title:
Scintillation light detection in the 6-m drift-length ProtoDUNE Dual
  Phase liquid argon TPC

abstract:
  DUNE is a dual-site experiment for long-baseline neutrino oscillation
studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase
(DP) is a 6x6x6m3 liquid argon time-projection-chamber (LArTPC) that recorded
cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of
the DUNE Far Detector. Charged particles propagating through the LArTPC produce
ionization and scintillation light. The scintillation light signal in these
detectors can provide the trigger for non-beam events. In addition, it adds
precise timing capabilities and improves the calorimetry measurements. In
ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic
muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away
from the ionizing track. In this paper, the ProtoDUNE-DP photon detection
system performance is evaluated with a particular focus on the different
wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr,
considering its future use in giant LArTPCs. The scintillation light production
and propagation processes are analyzed and a comparison of simulation to data
is performed, improving understanding of the liquid argon properties


title:
Kron-based Model-order Reduction of Open Mass-action Kinetics Chemical
  Reaction Networks

abstract:
  We propose a Kron-based model-order reduction method for mass-action kinetics
chemical reaction networks (CRN) with constant inflow and proportional outflow.
The reduced-order models preserve the CRN structure and we establish that the
resulting reduced-order models have the same DC-gain or zero-moment as that of
the full-order ones. Subsequently, we present the spectrum interlacing property
of the Kron-reduced open CRN and propose the use of Gramians-based approach for
single-species single-substrate chemical network to get the upper-bound of
approximation error and to use it in determining a good set of nodes to be
removed systematically. Finally, we evaluate the applicability and efficacy of
our results in two well-known biochemical kinetic models: the activated sludge
model (ASM) 1 and McKeithan's T-cell receptor model.


title: Disparities in greenspace access during COVID-19 mobility restrictions

abstract:
  More than half of the human population lives in cities meaning that most
people predominantly experience nature in urban greenspace. Nature exposure is
an important contributor to social, mental and physical health. As the world
faces a pandemic which threatens the physical and mental health of billions of
people, it is crucial to understand that all have the possibility to access
nature exposure to alleviate some of these challenges. Here, for the first
time, we integrate data from Facebook, Twitter, and Google Search users to show
that people looked for greenspace during COVID-19 mobility restrictions but may
not have always managed to reach it. People spent more time in areas with
greenspace when they could and that depended on the level of multiple
deprivation in the neighbourhood where the greenspace was embedded.
Importantly, while people sought greenspace throughout the first 20 months of
the pandemic, this preference intensified through the waves of lockdown. Living
in an affluent area conferred a greenspace advantage in London and Paris but we
find that in Berlin more deprived neighbourhoods sought greenspace more,
including outside their neighbourhood. This highlights the need to understand
how greenspace access and deprivation interact to create more sustainable
communities.


title: Quantitative De Giorgi methods in kinetic theory for non-local operators

abstract:
  We derive quantitatively the weak and strong Harnack inequality for kinetic
Fokker-Planck type equations with a non-local diffusion operator for the full
range of the non-locality exponent $s \in (0,1)$. This implies H\"older
continuity. We give novel proofs on the boundedness of the bilinear form
associated to the non-local operator and on the construction of a geometric
covering accounting for the non-locality to obtain the Harnack inequalities.
Our results apply to the inhomogeneous Boltzmann equation in the non-cutoff
case.


title:
Theory on electron-phonon spin dehphasing in GaAs multi-electron double
  quantum dots

abstract:
  Recent studies reveal that a double-quantum-dot system hosting more than two
electrons may be superior in certain aspects as compared to the traditional
case in which only two electrons are confined (a singlet-triplet qubit). We
study the electron-phonon dephasing occurring in a GaAs multi-electron
double-quantum-dot system, in a biased case in which the singlet state is
hybridized, as well as in an unbiased case in which the hybridization is
absent. We have found that while the electron-phonon dephasing rate increases
with the number of electrons confined in the unbiased case, this does not hold
in the biased case. We define a merit figure as a ratio between the exchange
energy and the dephasing rate, and have shown that in experimentally relevant
range of the exchange energy, the merit figure actually increases with the
number of electrons in the biased case. Our results show that the
multi-electron quantum-dot system has another advantage in mitigating the
effect of electron-phonon dephasing, which is previously under-appreciated in
the literature.


title: Modular class of Lie $\infty$-algebroids and adjoint representation

abstract:
  We study the modular class of $Q$-manifolds, and in particular of negatively
graded Lie $\infty$-algebroid. We show the equivalence of several descriptions
of those classes, that it matches the classes introduced by various authors and
that the notion is homotopy invariant. In the process, the adjoint and
coadjoint actions up to homotopy of a Lie $\infty$-algebroid are spelled out.
We also wrote down explicitly some dualities, e.g. between representations up
to homotopies of Lie $\infty$-algebroids and their $Q$-manifold equivalent,
which we hope to be of use for future reference.


title:
Perturbing the catenoid: stability and mechanical properties of
  non-axisymmetric minimal surfaces

abstract:
  Minimal surface problems arise naturally in many soft matter systems whose
free energies are dominated by surface or interface energies. Of particular
interest are the shapes, stability and mechanical stresses of minimal surfaces
spanning specific geometric boundaries. The "catenoid" is the best-known
example where an analytical solution is known which describes the form and
stability of a minimal surface held between two parallel, concentric circular
frames. Here we extend this problem to non-axisymmetric, parallel frame shapes
of different orientations, by developing a perturbation approach around the
known catenoid solution. We show that the predictions of the perturbation
theory are in good agreement with experiments on soap films and finite element
simulations (Surface Evolver). Combining theory, experiment and simulation, we
analyse in depth how the shapes, stability and mechanical properties of the
minimal surfaces depend on the type and orientation of elliptical and
three-leaf clover shaped frames. In the limit of perfectly aligned
non-axisymmetric frames, our predictions show excellent agreement with a recent
theory established by Alimov et al (M. M. Alimov, A. V. Bazilevsky and K. G.
Kornev, Physics of Fluids, 2021, 33, 052104). Moreover, we put in evidence the
intriguing capacity of minimal surfaces between non-axisymmetric frames to
transmit a mechanical torque despite being completely liquid. These forces
could be interesting to exploit for mechanical self-assembly of soft matter
systems or as highly sensitive force captors.


title:
Example-based Explanations with Adversarial Attacks for Respiratory
  Sound Analysis

abstract:
  Respiratory sound classification is an important tool for remote screening of
respiratory-related diseases such as pneumonia, asthma, and COVID-19. To
facilitate the interpretability of classification results, especially ones
based on deep learning, many explanation methods have been proposed using
prototypes. However, existing explanation techniques often assume that the data
is non-biased and the prediction results can be explained by a set of
prototypical examples. In this work, we develop a unified example-based
explanation method for selecting both representative data (prototypes) and
outliers (criticisms). In particular, we propose a novel application of
adversarial attacks to generate an explanation spectrum of data instances via
an iterative fast gradient sign method. Such unified explanation can avoid
over-generalisation and bias by allowing human experts to assess the model
mistakes case by case. We performed a wide range of quantitative and
qualitative evaluations to show that our approach generates effective and
understandable explanation and is robust with many deep learning models


title:
Cohomology of $\mathfrak{sl}_3$ and $\mathfrak{gl}_3$ with Coefficients
  in Simple Modules and Weyl Modules in Positive Characteristics

abstract:
  We calculate the cohomology of $\mathfrak{sl}_3(k)$ over an algebraically
closed field $k$ of characteristic $p>3$ with coefficients in simple modules
and Weyl modules. We also give descriptions of the corresponding cohomology of
$\mathfrak{gl}_3(k)$.


title:
On the retrieval of forward-scattered waveforms from acoustic reflection
  and transmission data with the Marchenko equation

abstract:
  A Green's function in an acoustic medium can be retrieved from reflection
data by solving a multidimensional Marchenko equation. This procedure requires
a-priori knowledge of the initial focusing function, which can be interpreted
as the inverse of a transmitted wavefield as it would propagate through the
medium, excluding (multiply) reflected waveforms. In practice, the initial
focusing function is often replaced by a time-reversed direct wave, which is
computed with help of a macro velocity model. Green's functions that are
retrieved under this (direct-wave) approximation typically lack
forward-scattered waveforms and their associated multiple reflections. We
examine whether this problem can be mitigated by incorporating transmission
data. Based on these transmission data, we derive an auxiliary equation for the
forward-scattered components of the initial focusing function. We demonstrate
that this equation can be solved in an acoustic medium with mass density
contrast and constant propagation velocity. By solving the auxiliary and
Marchenko equation successively, we can include forward-scattered waveforms in
our Green's function estimates, as we demonstrate with a numerical example.


title:
Angular momentum transfer via relativistic spin-lattice coupling from
  first principles

abstract:
  The transfer and control of angular momentum is a key aspect for spintronic
applications. Only recently, it was shown that it is possible to transfer
angular momentum from the spin system to the lattice on ultrashort time scales.
In an attempt to contribute to the understanding of angular momentum transfer
between spin and lattice degrees of freedom we present a scheme to calculate
fully-relativistic spin-lattice coupling parameters from first-principles. By
treating changes in the spin configuration and atomic positions at the same
level, closed expressions for the atomic spin-lattice coupling parameters can
be derived in a coherent manner up to any order. Analyzing the properties of
these parameters, in particular their dependence on spin-orbit coupling, we
find that even in bcc Fe the leading term for the angular momentum exchange
between the spin system and the lattice is a Dzyaloshiskii-Moriya-type
interaction, which is due to the symmetry breaking distortion of the lattice.


title:
Fringe visibility and correlation in Mach-Zehnder interferometer with an
  asymmetric beam splitter

abstract:
  We study the wave-particle duality in a general Mach-Zehnder interferometer
with an asymmetric beam splitter from the viewpoint of quantum information
theory. The correlations(including the classical correlation and the quantum
correlation) between the particle and the which-path detector are derived when
they are in pure state or mixed state at the output of Mach-Zehnder
interferometer. It is found that the fringe visibility and the correlations are
effected by the asymmetric beam splitter and the input state of the particle.
The complementary relations between the fringe visibility and the correlations
are also presented.


title:
On the geometry of Einstein-type manifolds with some structural
  conditions

abstract:
  In this paper, we investigate the geometry of Einstein-type equation on a
Riemannian manifold, unifying various particular geometric structures recently
studied in the literature, such as critical point equation and vacuum static
equation. We show various rigidity results of Einstein-type manifolds under
assumptions of several curvature conditions.


title: Density-Diffusion Relationship in Soda-Lime Phosphosilicate

abstract:
  Bioactive glasses release ions such as sodium when implanted in the human
body. However, an excess of the released ions can cause problems related to
cytotoxicity. The ion release control is considered one of the primary
challenges in developing new bioactive glasses. Here, we use molecular dynamics
simulations to investigate the effect of the density on atoms' dynamics in an
archetypal phosphosilicate bioactive system. The diffusion coefficient displays
three main regimes with increasing density. The mobility of the modifiers is
significantly affected by the increase of the density, especially Na, compared
to other elements. We use a modified Arrhenian model to access the complex
dynamic behavior of 45S5 melts and correlate it to the structural changes by
evaluating the network connectivity and pair-excess entropy. Overall, our
results present a step toward the rational design of bioactive glasses and a
key to controlling the ion release of bioactive glasses.


title:
Applying Model Checking to Highly-Configurable Safety Critical Software:
  The SPS-PPS PLC Program

abstract:
  An important aspect of many particle accelerators is the constant evolution
and frequent configuration changes that are needed to perform the experiments
they are designed for. This often leads to the design of configurable software
that can absorb these changes and perform the required control and protection
actions. This design strategy minimizes the engineering and maintenance costs,
but it makes the software verification activities more challenging since safety
properties must be guaranteed for any of the possible configurations. Software
model checking is a popular automated verification technique in many
industries. This verification method explores all possible combinations of the
system model to guarantee its compliance with certain properties or
specification. This is a very appropriate technique for highly configurable
software, since there is usually an enormous amount of combinations to be
checked. This paper presents how PLCverif, a CERN model checking platform, has
been applied to a highly configurable Programmable Logic Controller (PLC)
program, the SPS Personnel Protection System (PPS). The benefits and challenges
of this verification approach are also discussed.


title: Tampered VAE for Improved Satellite Image Time Series Classification

abstract:
  The unprecedented availability of spatial and temporal high-resolution
satellite image time series (SITS) for crop type mapping is believed to
necessitate deep learning architectures to accommodate challenges arising from
both dimensions. Recent state-of-the-art deep learning models have shown
promising results by stacking spatial and temporal encoders. However, we
present a Pyramid Time-Series Transformer (PTST) that operates solely on the
temporal dimension, i.e., neglecting the spatial dimension, can produce
superior results with a drastic reduction in GPU memory consumption and easy
extensibility. Furthermore, we augment it to perform semi-supervised learning
by proposing a classification-friendly VAE framework that introduces clustering
mechanisms into latent space and can promote linear separability therein.
Consequently, a few principal axes of the latent space can explain the majority
of variance in raw data. Meanwhile, the VAE framework with proposed tweaks can
maintain competitive classification performance as its purely discriminative
counterpart when only $40\%$ of labelled data is used. We hope the proposed
framework can serve as a baseline for crop classification with SITS for its
modularity and simplicity.


title:
The Ginzburg-Landau energy with a pinning term oscillating faster than
  the coherence length

abstract:
  The aim of this article is to study the magnetic Ginzburg-Landau functional
with an oscillating pinning term. We consider here oscillations of the pinning
term that are much faster than the coherence length \(\varepsilon>0\) which is
also the inverse of the Ginzburg-Landau parameter. We study both the case of a
periodic potential and of a random stationary ergodic one. We prove that we can
reduce the study of the problem to the case where the pinning term is replaced
by its average, in the periodic case, and by its expectation with respect to
the random parameter in the random case. In order to do that we use a
decoupling of the energy due to Lassoued-Mironescu. This leads us to the study
of the convergence of a scalar positive minimizer of the Ginzburg-Landau energy
with pinning term and with homogeneous Neumann boundary conditions. We prove
uniform convergence of this minimizer towards the mean value of the pinning
term by using a blow-up argument and a Liouville type result for non-vanishing
entire solutions of the real Ginzburg-Landau/Allen-Cahn equation, due to
Farina.


title: Manipulative Attacks and Group Identification

abstract:
  The group identification problem asks to identify a socially qualified
subgroup among a group of individuals based on their pairwise valuations. There
are several different rules that can be used to determine the social
qualification status. In this work, we consider the consent rules, the
consensus-start-respecting rule, and the liberal-start-respecting rule.
  In the context of group identification, a manipulative attack is the attempt
by an outsider to influence the outcome of the selection process through
certain means of manipulation. These means include adding, removing, or
partitioning individuals, as well as bribing individuals to change their
opinion.
  In this work, we provide an overview of manipulative attacks in group
identification as well as group identification with partial profiles. In
particular, we study the computational complexity of the corresponding
problems. Most results presented in this work are aggregated from the
literature, but we also show results for previously unstudied problems; these
include general and exact group control in binary profiles and in ternary
profiles, as well as constructive group control in $r$-profiles. For many
considered problems, we also study the parameterized complexity.


title:
Spline-Based Space-Time Finite Element Approach for Fluid-Structure
  Interaction Problems With a Focus on Fully Enclosed Domains

abstract:
  Non-Uniform Rational B-Spline (NURBS) surfaces are commonly used within
Computer-Aided Design (CAD) tools to represent geometric objects. When using
isogeometric analysis (IGA), it is possible to use such NURBS geometries for
numerical analysis directly. Analyzing fluid flows, however, requires complex
three-dimensional geometries to represent flow domains. Defining a
parametrization of such volumetric domains using NURBS can be challenging and
is still an ongoing topic in the IGA community. With the recently developed
NURBS-enhanced finite element method (NEFEM), the favorable geometric
characteristics of NURBS are used within a standard finite element method. This
is achieved by enhancing the elements touching the boundary by using the NURBS
geometry itself. In the current work, a new variation of NEFEM is introduced,
which is suitable for three-dimensional space-time finite element formulations.
The proposed method makes use of a new mapping which results in a non-Cartesian
formulation suitable for fluid-structure interaction (FSI). This is
demonstrated by combining the method with an IGA formulation in a
strongly-coupled partitioned framework for solving FSI problems. The framework
yields a fully spline-based representation of the fluid-structure interface
through a single NURBS. The coupling conditions at the fluid-structure
interface are enforced through a Robin-Neumann type coupling scheme. This
scheme is particularly useful when considering incompressible fluids in fully
Dirichlet-bounded and curved problems, as it satisfies the incompressibility
constraint on the fluid for each step within the coupling procedure. The
accuracy and performance of the introduced spline-based space-time finite
element approach and its use within the proposed coupled FSI framework are
demonstrated using a series of two- and three-dimensional benchmark problems.


title:
Topological Electromagnetic Effects and Higher Second Chern Numbers in
  Four-Dimensional Gapped Phases

abstract:
  Higher-dimensional topological phases play a key role in understanding the
lower-dimensional topological phases and the related topological responses
through a dimensional reduction procedure. In this work, we present a
Dirac-type model of four-dimensional (4D) $\mathbb{Z}_2$ topological insulator
(TI) protected by $\mathcal{CP}$-symmetry, whose 3D boundary supports an odd
number of Dirac cones. A specific perturbation splits each bulk massive Dirac
cone into two valleys separated in energy-momentum space with opposite second
Chern numbers, in which the 3D boundary modes become a nodal sphere or a Weyl
semimetallic phase. By introducing the electromagnetic (EM) and pseudo-EM
fields, exotic topological responses of our 4D system are revealed, which are
found to be described by the (4+1)D mixed Chern-Simons theories in the
low-energy regime. Notably, several topological phase transitions occur from a
$\mathcal{CP}$-broken $\mathbb{Z}_2$ TI to a $\mathbb{Z}$ TI when the bulk gap
closes by giving rise to exotic double-nodal-line/nodal-hyper-torus gapless
phases. Finally, we propose to probe experimentally these topological effects
in cold atoms.


title:
Learning to Socially Navigate in Pedestrian-rich Environments with
  Interaction Capacity

abstract:
  Existing navigation policies for autonomous robots tend to focus on collision
avoidance while ignoring human-robot interactions in social life. For instance,
robots can pass along the corridor safer and easier if pedestrians notice them.
Sounds have been considered as an efficient way to attract the attention of
pedestrians, which can alleviate the freezing robot problem. In this work, we
present a new deep reinforcement learning (DRL) based social navigation
approach for autonomous robots to move in pedestrian-rich environments with
interaction capacity. Most existing DRL based methods intend to train a general
policy that outputs both navigation actions, i.e., expected robot's linear and
angular velocities, and interaction actions, i.e., the beep action, in the
context of reinforcement learning. Different from these methods, we intend to
train the policy via both supervised learning and reinforcement learning. In
specific, we first train an interaction policy in the context of supervised
learning, which provides a better understanding of the social situation, then
we use this interaction policy to train the navigation policy via multiple
reinforcement learning algorithms. We evaluate our approach in various
simulation environments and compare it to other methods. The experimental
results show that our approach outperforms others in terms of the success rate.
We also deploy the trained policy on a real-world robot, which shows a nice
performance in crowded environments.


title:
RICON: A ML framework for real-time and proactive intervention to
  prevent customer churn

abstract:
  We consider the problem of churn prediction in real-time. Because of the
batch mode of inference generation, the traditional methods can only support
retention campaigns with offline interventions, e.g., test messages, emails or
static in-product nudges. Other recent works in real-time churn predictions do
not assess the cost to accuracy trade-off to deploy such models in production.
In this paper we present RICON, a flexible, cost-effective and robust machine
learning system to predict customer churn propensities in real-time using
clickstream data. In addition to churn propensity prediction, RICON provides
insights based on product usage intelligence. Through application on a real big
data of QBO Advanced customers we showcase how RICON has achieved a top decile
lift of 2.68 in the presence of strong class imbalance. Moreover, we execute an
extensive comparative study to justify our modeling choices for RICON. Finally,
we mention how RICON can be integrated with intervention platforms within
Intuit to run large-scale retention campaigns with real-time in-product
contextual helps.


title: Microscopic origins of the viscosity of a Lennard-Jones liquid

abstract:
  In contrast with crystalline solids or ideal gases, transport properties
remain difficult to describe from a microscopic point of view in liquids, whose
dynamics results from complex energetic and entropic contributions at the
atomic scale. Two scenarii are generally proposed: one pictures dynamics in a
fluid as a series of energy barrier crossings, leading to Arrhenius-like laws,
while the other one assumes atoms rearrange through collisions, as exemplified
by the free volume model. To assess the validity of these two viewpoints, we
computed with molecular dynamics simulations the transport properties of the
Lennard-Jones fluid, and tested how well the Arrhenius equation and the free
volume model describe the temperature dependence of the viscosity and of the
diffusion coefficient at fixed pressure. Although both models reproduce the
simulation results in a large range of pressure and temperature covering the
liquid and supercritical states of the Lennard-Jones fluid, we found that the
parameters of the free volume model can be estimated directly from local
structural parameters, also obtained in the simulations. This consistency of
the results gives more credibility to the free volume description of transport
properties in liquids.


title: Centralised multi link measurement compression with side information

abstract:
  We prove new one shot achievability results for measurement compression of
quantum instruments with side information at the receiver. Unlike previous one
shot results for this problem, our one shot bounds are nearly optimal and do
not need catalytic randomness. In fact, we state a more general problem called
centralised multi link measurement compression with quantum side information
and provide one shot achievability results for it. As a simple corollary, we
obtain one shot measurement compression results for quantum instruments with
side information that we mentioned earlier. All our one shot results lead to
the standard results for this problem in the asymptotic iid setting. We prove
our achievability bounds by first proving a novel sequential classical quantum
multipartite covering lemma, which should be of independent interest.


title: Detecting weak beryllium lines with CUBES

abstract:
  Beryllium is a light element with one single stable isotope, 9Be, which is a
pure product of cosmic-ray spallation in the interstellar medium. Beryllium
abundances in late-type stars can be used in studies about evolutionary mixing,
Galactic chemical evolution, planet engulfment, and the formation of globular
clusters. Some of these uses of Be abundances figure among the science cases of
the Cassegrain U-Band Efficient Spectrograph (CUBES), a new near-UV low- and
medium-resolution spectrograph under development for the Very Large Telescope.
Here, we report on a study about beryllium abundances in extremely metal-poor
stars in the context of the phase A of CUBES. Our motivation is to understand
the limits for the detection of weak lines in extremely metal-poor stars of low
Be abundances. We analyze simulated CUBES observations, performed in
medium-resolution mode, based on synthetic spectra for four mock stars with
[Fe/H] \leq -3.0. We find that detecting the Be lines is possible in certain
cases, but is very challenging and requires high signal-to-noise ratio.
Depending on the atmospheric parameters of the target stars, and if
signal-to-noise per pixel of about 400 can be achieved, it should be possible
to detect Be abundances between log(Be/H) = -13.1 and -13.6, with a typical
uncertainty of \pm 0.15 dex. Using CUBES, the required data for such studies
can be obtained for stars that are fainter by two magnitudes with respect to
what is possible with current instrumentation.


title: Reverse resonant nonspecular reflection controlled by intensity of light

abstract:
  Nonlinear optical features of a plane structure consisted of silicon disks
placed periodically on a silver substrate have been studied in the Littrow
reflection scenario. The structure manifests a bistable resonant reflective
ability. Values of both specular reflectance and reverse one can be tuned by
varying an intensity of incident light.


title:
On the long time behaviour of single stochastic Hodgkin-Huxley neurons
  with constant signal, and a construction of circuits of interacting neurons
  showing self-organized rhythmic oscillations

abstract:
  The stochastic Hodgkin-Huxley neurons considered in this paper replace
time-constant deterministic input $a dt$ of the classical deterministic model
by increments $\vartheta dt + dX_t$ of a stochastic process: $X$ is
Ornstein-Uhlenbeck with volatility $\sigma>0$ and backdriving force $\tau>0$,
and we call $\vartheta>0$ the signal. We have ergodicity and strong laws of
large numbers for various functionals of the process, and characterize 'quiet
behaviour' and 'regular spiking' as events whose probability depends on the
parameters $(\tau,\sigma)$ and on the signal $\vartheta$. The notions of quiet
behaviour and regular spiking allow for a construction of circuits of
interacting stochastic Hodgkin-Huxley neurons, combining excitation with
inhibition according to a bloc structure along the circuit, on which
self-organized rhythmic oscillations can be observed.


title: Recommendation of Compatible Outfits Conditioned on Style

abstract:
  Recommendation in the fashion domain has seen a recent surge in research in
various areas, for example, shop-the-look, context-aware outfit creation,
personalizing outfit creation, etc. The majority of state of the art approaches
in the domain of outfit recommendation pursue to improve compatibility among
items so as to produce high quality outfits. Some recent works have realized
that style is an important factor in fashion and have incorporated it in
compatibility learning and outfit generation. These methods often depend on the
availability of fine-grained product categories or the presence of rich item
attributes (e.g., long-skirt, mini-skirt, etc.). In this work, we aim to
generate outfits conditional on styles or themes as one would dress in real
life, operating under the practical assumption that each item is mapped to a
high level category as driven by the taxonomy of an online portal, like
outdoor, formal etc and an image. We use a novel style encoder network that
renders outfit styles in a smooth latent space. We present an extensive
analysis of different aspects of our method and demonstrate its superiority
over existing state of the art baselines through rigorous experiments.


title: AdaGrid: Adaptive Grid Search for Link Prediction Training Objective

abstract:
  One of the most important factors that contribute to the success of a machine
learning model is a good training objective. Training objective crucially
influences the model's performance and generalization capabilities. This paper
specifically focuses on graph neural network training objective for link
prediction, which has not been explored in the existing literature. Here, the
training objective includes, among others, a negative sampling strategy, and
various hyperparameters, such as edge message ratio which controls how training
edges are used. Commonly, these hyperparameters are fine-tuned by complete grid
search, which is very time-consuming and model-dependent. To mitigate these
limitations, we propose Adaptive Grid Search (AdaGrid), which dynamically
adjusts the edge message ratio during training. It is model agnostic and highly
scalable with a fully customizable computational budget. Through extensive
experiments, we show that AdaGrid can boost the performance of the models up to
$1.9\%$ while being nine times more time-efficient than a complete search.
Overall, AdaGrid represents an effective automated algorithm for designing
machine learning training objectives.


title: Rabbit, toad, and the Moon: Can machine categorize them into one class?

abstract:
  Recent machine learning algorithms such as neural networks can classify
objects and actions in video frames with high accuracy. Here, I discuss a
classification of objects based on basal dynamic patterns referencing one
tradition, the link between rabbit, toad, and the Moon, which can be seen in
several cultures. In order for them to be classified into one class, a basic
pattern of behavior (cyclic appearance and disappearance) works as a feature
point. A static character such as the shape and time scale of the behavior are
not essential for this classification. In cognitive semantics, image schemas
are introduced to describe basal patterns of events. If learning of these image
schemas is attained, a machine may be able to categorize rabbit, toad, and the
Moon as the same class. For learning, video frames that show boundary boxes or
segmentation may be helpful. Although this discussion is preliminary and many
tasks remain to be solved, the classification based on basal behaviors can be
an important topic for cognitive processes and computer science.


title:
Compressed Channel Estimation for IRS-Assisted Millimeter Wave OFDM
  Systems: A Low-Rank Tensor Decomposition-Based Approach

abstract:
  We consider the problem of downlink channel estimation for intelligent
reflecting surface (IRS)-assisted millimeter Wave (mmWave) orthogonal frequency
division multiplexing (OFDM) systems. By exploring the inherent sparse
scattering characteristics of mmWave channels, we show that the received
signals can be expressed as a low-rank third-order tensor that admits a tensor
rank decomposition, also known as canonical polyadic decomposition (CPD). A
structured CPD-based method is then developed to estimate the channel
parameters. Our analysis reveals that the training overhead required by our
proposed method is as low as O(U^2), where U denotes the sparsity of the
cascade channel. Simulation results are provided to illustrate the efficiency
of the proposed method.


title: Symbolic music generation conditioned on continuous-valued emotions

abstract:
  In this paper we present a new approach for the generation of
multi-instrument symbolic music driven by musical emotion. The principal
novelty of our approach centres on conditioning a state-of-the-art transformer
based on continuous-valued valence and arousal labels. In addition, we provide
a new large-scale dataset of symbolic music paired with emotion labels in terms
of valence and arousal. We evaluate our approach in a quantitative manner in
two ways, first by measuring its note prediction accuracy, and second via a
regression task in the valence-arousal plane. Our results demonstrate that our
proposed approaches outperform conditioning using control tokens which is
representative of the current state of the art.


title:
Kalman Filter Design for Intermittent Optical Wireless Communication
  Systems on Time Scales

abstract:
  Time-scale theory, due to its ability to unify the continuous and discrete
cases, allows handling intractable non-uniform measurements, such as
intermittent received signals. In this work, we address the state estimation
problem of a vibration-induced intermittent optical wireless communication
(OWC) system by designing a Kalman filter on time scales. First, the algorithm
of the time-scale Kalman filter is introduced and a numerical example is given
for illustration. Then the studied intermittent OWC system is presented, and
experimental data are collected to determine the time scale's form, which has
bounded graininess (a.k.a, bounded time jumps). Finally, we design a Kalman
filter on the previously defined time scale for the intermittent OWC system and
critically analyzed its estimation performance. Moreover, the obtained
conclusions are further validated on a reference system. The simulation results
corroborate that the time-scale Kalman filtering technique is considerably
promising to solve the state estimation problem with non-uniform measurements.
This study reveals for the first time the feasibility of applying the
time-scale Kalman filter theory to practical applications.


title: Quantum Monte Carlo in Configuration Space with Three-Nucleon Forces

abstract:
  Neutron matter, through its connection to neutron stars as well as systems
like cold atom gases, is one of the most interesting yet accessible systems in
nuclear physics. The Configuration-Interaction Monte Carlo (CIMC) method is a
stochastic many-body technique allowing to tackle strongly coupled systems. In
contrast to other Quantum Monte Carlo methods employed in nuclear physics, the
CIMC method can be formulated directly in momentum space allowing for an
efficient use of non-local interactions. In this work we extend CIMC method to
include three-nucleon interactions through the normal-ordered two-body
approximation. We present results for the equation of state of neutron matter
in line with other many-body calculations with low resolution chiral
interactions, and provide predictions for the momentum distribution and the
static structure factor.


title:
FLOAT: Factorized Learning of Object Attributes for Improved
  Multi-object Multi-part Scene Parsing

abstract:
  Multi-object multi-part scene parsing is a challenging task which requires
detecting multiple object classes in a scene and segmenting the semantic parts
within each object. In this paper, we propose FLOAT, a factorized label space
framework for scalable multi-object multi-part parsing. Our framework involves
independent dense prediction of object category and part attributes which
increases scalability and reduces task complexity compared to the monolithic
label space counterpart. In addition, we propose an inference-time 'zoom'
refinement technique which significantly improves segmentation quality,
especially for smaller objects/parts. Compared to state of the art, FLOAT
obtains an absolute improvement of 2.0% for mean IOU (mIOU) and 4.8% for
segmentation quality IOU (sqIOU) on the Pascal-Part-58 dataset. For the larger
Pascal-Part-108 dataset, the improvements are 2.1% for mIOU and 3.9% for sqIOU.
We incorporate previously excluded part attributes and other minor parts of the
Pascal-Part dataset to create the most comprehensive and challenging version
which we dub Pascal-Part-201. FLOAT obtains improvements of 8.6% for mIOU and
7.5% for sqIOU on the new dataset, demonstrating its parsing effectiveness
across a challenging diversity of objects and parts. The code and datasets are
available at floatseg.github.io.


title:
Detecting Unassimilated Borrowings in Spanish: An Annotated Corpus and
  Approaches to Modeling

abstract:
  This work presents a new resource for borrowing identification and analyzes
the performance and errors of several models on this task. We introduce a new
annotated corpus of Spanish newswire rich in unassimilated lexical borrowings
-- words from one language that are introduced into another without
orthographic adaptation -- and use it to evaluate how several sequence labeling
models (CRF, BiLSTM-CRF, and Transformer-based models) perform. The corpus
contains 370,000 tokens and is larger, more borrowing-dense, OOV-rich, and
topic-varied than previous corpora available for this task. Our results show
that a BiLSTM-CRF model fed with subword embeddings along with either
Transformer-based embeddings pretrained on codeswitched data or a combination
of contextualized word embeddings outperforms results obtained by a
multilingual BERT-based model.


title:
A Universal Profile for Stacked Filaments from Cold Dark Matter
  Simulations

abstract:
  We study the stacked filaments connecting group-mass halo pairs, using
dark-matter-only N-body simulations. We calculate the dark matter over-density
profile of these stacked filaments at different redshifts as a function of the
distance perpendicular to the filament axis. A four-parameter universal
functional form, including three comoving scale radii and one amplitude
parameter (core density), provides a good fit out to a radius of 20 cMpc/h for
stacked filaments over a range of redshifts, lengths and masses. The scale
radii are approximately independent of redshift but increase as power-laws with
the comoving filament length. Lastly, we compare the scaling of the filament
mass measured directly from the simulations to the predicted scaling from the
halo-halo-matter three-point correlation function as a function of redshift and
of the mass of the halo pairs. We find that both measured scalings are similar
to, but somewhat shallower than the predictions, by 10% and 30%, respectively.
These results provide a template to interpret present and upcoming
observational results based on stacking, for example, weak lensing, thermal and
kinetic Sunyaev-Zel'dovich, or X-ray observations.


title: Anticipatory Counterplanning

abstract:
  In competitive environments, commonly agents try to prevent opponents from
achieving their goals. Most previous preventing approaches assume the
opponent's goal is known a priori. Others only start executing actions once the
opponent's goal has been inferred. In this work we introduce a novel
domain-independent algorithm called Anticipatory Counterplanning. It combines
inference of opponent's goals with computation of planning centroids to yield
proactive counter strategies in problems where the opponent's goal is unknown.
Experimental results show how this novel technique outperforms reactive
counterplanning, increasing the chances of stopping the opponent from achieving
its goals.


title:
Self-Distillation from the Last Mini-Batch for Consistency
  Regularization

abstract:
  Knowledge distillation (KD) shows a bright promise as a powerful
regularization strategy to boost generalization ability by leveraging learned
sample-level soft targets. Yet, employing a complex pre-trained teacher network
or an ensemble of peer students in existing KD is both time-consuming and
computationally costly. Various self KD methods have been proposed to achieve
higher distillation efficiency. However, they either require extra network
architecture modification or are difficult to parallelize. To cope with these
challenges, we propose an efficient and reliable self-distillation framework,
named Self-Distillation from Last Mini-Batch (DLB). Specifically, we rearrange
the sequential sampling by constraining half of each mini-batch coinciding with
the previous iteration. Meanwhile, the rest half will coincide with the
upcoming iteration. Afterwards, the former half mini-batch distills on-the-fly
soft targets generated in the previous iteration. Our proposed mechanism guides
the training stability and consistency, resulting in robustness to label noise.
Moreover, our method is easy to implement, without taking up extra run-time
memory or requiring model structure modification. Experimental results on three
classification benchmarks illustrate that our approach can consistently
outperform state-of-the-art self-distillation approaches with different network
architectures. Additionally, our method shows strong compatibility with
augmentation strategies by gaining additional performance improvement. The code
is available at https://github.com/Meta-knowledge-Lab/DLB.


title:
Analytical scaling solutions for the evolution of cosmic domain walls in
  a parameter-free velocity-dependent one-scale model

abstract:
  We derive an analytical approximation for the linear scaling evolution of the
characteristic length $L$ and the root-mean-squared velocity $\sigma_v$ of
standard frictionless domain wall networks in
Friedmann-Lema\^itre-Robertson-Walker universes with a power law evolution of
the scale factor $a$ with the cosmic time $t$ ($a \propto t^\lambda$). This
approximation, obtained using a recently proposed parameter-free
velocity-dependent one-scale model for domain walls, reproduces well the model
predictions for $\lambda$ close to unity, becoming exact in the $\lambda \to
1^-$ limit. We use this approximation, in combination with the exact results
found for $\lambda=0$, to obtain a fit to the model predictions valid for
$\lambda \in [0, 1[$ with a maximum error of the order of $1 \%$. This fit is
also in good agreement with the results of field theory numerical simulations,
specially for $\lambda \in [0.9, 1[$. Finally, we explicitly show that the
phenomenological energy-loss parameter of the original velocity-dependent
one-scale model for domain walls vanishes in the $\lambda \to 1^-$ limit and
discuss the implications of this result.


title:
Status and plans of the Gravitational Behaviour of Antihydrogen at Rest
  experiment

abstract:
  The GBAR experiment aims to directly test the Weak Equivalence Principle of
ultracold antihydrogen in Earth's gravitational field. The gravitational
acceleration $\bar{g}$ will be measured to a precision of $1\,\%$ using a
classical free fall of the anti-atoms from a fixed height. Reaching a precision
of $10^{-6}$ is planned by performing a "quantum free fall" experiment by
detecting quantum interference of $\mathrm{\bar{H}}$ due to quantum
gravitational bound states above a reflecting surface. Additionally, a
$\mathrm{\bar{H}}$ Lamb shift measurement is being prepared in parallel that
will allow to determine the antiproton charge radius at the level of $10\,\%$.


title:
When Gamification Spoils Your Learning: A Qualitative Case Study of
  Gamification Misuse in a Language-Learning App

abstract:
  More and more learning apps like Duolingo are using some form of gamification
(e.g., badges, points, and leaderboards) to enhance user learning. However,
they are not always successful. Gamification misuse is a phenomenon that occurs
when users become too fixated on gamification and get distracted from learning.
This undesirable phenomenon wastes users' precious time and negatively impacts
their learning performance. However, there has been little research in the
literature to understand gamification misuse and inform future gamification
designs. Therefore, this paper aims to fill this knowledge gap by conducting
the first extensive qualitative research on gamification misuse in a popular
learning app called Duolingo. Duolingo is currently the world's most downloaded
learning app used to learn languages. This study consists of two phases: (I) a
content analysis of data from Duolingo forums (from the past nine years) and
(II) semi-structured interviews with 15 international Duolingo users. Our
research contributes to the Human-Computer Interaction (HCI) and Learning at
Scale (L@S) research communities in three ways: (1) elaborating the
ramifications of gamification misuse on user learning, well-being, and ethics,
(2) identifying the most common reasons for gamification misuse (e.g.,
competitiveness, overindulgence in playfulness, and herding), and (3) providing
designers with practical suggestions to prevent (or mitigate) the occurrence of
gamification misuse in their future designs of gamified learning apps.


title:
Enabling Trade-offs in Machine Learning-based Matching for Refugee
  Resettlement

abstract:
  The Swiss State Secretariat for Migration recently announced a pilot project
for a machine learning-based assignment process for refugee resettlement. This
approach has the potential to substantially increase the overall employment
rate of refugees in Switzerland. However, the currently proposed method ignores
families' preferences. In this paper, we build on this prior work and propose
two matching mechanisms that additionally take families' preferences over
locations into account. The first mechanism is strategyproof while the second
is not but achieves higher family welfare. Importantly, we parameterize both
mechanisms, giving placement officers precise control how to trade off family
welfare against overall employment success. Preliminary simulations on
synthetic data show that both mechanisms can significantly increase family
welfare even with only a small loss on the overall employment rate of refugees.


title: Marginalized Operators for Off-policy Reinforcement Learning

abstract:
  In this work, we propose marginalized operators, a new class of off-policy
evaluation operators for reinforcement learning. Marginalized operators
strictly generalize generic multi-step operators, such as Retrace, as special
cases. Marginalized operators also suggest a form of sample-based estimates
with potential variance reduction, compared to sample-based estimates of the
original multi-step operators. We show that the estimates for marginalized
operators can be computed in a scalable way, which also generalizes prior
results on marginalized importance sampling as special cases. Finally, we
empirically demonstrate that marginalized operators provide performance gains
to off-policy evaluation and downstream policy optimization algorithms.


title: No Periodic Geodesics in Jet Space

abstract:
  The $J^k$ space of $k$-jets of a real function of one real variable $x$
admits the structure of a \sR manifold, which then has an associated
Hamiltonian geodesic flow, which is integrable. As in any Hamiltonian flow, a
natural question is the existence of periodic solutions. Does $J^k$ have
periodic geodesics? This study will demonstrate that geodesics on $J^k$ are
never periodic. To answer this question the action-angle coordinates will be
constructed.


title: Matrices and Spans and internalization and enrichment in tricategories

abstract:
  We introduce categories $\M$ and $\S$ internal in the tricategory $\Bicat_3$
of bicategories, pseudofunctors, pseudonatural transformations and
modifications, for matrices and spans in a 1-strict tricategory $V$. Their
horizontal tricategories are the tricategories of matrices and spans in $V$.
Both the internal and the enriched constructions are tricategorifications of
the corresponding constructions in 1-categories. Following \cite{FGK} we
introduce monads and their vertical morphisms in categories internal in
tricategories. We prove an equivalent condition for when the internal
categories for matrices $\M$ and spans $\S$ in a 1-strict tricategory $V$ are
equivalent, and deduce that in that case their corresponding categories of
(strict) monads and vertical monad morphisms are equivalent, too. We prove that
the latter categories are isomorphic to those of categories enriched and
discretely internal in $V$, respectively. As a byproduct of our tricategorical
constructions we recover some results from \cite{Fem}. Truncating to
1-categories we recover results from \cite{CFP} and \cite{Ehr} on the
equivalence of enriched and discretely internal 1-categories.


title:
Millimeter-Wave Sensing for Avoidance of High-Risk Ground Conditions for
  Mobile Robots

abstract:
  Mobile robot autonomy has made significant advances in recent years, with
navigation algorithms well developed and used commercially in certain
well-defined environments, such as warehouses. The common link in usage
scenarios is that the environments in which the robots are utilized have a high
degree of certainty. Operating environments are often designed to be robot
friendly, for example augmented reality markers are strategically placed and
the ground is typically smooth, level, and clear of debris. For robots to be
useful in a wider range of environments, especially environments that are not
sanitized for their use, robots must be able to handle uncertainty. This
requires a robot to incorporate new sensors and sources of information, and to
be able to use this information to make decisions regarding navigation and the
overall mission. When using autonomous mobile robots in unstructured and poorly
defined environments, such as a natural disaster site or in a rural
environment, ground condition is of critical importance and is a common cause
of failure. Examples include loss of traction due to high levels of ground
water, hidden cavities, or material boundary failures. To evaluate a
non-contact sensing method to mitigate these risks, Frequency Modulated
Continuous Wave (FMCW) radar is integrated with an Unmanned Ground Vehicle
(UGV), representing a novel application of FMCW to detect new measurands for
Robotic Autonomous Systems (RAS) navigation, informing on terrain integrity and
adding to the state-of-the-art in sensing for optimized autonomous path
planning. In this paper, the FMCW is first evaluated in a desktop setting to
determine its performance in anticipated ground conditions. The FMCW is then
fixed to a UGV and the sensor system is tested and validated in a
representative environment containing regions with significant levels of ground
water saturation.


title: Dynamic Model Tree for Interpretable Data Stream Learning

abstract:
  Data streams are ubiquitous in modern business and society. In practice, data
streams may evolve over time and cannot be stored indefinitely. Effective and
transparent machine learning on data streams is thus often challenging.
Hoeffding Trees have emerged as a state-of-the art for online predictive
modelling. They are easy to train and provide meaningful convergence guarantees
under a stationary process. Yet, at the same time, Hoeffding Trees often
require heuristic and costly extensions to adjust to distributional change,
which may considerably impair their interpretability. In this work, we revisit
Model Trees for machine learning in evolving data streams. Model Trees are able
to maintain more flexible and locally robust representations of the active data
concept, making them a natural fit for data stream applications. Our novel
framework, called Dynamic Model Tree, satisfies desirable consistency and
minimality properties. In experiments with synthetic and real-world tabular
streaming data sets, we show that the proposed framework can drastically reduce
the number of splits required by existing incremental decision trees. At the
same time, our framework often outperforms state-of-the-art models in terms of
predictive quality -- especially when concept drift is involved. Dynamic Model
Trees are thus a powerful online learning framework that contributes to more
lightweight and interpretable machine learning in data streams.


title: Groups with $\mathsf A_\ell$-commutator relations

abstract:
  If $A$ is a unital associative ring and $\ell \geq 2$, then the general
linear group $\mathrm{GL}(\ell, A)$ has root subgroups $U_\alpha$ and Weyl
elements $n_\alpha$ for $\alpha$ from the root system of type $\mathsf A_{\ell
- 1}$. Conversely, if an arbitrary group has such root subgroups and Weyl
elements for $\ell \geq 4$ satisfying natural conditions, then there is a way
to recover the ring $A$. We prove a generalization of this result not using the
Weyl elements, so instead of the matrix ring $\mathrm M(\ell, A)$ we construct
a non-unital associative ring with a well-behaved Peirce decomposition.


title:
On the global regularity for minimizers of variational integrals:
  splitting-type problems in 2D and extensions to the general anisotropic
  setting

abstract:
  We mainly discuss superquadratic minimization problems for splitting-type
variational integrals on a bounded Lipschitz domain $\Omega \subset
\mathbb{R}^2$ and prove higher integrability of the gradient up to the boundary
by incorporating an appropriate weight-function measuring the distance of the
solution to the boundary data. As a corollary, the local H\"older coefficient
with respect to some improved H\"older continuity is quantified in terms of the
function ${\rm dist}(\cdot,\partial \Omega)$. The results are extended to
anisotropic problems without splitting structure under natural growth and
ellipticity conditions. In both cases we argue with variants of Caccioppoli's
inequality involving small weights


title:
Reduction of the lasing threshold in optically pumped AlGaN/GaN lasers
  with two-step etched facets

abstract:
  We report a two-step process to obtain smooth and vertical {10-10} m-plane
facets in AlGaN/GaN separate confinement heterostructures designed to fabricate
UV lasers emitting at 355 nm. The process consists in a dry etching by RIE-ICP
combined with a crystallographic-selective wet etching process using a
KOH-based solution. The anisotropy in the wet etching rates between the
different crystallographic planes of the AlGaN structure, allows the
fabrication of flat and parallel facets without a degradation of the
multilayered ensemble. The optical performance of the lasers display a major
improved when using the two-step process for the definition of the cavity, in
comparison to cavities fabricated by mechanical cleaving, with the lasing
threshold under optical pumping being reduced to almost half.

categories: physics.optics cond-mat.mes-hall cond-mat.mtrl-sci physics.app-ph


title: Reshaping the Jaynes-Cummings ladder with Majorana bound states

abstract:
  We study the optical properties of a hybrid device composed by a quantum dot
(QD) resonantly coupled to a photonic mode of an optical microcavity and a
Majorana nanowire: a topological superconducting segment hosting Majorana bound
states (MBSs) at the opposite ends. In the regime of strong light-matter
coupling, it is demonstrated that the leakage of the Majorana mode into the QD
opens new optical transitions between polaritonic states formed due to
hybridisation of material excitation with cavity photons, which leads to the
reshaping of the Jaynes-Cummings ladder and can lead to the formation of a
robust single-peak at cavity eigenfrequency in the emission spectrum. Moreover,
weak satellite peaks in the low and high frequency regions are revealed for the
distinct cases of highly isolated MBSs, overlapped MBSs and MBSs not well
localized at the nanowire ends.


title: On the eccentricity matrices of trees: Inertia and spectral symmetry

abstract:
  The \textit{eccentricity matrix} $\mathcal{E}(G)$ of a connected graph $G$ is
obtained from the distance matrix of $G$ by keeping the largest non-zero
entries in each row and each column, and leaving zeros in the remaining ones.
The eigenvalues of $\mathcal{E}(G)$ are the \textit{$\mathcal{E}$-eigenvalues}
of $G$. In this article, we find the inertia of the eccentricity matrices of
trees. Interestingly, any tree on more than $4$ vertices with odd diameter has
two positive and two negative $\mathcal{E}$-eigenvalues (irrespective of the
structure of the tree). A tree with even diameter has the same number of
positive and negative $\mathcal{E}$-eigenvalues, which is equal to the number
of 'diametrically distinguished' vertices (see Definition 3.1). Besides we
prove that the spectrum of the eccentricity matrix of a tree is symmetric with
respect to the origin if and only if the tree has odd diameter. As an
application, we characterize the trees with three distinct
$\mathcal{E}$-eigenvalues.


title:
Auto-MLM: Improved Contrastive Learning for Self-supervised
  Multi-lingual Knowledge Retrieval

abstract:
  Contrastive learning (CL) has become a ubiquitous approach for several
natural language processing (NLP) downstream tasks, especially for question
answering (QA). However, the major challenge, how to efficiently train the
knowledge retrieval model in an unsupervised manner, is still unresolved.
Recently the commonly used methods are composed of CL and masked language model
(MLM). Unexpectedly, MLM ignores the sentence-level training, and CL also
neglects extraction of the internal info from the query. To optimize the CL
hardly obtain internal information from the original query, we introduce a
joint training method by combining CL and Auto-MLM for self-supervised
multi-lingual knowledge retrieval. First, we acquire the fixed dimensional
sentence vector. Then, mask some words among the original sentences with random
strategy. Finally, we generate a new token representation for predicting the
masked tokens. Experimental results show that our proposed approach
consistently outperforms all the previous SOTA methods on both AliExpress $\&$
LAZADA service corpus and openly available corpora in 8 languages.


title:
From Pandemic to a New Normal: Strategies to Optimise Governmental
  Interventions in Indonesia Based on an SVEIQHR-Type Mathematical Model

abstract:
  There are five different forms of intervention presently realised by the
Indonesian government in an effort to end the COVID-19 pandemic: vaccinations,
social restrictions, tracings, testings, and treatments. In this paper, we
construct an SVEIQHR-type mathematical model for the disease's spread in the
country, which incorporates as parameters the rates of the above interventions,
as well as the vaccine's efficacy. We determine the model's equilibria and
basic reproduction number. Using the model, we formulate strategies by which
the interventions should be realised in order to optimise their impact. The
results show that, in a disease-free state, when the number of new cases rises,
the best strategy is to implement social restrictions, whereas in an endemic
state, if a near-lockdown policy is undesirable, carrying out vaccinations is
the best strategy; however, efforts should be aimed not primarily towards
increasing the vaccination rate, but towards the use of high-efficacy vaccines.


title:
Quantitative determination of interlayer electronic coupling at various
  critical points in bilayer MoS2

abstract:
  Tailoring interlayer coupling has emerged as a powerful tool to tune the
electronic structure of van der Waals (vdW) bilayers. One example is the usage
of the moire pattern to create controllable two-dimensional electronic
superlattices through the configurational dependence of interlayer electronic
couplings. This approach has led to some remarkable discoveries in twisted
graphene bilayers, and transition metal dichalcogenide (TMD) homo- and
hetero-bilayers. However, a largely unexplored factor is the interlayer
distance, d, which can impact the interlayer coupling strength exponentially.
In this letter, we quantitatively determine the coupling strengths as a
function of interlayer spacing at various critical points of the Brillouin zone
in bilayer MoS2. The exponential dependence of the coupling parameter on the
gap distance is demonstrated. Most significantly, we achieved a 280%
enhancement of K-valley coupling strength with an 8% reduction of the vdW gap,
pointing to a new strategy in designing a novel electronic system in vdW
bilayers.


title: Multidimensional hyperspin machine

abstract:
  From condensed matter to quantum chromodynamics, multidimensional spins are a
fundamental paradigm, with a pivotal role in combinatorial optimization and
machine learning. Machines formed by coupled parametric oscillators can
simulate spin models, but only for Ising or low-dimensional spins. Currently,
machines implementing arbitrary dimensions remain a challenge. Here, we
introduce and validate a hyperspin machine to simulate multidimensional
continuous spin models. We realize high-dimensional spins by pumping groups of
parametric oscillators, and study NP-hard graphs of hyperspins. The hyperspin
machine can interpolate between different dimensions by tuning the coupling
topology, a strategy that we call "dimensional annealing". When interpolating
between the XY and the Ising model, the dimensional annealing impressively
increases the success probability compared to conventional Ising simulators.
Hyperspin machines are a new computational model for combinatorial
optimization. They can be realized by off-the-shelf hardware for ultrafast,
large-scale applications in classical and quantum computing, condensed-matter
physics, and fundamental studies.


title: An alternative proof of Tataru's dispersive estimates

abstract:
  The aim of this article is to give an alternative proof of Tataru's
dispersive estimates for wave equations posed on the hyperbolic space. Based on
the formula for the wave kernel on hyperbolic spaces, in \cite{MR2743652}, we
give the proof from the perspective of Bessel potentials, by exploiting various
facts about Gamma functions, modified Bessel functions, and Bessel potentials.
This leads to our proof being more self-contained than that in Tataru
\cite{MR1804518}.


title: Large surface acoustic wave nonreciprocity in synthetic antiferromagnets

abstract:
  We have studied the transmission of surface acoustic waves (SAWs) in
ferromagnetic/non-magnetic/ferromagnetic tryilayers. The SAW scattering matrix
is studied for devices with various non-magnetic spacer thickness, which
defines the strength of the interlayer exchange coupling. We find the SAW
transmission amplitude depends on their propagation direction when the two
ferromagnetic layers are coupled antiferromagnetically. The degree of such SAW
nonreciprocity increases with increasing exchange coupling strength and reaches
37 dB/mm for a device with the thinnest spacer layer. These results show the
potential of interlayer exchange coupled synthetic antiferromagnets for viable
acoustic nonreciprocal transmission devices, such as circulators and isolators.


title:
Probing phoneme, language and speaker information in unsupervised speech
  representations

abstract:
  Unsupervised models of representations based on Contrastive Predictive Coding
(CPC)[1] are primarily used in spoken language modelling in that they encode
phonetic information. In this study, we ask what other types of information are
present in CPC speech representations. We focus on three categories: phone
class, gender and language, and compare monolingual and bilingual models. Using
qualitative and quantitative tools, we find that both gender and phone class
information are present in both types of models. Language information, however,
is very salient in the bilingual model only, suggesting CPC models learn to
discriminate languages when trained on multiple languages. Some language
information can also be retrieved from monolingual models, but it is more
diffused across all features. These patterns hold when analyses are carried on
the discrete units from a downstream clustering model. However, although there
is no effect of the number of target clusters on phone class and language
information, more gender information is encoded with more clusters. Finally, we
find that there is some cost to being exposed to two languages on a downstream
phoneme discrimination task.


title: FlowFormer: A Transformer Architecture for Optical Flow

abstract:
  We introduce Optical Flow TransFormer (FlowFormer), a transformer-based
neural network architecture for learning optical flow. FlowFormer tokenizes the
4D cost volume built from an image pair, encodes the cost tokens into a cost
memory with alternate-group transformer (AGT) layers in a novel latent space,
and decodes the cost memory via a recurrent transformer decoder with dynamic
positional cost queries. On the Sintel benchmark clean pass, FlowFormer
achieves 1.178 average end-ponit-error (AEPE), a 15.1% error reduction from the
best published result (1.388). Besides, FlowFormer also achieves strong
generalization performance. Without being trained on Sintel, FlowFormer
achieves 1.00 AEPE on the Sintel training set clean pass, outperforming the
best published result (1.29) by 22.4%.


title: On the Road to Online Adaptation for Semantic Image Segmentation

abstract:
  We propose a new problem formulation and a corresponding evaluation framework
to advance research on unsupervised domain adaptation for semantic image
segmentation. The overall goal is fostering the development of adaptive
learning systems that will continuously learn, without supervision, in
ever-changing environments. Typical protocols that study adaptation algorithms
for segmentation models are limited to few domains, adaptation happens offline,
and human intervention is generally required, at least to annotate data for
hyper-parameter tuning. We argue that such constraints are incompatible with
algorithms that can continuously adapt to different real-world situations. To
address this, we propose a protocol where models need to learn online, from
sequences of temporally correlated images, requiring continuous, frame-by-frame
adaptation. We accompany this new protocol with a variety of baselines to
tackle the proposed formulation, as well as an extensive analysis of their
behaviors, which can serve as a starting point for future research.


title: $\tau_{B_c}$ in the Standard Model

abstract:
  The Standard Model prediction of the $B_c$ lifetime is discussed, together
with the dominant uncertainties and strategies on how to improve them.
Furthermore, a new method to compute the $B_c$ lifetime based on the operator
product expansion is proposed. It relies on differences of $B,\,D$ and $B_c$
meson decay rates, in which the free-quark contributions cancel out, reducing
the uncertainty of the theory prediction.


title:
Diffusion evolution of a pore in bounded particle in a hydrogen
  atmosphere

abstract:
  The problem of the diffusion evolution of a pore filled with molecular
hydrogen in a spherical granule in a hydrogen medium is solved. The initial
position of the pore is displaced relative to the center of the granule. A
nonlinear system of equations is obtained, which describes the behavior of the
size of the gas-filled pore, the amount of gas in it and its position relative
to the center of the bounded particle with time. Numerical calculations have
shown the existence of two stages of evolution. The first (fast) stage is
associated with the equalization of pressure in the pore with the external. The
second is the slow diffusion "healing" of the pore, when the amount of gas
adjusts to its size and the gas pressure is approximately equal to the
external.


title:
Evidence of hard power-law spectral cutoff and disc reflection from the
  X-ray transient XTE~J1739-285

abstract:
  We report on the nearly simultaneous \nicer{} and \nustar{} observations of
the known X-ray transient XTE~J1739-285. These observations provide the first
sensitive hard X-ray spectrum of this neutron star X-ray transient. The source
was observed on 2020 February 19 in the hard spectral state with a luminosity
of $0.007$ of the Eddington limit. The broadband $1-70 \kev{}$ \nicer{} and
\nustar{} observation clearly detects a cutoff of the hard spectral component
around $34-40 \kev{}$ when the continuum is fitted by a soft thermal component
and a hard power-law component. This feature has been detected for the first
time in this source. Moreover, the spectrum shows evidence for disc reflection
-- a relativistically broadened Fe K$\alpha$ line around $5-8 \kev{}$ and a
Compton hump in the $10-20 \kev{}$ energy band. The accretion disc reflection
features have not been identified before from this source. Through accretion
disc reflection modeling, we constrain the radius of the inner disc to be
$R_{in}=3.1_{-0.5}^{+1.8}\;R_{ISCO}$ for the first time. In addition, we find a
low inclination, $i\sim 33^{0}$. The magnetosphere may be responsible for the
truncation of the inner accretion disc above the stellar surface -- if so, it
will provide an upper limit of the magnetic field strength of $B\le
6.2\times10^8$ G at the poles.


title:
Momentum transport properties of a hot and dense QCD matter in a weak
  magnetic field

abstract:
  We have studied the momentum transport properties of a hot and dense QCD
matter in a weak magnetic field by determining the shear ($\eta$) and bulk
($\zeta$) viscosities in the relaxation time approximation of kinetic theory.
The dependence of $\eta$ and $\zeta$ on the temperature has been explored in
the presence of weak magnetic field ($B$-field) and finite chemical potential
($\mu$). It is observed that both shear and bulk viscosities get decreased in
the presence of a weak magnetic field, whereas the finite chemical potential
increases these viscosities, specifically at low temperatures. This study is
important to understand the sound attenuation through the Prandtl number (Pl),
the nature of the flow through the Reynolds number (Rl), the fluidity and
location of transition point of the matter through the ratios $\eta/s$ and
$\zeta/s$ ($s$ is the entropy density), respectively. The Prandtl number gets
increased in the weak magnetic field, whereas the finite chemical potential
reduces its magnitude as compared to the counterpart in the absence of
$B$-field and $\mu$. However, Pl still remains larger than unity, indicating
that the energy dissipation due to the sound attenuation is mostly governed by
the momentum diffusion. It is noticed that the weak magnetic field makes the
Reynolds number larger, whereas the chemical potential makes it smaller than
that in the absence of $B$-field and $\mu$. We have observed that the ratio
$\eta/s$ gets decreased in the weak magnetic field regime, whereas the finite
chemical potential increases its value, but the ratio $\zeta/s$ is found to be
decreased by both weak magnetic field and finite chemical potential.


title:
On extensions and spectral problems for fourth order differential
  operator equation

abstract:
  The aim of the paper is firstly to study domains of definitions in terms of
boundary conditions of minimal and maximal operators, as well as selfadjoint
extensions of a minimal operator associated with the fourth-order differential
operator equation. Further, we give necessary and sufficient conditions for
that operators to have a purely discrete or continuous spectrum, to exist
extension with resolvent from $\sigma_p,$ study asymptotics of spectrum in case
pure discrete spectrum. Finally, give the new and more general method for
evaluations of regularized traces of operators with discrete spectrum
associated with one class boundary value problems.


title: Quantum Chaos Control by Complex Trajectories

abstract:
  In recent years, analysis and control of quantum chaos are increasingly
important, but the lack of the concept of trajectory makes it impossible to
analyze quantum chaos by the methods used in classical chaos. This research
aims to connect Newton's world to the quantum world by the complex mechanics so
that quantum chaos can be analyzed and controlled by the complex-extended
Newtonian mechanics. Through the bridge of complex mechanics, in this article,
we model quantum motions for 2D charged anisotropic harmonic oscillator by
complex-valued dynamic equations, based on which quantum chaos can be analyzed
by using well-known methods used in classical chaos. With the established
quantum dynamic model, we then apply the sliding-mode control method to control
the chaotic quantum behavior of the considered quantum system. The simulation
results show that chaotic motions can be changed into periodic motions by the
proposed chaos control and meanwhile, chaos synchronization can be achieved in
the presence of variations of initial conditions. Several signatures of chaos
are introduced here to justify the chaos of the periodicity process under the
sliding-mode control law.


title: Spatial-Temporal Parallel Transformer for Arm-Hand Dynamic Estimation

abstract:
  We propose an approach to estimate arm and hand dynamics from monocular video
by utilizing the relationship between arm and hand. Although monocular full
human motion capture technologies have made great progress in recent years,
recovering accurate and plausible arm twists and hand gestures from in-the-wild
videos still remains a challenge. To solve this problem, our solution is
proposed based on the fact that arm poses and hand gestures are highly
correlated in most real situations. To fully exploit arm-hand correlation as
well as inter-frame information, we carefully design a Spatial-Temporal
Parallel Arm-Hand Motion Transformer (PAHMT) to predict the arm and hand
dynamics simultaneously. We also introduce new losses to encourage the
estimations to be smooth and accurate. Besides, we collect a motion capture
dataset including 200K frames of hand gestures and use this data to train our
model. By integrating a 2D hand pose estimation model and a 3D human pose
estimation model, the proposed method can produce plausible arm and hand
dynamics from monocular video. Extensive evaluations demonstrate that the
proposed method has advantages over previous state-of-the-art approaches and
shows robustness under various challenging scenarios.


title: Predicting Solid State Material Platforms for Quantum Technologies

abstract:
  Semiconductor materials provide a compelling platform for quantum
technologies (QT), and the properties of a vast amount of materials can be
found in databases containing information from both experimental and
theoretical explorations. However, searching these databases to find promising
candidate materials for quantum technology applications is a major challenge.
Therefore, we have developed a framework for the automated discovery of
semiconductor host platforms for QT using material informatics and machine
learning methods, resulting in a dataset consisting of over $25.000$ materials
and nearly $5000$ physics-informed features. Three approaches were devised,
named the Ferrenti, extended Ferrenti and the empirical approach, to label data
for the supervised machine learning (ML) methods logistic regression, decision
trees, random forests and gradient boosting. We find that of the three, the
empirical approach relying exclusively on findings from the literature
predicted substantially fewer candidates than the other two approaches with a
clear distinction between suitable and unsuitable candidates when comparing the
two largest eigenvalues in the covariance matrix. In contrast to expectations
from the literature and that found for the Ferrenti and extended Ferrenti
approaches focusing on band gap and ionic character, the ML methods from the
empirical approach highlighted features related to symmetry and crystal
structure, including bond length, orientation and radial distribution, as
influential when predicting a material as suitable for QT. All three approaches
and all four ML methods agreed on a subset of $47$ eligible candidates %(to a
probability of $>50 \ \%$) of $8$ elemental, $29$ binary, and $10$ tertiary
compounds, and provide a basis for further material explorations towards
quantum technology.


title:
Geometrical Scaling of Direct Photons in Relativistic Heavy Ion and d+Au
  Collisions

abstract:
  In this paper, we show that multiplicity spectra of direct photons in A+A and
d+Au collisions at different centrality classes and different energies exhibit
geometrical scaling, {\em i.e.}, they depend on a specific combination of
number of participants $N_{\rm part}$, collisions energy $W$, and transverse
momentum $p_{T}$ -- called saturation scale -- rather than on all these three
variables separately. In particular, the dependence on the geometry of
collisions encoded in the dependence on $N_{\rm part}$ is in agreement with the
expectations based on the Color Glass Condensate theory.


title: Automatic Identification of Chemical Moieties

abstract:
  In recent years, the prediction of quantum mechanical observables with
machine learning methods has become increasingly popular. Message-passing
neural networks (MPNNs) solve this task by constructing atomic representations,
from which the properties of interest are predicted. Here, we introduce a
method to automatically identify chemical moieties (molecular building blocks)
from such representations, enabling a variety of applications beyond property
prediction, which otherwise rely on expert knowledge. The required
representation can either be provided by a pretrained MPNN, or learned from
scratch using only structural information. Beyond the data-driven design of
molecular fingerprints, the versatility of our approach is demonstrated by
enabling the selection of representative entries in chemical databases, the
automatic construction of coarse-grained force fields, as well as the
identification of reaction coordinates.


title:
Constructions of helicoidal minimal surfaces and minimal annuli in
  $\widetilde{E(2)}$

abstract:
  In this article, we construct two one-parameter families of properly embedded
minimal surfaces in a three-dimensional Lie group $\widetilde{E(2)}$, which is
the universal covering of the group of rigid motions of Euclidean plane endowed
with a left-invariant Riemannian metric. The first one can be seen as a family
of helicoids, while the second one is a family of catenoidal minimal surfaces.
The main tool that we use for the construction of these surfaces is a
Weierstrass-type representation introduced by Meeks, Mira, P\'erez and Ros for
minimal surfaces in Lie groups of dimension three. In the end, we study the
limit of the catenoidal minimal surfaces. As an application of this limit case,
we get a new proof of a half-space theorem for minimal surfaces in
$\widetilde{E(2)}$.


title:
Entanglement swapping and quantum correlations via Elegant Joint
  Measurements

abstract:
  We use hyper-entanglement to experimentally realize deterministic
entanglement swapping based on quantum Elegant Joint Measurements. These are
joint projections of two qubits onto highly symmetric, iso-entangled, bases. We
report measurement fidelities no smaller than $97.4\%$. We showcase the
applications of these measurements by using the entanglement swapping procedure
to demonstrate quantum correlations in the form of proof-of-principle
violations of both bilocal Bell inequalities and more stringent correlation
criteria corresponding to full network nonlocality. Our results are a foray
into entangled measurements and nonlocality beyond the paradigmatic Bell state
measurement and they show the relevance of more general measurements in
entanglement swapping scenarios.


title:
Superconductivity enhanced in the polar metal region of
  Sr$_{0.95}$Ba$_{0.05}$TiO$_3$ and Sr$_{0.985}$Ca$_{0.015}$TiO$_3$ revealed by
  the systematic Nb doping

abstract:
  Different ferroelectrics, Sr$_{0.95}$Ba$_{0.05}$TiO$_3$ and
Sr$_{0.985}$Ca$_{0.015}$TiO$_3$, turn into metallic with centrosymmetry
breaking by the electron doping. The systematic substitution of Nb$^{5+}$ for
Ti$^{4+}$ has revealed that the two polar metals commonly exhibit a simple
superconducting dome with a single convex. Interestingly, the superconducting
transition temperature $T_{\mathrm{c}}$ is more enhanced in these polar metals
compared with the non-polar matrix Sr(Ti,Nb)O$_3$ . The maximum
$T_{\mathrm{c}}$ reaches 0.75\,K, the highest in the families of SrTiO$_3$ ever
reported. However, the $T_{\mathrm{c}}$ enhancement is unexpectedly lower in
the vicinity of the ferroelectric quantum critical point (QCP). It becomes much
more prominent by further going into the dilute carrier-density region, where
the screening is less effective. The results suggest that the centrosymmetry
breaking, i.e., the ferroelectric nature, does not kill the superconductivity.
It rather directly enhances the superconductivity without the help of quantum
fluctuation.


title: Fair Contrastive Learning for Facial Attribute Classification

abstract:
  Learning visual representation of high quality is essential for image
classification. Recently, a series of contrastive representation learning
methods have achieved preeminent success. Particularly, SupCon outperformed the
dominant methods based on cross-entropy loss in representation learning.
However, we notice that there could be potential ethical risks in supervised
contrastive learning. In this paper, we for the first time analyze unfairness
caused by supervised contrastive learning and propose a new Fair Supervised
Contrastive Loss (FSCL) for fair visual representation learning. Inheriting the
philosophy of supervised contrastive learning, it encourages representation of
the same class to be closer to each other than that of different classes, while
ensuring fairness by penalizing the inclusion of sensitive attribute
information in representation. In addition, we introduce a group-wise
normalization to diminish the disparities of intra-group compactness and
inter-class separability between demographic groups that arouse unfair
classification. Through extensive experiments on CelebA and UTK Face, we
validate that the proposed method significantly outperforms SupCon and existing
state-of-the-art methods in terms of the trade-off between top-1 accuracy and
fairness. Moreover, our method is robust to the intensity of data bias and
effectively works in incomplete supervised settings. Our code is available at
https://github.com/sungho-CoolG/FSCL.


title: Learning of Global Objective for Network Flow in Multi-Object Tracking

abstract:
  This paper concerns the problem of multi-object tracking based on the
min-cost flow (MCF) formulation, which is conventionally studied as an instance
of linear program. Given its computationally tractable inference, the success
of MCF tracking largely relies on the learned cost function of underlying
linear program. Most previous studies focus on learning the cost function by
only taking into account two frames during training, therefore the learned cost
function is sub-optimal for MCF where a multi-frame data association must be
considered during inference. In order to address this problem, in this paper we
propose a novel differentiable framework that ties training and inference
together during learning by solving a bi-level optimization problem, where the
lower-level solves a linear program and the upper-level contains a loss
function that incorporates global tracking result. By back-propagating the loss
through differentiable layers via gradient descent, the globally parameterized
cost function is explicitly learned and regularized. With this approach, we are
able to learn a better objective for global MCF tracking. As a result, we
achieve competitive performances compared to the current state-of-the-art
methods on the popular multi-object tracking benchmarks such as MOT16, MOT17
and MOT20.


title: Lay-it-out: Interactive Design of Layout-Sensitive Grammars

abstract:
  Layout-sensitive grammars have been adopted in many modern programming
languages. However, tool support for this kind of grammars still remains
limited and immature. In this paper, we present Lay-it-out, an interactive
framework for layout-sensitive grammar design. Beginning with a user-defined
ambiguous grammar, our framework refines it by synthesizing layout constraints
through user interaction. For ease of interaction, a shortest nonempty
ambiguous sentence (if exists) is automatically generated by our bounded
ambiguity checker via SMT solving. The soundness and completeness of our SMT
encoding are mechanized in the Coq proof assistant. Case studies on real
grammars, including a full grammar, demonstrate the practicality and
scalability of our approach.


title:
Temperature-dependent structure of an intermetallic ErPd$_2$Si$_2$
  single crystal: A combined synchrotron and in-house X-ray diffraction study

abstract:
  We have grown intermetallic ErPd$_2$Si$_2$ single crystals employing
laser-diodes with the floating-zone method. The temperature-dependent
crystallography was determined using synchrotron and in-house X-ray powder
diffraction measurements from 20 to 500 K. The diffraction patterns fit well
with the tetragonal $I$4/$mmm$ space group (No. 139) with two chemical formulas
within one unit cell. Our synchrotron X-ray powder diffraction study shows that
the refined lattice constants are $a$ = 4.10320(2) {\AA}, $c$ = 9.88393(5)
{\AA} at 298 K and $a$ = 4.11737(2) {\AA}, $c$ = 9.88143(5) {\AA} at 500 K,
resulting in the unit-cell volume $V$ = 166.408(1) {\AA}$^3$ (298 K) and
167.517(2) {\AA}$^3$ (500 K). In the whole studied temperature range, we did
not find any structural phase transition. Upon cooling, the lattice constants a
and c are shortened and elongated, respectively.


title: Exclusive B-meson semileptonic decays from unitarity and lattice QCD

abstract:
  We examine the semileptonic $B \to D^{(*)} \ell \nu_\ell$ and $B \to \pi \ell
\nu_\ell$ decays adopting the unitarity-based Dispersive Matrix (DM) method,
which allows to determine the shape of the relevant hadronic form factors (FFs)
in their whole kinematical range, using only lattice QCD results available at
large values of the 4-momentum transfer without making any assumption on their
momentum dependence. Moreover, the experimental data are not used to constrain
the shape of the FFs, but only to obtain our final exclusive determination of
$\vert V_{cb} \vert$ and $\vert V_{ub} \vert$, namely: $\vert V_{cb} \vert
\cdot 10^3 = 41.1 \pm 1.0$ and $\vert V_{ub} \vert \cdot 10^3 = 3.88 \pm 0.32$,
which are consistent with the latest inclusive determinations at the $1 \sigma$
level or better. Our calculation of the FFs allows to obtain pure theoretical
estimates of the $\tau / \mu$ ratios of differential decay rates, $R(D) = 0.296
\pm 0.008$ and $R(D^*) = 0.275 \pm 0.008$, which turn out to be compatible with
the experimental world averages within $\simeq 1.4$ standard deviations.


title: Adaptive Divergence-based Non-negative Latent Factor Analysis

abstract:
  High-Dimensional and Incomplete (HDI) data are frequently found in various
industrial applications with complex interactions among numerous nodes, which
are commonly non-negative for representing the inherent non-negativity of node
interactions. A Non-negative Latent Factor (NLF) model is able to extract
intrinsic features from such data efficiently. However, existing NLF models all
adopt a static divergence metric like Euclidean distance or {\alpha}-\b{eta}
divergence to build its learning objective, which greatly restricts its
scalability of accurately representing HDI data from different domains. Aiming
at addressing this issue, this study presents an Adaptive Divergence-based
Non-negative Latent Factor (ADNLF) model with three-fold ideas: a) generalizing
the objective function with the {\alpha}-\b{eta}-divergence to expand its
potential of representing various HDI data; b) adopting a non-negative bridging
function to connect the optimization variables with output latent factors for
fulfilling the non-negativity constraints constantly; and c) making the
divergence parameters adaptive through particle swarm optimization, thereby
facilitating adaptive divergence in the learning objective to achieve high
scalability. Empirical studies are conducted on four HDI datasets from real
applications, whose results demonstrate that in comparison with
state-of-the-art NLF models, an ADNLF model achieves significantly higher
estimation accuracy for missing data of an HDI dataset with high computational
efficiency.


title: On the distribution of index of Farey Sequences

abstract:
  In this article, we study the distribution of index of Farey fractions which
was first introduced and studied by Hall and Shiu. We provide asymptotic
formulas for moments of index of Farey fractions twisted by Dirichlet
characters for Farey fractions with $\mathcal{B}$-free denominators.
Additionally, we reconsider the squarefree case earlier done in [ALVZ08], and
obtain new results for moments of indices with square-free denominators. We
also study higher level correlations of the index function generalizing earlier
known results on two level correlations.


title: The Precession Caused by Gravitational Waves

abstract:
  We show that gravitational waves cause freely falling gyroscopes to precess
relative to fixed distant stars, extending the stationary Lense-Thirring
effect. The precession rate decays as the square of the inverse distance to the
source, and is proportional to a suitable Noether current for dual asymptotic
symmetries at null infinity. Integrating the rate over time yields a net
rotation -- a `gyroscopic memory' -- whose angle reproduces the known spin
memory effect but also contains an extra contribution due to the generator of
gravitational electric-magnetic duality. The angle's order of magnitude for the
first LIGO signal is estimated to be $\Phi\sim 10^{-35}$ arcseconds near Earth,
but the effect may be substantially larger for supermassive black hole mergers.


title:
Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with
  Variance Reduction and its Application to Optimization

abstract:
  The stochastic gradient Langevin Dynamics is one of the most fundamental
algorithms to solve sampling problems and non-convex optimization appearing in
several machine learning applications. Especially, its variance reduced
versions have nowadays gained particular attention. In this paper, we study two
variants of this kind, namely, the Stochastic Variance Reduced Gradient
Langevin Dynamics and the Stochastic Recursive Gradient Langevin Dynamics. We
prove their convergence to the objective distribution in terms of KL-divergence
under the sole assumptions of smoothness and Log-Sobolev inequality which are
weaker conditions than those used in prior works for these algorithms. With the
batch size and the inner loop length set to $\sqrt{n}$, the gradient complexity
to achieve an $\epsilon$-precision is
$\tilde{O}((n+dn^{1/2}\epsilon^{-1})\gamma^2 L^2\alpha^{-2})$, which is an
improvement from any previous analyses. We also show some essential
applications of our result to non-convex optimization.


title:
APG: Adaptive Parameter Generation Network for Click-Through Rate
  Prediction

abstract:
  In many web applications, deep learning-based CTR prediction models (deep CTR
models for short) are widely adopted. Traditional deep CTR models learn
patterns in a static manner, i.e., the network parameters are the same across
all the instances. However, such a manner can hardly characterize each of the
instances which may have different underlying distribution. It actually limits
the representation power of deep CTR models, leading to sub-optimal results. In
this paper, we propose an efficient, effective, and universal module, Adaptive
Parameter Generation network (APG), where the parameters of deep CTR models are
dynamically generated on-the-fly based on different instances. Extensive
experimental evaluation results show that APG can be applied to a variety of
deep CTR models and significantly improve their performance. We have deployed
APG in the Taobao sponsored search system and achieved 3\% CTR gain and 1\% RPM
gain respectively.


title:
Acknowledging the Unknown for Multi-label Learning with Single Positive
  Labels

abstract:
  Due to the difficulty of collecting exhaustive multi-label annotations,
multi-label training data often contains partial labels. We consider an extreme
of this problem, called single positive multi-label learning (SPML), where each
multi-label training image has only one positive label. Traditionally, all
unannotated labels are assumed as negative labels in SPML, which would
introduce false negative labels and make model training be dominated by assumed
negative labels. In this work, we choose to treat all unannotated labels from a
different perspective, \textit{i.e.} acknowledging they are unknown. Hence, we
propose entropy-maximization (EM) loss to maximize the entropy of predicted
probabilities for all unannotated labels. Considering the positive-negative
label imbalance of unannotated labels, we propose asymmetric pseudo-labeling
(APL) with asymmetric-tolerance strategies and a self-paced procedure to
provide more precise supervision. Experiments show that our method
significantly improves performance and achieves state-of-the-art results on all
four benchmarks.


title:
Target-aware Dual Adversarial Learning and a Multi-scenario
  Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection

abstract:
  This study addresses the issue of fusing infrared and visible images that
appear differently for object detection. Aiming at generating an image of high
visual quality, previous approaches discover commons underlying the two
modalities and fuse upon the common space either by iterative optimization or
deep networks. These approaches neglect that modality differences implying the
complementary information are extremely important for both fusion and
subsequent detection task. This paper proposes a bilevel optimization
formulation for the joint problem of fusion and detection, and then unrolls to
a target-aware Dual Adversarial Learning (TarDAL) network for fusion and a
commonly used detection network. The fusion network with one generator and dual
discriminators seeks commons while learning from differences, which preserves
structural information of targets from the infrared and textural details from
the visible. Furthermore, we build a synchronized imaging system with
calibrated infrared and optical sensors, and collect currently the most
comprehensive benchmark covering a wide range of scenarios. Extensive
experiments on several public datasets and our benchmark demonstrate that our
method outputs not only visually appealing fusion but also higher detection mAP
than the state-of-the-art approaches.