title: New power-law tailed distributions emerging in $\kappa$-statistics

abstract:
  Over the last two decades, it has been argued that the Lorentz transformation
mechanism, which imposes the generalization of Newton's classical mechanics
into Einstein's special relativity, implies a generalization, or deformation,
of the ordinary statistical mechanics. The exponential function, which defines
the Boltzmann's factor, emerges properly deformed within this formalism.
Starting from this, so-called $\kappa$-deformed exponential function, we
introduce new classes of statistical distributions emerging as the
$\kappa$-deformed version of already known distribution as the Generalized
Gamma, Weibull, Logistic which can be adopted in the analysis of statistical
data that exhibit power-law tails.

categories: math.ST cond-mat.stat-mech math-ph math.MP math.PR physics.data-an stat.TH


title: Accelerated SGD for Non-Strongly-Convex Least Squares

abstract:
  We consider stochastic approximation for the least squares regression problem
in the non-strongly convex setting. We present the first practical algorithm
that achieves the optimal prediction error rates in terms of dependence on the
noise of the problem, as $O(d/t)$ while accelerating the forgetting of the
initial conditions to $O(d/t^2)$. Our new algorithm is based on a simple
modification of the accelerated gradient descent. We provide convergence
results for both the averaged and the last iterate of the algorithm. In order
to describe the tightness of these new bounds, we present a matching lower
bound in the noiseless setting and thus show the optimality of our algorithm.


title:
The White Dwarf Binary Pathways Survey VI: two close post common
  envelope binaries with TESS light curves

abstract:
  Establishing a large sample of post common envelope binaries (PCEBs) that
consist of a white dwarf plus an intermediate mass companion star of spectral
type AFGK, offers the potential to provide new constraints on theoretical
models of white dwarf binary formation and evolution. Here we present a
detailed analysis of two new systems, TYC 110-755-1 and TYC 3858-1215-1. Based
on radial velocity measurements we find the orbital periods of the two systems
to be $\sim$ 0.85 and $\sim$ 1.64 days, respectively. In addition, HST
spectroscopy of TYC 110-755-1 allowed us to measure the mass of the white dwarf
in this system (0.78 M$_\odot$). We furthermore analysed TESS high time
resolution photometry and find both secondary stars to be magnetically
extremely active. Differences in the photometric and spectroscopic periods of
TYC 110-755-1 indicate that the secondary in this system is differentially
rotating. Finally, studying the past and future evolution of both systems, we
conclude that the common envelope efficiency is likely similar in close white
dwarf plus AFGK binaries and PCEBs with M-dwarf companions and find a wide
range of possible evolutionary histories for both systems. While TYC
3858-1215-1 will run into dynamically unstable mass transfer that will cause
the two stars to merge and evolve into a single white dwarf, TYC 110-755-1 is a
progenitor of a cataclysmic variable system with an evolved donor star.


title: SaPHyRa: A Learning Theory Approach to Ranking Nodes in Large Networks

abstract:
  Ranking nodes based on their centrality stands a fundamental, yet,
challenging problem in large-scale networks. Approximate methods can quickly
estimate nodes' centrality and identify the most central nodes, but the ranking
for the majority of remaining nodes may be meaningless. For example, ranking
for less-known websites in search queries is known to be noisy and unstable. To
this end, we investigate a new node ranking problem with two important
distinctions: a) ranking quality, rather than the centrality estimation
quality, as the primary objective; and b) ranking only nodes of interest, e.g.,
websites that matched search criteria. We propose Sample space Partitioning
Hypothesis Ranking, or SaPHyRa, that transforms node ranking into a hypothesis
ranking in machine learning. This transformation maps nodes' centrality to the
expected risks of hypotheses, opening doors for theoretical machine learning
(ML) tools. The key of SaPHyRa is to partition the sample space into exact and
approximate subspaces. The exact subspace contains samples related to the nodes
of interest, increasing both estimation and ranking qualities. The approximate
space can be efficiently sampled with ML-based techniques to provide
theoretical guarantees on the estimation error. Lastly, we present SaPHyRa_bc,
an illustration of SaPHyRa on ranking nodes' betweenness centrality (BC). By
combining a novel bi-component sampling, a 2-hop sample partitioning, and
improved bounds on the Vapnik-Chervonenkis dimension, SaPHyRa_bc can
effectively rank any node subset in BC. Its performance is up to 200x faster
than state-of-the-art methods in approximating BC, while its rank correlation
to the ground truth is improved by multifold.


title: The puzzle of bicriticality in the XXZ antiferromagnet

abstract:
  Renormalization-group predicts that the XXZ antiferromagnet in a magnetic
field along the easy axis Z should exhibit either a tetracritical phase diagram
in the field-temperature plane, with a mixed phase of the longitudinal and the
spin-flopped order parameters, or a triple point, at which three first-order
transition lines (between the longitudinal and spin-flopped phases and between
these phases and the paramagnetic phase) meet. The former arises when the
renormalization group iterations flow to the biconical fixed point, and the
latter -- otherwise. Neither experiments nor Monte Carlo simulations procure
such phase diagrams. Instead, they find a bicritical phase diagram, with only
one first-order transition line between the two ordered phases. Here this
discrepancy is resolved: the renormalization-group predictions describe {\it
asymptotic} phase diagrams, which apply only for very large correlation
lengths. However, away from these asymptotic regions the renormalization group
predicts an apparent bicritical point, with effective critical exponents (which
vary with the distance from the multicritical point), whose measurements can
confirm the theory. A similar scenario may explain the puzzling discrepancy
between the Monte Carlo simulations and the renormalization-group predictions
for the competing superconductivity and antiferromagnetism in the SO(5) model,
and be relevant to other models with competing order parameters.


title: Determination of the argon spectral function from (e,e'p) data

abstract:
  The E12-14-012 experiment, performed in Jefferson Lab Hall A, has measured
the $(e, e'p)$ cross section in parallel kinematics using a natural argon
target. Here, we report the full results of the analysis of the data set
corresponding to beam energy 2.222 GeV, and spanning the missing momentum and
missing energy range $15 \lesssim p_m \lesssim 300$ MeV/c and $12 \lesssim E_m
\lesssim 80$ MeV. The reduced cross section, determined as a function of $p_m$
and $E_m$ with $\approx$4\% accuracy, has been fitted using the results of
Monte Carlo simulations involving a model spectral function and including the
effects of final state interactions. The overall agreement between data and
simulations turns out to be quite satisfactory ($\chi^2$/n.d.o.f.=1.9). The
resulting spectral function will provide valuable new information, needed for
the interpretation of neutrino interactions in liquid argon detectors.


title:
Different configurations of transferred atmospheric pressure plasma jet
  and their application to polymer treatment

abstract:
  The employment of atmospheric pressure plasma jets (APPJs) in a large sort of
applications is limited by the adversities related to the size of the treated
area and the difficulty to reach the target. The use of devices that employ
long tubes in their structure has contributed significantly to overcome these
challenges. In this work, a comparison between two different plasma systems
employing the jet transfer technique is presented. The main difference between
the two devices is how the long plastic tube was assembled. The first one uses
a copper wire placed inside a long plastic tube. The other device has a
metallic mesh installed in a concentric arrangement between two coaxial plastic
tubes. As a result, the two APPJ systems exhibit different properties, with the
wire assembly being more powerful, also presenting higher values for the
electrical current and rotational temperature when compared to the mesh
mounting. X-ray photoelectron spectroscopy (XPS) demonstrates that both
configurations were capable of inserting O-containing functional groups on the
polypropylene (PP) surface. However, the transferred plasma jet with wire
assembly was able to add more functional groups on the PP surface. The results
from XPS analysis were corroborated with water contact angle measurements
(WCA), being that lower WCA values were obtained when the PP surface presented
higher amounts of O-containing groups. Surface morphology evaluation showed
that the plasma treatment tends to slightly increase the surface roughness of
the PP samples with both wire and mesh configurations. Furthermore, the
findings in this work suggest that the APPJ with wire configuration is more
appropriate for material treatments, while the transferred jet with mesh
arrangement tends to present lower electrical current values which makes it
more suitable for biological applications.


title:
One year of AU Mic with HARPS: I -- measuring the masses of the two
  transiting planets

abstract:
  The system of two transiting Neptune-sized planets around the bright, young
M-dwarf AU Mic provides a unique opportunity to test models of planet
formation, early evolution, and star-planet interaction. However, the intense
magnetic activity of the host star makes measuring the masses of the planets
via the radial velocity (RV) method very challenging. We report on a 1-year,
intensive monitoring campaign of the system using 91 observations with the
HARPS spectrograph, allowing for detailed modelling of the $\sim 600\,
\rm{m\,s^{-1}}$ peak-to-peak activity-induced RV variations. We used a
multidimensional Gaussian Process framework to model these and the planetary
signals simultaneously. We detect the latter with semi-amplitudes of $\rm{K_b}
= 5.8 \pm 2.5\, \rm{m\,s^{-1}}$ and $\rm{K_c} = 8.5 \pm 2.5\, \rm{m\, s^{-1}}$,
respectively. The resulting mass estimates, $\rm{M_b} = 11.7 \pm 5.0\,
\rm{M_{\rm \oplus}}$ and $\rm{M_c} = 22.2 \pm 6.7\, \rm{M_{\rm \oplus}}$,
suggest that planet b might be less dense, and planet c considerably denser
than previously thought. These results are in tension with the current standard
models of core-accretion. They suggest that both planets accreted a H/He
envelope that is smaller than expected, and the trend between the two planets'
envelope fractions is the opposite of what is predicted by theory.


title:
BITKOMO: Combining Sampling and Optimization for Fast Convergence in
  Optimal Motion Planning

abstract:
  Optimal sampling based motion planning and trajectory optimization are two
competing frameworks to generate optimal motion plans. Both frameworks have
complementary properties: Sampling based planners are typically slow to
converge, but provide optimality guarantees. Trajectory optimizers, however,
are typically fast to converge, but do not provide global optimality guarantees
in nonconvex problems, e.g. scenarios with obstacles. To achieve the best of
both worlds, we introduce a new planner, BITKOMO, which integrates the
asymptotically optimal Batch Informed Trees (BIT*) planner with the K-Order
Markov Optimization (KOMO) trajectory optimization framework. Our planner is
anytime and maintains the same asymptotic optimality guarantees provided by
BIT*, while also exploiting the fast convergence of the KOMO trajectory
optimizer. We experimentally evaluate our planner on manipulation scenarios
that involve high dimensional configuration spaces, with up to two 7-DoF
manipulators, obstacles and narrow passages. BITKOMO performs better than KOMO
by succeeding even when KOMO fails, and it outperforms BIT* in terms of
convergence to the optimal solution.


title:
Vertical Federated Principal Component Analysis and Its Kernel Extension
  on Feature-wise Distributed Data

abstract:
  Despite enormous research interest and rapid application of federated
learning (FL) to various areas, existing studies mostly focus on supervised
federated learning under the horizontally partitioned local dataset setting.
This paper will study the unsupervised FL under the vertically partitioned
dataset setting. Accordingly, we propose the federated principal component
analysis for vertically partitioned dataset (VFedPCA) method, which reduces the
dimensionality across the joint datasets over all the clients and extracts the
principal component feature information for downstream data analysis. We
further take advantage of the nonlinear dimensionality reduction and propose
the vertical federated advanced kernel principal component analysis (VFedAKPCA)
method, which can effectively and collaboratively model the nonlinear nature
existing in many real datasets. In addition, we study two communication
topologies. The first is a server-client topology where a semi-trusted server
coordinates the federated training, while the second is the fully-decentralized
topology which further eliminates the requirement of the server by allowing
clients themselves to communicate with their neighbors. Extensive experiments
conducted on five types of real-world datasets corroborate the efficacy of
VFedPCA and VFedAKPCA under the vertically partitioned FL setting. Code is
available at: https://github.com/juyongjiang/VFedPCA-VFedAKPCA


title: Spin Hall effects and the localization of massless spinning particles

abstract:
  The spin Hall effects of light represent a diverse class of
polarization-dependent physical phenomena involving the dynamics of
electromagnetic wave packets. In a medium with an inhomogeneous refractive
index, wave packets can be effectively described by massless spinning particles
following polarization-dependent trajectories. Similarly, in curved spacetime
the gravitational spin Hall effect of light is represented by
polarization-dependent deviations from null geodesics. In this paper, we
analyze the equations of motion describing the gravitational spin Hall effect
of light. We show that these equations are a special case of the
Mathisson-Papapetrou equations for spinning objects in general relativity. This
allows us to use several known results for the Mathisson-Papapetrou equations,
and apply them to the study of electromagnetic wave packets. We derive
conservation laws, we discuss the limits of validity of the spin Hall
equations, and we study how the energy centroids of wave packets, effectively
described as massless spinning particles, depend on the external choice of a
timelike vector field, representing a family of observers. In flat spacetime,
the relativistic Hall effect and the Wigner(-Souriau) translations are
recovered, while our equations also provide a generalization of these effects
in arbitrary spacetimes. We construct a large class of wave packets that can be
described by the spin Hall equations, but also find its limits by giving
examples of wave packets which are more general and are not described by the
spin Hall equations. Lastly, we examine the assumption that electromagnetic
wave packets are massless. While this is approximately true in many contexts,
it is not exact. We show that failing to carefully account for the limitations
of the massless approximation results in the appearance of unphysical centroids
which are nowhere near the wave packet itself.


title:
PINA: Learning a Personalized Implicit Neural Avatar from a Single RGB-D
  Video Sequence

abstract:
  We present a novel method to learn Personalized Implicit Neural Avatars
(PINA) from a short RGB-D sequence. This allows non-expert users to create a
detailed and personalized virtual copy of themselves, which can be animated
with realistic clothing deformations. PINA does not require complete scans, nor
does it require a prior learned from large datasets of clothed humans. Learning
a complete avatar in this setting is challenging, since only few depth
observations are available, which are noisy and incomplete (i.e. only partial
visibility of the body per frame). We propose a method to learn the shape and
non-rigid deformations via a pose-conditioned implicit surface and a
deformation field, defined in canonical space. This allows us to fuse all
partial observations into a single consistent canonical representation. Fusion
is formulated as a global optimization problem over the pose, shape and
skinning parameters. The method can learn neural avatars from real noisy RGB-D
sequences for a diverse set of people and clothing styles and these avatars can
be animated given unseen motion sequences.


title: Modeling the Energy Consumption of HEVC Intra Decoding

abstract:
  Battery life is one of the major limitations to mobile device use, which
makes research on energy efficient soft- and hardware an important task. This
paper investigates the energy required by a CPU when decoding compressed
bitstream videos on mobile platforms. A model is derived that describes the
energy consumption of the new HEVC decoder for intra coded videos. We show that
the relative estimation error of the model is smaller than 3.2% and that the
model can be used to build encoders aiming at minimizing decoding energy.


title:
Nonlocal problems with Neumann and Robin boundary condition in
  fractional Musielak-Sobolev spaces

abstract:
  In this paper, we develop some properties of the $a_{x,y}(.)$-Neumann
derivative for the fractional $a_{x,y}(.)$-Laplacian operator. Therefore we
prove the basic proprieties of the correspondent function spaces. In the second
part of this paper, by means of Ekeland's variational principal and direct
variational approach, we prove the existence of weak solutions for a nonlocal
problem with nonhomogeneous Neumann and Robin boundary condition.


title:
First-Order Objective-Function-Free Optimization Algorithms and Their
  Complexity

abstract:
  A class of algorithms for unconstrained nonconvex optimization is considered
where the value of the objective function is never computed. The class contains
a deterministic version of the first-order Adagrad method typically used for
minimization of noisy function, but also allows the use of second-order
information when available. The rate of convergence of methods in the class is
analyzed and is shown to be identical to that known for first-order
optimization methods using both function and gradients values. The result is
essentially sharp and improves on previously known complexity bounds (in the
stochastic context) by Defossez et al. (2020) and Gratton et al. (2022). A new
class of methods is designed, for which a slightly worse and essentially sharp
complexity result holds. Limited numerical experiments show that the new
methods' performance may be comparable to that of standard steepest descent,
despite using significantly less information, and that this performance is
relatively insensitive to noise.


title:
On Practical Reinforcement Learning: Provable Robustness, Scalability,
  and Statistical Efficiency

abstract:
  This thesis rigorously studies fundamental reinforcement learning (RL)
methods in modern practical considerations, including robust RL, distributional
RL, and offline RL with neural function approximation. The thesis first
prepares the readers with an overall overview of RL and key technical
background in statistics and optimization. In each of the settings, the thesis
motivates the problems to be studied, reviews the current literature, provides
computationally efficient algorithms with provable efficiency guarantees, and
concludes with future research directions. The thesis makes fundamental
contributions to the three settings above, both algorithmically, theoretically,
and empirically, while staying relevant to practical considerations.


title:
Berry-phase switch in electrostatically-confined topological surface
  states

abstract:
  Here we visualize the trapping of topological surface states in the circular
n-p junctions on the top surface of the 7-quintuple-layer three dimensional
(3D) topological insulator (TI) Sb2Te3 epitaxial films. As shown by spatially-
and field-dependent tunneling spectra, these trapped resonances show
field-induced splittings between the degenerate time-reversal-symmetric states
at zero magnetic field. These behaviors are attributed unambiguously to
Berry-phase switch by comparing the experimental data with both numerical and
semi-classical simulations. The successful electrostatic trapping of
topological surface states in epitaxial films and the observation of
Berry-phase switch provide a rich platform of exploiting new ideas for TI-based
quantum devices.


title:
Quantum simulator of extended bipartite Hubbard model with broken
  sublattice symmetry: magnetism, correlations, and phase transitions

abstract:
  We describe here a quantum simulator of extended bipartite Hubbard model with
broken sublattice symmetry. The simulator consists of a structured lateral gate
confining two dimensional electrons in a quantum well into artificial minima
arranged in a hexagonal lattice. The sublattice symmetry breaking is generated
by forming an artificial triangular graphene quantum dot (ATGQD) with zigzag
edges. The resulting extended Hubbard model generates tunable ratio of
tunneling strength to electron-electron interactions and of sublattice symmetry
with control over shape. The validity of the simulator is confirmed for small
systems using mean-field and exact diagonalization many-body approaches which
show that the ground state changes from a metallic to an antiferromagnetic (AF)
phase by varying the distance between sites or depth of the confining
potential. The one-electron spectrum of these triangular dots contains a
macroscopically degenerate shell at the Fermi level. The shell persists at the
mean-field level for weak interactions (metallic phase) but disappears for
strong interactions, in the AF phase. We determine the effects of
electron-electron interactions on the ground state, the total spin, and the
excitation spectrum as a function of filling of the ATGQD. We find that the
half-filled charge neutral shell leads to a partially spin polarized state in
both metallic and AF regimes in accordance with Liebs theorem. In both regimes
a relatively large gap separates the spin polarized ground state to the first
excited many-body state at half filling of the degenerate shell. By adding or
removing an electron, this gap drops dramatically, and alternate total spin
states emerge with energies nearly degenerate to a spin polarized ground state.


title: Doubly Robust Calibration of Prediction Sets under Covariate Shift

abstract:
  Conformal prediction has received tremendous attention in recent years and
has offered new solutions to problems in missing data and causal inference; yet
these advances have not leveraged modern semiparametric efficiency theory for
more robust and efficient uncertainty quantification. In this paper, we
consider the problem of obtaining distribution-free prediction regions
accounting for a shift in the distribution of the covariates between the
training and test data. Under an explainable covariate shift assumption
analogous to the standard missing at random assumption, we propose three
variants of a general framework to construct well-calibrated prediction regions
for the unobserved outcome in the test sample. Our approach is based on the
efficient influence function for the quantile of the unobserved outcome in the
test population combined with an arbitrary machine learning prediction
algorithm, without compromising asymptotic coverage. Next, we extend our
approach to account for departure from the explainable covariate shift
assumption in a semiparametric sensitivity analysis for potential latent
covariate shift. In all cases, we establish that the resulting prediction sets
eventually attain nominal average coverage in large samples. This guarantee is
a consequence of the product bias form of our proposal which implies correct
coverage if either the propensity score or the conditional distribution of the
response is estimated sufficiently well. Our results also provide a framework
for construction of doubly robust prediction sets of individual treatment
effects, under both unconfoundedness and allowing for some degree of unmeasured
confounding. Finally, we discuss aggregation of prediction sets from different
machine learning algorithms for optimal prediction and illustrate the
performance of our methods in both synthetic and real data.


title:
NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural
  Radiance Fields

abstract:
  Deep learning has shown great potential for modeling the physical dynamics of
complex particle systems such as fluids (in Lagrangian descriptions). Existing
approaches, however, require the supervision of consecutive particle
properties, including positions and velocities. In this paper, we consider a
partially observable scenario known as fluid dynamics grounding, that is,
inferring the state transitions and interactions within the fluid particle
systems from sequential visual observations of the fluid surface. We propose a
differentiable two-stage network named NeuroFluid. Our approach consists of (i)
a particle-driven neural renderer, which involves fluid physical properties
into the volume rendering function, and (ii) a particle transition model
optimized to reduce the differences between the rendered and the observed
images. NeuroFluid provides the first solution to unsupervised learning of
particle-based fluid dynamics by training these two models jointly. It is shown
to reasonably estimate the underlying physics of fluids with different initial
shapes, viscosity, and densities. It is a potential alternative approach to
understanding complex fluid mechanics, such as turbulence, that are difficult
to model using traditional methods of mathematical physics.


title:
A central limit theorem for star-generators of $S_{\infty}$, which
  relates to traceless CCR-GUE matrices

abstract:
  We prove a limit theorem concerning the sequence of star-generators of
$S_{\infty}$, where the expectation functional is provided by a character of
$S_{\infty}$ with weights $(w_1, \ldots , w_d, 0,0, \ldots )$ in the Thoma
classification. The limit law turns out to be the law of a "traceless CCR-GUE"
matrix, an analogue of the traceless GUE where the off-diagonal entries
$g_{i,j}$ satisfy the commutation relation $g_{i,j}g_{j,i} = g_{j,i}g_{i,j} +
(w_j - w_i)$. The special case $w_1 = \cdots = w_d = 1/d$ yields the law of a
bona fide traceless GUE matrix, and we retrieve a result of K\"ostler and Nica
from 2021, which in turn extended a result of Biane from 1995.


title: Random Quantum Neural Networks (RQNN) for Noisy Image Recognition

abstract:
  Classical Random Neural Networks (RNNs) have demonstrated effective
applications in decision making, signal processing, and image recognition
tasks. However, their implementation has been limited to deterministic digital
systems that output probability distributions in lieu of stochastic behaviors
of random spiking signals. We introduce the novel class of supervised Random
Quantum Neural Networks (RQNNs) with a robust training strategy to better
exploit the random nature of the spiking RNN. The proposed RQNN employs hybrid
classical-quantum algorithms with superposition state and amplitude encoding
features, inspired by quantum information theory and the brain's
spatial-temporal stochastic spiking property of neuron information encoding. We
have extensively validated our proposed RQNN model, relying on hybrid
classical-quantum algorithms via the PennyLane Quantum simulator with a limited
number of \emph{qubits}. Experiments on the MNIST, FashionMNIST, and KMNIST
datasets demonstrate that the proposed RQNN model achieves an average
classification accuracy of $94.9\%$. Additionally, the experimental findings
illustrate the proposed RQNN's effectiveness and resilience in noisy settings,
with enhanced image classification accuracy when compared to the classical
counterparts (RNNs), classical Spiking Neural Networks (SNNs), and the
classical convolutional neural network (AlexNet). Furthermore, the RQNN can
deal with noise, which is useful for various applications, including computer
vision in NISQ devices. The PyTorch code (https://github.com/darthsimpus/RQN)
is made available on GitHub to reproduce the results reported in this
manuscript.


title:
Joint Optimization of Rate, Distortion, and Decoding Energy for HEVC
  Intraframe Coding

abstract:
  This paper presents a novel algorithm that aims at minimizing the required
decoding energy by exploiting a general energy model for HEVC-decoder
solutions. We incorporate the energy model into the HEVC encoder such that it
is capable of constructing a bit stream whose decoding process consumes less
energy than the decoding process of a conventional bit stream. To achieve this,
we propose to extend the traditional Rate-Distortion-Optimization scheme to a
Decoding-Energy-Rate-Distortion approach. To obtain fast encoding decisions in
the optimization process, we derive a fixed relation between the quantization
parameter and the Lagrange multiplier for energy optimization. Our experiments
show that this concept is applicable for intraframe-coded videos and that for
local playback as well as online streaming scenarios, up to 15% of the decoding
energy can be saved at the expense of a bitrate increase of approximately the
same magnitude.


title:
Holographic RG flows and symplectic deformations of N=4 gauged
  supergravity

abstract:
  We study four-dimensional $N=4$ gauged supergravity with $SO(4)\times
SO(4)\sim SO(3)\times SO(3)\times SO(3)\times SO(3)$ gauge group in the
presence of symplectic deformations. There are in general four
electric-magnetic phases corresponding to each $SO(3)$ factor, but two phases
of the $SO(3)$ factors embedded in the $SO(6)$ R-symmetry are fixed. One phase
can be set to zero by $SL(2,\mathbb{R})$ transformations. The second one gives
equivalent theories for any non-vanishing values and can be set to
$\frac{\pi}{2}$ resulting in gauged supergravities that admit $N=4$
supersymmetric $AdS_4$ vacua. The remaining two phases are truely deformation
parameters leading to different $SO(4)\times SO(4)$ gauged supergravities. As
in the $\omega$-deformed $SO(8)$ maximal gauged supergravity, the cosmological
constant and scalar masses of the $AdS_4$ vacuum at the origin of the scalar
manifold with $SO(4)\times SO(4)$ symmetry do not depend on the
electric-magnetic phases. We find $N=1$ holographic RG flow solutions between
$N=4$ critical points with $SO(4)\times SO(4)$ and $SO(3)_{\textrm{diag}}\times
SO(3)\times SO(3)$ or $SO(3)\times SO(3)_{\textrm{diag}}\times SO(3)$
symmetries. We also give $N=2$ and $N=1$ RG flows from these critical points to
various non-conformal phases. However, contrary to the $\omega$-deformed
$SO(8)$ gauged supergravity, there exist non-trivial supersymmetric $AdS_4$
critical points only for particular values of the deformation parameters within
the scalar sectors under consideration.


title: Estimating the HEVC Decoding Energy Using the Decoder Processing Time

abstract:
  This paper presents a method to accurately estimate the required decoding
energy for a given HEVC software decoding solution. We show that the decoder's
processing time as returned by common C++ and UNIX functions is a highly
suitable parameter to obtain valid estimations for the actual decoding energy.
We verify this hypothesis by performing an exhaustive measurement series using
different decoder setups and video bit streams. Our findings can be used by
developers and researchers in the search for new energy saving video
compression algorithms.


title:
The Jacobson--Morozov morphism for Langlands parameters in the relative
  setting

abstract:
  In this article we construct a moduli space $\mathsf{LP}_G$ of
$\mathrm{SL}_2$-parameters over $\mathbb{Q}$, and show that it has good
geometric properties (e.g. explicitly parametrized geometric connected
components and smoothness). We construct a Jacobson--Morozov morphism
$\mathsf{JM}\colon \mathsf{LP}_G\to\mathsf{WDP}_G$ (where $\mathsf{WDP}_G$ is
the moduli space of Weil--Deligne parameters considered by several other
authors). We show that $\mathsf{JM}$ is an isomorphism over a dense open of
$\mathsf{WDP}_G$, that it induces an isomorphism between the discrete loci
$\mathsf{LP}^{\mathrm{disc}}_G\to\mathsf{WDP}_G^{\mathrm{disc}}$, and that for
any $\mathbb{Q}$-algebra $A$ it induces a bijection between Frobenius
semi-simple equivalence classes in $\mathsf{LP}_G(A)$ and Frobenius semi-simple
equivalence classes in $\mathsf{WDP}_G(A)$ with constant (up to conjugacy)
monodromy operator.


title:
PeerSum: A Peer Review Dataset for Abstractive Multi-document
  Summarization

abstract:
  We present PeerSum, a new MDS dataset using peer reviews of scientific
publications. Our dataset differs from the existing MDS datasets in that our
summaries (i.e., the meta-reviews) are highly abstractive and they are real
summaries of the source documents (i.e., the reviews) and it also features
disagreements among source documents. We found that current state-of-the-art
MDS models struggle to generate high-quality summaries for PeerSum, offering
new research opportunities.


title:
Forward-modulated damping estimates: remark on a paper of Haragus,
  Johnson, Perkins, and de Rijk

abstract:
  We remark on an interesting recent analysis by Haragus, Johnson, Perkins, and
de Rijk of modulational stability of steady periodic solutions of the
Lugiato-Lefever equation. In this work, the authors point out a difficulty in
obtaining standard "nonlinear damping estimates" on modulated perturbation
variables to control regularity of solutions. In place of this standard device,
they introduce a different formalism interpolating between damping estimates on
the unmodulated perturbation variable and linearized bounds on the modulated
one. Here, we point out that one can alternatively carry out a modulated
damping estimate on the "forward-modulated" perturbation, noting that norms of
forward- and inverse-modulated variables are equivalent modulo absorbable
errors, thus recovering the classical argument structure of Johnson, Noble,
Rodrigues, and Zumbrun for parabolic systems. This observation seems of general
use in situations of delicate regularity


title:
Estimation of Non-Functional Properties for Embedded Hardware with
  Application to Image Processing

abstract:
  In recent years, due to a higher demand for portable devices, which provide
restricted amounts of processing capacity and battery power, the need for
energy and time efficient hard- and software solutions has increased.
Preliminary estimations of time and energy consumption can thus be valuable to
improve implementations and design decisions. To this end, this paper presents
a method to estimate the time and energy consumption of a given software
solution, without having to rely on the use of a traditional Cycle Accurate
Simulator (CAS). Instead, we propose to utilize a combination of high-level
functional simulation with a mechanistic extension to include non-functional
properties: Instruction counts from virtual execution are multiplied with
corresponding specific energies and times. By evaluating two common image
processing algorithms on an FPGA-based CPU, where a mean relative estimation
error of 3% is achieved for cacheless systems, we show that this estimation
tool can be a valuable aid in the development of embedded processor
architectures. The tool allows the developer to reach well-suited design
decisions regarding the optimal processor hardware configuration for a given
algorithm at an early stage in the design process.


title:
Direct observation of local antiferroelectricity induced phonon
  softening at a SrTiO3 defect

abstract:
  Defects in oxides usually exhibit exotic properties that may be associated
with the local lattice dynamics. Here, at atomic spatial resolution, we
directly measure phonon modes of an antiphase boundary (APB) in SrTiO3
freestanding membrane and correlate them with the picometer-level structural
distortion. We find that the SrTiO3 APB introduces new defect phonon modes that
are absent in bulk SrTiO3. These modes are highly sensitive to the subtle
structure distortion, i.e., the SrTiO3 APB generates the local electric dipoles
forming an antiferroelectric configuration, which significantly softens the
transverse optical (TO) and longitudinal optical (LO) modes at {\Gamma} point.
Correlating the local phonons with the subtle structural distortion, our
findings provide valuable insights into understanding the defect properties in
complex oxides and essential information for their applications such as
thermoelectric devices.


title:
Constraints on r-modes and mountains on millisecond neutron stars in
  binary systems

abstract:
  Continuous gravitational waves are nearly monochromatic signals emitted by
asymmetries in rotating neutron stars. These signals have not yet been
detected. Deep all-sky searches for continuous gravitational waves from
isolated neutron stars require significant computational expense. Deep searches
for neutron stars in binary systems are even more expensive, but potentially
these targets are more promising emitters, especially in the hundreds-Hz
region, where ground-based gravitational wave detectors are most sensitive. We
present here an all-sky search for continuous signals with frequency between
300 and 500 Hz, from neutron stars in binary systems with orbital period
between 15 and 60 days, and projected semi-major axis between 10 and 40
light-seconds. This is the only binary search on Advanced-LIGO data that probes
this frequency range. Compared to previous results, our search is over an order
of magnitude more sensitive. We do not detect any signals, but our results
exclude plausible and unexplored neutron star configurations, for example,
neutron stars with relative deformations greater than 3e-6 within 1 kpc from
Earth and r-mode emission at the level of alpha ~ few 1e-4 within the same
distance.


title: Novel electron and photon recording concepts in noble-liquid detectors

abstract:
  We present several novel ionization-electron and scintillation-photon
recording concepts in noble-liquid detectors, for future applications in
particle and astroparticle physics and in other fields. These involve both
single- and dual-phase detector configurations with combined
electroluminescence and small charge multiplication in gas and liquid media.


title:
Achieving high-temperature ferromagnetism by means of magnetic ions
  dimerization

abstract:
  Magnetic two-dimensional materials have potential application in
next-generation electronic devices and have stimulated extensive interest in
condensed matter physics and material fields. However, how to realize
high-temperature ferromagnetism in two-dimensional materials remains a great
challenge in physics.
  Herein, we propose an effective approach that the dimerization of magnetic
ions in two-dimensional materials can enhance the exchange coupling and
stabilize the ferromagnetism.
  Manganese carbonitride Mn$_2$N$_6$C$_6$ with a planar monolayer structure is
taken as an example to clarify the method, in which two Mn atoms are gathered
together to form a ferromagnetic dimer of Mn atoms and further these dimers are
coupled together to form the overall ferromagnetism of the two-dimensional
material.
  In Mn$_2$N$_6$C$_6$ monolayer, the high-temperature ferromagnetism with the
Curie temperature of 272.3 K is determined by solving Heisenberg model using
Monte Carlo simulations method.


title: First quantization of braided Majorana fermions

abstract:
  A ${\mathbb Z}_2$-graded qubit represents an even (bosonic) "vacuum state"
and an odd, excited, Majorana fermion state. The multiparticle sectors of $N$,
braided, indistinguishable Majorana fermions are constructed via first
quantization. The framework is that of a graded Hopf algebra endowed with a
braided tensor product. The Hopf algebra is ${U}({\mathfrak {gl}}(1|1))$, the
Universal Enveloping Algebra of the ${\mathfrak{gl}}(1|1)$ superalgebra. A
$4\times 4$ braiding matrix $B_t$ defines the braided tensor product. $B_t$,
which is related to the $R$-matrix of the Alexander-Conway polynomial, depends
on the braiding parameter $t$ belonging to the punctured plane ($t\in {\mathbb
C}^\ast$); the ordinary antisymmetry property of fermions is recovered for
$t=1$. For each $N$, the graded dimension $m|n$ of the graded multiparticle
Hilbert space is computed. Besides the generic case, truncations occur when $t$
coincides with certain roots of unity which appear as solutions of an ordered
set of polynomial equations. The roots of unity are organized into levels which
specify the maximal number of allowed braided Majorana fermions in a
multiparticle sector. By taking into account that the even/odd sectors in a
${\mathbb Z}_2$-graded Hilbert space are superselected, a nontrivial braiding
with $t\neq 1$ is essential to produce a nontrivial Hilbert space described by
qubits, qutrits, etc., since at $t=1$ the $N$-particle vacuum and the
antisymmetrized excited state encode the same information carried by a
classical $1$-bit.

categories: hep-th cond-mat.stat-mech math-ph math.MP quant-ph


title: Characterizing Grounded Theory Approaches in Visualization

abstract:
  Grounded theory (GT) is a research methodology that entails a systematic
workflow for theory generation grounded on emergent data. In this paper, we
juxtapose GT workflows with typical workflows in visualization and visual
analytics, shortly VIS, and underline the characteristics shared by these
workflows. We explore the research landscape of VIS to observe where GT has
been applied to generate VIS theories, explicitly as well as implicitly. We
propose a "why" typology for characterizing aspects in VIS where GT can
potentially play a significant role. We outline a "how" methodology for
conducting GT research in VIS, which addresses the need for theoretical
advancement in VIS while benefitting from other methods and techniques in VIS.
We exemplify this "how" methodology by adopting GT approaches in studying the
messages posted on VisGuides - an Open Discourse Forum for discussing
visualization guidelines.


title:
Who pays for gifts to physicians? Heterogeneous effects of industry
  payments on drug costs

abstract:
  This paper estimates the impact of gifts - monetary or in-kind payments -
from pharmaceutical firms on physicians' prescription decisions and drug costs
in the US. Using exhaustive micro data on prescriptions for anti-diabetic drugs
from Medicare Part D, we find that payments cause physicians to prescribe more
brand drugs. On average, for every dollar spent, payments generate a $6
increase in drug costs. We then estimate heterogeneous causal effects via
machine-learning methods. We find large heterogeneity in responses to payments
across physicians. Differences are predominantly explained by the insurance
coverage of patients: physicians prescribe more brand drugs in response to
payments when patients benefit from subsidies that reduce out-of-pocket drug
costs. Finally, we estimate that a gift ban would reduce drug costs to treat
diabetes by 3%.


title: Exchange distance of basis pairs in split matroids

abstract:
  The basis exchange axiom has been a driving force in the development of
matroid theory. However, the axiom gives only a local characterization of the
relation of bases, which is a major stumbling block to further progress, and
providing a global understanding of the structure of matroid bases is a
fundamental goal in matroid optimization.
  While studying the structure of symmetric exchanges, Gabow proposed the
problem that any pair of bases admits a sequence of symmetric exchanges. A
different extension of the exchange axiom was proposed by White, who
investigated the equivalence of compatible basis sequences. Farber studied the
structure of basis pairs, and conjectured that the basis pair graph of any
matroid is connected. These conjectures suggest that the family of bases of a
matroid possesses much stronger structural properties than we are aware of.
  In the present paper, we study the distance of basis pairs of a matroid in
terms of symmetric exchanges. In particular, we give an upper bound on the
minimum number of exchanges needed to transform a basis pair into another for
split matroids, a class that was motivated by the study of matroid polytopes
from a tropical geometry point of view. As a corollary, we verify the above
mentioned long-standing conjectures for this large class. Being a subclass of
split matroids, our result settles the conjectures for paving matroids as well.


title:
The VMC Survey -- XLVIII. Classical Cepheids unveil the 3D geometry of
  the LMC

abstract:
  We employed the {\it VISTA near-infrared $YJK_\mathrm{s}$ survey of the
Magellanic System} (VMC), to analyse the $Y,\,J,\,K_\mathrm{s}$ light curves of
$\delta$ Cepheid stars (DCEPs) in the Large Magellanic Cloud (LMC). Our sample
consists of 4408 objects accounting for 97 per cent of the combined list of
OGLE\,IV and {\it Gaia}\,DR2 DCEPs. We determined a variety of
period-luminosity ($PL$) and period-Wesenheit $PW$ relationships for
Fundamental (F) and First Overtone (1O) pulsators. We discovered for the first
time a break in these relationships for 1O DCEPs at $P$=0.58 d. We derived
relative individual distances for DCEPs in the LMC with a precision of $\sim$1
kpc, calculating the position angle of the line of nodes and inclination of the
galaxy: $\theta$=145.6$\pm$1.0 deg and $i$=25.7$\pm$0.4 deg. The bar and the
disc are seen under different viewing angles. We calculated the ages of the
pulsators, finding two main episodes of DCEP formation lasting $\sim$40 Myr
which happened 93 and 159 Myr ago. Likely as a result of its past interactions
with the SMC, the LMC shows a non-planar distribution, with considerable
structuring: the bar is divided into two distinct portions, the eastern and the
western displaced by more than 1 kpc from each other. Similar behaviour is
shown by the spiral arms. The LMC disc appears "flared" and thick, with a disc
scale height of $h\sim 0.97$ kpc. This feature can be explained by strong tidal
interactions with the Milky Way and/or the Small Magellanic Cloud or past
merging events with now disrupted LMC satellites.


title:
Censoring and censoring mechanisms in oncology in light of the estimands
  framework

abstract:
  In oncology clinical trials with time-to-event endpoints, censoring rules
have traditionally been defined and applied following standard approaches based
on longstanding regulatory guidelines. The estimand framework (addendum to the
ICH E9 guideline) calls for precisely defining the treatment effect of interest
to align with the clinical question of interest, and requires predefining the
handling of intercurrent events that occur after treatment initiation and
either preclude the observation of an event of interest or impact the
interpretation of the treatment effect. In the context of time to event
endpoints, this requires a careful discussion on how censoring rules are
applied. This paper explains the importance of distinguishing censoring
concepts that have traditionally been merged. Specifically, noumenal censoring
as an estimation method to address an intercurrent event which influences the
clinical question itself (i.e. defines the pre-specified estimand),
distinguishing it from phenomenal censoring that addresses administratively
missing information (i.e. defines missing data handling in the estimate).
Strategies for dealing with the most relevant intercurrent events, the need for
close alignment of the clinical question of interest and study design, impact
on data collection and other practical implications will be discussed. The
authors recommend defining trial-specific rules as well as considering when
noumenal and phenomenal censoring is used. These considerations also apply to
defining relevant and interpretable sensitivity and supplementary analyses.


title:
Multi-Objective Design Space Exploration for the Optimization of the
  HEVC Mode Decision Process

abstract:
  Finding the best possible encoding decisions for compressing a video sequence
is a highly complex problem. In this work, we propose a multi-objective Design
Space Exploration (DSE) method to automatically find HEVC encoder
implementations that are optimized for several different criteria. The DSE
shall optimize the coding mode evaluation order of the mode decision process
and jointly explore early skip conditions to minimize the four objectives a)
bitrate, b) distortion, c) encoding time, and d) decoding energy. In this
context, we use a SystemC-based actor model of the HM test model encoder for
the evaluation of each explored solution. The evaluation that is based on real
measurements shows that our framework can automatically generate encoder
solutions that save more than 60% of encoding time or 3% of decoding energy
when accepting bitrate increases of around 3%.


title:
Critical behavior of the specific heat in Ti-Si amorphous alloys at the
  metal-insulator transition

abstract:
  In this paper, we report the measurements of specific heat of an amorphous
$Ti_{9.5}Si_{90.5}$ alloy located very close to the critical point of the
metal-insulator transition. In the presence of a magnetic field, the specific
heat is dominated by the Schottky anomaly caused by magnetic moments associated
with the dangling bonds in the matrix of amorphous Si. Subtraction of this
contribution exposes the behavior of the electronic specific heat coefficient
$\gamma$. The coefficient is temperature-independent above 2 K and is, in order
of magnitude, close to the value expected in the absence of electron-electron
interactions. In the temperature range 0.4-1.5 K, the coefficient $\gamma$
shows an anomalous downturn, which can be approximated by the dependence
$\gamma\left(T\right)=\gamma_0ln{(T/T_0)}$, with $T_0\approx0.2$ K . In a
companion paper, we found that the Hall coefficient in Ti-Si alloys is affected
by the electron-electron interaction up to much higher temperature of 150 K and
also varies critically across the metal-insulator transition. We compare our
results with theoretical predictions for three models, which can potentially
explain the anomalous behavior of the specific heat: generalized non-linear
$\sigma$ model, Coulomb glass, and many-body localization.


title: Revisiting Click-based Interactive Video Object Segmentation

abstract:
  While current methods for interactive Video Object Segmentation (iVOS) rely
on scribble-based interactions to generate precise object masks, we propose a
Click-based interactive Video Object Segmentation (CiVOS) framework to simplify
the required user workload as much as possible. CiVOS builds on de-coupled
modules reflecting user interaction and mask propagation. The interaction
module converts click-based interactions into an object mask, which is then
inferred to the remaining frames by the propagation module. Additional user
interactions allow for a refinement of the object mask. The approach is
extensively evaluated on the popular interactive~DAVIS dataset, but with an
inevitable adaptation of scribble-based interactions with click-based
counterparts. We consider several strategies for generating clicks during our
evaluation to reflect various user inputs and adjust the DAVIS performance
metric to perform a hardware-independent comparison. The presented CiVOS
pipeline achieves competitive results, although requiring a lower user
workload.


title: On Learning Contrastive Representations for Learning with Noisy Labels

abstract:
  Deep neural networks are able to memorize noisy labels easily with a softmax
cross-entropy (CE) loss. Previous studies attempted to address this issue focus
on incorporating a noise-robust loss function to the CE loss. However, the
memorization issue is alleviated but still remains due to the non-robust CE
loss. To address this issue, we focus on learning robust contrastive
representations of data on which the classifier is hard to memorize the label
noise under the CE loss. We propose a novel contrastive regularization function
to learn such representations over noisy data where label noise does not
dominate the representation learning. By theoretically investigating the
representations induced by the proposed regularization function, we reveal that
the learned representations keep information related to true labels and discard
information related to corrupted labels. Moreover, our theoretical results also
indicate that the learned representations are robust to the label noise. The
effectiveness of this method is demonstrated with experiments on benchmark
datasets.


title:
Generative Modeling for Low Dimensional Speech Attributes with Neural
  Spline Flows

abstract:
  Despite recent advances in generative modeling for text-to-speech synthesis,
these models do not yet have the same fine-grained adjustability of
pitch-conditioned deterministic models such as FastPitch and FastSpeech2. Pitch
information is not only low-dimensional, but also discontinuous, making it
particularly difficult to model in a generative setting. Our work explores
several techniques for handling the aforementioned issues in the context of
Normalizing Flow models. We also find this problem to be very well suited for
Neural Spline flows, which is a highly expressive alternative to the more
common affine-coupling mechanism in Normalizing Flows.


title: Testing Gravitational Self-interaction via Matter-Wave Interferometry

abstract:
  The Schrodinger-Newton equation has frequently been studied as a nonlinear
modification of the Schrodinger equation incorporating gravitational
self-interaction. However, there is no evidence yet as to whether nature
actually behaves this way. This work investigates a possible way to
experimentally test gravitational self-interaction. The effect of self-gravity
on interference of massive particles is studied by numerically solving the
Schrodinger-Newton equation for a particle passing through a double-slit. The
results show that the presence of gravitational self-interaction has an effect
on the fringe width of the interference that can be tested in matter-wave
interferometry experiments. Notably, this approach can distinguish between
gravitational self-interaction and environment induced decoherence, as the
latter does not affect the fringe width. This result will also provide a way to
test if gravity requires to be quantized on the scale of ordinary quantum
mechanics.


title: Twisted Arrow Construction for Segal Spaces

abstract:
  We define the twisted arrow construction for simplicial spaces and show that
the twisted arrow construction of a complete Segal space $W$, $Tw(W)$ is a
complete Segal space. Further, we show that the projection map $Tw(W)\to
W^{op}\times W$ is a left fibration of Segal spaces.


title:
Quantum circuit compilation and hybrid computation using Pauli-based
  computation

abstract:
  Pauli-based computation (PBC) is driven by a sequence of adaptively chosen,
non-destructive measurements of Pauli observables. Any quantum circuit written
in terms of the Clifford+$T$ gate set and having $t$ $T$ gates can be compiled
into a PBC on $t$ qubits. Here we propose practical ways of implementing PBC as
adaptive quantum circuits, and provide code to do the required classical
side-processing. Our first scheme reduces the number of quantum gates to
$O(t^2)$ (from a previous $O(t^3 / \log t)$ scaling) at the cost of one extra
auxiliary qubit, with a possible reduction of the depth to $O(t \log t$), at
the cost of $t$ additional auxiliary qubits (second scheme). We compile
examples of random and hidden-shift quantum circuits into adaptive PBC
circuits. We also simulate hybrid quantum computation, where a classical
computer effectively extends the working memory of a small quantum computer by
$k$ virtual qubits, at a cost exponential in $k$. Our results demonstrate the
practical advantage of PBC techniques for circuit compilation and hybrid
computation.


title:
Effect of symmetry reduction on the magnetic properties of
  LnIr$_2$Si$_2$ polymorphs

abstract:
  Many tetragonal compounds LnIr$_2$Si$_2$ (Ln = lanthanoid) occur in two
polymorphous phases and are therefore well suited to study the relationship
between crystal structure and magnetic properties. We have grown GdIr$_2$Si$_2$
single crystals of both polymorphs from a high-temperature indium flux and
investigated their anisotropic magnetic properties. The higher symmetric form
with the ThCr$_2$Si$_2$ structure (space group $I4/mmm$) orders
antiferromagnetically at $T_{\rm N}=87\,\rm K$ while for the lower symmetric
compound in the CaBe$_2$Ge$_2$ structure (space group $P4/nmm$) we determined a
much lower \neel temperature $T_{\rm N}=12\,\rm K$. Our magnetic
characterization of the single crystals reveals that for both compounds the
magnetic moments are aligned in the $a-a$ plane of the tetragonal lattice, but
that the change of the symmetry strongly effects the inplane alignment of the
moment orientation. For Ln$=$Er and Ho, we confirmed the existence of
LnIr$_2$Si$_2$ in the space group $P4/nmm$. The magnetic properties of these
lower symmetric compounds are in remarkable difference to their related
compounds in the space group $I4/mmm$.


title:
Cavitation on single electron bubbles in liquid helium at small negative
  pressure

abstract:
  Liquid helium under negative pressure represents a unique possibility for
studying the macroscopic quantum nucleation phenomena in condensed media. We
analyze the quantum cavitation rate of single electron bubbles at low
temperatures down to absolute zero. The energy dissipation and sound emission
processes result in the different temperature behavior of quantum cavitation
rate in normal fluid $^3$He and superfluid $^4$He below the thermal-quantum
crossover temperature. The position of rapid nucleation line in the
temperature-pressure phase diagram is discussed as well.


title: Design Automation of Photonic Resonator Weights

abstract:
  Neuromorphic photonic processors based on resonator weight banks are an
emerging candidate technology for enabling modern artificial intelligence (AI)
in high speed, analog systems. These purpose-built analog devices implement
vector multiplications with the physics of resonator devices, offering
efficiency, latency, and throughput advantages over equivalent electronic
circuits. Along with these advantages, however, often comes the difficult
challenges of compensation for fabrication variations and environmental
disturbances. In this paper we review sources of variation and disturbances
from our experiments, as well as mathematically define quantities that model
them. Then, we introduce how the physics of resonators can be exploited to
weight and sum multiwavelength signals. Finally, we outline automated design
and control methodologies necessary to create practical, manufacturable, and
high accuracy/precision resonator weight banks that can withstand operating
conditions in the field. This represents a road map for unlocking the potential
of resonator weight banks in practical deployment scenarios.


title:
Deep Learning-Based Joint Control of Acoustic Echo Cancellation,
  Beamforming and Postfiltering

abstract:
  We introduce a novel method for controlling the functionality of a hands-free
speech communication device which comprises a model-based acoustic echo
canceller (AEC), minimum variance distortionless response (MVDR) beamformer
(BF) and spectral postfilter (PF). While the AEC removes the early echo
component, the MVDR BF and PF suppress the residual echo and background noise.
As key innovation, we suggest to use a single deep neural network (DNN) to
jointly control the adaptation of the various algorithmic components. This
allows for rapid convergence and high steady-state performance in the presence
of high-level interfering double-talk. End-to-end training of the DNN using a
time-domain speech extraction loss function avoids the design of individual
control strategies.


title: Efficient Fr\'echet distance queries for segments

abstract:
  We study the problem of constructing a data structure that can store a
two-dimensional polygonal curve $P$, such that for any query segment
$\overline{ab}$ one can efficiently compute the Fr\'{e}chet distance between
$P$ and $\overline{ab}$. First we present a data structure of size $O(n \log
n)$ that can compute the Fr\'{e}chet distance between $P$ and a horizontal
query segment $\overline{ab}$ in $O(\log n)$ time, where $n$ is the number of
vertices of $P$. In comparison to prior work, this significantly reduces the
required space. We extend the type of queries allowed, as we allow a query to
be a horizontal segment $\overline{ab}$ together with two points $s, t \in P$
(not necessarily vertices), and ask for the Fr\'{e}chet distance between
$\overline{ab}$ and the curve of $P$ in between $s$ and $t$. Using
$O(n\log^2n)$ storage, such queries take $O(\log^3 n)$ time, simplifying and
significantly improving previous results. We then generalize our results to
query segments of arbitrary orientation. We present an
$O(nk^{3+\varepsilon}+n^2)$ size data structure, where $k \in [1..n]$ is a
parameter the user can choose, and $\varepsilon > 0$ is an arbitrarily small
constant, such that given any segment $\overline{ab}$ and two points $s, t \in
P$ we can compute the Fr\'{e}chet distance between $\overline{ab}$ and the
curve of $P$ in between $s$ and $t$ in $O((n/k)\log^2n+\log^4 n)$ time. This is
the first result that allows efficient exact Fr\'{e}chet distance queries for
arbitrarily oriented segments.
  We also present two applications of our data structure: we show that we can
compute a local $\delta$-simplification (with respect to the Fr\'{e}chet
distance) of a polygonal curve in $O(n^{5/2+\varepsilon})$ time, and that we
can efficiently find a translation of an arbitrary query segment
$\overline{ab}$ that minimizes the Fr\'{e}chet distance with respect to a
subcurve of $P$.


title: Force density functional theory in- and out-of-equilibrium

abstract:
  When a fluid is subject to an external field, as is the case near an
interface or under spatial confinement, then the density becomes spatially
inhomogeneous. Although the one-body density provides much useful information,
a higher level of resolution is provided by the two-body correlations. These
give a statistical description of the internal microstructure of the fluid and
enable calculation of the average interparticle force, which plays an essential
role in determining both the equilibrium and dynamic properties of interacting
fluids. We present a theoretical framework for the description of inhomogeneous
(classical) many-body systems, based explicitly on the two-body correlation
functions. By consideration of local Noether-invariance against spatial
distortion of the system we demonstrate the fundamental status of the
Yvon-Born-Green (YBG) equation as a local force-balance within the fluid. Using
the inhomogeneous Ornstein-Zernike equation we show that the two-body
correlations are density functionals and, thus, that the average interparticle
force entering the YBG equation is also a functional of the one-body density.
The force-based theory we develop provides an alternative to standard density
functional theory for the study of inhomogeneous systems both in- and
out-of-equilibrium. We compare force-based density profiles to the results of
the standard potential-based (dynamical) density functional theory. In
equilibrium, we confirm both analytically and numerically that the standard
approach yields profiles that are consistent with the compressibility pressure,
whereas the force-density functional gives profiles consistent with the virial
pressure. The structure of the theory offers deep insights into the nature of
correlation in dense and inhomogeneous systems.


title: Sleptonic SUSY: From UV Framework to IR Phenomenology

abstract:
  We study an attractive scenario, "Sleptonic SUSY", which reconciles the $125$
GeV Higgs scalar and the non-observation of superpartners thus far with
potentially pivotal roles for slepton phenomenology: providing viable ongoing
targets for LHC discovery, incorporating a co-annihilation partner for
detectable thermal relic dark matter, and capable of mediating the existing
muon $g-2$ anomaly. This is accomplished by a modestly hierarchical spectrum,
with sub-TeV sleptons and electroweakinos and with multi-TeV masses for the
other new states. We study new elements in the UV MSSM realization of Sleptonic
SUSY based on higher-dimensional sequestering and the synergy between the
resulting gaugino-mediation, hypercharge $D$-term mediation and Higgs-mediation
of SUSY-breaking, so as to more fully capture the range of possibilities. This
framework stands out by harmoniously solving the flavor, CP and $\mu - B\mu$
problems of the supersymmetric paradigm. We discuss its extension to orbifold
GUTs, including gauge-coupling and $b$-tau unification. We also develop a
non-minimal model with extra Higgs fields, in which the electroweak vacuum is
more readily cosmologically stable against decay to a charge-breaking vacuum,
allowing a broader range of sleptonic spectra than in the MSSM alone. We survey
the rich set of signals possible at the LHC and future colliders, covering both
$R$-parity conservation and violation, as well as for dark matter detection.
While the multi-TeV squarks imply a Little Hierarchy Problem, intriguingly,
small changes in parameter space to improve naturalness result in dramatic
phase transitions to either electroweak-preservation or charge-breaking. In a
Multiverse setting, the modest unnaturalness may then be explained by the
"principle of living dangerously".


title:
Electronic Density Response of Warm Dense Hydrogen: Ab initio Path
  Integral Monte Carlo Simulations

abstract:
  The properties of hydrogen under extreme conditions are important for many
applications, including inertial confinement fusion and astrophysical models. A
key quantity is given by the electronic density response to an external
perturbation, which is probed in X-ray Thomson scattering (XRTS) experiments --
the state of the art diagnostics from which system parameters like the free
electron density $n_e$, the electronic temperature $T_e$, and the charge state
$Z$ can be inferred. In this work, we present highly accurate path integral
Monte Carlo (PIMC) results for the electronic density response of hydrogen. We
obtain the exchange-correlation (XC) kernel $K_{xc}$, which is of central
relevance for many applications, such as time-dependent density functional
theory (TD-DFT). This gives us a first unbiased look into the electronic
density response of hydrogen in the warm-dense matter regime, thereby opening
up a gamut of avenues for future research.


title:
Spectrally accurate solutions to inhomogeneous elliptic PDE in smooth
  geometries using function intension

abstract:
  We present a spectrally accurate embedded boundary method for solving linear,
inhomogeneous, elliptic partial differential equations (PDE) in general smooth
geometries, focusing in this manuscript on the Poisson, modified Helmholtz, and
Stokes equations. Unlike several recently proposed methods which rely on
function extension, we propose a method which instead utilizes function
`intension', or the smooth truncation of known function values. Similar to
those methods based on extension, once the inhomogeneity is truncated we may
solve the PDE using any of the many simple, fast, and robust solvers that have
been developed for regular grids on simple domains. Function intension is
inherently stable, as are all steps in the proposed solution method, and can be
used on domains which do not readily admit extensions. We pay a price in
exchange for improved stability and flexibility: in addition to solving the PDE
on the regular domain, we must additionally (1) solve the PDE on a small
auxiliary domain that is fitted to the boundary, and (2) ensure consistency of
the solution across the interface between this auxiliary domain and the rest of
the physical domain. We show how these tasks may be accomplished efficiently
(in both the asymptotic and practical sense), and compare convergence to
several recent high-order embedded boundary schemes.


title: Coupling Deep Learning with Full Waveform Inversion

abstract:
  Full waveform inversion (FWI) aims at reconstructing unknown physical
coefficients in wave equations using the wave field data generated from
multiple incoming sources. In this work, we propose an offline-online
computational strategy for coupling classical least-squares based computational
inversion with modern deep learning based approaches for FWI to achieve
advantages that can not be achieved with only one of the components. In a
nutshell, we develop an offline learning strategy to construct a robust
approximation to the inverse operator and utilize it to design a new objective
function for the online inversion with new datasets. We demonstrate through
numerical simulations that our coupling strategy improves the computational
efficiency of FWI with reliable offline training on moderate computational
resources (in terms of both the size of the training dataset and the
computational cost needed).


title: Automatic Facial Paralysis Estimation with Facial Action Units

abstract:
  Facial palsy is unilateral facial nerve weakness or paralysis of rapid onset
with unknown causes. Automatically estimating facial palsy severeness can be
helpful for the diagnosis and treatment of people suffering from it across the
world. In this work, we develop and experiment with a novel model for
estimating facial palsy severity. For this, an effective Facial Action Units
(AU) detection technique is incorporated into our model, where AUs refer to a
unique set of facial muscle movements used to describe almost every
anatomically possible facial expression. In this paper, we propose a novel
Adaptive Local-Global Relational Network (ALGRNet) for facial AU detection and
use it to classify facial paralysis severity. ALGRNet mainly consists of three
main novel structures: (i) an adaptive region learning module that learns the
adaptive muscle regions based on the detected landmarks; (ii) a skip-BiLSTM
that models the latent relationships among local AUs; and (iii) a feature
fusion&refining module that investigates the complementary between the local
and global face. Quantitative results on two AU benchmarks, i.e., BP4D and
DISFA, demonstrate our ALGRNet can achieve promising AU detection accuracy. We
further demonstrate the effectiveness of its application to facial paralysis
estimation by migrating ALGRNet to a facial paralysis dataset collected and
annotated by medical professionals.


title: 20-Mode Universal Quantum Photonic Processor

abstract:
  Integrated photonics is an essential technology for optical quantum
computing. Universal, phase-stable, reconfigurable multimode interferometers
(quantum photonic processors) enable manipulation of photonic quantum states
and are one of the main components of photonic quantum computers in various
architectures. In this paper, we report the realization of the largest quantum
photonic processor to date. The processor enables arbitrary unitary
transformations on its 20 input modes with a fidelity of ($F_{Haar} = 97.4 \%$,
$F_{Perm} = 99.5 \%$), an average optical loss of 2.9 dB/mode, and
high-visibility quantum interference ($V_{HOM} = 98 \%$). The processor is
realized in Si$_3$N$_4$ waveguides.


title: Shortest Minkowski billiard trajectories on convex bodies

abstract:
  We rigorously investigate closed Minkowski/Finsler billiard trajectories on
$n$-dimensional convex bodies. We outline the central properties in comparison
and differentiation from the Euclidean special case and establish two main
results for length-minimizing closed Minkowski/Finsler billiard trajectories:
one is a regularity result, the other is of geometric nature. Building on these
results, we develop an algorithm for computing length-minimizing closed
Minkowski/Finsler billiard trajectories in the plane.


title:
The EDIBLES survey V: Line profile variations in the
  $\lambda\lambda$5797, 6379, and 6614 diffuse interstellar bands as a tool to
  constrain carrier sizes

abstract:
  Several diffuse interstellar bands (DIBs) have profiles with resolved
sub-peaks that resemble rotational bands of large molecules. Analysis of these
profiles can constrain the sizes and geometries of the DIB carriers, especially
if the profiles exhibit clear variations along lines of sight probing different
physical conditions. Using the extensive data set from the EDIBLES survey we
searched for systematic variations in the peak-to-peak separation of these
sub-peaks for the $\lambda\lambda$5797, 6379, and 6614 DIBs in lines of sight
with a single dominant interstellar cloud. We used the spectra of twelve
single-cloud sight lines to measure the peak-to-peak separation in the band
profile substructures for these DIBs. We adopted the rotational contour
formalism to infer the rotational constant for each DIB carrier and the
rotational excitation temperature in the sight lines. We compared these to
rotational constants for linear and spherical molecules to estimate the DIB
carrier sizes. All three DIBs have peak separations that vary systematically
between lines of sight, indicating correlated changes in the rotational
excitation temperatures. We derived $B_{6614}$=$(22.2\pm8.9)\times 10^{-3}$
cm$^{-1}$, consistent with previous estimates. Assuming a similar rotational
temperature for the $\lambda$6614 DIB carrier and assuming a linear carrier, we
found B$_{5797}^{\rm linear}=(5.1\pm2.0)\times10^{-3}~{\rm cm}^{-1}$ and
B$_{6379}^{\rm linear} =(2.3\pm0.9)\times10^{-3}~{\rm cm}^{-1}$. If the
carriers of those DIBs however are spherical species, their rotational
constants are half that value, $B_{5797}^{\rm spherical} =
(2.6\pm1.0)\times10^{-3}~{\rm cm}^{-1}$ and $B_{6379}^{\rm spherical} =
(1.1\pm0.4)\times10^{-3}~{\rm cm}^{-1}$. We estimate molecule sizes that range
from 7--9 carbon atoms ($\lambda$6614 carrier, linear) to 77--114 carbon atoms
($\lambda$6379, spherical).


title:
Comment on: Calculation of an Enhanced $A_{1g}$ Symmetry Mode Induced by
  Higgs Oscillations in the Raman Spectrum of High-Temperature Cuprate
  Superconductors

abstract:
  In a recent manuscript Phys. Rev. Lett. 127, 197001 (2021) Puviani et al.
claim that the Higgs mode gives an enhancement of the Raman response in the
symmetric $A_{1g}$ Raman channel of cuprates. Here we report a series of
technical mistakes in their derivation that completely invalidate this
conclusion. In particular, the Raman response function contains two
renormalized vertices, with a clearly overcounting of diagrams and uncontrolled
results for the Higgs-mode contribution.


title: Multi-Radical Lipkin-Meshkov-Glick Model for Avian Navigation

abstract:
  The mechanism of avian navigation is an important question for global
scientists. One of the most famous candidates is the radical pair mechanism
(RPM), which shows that avian navigation is achieved by detecting the amount of
products from chemical reactions, which exhibit different properties for
singlet and triplet spin states. Based on the theory, we explore the amount of
the the singlet recombination product by changing the coupling strength and
direction of the magnetic field. The radical pair in our model is coupled with
a Lipkin-Meshkov-Glick (LMG) bath, which proposes attractive results at quantum
phase transition (QPT) point, especially when the magnetic field is parallel
with the original direction of the pair. We find weakest navigation ability in
QPT point according to one of our proposing methods. And we find invariant
subspace in our Hamiltonian when the magnetic field is vertical to the plane of
the radical pair and the bath, which will greatly speed up our calculation. Our
results could help the exploration of the design principle of artificial avian
navigation.

categories: physics.bio-ph cond-mat.stat-mech physics.chem-ph quant-ph


title: Time Evolution of the CMB Quadrupole

abstract:
  I show that the quadrupole of the Cosmic Microwave Background (CMB) evolves
more rapidly than previously expected, as a result of the acceleration of the
Sun towards the Galactic center. The acceleration, measured most recently by
Gaia EDR3, implies a fractional change in the quadrupole of ~10^{-9} per year,
an order of magnitude larger than expected from the evolution in the last
scattering surface of the CMB.


title:
Quadrotor Formation Flying Resilient to Abrupt Vehicle Failures via a
  Fluid Flow Navigation Function

abstract:
  This paper develops and experimentally evaluates a navigation function for
quadrotor formation flight that is resilient to abrupt quadrotor failures and
other obstacles. The navigation function is based on modeling healthy
quadrotors as particles in an ideal fluid flow. We provide three key
contributions: (i) A Containment Exclusion Mode (CEM) safety theorem and proof
which guarantees safety and formally specifies a minimum safe distance between
quadrotors in formation, (ii) A real-time, computationally efficient CEM
navigation algorithm, (iii) Simulation and experimental algorithm validation.
Simulations were first performed with a team of six virtual quadrotors to
demonstrate velocity tracking via dynamic slide speed, maintaining sufficient
inter-agent distances, and operating in real-time. Flight tests with a team of
two custom quadrotors were performed in an indoor motion capture flight
facility, successfully validating that the navigation algorithm can handle
non-trivial bounded tracking errors while guaranteeing safety.


title:
Study of $B_c^+\to J/\psi D_s^+$ and $B_c^+\to J/\psi D_s^{*+}$ decays
  in $pp$ collisions at $\sqrt{s} = 13$ TeV with the ATLAS detector

abstract:
  A study of $B_c^+\to J/\psi D_s^+$ and $B_c^+\to J/\psi D_s^{*+}$ decays
using 139 fb$^{-1}$ of integrated luminosity collected with the ATLAS detector
from $\sqrt{s} = 13$ TeV $pp$ collisions at the LHC is presented. The ratios of
the branching fractions of the two decays to the branching fraction of the
$B_c^+\to J/\psi \pi^+$ decay are measured: $\mathcal B(B_c^+\to J/\psi
D_s^+)/\mathcal B(B_c^+\to J/\psi \pi^+) = 2.76\pm 0.47$ and $\mathcal
B(B_c^+\to J/\psi D_s^{*+})/\mathcal B(B_c^+\to J/\psi \pi^+) = 5.33\pm 0.96$.
The ratio of the branching fractions of the two decays is found to be $\mathcal
B(B_c^+\to J/\psi D_s^{*+})/\mathcal B(B_c^+\to J/\psi D_s^+) = 1.93\pm0.26$.
For the $B_c^+\to J/\psi D_s^{*+}$ decay, the transverse polarization fraction,
$\Gamma_{\pm\pm}/\Gamma$, is measured to be $0.70\pm0.11$. The reported
uncertainties include both the statistical and systematic components added in
quadrature. The precision of the measurements exceeds that in all previous
studies of these decays. These results supersede those obtained in the earlier
ATLAS study of the same decays with $\sqrt{s} = 7$ and 8 TeV $pp$ collision
data. A comparison with available theoretical predictions for the measured
quantities is presented.


title:
Unique continuation results for certain generalized ray transforms of
  symmetric tensor fields

abstract:
  Let $I_{m}$ denote the Euclidean ray transform acting on compactly supported
symmetric $m$-tensor field distributions $f$, and $I_{m}^{*}$ be its formal
$L^2$ adjoint. We study a unique continuation result for the normal operator
$N_{m}=I_{m}^{*}I_{m}$. More precisely, we show that if $N_{m}$ vanishes to
infinite order at a point $x_0$ and if the Saint-Venant operator $W$ acting on
$f$ vanishes on an open set containing $x_0$, then $f$ is a potential tensor
field. This generalizes two recent works of Ilmavirta and M\"onkk\"onen who
proved such unique continuation results for the ray transform of functions and
vector fields/1-forms. One of the main contributions of this work is
identifying the Saint-Venant operator acting on higher order tensor fields as
the right generalization of the exterior derivative operator acting on 1-forms,
which makes unique continuation results for ray transforms of higher order
tensor fields possible. In the second half of the paper, we prove analogous
unique continuation results for momentum ray and transverse ray transforms.


title:
Integrating Contrastive Learning with Dynamic Models for Reinforcement
  Learning from Images

abstract:
  Recent methods for reinforcement learning from images use auxiliary tasks to
learn image features that are used by the agent's policy or Q-function. In
particular, methods based on contrastive learning that induce linearity of the
latent dynamics or invariance to data augmentation have been shown to greatly
improve the sample efficiency of the reinforcement learning algorithm and the
generalizability of the learned embedding. We further argue, that explicitly
improving Markovianity of the learned embedding is desirable and propose a
self-supervised representation learning method which integrates contrastive
learning with dynamic models to synergistically combine these three objectives:
(1) We maximize the InfoNCE bound on the mutual information between the state-
and action-embedding and the embedding of the next state to induce a linearly
predictive embedding without explicitly learning a linear transition model, (2)
we further improve Markovianity of the learned embedding by explicitly learning
a non-linear transition model using regression, and (3) we maximize the mutual
information between the two nonlinear predictions of the next embeddings based
on the current action and two independent augmentations of the current state,
which naturally induces transformation invariance not only for the state
embedding, but also for the nonlinear transition model. Experimental evaluation
on the Deepmind control suite shows that our proposed method achieves higher
sample efficiency and better generalization than state-of-art methods based on
contrastive learning or reconstruction.


title: Cubic Lovelock Gravity in Lower Dimensions

abstract:
  In this paper, we obtain the lower-dimensional limits $(p=2,3,4,5,6)$ of
cubic Lovelock gravity through a regularized Kaluza-Klein reduction. By taking
a flat internal space for simplicity, we also study the static black hole
solutions in the resulting theories. We show that the solutions match with the
ones obtained from the "naive limit" of $D$-dimensional equation for the metric
function, which is obtained by first scaling the relevant couplings by a factor
of $\frac{1}{D-p}$ and then taking the limit $D\to p$, with one important
exception: In 4D, one obtains the expected solution only for the black hole
with a planar horizon.


title: Casimir effect and Lorentz invariance violation

abstract:
  The Casimir effect is one of the most direct manifestations of the existence
of the vacuum quantum fluctuations, discovered by H. B Casimir in 1948. On the
other hand, Lorentz invariance is one of the main and basic concepts in special
relativity, which states that, the laws of physics are invariant under Lorentz
transformation. In this work, we calculate the corrections imposed by LIV on
Casimir effect (force). This may provide a direct probe to test LIV in nature.


title:
Computation offloading to hardware accelerators in Intel SGX and Gramine
  Library OS

abstract:
  The Intel Software Guard Extensions (SGX) technology enables applications to
run in an isolated SGX enclave environment, with elevated confidentiality and
integrity guarantees. Gramine Library OS facilitates execution of existing
unmodified applications in SGX enclaves, requiring only an accompanying
manifest file that describes the application's security posture and
configuration. However, Intel SGX is a CPU-only technology, thus Gramine
currently supports CPU-only workloads. To enable a broader class of
applications that offload computations to hardware accelerators - GPU offload,
NIC offload, FPGA offload, TPM communications - Gramine must be augmented with
device-backed mmap support and generic ioctl support. In this paper, we
describe the design and implementation of this newly added support, the
corresponding changes to the manifest-file syntax and the requisite deep copy
algorithm. We evaluate our implementation on Intel Media SDK workloads and
discuss the encountered caveats and limitations. Finally, we outline a use case
for the presented mmap/ioctl support beyond mere device communication, namely
the mechanism to slice the application into the trusted enclave part (where the
core application executes) and the untrusted shared-memory part (where insecure
shared libraries execute).


title: The spectrum of Hawking radiation in Tsallis statistical mechanics

abstract:
  Hawking radiation is one of the cores in modern gratitational theory. Several
articles have calculated the spectrum of Hawking radiation in Boltzmann-Gibbs
statistical mechanics. However, based on recent researches, gravitational
systems cannot be studied by the standard statistical mechanics. In this
article, we calculate the modification to the spectrum of Hawking radiation in
Tsallis statistical mechanics. We obtain the modified Stefan-Boltzmann's law
and modified power of Hawking radiation. We confirm the conclusion proposed by
Giddings, namely, the radiation should originate from the effective radius,
which extends well outside the horizon of black-hole. The lifetime of black
hole and the effect of large q are discussed as well.


title:
A multilevel Analysis of Saudi Arabian Student 8th Grade Mathematics
  Achievement TIMSS 2011

abstract:
  This article uses the hierarchical linear modelling (HLM) technique to
explain causally the Mathematics Performance achievement of students in Saudi
Arabia. Particularly, the HLM technique was applied to the TIMSS 2011 data set
where five variables (Home educational resources (BSBGHER), Like learning
mathematics (BSBGSLM), Self-confidence in mathematics (BSBGSCM), Engaged in
mathematics learning (BSBGEML), Value learning math (BSBGSVM)) at the student
level and three variables (Emphasis on academic success (BCBGEAS), School
discipline and safety (BCBGDAS), and Instruction affected by mathematics
resources shortages (BCBGMRS) at the school level, were used to build the
hierarchical linear model so as to predict the status of mathematically 8th
grader. The final model suggested that all the student level factors are found
to be significant but their impact on achievement do not vary significantly
across the population of schools, i.e. BSBGHER, BSBGSLM, BSBGSCM, BSBGEML, and
BSBGSVM significantly predicted the status of Mathematics Performance
achievement of students in Saudi Arabia. At the school level, it is found that
BCBGDAS and BCBGMRS have a significant impact on performance. However, a scale
point increase in the availability of school resources for mathematics
decreased achievement by 4 points.


title: Intensity Image-based LiDAR Fiducial Marker System

abstract:
  The fiducial marker system for LiDAR is crucial for the robotic application
but it is still rare to date. In this paper, an Intensity Image-based LiDAR
Fiducial Marker (IILFM) system is developed. This system only requires an
unstructured point cloud with intensity as the input and it has no restriction
on marker placement and shape. A marker detection method that locates the
predefined 3D fiducials in the point cloud through the intensity image is
introduced. Then, an approach that utilizes the detected 3D fiducials to
estimate the LiDAR 6-DOF pose that describes the transmission from the world
coordinate system to the LiDAR coordinate system is developed. Moreover, all
these processes run in real-time (approx 40 Hz on Livox Mid-40 and approx 143
Hz on VLP-16). Qualitative and quantitative experiments are conducted to
demonstrate that the proposed system has similar convenience and accuracy as
the conventional visual fiducial marker system. The codes and results are
available at: https://github.com/York-SDCNLab/IILFM.


title:
Gaussian discriminators between $\Lambda$CDM and wCDM cosmologies using
  expansion data

abstract:
  The Gaussian linear model provides a unique way to obtain the posterior
probability distribution as well as the Bayesian evidence analytically.
Considering the expansion rate data, the Gaussian linear model can be applied
for $\Lambda$CDM, wCDM, and a non-flat $\Lambda$CDM. In this paper, we simulate
the expansion data with various precision and obtain the Bayesian evidence,
then it has been used to discriminate the models. The data uncertainty is in
the range $\sigma\in(0.5,10)\%$ and two different sampling rates have been
considered. Our results indicate that it is possible to discriminate $w=-1.02$
(or $w=-0.98$) model from the $\Lambda$CDM $(w=-1)$ with $\sigma=0.5\%$
uncertainty in expansion rate data. Finally, we perform a parameters inference
in both the MCMC and Gaussian linear model, using currently available expansion
rate data, and compare the results.


title: ADPCM with nonlinear prediction

abstract:
  Many speech coders are based on linear prediction coding (LPC), nevertheless
with LPC is not possible to model the nonlinearities present in the speech
signal. Because of this there is a growing interest for nonlinear techniques.
In this paper we discuss ADPCM schemes with a nonlinear predictor based on
neural nets, which yields an increase of 1-2.5dB in the SEGSNR over classical
methods. This paper will discuss the block-adaptive and sample-adaptive
predictions.


title: Speech segmentation using multilevel hybrid filters

abstract:
  A novel approach for speech segmentation is proposed, based on Multilevel
Hybrid (mean/min) Filters (MHF) with the following features: An accurate
transition location. Good performance in noisy environments (gaussian and
impulsive noise). The proposed method is based on spectral changes, with the
goal of segmenting the voice into homogeneous acoustic segments. This algorithm
is being used for phoneticallysegmented speech coder, with successful results.


title:
An Effective Graph Learning based Approach for Temporal Link Prediction:
  The First Place of WSDM Cup 2022

abstract:
  Temporal link prediction, as one of the most crucial work in temporal graphs,
has attracted lots of attention from the research area. The WSDM Cup 2022 seeks
for solutions that predict the existence probabilities of edges within time
spans over temporal graph. This paper introduces the solution of AntGraph,
which wins the 1st place in the competition. We first analysis the theoretical
upper-bound of the performance by removing temporal information, which implies
that only structure and attribute information on the graph could achieve great
performance. Based on this hypothesis, then we introduce several well-designed
features. Finally, experiments conducted on the competition datasets show the
superiority of our proposal, which achieved AUC score of 0.666 on dataset A and
0.902 on dataset B, the ablation studies also prove the efficiency of each
feature. Code is publicly available at
https://github.com/im0qianqian/WSDM2022TGP-AntGraph.


title:
Socially Aware Robot Crowd Navigation with Interaction Graphs and Human
  Trajectory Prediction

abstract:
  We study the problem of safe and socially aware robot navigation in dense and
interactive human crowds. Previous works use simplified methods to model the
personal spaces of pedestrians and ignore the social compliance of the robot
behaviors. In this paper, we provide a more accurate representation of personal
zones of walking pedestrians with their future trajectories. The predicted
personal zones are incorporated into a reinforcement learning framework to
prevent the robot from intruding into the personal zones. To learn socially
aware navigation policies, we propose a novel recurrent graph neural network
with attention mechanisms to capture the interactions among agents through
space and time. We demonstrate that our method enables the robot to achieve
good navigation performance and non-invasiveness in challenging crowd
navigation scenarios. We successfully transfer the policy learned in the
simulator to a real-world TurtleBot 2i.


title: Interpolation Polynomials and Linear Algebra

abstract:
  We reconsider the theory of Lagrange interpolation polynomials with multiple
interpolation points and apply it to linear algebra. For instance, $A$ be a
linear operator satisfying a degree $n$ polynomial equation $P(A)=0$. One can
see that the evaluation of a meromorphic function $F$ at $A$ is equal to
$Q(A)$, where $Q$ is the degree $<n$ interpolation polynomial of $F$ with the
the set of interpolation points equal to the set of roots of the polynomial
$P$. In particular, for $A$ an $n \times n$ matrix, there is a common belief
that for computing $F(A)$ one has to reduce $A$ to its Jordan form. Let $P$ be
the characteristic polynomial of $A$. Then by the Cayley-Hamilton theorem,
$P(A)=0$. And thus the matrix $F(A)$ can be found without reducing $A$ to its
Jordan form. Computation of the Jordan form for $A$ involves many extra
computations. In the paper we show that it is not needed. One application is to
compute the matrix exponential for a matrix with repeated eigenvalues, thereby
solving arbitrary order linear differential equations with constant
coefficients.


title: Mobile device users' susceptibility to phishing attacks

abstract:
  The mobile device is one of the fasted growing technologies that is widely
used in a diversifying sector. Mobile devices are used for everyday life, such
as personal information exchange - chatting, email, shopping, and mobile
banking, contributing to information security threats. Users' behavior can
influence information security threats. More research is needed to understand
users' threat avoidance behavior and motivation. Using Technology threat
avoidance theory (TTAT), this study assessed factors that influenced mobile
device users' threat avoidance motivations and behaviors as it relates to
phishing attacks. From the data collected from 137 mobile device users using a
questionnaire, the findings indicate that (1) mobile device users' perceived
susceptibility and severity of phishing attacks have a significant correlation
with a users' perception of the threat; (2) mobile device users' motivation to
avoid a threat is correlated to a users' behavior in avoiding threat; and (3) a
mobile device user's susceptibility to phishing attacks can be reduced by their
perception of the threat. These findings reveal that a user's perception of
threat increases if they perceive that the consequence of such threat to their
mobile devices will be severe, thereby increasing a user's motivation and
behavior to avoid phishing attack threats. This study is beneficial to mobile
device users in personal and organizational settings.


title:
LGT-Net: Indoor Panoramic Room Layout Estimation with Geometry-Aware
  Transformer Network

abstract:
  3D room layout estimation by a single panorama using deep neural networks has
made great progress. However, previous approaches can not obtain efficient
geometry awareness of room layout with the only latitude of boundaries or
horizon-depth. We present that using horizon-depth along with room height can
obtain omnidirectional-geometry awareness of room layout in both horizontal and
vertical directions. In addition, we propose a planar-geometry aware loss
function with normals and gradients of normals to supervise the planeness of
walls and turning of corners. We propose an efficient network, LGT-Net, for
room layout estimation, which contains a novel Transformer architecture called
SWG-Transformer to model geometry relations. SWG-Transformer consists of
(Shifted) Window Blocks and Global Blocks to combine the local and global
geometry relations. Moreover, we design a novel relative position embedding of
Transformer to enhance the spatial identification ability for the panorama.
Experiments show that the proposed LGT-Net achieves better performance than
current state-of-the-arts (SOTA) on benchmark datasets.


title:
What Makes Transfer Learning Work For Medical Images: Feature Reuse &
  Other Factors

abstract:
  Transfer learning is a standard technique to transfer knowledge from one
domain to another. For applications in medical imaging, transfer from ImageNet
has become the de-facto approach, despite differences in the tasks and image
characteristics between the domains. However, it is unclear what factors
determine whether - and to what extent - transfer learning to the medical
domain is useful. The long-standing assumption that features from the source
domain get reused has recently been called into question. Through a series of
experiments on several medical image benchmark datasets, we explore the
relationship between transfer learning, data size, the capacity and inductive
bias of the model, as well as the distance between the source and target
domain. Our findings suggest that transfer learning is beneficial in most
cases, and we characterize the important role feature reuse plays in its
success.


title:
Improving Non-native Word-level Pronunciation Scoring with Phone-level
  Mixup Data Augmentation and Multi-source Information

abstract:
  Deep learning-based pronunciation scoring models highly rely on the
availability of the annotated non-native data, which is costly and has
scalability issues. To deal with the data scarcity problem, data augmentation
is commonly used for model pretraining. In this paper, we propose a phone-level
mixup, a simple yet effective data augmentation method, to improve the
performance of word-level pronunciation scoring. Specifically, given a phoneme
sequence from lexicon, the artificial augmented word sample can be generated by
randomly sampling from the corresponding phone-level features in training data,
while the word score is the average of their GOP scores. Benefit from the
arbitrary phone-level combination, the mixup is able to generate any word with
various pronunciation scores. Moreover, we utilize multi-source information
(e.g., MFCC and deep features) to further improve the scoring system
performance. The experiments conducted on the Speechocean762 show that the
proposed system outperforms the baseline by adding the mixup data for
pretraining, with Pearson correlation coefficients (PCC) increasing from 0.567
to 0.61. The results also indicate that proposed method achieves similar
performance by using 1/10 unlabeled data of baseline. In addition, the
experimental results also demonstrate the efficiency of our proposed
multi-source approach.