary trust-region steepest descent

abstract:
  Combinatorial integral approximation and binary trust-region steepest descent
are two approaches for the solution of optimal control problems with discrete
(binary in the latter case) control inputs. While the former method
approximates solutions of a continuous relaxation with discrete controls, the
latter produces a sequence of discrete and objective-decreasing controls by
manipulating the level sets of the control inputs. The main difference in their
analyses is an additional compactness assumption on the reduced objective
functional for the combinatorial integral decomposition that implies an
approximation of stationary points or (local) minimizers of the continuous
relaxation and their objective function values. We prove that by imposing a
suitable compactness assumption on the derivative of the reduced objective, the
iterates produced by binary trust-region steepest descent have the same
convergence properties. The methods achieve comparable results under comparable
structural assumptions on the problem. We provide computational results to
illustrate qualitative and quantitative similarities and differences of the
iterates of both methods.


title:
Comparison of Supervised and Unsupervised Anomaly Detection in Belle II
  Pixel Detector Data

abstract:
  Machine learning has become a popular instrument for the identification of
dark matter candidates at particle collider experiments. They enable the
processing of large datasets and are therefore suitable to operate directly on
raw data coming from the detector, instead of reconstructed objects. Here, we
investigate patterns of raw pixel hits recorded by the Belle II pixel detector,
that is operational since 2019 and presently features 4 M pixels and trigger
rates up to 5 kHz. In particular, we focus on unsupervised techniques that
operate without the need for a theoretical model. These model-agnostic
approaches allow for an unbiased exploration of data, while filtering out
anomalous detector signatures that could hint at new physics scenarios. We
present the identification of hypothetical magnetic monopoles against Belle II
beam background using Self-Organizing Kohonen Maps and Autoencoders. The two
unsupervised algorithms are compared to a convolutional Multilayer Perceptron
and a superior signal efficiency is found at high background rejection levels.
Our results strengthen the case for using unsupervised machine learning
techniques to complement traditional search strategies at particle colliders
and pave the way to potential online applications of the algorithms in the near
future.


title:
Static and Dynamic Bethe-Salpeter Equations in the $T$-Matrix
  Approximation

abstract:
  While the well-established $GW$ approximation corresponds to a resummation of
the direct ring diagrams and is particularly well suited for weakly-correlated
systems, the $T$-matrix approximation does sum ladder diagrams up to infinity
and is supposedly more appropriate in the presence of strong correlation. Here,
we derive and implement, for the first time, the static and dynamic
Bethe-Salpeter equations when one considers $T$-matrix quasiparticle energies
as well as a $T$-matrix-based kernel. The performance of the static scheme and
its perturbative dynamical correction are assessed by computing the neutral
excited states of molecular systems. Comparison with more conventional schemes
as well as other wave function methods are also reported. Our results suggest
that the $T$-matrix-based formalism performs best in few-electron systems where
the electron density remains low.

categories: physics.chem-ph cond-mat.mtrl-sci cond-mat.str-el nucl-th physics.comp-ph


title: Separation and Estimation of Periodic/Aperiodic State

abstract:
  Periodicity and aperiodicity can exist in a state simultaneously and
typically become quasi-periodicity and quasi-aperiodicity in a dynamically
changing state. The quasi-periodic and quasi-aperiodic states existing in the
periodic/aperiodic state mostly correspond to different phenomena and require
different controls. For separation control of these states, this paper defines
the periodic/aperiodic, quasi-periodic, and quasi-aperiodic states to construct
a periodic/aperiodic separation filter that separates the periodic/aperiodic
state into the quasi-periodic and quasi-aperiodic states. Based on these
definitions, the linearity of periodic-pass and aperiodic-pass functions and
the orthogonality of quasi-periodic and quasi-aperiodic state functions are
proved. Subsequently, the periodic/aperiodic separation filter composed of
periodic-pass and aperiodic-pass filters that realize the periodic-pass and
aperiodic-pass functions is designed and integrated with a Kalman filter for
estimation of the quasi-periodic and quasi-aperiodic states.


title: Magnetic field induced hair structure in charmonium gluon-dissociation

abstract:
  We study electromagnetic field effect on charmonium gluon-dissociation in
quark-gluon plasma. With the effective Hamiltonian derived from QCD multipole
expansion under an external electromagnetic field, we first solve the two-body
Schr\"odinger equation for a pair of charm quarks with mean field potentials
for color and electromagnetic interactions and obtain the charmonium binding
energies and wave functions, and then calculate the gluon-dissociation
cross-section and decay width by taking the color electric and magnetic dipole
interactions as perturbations above the mean field and employing Fermi's Golden
Rule. Considering the charmonium deformation in magnetic field, the discrete
Landau energy levels make the dissociation cross-section grow hair, and the
electric dipole channel is significantly changed, especially for the $P-$wave
states $\chi_{c0}$ and $\chi_{c\pm}$. From our numerical calculation, the
magnetic field strength $eB=5~m_\pi^2$ already changes the gluon dissociation
strongly, which may indicate measurable effects in high-energy nuclear
collisions.