title:
The Microscopic Diamond Anvil Cell: Stabilization of Superhard,
  Superconducting Carbon Allotropes at Ambient Pressure

abstract:
  A metallic covalently bonded carbon allotrope is predicted via first
principles calculations. It is composed of an $sp^3$ carbon framework that acts
as a diamond anvil cell by constraining the distance between parallel
cis-polyacetylene chains. The distance between these $sp^2$ carbon atoms
renders the phase metallic, and yields two well-nested nearly parallel bands
that span the Fermi level. Calculations show that this phase is a conventional
superconductor, with the motions of the $sp^2$ carbons being key contributors
to the electron phonon coupling. The $sp^3$ carbon atoms impart superior
mechanical properties, with a predicted Vickers hardness of 48~GPa. This phase,
metastable at ambient conditions, could be made via cold compression of
graphite to 40~GPa. A family of multifunctional materials with tunable
superconducting and mechanical properties could be derived from this phase by
varying the $sp^2$ versus $sp^3$ carbon content and by doping.


title:
Leveraging Real Conversational Data for Multi-Channel Continuous Speech
  Separation

abstract:
  Existing multi-channel continuous speech separation (CSS) models are heavily
dependent on supervised data - either simulated data which causes data mismatch
between the training and real-data testing, or the real transcribed overlapping
data, which is difficult to be acquired, hindering further improvements in the
conversational/meeting transcription tasks. In this paper, we propose a
three-stage training scheme for the CSS model that can leverage both supervised
data and extra large-scale unsupervised real-world conversational data. The
scheme consists of two conventional training approaches -- pre-training using
simulated data and ASR-loss-based training using transcribed data -- and a
novel continuous semi-supervised training between the two, in which the CSS
model is further trained by using real data based on the teacher-student
learning framework. We apply this scheme to an array-geometry-agnostic CSS
model, which can use the multi-channel data collected from any microphone
array. Large-scale meeting transcription experiments are carried out on both
Microsoft internal meeting data and the AMI meeting corpus. The steady
improvement by each training stage has been observed, showing the effect of the
proposed method that enables leveraging real conversational data for CSS model
training.


title:
Muonic X-Ray Measurement for the Nuclear Charge Distribution: the Case
  of Stable Palladium Isotopes

abstract:
  Background: The nuclear charge radius and distribution are the most
fundamental quantities of the atomic nucleus. From the muonic transition
energies, the absolute charge radius has been experimentally obtained, while
there have been no established methods to discuss the distribution.
  Purpose: The muonic transition energies for five palladium isotopes with the
mass number $A = 104$, $105$, $106$, $108$ and $110$ were measured. The
procedure to deduce the charge radii and the method to discuss the charge
distribution from the muonic transition energies are proposed.
  Method: The experiment was performed at the MuSIC-M1 beamline at Research
Center for Nuclear Physics, Osaka University. A continuous muon beam impinged
on the enriched palladium targets. Muonic X rays were measured by high-purity
germanium detectors.
  Results: The muonic transition energies up to $4f$-$3d$ transitions were
determined for five palladium isotopes.
  Discussion and conclusion: The root-mean-square charge radii are deduced
assuming the two-parameter Fermi distribution. The charge distribution of the
nucleus is discussed employing the Barrett model. The muonic transition
energies of the $3d$-$2p$ transitions are crucial to discuss both the charge
radius and the charge distribution.


title:
Local and 2-local derivations on Lie matrix rings over commutative
  involutive rings

abstract:
  In the present paper we prove that every 2-local inner derivation on the Lie
ring of skew-adjoint matrices over a commutative $*$-ring is an inner
derivation. We also apply our technique to various Lie algebras of
infinite-dimensional skew-adjoint matrix-valued maps on a set and prove that
every 2-local spatial derivation on such algebras is a spatial derivation. We
also show that every local spatial derivation on the above Lie algebras is a
derivation.


title:
Reverse isoperimetric inequality for the lowest Robin eigenvalue of a
  triangle

abstract:
  We consider the Laplace operator on a triangle, subject to attractive Robin
boundary conditions. We prove that the equilateral triangle is a local
maximiser of the lowest eigenvalue among all triangles of a given area provided
that the negative boundary parameter is sufficiently small in absolute value,
with the smallness depending on the area only. Moreover, using various trial
functions, we obtain sufficient conditions for the global optimality of the
equilateral triangle under fixed area constraint in the regimes of small and
large couplings. We also discuss the constraint of fixed perimeter.


title:
Learning to Solve Travelling Salesman Problem with Hardness-adaptive
  Curriculum

abstract:
  Various neural network models have been proposed to tackle combinatorial
optimization problems such as the travelling salesman problem (TSP). Existing
learning-based TSP methods adopt a simple setting that the training and testing
data are independent and identically distributed. However, the existing
literature fails to solve TSP instances when training and testing data have
different distributions. Concretely, we find that different training and
testing distribution will result in more difficult TSP instances, i.e., the
solution obtained by the model has a large gap from the optimal solution. To
tackle this problem, in this work, we study learning-based TSP methods when
training and testing data have different distributions using adaptive-hardness,
i.e., how difficult a TSP instance can be for a solver. This problem is
challenging because it is non-trivial to (1) define hardness measurement
quantitatively; (2) efficiently and continuously generate sufficiently hard TSP
instances upon model training; (3) fully utilize instances with different
levels of hardness to learn a more powerful TSP solver. To solve these
challenges, we first propose a principled hardness measurement to quantify the
hardness of TSP instances. Then, we propose a hardness-adaptive generator to
generate instances with different hardness. We further propose a curriculum
learner fully utilizing these instances to train the TSP solver. Experiments
show that our hardness-adaptive generator can generate instances ten times
harder than the existing methods, and our proposed method achieves significant
improvement over state-of-the-art models in terms of the optimality gap.


title: Rule-based Procedural Tree Modeling Approach

abstract:
  In some entertainment and virtual reality applications, it is necessary to
model and draw the real world realistically, so as to improve the fidelity of
natural scenes and make users have a better sense of immersion. However, due to
the morphological structure of trees The complexity and variety present many
challenges for photorealistic modeling and rendering of trees. This paper
reviews the progress achieved in photorealistic modeling and rendering of tree
branches, leaves, and bark over the past few decades. The main achievement is
mainly a rule-based procedural tree modeling method.


title:
Unsupervised Quantized Prosody Representation for Controllable Speech
  Synthesis

abstract:
  In this paper, we propose a novel prosody disentangle method for prosodic
Text-to-Speech (TTS) model, which introduces the vector quantization (VQ)
method to the auxiliary prosody encoder to obtain the decomposed prosody
representations in an unsupervised manner. Rely on its advantages, the speaking
styles, such as pitch, speaking velocity, local pitch variance, etc., are
decomposed automatically into the latent quantize vectors. We also investigate
the internal mechanism of VQ disentangle process by means of a latent variables
counter and find that higher value dimensions usually represent prosody
information. Experiments show that our model can control the speaking styles of
synthesis results by directly manipulating the latent variables. The objective
and subjective evaluations illustrated that our model outperforms the popular
models.


title:
Spin dynamics and exchange interaction in orthoferrite TbFeO$_3$ with
  non-Kramers rare-earth ion

abstract:
  The low-temperature spin dynamics of the orthorhombic TbFeO$_3$ perovskite
has been studied. It has been found that the inelastic neutron scattering (INS)
spectrum contains two modes corresponding to different sublattices in the
compound. The iron subsystem orders antiferromagnetically at T$_N$ = 632 K and
exhibits the high-energy magnon dispersion. Magnetic dynamics of this subsystem
has been described using the linear spin wave theory and our solution yields
sizable anisotropy between in-plane and out-of-plane exchange interactions.
This approach was previously used to describe the magnon dispersion in the
TmFeO$_3$ compound. Three non-dispersive crystal electric field levels
corresponding to Tb$^{3+}$ ions have been established in the region below 40
meV at about 17, 26, and 35 meV. Study of diffuse scattering at different
temperatures has elucidated the behavior of the magnetic correlation length.
The behavior of the Tb$^{3+}$ ion subsystem has been numerically described in
the framework of the point charge model. The numerical data agree
satisfactorily with the experiment and with the general concept of the
single-ion approximation applied to the rare-earth subsystem of orthorhombic
perovskites.


title: Speech Pre-training with Acoustic Piece

abstract:
  Previous speech pre-training methods, such as wav2vec2.0 and HuBERT,
pre-train a Transformer encoder to learn deep representations from audio data,
with objectives predicting either elements from latent vector quantized space
or pre-generated labels (known as target codes) with offline clustering.
However, those training signals (quantized elements or codes) are independent
across different tokens without considering their relations. According to our
observation and analysis, the target codes share obvious patterns aligned with
phonemized text data. Based on that, we propose to leverage those patterns to
better pre-train the model considering the relations among the codes. The
patterns we extracted, called "acoustic piece"s, are from the sentence piece
result of HuBERT codes. With the acoustic piece as the training signal, we can
implicitly bridge the input audio and natural language, which benefits
audio-to-text tasks, such as automatic speech recognition (ASR). Simple but
effective, our method "HuBERT-AP" significantly outperforms strong baselines on
the LibriSpeech ASR task.


title: Three Laws of Technology Rise or Fall

abstract:
  Newton's laws of motion perfectly explain or approximate physical phenomena
in our everyday life. Are there any laws that explain or approximate
technology's rise or fall? After reviewing thirteen information technologies
that succeeded, this article concludes three laws of technology and derives
five corollaries to explain or approximate the rise or fall of technology.
Three laws are the laws of technology inertia, technology change force, and
technology action and reaction. Five corollaries are the corollaries of
measurement of technology change force, technology breakthrough, technology
monopoly, technology openness, and technology business opportunity. I present
how to use the laws and the corollaries to analyze an emerging technology --
the open-source RISC-V processor. Also, I elaborate on benchmarks' role in
applying those laws.


title: An odd-frequency Cooper pair around a magnetic impurity

abstract:
  The Yu-Shiba-Rusinov (YSR) state appears as a bound state of a quasiparticle
at a magnetic atom embedded in a superconductor. We discuss why the YSR state
has energy below the superconducting gap and why the pair potential changes the
sign at the magnetic atom. Although a magnetic atom in a superconductor has
been considered as a pair breaker since 1960s, we propose an alternative
physical picture to explain these reasons. We show that a magnetic atom
converts a spin-singlet s-wave Cooper pair into an odd-frequency pair rather
than breaking it. The odd-frequency pairing correlations always coexist with
the quasiparticle states below the gap. The YSR state is an example of such a
subgap quasiparticle state. The paramagnetic property of an odd-frequency pair
explains the sign change of the pair potential at a magnetic atom and the
decrease of superconducting transition temperature in the presence of many
magnetic impurities.


title:
Pretraining Text Encoders with Adversarial Mixture of Training Signal
  Generators

abstract:
  We present a new framework AMOS that pretrains text encoders with an
Adversarial learning curriculum via a Mixture Of Signals from multiple
auxiliary generators. Following ELECTRA-style pretraining, the main encoder is
trained as a discriminator to detect replaced tokens generated by auxiliary
masked language models (MLMs). Different from ELECTRA which trains one MLM as
the generator, we jointly train multiple MLMs of different sizes to provide
training signals at various levels of difficulty. To push the discriminator to
learn better with challenging replaced tokens, we learn mixture weights over
the auxiliary MLMs' outputs to maximize the discriminator loss by
backpropagating the gradient from the discriminator via Gumbel-Softmax. For
better pretraining efficiency, we propose a way to assemble multiple MLMs into
one unified auxiliary model. AMOS outperforms ELECTRA and recent
state-of-the-art pretrained models by about 1 point on the GLUE benchmark for
BERT base-sized models.


title:
Domain wall formation and magnon localization in twisted chromium
  trihalides

abstract:
  The rise of twistronics has revolutionized the field of condensed matter
physics, and more specifically the future applications of two-dimensional
materials. At small twist angles, the microscopic world becomes strongly
correlated, and unexpected physical phenomena such as superconductivity emerge.
For magnetic layers, stacking plays a crucial role in the magnetic exchange
coupling between the layers leading to non-trivial spin configurations and flat
spin-wave dispersion when twisted. In this work, we give a short overview of
the most recent theoretical and experimental works reporting the effect of
twist angles on two-dimensional magnets. Besides, we discuss the effect of the
twist angle and the local antiferromagnetic interlayer exchange coupling on the
formation of antiferromagnetic domains in chromium trihalides. Finally, we show
some preliminary results on the effect of the stacking and the twist angle on
the spin-wave dispersion of bilayer CrI$_3$


title:
HIT-UAV: A High-altitude Infrared Thermal Dataset for Unmanned Aerial
  Vehicles

abstract:
  This paper presents a High-altitude infrared thermal dataset, HIT-UAV, for
object detection applications on Unmanned Aerial Vehicles (UAVs). HIT-UAV
contains 2898 infrared thermal images extracted from 43470 frames. These images
are collected by UAV from schools, parking lots, roads, playgrounds, etc.
HIT-UAV provides different flight data for each place, including flight
altitude (from 60 to 130 meters), camera perspective (from 30 to 90 degrees),
date, and daylight intensity. For each image, the HIT-UAV manual annotates
object instances with two types of the bounding box (oriented and standard) to
address the challenge that object instances have a significant overlap in
aerial images. To the best of our knowledge, HIT-UAV is the first publicly
available high-altitude infrared thermal UAV dataset for persons and vehicles
detection. Moreover, we trained and evaluated the benchmark detection
algorithms (YOLOv4 and YOLOv4-tiny) on HIT-UAV. Compared to the visual light
dataset, the detection algorithms have excellent performance on HIT-UAV because
the infrared thermal images do not contain a significant quantity of irrelevant
information with detection objects. This indicates that infrared thermal
datasets can significantly promote the development of object detection
applications. We hope HIT-UAV contributes to UAV applications such as traffic
surveillance and city monitoring at night. The dataset is available at
https://github.com/suojiashun/HIT-UAV-Infrared-Thermal-Dataset.


title:
Analysis of a class of globally divergence-free HDG methods for
  stationary Navier-Stokes equations

abstract:
  This paper analyzes a class of globally divergence-free (and therefore
pressure-robust) hybridizable discontinuous Galerkin (HDG) finite element
methods for stationary Navier-Stokes equations. The methods use the
$\mathcal{P}_{k}/\mathcal{P}_{k-1}$ $(k\geq1)$ discontinuous finite element
combination for the velocity and pressure approximations in the interior of
elements, and piecewise $\mathcal{P}_k/\mathcal{P}_{k}$ for the trace
approximations of the velocity and pressure on the inter-element boundaries. It
is shown that the uniqueness condition for the discrete solution is guaranteed
by that for the continuous solution together with a sufficiently small mesh
size. Based on the derived discrete HDG Sobolev embedding properties, optimal
error estimates are obtained. Numerical experiments are performed to verify the
theoretical analysis.


title: Emergent SUSY in Two Dimensions

abstract:
  We propose a renormalization group flow with emergent supersymmetry in two
dimensions from a non-Lagrangian theory. The ultraviolet theory does not have
supersymmetry while the infrared theory does. The flow is constrained
analytically by topological defect lines including a new spin constraint, and
further supported by numerics from the truncated conformal space approach.


title:
Composite Spatial Monte Carlo Integration Based on Generalized Least
  Squares

abstract:
  Although evaluation of the expectations on the Ising model is essential in
various applications, this is frequently infeasible because of intractable
multiple summations (or integrations). Spatial Monte Carlo integration (SMCI)
is a sampling-based approximation, and can provide high-accuracy estimations
for such intractable expectations. To evaluate the expectation of a function of
variables in a specific region (called target region), SMCI considers a larger
region containing the target region (called sum region). In SMCI, the multiple
summation for the variables in the sum region is precisely executed, and that
in the outer region is evaluated by the sampling approximation such as the
standard Monte Carlo integration. It is guaranteed that the accuracy of the
SMCI estimator is monotonically improved as the size of the sum region
increases. However, a haphazard expansion of the sum region could cause a
combinatorial explosion. Therefore, we hope to improve the accuracy without
such region expansion. In this study, based on the theory of generalized least
squares, a new effective method is proposed by combining multiple SMCI
estimators. The validity of the proposed method is demonstrated theoretically
and numerically. The results indicate that the proposed method can be effective
in the inverse Ising problem (or Boltzmann machine learning).

categories: stat.CO cond-mat.dis-nn cs.LG physics.data-an stat.ML


title:
Expressive Singing Synthesis Using Local Style Token and Dual-path Pitch
  Encoder

abstract:
  This paper proposes a controllable singing voice synthesis system capable of
generating expressive singing voice with two novel methodologies. First, a
local style token module, which predicts frame-level style tokens from an input
pitch and text sequence, is proposed to allow the singing voice system to
control musical expression often unspecified in sheet music (e.g., breathing
and intensity). Second, we propose a dual-path pitch encoder with a choice of
two different pitch inputs: MIDI pitch sequence or f0 contour. Because the
initial generation of a singing voice is usually executed by taking a MIDI
pitch sequence, one can later extract an f0 contour from the generated singing
voice and modify the f0 contour to a finer level as desired. Through
quantitative and qualitative evaluations, we confirmed that the proposed model
could control various musical expressions while not sacrificing the sound
quality of the singing voice synthesis system.


title: Cascade of magnetic-field-driven quantum phase transitions in Ce3Pd20Si6

abstract:
  Magnetically hidden order is a hypernym for electronic ordering phenomena
that are visible to macroscopic thermodynamic probes but whose microscopic
symmetry cannot be revealed with conventional neutron or x-ray diffraction. In
a handful of f-electron systems, the ordering of odd-rank multipoles leads to
order parameters with a vanishing neutron cross-section. Among them,
Ce$_3$Pd$_{20}$Si$_6$ is known for its unique phase diagram exhibiting two
distinct multipolar-ordered ground states (phases II and II'), separated by a
field-driven quantum phase transition associated with a putative change in the
ordered quadrupolar moment from $O_2^0$ to $O_{xy}$. Using torque magnetometry
at subkelvin temperatures, here we find another phase transition at higher
fields above 12 T, which appears only for low-symmetry magnetic field
directions $\mathbf{B} \parallel \langle11L\rangle$ with $1 < L \leq 2$. While
the order parameter of this new phase II'' remains unknown, the discovery
renders Ce$_3$Pd$_{20}$Si$_6$ a unique material with two field-driven phase
transitions between distinct multipolar phases. They are both clearly
manifested in the magnetic-field dependence of the field-induced (111) Bragg
intensities measured with neutron scattering for $\mathbf{B} \parallel [112]$.
We also find from inelastic neutron scattering that the number of nondegenerate
collective excitations induced by the magnetic field correlates with the number
of phases in the magnetic phase diagram for the same field direction.
Furthermore, the magnetic excitation spectrum suggests that the new phase II''
may have a different propagation vector, revealed by the minimum in the
dispersion that may represent the Goldstone mode of this hidden-order phase.


title:
Automatic WordNet Construction using Word Sense Induction through
  Sentence Embeddings

abstract:
  Language resources such as wordnets remain indispensable tools for different
natural language tasks and applications. However, for low-resource languages
such as Filipino, existing wordnets are old and outdated, and producing new
ones may be slow and costly in terms of time and resources. In this paper, we
propose an automatic method for constructing a wordnet from scratch using only
an unlabeled corpus and a sentence embeddings-based language model. Using this,
we produce FilWordNet, a new wordnet that supplants and improves the outdated
Filipino WordNet. We evaluate our automatically-induced senses and synsets by
matching them with senses from the Princeton WordNet, as well as comparing the
synsets to the old Filipino WordNet. We empirically show that our method can
induce existing, as well as potentially new, senses and synsets automatically
without the need for human supervision.


title:
A note on asymptotically exact a posteriori error estimates for mixed
  Laplace eigenvalue problems

abstract:
  We derive optimal and asymptotically exact a posteriori error estimates for
the approximation of the Laplace eigenvalue problem. To do so, we combine two
results from the literature. First, we use the hypercircle techniques developed
for mixed eigenvalue approximations with Raviart-Thomas Finite elements. In
addition, we use the post-processings introduced for the eigenvalue and
eigenfunction based on mixed approximations with the Brezzi-Douglas-Marini
Finite element. To combine these approaches, we define a novel additional local
post-processing for the fluxes that appropriately modifies the divergence.
Consequently, the new flux can be used to derive upper bounds and still shows
good approximation properties. Numerical examples validate the theory and
motivate the use of an adaptive mesh refinement.


title:
Quasi-periodic oscillations of the X-ray burst from the magnetar SGR
  J1935+2154 and associated with the fast radio burst FRB 200428

abstract:
  The origin(s) and mechanism(s) of fast radio bursts (FRBs), which are short
radio pulses from cosmological distances, have remained a major puzzle since
their discovery. We report a strong Quasi-Periodic Oscillation(QPO) of 40 Hz in
the X-ray burst from the magnetar SGR J1935+2154 and associated with FRB
200428, significantly detected with the Hard X-ray Modulation Telescope
(Insight-HXMT) and also hinted by the Konus-Wind data. QPOs from magnetar
bursts have only been rarely detected; our 3.4 sigma (p-value is 2.9e-4)
detection of the QPO reported here reveals the strongest QPO signal observed
from magnetars (except in some very rare giant flares), making this X-ray burst
unique among magnetar bursts. The two X-ray spikes coinciding with the two FRB
pulses are also among the peaks of the QPO. Our results suggest that at least
some FRBs are related to strong oscillation processes of neutron stars. We also
show that we may overestimate the significance of the QPO signal and
underestimate the errors of QPO parameters if QPO exists only in a fraction of
the time series of a X-ray burst which we use to calculate the Leahy-normalized
periodogram.


title: The General Index of Software Engineering Papers

abstract:
  We introduce the General Index of Software Engineering Papers, a dataset of
fulltext-indexed papers from the most prominent scientific venues in the field
of Software Engineering. The dataset includes both complete bibliographic
information and indexed ngrams (sequence of contiguous words after removal of
stopwords and non-words, for a total of 577 276 382 unique n-grams in this
release) with length 1 to 5 for 44 581 papers retrieved from 34 venues over the
1971-2020 period.The dataset serves use cases in the field of meta-research,
allowing to introspect the output of software engineering research even when
access to papers or scholarly search engines is not possible (e.g., due to
contractual reasons). The dataset also contributes to making such analyses
reproducible and independently verifiable, as opposed to what happens when they
are conducted using 3rd-party and non-open scholarly indexing services.The
dataset is available as a portable Postgres database dump and released as open
data.


title: Arabic Text-To-Speech (TTS) Data Preparation

abstract:
  People may be puzzled by the fact that voice over recordings data sets exist
in addition to Text-to-Speech (TTS), Synthesis system advancements, albeit this
is not the case. The goal of this study is to explain the relevance of TTS as
well as the data preparation procedures. TTS relies heavily on recorded data
since it can have a substantial influence on the outcomes of TTS modules.
Furthermore, whether the domain is specialized or general, appropriate data
should be developed to address all predicted language variants and domains.
Different recording methodologies, taking into account quality and behavior,
may also be advantageous in the development of the module. In light of the lack
of Arabic language in present synthesizing systems, numerous variables that
impact the flow of recorded utterances are being considered in order to
manipulate an Arabic TTS module. In this study, two viewpoints will be
discussed: linguistics and the creation of high-quality recordings for TTS. The
purpose of this work is to offer light on how ground-truth utterances may
influence the evolution of speech systems in terms of naturalness,
intelligibility, and understanding. Well provide voice actor specs as well as
data specs that will assist both voice actors and voice coaches in the studio
as well as the annotators who will be evaluating the audios.


title: Calibration Strategy of the JUNO-TAO Experiment

abstract:
  The Taishan Antineutrino Observatory (JUNO-TAO, or TAO) is a satellite
detector for the Jiangmen Underground Neutrino Observatory (JUNO). Located near
the Taishan reactor, TAO independently measures the reactor's antineutrino
energy spectrum with unprecedented energy resolution. To achieve this goal,
energy response must be well calibrated. Using the Automated Calibration Unit
(ACU) and the Cable Loop System (CLS) of TAO, multiple radioactive sources are
deployed to various positions in the detector to perform a precise calibration
of energy response. The non-linear energy response can be controlled within
0.6% with different energy points of these radioactive sources. It can be
further improved by using $^{12}\rm B$ decay signals produced by cosmic muons.
Through the energy non-uniformity calibration, residual non-uniformity is less
than 0.2%. The energy resolution degradation and energy bias caused by the
residual non-uniformity can be controlled within 0.05% and 0.3%, respectively.
In addition, the stability of other detector parameters, such as the gain of
each silicon photo-multiplier, can be monitored with a special ultraviolet LED
calibration system.


title:
Pan-cancer computational histopathology reveals tumor mutational burden
  status through weakly-supervised deep learning

abstract:
  Tumor mutational burden (TMB) is a potential genomic biomarker that can help
identify patients who will benefit from immunotherapy across a variety of
cancers. We included whole slide images (WSIs) of 3228 diagnostic slides from
the Cancer Genome Atlas and 531 WSIs from the Clinical Proteomic Tumor Analysis
Consortium for the development and verification of a pan-cancer TMB prediction
model (PC-TMB). We proposed a multiscale weakly-supervised deep learning
framework for predicting TMB of seven types of tumors based only on routinely
used hematoxylin-eosin (H&E)-stained WSIs. PC-TMB achieved a mean area under
curve (AUC) of 0.818 (0.804-0.831) in the cross-validation cohort, which was
superior to the best single-scale model. In comparison with the
state-of-the-art TMB prediction model from previous publications, our
multiscale model achieved better performance over previously reported models.
In addition, the improvements of PC-TMB over the single-tumor models were also
confirmed by the ablation tests on 10x magnification. The PC-TMB algorithm also
exhibited good generalization on external validation cohort with AUC of 0.732
(0.683-0.761). PC-TMB possessed a comparable survival-risk stratification
performance to the TMB measured by whole exome sequencing, but with low cost
and being time-efficient for providing a prognostic biomarker of multiple solid
tumors. Moreover, spatial heterogeneity of TMB within tumors was also
identified through our PC-TMB, which might enable image-based screening for
molecular biomarkers with spatial variation and potential exploring for
genotype-spatial heterogeneity relationships.


title: Amplitude Factorization in the Electroweak Standard Model

abstract:
  We lay out the basis of factorization at the amplitude level for processes
involving the entire Standard Model. The factorization appears in a generalized
eikonal approximation in which we expand around a quasi-soft limit for massive
gauge bosons, fermions, and scalars. We use the chirality-flow formalism
together with a flow basis for isospin to express loop exchanges or emissions
as operators in chirality and isospin flow. This forms the basis for amplitude
evolution with parton exchange and branching in the full Standard Model,
including the electroweak sector.


title: Quasinormal Modes of Self-dual Black Holes in Loop Quantum Gravity

abstract:
  We study the evolution of a test scalar field on the background geometry of a
regular loop quantum black hole (LQBH) characterized by two loop quantum
gravity (LQG) correction parameters, namely, the polymeric function and the
minimum area gap. The calculations of quasinormal frequencies in asymptotically
flat spacetime are performed with the help of higher-order WKB expansion and
related Pad\'{e} approximants, the improved asymptotic iteration method (AIM),
and time-domain integration. The effects of free parameters of the theory on
the quasinormal modes are studied and deviations from those of the
Schwarzschild BHs are investigated. We show that the LQG correction parameters
have opposite effects on the quasinormal frequencies and the LQBHs are
dynamically stable.


title: Some special curves in the unit tangent bundles of surfaces

abstract:
  The aim of this paper is to give some characterizations for N-Legendre and
N-slant curves in the unit tangent bundles of surfaces endowed with natural
diagonal lifted structures.


title:
Texture-Dependent Frequency Selective Reconstruction of Non-Regularly
  Sampled Images

abstract:
  There exist many scenarios where pixel information is available only on a
non-regular subset of pixel positions. For further processing, however, it is
required to reconstruct such images on a regular grid. Besides many other
algorithms, frequency selective reconstruction can be applied for this task. It
performs a block-wise generation of a sparse signal model as an iterative
superposition of Fourier basis functions and uses this model to replace missing
or corrupted pixels in an image. In this paper, it is shown that it is not
required to spend the same amount of iterations on both homogeneous and
heterogeneous regions. Hence, a new texture-dependent approach for frequency
selective reconstruction is introduced that distributes the number of
iterations depending on the texture of the regions to be reconstructed.
Compared to the original frequency selective reconstruction and depending on
the number of iterations, visually noticeable gains in PSNR of up to 1.47 dB
can be achieved.


title:
Korean Online Hate Speech Dataset for Multilabel Classification: How Can
  Social Science Improve Dataset on Hate Speech?

abstract:
  We suggest a multilabel Korean online hate speech dataset that covers seven
categories of hate speech: (1) Race and Nationality, (2) Religion, (3)
Regionalism, (4) Ageism, (5) Misogyny, (6) Sexual Minorities, and (7) Male. Our
35K dataset consists of 24K online comments with Krippendorff's Alpha label
accordance of .713, 2.2K neutral sentences from Wikipedia, 1.7K additionally
labeled sentences generated by the Human-in-the-Loop procedure and
rule-generated 7.1K neutral sentences. The base model with 24K initial dataset
achieved the accuracy of LRAP .892, but improved to .919 after being combined
with 11K additional data. Unlike the conventional binary hate and non-hate
dichotomy approach, we designed a dataset considering both the cultural and
linguistic context to overcome the limitations of western culture-based English
texts. Thus, this paper is not only limited to presenting a local hate speech
dataset but extends as a manual for building a more generalized hate speech
dataset with diverse cultural backgrounds based on social science perspectives.


title:
The chirality-dependent second-order spin current in systems with
  time-reversal symmetry

abstract:
  Spin currents proportional to the first- and second-order of the electric
field are calculated in a specific tight-binding model with time-reversal
symmetry. Specifically, a tight-binding model with time-reversal symmetry is
constructed with chiral hopping and spin-orbit coupling. The spin conductivity
of the model is calculated using the Boltzmann equation. As a result, it is
clarified that the first-order spin current of the electric field vanishes,
while the second-order spin current can be finite. Furthermore, the spin
current changes its sign by reversing the chirality of the model. The present
results reveal the existence of spin currents in systems with time-reversal
symmetry depending on the chirality of the system. They may provide useful
information for understanding the chirality-dependent spin polarization
phenomena in systems with time-reversal symmetry.


title: Deficit hawks: robust new physics searches with unknown backgrounds

abstract:
  Searches for new physics often face unknown backgrounds, causing false
detections or weakened upper limits. This paper introduces the deficit hawk
technique, which mitigates unknown backgrounds by testing multiple options for
data cuts, such as fiducial volumes or energy thresholds. Combining the power
of likelihood ratios with the robustness of the interval-searching techniques,
deficit hawks could double the physics reach of experiments with partial or
speculative background knowledge. Deficit hawks are well-suited to analyses
that use machine learning or other multidimensional discrimination techniques,
and permit discoveries in regions without unknown background.


title:
The OmegaWhite Survey for Short-Period Variable Stars VII: High
  amplitude, short period blue variables

abstract:
  Blue Large Amplitude Pulsators (BLAPs) are a relatively new class of blue
variable stars showing periodic variations in their light curves with periods
shorter than a few tens of mins and amplitudes of more than ten percent. We
report nine blue variable stars identified in the OmegaWhite survey conducted
using ESO's VST, which show a periodic modulation in the range 7-37 min and an
amplitude in the range 0.11-0.28 mag. We have obtained a series of followup
photometric and spectroscopic observations made primarily using SALT and
telescopes at SAAO. We find four stars which we identify as BLAPs, one of which
was previously known. One star, OW J0820--3301, appears to be a member of the
V361 Hya class of pulsating stars and is spatially close to an extended nebula.
One further star, OW J1819--2729, has characteristics similar to the sdAV
pulsators. In contrast, OW J0815--3421 is a binary star containing an sdB and a
white dwarf with an orbital period of 73.7 min, making it only one of six white
dwarf-sdB binaries with an orbital period shorter than 80 min. Finally, high
cadence photometry of four of the candidate BLAPs show features which we
compare with notch-like features seen in the much longer period Cepheid
pulsators.


title: Lower Bounds for Restricted Schemes in the Two-Adaptive Bitprobe Model

abstract:
  In the adaptive bitprobe model answering membership queries in two bitprobes,
we consider the class of restricted schemes as introduced by Kesh and Sharma
(Discrete Applied Mathematics 2021). In that paper, the authors showed that
such restricted schemes storing subsets of size 2 require
$\Omega(m^\frac{2}{3})$ space. In this paper, we generalise the result to
arbitrary subsets of size $n$, and prove that the space required for such
restricted schemes will be $\Omega(\left(\frac{m}{n}\right)^{1 -
\frac{1}{\lfloor n / 4 \rfloor + 2}})$.


title:
Pure and truly nonclassical noise-induced transitions at the microscopic
  scale

abstract:
  It is universally accepted that noise may bring order to complex
nonequilibrium systems. Most strikingly, entirely new states not seen in the
noiseless system can be induced purely by including multiplicative noise - an
effect known as pure noise-induced transitions. It was first observed in
superfluids in the 1980s. Recent results in complex nonequilibrium systems have
also shown how new collective states emerge from such pure noise-induced
transitions, such as the foraging behavior of insect colonies, and schooling in
fish. Here we report such effects of noise in a quantum-mechanical system. We
find that multiplicative quantum noise can induce a classically forbidden
transition. We use a minimal model of a nonlinearly damped oscillator in a
fluctuating environment that is analytically tractable, and whose microscopic
physics can be understood. When environmental noise is included, the system is
seen to transition to a limit-cycle state. The noise-induced quantum limit
cycle exhibits genuine nonclassical traits such as Wigner negativity and
number-parity sensitive circulation in phase space. Such quantum limit cycles
are also conservative. These properties are in stark contrast to those of a
widely used limit cycle in the literature, which is dissipative and loses all
Wigner negativity. Our results establish the existence of a pure noise-induced
transition that is nonclassical and unique to open quantum systems. They
illustrate a fundamental difference between quantum and classical noise.


title:
Dynamic Non-Regular Sampling Sensor Using Frequency Selective
  Reconstruction

abstract:
  Both a high spatial and a high temporal resolution of images and videos are
desirable in many applications such as entertainment systems, monitoring
manufacturing processes, or video surveillance. Due to the limited throughput
of pixels per second, however, there is always a trade-off between acquiring
sequences with a high spatial resolution at a low temporal resolution or vice
versa. In this paper, a modified sensor concept is proposed which is able to
acquire both a high spatial and a high temporal resolution. This is achieved by
dynamically reading out only a subset of pixels in a non-regular order to
obtain a high temporal resolution. A full high spatial resolution is then
obtained by performing a subsequent three-dimensional reconstruction of the
partially acquired frames. The main benefit of the proposed dynamic readout is
that for each frame, different sampling points are available which is
advantageous since this information can significantly enhance the
reconstruction quality of the proposed reconstruction algorithm. Using the
proposed dynamic readout strategy, gains in PSNR of up to 8.55 dB are achieved
compared to a static readout strategy. Compared to other state-of-the-art
techniques like frame rate up-conversion or super-resolution which are also
able to reconstruct sequences with both a high spatial and a high temporal
resolution, average gains in PSNR of up to 6.58 dB are possible.


title: Free-Floating Planets, the Einstein Desert, and 'Oumuamua

abstract:
  We complete the survey for finite-source/point-lens (FSPL) giant-source
events in 2016-2019 KMTNet microlensing data. The 30 FSPL events show a clear
gap in Einstein radius, $9\,\mu{\rm as}<\theta_{\rm E} <26\,\mu{\rm as}$, which
is consistent with the gap in Einstein timescales near $t_{\rm E}\sim
0.5\,$days found by Mroz et al. (2017) in an independent sample of
point-source/point-lens (PSPL) events. We demonstrate that the two surveys are
consistent. We estimate that the 4 events below this gap are due to a power-law
distribution of free-floating planet candidates (FFPs) $dN_{\rm FFP}/d\log M =
(0.4\pm 0.2)\,(M/38 M_\oplus)^{-p}$/star, with $0.9\lesssim p \lesssim 1.2$.
There are substantially more FFPs than known bound planets, implying that the
bound planet power-law index $\gamma=0.6$ is likely shaped by the ejection
process at least as much as by formation. The mass density per decade of FFPs
in the Solar neighborhood is of the same order as that of 'Oumuamua-like
objects. In particular, if we assume that 'Oumuamua is part of the same process
that ejected the FFPs to very wide or unbound orbits, the power-law index is
$p=0.92\pm 0.06$. If the Solar System's endowment of Neptune-mass objects in
Neptune-like orbits is typical, which is consistent with the results of Poleski
et al. (2021), then these could account for a substantial fraction of the FFPs
in the Neptune-mass range.


title: Context-Sensitive Temporal Feature Learning for Gait Recognition

abstract:
  Although gait recognition has drawn increasing research attention recently,
it remains challenging to learn discriminative temporal representation, since
the silhouette differences are quite subtle in spatial domain. Inspired by the
observation that human can distinguish gaits of different subjects by
adaptively focusing on temporal clips with different time scales, we propose a
context-sensitive temporal feature learning (CSTL) network for gait
recognition. CSTL produces temporal features in three scales, and adaptively
aggregates them according to the contextual information from local and global
perspectives. Specifically, CSTL contains an adaptive temporal aggregation
module that subsequently performs local relation modeling and global relation
modeling to fuse the multi-scale features. Besides, in order to remedy the
spatial feature corruption caused by temporal operations, CSTL incorporates a
salient spatial feature learning (SSFL) module to select groups of
discriminative spatial features. Particularly, we utilize transformers to
implement the global relation modeling and the SSFL module. To the best of our
knowledge, this is the first work that adopts transformer in gait recognition.
Extensive experiments conducted on three datasets demonstrate the
state-of-the-art performance. Concretely, we achieve rank-1 accuracies of
98.7%, 96.2% and 88.7% under normal-walking, bag-carrying and coat-wearing
conditions on CASIA-B, 97.5% on OU-MVLP and 50.6% on GREW.


title:
Asymptotically Efficient Estimation of Ergodic Rough Fractional
  Ornstein-Uhlenbeck Process under Continuous Observations

abstract:
  We consider the problem of asymptotically efficient estimation of drift
parameters of the ergodic fractional Ornstein-Uhlenbeck process under
continuous observations when the Hurst parameter $H<1/2$ and the mean of its
stationary distribution is not equal to zero. In this paper, we derive
asymptotically efficient rates and variances of estimators of drift parameters
and prove an asymptotic efficiency of a maximum likelihood estimator of drift
parameters.


title: mulEEG: A Multi-View Representation Learning on EEG Signals

abstract:
  Modeling effective representations using multiple views that positively
influence each other is challenging, and the existing methods perform poorly on
Electroencephalogram (EEG) signals for sleep-staging tasks. In this paper, we
propose a novel multi-view self-supervised method (mulEEG) for unsupervised EEG
representation learning. Our method attempts to effectively utilize the
complementary information available in multiple views to learn better
representations. We introduce diverse loss that further encourages
complementary information across multiple views. Our method with no access to
labels beats the supervised training while outperforming multi-view baseline
methods on transfer learning experiments carried out on sleep-staging tasks. We
posit that our method was able to learn better representations by using
complementary multi-views.


title:
Dust SEDs in Milky Way-like galaxies in the IllustrisTNG simulations
  based on the evolution of grain size distribution

abstract:
  To understand how the evolution of grain size distribution in galaxies
affects observed dust properties, we apply a post-processing dust evolution
model to galaxy merger trees from the IllustrisTNG cosmological hydrodynamical
simulation. Our dust model includes stellar dust production, sputtering in hot
gas, dust growth by accretion and coagulation in the dense interstellar medium
(ISM), and shattering in the diffuse ISM. We decompose the grain size
distribution into different dust species depending on the elemental abundances
and the dense ISM fraction given by the simulation. In our previous work, we
focused on Milky Way (MW) analogs and reproduced the observed MW extinction
curve. In this study, we compute dust spectral energy distributions (SEDs) for
the MW analogues. Our simulated SEDs broadly reproduce the observed MW SED
within their dispersion and so does the observational data of nearby galaxies,
although they tend to underpredict the MW SED at short wavelengths where
emission is dominated by polycyclic aromatic hydrocarbons (PAHs). We find that
metallicity and dense gas fraction are the most critical factors for the SED
shape, through their influence on coagulation and shattering.The overall
success of our models in reproducing the MW SED further justifies the dust
evolution processes included in the model and predicts the dispersion in the
SEDs caused by the variety in the assembly history. We also show that the most
significant increase in the dust SED occurs between redshifts $z\sim 3$ and 2
in the progenitors of the simulated MW-like galaxies.


title: A direct construction of a full family of Whitham solitary waves

abstract:
  Starting with the periodic waves earlier constructed for the gravity Whitham
equation, we parameterise the solution curves through relative wave height, and
use a limiting argument to obtain a full family of solitary waves. The
resulting branch starts from the zero solution, traverses unique points in the
wave speed-wave height space, and reaches a singular highest wave at
$\varphi(0) = \frac{\mu}{2}$. The construction is based on uniform estimates
improved from earlier work on periodic waves for the same equation, together
with limiting arguments and a Galilean transform to exclude vanishing waves and
waves levelling off at negative surface depth. In fact, the periodic waves can
be proved to converge locally uniformly to a wave with negative tails, which is
then transformed to the desired branch of solutions. The paper also contains
some proof concerning uniqueness and continuity for signed solutions (improved
touching lemma).


title: Three-species drift-diffusion models for memristors

abstract:
  A system of drift-diffusion equations for the electron, hole, and oxygene
vacancy densities in a semiconductor, coupled to the Poisson equation for the
electric potential, is analyzed in a bounded domain with mixed
Dirichlet-Neumann boundary conditions. This system describes the dynamics of
charge carriers in a memristor device. Memristors can be seen as nonlinear
resistors with memory, mimicking the conductance response of biological
synapses. In the fast-relaxation limit, the system reduces to a drift-diffusion
system for the oxygene vacancy density and electric potential, which is often
used in neuromorphic applications. The following results are proved: the global
existence of weak solutions to the full system in any space dimension; the
uniform-in-time boundedness of the solutions to the full system and the
fast-relaxation limit in two space dimensions; the global existence and
weak-strong uniqueness analysis of the reduced system. Numerical experiments in
one space dimension illustrate the behavior of the solutions and reproduce
hysteresis effects in the current-voltage characteristics.


title: PALBERT: Teaching ALBERT to Ponder

abstract:
  Currently, pre-trained models can be considered the default choice for a wide
range of NLP tasks. Despite their SoTA results, there is practical evidence
that these models may require a different number of computing layers for
different input sequences, since evaluating all layers leads to overconfidence
on wrong predictions (namely overthinking). This problem can potentially be
solved by implementing adaptive computation time approaches, which were first
designed to improve inference speed. Recently proposed PonderNet may be a
promising solution for performing an early exit by treating the exit layers
index as a latent variable. However, the originally proposed exit criterion,
relying on sampling from trained posterior distribution on the probability of
exiting from i-th layer, introduces major variance in model outputs,
significantly reducing the resulting models performance. In this paper, we
propose Ponder ALBERT (PALBERT): an improvement to PonderNet with a novel
deterministic Q-exit criterion and a revisited model architecture. We compared
PALBERT with recent methods for performing an early exit. We observed that the
proposed changes can be considered significant improvements on the original
PonderNet architecture and outperform PABEE on a wide range of GLUE tasks. In
addition, we also performed an in-depth ablation study of the proposed
architecture to further understand Lambda layers and their performance.


title:
Recursive Frequency Selective Reconstruction of Non-Regularly Sampled
  Video Data

abstract:
  High resolution images can be acquired using a non-regular sampling sensor
which consists of an underlying low resolution sensor that is covered with a
non-regular sampling mask. The reconstructed high resolution image is then
obtained during post-processing. Recently, it has been shown that the temporal
correlation between neighboring frames can be exploited in order to enhance the
reconstruction quality of non-regularly sampled video data. In this paper, a
new recursive multi-frame reconstruction approach is proposed in order to
further increase the reconstruction quality. By using a new reference order,
previously reconstructed frames can be used for the subsequent motion
estimation and a new weighting function allows for the incorporation of
multiple pixels projected onto the same position. With the new recursive
multi-frame approach, a visually noticeable average gain in PSNR of up to 1.13
dB with respect to a state-of-the-art single-frame reconstruction approach can
be achieved. Compared to the existing multi-frame approach, a gain of 0.31 dB
is possible. SSIM results show the same behavior as PSNR results. Additionally,
the pre-reconstruction step of the existing multi-frame approach can be avoided
and the new algorithm is, in general, capable of real-time processing.


title:
Finiteness properties of some groups of piecewise projective
  homeomorphisms

abstract:
  The Lodha-Moore group $G$ first arose as a finitely presented counterexample
to von Neumann's conjecture. The group $G$ acts on the unit interval via
piecewise projective homemorphisms. A result of Lodha shows that $G$ in fact
has type $F_{\infty}$.
  Here we will describe $G$ as a group that is "locally determined" by an
inverse semigroup $S_{2}$, in the sense of the author's joint work with Hughes.
The semigroup $S_{2}$ is generated by three linear fractional transformations
$A$, $B$, and $C_{2}$, where $A$ and $B$ are elliptical transformations of the
hyperbolic plane and $C_{2}$ is a hyperbolic translation. Following a general
procedure delineated by Farley and Hughes, we offer a new proof that $G$ has
type $F_{\infty}$. Our proof simultaneously shows that various groups acting on
the line, the circle, and the Cantor set have type $F_{\infty}$. We also prove
analogous results for the groups that are locally determined by an inverse
semigroup $S_{3}$, which shares the generators $A$ and $B$ with $S_{2}$, but
replaces $C_{2}$ with a different hyperbolic translation $C_{3}$.


title: Non-universality of free fall in quantum theory

abstract:
  We show by respecting the Einstein equivalence principle and general
covariance in quantum theory that wave-function spreading rules out
universality of free fall, and vice versa. Assuming the former is more
fundamental than the latter, we gain a quantitative estimate of the free-fall
non-universality, which turns out to be empirically testable in atom
interferometry.


title:
Global dynamics of the $2d$ NLS with white noise potential and generic
  polynomial nonlinearity

abstract:
  Using an approach introduced by Hairer-Labb\' e we construct a unique global
dynamics for the NLS on $\T^2$ with a white noise potential and an arbitrary
polynomial nonlinearity. We build the solutions as a limit of classical
solutions (up to a phase shift) of the same equation with smoothed potentials.
This is an improvement on previous contributions of us and Debussche-Weber
dealing with quartic nonlinearities and cubic nonlinearities respectively.


title: Single-shot Embedding Dimension Search in Recommender System

abstract:
  As a crucial component of most modern deep recommender systems, feature
embedding maps high-dimensional sparse user/item features into low-dimensional
dense embeddings. However, these embeddings are usually assigned a unified
dimension, which suffers from the following issues: (1) high memory usage and
computation cost. (2) sub-optimal performance due to inferior dimension
assignments. In order to alleviate the above issues, some works focus on
automated embedding dimension search by formulating it as hyper-parameter
optimization or embedding pruning problems. However, they either require
well-designed search space for hyperparameters or need time-consuming
optimization procedures. In this paper, we propose a Single-Shot Embedding
Dimension Search method, called SSEDS, which can efficiently assign dimensions
for each feature field via a single-shot embedding pruning operation while
maintaining the recommendation accuracy of the model. Specifically, it
introduces a criterion for identifying the importance of each embedding
dimension for each feature field. As a result, SSEDS could automatically obtain
mixed-dimensional embeddings by explicitly reducing redundant embedding
dimensions based on the corresponding dimension importance ranking and the
predefined parameter budget. Furthermore, the proposed SSEDS is model-agnostic,
meaning that it could be integrated into different base recommendation models.
The extensive offline experiments are conducted on two widely used public
datasets for CTR prediction tasks, and the results demonstrate that SSEDS can
still achieve strong recommendation performance even if it has reduced 90\%
parameters. Moreover, SSEDS has also been deployed on the WeChat Subscription
platform for practical recommendation services. The 7-day online A/B test
results show that SSEDS can significantly improve the performance of the online
recommendation model.


title: From Berry-Esseen to super-exponential

abstract:
  For any integer $m<n$, where $m$ can depend on $n$, we study the rate of
convergence of $\frac{1}{\sqrt{m}}\mathrm{Tr} \mathbf{U}^m$ to its limiting
Gaussian as $n\to\infty$ for orthogonal, unitary and symplectic Haar
distributed random matrices $\mathbf{U}$ of size $n$. In the unitary case, we
prove that the total variation distance is less than $\Gamma(\lfloor n/m
\rfloor+2)^{-1} m^{- \lfloor n/m\rfloor} \lfloor n/m \rfloor^{1/4}\sqrt{\log
n}$ times a constant. This result interpolates between the super-exponential
bound obtained for fixed $m$ and the $1/n$ bound coming from the Berry-Esseen
theorem applicable when $m\ge n$ by a result of Rains. We obtain analogous
results for the orthogonal and symplectic groups. In these cases, our total
variation upper bound takes the form $\Gamma(2\lfloor
n/m\rfloor+1)^{-1/2}m^{-\lfloor n/m\rfloor +1}(\log n)^{1/4}$ times a constant
and the result holds provided $n \geq 2m$. For $m=1$, we obtain complementary
lower bounds and precise asymptotics for the $L^2$-distances as $n\to\infty$,
which show how sharp our results are.


title: Optimal convergence order for multi-scale stochastic Burgers equation

abstract:
  In this paper, we study the strong and weak convergence rates for multi-scale
one-dimensional stochastic Burgers equation. Based on the techniques of
Galerkin approximation, Kolmogorov equation and Poisson equation, we obtain the
slow component strongly and weakly converges to the solution of the
corresponding averaged equation with optimal orders 1/2 and 1 respectively. The
highly nonlinear term in system brings us huge difficulties, we develop new
technique to overcome these difficulties. To the best of our knowledge, this
work seems to be the first result in which the optimal convergence orders in
strong and weak sense for multi-scale stochastic partial differential equations
with highly nonlinear term.


title: First detection of AlF line emission towards M-type AGB stars

abstract:
  The nucleosynthesis production of fluorine (F) is still a matter of debate.
Asymptotic giant branch (AGB) stars are one of the main candidates for F
production. However, their contribution to the total F budget is not fully
known due to the lack of observations. In this paper, we report the detection
of AlF line emission, one of the two main carriers of F in the gas-phase in the
outflow of evolved stars, towards five nearby oxygen-rich AGB stars, $o$ Ceti,
R Leo, IK Tau, R Dor, and W Hya. From spatially resolved observations, we
estimated the AlF emitting region with a radius $\sim11R_{\star}$ for $o$ Ceti
and $\sim9R_{\star}$ for R Leo. From population diagram analysis, we report the
AlF column densities of $\sim 5.8\times10^{15}$ cm$^{-2}$ and $\sim
3\times10^{15}$ cm$^{-2}$ for $o$ Ceti and R Leo, respectively, within these
regions. For $o$ Ceti, we used the C$^{18}$O ($v=0$, $J=3-2$) observations to
estimate the H$_2$ column density of the emitting region. We found a fractional
abundance of $f_{\rm AlF/H_2}\sim(2.5\pm1.7)\times10^{-8}$. This gives a lower
limit on the F budget in $o$ Ceti and is compatible with the solar F budget
$f_{\rm F/H_2}=(5\pm2)\times10^{-8}$. For R Leo, a fractional abundance $f_{\rm
AlF/H_2}=(1.2\pm0.5)\times10^{-8}$ is estimated. For other sources, we cannot
precisely determine the emitting region based on the available data. Assuming
an emitting region with a radius of $\sim 11R_{\star}$ and the rotational
temperatures derived for $o$ Ceti and R Leo, we crudely approximated the AlF
column density to be $\sim(1.2-1.5)\times10^{15}$ cm$^{-2}$ in W Hya,
$\sim(2.5-3.0)\times10^{14}$ cm$^{-2}$ in R Dor, and
$\sim(0.6-1.0)\times10^{16}$ cm$^{-2}$ in IK Tau. These result in fractional
abundances within a range of $f_{\rm AlF/H_2}\sim(0.1-4)\times10^{-8}$ in W
Hya, R Dor, and IK Tau.


title:
Fast estimation of Kendall's Tau and conditional Kendall's Tau matrices
  under structural assumptions

abstract:
  Kendall's tau and conditional Kendall's tau matrices are multivariate
(conditional) dependence measures between the components of a random vector.
For large dimensions, available estimators are computationally expensive and
can be improved by averaging. Under structural assumptions on the underlying
Kendall's tau and conditional Kendall's tau matrices, we introduce new
estimators that have a significantly reduced computational cost while keeping a
similar error level. In the unconditional setting we assume that, up to
reordering, the underlying Kendall's tau matrix is block-structured with
constant values in each of the off-diagonal blocks. Consequences on the
underlying correlation matrix are then discussed. The estimators take advantage
of this block structure by averaging over (part of) the pairwise estimates in
each of the off-diagonal blocks. Derived explicit variance expressions show
their improved efficiency. In the conditional setting, the conditional
Kendall's tau matrix is assumed to have a constant block structure,
independently of the conditioning variable. Conditional Kendall's tau matrix
estimators are constructed similarly as in the unconditional case by averaging
over (part of) the pairwise conditional Kendall's tau estimators. We establish
their joint asymptotic normality, and show that the asymptotic variance is
reduced compared to the naive estimators. Then, we perform a simulation study
which displays the improved performance of both the unconditional and
conditional estimators. Finally, the estimators are used for estimating the
value at risk of a large stock portfolio; backtesting illustrates the obtained
improvements compared to the previous estimators.


title: Entailment Graph Learning with Textual Entailment and Soft Transitivity

abstract:
  Typed entailment graphs try to learn the entailment relations between
predicates from text and model them as edges between predicate nodes. The
construction of entailment graphs usually suffers from severe sparsity and
unreliability of distributional similarity. We propose a two-stage method,
Entailment Graph with Textual Entailment and Transitivity (EGT2). EGT2 learns
local entailment relations by recognizing possible textual entailment between
template sentences formed by typed CCG-parsed predicates. Based on the
generated local graph, EGT2 then uses three novel soft transitivity constraints
to consider the logical transitivity in entailment structures. Experiments on
benchmark datasets show that EGT2 can well model the transitivity in entailment
graph to alleviate the sparsity issue, and lead to significant improvement over
current state-of-the-art methods.


title:
Calibration of a bumble bee foraging model using Approximate Bayesian
  Computation

abstract:
  1. Challenging calibration of complex models can be approached by using prior
knowledge on the parameters. However, the natural choice of Bayesian inference
can be computationally heavy when relying on Markov Chain Monte Carlo (MCMC)
sampling. When the likelihood of the data is intractable, alternative Bayesian
methods have been proposed. Approximate Bayesian Computation (ABC) only
requires sampling from the data generative model, but may be problematic when
the dimension of the data is high.
  2. We studied alternative strategies to handle high dimensional data in ABC
applied to the calibration of a spatially explicit foraging model for
\textit{Bombus terrestris}. The first step consisted in building a set of
summary statistics carrying enough biological meaning, i.e. as much as the
original data, and then applying ABC on this set. Two ABC strategies, the use
of regression adjustment leading to the production of ABC posterior samples,
and the use of machine learning approaches to approximate ABC posterior
quantiles, were compared with respect to coverage of model estimates and true
parameter values. The comparison was made on simulated data as well as on data
from two field studies.
  3. Results from simulated data showed that some model parameters were easier
to calibrate than others. Approaches based on random forests in general
performed better on simulated data. They also performed well on field data,
even though the posterior predictive distribution exhibited a higher variance.
Nonlinear regression adjustment performed better than linear ones, and the
classical ABC rejection algorithm performed badly.
  4. ABC is an interesting and appealing approach for the calibration of
complex models in biology, such as spatially explicit foraging models. However,
while ABC methods are easy to implement, they require considerable tuning.


title: Study of the band-gap energy of radiation-damaged silicon

abstract:
  The transmission of silicon crystals irradiated by 24 GeV/c protons and
reactor neutrons has been measured for photon energies, $E{\gamma}$, between
0.95 and 1.3 eV. From the transmission data the absorption coefficient
${\alpha}$ is calculated, and from ${\alpha}(E_{\gamma})$ the fluence
dependence of the band-gap energy, $E_{gap}$, and the energy of transverse
optical phonons, $E_{ph}$, determined. It is found that within the experimental
uncertainties of about 1 meV neither $E_{gap}$ nor $E_{ph}$ depend on fluence
up to the maximum fluence of $1 \times 10^{17}$ cm$^{-2}$ of the measurements.
The value of $E_{gap}$ agrees within about 1 meV with the generally accepted
value, if an exciton-binding energy of 15 meV is assumed. A similar agreement
is found for $E_{ph}$. For the extraction of $E_{gap}$ and $E_{ph}$ the second
derivative of $\sqrt{{\alpha}(E{\gamma} )}$ smoothed with a Gaussian kernel has
been used.


title:
Persistent Memory Objects: Fast and Easy Crash Consistency for
  Persistent Memory

abstract:
  DIMM-compatible persistent memory unites memory and storage. Prior works
utilize persistent memory either by combining the filesystem with direct access
on memory mapped files or by managing it as a collection of objects while
abolishing the POSIX abstraction. In contrast, we propose retaining the POSIX
abstraction and extending it to provide support for persistent memory, using
Persistent Memory Objects (PMOs). In this work, we design and implement PMOs, a
crash-consistent abstraction for managing persistent memory. We introduce
psync, a single system call, that a programmer can use to specify crash
consistency points in their code, without needing to orchestrate durability
explicitly. When rendering data crash consistent, our design incurs a overhead
of $\approx 25\%$ and $\approx 21\%$ for parallel workloads and FileBench,
respectively, compared to a system without crash consistency. Compared to
NOVA-Fortis, our design provides a speedup of $\approx 1.67\times$ and $\approx
3\times$ for the two set of benchmarks, respectively.


title:
Memory Performance of AMD EPYC Rome and Intel Cascade Lake SP Server
  Processors

abstract:
  Modern processors, in particular within the server segment, integrate more
cores with each generation. This increases their complexity in general, and
that of the memory hierarchy in particular. Software executed on such
processors can suffer from performance degradation when data is distributed
disadvantageously over the available resources. To optimize data placement and
access patterns, an in-depth analysis of the processor design and its
implications for performance is necessary. This paper describes and
experimentally evaluates the memory hierarchy of AMD EPYC Rome and Intel Xeon
Cascade Lake SP server processors in detail. Their distinct microarchitectures
cause different performance patterns for memory latencies, in particular for
remote cache accesses. Our findings illustrate the complex NUMA properties and
how data placement and cache coherence states impact access latencies to local
and remote locations. This paper also compares theoretical and effective
bandwidths for accessing data at the different memory levels and main memory
bandwidth saturation at reduced core counts. The presented insight is a
foundation for modeling performance of the given microarchitectures, which
enables practical performance engineering of complex applications. Moreover,
security research on side-channel attacks can also leverage the presented
findings.


title:
Energy-stable global radial basis function methods on summation-by-parts
  form

abstract:
  Radial basis function methods are powerful tools in numerical analysis and
have demonstrated good properties in many different simulations. However, for
time-dependent partial differential equations, only a few stability results are
known. In particular, if boundary conditions are included, stability issues
frequently occur. The question we address in this paper is how provable
stability for RBF methods can be obtained. We develop and construct
energy-stable radial basis function methods using the general framework of
summation-by-parts operators often used in the Finite Difference and Finite
Element communities.


title: Robust Controllers for a Flexible Satellite Model

abstract:
  We consider a PDE-ODE model of a flexible satellite that is composed of two
identical flexible solar panels and a center rigid body. We prove that the
satellite model is exponentially stable in the sense that the energy of the
solutions decays to zero exponentially. In addition, we construct two internal
model based controllers, a passive controller and an observer based controller,
such that the linear and angular velocities of the center rigid body converge
to the given sinusoidal signals asymptotically. A numerical simulation is
presented to compare the performances of the two controllers.


title:
Enhancing Semantic Code Search with Multimodal Contrastive Learning and
  Soft Data Augmentation

abstract:
  Code search aims to retrieve the most semantically relevant code snippet for
a given natural language query. Recently, large-scale code pre-trained models
such as CodeBERT and GraphCodeBERT learn generic representations of source code
and have achieved substantial improvement on code search task. However, the
high-quality sequence-level representations of code snippets have not been
sufficiently explored. In this paper, we propose a new approach with multimodal
contrastive learning and soft data augmentation for code search. Multimodal
contrastive learning is used to pull together the representations of code-query
pairs and push apart the unpaired code snippets and queries. Moreover, data
augmentation is critical in contrastive learning for learning high-quality
representations. However, only semantic-preserving augmentations for source
code are considered in existing work. In this work, we propose to do soft data
augmentation by dynamically masking and replacing some tokens in code sequences
to generate code snippets that are similar but not necessarily
semantic-preserving as positive samples for paired queries. We conduct
extensive experiments to evaluate the effectiveness of our approach on a
large-scale dataset with six programming languages. The experimental results
show that our approach significantly outperforms the state-of-the-art methods.
We also adapt our techniques to several pre-trained models such as RoBERTa and
CodeBERT, and significantly boost their performance on the code search task.


title:
Heterogeneous Ultra-Dense Networks with Traffic Hotspots: A Unified
  Handover Analysis

abstract:
  With the ever-growing communication demands and the unceasing miniaturization
of mobile devices, the Internet of Things is expanding the amount of mobile
terminals to an enormous level. To deal with such numbers of communication
data, plenty of base stations (BSs) need to be deployed. However, denser
deployments of heterogeneous networks (HetNets) lead to more frequent
handovers, which could increase network burden and degrade the users
experience, especially in traffic hotspot areas. In this paper, we develop a
unified framework to investigate the handover performance of wireless networks
with traffic hotspots. Using the stochastic geometry, we derive the theoretical
expressions of average distances and handover metrics in HetNets, where the
correlations between users and BSs in hotspots are captured. Specifically, the
distributions of macro cells are modeled as independent Poisson point processes
(PPPs), and the two tiers of small cells outside and inside the hotspots are
modeled as PPP and Poisson cluster process (PCP) separately. A modified random
waypoint (MRWP) model is also proposed to eliminate the density wave phenomenon
in traditional models and to increase the accuracy of handover decision. By
combining the PCP and MRWP model, the distributions of distances from a typical
terminal to the BSs in different tiers are derived. Afterwards, we derive the
expressions of average distances from a typical terminal to different BSs, and
reveal that the handover rate, handover failure rate, and ping-pong rate are
deduced as the functions of BS density, scattering variance of clustered small
cell, user velocity, and threshold of triggered time. Simulation results verify
the accuracy of the proposed analytical model and closed-form theoretical
expressions.


title: Quantum algorithm for structured problems

abstract:
  Quantum algorithm can achieve exponential speedup over its classical
counterparts in solving some problems by considering their structures, e.g. the
Abelian hidden subgroup problems (HSP). In this work, we apply a quantum
algorithm of finding the ground state of a Hamiltonian via a multi-step quantum
computation process (arxiv: 1912.06959) for solving some structured problems,
where the solution to a problem is encoded in the ground state of the
corresponding problem Hamiltonian. By decomposing the problem based on its
structure, we construct a sequence of intermediate Hamiltonians to approach the
problem Hamiltonian, and evolve the system through the ground states of the
intermediate Hamiltonians via quantum resonant transitions sequentially,
finally obtain the ground state of the problem Hamiltonian. We find that this
algorithm achieves exponential speedup over classical algorithms in solving
some structured problems, including problems that can be reduced to both the
Abelian and the non-Abelian HSP. The HSP and the unstructured search problem
can be casted in the same framework in this algorithm.


title:
Deep Learning for Real Time Satellite Pose Estimation on Low Power Edge
  TPU

abstract:
  Pose estimation of an uncooperative space resident object is a key asset
towards autonomy in close proximity operations. In this context monocular
cameras are a valuable solution because of their low system requirements.
However, the associated image processing algorithms are either too
computationally expensive for real time on-board implementation, or not enough
accurate. In this paper we propose a pose estimation software exploiting neural
network architectures which can be scaled to different accuracy-latency
trade-offs. We designed our pipeline to be compatible with Edge Tensor
Processing Units to show how low power machine learning accelerators could
enable Artificial Intelligence exploitation in space. The neural networks were
tested both on the benchmark Spacecraft Pose Estimation Dataset, and on the
purposely developed Cosmo Photorealistic Dataset, which depicts a COSMO-SkyMed
satellite in a variety of random poses and steerable solar panels orientations.
The lightest version of our architecture achieves state-of-the-art accuracy on
both datasets but at a fraction of networks complexity, running at 7.7 frames
per second on a Coral Dev Board Mini consuming just 2.2W.


title:
A Multi-Transformation Evolutionary Framework for Influence Maximization
  in Social Networks

abstract:
  Influence maximization is a key issue for mining the deep information of
social networks, which aims to select a seed set from the network to maximize
the number of influenced nodes. To evaluate the influence spread of a seed set
efficiently, existing works have proposed some proxy models (transformations)
with lower computational costs to replace the expensive Monte Carlo simulation
process. These alternate transformations based on network prior knowledge
induce different search behaviors with similar characteristics from various
perspectives. For a specific case, it is difficult for users to determine a
suitable transformation a priori. Keeping those in mind, we propose a
multi-transformation evolutionary framework for influence maximization (MTEFIM)
to exploit the potential similarities and unique advantages of alternate
transformations and avoid users to determine the most suitable one manually. In
MTEFIM, multiple transformations are optimized simultaneously as multiple
tasks. Each transformation is assigned an evolutionary solver. Three major
components of MTEFIM are conducted: 1) estimating the potential relationship
across transformations based on the degree of overlap across individuals (seed
sets) of different populations, 2) transferring individuals across populations
adaptively according to the inter-transformation relationship, 3) selecting the
final output seed set containing all the proxy model knowledge. The
effectiveness of MTEFIM is validated on four real-world social networks.
Experimental results show that MTEFIM can efficiently utilize the potentially
transferable knowledge across multiple transformations to achieve highly
competitive performance compared to several popular IM-specific methods. The
implementation of MTEFIM can be accessed at
https://github.com/xiaofangxd/MTEFIM.


title: Around Van den Bergh's double brackets for different bimodule structures

abstract:
  A double Poisson bracket, in the sense of M. Van den Bergh, is an operation
on an associative algebra $A$ which induces a Poisson bracket on each
representation space $\operatorname{Rep}(A,n)$ in an explicit way. In this
note, we study the impact of changing the Leibniz rules underlying a double
bracket. This change amounts to make a suitable choice of $A$-bimodule
structure on $A\otimes A$. In the most important cases, we describe how the
choice of $A$-bimodule structure fixes an analogue to Jacobi identity, and we
obtain induced Poisson brackets on representation spaces. The present theory
also encodes a formalisation of the widespread tensor notation used to write
Poisson brackets of matrices in mathematical physics.


title: On the Impact of Social Media Recommendations on Opinion Consensus

abstract:
  We consider a discrete opinion formation problem in a setting where agents
are influenced by both information diffused by their social relations and from
recommendations received directly from the social media manager. We study how
the "strength" of the influence of the social media and the homophily ratio
affect the probability of the agents of reaching a consensus and how these
factors can determine the type of consensus reached. In a simple 2-symmetric
block model we prove that agents converge either to a consensus or to a
persistent disagreement. In particular, we show that when the homophily ratio
is large, the social media has a very low capacity of determining the outcome
of the opinion dynamics. On the other hand, when the homophily ratio is low,
the social media influence can have an important role on the dynamics, either
by making harder to reach a consensus or inducing it on extreme opinions.
Finally, in order to extend our analysis to more general and realistic settings
we give some experimental evidences that our results still hold on general
networks.


title: Approximating Nash Equilibrium for Production Control with Sticky Price

abstract:
  We study a mean field game problem arising from the production control for
multiple firms with price stickiness in the commodity market. The price
dynamics for each firm is described as a (controlled) jump-diffusion process
with mean-field interaction. Each firm aims to maximize her expectation of
cumulative net profit coupled with each other through price processes. By
solving the limiting control problem and a fixed-point problem, we construct an
explicit approximating Nash equilibrium when the number of firms grows large.


title: Sequence-Based Extractive Summarisation for Scientific Articles

abstract:
  This paper presents the results of research on supervised extractive text
summarisation for scientific articles. We show that a simple sequential tagging
model based only on the text within a document achieves high results against a
simple classification model. Improvements can be achieved through additional
sentence-level features, though these were minimal. Through further analysis,
we show the potential of the sequential model relying on the structure of the
document depending on the academic discipline which the document is from.


title:
Fast inverse elastic scattering of multiple particles in three
  dimensions

abstract:
  Many applications require recovering the geometry information of multiple
elastic particles based on the scattering information. In this paper, we
consider the inverse time-harmonic elastic scattering of multiple rigid
particles in three dimensions. We measure the far field information and apply
the time reversal method to recover the unknown elastic particles. Two regimes
are considered depending on the size and distance among particles. First, an
asymptotic analysis for the imaging of small and distant particles is given
based on the scattering property of a single particle, which can be used for
selective focusing. Second, when particles are not small but well-separated, a
fast algorithm, based on the combination of multiple scattering theory and fast
multipole method, is proposed to efficiently simulate the forward multiple
scattering problem and applied in the inverse elastic scattering. Numerical
experiments demonstrate the proposed method can determine the locations and
shapes of multiple particles instantly.


title:
A review of exact results for fluctuation formulas in random matrix
  theory

abstract:
  Covariances and variances of linear statistics of a point process can be
written as integrals over the truncated two-point correlation function. When
the point process consists of the eigenvalues of a random matrix ensemble,
there are often large $N$ universal forms for this correlation after smoothing,
which results in particularly simple limiting formulas for the fluctuation of
the linear statistics. We review these limiting formulas, derived in the
simplest cases as corollaries of explicit knowledge of the truncated two-point
correlation. One of the large $N$ limits is to scale the eigenvalues so that
limiting support is compact, and the linear statistics vary on the scale of the
support. This is a global scaling. The other, where a thermodynamic limit is
first taken so that the spacing between eigenvalues is of order unity, and then
a scale imposed on the test functions so they are slowly varying, is the bulk
scaling. The latter was already identified as a probe of random matrix
characteristics for quantum spectra in the pioneering work of Dyson and Mehta.


title:
Federated Learning from Only Unlabeled Data with
  Class-Conditional-Sharing Clients

abstract:
  Supervised federated learning (FL) enables multiple clients to share the
trained model without sharing their labeled data. However, potential clients
might even be reluctant to label their own data, which could limit the
applicability of FL in practice. In this paper, we show the possibility of
unsupervised FL whose model is still a classifier for predicting class labels,
if the class-prior probabilities are shifted while the class-conditional
distributions are shared among the unlabeled data owned by the clients. We
propose federation of unsupervised learning (FedUL), where the unlabeled data
are transformed into surrogate labeled data for each of the clients, a modified
model is trained by supervised FL, and the wanted model is recovered from the
modified model. FedUL is a very general solution to unsupervised FL: it is
compatible with many supervised FL methods, and the recovery of the wanted
model can be theoretically guaranteed as if the data have been labeled.
Experiments on benchmark and real-world datasets demonstrate the effectiveness
of FedUL. Code is available at https://github.com/lunanbit/FedUL.


title:
MBI-Net: A Non-Intrusive Multi-Branched Speech Intelligibility
  Prediction Model for Hearing Aids

abstract:
  Improving the user's hearing ability to understand speech in noisy
environments is critical to the development of hearing aid (HA) devices. For
this, it is important to derive a metric that can fairly predict speech
intelligibility for HA users. A straightforward approach is to conduct a
subjective listening test and use the test results as an evaluation metric.
However, conducting large-scale listening tests is time-consuming and
expensive. Therefore, several evaluation metrics were derived as surrogates for
subjective listening test results. In this study, we propose a multi-branched
speech intelligibility prediction model (MBI-Net), for predicting the
subjective intelligibility scores of HA users. MBI-Net consists of two branches
of models, with each branch consisting of a hearing loss model, a cross-domain
feature extraction module, and a speech intelligibility prediction model, to
process speech signals from one channel. The outputs of the two branches are
fused through a linear layer to obtain predicted speech intelligibility scores.
Experimental results confirm the effectiveness of MBI-Net, which produces
higher prediction scores than the baseline system in Track 1 and Track 2 on the
Clarity Prediction Challenge 2022 dataset.


title: Music-robust Automatic Lyrics Transcription of Polyphonic Music

abstract:
  Lyrics transcription of polyphonic music is challenging because singing
vocals are corrupted by the background music. To improve the robustness of
lyrics transcription to the background music, we propose a strategy of
combining the features that emphasize the singing vocals, i.e. music-removed
features that represent singing vocal extracted features, and the features that
capture the singing vocals as well as the background music, i.e. music-present
features. We show that these two sets of features complement each other, and
their combination performs better than when they are used alone, thus improving
the robustness of the acoustic model to the background music. Furthermore,
language model interpolation between a general-purpose language model and an
in-domain lyrics-specific language model provides further improvement in
transcription results. Our experiments show that our proposed strategy
outperforms the existing lyrics transcription systems for polyphonic music.
Moreover, we find that our proposed music-robust features specially improve the
lyrics transcription performance in metal genre of songs, where the background
music is loud and dominant.


title:
Genre-conditioned Acoustic Models for Automatic Lyrics Transcription of
  Polyphonic Music

abstract:
  Lyrics transcription of polyphonic music is challenging not only because the
singing vocals are corrupted by the background music, but also because the
background music and the singing style vary across music genres, such as pop,
metal, and hip hop, which affects lyrics intelligibility of the song in
different ways. In this work, we propose to transcribe the lyrics of polyphonic
music using a novel genre-conditioned network. The proposed network adopts
pre-trained model parameters, and incorporates the genre adapters between
layers to capture different genre peculiarities for lyrics-genre pairs, thereby
only requiring lightweight genre-specific parameters for training. Our
experiments show that the proposed genre-conditioned network outperforms the
existing lyrics transcription systems.


title: On extremal properties of perfect 2-colorings

abstract:
  A coloring of vertices of a graph is called perfect if, for every vertex, the
collection of colors of its neighbors depends only on its own color. The
correspondent color partition of vertices is called equitable. We note that a
number of bounds (Hoffman bound, Cheeger bound, Bierbrauer--Friedman bound and
other) is only reached on perfect $2$-colorings. We show that the Expander
Mixing Lemma is another example of an inequality that generates a perfect
$2$-coloring. We prove a new upper bound for the size of $S\subset V(G)$ with
the fixed average internal degree for an amply regular graph $G$. This bound is
reached on the set $S$ if and only if $\{S, V(G)\setminus S\}$ is an equitable
partition.


title: Dark radiation as a probe for phase transition in the early universe

abstract:
  The cosmological constant is not necessarily small in the early universe. If
a scalar field obtains a vacuum expectation value after a phase transition
(PT), a possibly large cosmological constant could present before PT. The early
cosmological constant (ECC) and the PT process may be detectable from dark
radiation (DR) today, such as in the cosmic axion background. We show that for
a broad class of DR models, the DR density and spectrum are significantly
modified by the presence of an ECC. From the density and the spectrum of the DR
today, we can deduce the temperature and the strength of the PT.


title: MTI-Net: A Multi-Target Speech Intelligibility Prediction Model

abstract:
  Recently, deep learning (DL)-based non-intrusive speech assessment models
have attracted great attention. Many studies report that these DL-based models
yield satisfactory assessment performance and good flexibility, but their
performance in unseen environments remains a challenge. Furthermore, compared
to quality scores, fewer studies elaborate deep learning models to estimate
intelligibility scores. This study proposes a multi-task speech intelligibility
prediction model, called MTI-Net, for simultaneously predicting human and
machine intelligibility measures. Specifically, given a speech utterance,
MTI-Net is designed to predict subjective listening test results and word error
rate (WER) scores. We also investigate several methods that can improve the
prediction performance of MTI-Net. First, we compare different features
(including low-level features and embeddings from self-supervised learning
(SSL) models) and prediction targets of MTI-Net. Second, we explore the effect
of transfer learning and multi-tasking learning on training MTI-Net. Finally,
we examine the potential advantages of fine-tuning SSL embeddings. Experimental
results demonstrate the effectiveness of using cross-domain features,
multi-task learning, and fine-tuning SSL embeddings. Furthermore, it is
confirmed that the intelligibility and WER scores predicted by MTI-Net are
highly correlated with the ground-truth scores.


title: How to design a network architecture using availability

abstract:
  The best way to design a network is to take into account Availability values
and Capacity Planning. You already saw Availability expressed with numbers such
as 99.99%. The purpose of this document is to introduce the way to compute
Availability values using Reliability Block Diagrams.


title:
ESPRIT versus ESPIRA for reconstruction of short cosine sums and its
  application

abstract:
  In this paper we introduce two new algorithms for stable approximation with
and recovery of short cosine sums. The used signal model contains cosine terms
with arbitrary real positive frequency parameters and therefore strongly
generalizes usual Fourier sums. The proposed methods both employ a set of
equidistant signal values as input data. The ESPRIT method for cosine sums is a
Prony-like method and applies matrix pencils of Toeplitz+Hankel matrices while
the ESPIRA method is based on rational approximation of DCT data and can be
understood as a matrix pencil method for special Loewner matrices. Compared to
known numerical methods for recovery of exponential sums, the design of the
considered new algorithms directly exploits the special real structure of the
signal model and therefore usually provides real parameter estimates for noisy
input data, while the known general recovery algorithms for complex exponential
sums tend to yield complex parameters in this case.


title:
Situation Awareness for Autonomous Vehicles Using Blockchain-based
  Service Cooperation

abstract:
  Efficient Vehicle-to-Everything enabling cooperation and enhanced
decision-making for autonomous vehicles is essential for optimized and safe
traffic. Real-time decision-making based on vehicle sensor data, other traffic
data, and environmental and contextual data becomes imperative. As a part of
such Intelligent Traffic Systems, cooperation between different stakeholders
needs to be facilitated rapidly, reliably, and securely. The Internet of Things
provides the fabric to connect these stakeholders who share their data, refined
information, and provided services with each other. However, these cloud-based
systems struggle to meet the real-time requirements for smart traffic due to
long distances across networks. Here, edge computing systems bring the data and
services into the close proximity of fast-moving vehicles, reducing information
delivery latencies and improving privacy as sensitive data is processed
locally. To solve the issues of trust and latency in data sharing between these
stakeholders, we propose a decentralized framework that enables smart contracts
between traffic data producers and consumers based on blockchain. Autonomous
vehicles connect to a local edge server, share their data, or use services
based on agreements, for which the cooperating edge servers across the system
provide a platform. We set up proof-of-concept experiments with Hyperledger
Fabric and virtual cars to analyze the system throughput with secure unicast
and multicast data transmissions. Our results show that multicast transmissions
in such a scenario boost the throughput up to 2.5 times where the data packets
of different sizes can be transmitted in less than one second.


title: Super-Penrose process with charged particles near naked singularity

abstract:
  We consider the Penrose process near the naked singularity in the
Reissner-Nordstr\"{o}m metric. Particle 0 falls from infinity and decays to two
fragments at some point $r_{0}$. We show that the energy extraction due to this
process can be indefinitely large in the limit $r_{0}\rightarrow 0$. In doing
so, the value of the particle charge can remain bounded, in contrast to the
previously known examples of the Penrose process in the electric field with
unbounded energy extraction. The effect persists even in the limit of the flat
pace-time.


title:
Three-Module Modeling For End-to-End Spoken Language Understanding Using
  Pre-trained DNN-HMM-Based Acoustic-Phonetic Model

abstract:
  In spoken language understanding (SLU), what the user says is converted to
his/her intent. Recent work on end-to-end SLU has shown that accuracy can be
improved via pre-training approaches. We revisit ideas presented by Lugosch et
al. using speech pre-training and three-module modeling; however, to ease
construction of the end-to-end SLU model, we use as our phoneme module an
open-source acoustic-phonetic model from a DNN-HMM hybrid automatic speech
recognition (ASR) system instead of training one from scratch. Hence we
fine-tune on speech only for the word module, and we apply multi-target
learning (MTL) on the word and intent modules to jointly optimize SLU
performance. MTL yields a relative reduction of 40% in intent-classification
error rates (from 1.0% to 0.6%). Note that our three-module model is a
streaming method. The final outcome of the proposed three-module modeling
approach yields an intent accuracy of 99.4% on FluentSpeech, an intent error
rate reduction of 50% compared to that of Lugosch et al. Although we focus on
real-time streaming methods, we also list non-streaming methods for comparison.


title:
Structured Gradient Descent for Fast Robust Low-Rank Hankel Matrix
  Completion

abstract:
  We study the robust matrix completion problem for the low-rank Hankel matrix,
which detects the sparse corruptions caused by extreme outliers while we try to
recover the original Hankel matrix from the partial observation. In this paper,
we explore the convenient Hankel structure and propose a novel non-convex
algorithm, coined Hankel Structured Gradient Descent (HSGD), for large-scale
robust Hankel matrix completion problems. HSGD is highly computing- and
sample-efficient compared to the state-of-the-arts. The recovery guarantee with
a linear convergence rate has been established for HSGD under some mild
assumptions. The empirical advantages of HSGD are verified on both synthetic
datasets and real-world nuclear magnetic resonance signals.


title:
Rheology and structure of a suspension of deformable particles in plane
  Poiseuille flow

abstract:
  We present an experimental study of the rheology and structure of a confined
suspension of deformable particles flowing in a quasi-two-dimensional
Poiseuille flow. Thanks to a precise microfluidic viscosimetry technique
combined with measurements of concentration profiles, our study provides the
first experimental confirmation {with three-dimensional particles} of a strong
relationship between structuring effects and rheology, previously only reported
in numerical simulations of purely two-dimensional systems. In conditions where
strong structuring effects take place due to confinement, the evolution of the
effective viscosity with particle concentration (here, red blood cells) shows a
remarkable succession of ranges of rapid growth and plateaus that are
associated to qualitative transitions in the structure of the suspension.