title: Spatial Loss for Unsupervised Multi-channel Source Separation

abstract:
  We propose a spatial loss for unsupervised multi-channel source separation.
The proposed loss exploits the duality of direction of arrival (DOA) and
beamforming: the steering and beamforming vectors should be aligned for the
target source, but orthogonal for interfering ones. The spatial loss encourages
consistency between the mixing and demixing systems from a classic DOA
estimator and a neural separator, respectively. With the proposed loss, we
train the neural separators based on minimum variance distortionless response
(MVDR) beamforming and independent vector analysis (IVA). We also investigate
the effectiveness of combining our spatial loss and a signal loss, which uses
the outputs of blind source separation as the reference. We evaluate our
proposed method on synthetic and recorded (LibriCSS) mixtures. We find that the
spatial loss is most effective to train IVA-based separators. For the neural
MVDR beamformer, it performs best when combined with a signal loss. On
synthetic mixtures, the proposed unsupervised loss leads to the same
performance as a supervised loss in terms of word error rate. On LibriCSS, we
obtain close to state-of-the-art performance without any labeled training data.


title: Random diffusivity processes in an external force field

abstract:
  Brownian yet non-Gaussian processes have recently been observed in numerous
biological systems and the corresponding theories have been built based on
random diffusivity models. Considering the particularity of random diffusivity,
this paper studies the effect of an external force acting on two kinds of
random diffusivity models whose difference is embodied in whether the
fluctuation-dissipation theorem is valid. Based on the two random diffusivity
models, we derive the Fokker-Planck equations with an arbitrary external force,
and analyse various observables in the case with a constant force, including
the Einstein relation, the moments, the kurtosis, and the asymptotic behaviors
of the probability density function of particle's displacement at different
time scales. Both the theoretical results and numerical simulations of these
observables show significant difference between the two kinds of random
diffusivity models, which implies the important role of the
fluctuation-dissipation theorem in random diffusivity systems.


title:
Effect and Analysis of Large-scale Language Model Rescoring on
  Competitive ASR Systems

abstract:
  Large-scale language models (LLMs) such as GPT-2, BERT and RoBERTa have been
successfully applied to ASR N-best rescoring. However, whether or how they can
benefit competitive, near state-of-the-art ASR systems remains unexplored. In
this study, we incorporate LLM rescoring into one of the most competitive ASR
baselines: the Conformer-Transducer model. We demonstrate that consistent
improvement is achieved by the LLM's bidirectionality, pretraining, in-domain
finetuning and context augmentation. Furthermore, our lexical analysis sheds
light on how each of these components may be contributing to the ASR
performance.


title:
Optimizing Pilot Spacing in MU-MIMO Systems Operating Over Aging
  Channels

abstract:
  In the uplink of multiuser multiple input multiple output (MU-MIMO) systems
operating over aging channels, pilot spacing is crucial for acquiring channel
state information and achieving high signal-to-interference-plus-noise ratio
(SINR). Somewhat surprisingly, very few works examine the impact of pilot
spacing on the correlation structure of subsequent channel estimates and the
resulting quality of channel state information considering channel aging. In
this paper, we consider a fast-fading environment characterized by its
exponentially decaying autocorrelation function, and model pilot spacing as a
sampling problem to capture the inherent trade-off between the quality of
channel state information and the number of symbols available for information
carrying data symbols. We first establish a quasi-closed form for the
achievable asymptotic deterministic equivalent SINR when the channel estimation
algorithm utilizes multiple pilot signals. Next, we establish upper bounds on
the achievable SINR and spectral efficiency, as a function of pilot spacing,
which helps to find the optimum pilot spacing within a limited search space.
Our key insight is that to maximize the achievable SINR and the spectral
efficiency of MU-MIMO systems, proper pilot spacing must be applied to control
the impact of the aging channel and to tune the trade-off between pilot and
data symbols.


title: Toric varieties of Schr\"{o}der type

abstract:
  A dissection of a polygon is obtained by drawing diagonals such that no two
diagonals intersect in their interiors. In this paper, we define a toric
variety of Schr\"{o}der type as a smooth toric variety associated with a
polygon dissection. Toric varieties of Schr\"{o}der type are Fano generalized
Bott manifolds, and they are isomorphic if and only if the associated
Schr\"{o}der trees are the same as unordered rooted trees. We describe the
cohomology ring of a toric variety of Schr\"{o}der type using the associated
Schr\"{o}der tree and discuss the cohomological rigidity problem.


title:
Federated Learning Framework Coping with Hierarchical Heterogeneity in
  Cooperative ITS

abstract:
  In this paper, we introduce a federated learning framework coping with
Hierarchical Heterogeneity (H2-Fed), which can notably enhance the conventional
pre-trained deep learning model. The framework exploits data from connected
public traffic agents in vehicular networks without affecting user data
privacy. By coordinating existing traffic infrastructure, including roadside
units and road traffic clouds, the model parameters are efficiently
disseminated by vehicular communications and hierarchically aggregated.
Considering the individual heterogeneity of data distribution, computational
and communication capabilities across traffic agents and roadside units, we
employ a novel method that addresses the heterogeneity of different aggregation
layers of the framework architecture, i.e., aggregation in layers of roadside
units and cloud. The experiment results indicate that our method can well
balance the learning accuracy and stability according to the knowledge of
heterogeneity in current communication networks. Compared to other baseline
approaches, the evaluation on a Non-IID MNIST dataset shows that our framework
is more general and capable especially in application scenarios with low
communication quality. Even when 90% of the agents are timely disconnected, the
pre-trained deep learning model can still be forced to converge stably, and its
accuracy can be enhanced from 68% to over 90% after convergence.


title: Sequential Recommendation with Causal Behavior Discovery

abstract:
  The key of sequential recommendation lies in the accurate item correlation
modeling. Previous models infer such information based on item co-occurrences,
which may fail to capture the real causal relations, and impact the
recommendation performance and explainability. In this paper, we equip
sequential recommendation with a novel causal discovery module to capture
causalities among user behaviors. Our general idea is firstly assuming a causal
graph underlying item correlations, and then we learn the causal graph jointly
with the sequential recommender model by fitting the real user behavior data.
More specifically, in order to satisfy the causality requirement, the causal
graph is regularized by a differentiable directed acyclic constraint.
Considering that the number of items in recommender systems can be very large,
we represent different items with a unified set of latent clusters, and the
causal graph is defined on the cluster level, which enhances the model
scalability and robustness. In addition, we provide theoretical analysis on the
identifiability of the learned causal graph. To the best of our knowledge, this
paper makes a first step towards combining sequential recommendation with
causal discovery. For evaluating the recommendation performance, we implement
our framework with different neural sequential architectures, and compare them
with many state-of-the-art methods based on real-world datasets. Empirical
studies manifest that our model can on average improve the performance by about
7% and 11% on f1 and NDCG, respectively. To evaluate the model explainability,
we build a new dataset with human labeled explanations for both quantitative
and qualitative analysis.


title:
Dynamics of particle-laden turbulent Couette flow. Part1: Turbulence
  modulation by inertial particles

abstract:
  In particle-laden turbulent flows the turbulence in carrier fluid phase gets
affected by the dispersed particle phase for volume fraction above $10^{-4}$
and hence reverse coupling or two-way coupling becomes relevant in that volume
fraction regime. In a recent study by Muramulla $et.al.^1$, a discontinuous
decrease of turbulence intensity is observed in a vertical particle-laden
turbulent channel-flow for a critical volume fraction O($10^{-3}$). The
collapse of turbulent intensity is found out to be a result of catastrophic
reduction of turbulent energy production rate. Mechanistically, particle-fluid
coupling in particle-laden turbulent Couette-flow differs from that in a closed
channel flow. In this article, the turbulence modulation in Couette-flow by
inertial particles is explored through two-way coupled DNS where particle
volume fraction ($\phi$) is varied from $1.75\times10^{-4}$ to
$1.05\times10^{-3}$ and Reynolds Number based on half-channel width ($\delta$)
and wall velocity ($U$) ($Re_{\delta}$) is $750$. The particles are heavy point
particles with $St\sim367$ based on fluid integral time-scale represented by
$\delta/U$. A discontinuous decrease of fluid turbulence intensity, mean square
velocity and Reynolds stress is observed beyond a critical volume fraction
$\phi_{cr}\sim7.875\times10^{-4}$. The drastic reduction of shear production of
turbulence and in turn the reduction of viscous dissipation of turbulent
kinetic energy are two important phenomena for the occurrence of discontinuous
transition similar to channelflow. The step-wise particle injection and
step-wise removal study confirms that it is the presence of particles which is
majorly behind this discontinuous transition.


title: End-to-End Multi-speaker ASR with Independent Vector Analysis

abstract:
  We develop an end-to-end system for multi-channel, multi-speaker automatic
speech recognition. We propose a frontend for joint source separation and
dereverberation based on the independent vector analysis (IVA) paradigm. It
uses the fast and stable iterative source steering algorithm together with a
neural source model. The parameters from the ASR module and the neural source
model are optimized jointly from the ASR loss itself. We demonstrate
competitive performance with previous systems using neural beamforming
frontends. First, we explore the trade-offs when using various number of
channels for training and testing. Second, we demonstrate that the proposed IVA
frontend performs well on noisy data, even when trained on clean mixtures only.
Furthermore, it extends without retraining to the separation of more speakers,
which is demonstrated on mixtures of three and four speakers.


title:
Same environment, stratified impacts? Air pollution, extreme
  temperatures, and birth weight in south China

abstract:
  This paper investigates whether associations between birth weight and
prenatal ambient environmental conditions--pollution and extreme
temperatures--differ by 1) maternal education; 2) children's innate health; and
3) interactions between these two. We link birth records from Guangzhou, China,
during a period of high pollution, to ambient air pollution (PM10 and a
composite measure) and extreme temperature data. We first use mean regressions
to test whether, overall, maternal education is an "effect modifier" in the
relationships between ambient air pollution, extreme temperature, and birth
weight. We then use conditional quantile regressions to test for effect
heterogeneity according to the unobserved innate vulnerability of babies after
conditioning on other confounders. Results show that 1) the negative
association between ambient exposures and birth weight is twice as large at
lower conditional quantiles of birth weights as at the median; 2) the
protection associated with college-educated mothers with respect to pollution
and extreme heat is heterogeneous and potentially substantial: between 0.02 and
0.34 standard deviations of birth weights, depending on the conditional
quantiles; 3) this protection is amplified under more extreme ambient
conditions and for infants with greater unobserved innate vulnerabilities.


title:
Bridging the Gap between Classification and Localization for Weakly
  Supervised Object Localization

abstract:
  Weakly supervised object localization aims to find a target object region in
a given image with only weak supervision, such as image-level labels. Most
existing methods use a class activation map (CAM) to generate a localization
map; however, a CAM identifies only the most discriminative parts of a target
object rather than the entire object region. In this work, we find the gap
between classification and localization in terms of the misalignment of the
directions between an input feature and a class-specific weight. We demonstrate
that the misalignment suppresses the activation of CAM in areas that are less
discriminative but belong to the target object. To bridge the gap, we propose a
method to align feature directions with a class-specific weight. The proposed
method achieves a state-of-the-art localization performance on the CUB-200-2011
and ImageNet-1K benchmarks.


title:
Nanometer-Scale Nuclear Magnetic Resonance Diffraction with Sub-\AA
  ngstrom Precision

abstract:
  Achieving atomic resolution is the ultimate limit of magnetic resonance
imaging (MRI), and attaining this capability offers enormous technological and
scientific opportunities, from drug development to understanding the dynamics
in interacting quantum systems. In this work, we present a new approach to
nanoMRI utilizing nuclear magnetic resonance diffraction (NMRd) -- a method
that extends NMR imaging to probe the structure of periodic spin systems. The
realization of NMRd on the atomic scale would create a powerful new methodology
for materials characterization utilizing the spectroscopic capabilities of NMR.
We describe two experiments that realize NMRd measurement of $^{31}$P spins in
an indium-phosphide (InP) nanowire with sub-\r{A}ngstrom precision. In the
first experiment, we encode a nanometer-scale spatial modulation of the
$z$-axis magnetization by periodically inverting the $^{31}$P spins, and detect
the period and position of the modulation with a precision of $<0.8$ \r{A}. In
the second experiment, we demonstrate an interferometric technique, utilizing
NMRd, for detecting an \r{A}ngstrom-scale displacement of the InP sample with a
precision of 0.07 \r{A}. The diffraction-based techniques developed in this
work represent new measurement modalities in NMR for probing the structure and
dynamics of spins on sub-\r{A}ngstrom length scales, and demonstrate the
feasibility of crystallographic MRI measurements.


title: Motor-free contractility of active biopolymer networks

abstract:
  Contractility in animal cells is often generated by molecular motors such as
myosin, which require polar substrates for their function. Motivated by recent
experimental evidence of motor-independent contractility, we propose a robust
motor-free mechanism that can generate contraction in biopolymer networks
without the need for substrate polarity. We show that contractility is a
natural consequence of active binding/unbinding of crosslinkers that breaks the
Principle of Detailed Balance, together with the asymmetric force-extension
response of semiflexible biopolymers. We calculate the resulting contractile
velocity using both a simple coarse-grained model and a more detailed
microscopic model for a viscoelastic biopolymer network. Our model may provide
an explanation of recent reports of motor-independent contractility in cells.
Our results also suggest a mechanism for generating contractile forces in
synthetic active materials.


title: Generalised Ellis-Bronnikov Wormholes in $f(R)$ Gravity

abstract:
  In this manuscript, we construct generalized Ellis-Bronnikov wormholes in the
context of $f(R)$ modified theories of gravity. We consider that the matter
driving the wormhole satisfies the energy conditions so that it is the
effective energy-momentum tensor containing the higher-order derivatives of
curvature terms that violate the null energy condition. Thus, the gravitational
fluid is interpreted by the higher-order derivatives of curvature terms to
represent the wormhole geometries and is fundamentally different from its
counter representation in general relativity. In particular, we explore the
wormhole geometries by presuming various well-known forms of Lagrangian $f(R)$.
In addition, for the seek of completeness, we discuss modified
Tolman-Oppenheimer-Volkov, volume integral quantifier, and total gravitational
energy.


title:
Of fiery sparks & glittering spots: Melting-resolidification and
  spherical particle formation in abrasion

abstract:
  The curious occurrence of perfectly spherical particles when a steel
substrate is slid against a hard abrasive was first observed and documented by
Robert Hooke in the 17$^{th}$ century. Similar particles have subsequently been
observed in numerous other abrasion-type processes, ranging from grinding of
steels to sliding rock faults. The prevalent hypothesis, originally proposed by
Hooke, is that these particles are formed due to high local temperatures
between the abrasive and the substrate, resulting in melting, droplet ejection
and subsequent resolidification -- the melting-resolidification hypothesis. In
this work, we revisit this phenomenon using \emph{in situ} analysis of a model
steel-abrasive contact geometry, complemented by analytical calculations. It is
found that the temperature within the contact zone, for typical contact
conditions used, is far from the melting point and that spherical particles do
not form in the absence of oxygen. We thereby propose a modification of the
melting-resolidification hypothesis, involving an intermediate exothermic
oxidation stage, and provide quantitative evidence for each step of the
process. Our results have implications for a wide class of abrasive systems
that involve the formation and utilization of spherical metallic particles


title:
Causal Lie products of free fields and the emergence of quantum field
  theory

abstract:
  All causal Lie products of solutions of the Klein-Gordon equation and the
wave equation in Minkowski space are determined. The results shed light on the
origin of the algebraic structures underlying quantum field theory.


title: Multiple Confidence Gates For Joint Training Of SE And ASR

abstract:
  Joint training of speech enhancement model (SE) and speech recognition model
(ASR) is a common solution for robust ASR in noisy environments. SE focuses on
improving the auditory quality of speech, but the enhanced feature distribution
is changed, which is uncertain and detrimental to the ASR. To tackle this
challenge, an approach with multiple confidence gates for jointly training of
SE and ASR is proposed. A speech confidence gates prediction module is designed
to replace the former SE module in joint training. The noisy speech is filtered
by gates to obtain features that are easier to be fitting by the ASR network.
The experimental results show that the proposed method has better performance
than the traditional robust speech recognition system on test sets of clean
speech, synthesized noisy speech, and real noisy speech.


title: Perception Prioritized Training of Diffusion Models

abstract:
  Diffusion models learn to restore noisy data, which is corrupted with
different levels of noise, by optimizing the weighted sum of the corresponding
loss terms, i.e., denoising score matching loss. In this paper, we show that
restoring data corrupted with certain noise levels offers a proper pretext task
for the model to learn rich visual concepts. We propose to prioritize such
noise levels over other levels during training, by redesigning the weighting
scheme of the objective function. We show that our simple redesign of the
weighting scheme significantly improves the performance of diffusion models
regardless of the datasets, architectures, and sampling strategies.


title:
Effective Field Theory of Black Hole Perturbations with Timelike Scalar
  Profile: Formulation

abstract:
  We formulate the Effective Field Theory (EFT) of perturbations within
scalar-tensor theories on an inhomogeneous background. The EFT is constructed
while keeping a background of a scalar field to be $\textit{timelike}$, which
spontaneously breaks the time diffeomorphism. We find a set of consistency
relations that are imposed by the invariance of the EFT under the 3d spatial
diffeomorphism. This EFT can be generically applied to any inhomogeneous
background metric as long as the scalar profile is everywhere timelike. For
completeness, we report a dictionary between our EFT parameters to those of
Horndernski theories. Finally, we compute background equations for a class of
spherically symmetric, static black hole backgrounds, including a stealth
Schwarzschild-de Sitter solution.


title: Turbulence-free computational ghost imaging

abstract:
  Turbulence-free images cannot be produced by conventional computational ghost
imaging because calculated light is not affected by the same atmospheric
turbulence as real light. In this article, we first addressed this issue by
measuring the photon number fluctuation autocorrelation of the signals
generated by a conventional computational ghost imaging device. Our results
illustrate how conventional computational ghost imaging without structural
changes can be used to produce turbulence-free images.


title: Reachability Analysis of Linear System

abstract:
  In this paper, we propose a decision procedure of reachability for linear
system {\xi}' = A{\xi} + u, where the matrix A's eigenvalues can be arbitrary
algebraic numbers and the input u is a vector of trigonometric-exponential
polynomials. If the initial set contains only one point, the reachability
problem under consideration is resorted to the decidability of the sign of
trigonometric-exponential polynomial and then achieved by being reduced to
verification of a series of univariate polynomial inequalities through Taylor
expansions of the related exponential functions and trigonometric functions. If
the initial set is open semi-algebraic, we will propose a decision procedure
based on openCAD and an algorithm of real roots isolation derivated from the
sign-deciding procedure for the trigonometric-exponential polynomials. The
experimental results indicate the efficiency of our approach. Furthermore, the
above procedures are complete under the assumption of Schanuel Conjecture


title: Online panoptic 3D reconstruction as a Linear Assignment Problem

abstract:
  Real-time holistic scene understanding would allow machines to interpret
their surrounding in a much more detailed manner than is currently possible.
While panoptic image segmentation methods have brought image segmentation
closer to this goal, this information has to be described relative to the 3D
environment for the machine to be able to utilise it effectively. In this
paper, we investigate methods for sequentially reconstructing static
environments from panoptic image segmentations in 3D. We specifically target
real-time operation: the algorithm must process data strictly online and be
able to run at relatively fast frame rates. Additionally, the method should be
scalable for environments large enough for practical applications. By applying
a simple but powerful data-association algorithm, we outperform earlier similar
works when operating purely online. Our method is also capable of reaching
frame-rates high enough for real-time applications and is scalable to larger
environments as well. Source code and further demonstrations are released to
the public at: \url{https://tutvision.github.io/Online-Panoptic-3D/}


title: Path Integrals on sl(2,R) Orbits

abstract:
  We quantise orbits of the adjoint group action on elements of the sl(2,R) Lie
algebra. The path integration along elliptic slices is akin to the coadjoint
orbit quantization of compact Lie groups, and the calculation of the characters
of elliptic group elements proceeds along the same lines as in compact groups.
The computation of the trace of hyperbolic group elements in a diagonal basis
as well as the calculation of the full group action on a hyperbolic basis
requires considerably more technique. We determine the action of hyperbolic
one-parameter subgroups of PSL(2,R) on the adjoint orbits and discuss global
subtleties in choices of adapted coordinate systems. Using the hyperbolic
slicing of orbits, we describe the quantum mechanics of an irreducible sl(2,R)
representation in a hyperbolic basis and relate the basis to the mathematics of
the Mellin integral transform. We moreover discuss the representation theory of
the double cover SL(2,R) of PSL(2,R) as well as that of its universal cover.
Traces in the representations of these groups for both elliptic and hyperbolic
elements are computed. Finally, we motivate our treatment of this elementary
quantisation problem by indicating applications.


title:
An implicit--explicit second order BDF numerical scheme with variable
  steps for gradient flows

abstract:
  In this paper, we propose and analyze an efficient implicit--explicit (IMEX)
second order in time backward differentiation formulation (BDF2) scheme with
variable time steps for gradient flow problems using the scalar auxiliary
variable (SAV) approach. We prove the unconditional energy stability of the
scheme for a modified discrete energy with the adjacent time step ratio
$\gamma_{n+1}:=\Dt_{n+1}/\Dt_{n}\leq 4.8645$. The uniform $H^{2}$ bound for the
numerical solution is derived under a mild regularity restriction on the
initial condition, that is $\phi(\x,0)\in H^{2}$. Based on this uniform bound,
a rigorous error estimate of the numerical solution is carried out on the
temporal nonuniform mesh. Finally, serval numerical tests are provided to
validate the theoretical claims. With the application of an adaptive
time-stepping strategy, the efficiency of our proposed scheme can be clearly
observed in the coarsening dynamics simulation.


title: Periodicity of power Fibonacci sequences modulus a Fibonacci number

abstract:
  Let ${\mathcal F}=(F_i:i\ge 0)$ be the sequence of Fibonacci numbers, and $j$
and $e$ be non negative integers. We study the periodicity of the power
Fibonacci sequences ${\mathcal F}^e(F_j)=(F_i^e\pmod{F_j}: i\ge 0)$. It is
shown that for every $j,e\ge 1$ the sequence ${\mathcal F}^e(F_j)$ is periodic
and its periodicity is computed. The result was previously known for ${\mathcal
F}(F_j)$; that is, for $e=1$. For $e\in \{1, 2\}$, the values of the normalized
residues $\rho_i\equiv F_i^e\pmod{F_j}$ with $0\le \rho_i<F_j-1$ are obtained.


title:
Production and anisotropic flow of thermal photons in most-central
  $\alpha$-clustered C+Au collisions at BNL Relativistic Heavy Ion Collider

abstract:
  The presence of $\alpha$-clustered structure in the light nuclei produces
different exotic shapes in nuclear structure studies at low energies. Recent
phenomenological studies suggest that collision of heavy nuclei with
$\alpha$-clustered carbon ($^{12}$C) at relativistic energies can lead to large
initial state anisotropies. This is expected to impact the final momentum
anisotropies of the produced particles significantly. The emission of
electromagnetic radiations is considered to be more sensitive to the initial
state compared to hadronic observables and thus photon observables are expected
to be affected by the initial clustered structure profoundly. In this work we
estimate the production and anisotropic flow of photons from most-central
collisions of triangular $\alpha$- clustered carbon and gold at $\sqrt{s_{\rm
NN}}=200$ GeV at RHIC using an event-by-event hydrodynamic framework and
compare the results with those obtained from unclustered carbon and gold
collisions. We show that the thermal photon $v_3$ for most central collisions
is significantly large for the clustered case compared to the case with
unclustered carbon, whereas the elliptic flow parameter does not show much
difference for the two cases. In addition, the ratio of anisotropic flow
coefficients is found to be a potential observable to constrain the initial
state produced in relativistic heavy-ion collisions and also to know more about
the $\alpha$-clustered structure in carbon nucleus.


title:
Dynamics of particle-laden turbulent Couette flow. Part2: Modified
  fluctuating force model (M-FFS)

abstract:
  Two-way coupled DNS simulation of particle-laden turbulent Couette-flow [1],
in the volume fraction regime $\phi>10^{-4}$, showed a discontinuous decrease
of turbulence intensity beyond a critical volume fraction
$\phi_{cr}\sim7.875\times10^{-4}$. Due to the presence of high inertial
particles, the drastic reduction of shear production of turbulence is found to
be the main cause for the discontinuous attenuation of turbulence. In this
article, particle-phase statistics is explored. The two-way coupled DNS reveal
that the mean-square velocity profiles in cross-stream (y) and span-wise (z)
directions are flat and increase with $\phi$ as the higher frequency of
collision helps in transferring streamwise momentum to span-wise and
wall-normal directions. Whereas, streamwise fluctuations decrease and tend
become flatter with increase in loading. In the regime with $\phi>\phi_{cr}$,
the particle velocity fluctuations drive the fluid phase velocity fluctuations.
Additionally it is observed that one-way coupled DNS and Fluctuating Force
Simulation (FFS) [2] are capable to predict the particle phase statistics with
reasonable accuracy in the regime $\phi<\phi_{cr}$ where wall-particle
collision time and inter-particle collision time is lesser than viscous
relaxation time of the particles. For, $\phi>\phi_{cr}$, a significant error in
the prediction from one-way coupled DNS and FFS is observed due to the
limitation of FFS in capturing the turbulence attenuation and the change in
mean fluid velocity profile. A modified FFS model (M-FFS) is successfully
developed in this article with modified mean fluid velocity profile and
zero-diffusivity.


title: Approximate Gibbs sampler for Bayesian Huberized lasso

abstract:
  The Bayesian lasso is well-known as a Bayesian alternative for lasso.
Although the advantage of the Bayesian lasso is capable of full probabilistic
uncertain quantification for parameters, the corresponding posterior
distribution can be sensitive to outliers. To overcome such problem, robust
Bayesian regression models have been proposed in recent years. In this paper,
we consider the robust and efficient estimation for the Bayesian Huberized
lasso regression in fully Bayesian perspective. A new posterior computation
algorithm for the Bayesian Huberized lasso regression is proposed. The proposed
approximate Gibbs sampler is based on the approximation of full conditional
distribution and it is possible to estimate a tuning parameter for robustness
of the pseudo-Huber loss function. Some theoretical properties of the posterior
distribution are also derived. We illustrate performance of the proposed method
through simulation studies and real data examples.


title: Bimodues associated to twisted modules of vertex operator algebras

abstract:
  Let $V$ be a vertex operator algebra, $T\in \mathbb{N}$ and $(M_k, Y_{M_k})$,
for $k=1, 2, 3$, be a $g_k$ twisted module respectively, where $g_k$ are
commuting automorphisms of $V$ such that $g_k^T=1$ for $k=1, 2, 3$ and
$g_3=g_1g_2$. Given an intertwining operator of type $\left(\begin{array}{c}
M_{3} \\ M_{1} M_{2} \end{array}\right) $, we constructed an
$A_{g_1g_2}(V)$-$A_{g_2}(V)$-bimodule $A_{g_1g_2, g_2}(M_1)$ which determines
the action of $M_1$ from the bottom level of $M_2$ to the bottom level of
$M_3$.


title:
ObjectMix: Data Augmentation by Copy-Pasting Objects in Videos for
  Action Recognition

abstract:
  In this paper, we propose a data augmentation method for action recognition
using instance segmentation. Although many data augmentation methods have been
proposed for image recognition, few methods have been proposed for action
recognition. Our proposed method, ObjectMix, extracts each object region from
two videos using instance segmentation and combines them to create new videos.
Experiments on two action recognition datasets, UCF101 and HMDB51, demonstrate
the effectiveness of the proposed method and show its superiority over
VideoMix, a prior work.


title:
A fast tunable 3D-transmon architecture for superconducting qubit-based
  hybrid devices

abstract:
  Superconducting qubits utilize the strong non-linearity of the Josephson
junctions. Control over the Josephson nonlinearity, either by a current bias or
by the magnetic flux, can be a valuable resource that brings tunability in the
hybrid system consisting of superconducting qubits. To enable such a control,
here we incorporate a fast-flux line for a frequency tunable transmon qubit in
3D cavity architecture. We investigate the flux-dependent dynamic range,
relaxation from unconfined states, and the bandwidth of the flux-line. Using
time-domain measurements, we probe transmon's relaxation from higher energy
levels after populating the cavity with $\approx 2.1\times10^4$ photons. For
the device used in the experiment, we find a resurgence time corresponding to
the recovery of coherence to be 4.8~$\mu$s. We use a fast-flux line to tune the
qubit frequency and demonstrate the swap of a single excitation between cavity
and qubit mode. By measuring the deviation in the transferred population from
the theoretical prediction, we estimate the bandwidth of the flux line to be
$\approx$~100~MHz, limited by the parasitic effect in the design. These results
suggest that the approach taken here to implement a fast-flux line in a 3D
cavity could be helpful for the hybrid devices based on the superconducting
qubit.


title:
FaiRIR: Mitigating Exposure Bias from Related Item Recommendations in
  Two-Sided Platforms

abstract:
  Related Item Recommendations (RIRs) are ubiquitous in most online platforms
today, including e-commerce and content streaming sites. These recommendations
not only help users compare items related to a given item, but also play a
major role in bringing traffic to individual items, thus deciding the exposure
that different items receive. With a growing number of people depending on such
platforms to earn their livelihood, it is important to understand whether
different items are receiving their desired exposure. To this end, our
experiments on multiple real-world RIR datasets reveal that the existing RIR
algorithms often result in very skewed exposure distribution of items, and the
quality of items is not a plausible explanation for such skew in exposure. To
mitigate this exposure bias, we introduce multiple flexible interventions
(FaiRIR) in the RIR pipeline. We instantiate these mechanisms with two
well-known algorithms for constructing related item recommendations --
rating-SVD and item2vec -- and show on real-world data that our mechanisms
allow for a fine-grained control on the exposure distribution, often at a small
or no cost in terms of recommendation quality, measured in terms of relatedness
and user satisfaction.


title:
The lifespan estimates of classical solutions of one dimensional
  semilinear wave equations with characteristic weights

abstract:
  In this paper, we study the lifespan estimates of classical solutions for
semilinear wave equations with characteristic weights and compactly supported
data in one space dimension. The results include those for weights by
time-variable, but exclude those for weights by space-variable in some cases.
We have interactions of two characteristic directions.


title: Cartan Motion Group and Orbital Integrals

abstract:
  In this short note, we study the variation of orbital integrals, as traces on
the group algebra $G$, under the deformation groupoid. We show that orbital
integrals are continuous under the deformation. And we prove that the pairing
between orbital integrals and $K$-theory element of $C^*_r(G)$ stays constant
with respect to the deformation for regular group elements, but vary at
singular elements.


title: Can a Ground-Based Vehicle Hear the Shape of a Room?

abstract:
  Assume that a ground-based vehicle moves in a room with walls or other planar
surfaces. Can the vehicle reconstruct the positions of the walls from the
echoes of a single sound event? We assume that the vehicle carries some
microphones and that a loudspeaker is either also mounted on the vehicle or
placed at a fixed location in the room. We prove that the reconstruction is
almost always possible if (1) no echoes are received from floors, ceilings or
sloping walls and the vehicle carries at least three non-collinear microphones,
or if (2) walls of any inclination may occur, the loudspeaker is fixed in the
room and there are four non-coplanar microphones.
  The difficulty lies in the echo-matching problem: how to determine which
echoes come from the same wall. We solve this by using a Cayley-Menger
determinant. Our proofs use methods from computational commutative algebra.


title: Adaptive hybrid speech coding with a MLP LPC structure

abstract:
  In the last years there has been a growing interest for nonlinear speech
models. Several works have been published revealing the better performance of
nonlinear techniques, but little attention has been dedicated to the
implementation of the nonlinear model into real applications. This work is
focused on the study of the behaviour of a combined linear/nonlinear predictive
model based on linear predictive coding (LPC-10) and neural nets, in a speech
waveform coder. Our novel scheme obtains an improvement in SEGSNR between 1 and
2.5 dB for an adaptive quantization ranging from 2 to 5 bits.


title: Fluctuation assisted collapses of Bose-Einstein condensates

abstract:
  We study the collapse dynamics of a Bose-Einstein condensate subjected to a
sudden change of the scattering length to a negative value by adopting the
self-consistent Gaussian state theory for mixed states. Compared to the
Gross-Pitaevskii and the Hartree-Fock-Bogoliubov approaches, both fluctuations
and three-body loss are properly treated in our theory. We find a new type of
collapse assisted by fluctuations which amplify the attractive interaction
between atoms. Moreover, the calculation of the fluctuated atoms, the entropy,
and the second-order correlation function showed that the collapsed gas was
significantly deviated from a pure state.


title: Infinite Combinatorics revisited in the absence of Axiom of Choice

abstract:
  Although the classical combinatorial theorems on infinite cardinals can not
be proved in ZF, we show that certain weakening of these results can be
obtained without using the Axiom of Choice.
  In particular, we prove in ZF that for each infinite cardinal ${\kappa}\in On
$
  (1) a ${\kappa}$-branching tree $T\subset {On}^{<{\omega}}$ has cardinality
at most ${\kappa}^+$,
  (2) ${\kappa}^+\to ({\kappa},{\omega}+1)^2$,
  (3) any family $\mathcal A\subset [{On}]^{<{\omega}}$ of size ${\kappa}^+$
contains a $\Delta$-system of size ${\kappa}$,
  (4) any regressive function $f:{\kappa}^+\to {\kappa}^+$ is constant on a set
of size ${\kappa}$,
  (5) given a set mapping $F:{\kappa}^+\to {[{\kappa}^+]}^{<{\omega}}$, there
is an $F$-free set of cardinality ${\kappa}$,
  (6) given a set mapping $F:{\kappa}\to {[{\kappa}]}^{<{\omega}}$, ${\kappa}$
can be partitioned into ${\omega}_1$-many $F$-free sets.
  We say that a function $f$ is a uniform denumeration of ${\omega}_1$ iff
$dom(f)={\omega}_1$ and for each ${\alpha}<{\omega}_1$, $f({\alpha})$ is a
function from ${\omega}$ onto ${\alpha}$.
  Clearly the existence of a uniform denumeration of ${\omega}_1$ implies
$cf({\omega}_1)={\omega}_1$. We prove that the reversed implication does not
hold in ZF.
  We also show that ${\omega}_1\to ({\omega}_1,{\omega}+1)$ holds iff
$cf({\omega}_1)={\omega}_1$, but the partition relation ${\omega}_1\to
({\omega}_1,{\omega})$ is a strictly weaker assumption.


title: Study of Classical Be stars in open clusters older than 100 Myr

abstract:
  We performed the slitless spectroscopic survey to identify Classical (CBe)
stars in open clusters older than 100 Myr. Observing a sample of 71 open
clusters, we identified 13 CBe stars in 11 open clusters, one of, which (TYC
2679-432-1) is a new detection. The 13 CBe stars show both H$\alpha$ in
emission and IR excess, which confirm that they possess gaseous circumstellar
discs. Two more CBe stars are found to exhibit H$\alpha$ in absorption for the
first time, indicating that might be passing through disc-less episode
presently. The spectral type estimation is done for all these 15 stars and it
is noticed that they belong to B0.5 -- B8 type. Moreover, we found that the
distribution of our sample is peaking near late B-types as expected.


title: GRMHD Simulations of Accreting Neutron Stars with Non-Dipole Fields

abstract:
  NASA's NICER telescope has recently provided evidence for non-dipolar
magnetic field structures in rotation-powered millisecond pulsars. These stars
are assumed to have gone through a prolonged accretion spin-up phase, begging
the question of what accretion flows onto stars with complex magnetic fields
would look like. We present results from a suite of GRMHD simulations of
accreting neutron stars for dipole, quadrupole, and quadrudipolar stellar field
geometries. This is a first step towards simulating realistic hotspot shapes in
a general relativistic framework to understand hotspot variability in accreting
millisecond pulsars. We find that the location and size of the accretion
columns resulting in hotspots changes significantly depending on initial
stellar field strength and geometry. We also find that the strongest
contributions to the stellar torque are from disk-connected fieldlines and the
pulsar wind, leading to spin-down in almost all of the parameter regime
explored here. We further analyze angular momentum transport in the accretion
disk due to large scale magnetic stresses, turbulent stresses, wind- and
compressible effects which we identify with convective motions. The disk
collimates the initial open stellar flux forming jets. For dipoles, the
disk-magnetosphere interaction can either enhance or reduce jet power compared
to the isolated case. However for quadrupoles, the disk always leads to an
enhanced net open flux making the jet power comparable to the dipolar case. We
discuss our results in the context of observed neutron star jets and provide a
viable mechanism to explain radio power both in the low- and high-magnetic
field case.


title:
Messenger Monte-Carlo MAPPINGS V (M^3) -- A self-consistent
  three-dimensional photoionization code

abstract:
  The Messenger Interface Monte-Carlo Mappings V (M^3) is a photoionization
code adopting the fully self-consistent Monte-Carlo radiative transfer
technique, which presents a major advance over previous photoionization models
with simple geometries. M^3 is designed for modeling nebulae in arbitrary
three-dimensional geometries. In this paper, we describe the Monte-Carlo
radiative transfer technique and the microphysics implemented in M^3, including
the photoionization, collisional ionization, the free-free and free-bound
recombination, and two-photon radiation. We put M^3 through the
Lexington/Meudon benchmarks to test the reliability of the new code. We apply
M^3 to three HII region models with fiducial geometries, demonstrating that M^3
is capable of dealing with nebulae with complex geometries. M^3 is a promising
tool for understanding emission-line behavior in the era of SDSS-V/LVM and
JWST, which will provide high-quality data of spatially-resolved nearby HII
regions and highly turbulent local and high-redshift HII regions.


title: The return of (I)DeFiX

abstract:
  Decentralized Finance (DeFi) is a nascent set of financial services, using
tokens, smart contracts, and blockchain technology as financial instruments. We
investigate four possible drivers of DeFi returns: exposure to cryptocurrency
market, the network effect, the investor's attention, and the valuation ratio.
As DeFi tokens are distinct from classical cryptocurrencies, we design a new
dedicated market index, denoted DeFiX. First, we show that DeFi tokens returns
are driven by the investor's attention on technical terms such as
"decentralized finance" or "DeFi", and are exposed to their own network
variables and cryptocurrency market. We construct a valuation ratio for the
DeFi market by dividing the Total Value Locked (TVL) by the Market
Capitalization (MC). Our findings do not support the TVL/MC predictive power
assumption. Overall, our empirical study shows that the impact of the
cryptocurrency market on DeFi returns is stronger than any other considered
driver and provides superior explanatory power.


title:
Reduced scaling of optimal regional orbital localization via sequential
  exhaustion of the single-particle space

abstract:
  Wannier functions have become a powerful tool in the electronic structure
calculations of extended systems. The generalized Pipek-Mezey Wannier functions
exhibit appealing characteristics (e.g., reaching an optimal localization and
the separation of the $\sigma$-$\pi$ orbitals) when compared with other
schemes. However, when applied to giant nanoscale systems, the orbital
localization suffers from a large computational cost overhead when one is
interested in localized states in a small fragment of the system. Herein we
present a swift, efficient, and robust approach for obtaining regionally
localized orbitals of a subsystem within the generalized Pipek-Mezey scheme.
The proposed algorithm introduces a reduced workspace and sequentially exhausts
the entire orbital space until the convergence of the localization functional.
It tackles systems with $\sim$10000 electrons within 0.5 hours with no loss in
localization quality compared to the traditional approach. Regionally localized
orbitals with a higher extent of localization are obtained via judiciously
extending the subsystem's size. Exemplifying on large bulk and a 4-nm wide slab
of diamond with NV$^-$ center, we demonstrate the methodology and discuss how
the choice of the localization region affects the excitation energy of the
defect. Furthermore, we show how the sequential algorithm is easily extended to
stochastic methodologies that do not provide individual single-particle
eigenstates. It is thus a promising tool to obtain regionally localized states
for solving the electronic structure problems of a subsystem embedded in giant
condensed systems.


title: Kakeya-type sets for Geometric Maximal Operators

abstract:
  Given a family G of rectangles, to which one associates a tree [G], one
defines a natural number $\lambda$ [G] called its analytic split and
satisfying, for all 1 < p < $\infty$ log($\lambda$ [G]) p MG p p where MG is
the Hardy-Littlewood type maximal operator associated to the family G. As an
application, we completely characterize the boundeness of planar rarefied
directional maximal operators on L p for 1 < p < $\infty$. Precisely, if
$\Omega$ is an arbitrary set of angles in [0, $\pi$ 4), we prove that any
rarefied basis B of the directional basis R $\Omega$ yields an operator MB that
has the same L p-behavior than the directional maximal operator M $\Omega$ for
1 < p < $\infty$.


title:
Stress Concentration between Two Adjacent Rigid Inclusions for the
  Stokes Flow in 2D and 3D

abstract:
  To study the field enhancements in the narrow region between two inclusions
is vital important in material sciences and fluid mechanics. Complex fluids
including particle suspensions usually result in complicated flow behavior. In
this paper we establish the pointwise upper bounds of the gradient estimates
for the Stokes flow when two rigid particles are closely spaced suspending in
an open bounded domain and away from the boundary in dimensions two and three.
Moreover, the lower bounds at the narrowest place of the neck region show the
optimality of these blow-up rates. These results are valid for inclusions with
arbitrary shape.


title:
NC-DRE: Leveraging Non-entity Clue Information for Document-level
  Relation Extraction

abstract:
  Document-level relation extraction (RE), which requires reasoning on multiple
entities in different sentences to identify complex inter-sentence relations,
is more challenging than sentence-level RE. To extract the complex
inter-sentence relations, previous studies usually employ graph neural networks
(GNN) to perform inference upon heterogeneous document-graphs. Despite their
great successes, these graph-based methods, which normally only consider the
words within the mentions in the process of building graphs and reasoning, tend
to ignore the non-entity clue words that are not in the mentions but provide
important clue information for relation reasoning. To alleviate this problem,
we treat graph-based document-level RE models as an encoder-decoder framework,
which typically uses a pre-trained language model as the encoder and a GNN
model as the decoder, and propose a novel graph-based model NC-DRE that
introduces decoder-to-encoder attention mechanism to leverage Non-entity Clue
information for Document-level Relation Extraction.


title: A Large-scale Dataset of (Open Source) License Text Variants

abstract:
  We introduce a large-scale dataset of the complete texts of free/open source
software (FOSS) license variants. To assemble it we have collected from the
Software Heritage archive-the largest publicly available archive of FOSS source
code with accompanying development history-all versions of files whose names
are commonly used to convey licensing terms to software users and
developers.The dataset consists of 6.5 million unique license files that can be
used to conduct empirical studies on open source licensing, training of
automated license classifiers, natural language processing (NLP) analyses of
legal texts, as well as historical and phylogenetic studies on FOSS licensing.
Additional metadata about shipped license files are also provided, making the
dataset ready to use in various contexts; they include: file length measures,
detected MIME type, detected SPDX license (using ScanCode), example origin
(e.g., GitHub repository), oldest public commit in which the license
appeared.The dataset is released as open data as an archive file containing all
deduplicated license files, plus several portable CSV files for metadata,
referencing files via cryptographic checksums.


title: Solving Singular Nonlinear PDEs Using Distribution Dependent SDEs

abstract:
  The existence, uniqueness and non-explosion are derived for the equation
$$\partial_t u_t= (L_t+V_t) u_t + F_t(\cdot, u_t,\nabla u_t)\cdot\nabla u_t+g_t
(\cdot, u_t,\nabla u_t, \nabla^2 u_t)$$ of $u: [0,\infty)\times E\to \mathbb
R^m (m\ge 1),$ where $E= \mathbb R^d$ or $\mathbb T^d (d\ge 1)$, $L_t$ is a
time dependent second order differential operator on $E$, and \begin{align*} &
g : [0,\infty)\times E\times\mathbb R^m\times\mathbb R^{d\otimes m}\times
\mathbb R^{d\otimes d\otimes m} \to \mathbb R^m, &F: [0,\infty)\times
E\times\mathbb R^m\times\mathbb R^{d\otimes m} \to \mathbb R^d,\ \ \ V:
[0,\infty)\times E\to \mathbb R\end{align*} are singular in the space-time
variable $(t,x)\in [0,\infty)\times E,$ and (local) Lipschitz continuous in
other arguments. To prove the main result, we solve a new type distribution
dependent SDEs and establish the correspondence to nonlinear PDEs, which should
be interesting by itself. The main result is illustrated by singular
KPZ/Navier-Stokes type equations.


title:
Secular variation signals in magnetic field gradient tensor elements
  derived from satellite-based geomagnetic virtual observatories

abstract:
  We present local time-series of the magnetic field gradient tensor elements
at satellite altitude derived using a Geomagnetic Virtual Observatory (GVO)
approach. Gradient element timeseries are computed in 4-monthly bins on an
approximately equal-area distributed worldwide network. This enables global
investigations of spatio temporal variations in the gradient tensor elements.
Series are derived from data collected by the Swarm and CHAMP satellite
missions, using vector field measurements and their along-track and east-west
differences, when available. We find evidence for a regional secular variation
impulse (jerk) event in 2017 in the first time derivative of the gradient
tensor elements. This event is located at low latitudes in the Pacific region.
It has a similar profile and amplitude regardless of the adopted data selection
criteria and is well fit by an internal potential field. Spherical harmonic
models of the internal magnetic field built from the GVO gradient series show
lower scatter in near-zonal harmonics compared with models built using standard
GVO vector field series. The GVO gradient element series are an effective means
of compressing the spatio temporal information gathered by low-Earth orbit
satellites on geomagnetic field variations, which may prove useful for core
flow inversions and in geodynamo data assimilation studies.


title:
On the Fujita exponent for a Hardy-H\'{e}non equation with a
  spatial-temporal forcing term

abstract:
  The purpose of this work is to analyze the wellposedness and the blow-up of
solutions of the higher-order parabolic semilinear equation \[
  u_t+(-\Delta)^{d}u=|x|^{\alpha}|u|^{p}+\zeta(t){\mathbf w}(x) \
\quad\mbox{for }
  (x,t)\in\mathbb{R}^{N}\times(0,\infty), \] where $d\in (0,1)\cup \mathbb{N}$,
$p>1$, $-\alpha\in(0,\min(2d,N))$ or $\alpha\geq 0$ and $\zeta$ as well as
${\mathbf w}$ are suitable given functions. Given $p\geq
\frac{N-2d\sigma+\alpha}{N-2d\sigma-2d}$ and setting
$p_c=\frac{N(p-1)}{2d+\alpha}$, $\ell=\frac{N p_c}{N+2(\sigma+1)d p_c}$, we
prove that for any data $u_0\in L^{p_c,\infty}(\mathbb{R}^N)$ and
$\textbf{w}\in L^{\ell,\infty}(\mathbb{R}^N)$ with small norms there exists a
unique global-in-time solution under the hypotheses $\zeta(t)=t^{\sigma}$,
$\sigma\in (-1,0)$ and $N>2d$ in the space
$C_{b}([0,\infty);L^{p_c,\infty}(\mathbb{R}^N))$. As a by-product, small
Lebesgue data global existence follows and in particular, unconditional
uniqueness holds in $C_{b}([0,\infty);L^{p_c}(\mathbb{R}^N))$ provided $p\in
(\frac{N+\alpha}{N-2d},\infty)$. If either $m\in (\infty,0]$ and $p\in
(1,\frac{N-2d\sigma+\alpha}{N-2d\sigma-2d})$ or $m>0$ and
$p>\frac{N-2dm+\alpha}{N-2dm-2d}$ where $\zeta(t)=O(t^m)$, $t\rightarrow\infty$
($m\in \mathbb{R}$), then all solutions blow up under the additional condition
$\int_{\mathbb{R}^N}\textbf{w}(x)\,dx>0$. As a consequence, we deduce that the
corresponding Fujita critical exponent is a function of $\sigma$ and reads
$p_{F}(\sigma)=\frac{N-2d\sigma+\alpha}{N-2d\sigma-2d}$ if $-1<\sigma<0$ and
infinity otherwise.


title:
MS-HLMO: Multi-scale Histogram of Local Main Orientation for Remote
  Sensing Image Registration

abstract:
  Multi-source image registration is challenging due to intensity, rotation,
and scale differences among the images. Considering the characteristics and
differences of multi-source remote sensing images, a feature-based registration
algorithm named Multi-scale Histogram of Local Main Orientation (MS-HLMO) is
proposed. Harris corner detection is first adopted to generate feature points.
The HLMO feature of each Harris feature point is extracted on a Partial Main
Orientation Map (PMOM) with a Generalized Gradient Location and Orientation
Histogram-like (GGLOH) feature descriptor, which provides high intensity,
rotation, and scale invariance. The feature points are matched through a
multi-scale matching strategy. Comprehensive experiments on 17 multi-source
remote sensing scenes demonstrate that the proposed MS-HLMO and its simplified
version MS-HLMO$^+$ outperform other competitive registration algorithms in
terms of effectiveness and generalization.


title:
Rationality of the inner products of spherical $s$-distance $t$-designs
  for $t \geq 2s-2$, $s \geq 3$

abstract:
  We prove that the inner products of spherical $s$-distance $t$-designs with
$t \geq 2s-2$ (Delsarte codes) and $s \geq 3$ are rational with the only
exception being the icosahedron. In other formulations, we prove that all sharp
configurations have rational inner products and all spherical codes which
attain the Levenshtein bound, have rational inner products, except for the
icosahedron.


title: On the kinematic constraint in BFKL near threshold region

abstract:
  We introduce a modified Balitskii-Fadin-Kuraev-Lipatov (BFKL) equation with
the rapidity veto that originates from external kinematic constraint. Though it
is a formally sub-leading power contribution, such kinematic effect becomes
very important especially in the threshold region, where there is no sufficient
phase space for the small $x$ evolution to be fully developed. We also
investigate the phenomenological consequences of the kinematic constraint BFKL
equation in the forward particle production processes in pp and eA collisions.
It is shown that particle yield at large transverse momentum is significantly
suppressed in the threshold region.


title:
Wasserstein convergence rate in the invariance principle for
  deterministic dynamical systems

abstract:
  In this paper, we consider the convergence rate with respect to Wasserstein
distance in the invariance principle for deterministic nonuniformly hyperbolic
systems. Our results apply to uniformly hyperbolic systems and large classes of
nonuniformly hyperbolic systems including intermittent maps, Viana maps,
unimodal maps and others. Furthermore, as a nontrivial application to
homogenization problem, we investigate the $\mathcal{W}_2$-convergence rate of
a fast-slow discrete deterministic system to a stochastic differential
equation.


title: Minkowski and (A)dS ground states in general 2d dilaton gravity

abstract:
  We reorganize the derivative expansion of general (power-counting
non-renormalizable) 2d dilaton gravity such that the mass function is
integrable. As an example, we consider a three-parameter family of models and
provide conditions on the parameters such that the ground state is either
Minkowski, Rindler, or (A)dS.


title: Copula-based statistical dependence visualizations

abstract:
  A frequent task in exploratory data analysis consists in examining pairwise
dependencies between data variables. Popular approaches include visualizing
correlation or scatter plot matrices. However, both methods can be misleading.
The former is primarily limited because it reports a single value for a pair of
random variables. Furthermore, scatter plots can fail to convey the dependency
structure between variables properly. In this paper we discuss these
shortcomings and present alternative and richer visualizations based on copula
functions, which fully determine the dependency between continuous random
variables. Since copulas seldom appear in the data visualization literature we
first review essential theory, and propose alternative scatter plots and
several heatmaps for assessing the statistical association between two
continuous random variables. These visualizations not only allow users to
detect independence, but also increasing and/or decreasing trends in the data
through a color coding, which can also be applied in other methods such as
parallel coordinates.


title:
Multifaceted Improvements for Conversational Open-Domain Question
  Answering

abstract:
  Open-domain question answering (OpenQA) is an important branch of textual QA
which discovers answers for the given questions based on a large number of
unstructured documents. Effectively mining correct answers from the open-domain
sources still has a fair way to go. Existing OpenQA systems might suffer from
the issues of question complexity and ambiguity, as well as insufficient
background knowledge. Recently, conversational OpenQA is proposed to address
these issues with the abundant contextual information in the conversation.
Promising as it might be, there exist several fundamental limitations including
the inaccurate question understanding, the coarse ranking for passage
selection, and the inconsistent usage of golden passage in the training and
inference phases. To alleviate these limitations, in this paper, we propose a
framework with Multifaceted Improvements for Conversational open-domain
Question Answering (MICQA). Specifically, MICQA has three significant
advantages. First, the proposed KL-divergence based regularization is able to
lead to a better question understanding for retrieval and answer reading.
Second, the added post-ranker module can push more relevant passages to the top
placements and be selected for reader with a two-aspect constrains. Third, the
well designed curriculum learning strategy effectively narrows the gap between
the golden passage settings of training and inference, and encourages the
reader to find true answer without the golden passage assistance. Extensive
experiments conducted on the publicly available dataset OR-QuAC demonstrate the
superiority of MICQA over the state-of-the-art model in conversational OpenQA
task.


title:
Spatial extension of dark subhalos as seen by Fermi-LAT and implications
  for WIMP constraints

abstract:
  Spatial extension has been hailed as a "smoking gun" in the gamma-ray search
of dark galactic subhalos, which would appear as unidentified sources for
gamma-ray telescopes. In this work, we study the sensitivity of the Fermi-LAT
to extended subhalos using simulated data based on a realistic sky model. We
simulate spatial templates for a set of representative subhalos, whose
parameters were derived from our previous work with N-body cosmological
simulation data. We find that detecting an extended subhalo and finding an
unequivocal signal of angular extension requires, respectively, a flux 2 to 10
times larger than in the case of a point-like source. By studying a large grid
of models, where parameters such as the WIMP mass, annihilation channel or
subhalo model are varied significantly, we obtain the response of the LAT as a
function of the product of annihilation cross section times the J-factor.
Indeed, we show that spatial extension can be used as an additional filter to
reject subhalos candidates among the pool of unidentified LAT sources, as well
as a "smoking gun" for positive identification. For instance, typical angular
extensions of a few tenths of degree are expected for the considered scenarios.
Finally, we also study the impact of the obtained LAT sensitivity to such
extended subhalos on the achievable dark matter constraints, which are a few
times less constraining than comparable point-source limits.


title:
To Explore or Not to Explore: Regret-Based LTL Planning in
  Partially-Known Environments

abstract:
  In this paper, we investigate the optimal robot path planning problem for
high-level specifications described by co-safe linear temporal logic (LTL)
formulae. We consider the scenario where the map geometry of the workspace is
partially-known. Specifically, we assume that there are some unknown regions,
for which the robot does not know their successor regions a priori unless it
reaches these regions physically. In contrast to the standard game-based
approach that optimizes the worst-case cost, in the paper, we propose to use
regret as a new metric for planning in such a partially-known environment. The
regret of a plan under a fixed but unknown environment is the difference
between the actual cost incurred and the best-response cost the robot could
have achieved if it realizes the actual environment with hindsight. We provide
an effective algorithm for finding an optimal plan that satisfies the LTL
specification while minimizing its regret. A case study on firefighting robots
is provided to illustrate the proposed framework. We argue that the new metric
is more suitable for the scenario of partially-known environment since it
captures the trade-off between the actual cost spent and the potential benefit
one may obtain for exploring an unknown region.


title:
Comments to "A non-thermal laser-driven mixed fuel nuclear fusion
  reactor concept" by H. Ruhl and G. Korn (Marvel Fusion, Munich)

abstract:
  The declared aim of Marvel Fusion is the realization of a reactor based on
the aneutronic fusion of proton and boron-11 nuclei in the near future, making
use of latest-day laser technology and nano-structured materials. The aim of
the preprint quoted above is to demonstrate the feasibility of fusion energy
gain from the irradiation of an initially uncompressed target consisting of an
array of nano-wires, by a femto-second laser. This proposal is in apparent
contrast to text-book wisdom, which postulates - even for the fast-ignitor
concept, and a DT fuel mix - a target density about 1000 times that of solid
state. The novel, optimistic predictions of Ruhl and Korn are based, however,
not on rigorous estimates, but only on parametric dependencies, extrapolated
far beyond their conventional limits of validity. The authors invoke the
effects of self-arising magnetic fields to result in additional magnetic
confinement, but we show their model to contain intrinsic contradictions. The
conclusion from out note is that ultra-fast, high power lasers can be of use to
inertial fusion energy production only, if the intrinsic time-scale for fusion
reactions can be brought down to a level more commensurate with the Laser pulse
length. Involving magnetic fields does not change this basic limitation, as the
Alfv\'en time will be similar to the kinetic disintegration time. Given that
the fusion energy gain as function of particle energy has a maximum (for DT at
about 170 keV) overheating will not be helpful so that the required enhancement
of the fusion reaction rate can only be achieved by strong pre-compression of
the target.


title:
Rethinking Position Bias Modeling with Knowledge Distillation for CTR
  Prediction

abstract:
  Click-through rate (CTR) Prediction is of great importance in real-world
online ads systems. One challenge for the CTR prediction task is to capture the
real interest of users from their clicked items, which is inherently biased by
presented positions of items, i.e., more front positions tend to obtain higher
CTR values. A popular line of existing works focuses on explicitly estimating
position bias by result randomization which is expensive and inefficient, or by
inverse propensity weighting (IPW) which relies heavily on the quality of the
propensity estimation. Another common solution is modeling position as features
during offline training and simply adopting fixed value or dropout tricks when
serving. However, training-inference inconsistency can lead to sub-optimal
performance. Furthermore, post-click information such as position values is
informative while less exploited in CTR prediction. This work proposes a simple
yet efficient knowledge distillation framework to alleviate the impact of
position bias and leverage position information to improve CTR prediction. We
demonstrate the performance of our proposed method on a real-world production
dataset and online A/B tests, achieving significant improvements over competing
baseline models. The proposed method has been deployed in the real world online
ads systems, serving main traffic on one of the world's largest e-commercial
platforms.


title:
Energetic stability and spatial inhomogeneity in the local electronic
  structure of relaxed twisted trilayer graphene

abstract:
  We study the energetic stability and the local electronic structure of the
general twisted trilayer graphene (TTG) with the top and bottom layers rotated
with respect to the middle layer respectively by $\theta$ and $\theta'$.
Approximate supercells of the moir\'{e}-of-moir\'{e} superlattices with
$\theta$ and $\theta^{\prime}$ within $1^{\circ}\sim 2^{\circ}$ are established
to describe the structural and electronic properties of relaxed TTG with the
periodic boundary condition. Full relaxation demonstrates that the commensurate
TTG with $\theta=\theta^{\prime}$ has the local minimum total energy
($E_{tol}$) at a fixed $\theta$, while $E_{tol}$ first reaches a local maximum
and begins to drop with decreasing $\theta^{\prime}$ for $\theta^{\prime} <
\theta$. Some regions exhibit enhanced in-plane relaxation in the top and
bottom layers but suppressed relaxation in the middle layer and form a
hexagonal network with the moir\'{e}-of-moir\'{e} length scale. The stacking
configurations with the atoms in the three layers vertically aligned at the
origin of the relaxed TTG supercells at $\theta$ around $1.6^{\circ}$ and
$\theta^{\prime}$ around $1.4^{\circ}$ have a high density of states (DOS) near
the Fermi level ($E_F$), which can reach that of the mirror symmetric TTG with
equal twist angles of about $1.7^{\circ}$. In contrast, some other stackings
can have rather low DOS around $E_F$. The significant stacking dependence of
DOS for some TTG supercells demonstrates that the local electronic structure of
TTG can exhibit strong spatial inhomogeneity when the twist angles are slightly
away from those of the small supercells with large variations of DOS among
different stackings. Moreover, the structural relaxation of TTG plays a crucial
role in the high DOS and its strong stacking dependence.


title:
Fusing Interpretable Knowledge of Neural Network Learning Agents For
  Swarm-Guidance

abstract:
  Neural-based learning agents make decisions using internal artificial neural
networks. In certain situations, it becomes pertinent that this knowledge is
re-interpreted in a friendly form to both the human and the machine. These
situations include: when agents are required to communicate the knowledge they
learn to each other in a transparent way in the presence of an external human
observer, in human-machine teaming settings where humans and machines need to
collaborate on a task, or where there is a requirement to verify the knowledge
exchanged between the agents. We propose an interpretable knowledge fusion
framework suited for neural-based learning agents, and propose a Priority on
Weak State Areas (PoWSA) retraining technique. We first test the proposed
framework on a synthetic binary classification task before evaluating it on a
shepherding-based multi-agent swarm guidance task. Results demonstrate that the
proposed framework increases the success rate on the swarm-guidance environment
by 11% and better stability in return for a modest increase in computational
cost of 14.5% to achieve interpretability. Moreover, the framework presents the
knowledge learnt by an agent in a human-friendly representation, leading to a
better descriptive visual representation of an agent's knowledge.


title:
Globally Optimal Spectrum- and Energy-Efficient Beamforming for Rate
  Splitting Multiple Access

abstract:
  Rate splitting multiple access (RSMA) is a promising non-orthogonal
transmission strategy for next-generation wireless networks. It has been shown
to outperform existing multiple access schemes in terms of spectral and energy
efficiency when suboptimal beamforming schemes are employed. In this work, we
fill the gap between suboptimal and truly optimal beamforming schemes and
conclusively establish the superior spectral and energy efficiency of RSMA. To
this end, we propose a successive incumbent transcending (SIT) branch and bound
(BB) algorithm to find globally optimal beamforming solutions that maximize the
weighted sum rate or energy efficiency of RSMA in Gaussian multiple-input
single-output (MISO) broadcast channels. Numerical results show that RSMA
exhibits an explicit globally optimal spectral and energy efficiency gain over
conventional multi-user linear precoding (MU-LP) and power-domain
non-orthogonal multiple access (NOMA). Compared to existing globally optimal
beamforming algorithms for MU-LP, the proposed SIT BB not only improves the
numerical stability but also achieves faster convergence. Moreover, for the
first time, we show that the spectral/energy efficiency of RSMA achieved by
suboptimal beamforming schemes (including weighted minimum mean squared error
(WMMSE) and successive convex approximation) almost coincides with the
corresponding globally optimal performance, making it a valid choice for
performance comparisons. The globally optimal results provided in this work are
imperative to the ongoing research on RSMA as they serve as benchmarks for
existing suboptimal beamforming strategies and those to be developed in
multi-antenna broadcast channels.


title: Random walks on the circle and Diophantine approximation

abstract:
  Random walks on the circle group $\mathbb{R}/\mathbb{Z}$ whose elementary
steps are lattice variables with span $\alpha \not\in \mathbb{Q}$ or $p/q \in
\mathbb{Q}$ taken mod $\mathbb{Z}$ exhibit delicate behavior. In the rational
case we have a random walk on the finite cyclic subgroup $\mathbb{Z}_q$, and
the central limit theorem and the law of the iterated logarithm follow from
classical results on finite state space Markov chains. In this paper we extend
these results to random walks with irrational span $\alpha$, and explicitly
describe the transition of these Markov chains from finite to general state
space as $p/q \to \alpha$ along the sequence of best rational approximations.
We also consider the rate of weak convergence to the stationary distribution in
the Kolmogorov metric, and in the rational case observe a surprising transition
from polynomial to exponential decay after $\approx q^2$ steps; this seems to
be a new phenomenon in the theory of random walks on compact groups. In
contrast, the rate of weak convergence to the stationary distribution in the
total variation metric is purely exponential.


title:
Random Douglas-Rachford algorithms for solving convex feasibility
  problems in Hilbert space

abstract:
  In this work we focus on the convex feasibility problem (CFP) in Hilbert
space. A specific method in this area that has gained a lot of interest in
recent years is the Douglas-Rachford (DR) algorithm. This algorithm was
originally introduced in 1956 for solving stationary and non-stationary heat
equations. Then in 1979, Lions and Mercier adjusted and extended the algorithm
with the aim of solving CFPs and even more general problems, such as finding
zeros of the sum of two maximally monotone operators. Many developments which
implement various concepts concerning this algorithm have occurred during the
last decade. We introduce a random DR algorithm, which provides a general
framework for such concepts. Using random products of a finite number of
strongly nonexpansive operators, we apply such concepts to provide new
iterative methods, where, \textit{inter alia}, such operators may be interlaced
between the operators used in the scheme of our random DR algorithm.


title:
Ising machines as hardware solvers of combinatorial optimization
  problems

abstract:
  Ising machines are hardware solvers which aim to find the absolute or
approximate ground states of the Ising model. The Ising model is of fundamental
computational interest because it is possible to formulate any problem in the
complexity class NP as an Ising problem with only polynomial overhead. A
scalable Ising machine that outperforms existing standard digital computers
could have a huge impact for practical applications for a wide variety of
optimization problems. In this review, we survey the current status of various
approaches to constructing Ising machines and explain their underlying
operational principles. The types of Ising machines considered here include
classical thermal annealers based on technologies such as spintronics, optics,
memristors, and digital hardware accelerators; dynamical-systems solvers
implemented with optics and electronics; and superconducting-circuit quantum
annealers. We compare and contrast their performance using standard metrics
such as the ground-state success probability and time-to-solution, give their
scaling relations with problem size, and discuss their strengths and
weaknesses.


title:
Calculation of the exact Lyapunov exponent of affine Boole
  transformations using Feynman's trick

abstract:
  We show an elementary way for calculation of the exact Lyapunov exponent of
affine Boole transformations using a method for evaluation of integrals via
differentiating under the integral sign, a trick originated to Richard Feynman.


title:
On the intrinsic pinning and shape of charge-density waves in 1D Peierls
  systems

abstract:
  Within the standard perturbative approach of Peierls, a charge-density wave
is usually assumed to have a cosine shape of weak amplitude. In nonlinear
physics, we know that waves can be deformed. What are the effects of the
nonlinearities of the electron-lattice models in the physical properties of
Peierls systems? A nonlinear discrete model, introduced by Brazovskii,
Dzyaloshinskii and Krichever, allows to address this issue beyond perturbation
theory. For some special parameters of the model, at integrable points, it has
exact solutions: the wave is cnoidal rather than simply harmonic but its
envelope function remains continuous. As a consequence, the charge-density wave
may slide over the lattice potential at no energy cost, in agreement with the
Fr\"ohlich's argument. When the integrability is broken, however, the envelope
function may be discontinuous, electrons tend to form some stronger distinct
chemical bonds, i.e. local dimers or oligomers. The wave is intrinsically
pinned by the lattice. We argue that an Aubry transition from the sliding phase
to the insulating pinned phase occurs when the nonlinearities become strong.


title: Proactively Control Privacy in Recommender Systems

abstract:
  Recently, privacy issues in web services that rely on users' personal data
have raised great attention. Unlike existing privacy-preserving technologies
such as federated learning and differential privacy, we explore another way to
mitigate users' privacy concerns, giving them control over their own data. For
this goal, we propose a privacy aware recommendation framework that gives users
delicate control over their personal data, including implicit behaviors, e.g.,
clicks and watches. In this new framework, users can proactively control which
data to disclose based on the trade-off between anticipated privacy risks and
potential utilities. Then we study users' privacy decision making under
different data disclosure mechanisms and recommendation models, and how their
data disclosure decisions affect the recommender system's performance.
  To avoid the high cost of real-world experiments, we apply simulations to
study the effects of our proposed framework. Specifically, we propose a
reinforcement learning algorithm to simulate users' decisions (with various
sensitivities) under three proposed platform mechanisms on two datasets with
three representative recommendation models. The simulation results show that
the platform mechanisms with finer split granularity and more unrestrained
disclosure strategy can bring better results for both end users and platforms
than the "all or nothing" binary mechanism adopted by most real-world
applications. It also shows that our proposed framework can effectively protect
users' privacy since they can obtain comparable or even better results with
much less disclosed data.


title:
A Versatile Framework for Evaluating Ranked Lists in terms of Group
  Fairness and Relevance

abstract:
  We present a simple and versatile framework for evaluating ranked lists in
terms of group fairness and relevance, where the groups (i.e., possible
attribute values) can be either nominal or ordinal in nature. First, we
demonstrate that, if the attribute set is binary, our framework can easily
quantify the overall polarity of each ranked list. Second, by utilising an
existing diversified search test collection and treating each intent as an
attribute value, we demonstrate that our framework can handle soft group
membership, and that our group fairness measures are highly correlated with
both adhoc IR and diversified IR measures under this setting. Third, we
demonstrate how our framework can quantify intersectional group fairness based
on multiple attribute sets. We also show that the similarity function for
comparing the achieved and target distributions over the attribute values
should be chosen carefully.


title:
i-Razor: A Neural Input Razor for Feature Selection and Dimension Search
  in Large-Scale Recommender Systems

abstract:
  Input features play a crucial role in the predictive performance of DNN-based
industrial recommender systems with thousands of categorical and continuous
fields from users, items, contexts, and their interactions. Noisy features and
inappropriate embedding dimension assignments can impair the performance of
recommender systems and introduce unnecessary complexity in model training and
online serving. Optimizing the input configuration of DNN models, including
feature selection and embedding dimension assignment, has become one of the
essential topics in feature engineering. Typically, feature selection and
embedding dimension search are optimized sequentially, i.e., feature selection
is performed first, followed by embedding dimension search to determine the
optimal dimension size for each selected feature. In contrast, this paper
studies the joint optimization of feature selection and embedding dimension
search. To this end, we propose a differentiable neural \textbf{i}nput
\textbf{razor}, namely \textbf{i-Razor}. Specifically, inspired by recent
advances in neural architecture search, we introduce an end-to-end
differentiable model to learn the relative importance between different
embedding regions of each feature. Furthermore, a flexible pruning algorithm is
proposed to simultaneously achieve feature filtering and dimension size
derivation. Extensive experiments on two large-scale public datasets in the
Click-Through-Rate (CTR) prediction task demonstrate the efficacy and
superiority of i-Razor in balancing model complexity and performance.


title:
A simple proof of the Baillon-Haddad theorem on open subsets of Hilbert
  spaces

abstract:
  We give a simple proof of the Baillon-Haddad theorem for convex functions
defined on open and convex subsets of Hilbert spaces. We also state some
generalizations and limitations. In particular, we discuss equivalent
characterizations of the Lipschitz continuity of the derivative of convex
functions on open and convex subsets of Banach spaces.


title:
Stability of pizoelectric beam with magnetic effect under (Coleman or
  Pipkin)-Gurtin thermal law

abstract:
  In this paper, we investigate the stabilization of a system of piezoelectric
beams under (Coleman or Pipkin )-Gurtin thermal law with magnetic effect.
First, we study the Piezoelectric-Coleman-Gurtin system and we obtain an
exponential stability result. Next, we consider the Piezoelectric-Gurtin-Pipkin
system and we establish a polynomial energy decay rate of type $t^{-1}$


title:
Understanding temperature modulated calorimetry through studies of a
  model system

abstract:
  Temperature Modulated calorimetry is widely used but still raises some
fundamental questions. In this paper we study a model system as a test sample
to address some of them. The model has a nontrivial spectrum of relaxation
times. We investigate temperature modulated calorimetry at constant average
temperature to precise the meaning of the frequency-dependent heat capacity,
its relation with entropy production, and how such measurements can observe the
aging of a glassy sample leading to a time-dependent heat capacity. The study
of the Kovacs effect for an out-of-equilibrium system shows how temperature
modulated calorimetry could contribute to the understanding of this memory
effect. Then we compare measurements of standard scanning calorimetry and
temperature-modulated calorimetry and show how the two methods are
complementary because they do not observe the same features. While it can probe
the time scales of energy transfers in a system, even in the limit of low
frequency temperature modulated calorimetry does not probe some relaxation
phenomena which can be measured by scanning calorimetry, as suggested by
experiments with glasses.


title:
KilonovaNet: Surrogate Models of Kilonova Spectra with Conditional
  Variational Autoencoders

abstract:
  Detailed radiative transfer simulations of kilonova spectra play an essential
role in multimessenger astrophysics. Using the simulation results in parameter
inference studies requires building a surrogate model from the simulation
outputs to use in algorithms requiring sampling. In this work, we present
KilonovaNet, an implementation of conditional variational autoencoders (cVAEs)
for the construction of surrogate models of kilonova spectra. This method can
be trained on spectra directly, removing overhead time of pre-processing
spectra, and greatly speeds up parameter inference time. We build surrogate
models of three state-of-the-art kilonova simulation data sets and present
in-depth surrogate error evaluation methods, which can in general be applied to
any surrogate construction method. By creating synthetic photometric
observations from the spectral surrogate, we perform parameter inference for
the observed light curve data of GW170817 and compare the results with previous
analyses. Given the speed with which KilonovaNet performs during parameter
inference, it will serve as a useful tool in future gravitational wave
observing runs to quickly analyze potential kilonova candidates


title:
Net-proton number cumulant ratios as function of beam energy from a
  nonequilibrium chiral Bjorken model

abstract:
  STAR's beam energy scan program at RHIC provides data on net-proton number
fluctuations with the goal to detect the QCD critical point and first-order
phase transition. Interpreting these experimental signals requires a vital
understanding of the interplay of critical phenomena and the nonequilibrium
dynamics of the rapidly expanding fireball. We study these aspects with a fluid
dynamic expansion coupled to the explicit propagation of the chiral order
parameter sigma via a Langevin equation. Assuming a sigma-proton coupling
through an effective proton mass, we relate cumulants of the order parameter
and the net-proton number at freeze-out and obtain observable cumulant ratios
as a function of beam energy. We emphasize the role of the nonequilibrium
first-order phase transition twofold: First, the presence of an unstable phase
causes the well-known bending of the trajectories in the space of temperature
and baryochemical potential. For these cases at lower beam energies, the system
crosses the freeze-out line more than once, allowing us to calculate a wide
range of cumulants for each initial condition. Second, the thermodynamic
susceptibilities diverge along the spinodal lines in nonequilibrium. Depending
on the freeze-out parameters, these divergences can have a dramatic impact on
the calculated cumulants and cumulant ratios.


title: Existence of Ground States in the Infrared-Critial Spin Boson Model

abstract:
  We review recent results on the existence of ground states for the
infrared-critical spin boson model, which describes the interaction of a
massless bosonic field with a two-state quantum system. Explicitly, we derive a
critical coupling $\lambda_{\mathsf c}>0$ such that the spin boson model
exhibits a ground state for coupling constants $\lambda$ with
$|\lambda|<\lambda_{\mathsf c}$. The proof combines a Feynman-Kac-Nelson
formula for the spin boson model with external magnetic field, a 1D-Ising model
correlation bound and a compactness argument in Fock space. Elaborating on the
connection to a long-range 1D-Ising model, we briefly discuss the conjecture
that the spin boson model does not have a ground state at large coupling. This
note is based on joint work with David Hasler and Oliver Siebert.


title: Symbolic Search for Optimal Planning with Expressive Extensions

abstract:
  In classical planning, the goal is to derive a course of actions that allows
an intelligent agent to move from any situation it finds itself in to one that
satisfies its goals. Classical planning is considered domain-independent, i.e.,
it is not limited to a particular application and can be used to solve
different types of reasoning problems. In practice, however, some properties of
a planning problem at hand require an expressive extension of the standard
classical planning formalism to capture and model them. Although the importance
of many of these extensions is well known, most planners, especially optimal
planners, do not support these extended planning formalisms. The lack of
support not only limits the use of these planners for certain problems, but
even if it is possible to model the problems without these extensions, it often
leads to increased effort in modeling or makes modeling practically impossible
as the required problem encoding size increases exponentially.
  In this thesis, we propose to use symbolic search for cost-optimal planning
for different expressive extensions of classical planning, all capturing
different aspects of the problem. In particular, we study planning with axioms,
planning with state-dependent action costs, oversubscription planning, and
top-k planning. For all formalisms, we present complexity and compilability
results, highlighting that it is desirable and even necessary to natively
support the corresponding features. We analyze symbolic heuristic search and
show that the search performance does not always benefit from the use of a
heuristic and that the search performance can exponentially deteriorate even
under the best possible circumstances, namely the perfect heuristic. This
reinforces that symbolic blind search is the dominant symbolic search strategy
nowadays, on par with other state-of-the-art cost-optimal planning
strategies...


title:
Selecting task with optimal transport self-supervised learning for
  few-shot classification

abstract:
  Few-Shot classification aims at solving problems that only a few samples are
available in the training process. Due to the lack of samples, researchers
generally employ a set of training tasks from other domains to assist the
target task, where the distribution between assistant tasks and the target task
is usually different. To reduce the distribution gap, several lines of methods
have been proposed, such as data augmentation and domain alignment. However,
one common drawback of these algorithms is that they ignore the similarity task
selection before training. The fundamental problem is to push the auxiliary
tasks close to the target task. In this paper, we propose a novel task
selecting algorithm, named Optimal Transport Task Selecting (OTTS), to
construct a training set by selecting similar tasks for Few-Shot learning.
Specifically, the OTTS measures the task similarity by calculating the optimal
transport distance and completes the model training via a self-supervised
strategy. By utilizing the selected tasks with OTTS, the training process of
Few-Shot learning become more stable and effective. Other proposed methods
including data augmentation and domain alignment can be used in the meantime
with OTTS. We conduct extensive experiments on a variety of datasets, including
MiniImageNet, CIFAR, CUB, Cars, and Places, to evaluate the effectiveness of
OTTS. Experimental results validate that our OTTS outperforms the typical
baselines, i.e., MAML, matchingnet, protonet, by a large margin (averagely
1.72\% accuracy improvement).


title:
Predicting Intervention Approval in Clinical Trials through
  Multi-Document Summarization

abstract:
  Clinical trials offer a fundamental opportunity to discover new treatments
and advance the medical knowledge. However, the uncertainty of the outcome of a
trial can lead to unforeseen costs and setbacks. In this study, we propose a
new method to predict the effectiveness of an intervention in a clinical trial.
Our method relies on generating an informative summary from multiple documents
available in the literature about the intervention under study. Specifically,
our method first gathers all the abstracts of PubMed articles related to the
intervention. Then, an evidence sentence, which conveys information about the
effectiveness of the intervention, is extracted automatically from each
abstract. Based on the set of evidence sentences extracted from the abstracts,
a short summary about the intervention is constructed. Finally, the produced
summaries are used to train a BERT-based classifier, in order to infer the
effectiveness of an intervention. To evaluate our proposed method, we introduce
a new dataset which is a collection of clinical trials together with their
associated PubMed articles. Our experiments, demonstrate the effectiveness of
producing short informative summaries and using them to predict the
effectiveness of an intervention.


title: Text-To-Speech Data Augmentation for Low Resource Speech Recognition

abstract:
  Nowadays, the main problem of deep learning techniques used in the
development of automatic speech recognition (ASR) models is the lack of
transcribed data. The goal of this research is to propose a new data
augmentation method to improve ASR models for agglutinative and low-resource
languages. This novel data augmentation method generates both synthetic text
and synthetic audio. Some experiments were conducted using the corpus of the
Quechua language, which is an agglutinative and low-resource language. In this
study, a sequence-to-sequence (seq2seq) model was applied to generate synthetic
text, in addition to generating synthetic speech using a text-to-speech (TTS)
model for Quechua. The results show that the new data augmentation method works
well to improve the ASR model for Quechua. In this research, an 8.73%
improvement in the word-error-rate (WER) of the ASR model is obtained using a
combination of synthetic text and synthetic speech.


title: Preventing Distillation-based Attacks on Neural Network IP

abstract:
  Neural networks (NNs) are already deployed in hardware today, becoming
valuable intellectual property (IP) as many hours are invested in their
training and optimization. Therefore, attackers may be interested in copying,
reverse engineering, or even modifying this IP. The current practices in
hardware obfuscation, including the widely studied logic locking technique, are
insufficient to protect the actual IP of a well-trained NN: its weights. Simply
hiding the weights behind a key-based scheme is inefficient (resource-hungry)
and inadequate (attackers can exploit knowledge distillation). This paper
proposes an intuitive method to poison the predictions that prevent
distillation-based attacks; this is the first work to consider such a poisoning
approach in hardware-implemented NNs. The proposed technique obfuscates a NN so
an attacker cannot train the NN entirely or accurately. We elaborate a threat
model which highlights the difference between random logic obfuscation and the
obfuscation of NN IP. Based on this threat model, our security analysis shows
that the poisoning successfully and significantly reduces the accuracy of the
stolen NN model on various representative datasets. Moreover, the accuracy and
prediction distributions are maintained, no functionality is disturbed, nor are
high overheads incurred. Finally, we highlight that our proposed approach is
flexible and does not require manipulation of the NN toolchain.


title:
The role of living laboratories in unlocking the potential of low-carbon
  energy technologies on the journey to net-zero

abstract:
  We demonstrate the potential role of one of the largest at scale multi-vector
Smart Energy Network Demonstrator (SEND).


title:
GrowliFlower: An image time series dataset for GROWth analysis of
  cauLIFLOWER

abstract:
  This article presents GrowliFlower, a georeferenced, image-based UAV time
series dataset of two monitored cauliflower fields of size 0.39 and 0.60 ha
acquired in 2020 and 2021. The dataset contains RGB and multispectral
orthophotos from which about 14,000 individual plant coordinates are derived
and provided. The coordinates enable the dataset users the extraction of
complete and incomplete time series of image patches showing individual plants.
The dataset contains collected phenotypic traits of 740 plants, including the
developmental stage as well as plant and cauliflower size. As the harvestable
product is completely covered by leaves, plant IDs and coordinates are provided
to extract image pairs of plants pre and post defoliation, to facilitate
estimations of cauliflower head size. Moreover, the dataset contains
pixel-accurate leaf and plant instance segmentations, as well as stem
annotations to address tasks like classification, detection, segmentation,
instance segmentation, and similar computer vision tasks. The dataset aims to
foster the development and evaluation of machine learning approaches. It
specifically focuses on the analysis of growth and development of cauliflower
and the derivation of phenotypic traits to foster the development of automation
in agriculture. Two baseline results of instance segmentation at plant and leaf
level based on the labeled instance segmentation data are presented. The entire
data set is publicly available.


title: Cocartesian fibrations and straightening internal to an $\infty$-topos

abstract:
  We define and study cartesian and cocartesian fibrations between categories
internal to an $\infty$-topos and prove a straightening equivalence in this
context.


title: Scalable Semi-Modular Inference with Variational Meta-Posteriors

abstract:
  The Cut posterior and related Semi-Modular Inference are Generalised Bayes
methods for Modular Bayesian evidence combination. Analysis is broken up over
modular sub-models of the joint posterior distribution. Model-misspecification
in multi-modular models can be hard to fix by model elaboration alone and the
Cut posterior and SMI offer a way round this. Information entering the analysis
from misspecified modules is controlled by an influence parameter $\eta$
related to the learning rate. This paper contains two substantial new methods.
First, we give variational methods for approximating the Cut and SMI posteriors
which are adapted to the inferential goals of evidence combination. We
parameterise a family of variational posteriors using a Normalising Flow for
accurate approximation and end-to-end training. Secondly, we show that analysis
of models with multiple cuts is feasible using a new Variational
Meta-Posterior. This approximates a family of SMI posteriors indexed by $\eta$
using a single set of variational parameters.