title:
Semi-classical kinetic theory for massive spin-half fermions with
  leading-order spin effects

abstract:
  We consider the quantum kinetic-theory description for interacting massive
spin-half fermions using the Wigner function formalism. We derive general
collision kernel involving local as well as non-local collisions between
particles assuming that the spin effects can have both classical and quantum
origins. To track the effect of such different contributions we use the
semi-classical expansion method to obtain the generalized kinetic equations
with spin. Employing the ansatz for the equilibrium distribution function we
derive global equilibrium conditions, assuming the total angular momentum is
conserved in the microscopic collisions.


title: Attacker Attribution of Audio Deepfakes

abstract:
  Deepfakes are synthetically generated media often devised with malicious
intent. They have become increasingly more convincing with large training
datasets advanced neural networks. These fakes are readily being misused for
slander, misinformation and fraud. For this reason, intensive research for
developing countermeasures is also expanding. However, recent work is almost
exclusively limited to deepfake detection - predicting if audio is real or
fake. This is despite the fact that attribution (who created which fake?) is an
essential building block of a larger defense strategy, as practiced in the
field of cybersecurity for a long time. This paper considers the problem of
deepfake attacker attribution in the domain of audio. We present several
methods for creating attacker signatures using low-level acoustic descriptors
and machine learning embeddings. We show that speech signal features are
inadequate for characterizing attacker signatures. However, we also demonstrate
that embeddings from a recurrent neural network can successfully characterize
attacks from both known and unknown attackers. Our attack signature embeddings
result in distinct clusters, both for seen and unseen audio deepfakes. We show
that these embeddings can be used in downstream-tasks to high-effect, scoring
97.10% accuracy in attacker-id classification.


title:
No scalar condensations outside reflecting stars with coupling terms
  from Ginzburg-Landau models

abstract:
  We consider static scalar fields coupled to the gradient where the coupling
also appears in next-to-leading order Ginzburg-Landau models. We study
condensation behaviors of scalar fields outside regular compact reflecting
stars in the asymptotically flat background. For non-negative coupling
parameters, we prove that the reflecting star cannot support coupled static
scalar fields.


title:
Killing Two Birds with One Stone:Efficient and Robust Training of Face
  Recognition CNNs by Partial FC

abstract:
  Learning discriminative deep feature embeddings by using million-scale
in-the-wild datasets and margin-based softmax loss is the current
state-of-the-art approach for face recognition. However, the memory and
computing cost of the Fully Connected (FC) layer linearly scales up to the
number of identities in the training set. Besides, the large-scale training
data inevitably suffers from inter-class conflict and long-tailed distribution.
In this paper, we propose a sparsely updating variant of the FC layer, named
Partial FC (PFC). In each iteration, positive class centers and a random subset
of negative class centers are selected to compute the margin-based softmax
loss. All class centers are still maintained throughout the whole training
process, but only a subset is selected and updated in each iteration.
Therefore, the computing requirement, the probability of inter-class conflict,
and the frequency of passive update on tail class centers, are dramatically
reduced. Extensive experiments across different training data and backbones
(e.g. CNN and ViT) confirm the effectiveness, robustness and efficiency of the
proposed PFC. The source code is available at
\https://github.com/deepinsight/insightface/tree/master/recognition.


title: Core Risk Minimization using Salient ImageNet

abstract:
  Deep neural networks can be unreliable in the real world especially when they
heavily use spurious features for their predictions. Recently, Singla & Feizi
(2022) introduced the Salient Imagenet dataset by annotating and localizing
core and spurious features of ~52k samples from 232 classes of Imagenet. While
this dataset is useful for evaluating the reliance of pretrained models on
spurious features, its small size limits its usefulness for training models. In
this work, we first introduce the Salient Imagenet-1M dataset with more than 1
million soft masks localizing core and spurious features for all 1000 Imagenet
classes. Using this dataset, we first evaluate the reliance of several Imagenet
pretrained models (42 total) on spurious features and observe that: (i)
transformers are more sensitive to spurious features compared to Convnets, (ii)
zero-shot CLIP transformers are highly susceptible to spurious features. Next,
we introduce a new learning paradigm called Core Risk Minimization (CoRM) whose
objective ensures that the model predicts a class using its core features. We
evaluate different computational approaches for solving CoRM and achieve
significantly higher (+12%) core accuracy (accuracy when non-core regions
corrupted using noise) with no drop in clean accuracy compared to models
trained via Empirical Risk Minimization.


title:
Analytic theory for the tangential YORP produced by the asteroid
  regolith

abstract:
  The tangential YORP effect is a radiation pressure torque produced by
asymmetric thermal emission by structures on the asteroid surface. As such
structures, previous works considered boulders of different shapes lying on the
surface of the asteroid.
  We study the tangential YORP produced by the rough interface of the
asteroid's regolith.
  We create an approximate analytic theory of heat conduction on a slightly
non-flat sinusoidal surface. We analyze the published data on the small-scale
shape of the asteroid (162173) Ryugu and estimate its tangential YORP due to
the surface roughness.
  We derive an analytic formula that expresses the TYORP of a sinusoidal
surface in terms of its geometric and thermal properties. TYORP is maximal at
the thermal parameter of the order of unity and for the shape irregularities of
the order of the thermal wavelength. Application of this equation to Ryugu
predicts TYORP, which is 5-70 times greater than its normal YORP effect.
  The contribution of the small-scale regolith roughness to the YORP effect of
the asteroid can be comparable to the normal YORP and the tangential YORP
produced by boulders. The same theory can describe roughness on the asteroid
boulders, thus adding a new term to the previously considered TYORP created by
boulders.


title: A Dataset for Speech Emotion Recognition in Greek Theatrical Plays

abstract:
  Machine learning methodologies can be adopted in cultural applications and
propose new ways to distribute or even present the cultural content to the
public. For instance, speech analytics can be adopted to automatically generate
subtitles in theatrical plays, in order to (among other purposes) help people
with hearing loss. Apart from a typical speech-to-text transcription with
Automatic Speech Recognition (ASR), Speech Emotion Recognition (SER) can be
used to automatically predict the underlying emotional content of speech
dialogues in theatrical plays, and thus to provide a deeper understanding how
the actors utter their lines. However, real-world datasets from theatrical
plays are not available in the literature. In this work we present GreThE, the
Greek Theatrical Emotion dataset, a new publicly available data collection for
speech emotion recognition in Greek theatrical plays. The dataset contains
utterances from various actors and plays, along with respective valence and
arousal annotations. Towards this end, multiple annotators have been asked to
provide their input for each speech recording and inter-annotator agreement is
taken into account in the final ground truth generation. In addition, we
discuss the results of some indicative experiments that have been conducted
with machine and deep learning frameworks, using the dataset, along with some
widely used databases in the field of speech emotion recognition.


title:
The finite generation ideal for Daigle & Freudenberg's counterexample to
  Hilbert's fourteenth problem

abstract:
  We compute the finite generation ideal for Daigle and Freudenberg's
counterexample to Hilbert's fourteenth problem. This ideal is helps to
understand how far the ring of invariants in this counterexample is from being
finitely generated. Our calculations yield that the finite generation ideal is
the radical of an ideal generated by three invariants. We also obtain a
Gr\"obner basis for this ideal which consists of three infinite families of
invariants. We show that these three families together with an additional
invariant form a SAGBI-basis. A SAGBI-basis with three infinite families with
the same leading terms as those we constructed is already known to exist. We
use the properties of our SAGBI-basis in our computation of the finite
generation ideal.


title: Fast and stable schemes for non-linear osmosis filtering

abstract:
  We consider a non-linear variant of the transport-diffusion osmosis model for
solving a variety of imaging problems such as shadow/soft-light removal and
compact data representation. The non-linear behaviour is encoded in terms of a
general scalar function g with suitable properties, which allows to balance the
diffusion intensity on the different regions of the image while preventing
smoothing artefacts. For the proposed model, conservation properties (intensity
and non-negativity) are proved and a variational interpretation is showed for
specific choices of g. Upon suitable spatial discretisation, both an explicit
and a semi-implicit iterative scheme are considered, for which convergence
restrictions and unconditional stability are proved, respectively. To validate
the proposed modelling and the computational speed of the numerical schemes
considered, we report several results and comparisons for the problem of
shadow/light-spot removal and compact data representation, showing that
artefact-free and computationally efficient results are obtained in comparison
to standard linear and anisotropic models, and state-of-the art approaches.


title:
Determining the transport coefficients of the quark-gluon plasma using a
  new relaxation time approximation of the Boltzmann equation

abstract:
  We investigate the transport properties of a kinetic theory model that is
tuned to describe the thermodynamic properties of QCD at zero chemical
potential using a new formulation of the relaxation time approximation. In
contrast to previous approaches, the latter is constructed to preserve the
fundamental properties of the collision term of the Boltzmann equation for any
energy-dependence of the relaxation time. A novel choice of matching conditions
is implemented to ensure that the background mean-field depends only on the
temperature even when the system is out of equilibrium. We provide a consistent
analysis of how the transport coefficients of relativistic Navier-Stokes theory
vary with the energy dependence of the relaxation time. We also show that the
entropy production of this theory is consistent with the second law of
thermodynamics and verify that it is independent of the matching conditions
employed. We used this fact to calculate the matching independent combination
of transport coefficients.


title: New Proofs of Some Double Sum Rogers-Ramanujan Type Identities

abstract:
  Recently, Rosengren utilized an integral method to prove a number of
conjectural identities found by Kanade and Russell. Using this integral method,
we give new proofs to some double sum identities of Rogers-Ramanujan type.
These identities were earlier proved by approaches such as combinatorial
arguments or by using $q$-difference equations. Our proofs are based on
streamlined calculations, which relate these double sum identities to some
known Rogers-Ramanujan type identities with single sums. Moreover, we prove a
conjectural identity of Andrews and Uncu which was earlier confirmed by Chern.


title:
Flat-band ferromagnetism in a correlated topological insulator on a
  honeycomb lattice

abstract:
  We study the flat-band ferromagnetic phase of a spinfull and time-reversal
symmetric Haldane-Hubbard model on a honeycomb lattice within a bosonization
formalism for flat-band Z$_2$ topological insulators. Such a study extend our
previous one [L. S. G. Leite and R. L. Doretto, Phys. Rev. B {\bf 104}, 155129
(2021)] concerning the flat-band ferromagnetic phase of a correlated Chern
insulator described by a Haldane-Hubbard model. We consider the topological
Hubbard model at $1/4$ filling of its corresponding noninteracting limit and in
the nearly flat band limit of its lower free-electronic bands. We show that it
is possible to define boson operators associated with two distinct spin-flip
excitations, one that changes (mixed-lattice excitations) and a second one that
preserves (same-lattice excitations) the index related with the two triangular
sublattices. Within the bosonization scheme, the fermionic model is mapped into
an effective interacting boson model, whose quadratic term is considered at the
harmonic approximation in order to determine the spin-wave excitation spectrum.
For both mixed and same-lattice excitations, we find that the spin-wave
spectrum is gapped and has two branches, with an energy gap between the lower
and the upper bands at the $K$ and $K'$ points of the first Brillouin zone.
Such a behavior is distinct from the one of the corresponding correlated Chern
insulator, whose spin-wave spectrum has a Goldstone mode at the center of the
first Brillouin zone and Dirac points at $K$ and $K'$ points. We also find some
evidences that the spin-wave bands for the same-lattice excitations might be
topologically nontrivial even in the completely flat band limit.


title:
Circuit encapsulation for efficient quantum computing based on
  controlled many-body dynamics

abstract:
  Controlling the time evolution of interacting spin systems is an important
approach of implementing quantum computing. Different from the approaches by
compiling the circuits into the product of multiple elementary gates, we here
propose the quantum circuit encapsulation (QCE), where we encapsulate the
circuits into different parts, and optimize the magnetic fields to realize the
unitary transformation of each part by the time evolution. The QCE is
demonstrated to possess well-controlled error and time cost, which avoids the
error accumulations by aiming at finding the shortest path directly to the
target unitary. We test four different encapsulation ways to realize the
multi-qubit quantum Fourier transformations by controlling the time evolution
of the quantum Ising chain. The scaling behaviors of the time costs and errors
against the number of two-qubit controlled gates are demonstrated. The QCE
provides an alternative compiling scheme that translates the circuits into a
physically-executable form based on the quantum many-body dynamics, where the
key issue becomes the encapsulation way to balance between the efficiency and
flexibility.


title:
Digraphs with all induced directed cycles of the same length are not
  $\vec{\chi}$-bounded

abstract:
  For $t \ge 2$, let us call a digraph $D$ \emph{t-chordal} if all induced
directed cycles in $D$ have length equal to $t$. In a previous paper, we asked
for which $t$ it is true that $t$-chordal graphs with bounded clique number
have bounded dichromatic number. Recently, Aboulker, Bousquet, and de Verclos
answered this in the negative for $t=3$, that is, they gave a construction of
$3$-chordal digraphs with clique number at most $3$ and arbitrarily large
dichromatic number. In this paper, we extend their result, giving for each $t
\ge 3$ a construction of digraphs with clique number at most $3$ and
arbitrarily large dichromatic number, thus answering our question in the
negative. On the other hand, we show that a more restricted class, digraphs
with no induced directed cycle of length less than $t$, and no induced directed
$t$-vertex path, have bounded dichromatic number if their clique number is
bounded. We also show the following complexity result: for fixed $t \ge 2$, the
problem of determining whether a digraph is $t$-chordal is coNP-complete.


title:
Subspace-based Representation and Learning for Phonotactic Spoken
  Language Recognition

abstract:
  Phonotactic constraints can be employed to distinguish languages by
representing a speech utterance as a multinomial distribution or phone events.
In the present study, we propose a new learning mechanism based on
subspace-based representation, which can extract concealed phonotactic
structures from utterances, for language verification and dialect/accent
identification. The framework mainly involves two successive parts. The first
part involves subspace construction. Specifically, it decodes each utterance
into a sequence of vectors filled with phone-posteriors and transforms the
vector sequence into a linear orthogonal subspace based on low-rank matrix
factorization or dynamic linear modeling. The second part involves subspace
learning based on kernel machines, such as support vector machines and the
newly developed subspace-based neural networks (SNNs). The input layer of SNNs
is specifically designed for the sample represented by subspaces. The topology
ensures that the same output can be derived from identical subspaces by
modifying the conventional feed-forward pass to fit the mathematical definition
of subspace similarity. Evaluated on the "General LR" test of NIST LRE 2007,
the proposed method achieved up to 52%, 46%, 56%, and 27% relative reductions
in equal error rates over the sequence-based PPR-LM, PPR-VSM, and PPR-IVEC
methods and the lattice-based PPR-LM method, respectively. Furthermore, on the
dialect/accent identification task of NIST LRE 2009, the SNN-based system
performed better than the aforementioned four baseline methods.


title:
Understanding Code Snippets in Code Reviews: A Preliminary Study of the
  OpenStack Community

abstract:
  Code review is a mature practice for software quality assurance in software
development with which reviewers check the code that has been committed by
developers, and verify the quality of code. During the code review discussions,
reviewers and developers might use code snippets to provide necessary
information (e.g., suggestions or explanations). However, little is known about
the intentions and impacts of code snippets in code reviews. To this end, we
conducted a preliminary study to investigate the nature of code snippets and
their purposes in code reviews. We manually collected and checked 10,790 review
comments from the Nova and Neutron projects of the OpenStack community, and
finally obtained 626 review comments that contain code snippets for further
analysis. The results show that: (1) code snippets are not prevalently used in
code reviews, and most of the code snippets are provided by reviewers. (2) We
identified two high-level purposes of code snippets provided by reviewers
(i.e., Suggestion and Citation) with six detailed purposes, among which,
Improving Code Implementation is the most common purpose. (3) For the code
snippets in code reviews with the aim of suggestion, around 68.1% was accepted
by developers. The results highlight promising research directions on using
code snippets in code reviews.


title: Disentangling speech from surroundings in a neural audio codec

abstract:
  We present a method to separate speech signals from noisy environments in the
compressed domain of a neural audio codec. We introduce a new training
procedure that allows our model to produce structured encodings of audio
waveforms given by embedding vectors, where one part of the embedding vector
represents the speech signal, and the rest represents the environment. We
achieve this by partitioning the embeddings of different input waveforms and
training the model to faithfully reconstruct audio from mixed partitions,
thereby ensuring each partition encodes a separate audio attribute. As use
cases, we demonstrate the separation of speech from background noise or from
reverberation characteristics. Our method also allows for targeted adjustments
of the audio output characteristics.


title: Cometary science with CUBES

abstract:
  The proposed CUBES spectrograph for ESO's Very Large Telescope will be an
exceptionally powerful instrument for the study of comets. The gas coma of a
comet contains a large number of emission features in the near-UV range covered
by CUBES (305-400 nm), which are diagnostic of the composition of the ices in
its nucleus and the chemistry in the coma. Production rates and relative ratios
between different species reveal how much ice is present and inform models of
the conditions in the early solar system. In particular, CUBES will lead to
advances in detection of water from very faint comets, revealing how much ice
may be hidden in the main asteroid belt, and in measuring isotopic and
molecular composition ratios in a much wider range of comets than currently
possible, provide constraints on their formation temperatures. CUBES will also
be sensitive to emissions from gaseous metals (e.g., FeI and NiI), which have
recently been identified in comets and offer an entirely new area of
investigation to understand these enigmatic objects.


title:
Learning a Structured Latent Space for Unsupervised Point Cloud
  Completion

abstract:
  Unsupervised point cloud completion aims at estimating the corresponding
complete point cloud of a partial point cloud in an unpaired manner. It is a
crucial but challenging problem since there is no paired partial-complete
supervision that can be exploited directly. In this work, we propose a novel
framework, which learns a unified and structured latent space that encoding
both partial and complete point clouds. Specifically, we map a series of
related partial point clouds into multiple complete shape and occlusion code
pairs and fuse the codes to obtain their representations in the unified latent
space. To enforce the learning of such a structured latent space, the proposed
method adopts a series of constraints including structured ranking
regularization, latent code swapping constraint, and distribution supervision
on the related partial point clouds. By establishing such a unified and
structured latent space, better partial-complete geometry consistency and shape
completion accuracy can be achieved. Extensive experiments show that our
proposed method consistently outperforms state-of-the-art unsupervised methods
on both synthetic ShapeNet and real-world KITTI, ScanNet, and Matterport3D
datasets.


title: Accretion and Outflows in Young Stars with CUBES

abstract:
  The science case on studies of accretion and outflows in low-mass ($<$1.5
$M_{\odot}$) young stellar objects (YSOs) with the new CUBES instrument is
presented. We show the need for a high-sensitivity, near-ultraviolet (NUV)
spectrograph like CUBES, with a resolving power at least four times that of
X-Shooter and combined with UVES via a fibrelink for simultaneous observations.
Simulations with the CUBES exposure time calculator and the end-to-end software
show that a significant gain in signal-to -noise can be achieved compared to
current instruments, for both the spectral continuum and emission lines,
including for relatively embedded YSOs. Our simulations also show that the
low-resolution mode of CUBES will be able to observe much fainter YSOs (V
$\sim$22 mag) in the NUV than we can today, allowing us extend studies to YSOs
with background-limited magnitudes. The performance of CUBES in terms of
sensitivity in the NUV will provide important new insights into the evolution
of circumstellar disks, by studying the accretion, jets/winds and
photo-evaporation processes, down to the low-mass brown dwarf regime. CUBES
will also open-up new science as it will be able to observe targets that are
several magnitudes fainter than those reachable with current instruments,
facilitating studies of YSOs at distances of $\sim$ kpc scale. This means a
step-change in the field of low-mass star formation, as it will be possible to
expand the science case from relatively local star-forming regions to a large
swathe of distances within the Milky Way.


title:
Molecular hydrogen in absorption at high redshifts. Science cases for
  CUBES

abstract:
  Absorption lines from molecular hydrogen ($\rm H_2$) in the spectra of
background sources are a powerful probe of the physical conditions in
intervening cold neutral medium. At high redshift, $z>2$, $\rm H_2$ lines are
conveniently shifted in the optical domain, allowing the use of ground-based
telescopes to perform high-resolution spectroscopy, which is essential for a
proper analysis of the cold gas. We describe recent observational progress,
based on the development of efficient pre-selection techniques in
low-resolution spectroscopic surveys such as the Sloan Digital Sky Survey
(SDSS). The next generation of spectrographs with high blue-throughput, such as
CUBES, will certainly significantly boost the efficiency and outcome of
follow-up observations. In this paper, we discuss high priority science cases
for CUBES, building on recent $\rm H_2$ observations at high-z: probing the
physical conditions in the cold phase of regular galaxies and outflowing gas
from active galactic nucleus.


title: The Master Equation in a Bounded Domain with Absorption

abstract:
  We analyze the well-posedness and the regularity properties of the solution
of the Master Equation in a bounded domain with homogeneous Dirichlet
conditions. Furthermore, our aim is to prove that the solution of the related
Nash system converges, in a suitable sense, to the solution of the Master
Equation with Dirichlet boundary conditions when the number of the players goes
to infinite. Finally, we also prove that optimal trajectories also converge.


title: New Exact Plasma Equilibria with Axial and Helical Symmetry

abstract:
  Exact solutions to the static equilibrium magnetohydrodynamic equations are
presented and discussed for both axially and helically reduced systems. For
both symmetries, physical restrictions on the solutions are discussed and it is
seen that there arise two different families of physical solutions
corresponding to two distinct pressure profiles. One is suitable for
atmospherically confined plasma whereas the other is suitable for plasma
residing in a vacuum. For solutions in vacuum, the radial dependence is found
to behave periodically for both axial and helical symmetries. For these
physical solutions, only a truncated solution inside of some chosen domain can
be considered. The magnetic field boundary can than be described by a current
sheet. In the last part of the paper, these static equilibrium solutions are
transformed into solutions with non-zero fluid velocity.


title:
Advanced testing of low, medium and high ECS CMIP6 GCM simulations
  versus ERA5-T2m

abstract:
  The equilibrium climate sensitivity (ECS) of the CMIP6 global circulation
models (GCMs) varies from 1.83 {\deg}C to 5.67 {\deg}C. Herein, 38 GCMs are
grouped into three ECS classes (low, 1.80-3.00 {\deg}C; medium, 3.01-4.50
{\deg}C; high, 4.51-6.00 {\deg}C) and compared against the ERA5-T2m records
from 1980-1990 to 2011-2021. We found that all models with ECS > 3.0 {\deg}C
overestimate the observed global surface warming and that spatial t-statistics
rejects the data-model agreement over 60% (using low-ECS GCMs) to 81% (using
high-ECS GCMs) of the Earth's surface. Thus, the high and medium-ECS GCMs are
unfit for prediction purposes. The low-ECS GCMs are not fully satisfactory yet,
but they are found unalarming because by 2050 they predict a moderate warming
($\Delta T_{preindustrial\rightarrow2050}\lesssim2\:{^\circ}C$).


title:
Discovering Governing Equations by Machine Learning implemented with
  Invariance

abstract:
  The partial differential equation (PDE) plays a significantly important role
in many fields of science and engineering. The conventional case of the
derivation of PDE mainly relies on first principles and empirical observation.
However, the development of machine learning technology allows us to mine
potential control equations from the massive amounts of stored data in a fresh
way. Although there has been considerable progress in the data-driven discovery
of PDE, the extant literature mostly focuses on the improvements of discovery
methods, without substantial breakthroughs in the discovery process itself,
including the principles for the construction of candidates and how to
incorporate physical priors. In this paper, through rigorous derivation of
formulas, novel physically enhanced machining learning discovery methods for
control equations: GSNN (Galileo Symbolic Neural Network) and LSNN (Lorentz
Symbolic Neural Network) are firstly proposed based on Galileo invariance and
Lorentz invariance respectively, setting forth guidelines for building the
candidates of discovering equations. The adoption of mandatory embedding of
physical constraints is fundamentally different from PINN in the form of the
loss function, thus ensuring that the designed Neural Network strictly obeys
the physical prior of invariance and enhancing the interpretability of the
network. By comparing the results with PDE-NET in numerical experiments of
Burgers equation and Sine-Gordon equation, it shows that the method presented
in this study has better accuracy, parsimony, and interpretability.


title: RGB-D Neural Radiance Fields: Local Sampling for Faster Training

abstract:
  Learning a 3D representation of a scene has been a challenging problem for
decades in computer vision. Recent advances in implicit neural representation
from images using neural radiance fields(NeRF) have shown promising results.
Some of the limitations of previous NeRF based methods include longer training
time, and inaccurate underlying geometry. The proposed method takes advantage
of RGB-D data to reduce training time by leveraging depth sensing to improve
local sampling. This paper proposes a depth-guided local sampling strategy and
a smaller neural network architecture to achieve faster training time without
compromising quality.


title:
Deep Multi-modal Fusion of Image and Non-image Data in Disease Diagnosis
  and Prognosis: A Review

abstract:
  The rapid development of diagnostic technologies in healthcare is leading to
higher requirements for physicians to handle and integrate the heterogeneous,
yet complementary data that are produced during routine practice. For instance,
the personalized diagnosis and treatment planning for a single cancer patient
relies on the various images (e.g., radiological, pathological, and camera
images) and non-image data (e.g., clinical data and genomic data). However,
such decision-making procedures can be subjective, qualitative, and have large
inter-subject variabilities. With the recent advances in multi-modal deep
learning technologies, an increasingly large number of efforts have been
devoted to a key question: how do we extract and aggregate multi-modal
information to ultimately provide more objective, quantitative computer-aided
clinical decision making? This paper reviews the recent studies on dealing with
such a question. Briefly, this review will include the (1) overview of current
multi-modal learning workflows, (2) summarization of multi-modal fusion
methods, (3) discussion of the performance, (4) applications in disease
diagnosis and prognosis, and (5) challenges and future directions.


title: On Kernelized Multi-Armed Bandits with Constraints

abstract:
  We study a stochastic bandit problem with a general unknown reward function
and a general unknown constraint function. Both functions can be non-linear
(even non-convex) and are assumed to lie in a reproducing kernel Hilbert space
(RKHS) with a bounded norm. This kernelized bandit setup strictly generalizes
standard multi-armed bandits and linear bandits. In contrast to safety-type
hard constraints studied in prior works, we consider soft constraints that may
be violated in any round as long as the cumulative violations are small, which
is motivated by various practical applications. Our ultimate goal is to study
how to utilize the nature of soft constraints to attain a finer
complexity-regret-constraint trade-off in the kernelized bandit setting. To
this end, leveraging primal-dual optimization, we propose a general framework
for both algorithm design and performance analysis. This framework builds upon
a novel sufficient condition, which not only is satisfied under general
exploration strategies, including \emph{upper confidence bound} (UCB),
\emph{Thompson sampling} (TS), and new ones based on \emph{random exploration},
but also enables a unified analysis for showing both sublinear regret and
sublinear or even zero constraint violation. We demonstrate the superior
performance of our proposed algorithms via numerical experiments based on both
synthetic and real-world datasets. Along the way, we also make the first
detailed comparison between two popular methods for analyzing constrained
bandits and Markov decision processes (MDPs) by discussing the key difference
and some subtleties in the analysis, which could be of independent interest to
the communities.


title:
Heuristic-based Inter-training to Improve Few-shot Multi-perspective
  Dialog Summarization

abstract:
  Many organizations require their customer-care agents to manually summarize
their conversations with customers. These summaries are vital for decision
making purposes of the organizations. The perspective of the summary that is
required to be created depends on the application of the summaries. With this
work, we study the multi-perspective summarization of customer-care
conversations between support agents and customers. We observe that there are
different heuristics that are associated with summaries of different
perspectives, and explore these heuristics to create weak-labeled data for
intermediate training of the models before fine-tuning with scarce human
annotated summaries. Most importantly, we show that our approach supports
models to generate multi-perspective summaries with a very small amount of
annotated data. For example, our approach achieves 94\% of the performance
(Rouge-2) of a model trained with the original data, by training only with 7\%
of the original data.


title: Earnings-22: A Practical Benchmark for Accents in the Wild

abstract:
  Modern automatic speech recognition (ASR) systems have achieved superhuman
Word Error Rate (WER) on many common corpora despite lacking adequate
performance on speech in the wild. Beyond that, there is a lack of real-world,
accented corpora to properly benchmark academic and commercial models. To
ensure this type of speech is represented in ASR benchmarking, we present
Earnings-22, a 125 file, 119 hour corpus of English-language earnings calls
gathered from global companies. We run a comparison across 4 commercial models
showing the variation in performance when taking country of origin into
consideration. Looking at hypothesis transcriptions, we explore errors common
to all ASR systems tested. By examining Individual Word Error Rate (IWER), we
find that key speech features impact model performance more for certain accents
than others. Earnings-22 provides a free-to-use benchmark of real-world,
accented audio to bridge academic and industrial research.


title: Demystifying Software Release Note Issues on GitHub

abstract:
  Release notes (RNs) summarize main changes between two consecutive software
versions and serve as a central source of information when users upgrade
software. While producing high quality RNs can be hard and poses a variety of
challenges to developers, a comprehensive empirical understanding of these
challenges is still lacking. In this paper, we bridge this knowledge gap by
manually analyzing 1,731 latest GitHub issues to build a comprehensive taxonomy
of RN issues with four dimensions: Content, Presentation, Accessibility, and
Production. Among these issues, nearly half (48.47%) of them focus on
Production; Content, Accessibility, and Presentation take 25.61%, 17.65%, and
8.27%, respectively. We find that: 1) RN producers are more likely to miss
information than to include incorrect information, especially for breaking
changes; 2) improper layout may bury important information and confuse users;
3) many users find RNs inaccessible due to link deterioration, lack of
notification, and obfuscate RN locations; 4) automating and regulating RN
production remains challenging despite the great needs of RN producers. Our
taxonomy not only pictures a roadmap to improve RN production in practice but
also reveals interesting future research directions for automating RN
production.


title:
Triple-junction solar cells with 39.5% terrestrial and 34.2% space
  efficiency enabled by thick quantum well superlattices

abstract:
  Multijunction solar cell design is guided by both the theoretical optimal
bandgap combination as well as the realistic limitations to materials with
these bandgaps. For instance, triple-junction III-V multijunction solar cells
commonly use GaAs as a middle cell because of its near-perfect material
quality, despite its bandgap being higher than optimal for the global spectrum.
Here, we modify the GaAs bandgap using thick GaInAs/GaAsP strain-balanced
quantum well (QW) solar cells with excellent voltage and absorption. These
high-performance QWs are incorporated into a triple-junction inverted
metamorphic multijunction device consisting of a GaInP top cell, GaInAs/GaAsP
QW middle cell, and lattice-mismatched GaInAs bottom cell, each of which has
been highly optimized. We demonstrate triple-junction efficiencies of 39.5% and
34.2% under the global and space spectra, respectively, which are higher than
previous record six-junction devices.


title: Single pion production in electron-proton interactions

abstract:
  This paper presents an extension of the MK single pion production model
\cite{MK, MK2} to high hadron invariant mass ($W$) and high momentum transfer
to conform to the predictions of perturbative QCD due to quark-hadron duality.
New form factors for several resonant interactions and non-resonant background
in the electron-nucleon cross-sections are determined taking into account the
experimental data and improved evaluation techniques. Fits to electron-proton
scattering data are used to calculate the vector-current form factors, and to
assign uncertainties to the constrained free parameters of the model. The
%hadron currents results from this work can be used to determine the vector
current in the corresponding neutrino-nucleon cross-sections, which are an
important input for event generators in long baseline neutrino oscillation
measurements.


title:
Cross-Media Scientific Research Achievements Retrieval Based on Deep
  Language Model

abstract:
  Science and technology big data contain a lot of cross-media
information.There are images and texts in the scientific paper.The s ingle
modal search method cannot well meet the needs of scientific researchers.This
paper proposes a cross-media scientific research achievements retrieval method
based on deep language model (CARDL).It achieves a unified cross-media semantic
representation by learning the semantic association between different modal
data, and is applied to the generation of text semantic vector of scientific
research achievements, and then cross-media retrieval is realized through
semantic similarity matching between different modal data.Experimental results
show that the proposed CARDL method achieves better cross-modal retrieval
performance than existing methods. Key words science and technology big data ;
cross-media retrieval; cross-media semantic association learning; deep language
model; semantic similarity


title:
Effects of transverse geometry on the thermal conductivity of Si and Ge
  nanowires

abstract:
  We explore the effects of geometry on the thermal conductivity (kappa) of
silicon and germanium nanowires, with lengths between 10-120 nm and diameters
up to 5-6 nm. To this end we perform molecular dynamics simulations with the
LAMMPS software, using Tersoff interatomic potentials. We consider nanowires
with polygonal cross section and we discuss the effect of the transverse
geometry on the thermal conductivity. We also consider tubular (hollow)
nanowires and core/shell combinations of Si/Ge and Ge/Si, and we compare the
heat transport of the core/shell structure with that of the separated core and
shell components.


title: Sparse Pose Graph Optimization in Cycle Space

abstract:
  The state-of-the-art modern pose-graph optimization (PGO) systems are vertex
based. In this context the number of variables might be high, albeit the number
of cycles in the graph (loop closures) is relatively low. For sparse problems
particularly, the cycle space has a significantly smaller dimension than the
number of vertices. By exploiting this observation, in this paper we propose an
alternative solution to PGO, that directly exploits the cycle space. We
characterize the topology of the graph as a cycle matrix, and re-parameterize
the problem using relative poses, which are further constrained by a cycle
basis of the graph. We show that by using a minimum cycle basis, the
cycle-based approach has superior convergence properties against its
vertex-based counterpart, in terms of convergence speed and convergence to the
global minimum. For sparse graphs, our cycle-based approach is also more time
efficient than the vertex-based. As an additional contribution of this work we
present an effective algorithm to compute the minimum cycle basis. Albeit known
in computer science, we believe that this algorithm is not familiar to the
robotics community. All the claims are validated by experiments on both
standard benchmarks and simulated datasets. To foster the reproduction of the
results, we provide a complete open-source C++ implementation (Code:
\url{https://bitbucket.org/FangBai/cycleBasedPGO) of our approach.


title:
Angular Super-Resolution in Diffusion MRI with a 3D Recurrent
  Convolutional Autoencoder

abstract:
  High resolution diffusion MRI (dMRI) data is often constrained by limited
scanning time in clinical settings, thus restricting the use of downstream
analysis techniques that would otherwise be available. In this work we develop
a 3D recurrent convolutional neural network (RCNN) capable of super-resolving
dMRI volumes in the angular (q-space) domain. Our approach formulates the task
of angular super-resolution as a patch-wise regression using a 3D autoencoder
conditioned on target b-vectors. Within the network we use a convolutional long
short term memory (ConvLSTM) cell to model the relationship between q-space
samples. We compare model performance against a baseline spherical harmonic
interpolation and a 1D variant of the model architecture. We show that the 3D
model has the lowest error rates across different subsampling schemes and
b-values. The relative performance of the 3D RCNN is greatest in the very low
angular resolution domain. Code for this project is available at
https://github.com/m-lyon/dMRI-RCNN.


title:
Signature of primordial non-Gaussianity on 21-cm power spectrum from
  dark ages

abstract:
  We study the signature of primordial non-Gaussianity imprinted on the power
spectrum of the 21-cm line differential brightness temperature during dark
ages. Employing the perturbative treatment of gravitational clustering, we
quantitatively estimate the effects of the non-Gaussian and one-loop
corrections on the 21-cm power spectrum. The potential impact of the use of the
21-cm power spectrum for the constraint on local-type primordial
non-Gaussianity is investigated based on the Fisher matrix analysis. Our
results show that the 21-cm power spectrum for an array with a baseline of
several tens of kilometers can constrain the primordial non-Gaussianity to a
level severer than that from cosmic microwave background measurements and its
constraining power is stronger than that of the 21-cm bispectrum, while in the
ultimate situation the 21-cm bispectrum eventually becomes more powerful.


title: Theory of electronic resonances: Fundamental aspects and recent advances

abstract:
  Electronic resonances are states that are unstable towards loss of electrons.
They play critical roles in high-energy environments across chemistry, physics,
and biology but are also relevant to processes under ambient conditions that
involve unbound electrons. This feature article focuses on complex-variable
techniques such as complex scaling and complex absorbing potentials that afford
a treatment of electronic resonances in terms of discrete square-integrable
eigenstates of non-Hermitian Hamiltonians with complex energy. Fundamental
aspects of these techniques as well their integration into molecular
electronic-structure theory are discussed and an overview of some recent
developments is given: analytic gradient theory for electronic resonances, the
application of rank-reduction techniques and quantum embedding to them, as well
as approaches for evaluating partial decay widths.


title:
Photographic Visualization of Weather Forecasts with Generative
  Adversarial Networks

abstract:
  Outdoor webcam images are an information-dense yet accessible visualization
of past and present weather conditions, and are consulted by meteorologists and
the general public alike. Weather forecasts, however, are still communicated as
text, pictograms or charts. We therefore introduce a novel method that uses
photographic images to also visualize future weather conditions.
  This is challenging, because photographic visualizations of weather forecasts
should look real, be free of obvious artifacts, and should match the predicted
weather conditions. The transition from observation to forecast should be
seamless, and there should be visual continuity between images for consecutive
lead times. We use conditional Generative Adversarial Networks to synthesize
such visualizations. The generator network, conditioned on the analysis and the
forecasting state of the numerical weather prediction (NWP) model, transforms
the present camera image into the future. The discriminator network judges
whether a given image is the real image of the future, or whether it has been
synthesized. Training the two networks against each other results in a
visualization method that scores well on all four evaluation criteria.
  We present results for three camera sites across Switzerland that differ in
climatology and terrain. We show that users find it challenging to distinguish
real from generated images, performing not much better than if they guessed
randomly. The generated images match the atmospheric, ground and illumination
conditions of the COSMO-1 NWP model forecast in at least 89 % of the examined
cases. Nowcasting sequences of generated images achieve a seamless transition
from observation to forecast and attain visual continuity.


title: The complex of free factors of a free group

abstract:
  This paper corrects an error in a proof in the original version of the paper
published in 1998 in the Oxford Quarterly. The main theorem remains the same:
The geometric realization of the partially ordered set of proper free factors
in a finitely generated free group of rank $n$ is homotopy equivalent to a
wedge of spheres of dimension $n-2$.


title: Estimating Nonlinear Network Data Models with Fixed Effects

abstract:
  This paper considers estimation of a directed network model in which outcomes
are driven by dyad-specific variables (such as measures of homophily) as well
as unobserved agent-specific parameters that capture degree heterogeneity. I
develop a jackknife bias correction to deal with the incidental parameters
problem that arises from fixed effect estimation of the model. In contrast to
previous proposals, the jackknife approach is easily adaptable to different
models and allows for non-binary outcome variables. Additionally, since the
jackknife estimates all parameters in the model, including fixed effects, it
allows researchers to construct estimates of average effects and counterfactual
outcomes. I also show how the jackknife can be used to bias-correct fixed
effect averages over functions that depend on multiple nodes, e.g. triads or
tetrads in the network. As an example, I implement specification tests for
dependence across dyads, such as reciprocity or transitivity. Finally, I
demonstrate the usefulness of the estimator in an application to a gravity
model for import/export relationships across countries.


title:
Beryllium abundances in turn-off stars of globular clusters with the
  CUBES spectrograph

abstract:
  Globular clusters host multiple stellar populations that display star-to-star
variation of light elements that are affected by hot hydrogen burning (e.g.,
He, C, N, O). Several scenarios have been suggested to explain these
variations. Most involve multiple star formation episodes, where later
generations are born from material contaminated by the nucleosynthetic products
of the previous stellar generation(s). One difficulty in the modelling of such
scenarios is knowing the extent to which processed and pristine material are
mixed. In this context, beryllium abundances measured in turn-off stars of
different generations can provide new information. Beryllium originates from
cosmic-ray spallation and can only be destroyed inside stars. Beryllium
abundances can thus directly measure the degree of pollution of the material
that formed stars in globular clusters. Turn-off stars in globular clusters are
however faint and such studies are beyond the capabilities of current
instrumentation. In this work, we show the progress that the CUBES spectrograph
will bring to this area. Our simulations indicate that CUBES will enable the
detection of variations of about 0.6 dex in the Be abundances between stars
from different generations, in several nearby globular clusters with turn-off
magnitude down to $V$ = 18 mag.


title:
The effect of multiple features on the power spectrum in two-field
  inflation

abstract:
  We extend our previous work on the enhancement of the curvature spectrum
during inflation to the two-field case. We identify the slow-roll parameter
$\eta$ as the quantity that can trigger the rapid growth of perturbations. Its
two components, $\eta_\parallel$ along the background trajectory and
$\eta_\perp$ perpendicular to it, remain small during most of the evolution,
apart from short intervals during which they take large, positive or negative,
values. The typical reason for the appearance of strong features in
$\eta_\parallel$ is sharp steps or inflection points in the inflaton potential,
while $\eta_\perp$ grows large during sharp turns in field space. We focus on
the additive effect of several features leading to the resonant growth of the
curvature spectrum. Three or four features in the evolution of $\eta$ are
sufficient in order to induce an enhancement of the power spectrum by six or
seven orders of magnitude, which can lead to the significant production of
primordial black holes and stochastic gravitational waves. A big part of our
study focuses on understanding the evolution of the perturbations and the
resulting spectra through analytic means. The presence of multiple features in
the background evolution points to a more complex inflationary paradigm, which
is also more natural in the multi-field case. The critical examination of this
possibility is within the reach of experiment.


title:
Spontaneous spinning of a dichloromethane drop on an aqueous surfactant
  solution

abstract:
  We report a series of experiments carried out with a dichloromethane drop
deposited on the surface of an aqueous solution containing a surfactant,
cetyltrimethylammonium bromide. After an induction stage during which the drop
stays axisymmetric, oscillations occur along the contact line. These
oscillations are succeeded by a spectacular spontaneous spinning of the drop.
The latter quickly takes the form of a two-tip `rotor' and the spinning rate
stabilizes at a constant value, no longer varying despite the gradual changes
of the drop shape and size. The drop eventually disappears due to the continual
dissolution and evaporation of dichloromethane. Schlieren visualizations and
particle image velocimetry are used to establish a consistent scenario capable
of explaining the evolution of the system. The Marangoni effect induced by the
dissolution of dichloromethane in the drop vicinity is shown to be responsible
for the observed dynamics. Arguments borrowed from dynamical systems theory and
from an existing low-order model allow us to explain qualitatively why the
system selects the spinning configuration. The geometry of the immersed part of
the drop is shown to play a crucial role in this selection process, as well as
in the regulation of the spinning rate.


title: Typical Error Exponents: A Dual Domain Derivation

abstract:
  This paper shows that the probability that the error exponent of a given code
randomly generated from a pairwise independent ensemble being smaller than a
lower bound on the typical random-coding exponent tends to zero as the codeword
length tends to infinity. This lower bound is known to be tight for i.i.d.
ensembles over the binary symmetric channel and for constant-composition codes
over memoryless channels. Our results recover both as special cases and remain
valid for arbitrary alphabets, arbitrary channels -- for example finite-state
channels with memory -- and arbitrary pairwise-independent ensembles. We
specialize our results to the i.i.d., constant-composition and cost-constrained
ensembles over discrete memoryless channels and to ensembles over finite-state
channels.


title:
Hybrid helical state and superconducting diode effect in S/F/TI
  heterostructures

abstract:
  It is well-known that the ground state of homogeneous superconducting systems
with spin-orbit coupling (SOC) in the presence of the Zeeman field is the
so-called helical state, which is characterized by the phase modulation of the
order parameter, but zero supercurrent density. In this work we investigate the
realization of the helical state in a hybrid system with spatially separated
superconductivity and exchange field by considering S/F bilayer on top of a 3D
topological insulator. This system is characterized by strong spin-momentum
locking and, consequently, provides the most favorable conditions for the
helical state generation. The analysis is based on the microscopic theory in
terms of the quasiclassical Green's functions. We demonstrate that in the
bilayer the helical state survives if the exchange field has non-zero component
perpendicular to the S/F interface even in spite of the fact that the
superconducting order parameter and the exchange field are spatially separated.
At the same time, in this spatially inhomogeneous situation the helical state
is accompanied by the spontaneous currents distributed over the bilayer in such
a way as to have zero average. Further, we show that this hybrid helical state
gives rise to nonreciprocity in the system. We demonstrate the realization of
the superconducting diode effect and show that the degree of nonreciprocity is
very large in the considered system due to the strongest possible value of the
spin-momentum locking.


title:
Locality Matters: A Locality-Biased Linear Attention for Automatic
  Speech Recognition

abstract:
  Conformer has shown a great success in automatic speech recognition (ASR) on
many public benchmarks. One of its crucial drawbacks is the quadratic
time-space complexity with respect to the input sequence length, which
prohibits the model to scale-up as well as process longer input audio
sequences. To solve this issue, numerous linear attention methods have been
proposed. However, these methods often have limited performance on ASR as they
treat tokens equally in modeling, neglecting the fact that the neighbouring
tokens are often more connected than the distanced tokens. In this paper, we
take this fact into account and propose a new locality-biased linear attention
for Conformer. It not only achieves higher accuracy than the vanilla Conformer,
but also enjoys linear space-time computational complexity. To be specific, we
replace the softmax attention with a locality-biased linear attention (LBLA)
mechanism in Conformer blocks. The LBLA contains a kernel function to ensure
the linear complexities and a cosine reweighing matrix to impose more weights
on neighbouring tokens. Extensive experiments on the LibriSpeech corpus show
that by introducing this locality bias to the Conformer, our method achieves a
lower word error rate with more than 22% inference speed.


title:
LightHuBERT: Lightweight and Configurable Speech Representation Learning
  with Once-for-All Hidden-Unit BERT

abstract:
  Self-supervised speech representation learning has shown promising results in
various speech processing tasks. However, the pre-trained models, e.g., HuBERT,
are storage-intensive Transformers, limiting their scope of applications under
low-resource settings. To this end, we propose LightHuBERT, a once-for-all
Transformer compression framework, to find the desired architectures
automatically by pruning structured parameters. More precisely, we create a
Transformer-based supernet that is nested with thousands of weight-sharing
subnets and design a two-stage distillation strategy to leverage the
contextualized latent representations from HuBERT. Experiments on automatic
speech recognition (ASR) and the SUPERB benchmark show the proposed LightHuBERT
enables over $10^9$ architectures concerning the embedding dimension, attention
dimension, head number, feed-forward network ratio, and network depth.
LightHuBERT outperforms the original HuBERT on ASR and five SUPERB tasks with
the HuBERT size, achieves comparable performance to the teacher model in most
tasks with a reduction of 29% parameters, and obtains a $3.5\times$ compression
ratio in three SUPERB tasks, e.g., automatic speaker verification, keyword
spotting, and intent classification, with a slight accuracy loss. The code and
pre-trained models are available at
https://github.com/mechanicalsea/lighthubert.


title:
Galaxy And Mass Assembly (GAMA): Self-Organizing Map Application on
  Nearby Galaxies

abstract:
  Galaxy populations show bimodality in a variety of properties: stellar mass,
colour, specific star-formation rate, size, and S\'ersic index. These
parameters are our feature space. We use an existing sample of 7556 galaxies
from the Galaxy and Mass Assembly (GAMA) survey, represented using five
features and the K-means clustering technique, showed that the bimodalities are
the manifestation of a more complex population structure, represented by
between 2 and 6 clusters.
  Here we use Self Organizing Maps (SOM), an unsupervised learning technique
which can be used to visualize similarity in a higher dimensional space using a
2D representation, to map these five-dimensional clusters in the feature space
onto two-dimensional projections. To further analyze these clusters, using the
SOM information, we agree with previous results that the sub-populations found
in the feature space can be reasonably mapped onto three or five clusters. We
explore where the "green valley" galaxies are mapped onto the SOM, indicating
multiple interstitial populations within the green valley population.
  Finally, we use the projection of the SOM to verify whether morphological
information provided by GalaxyZoo users, for example, if features are visible,
can be mapped onto the SOM-generated map. Voting on whether galaxies are
smooth, likely ellipticals, or "featured" can reasonably be separated but
smaller morphological features (bar, spiral arms) can not. SOMs promise to be a
useful tool to map and identify instructive sub-populations in multidimensional
galaxy survey feature space, provided they are large enough.


title:
Three-Dimensional Spectrum Occupancy Measurement using UAV: Performance
  Analysis and Algorithm Design

abstract:
  Spectrum sharing, as an approach to significantly improve spectrum efficiency
in the era of 6th generation mobile networks (6G), has attracted extensive
attention. Radio Environment Map (REM) based low-complexity spectrum sharing is
widely studied where the spectrum occupancy measurement (SOM) is vital to
construct REM. The SOM in three-dimensional (3D) space is becoming increasingly
essential to support the spectrum sharing with space-air-ground integrated
network being a great momentum of 6G. In this paper, we analyze the performance
of 3D SOM to further study the tradeoff between accuracy and efficiency in 3D
SOM. We discover that the error of 3D SOM is related with the area of the
boundary surfaces of licensed networks, the number of discretized cubes, and
the length of the edge of 3D space. Moreover, we design a fast and accurate 3D
SOM algorithm that utilizes unmanned aerial vehicle (UAV) to measure the
spectrum occupancy considering the path planning of UAV, which improves the
measurement efficiency by requiring less measurement time and flight time of
the UAV for satisfactory performance. The theoretical results obtained in this
paper reveal the essential dependencies that describe the 3D SOM methodology,
and the proposed algorithm is beneficial to improve the efficiency of 3D SOM.
It is noted that the theoretical results and algorithm in this paper may
provide a guideline for more areas such as spectrum monitoring, spectrum
measurement, network measurement, planning, etc.


title:
Dynamic Latency for CTC-Based Streaming Automatic Speech Recognition
  With Emformer

abstract:
  An inferior performance of the streaming automatic speech recognition models
versus non-streaming model is frequently seen due to the absence of future
context. In order to improve the performance of the streaming model and reduce
the computational complexity, a frame-level model using efficient augment
memory transformer block and dynamic latency training method is employed for
streaming automatic speech recognition in this paper. The long-range history
context is stored into the augment memory bank as a complement to the limited
history context used in the encoder. Key and value are cached by a cache
mechanism and reused for next chunk to reduce computation. Afterwards, a
dynamic latency training method is proposed to obtain better performance and
support low and high latency inference simultaneously. Our experiments are
conducted on benchmark 960h LibriSpeech data set. With an average latency of
640ms, our model achieves a relative WER reduction of 6.0% on test-clean and
3.0% on test-other versus the truncate chunk-wise Transformer.


title: Integrate Lattice-Free MMI into End-to-End Speech Recognition

abstract:
  In automatic speech recognition (ASR) research, discriminative criteria have
achieved superior performance in DNN-HMM systems. Given this success, the
adoption of discriminative criteria is promising to boost the performance of
end-to-end (E2E) ASR systems. With this motivation, previous works have
introduced the minimum Bayesian risk (MBR, one of the discriminative criteria)
into E2E ASR systems. However, the effectiveness and efficiency of the
MBR-based methods are compromised: the MBR criterion is only used in system
training, which creates a mismatch between training and decoding; the
on-the-fly decoding process in MBR-based methods results in the need for
pre-trained models and slow training speeds. To this end, novel algorithms are
proposed in this work to integrate another widely used discriminative
criterion, lattice-free maximum mutual information (LF-MMI), into E2E ASR
systems not only in the training stage but also in the decoding process. The
proposed LF-MMI training and decoding methods show their effectiveness on two
widely used E2E frameworks: Attention-Based Encoder-Decoders (AEDs) and Neural
Transducers (NTs). Compared with MBR-based methods, the proposed LF-MMI method:
maintains the consistency between training and decoding; eschews the on-the-fly
decoding process; trains from randomly initialized models with superior
training efficiency. Experiments suggest that the LF-MMI method outperforms its
MBR counterparts and consistently leads to statistically significant
performance improvements on various frameworks and datasets from 30 hours to
14.3k hours. The proposed method achieves state-of-the-art (SOTA) results on
Aishell-1 (CER 4.10%) and Aishell-2 (CER 5.02%) datasets. Code is released.


title: Learning Sparse Mixture Models

abstract:
  This work approximates high-dimensional density functions with an ANOVA-like
sparse structure by the mixture of wrapped Gaussian and von Mises
distributions. When the dimension $d$ is very large, it is complex and
impossible to train the model parameters by the usually known learning
algorithms due to the curse of dimensionality. Therefore, assuming that each
component of the model depends on an a priori unknown much smaller number of
variables than the space dimension $d,$ we first define an algorithm that
determines the mixture model's set of active variables by the
Kolmogorov-Smirnov and correlation test. Then restricting the learning
procedure to the set of active variables, we iteratively determine the set of
variable interactions of the marginal density function and simultaneously learn
the parameters by the Kolmogorov and correlation coefficient statistic test and
the proximal Expectation-Maximization algorithm. The learning procedure
considerably reduces the algorithm's complexity for the input dimension $d$ and
increases the model's accuracy for the given samples, as the numerical examples
show.


title: QCD sum rule study for hidden-strange pentaquarks

abstract:
  Inspired by the LHCb's observations of hidden-charm $P_{c(s)}$ states, we
study their hidden-strange analogues $P_s$ states in both $[udu][\bar ss]$ and
$[uds][\bar su]$ configurations. We investigate the $P_s$ pentaquark states in
$p\eta^\prime$, $p\phi$, $\Lambda K$, $\Sigma K$ and $\Sigma^\ast K^\ast$
structures with $J^P = \frac{1}{2}^-$ and calculate their masses in the
framework of QCD sum rules. Our numerical results can not exclude the
possibility of the $p\phi$ bound state. For the $p\eta^\prime$, $\Sigma K$ and
$\Sigma^\ast K^\ast$ channels, the extracted hadron masses are much higher than
the two-hadron thresholds so that no bound state exists in such structures,
which is consistent with the experimental status to date.


title: Viscoplastic sessile drop coalescence

abstract:
  The evolution of the liquid bridge formed between two coalescing sessile
yield-stress drops is studied experimentally. We find that the height of the
bridge evolves similar to a viscous Newtonian fluid, $h_0\sim t$, before
arresting at long time prior to minimizing its liquid/gas interfacial energy.
We numerically solve for the final arrested profile shape and find it depends
on the fluid's yield stress $\tau_y$ and coalescence angle $\alpha$,
represented by the Bingham number $\tau_y h_{drop} / \sigma$ modified by the
drop's height-width aspect ratio. We present a scaling argument for the
bridge's temporal evolution using the length scale found from an analysis of
the arrested shape as well as from the similarity solution derived for the
bridge's evolution.


title: Exploring Body Texture from mmW Images for Person Recognition

abstract:
  Imaging using millimeter waves (mmWs) has many advantages including the
ability to penetrate obscurants such as clothes and polymers. After having
explored shape information retrieved from mmW images for person recognition, in
this work we aim to gain some insight about the potential of using mmW texture
information for the same task, considering not only the mmW face, but also mmW
torso and mmW wholebody. We report experimental results using the mmW TNO
database consisting of 50 individuals based on both hand-crafted and learned
features from Alexnet and VGG-face pretrained Convolutional Neural Networks
(CNN) models. First, we analyze the individual performance of three mmW body
parts, concluding that: i) mmW torso region is more discriminative than mmW
face and the whole body, ii) CNN features produce better results compared to
hand-crafted features on mmW faces and the entire body, and iii) hand-crafted
features slightly outperform CNN features on mmW torso. In the second part of
this work, we analyze different multi-algorithmic and multi-modal techniques,
including a novel CNN-based fusion technique, improving verification results to
2% EER and identification rank-1 results up to 99%. Comparative analyses with
mmW body shape information and face recognition in the visible and NIR spectral
bands are also reported.


title:
Classification of Hyperspectral Images Using SVM with Shape-adaptive
  Reconstruction and Smoothed Total Variation

abstract:
  In this work, a novel algorithm called SVM with Shape-adaptive Reconstruction
and Smoothed Total Variation (SaR-SVM-STV) is introduced to classify
hyperspectral images, which makes full use of spatial and spectral information.
The Shape-adaptive Reconstruction (SaR) is introduced to preprocess each pixel
based on the Pearson Correlation between pixels in its shape-adaptive (SA)
region. Support Vector Machines (SVMs) are trained to estimate the pixel-wise
probability maps of each class. Then the Smoothed Total Variation (STV) model
is applied to denoise and generate the final classification map. Experiments
show that SaR-SVM-STV outperforms the SVM-STV method with a few training
labels, demonstrating the significance of reconstructing hyperspectral images
before classification.


title: Properties Preserved by Groupoid Equivalence

abstract:
  We show that groupoid equivalence preserves a number of groupoid properties
such as properness or the property of being topologically principal.


title: Impact of COVID-19 on Astronomy: Two Years In

abstract:
  We study the impact of the COVID-19 pandemic on astronomy using public
records of astronomical publications. We show that COVID-19 has had both
positive and negative impacts on research in astronomy. We find that the
overall output of the field, measured by the yearly paper count, has increased.
This is mainly driven by boosted individual productivity seen across most
countries, possibly the result of cultural and technological changes in the
scientific community during COVID. However, a decreasing number of incoming new
researchers is seen in most of the countries we studied, indicating larger
barriers for new researchers to enter the field or for junior researchers to
complete their first project during COVID. Unfortunately, the overall
improvement in productivity seen in the field is not equally shared by female
astronomers. By fraction, fewer papers are written by women and fewer women are
among incoming new researchers in most countries. Even though female
astronomers also became more productive during COVID, the level of improvement
is smaller than for men. Pre-COVID, female astronomers in the Netherlands,
Australia, Switzerland were equally as or even more productive than their male
colleagues. During COVID, no single country's female astronomers were able to
be more productive than their male colleagues on average.


title: On averaging and mixing for stochastic PDEs

abstract:
  We examine the convergence in the Krylov--Bogolyubov averaging for nonlinear
stochastic perturbations of linear PDEs with pure imaginary spectrum and show
that if the involved effective equation is mixing, then the convergence is
uniform in time.


title: On Choquet integrals and Poincar\'e-Sobolev inequalities

abstract:
  We consider integral inequalities in the sense of Choquet with respect to the
Hausdorff content $\mathcal{H}_\infty^{\delta}$. In particular, if $\Omega$ is
a bounded John domain in $\mathbb{R}^n$, $n\geq 2$, and $0 <\delta \le n$, we
prove that the corresponding $(\delta p/(\delta -p),p)$-Poincar\'e-Sobolev
inequalities hold for all continuously differentiable functions defined on
$\Omega$ whenever $\delta /n < p < \delta$. We prove also that the
$(p,p)$-Poincar\'e inequality is valid for all $p>\delta /n$.


title:
Microphysics of diffusive shock acceleration: impact on the spectrum of
  accelerated particles

abstract:
  Diffusive shock acceleration at collisionless shocks remains the most likely
process for accelerating particles in a variety of astrophysical sources. While
the standard prediction for strong shocks is that the spectrum of accelerated
particles is universal, $f(p)\propto p^{-4}$, numerous phenomena affect this
simple conclusion. In general, the non-linear dynamical reaction of accelerated
particles leads to a concave spectrum, steeper than $p^{-4}$ at momenta below a
few tens of GeV/c and harder than the standard prediction at high energies.
However, the non-linear effects become important in the presence of magnetic
field amplification, which in turn leads to higher values of the maximum
momentum $p_{max}$. It was recently discovered that the self-generated
perturbations that enhance particle scattering, when advected downstream, move
in the same direction as the background plasma, so that the effective
compression factor at the shock decreases and the spectrum becomes steeper. We
investigate the implications of the excitation of the non-resonant streaming
instability on these spectral deformations, the dependence of the spectral
steepening on the shock velocity and the role played by the injection momentum.


title:
PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and
  Hallucination under Self-supervision

abstract:
  Existing self-supervised 3D human pose estimation schemes have largely relied
on weak supervisions like consistency loss to guide the learning, which,
inevitably, leads to inferior results in real-world scenarios with unseen
poses. In this paper, we propose a novel self-supervised approach that allows
us to explicitly generate 2D-3D pose pairs for augmenting supervision, through
a self-enhancing dual-loop learning framework. This is made possible via
introducing a reinforcement-learning-based imitator, which is learned jointly
with a pose estimator alongside a pose hallucinator; the three components form
two loops during the training process, complementing and strengthening one
another. Specifically, the pose estimator transforms an input 2D pose sequence
to a low-fidelity 3D output, which is then enhanced by the imitator that
enforces physical constraints. The refined 3D poses are subsequently fed to the
hallucinator for producing even more diverse data, which are, in turn,
strengthened by the imitator and further utilized to train the pose estimator.
Such a co-evolution scheme, in practice, enables training a pose estimator on
self-generated motion data without relying on any given 3D data. Extensive
experiments across various benchmarks demonstrate that our approach yields
encouraging results significantly outperforming the state of the art and, in
some cases, even on par with results of fully-supervised methods. Notably, it
achieves 89.1% 3D PCK on MPI-INF-3DHP under self-supervised cross-dataset
evaluation setup, improving upon the previous best self-supervised methods by
8.6%. Code can be found at: https://github.com/Garfield-kh/PoseTriplet


title: Hubble Space Telescope STIS Spectroscopy of Nova T Aurigae 1891

abstract:
  T Aurigae is an eclipsing old nova which exploded in 1891. At a Gaia EDR3
distance of 815-871 pc, it is a relatively nearby old nova. Through ultraviolet
spectral modeling and using the new precise Gaia distance, we find that the
HST/STIS spectrum of T Aurigae is consistent with an accretion disk with a mass
transfer rate $\dot{M}$ of the order of $10^{-8}M_{\odot}$/yr, for a white
dwarf mass of $M_{\rm wd} \approx 0.7 \pm 0.2 M_{\odot}$, an inclination of $i
\sim 60^{\circ}$, and a Gaia distance of of $840_{-25}^{+31}$~pc. The sharp
absorption lines of metals cannot form in the disk and are likely forming in
material above the disk (e.g. due stream disk overflow), in circumbinary
material, and/or in material associated with the ejected shell from the 1891
nova explosion. The saturated hydrogen Ly$\alpha$ absorption feature is
attributed to a large interstellar medium hydrogen column density of the order
of $10^{21}$cm$^{-2}$ towards T Aur, as corroborated by the value of its
reddening $E(B-V)=0.42 \pm 0.08$.


title: Low Treewidth Embeddings of Planar and Minor-Free Metrics

abstract:
  Cohen-Addad, Filtser, Klein and Le [FOCS'20] constructed a stochastic
embedding of minor-free graphs of diameter $D$ into graphs of treewidth
$O_{\epsilon}(\log n)$ with expected additive distortion $+\epsilon D$.
Cohen-Addad et al. then used the embedding to design the first quasi-polynomial
time approximation scheme (QPTAS) for the capacitated vehicle routing problem.
Filtser and Le [STOC'21] used the embedding (in a different way) to design a
QPTAS for the metric Baker's problems in minor-free graphs. In this work, we
devise a new embedding technique to improve the treewidth bound of Cohen-Addad
et al. exponentially to $O_{\epsilon}(\log\log n)^2$. As a corollary, we obtain
the first efficient PTAS for the capacitated vehicle routing problem in
minor-free graphs. We also significantly improve the running time of the QPTAS
for the metric Baker's problems in minor-free graphs from
$n^{O_{\epsilon}(\log(n))}$ to $n^{O_{\epsilon}(\log\log(n))^3}$.
  Applying our embedding technique to planar graphs, we obtain a deterministic
embedding of planar graphs of diameter $D$ into graphs of treewidth
$O((\log\log n)^2)/\epsilon)$ and additive distortion $+\epsilon D$ that can be
constructed in nearly linear time. Important corollaries of our result include
a bicriteria PTAS for metric Baker's problems and a PTAS for the vehicle
routing problem with bounded capacity in planar graphs, both run in
almost-linear time. The running time of our algorithms is significantly better
than previous algorithms that require quadratic time.
  A key idea in our embedding is the construction of an (exact) emulator for
tree metrics with treewidth $O(\log\log n)$ and hop-diameter $O(\log \log n)$.
This result may be of independent interest.


title:
Exploring Opportunities in Usable Hazard Analysis Processes for AI
  Engineering

abstract:
  Embedding artificial intelligence into systems introduces significant
challenges to modern engineering practices. Hazard analysis tools and processes
have not yet been adequately adapted to the new paradigm. This paper describes
initial research and findings regarding current practices in AI-related hazard
analysis and on the tools used to conduct this work. Our goal with this initial
research is to better understand the needs of practitioners and the emerging
challenges of considering hazards and risks for AI-enabled products and
services. Our primary research question is: Can we develop new structured
thinking methods and systems engineering tools to support effective and
engaging ways for preemptively considering failure modes in AI systems? The
preliminary findings from our review of the literature and interviews with
practitioners highlight various challenges around integrating hazard analysis
into modern AI development processes and suggest opportunities for exploration
of usable, human-centered hazard analysis tools.


title: Stochastic Conservative Contextual Linear Bandits

abstract:
  Many physical systems have underlying safety considerations that require that
the strategy deployed ensures the satisfaction of a set of constraints.
Further, often we have only partial information on the state of the system. We
study the problem of safe real-time decision making under uncertainty. In this
paper, we formulate a conservative stochastic contextual bandit formulation for
real-time decision making when an adversary chooses a distribution on the set
of possible contexts and the learner is subject to certain safety/performance
constraints. The learner observes only the context distribution and the exact
context is unknown, and the goal is to develop an algorithm that selects a
sequence of optimal actions to maximize the cumulative reward without violating
the safety constraints at any time step. By leveraging the UCB algorithm for
this setting, we propose a conservative linear UCB algorithm for stochastic
bandits with context distribution. We prove an upper bound on the regret of the
algorithm and show that it can be decomposed into three terms: (i) an upper
bound for the regret of the standard linear UCB algorithm, (ii) a constant term
(independent of time horizon) that accounts for the loss of being conservative
in order to satisfy the safety constraint, and (ii) a constant term
(independent of time horizon) that accounts for the loss for the contexts being
unknown and only the distribution being known. To validate the performance of
our approach we perform extensive simulations on synthetic data and on
real-world maize data collected through the Genomes to Fields (G2F) initiative.


title: Adaptive Hermite Spectral Methods in Unbounded Domains

abstract:
  Recently, new adaptive techniques were developed that greatly improved the
efficiency of solving PDEs using spectral methods. These adaptive spectral
techniques are especially suited for accurately solving problems in unbounded
domains and require the monitoring and dynamic adjustment of three key tunable
parameters: the scaling factor, the displacement of the basis functions, and
the spectral expansion order. There have been few analyses of numerical methods
for unbounded domain problems. Specifically, there is no analysis of adaptive
spectral methods to provide insight into how to increase efficiency and
accuracy through dynamical adjustment of parameters. In this paper, we perform
the first numerical analysis of the adaptive spectral method using generalized
Hermite functions defined on the whole line. We investigate how the
implementation of the adaptive spectral methods affects numerical results,
thereby providing guidelines for the proper tuning of parameters. Finally, we
further improve performance by extending the adaptive methods to allow
bidirectional basis function translation.


title:
Scintillation detectors with silicon photomultiplier readout in a
  dilution refrigerator at temperatures down to 0.2 K

abstract:
  We are developing a novel high-brightness atomic beam, comprised of a
two-body exotic atom called muonium (M $ = \mu^+ + e^-$), for next-generation
atomic physics and gravitational interaction measurements. This M source
originates from a thin sheet of superfluid helium (SFHe), hence diagnostics and
later measurements require a detection system which is operational in a
dilution cryostat at temperatures below 1 K. In this paper, we describe the
operation and characterization of silicon photomultipliers (SiPMs) at ultra-low
temperatures in SFHe targets. We show the temperature dependence of the signal
shape, breakdown voltage, and single photon detection efficiency, concluding
that single photon detection with SiPMs below 0.85 K is feasible. Furthermore,
we show the development of segmented scintillation detectors, where 16 channels
at 1.7 K and one channel at 170 mK were commissioned using a muon beam.


title:
Error propagation in NISQ devices for solving classical optimization
  problems

abstract:
  We propose a random circuit model to analyze the impact of noise on the
performance of variational quantum circuits for classical optimization
problems. Our model accounts for the propagation of arbitrary single qubit
errors through the circuit. We find that even with a small noise rate, the
quality of the obtained classical optima is low on average and a single-qubit
error rate of $1 / nD$, where $n$ is the number of qubits and $D$ is the
circuit depth, is needed for the possibility of a quantum advantage. We
estimate that this translates to an error rate lower than $10^{-6}$ using QAOA
for classical optimization problems with 2D circuits.


title: Lenses for Composable Servers

abstract:
  We implement the semantics of server operations using parameterised lenses.
They allow us to define endpoints and extend them using classical lens
composition. The parameterised nature of lenses models state updates while the
lens laws mimic properties expected from HTTP.
  This first approach to server development is extended to use dependent
parameterised lenses. An upgrade necessary to model not only endpoints, but
entire servers, unlocking the ability to compose them together.


title: Convex Non-negative Matrix Factorization Through Quantum Annealing

abstract:
  In this paper we provide the quantum version of the Convex Non-negative
Matrix Factorization algorithm (Convex-NMF) by using the D-wave quantum
annealer. More precisely, we use D-wave 2000Q to find the low rank
approximation of a fixed real-valued matrix X by the product of two
non-negative matrices factors W and G such that the Frobenius norm of the
difference X-XWG is minimized. In order to solve this optimization problem we
proceed in two steps. In the first step we transform the global real
optimization problem depending on W,G into two quadratic unconstrained binary
optimization problems (QUBO) depending on W and G respectively. In the second
step we use an alternative strategy between the two QUBO problems corresponding
to W and G to find the global solution. The running of these two QUBO problems
on D-wave 2000Q need to use an embedding to the chimera graph of D-wave 2000Q,
this embedding is limited by the number of qubits of D-wave 2000Q. We perform a
study on the maximum number of real data to be used by our approach on D-wave
2000Q. The proposed study is based on the number of qubits used to represent
each real variable. We also tested our approach on D-Wave 2000Q with several
randomly generated data sets to prove that our approach is faster than the
classical approach and also to prove that it gets the best results.


title:
BASiNETEntropy: an alignment-free method for classification of
  biological sequences through complex networks and entropy maximization

abstract:
  The discovery of nucleic acids and the structure of DNA have brought
considerable advances in the understanding of life. The development of
next-generation sequencing technologies has led to a large-scale generation of
data, for which computational methods have become essential for analysis and
knowledge discovery. In particular, RNAs have received much attention because
of the diversity of their functionalities in the organism and the discoveries
of different classes with different functions in many biological processes.
Therefore, the correct identification of RNA sequences is increasingly
important to provide relevant information to understand the functioning of
organisms. This work addresses this context by presenting a new method for the
classification of biological sequences through complex networks and entropy
maximization. The maximum entropy principle is proposed to identify the most
informative edges about the RNA class, generating a filtered complex network.
The proposed method was evaluated in the classification of different RNA
classes from 13 species. The proposed method was compared to PLEK, CPC2 and
BASiNET methods, outperforming all compared methods. BASiNETEntropy classified
all RNA sequences with high accuracy and low standard deviation in results,
showing assertiveness and robustness. The proposed method is implemented in an
open source in R language and is freely available at
https://cran.r-project.org/web/packages/BASiNETEntropy.

categories: q-bio.MN cs.CE cs.IT cs.LG math.IT q-bio.GN


title: Diffusion Models for Counterfactual Explanations

abstract:
  Counterfactual explanations have shown promising results as a post-hoc
framework to make image classifiers more explainable. In this paper, we propose
DiME, a method allowing the generation of counterfactual images using the
recent diffusion models. By leveraging the guided generative diffusion process,
our proposed methodology shows how to use the gradients of the target
classifier to generate counterfactual explanations of input instances. Further,
we analyze current approaches to evaluate spurious correlations and extend the
evaluation measurements by proposing a new metric: Correlation Difference. Our
experimental validations show that the proposed algorithm surpasses previous
State-of-the-Art results on 5 out of 6 metrics on CelebA.


title: High efficiency transmission grating for the ESO CUBES UV spectrograph

abstract:
  CUBES is the Cassegrain U-Band Efficient Spectrograph, a high-efficiency
instrument operating in the UV spectral range between 300nm and 400nm with a
resolution not less than 20000. CUBES is to be installed at a Cassegrain focus
of the Very Large Telescope of the European Southern Observatory. The paper
briefly reviews various types of devices used as dispersing elements in
astronomical spectrographs to achieve high resolution, before identifying
binary transmission gratings produced by microlithography as the best candidate
technology for the CUBES instrument. We describe the lithographic fabrication
technology in general, two different design considerations to achieve the
required high-resolution transmission grating, its prototyping by a
direct-write lithographic fabrication technology, and the characterization of
the achieved optical performance. An outlook to the realization of the grating
for the final instrument, taking the most recent developments of lithographic
writing capabilities into consideration is given.


title: NL-FCOS: Improving FCOS through Non-Local Modules for Object Detection

abstract:
  During the last years, we have seen significant advances in the object
detection task, mainly due to the outperforming results of convolutional neural
networks. In this vein, anchor-based models have achieved the best results.
However, these models require prior information about the aspect and scales of
target objects, needing more hyperparameters to fit. In addition, using anchors
to fit bounding boxes seems far from how our visual system does the same visual
task. Instead, our visual system uses the interactions of different scene parts
to semantically identify objects, called perceptual grouping. An object
detection methodology closer to the natural model is anchor-free detection,
where models like FCOS or Centernet have shown competitive results, but these
have not yet exploited the concept of perceptual grouping. Therefore, to
increase the effectiveness of anchor-free models keeping the inference time
low, we propose to add non-local attention (NL modules) modules to boost the
feature map of the underlying backbone. NL modules implement the perceptual
grouping mechanism, allowing receptive fields to cooperate in visual
representation learning. We show that non-local modules combined with an FCOS
head (NL-FCOS) are practical and efficient. Thus, we establish state-of-the-art
performance in clothing detection and handwritten amount recognition problems.


title:
Nuclear coherent population transfer to the $^{229m}$Th isomer using
  x-ray pulses

abstract:
  Population of the 8 eV $^{229m}$Th isomer via the second nuclear excited
state at 29.19 keV by means of coherent x-ray pulses is investigated
theoretically. We focus on two nuclear coherent population transfer schemes
using partially overlapping x-ray pulses known from quantum optics: stimulated
Raman adiabatic passage (STIRAP), and successive $\pi$ pulses. Numerical
results are presented for three possible experimental setups. Our results
identify the Gamma Factory as the most promising scenario, where two
ultraviolet pulses combined with relativistically accelerated ions deliver the
required intensities for efficient isomer population. Our simulations require
knowledge of the in-band and cross-band nuclear transition probabilities. We
give theoretically predicted values for the latter and discuss them in the
context of recent experiments.


title:
Synthesis and Execution of Communicative Robotic Movements with
  Generative Adversarial Networks

abstract:
  Object manipulation is a natural activity we perform every day. How humans
handle objects can communicate not only the willfulness of the acting, or key
aspects of the context where we operate, but also the properties of the objects
involved, without any need for explicit verbal description. Since human
intelligence comprises the ability to read the context, allowing robots to
perform actions that intuitively convey this kind of information would greatly
facilitate collaboration. In this work, we focus on how to transfer on two
different robotic platforms the same kinematics modulation that humans adopt
when manipulating delicate objects, aiming to endow robots with the capability
to show carefulness in their movements. We choose to modulate the velocity
profile adopted by the robots' end-effector, inspired by what humans do when
transporting objects with different characteristics. We exploit a novel
Generative Adversarial Network architecture, trained with human kinematics
examples, to generalize over them and generate new and meaningful velocity
profiles, either associated with careful or not careful attitudes. This
approach would allow next generation robots to select the most appropriate
style of movement, depending on the perceived context, and autonomously
generate their motor action execution.


title:
Power and Sample Size Computation for Genetic Association Studies of
  Binary Traits: Accounting for Covariate Effects

abstract:
  Power and sample size computation plays an important role in the design and
analysis of genetic association studies. Unlike when analyzing a continuous
trait, the power of association testing between a binary trait and a genetic
variant is influenced by covariate effect sizes, in addition to the genetic
effect size. Motivated by this phenomenon, we thus propose and implement a
unified methodology for power and sample size computation that can account for
the presence of covariate effects of different structures. Extensive simulation
studies show that the proposed method is accurate and computationally efficient
for both prospective and retrospective sampling designs with various covariate
structures. A proof-of-principle application to the UK Biobank data, focusing
on the understudied African sample, shows that ignoring the covariate age
effect leads to overestimated power (underestimated replication sample size)
when analyzing the binary hypertension trait, while the computation for the
continuous blood pressure trait is invariant to covariate effect size.


title: Generalized multiple q-zeta values and characters of vertex algebras

abstract:
  In this note, we analyze several families of vertex algebras whose characters
can be expressed using (generalized) q-MZVs. We analyze: (i) characters of
vertex algebras associated to arc spaces, (ii) characters (or indices) of
certain $\mathcal{S}$-class VOAs in 4d $\mathcal{N}=2$ SCFT, (iii)
supercharacters of the $\mathcal{U}$ family of vertex algebras. Along the way
we also introduce a family of multiple $q$-zeta values associated to simple Lie
algebras and present their conjectural properties.


title:
Nix-TTS: An Incredibly Lightweight End-to-End Text-to-Speech Model via
  Non End-to-End Distillation

abstract:
  We propose Nix-TTS, a lightweight neural TTS (Text-to-Speech) model achieved
by applying knowledge distillation to a powerful yet large-sized generative TTS
teacher model. Distilling a TTS model might sound unintuitive due to the
generative and disjointed nature of TTS architectures, but pre-trained TTS
models can be simplified into encoder and decoder structures, where the former
encodes text into some latent representation and the latter decodes the latent
into speech data. We devise a framework to distill each component in a non
end-to-end fashion. Nix-TTS is end-to-end (vocoder-free) with only 5.23M
parameters or up to 82\% reduction of the teacher model, it achieves over
3.26$\times$ and 8.36$\times$ inference speedup on Intel-i7 CPU and Raspberry
Pi respectively, and still retains a fair voice naturalness and intelligibility
compared to the teacher model. We publicly release Nix-TTS pretrained models
and audio samples in English (https://github.com/rendchevi/nix-tts).


title:
Floquet topological properties in the Non-Hermitian long-range system
  with complex hopping amplitudes

abstract:
  Non-equilibrium phases of matter have attracted much attention in recent
years, among which the Floquet phase is a hot point. In this work, based on the
Periodic driving Non-Hermitian model, we reveal that the winding number
calculated in the framework of the Bloch band theory has a direct connection
with the number of edge states even the Non-Hermiticity is present. Further, we
find that the change of the phase of the hopping amplitude can induce the
topological phase transitions. Precisely speaking, the increase of the value of
the phase can bring the system into the larger topological phase. Moreover, it
can be unveiled that the introduction of the purely imaginary hopping term
brings an extremely rich phase diagram. In addition, we can select the even
topological invariant exactly from the unlimited winding numbers if we only
consider the next-nearest neighbor hopping term. Here, the results obtained may
be useful for understanding the Periodic driving Non-Hermitian theory.


title: Equivalent birational embedding IV: reduced varieties

abstract:
  Two reduced projective schemes are said to be Cremona equivalent if there is
a Cremona map that maps one in the other. In this paper I revise some of the
known results about Cremona equivalence and extend the main result of [MP09] to
reduced schemes. This allows to prove a very general contractibility result for
union of rational subvarieties.


title: Difference-in-Differences for Policy Evaluation

abstract:
  Difference-in-differences is one of the most used identification strategies
in empirical work in economics. This chapter reviews a number of important,
recent developments related to difference-in-differences. First, this chapter
reviews recent work pointing out limitations of two way fixed effects
regressions (these are panel data regressions that have been the dominant
approach to implementing difference-in-differences identification strategies)
that arise in empirically relevant settings where there are more than two time
periods, variation in treatment timing across units, and treatment effect
heterogeneity. Second, this chapter reviews recently proposed alternative
approaches that are able to circumvent these issues without being substantially
more complicated to implement. Third, this chapter covers a number of
extensions to these results, paying particular attention to (i) parallel trends
assumptions that hold only after conditioning on observed covariates and (ii)
strategies to partially identify causal effect parameters in
difference-in-differences applications in cases where the parallel trends
assumption may be violated.