title:
Neighbors Are Not Strangers: Improving Non-Autoregressive Translation
  under Low-Frequency Lexical Constraints

abstract:
  However, current autoregressive approaches suffer from high latency. In this
paper, we focus on non-autoregressive translation (NAT) for this problem for
its efficiency advantage. We identify that current constrained NAT models,
which are based on iterative editing, do not handle low-frequency constraints
well. To this end, we propose a plug-in algorithm for this line of work, i.e.,
Aligned Constrained Training (ACT), which alleviates this problem by
familiarizing the model with the source-side context of the constraints.
Experiments on the general and domain datasets show that our model improves
over the backbone constrained NAT model in constraint preservation and
translation quality, especially for rare constraints.


title:
General analytical nuclear force and molecular potential energy surface
  from full configuration interaction quantum Monte Carlo

abstract:
  Full configuration interaction quantum Monte Carlo (FCIQMC) is a
state-of-the-art stochastic electronic structure method, providing a
methodology to compute FCI-level state energies of molecular systems within a
quantum chemical basis. However, especially to probe {\em dynamics} at the
FCIQMC level, it is necessary to devise more efficient schemes to produce
nuclear forces and potential energy surfaces (PES) from FCIQMC. In this work,
we derive the general formula for nuclear force from FCIQMC, and clarify
different contributions of the total force. This method to obtain FCIQMC forces
eliminates previous restrictions, and can be used with frozen core
approximation and free selection of orbitals, making it promising for more
efficient nuclear force calculations. After numerical check of this procedure
on the binding curve of N$_2$ molecule, we use the FCIQMC energy and force to
obtain the full-dimensional ground state PES of water molecule via Gaussian
processes regression. The new water FCIQMC PES can be used as the basis for
H$_2$O ground state nuclear dynamics, structure optimization, and
rotation-vibrational spectrum calculation.


title:
EvTL: A Temporal Logic for the Transient Analysis of Cyber-Physical
  Systems

abstract:
  The behaviour of systems characterised by a closed interaction of software
components with the environment is inevitably subject to perturbations and
uncertainties. In this paper we propose a general framework for the
specification and verification of requirements on the behaviour of these
systems. We introduce the Evolution Temporal Logic (EvTL), a stochastic
extension of STL allowing us to specify properties of the probability
distributions describing the transient behaviour of systems, and to include the
presence of uncertainties in the specification. We equip EvTL with a robustness
semantics and we prove it sound and complete with respect to the semantics
induced by the evolution metric, i.e., a hemimetric expressing how well a
system is fulfilling its tasks with respect to another one. Finally, we develop
a statistical model checking algorithm for EvTL specifications. As an example
of an application of our framework, we consider a three-tanks laboratory
experiment.


title: Spherical Gravitational Collapse in 4D Einstein- Gauss- Bonnet theory

abstract:
  In this paper, we study spherical gravitational collapse of inhomogeneous
pressureless matter in $n \rightarrow4$d Einstein- Gauss- Bonnet gravity. The
collapse leads to either a black hole or a massive naked singularity depending
on time of formation of trapped surfaces. More precisely, horizon formation and
its time development is controlled by relative strengths of the Gauss- Bonnet
coupling $(\lambda)$ and the mass function $F(r,t)$ of collapsing sphere. We
find that, if there is no black hole on the initial Cauchy hypersurface and
$F(r,t)< 2\sqrt{\lambda}$, the central singularity is massive and naked. When
this inequality is equalised or reversed, the central singularity is always
censored by spacelike/timelike spherical marginally trapped surface of topology
$S^{2}\times \mathbb{R}$, which eventually becomes null and coincides with the
event horizon at equilibrium. These conclusions are verified for a wide class
of mass profiles admitting different initial velocity conditions.


title:
Poly-CAM: High resolution class activation map for convolutional neural
  networks

abstract:
  The need for Explainable AI is increasing with the development of deep
learning. The saliency maps derived from convolutional neural networks
generally fail in localizing with accuracy the image features justifying the
network prediction. This is because those maps are either low-resolution as for
CAM [Zhou et al., 2016], or smooth as for perturbation-based methods [Zeiler
and Fergus, 2014], or do correspond to a large number of widespread peaky spots
as for gradient-based approaches [Sundararajan et al., 2017, Smilkov et al.,
2017]. In contrast, our work proposes to combine the information from earlier
network layers with the one from later layers to produce a high resolution
Class Activation Map that is competitive with the previous art in term of
insertion-deletion faithfulness metrics, while outperforming it in term of
precision of class-specific features localization.


title: Probabilistic Voting Models with Varying Speeds of Correlation Decay

abstract:
  We model voting behaviour in the multi-group setting of a two-tier voting
system using sequences of de Finetti measures. Our model is defined by using
what we call the de Finetti representation of a probability measure describing
voting behaviour. The de Finetti measures describe the interaction between
voters and possible outside influences on them. We assume these sequences
converge weakly to the Dirac measure in the origin, representing a tendency
toward weakening social cohesion as the population grows large. The resulting
model covers a wide variety of behaviour, ranging from independent voting in
the limit under fast converge and a critical converge speed with its own
pattern of behaviour, to a subcritical converge speed which yields a model in
line with empirical evidence of real-world voting data. These models can be
used e.g. to study the problem of optimal voting weights in two-tier voting
systems.


title: It's DONE: Direct ONE-shot learning without training optimization

abstract:
  Learning a new concept from one example is a superior function of human brain
and it is drawing attention in the field of machine learning as one-shot
learning task. In this paper, we propose the simplest method for this task,
named Direct ONE-shot learning (DONE). DONE adds a new class to a pretrained
deep neural network (DNN) classifier with neither training optimization nor
other-classes modification. DONE is inspired by Hebbian theory and directly
uses the neural activity input of the final dense layer obtained from a data
that belongs to the new additional class as the connectivity weight (synaptic
strength) with a newly-provided-output neuron for the new class. DONE requires
just one inference for obtaining the output of the final dense layer and its
procedure is simple, deterministic, not requiring parameter tuning and
hyperparameters. The performance of DONE depends entirely on the pretrained DNN
model used as a backbone model, and we confirmed that DONE with a well-trained
backbone model performs a practical-level accuracy. DONE has some advantages
including a DNN's practical use that is difficult to spend high cost for a
training, an evaluation of existing DNN models, and the understanding of the
brain. DONE might be telling us one-shot learning is an easy task that can be
achieved by a simple principle not only for humans but also for current
well-trained DNN models.


title:
Tailor: A Prompt-Based Approach to Attribute-Based Controlled Text
  Generation

abstract:
  Attribute-based Controlled Text Generation (CTG) refers to generating
sentences that satisfy desirable attributes (e.g., emotions and topics).
Existing works often utilize fine-tuning or resort to extra attribute
classifiers, yet suffer from storage and inference time increases. To address
these concerns, we explore attribute-based CTG in a prompt-based manner. In
short, the proposed Tailor represents each attribute as a pre-trained
continuous vector (i.e., single-attribute prompt) and guides the generation of
a fixed PLM switch to a pre-specified attribute. We experimentally find that
these prompts can be simply concatenated as a whole to multi-attribute CTG
without any re-training, yet raises problems of fluency decrease and position
sensitivity. To this end, Tailor provides a multi-attribute prompt mask and a
re-indexing position-ids sequence to bridge the gap between the training (one
prompt for each task) and testing stage (concatenating more than one prompt).
To further enhance such single-attribute prompt combinations, Tailor also
introduces a trainable prompt connector, which can be concatenated with any two
single-attribute prompts to multi-attribute text generation. Experiments on 11
attribute-specific generation tasks demonstrate strong performances of Tailor
on both single-attribute and multi-attribute CTG, with 0.08\% training
parameters of a GPT-2.


title:
Accretion Scenario of MAXI J1820+070 during 2018 Outbursts with
  Multi-mission Observations

abstract:
  We present a comprehensive spectral and temporal study of the black hole
X-ray transient MAXI J1820+070 during its outbursts in 2018 using Swift/XRT,
NICER, NuSTAR and AstroSat observations. The Swift/XRT and NICER spectral study
shows a plateau in the light curve with spectral softening (hardness changes
from $\sim$ $2.5$ to $2$) followed by a gradual decline without spectral
softening during the first outburst. Also, spectral modelling suggests that the
first outburst is in the low/hard state throughout with a truncated disk
whereas the thermal disk emission dominates during the second outburst. During
the entire outburst, strong reflection signature (reflection fraction varies
between $\sim$ $0.38 - 3.8$) is observed in the simultaneous wideband
(NICER-NuSTAR, XRT-NuSTAR, AstroSat) data due to the presence of a dynamically
evolving corona. The NICER timing analysis shows Quasi-periodic Oscillation
(QPO) signatures and the characteristic frequency increases (decreases) in the
plateau (decline) phase with time during the first outburst. We understand that
the reduction of the electron cooling timescale in the corona due to spectral
softening and the resonance oscillation with the local dynamical timescale may
explain the above behavior of the source during the outburst. Also, we propose
a possible scenario of outburst triggering and the associated accretion
geometry of the source.


title:
Phase transition model of high-nickel positive electrodes: Effects of
  loss of active material and cyclable lithium on capacity fade

abstract:
  Nickel-rich layered oxides have been widely used as positive electrode (PE)
materials for higher-energy-density lithium ion batteries. However, their
severe degradation has been limiting battery lifetime. Here we present a phase
transition model at the particle level to describe the structural degradation
in terms of loss of active material (LAM), loss of lithium inventory (LLI), and
resistance increase. The particle degradation model is then incorporated into a
cell-level physics-based model (e.g., P2D) to explore the effects of LAM and
LLI on capacity fade in cyclic aging tests. It is found that loss of PE active
material will terminate the discharging earlier with less PE material to take
lithium, thereby diminishing the discharging capacity. The loss of cyclable
lithium changes the stoichiometry range of the NE but does not contribute to
capacity loss in the simulated cases where LAM dominates the collective
response. The shell layer resistance, similar to the SEI resistance at the
negative electrode side, suppresses the accessible voltage and power. The work
offers insights into the diagnosis of degradation modes from the change pattern
of the state of charge curve. The model has been implemented into PyBaMM and
made available as open source codes.


title:
Running coupling and non-perturbative corrections for O$(N)$ free energy
  and for disk capacitor

abstract:
  We reconsider the complete solution of the linear TBA equation describing the
energy density of finite density states in the $O(N)$ nonlinear sigma models by
the Wiener-Hopf method. We keep all perturbative and non-perturbative
contributions and introduce a running coupling in terms of which all asymptotic
series appearing in the problem can be represented as pure power series without
logs. We work out the first non-perturbative contribution in the $O(3)$ case
and show that (presumably because of the instanton corrections) resurgence
theory fails in this example. Using the relation of the $O(3)$ problem to the
coaxial disks capacitor problem we work out the leading non-perturbative terms
for the latter and show that (at least to this order) resurgence theory, in
particular the median resummation prescription, gives the correct answer. We
demonstrate this by comparing the Wiener-Hopf results to the high precision
numerical solution of the original integral equation.


title: Semantic Communication: An Information Bottleneck View

abstract:
  Motivated by recent success of machine learning tools at the PHY layer and
driven by high bandwidth demands of the next wireless communication standard
6G, the old idea of semantic communication by Weaver from 1949 has received
considerable attention. It breaks with the classic design paradigm according to
Shannon by aiming to transmit the meaning of a message rather than its exact
copy and thus potentially allows for savings in bandwidth.
  In this work, inspired by Weaver, we propose an information-theoretic
framework where the semantic context is explicitly introduced into
probabilistic models. In particular, for bandwidth efficient transmission, we
define semantic communication system design as an Information Bottleneck
optimization problem and consider important implementation aspects. Further, we
uncover the restrictions of the classic 5G communication system design w.r.t.
semantic context. Notably, based on the example of distributed image
classification, we reveal the huge potential of a semantic communication system
design. Numerical results show a tremendous saving in bandwidth of 20 dB with
our proposed approach ISCNet compared to a classic PHY layer design.

categories: cs.IT cs.AI cs.LG eess.SP math.IT stat.ML


title:
Coupling between turbulence and solar-like oscillations: A combined
  Lagrangian PDF/SPH approach. II - Mode driving, damping and modal surface
  effect

abstract:
  The first paper of this series established a linear stochastic wave equation
for solar-like p-modes, correctly taking the effect of turbulence thereon into
account. In this second paper, we aim at deriving simultaneous expressions for
the excitation rate, damping rate, and modal surface effect associated with any
given p-mode, as an explicit function of the statistical properties of the
turbulent velocity field. We reduce the stochastic wave equation to complex
amplitude equations for the normal oscillating modes of the system. We then
derive the equivalent Fokker-Planck equation for the real amplitudes and phases
of all the oscillating modes of the system simultaneously. The effect of the
finite-memory time of the turbulent fluctuations (comparable to the period of
the modes) on the modes themselves is consistently and rigorously accounted
for, by means of the simplified amplitude equation formalism. This formalism
accounts for mutual linear mode coupling in full, and we then turn to the
special single-mode case. This allows us to derive evolution equations for the
mean energy and mean phase of each mode, from which the excitation rate, the
damping rate, and the modal surface effect naturally arise.
  We show that the expression for the excitation rate of the modes is identical
to previous results obtained through a different modelling approach, thus
supporting the validity of the formalism presented here. We also recover the
fact that the damping rate and modal surface effect correspond to the real and
imaginary part of the same single complex quantity. We explicitly separate the
different physical contributions to these observables, in particular the
turbulent pressure contribution and the joint effect of the
pressure-rate-of-strain correlation and the turbulent dissipation. We show that
the former dominates for high-frequency modes and the latter for low-frequency
modes.


title: Stueckelberg and Higgs Mechanisms: Frames and Scales

abstract:
  We consider Yang-Mills theory with a compact gauge group $G$ on Minkowski
space ${\mathbb R}^{3,1}$ and compare the introduction of masses of gauge
bosons using the Stueckelberg and Higgs mechanisms. The Stueckelberg field
$\phi$ is identified with a $G$-frame on the gauge vector bundle $E$ and the
kinetic term for $\phi$ leads to the mass of the gauge bosons. The Stueckelberg
mechanism is extended to the Higgs mechanism by adding to the game a scalar
field describing rescaling of metric on fibres of $E$. Thus, we associate Higgs
fields as well as running coupling parameters with conformal geometry on fibres
of gauge bundles. In particular, a running coupling tending to zero or to
infinity is equivalent to an unbounded expansion of $G$-fibres or its
contraction to a point. We also discuss scale connection, space-time dependent
Higgs vacua and compactly supported gauge and quark fields as an attribute of
confinement.


title:
Joint Sum Rate and Blocklength Optimization in RIS-aided Short Packet
  URLLC Systems

abstract:
  In this paper, a multi-objective optimization problem (MOOP) is proposed for
maximizing the achievable finite blocklength (FBL) rate while minimizing the
utilized channel blocklengths (CBLs) in a reconfigurable intelligent surface
(RIS)-assisted short packet communication system. The formulated MOOP has two
objective functions namely maximizing the total FBL rate with a target error
probability, and minimizing the total utilized CBLs which is directly
proportional to the transmission duration. The considered MOOP variables are
the base station (BS) transmit power, number of CBLs, and passive beamforming
at the RIS. Since the proposed non-convex problem is intractable to solve, the
Tchebyshev method is invoked to transform it into a single-objective OP, then
the alternating optimization (AO) technique is employed to iteratively obtain
optimized parameters in three main sub-problems. The numerical results show a
fundamental trade-off between maximizing the achievable rate in the FBL regime
and reducing the transmission duration. Also, the applicability of RIS
technology is emphasized in reducing the utilized CBLs while increasing the
achievable rate significantly.


title: Unconstrained optimization using the directional proximal point method

abstract:
  This paper presents a directional proximal point method (DPPM) to derive the
minimum of any C1-smooth function f. The proposed method requires a function
persistent a local convex segment along the descent direction at any
non-critical point (referred to a DLC direction at the point). The proposed
DPPM can determine a DLC direction by solving a two-dimensional quadratic
optimization problem, regardless of the dimensionally of the function
variables. Along that direction, the DPPM then updates by solving a
one-dimensional optimization problem. This gives the DPPM advantage over
competitive methods when dealing with large-scale problems, involving a large
number of variables. We show that the DPPM converges to critical points of f.
We also provide conditions under which the entire DPPM sequence converges to a
single critical point. For strongly convex quadratic functions, we demonstrate
that the rate at which the error sequence converges to zero can be
R-superlinear, regardless of the dimension of variables.


title:
On the Role of Field of View for Occlusion Removal with Airborne Optical
  Sectioning

abstract:
  Occlusion caused by vegetation is an essential problem for remote sensing
applications in areas, such as search and rescue, wildfire detection, wildlife
observation, surveillance, border control, and others. Airborne Optical
Sectioning (AOS) is an optical, wavelength-independent synthetic aperture
imaging technique that supports computational occlusion removal in real-time.
It can be applied with manned or unmanned aircrafts, such as drones. In this
article, we demonstrate a relationship between forest density and field of view
(FOV) of applied imaging systems. This finding was made with the help of a
simulated procedural forest model which offers the consideration of more
realistic occlusion properties than our previous statistical model. While AOS
has been explored with automatic and autonomous research prototypes in the
past, we present a free AOS integration for DJI systems. It enables bluelight
organizations and others to use and explore AOS with compatible, manually
operated, off-the-shelf drones. The (digitally cropped) default FOV for this
implementation was chosen based on our new finding.


title:
Phase Shift Design in RIS Empowered Wireless Networks: From Optimization
  to AI-Based Methods

abstract:
  Reconfigurable intelligent surfaces (RISs) have a revolutionary capability to
customize the radio propagation environment for wireless networks. To fully
exploit the advantages of RISs in wireless systems, the phases of the
reflecting elements must be jointly designed with conventional communication
resources, such as beamformers, transmit power, and computation time. However,
due to the unique constraints on the phase shift, and massive numbers of
reflecting units and users in large-scale networks, the resulting optimization
problems are challenging to solve. This paper provides a review of current
optimization methods and artificial intelligence-based methods for handling the
constraints imposed by RIS and compares them in terms of solution quality and
computational complexity. Future challenges in phase shift optimization
involving RISs are also described and potential solutions are discussed.


title:
NIHAO XXVIII: Collateral effects of AGN on dark matter concentration and
  stellar kinematics

abstract:
  Although active galactic nuclei (AGN) feedback is required in simulations of
galaxies to regulate star formation, further downstream effects on the dark
matter distribution of the halo and stellar kinematics of the central galaxy
can be expected. We combine simulations of galaxies with and without AGN
physics from the Numerical Investigation of a Hundred Astrophysical Objects
(NIHAO) to investigate the effect of AGN on the dark matter profile and central
stellar rotation of the host galaxies. Specifically, we study how the
concentration-halo mass ($c-M$) relation and the stellar spin parameter
($\lambda_R$) are affected by AGN feedback. We find that AGN physics is crucial
to reduce the central density of simulated massive ($\gtrsim 10^{12}$
M$_\odot$) galaxies and bring their concentration to agreement with results
from the Spitzer Photometry & Accurate Rotation Curves (SPARC) sample.
Similarly, AGN feedback has a key role in reproducing the dichotomy between
slow and fast rotators as observed by the ATLAS$^{3\text{D}}$ survey. Without
star formation suppression due to AGN feedback, the number of fast rotators
strongly exceeds the observational constraints. Our study shows that there are
several collateral effects that support the importance of AGN feedback in
galaxy formation, and these effects can be used to constrain its implementation
in numerical simulations.


title: Morphing Attack Potential

abstract:
  In security systems the risk assessment in the sense of common criteria
testing is a very relevant topic; this requires quantifying the attack
potential in terms of the expertise of the attacker, his knowledge about the
target and access to equipment. Contrary to those attacks, the recently
revealed morphing attacks against Face Recognition Systems (FRSs) can not be
assessed by any of the above criteria. But not all morphing techniques pose the
same risk for an operational face recognition system. This paper introduces
with the Morphing Attack Potential (MAP) a consistent methodology, that can
quantify the risk, which a certain morphing attack creates.


title:
Finite subgroups of the homeomorphism group of a compact topological
  manifold are almost nilpotent

abstract:
  Around twenty years ago Ghys conjectured that finite subgroups of the
diffeomorphism group of a compact smooth manifold M have an abelian normal
subgroup of index at most a(M), where a(M) depends only on M. First we
construct a family of counterexamples to this conjecture including, for
example, the product space $T^2\times S^2$. Following the first appearance of
our counterexample on the arXiv Ghys put forward a revised conjecture, which
predicts only the existence of a nilpotent normal subgroup of index at most
n(M). Our main result is the proof of the revised Ghys conjecture. More
generally, we show that the same result holds for homeomorphism groups of not
necessarily compact topological manifolds with finitely generated homology
groups. Our proofs are based on finite group theoretic results which provide a
general strategy for proving similar Jordan-type theorems.


title:
Machine learning for knowledge acquisition and accelerated
  inverse-design for non-Hermitian systems

abstract:
  Non-Hermitian systems offer new platforms for unusual physical properties
that can be flexibly manipulated by redistribution of the real and imaginary
parts of refractive indices, whose presence breaks conventional wave
propagation symmetries, leading to asymmetric reflection and symmetric
transmission with respect to the wave propagation direction. Here, we use
supervised and unsupervised learning techniques for knowledge acquisition in
non-Hermitian systems which accelerate the inverse design process. In
particular, we construct a deep learning model that relates the transmission
and asymmetric reflection in non-conservative settings and proposes
sub-manifold learning to recognize non-Hermitian features from transmission
spectra. The developed deep learning framework determines the feasibility of a
desired spectral response for a given structure and uncovers the role of
effective gain-loss parameters to tailor the spectral response. These findings
pave the way for intelligent inverse design and shape our understanding of the
physical mechanism in general non-Hermitian systems.


title:
Acylindrical hyperbolicity for Artin groups associated to graphs that
  are not cones

abstract:
  Charney and Morris-Wright showed acylindrical hyperbolicity for Artin groups
of infinite type associated to graphs that are not joins, by studying
clique-cube complexes and actions on them. In this paper, by developing their
study with additional discussion, we show acylindrical hyperbolicity for more
general Artin groups. Indeed we treat Artin groups of infinite type associated
to graphs that are not cones.


title:
Learning General Inventory Management Policy for Large Supply Chain
  Network

abstract:
  Inventory management in warehouses directly affects profits made by
manufacturers. Particularly, large manufacturers produce a very large variety
of products that are handled by a significantly large number of retailers. In
such a case, the computational complexity of classical inventory management
algorithms is inordinately large. In recent years, learning-based approaches
have become popular for addressing such problems. However, previous studies
have not been managed systems where both the number of products and retailers
are large. This study proposes a reinforcement learning-based warehouse
inventory management algorithm that can be used for supply chain systems where
both the number of products and retailers are large. To solve the computational
problem of handling large systems, we provide a means of approximate simulation
of the system in the training phase. Our experiments on both real and
artificial data demonstrate that our algorithm with approximated simulation can
successfully handle large supply chain networks.


title:
Efficiency calibration of the BRIKEN detector: the world largest counter
  for beta-delayed neutrons

abstract:
  Beta-delayed neutron emission plays a fundamental role in the explosive
nucleosynthesis of elements heavier than iron by the rapid neutron capture
(r-process). The most ambitious project related to beta-delayed neutron
detection of very exotic nuclei is carried out by the BRIKEN collaboration at
RIKEN Nishima Center. In this work, a brief description of the BRIKEN project
is presented. A methodology for the precise characterization of the BRIKEN
neutron counter efficiency, for fast neutrons, using an uncalibrated 252Cf
neutron source is described. The method relies on the well-known neutron
multiplicity distribution of such source and correlation counting method. A
detailed experimental study with the BRIKEN neutron counter and a 252 Cf
neutron source at the RIKEN Nishina center is presented. The result of this
work is the determination of the neutron detection efficiency of the BRIKEN
neutron counter with high accuracy.


title: Safety-Aware Optimal Control in Motion Planning

abstract:
  Optimisation is a popular tool of generating control laws for motion planning
in a complex environment. The existence of multiple irregular obstacles in the
working space introduces nonconvex collision avoidance constraints, making the
optimal control problem intractable. One efficient approach to address this
issue is successive convex approximation (SCA), where the nonconvex problem is
convexified and solved successively. However, this approach still faces two
main challenges: 1) high computational complexity, incurred by multiple
constraints when solving the optimal control problem with a long planning
horizon and multiple obstacles; 2) infeasibility, caused by linearization about
infeasible reference points. To address these challenges, this paper proposes a
backward receding SCA (BRSCA) approach, which leverages dynamic programming
with a primal-dual iteration to decrease computational complexity; and designs
a dynamic constraints-selection rule to avoid infeasibility. Moreover, it is
found that the proposed BRSCA approach is applicable to time-varying control
limits. Numerical simulations and hardware experiments demonstrate that BRSCA
has a higher probability of finding feasible solutions and reduces the
computation time by at least 17.4%, compared to other methodologies in the
literature.


title:
The Hodge filtrations of monodromic mixed Hodge modules and the
  irregular Hodge filtrations

abstract:
  For an algebraic vector bundle $E$ over a smooth algebraic variety $X$, a
monodromic $D$-module on $E$ is decomposed into a direct sum of some
$O$-modules on $X$. We show that the Hodge filtration of a monodromic mixed
Hodge module is decomposed with respect to the decomposition of the underlying
$D$-module. By using this result, we endow the Fourier-Laplace transform
$M^{\wedge}$ of the underlying $D$-module $M$ of a monodromic mixed Hodge
module with a mixed Hodge module structure. Moreover, we describe the irregular
Hodge filtration on $M^{\wedge}$ concretely and show that it coincides with the
Hodge filtration at all integer indices.


title:
Keep the Caption Information: Preventing Shortcut Learning in
  Contrastive Image-Caption Retrieval

abstract:
  To train image-caption retrieval (ICR) methods, contrastive loss functions
are a common choice for optimization functions. Unfortunately, contrastive ICR
methods are vulnerable to learning shortcuts: decision rules that perform well
on the training data but fail to transfer to other testing conditions. We
introduce an approach to reduce shortcut feature representations for the ICR
task: latent target decoding (LTD). We add an additional decoder to the
learning framework to reconstruct the input caption, which prevents the image
and caption encoder from learning shortcut features. Instead of reconstructing
input captions in the input space, we decode the semantics of the caption in a
latent space. We implement the LTD objective as an optimization constraint, to
ensure that the reconstruction loss is below a threshold value while primarily
optimizing for the contrastive loss. Importantly, LTD does not depend on
additional training data or expensive (hard) negative mining strategies. Our
experiments show that, unlike reconstructing the input caption, LTD reduces
shortcut learning and improves generalizability by obtaining higher recall@k
and r-precision scores. Additionally, we show that the evaluation scores
benefit from implementing LTD as an optimization constraint instead of a dual
loss.


title:
Strong coupling of quantum emitters and the exciton-polariton in MoS$_2$
  nanodisk

abstract:
  As a quasiparticle formed by light and exciton in semiconductors,
exciton-polariton (EP) as a quantum bus is promising for developing quantum
interconnect devices at room temperature. However, the significant damping of
EP in the material generally causes the loss of quantum information, which
greatly hinders its application. We here propose a mechanism to overcome the
destructive effect of the damping EP on its mediated correlation dynamics of
quantum emitters (QEs). Via investigating the near-field coupling between two
QEs and the EP in a monolayer MoS$_{2}$ nanodisk, we find that, with the
complete dissipation of the QEs efficiently avoided, a persistent quantum
correlation between the QEs can be stabilized even to their steady state. This
is caused by that, with decreasing the QE-MoS$_2$ distance, the QEs become so
hybridized with the EP that one or two bound states are formed between them.
Our result supplies a useful way to avoid the impact of the EP damping, which
lays a foundation for developing quantum polaritonic devices.


title:
D3: A Massive Dataset of Scholarly Metadata for Analyzing the State of
  Computer Science Research

abstract:
  DBLP is the largest open-access repository of scientific articles on computer
science and provides metadata associated with publications, authors, and
venues. We retrieved more than 6 million publications from DBLP and extracted
pertinent metadata (e.g., abstracts, author affiliations, citations) from the
publication texts to create the DBLP Discovery Dataset (D3). D3 can be used to
identify trends in research activity, productivity, focus, bias, accessibility,
and impact of computer science research. We present an initial analysis focused
on the volume of computer science research (e.g., number of papers, authors,
research activity), trends in topics of interest, and citation patterns. Our
findings show that computer science is a growing research field (approx. 15%
annually), with an active and collaborative researcher community. While papers
in recent years present more bibliographical entries in comparison to previous
decades, the average number of citations has been declining. Investigating
papers' abstracts reveals that recent topic trends are clearly reflected in D3.
Finally, we list further applications of D3 and pose supplemental research
questions. The D3 dataset, our findings, and source code are publicly available
for research purposes.


title:
Fuzzy Expert System for Stock Portfolio Selection: An Application to
  Bombay Stock Exchange

abstract:
  Selection of proper stocks, before allocating investment ratios, is always a
crucial task for the investors. Presence of many influencing factors in stock
performance have motivated researchers to adopt various Artificial Intelligence
(AI) techniques to make this challenging task easier. In this paper a novel
fuzzy expert system model is proposed to evaluate and rank the stocks under
Bombay Stock Exchange (BSE). Dempster-Shafer (DS) evidence theory is used for
the first time to automatically generate the consequents of the fuzzy rule base
to reduce the effort in knowledge base development of the expert system. Later
a portfolio optimization model is constructed where the objective function is
considered as the ratio of the difference of fuzzy portfolio return and the
risk free return to the weighted mean semi-variance of the assets that has been
used. The model is solved by applying Ant Colony Optimization (ACO) algorithm
by giving preference to the top ranked stocks. The performance of the model
proved to be satisfactory for short-term investment period when compared with
the recent performance of the stocks.


title: Audio-Visual Contrastive Learning for Self-supervised Action Recognition

abstract:
  The underlying correlation between audio and visual modalities within videos
can be utilized to learn supervised information for unlabeled videos. In this
paper, we present an end-to-end self-supervised framework named Audio-Visual
Contrastive Learning (AVCL), to learn discriminative audio-visual
representations for action recognition. Specifically, we design an attention
based multi-modal fusion module (AMFM) to fuse audio and visual modalities. To
align heterogeneous audio-visual modalities, we construct a novel
co-correlation guided representation alignment module (CGRA). To learn
supervised information from unlabeled videos, we propose a novel
self-supervised contrastive learning module (SelfCL). Furthermore, to expand
the existing audio-visual action recognition datasets and better evaluate our
framework AVCL, we build a new audio-visual action recognition dataset named
Kinetics-Sounds100. Experimental results on Kinetics-Sounds32 and
Kinetics-Sounds100 datasets demonstrate the superiority of our AVCL over the
state-of-the-art methods on large-scale action recognition benchmark.


title:
Structural and thermodynamic properties of fluids whose molecules
  interact via one-, two-, and three-step potentials

abstract:
  The structural and thermodynamic properties of fluids whose molecules
interact via potentials with a hard-core plus a square well, a square shoulder,
and a second square well, are considered. Those properties are derived by using
a (semi-analytical) rational-function approximation method as a particular case
of the more general formulation provided earlier involving potentials with a
hard-core plus $n$ piecewise constant sections. Comparison of the results with
recent simulation data confirms the usefulness of the approach.


title:
${ \kappa }${\ell$}-cast: On the Foundations of Byzantine Reliable
  Broadcast in the Presence of Message Adversaries

abstract:
  This article is on reliable broadcast in asynchronous signature-free
$n$-process message-passing systems in which up to \tb processes are Byzantine
and where, at the network level, for each message broadcast by a correct
process, an adversary can prevent up to \tm processes from receiving it (the
integer \tm defines the power of the message adversary). So, differently from
previous works, this article considers that not only computing entities can be
faulty (Byzantine processes), but also that the network can lose messages. To
this end the article first introduces a new basic communication abstraction
denoted \kl-cast and studies its properties in the previous bi-dimensional
adversary context. Then, the article deconstructs existing Byzantine-tolerant
asynchronous broadcast algorithms and, with the help of the \kl-cast
communication abstraction, reconstructs versions of them that tolerate both
Byzantine processes and message adversaries. Interestingly, some of these
reconstructed algorithms are also more efficient than the
Byzantine-tolerant-only algorithms from which they originate. The article also
shows that the condition $n>3\tb+2\tm$ is necessary and sufficient (with
signatures) to design such reliable broadcast algorithms.


title: Backward Stochastic Evolution Inclusions in UMD Banach Spaces

abstract:
  In this paper, we prove the existence of a mild $L^p$-solution for the
backward stochastic evolution inclusion (BSEI for short) of the form
\begin{align*}%\label{BSDI3}
  \begin{cases}
  dY_t+AY_tdt\in G(t,Y_t,Z_t)dt+Z_tdW_t,\quad t\in [0,T]
  Y_T =\xi,
  \end{cases} \end{align*} where $W=(W_t)_{t\in [0,T]}$ is a standard Brownian
motion, $A$ is the generator of a $C_0$-semigroup on a UMD Banach space $E$,
$\xi$ is a terminal condition from $L^p(\Omega,\mathscr{F}_T;E)$, with $p>1$
and $G$ is a set-valued function satisfying some suitable conditions.
  The case when the processes with values in spaces that have martingale type
$2$, has been also studied.


title:
Kinetics and the crystallographic structure of bismuth during
  liquefaction and solidification on the insulating substrate

abstract:
  Here we study the kinetics of liquefaction and solidification of thin bismuth
films grown on the insulating substrate by the pulsed laser deposited (PLD) and
molecular beam epitaxy (MBE) and investigated by in situ electron and X-ray
diffraction. By PLD, we can grow films similar to those obtained using MBE,
studied by ex-situ AFM, KPFM, XRR, and XRD. The liquefaction-solidification
transition is monitored in real-time by RHEED and synchrotron XRD, resulting in
a dewetting phenomenon and the formation of spherical droplets which size
depends on the initial film thickness. Studying this phase transition in more
detail, we find instantaneous liquefaction and solidification, resulting in
formation of the nanodots oriented with a (110) crystallographic plane parallel
to the substrate. Furthermore, we propose a two-step growth mechanism by
analyzing the recorded specular diffraction rods. Overall, we show that the PLD
and MBE can be used as a method for the highly controlled growth of Bi
nanostructures, including their crystallographic orientation on the substrate.


title:
Local Rotational Jamming and Multi-Scale Hyperuniformities in an Active
  Spinner System

abstract:
  An active system consisting of many self-spinning dimers is simulated, and a
distinct local rotational jamming transition is observed as the density
increases. In the low density regime, the system stays in an absorbing state,
in which each dimer rotates independently subject to the applied torque. While
in the high density regime, a fraction of the dimers become rotationally jammed
into local clusters, and the system exhibits spinodal-decomposition like
two-phase morphologies. For high enough densities, the system becomes
completely jammed in both rotational and translational degrees of freedom. Such
a simple system is found to exhibit rich and multiscale disordered
hyperuniformities among the above phases: the absorbing state shows a critical
hyperuniformity of the strongest class and subcritically preserves the
vanishing density-fluctuation scaling up to some length scale; the
locally-jammed state shows a two-phase hyperuniformity conversely beyond some
length scale with respect to the phase cluster sizes; the totally jammed state
appears to be a monomer crystal, but intrinsically loses large-scale
hyperuniformity. These results are inspiring for designing novel
phase-separation and disordered hyperuniform systems through dynamical
organization.


title: Dynamic screening

abstract:
  We study dynamic screening problems where elements are subjected to noisy
evaluations and, in every stage, some of the elements are rejected while the
remaining ones are independently re-evaluated in subsequent stages. We prove
that, ceteris paribus, the quality of a dynamic screening process is not
monotonic in the number of stages. Specifically, we examine the accepted
elements' values and show that adding a single stage to a screening process may
produce inferior results, in terms of stochastic dominance, whereas increasing
the number of stages substantially leads to a first-best outcome.


title: A Hardware-aware and Stable Orthogonalization Framework

abstract:
  The orthogonalization process is an essential building block in Krylov space
methods, which takes up a large portion of the computational time. Commonly
used methods, like the Gram-Schmidt method, consider the projection and
normalization separately and store the orthogonal base explicitly. We consider
the problem of orthogonalization and normalization as a QR decomposition
problem on which we apply known algorithms, namely CholeskyQR and TSQR. This
leads to methods that solve the orthogonlization problem with reduced
communication costs, while maintaining stability and stores the orthogonal base
in a locally orthogonal representation. Furthermore, we discuss the novel
method as a framework which allows us to combine different orthogonalization
algorithms and use the best algorithm for each part of the hardware. After the
formulation of the methods, we show their advantageous performance properties
based on a performance model that takes data transfers within compute nodes as
well as message passing between compute nodes into account. The theoretic
results are validated by numerical experiments.


title: Searches for new physics with high-intensity kaon beams

abstract:
  The availability of high-intensity kaon beams at the J-PARC Hadron
Experimental Facility and the CERN SPS North Area, together with the abundant
forward production of kaons at the LHC, gives rise to unique possibilities for
sensitive tests of the Standard Model in the quark flavor sector. Precise
measurements of the branching ratios for the flavor-changing neutral current
decays $K\to\pi\nu\bar{\nu}$ can provide unique constraints on CKM unitarity
and, potentially, evidence for new physics. Building on the success of the
current generation of fixed-target experiments, initiatives are taking shape in
both Europe and Japan to measure the branching ratio for
$K^+\to\pi^+\nu\bar\nu$ to ~5% and for $K_L\to\pi^0\nu\bar\nu$ to ~20\%
precision. These planned experiments would also carry out lepton flavor
universality tests, lepton number and flavor conservation tests, and perform
other precision measurements in the kaon sector, as well as searches for exotic
particles in kaon decays. Meanwhile, the LHCb experiment is ready to restart
data taking with a trigger upgrade that will vastly increase its sensitivity
for rare $K_S$ decays and complementary hyperon decays. We overview the
initiatives for next-generation experiments in kaon physics in Europe and
Japan, identifying potential contributions from the US high-energy physics
community.


title:
Theoretical analysis of the extraction of neutron skin thickness from
  coherent {\pi}0 photoproduction off nuclei

abstract:
  Background: Coherent {\pi}0 photoproduction on heavy nuclei has been
suggested as a reliable tool to infer neutron skin thicknesses. To this aim,
various experiments have been performed, especially on 208Pb. Purpose: We
analyze the sensitivity of that reaction process to the nucleonic density, and
especially to the neutron skin thickness, for 12C, 40Ca and 208Pb, for which
reliable data exist, and on 116,124Sn, for which measurements have been
performed in Mainz. We study also the role played by the {\pi}0-nucleus
final-state interaction. Method: A model of the reaction is developed at the
impulse approximation considering either plane waves or distorted waves to
describe the {\pi}0-nucleus scattering in the outgoing channel. Results: Our
calculations are in good agreement with existing data, especially for 208Pb.
The sensitivity of the theoretical cross sections to the choice of the
nucleonic density is small, and below the experimental resolution. Conclusions:
Coherent {\pi}0 photoproduction is mostly an isoscalar observable that bares no
practical sensitivity to the neutron skin thickness. To infer that structure
observable it should be coupled to other reaction measurements, such as
electron scattering, or by comparing experiments performed on isotopes of the
same chemical element.


title:
Interpretation of the $\Omega_c \to \pi^+ \Omega(2012) \to \pi^+(\bar{K}
  \Xi)$ relative to $\Omega_c \to \pi^+\bar{K} \Xi$ from the $\Omega(2012)$
  molecular perspective

abstract:
  We present a mechanism for $\Omega_c \to \pi^+ \Omega(2012)$ production
through an external emission Cabibbo favored weak decay mode, where the
$\Omega(2012)$ is dynamically generated from the interaction of
$\bar{K}\Xi^*(1530)$, $\eta\Omega$, with $\bar{K}\Xi$ as the main decay
channel. The $\Omega(2012)$ decays latter to $\bar{K}\Xi$ in this picture, with
results compatible with previous Belle data. The picture has as a consequence
that one can evaluate the direct decay $\Omega_c^0 \to \pi^+K^- \Xi^0$ and the
decay $\Omega_c^0 \to \pi^+\bar{K} \Xi^*$, $\pi^+\eta\Omega$ with direct
coupling of $\bar{K}\Xi^*$ and $\eta\Omega$ to $K^- \Xi^0$. We show that,
within uncertainties and using data from a recent Belle measurement, all these
three channels account for about (12-20)% of the total $\Omega_c \to \pi^+K^-
\Xi^0$ decay rate. The consistency of the molecular picture with all the data
is established by showing that $\Omega_c \to \Xi^0 \bar{K}^{*0} \to \Xi^0K^-
\pi^+$ together with $\Omega_c \to \pi^+ \Omega^* \to \pi^+K^- \Xi^0 $ account
for about 85% of the total $\Omega_c \to \pi^+K^- \Xi^0 $.


title: A symmetric extensible protocol for quantum secret sharing

abstract:
  This paper introduces the Symmetric Extensible Quantum Secret Sharing
protocol, which is a novel quantum protocol for secret sharing. At its heart,
it is an entanglement based protocol that relies on the use of maximally
entangled GHZ tuples, evenly distributed among the players, endowing the
spymaster with the ability to securely share a secret message with her agents.
It offers uncompromising security, making virtually impossible for a malicious
eavesdropper or a rogue double agent to disrupt its successful execution. It is
characterized by symmetry, since all agents are treated indiscriminately,
utilizing identical quantum circuits. Furthermore, it can be seamlessly
extended to an arbitrary number of agents. After the completion of the quantum
part of the protocol, the spymaster will have to publicly transmit some
information, in order to enable the agents to discover the secret message.
Finally, it has the additional advantage that the spymaster has the privilege
to decide when it is the right time for the agents to discover the secret
message.


title: Portfolio Diversification Revisited

abstract:
  We relax a number of assumptions in Alexeev and Tapon (2012) in order to
account for non-normally distributed, skewed, multi-regime, and leptokurtic
asset return distributions. We calibrate a Markov-modulated Levy process model
to equity market data to demonstrate the merits of our approach, and show that
the calibrated models do a good job of matching the empirical moments. Finally,
we argue that much of the related literature on portfolio diversification
relies on assumptions that are in tension with certain observable regularities
and which, if ignored, may lead to underestimation of risk.


title:
Federated Learning on Heterogeneous and Long-Tailed Data via Classifier
  Re-Training with Federated Features

abstract:
  Federated learning (FL) provides a privacy-preserving solution for
distributed machine learning tasks. One challenging problem that severely
damages the performance of FL models is the co-occurrence of data heterogeneity
and long-tail distribution, which frequently appears in real FL applications.
In this paper, we reveal an intriguing fact that the biased classifier is the
primary factor leading to the poor performance of the global model. Motivated
by the above finding, we propose a novel and privacy-preserving FL method for
heterogeneous and long-tailed data via Classifier Re-training with Federated
Features (CReFF). The classifier re-trained on federated features can produce
comparable performance as the one re-trained on real data in a
privacy-preserving manner without information leakage of local data or class
distribution. Experiments on several benchmark datasets show that the proposed
CReFF is an effective solution to obtain a promising FL model under
heterogeneous and long-tailed data. Comparative results with the
state-of-the-art FL methods also validate the superiority of CReFF. Our code is
available at https://github.com/shangxinyi/CReFF-FL.


title:
Placing M-Phasis on the Plurality of Hate: A Feature-Based Corpus of
  Hate Online

abstract:
  Even though hate speech (HS) online has been an important object of research
in the last decade, most HS-related corpora over-simplify the phenomenon of
hate by attempting to label user comments as "hate" or "neutral". This ignores
the complex and subjective nature of HS, which limits the real-life
applicability of classifiers trained on these corpora. In this study, we
present the M-Phasis corpus, a corpus of ~9k German and French user comments
collected from migration-related news articles. It goes beyond the
"hate"-"neutral" dichotomy and is instead annotated with 23 features, which in
combination become descriptors of various types of speech, ranging from
critical comments to implicit and explicit expressions of hate. The annotations
are performed by 4 native speakers per language and achieve high (0.77 <= k <=
1) inter-annotator agreements. Besides describing the corpus creation and
presenting insights from a content, error and domain analysis, we explore its
data characteristics by training several classification baselines.


title: Positive (Modal) Logic Beyond Distributivity

abstract:
  We present a duality for non-necessarily-distributive (modal) lattices and
use this to study non-necessarily-distributive positive (modal) logic. Our
duality is similar to Priestley duality and as such allows us to use similar
tools and techniques to study logic. As a result, we prove Sahlqvist
correspondence and canonicity for both the propositional logic as well as a
modal extension.


title:
Generalized polarizabilites for an exact multipole analysis of complex
  nanostructures under inhomogeneous illumination

abstract:
  The multipole expansion of a nano-photonic structure's electromagnetic
response is a versatile tool to interpret optical effects in nano-optics, but
it requires knowledge of the internal field distribution, for which in general
expensive full-field simulations are necessary. We present a generalized
polarizability model based on the Green's Dyadic Method (GDM), capable to
describe resonant nanostructures, supporting both electric and magnetic dipole
and quadrupole modes. Our formalism combines the recently developed exact
multipole decomposition [Alaee et al., Opt. Comms. 407, 17-21 (2018)] with the
concept of a generalized field propagator. It reproduces the exact multipole
moments for any particle size and for arbitrary, inhomogeneous illumination
fields such as focused vector beams or local light sources. After an initial
computation step, our approach allows to instantaneously obtain the exact
multipole decomposition for any possible illumination, and it allows to
calculate spectra of the total density of multipole modes. Furthermore, the
technique can be used to visualize spatial zones inside a nanostructure, where
an external illumination couples strongly to a specific multipole moment. The
formalism will be very useful for various applications in nano-optics like
illumination-field engineering, or meta-atom design e.g. for Huygens
metasurfaces. We provide a numerical open source implementation compatible with
the pyGDM python package.


title: Random number generation with a chaotic electromechanical resonator

abstract:
  Chaos enables the emergence of randomness in deterministic physical systems.
Therefore it can be exploited for the conception of true random number
generators (RNG) mandatory in classical cryptography applications. Meanwhile,
nanomechanical oscillators, at the core of many on-board functionalities such
as sensing, reveal as excellent candidates to behave chaotically. This is made
possible thanks to intrinsic mechanical nonlinearities emerging at the
nanoscale. Here we present a platform gathering a nanomechanical oscillator and
its integrated capacitive actuation. Using a modulation of the resonant force
induced by the electrodes, we demonstrate chaotic dynamics and study how it
depends on the dissipation of the system. The randomness of a binary sequence
generated from a chaotic time trace is evaluated and discussed such that the
generic parameters enabling successful random number generation can be
established. This demonstration makes use of concepts which are sufficiently
general to be applied to the next generation of nano-electro-optomechanical
systems.


title: List-Mode PET Image Reconstruction Using Deep Image Prior

abstract:
  List-mode positron emission tomography (PET) image reconstruction is an
important tool for PET scanners with many lines-of-response (LORs) and
additional information such as time-of-flight and depth-of-interaction. Deep
learning is one possible solution to enhance the quality of PET image
reconstruction. However, the application of deep learning techniques to
list-mode PET image reconstruction have not been progressed because list data
is a sequence of bit codes and unsuitable for processing by convolutional
neural networks (CNN). In this study, we propose a novel list-mode PET image
reconstruction method using an unsupervised CNN called deep image prior (DIP)
and a framework of alternating direction method of multipliers. The proposed
list-mode DIP reconstruction (LM-DIPRecon) method alternatively iterates
regularized list-mode dynamic row action maximum likelihood algorithm
(LM-DRAMA) and magnetic resonance imaging conditioned DIP (MR-DIP). We
evaluated LM-DIPRecon using both simulation and clinical data, and it achieved
sharper images and better tradeoff curves between contrast and noise than the
LM-DRAMA and MR-DIP. These results indicated that the LM-DIPRecon is useful for
quantitative PET imaging with limited events. In addition, as list data has
finer temporal information than dynamic sinograms, list-mode deep image prior
reconstruction is expected to be useful for 4D PET imaging and motion
correction.


title:
Known unknowns: assessing the impact of instrumental calibration
  uncertainty on LISA science

abstract:
  The primary scientific results of the future space-based gravitational wave
interferometer LISA will come from the parameter inference of a large variety
of gravitational wave sources. However, the presence of calibration errors
could potentially degrade the measurement precision of the system parameters.
Here, we assess the impact of calibration uncertainties on parameter estimation
for individual sources, focusing on massive black holes, extreme-mass-ratio
inspirals (EMRIs), galactic binaries, and stellar origin black hole binaries.
Using a Fisher matrix formalism, we investigate how the measurement precision
of source parameters degrades as a function of the size of the assumed
calibration uncertainties. If we require that parameter measurements are
degraded by no more than a factor of two relative to their value in the absence
of calibration error, we find that calibration errors should be smaller than a
few tenths of a percent in amplitude and $10^{-3}$ in phase. We also
investigate the possibility of using verification binaries and EMRIs to
constrain calibration uncertainties. Verification binaries can constrain
amplitude calibration uncertainties at the level of a few percent, while both
source types can provide constrain phase calibration at the level of a
few$\times10^{-2}$.


title:
On the regularity of axisymmetric, swirl-free solutions of the Euler
  equation in four and higher dimensions

abstract:
  In this paper, we will discuss the axisymetric, swirl-free Euler equation in
four and higher dimensions. We will show that in four and higher dimensions the
axisymetric, swirl-free Euler equation has properties which could allow
finite-time singularity formation of a form that is excluded in three
dimensions. We will also consider a model equation that is obtained by taking
the infinite-dimensional limit of the vorticity equation in this setup. This
model exhibits finite-time blowup of a Burgers shock type. The blowup result
for the infinite dimensional model equation heavily suggests that smooth
solutions of the Euler equation exhibit finite-time blowup in sufficiently high
dimensions.


title:
Implementing Bogoliubov Transformations Beyond the Shale-Stinespring
  Condition

abstract:
  We provide two extensions of a dense subspace of Fock space, such that
Bogoliubov transformations become implementable on them, even though they
violate the Shale-Stinespring condition, so they are not implementable on Fock
space. Both the bosonic and fermionic case are covered. Conditions for
implementability in the extended sense are stated and proved. From these, we
derive conditions for a quadratic Hamiltonian to be diagonalizable by a
Bogoliubov transformation that is implementable in the extended sense. Three
examples illustrate situations, in which an implementation in the extended
sense is possible although the Shale-Stinespring condition fails to hold.


title:
Disc cloaking: Establishing a lower limit to the number density of local
  compact massive spheroids/bulges and the potential fate of some high-z red
  nuggets

abstract:
  The near-absence of compact massive quiescent galaxies in the local Universe
implies a size evolution since $z\sim2.5$. It is often theorised that such `red
nuggets' have evolved into today's elliptical (E) galaxies via an E-to-E
transformation. We examine an alternative scenario in which a red nugget
develops a rotational disc through mergers and accretion, say, at $1\lesssim
z\lesssim2$, thereby cloaking the nugget as the extant bulge/spheroid component
of a larger, now old, galaxy. We have performed detailed, physically-motivated,
multi-component decompositions of a volume-limited sample of 103 massive
($M_*/\rm M_{\odot} \gtrsim 1\times 10^{11}$) galaxies within 110\,Mpc. Among
our 28 galaxies with existing elliptical classifications, we found that 18 have
large-scale discs, and two have intermediate-scale discs, and are reclassified
here as lenticulars (S0) and elliculars (ES). The local spheroid stellar mass
function, size-mass diagram and bulge-to-total ($B/T$) flux ratio are
presented. We report lower-limits for the volume number density of compact
massive spheroids, $n_\mathrm{c,Sph}\sim (0.17$-$1.2) \times 10^{-4}\,\rm
Mpc^{-3}$, based on different definitions of `red nuggets' in the literature.
Similar number densities of local compact massive bulges were reported by de la
Rosa et al. using automated two-component decompositions and their existence is
now abundantly clear with our multi-component decompositions. We find
disc-cloaking to be a salient alternative for galaxy evolution. In particular,
instead of an E-to-E process, disc growth is the dominant evolutionary pathway
for at least low-mass ($1\times10^{10}<M_*/\rm M_{\odot} \lessapprox 4 \times
10^{10}$) red nuggets, while our current lower-limits are within an alluring
factor of a few of the peak abundance of high-mass red nuggets at $1\lesssim
z\lesssim2$.


title: WeaNF: Weak Supervision with Normalizing Flows

abstract:
  A popular approach to decrease the need for costly manual annotation of large
data sets is weak supervision, which introduces problems of noisy labels,
coverage and bias. Methods for overcoming these problems have either relied on
discriminative models, trained with cost functions specific to weak
supervision, and more recently, generative models, trying to model the output
of the automatic annotation process. In this work, we explore a novel direction
of generative modeling for weak supervision: Instead of modeling the output of
the annotation process (the labeling function matches), we generatively model
the input-side data distributions (the feature space) covered by labeling
functions. Specifically, we estimate a density for each weak labeling source,
or labeling function, by using normalizing flows. An integral part of our
method is the flow-based modeling of multiple simultaneously matching labeling
functions, and therefore phenomena such as labeling function overlap and
correlations are captured. We analyze the effectiveness and modeling
capabilities on various commonly used weak supervision data sets, and show that
weakly supervised normalizing flows compare favorably to standard weak
supervision baselines.


title:
Information Inequalities via Submodularity and a Problem in Extremal
  Graph Theory

abstract:
  The present paper offers, in its first part, a unified approach for the
derivation of families of inequalities for set functions which satisfy
sub/supermodularity properties. It applies this approach for the derivation of
information inequalities with Shannon information measures. Connections of the
considered approach to a generalized version of Shearer's lemma, and other
related results in the literature are considered. Some of the derived
information inequalities are new, and also known results (such as a generalized
version of Han's inequality) are reproduced in a simple and unified way. In its
second part, this paper applies the generalized Han's inequality to analyze a
problem in extremal graph theory. This problem is motivated and analyzed from
the perspective of information theory, and the analysis leads to generalized
and refined bounds. The two parts of this paper are meant to be independently
accessible to the reader.


title: Some developments around the Katznelson-Tzafriri theorem

abstract:
  This paper is a survey article on developments arising from a theorem proved
by Katznelson and Tzafriri in 1986 showing that $\lim_{n\to\infty} \|T^n(I-T)\|
=0$ if $T$ is a power-bounded operator on a Banach space and $\sigma(T) \cap \T
\subseteq \{1\}$. Many variations and consequences of the original theorem have
been proved subsequently, and we provide an account of this branch of operator
theory.


title:
Viscous inertial modes on a differentially rotating sphere: Comparison
  with solar observations

abstract:
  In a previous paper we studied the effect of latitudinal rotation on solar
equatorial Rossby modes in the beta-plane approximation. Since then, a rich
spectrum of inertial modes has been observed on the Sun, which is not limited
to the equatorial Rossby modes and includes high-latitude modes.
  Here we extend the computation of toroidal modes in 2D to spherical geometry,
using realistic solar differential rotation and including viscous damping. The
aim is to compare the computed mode spectra with the observations and to study
mode stability.
  At fixed radius, we solve the eigenvalue problem numerically using a
spherical harmonics decomposition of the velocity stream function. Due to the
presence of viscous critical layers, the spectrum consists of four different
families: Rossby modes, high-latitude modes, critical-latitude modes, and
strongly damped modes. For each longitudinal wavenumber m<4, up to three
Rossby-like modes are present on the sphere, in contrast to the equatorial beta
plane where only the equatorial Rossby mode is present. The least damped modes
in the model have eigenfrequencies and eigenfunctions that resemble the
observed modes; the comparison improves when the radius is taken in the lower
half of the convection zone. For radii above 0.75R and Ekman numbers E<10^{-4},
at least one mode is unstable. For either m=1 or m=2, up to two Rossby modes
are unstable when the radial dependence of the Ekman number follows a quenched
diffusivity model (E=2. 10^{-5} at the base of the convection zone). For m=3,
up to two Rossby modes can be unstable, including the equatorial Rossby mode.
  Although the 2D model discussed here is highly simplified, the spectrum of
toroidal modes appears to include many of the observed solar inertial modes.
The self-excited modes in the model have frequencies close to those of the
observed modes with the largest amplitudes.


title: HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification

abstract:
  Hierarchical text classification (HTC) is a challenging subtask of
multi-label classification due to its complex label hierarchy. Recently, the
pretrained language models (PLM) have been widely adopted in HTC through a
fine-tuning paradigm. However, in this paradigm, there exists a huge gap
between the classification tasks with sophisticated label hierarchy and the
masked language model (MLM) pretraining tasks of PLMs and thus the potentials
of PLMs can not be fully tapped. To bridge the gap, in this paper, we propose
HPT, a Hierarchy-aware Prompt Tuning method to handle HTC from a multi-label
MLM perspective. Specifically, we construct dynamic virtual template and label
words which take the form of soft prompts to fuse the label hierarchy knowledge
and introduce a zero-bounded multi-label cross entropy loss to harmonize the
objectives of HTC and MLM. Extensive experiments show HPT achieves the
state-of-the-art performances on 3 popular HTC datasets and is adept at
handling the imbalance and low resource situations.


title:
Improving the Robustness of Federated Learning for Severely Imbalanced
  Datasets

abstract:
  With the ever increasing data deluge and the success of deep neural networks,
the research of distributed deep learning has become pronounced. Two common
approaches to achieve this distributed learning is synchronous and asynchronous
weight update. In this manuscript, we have explored very simplistic synchronous
weight update mechanisms. It has been seen that with an increasing number of
worker nodes, the performance degrades drastically. This effect has been
studied in the context of extreme imbalanced classification (e.g. outlier
detection). In practical cases, the assumed conditions of i.i.d. may not be
fulfilled. There may also arise global class imbalance situations like that of
outlier detection where the local servers receive severely imbalanced data and
may not get any samples from the minority class. In that case, the DNNs in the
local servers will get completely biased towards the majority class that they
receive. This would highly impact the learning at the parameter server (which
practically does not see any data). It has been observed that in a parallel
setting if one uses the existing federated weight update mechanisms at the
parameter server, the performance degrades drastically with the increasing
number of worker nodes. This is mainly because, with the increasing number of
nodes, there is a high chance that one worker node gets a very small portion of
the data, either not enough to train the model without overfitting or having a
highly imbalanced class distribution. The chapter, hence, proposes a workaround
to this problem by introducing the concept of adaptive cost-sensitive momentum
averaging. It is seen that for the proposed system, there was no to minimal
degradation in performance while most of the other methods hit their bottom
performance before that.


title: Torsion-induced chiral magnetic current in equilibrium

abstract:
  We study equilibrium transport properties of massless Dirac fermions at
finite temperature and chemical potential in spacetime accompanied by torsion,
which in four dimensions couples with Dirac fermions as an axial gauge field.
In particular, we compute the current density at the linear order in the
torsion as well as in an external magnetic field with the Pauli-Villars
regularization, finding that an equilibrium current akin to the chiral magnetic
current is locally induced. Such torsion can be realized in condensed matter
systems along a screw dislocation line, around which localized and extended
current distributions are predicted so as to be relevant to Dirac and Weyl
semimetals. Furthermore, we compute the current density at the linear order in
the torsion as well as in a Weyl node separation, which turns out to vanish in
spite of being allowed from the symmetry perspective. Contrasts of our findings
with torsion-induced currents from previous work are also discussed.


title:
A continuous-parameter Katznelson-Tzafriri theorem for analytic Besov
  functions

abstract:
  We prove a continuous-parameter version of the recent theorem of
Katznelson-Tzafiri type for power-bounded operators which have a bounded
calculus for analytic Besov functions.


title: Skolem Meets Schanuel

abstract:
  The celebrated Skolem-Mahler-Lech Theorem states that the set of zeros of a
linear recurrence sequence is the union of a finite set and finitely many
arithmetic progressions. The corresponding computational question, the Skolem
Problem, asks to determine whether a given linear recurrence sequence has a
zero term. Although the Skolem-Mahler-Lech Theorem is almost 90 years old,
decidability of the Skolem Problem remains open. The main contribution of this
paper is an algorithm to solve the Skolem Problem for simple linear recurrence
sequences (those with simple characteristic roots). Whenever the algorithm
terminates, it produces a stand-alone certificate that its output is correct --
a set of zeros together with a collection of witnesses that no further zeros
exist. We give a proof that the algorithm always terminates assuming two
classical number-theoretic conjectures: the Skolem Conjecture (also known as
the Exponential Local-Global Principle) and the $p$-adic Schanuel Conjecture.
Preliminary experiments with an implementation of this algorithm within the
tool \textsc{Skolem} point to the practical applicability of this method.


title:
Simplifying Multilingual News Clustering Through Projection From a
  Shared Space

abstract:
  The task of organizing and clustering multilingual news articles for media
monitoring is essential to follow news stories in real time. Most approaches to
this task focus on high-resource languages (mostly English), with low-resource
languages being disregarded. With that in mind, we present a much simpler
online system that is able to cluster an incoming stream of documents without
depending on language-specific features. We empirically demonstrate that the
use of multilingual contextual embeddings as the document representation
significantly improves clustering quality. We challenge previous crosslingual
approaches by removing the precondition of building monolingual clusters. We
model the clustering process as a set of linear classifiers to aggregate
similar documents, and correct closely-related multilingual clusters through
merging in an online fashion. Our system achieves state-of-the-art results on a
multilingual news stream clustering dataset, and we introduce a new evaluation
for zero-shot news clustering in multiple languages. We make our code available
as open-source.


title: Scattering bound states in AdS

abstract:
  We initiate the study of bound state scattering in AdS space at the level of
Witten diagrams. For concreteness, we focus on the case with only scalar fields
and analyze several basic diagrams which more general diagrams reduce to. We
obtain closed form expressions for their Mellin amplitudes with arbitrary
conformal dimensions, which exhibit interesting behavior. In particular, we
observe that certain tree-level bound state Witten diagrams have the same
structure as loop diagrams in AdS.


title:
Semi-MoreGAN: A New Semi-supervised Generative Adversarial Network for
  Mixture of Rain Removal

abstract:
  Rain is one of the most common weather which can completely degrade the image
quality and interfere with the performance of many computer vision tasks,
especially under heavy rain conditions. We observe that: (i) rain is a mixture
of rain streaks and rainy haze; (ii) the scene depth determines the intensity
of rain streaks and the transformation into the rainy haze; (iii) most existing
deraining methods are only trained on synthetic rainy images, and hence
generalize poorly to the real-world scenes. Motivated by these observations, we
propose a new SEMI-supervised Mixture Of rain REmoval Generative Adversarial
Network (Semi-MoreGAN), which consists of four key modules: (I) a novel
attentional depth prediction network to provide precise depth estimation; (ii)
a context feature prediction network composed of several well-designed detailed
residual blocks to produce detailed image context features; (iii) a pyramid
depth-guided non-local network to effectively integrate the image context with
the depth information, and produce the final rain-free images; and (iv) a
comprehensive semi-supervised loss function to make the model not limited to
synthetic datasets but generalize smoothly to real-world heavy rainy scenes.
Extensive experiments show clear improvements of our approach over twenty
representative state-of-the-arts on both synthetic and real-world rainy images.


title:
Higher-curvature Gravities from Braneworlds and the Holographic
  c-theorem

abstract:
  We study the structure of the higher-curvature gravitational densities that
are induced from holographic renormalization in AdS$_{d+1}$. In a braneworld
construction, such densities define a d-dimensional higher-curvature
gravitational theory on the brane, which in turn is dual to a (d-1)-dimensional
CFT living at its boundary. We show that this CFT$_{d-1}$ satisfies a
holographic c-theorem in general dimensions (different than the g-theorem of
holographic boundary CFTs), since at each and every order the higher-curvature
densities satisfy c-theorems on their own. We find that, in these densities,
the terms that affect the monotonicity of the holographic c-function are
algebraic in the curvature, and do not involve covariant derivatives of the
Riemann tensor. We examine various other features of the holographically
induced higher-curvature densities, such as the presence of reduced-order
traced equations, and their connection to Born-Infeld-type gravitational
Lagrangians.


title:
Autoencoder based Hybrid Multi-Task Predictor Network for Daily
  Open-High-Low-Close Prices Prediction of Indian Stocks

abstract:
  Stock prices are highly volatile and sudden changes in trends are often very
problematic for traditional forecasting models to handle. The standard Long
Short Term Memory (LSTM) networks are regarded as the state-of-the-art models
for such predictions. But, these models fail to handle sudden and drastic
changes in the price trend. Moreover, there are some inherent constraints with
the open, high, low and close (OHLC) prices of the stocks. Literature lacks the
study on the inherent property of OHLC prices. We argue that predicting the
OHLC prices for the next day is much more informative than predicting the
trends of the stocks as the trend is mostly calculated using these OHLC prices
only. The problem mainly is focused on Buy-Today Sell-Tomorrow (BTST) trading.
In this regard, AEs when pre-trained with the stock prices, may be beneficial.
A novel framework is proposed where a pre-trained encoder is cascaded in front
of the multi-task predictor network. This hybrid network can leverage the power
of a combination of networks and can both handle the OHLC constraints as well
as capture any sudden drastic changes in the prices. It is seen that such a
network is much more efficient at predicting stock prices. The experiments have
been extended to recommend the most profitable and most overbought stocks on
the next day. The model has been tested for multiple Indian companies and it is
found that the recommendations from the proposed model have not resulted in a
single loss for a test period of 300 days.


title: Hybrid Relation Guided Set Matching for Few-shot Action Recognition

abstract:
  Current few-shot action recognition methods reach impressive performance by
learning discriminative features for each video via episodic training and
designing various temporal alignment strategies. Nevertheless, they are limited
in that (a) learning individual features without considering the entire task
may lose the most relevant information in the current episode, and (b) these
alignment strategies may fail in misaligned instances. To overcome the two
limitations, we propose a novel Hybrid Relation guided Set Matching (HyRSM)
approach that incorporates two key components: hybrid relation module and set
matching metric. The purpose of the hybrid relation module is to learn
task-specific embeddings by fully exploiting associated relations within and
cross videos in an episode. Built upon the task-specific features, we
reformulate distance measure between query and support videos as a set matching
problem and further design a bidirectional Mean Hausdorff Metric to improve the
resilience to misaligned instances. By this means, the proposed HyRSM can be
highly informative and flexible to predict query categories under the few-shot
settings. We evaluate HyRSM on six challenging benchmarks, and the experimental
results show its superiority over the state-of-the-art methods by a convincing
margin. Project page: https://hyrsm-cvpr2022.github.io/.


title: Interpretable collective intelligence of non-rational human agents

abstract:
  We outline how to create a mechanism that provides an optimal way to elicit,
from an arbitrary group of experts, the probability of the truth of an
arbitrary logical proposition together with collective information that has an
explicit form and interprets this probability. Such a system could, in
particular, incentivize experts from all over the world to collectively solve
scientific or medical problems in a very efficient manner. In our main
considerations about real experts, they are not assumed to be Bayesian and
their behavior is described by utilities that satisfy the von
Neumann-Morgenstern axioms only locally.


title: Electroweak Phase Transition in a Dark Sector with CP Violation

abstract:
  In this paper, we investigate the possibility of a strong first-order
electroweak phase transition (SFOEWPT) in the model `CP in the Dark'. The Higgs
sector of the model consists of two scalar doublets and one scalar singlet with
a specific discrete symmetry. After spontaneous symmetry breaking the model has
a Standard-Model-like phenomenology and a hidden scalar sector with a viable
Dark Matter candidate supplemented by explicit CP violation that solely occurs
in the hidden sector. The model `CP in the Dark' has been implemented in the
C++ code BSMPT v2.3 which performs a global minimisation of the
finite-temperature one-loop corrected effective potential and searches for
SFOEWPTs. An SFOEWPT is found to be allowed in a broad range of the parameter
space. Furthermore, there are parameter scenarios where spontaneous CP
violation is generated at finite temperature. The in addition spontaneously
broken $\mathbb{Z}_2$ symmetry then leads to mixing between the dark and the
visible sector so that CP violation in the dark is promoted at finite
temperature to the visible sector and thereby provides additional sources of CP
violation that are not restricted by the electric dipole moment measurements at
zero temperature. Thus, `CP in the Dark' provides a promising candidate for the
generation of the baryon asymmetry of the universe through electroweak
baryogenesis.


title:
AE-NeRF: Auto-Encoding Neural Radiance Fields for 3D-Aware Object
  Manipulation

abstract:
  We propose a novel framework for 3D-aware object manipulation, called
Auto-Encoding Neural Radiance Fields (AE-NeRF). Our model, which is formulated
in an auto-encoder architecture, extracts disentangled 3D attributes such as 3D
shape, appearance, and camera pose from an image, and a high-quality image is
rendered from the attributes through disentangled generative Neural Radiance
Fields (NeRF). To improve the disentanglement ability, we present two losses,
global-local attribute consistency loss defined between input and output, and
swapped-attribute classification loss. Since training such auto-encoding
networks from scratch without ground-truth shape and appearance information is
non-trivial, we present a stage-wise training scheme, which dramatically helps
to boost the performance. We conduct experiments to demonstrate the
effectiveness of the proposed model over the latest methods and provide
extensive ablation studies.


title:
Electrically reduced optical switching threshold of VO2-based THz
  metasurface

abstract:
  The VO2-based THz metasurface optical switching threshold is reduced by more
than five orders of magnitude to 0.32-0.4 W/cm2 due to the supply of a
subthreshold electric current. Moreover, additional deposition of Au
nanoparticles can further reduce the threshold by 30% due to plasmonic effects.
The proposed designs and a combined approach to controlling the sub-THz/THz
transmission by electric current and light make it possible to overcome the
existing bottleneck in design of dynamically programmable THz mirror array
based on metasurfaces.


title:
Relativistically calculated K-shell level widths and fluorescence yields
  for atoms with 20$\le$Z$\le$30

abstract:
  The $K$-shell level radiative, non-radiative, and total widths, $K$-shell
fluorescence yields and $K$-shell hole state lifetimes for atoms with
20$\le$Z$\le$30 have been calculated in fully-relativistic way using the
extensive multiconfiguration Dirac-Fock calculations with the inclusion of the
Breit interaction and QED corrections and also using multiconfiguration
Dirac-Hartree-Slater calculations.


title: DOTIN: Dropping Task-Irrelevant Nodes for GNNs

abstract:
  Scalability is an important consideration for deep graph neural networks.
Inspired by the conventional pooling layers in CNNs, many recent graph learning
approaches have introduced the pooling strategy to reduce the size of graphs
for learning, such that the scalability and efficiency can be improved.
However, these pooling-based methods are mainly tailored to a single
graph-level task and pay more attention to local information, limiting their
performance in multi-task settings which often require task-specific global
information. In this paper, departure from these pooling-based efforts, we
design a new approach called DOTIN (\underline{D}r\underline{o}pping
\underline{T}ask-\underline{I}rrelevant \underline{N}odes) to reduce the size
of graphs. Specifically, by introducing $K$ learnable virtual nodes to
represent the graph embeddings targeted to $K$ different graph-level tasks,
respectively, up to 90\% raw nodes with low attentiveness with an attention
model -- a transformer in this paper, can be adaptively dropped without notable
performance decreasing. Achieving almost the same accuracy, our method speeds
up GAT by about 50\% on graph-level tasks including graph classification and
graph edit distance (GED) with about 60\% less memory, on D\&D dataset. Code
will be made publicly available in https://github.com/Sherrylone/DOTIN.


title: Pseudo strong labels for large scale weakly supervised audio tagging

abstract:
  Large-scale audio tagging datasets inevitably contain imperfect labels, such
as clip-wise annotated (temporally weak) tags with no exact on- and offsets,
due to a high manual labeling cost. This work proposes pseudo strong labels
(PSL), a simple label augmentation framework that enhances the supervision
quality for large-scale weakly supervised audio tagging. A machine annotator is
first trained on a large weakly supervised dataset, which then provides finer
supervision for a student model. Using PSL we achieve an mAP of 35.95 balanced
train subset of Audioset using a MobileNetV2 back-end, significantly
outperforming approaches without PSL. An analysis is provided which reveals
that PSL mitigates missing labels. Lastly, we show that models trained with PSL
are also superior at generalizing to the Freesound datasets (FSD) than their
weakly trained counterparts.


title:
Direct Determination of the Topological Thermal Conductance via Local
  Power Measurement

abstract:
  Thermal conductance measurements, sensitive to charge and chargeless energy
flow, are evolving as an essential measurement technique in Condensed Matter
Physics. For two-dimensional topological insulators, the measurements of the
thermal Hall conductance, $\kappa_{xy}T$, and the longitudinal one
$\kappa_{xx}T$, are crucial for the understanding of their underlying
topological order. Such measurements are thus far lacking, even in the
extensively studied quantum Hall effect (QHE) regime. Here, we report a new
local power measurement technique that reveals the topological thermal Hall
conductance (not the ubiquitous two-terminal one). For example, we find
$\kappa_{xy}\sim0$ of the challenging $\nu=2/3$ particle-hole conjugated state.
This is in contrast to the two-terminal measurement, which provides a
non-universal value that depends on the extent of thermal equilibration between
the counter-propagating edge modes. Moreover, we use this technique to study
the power carried by the current fluctuations in a partitioned edge mode with
an out-of-equilibrium distribution.


title: Iterative Quantum Optimization with Adaptive Problem Hamiltonian

abstract:
  Quantum optimization algorithms hold the promise of solving classically hard,
discrete optimization problems in practice. The requirement of encoding such
problems in a Hamiltonian realized with a finite -- and currently small --
number of qubits, however, poses the risk of finding only the optimum within
the restricted space supported by this Hamiltonian. We describe an iterative
algorithm in which a solution obtained with such a restricted problem
Hamiltonian is used to define a new problem Hamiltonian that is better suited
than the previous one. In numerical examples of the shortest vector problem, we
show that the algorithm with a sequence of improved problem Hamiltonians
converges to the desired solution.


title: Reductive homogeneous Lorentzian manifolds

abstract:
  We study homogeneous Lorentzian manifolds $M = G/L$ of a connected reductive
Lie group $G$ modulo a connected reductive subgroup $L$, under the assumption
that $M$ is (almost) $G$-effective and the isotropy representation is totally
reducible. We show that the description of such manifolds reduces to the case
of semisimple Lie groups $G$. Moreover, we prove that such a homogeneous space
is reductive. We describe all totally reducible subgroups of the Lorentz group
and divide them into three types. The subgroups of Type I are compact, while
the subgroups of Type II and Type III are non-compact. The explicit description
of the corresponding homogeneous Lorentzian spaces of Type II and III (under
some mild assumption) is given. We also show that the description of Lorentz
homogeneous manifolds $M = G/L$ of Type I, reduces to the description of
subgroups $L$ such that $M=G/L$ is an admissible manifold, i.e., an effective
homogeneous manifold that admits an invariant Lorentzian metric. Whenever the
subgroup $L$ is a maximal subgroup with these properties, we call such a
manifold minimal admissible. We classify all minimal admissible homogeneous
manifolds $G/L$ of a compact semisimple Lie group $G$ and describe all
invariant Lorentzian metrics on them.


title:
Graphene on a ferromagnetic substrate: instability of the electronic
  liquid

abstract:
  We previously show [JETP Letters, {\bf 114}, 763 (2021)] that a graphene
sample placed on a ferromagnetic substrate demonstrates a cooperative
magnetoelectronic instability. The instability induces a gap in the electronic
spectrum and a canting deformation of the magnetization near the
graphene-substrate interface. In this paper we prove that the interaction
between the electrons in graphene strongly enhances the instability. Our
estimates suggest that in the presence of even a moderate interaction the
instability can be sufficiently pronounced to be detected experimentally in a
realistic setting.


title:
Correlation function and linear response function of homogeneous
  isotropic turbulence in the Eulerian and Lagrangian coordinates

abstract:
  We study the correlation function and mean linear response function of the
velocity Fourier mode of statistically steady-state, homogeneous and isotropic
turbulence in the Eulerian and Lagrangian coordinates through direct numerical
simulation (DNS). As the Lagrangian velocity, we here adopt Kraichnan's
Lagrangian history framework where Lagrangian particles are labelled with
current positions and their velocity are measured at some time before. This
Lagrangian velocity is numerically calculated with a method known as passive
vector method. Our first goal is to study relation between the correlation
function and the mean linear response function in the Eulerian and Lagrangian
coordinates. Such a relation is known to be important in analysing the closed
set of equations for the two functions, which are obtained by
direct-interaction-approximation type closures. We demonstrate numerically that
the fluctuation-dissipation theorem (proportionality between the two functions)
does not hold. The relation is further investigated with general analytical
expressions of the mean linear response function under stochastic settings,
which are known as the fluctuation-response relations in non-equilibrium
statistical mechanics. Our second goal is to identify characteristic times
associated with the two functions and to compare the times between the Eulerian
and Lagrangian coordinates. Our DNS result supports the common view that the
Eulerian characteristic times have the sweeping-time scaling ($\propto k^{-1}$,
where $k$ is the wavenumber) for both functions and the Lagrangian
characteristic times in the inertial range have the Kolmogorov-time scaling
($\propto k^{-2/3}$) for both functions.


title:
A measurement of the Integrated Sachs-Wolfe Effect with the Rapid ASKAP
  Continuum Survey

abstract:
  The evolution of the gravitational potentials on large scales due to the
accelerated expansion of the Universe is an important and independent probe of
dark energy, known as the integrated Sachs-Wolfe (ISW) effect. We measure this
ISW effect through cross-correlating the cosmic microwave background maps from
the \textit{Planck} satellite with a radio continuum galaxy distribution map
from the recent Rapid ASKAP Continuum Survey (RACS). We detect a positive
cross-correlation at $\sim 2.8\,\sigma$ relative to the null hypothesis of no
correlation. We parameterise the strength of the ISW effect through an
amplitude parameter and find the constraints to be $A_{\mathrm{ISW}} =
0.94^{+0.42}_{-0.41}$, which is consistent with the prediction of an
accelerating universe within the current concordance cosmological model,
$\Lambda$CDM. The credible interval on this parameter is independent of the
different bias models and redshift distributions that were considered when
marginalising over the nuisance parameters. We also detect a power excess in
the galaxy auto-correlation angular power spectrum on large scales ($\ell \leq
40$), and investigate possible systematic causes.


title:
Regotron: Regularizing the Tacotron2 architecture via monotonic
  alignment loss

abstract:
  Recent deep learning Text-to-Speech (TTS) systems have achieved impressive
performance by generating speech close to human parity. However, they suffer
from training stability issues as well as incorrect alignment of the
intermediate acoustic representation with the input text sequence. In this
work, we introduce Regotron, a regularized version of Tacotron2 which aims to
alleviate the training issues and at the same time produce monotonic
alignments. Our method augments the vanilla Tacotron2 objective function with
an additional term, which penalizes non-monotonic alignments in the
location-sensitive attention mechanism. By properly adjusting this
regularization term we show that the loss curves become smoother, and at the
same time Regotron consistently produces monotonic alignments in unseen
examples even at an early stage (13\% of the total number of epochs) of its
training process, whereas the fully converged Tacotron2 fails to do so.
Moreover, our proposed regularization method has no additional computational
overhead, while reducing common TTS mistakes and achieving slighlty improved
speech naturalness according to subjective mean opinion scores (MOS) collected
from 50 evaluators.


title: Mass Spectroscopy and Decay Properties of $\Xi_{cb}$, $\Xi_{bb}$ Baryons

abstract:
  Using the Hypercentral Constituent Quark Model (hCQM), the mass spectra of
doubly heavy baryons $\Xi_{cb}$ and $\Xi_{bb}$ are determined. The model
describes an interaction inside the baryonic system. Screened potential has
been considered as confining potential with color-Coloumb potential to
enumerate the masses of baryonic states. Regge trajectories have been plotted
in $(J, M^2)$ plane. The properties like magnetic moment and radiative decay
width have been determined using the obtained results.


title:
Mahalanobis balancing: a multivariate perspective on approximate
  covariate balancing

abstract:
  In the past decade, various exact balancing-based weighting methods were
introduced to the causal inference literature. Exact balancing alleviates the
extreme weight and model misspecification issues that may incur when one
implements inverse probability weighting. It eliminates covariate imbalance by
imposing balancing constraints in an optimization problem. The optimization
problem can nevertheless be infeasible when there is bad overlap between the
covariate distributions in the treated and control groups or when the
covariates are high-dimensional. Recently, approximate balancing was proposed
as an alternative balancing framework, which resolves the feasibility issue by
using inequality moment constraints instead. However, it can be difficult to
select the threshold parameters when the number of constraints is large.
Moreover, moment constraints may not fully capture the discrepancy of covariate
distributions. In this paper, we propose Mahalanobis balancing, which
approximately balances covariate distributions from a multivariate perspective.
We use a quadratic constraint to control overall imbalance with a single
threshold parameter, which can be tuned by a simple selection procedure. We
show that the dual problem of Mahalanobis balancing is an l_2 norm-based
regularized regression problem, and establish interesting connection to
propensity score models. We further generalize Mahalanobis balancing to the
high-dimensional scenario. We derive asymptotic properties and make extensive
comparisons with existing balancing methods in the numerical studies.