title:
On Equivariant Gromov--Witten Invariants of Resolved Conifold with
  Diagonal and Anti-Diagonal Actions

abstract:
  We propose two conjectural relationships between the equivariant
Gromov-Witten invariants of the resolved conifold under diagonal and
anti-diagonal actions and the Gromov-Witten invariants of $\mathbb{P}^1$, and
verify their validity in genus zero approximation. We also provide evidences to
support the validity of these relationships in genus one and genus two.


title: Automatic detection of low surface brightness galaxies from SDSS images

abstract:
  Low surface brightness (LSB) galaxies are galaxies with central surface
brightness fainter than the night sky. Due to the faint nature of LSB galaxies
and the comparable sky background, it is difficult to search LSB galaxies
automatically and efficiently from large sky survey. In this study, we
established the Low Surface Brightness Galaxies Auto Detect model (LSBG-AD),
which is a data-driven model for end-to-end detection of LSB galaxies from
Sloan Digital Sky Survey (SDSS) images. Object detection techniques based on
deep learning are applied to the SDSS field images to identify LSB galaxies and
estimate their coordinates at the same time. Applying LSBG-AD to 1120 SDSS
images, we detected 1197 LSB galaxy candidates, of which 1081 samples are
already known and 116 samples are newly found candidates. The B-band central
surface brightness of the candidates searched by the model ranges from 22 mag
arcsec $^ {- 2} $ to 24 mag arcsec $^ {- 2} $, quite consistent with the
surface brightness distribution of the standard sample. 96.46\% of LSB galaxy
candidates have an axis ratio ($b/a$) greater than 0.3, and 92.04\% of them
have $fracDev\_r$\textless 0.4, which is also consistent with the standard
sample. The results show that the LSBG-AD model learns the features of LSB
galaxies of the training samples well, and can be used to search LSB galaxies
without using photometric parameters. Next, this method will be used to develop
efficient algorithms to detect LSB galaxies from massive images of the next
generation observatories.


title:
Effect of external magnetic field and dust grains on the properties of
  Ion Acoustic Waves

abstract:
  An experimental study to investigate the effect of an external magnetic field
on the propagation of ion-acoustic waves (IAWs) has been carried out in
hydrogen plasma containing two-temperature electrons and dust grains. A
low-pressure hot cathode discharge method is opted for plasma production. The
desired two electron groups with distinct temperatures are achieved by
inserting two magnetic cages with a cusp-shaped magnetic field of different
surface field strengths in the same chamber. The dust grains are dropped into
the plasma with the help of a dust dropper, which gain negative charges by
interacting with the plasma. The IAWs are excited with the help of a mesh-grid
inserted into the plasma. A planar Langmuir probe is used as a detector to
detect the IAWs. The time of flight technique has been applied to measure the
phase velocity of the IAWs. The results suggest that in the presence of a
magnetic field, the phase velocity of IAWs increases, whereas introducing the
dust particles leads to the lower phase velocity. The magnetic field is
believed to have a significant effect on the wave damping. This study will aid
in utilising IAWs as a diagnostic tool to estimate plasma parameters in the
presence of an external magnetic field. Moreover, the study might be useful for
estimating the relative ion concentrations in a two positive ion species
plasma, as well as the relative concentration of the negative ions in the
presence of an external magnetic field.


title: The Wiener criterion for nonlocal Dirichlet problems

abstract:
  We study the boundary behavior of solutions to the Dirichlet problems for
integro-differential operators with order of differentiability $s \in (0, 1)$
and summability $p>1$. We establish a nonlocal counterpart of the Wiener
criterion, which characterizes a regular boundary point in terms of the
nonlocal nonlinear potential theory.


title: Budget-Constrained Auctions with Unassured Priors

abstract:
  In today's online advertising markets, it is common for an advertiser to set
a long-period budget. Correspondingly, advertising platforms adopt budget
control methods to ensure that any advertiser's payment is within her budget.
Most budget control methods rely on value distributions of advertisers.
However, due to the complex environment advertisers stand in and privacy
issues, the platform hardly learns their true priors. Therefore, it is
essential to understand how budget control auction mechanisms perform under
unassured priors.
  This paper gives a two-fold answer. First, we propose a bid-discount method
barely studied in the literature. We show that such a method exhibits desirable
properties in revenue-maximizing and computation when fitting into first-price
auction. Second, we compare this mechanism with another four in the prior
manipulation model, where an advertiser can arbitrarily report a value
distribution to the platform. These four mechanisms include the optimal
mechanism satisfying budget-constrained IC, first-price/second-price mechanisms
with the widely-studied pacing method, and an application of bid-discount in
second-price mechanism. We consider three settings under the model, depending
on whether the reported priors are fixed and advertisers are symmetric or not.
We show that under all three cases, the bid-discount first-price auction we
introduce dominates the other four mechanisms concerning the platform's
revenue. For the advertisers' side, we show a surprising strategic-equivalence
result between this mechanism and the optimal auction. Extensive revenue
dominance and strategic relationships among these mechanisms are also revealed.
Based on these findings, we provide a thorough understanding of prior
dependency in repeated auctions with budgets. The bid-discount first-price
auction itself may also be of further independent research interest.


title:
Structural Phase Transition and Possible Valence Instability of Ce$-4f$
  Electron Induced by Pressure in CeCoSi

abstract:
  X-ray powder diffraction and electrical resistivity measurements were
performed on the tetragonal compound CeCoSi under pressure to elucidate the
phase boundary of the pressure-induced structural transition and the change in
the 4$f$ electronic state. The temperature-pressure phase diagram has been
determined from the shift of the Bragg peaks and from the anomaly in the
resistivity. The critical pressure, $P_{\rm s}$ $\sim$ 4.9 GPa at 300 K,
decreases to $P_{\rm s}$ $\sim$ 3.6 GPa at 10 K. The decrease of $P_{\rm s}$ is
due not only to the decrease in volume of the unit cell but also to an
anisotropic shrinkage by cooling. When crossing the boundary to the
high-pressure phase, the resistivity shows a significant drop to exhibit a
metallic temperature dependence. The results of this study strongly suggest
that the structural phase transition can be ascribed to valence instability of
Ce-$4f$ electron.


title:
Online Primal-Dual Algorithms For Stochastic Resource Allocation
  Problems

abstract:
  This paper studies the online stochastic resource allocation problem (RAP)
with chance constraints and conditional expectation constraints. The online RAP
is an integer linear programming problem where resource consumption
coefficients are revealed column by column along with the corresponding revenue
coefficients. When a column is revealed, the corresponding decision variables
are determined instantaneously without future information. In online
applications, the resource consumption coefficients are often obtained by
prediction. An application for such scenario rises from the online order
fulfilment task. When the timeliness constraints are considered, the
coefficients are generated by the prediction for the transportation time from
origin to destination. To model their uncertainties, we take the chance
constraints and conditional expectation constraints into the consideration.
Assuming that the uncertain variables have known Gaussian distributions, the
stochastic RAP can be transformed into a deterministic but nonlinear problem
with integer second-order cone constraints. Next, we linearize this nonlinear
problem and theoretically analyze the performance of vanilla online primal-dual
algorithm for solving the linearized stochastic RAP. Under mild technical
assumptions, the optimality gap and constraint violation are both on the order
of $\sqrt{n}$. Then, to further improve the performance of the algorithm,
several modified online primal-dual algorithms with heuristic corrections are
proposed. Finally, extensive numerical experiments demonstrate the
applicability and effectiveness of our methods.


title:
Non-diagonal contributions to $Z\gamma V^\ast$ vertex and bounds on
  $Z\overline{t}q$ couplings

abstract:
  The non-diagonal contributions to the trilinear gauge bosons couplings
$Z\gamma V^\ast$ ($V=Z$, $\gamma$) at the one-loop level are calculated through
complex flavor changing neutral currents mediated by the $Z$ boson. We find
that it is not possible to induce non-diagonal contributions to the vertex
$Z\gamma\gamma^\ast$ using these couplings. On the other hand, for the vertex
$Z\gamma Z^\ast$ only the $CP$-conserving form factor $h_3^Z$ is generated.
Constrains on the $Z\overline{t}c$ couplings are obtained from the most recent
limits on the branching ratio $t\rightarrow Zq$ decays. Our new bounds are
$|g_{\text{V}}^{tu}|\text{,}|g_{\text{A}}^{tu}|\leqslant 0.011$ and
$|g_{\text{V}}^{tc}|\text{,}|g_{\text{A}}^{tc}|\leqslant 0.016$ in terms of the
vector and axial couplings, whereas in terms of the coefficients of the
effective field theory framework these bounds are $|C^{(1+3)}_{\phi
\mu}|\text{,}|C^{-(1+3)}_{\phi q}|\leqslant 0.2$ and $|C^{(2+3)}_{\phi
\mu}|\text{,}|C^{-(2+3)}_{\phi q}|\leqslant 0.28$. In both cases, our new
limits are tighter than those reported in the literature. It is found that the
relevant contributions for the real part of the $CP$-conserving form factor
arise from the quarks up and down, whereas the imaginary part only receives
non-vanishing contributions from quarks up. Our results are in general of order
$10^{-6}-10^{-7}$, but they could be one order of magnitude bigger for the
upper limits of the $Z\overline{t}c$ coupling.


title:
Assessment of anisotropy and revisiting Kolmogorov constant in
  particle-laden turbulent channel flows

abstract:
  A large number of models which address the dynamics of particle-laden
turbulent flows have been developed based on the assumption of local isotropy
and use the Kolmogorov constant that correlates the spectral distribution of
turbulent kinetic energy with the turbulent dissipation rate. Many research
works have been performed to estimate the Kolmogorov constant for a wide range
of single-phase turbulent flows like homogeneous isotropic turbulence,
turbulent boundary layers, etc. In earlier work, Sreenivasan (1995) has
consolidated and analyzed a large number of experimental data and concluded
that the Kolmogorov constant is independent of the Reynolds number and may be
considered a universal constant. In the present work, we assess the extent of
local anisotropy in the inertial and dissipation range of particle-laden
channel flows. We estimate the Kolmogorov constant using a second-order
velocity structure function and compensated spectra. Our study reveals that the
Kolmogorov constant decreases with an increase in the particle volume fraction.
The present analysis of variation of the Kolmogorov constant with particle
volume loading will provide insight to develop advanced models for two-phase
turbulent flows.


title:
Necessary and Sufficient Condition for zeros of Derivative of
  Meromorphic and Entire Functions

abstract:
  The main result of this paper shows a totally new necessary and sufficient
condition to determine both real and complex zeros of derivative of all entire
and meromorphic functions of one complex variable in the extended complex
plane. By using the theorem, we reprove some results about zeros of derivative
of Xi function,Gamma function and digamma function in a new way.


title:
How Does Pre-trained Wav2Vec2.0 Perform on Domain Shifted ASR? An
  Extensive Benchmark on Air Traffic Control Communications

abstract:
  Recent work on self-supervised pre-training focus on leveraging large-scale
unlabeled speech data to build robust end-to-end (E2E) acoustic models (AM)
that can be later fine-tuned on downstream tasks e.g., automatic speech
recognition (ASR). Yet, few works investigated the impact on performance when
the data substantially differs between the pre-training and downstream
fine-tuning phases (i.e., domain shift). We target this scenario by analyzing
the robustness of Wav2Vec2.0 and XLS-R models on downstream ASR for a
completely unseen domain, i.e., air traffic control (ATC) communications. We
benchmark the proposed models on four challenging ATC test sets
(signal-to-noise ratio varies between 5 to 20 dB). Relative word error rate
(WER) reduction between 20% to 40% are obtained in comparison to hybrid-based
state-of-the-art ASR baselines by fine-tuning E2E acoustic models with a small
fraction of labeled data. We also study the impact of fine-tuning data size on
WERs, going from 5 minutes (few-shot) to 15 hours.


title:
Effectiveness of text to speech pseudo labels for forced alignment and
  cross lingual pretrained models for low resource speech recognition

abstract:
  In the recent years end to end (E2E) automatic speech recognition (ASR)
systems have achieved promising results given sufficient resources. Even for
languages where not a lot of labelled data is available, state of the art E2E
ASR systems can be developed by pretraining on huge amounts of high resource
languages and finetune on low resource languages. For a lot of low resource
languages the current approaches are still challenging, since in many cases
labelled data is not available in open domain. In this paper we present an
approach to create labelled data for Maithili, Bhojpuri and Dogri by utilising
pseudo labels from text to speech for forced alignment. The created data was
inspected for quality and then further used to train a transformer based
wav2vec 2.0 ASR model. All data and models are available in open domain.


title: Note About D-Branes in Carrollian Background

abstract:
  This paper is devoted to the construction of stable and unstable Dp-branes in
generalized Carroll space-times. We also study tachyon solutions on the
world-volume of unstable Dp-brane in Carrollian background and discuss their
physical interpretations.


title:
indic-punct: An automatic punctuation restoration and inverse text
  normalization framework for Indic languages

abstract:
  Automatic Speech Recognition (ASR) generates text which is most of the times
devoid of any punctuation. Absence of punctuation is text can affect
readability. Also, down stream NLP tasks such as sentiment analysis, machine
translation, greatly benefit by having punctuation and sentence boundary
information. We present an approach for automatic punctuation of text using a
pretrained IndicBERT model. Inverse text normalization is done by hand writing
weighted finite state transducer (WFST) grammars. We have developed this tool
for 11 Indic languages namely Hindi, Tamil, Telugu, Kannada, Gujarati, Marathi,
Odia, Bengali, Assamese, Malayalam and Punjabi. All code and data is publicly.
available


title:
Remote State Estimation of Multiple Systems over Semi-Markov Wireless
  Fading Channels

abstract:
  This work studies remote state estimation of multiple linear time-invariant
systems over shared wireless time-varying communication channels. We model the
channel states by a semi-Markov process which captures both the random holding
period of each channel state and the state transitions. The model is
sufficiently general to be used in both fast and slow fading scenarios. We
derive necessary and sufficient stability conditions of the
multi-sensor-multi-channel system in terms of the system parameters. We further
investigate how the delay of the channel state information availability and the
holding period of channel states affect the stability. In particular, we show
that, from a system stability perspective, fast fading channels may be
preferable to slow fading ones.


title:
Pasta properties of the neutron star within effective relativistic
  mean-field model

abstract:
  We study the properties of pasta structures and their influence on the
neutron star observables employing the effective relativistic mean-field theory
(E-RMF). The compressible liquid drop model is used to incorporate the finite
size effects, considering the possibility of non-spherical structures in the
inner crust. The unified equation of state are constructed for several E-RMF
parameters to study various properties such as pasta mass and thickness in the
neutron star's crust. The majority of the pasta properties are sensitive to the
symmetry energy in the subsaturation density region. Using the results from
Monte Carlo simulations, we estimate the shear modulus of the crust in context
of quasiperiodic oscillations from soft gamma-ray repeaters and calculate the
frequency of fundamental torsional oscillation mode in the inner crust. Global
properties of the neutron star such as mass-radius profile, the moment of
inertia, crustal mass, crustal thickness and fractional crustal moment of
inertia are worked out. The results are consistent with various observational
and theoretical constraints.


title: Rethinking Portrait Matting with Privacy Preserving

abstract:
  Recently, there has been an increasing concern about the privacy issue raised
by using personally identifiable information in machine learning. However,
previous portrait matting methods were all based on identifiable portrait
images. To fill the gap, we present P3M-10k in this paper, which is the first
large-scale anonymized benchmark for Privacy-Preserving Portrait Matting (P3M).
P3M-10k consists of 10,000 high-resolution face-blurred portrait images along
with high-quality alpha mattes. We systematically evaluate both trimap-free and
trimap-based matting methods on P3M-10k and find that existing matting methods
show different generalization abilities under the privacy preserving training
setting, i.e., training only on face-blurred images while testing on arbitrary
images. Based on the gained insights, we propose a unified matting model named
P3M-Net consisting of three carefully designed integration modules that can
perform privacy-insensitive semantic perception and detail-reserved matting
simultaneously. We further design multiple variants of P3M-Net with different
CNN and transformer backbones and identify the difference in their
generalization abilities. To further mitigate this issue, we devise a simple
yet effective Copy and Paste strategy (P3M-CP) that can borrow facial
information from public celebrity images without privacy concerns and direct
the network to reacquire the face context at both data and feature levels.
P3M-CP only brings a few additional computations during training, while
enabling the matting model to process both face-blurred and normal images
without extra effort during inference. Extensive experiments on P3M-10k
demonstrate the superiority of P3M-Net over state-of-the-art methods and the
effectiveness of P3M-CP in improving the generalization ability of P3M-Net,
implying a great significance of P3M for future research and real-world
applications.


title:
$S^1$-bounded Fourier multipliers on $H^1({\mathbb R})$ and functional
  calculus for semigroups

abstract:
  Let $T\colon H^1({\mathbb R})\to H^1({\mathbb R})$ be a bounded Fourier
multiplier on the analytic Hardy space $H^1({\mathbb R})\subset L^1({\mathbb
R})$ and let $m\in L^\infty({\mathbb R}_+)$ be its symbol, that is,
$\widehat{T(h)}=m\widehat{h}$ for all $h\in H^1({\mathbb R})$.Let $S^1$ be the
Banach space of all trace class operators on $\ell^2$. We show that $T$ admits
a bounded tensor extension $T\overline{\otimes} I_{S_1}\colon H^1({\mathbb
R};S^1) \to H^1({\mathbb R};S^1)$ if and only if there exist a Hilbert space
$\mathcal H$ and two functions $\alpha, \beta \in L^\infty({\mathbb
R}_+;{\mathcal H})$ such that $m(s+t) =
\langle\alpha(t),\beta(s)\rangle_{\mathcal H}$ for almost every
$(s,t)\in{\mathbb R}_+^2$. Such Fourier multipliers arecalled $S^1$-bounded and
we let ${\mathcal M}_{S^1}(H^1({\mathbb R}))$ denote the Banach space of all
$S^1$-bounded Fourier multipliers. Next we apply this result to functional
calculus estimates, in two steps. First we introduce a new Banach algebra
${\mathcal A}_{0,S^1}({\mathbb C}_+)$ of bounded analytic functions on
${\mathbb C}_+ =\bigl\{z\in{\mathbb C}\, :\, {\rm Re}(z)>0\bigr\}$ and show
that its dual space coincides with ${\mathcal M}_{S^1}(H^1({\mathbb R}))$.
Second, given any bounded $C_0$-semigroup $(T_t)_{t\geq 0}$ on Hilbert space,
and any $b\in L^1({\mathbb R}_+)$, we establish an estimate
$\bigl\Vert\int_0^\infty b(t) T_t\, dt\bigr\Vert\lesssim \Vert
L_b\Vert_{{\mathcal A}_{0,S^1}({\mathbb R})}$, where $L_b$ denotes the Laplace
transform of $b$. This improves previous functional calculus estimates recently
obtained by the first two authors.


title:
The minimal dimension of a sphere with an equivariant embedding of the
  bouquet of $g$ circles is $2g-1$

abstract:
  To embed the bouquet of $g$ circles $B_g$ into the $n$-sphere $S^n$ so that
its full symmetry group action extends to an orthogonal actions on $S^n$, the
minimal $n$ is $2g-1$. This answers a question raised by B. Zimmermann.


title:
The Dukhin number as a scaling parameter for selectivity in the
  infinitely long nanopore limit: extension to multivalent electrolytes

abstract:
  Scaling of the behavior of a nanodevice means that the device function
(selectivity, in this work) is a unique function of a scaling parameter that is
an appropriate combination of the device parameters. Although nanopores
facilitate the transport of ions through a membrane of finite length if the
pore is long compared to the pore radius, we deal with an important limiting
case, the infinitely long nanopore (nanotube). While in our previous study
(Sarkadi et al., J. Chem. Phys. 154 (2021) 154704.) we showed that the Dukhin
number is an appropriate scaling parameter in the nanotube limit for 1:1
electrolytes, in this work we obtain the Dukhin number from first principles on
the basis of the Poisson-Boltzmann (PB) theory and generalize it to
electrolytes containing multivalent ions as well. We show that grand canonical
Monte Carlo simulations for charged hard spheres in an implicit solvent give
results that are similar to those obtained from the PB theory with deviations
that are the consequences of ionic correlations (including finite size of ions)
beyond the mean-field level of the PB theory. Such a deviation occurs when
charge inversion is present, in 2:2 and 3:1 electrolytes, for example.

categories: cond-mat.soft cond-mat.mes-hall cond-mat.stat-mech physics.chem-ph


title: Point Scene Understanding via Disentangled Instance Mesh Reconstruction

abstract:
  Semantic scene reconstruction from point cloud is an essential and
challenging task for 3D scene understanding. This task requires not only to
recognize each instance in the scene, but also to recover their geometries
based on the partial observed point cloud. Existing methods usually attempt to
directly predict occupancy values of the complete object based on incomplete
point cloud proposals from a detection-based backbone. However, this framework
always fails to reconstruct high fidelity mesh due to the obstruction of
various detected false positive object proposals and the ambiguity of
incomplete point observations for learning occupancy values of complete
objects. To circumvent the hurdle, we propose a Disentangled Instance Mesh
Reconstruction (DIMR) framework for effective point scene understanding. A
segmentation-based backbone is applied to reduce false positive object
proposals, which further benefits our exploration on the relationship between
recognition and reconstruction. Based on the accurate proposals, we leverage a
mesh-aware latent code space to disentangle the processes of shape completion
and mesh generation, relieving the ambiguity caused by the incomplete point
observations. Furthermore, with access to the CAD model pool at test time, our
model can also be used to improve the reconstruction quality by performing mesh
retrieval without extra training. We thoroughly evaluate the reconstructed mesh
quality with multiple metrics, and demonstrate the superiority of our method on
the challenging ScanNet dataset.


title:
Massive Young Stellar Objects in the Galactic Center. II. Seeing Through
  the Ice-rich Envelopes

abstract:
  To study the demographics of interstellar ices in the Central Molecular Zone
(CMZ) of the Milky Way, we obtain near-infrared spectra of $109$ red point
sources using NASA IRTF/SpeX at Maunakea. We select the sample from near- and
mid-infrared photometry, including $12$ objects in the previous paper of this
series, to ensure that these sources trace a large amount of absorption through
clouds in each line of sight. We find that most of the sample ($100$ objects)
show CO band-head absorption at $2.3\ \mu$m, tagging them as red (super-)
giants. Despite the photospheric signature, however, a fraction of the sample
with $L$-band spectra ($9/82=0.11$) exhibit large H$_2$O ice column densities
($N > 2\times10^{18}\ {\rm cm}^{-2}$), and six of them also reveal CH$_3$OH ice
absorption. As one of such objects is identified as a young stellar object
(YSO) in our previous work, these ice-rich sight lines are likely associated
with background stars in projection to an extended envelope of a YSO or a dense
cloud core. The low frequency of such objects in the early stage of stellar
evolution implies a low star-formation rate ($<0.02\ M_\odot$ yr$^{-1}$),
reinforcing the previous claim on the suppressed star-formation activity in the
CMZ. Our data also indicate that the strong "shoulder" CO$_2$ ice absorption at
$15.4\ \mu$m observed in YSO candidates in the previous paper arises from
CH$_3$OH-rich ice grains having a large CO$_2$ concentration [$N {\rm (CO_2)} /
N {\rm (CH_3OH)} \approx 1/3$].


title:
A Comparative Study on Speaker-attributed Automatic Speech Recognition
  in Multi-party Meetings

abstract:
  In this paper, we conduct a comparative study on speaker-attributed automatic
speech recognition (SA-ASR) in the multi-party meeting scenario, a topic with
increasing attention in meeting rich transcription. Specifically, three
approaches are evaluated in this study. The first approach, FD-SOT, consists of
a frame-level diarization model to identify speakers and a multi-talker ASR to
recognize utterances. The speaker-attributed transcriptions are obtained by
aligning the diarization results and recognized hypotheses. However, such an
alignment strategy may suffer from erroneous timestamps due to the modular
independence, severely hindering the model performance. Therefore, we propose
the second approach, WD-SOT, to address alignment errors by introducing a
word-level diarization model, which can get rid of such timestamp alignment
dependency. To further mitigate the alignment issues, we propose the third
approach, TS-ASR, which trains a target-speaker separation module and an ASR
module jointly. By comparing various strategies for each SA-ASR approach,
experimental results on a real meeting scenario corpus, AliMeeting, reveal that
the WD-SOT approach achieves 10.7% relative reduction on averaged
speaker-dependent character error rate (SD-CER), compared with the FD-SOT
approach. In addition, the TS-ASR approach also outperforms the FD-SOT approach
and brings 16.5% relative average SD-CER reduction.


title:
On Estimating the Autoregressive Coefficients of Time-Varying Fading
  Channels

abstract:
  As several previous works have pointed out, the evolution of the wireless
channels in multiple input multiple output systems can be advantageously
modeled as an autoregressive process. Therefore, estimating the coefficients,
and, in particular, the state transition matrix of this autoregressive process
is a key to accurate channel estimation, tracking, and prediction in fast
fading environments. In this paper we assume a time varying spatially
uncorrelated channel, which is approximately the case with proper antenna
spacing at the base station in rich scattering environments. We propose a
method for autoregressive parameter estimation for the single input multiple
output (SIMO) channel. We show an almost sure convergence of the estimated
coefficients to the true autoregressive coefficients in large dimensions. We
apply the proposed method to SIMO channel tracking.


title:
Exponential convergence of a dissipative quantum system towards
  finite-energy grid states of an oscillator

abstract:
  Based on the stabilizer formalism underlying Quantum Error Correction (QEC),
the design of an original Lindblad master equation for the density operator of
a quantum harmonic oscillator is proposed. This Lindblad dynamics stabilizes
exactly the finite-energy grid states introduced in 2001 by Gottesman, Kitaev
and Preskill for quantum computation. Stabilization results from an exponential
Lyapunov function with an explicit lower-bound on the convergence rate.
Numerical simulations indicate the potential interest of such autonomous QEC in
presence of non-negligible photon-losses.


title: Discrete Ollivier-Ricci curvature

abstract:
  We analyze both continuous and discrete-time Ollivier-Ricci curvatures of
locally-finite weighted graphs $\G$ equipped with a given distance "$\dist$"
(w.r.t. which $\G$ is metrically complete) and for general random walks. We
show the continuous-time Ollivier-Ricci curvature is well-defined for a large
class of Markovian and non-Markovian random walks and provide a criterion for
existence of continuous-time Ollivier-Ricci curvature; the said results
generalize the previous rather limited constructions in the literature. In
addition, important properties of both discrete-time and continuous-time
Ollivier-Ricci curvatures are obtained including -- to name a few -- Lipschitz
continuity, concavity properties, piece-wise regularity (piece-wise linearity
in the case of linear walks) for the discrete-time Ollivier-Ricci as well as
Lipschitz continuity and limit-free formulation for the continuous-time
Ollivier-Ricci. these properties were previously known only for very specific
distances and very specific random walks. As an application of Lipschitz
continuity, we obtain existence and uniqueness of generalized continuous-time
Ollivier-Ricci curvature flows. Along the way, we obtain -- by optimizing
McMullen's upper bounds -- a sharp upper bound estimate on the number of
vertices of a convex polytope in terms of number of its facets and the ambient
dimension, which might be of independent interest in convex geometry. The said
upper bound allows us to bound the number of polynomial pieces of the
discrete-time Ollivier-Ricci curvature as a function of time in the
time-polynomial random walk. The limit-free formulation we establish allows us
to define an operator theoretic Ollivier-Ricci curvature which is a non-linear
concave functional on suitable operator spaces.


title:
NeuFA: Neural Network Based End-to-End Forced Alignment with
  Bidirectional Attention Mechanism

abstract:
  Although deep learning and end-to-end models have been widely used and shown
superiority in automatic speech recognition (ASR) and text-to-speech (TTS)
synthesis, state-of-the-art forced alignment (FA) models are still based on
hidden Markov model (HMM). HMM has limited view of contextual information and
is developed with long pipelines, leading to error accumulation and
unsatisfactory performance. Inspired by the capability of attention mechanism
in capturing long term contextual information and learning alignments in ASR
and TTS, we propose a neural network based end-to-end forced aligner called
NeuFA, in which a novel bidirectional attention mechanism plays an essential
role. NeuFA integrates the alignment learning of both ASR and TTS tasks in a
unified framework by learning bidirectional alignment information from a shared
attention matrix in the proposed bidirectional attention mechanism. Alignments
are extracted from the learnt attention weights and optimized by the ASR, TTS
and FA tasks in a multi-task learning manner. Experimental results demonstrate
the effectiveness of our proposed model, with mean absolute error on test set
drops from 25.8 ms to 23.7 ms at word level, and from 17.0 ms to 15.7 ms at
phoneme level compared with state-of-the-art HMM based model.


title:
Graphop Mean-Field Limits and Synchronization for the Stochastic
  Kuramoto Model

abstract:
  Models of coupled oscillator networks play an important role in describing
collective synchronization dynamics in biological and technological systems.
The Kuramoto model describes oscillator's phase evolution and explains the
transition from incoherent to coherent oscillations under simplifying
assumptions including all-to-all coupling with uniform strength. Real world
networks, however, often display heterogeneous connectivity and coupling
weights that influence the critical threshold for this transition. We ormulate
a general mean field theory (Vlasov-Focker Planck equation) for stochastic
Kuramoto-type phase oscillator models, valid for coupling graphs/networks with
heterogeneous connectivity and coupling strengths, by using graphop theory in
the mean field limit. Considering symmetric odd valued coupling functions, we
mathematically prove an exact formula for the critical threshold for the
incoherence-coherence transition. We numerically test the predicted threshold
using large finite-size representations of the network model. For a large class
of graph models we find that the numerical tests agree very well with the
predicted threshold obtained from mean field theory. However, the prediction is
more difficult in practice for graph structures that are sufficiently sparse.
Our findings open future research avenues towards a deeper understanding of
mean-field theories for heterogeneous systems.


title: Speaker Extraction with Co-Speech Gestures Cue

abstract:
  Speaker extraction seeks to extract the clean speech of a target speaker from
a multi-talker mixture speech. There have been studies to use a pre-recorded
speech sample or face image of the target speaker as the speaker cue. In human
communication, co-speech gestures that are naturally timed with speech also
contribute to speech perception. In this work, we explore the use of co-speech
gestures sequence, e.g. hand and body movements, as the speaker cue for speaker
extraction, which could be easily obtained from low-resolution video
recordings, thus more available than face recordings. We propose two networks
using the co-speech gestures cue to perform attentive listening on the target
speaker, one that implicitly fuses the co-speech gestures cue in the speaker
extraction process, the other performs speech separation first, followed by
explicitly using the co-speech gestures cue to associate a separated speech to
the target speaker. The experimental results show that the co-speech gestures
cue is informative in associating the target speaker, and the quality of the
extracted speech shows significant improvements over the unprocessed mixture
speech.


title:
Quasielastic Charged-Current Neutrino-Nucleus Scattering with
  Nonrelativistic Nuclear Energy Density Functionals

abstract:
  Charged-current neutrino-nucleus scattering is studied in the quasielastic
region with the KIDS (Korea-IBS-Daegu-SKKU) nuclear energy density functional.
We focus on the uncertainties stemming from the axial mass and the in-medium
effective mass of the nucleon. Comparing the result of theory to the
state-of-the-art data from MiniBooNE, T2K, and MINER$\nu$A, we constrain the
axial mass and the effective mass that are compatible with the data. We find
that the total cross section is insensitive to the effective mass, so the axial
mass could be determined independently of the uncertainty in the effective
mass. Differential cross sections at different kinematics are, on the other
hand, sensitive to the effective mass as well as the axial mass. Within the
uncertainty of the axial mass constrained from the total cross section,
dependence on the effective mass is examined. As a result we obtain the axial
mass and the effective mass that are consistent with the experimental data.


title: X-ray emission of massive stars and their winds

abstract:
  Most types of massive stars display X-ray emission that is affected by the
properties of their stellar winds. Single non-magnetic OB stars have an X-ray
luminosity that scales with their bolometric luminosity and their emission is
thought to arise from a distribution of wind-embedded shocks. The lack of
significant short-term stochastic variability indicates that the winds consist
of a large number of independent fragments. Detailed variability studies
unveiled a connection between the photosphere and the wind: well-studied O-type
stars exhibit a ~ 10% modulation of their emission on timescales consistent
with the rotation period, and a few early B-type pulsators display ~ 10%
modulations of their X-ray flux with the pulsation period. Unlike OB stars,
their evolved descendants (WR and LBV stars) lack a well-defined relation
between their X-ray and bolometric luminosities, and several subcategories of
objects remain undetected. These properties most likely stem from the combined
effects of wind optical depth and wind velocity. Magnetic OB stars display an
enhanced X-ray emission frequently modulated by the rotation of the star. These
properties are well explained by the magnetically confined wind shock model and
an oblique magnetic rotator configuration. Some massive binaries display
phase-dependent excess emission arising from the collision between the winds of
the binary components. Yet, the majority of the massive binaries do not show
such an emission, probably as a consequence of radiative cooling of the
shock-heated plasma. Finally, a growing subset of the Be stars, the so-called
gamma Cas stars, feature an unusually hard and strong thermal X-ray emission
that varies over a wide range of timescales. Several scenarios have been
proposed to explain these properties, but the origin of the phenomenon remains
currently one of the major unsolved puzzles in stellar X-ray astrophysics.


title:
A Hybrid Continuity Loss to Reduce Over-Suppression for Time-domain
  Target Speaker Extraction

abstract:
  Speaker extraction algorithm extracts the target speech from a mixture speech
containing interference speech and background noise. The extraction process
sometimes over-suppresses the extracted target speech, which not only creates
artifacts during listening but also harms the performance of downstream
automatic speech recognition algorithms. We propose a hybrid continuity loss
function for time-domain speaker extraction algorithms to settle the
over-suppression problem. On top of the waveform-level loss used for superior
signal quality, i.e., SI-SDR, we introduce a multi-resolution delta spectrum
loss in the frequency-domain, to ensure the continuity of an extracted speech
signal, thus alleviating the over-suppression. We examine the hybrid continuity
loss function using a time-domain audio-visual speaker extraction algorithm on
the YouTube LRS2-BBC dataset. Experimental results show that the proposed loss
function reduces the over-suppression and improves the word error rate of
speech recognition on both clean and noisy two-speakers mixtures, without
harming the reconstructed speech quality.


title:
Open Source MagicData-RAMC: A Rich Annotated Mandarin
  Conversational(RAMC) Speech Dataset

abstract:
  This paper introduces a high-quality rich annotated Mandarin conversational
(RAMC) speech dataset called MagicData-RAMC. The MagicData-RAMC corpus contains
180 hours of conversational speech data recorded from native speakers of
Mandarin Chinese over mobile phones with a sampling rate of 16 kHz. The dialogs
in MagicData-RAMC are classified into 15 diversified domains and tagged with
topic labels, ranging from science and technology to ordinary life. Accurate
transcription and precise speaker voice activity timestamps are manually
labeled for each sample. Speakers' detailed information is also provided. As a
Mandarin speech dataset designed for dialog scenarios with high quality and
rich annotations, MagicData-RAMC enriches the data diversity in the Mandarin
speech community and allows extensive research on a series of speech-related
tasks, including automatic speech recognition, speaker diarization, topic
detection, keyword search, text-to-speech, etc. We also conduct several
relevant tasks and provide experimental results to help evaluate the dataset.


title: Multi-access Coded Caching with Decentralized Prefetching

abstract:
  An extension of coded caching referred to as multi-access coded caching where
each user can access multiple caches and each cache can serve multiple users is
considered in this paper. Most of the literature in multi-access coded caching
focuses on cyclic wrap-around cache access where each user is allowed to access
an exclusive set of consecutive caches only. In this paper, a more general
framework of multi-access caching problem is considered in which each user is
allowed to randomly connect to a specific number of caches and multiple users
can access the same set of caches. For the proposed system model considering
decentralized prefetching, a new delivery scheme is proposed and an expression
for per user delivery rate is obtained. A lower bound on the delivery rate is
derived using techniques from index coding. The proposed scheme is shown to be
optimal among all the linear schemes under certain conditions. An improved
delivery rate and a lower bound for the decentralized multi-access coded
caching scheme with cyclic wrap-around cache access can be obtained as a
special case. By giving specific values to certain parameters, the results of
decentralized shared caching scheme and of conventional decentralized caching
scheme can be recovered.


title: Active Galactic Nuclei and their demography through cosmic time

abstract:
  Active Galactic Nuclei (AGN) are highly energetic astrophysical sources
powered by accretion onto supermassive black holes in galaxies, which present
unique observational signatures covering the full electromagnetic spectrum (and
more) over about twenty orders of magnitude in frequency. We first review the
main AGN properties and diversities and show that they can be explained by a
small number of parameters. We then discuss the so-called Unification Models
for non-jetted AGN, according to which these sources are believed to have the
same nuclear engine and circumnuclear matter, with the same geometry for the
obscuring structure. This simplified scenario, however, cannot explain all the
observed complexities, such as the presence of multiple absorbers on different
physical scales, including recent X-ray observations of circumnuclear matter.
Finally, we touch upon AGN evolution in the X-ray and $\gamma$-ray bands.


title:
Hierarchical Attention Network for Evaluating Therapist Empathy in
  Counseling Session

abstract:
  Counseling typically takes the form of spoken conversation between a
therapist and a client. The empathy level expressed by the therapist is
considered to be an essential quality factor of counseling outcome. This paper
proposes a hierarchical recurrent network combined with two-level attention
mechanisms to determine the therapist's empathy level solely from the acoustic
features of conversational speech in a counseling session. The experimental
results show that the proposed model can achieve an accuracy of 72.1% in
classifying the therapist's empathy level as being "high" or "low". It is found
that the speech from both the therapist and the client are contributing to
predicting the empathy level that is subjectively rated by an expert observer.
By analyzing speaker turns assigned with high attention weights, it is observed
that 2 to 6 consecutive turns should be considered together to provide useful
clues for detecting empathy, and the observer tends to take the whole session
into consideration when rating the therapist empathy, instead of relying on a
few specific speaker turns.


title:
Activity-induced Nonequilibrium Vaporization Leads to Reentrant Phase
  Separation

abstract:
  Active Brownian particles (ABPs) with pure repulsion is an ideal model to
understand the effect of nonequilibrium on collective behaviors. It has long
been established that activity can create effective attractions leading to
motility-induced phase separation (MIPS), whose role is similar to that of
(inverse) temperature in the simplest equilibrium system with attractive
inter-particle interactions. Here, our theoretical analysis based on a kinetic
theory of MIPS shows that a new type of activity-induced nonequilibrium
vaporization is able to hinder the formation of dense phase when activity is
large enough. Such nonequilibrium vaporization along with the activity-induced
effective attraction thus lead to a MIPS reentrance. Numerical simulations
verify such nonequilibrium effect induced solely by activity on phase behaviors
of ABPs, and further demonstrate the dependence of MIPS on activity and the
strength of inter-particle interaction predicted by our theoretical analysis.
Our findings highlight the unique role played by the nonequilibrium nature of
activity on phase behaviors of active systems, which may inspire deep insights
into the essential difference between equilibrium and nonequilibrium systems.


title: The effect of three matters on KSS bound

abstract:
  Our goal in this manuscript is to introduce black brane solutions in AdS
4-dimensional (4D) Einstein-Gauss-Bonnet-Yang-Mills theory in presence of
string cloud and quintessence. Shear viscosity to entropy density ratio is
computed via fluid-gravity duality, as a transport coefficient for this model.


title: Revisiting Document Image Dewarping by Grid Regularization

abstract:
  This paper addresses the problem of document image dewarping, which aims at
eliminating the geometric distortion in document images for document
digitization. Instead of designing a better neural network to approximate the
optical flow fields between the inputs and outputs, we pursue the best
readability by taking the text lines and the document boundaries into account
from a constrained optimization perspective. Specifically, our proposed method
first learns the boundary points and the pixels in the text lines and then
follows the most simple observation that the boundaries and text lines in both
horizontal and vertical directions should be kept after dewarping to introduce
a novel grid regularization scheme. To obtain the final forward mapping for
dewarping, we solve an optimization problem with our proposed grid
regularization. The experiments comprehensively demonstrate that our proposed
approach outperforms the prior arts by large margins in terms of readability
(with the metrics of Character Errors Rate and the Edit Distance) while
maintaining the best image quality on the publicly-available DocUNet benchmark.


title: Towards Driving-Oriented Metric for Lane Detection Models

abstract:
  After the 2017 TuSimple Lane Detection Challenge, its dataset and evaluation
based on accuracy and F1 score have become the de facto standard to measure the
performance of lane detection methods. While they have played a major role in
improving the performance of lane detection methods, the validity of this
evaluation method in downstream tasks has not been adequately researched. In
this study, we design 2 new driving-oriented metrics for lane detection:
End-to-End Lateral Deviation metric (E2E-LD) is directly formulated based on
the requirements of autonomous driving, a core downstream task of lane
detection; Per-frame Simulated Lateral Deviation metric (PSLD) is a lightweight
surrogate metric of E2E-LD. To evaluate the validity of the metrics, we conduct
a large-scale empirical study with 4 major types of lane detection approaches
on the TuSimple dataset and our newly constructed dataset Comma2k19-LD. Our
results show that the conventional metrics have strongly negative correlations
($\leq$-0.55) with E2E-LD, meaning that some recent improvements purely
targeting the conventional metrics may not have led to meaningful improvements
in autonomous driving, but rather may actually have made it worse by
overfitting to the conventional metrics. As autonomous driving is a
security/safety-critical system, the underestimation of robustness hinders the
sound development of practical lane detection models. We hope that our study
will help the community achieve more downstream task-aware evaluations for lane
detection.


title:
JETS: Jointly Training FastSpeech2 and HiFi-GAN for End to End Text to
  Speech

abstract:
  In neural text-to-speech (TTS), two-stage system or a cascade of separately
learned models have shown synthesis quality close to human speech. For example,
FastSpeech2 transforms an input text to a mel-spectrogram and then HiFi-GAN
generates a raw waveform from a mel-spectogram where they are called an
acoustic feature generator and a neural vocoder respectively. However, their
training pipeline is somewhat cumbersome in that it requires a fine-tuning and
an accurate speech-text alignment for optimal performance. In this work, we
present end-to-end text-to-speech (E2E-TTS) model which has a simplified
training pipeline and outperforms a cascade of separately learned models.
Specifically, our proposed model is jointly trained FastSpeech2 and HiFi-GAN
with an alignment module. Since there is no acoustic feature mismatch between
training and inference, it does not requires fine-tuning. Furthermore, we
remove dependency on an external speech-text alignment tool by adopting an
alignment learning objective in our joint training framework. Experiments on
LJSpeech corpus shows that the proposed model outperforms publicly available,
state-of-the-art implementations of ESPNet2-TTS on subjective evaluation (MOS)
and some objective evaluations.


title: Thermal Hall responses in frustrated honeycomb spin systems

abstract:
  We study geometrical responses of magnons driven by a temperature gradient in
frustrated spin systems. While Dzyaloshinskii-Moriya (DM) interactions are
usually incorporated to obtain geometrically nontrivial magnon bands, here we
investigate thermal Hall responses of magnons that do no rely on the DM
interactions. Specifically, we focus on frustrated spin systems with sublattice
degrees of freedom and show that a nonzero Berry curvature requires breaking of
an effective $PT$ symmetry. According to this symmetry consideration, we study
the $J_1$-$J_2$-$J_2^\prime$ Heisenberg models on a honeycomb lattice as a
representative example, and demonstrate that magnons in the spiral phase
support the thermal Hall effect once we introduce a magnetic field and
asymmetry between the two sublattices. We also show that driving the magnons by
a temperature gradient induces spin current generation (i.e., magnon spin
Nernst effect) in the $J_1$-$J_2$-$J_2^\prime$ Heisenberg models.


title:
Identifying Cost-effective Debunkers for Multi-stage Fake News
  Mitigation Campaigns

abstract:
  Online social networks have become a fertile ground for spreading fake news.
Methods to automatically mitigate fake news propagation have been proposed.
Some studies focus on selecting top k influential users on social networks as
debunkers, but the social influence of debunkers may not translate to wide
mitigation information propagation as expected. Other studies assume a given
set of debunkers and focus on optimizing intensity for debunkers to publish
true news, but as debunkers are fixed, even if with high social influence
and/or high intensity to post true news, the true news may not reach users
exposed to fake news and therefore mitigation effect may be limited. In this
paper, we propose the multi-stage fake news mitigation campaign where debunkers
are dynamically selected within budget at each stage. We formulate it as a
reinforcement learning problem and propose a greedy algorithm optimized by
predicting future states so that the debunkers can be selected in a way that
maximizes the overall mitigation effect. We conducted extensive experiments on
synthetic and real-world social networks and show that our solution outperforms
state-of-the-art baselines in terms of mitigation effect.


title:
Insight-HXMT dedicated 33-day observation of SGR J1935+2154 I. Burst
  Catalog

abstract:
  Magnetars are neutron stars with extreme magnetic field and sometimes
manifest as soft gamma-ray repeaters (SGRs). SGR J1935+2154 is one of the most
prolific bursters and the first confirmed source of fast radio burst (i.e. FRB
200428). Encouraged by the discovery of the first X-ray counterpart of FRB,
Insight-Hard X-ray Modulation Telescope (Insight-HXMT) implemented a dedicated
33-day long ToO observation of SGR J1935+2154 since April 28, 2020. With the
HE, ME, and LE telescopes, Insight-HXMT provides a thorough monitoring of burst
activity evolution of SGR J1935+2154, in a very broad energy range (1-250 keV)
with high temporal resolution and high sensitivity, resulting in a unique
valuable data set for detailed studies of SGR J1935+2154. In this work, we
conduct a comprehensive analysis of this observation including detailed burst
search, identification and temporal analyses. After carefully removing false
triggers, we find a total of 75 bursts from SGR J1935+2154, out of which 70 are
single-pulsed. The maximum burst rate is about 56 bursts/day. Both the burst
duration and the waiting time between two successive bursts follow log-normal
distributions, consistent with previous studies. We also find that bursts with
longer duration (some are multi-pulsed) tend to occur during the period with
relatively high burst rate. There is no correlation between the waiting time
and the fluence or duration of either the former or latter burst. It also seems
that there is no correlation between burst duration and hardness ratio, in
contrast to some previous reports. In addition, we do not find any X-ray burst
associated with any reported radio bursts except for FRB 200428.


title:
The PEPSI Exoplanet Transit Survey (PETS) I: Investigating the presence
  of a silicate atmosphere on the super-Earth 55 Cnc e

abstract:
  The study of exoplanets and especially their atmospheres can reveal key
insights on their evolution by identifying specific atmospheric species. For
such atmospheric investigations, high-resolution transmission spectroscopy has
shown great success, especially for Jupiter-type planets. Towards the
atmospheric characterization of smaller planets, the super-Earth exoplanet 55
Cnc e is one of the most promising terrestrial exoplanets studied to date.
Here, we present a high-resolution spectroscopic transit observation of this
planet, acquired with the PEPSI instrument at the Large Binocular Telescope.
Assuming the presence of Earth-like crust species on the surface of 55 Cnc e,
from which a possible silicate-vapor atmosphere could have originated, we
search in its transmission spectrum for absorption of various atomic and
ionized species such as Fe , Fe+, Ca , Ca+, Mg and K , among others. Not
finding absorption for any of the investigated species, we are able to set
absorption limits with a median value of 1.9 x RP. In conclusion, we do not
find evidence of a widely extended silicate envelope on this super-Earth
reaching several planetary radii.


title: Lifeline: Emergency Ad Hoc Network

abstract:
  Lifeline is a group of systems designed for mobile phones and battery powered
wireless routers for forming emergency Ad hoc networks. Devices installed with
Lifeline program can automatically form Ad hoc networks when cellular signal is
unavailable or disrupted during natural disasters. For instance, large scale
earthquakes can cause extensive damages to land-based telecommunication
infrastructures. In such circumstances, mobile phones installed with Lifeline
program can be used to send emergency messages by the victims who are trapped
under collapsed buildings. In addition, Lifeline also provides a function for
the rescuers to estimate the positions of the victims based on network
propagation techniques. Lifeline also has the ability to recover from partial
crash of network and nodes lost.


title:
Direct visualization of percolating metal-insulator transition in V2O3
  using scanning microwave impedance microscopy

abstract:
  Using the extensively studied V2O3 as a prototype system, we investigate the
role of percolation in metal-insulator transition (MIT). We apply scanning
microwave impedance microscopy to directly determine the metallic phase
fraction p and relate it to the macroscopic conductance G, which shows a sudden
jump when p reaches the percolation threshold. Interestingly, the conductance G
exhibits a hysteretic behavior against p, suggesting two different percolating
processes upon cooling and warming. Based on our image analysis and model
simulation, we ascribe such hysteretic behavior to different domain nucleation
and growth processes between cooling and warming, which is likely caused by the
decoupled structural and electronic transitions in V2O3 during MIT. Our work
provides a microscopic view of how the interplay of structural and electronic
degrees of freedom affects MIT in strongly correlated systems.


title:
Boundary Node Detection and Unfolding of Complex Non-Convex Ad Hoc
  Networks

abstract:
  Complex non-convex ad hoc networks (CNCAH) contain intersecting polygons and
edges. In many instances, the layouts of these networks are not entirely convex
in shape. In this article, we propose a Kamada-Kawai-based algorithm called
W-KK-MS for boundary node detection problems, which is capable of aligning node
positions while achieving high sensitivity, specificity, and accuracy in
producing a visual drawing from the input network topology. The algorithm put
forward in this article selects and assigns weights to top-k nodes in each
iteration to speed up the updating process of nodes. We also propose a novel
approach to detect and unfold stacked regions in CNCAH networks. Experimental
results show that the proposed algorithms can achieve fast convergence on
boundary node detection in CNCAH networks and are able to successfully unfold
stacked regions. The design and implementation of a prototype system called
ELnet for analyzing CNCAH networks is also described in this article. The ELnet
system is capable of generating synthetic networks for testing, integrating
with force-directed algorithms, and visualizing and analyzing algorithms'
outcomes.


title: Investigating Modality Bias in Audio Visual Video Parsing

abstract:
  We focus on the audio-visual video parsing (AVVP) problem that involves
detecting audio and visual event labels with temporal boundaries. The task is
especially challenging since it is weakly supervised with only event labels
available as a bag of labels for each video. An existing state-of-the-art model
for AVVP uses a hybrid attention network (HAN) to generate cross-modal features
for both audio and visual modalities, and an attentive pooling module that
aggregates predicted audio and visual segment-level event probabilities to
yield video-level event probabilities. We provide a detailed analysis of
modality bias in the existing HAN architecture, where a modality is completely
ignored during prediction. We also propose a variant of feature aggregation in
HAN that leads to an absolute gain in F-scores of about 2% and 1.6% for visual
and audio-visual events at both segment-level and event-level, in comparison to
the existing HAN model.


title: On Reconfiguration Graph of Independent Sets under Token Sliding

abstract:
  An independent set of a graph $G$ is a vertex subset $I$ such that there is
no edge joining any two vertices in $I$. Imagine that a token is placed on each
vertex of an independent set of $G$. The $\mathsf{TS}$- ($\mathsf{TS}_k$-)
reconfiguration graph of $G$ takes all non-empty independent sets (of size $k$)
as its nodes, where $k$ is some given positive integer. Two nodes are adjacent
if one can be obtained from the other by sliding a token on some vertex to one
of its unoccupied neighbors. This paper focuses on the structure and
realizability of these reconfiguration graphs. More precisely, we study two
main questions for a given graph $G$: (1) Whether the
$\mathsf{TS}_k$-reconfiguration graph of $G$ belongs to some graph class
$\mathcal{G}$ (including complete graphs, paths, cycles, complete bipartite
graphs, and connected split graphs) and (2) If $G$ satisfies some property
$\mathcal{P}$ (including $s$-partitedness, planarity, Eulerianity, girth, and
the clique's size), whether the corresponding $\mathsf{TS}$- ($\mathsf{TS}_k$-)
reconfiguration graph of $G$ also satisfies $\mathcal{P}$, and vice versa.


title:
Regular solutions of a functional equation derived from the invariance
  problem of Matkowski means

abstract:
  The main result of the present paper is about the solutions of the functional
equation \Eq{*}{ F\Big(\frac{x+y}2\Big)+f_1(x)+f_2(y)=G(g_1(x)+g_2(y)),\qquad
x,y\in I, } derived originally, in a natural way, from the invariance problem
of generalized weighted quasi-arithmetic means, where
$F,f_1,f_2,g_1,g_2:I\to\mathbb{R}$ and $G:g_1(I)+g_2(I)\to\mathbb{R}$ are the
unknown functions assumed to be continuously differentiable with $0\notin
g'_1(I)\cup g'_2(I)$, and the set $I$ stands for a nonempty open subinterval of
$\mathbb{R}$.
  In addition to these, we will also touch upon solutions not necessarily
regular. More precisely, we are going to solve the above equation assuming
first that $F$ is affine on $I$ and $g_1$ and $g_2$ are continuous functions
strictly monotone in the same sense, and secondly that $g_1$ and $g_2$ are
invertible affine functions with a common additive part.


title:
Cross-Domain Recommendation to Cold-Start Users via Variational
  Information Bottleneck

abstract:
  Recommender systems have been widely deployed in many real-world
applications, but usually suffer from the long-standing user cold-start
problem. As a promising way, Cross-Domain Recommendation (CDR) has attracted a
surge of interest, which aims to transfer the user preferences observed in the
source domain to make recommendations in the target domain. Previous CDR
approaches mostly achieve the goal by following the Embedding and Mapping
(EMCDR) idea which attempts to learn a mapping function to transfer the
pre-trained user representations (embeddings) from the source domain into the
target domain. However, they pre-train the user/item representations
independently for each domain, ignoring to consider both domain interactions
simultaneously. Therefore, the biased pre-trained representations inevitably
involve the domain-specific information which may lead to negative impact to
transfer information across domains. In this work, we consider a key point of
the CDR task: what information needs to be shared across domains? To achieve
the above idea, this paper utilizes the information bottleneck (IB) principle,
and proposes a novel approach termed as CDRIB to enforce the representations
encoding the domain-shared information. To derive the unbiased representations,
we devise two IB regularizers to model the cross-domain/in-domain user-item
interactions simultaneously and thereby CDRIB could consider both domain
interactions jointly for de-biasing.


title:
Saving lives: design and implementation of lifeline emergency ad hoc
  network

abstract:
  This paper aims to propose a system for automatically forming ad hoc networks
using mobile phones and battery-powered wireless routers for emergency
situations. The system also provides functions to send emergency messages and
identify the location of victims based on the network topology information.
  Optimized link state routing protocol is used to instantly form an ad hoc
emergency network based on WiFi signals from mobile phones of the victims,
backup battery-powered wireless routers preinstalled in buildings and mobile
devices deployed by search and rescue teams. The proposed system is also
designed to recover from partial crash of network and nodes lost.
  Experimental results demonstrate the effectiveness of the proposed system in
terms of battery life, transmission distance and noises.
  A novel message routing schedule is proposed for conserving battery life. A
novel function to estimate the location of a mobile device which sent an
emergency message is proposed in this paper.


title:
Error estimates for the numerical approximation of a non-smooth
  quasilinear elliptic control problem

abstract:
  In this paper, we carry out the numerical analysis of a non-smooth
quasilinear elliptic optimal control problem, where the coefficient in the
divergence term of the corresponding state equation is a finitely $PC^2$
(continuous and $C^2$ apart from finitely many points) function in the state
variable. Although the nonlinearity of the quasilinear elliptic equation is
non-smooth, the corresponding control-to-state operator is of class $C^1$ but
not of class $C^2$. Analogously, the discrete control-to-state operators
associated with the approximated control problems are proven to be of class
$C^1$ only. An explicit formula of a second-order generalized derivative of the
cost functional is also established. We then exploit a second-order sufficient
optimality condition to prove a priori error estimates for a variational and a
piecewise constant approximation of the continuous optimal control problem.


title:
Transient behavior of damage spreading in the two-dimensional
  Blume-Capel ferromagnet

abstract:
  We study the transient behaviour of the propagation of damage in the
two-dimensional spin-$1$ Blume-Capel model using Monte Carlo simulations with
Metropolis dynamics. We find that the Hamming distance decreases exponentially
with time in the weakly damaged system and its rate of decay appears to depend
linearly on a number of Hamiltonian parameters, namely the crystal field,
temperature, applied magnetic field, but also on the amount of damage.
Furthermore, a comparative study using Metropolis and Glauber dynamics
indicates a slower decay rate of the Hamming distance for the Glauber protocol.


title: Snapshot Visualization of Complex Graphs with Force-Directed Algorithms

abstract:
  Force-directed algorithms are widely used for visualizing graphs. However,
these algorithms are computationally expensive in producing good quality
layouts for complex graphs. The layout quality is largely influenced by
execution time and methods' input parameters especially for large complex
graphs. The snapshots of visualization generated from these algorithms are
useful in presenting the current view or a past state of an information on
timeslices. Therefore, researchers often need to make a trade-off between the
quality of visualization and the selection of appropriate force-directed
algorithms. In this paper, we evaluate the quality of snapshots generated from
7 force-directed algorithms in terms of number of edge crossing and the
standard deviations of edge length. Our experimental results showed that KK,
FA2 and DH algorithms cannot produce satisfactory visualizations for large
graphs within the time limit. KK-MS-DS algorithm can process large and planar
graphs but it does not perform well for graphs with low average degrees. KK-MS
algorithm produces better visualizations for sparse and non-clustered graphs
than KK-MS-DS algorithm.


title: Memory-Efficient Training of RNN-Transducer with Sampled Softmax

abstract:
  RNN-Transducer has been one of promising architectures for end-to-end
automatic speech recognition. Although RNN-Transducer has many advantages
including its strong accuracy and streaming-friendly property, its high memory
consumption during training has been a critical problem for development. In
this work, we propose to apply sampled softmax to RNN-Transducer, which
requires only a small subset of vocabulary during training thus saves its
memory consumption. We further extend sampled softmax to optimize memory
consumption for a minibatch, and employ distributions of auxiliary CTC losses
for sampling vocabulary to improve model accuracy. We present experimental
results on LibriSpeech, AISHELL-1, and CSJ-APS, where sampled softmax greatly
reduces memory consumption and still maintains the accuracy of the baseline
model.


title: Cyberbullying Indicator as a Precursor to a Cyber Construct Development

abstract:
  The current global pandemic occasioned by the SARS-CoV-2 virus has been
attributed, partially, to the growing range of cyber vises within the cyber
ecosystem. One area of such impact is the increasing tendencies of
cyber-bullying among students. Cyberbullying -- the act of subjugating others
using a cyber platform -- is a growing concern among educators, especially in
High-S chools. Whilst studies have been carried out towards understanding this
menace, the approach towards id entifying indicators of cyberbullying is
largely missing in the literature. To address this research gap, this study
proposed a cyberbullying framework based on the identification of some
observable behavioral indicators. Using a self-administered measurement
instrument from 30-respondents, the study observed the probability of a
cyberbully construct, as a potential measure of the presence of cyberbullying;
a probability that has been largely ignored in extant literature. This
observation presents a veritable tool for the development of an active and
integrated learning platform void of abuse among students. Furthermore, within
the cyber education ecosystem, a cyberbullying construct would provide a
mechanism for the development of an appropriate online learning platform, which
would be useful to the information system and cyber education research
communities.


title:
A Convex Optimal Control Framework for Autonomous Vehicle Intersection
  Crossing

abstract:
  Cooperative vehicle management emerges as a promising solution to improve
road traffic safety and efficiency. This paper addresses the speed planning
problem for connected and autonomous vehicles (CAVs) at an unsignalized
intersection with consideration of turning maneuvers. The problem is approached
by a hierarchical centralized coordination scheme that successively optimizes
the crossing order and velocity trajectories of a group of vehicles so as to
minimize their total energy consumption and travel time required to pass the
intersection. For an accurate estimate of the energy consumption of each CAV,
the vehicle modeling framework in this paper captures 1) friction losses that
affect longitudinal vehicle dynamics, and 2) the powertrain of each CAV in line
with a battery-electric architecture. It is shown that the underlying
optimization problem subject to safety constraints for powertrain operation,
cornering and collision avoidance, after convexification and relaxation in some
aspects can be formulated as two second-order cone programs, which ensures a
rapid solution search and a unique global optimum. Simulation case studies are
provided showing the tightness of the convex relaxation bounds, the overall
effectiveness of the proposed approach, and its advantages over a benchmark
solution invoking the widely used first-in-first-out policy. The investigation
of Pareto optimal solutions for the two objectives (travel time and energy
consumption) highlights the importance of optimizing their trade-off, as small
compromises in travel time could produce significant energy savings.


title: Ransomware Detection using Process Memory

abstract:
  Ransomware attacks have increased significantly in recent years, causing
great destruction and damage to critical systems and business operations.
Attackers are unfailingly finding innovative ways to bypass detection
mechanisms, whichencouraged the adoption of artificial intelligence. However,
most research summarizes the general features of AI and induces many false
positives, as the behavior of ransomware constantly differs to bypass
detection. Focusing on the key indicating features of ransomware becomes vital
as this guides the investigator to the inner workings and main function of
ransomware itself. By utilizing access privileges in process memory, the main
function of the ransomware can be detected more easily and accurately.
Furthermore, new signatures and fingerprints of ransomware families can be
identified to classify novel ransomware attacks correctly. The current research
used the process memory access privileges of the different memory regions of
the behavior of an executable to quickly determine its intent before serious
harm can occur. To achieve this aim, several well-known machine learning
algorithms were explored with an accuracy range of 81.38 to 96.28 percents. The
study thus confirms the feasibility of utilizing process memory as a detection
mechanism for ransomware.


title:
Group Control for Procedural Rules: Parameterized Complexity and
  Consecutive Domains

abstract:
  We consider {\sc{Group Control by Adding Individuals}} (\sc{GCAI}) for two
procedural rules -- the consensus-start-respecting rule and the
liberal-start-respecting rule. It is known that {\sc{GCAI}} for both rules are
NP-hard, but whether they are fixed-parameter tractable with respect to the
number of distinguished candidates remained open. We resolve both open problems
in the affirmative. In addition, we strengthen the NP-hardness of GCAI by
showing that, with respect to the natural parameter the number of added
individuals, GCAI for both rules is W[2]-hard. Notably, the W[2]-hardness for
the liberal-start-respecting rule holds even when restricted to a very special
case where the qualifications of individuals satisfy the so-called consecutive
ones property. However, for the consensus-start-respecting rule, the problem
becomes polynomial-time solvable in this special case. We also study a duality
restriction where the disqualifications of individuals fulfill the consecutive
ones property, and we show that under this restriction {\sc{GCAI}} for both
rules turn out to be polynomial-time solvable. Our reductions for showing
W[2]-hardness also imply several other lower bounds concerning kernelization
and exact algorithms.


title:
Extremal marginals of an unbounded local completely positive and local
  completely contractive map

abstract:
  In this paper we consider the unbounded local completely positive and local
completely contractive maps on maximal tensor product of unital locally
C*-algebras and discuss on extremal points of certain convex subsets in the set
of such maps.


title:
An Optimal Control Method to Compute the Most Likely Transition Path for
  Stochastic Dynamical Systems with Jumps

abstract:
  Many complex real world phenomena exhibit abrupt, intermittent or jumping
behaviors, which are more suitable to be described by stochastic differential
equations under non-Gaussian L\'evy noise. Among these complex phenomena, the
most likely transition paths between metastable states are important since
these rare events may have high impact in certain scenarios. Based on the large
deviation principle, the most likely transition path could be treated as the
minimizer of the rate function upon paths that connect two points. One of the
challenges to calculate the most likely transition path for stochastic
dynamical systems under non-Gaussian L\'evy noise is that the associated rate
function can not be explicitly expressed by paths. For this reason, we
formulate an optimal control problem to obtain the optimal state as the most
likely transition path. We then develop a neural network method to solve this
issue. Several experiments are investigated for both Gaussian and non-Gaussian
cases.


title: MPS-NeRF: Generalizable 3D Human Rendering from Multiview Images

abstract:
  There has been rapid progress recently on 3D human rendering, including novel
view synthesis and pose animation, based on the advances of neural radiance
fields (NeRF). However, most existing methods focus on person-specific training
and their training typically requires multi-view videos. This paper deals with
a new challenging task -- rendering novel views and novel poses for a person
unseen in training, using only multiview images as input. For this task, we
propose a simple yet effective method to train a generalizable NeRF with
multiview images as conditional input. The key ingredient is a dedicated
representation combining a canonical NeRF and a volume deformation scheme.
Using a canonical space enables our method to learn shared properties of human
and easily generalize to different people. Volume deformation is used to
connect the canonical space with input and target images and query image
features for radiance and density prediction. We leverage the parametric 3D
human model fitted on the input images to derive the deformation, which works
quite well in practice when combined with our canonical NeRF. The experiments
on both real and synthetic data with the novel view synthesis and pose
animation tasks collectively demonstrate the efficacy of our method.


title:
Evidence for the transition of a Jacobi ellipsoid into a Maclaurin
  spheroid in gamma-ray bursts

abstract:
  In the binary-driven hypernova (BdHN) scenario, long gamma-ray bursts (GRBs)
originate in a cataclysmic event that occurs in a binary system composed of a
carbon-oxygen (CO) star and a neutron star (NS) companion in close orbit. The
collapse of the CO star generates at its center a newborn NS ($\nu$NS), and a
supernova (SN) explosion. Matter from the ejecta is accreted both onto the
$\nu$NS because of fallback and onto the NS companion, leading to the collapse
of the latter into a black hole (BH). Each of the ingredients of the above
system leads to observable emission episodes in a GRB. In particular, the
$\nu$NS is expected to show up (hereafter $\nu$NS-rise) in the early GRB
emission, nearly contemporary or superimposed to the ultrarelativistic prompt
emission (UPE) phase, but with a different spectral signature. Following the
$\nu$NS-rise, the $\nu$NS powers the afterglow emission by injecting energy
into the expanding ejecta leading to synchrotron radiation. We here show that
the $\nu$NS-rise and the subsequent afterglow emission in both systems, GRB
180720B and GRB 190114C, are powered by the release of rotational energy of a
Maclaurin spheroid, starting from the bifurcation point to the Jacobi ellipsoid
sequence. This implies that the $\nu$NS evolves from a triaxial Jacobi
configuration, prior to the $\nu$NS-rise, into the axially symmetric Maclaurin
configuration observed in the GRB. The triaxial $\nu$NS configuration is
short-lived (less than a second) due to a copious emission of gravitational
waves, before the GRB emission, and it could be in principle detected for
sources located at distances closer than $100$ Mpc. This appears to be the sole
process of emission of gravitational waves in long GRBs.


title: Asymptotic behavior of the Dirichlet energy on Poisson point clouds

abstract:
  We prove that quadratic pair interactions for functions defined on planar
Poisson clouds and taking into account pairs of sites of distance up to a
certain (large-enough) threshold can be almost surely approximated by the
multiple of the Dirichlet energy by a deterministic constant. This is achieved
by scaling the Poisson cloud and the corresponding energies and computing a
compact discrete-to-continuum limit. In order to avoid the effect of
exceptional regions of the Poisson cloud, with an accumulation of sites or with
"disconnected sites", a suitable "coarse-grained" notion of convergence of
functions defined on scaled Poisson clouds must be given.


title: A Degenerate Hopf Bifurcation Theorem in Infinite Dimensions

abstract:
  A Hopf bifurcation theorem is established for the abstract evolution equation
$\frac{\mathrm{d}x}{\mathrm{d}t}=F(x,\lambda)$ in infinite dimensions under the
degeneracy condition $Re \mu ^{\prime}(\lambda_0)= 0$ and suitable assumptions.
The stability properties of bifurcating periodic solutions are also derived.
Interestingly, it is shown that a transcritical Hopf bifurcation still can
occur at $\lambda_0$ although the stability property of the trivial solutions
does not change near $\lambda_0$. Our results do not require the analyticity of
$F$. The main tools are the Lyapunov--Schmidt reduction and a Morse lemma.
Applications to a multi-parameter diffusive predator--prey system discover new
branches of periodic solutions.


title:
Commensurate-Incommensurate Transitions of the 1D Disordered Chiral
  Clock Model

abstract:
  We study the effects of quenched disorder on the commensurate-incommensurate
transitions in the 1D $\mathbb{Z}_N$ chiral clock model. The interplay of
domain walls and rare regions rounds the sharp transitions of the pure model.
The density of domain walls displays an essential singularity, while the order
parameter develops a discontinuity at the transition. We perform extensive
density-matrix renormalization group calculations to support theoretical
predictions. Our results provide a distinct rounding mechanism of continuous
phase transitions in disordered systems.


title: Bootstrap methods in bounding discrete Radon operators

abstract:
  The aim of this paper is to develop bootstrap arguments to establish maximal,
oscillation, variational and jump inequalities for the discrete averaging Radon
operators on $\ell^p(\mathbb Z^d)$.


title: PV-Reduction of Sunset Topology with Auxiliary Vector

abstract:
  Passarino-Veltman (PV) reduction method has been proved to be very useful for
the computation of general one-loop integrals. However, not much progress has
been made when applying to higher loops. Recently, we have improved the
PV-reduction method by introducing an auxiliary vector. In this paper, we apply
our new method to the simplest two-loop integrals, i.e., the sunset topology.
We show how to use differential operators to establish algebraic recursion
relations for reduction coefficients. Our algorithm can be easily applied to
the reduction of integrals with arbitrary high-rank tensor structures. We
demonstrate the efficiency of our algorithm by computing the reduction with the
total tensor rank up to four.


title:
A Contextual Framework for Adaptive User Interfaces: Modelling the
  Interaction Environment

abstract:
  The interaction context (or environment) is key to any HCI task and
especially to adaptive user interfaces (AUIs), since it represents the
conditions under which users interact with computers. Unfortunately, there are
currently no formal representations to model said interaction context. In order
to address this gap, we propose a contextual framework for AUIs and illustrate
a practical applica- tion using learning management systems as a case study. We
also discuss limitations of our framework and offer discussion points about the
realisation of truly context-aware AUIs.


title:
An Overview of Application of Optimization Models Under Uncertainty to
  the Unit Commitment Problem

abstract:
  Optimization models have been broadly used within side the energy industry as
useful decision-making systems for scheduling and dispatching electric powered
energy resources; this is applied in a system called unit commitment (UC).
  Unit Commitment seeks the maximum price adequate generator commitment scheme
for an electric powered energy device to satisfy a certain demand, at the same
time as fulfilling the operational constraints on transmission models and
computational resources. Taking into account risk variability in those
processes and checking out their comparative overall performance for a single
or multi-stage energy model as a function of monetary performance in addition
to the risk related to the commitment decisions.
  Stochastic programming and Robust optimization are by a vast majority the
most widely studied methodologies for UC under net load uncertainty. These
techniques will be discussed in this paper.


title:
Dynamic scaling properties of multistep polarization response in
  ferroelectrics

abstract:
  Ferroelectrics are multifunctional smart materials finding applications in
sensor technology, micromechanical actuation, digital information storage etc.
Their most fundamental property is the ability of polarization switching under
applied electric field. In particular, understanding of switching kinetics is
essential for digital information storage. In this regard, scaling properties
of the temporal polarization response are well-known for 180{\deg}-switching
processes in ferroelectrics characterized by a unique field-dependent local
switching time. Unexpectedly, these properties were now observed in multiaxial
polycrystalline ferroelectrics, exhibiting a number of parallel and sequential
non-180{\deg}-switching processes with distinct switching times. This behaviour
can be explained by a combination of the multistep stochastic mechanism and the
inhomogeneous field mechanism models of polarization reversal. Scaling
properties are predicted for polycrystalline ferroelectrics of tetragonal,
rhombohedral and orthorhombic symmetries and exemplarily demonstrated by
measurements of polarization kinetics in (K,Na)NbO3-based ferroelectric ceramic
over a timescale of 7 orders of magnitude. Dynamic scaling properties allow
insight into the microscopic switching mechanisms, on the one hand, and into
statistical material characteristics, on the other hand, providing thereby the
description of temporal polarization with high accuracy. The gained deeper
insight into the mechanisms of multistep polarization switching is crucial for
future ultrafast and multilevel digital information storage.


title:
A bilingual approach to specialised adjectives through word embeddings
  in the karstology domain

abstract:
  We present an experiment in extracting adjectives which express a specific
semantic relation using word embeddings. The results of the experiment are then
thoroughly analysed and categorised into groups of adjectives exhibiting formal
or semantic similarity. The experiment and analysis are performed for English
and Croatian in the domain of karstology using data sets and methods developed
in the TermFrame project. The main original contributions of the article are
twofold: firstly, proposing a new and promising method of extracting
semantically related words relevant for terminology, and secondly, providing a
detailed evaluation of the output so that we gain a better understanding of the
domain-specific semantic structures on the one hand and the types of
similarities extracted by word embeddings on the other.


title:
The geodesic ray transform on spherically symmetric reversible Finsler
  manifolds

abstract:
  We show that the geodesic ray transform is injective on scalar functions on
spherically symmetric reversible Finsler manifolds where the Finsler norm
satisfies a Herglotz condition. We use angular Fourier series to reduce the
injectivity problem to the invertibility of generalized Abel transforms and by
Taylor expansions of geodesics we show that these Abel transforms are
injective. Our result has applications in linearized boundary rigidity problem
on Finsler manifolds and especially in linearized elastic travel time
tomography.


title: Mutual information estimation for graph convolutional neural networks

abstract:
  Measuring model performance is a key issue for deep learning practitioners.
However, we often lack the ability to explain why a specific architecture
attains superior predictive accuracy for a given data set. Often, validation
accuracy is used as a performance heuristic quantifying how well a network
generalizes to unseen data, but it does not capture anything about the
information flow in the model. Mutual information can be used as a measure of
the quality of internal representations in deep learning models, and the
information plane may provide insights into whether the model exploits the
available information in the data. The information plane has previously been
explored for fully connected neural networks and convolutional architectures.
We present an architecture-agnostic method for tracking a network's internal
representations during training, which are then used to create the mutual
information plane. The method is exemplified for graph-based neural networks
fitted on citation data. We compare how the inductive bias introduced in
graph-based architectures changes the mutual information plane relative to a
fully connected neural network.


title:
Thermally enhanced photoluminescence and temperature sensing properties
  of Sc$_2$W$_3$O$_{12}$:Eu$^{3+}$ phosphors

abstract:
  Currently,lanthanide ions doped luminescence materials applying as optical
thermometers have arose much concern. Basing on the different responses of two
emissions to temperature, the fluorescence intensity ratio (FIR) technique can
be executed and further estimate the sensitivities to assess the optical
thermometry performances. In this study, we introduce different doping
concentrations of Eu$^{3+}$ ions into negative expansion material
Sc$_2$W$_3$O$_{12}$:Eu$^{3+}$, accessing to the thermal enhanced luminescence
from 373 to 548 K, and investigate the temperature sensing properties in
detail. All samples exhibit good thermally enhanced luminescence behavior. The
emission intensity of Sc$_2$W$_3$O$_{12}$: 6 mol% Eu$^{3+}$ phosphors reaches
at 147.81% of initial intensity at 473 K. As the Eu doping concentration
increases, the resistance of the samples to thermal quenching decreases. The
FIR technique based on the transitions 5D0-7F1 (592 nm) and 5D0-7F2 (613 nm) of
Eu$^{3+}$ ions demonstrate a maximum relative temperature sensitivity of 3.063%
K-1 at 298 K for Sc$_2$W$_3$O$_{12}$:Eu$^{3+}$: 6 mol% Eu$^{3+}$ phosphors. The
sensitivity of sample decreases with the increase of Eu$^{3+}$ concentration.
Benefiting from the thermal enhanced luminescence performance and good
temperature sensing properties, the Sc$_2$W$_3$O$_{12}$:Eu$^{3+}$: Eu$^{3+}$
phosphors can be applies as optical thermometers.


title:
Exactly solvable anharmonic oscillator, degenerate orthogonal
  polynomials and Painlev\'e II

abstract:
  The paper addresses a conjecture of Shapiro and Tater on the similarity
between two sets of points in the complex plane; on one side is the values of
$t\in \mathbb{C}$ for which the spectrum of the quartic anharmonic oscillator
in the complex plane $$\frac{{\rm d}^2 y}{{\rm d}x^2} - ( x^4 + tx^2 + 2Jx )y =
\Lambda y, $$ with certain boundary conditions, has repeated eigenvalues. On
the other side is the set of zeroes of the Vorob'ev-Yablonskii polynomials,
i.e. the poles of rational solutions of the second Painlev\'e equation. Along
the way, we indicate a surprising and deep connection between the anharmonic
oscillator problem and certain degenerate orthogonal polynomials.


title: A generalization of falsity in finitely-many valued logics

abstract:
  The paper proposes a new type of negation in multi-valued logics, providing a
different way to answer the following question: what does it mean that some
object language formula does not have a given truth-value. Along the way, the
paper provides a general definition of truth and falsity in an arbitrary
many-valued logic.


title:
A survey of neural models for the automatic analysis of conversation:
  Towards a better integration of the social sciences

abstract:
  Some exciting new approaches to neural architectures for the analysis of
conversation have been introduced over the past couple of years. These include
neural architectures for detecting emotion, dialogue acts, and sentiment
polarity. They take advantage of some of the key attributes of contemporary
machine learning, such as recurrent neural networks with attention mechanisms
and transformer-based approaches. However, while the architectures themselves
are extremely promising, the phenomena they have been applied to to date are
but a small part of what makes conversation engaging. In this paper we survey
these neural architectures and what they have been applied to. On the basis of
the social science literature, we then describe what we believe to be the most
fundamental and definitional feature of conversation, which is its
co-construction over time by two or more interlocutors. We discuss how neural
architectures of the sort surveyed could profitably be applied to these more
fundamental aspects of conversation, and what this buys us in terms of a better
analysis of conversation and even, in the longer term, a better way of
generating conversation for a conversational system.


title:
Dynamical systems of cosmological models for different possibilities of
  $G$ and $\rho_{\Lambda}$

abstract:
  The present paper deals with the dynamics of spatially flat
Friedmann-Lemaitre-Robertson-Walker (FLRW) cosmological model with a time
varying cosmological constant $\Lambda$ where $\Lambda$ evolves with the cosmic
time (t) through the Hubble parameter (H). We consider that the model dynamics
has a reflection symmetry $H \rightarrow -H $ with $\Lambda(H)$ expressed in
the form of Taylor series with respect to H. Dynamical systems for three
different cases based on the possibilities of gravitational constant G and the
vacuum energy density $\rho_{\Lambda}$ have been analysed. In Case I, both G
and $\rho_{\Lambda}$ are taken to be constant. We analyse stability of the
system by using the notion of spectral radius, behavior of perturbation along
each of the axis with respect to cosmic time and Poincare sphere. In Case II,
we have dynamical system analysis for G=constant and $\rho_{\Lambda} \neq $
constant where we study stability by using the concept of spectral radius and
perturbation function. In Case III, we take $G \neq$ constant and
$\rho_{\Lambda} \neq$ constant where we introduce a new set of variables to set
up the corresponding dynamical system. We find out the fixed points of the
system and analyse the stability from different directions: by analysing
behaviour of the perturbation along each of the axis, Center Manifold Theory
and stability at infinity using Poincare sphere respectively. Phase plots and
perturbation plots have been presented. We deeply study the cosmological
scenario with respect to the fixed points obtained and analyse the late time
behavior of the Universe. Our model agrees with the fact that the Universe is
in the epoch of accelerated expansion. The EOS parameter $\omega_{eff}$, total
energy density $\Omega_{tt}$ are also evaluated at the fixed points for each of
the three cases and these values are in agreement with the observational values
in [1].


title:
Reassignment of magic numbers for icosahedral Au clusters: 310, 564, 928
  and 1426

abstract:
  Icosahedral Au clusters with three and four shells of atoms are found to
deviate significantly from the commonly assumed Mackay structures. By
introducing additional atoms in the surface shell and creating a vacancy in the
center of the cluster, the calculated energy per atom can be lowered
significantly, according to several different descriptions of the interatomic
interaction. Analogous icosahedral structures with five and six shells of atoms
are generated using the same structural motifs and are similarly found to be
more stable than Mackay icosahedra. The lowest energy per atom is obtained with
clusters containing 310, 564, 928 and 1426 atoms, as compared with the commonly
assumed magic numbers of 309, 561, 923 and 1415. Some of the vertices in the
optimized clusters have a hexagonal ring of atoms, rather than a pentagon, with
the vertex atom missing. An inner shell atom in some cases moves outwards by
more than an \AA{}ngstr\"om into the surface shell at the vertex site. This
feature, as well as the wide distribution of nearest-neighbor distances in the
surface layer, can strongly influence the catalytic properties of icosahedral
clusters. The structural optimization is initially carried out using the GOUST
method with atomic forces estimated with the EMT empirical potential function,
but the atomic coordinates are then refined by minimization using electron
density functional theory (DFT) or Gaussian approximation potential (GAP). A
single energy barrier is found to separate the Mackay icosahedron from a lower
energy structure where a string of atoms moves outwards in a concerted manner
from the center so as to create a central vacancy while placing an additional
atom in the surface shell.


title:
Analysis and Optimization of A Double-IRS Cooperatively Assisted System
  with A Quasi-Static Phase Shift Design

abstract:
  The analysis and optimization of single intelligent reflecting surface
(IRS)-assisted systems have been extensively studied, whereas little is known
regarding multiple-IRS-assisted systems. This paper investigates the analysis
and optimization of a double-IRS cooperatively assisted downlink system, where
a multi-antenna base station (BS) serves a single-antenna user with the help of
two multi-element IRSs, connected by an inter-IRS channel. The channel between
any two nodes is modeled with Rician fading. The BS adopts the instantaneous
CSI-adaptive maximum-ratio transmission (MRT) beamformer, and the two IRSs
adopt a cooperative quasi-static phase shift design. The goal is to maximize
the average achievable rate, which can be reflected by the average channel
power of the equivalent channel between the BS and user, at a low phase
adjustment cost and computational complexity. First, we obtain tractable
expressions of the average channel power of the equivalent channel in the
general Rician factor, pure line of sight (LoS), and pure non-line of sight
(NLoS) regimes, respectively. Then, we jointly optimize the phase shifts of the
two IRSs to maximize the average channel power of the equivalent channel in
these regimes. The optimization problems are challenging non-convex problems.
We obtain globally optimal closed-form solutions for some cases and propose
computationally efficient iterative algorithms to obtain stationary points for
the other cases. Next, we compare the computational complexity for optimizing
the phase shifts and the optimal average channel power of the double-IRS
cooperatively assisted system with those of a counterpart single-IRS-assisted
system at a large number of reflecting elements in the three regimes. Finally,
we numerically demonstrate notable gains of the proposed solutions over the
existing solutions at different system parameters.