title:
Anisotropic three-dimensional quantum Hall effect and magnetotransport
  in mesoscopic Weyl semimetals

abstract:
  Weyl semimetals are emerging to become a new class of quantum-material
platform for various novel phenomena. Especially, the Weyl orbit made from
surface Fermi arcs and bulk relativistic states is expected to play a key role
in magnetotransport, leading even to a three-dimensional quantum Hall effect
(QHE). It is experimentally and theoretically important although yet unclear
whether it bears essentially the same phenomenon as the conventional
two-dimensional QHE. We discover an unconventional fully three-dimensional
anisotropy in the quantum transport under magnetic field. Strong suppression
and even disappearance of QHE occur when Hall-bar current is rotated away from
being transverse to parallel with respect to the Weyl point alignment, which is
attributed to a peculiar absence of conventional bulk-boundary correspondence.
Besides, transport along the magnetic field can exhibit a remarkable reversal
from negative to positive magnetoresistance. These results establish the
uniqueness of this QHE system as a novel three-dimensional quantum matter.


title:
Tuning Particle Accelerators with Safety Constraints using Bayesian
  Optimization

abstract:
  Tuning machine parameters of particle accelerators is a repetitive and
time-consuming task, that is challenging to automate. While many off-the-shelf
optimization algorithms are available, in practice their use is limited because
most methods do not account for safety-critical constraints that apply to each
iteration, including loss signals or step-size limitations. One notable
exception is safe Bayesian optimization, which is a data-driven tuning approach
for global optimization with noisy feedback. We propose and evaluate a step
size-limited variant of safe Bayesian optimization on two research faculties of
the Paul Scherrer Institut (PSI): a) the Swiss Free Electron Laser (SwissFEL)
and b) the High-Intensity Proton Accelerator (HIPA). We report promising
experimental results on both machines, tuning up to 16 parameters subject to
more than 200 constraints.


title:
Preventing Outages under Coordinated Cyber-Physical Attack with Secured
  PMUs

abstract:
  Due to the potentially severe consequences of coordinated cyber-physical
attacks (CCPA), the design of defenses has gained significant attention. A
popular approach is to eliminate the existence of attacks by either securing
existing sensors or deploying secured PMUs. In this work, we improve this
approach by lowering the defense target from eliminating attacks to preventing
outages and reducing the required number of PMUs. To this end, we formulate the
problem of PMU Placement for Outage Prevention (PPOP) under DC power flow model
as a tri-level non-linear optimization problem and transform it into a bi-level
mixed-integer linear programming (MILP) problem. Then, we propose an
alternating optimization framework to solve PPOP by iteratively adding
constraints, for which we develop two constraint generation algorithms. In
addition, for large-scale grids, we propose a polynomial-time heuristic
algorithm to obtain suboptimal solutions. Next, we extend our solution to
achieve the defense goal under AC power flow model. Finally, we evaluate our
algorithm on IEEE 30-bus, 57-bus, 118-bus, and 300-bus systems, which
demonstrates the potential of the proposed approach in greatly reducing the
required number of PMUs.


title: An Epistemic Interpretation of Tensor Disjunction

abstract:
  This paper aims to give an epistemic interpretation to the tensor disjunction
in dependence logic, through a rather surprising connection to the so-called
weak disjunction in Medvedev's early work on intermediate logic under the
Brouwer-Heyting-Kolmogorov (BHK)-interpretation. We expose this connection in
the setting of inquisitive logic with tensor disjunction discussed by Ciardelli
and Barbero (2019}, but from an epistemic perspective. More specifically, we
translate the propositional formulae of inquisitive logic with tensor into
modal formulae in a powerful epistemic language of "knowing how" following the
proposal by Wang (2021). We give a complete axiomatization of the logic of our
full language based on Fine's axiomatization of S5 modal logic with
propositional quantifiers. Finally, we generalize the tensor operator with
parameters $k$ and $n$, which intuitively captures the epistemic situation that
one knows $n$ potential answers to $n$ questions and is sure $k$ answers of
them must be correct. The original tensor disjunction is the special case when
$k=1$ and $n=2$. We show that the generalized tensor operators do not increase
the expressive power of our logic, the inquisitive logic, and propositional
dependence logic, though most of these generalized tensors are not uniformly
definable in these logics, except in our dynamic epistemic logic of knowing
how.


title: There are infinitely many monotone games over $L_5$

abstract:
  A notion of combinatorial game over a partially ordered set of atomic
outcomes was recently introduced by Selinger. These games are appropriate for
describing the value of positions in Hex and other monotone set coloring games.
It is already known that there are infinitely many distinct monotone game
values when the poset of atoms is not linearly ordered, and that there are only
finitely many such values when the poset of atoms is linearly ordered with 4 or
fewer elements. In this short paper, we settle the remaining case: when the
atom poset has 5 or more elements, there are infinitely many distinct monotone
values.


title:
Autoregressive Linguistic Steganography Based on BERT and Consistency
  Coding

abstract:
  Linguistic steganography (LS) conceals the presence of communication by
embedding secret information into a text. How to generate a high-quality text
carrying secret information is a key problem. With the widespread application
of deep learning in natural language processing, recent algorithms use a
language model (LM) to generate the steganographic text, which provides a
higher payload compared with many previous arts. However, the security still
needs to be enhanced. To tackle with this problem, we propose a novel
autoregressive LS algorithm based on BERT and consistency coding, which
achieves a better trade-off between embedding payload and system security. In
the proposed work, based on the introduction of the masked LM, given a text, we
use consistency coding to make up for the shortcomings of block coding used in
the previous work so that we can encode arbitrary-size candidate token set and
take advantages of the probability distribution for information hiding. The
masked positions to be embedded are filled with tokens determined by an
autoregressive manner to enhance the connection between contexts and therefore
maintain the quality of the text. Experimental results have shown that,
compared with related works, the proposed work improves the fluency of the
steganographic text while guaranteeing security, and also increases the
embedding payload to a certain extent.


title:
Soliton microcombs in whispering gallery mode crystalline resonators
  with broadband intermode interactions

abstract:
  Soliton microcombs have shown great potential in a variety of applications
ranging from chip scale frequency metrology to optical communications and
photonic data center, in which light coupling among cavity transverse modes,
termed as intermode interactions, are long-existing and usually give rise to
localized impacts on the soliton state. Of particular interest are whispering
gallery mode based crystalline resonators, which with dense mode families,
potentially feature interactions of all kind. While effects of narrow-band
interactions such as spectral power spikes have been well recognized in
crystalline resonators, effects of broadband interactions remains unexplored.
Here, we demonstrate soliton microcombs with broadband intermode interactions,
in home-developed magnesium fluoride microresonators with an intrinsic
$\mathbf{Q}$-factor approaching $\mathbf{10^{10}}$. Soliton combs with featured
spectral tailoring effect have been observed, which is found determined by
dispersive effects of the coupled mode family. Remarkably, we demonstrate a
broadband power enhanced soliton comb whose spectrum is beyond the standard
soliton profile and the power efficiency is largely increased to 45%. Our
results not only contribute to the understanding of dissipative soliton
dynamics in multi-mode or coupled resonator systems, but also open an access to
stable yet efficient soliton combs in crystalline microresonators where mode
control and dispersion engineering are hardly performed.


title: Single Pair of Weyl Points in Nonmagnetic Crystals

abstract:
  Topological semimetal states having the minimal number, i.e., only a single
pair, of Weyl points are desirable for the study of effects associated with
chiral topological charges. So far, the search for such states is focused on
magnetic spinful systems. Here, we find that nonmagnetic spinless systems can
host a class of single-pair-Weyl-point (SP-WP) states, where the two Weyl
points are located at two high-symmetry time-reversal-invariant momenta. We
identify 32 candidate space groups that host such states, and we show that the
chiral charge of each Weyl point in the SP-WP state must be an even integer.
Besides achieving the minimal number, Weyl points in SP-WP states are far
separated in momentum space, making the physics of each individual point better
exposed. The large separation combined with the even topological charge lead to
extended surface Fermi loops with a non-contractible winding topology on the
surface Brillouin zone torus, distinct from conventional Weyl semimetals. We
confirm our proposal in the phonon spectra of two concrete materials TlBO$_2$
and KNiIO$_6$. Our finding applies to a wide range of systems, including
electronic, phononic, and various artificial systems. It offers a new direction
for the search of ideal platforms to study chiral particles.


title: Gaia22ayj: outburst from a deeply eclipsing 9.36-min binary?

abstract:
  Gaia22ayj (=ZTF19aagmvuk) was detected as a transient on 2022 March 3 both by
the Gaia satellite and the Zwicky Transient Facility (ZTF). I analyzed the past
public ZTF data and found that Gaia22ayj showed coherent large-amplitude
double-wave variations with a period of 0.00649910257(13) d = 9.36 min. The
period can be either from the spin period of the white dwarf or the orbital
period of the binary. I consider the latter possibility more likely based on
the light curve resembling an eclipsing binary and on the stability of the
profile. The presence of an outburst lasting at least ~1 d suggests that this
system has an accretion disk. If Gaia22ayj is indeed an eclipsing binary with a
period of 9.36 min, this is the shortest orbital period ever measured in
eclipsing accreting binaries and is the shortest record of a system with a
dwarf nova-type outburst. The presence of an outburst is unusual for an AM CVn
system with this orbital period and this object might be in the turn-on phase
of the mass-transfer. If the ultrashort orbital period of Gaia22ayj is
confirmed, this object would an ideal target to detect period variations within
several years to confirm its evolutionary state and to identify the future
evolutionary consequence.


title:
Driver Side and Traffic Based Evaluation Model for On-Street Parking
  Solutions

abstract:
  Parking has been a painful problem for urban drivers. The parking pain
exacerbates as more people tend to live in cities in the context of global
urbanization. Thus, it is demanding to find a solution to mitigate d rivers'
parking headaches. Many solutions tried to resolve the parking issue by
predicting parking occupancy. Their focuses were on the accuracy of the
theoretical side but lacked a standardized model to evaluate these proposals in
practice. This paper develops a Driver Side and Traffic Based Evaluation Model
(DSTBM), which provides a general evaluation scheme for different parking
solutions. Two common parking detection methods, fixed sensing and mobile
sensing are analyzed using DSTBM. The results indicate first, DSTBM examines
different solutions from the driver's perspective and has no conflicts with
other evaluation schemes; second, DSTBM confirms that fixed sensing performs
better than mobile sensing in terms of prediction accuracy.


title: Exploring Self-Attention for Visual Intersection Classification

abstract:
  In robot vision, self-attention has recently emerged as a technique for
capturing non-local contexts. In this study, we introduced a self-attention
mechanism into the intersection recognition system as a method to capture the
non-local contexts behind the scenes. An intersection classification system
comprises two distinctive modules: (a) a first-person vision (FPV) module,
which uses a short egocentric view sequence as the intersection is passed, and
(b) a third-person vision (TPV) module, which uses a single view immediately
before entering the intersection. The self-attention mechanism is effective in
the TPV module because most parts of the local pattern (e.g., road edges,
buildings, and sky) are similar to each other, and thus the use of a non-local
context (e.g., the angle between two diagonal corners around an intersection)
would be effective. This study makes three major contributions. First, we
proposed a self-attention-based approach for intersection classification using
TPVs. Second, we presented a practical system in which a self-attention-based
TPV module is combined with an FPV module to improve the overall recognition
performance. Finally, experiments using the public KITTI dataset show that the
above self-attention-based system outperforms conventional recognition based on
local patterns and recognition based on convolution operations.


title:
Long term measurement of the $^{222}$Rn concentration in the Canfranc
  Underground Laboratory

abstract:
  We report the results of six years (2013-2018) of measurements of $^{222}$Rn
air concentration, relative humidity, atmospheric pressure and temperature in
the halls A, B and C of the Canfranc Underground Laboratory (LSC). We have
calculated all the Pearson correlation coefficients among these parameters and
we have found a positive correlation between the $^{222}$Rn concentration and
the relative humidity. Both correlated variables show a seasonal periodicity.
The joint analysis of laboratory data and four years (2015-2018) of the
meteorological variables outside the laboratory shows the correlation between
the $^{222}$Rn concentration and the outside temperature. The collected
information stresses the relevance of designing good Rn-mitigation strategies
in current and future experiments at LSC; in particular, we have checked for
two years (2017-2018) the good performance of the mitigation procedure of the
ANAIS--112 experiment. Finally, in another measurement (2019-2021) for two
years of live time, we report an upper limit to the residual $^{222}$Rn content
of the radon-free air provided by the radon abatement system installed in the
laboratory.


title: Japan's Strategy for Future Projects in High Energy Physics

abstract:
  The current strategy for future projects of the Japanese high energy physics
community, Japan Association of High Energy Physicists (JAHEP), remains as
described in the Final Report of the Committee on Future Projects in High
Energy Physics, published in 2017. The Recommendation part of the Final Report
is excerpted in the following page. This document updates the Final Report by
adding developments and advances that have occurred since 2017.

categories: hep-ex hep-ph physics.acc-ph physics.ins-det


title:
Variational approximation method for the long-range force transmission
  in biopolymer gels

abstract:
  The variational principle of minimum free energy (MFEVP) has been widely used
in the study of soft matter statics. MFEVP can be used not only to derive
equilibrium equations (including both bulk equations and boundary conditions),
but also to develop direct variational methods (such as Ritz method) to find
approximate solutions to these equilibrium equations. In this work, we applied
these variational methods to study long-range force transmission in nonlinear
elastic biopolymer gels. We showed that the slow decay of cell-induced
displacements measured experimentally for fibroblast spheroids in
three-dimensional fibrin gels can be well explained by variational
approximations based on the three-chain model of biopolymer gels.


title: Current Source Localization Using Deep Prior with Depth Weighting

abstract:
  This paper proposes a novel neuronal current source localization method based
on Deep Prior that represents a more complicated prior distribution of current
source using convolutional networks. Deep Prior has been suggested as a means
of an unsupervised learning approach that does not require learning using
training data, and randomly-initialized neural networks are used to update a
source location using a single observation. In our previous work, a
Deep-Prior-based current source localization method in the brain has been
proposed but the performance was not almost the same as those of conventional
approaches, such as sLORETA. In order to improve the Deep-Prior-based approach,
in this paper, a depth weight of the current source is introduced for Deep
Prior, where depth weighting amounts to assigning more penalty to the
superficial currents. Its effectiveness is confirmed by experiments of current
source estimation on simulated MEG data.


title:
Implications of Mortality Displacement for Effect Modification and
  Selection Bias

abstract:
  Mortality displacement is the concept that deaths are moved forward in time
(e.g., a few days, several months, and years) by exposure from when they would
occur without the exposure, which is common in environmental time-series
studies. Using concepts of a frail population and loss of life expectancy, it
is understood that mortality displacement may decrease rate ratio (RR). Such
decreases are thought to be minimal or substantial depending on study
populations. Environmental epidemiologists have interpreted RR considering
mortality displacement. This theoretical paper reveals that mortality
displacement can be formulated as a built-in selection bias of RR in Cox models
due to unmeasured risk factors independent from exposure of interest, and
mortality displacement can also be viewed as an effect modifier by integrating
the concepts of rate and loss of life expectancy. Thus, depending on the
framework through which we view bias, mortality displacement can be categorized
as selection bias in the bias taxonomy of epidemiology, and simultaneously
mortality displacement can be seen as an effect modifier. This dichotomy
provides useful implications regarding policy, effect modification, exposure
time-windows selection, and generalizability, specifically why research in
epidemiology may produce unexpected and heterogeneous RR over different studies
and sub-populations.


title: Coherence interpretation of the Hong-Ou-Mandel effect

abstract:
  A photon bunching phenomenon of the Hong-Ou-Mandel (HOM) effect has been
intensively studied over the last several decades for one of the most
interesting nonclassical features of two-photon correlation. Due to the rule of
thumb that a photon never interferes with others, the two-photon correlation in
the HOM effect is understood as superposition of the probability amplitudes
between indistinguishably paired photons on a beam splitter. On the contrary of
this common understanding based on the particle nature of photons, here, a
coherence interpretation is conducted for the HOM effect to understand the
quintessence of the photon bunching. For this, a complete solution of the HOM
effect is driven using typical entangled photon pairs generated from
spontaneous parametric down conversion processes. This coherence interpretation
can view quantum features in a deterministic way, otherwise remains a
mysterious quantum feature.


title:
Criticality of the excess energy cost due to the unit-flux-quantum
  external field for the $(2+1)$D superfluid-insulator transition

abstract:
  The two-dimensional ($2$D) spin-$S=1$ $XY$ model was investigated numerically
as a realization of the $(2+1)$D superfluid-Mott-insulator (SF-MI) transition.
The interaction parameters are extended so as to suppress corrections to
finite-size scaling. Thereby, the external field of a unit flux quantum
($\Phi=2\pi$) is applied to the 2D cluster by incorporating the phase factor
$e^{i\phi_{ij}}$ ($\phi_{ij}$: gauge angle between the $i$ and $j$ sites) into
the hopping amplitudes. Taking the advantage in that the exact-diagonalization
method allows us to treat such a complex-valued matrix element, we evaluated
the excess energy cost $\Delta E(2\pi)$ due to the magnetic flux $\Phi=2\pi$
explicitly in the SF ($XY$) phase. As a result, we found that the amplitude
ratio $\rho_s / \Delta E(2\pi)$ ($\rho_s$: spin stiffness) makes sense in
proximity to the critical point, exhibiting a notable plateau in the SF-phase
side. The plateau height is estimated, and compared to the related studies.


title:
A Strategic Approach to Advance Magnet Technology for Next Generation
  Colliders

abstract:
  Colliders are built on a foundation of superconducting magnet technology that
provides strong dipole magnets to maintain the beam orbit and strong focusing
magnets to enable the extraordinary luminosity required to probe physics at the
energy frontier. The dipole magnet strength plays a critical role in dictating
the energy reach of a collider, and the superconducting magnets are arguably
the dominant cost driver for future collider facilities. As the community
considers opportunities to explore new energy frontiers, the importance of
advanced magnet technology - both in terms of magnet performance and in the
magnet technology's potential for cost reduction - is evident, as the
technology status is essential for informed decisions on targets for physics
reach and facility feasibility.


title:
MWA Tied-Array Processing IV: A Multi-Pixel Beamformer for Pulsar
  Surveys and Ionospheric Corrected Localisation

abstract:
  The Murchison Widefield Array (MWA) is a low-frequency aperture array capable
of high-time and frequency resolution astronomy applications such as pulsar
studies. The large field-of-view of the MWA (hundreds of square degrees) can
also be exploited to attain fast survey speeds for all-sky pulsar search
applications, but to maximise sensitivity requires forming thousands of
tied-array beams from each voltage-capture observation. The necessity of using
calibration solutions that are separated from the target observation both
temporally and spatially makes pulsar observations vulnerable to uncorrected,
frequency-dependent positional offsets due to the ionosphere. These offsets may
be large enough to move the source away from the centre of the tied-array beam,
incurring sensitivity drops of $\sim$30-50\% in Phase II extended array
configuration. We analyse these offsets in pulsar observations and develop a
method for mitigating them, improving both the source position accuracy and the
sensitivity. This analysis prompted the development of a multi-pixel
beamforming functionality that can generate dozens of tied-array beams
simultaneously, which runs a factor of ten times faster compared to the
original single-pixel version. This enhancement makes it feasible to observe
multiple pulsars within the vast field of view of the MWA and supports the
ongoing large-scale pulsar survey efforts with the MWA. We explore the extent
to which ionospheric offset correction will be necessary for the MWA Phase III
and the low-frequency Square Kilometre Array (SKA-Low).


title:
Does Monocular Depth Estimation Provide Better Pre-training than
  Classification for Semantic Segmentation?

abstract:
  Training a deep neural network for semantic segmentation is labor-intensive,
so it is common to pre-train it for a different task, and then fine-tune it
with a small annotated dataset. State-of-the-art methods use image
classification for pre-training, which introduces uncontrolled biases. We test
the hypothesis that depth estimation from unlabeled videos may provide better
pre-training. Despite the absence of any semantic information, we argue that
estimating scene geometry is closer to the task of semantic segmentation than
classifying whole images into semantic classes. Since analytical validation is
intractable, we test the hypothesis empirically by introducing a pre-training
scheme that yields an improvement of 5.7% mIoU and 4.1% pixel accuracy over
classification-based pre-training. While annotation is not needed for
pre-training, it is needed for testing the hypothesis. We use the KITTI
(outdoor) and NYU-V2 (indoor) benchmarks to that end, and provide an extensive
discussion of the benefits and limitations of the proposed scheme in relation
to existing unsupervised, self-supervised, and semi-supervised pre-training
protocols.


title:
Electromagnetic transition amplitude for Roper resonance from
  holographic QCD

abstract:
  The Roper resonance, the first excited state of the nucleon, Is one of the
best-established baryon resonances. Yet, its properties have not been
consistently explained by effective models of QCD, such as the non-relativistic
quark model. In this letter, we propose an alternative approach in the
Sakai-Sugimoto model that is one of the holographic models of QCD. In
particular, we analyze the helicity amplitude of the electromagnetic
transitions at the leading of 't~Hooft coupling $1/\lambda$. The model
incorporates baryon structure at short distance by non-linear mesons surrounded
by meson clouds at long distance. We demonstrate that the recently observed
data by CLAS are explained in the present approach.


title:
Decay estimates for matrix coefficients of unitary representations of
  semisimple Lie groups

abstract:
  Let $G$ be a connected semisimple Lie group with finite centre and $K$ be a
maximal compact subgroup thereof. Given a function $u$ on $G$, we define
$\mathcal{A} u$ to be the root mean square average over $K$, acting both on the
left and the right, of $u$. We show that for all unitary representations $\pi$
of $G$, there exists a unique minimal positive-real-valued spherical function
$\phi_{\lambda}$ on $G$ such that $\mathcal{A} \langle \pi(\cdot) \xi, \eta
\rangle \leq \Vert \xi \Vert_{\mathcal{H}_\pi} \Vert \eta
\Vert_{\mathcal{H}_\pi} \phi_{\lambda}$. This estimate has nice features of
both asymptotic pointwise estimates and Lebesgue space estimates; indeed it is
equivalent to pointwise estimates $\vert \langle \pi(\cdot) \xi, \eta \rangle
\vert \leq C(\xi, \eta) \,\phi_{\lambda}$ for $K$-finite or smooth vectors
$\xi$ and $\eta$, and it exhibits different decay rates in different directions
at infinity in $G$. Further, if we assume the latter inequality with arbitrary
$C( \xi, \eta )$, we can prove the former inequality and then return to the
latter inequality with explicit knowledge of $C( \xi, \eta )$. On the other
hand, it holds everywhere in $G$, in contrast to asymptotic estimates which are
not global. We also provide some applications.


title:
Minimal volume product of convex bodies with certain discrete symmetries
  and its applications

abstract:
  We give the sharp lower bound of the volume product of $n$-dimensional convex
bodies which are invariant under a discrete subgroup $SO(K)=\{ g \in SO(n);
g(K)=K \}$, where $K$ is an $n$-cube or $n$-simplex. This provides new partial
results of Mahler's conjecture and its non-symmetric version. In addition, we
give partial answers for Viterbo's isoperimetric type conjecture in symplectic
geometry from the view point of Mahler's conjecture.


title:
Risk Assessment with Generic Energy Storage under Exogenous and
  Endogenous Uncertainty

abstract:
  Current risk assessment ignores the stochastic nature of energy storage
availability itself and thus lead to potential risk during operation. This
paper proposes the redefinition of generic energy storage (GES) that is allowed
to offer probabilistic reserve. A data-driven unified model with exogenous and
endogenous uncertainty (EXU & EDU) description is presented for four typical
types of GES. Moreover, risk indices are proposed to assess the impact of
overlooking (EXU & EDU) of GES. Comparative results between EXU & EDU are
illustrated in distribution system with day-ahead chance-constrained
optimization (CCO) and more severe risks are observed for the latter, which
indicate that system operator (SO) should adopt novel strategies for EDU
uncertainty.


title: Reconstructing cosmic evolution with a density parametrization

abstract:
  The current paper provides a comprehensive examination of a dark energy
cosmological model in the classical regime, in which a generic scalar field is
regarded as a dark energy source. Einstein's field equations are solved in
model independent way i.e. using a scheme of cosmological parametrization. A
parametrization of the density parameter as a function of the cosmic scale
factor has been investigated in this line. The result is noteworthy because it
shows a smooth transition from a decelerating to an accelerating phase in the
recent past. The model parameters involved in the functional form of the
parametrization approach utilized here were constrained using certain external
datasets. The updated Hubble datasets containing 57 datapoints, 1048 points of
recently compiled Pantheon datasets, and also the Baryon Acoustic Oscillation
(BAO) datasets are used here to determine the best-fitting model parameter
values. The expressions of several significant cosmological parameters are
represented as a function of redshift `$z$' and illustrated visually for the
best fit values of the model parameters to better comprehend cosmic evolution.
The obtained model is also compared with the $\Lambda CDM$ model. Our model has
a distinct behavior in future and shown a big crunch type collapse. The best
fit values of the model parameters are also used to compute the current values
of several physical and geometrical parameters, as well as phase transition
redshift. To examine the nature of dark energy, certain cosmological tests and
diagnostic analyses are done on the derived model.


title: RSCFed: Random Sampling Consensus Federated Semi-supervised Learning

abstract:
  Federated semi-supervised learning (FSSL) aims to derive a global model by
training fully-labeled and fully-unlabeled clients or training partially
labeled clients. The existing approaches work well when local clients have
independent and identically distributed (IID) data but fail to generalize to a
more practical FSSL setting, i.e., Non-IID setting. In this paper, we present a
Random Sampling Consensus Federated learning, namely RSCFed, by considering the
uneven reliability among models from fully-labeled clients, fully-unlabeled
clients or partially labeled clients. Our key motivation is that given models
with large deviations from either labeled clients or unlabeled clients, the
consensus could be reached by performing random sub-sampling over clients. To
achieve it, instead of directly aggregating local models, we first distill
several sub-consensus models by random sub-sampling over clients and then
aggregating the sub-consensus models to the global model. To enhance the
robustness of sub-consensus models, we also develop a novel distance-reweighted
model aggregation method. Experimental results show that our method outperforms
state-of-the-art methods on three benchmarked datasets, including both natural
and medical images. The code is available at
https://github.com/XMed-Lab/RSCFed.


title: Estimating the Ratio of Means in a Zero-inflated Poisson Mixture Model

abstract:
  The problem of estimating the ratio of the means of a two-component Poisson
mixture model is considered, when each component is subject to zero-inflation,
i.e., excess zero counts. The. resulting {\it zero-inflated Poisson mixture
(ZIPM) model} can be treated as a three-component Poisson mixture model with
one degenerate component. The EM algorithm is applied to obtain frequentist
estimators and their standard errors, the latter determined via an explicit
expression for the observed information matrix. Bayes and empirical Bayes
estimators also are obtained by means of conjugate priors and their data-based
variants. Lastly, the ZIPM distribution and the ZTP (zero-truncated Poisson)
distribution are compared.


title:
Transfer of codebook latent factors for cross-domain recommendation with
  non-overlapping data

abstract:
  Recommender systems based on collaborative filtering play a vital role in
many E-commerce applications as they guide the user in finding their items of
interest based on the user's past transactions and feedback of other similar
customers. Data Sparsity is one of the major drawbacks with collaborative
filtering technique arising due to the less number of transactions and feedback
data. In order to reduce the sparsity problem, techniques called transfer
learning/cross-domain recommendation has emerged. In transfer learning methods,
the data from other dense domain(s) (source) is considered in order to predict
the missing ratings in the sparse domain (target). In this paper, we come up
with a novel transfer learning approach for cross-domain recommendation,
wherein the cluster-level rating pattern(codebook) of the source domain is
obtained via a co-clustering technique. Thereafter we apply the Maximum Margin
Matrix factorization (MMMF) technique on the codebook in order to learn the
user and item latent features of codebook. Prediction of the target rating
matrix is achieved by introducing these latent features in a novel way into the
optimisation function. In the experiments we demonstrate that our model
improves the prediction accuracy of the target matrix on benchmark datasets.


title: Parametric Euler $T$-sums of odd harmonic numbers

abstract:
  In this paper, we define a parametric variant of generalized Euler sums and
call them the (alternating) parametric Euler $T$-sums. By using the contour
integration method and residue theorem, we establish several explicit formulae
for the linear parametric Euler $T$-sums. Furthermore, by applying the results,
we obtain explicit formulae for the Hoffman's (alternating) double $t$-values
and Kaneko-Tsumura's (alternating) double $T$-values.


title:
Learning to Predict RNA Sequence Expressions from Whole Slide Images
  with Applications for Search and Classification

abstract:
  Deep learning methods are widely applied in digital pathology to address
clinical challenges such as prognosis and diagnosis. As one of the most recent
applications, deep models have also been used to extract molecular features
from whole slide images. Although molecular tests carry rich information, they
are often expensive, time-consuming, and require additional tissue to sample.
In this paper, we propose tRNAsfomer, an attention-based topology that can
learn both to predict the bulk RNA-seq from an image and represent the whole
slide image of a glass slide simultaneously. The tRNAsfomer uses multiple
instance learning to solve a weakly supervised problem while the pixel-level
annotation is not available for an image. We conducted several experiments and
achieved better performance and faster convergence in comparison to the
state-of-the-art algorithms. The proposed tRNAsfomer can assist as a
computational pathology tool to facilitate a new generation of search and
classification methods by combining the tissue morphology and the molecular
fingerprint of the biopsy samples.


title:
A Work Proposal for a Collaborative Study of Magnet Technology for a
  Future Muon Collider

abstract:
  In this paper we elaborate on the nature and challenges for the magnet
systems of a muon collider as presently considered within the scope of the
International Muon Collider Collaboration (IMCC). We outline the structure of
the work proposed over the coming period of five years to study and demonstrate
relevant magnet technology. The proposal, which is part of the overall work
planned to establish feasibility of a muon collider, is in direct response to
the recent recommendations received from the Laboratories Directors Group
(LDG). The plan is to profit from joint activities, within the scope of the
IMCC and beyond, implemented through direct and EU-funded contributions.


title: Distributional Robust Portfolio Construction based on Investor Aversion

abstract:
  In behavioral finance, aversion affects investors' judgment of future
uncertainty when profit and loss occur. Considering investors' aversion to loss
and risk, and the ambiguous uncertainty characterizing asset returns, we
construct a distributional robust portfolio model (DRP) under the condition
that the distribution of risky asset returns is unknown. Specifically, our
objective is to find an optimal portfolio of assets that maximizes the
worst-case utility level on the Wasserstein ball, which is centered on the
empirical distribution of sample returns and the radius of the ball quantifies
the investor's ambiguity level. The model is also formulated as a mixed-integer
quadratic programming problem with cardinality constraints. In addition, we
propose a hybrid algorithm to improve the efficiency of the solution and make
it more suitable for large-scale problems. The distributional robust portfolio
model considering aversion is empirically tested for superior performance in
asset allocation, and we also compare common asset allocation strategies to
further enhance the credibility of the portfolio.


title:
On Time Stepping Schemes Considering Switching Behaviors for Power
  System Electromagnetic Transient Simulation

abstract:
  Several difficulties will appear when typical electromagnetic transient
simulation, using the implicit trapezoidal method and fixed step sizes, is
applied to power systems with switching behaviors. These difficulties are
addressed by different aspects of time stepping schemes in the literature. This
paper first details the different aspects and reviews corresponding methods.
Some misunderstanding in the literature is clarified. Issues that may be
encountered by the existing methods are concurrently revealed. Based on the
detailed review, the paper then puts forward a novel time stepping scheme which
fully addresses the difficulties. The effectiveness of the proposed scheme is
demonstrated via numerical case studies.


title: Knowledge Distillation with the Reused Teacher Classifier

abstract:
  Knowledge distillation aims to compress a powerful yet cumbersome teacher
model into a lightweight student model without much sacrifice of performance.
For this purpose, various approaches have been proposed over the past few
years, generally with elaborately designed knowledge representations, which in
turn increase the difficulty of model development and interpretation. In
contrast, we empirically show that a simple knowledge distillation technique is
enough to significantly narrow down the teacher-student performance gap. We
directly reuse the discriminative classifier from the pre-trained teacher model
for student inference and train a student encoder through feature alignment
with a single $\ell_2$ loss. In this way, the student model is able to achieve
exactly the same performance as the teacher model provided that their extracted
features are perfectly aligned. An additional projector is developed to help
the student encoder match with the teacher classifier, which renders our
technique applicable to various teacher and student architectures. Extensive
experiments demonstrate that our technique achieves state-of-the-art results at
the modest cost of compression ratio due to the added projector.


title:
On equicontinuity of mappings with a finite integral over spheres by
  prime ends

abstract:
  We study mappings that satisfy the inverse Poletsky-type inequality in a
domain of Euclidean space. Under some conditions, it was proved that such
mappings have a continuous extension to the boundary in terms of prime ends and
form equicontinuous families of mappings on it. Here we consider the case of
bad domains between which the mappings under study act.


title:
Optical Wireless Transmissions over Multi-layer Underwater Channels with
  Generalized Gamma Fading

abstract:
  Underwater optical communication (UWOC) is a potential solution for broadband
connectivity in oceans and seas for underwater applications providing high data
rate transmission with low latency and high reliability. Recent measurement
campaigns suggest generalized Gamma distribution as a viable model for oceanic
turbulence. In this paper, we analyze the performance of a UWOC system by
modeling the vertical underwater link as a multi-layer cascaded channel, each
distributed according to independent but not identically distributed (i.ni.d.)
generalized Gamma random variables and considering the zero bore-sight model
for pointing errors. We derive analytical expressions for probability density
function (PDF) and cumulative distribution function (CDF) for the
signal-to-noise ratios (SNR) of the combined channel and develop performance
metrics of the considered UWOC system using outage probability, average bit
error rate (BER), and ergodic capacity. We also derive the asymptotic
expressions for outage probability and average BER to determine the diversity
order of the proposed system for a better insight into the system performance.
We use Monte-Carlo simulation results to validate our exact and asymptotic
expressions and demonstrate the performance of the considered underwater UWOC
system using measurement-based parametric data available for turbulent oceanic
channels.


title: Finite-size scaling of critical avalanches

abstract:
  We examine probability distribution for avalanche sizes observed in
self-organized critical systems. While a power-law distribution with a cutoff
because of finite system size is typical behavior, a systematic investigation
reveals that it may decrease on increasing the system size at a fixed avalanche
size. We implement the scaling method and identify scaling functions. The data
collapse ensures a correct estimation of the critical exponents and
distinguishes two exponents related to avalanche size and system size. Our
simple analysis provides striking implications. While the exact value for
avalanches size exponent remains elusive for the prototype sandpile on a square
lattice, we suggest the exponent should be 1. The simulation results represent
that the distribution shows a logarithmic system size dependence, consistent
with the normalization condition. We also argue that for train or Oslo sandpile
model with bulk drive, the avalanche size exponent is slightly less than 1
which is significantly different from the previous estimate 1.11.


title: Learn to Adapt for Monocular Depth Estimation

abstract:
  Monocular depth estimation is one of the fundamental tasks in environmental
perception and has achieved tremendous progress in virtue of deep learning.
However, the performance of trained models tends to degrade or deteriorate when
employed on other new datasets due to the gap between different datasets.
Though some methods utilize domain adaptation technologies to jointly train
different domains and narrow the gap between them, the trained models cannot
generalize to new domains that are not involved in training. To boost the
transferability of depth estimation models, we propose an adversarial depth
estimation task and train the model in the pipeline of meta-learning. Our
proposed adversarial task mitigates the issue of meta-overfitting, since the
network is trained in an adversarial manner and aims to extract domain
invariant representations. In addition, we propose a constraint to impose upon
cross-task depth consistency to compel the depth estimation to be identical in
different adversarial tasks, which improves the performance of our method and
smoothens the training process. Experiments demonstrate that our method adapts
well to new datasets after few training steps during the test procedure.


title:
Continuity scaling: A rigorous framework for detecting and quantifying
  causality accurately

abstract:
  Data based detection and quantification of causation in complex, nonlinear
dynamical systems is of paramount importance to science, engineering and
beyond. Inspired by the widely used methodology in recent years, the
cross-map-based techniques, we develop a general framework to advance towards a
comprehensive understanding of dynamical causal mechanisms, which is consistent
with the natural interpretation of causality. In particular, instead of
measuring the smoothness of the cross map as conventionally implemented, we
define causation through measuring the {\it scaling law} for the continuity of
the investigated dynamical system directly. The uncovered scaling law enables
accurate, reliable, and efficient detection of causation and assessment of its
strength in general complex dynamical systems, outperforming those existing
representative methods. The continuity scaling based framework is rigorously
established and demonstrated using datasets from model complex systems and the
real world.

categories: math.DS cs.SY eess.SY physics.data-an q-bio.QM


title: EYNet: Extended YOLO for Airport Detection in Remote Sensing Images

abstract:
  Nowadays, airport detection in remote sensing images has attracted
considerable attention due to its strategic role in civilian and military
scopes. In particular, uncrewed and operated aerial vehicles must immediately
detect safe areas to land in emergencies. The previous schemes suffered from
various aspects, including complicated backgrounds, scales, and shapes of the
airport. Meanwhile, the rapid action and accuracy of the method are confronted
with significant concerns. Hence, this study proposes an effective scheme by
extending YOLOV3 and ShearLet transform. In this way, MobileNet and ResNet18,
with fewer layers and parameters retrained on a similar dataset, are parallelly
trained as base networks. According to airport geometrical characteristics, the
ShearLet filters with different scales and directions are considered in the
first convolution layers of ResNet18 as a visual attention mechanism. Besides,
the major extended in YOLOV3 concerns the detection Sub-Networks with novel
structures which boost object expression ability and training efficiency. In
addition, novel augmentation and negative mining strategies are presented to
significantly increase the localization phase's performance. The experimental
results on the DIOR dataset reveal that the framework reliably detects
different types of airports in a varied area and acquires robust results in
complex scenes compared to traditional YOLOV3 and state-of-the-art schemes.


title: Analysis of Parameter Space of Morse Oscillator

abstract:
  We present the analysis of the parameter space of the Morse oscillator. By
scrutinizing the mathematical relations that are related to Morse oscillator,
the parameter space of Morse oscillator is visualized. This parameter space is
the space of possible parameter values that are depended on the depth of the
Morse's potential well and other variables. The relationship between an
eigenvalue and other parameters of Morse oscillator is investigated. We show
the mathematical structures of operators which are dependent on the values of
the parameters of Morse oscillator may change our conventional expectations.
The algorithm that we present is also applicable for other quantum systems with
certain modifications.


title:
Combining Evolution and Deep Reinforcement Learning for Policy Search: a
  Survey

abstract:
  Deep neuroevolution and deep Reinforcement Learning have received a lot of
attention in the last years. Some works have compared them, highlighting theirs
pros and cons, but an emerging trend consists in combining them so as to
benefit from the best of both worlds. In this paper, we provide a survey of
this emerging trend by organizing the literature into related groups of works
and casting all the existing combinations in each group into a generic
framework. We systematically cover all easily available papers irrespective of
their publication status, focusing on the combination mechanisms rather than on
the experimental results. In total, we cover 45 algorithms more recent than
2017. We hope this effort will favor the growth of the domain by facilitating
the understanding of the relationships between the methods, leading to deeper
analyses, outlining missing useful comparisons and suggesting new combinations
of mechanisms.


title: A Neural Vocoder Based Packet Loss Concealment Algorithm

abstract:
  The packet loss problem seriously affects the quality of service in Voice
over IP (VoIP) sceneries. In this paper, we investigated online receiver-based
packet loss concealment which is much more portable and applicable. For
ensuring the speech naturalness, rather than directly processing time-domain
waveforms or separately reconstructing amplitudes and phases in frequency
domain, a flow-based neural vocoder is adopted to generate the substitution
waveform of lost packet from Mel-spectrogram which is generated from history
contents by a well-designed neural predictor. Furthermore, a waveform
similarity-based smoothing post-process is created to mitigate the
discontinuity of speech and avoid the artifacts. The experimental results show
the outstanding performance of the proposed method.


title:
Approximations for Optimal Experimental Design in Power System Parameter
  Estimation

abstract:
  This paper is about computationally tractable methods for power system
parameter estimation and Optimal Experiment Design (OED). Here, the main
motivation is that OED has the potential to significantly increase the accuracy
of power system parameter estimates, for example, if only a few batches of data
are available. The problem is, however, that solving the exact OED problem for
larger power grids turns out to be computationally expensive and, in many
cases, even computationally intractable. Therefore, the present paper proposes
three numerical approximation techniques, which increase the computational
tractability of OED for power systems. These approximation techniques are
bench-marked on a 5-bus and a 14-bus case studies.


title: Molecular conformer search with low-energy latent space

abstract:
  Identifying low-energy conformers with quantum mechanical accuracy for
molecules with many degrees of freedom is challenging. In this work, we use the
molecular dihedral angles as features and explore the possibility of performing
molecular conformer search in a latent space with a generative model named
variational auto-encoder (VAE). We bias the VAE towards low-energy molecular
configurations to generate more informative data. In this way, we can
effectively build a reliable energy model for the low-energy potential energy
surface. After the energy model has been built, we extract local-minimum
conformations and refine them with structure optimization. We have tested and
benchmarked our low-energy latent-space (LOLS) structure search method on
organic molecules with $5-9$ searching dimensions. Our results agree with
previous studies.


title:
Scalar Curvature, Injectivity Radius and Immersions with Small Second
  Fundamental Forms

abstract:
  We conjecture and prove in special cases bounds on the {\it conjugacy and
injectivity radii} of "topologically complicated" Riemannian manifolds with
{\it lower bounds} on their {\it scalar curvatures.}
  We show how this leads to {\it lower bounds} on {\it extrinsic curvatures} of
certain smooth manifolds, e.g tori, immersed to "small" Riemannin ones, e.g to
the unit balls in the Euclidean spaces.
  We conjecture and sometimes prove {\it parametric inradii inequalities} for
mean convex domains in the Euclidean spaces.
  At the present moment, our results are plagued by the {\it spin condition}
and the {\it $n\leq 8$ restriction.}


title: A possible structure of newly found exotic state $\eta_1(1855)$

abstract:
  Recently, a hadronic state, named $\eta_1(1855)$, about 1.86 GeV was observed
in BESIII experiment. This finding has a peculiar interest due to its exotic
quantum number $J^{PC}=1^{-+}$. In this paper, we examine the tetraquark
interpretation for the structure of $\eta_1(1855)$ in the configurations of
$[1_c]_{\bar{s}s}\otimes[1_c]_{\bar{q}q}$ and
$[1_c]_{\bar{s}q}\otimes[1_c]_{\bar{s}q}$, and perform a mass spectrum
calculation in the framework of QCD sum rules. The results show that the
observed $\eta_1(1855)$ could be embedded into the
$[1_c]_{\bar{s}s}\otimes[1_c]_{\bar{q}q}$ configuration. The possible
tetraquark and hybrid decay modes are analyzed, which are critical in decoding
its inner structure. By the same way, we evaluate as well the
$\bar{s}s\bar{s}s$ molecular state with $1^{-+}$ quantum number and find that
there might exist two such ground states.


title:
Interplay between nonlinear potential theory and fully nonlinear
  elliptic PDEs

abstract:
  We discuss one of the many topics that illustrate the interaction of Blaine
Lawson's deep geometric and analytic insights. The first author is extremely
grateful to have had the pleasure of collaborating with Blaine over many
enjoyable years. The topic to be discussed concerns the fruitful interplay
between nonlinear potential theory; that is, the study of subharmonics with
respect to a general constraint set in the 2-jet bundle and the study of
subsolutions and supersolutions of a nonlinear (degenerate) elliptic PDE. The
main results include (but are not limited to) the validity of the comparison
principle and the existence and uniqueness to solutions to the relevant
Dirichlet problems on domains which are suitably "pseudoconvex". The methods
employed are geometric and flexible as well as being very general on the
potential theory side, which is interesting in its own right. Moreover, in many
important geometric contexts no natutral operator may be present. On the other
hand, the potential theoretic approach can yield results on the PDE side in
terms of non standard structual conditions on a given differential operator.


title:
Expected Constant Time Self-stabilizing Byzantine Pulse
  Resynchronization

abstract:
  In extending fast digital clock synchronization to the bounded-delay model,
the expected constant time Byzantine pulse resynchronization problem is
investigated. In this problem, the synchronized state of the system should not
only be deterministically maintained but be reached from arbitrary states with
expected constant time in the presence of Byzantine faults. An intuitive
geometric representation of the problem is introduced, with which the classical
approximate agreement, randomized Byzantine agreement, and random walk are
integrated with some geometric operations. Efficient realizations are also
provided for practical uses. Compared with the state-of-the-art solutions, the
assumed common pulses need not be regularly generated, the message complexity
can be lowered as approximate agreement, and the expected stabilization time is
optimal. With this, the provided solution can efficiently convert irregularly
generated common pulses to self-stabilizing Byzantine pulse synchronization.


title:
Ferromagnetic Impurity Induced Majorana Zero Mode in Iron-Based
  Superconductor

abstract:
  Recent experiments reported the puzzling zero energy modes associated with
ferromagnetic impurities in some iron-based superconductors with topological
band structures. Here, we show that the sufficiently strong exchange coupling
between a ferromagnetic impurity and substrate can trigger a quantum phase
transition, beyond which, the phase of the superconducting order parameter
around the impurity acquires a sign-change. In such a case, we prove that a
Kramers degenerate pair of Majorana modes can be induced and trapped by the
impurity in the superconducting state of the electrons from the topological
bands with linear dispersion. Furthermore, we show that our theory can explain
the controversial observations and confusing features of the zero energy modes
from recent experiments in some iron-based superconductors.


title: Model Transformations for Ranking Functions and Total Preorders

abstract:
  In the field of knowledge representation, the considered epistemic states are
often based on propositional interpretations, also called worlds. E.g.,
epistemic states of agents can be modelled by ranking functions or total
preorders on worlds. However, there are usually different ways of how to
describe a real world situation in a propositional language; this can be seen
as different points of view on the same situation. In this paper we introduce
the concept of model transformations to convert an epistemic state from one
point of view to another point of view, yielding a novel notion of equivalence
of epistemic states. We show how the well-known advantages of syntax splitting,
originally developed for belief sets and later extended to representation of
epistemic states and to nonmonotonic reasoning, can be exploited for belief
revision via model transformation by uncovering splittings not being present
before. Furthermore, we characterize situations where belief change operators
commute with model transformations.


title:
TridentNetV2: Lightweight Graphical Global Plan Representations for
  Dynamic Trajectory Generation

abstract:
  We present a framework for dynamic trajectory generation for autonomous
navigation, which does not rely on HD maps as the underlying representation.
High Definition (HD) maps have become a key component in most autonomous
driving frameworks, which include complete road network information annotated
at a centimeter-level that include traversable waypoints, lane information, and
traffic signals. Instead, the presented approach models the distributions of
feasible ego-centric trajectories in real-time given a nominal graph-based
global plan and a lightweight scene representation. By embedding contextual
information, such as crosswalks, stop signs, and traffic signals, our approach
achieves low errors across multiple urban navigation datasets that include
diverse intersection maneuvers, while maintaining real-time performance and
reducing network complexity. Underlying datasets introduced are available
online.


title: Proposal of appropriate location calculations for environment adaptation

abstract:
  To use heterogeneous hardware, programmers must have sufficient technical
skills to utilize OpenMP, CUDA, and OpenCL. On the basis of this, I have
proposed environment-adaptive software that enables automatic conversion,
configuration, and high performance operation of once written code, in
accordance with the hardware. However, although it has been considered to
convert the code according to the offload devices, there has been no study
where to place the offloaded applications to satisfy users' requirements of
price and response time. In this paper, as a new element of environment-adapted
software, I examine a method to calculate appropriate locations using linear
programming method. I confirm that applications can be arranged appropriately
through simulation experiments when some conditions such as application type
and users' requirements are changed.


title:
Representation and normality of $\ast$-paranormal absolutely norm
  attaining operators

abstract:
  In this article, we give a representation of $\ast$-paranormal absolutely
norm attaining operator. Explicitly saying, every $\ast$-paranormal absolutely
norm attaining ($\mathcal{AN}$ in short) $T$ can be decomposed as $U\oplus D$,
where $U$ is a direct sum of scalar multiple of unitary operators and $D$ is a
$2\times 2$ upper diagonal operator matrix. By the representation it is clear
that the class of $\ast$-paranormal $\mathcal{AN}$-operators is bigger than the
class of normal $\mathcal{AN}$-operators but here we observe that a
$\ast$-paranormal $\mathcal{AN}$-operator is normal if either it is invertible
or dimension of its null space is same as dimension of null space of its
adjoint.


title:
Investigation of the microstructure of the fine-grained YPO$_4$:Gd
  ceramics with xenotime structure after Xe irradiation

abstract:
  The paper reports on the preparation of xenotime-structured ceramics by the
Spark Plasma Sintering (SPS) method. Phosphates Y$_{0.95}$Gd$_{0.05}$PO$_4$
(YPO$_4$:Gd) were obtained by the sol-gel method. The synthesized nanopowders
are collected in large agglomerates 10-50 mkm in size. Ceramics has a
fine-grained microstructure and a high relative density (98.67%). The total
time of the SPS process was approximately 18 min. High-density sintered
ceramics YPO$_4$:Gd with a xenotime structure were irradiated with Xe$^{+26}$
ions (E = 167 MeV) to fluences of $1\times10^{12}$-$3\times 10^{13}$ cm$^{-2}$.
Complete amorphization at maximum fluence was not achieved. As the fluence
increases, an insignificant increase in the depth of the amorphous layer is
observed. According to the results of grazing incidence XRD (GIXRD), with an
increase in fluence from $1\times10^{12}$-$3\times 10^{13}$ cm$^{-2}$, an
increase in the volume fraction of the amorphous structure from 20 to 70% is
observed. The intensity of XRD peak 200 YPO$_4$:Gd after recovery annealing
(700$^\circ$C, 18 h) reached a value of ~80% of the initial intensity I0.


title:
Constraining non-dissipative transport coefficients in global
  equilibrium

abstract:
  The fluid in global equilibrium must fulfill some constraints. These
constraints can be derived from quantum statistical theory or kinetic theory.
In this paper we will show that how these constraints can be applied to
determine the non-dissipative transport coefficients for chiral systems along
with the energy-momentum conservation, chiral anomaly for charge current and
trace anomaly in energy-momentum tensor.


title: On the component group of a real reductive group

abstract:
  For a connected linear algebraic group $G$ defined over $\mathbb{R}$, we
compute the component group $\pi_0G(\mathbb{R})$ of the real Lie group
$G(\mathbb{R})$ in terms of a maximal split torus $T_{\text{s}}\subseteq G$. In
particular, we recover a theorem of Matsumoto (1964) that each connected
component of $G(\mathbb{R})$ intersects $T_{\text{s}}(\mathbb{R})$. We provide
explicit elements of $T_{\text{s}}(\mathbb{R})$ which represent all connected
components of $G(\mathbb{R})$. The computation is based on structure results
for real loci of algebraic groups and on methods of Galois cohomology.


title:
SGDR: Semantic-guided Disentangled Representation for Unsupervised
  Cross-modality Medical Image Segmentation

abstract:
  Disentangled representation is a powerful technique to tackle domain shift
problem in medical image analysis in unsupervised domain adaptation
setting.However, previous methods only focus on exacting domain-invariant
feature and ignore whether exacted feature is meaningful for downstream
tasks.We propose a novel framework, called semantic-guided disentangled
representation (SGDR), an effective method to exact semantically meaningful
feature for segmentation task to improve performance of cross modality medical
image segmentation in unsupervised domain adaptation setting.To exact the
meaningful domain-invariant features of different modality, we introduce a
content discriminator to force the content representation to be embedded to the
same space and a feature discriminator to exact the meaningful
representation.We also use pixel-level annotations to guide the encoder to
learn features that are meaningful for segmentation task.We validated our
method on two public datasets and experiment results show that our approach
outperforms the state of the art methods on two evaluation metrics by a
significant margin.


title: Modeling Compact Binary Merger Waveforms Beyond General Relativity

abstract:
  The parameterized post-Einsteinian framework modifies inspiral waveform
models to incorporate effects beyond General Relativity. We extend the existing
model into the merger-ringdown regime. The modification introduced here adds a
single degree of freedom that corresponds to a change in the binary coalescence
time. Other merger properties remain as predicted by GR. We discuss parameter
estimation with this model, and how it can be used to extract information from
beyond-GR waveforms.


title:
Improved phenomenological nuclear charge radius formulae with kernel
  ridge regression

abstract:
  The kernel ridge regression (KRR) method with Gaussian kernel is used to
improve the description of the nuclear charge radius by several
phenomenological formulae. The widely used $A^{1/3}$, $N^{1/3}$ and $Z^{1/3}$
formulae, and their improved versions by considering the isospin dependence are
adopted as examples. The parameters in these six formulae are refitted using
the Levenberg-Marquardt method, which give better results than the previous
ones. The radius for each nucleus is predicted with the KRR network, which is
trained with the deviations between experimental and calculated nuclear charge
radii. For each formula, the resultant root-mean-square deviations of 884
nuclei with proton number $Z \geq 8$ and neutron number $N \geq 8$ can be
reduced to about 0.017~fm after considering the modification of the KRR method.
The extrapolation ability of the KRR method for the neutron-rich region is
examined carefully and compared with the radial basis function method. It is
found that the improved nuclear charge radius formulae by KRR method can avoid
the risk of overfitting and have a good extrapolation ability. The influence of
the ridge penalty term on the extrapolation ability of the KRR method is also
discussed. At last, the nuclear charge radii of several recently observed K and
Ca isotopes have been analyzed.


title:
A new type of nodal solutions to a singularly perturbed elliptic
  equations with supercritical growth

abstract:
  In this paper, we aim to investigate the following class of singularly
perturbed elliptic problem $$ \left\{
  \begin{array}{ll}
  \displaystyle -\varepsilon^2\triangle {u}+|x|^\eta u =|x|^\eta f(u)&
\mbox{in}\,\, A,
  u=0 & \mbox{on}\,\, \partial A,
  \end{array} \right. $$ where $\varepsilon>0$, $\eta\in\mathbb{R}$,
$A=\{x\in\R^{2N}:\,\,0<a<|x|<b\}$, $N\ge2$ and $f$ is a nonlinearity of $C^1$
class with supercritical growth. By a reduction argument, we show that there
exists a nodal solution $u_\e$ with exactly two positive and two negative
peaks, which concentrate on two different orthogonal spheres of dimension $N-1$
as $\e\rightarrow0$. In particular, we establish different concentration
phenomena of four peaks when the parameter $\eta>2$, $\eta=2$ and $\eta<2$.


title: Quasi-Chaotic Oscillators Based on Modular Quantum Circuits

abstract:
  Digital circuits based on residue number systems have been considered to
produce a pseudo-random behavior. The present work is an initial step towards
the complete implementation of those systems for similar applications using
quantum technology. We propose the implementation of a quasi-chaotic oscillator
based on quantum modular addition and multiplication and we prove that quantum
computing allows the parallel processing of data, paving the way for a fast and
robust multi-channel encryption/decryption scheme. The resulting structure is
assessed by several experiments in order to ascertain the desired noise-like
behavior.


title: Weighted Sum Formulas from Shuffle Products of Multiple Zeta-star Values

abstract:
  In this paper, we are going to perform the shuffle products of $Z_-(n) =
\sum_{a+b=m} (-1)^{b} \zeta(\{1\}^{a},b+2)$ and $Z_+^\star(n) = \sum_{c+d=n}
\zeta^{\star}(\{1\}^{c},d+2)$ with $m+n = p$. The resulted shuffle relation is
a weighted sum formula given by \begin{equation*}
  \frac{(p+1)(p+2)}{2} \zeta(p+4)
  =\sum_{m+n=p} \sum_{|\boldsymbol{\alpha}|=p+3}
  \zeta(\alpha_{0}, \alpha_{1}, \ldots, \alpha_{m}, \alpha_{m+1}+1)
  \sum_{a+b+c=m}
  \Bigl( W_{\boldsymbol\alpha}(a,b,c) + W_{\boldsymbol\alpha}(a,b,c=0)
  + W_{\boldsymbol\alpha}(a=0,b,c) + W_{\boldsymbol\alpha}(a=0,b=m,c=0) \Bigr),
\end{equation*} where $W_{\boldsymbol\alpha}(a,b,c) =
2^{\sigma(a+b+1)-\sigma(a)-(b+1)} (1-2^{1-\alpha_{a+b+1}}\ \ )$, with
$\sigma(r) = \sum_{j=0}^{r} \alpha_{j}$.


title:
Medicinal Boxes Recognition on a Deep Transfer Learning Augmented
  Reality Mobile Application

abstract:
  Taking medicines is a fundamental aspect to cure illnesses. However, studies
have shown that it can be hard for patients to remember the correct posology.
More aggravating, a wrong dosage generally causes the disease to worsen.
Although, all relevant instructions for a medicine are summarized in the
corresponding patient information leaflet, the latter is generally difficult to
navigate and understand. To address this problem and help patients with their
medication, in this paper we introduce an augmented reality mobile application
that can present to the user important details on the framed medicine. In
particular, the app implements an inference engine based on a deep neural
network, i.e., a densenet, fine-tuned to recognize a medicinal from its
package. Subsequently, relevant information, such as posology or a simplified
leaflet, is overlaid on the camera feed to help a patient when taking a
medicine. Extensive experiments to select the best hyperparameters were
performed on a dataset specifically collected to address this task; ultimately
obtaining up to 91.30\% accuracy as well as real-time capabilities.


title:
Continual learning of quantum state classification with gradient
  episodic memory

abstract:
  Continual learning is one of the many areas of machine learning research. For
the goal of strong artificial intelligence that can mimic human-level
intelligence, AI systems would have the ability to adapt to ever-changing
scenarios and learn new knowledge continuously without forgetting previously
acquired knowledge. A phenomenon called catastrophic forgetting emerges when a
machine learning model is trained across multiple tasks. The model's
performance on previously learned tasks may drop dramatically during the
learning process of the newly seen task. Some continual learning strategies
have been proposed to address the catastrophic forgetting problem. Recently,
continual learning has also been studied in the context of quantum machine
learning. By leveraging the elastic weight consolidation method, a single
quantum classifier can perform multiple tasks after being trained consecutively
on those tasks. In this work, we incorporate the gradient episodic memory
method to train a variational quantum classifier. The gradient of the current
task is projected to the closest gradient, avoiding the increase of the loss at
previous tasks, but allowing the decrease. We use six quantum state
classification tasks to benchmark this method. Numerical simulation results
show that better performance is obtained compared to the elastic weight
consolidation method. Furthermore, positive transfer of knowledge to previous
tasks is observed, which means the classifier's performance on previous tasks
is enhanced rather than compromised while learning a new task.


title:
Aggressive Quadrotor Flight Using Curiosity-Driven Reinforcement
  Learning

abstract:
  The ability to perform aggressive movements, which are called aggressive
flights, is important for quadrotors during navigation. However, aggressive
quadrotor flights are still a great challenge to practical applications. The
existing solutions to aggressive flights heavily rely on a predefined
trajectory, which is a time-consuming preprocessing step. To avoid such path
planning, we propose a curiosity-driven reinforcement learning method for
aggressive flight missions and a similarity-based curiosity module is
introduced to speed up the training procedure. A branch structure exploration
(BSE) strategy is also applied to guarantee the robustness of the policy and to
ensure the policy trained in simulations can be performed in real-world
experiments directly. The experimental results in simulations demonstrate that
our reinforcement learning algorithm performs well in aggressive flight tasks,
speeds up the convergence process and improves the robustness of the policy.
Besides, our algorithm shows a satisfactory simulated to real transferability
and performs well in real-world experiments.


title:
Spin and Contextuality in Extended de Broglie-Bohm-Bell Quantum
  Mechanics

abstract:
  This paper introduces an extension of the de Broglie-Bohm-Bell formulation of
quantum mechanics, which includes intrinsic particle degrees of freedom, such
as spin, as elements of reality. To evade constraints from the Kochen-Specker
theorem the discrete spin values refer to a specific basis -- i.e., a single
spin vector orientation for each particle; these spin orientations are,
however, not predetermined, but dynamic and guided by the (reduced, spin-only)
wave function of the system, which is conditional on the realized location
values of the particles. In this way, the unavoidable contextuality of spin is
provided by the wave function and its realized particle configuration, whereas
spin is still expressed as a local property of the individual particles. This
formulation, which furthermore features a rigorous discrete-time stochastic
dynamics, allows for numerical simulations of particle systems with entangled
spin, such as Bohm's version of the EPR experiment.


title: Locating quantum critical points with shallow quantum circuits

abstract:
  Quantum critical point is one key concept for studying many-body physics but
its investigation may be resource-demanding even for a quantum computer due to
the intrinsic complexity. In this paper, we propose an approach based on
variational quantum eigensolver(VQE), dubbed as Delta-VQE, for locating the
quantum critical point using only shallow quantum circuits. With Delta-VQE, the
critical point can be identified as a most confusing point, quantified as zero
difference between two variational energies that use two representative
reference states of distinct phases of matter. Remarkably, the signature of a
critical point as a minimal point can be sharper while using shallower quantum
circuits. We test the algorithm for different quantum systems and demonstrate
the usefulness of Delta-VQE. The scheme suggests a new avenue for investigating
quantum phases of matter on near-term quantum devices with limited quantum
resources.


title: On the treewidth of generalized Kneser graphs

abstract:
  The generalized Kneser graph $K(n,k,t)$ for integers $k>t>0$ and $n>2k-t$ is
the graph whose vertices are the $k$-subsets of $\{1,\dots,n\}$ with two
vertices adjacent if and only if they share less than $t$ elements. We
determine the treewidth of the generalized Kneser graphs $K(n,k,t)$ when $t\ge
2$ and $n$ is sufficiently large compared to $k$. The imposed bound on $n$ is a
significant improvement of a previously known bound. One consequence of our
result is the following. For each integer $c\ge 1$ there exists a constant
$K(c)\ge 2c$ such that $k\ge K(c)$ implies for $t=k-c$ that
$$tw(K(n,k,t))=\binom{n}{k}-\binom{n-t}{k-t}-1$$ if and only if $n\ge
(t+1)(k+1-t)$ .


title:
Data Augmentation Strategies for Improving Sequential Recommender
  Systems

abstract:
  Sequential recommender systems have recently achieved significant performance
improvements with the exploitation of deep learning (DL) based methods.
However, although various DL-based methods have been introduced, most of them
only focus on the transformations of network structure, neglecting the
importance of other influential factors including data augmentation. Obviously,
DL-based models require a large amount of training data in order to estimate
parameters well and achieve high performances, which leads to the early efforts
to increase the training data through data augmentation in computer vision and
speech domains. In this paper, we seek to figure out that various data
augmentation strategies can improve the performance of sequential recommender
systems, especially when the training dataset is not large enough. To this end,
we propose a simple set of data augmentation strategies, all of which transform
original item sequences in the way of direct corruption and describe how data
augmentation changes the performance. Extensive experiments on the latest
DL-based model show that applying data augmentation can help the model
generalize better, and it can be significantly effective to boost model
performances especially when the amount of training data is small. Furthermore,
it is shown that our proposed strategies can improve performances to a better
or competitive level to existing strategies suggested in the prior works.


title: Biolocomotion and premelting in ice

abstract:
  Biota are found in glaciers, ice sheets and permafrost. Ice bound
micro-organisms evolve in a complex mobile environment facilitated or hindered
by a range of bulk and surface interactions. When a particle is embedded in a
host solid near its bulk melting temperature, a melted film forms at the
surface of the particle in a process known as interfacial premelting. Under a
temperature gradient, the particle is driven by a thermomolecular pressure
gradient toward regions of higher temperatures in a process called thermal
regelation. When the host solid is ice and the particles are biota, thriving in
their environment requires the development of strategies, such as producing
exopolymeric substances (EPS) and antifreeze glycoproteins (AFP) that enhance
the interfacial water. Therefore, thermal regelation is enhanced and modified
by a process we term {\em bio-enhanced premelting}. Additionally, the motion of
bioparticles is influenced by chemical gradients influenced by nutrients within
the icy host body. We show how the overall trajectory of bioparticles is
controlled by a competion between thermal regelation and directed
biolocomotion. By re-casting this class of regelation phenomena in the
stochastic framework of active Ornstein-Uhlenbeck dynamics, and using multiple
scales analysis, we find that for an attractive (repulsive) nutrient source,
that thermal regelation is enhanced (suppressed) by biolocomotion. This
phenomena is important in astrobiology, the biosignatures of extremophiles and
in terrestrial paleoclimatology.


title: Tidal effects of dark matter halo around a galactic black hole

abstract:
  We have investigated the tidal forces and geodesic deviation motion in the
spacetime of a black hole in the galaxy with dark matter halo. Our results show
that the tidal force and geodesic deviation motion depend on the dark matter
halo mass and the typical lengthscale of galaxy. The effect of the typical
lengthscale of galaxy on tidal force is opposite to that of dark matter mass.
For the radial tidal force, with the increasing mass of dark matter, it
increases in the region far from the black hole, but decreases in the region
near black hole. For the angular tidal force, its absolute value of angular
tidal force monotonously increases with the dark matter halo mass. Especially,
the angular tidal force also depends on the particle's energy and the effects
of dark matter become more distinct for the test particle with high energy,
which is different from those in the usual static black hole spacetimes. We
also present the change of geodesic deviation vector with the dark matter halo
mass and the typical lengthscale of galaxy under two kinds of initial
conditions.


title: Visual Abductive Reasoning

abstract:
  Abductive reasoning seeks the likeliest possible explanation for partial
observations. Although abduction is frequently employed in human daily
reasoning, it is rarely explored in computer vision literature. In this paper,
we propose a new task and dataset, Visual Abductive Reasoning (VAR), for
examining abductive reasoning ability of machine intelligence in everyday
visual situations. Given an incomplete set of visual events, AI systems are
required to not only describe what is observed, but also infer the hypothesis
that can best explain the visual premise. Based on our large-scale VAR dataset,
we devise a strong baseline model, Reasoner (causal-and-cascaded reasoning
Transformer). First, to capture the causal structure of the observations, a
contextualized directional position embedding strategy is adopted in the
encoder, that yields discriminative representations for the premise and
hypothesis. Then, multiple decoders are cascaded to generate and progressively
refine the premise and hypothesis sentences. The prediction scores of the
sentences are used to guide cross-sentence information flow in the cascaded
reasoning procedure. Our VAR benchmarking results show that Reasoner surpasses
many famous video-language models, while still being far behind human
performance. This work is expected to foster future efforts in the
reasoning-beyond-observation paradigm.


title:
Principal Structure Identification: Fast Disentanglement of Multi-source
  Dataset

abstract:
  Analysis of multi-source data, where data on the same objects are collected
from multiple sources, is of rising importance in many fields, e.g.,
multi-omics biology. Major challenges in multi-source data analysis include
heterogeneity among different data sources and the entanglement of their
association structure among several groups of variables. Our goal is to
disentangle the association structure by identifying shared score subspaces
among all or some of data blocks. We propose a sequential algorithm that
gathers score subspaces of different data blocks within certain angle threshold
and identifies partially-shared score components, using the concept of
principal angles between subspaces of different dimensions. Our method shows
better performance in identifying the linear association structure than
competing methods in this field. In real data analysis, we apply our method to
an oncological multi-omics dataset associated with drug responses. The proposed
method boasts super-fast computational speed and results in revealing the
scores in the estimated shared component showing strong correlations with
well-known biological pathways.


title: Comments on a note by M. Waldschmidt

abstract: This note is the follow up to a paper by M. Waldschmidt.


title: Semantic Segmentation by Early Region Proxy

abstract:
  Typical vision backbones manipulate structured features. As a compromise,
semantic segmentation has long been modeled as per-point prediction on dense
regular grids. In this work, we present a novel and efficient modeling that
starts from interpreting the image as a tessellation of learnable regions, each
of which has flexible geometrics and carries homogeneous semantics. To model
region-wise context, we exploit Transformer to encode regions in a
sequence-to-sequence manner by applying multi-layer self-attention on the
region embeddings, which serve as proxies of specific regions. Semantic
segmentation is now carried out as per-region prediction on top of the encoded
region embeddings using a single linear classifier, where a decoder is no
longer needed. The proposed RegProxy model discards the common Cartesian
feature layout and operates purely at region level. Hence, it exhibits the most
competitive performance-efficiency trade-off compared with the conventional
dense prediction methods. For example, on ADE20K, the small-sized RegProxy-S/16
outperforms the best CNN model using 25% parameters and 4% computation, while
the largest RegProxy-L/16 achieves 52.9mIoU which outperforms the
state-of-the-art by 2.1% with fewer resources. Codes and models are available
at https://github.com/YiF-Zhang/RegionProxy.


title: Contrastive Graph Learning for Population-based fMRI Classification

abstract:
  Contrastive self-supervised learning has recently benefited fMRI
classification with inductive biases. Its weak label reliance prevents
overfitting on small medical datasets and tackles the high intraclass
variances. Nonetheless, existing contrastive methods generate resemblant pairs
only on pixel-level features of 3D medical images, while the functional
connectivity that reveals critical cognitive information is under-explored.
Additionally, existing methods predict labels on individual contrastive
representation without recognizing neighbouring information in the patient
group, whereas interpatient contrast can act as a similarity measure suitable
for population-based classification. We hereby proposed contrastive functional
connectivity graph learning for population-based fMRI classification.
Representations on the functional connectivity graphs are "repelled" for
heterogeneous patient pairs meanwhile homogeneous pairs "attract" each other.
Then a dynamic population graph that strengthens the connections between
similar patients is updated for classification. Experiments on a multi-site
dataset ADHD200 validate the superiority of the proposed method on various
metrics. We initially visualize the population relationships and exploit
potential subtypes.


title:
Adaptively Lighting up Facial Expression Crucial Regions via Local
  Non-Local Joint Network

abstract:
  Facial expression recognition (FER) is still one challenging research due to
the small inter-class discrepancy in the facial expression data. In view of the
significance of facial crucial regions for FER, many existing researches
utilize the prior information from some annotated crucial points to improve the
performance of FER. However, it is complicated and time-consuming to manually
annotate facial crucial points, especially for vast wild expression images.
Based on this, a local non-local joint network is proposed to adaptively light
up the facial crucial regions in feature learning of FER in this paper. In the
proposed method, two parts are constructed based on facial local and non-local
information respectively, where an ensemble of multiple local networks are
proposed to extract local features corresponding to multiple facial local
regions and a non-local attention network is addressed to explore the
significance of each local region. Especially, the attention weights obtained
by the non-local network is fed into the local part to achieve the interactive
feedback between the facial global and local information. Interestingly, the
non-local weights corresponding to local regions are gradually updated and
higher weights are given to more crucial regions. Moreover, U-Net is employed
to extract the integrated features of deep semantic information and low
hierarchical detail information of expression images. Finally, experimental
results illustrate that the proposed method achieves more competitive
performance compared with several state-of-the art methods on five benchmark
datasets. Noticeably, the analyses of the non-local weights corresponding to
local regions demonstrate that the proposed method can automatically enhance
some crucial regions in the process of feature learning without any facial
landmark information.


title:
A Survey of Robust Adversarial Training in Pattern Recognition:
  Fundamental, Theory, and Methodologies

abstract:
  In the last a few decades, deep neural networks have achieved remarkable
success in machine learning, computer vision, and pattern recognition. Recent
studies however show that neural networks (both shallow and deep) may be easily
fooled by certain imperceptibly perturbed input samples called adversarial
examples. Such security vulnerability has resulted in a large body of research
in recent years because real-world threats could be introduced due to vast
applications of neural networks. To address the robustness issue to adversarial
examples particularly in pattern recognition, robust adversarial training has
become one mainstream. Various ideas, methods, and applications have boomed in
the field. Yet, a deep understanding of adversarial training including
characteristics, interpretations, theories, and connections among different
models has still remained elusive. In this paper, we present a comprehensive
survey trying to offer a systematic and structured investigation on robust
adversarial training in pattern recognition. We start with fundamentals
including definition, notations, and properties of adversarial examples. We
then introduce a unified theoretical framework for defending against
adversarial samples - robust adversarial training with visualizations and
interpretations on why adversarial training can lead to model robustness.
Connections will be also established between adversarial training and other
traditional learning theories. After that, we summarize, review, and discuss
various methodologies with adversarial attack and defense/training algorithms
in a structured way. Finally, we present analysis, outlook, and remarks of
adversarial training.


title:
On the reflexivity of the spaces of variable integrability and
  summability

abstract:
  In this paper, we show that under the condition $ 1<p_-, q_-, p_+,
q_+<\infty$, the space $\ell^{q(\cdot)} (L^{p(\cdot)})$ is reflexive. In this
way we give an answer to open problem posed by H\"ast\"o in 2017 about the
reflexivity of the variable mixed Lebesgue-sequence spaces $\ell^{q(\cdot)}
(L^{p(\cdot)})$.
  What is important here is that the dual space of $\ell^{q(\cdot)}
(L^{p(\cdot)})$ is specified. As its direct corollary, we show that the
corresponding Besov space $B^{s(\cdot)}_{p(\cdot)q(\cdot)}$ is reflexive.


title: Learning Deep Implicit Functions for 3D Shapes with Dynamic Code Clouds

abstract:
  Deep Implicit Function (DIF) has gained popularity as an efficient 3D shape
representation. To capture geometry details, current methods usually learn DIF
using local latent codes, which discretize the space into a regular 3D grid (or
octree) and store local codes in grid points (or octree nodes). Given a query
point, the local feature is computed by interpolating its neighboring local
codes with their positions. However, the local codes are constrained at
discrete and regular positions like grid points, which makes the code positions
difficult to be optimized and limits their representation ability. To solve
this problem, we propose to learn DIF with Dynamic Code Cloud, named DCC-DIF.
Our method explicitly associates local codes with learnable position vectors,
and the position vectors are continuous and can be dynamically optimized, which
improves the representation ability. In addition, we propose a novel code
position loss to optimize the code positions, which heuristically guides more
local codes to be distributed around complex geometric details. In contrast to
previous methods, our DCC-DIF represents 3D shapes more efficiently with a
small amount of local codes, and improves the reconstruction quality.
Experiments demonstrate that DCC-DIF achieves better performance over previous
methods. Code and data are available at https://github.com/lity20/DCCDIF.


title: Joint Transformer/RNN Architecture for Gesture Typing in Indic Languages

abstract:
  Gesture typing is a method of typing words on a touch-based keyboard by
creating a continuous trace passing through the relevant keys. This work is
aimed at developing a keyboard that supports gesture typing in Indic languages.
We begin by noting that when dealing with Indic languages, one needs to cater
to two different sets of users: (i) users who prefer to type in the native
Indic script (Devanagari, Bengali, etc.) and (ii) users who prefer to type in
the English script but want the output transliterated into the native script.
In both cases, we need a model that takes a trace as input and maps it to the
intended word. To enable the development of these models, we create and release
two datasets. First, we create a dataset containing keyboard traces for 193,658
words from 7 Indic languages. Second, we curate 104,412 English-Indic
transliteration pairs from Wikidata across these languages. Using these
datasets we build a model that performs path decoding, transliteration, and
transliteration correction. Unlike prior approaches, our proposed model does
not make co-character independence assumptions during decoding. The overall
accuracy of our model across the 7 languages varies from 70-95%.


title:
Heat transfer in transversely coupled qubits: Optically controlled
  thermal modulator with common reservoirs

abstract:
  This paper systematically studied two transversely coupled qubits' heat
transfer in contact with independent and common heat reservoirs. We reveal that
common heat reservoirs can slightly suppress the steady-state heat currents
compared to independent heat reservoirs, and the transverse coupling of qubits
can enhance the suppression roles. In particular, in the case of resonant
coupling of two qubits and the proper dissipations, the steady state can be
decomposed into a stationary dark state which doesn't evolve and contributes
zero heat current, and a steady state which corresponds to the maximal heat
current. This dark state enables us to control steady-state heat current with
an external control field and design a thermal modulator. In addition, we find
that inverse heat currents could be present in the dissipative subchannels
between the system and reservoirs, which interprets the suppression roles of
common heat reservoirs. We also find that heat current for a given system has a
similar changing tendency to the concurrence of assistance with temperature.


title: Upper bounds for the critical values of homology classes of loops

abstract:
  In this short note we discuss upper bounds for the critical values of
homology classes in the based and free loop space of manifolds carrying a
Riemannian or Finsler metric of positive Ricci curvature. In particular it
follows that a shortest closed geodesic on a simply-connected $n$-dimensional
manifold of positive Ricci curvature $\textrm{Ric} \ge n-1$ has length $\le n
\pi.$


title: On a foundational conceptual principle of quantum mechanics

abstract:
  We argue that Anton Zeilinger's "foundational conceptual principle" for
quantum mechanics according to which an elementary system carries one bit of
information is an idealistic principle, which should be replaced by a realistic
principle of contextuality. Specific properties of quantum systems are a
consequence of impossibility to speak about them without reference to the tools
of their observation (identification) and, consequently, context in which these
tools are applied. In particular, the assumption of non-locality is not
required to explain quantum correlation. Correlated quantum events are related
with each other in a causal way. But this is not classical, but quantum
causality expressed by an entangled wave function. This or that particular
correlation does not arise in measurement; in measurement it is identified in
context. In contrast to Zeilinger's proposed principle of quantization of
information, the principle of contextuality explains it realistically.


title: Substrate Effects on the Speed Limiting Factor of WSe$_2$ Photodetectors

abstract:
  We investigate the time-resolved photoelectric response of WSe$_2$ crystals
on common glass and flexible polyimide substrates to determine the effect of
the dielectric environment on the speed of the photodetectors. We show that
varying the substrate material can alter the speed-limiting mechanism: while
the detectors on polyimide are RC limited, those on glass are limited by slower
excitonic diffusion processes. We attribute this to a shortening of the
depletion layer at the metal electrode/WSe2 interface caused by the higher
dielectric screening of glass compared to polyimide. The photodetectors on
glass show a tunable bandwidth which can be increased to 2.6 MHz with
increasing the electric field.


title: Universal Clone Algebra

abstract:
  We develop a new general framework for algebras and clones, called Universal
Clone Algebra. Algebras and clones of finitary operations are to Universal
Algebra what t-algebras and clone algebras are to Universal Clone Algebra.
Clone algebras have been recently introduced to found a one-sorted, purely
algebraic theory of clones, while t-algebras are first introduced in this
article. We present a method to codify algebras and clones into t-algebras and
clone algebras, respectively. We provide concrete examples showing that general
results in Universal Clone Algebra, when translated in terms of algebras and
clones, give more powerful versions of known theorems in Universal Algebra. We
apply this methodology to Birkhoff's HSP theorem and to the recent topological
versions of Birkhoff's theorem.


title: Aeroelastic characterisation of a bio-inspired flapping membrane wing

abstract:
  Natural fliers like bats exploit the complex fluid-structure interaction
between their flexible membrane wings and the air with great ease. Yet,
replicating and scaling the balance between the structural and fluid-dynamical
parameters of unsteady membrane wings for engineering applications remains
challenging. In this study, we introduce a novel bio-inspired membrane wing
design and systematically investigate the fluid-structure interactions of
flapping membrane wings. The membrane wing can passively camber and its leading
and trailing edges rotate with respect to the stroke plane. We find optimal
combinations of the membrane properties and flapping kinematics that
out-perform their rigid counterparts both in terms of increased stroke-average
lift and efficiency but the improvements are not persistent over the entire
input parameter space. The lift and efficiency optima occur at different angles
of attack and effective membrane stiffnesses which we characterise with the
aeroelastic number. At optimal aeroelastic numbers, the membrane has a moderate
camber between 15% and 20% and its leading and trailing edges align favourably
with the flow. Higher camber at lower aeroelastic numbers leads to reduced
aerodynamic performance due to negative angles of attack at the leading edge
and an over-rotation of the trailing edge. Most of the performance gain of the
membrane wings with respect to rigid wings is achieved in the second half of
the stroke when the wing is decelerating. The stroke-maximum camber is reached
around mid-stroke but is sustained during most of the remainder of the stroke
which leads to an increase in lift and a reduction in power. Our results show
that combining the effect of variable stiffness and angle of attack variation
can significantly enhance the aerodynamic performance of membrane wings and has
the potential to improve the control capabilities of micro air vehicles.