title:
Noncoherent Massive MIMO with Embedded One-Way Function Physical Layer
  Security

abstract:
  We propose a novel physical layer security scheme that exploits an
optimization method as a one-way function. The proposed scheme builds on
nonsquare differential multiple-input multiple-output (MIMO), which is capable
of noncoherent detection even in massive MIMO scenarios and thus resilient
against risky pilot insertion and pilot contamination attacks. In contrast to
conventional nonsquare differential MIMO schemes, which require space-time
projection matrices designed via highly complex, discrete, and combinatorial
optimization, the proposed scheme utilizes projection matrices constructed via
low-complexity continuous optimization designed to maximize the coding gain of
the system. Furthermore, using a secret key generated from the true randomness
nature of the wireless channel as an initial value, the proposed continuous
optimization-based projection matrix construction method becomes a one
way-function (in a cryptographic sense), making the proposed scheme a physical
layer secure differential MIMO system. An attack algorithm to challenge the
proposed scheme is also devised, which demonstrate that the security level
achieved improves as the number of transmit antennas increases, even in an
environment where the eavesdropper can perfectly estimate channel coefficients
and experiences asymptotically large signal-to-noise ratios.


title: Stability of geodesic vectors in low-dimensional Lie algebras

abstract:
  A naturally parameterised curve in a Lie group with a left invariant metric
is a geodesic, if its tangent vector left-translated to the identity satisfies
the Euler equation $\dot{Y}=\operatorname{ad}^t_YY$ on the Lie algebra
$\mathfrak{g}$ of $G$. Stationary points (equilibria) of the Euler equation are
called geodesic vectors: the geodesic starting at the identity in the direction
of a geodesic vector is a one-parameter subgroup of $G$. We give a complete
classification of Lyapunov stable and unstable geodesic vectors for metric Lie
algebras of dimension $3$ and for unimodular metric Lie algebras of dimension
$4$.


title:
The Impossibility of Testing for Dependence Using Kendall's $\tau$ Under
  Missing Data of Unknown Form

abstract:
  This paper discusses the statistical inference problem associated with
testing for dependence between two continuous random variables using Kendall's
$\tau$ in the context of the missing data problem. We prove the worst-case
identified set for this measure of association always includes zero. The
consequence of this result is that robust inference for dependence using
Kendall's $\tau$, where robustness is with respect to the form of the
missingness-generating process, is impossible.


title: Controlling Memorability of Face Images

abstract:
  Everyday, we are bombarded with many photographs of faces, whether on social
media, television, or smartphones. From an evolutionary perspective, faces are
intended to be remembered, mainly due to survival and personal relevance.
However, all these faces do not have the equal opportunity to stick in our
minds. It has been shown that memorability is an intrinsic feature of an image
but yet, it is largely unknown what attributes make an image more memorable. In
this work, we aimed to address this question by proposing a fast approach to
modify and control the memorability of face images. In our proposed method, we
first found a hyperplane in the latent space of StyleGAN to separate high and
low memorable images. We then modified the image memorability (while
maintaining the identity and other facial features such as age, emotion, etc.)
by moving in the positive or negative direction of this hyperplane normal
vector. We further analyzed how different layers of the StyleGAN augmented
latent space contribute to face memorability. These analyses showed how each
individual face attribute makes an image more or less memorable. Most
importantly, we evaluated our proposed method for both real and synthesized
face images. The proposed method successfully modifies and controls the
memorability of real human faces as well as unreal synthesized faces. Our
proposed method can be employed in photograph editing applications for social
media, learning aids, or advertisement purposes.


title: Deep learning on nuclear mass and $\alpha$ decay half-lives

abstract:
  Ab-initio calculations of nuclear masses, the binding energy and the $\alpha$
decay half-lives are intractable for heavy nucleus, because of the curse of
dimensionality in many body quantum simulations as proton number($\mathrm{N}$)
and neutron number($\mathrm{Z}$) grow. We take advantage of the powerful
non-linear transformation and feature representation ability of deep neural
network(DNN) to predict the nuclear masses and $\alpha$ decay half-lives. For
nuclear binding energy prediction problem we achieve standard deviation
$\sigma=0.263$ MeV on 10-fold cross validation on 2149 nuclei. Word-vectors
which are high dimensional representation of nuclei from the hidden layers of
mass-regression DNN help us to calculate $\alpha$ decay half-lives. For this
task, we get $\sigma=0.797$ on 100 times 10-fold cross validation on 350 nuclei
on $log_{10}T_{1/2}$ and $\sigma=0.731 $ on 486 nuclei. We also find physical a
priori such as shell structure, magic numbers and augmented inputs inspired by
Finite Range Droplet Model are important for this small data regression task.


title:
Improving Robustness of Convolutional Neural Networks Using Element-Wise
  Activation Scaling

abstract:
  Recent works reveal that re-calibrating the intermediate activation of
adversarial examples can improve the adversarial robustness of a CNN model. The
state of the arts [Baiet al., 2021] and [Yanet al., 2021] explores this feature
at the channel level, i.e. the activation of a channel is uniformly scaled by a
factor. In this paper, we investigate the intermediate activation manipulation
at a more fine-grained level. Instead of uniformly scaling the activation, we
individually adjust each element within an activation and thus propose
Element-Wise Activation Scaling, dubbed EWAS, to improve CNNs' adversarial
robustness. Experimental results on ResNet-18 and WideResNet with CIFAR10 and
SVHN show that EWAS significantly improves the robustness accuracy. Especially
for ResNet18 on CIFAR10, EWAS increases the adversarial accuracy by 37.65% to
82.35% against C&W attack. EWAS is simple yet very effective in terms of
improving robustness. The codes are anonymously available at
https://anonymous.4open.science/r/EWAS-DD64.


title:
An Efficient Binary Harris Hawks Optimization based on Quantum SVM for
  Cancer Classification Tasks

abstract:
  Cancer classification based on gene expression increases early diagnosis and
recovery, but high-dimensional genes with a small number of samples are a major
challenge. This work introduces a new hybrid quantum kernel support vector
machine (QKSVM) combined with a Binary Harris hawk optimization (BHHO) based
gene selection for cancer classification on a quantum simulator. This study
aims to improve the microarray cancer prediction performance with the quantum
kernel estimation based on the informative genes by BHHO. The feature selection
is a critical step in large-dimensional features, and BHHO is used to select
important features. The BHHO mimics the behavior of the cooperative action of
Harris hawks in nature. The principal component analysis (PCA) is applied to
reduce the selected genes to match the qubit numbers. After which, the quantum
computer is used to estimate the kernel with the training data of the reduced
genes and generate the quantum kernel matrix. Moreover, the classical computer
is used to draw the support vectors based on the quantum kernel matrix. Also,
the prediction stage is performed with the classical device. Finally, the
proposed approach is applied to colon and breast microarray datasets and
evaluated with all genes and the selected genes by BHHO. The proposed approach
is found to enhance the overall performance with two datasets. Also, the
proposed approach is evaluated with different quantum feature maps (kernels)
and classical kernel (RBF).


title:
SLRNet: Semi-Supervised Semantic Segmentation Via Label Reuse for Human
  Decomposition Images

abstract:
  Semantic segmentation is a challenging computer vision task demanding a
significant amount of pixel-level annotated data. Producing such data is a
time-consuming and costly process, especially for domains with a scarcity of
experts, such as medicine or forensic anthropology. While numerous
semi-supervised approaches have been developed to make the most from the
limited labeled data and ample amount of unlabeled data, domain-specific
real-world datasets often have characteristics that both reduce the
effectiveness of off-the-shelf state-of-the-art methods and also provide
opportunities to create new methods that exploit these characteristics. We
propose and evaluate a semi-supervised method that reuses available labels for
unlabeled images of a dataset by exploiting existing similarities, while
dynamically weighting the impact of these reused labels in the training
process. We evaluate our method on a large dataset of human decomposition
images and find that our method, while conceptually simple, outperforms
state-of-the-art consistency and pseudo-labeling-based methods for the
segmentation of this dataset. This paper includes graphic content of human
decomposition.


title: On the boundedness of multilinear Fourier multipliers on Hardy spaces

abstract:
  In this paper, we study multilinear Fourier multiplier operators on Hardy
spaces. In particular, we prove that the multilinear Fourier multiplier
operator of H\"ormander type is bounded from $H^{p_1} \times \cdots \times
H^{p_m}$ to $H^p$ for $0<p_1,\dots,p_m\le 1$ with $1/p_1 + \cdots 1/p_m = 1/p$,
under suitable cancellation conditions. As a result, we extend the trilinear
estimates of the authors(arXiv:2107.00225) to general multilinear ones and
improve the boundedness result of the authors, Lee, Heo, Hong, Park, and
Yang(Math. Ann. 381 : 499-555, 2021) in limiting situations.


title: A PTAS for Packing Hypercubes into a Knapsack

abstract:
  We study the d-dimensional hypercube knapsack problem where we are given a
set of d-dimensional hypercubes with associated profits, and a knapsack which
is a unit d-dimensional hypercube. The goal is to find an axis-aligned
non-overlapping packing of a subset of hypercubes such that the profit of the
packed hypercubes is maximized. For this problem, Harren (ICALP'06) gave an
algorithm with an approximation ratio of (1+1/2^d+epsilon). For d=2, Jansen and
Solis-Oba (IPCO'08) showed that the problem admits a polynomial-time
approximation scheme (PTAS); Heydrich and Wiese (SODA'17) further improved the
running time and gave an efficient polynomial-time approximation scheme
(EPTAS). Both the results use structural properties of 2-D packing, which do
not generalize to higher dimensions. For d>2, it remains open to obtain a PTAS,
and in fact, there has been no improvement since Harren's result.
  We settle the problem by providing a PTAS. Our main technical contribution is
a structural lemma which shows that any packing of hypercubes can be converted
into another structured packing such that a high profitable subset of
hypercubes is packed into a constant number of special hypercuboids, called
V-Boxes and N-Boxes. As a side result, we give an almost optimal algorithm for
a variant of the strip packing problem in higher dimensions. This might have
applications for other multidimensional geometric packing problems.


title: Deep Learning Unresolved Lensed Lightcurves

abstract:
  Gravitationally lensed sources may have unresolved or blended multiple
images, and for time varying sources the lightcurves from individual images can
overlap. We use convolutional neural nets to both classify the lightcurves as
due to unlensed, double, or quad lensed sources and fit for the time delays.
Focusing on lensed supernova systems with time delays $\Delta t\gtrsim6$ days,
we achieve 100\% precision and recall in identifying the number of images and
then estimating the time delays to $\sigma_{\Delta t}\approx1$ day, with a
$1000\times$ speedup relative to our previous Monte Carlo technique. This also
succeeds for flux noise levels $\sim10\%$. For $\Delta t\in[2,6]$ days we
obtain 94--98\% accuracy, depending on image configuration. We also explore
using partial lightcurves where observations only start near maximum light,
without the rise time data, and quantify the success.


title:
Unusual Hole-doping-dependent Electronic Instability and Electron-Phonon
  Coupling in Infinite-layer Nickelates

abstract:
  The interplay between superconductivity and charge density waves (CDWs) under
hole doping in cuprates is one of the central phenomena in condensed matter
physics. Recently, CDWs are also observed in CaCuO$_2$-analogous nickelates
RNiO$_2$ (R = La, Nd) but exhibit fundamentally different hole-doping-dependent
behaviors compared to that in cuprates, raising a challenging question on its
origin. In this article, we propose that electronic instability (EI) and
moment-dependent electron-phonon coupling (MEPC), mainly contributed by Ni
3dx2-y2 and R 5dz2, respectively, may be the possible reasons for CDW formation
in RNiO$_2$. Without hole doping, a strong Fermi surface nesting (FSN) induced
by the unique feature of van Hove singularity (VHS) across Fermi level exists
in RNiO$_2$ but not in CaCuO$_2$, and the unusual temperature-insensitive
feature of EI and MEPC could result in rather high temperature CDWs in
RNiO$_2$. Under hole doping, the reduced FSN of Ni 3dx2-y2 by the shift of VHS
and decreased occupation of R 5dz2 largely weaken EI and MEPC in RNiO$_2$,
respectively, suppressing the CDW formation. Our theory not only offers
possible explanations to some puzzling experimental observations, but also
establishes a unified understanding on the hole-doping-dependent EI and MEPC in
nickelates and cuprates.


title: Topological Anderson insulators induced by random binary disorders

abstract:
  Different disorders lead to various localization and topological phenomena in
condensed matter and artificial systems. Here we study the topological and
localization properties in one-dimensional Su-Schrieffer-Heeger model with
spatially correlated random binary disorders. It is found that random binary
disorders can induce the topological Anderson insulating phase from the trivial
insulator in various parameter regions. The topological Anderson insulators are
characterized by the disorder-averaged winding number and localized bulk states
revealed by the inverse participation ratio in both real and momentum spaces.
We show that the topological phase boundaries are consistent with the
analytical results of the self-consistent Born approach and the localization
length of zero-energy modes, and discuss how the bimodal probability affects
the disorder-induced topological phases. The topological characters can be
detected from the mean chiral displacement in atomic or photonic systems. Our
work provides an extension of the topological Anderson insulators to the case
of correlated disorders.


title:
The effect of loop quantum gravitational rainbow functions on the
  formation of naked singularities

abstract:
  In this paper, we will investigate the consequences of loop quantum
gravitational modifications on the formation of naked singularities. The loop
quantum gravitational effects will be incorporated in the collapsing system
using gravity's rainbow. This will be done by using rainbow functions, which
are constructed from loop quantum gravitational modifications to the energy
momentum dispersion. It will be observed that this modification will prevent
the formation of naked singularity. Thus, such a modification can ensures that
the weak cosmic censorship hypothesis will hold for any collapsing system.


title: Uncertainty-driven Planner for Exploration and Navigation

abstract:
  We consider the problems of exploration and point-goal navigation in
previously unseen environments, where the spatial complexity of indoor scenes
and partial observability constitute these tasks challenging. We argue that
learning occupancy priors over indoor maps provides significant advantages
towards addressing these problems. To this end, we present a novel planning
framework that first learns to generate occupancy maps beyond the field-of-view
of the agent, and second leverages the model uncertainty over the generated
areas to formulate path selection policies for each task of interest. For
point-goal navigation the policy chooses paths with an upper confidence bound
policy for efficient and traversable paths, while for exploration the policy
maximizes model uncertainty over candidate paths. We perform experiments in the
visually realistic environments of Matterport3D using the Habitat simulator and
demonstrate: 1) Improved results on exploration and map quality metrics over
competitive methods, and 2) The effectiveness of our planning module when
paired with the state-of-the-art DD-PPO method for the point-goal navigation
task.


title: Program Synthesis for the OEIS

abstract:
  We present a self-learning approach to synthesize programs for integer
sequences. Our method relies on a tree search equipped with a semantic quotient
guided by a learned policy. Through self-learning, our implementation discovers
in one week programs that generate the first 16 numbers of 43516 OEIS sequences
with sequences identical up to the 16th number counted only once.


title:
Ultrafast photothermoelectric effect in Dirac semimetallic Cd3As2
  revealed by terahertz emission

abstract:
  The thermoelectric effects of topological semimetals have attracted
tremendous research interest because many topological semimetals are excellent
thermoelectric materials and thermoelectricity serves as one of their most
important potential applications. In this work, we reveal the transient
photothermoelectric response of Dirac semimetallic Cd3As2, namely the
photo-Seebeck effect and photo-Nernst effect, by studying the terahertz (THz)
emission from the transient photocurrent induced by these effects. Our
excitation polarization and power dependence confirm that the observed THz
emission is due to photothermoelectric effect instead of other nonlinear
optical effect. Furthermore, when a weak magnetic field (~0.4 T) is applied,
the response clearly indicates an order of magnitude enhancement on transient
photothermoelectric current generation compared to the photo-Seebeck effect.
Such enhancement supports an ambipolar transport nature of the photo-Nernst
current generation in Cd3As2. These results highlight the enhancement of
thermoelectric performance can be achieved in topological Dirac semimetals
based on the Nernst effect, and our transient studies pave the way for
thermoelectric devices applicable for high field circumstance when
nonequilibrium state matters. The large THz emission due to highly efficient
photothermoelectric conversion is comparable to conventional semiconductors
through optical rectification and photo-Dember effect.


title: Robust Probabilistic Time Series Forecasting

abstract:
  Probabilistic time series forecasting has played critical role in
decision-making processes due to its capability to quantify uncertainties. Deep
forecasting models, however, could be prone to input perturbations, and the
notion of such perturbations, together with that of robustness, has not even
been completely established in the regime of probabilistic forecasting. In this
work, we propose a framework for robust probabilistic time series forecasting.
First, we generalize the concept of adversarial input perturbations, based on
which we formulate the concept of robustness in terms of bounded Wasserstein
deviation. Then we extend the randomized smoothing technique to attain robust
probabilistic forecasters with theoretical robustness certificates against
certain classes of adversarial perturbations. Lastly, extensive experiments
demonstrate that our methods are empirically effective in enhancing the
forecast quality under additive adversarial attacks and forecast consistency
under supplement of noisy observations.


title:
When Transformer Meets Robotic Grasping: Exploits Context for Efficient
  Grasp Detection

abstract:
  In this paper, we present a transformer-based architecture, namely TF-Grasp,
for robotic grasp detection. The developed TF-Grasp framework has two elaborate
designs making it well suitable for visual grasping tasks. The first key design
is that we adopt the local window attention to capture local contextual
information and detailed features of graspable objects. Then, we apply the
cross window attention to model the long-term dependencies between distant
pixels. Object knowledge, environmental configuration, and relationships
between different visual entities are aggregated for subsequent grasp
detection. The second key design is that we build a hierarchical
encoder-decoder architecture with skip-connections, delivering shallow features
from encoder to decoder to enable a multi-scale feature fusion. Due to the
powerful attention mechanism, the TF-Grasp can simultaneously obtain the local
information (i.e., the contours of objects), and model long-term connections
such as the relationships between distinct visual concepts in clutter.
Extensive computational experiments demonstrate that the TF-Grasp achieves
superior results versus state-of-art grasping convolutional models and attain a
higher accuracy of 97.99% and 94.6% on Cornell and Jacquard grasping datasets,
respectively. Real-world experiments using a 7DoF Franka Emika Panda robot also
demonstrate its capability of grasping unseen objects in a variety of
scenarios. The code and pre-trained models will be available at
https://github.com/WangShaoSUN/grasp-transformer


title:
A Rigorous Study of Integrated Gradients Method and Extensions to
  Internal Neuron Attributions

abstract:
  As the efficacy of deep learning (DL) grows, so do concerns about the lack of
transparency of these black-box models. Attribution methods aim to improve
transparency of DL models by quantifying an input feature's importance to a
model's prediction. The method of Integrated gradients (IG) sets itself apart
by claiming other methods failed to satisfy desirable axioms, while IG and
methods like it uniquely satisfied said axioms. This paper comments on
fundamental aspects of IG and its applications/extensions: 1) We identify key
unaddressed differences between DL-attribution function spaces and the
supporting literature's function spaces which problematize previous claims of
IG uniqueness. We show that with the introduction of an additional axiom,
$\textit{non-decreasing positivity}$, the uniqueness claim can be established.
2) We address the question of input sensitivity by identifying function spaces
where the IG is/is not Lipschitz continuous in the attributed input. 3) We show
how axioms for single-baseline methods in IG impart analogous properties for
methods where the baseline is a probability distribution over the input sample
space. 4) We introduce a means of decomposing the IG map with respect to a
layer of internal neurons while simultaneously gaining internal-neuron
attributions. Finally, we present experimental results validating the
decomposition and internal neuron attributions.


title:
Deflection of light by magnetars in the generalized Born-Infeld
  electrodynamics

abstract:
  We study the deflection of light by a magnetic dipole field in the
generalized Born-Infeld electrodynamics. Using the effective index of
refraction and the trajectory equation based on geometric optics, we compute
the weak bending angle of light passing on the equator of the magnetic dipole.
In the limit where the classical Born-Infeld parameter is infinite, the bending
angle agree with the one computed from the Euler-Heisenberg Lagrangian. We also
compute the bending angle using the geodesic equation of the effective metric
induced by a massive object with magnetic dipole. In the massless limit the
bending angle agrees with the computation using the trajectory equation. We
apply the result to magnetars to estimate the order of magnitude for the
bending angle.


title: An Efficient Adaptive Finite Element Method for Eigenvalue Problems

abstract:
  The aim of this paper is to propose an efficient adaptive finite element
method for eigenvalue problems based on the multilevel correction scheme and
inverse power method. This method involves solving associated boundary value
problems on each adaptive partitions and very low dimensional eigenvalue
problems on some special meshes which are controlled by the proposed algorithm.
Since we Hence the efficiency of solving eigenvalue problems can be improved to
be similar to the adaptive finite element method for the associated boundary
value problems. The convergence and optimal complexity is theoretically
verified and numerically demonstrated.


title:
Interpolation-based Contrastive Learning for Few-Label Semi-Supervised
  Learning

abstract:
  Semi-supervised learning (SSL) has long been proved to be an effective
technique to construct powerful models with limited labels. In the existing
literature, consistency regularization-based methods, which force the perturbed
samples to have similar predictions with the original ones have attracted much
attention for their promising accuracy. However, we observe that, the
performance of such methods decreases drastically when the labels get extremely
limited, e.g., 2 or 3 labels for each category. Our empirical study finds that
the main problem lies with the drifting of semantic information in the
procedure of data augmentation. The problem can be alleviated when enough
supervision is provided. However, when little guidance is available, the
incorrect regularization would mislead the network and undermine the
performance of the algorithm. To tackle the problem, we (1) propose an
interpolation-based method to construct more reliable positive sample pairs;
(2) design a novel contrastive loss to guide the embedding of the learned
network to change linearly between samples so as to improve the discriminative
capability of the network by enlarging the margin decision boundaries. Since no
destructive regularization is introduced, the performance of our proposed
algorithm is largely improved. Specifically, the proposed algorithm outperforms
the second best algorithm (Comatch) with 5.3% by achieving 88.73%
classification accuracy when only two labels are available for each class on
the CIFAR-10 dataset. Moreover, we further prove the generality of the proposed
method by improving the performance of the existing state-of-the-art algorithms
considerably with our proposed strategy.


title: Stationary accretion flow with nuclear burning

abstract:
  We present a series of numerical solutions of spherically symmetric
stationary flows with nuclear burning accreted by a neutron star (or black
hole). We consider the accretion of matter composed of carbon and oxygen, which
mimics the flow after a neutron star is engulfed by a CO star or CO core of a
massive star. It is found that there are two types of transonic solutions
depending on the accretion rate. The flow with a small accretion rate reaches
the center (or the surface of the central object) at supersonic speeds. The
other type with a large accretion rate has another sonic point inside the
transonic point and the flow truncates at the sonic point. The critical
accretion rate dividing these two types is derived as a function of the mass of
the central object and the specific enthalpy in the ambient matter. We discuss
implications from the solutions for a new mechanism of super-Chandrasekhar type
Ia supernovae and type Icn supernovae.


title:
Machine Learning for Intrusion Detection in Industrial Control Systems:
  Applications, Challenges, and Recommendations

abstract:
  Methods from machine learning are being applied to design Industrial Control
Systems resilient to cyber-attacks. Such methods focus on two major areas: the
detection of intrusions at the network-level using the information acquired
through network packets, and detection of anomalies at the physical process
level using data that represents the physical behavior of the system. This
survey focuses on four types of methods from machine learning in use for
intrusion and anomaly detection, namely, supervised, semi-supervised,
unsupervised, and reinforcement learning. Literature available in the public
domain was carefully selected, analyzed, and placed in a 7-dimensional space
for ease of comparison. The survey is targeted at researchers, students, and
practitioners. Challenges associated in using the methods and research gaps are
identified and recommendations are made to fill the gaps.


title:
Phase Continuity: Learning Derivatives of Phase Spectrum for Speech
  Enhancement

abstract:
  Modern neural speech enhancement models usually include various forms of
phase information in their training loss terms, either explicitly or
implicitly. However, these loss terms are typically designed to reduce the
distortion of phase spectrum values at specific frequencies, which ensures they
do not significantly affect the quality of the enhanced speech. In this paper,
we propose an effective phase reconstruction strategy for neural speech
enhancement that can operate in noisy environments. Specifically, we introduce
a phase continuity loss that considers relative phase variations across the
time and frequency axes. By including this phase continuity loss in a
state-of-the-art neural speech enhancement system trained with reconstruction
loss and a number of magnitude spectral losses, we show that our proposed
method further improves the quality of enhanced speech signals over the
baseline, especially when training is done jointly with a magnitude spectrum
loss.


title:
Threading the Needle of On and Off-Manifold Value Functions for Shapley
  Explanations

abstract:
  A popular explainable AI (XAI) approach to quantify feature importance of a
given model is via Shapley values. These Shapley values arose in cooperative
games, and hence a critical ingredient to compute these in an XAI context is a
so-called value function, that computes the "value" of a subset of features,
and which connects machine learning models to cooperative games. There are many
possible choices for such value functions, which broadly fall into two
categories: on-manifold and off-manifold value functions, which take an
observational and an interventional viewpoint respectively. Both these classes
however have their respective flaws, where on-manifold value functions violate
key axiomatic properties and are computationally expensive, while off-manifold
value functions pay less heed to the data manifold and evaluate the model on
regions for which it wasn't trained. Thus, there is no consensus on which class
of value functions to use. In this paper, we show that in addition to these
existing issues, both classes of value functions are prone to adversarial
manipulations on low density regions. We formalize the desiderata of value
functions that respect both the model and the data manifold in a set of axioms
and are robust to perturbation on off-manifold regions, and show that there
exists a unique value function that satisfies these axioms, which we term the
Joint Baseline value function, and the resulting Shapley value the Joint
Baseline Shapley (JBshap), and validate the effectiveness of JBshap in
experiments.


title: Quantum sensing on magnetic field inspired by Avian compass

abstract:
  Magnetic measurement can be performed by various sensors, such as SQUID and
Giant Magnetoresistance. This device can achieve high accuracy while losing
efficiency and convenience. The model of biological magnetic sensing in avian
proposes a radical pair response to the external field on the FLY results in
regulating animal behaviour. Inspired by the radical pair system found in
biological system, the effect of intra-radical coupling and the initial
condition is studied in this simplified radical pair model as the quantum
advantage results from entanglement and superposition are investigated in
metrology. To identify the sensing benefit from the cooperation between the
radical pairs, the inter-radical coupling is considered. We find the
intra-radical coupling determines the sensing regime while the coupled radical
pair system enables a new sensing scheme that provides a more general and
flexible way for magnetic sensing in a broader range through a easier
manipulation to the system.


title: Auto-scaling Vision Transformers without Training

abstract:
  This work targets automated designing and scaling of Vision Transformers
(ViTs). The motivation comes from two pain spots: 1) the lack of efficient and
principled methods for designing and scaling ViTs; 2) the tremendous
computational cost of training ViT that is much heavier than its convolution
counterpart. To tackle these issues, we propose As-ViT, an auto-scaling
framework for ViTs without training, which automatically discovers and scales
up ViTs in an efficient and principled manner. Specifically, we first design a
"seed" ViT topology by leveraging a training-free search process. This
extremely fast search is fulfilled by a comprehensive study of ViT's network
complexity, yielding a strong Kendall-tau correlation with ground-truth
accuracies. Second, starting from the "seed" topology, we automate the scaling
rule for ViTs by growing widths/depths to different ViT layers. This results in
a series of architectures with different numbers of parameters in a single run.
Finally, based on the observation that ViTs can tolerate coarse tokenization in
early training stages, we propose a progressive tokenization strategy to train
ViTs faster and cheaper. As a unified framework, As-ViT achieves strong
performance on classification (83.5% top1 on ImageNet-1k) and detection (52.7%
mAP on COCO) without any manual crafting nor scaling of ViT architectures: the
end-to-end model design and scaling process cost only 12 hours on one V100 GPU.
Our code is available at https://github.com/VITA-Group/AsViT.


title:
Proximity spin-orbit and exchange coupling in ABA and ABC trilayer
  graphene van der Waals heterostructures

abstract:
  We investigate the proximity spin-orbit and exchange couplings in ABA and ABC
trilayer graphene encapsulated within monolayers of semiconducting
transition-metal dichalcogenides and the ferromagnetic semiconductor
Cr$_2$Ge$_2$Te$_6$. Employing first-principles calculations we obtain the
electronic structures of the multilayer stacks and extract the relevant
proximity-induced orbital and spin interaction parameters by fitting the
low-energy bands to model Hamiltonians. We also demonstrate the tunability of
the proximity effects by a transverse electric field. Using the model
Hamiltonians we also study mixed spin-orbit/exchange coupling encapsulation,
which allows to tailor the spin interactions very efficiently by the applied
field. We also summarize the spin-orbit physics of bare ABA, ABC, and ABB
trilayers, and provide, along with the first-principles results of the
electronic band structures, density of states, spin splittings, and
electric-field tunabilities of the bands, qualitative understanding of the
observed behavior and realistic model parameters as a resource for model
simulations of transport and correlation physics in trilayer graphene.


title:
Welcome to the Modern World of Pronouns: Identity-Inclusive Natural
  Language Processing beyond Gender

abstract:
  The world of pronouns is changing. From a closed class of words with few
members to a much more open set of terms to reflect identities. However,
Natural Language Processing (NLP) is barely reflecting this linguistic shift,
even though recent work outlined the harms of gender-exclusive language
technology. Particularly problematic is the current modeling 3rd person
pronouns, as it largely ignores various phenomena like neopronouns, i.e.,
pronoun sets that are novel and not (yet) widely established. This omission
contributes to the discrimination of marginalized and underrepresented groups,
e.g., non-binary individuals. However, other identity-expression phenomena
beyond gender are also ignored by current NLP technology. In this paper, we
provide an overview of 3rd person pronoun issues for NLP. Based on our
observations and ethical considerations, we define a series of desiderata for
modeling pronouns in language technology. We evaluate existing and novel
modeling approaches w.r.t. these desiderata qualitatively, and quantify the
impact of a more discrimination-free approach on established benchmark data.


title:
On the Variation of Volumetric Evolution of CMEs from Inner to Outer
  Corona

abstract:
  Some of the major challenges faced in understanding the early evolution of
Coronal Mass Ejections (CMEs) are due to limited observations in the inner
corona ($<\,3$ R$_{\odot}$) and the plane of sky measurements. In this work, we
have thus extended the application of the Graduated Cylindrical Shell (GCS)
model to the inner coronal observations from the ground--based coronagraph
K--Cor of the Mauna Loa Solar Observatory (MLSO) along with the pair of
observations from COR--1 onboard the Solar Terrestrial Relations Observatory
(STEREO). We study the rapid initial acceleration and width expansion phase of
5 CMEs in white-light in the lower heights. We also study the evolution of the
modelled volume of these CMEs in inner corona and report for the first time, a
power law dependence of the CME volume with distance from the Sun. We further
find the volume of ellipsoidal leading front and the conical legs follow
different power laws, thus indicating differential volume expansion through a
CME. The study also reveals two distinct power laws for the total volume
evolution of CMEs in the inner and outer corona, thus suggesting different
expansion mechanisms at these different heights. These results besides aiding
our current understanding on CME evolution, will also provide better
constraints to CME initiation and propagation models. Also, since the loss of
STEREO-B (and hence COR--1B data) from 2016, this modified GCS model presented
here will still enable stereoscopy in the inner corona for the 3D study of CMEs
in white-light.


title: Formation and Morphology of the First Galaxies in the Cosmic Morning

abstract:
  We investigate the formation and morphological evolution of the first
galaxies in the cosmic morning ($10>z>4$) using the Horizon Run 5 (HR5)
cosmological hydrodynamical simulation. For galaxies above the stellar mass
$M_{\star} = 2\times10^9\,M_{\odot}$, we classify them into disk, spheroid, and
irregular types according to their asymmetry and stellar mass morphology. We
find that 2/3 of galaxies have a S\'{e}rsic index less than 1.5, reflecting the
dominance of disk-type morphology in the cosmic morning. The rest are evenly
distributed as irregulars or spheroids. We also find that these fractions are
roughly independent of redshift and stellar mass up to
$\sim10^{10}\,M_{\odot}$, while irregular or spheroidal morphology appears
incidental and transient. Almost all the first galaxies with
$M_{\star}>2\times10^9\,M_{\odot}$ at redshift 6 form at initial peaks of the
matter density field. Large-scale structures in the universe emerge and grow
like cosmic rhizomes as the underlying matter density fluctuations grow and
form associations of galaxies in rare overdense regions. The growth of the
density field further stretches the realm of the galactic world into relatively
lower-density regions along evolving filaments. The cosmic web of galaxies
forms at lower redshifts when most rhizomes globally percolate. The primordial
angular momentum produced by the induced tidal torques on protogalactic regions
is correlated with the internal kinematics of galaxies and tightly aligned with
the angular momentum of the total galaxy mass. However, the primordial angular
momentum only very weakly correlates with the instantaneous morphology and
orientation of the stellar component below $z=6$. The large-scale tidal field
imprinted in the initial conditions seems responsible for the dominance of disk
morphology, and for the tendency of galaxies to re-acquire a disk
post-distortion.


title: Dynamic control of ferroionic states in ferroelectric nanoparticles

abstract:
  The polar states of uniaxial ferroelectric nanoparticles interacting with a
surface system of electronic and ionic charges with a broad distribution of
mobilities is explored, which corresponds to the experimental case of
nanoparticles in solution or ambient conditions. The nonlinear interactions
between the ferroelectric dipoles and surface charges with slow relaxation
dynamics in an external field lead to the emergence of a broad range of
paraelectric-like, antiferroelectric-like ionic, and ferroelectric-like
ferroionic states. The crossover between these states can be controlled not
only by the static characteristics of the surface charges, but also by their
relaxation dynamics in the applied field. Obtained results are not only
promising for advanced applications of ferroelectric nanoparticles in
nanoelectronics and optoelectronics, they also offer strategies for
experimental verification.


title: Polynomial Kernels for Tracking Shortest Paths

abstract:
  Given an undirected graph $G=(V,E)$, vertices $s,t\in V$, and an integer $k$,
Tracking Shortest Paths requires deciding whether there exists a set of $k$
vertices $T\subseteq V$ such that for any two distinct shortest paths between
$s$ and $t$, say $P_1$ and $P_2$, we have $T\cap V(P_1)\neq T\cap V(P_2)$. In
this paper, we give the first polynomial size kernel for the problem.
Specifically we show the existence of a kernel with $\mathcal{O}(k^2)$ vertices
and edges in general graphs and a kernel with $\mathcal{O}(k)$ vertices and
edges in planar graphs for the Tracking Paths in DAG problem. This problem
admits a polynomial parameter transformation to Tracking Shortest Paths, and
this implies a kernel with $\mathcal{O}(k^4)$ vertices and edges for Tracking
Shortest Paths in general graphs and a kernel with $\mathcal{O}(k^2)$ vertices
and edges in planar graphs. Based on the above we also give a single
exponential algorithm for Tracking Shortest Paths in planar graphs.


title:
AutoCl : A Visual Interactive System for Automatic Deep Learning
  Classifier Recommendation Based on Models Performance

abstract:
  Nowadays, deep learning (DL) models being increasingly applied to various
fields, people without technical expertise and domain knowledge struggle to
find an appropriate model for their task. In this paper, we introduce AutoCl a
visual interactive recommender system aimed at helping non-experts to adopt an
appropriate DL classifier. Our system enables users to compare the performance
and behavior of multiple classifiers trained with various hyperparameter setups
as well as automatically recommends a best classifier with appropriate
hyperparameter. We compare features of AutoCl against several recent AutoML
systems and show that it helps non-experts better in choosing DL classifier.
Finally, we demonstrate use cases for image classification using publicly
available dataset to show the capability of our system.


title:
Word Segmentation on Discovered Phone Units with Dynamic Programming and
  Self-Supervised Scoring

abstract:
  Recent work on unsupervised speech segmentation has used self-supervised
models with a phone segmentation module and a word segmentation module that are
trained jointly. This paper compares this joint methodology with an older idea:
bottom-up phone-like unit discovery is performed first, and symbolic word
segmentation is then performed on top of the discovered units (without
influencing the lower level). I specifically describe a duration-penalized
dynamic programming (DPDP) procedure that can be used for either phone or word
segmentation by changing the self-supervised scoring network that gives segment
costs. For phone discovery, DPDP is applied with a contrastive predictive
coding clustering model, while for word segmentation it is used with an
autoencoding recurrent neural network. The two models are chained in order to
segment speech. This approach gives comparable word segmentation results to
state-of-the-art joint self-supervised models on an English benchmark. On
French and Mandarin data, it outperforms previous systems on the ZeroSpeech
benchmarks. Analysis shows that the chained DPDP system segments shorter filler
words well, but longer words might require an external top-down signal.


title:
Average abundancy of cooperation in multi-player games with random
  payoffs

abstract:
  We consider interactions between players in groups of size $d\geq2$ with
payoffs that not only depend on the strategies used in the group but also
fluctuate at random over time. An individual can adopt either cooperation or
defection as strategy and the population is updated from one-time step to the
next by a birth-death event according to a Moran model. Assuming recurrent
symmetric mutation and payoffs with expected values, variances, and covariances
of the same small order, we derive a first-order approximation of the average
abundance of cooperation in the selection-mutation equilibrium. We show that
increasing the variance of any payoff for defection or decreasing the variance
of any payoff for cooperation increases the average abundance of cooperation.
As for the effect of the covariance between any payoff for cooperation and any
payoff for defection, we show that it depends on the number of cooperators in
the group associated with these payoffs. We study in particular the public
goods game, the stag hunt game, and the snowdrift game, all social dilemmas
based on random benefit $b$ and cost $c$ for cooperation. We show that a
decrease in the scaled variance of $b$ or $c$, or an increase in their scaled
covariance, makes it easier for weak selection to favor the abundance of
cooperation in the stag hunt game and the snowdrift game. The same conclusion
holds for the public goods game except that the covariance of $b$ has no effect
on the average abundance of $C$. On the other hand, increasing the scaled
mutation rate or the group size can enhance or lessen the condition for weak
selection to favor the abundance of $C$.


title:
Explore-Bench: Data Sets, Metrics and Evaluations for Frontier-based and
  Deep-reinforcement-learning-based Autonomous Exploration

abstract:
  Autonomous exploration and mapping of unknown terrains employing single or
multiple robots is an essential task in mobile robotics and has therefore been
widely investigated. Nevertheless, given the lack of unified data sets,
metrics, and platforms to evaluate the exploration approaches, we develop an
autonomous robot exploration benchmark entitled Explore-Bench. The benchmark
involves various exploration scenarios and presents two types of quantitative
metrics to evaluate exploration efficiency and multi-robot cooperation.
Explore-Bench is extremely useful as, recently, deep reinforcement learning
(DRL) has been widely used for robot exploration tasks and achieved promising
results. However, training DRL-based approaches requires large data sets, and
additionally, current benchmarks rely on realistic simulators with a slow
simulation speed, which is not appropriate for training exploration strategies.
Hence, to support efficient DRL training and comprehensive evaluation, the
suggested Explore-Bench designs a 3-level platform with a unified data flow and
$12 \times$ speed-up that includes a grid-based simulator for fast evaluation
and efficient training, a realistic Gazebo simulator, and a remotely accessible
robot testbed for high-accuracy tests in physical environments. The
practicality of the proposed benchmark is highlighted with the application of
one DRL-based and three frontier-based exploration approaches. Furthermore, we
analyze the performance differences and provide some insights about the
selection and design of exploration methods. Our benchmark is available at
https://github.com/efc-robot/Explore-Bench.


title:
Collective Conditioned Reflex: A Bio-Inspired Fast Emergency Reaction
  Mechanism for Designing Safe Multi-Robot Systems

abstract:
  A multi-robot system (MRS) is a group of coordinated robots designed to
cooperate with each other and accomplish set tasks. Due to the uncertainties in
operating environments, the system may encounter unexpected emergencies, such
as unobserved obstacles, unexpectedly moving vehicles, explosions, and fires.
Animal groups such as bee colonies initiate collective emergency reaction
behaviors such as bypassing the front trees and avoiding back predators,
similar to muscle conditioned reflex which organizes local muscles to avoid
hazards in the first response without delaying passage through the central
brain. Inspired by this, we develop a similar collective conditioned reflex
mechanism for multi-robot systems to respond to emergencies. In this study,
Collective Conditioned Reflex (CCR), a bio-inspired emergency reaction
mechanism, is developed based on animal collective behavior analysis and
multi-agent reinforcement learning. The algorithm uses a physical model to
determine if the robots are experiencing an emergency; then, rewards for robots
involved in the emergency are augmented with corresponding heuristic rewards,
which evaluate emergency magnitudes and consequences and decide local robots'
participation. CCR is validated on three typical emergency scenarios:
unexpected turbulence, strong wind, and uncertain obstacle. Experimental
results demonstrate that CCR improves robot teams' emergency reaction
capability with faster reaction speed and safer trajectory adjustment compared
with traditional methods.


title:
Preferred Spin Excitations in the Bilayer Iron-Based Superconductor
  CaK(Fe$_{0.96}$Ni$_{0.04}$)$_4$As$_4$ with Spin-Vortex Crystal Order

abstract:
  The spin-orbit coupling (SOC) is a key to understand the magnetically driven
superconductivity in iron-based superconductors, where both local and itinerant
electrons are present and the orbital angular momentum is not completely
quenched. Here, we report a neutron scattering study on the bilayer compound
CaK(Fe$_{0.96}$Ni$_{0.04}$)$_4$As$_4$ with superconductivity coexisting with a
non-collinear spin-vortex crystal magnetic order that preserves the tetragonal
symmetry of Fe-Fe plane. In the superconducting state, two spin resonance modes
with odd and even $L$ symmetries due to the bilayer coupling are found similar
to the undoped compound CaKFe$_4$As$_4$ but at lower energies. Polarization
analysis reveals that the odd mode is $c-$axis polarized, and the low-energy
spin anisotropy can persist to the paramagnetic phase at high temperature,
which closely resembles other systems with in-plane collinear and $c-$axis
biaxial magnetic orders. These results provide the missing piece of the puzzle
on the SOC effect in iron-pnictide superconductors, and also establish a common
picture of $c-$axis preferred magnetic excitations below $T_c$ regardless of
the details of magnetic pattern or lattice symmetry.


title: On the resolution of the Diophantine equation $U_n + U_m = x^q$

abstract:
  Suppose that $U_n$ is a Lucas sequence of first kind and has a dominant root
$\alpha$ with $\alpha>1$ and the discriminant $D>0$. In this paper, we study
the Diophantine equation $U_n + U_m = x^q$ in integers $n \geq m \geq 0$, $x
\geq 2$, and $q \geq 2$. Firstly, we show that there are only finitely many of
them for a fixed $x$ using linear forms in logarithms. Secondly, we show that
there are only finitely many solutions in $(n, m, x, q)$ with $q, x\geq 2$
under the assumption of the {\em abc conjecture}. To prove this, we use several
classical results like Schmidt subspace theorem, a fundamental theorem on
linear equations in $S$-units and Siegels' theorem concerning the finiteness of
the number of solutions of a hyperelliptic equation.


title: Planet population synthesis: The role of stellar encounters

abstract:
  Depending on the stellar densities, protoplanetary discs in stellar clusters
undergo background heating, disc truncation-driven by stellar encounter, and
photo-evaporation. Disc truncation leads to reduced characteristic sizes and
disc masses that eventually halts gas giant planet formation. We investigate
how disc truncation impacts planet formation via pebble-based core accretion
paradigm, where pebble sizes were derived from the full grain-size distribution
within the disc lifetimes. We make the best-case assumption of one embryo and
one stellar encounter per disc. Using planet population syntheses techniques,
we find that disc truncation shifts the disc mass distributions to the lower
margins. This consequently lowered the gas giant occurrence rates. Despite the
reduced gas giant formation rates in clustered discs, the encounter models
mostly show as in the isolated field; the cold Jupiters are more frequent than
the hot Jupiters, consistent with observation. Moreover, the ratio of hot to
cold Jupiters depend on the periastron distribution of the perturbers with
linear distribution in periastron ratio showing enhanced hot to cold Jupiters
ratio in comparison to the remaining models. Our results are valid in the
best-case scenario corresponding to our assumptions of: only one disc encounter
with a perturber, ambient background heating and less rampant
photo-evaporation. It is not known exactly of how much gas giant planet
formation would be affected should disc encounter, background heating and
photo-evaporation act in a concert. Thus, our study will hopefully serve as
motivation for quantitative investigations of the detailed impact of stellar
cluster environments on planet formations.


title: Numerical Simulations of a Superwalking Droplet

abstract:
  A new class of walking droplets, coined superwalkers, has been observed when
a bath of silicone oil is vibrated simultaneously at a given frequency and its
subharmonic tone with a relative phase difference between them~[1]. In this
paper, we present the details of the numerical implementation of a theoretical
model for superwalkers that was developed by Valani \emph{et al.}~[2]. The
numerical analysis presented here provides the foundation for future numerical
studies of superwalking droplets.


title: Compositional Generalization Requires Compositional Parsers

abstract:
  A rapidly growing body of research on compositional generalization
investigates the ability of a semantic parser to dynamically recombine
linguistic elements seen in training into unseen sequences. We present a
systematic comparison of sequence-to-sequence models and models guided by
compositional principles on the recent COGS corpus (Kim and Linzen, 2020).
Though seq2seq models can perform well on lexical tasks, they perform with
near-zero accuracy on structural generalization tasks that require novel
syntactic structures; this holds true even when they are trained to predict
syntax instead of semantics. In contrast, compositional models achieve
near-perfect accuracy on structural generalization; we present new results
confirming this from the AM parser (Groschwitz et al., 2021). Our findings show
structural generalization is a key measure of compositional generalization and
requires models that are aware of complex structure.


title: Topological portals from matter to antimatter

abstract:
  We discuss possibilities of generating a Majorana mass for the neutron from
topological quantum gravity effects which survive at mesoscopic scales from
decoherence. We show how virtual micro-black hole (BH) pairs with skyrme/baryon
hairs induce a neutron-antineutron transition which can be tested in next
generation of experiments. Such effects do not destabilize the proton. We also
discuss how BHs with mix ordinary and mirror baryon hairs can mediate
neutron-mirror neutron mixings.


title:
Spectroscopic Study of M33 with LAMOST survey. I. Chemical gradients
  from nebulae

abstract:
  Morphological and chemical structures of M33 are investigated with LAMOST DR7
survey. Physical parameters, extinction, chemical composition of He, N, O, Ne,
S, Cl, Ar (where available), and radial velocities were determined for 110
nebulae (95 H\ii\ regions and 15 PNe) in M33. Among them, 8 PNe and 55 H\ii\
regions in M33 are newly discovered. We obtained the following O abundance
gradients: $-$0.199$_{-0.030}^{+0.030}$ dex $R_{25}^{-1}$ (based on 95 H\ii\
regions), $-$0.124$_{-0.036}^{+0.036}$ dex $R_{25}^{-1}$ (based on 93 H\ii\
regions), and $-$0.207$_{-0.174}^{+0.160}$ dex $R_{25}^{-1}$ (based on 21 H\ii\
region), utilizing abundances from N2 at O3N2 diagnostics and the $T_{\rm
e}$-sensitive method, respectively. The He, N, Ne, S, and Ar gradients resulted
in slopes of $-$0.179$_{-0.146}^{+0.145}$, $-$0.431$_{-0.281}^{+0.282}$,
$-$0.171$_{-0.239}^{+0.234}$, $-$0.417$_{-0.182}^{+0.174}$,
$-$0.340$_{-0.157}^{+0.156}$, respectively, utilizing abundances from the
$T_{\rm e}$-sensitive method. Our results confirm the existence of negative
axisymmetric global metallicity distribution that is assumed in the literature.
We noticed one new WC star candidate and one transition WR WN/C candidate. The
grand-design pattern of spiral structure of M33 is presented.


title: On Learning Mixture Models with Sparse Parameters

abstract:
  Mixture models are widely used to fit complex and multimodal datasets. In
this paper we study mixtures with high dimensional sparse latent parameter
vectors and consider the problem of support recovery of those vectors. While
parameter learning in mixture models is well-studied, the sparsity constraint
remains relatively unexplored. Sparsity of parameter vectors is a natural
constraint in variety of settings, and support recovery is a major step towards
parameter estimation. We provide efficient algorithms for support recovery that
have a logarithmic sample complexity dependence on the dimensionality of the
latent space. Our algorithms are quite general, namely they are applicable to
1) mixtures of many different canonical distributions including Uniform,
Poisson, Laplace, Gaussians, etc. 2) Mixtures of linear regressions and linear
classifiers with Gaussian covariates under different assumptions on the unknown
parameters. In most of these settings, our results are the first guarantees on
the problem while in the rest, our results provide improvements on existing
works.


title:
A Timing Yield Model for SRAM Cells in Sub/Near-threshold Voltages Based
  on A Compact Drain Current Model

abstract:
  Sub/Near-threshold static random-access memory (SRAM) design is crucial for
addressing the memory bottleneck in energy-constrained applications. However,
the high integration density and reliability under process variations demand an
accurate estimation of extremely small failure probabilities. To capture such a
rare event in memory circuits, the time and storage overhead of conventional
Monte Carlo (MC) simulations cannot be tolerated. On the other hand, classic
analytical methods predicting failure probabilities from a physical expression
become inaccurate in the sub/near-threshold region due to the assumed
distribution or the oversimplified drain current model for nanoscale devices.
This work first proposes a simple but efficient drain current model to describe
the drain-induced barrier lowering effect at low voltages. Based on that, the
probability density functions of the interest metrics in SRAM are derived. Two
analytical models are then put forward to evaluate SRAM dynamic stabilities
including access and write-time failures. The proposed models can be extended
easily to different types of SRAM with different read/write assist circuits.
The models are validated against MC simulations across different operating
voltages and temperatures. The average relative errors at 0.5V VDD are only
8.8% and 10.4% for the access-time and write failure models respectively. The
size of required data samples is 43.6X smaller than that of the
state-of-the-art method.


title:
Explaining the UHECR spectrum, composition and large-scale anisotropies
  with radio galaxies

abstract:
  Radio galaxies are promising candidates as the sources of ultrahigh energy
cosmic rays (UHECRs). In this work, we examine if the stringent constraints
imposed by the dipole and quadropole anisotropies as well as the UHECR spectrum
and composition allow that radio galaxies are the dominant extragalactic cosmic
ray sources. In order to calculate the UHECR flux emitted by individual radio
galaxies, we constrain their properties using information from the radio-CR
correlation and a dynamical evolution model. In addition to the UHECR flux from
individual, local sources, we include the diffuse flux emitted by the bulk of
non-local radio galaxies based on their radio luminosity distribution.
Analyzing the source parameters within a range around their expected
properties, we finally determine the configurations of local sources describing
well the UHECR spectrum, composition and large-scale anisotropies. We obtain a
good description of all data even in the case that we include only a small
number of local sources. In particular, we find that scenarios where few
sources like Fornax A and Virgo A dominate the flux above the ankle, while
low-luminosity radio galaxies contribute an isotropic background dominating
below the ankle, provide a good fit to the data.


title:
A unified approach towards the impossibility of finite time vanishing
  depth for incompressible free boundary flows

abstract:
  In this paper we study the motion of an internal water wave and an internal
wave in a porous medium. For these problems we establish that, if the free
boundary and, in the case of the Euler equations, also the tangential velocity
at the interface are sufficiently smooth, the depth cannot vanish in finite
time. This results holds regardless of gravity and surface tension effects or,
if applicable, the stratification in multiphase flows.


title:
Computer Aided Diagnosis and Out-of-Distribution Detection in Glaucoma
  Screening Using Color Fundus Photography

abstract:
  Artificial Intelligence for RObust Glaucoma Screening (AIROGS) Challenge is
held for developing solutions for glaucoma screening from color fundus
photography that are robust to real-world scenarios. This report describes our
method submitted to the AIROGS challenge. Our method employs convolutional
neural networks to classify input images to "referable glaucoma" or "no
referable glaucoma". In addition, we introduce an inference-time
out-of-distribution (OOD) detection method to identify ungradable images. Our
OOD detection is based on an energy-based method combined with activation
rectification.


title:
Performance of an iterative wavelet reconstructor for the
  Multi-conjugate Adaptive Optics RelaY of ESO's ELT

abstract:
  The Multi-conjugate Adaptive Optics RelaY (MAORY) is one of the key Adaptive
Optics (AO) systems on the European Southern Observatory's Extremely Large
Telescope. MAORY aims to achieve a good wavefront correction over a large field
of view, which involves a tomographic estimation of the 3D atmospheric
wavefront disturbance. Mathematically, the reconstruction of turbulent layers
in the atmosphere is severely ill-posed, hence, limits the achievable
reconstruction accuracy. Moreover, the reconstruction has to be performed in
real-time at a few hundred to one thousand Hertz frame rates. Huge amounts of
data have to be processed and thousands of actuators of the deformable mirrors
have to be controlled by elaborated algorithms. Even with extensive
parallelization and pipelining, direct solvers, such as the Matrix Vector
Multiplication (MVM) method, are extremely demanding. Thus, research in the
last years shifted into the direction of iterative methods. In this paper we
focus on the iterative Finite Element Wavelet Hybrid Algorithm (FEWHA). The key
feature of FEWHA is a matrix-free representation of all operators involved,
which makes the algorithm fast and enables on the fly system updates whenever
parameters at the telescope or in the atmosphere change. We provide a
performance analysis of the method regarding quality and run-time for the MAORY
instrument using the AO software package COMPASS.


title:
Temporal Efficient Training of Spiking Neural Network via Gradient
  Re-weighting

abstract:
  Recently, brain-inspired spiking neuron networks (SNNs) have attracted
widespread research interest because of their event-driven and energy-efficient
characteristics. Still, it is difficult to efficiently train deep SNNs due to
the non-differentiability of its activation function, which disables the
typically used gradient descent approaches for traditional artificial neural
networks (ANNs). Although the adoption of surrogate gradient (SG) formally
allows for the back-propagation of losses, the discrete spiking mechanism
actually differentiates the loss landscape of SNNs from that of ANNs, failing
the surrogate gradient methods to achieve comparable accuracy as for ANNs. In
this paper, we first analyze why the current direct training approach with
surrogate gradient results in SNNs with poor generalizability. Then we
introduce the temporal efficient training (TET) approach to compensate for the
loss of momentum in the gradient descent with SG so that the training process
can converge into flatter minima with better generalizability. Meanwhile, we
demonstrate that TET improves the temporal scalability of SNN and induces a
temporal inheritable training for acceleration. Our method consistently
outperforms the SOTA on all reported mainstream datasets, including
CIFAR-10/100 and ImageNet. Remarkably on DVS-CIFAR10, we obtained 83$\%$ top-1
accuracy, over 10$\%$ improvement compared to existing state of the art. Codes
are available at \url{https://github.com/Gus-Lab/temporal_efficient_training}.


title:
Quantized and unquantized thermal Hall conductance of Kitaev spin-liquid
  candidate $\alpha$-RuCl$_3$

abstract:
  Despite extensive investigations, a topological state that hosts Majorana
edge modes in the magnetic field-induced quantum disordered state of Kitaev
candidate material $\alpha$-RuCl$_3$ has been hotly debated. To gain more
insight into this issue, we measured the thermal Hall conductivity
$\kappa_{xy}$ of various samples grown by the Bridgman method. The results show
that the half-integer quantum thermal Hall effect is intimately related to the
magnitude of longitudinal thermal conductivity and the N\'{e}el temperature at
zero field, both of which are sample-dependent. Samples exhibiting the
half-integer quantum thermal Hall effect have larger zero-field thermal
conductivity values than a threshold value, implying that a long mean free path
of heat carriers is an important prerequisite. In addition, we find that
samples with higher N\'{e}el temperature exhibit a higher magnetic field at
which quantization starts to occur. These results indicate that the
quantization phenomenon is significantly affected by the impurity scatterings
and the non-Kitaev interactions.


title:
Domain Disentangled Generative Adversarial Network for Zero-Shot
  Sketch-Based 3D Shape Retrieval

abstract:
  Sketch-based 3D shape retrieval is a challenging task due to the large domain
discrepancy between sketches and 3D shapes. Since existing methods are trained
and evaluated on the same categories, they cannot effectively recognize the
categories that have not been used during training. In this paper, we propose a
novel domain disentangled generative adversarial network (DD-GAN) for zero-shot
sketch-based 3D retrieval, which can retrieve the unseen categories that are
not accessed during training. Specifically, we first generate domain-invariant
features and domain-specific features by disentangling the learned features of
sketches and 3D shapes, where the domain-invariant features are used to align
with the corresponding word embeddings. Then, we develop a generative
adversarial network that combines the domainspecific features of the seen
categories with the aligned domain-invariant features to synthesize samples,
where the synthesized samples of the unseen categories are generated by using
the corresponding word embeddings. Finally, we use the synthesized samples of
the unseen categories combined with the real samples of the seen categories to
train the network for retrieval, so that the unseen categories can be
recognized. In order to reduce the domain shift between the synthesized domain
and the real domain, we adopt the transductive setting to reduce the gap
between the distributions of the synthesized unseen categories and real unseen
categories. Extensive experiments on the SHREC'13 and SHREC'14 datasets show
that our method significantly improves the retrieval performance of the unseen
categories.


title:
SMILE: Sequence-to-Sequence Domain Adaption with Minimizing Latent
  Entropy for Text Image Recognition

abstract:
  Training recognition models with synthetic images have achieved remarkable
results in text recognition. However, recognizing text from real-world images
still faces challenges due to the domain shift between synthetic and real-world
text images. One of the strategies to eliminate the domain difference without
manual annotation is unsupervised domain adaptation (UDA). Due to the
characteristic of sequential labeling tasks, most popular UDA methods cannot be
directly applied to text recognition. To tackle this problem, we proposed a UDA
method with minimizing latent entropy on sequence-to-sequence attention-based
models with classbalanced self-paced learning. Our experiments show that our
proposed framework achieves better recognition results than the existing
methods on most UDA text recognition benchmarks. All codes are publicly
available.


title:
An efficient combined local and global search strategy for optimization
  of parallel kinematic mechanisms with joint limits and collision constraints

abstract:
  The optimization of parallel kinematic manipulators (PKM) involve several
constraints that are difficult to formalize, thus making optimal synthesis
problem highly challenging. The presence of passive joint limits as well as the
singularities and self-collisions lead to a complicated relation between the
input and output parameters. In this article, a novel optimization methodology
is proposed by combining a local search, Nelder-Mead algorithm, with global
search methodologies such as low discrepancy distribution for faster and more
efficient exploration of the optimization space. The effect of the dimension of
the optimization problem and the different constraints are discussed to
highlight the complexities of closed-loop kinematic chain optimization. The
work also presents the approaches used to consider constraints for passive
joint boundaries as well as singularities to avoid internal collisions in such
mechanisms. The proposed algorithm can also optimize the length of the
prismatic actuators and the constraints can be added in modular fashion,
allowing to understand the impact of given criteria on the final result. The
application of the presented approach is used to optimize two PKMs of different
degrees of freedom.


title:
Structure Analysis using Time-of-Flight Momentum Microscopy with Hard
  X-rays: Status and Prospects

abstract:
  X-ray photoelectron diffraction (XPD) has developed into a powerful technique
for the structural analysis of solids. Extension of the technique into the
hard-X-ray range (hXPD) gives access to true bulk information. Here we give a
status report on hXPD experiments using a novel full-field imaging technique:
Time-of-flight momentum microscopy (ToF-MM). A special variant of ToF-MM is
capable of recording high kinetic energies (up to >7keV) and enlarged
k-fields-of-view. We present applications that are specific for high kinetic
energies. The strong site specificity of hXPD is exemplified for NbSe2, the
cubic-to-tetragonal transition in SrTiO3 and the zinc-blende structure in
epitaxial GaAs films. Bloch-wave calculations show a very good agreement with
experiment and reveal fingerprint-like signatures of emitter sites in host
lattices. We show a dopant-site analysis in two semiconductors (Mn in GaAs and
Te in Si). Hard-X-ray ARPES plus core-level hXPD enable eliminating the strong
diffraction signature imprinted in HARPES maps.


title:
Dissipative time crystal in an atom-cavity system: Influence of trap and
  competing interactions

abstract:
  While the recently realized dissipative time crystal in a laser-pumped
atom-cavity system in the experiment of Ke{\ss}ler et al. [Phys. Rev. Lett.
127, 043602 (2021)] is qualitatively consistent with a theoretical description
in an idealized limit, here, we investigate the stability of this dissipative
time crystal in the presence of an inhomogeneous potential provided by a
harmonic trap, and competing short- and infinite-range interactions. We note
that these features are ubiquitous in any realization of atom-cavity systems.
By mapping out the dynamical phase diagram and studying how it is modified by
the harmonic trap and short-range interactions, we demonstrate the persistence
of long-lived dissipative time crystals beyond the idealized limit. We show the
emergence of metastable dissipative time crystals with and without
prethermalization plateaus for tight harmonic confinement and strong contact
interaction, respectively.


title:
Scaling limits for the random walk penalized by its range in dimension
  one

abstract:
  In this article we study a one dimensional model for a polymer in a poor
solvent: the random walk on $\mathbb{Z}$ penalized by its range. More
precisely, we consider a Gibbs transformation of the law of the simple
symmmetric random walk by a weight $exp(-{h_n |\mathcal{R}_n |})$, with
$|\mathcal{R}_n|$ the number of visited sites and $h_n$ a size-dependent
positive parameter. We use gambler's ruin estimates to obtain exact asymptotics
for the partition function, that enables us to obtain a precise description of
trajectories, in particular scaling limits for the center and the amplitude of
the range. A phase transition for the fluctuations around an optimal amplitude
is identified at $h_n \approx n^{1/4}$ , inherent to the underlying lattice
structure.


title:
XAutoML: A Visual Analytics Tool for Establishing Trust in Automated
  Machine Learning

abstract:
  In the last ten years, various automated machine learning (AutoML) systems
have been proposed to build end-to-end machine learning (ML) pipelines with
minimal human interaction. Even though such automatically synthesized ML
pipelines are able to achieve a competitive performance, recent studies have
shown that users do not trust models constructed by AutoML due to missing
transparency of AutoML systems and missing explanations for the constructed ML
pipelines. In a requirements analysis study with 26 domain experts, data
scientists, and AutoML researchers from different professions with vastly
different expertise in ML, we collect detailed informational needs to establish
trust in AutoML. We propose XAutoML, an interactive visual analytics tool for
explaining arbitrary AutoML optimization procedures and ML pipelines
constructed by AutoML. XAutoML combines interactive visualizations with
established techniques from explainable artificial intelligence (XAI) to make
the complete AutoML procedure transparent and explainable. By integrating
XAutoML with JupyterLab, experienced users can extend the visual analytics with
ad-hoc visualizations based on information extracted from XAutoML. We validate
our approach in a user study with the same diverse user group from the
requirements analysis. All participants were able to extract useful information
from XAutoML, leading to a significantly increased trust in ML pipelines
produced by AutoML and the AutoML optimization itself.


title: NP$^{\#P}$ = $\exists$PP and other remarks about maximized counting

abstract:
  We consider the following decision problem DMAX#SAT, and generalizations
thereof: given a quantifier-free propositional formula
$F(\mathbf{x},\mathbf{y})$, where $\mathbf{x},\mathbf{y}$ are tuples of
variables, and a bound $B$, determine if there is $\vec{x}$ such that
$\#\{\mathbf{y} \mid F(\mathbf{x},\mathbf{y})\} \geq B$. This is the decision
version of the problem of MAX#SAT: finding $\mathbf{x}$ and $B$ for maximal
$B$.


title:
In-plane epitaxy strain tuning intralayer and interlayer magnetic
  couplings in CrSe2 and CrTe2 mono- and bi-layers

abstract:
  Mismatched lattice-constants at a van-der-Waals epitaxy interface often
introduce in-plane strains to the lattice of the epitaxial layer, termed
epitaxy-strain, where the strains do not follow the intra-layer Poisson's
relation. Here, we presented the magnetic phase diagrams of the CrSe2 and CrTe2
mono- and bi-layers under epitaxy-strain up to 8 %, as predicted using
density-functional-theory calculations. They show that the in-plane
epitaxy-strain manipulates either the intra- or inter-layer magnetism of them.
The in-plane strain varies the interlayer distance, defined using an
inter-layer Poisson's ratio, which governs the inter-layer magnetism in two
opposite ways depending on the in-plane magnetism. The tunability of the
intra-layer magnetism is a result of competing intra-layer Cr-Cr super-exchange
interactions. A graphene substrate was introduced to examine the validity of
our diagrams in practice. Our work also gives a tentative explanation on the
controversially reported magnetizations in CrSe2 and CrTe2 epitaxial mono- or
bi-layers under epitaxy-strains.


title:
Efficient frequency conversion with geometric phase control in optical
  metasurfaces

abstract:
  Metasurfaces have appeared as a versatile platform for miniaturized
functional nonlinear optics due to their design freedom in tailoring
wavefronts. The key factor that limits its application in functional devices is
the low conversion efficiency. Recently, dielectric metasurfaces governed by
either high-quality factor modes (quasi-bound states in the continuum) or Mie
modes, enabling strong light-matter interaction, have become a prolific route
to achieve high nonlinear efficiency. Here, we demonstrate both numerically and
experimentally an effective way of spatial nonlinear phase control by using the
Pancharatnam-Berry phase principle with a high third harmonic conversion
efficiency of $10^{-4}$ $1/W^2$. We find that the magnetic Mie resonance
appears to be the main contributor to the third harmonic response, while the
contribution from the quasi-bound states in the continuum is negligible. This
is confirmed by a phenomenological model based on coupled anharmonic
oscillators. Besides, our metasurface provides experimentally a high
diffraction efficiency (80-90%) in both polarization channels. We show a
functional application of our approach by experimentally reconstructing an
encoded polarization-multiplexed vortex beam array with different topological
charges at the third harmonic frequency with high fidelity. Our approach has
the potential viability for future on-chip nonlinear signal processing and
wavefront control.


title: Cognitive Semantic Communication Systems Driven by Knowledge Graph

abstract:
  Semantic communication is envisioned as a promising technique to break
through the Shannon limit. However, the existing semantic communication
frameworks do not involve inference and error correction, which limits the
achievable performance. In this paper, in order to tackle this issue, a
cognitive semantic communication framework is proposed by exploiting knowledge
graph. Moreover, a simple, general and interpretable solution for semantic
information detection is developed by exploiting triples as semantic symbols.
It also allows the receiver to correct errors occurring at the symbolic level.
Furthermore, the pre-trained model is fine-tuned to recover semantic
information, which overcomes the drawback that a fixed bit length coding is
used to encode sentences of different lengths. Simulation results on the public
WebNLG corpus show that our proposed system is superior to other benchmark
systems in terms of the data compression rate and the reliability of
communication.


title:
Entropic trust region for densest crystallographic symmetry group
  packings

abstract:
  Molecular crystal structure prediction (CSP) seeks the most stable periodic
structure given a chemical composition of a molecule and pressure-temperature
conditions. Modern CSP solvers use global optimization methods to search for
structures with minimal free energy within a complex energy landscape induced
by intermolecular potentials. A major caveat of these methods is that initial
configurations are random, making thus the search susceptible to the
convergence at local minima. Providing initial configurations that are densely
packed with respect to the geometric representation of a molecule can
significantly accelerate CSP. Motivated by these observations we define a class
of periodic packings restricted to crystallographic symmetry groups (CSG) and
design a search method for densest CSG packings in an information geometric
framework. Since the CSG induce a toroidal topology on the configuration space,
a non-euclidean trust region method is performed on a statistical manifold
consisting of probability distributions defined on an $n$-dimensional flat unit
torus by extending the multivariate von Mises distribution. By introducing an
adaptive quantile reformulation of the fitness function into the optimization
schedule we provide the algorithm a geometric characterization through local
dual geodesic flows. Moreover, we examine the geometry of the adaptive
selection quantile defined trust region and show that the algorithm performs a
maximization of stochastic dependence among elements of the extended
multivariate von Mises distributed random vector. We experimentally evaluate
its behavior and performance on various densest packings of convex polygons in
$2$-dimensional CSG for which optimal solutions are known.

categories: math.OC cond-mat.mtrl-sci cs.IT cs.NE math.IT math.MG


title:
All You Need Is Supervised Learning: From Imitation Learning to Meta-RL
  With Upside Down RL

abstract:
  Upside down reinforcement learning (UDRL) flips the conventional use of the
return in the objective function in RL upside down, by taking returns as input
and predicting actions. UDRL is based purely on supervised learning, and
bypasses some prominent issues in RL: bootstrapping, off-policy corrections,
and discount factors. While previous work with UDRL demonstrated it in a
traditional online RL setting, here we show that this single algorithm can also
work in the imitation learning and offline RL settings, be extended to the
goal-conditioned RL setting, and even the meta-RL setting. With a general agent
architecture, a single UDRL agent can learn across all paradigms.


title:
"Is not the truth the truth?": Analyzing the Impact of User Validations
  for Bus In/Out Detection in Smartphone-based Surveys

abstract:
  Passenger flow allows the study of users' behavior through the public network
and assists in designing new facilities and services. This flow is observed
through interactions between passengers and infrastructure. For this task,
Bluetooth technology and smartphones represent the ideal solution. The latter
component allows users' identification, authentication, and billing, while the
former allows short-range implicit interactions, device-to-device. To assess
the potential of such a use case, we need to verify how robust Bluetooth signal
and related machine learning (ML) classifiers are against the noise of
realistic contexts. Therefore, we model binary passenger states with respect to
a public vehicle, where one can either be-in or be-out (BIBO). The BIBO label
identifies a fundamental building block of continuously-valued passenger flow.
This paper describes the Human-Computer interaction experimental setting in a
semi-controlled environment, which involves: two autonomous vehicles operating
on two routes, serving three bus stops and eighteen users, as well as a
proprietary smartphone-Bluetooth sensing platform. The resulting dataset
includes multiple sensors' measurements of the same event and two ground-truth
levels, the first being validation by participants, the second by three
video-cameras surveilling buses and track. We performed a Monte-Carlo
simulation of labels-flip to emulate human errors in the labeling process, as
is known to happen in smartphone surveys; next we used such flipped labels for
supervised training of ML classifiers. The impact of errors on model
performance bias can be large. Results show ML tolerance to label flips caused
by human or machine errors up to 30%.


title:
Large Scale Passenger Detection with Smartphone/Bus Implicit Interaction
  and Multisensory Unsupervised Cause-effect Learning

abstract:
  Intelligent Transportation Systems (ITS) underpin the concept of Mobility as
a Service (MaaS), which requires universal and seamless users' access across
multiple public and private transportation systems while allowing operators'
proportional revenue sharing. Current user sensing technologies such as
Walk-in/Walk-out (WIWO) and Check-in/Check-out (CICO) have limited scalability
for large-scale deployments. These limitations prevent ITS from supporting
analysis, optimization, calculation of revenue sharing, and control of MaaS
comfort, safety, and efficiency. We focus on the concept of implicit
Be-in/Be-out (BIBO) smartphone-sensing and classification.
  To close the gap and enhance smartphones towards MaaS, we developed a
proprietary smartphone-sensing platform collecting contemporary Bluetooth Low
Energy (BLE) signals from BLE devices installed on buses and Global Positioning
System (GPS) locations of both buses and smartphones. To enable the training of
a model based on GPS features against the BLE pseudo-label, we propose the
Cause-Effect Multitask Wasserstein Autoencoder (CEMWA). CEMWA combines and
extends several frameworks around Wasserstein autoencoders and neural networks.
As a dimensionality reduction tool, CEMWA obtains an auto-validated
representation of a latent space describing users' smartphones within the
transport system. This representation allows BIBO clustering via DBSCAN.
  We perform an ablation study of CEMWA's alternative architectures and
benchmark against the best available supervised methods. We analyze
performance's sensitivity to label quality. Under the na\"ive assumption of
accurate ground truth, XGBoost outperforms CEMWA. Although XGBoost and Random
Forest prove to be tolerant to label noise, CEMWA is agnostic to label noise by
design and provides the best performance with an 88\% F1 score.


title:
A general framework for adaptive two-index fusion attribute weighted
  naive Bayes

abstract:
  Naive Bayes(NB) is one of the essential algorithms in data mining. However,
it is rarely used in reality because of the attribute independent assumption.
Researchers have proposed many improved NB methods to alleviate this
assumption. Among these methods, due to high efficiency and easy
implementation, the filter attribute weighted NB methods receive great
attentions. However, there still exists several challenges, such as the poor
representation ability for single index and the fusion problem of two indexes.
To overcome above challenges, we propose a general framework for Adaptive
Two-index Fusion attribute weighted NB(ATFNB). Two types of data description
category are used to represent the correlation between classes and attributes,
intercorrelation between attributes and attributes, respectively. ATFNB can
select any one index from each category. Then, we introduce a switching factor
\{beta} to fuse two indexes, which can adaptively adjust the optimal ratio of
the two index on various datasets. And a quick algorithm is proposed to infer
the optimal interval of switching factor \{beta}. Finally, the weight of each
attribute is calculated using the optimal value \{beta} and is integrated into
NB classifier to improve the accuracy. The experimental results on 50 benchmark
datasets and a Flavia dataset show that ATFNB outperforms the basic NB and
state-of-the-art filter weighted NB models. In addition, the ATFNB framework
can improve the existing two-index NB model by introducing the adaptive
switching factor \{beta}. Auxiliary experimental results demonstrate the
improved model significantly increases the accuracy compared to the original
model without the adaptive switching factor \{beta}.


title:
Simultaneous dependence of the dark matter and gas clustering on the
  scale and environment

abstract:
  In this work, we propose new statistical tools that are capable of
characterizing the simultaneous dependence of the matter clustering on the
scale and density environment, which are the environment-dependent wavelet
power spectrum (env-WPS) and environment-dependent wavelet cross-correlation
(env-WCC). We apply these statistics to the projected 2D dark matter and
baryonic gas density fields of the IllustrisTNG simulation at redshifts from
$z=3.0$ to $z=0.0$. Measurement of the env-WPSs indicates that the clustering
strengths of both the dark matter and gas increase with density. At all
redshifts, we observe that the env-WPS of the gas is suppressed on intermediate
and small scales, which is caused by baryonic processes. Furthermore, by
computing the environmental bias function, we find that this suppression varies
with the environment, scale and redshift. A noteworthy feature is that at
$z=0.0$, the bias value increases with density on scales $2\lesssim k \lesssim
10 \ h\mathrm{Mpc}^{-1}$, which is the reversal of that in earlier times. On
the other hand, the env-WCC also shows strong environmental dependence on
scales $k \gtrsim 2 \ h\mathrm{Mpc}^{-1}$ and is sensitive to the redshift. We
find that at $z=3.0$, the coherence of the dark matter and gas decreases with
density. At lower redshifts, the coherence in denser environments becomes much
higher than that in less dense environments. These results suggest that
baryonic processes have less effect on the matter clustering in high-density
environments at later times. Obviously, these statistics provide us rich
information and hence could improve our understanding of the matter clustering.


title:
A Holistic View on Data Protection for Sharing, Communicating, and
  Computing Environments: Taxonomy and Future Directions

abstract:
  The data is an important asset of an organization and it is essential to keep
this asset secure. It requires security in whatever state is it i.e. data at
rest, data in use, and data in transit. There is a need to pay more attention
to it when the third party is included i.e. when the data is stored in the
cloud then it requires more security. Since confidential data can reside on a
variety of computing devices (physical servers, virtual servers, databases,
file servers, PCs, point-of-sale devices, flash drives, and mobile devices) and
move through a variety of network access points (wireline, wireless, VPNs,
etc.), there is a need of solutions or mechanism that can tackle the problem of
data loss, data recovery and data leaks. In this context, the paper presents a
holistic view of data protection for sharing and communicating environments for
any type of organization. A taxonomy of data leakage protection systems and
major challenges faced while protecting confidential data are discussed. Data
protection solutions, Data Leakage Protection System's analysis techniques,
and, a thorough analysis of existing state-of-the-art contributions empowering
machine learning-based approaches are entailed. Finally, the paper explores and
concludes various critical emerging challenges and future research directions
concerning data protection.


title: A Fair Empirical Risk Minimization with Generalized Entropy

abstract:
  Recently a parametric family of fairness metrics to quantify algorithmic
fairness has been proposed based on generalized entropy which have been
originally used in economics and public welfare. Since these metrics have
several advantages such as quantifying unfairness at the individual-level and
group-level, and unfold trade-off between the individual fairness and
group-level fairness, algorithmic fairness requirement may be given in terms of
generalized entropy for a fair classification problem. We consider a fair
empirical risk minimization with a fairness constraint specified by generalized
entropy. We theoretically investigate if the fair empirical fair classification
problem is learnable and how to find an approximate optimal classifier of it.


title:
Coherence of ion cyclotron resonance for damping ion cyclotron waves in
  space plasmas

abstract:
  Ion cyclotron resonance is one of the fundamental energy conversion processes
through field-particle interaction in collisionless plasmas. However, the key
evidence for ion cyclotron resonance (i.e., the coherence between
electromagnetic fields and the ion phase space density) and the resulting
damping of ion cyclotron waves (ICWs) has not yet been directly observed.
Investigating the high-quality measurements of space plasmas by the
Magnetospheric Multiscale (MMS) satellites, we find that both the wave
electromagnetic field vectors and the bulk velocity of the disturbed ion
velocity distribution rotate around the background magnetic field. Moreover, we
find that the absolute gyro-phase angle difference between the center of the
fluctuations in the ion velocity distribution functions and the wave electric
field vectors falls in the range of (0, 90) degrees, consistent with the
ongoing energy conversion from wave-fields to particles. By invoking plasma
kinetic theory, we demonstrate that the field-particle correlation for the
damping ion cyclotron waves in our theoretical model matches well with our
observations. Furthermore, the wave electric field vectors ($\delta
\mathbf{E'}_{\mathrm {wave,\perp}}$), the ion current density ($\delta
\mathbf{J}_\mathrm {i,\perp}$) and the energy transfer rate ($\delta
\mathbf{J}_\mathrm {i,\perp}\cdot \delta \mathbf{E'}_{\mathrm {wave,\perp}}$)
exhibit quasi-periodic oscillations, and the integrated work done by the
electromagnetic field on the ions are positive, indicates that ions are mainly
energized by the perpendicular component of the electric field via cyclotron
resonance. Therefore, our combined analysis of MMS observations and kinetic
theory provides direct, thorough, and comprehensive evidence for ICW damping in
space plasmas.


title:
Combining the target trial and estimand frameworks to define the causal
  estimand: an application using real-world data to contextualize a single-arm
  trial

abstract:
  Single-arm trials (SATs) may be used to support regulatory submissions in
settings where there is a high unmet medical need and highly promising early
efficacy data undermine the equipoise needed for randomization. In this
context, patient-level real-world data (RWD) may be used to create an external
control arm (ECA) to contextualize the SAT results. However, naive comparisons
of the SAT with its ECA will yield biased estimates of causal effects if groups
are imbalanced with regards to (un)measured prognostic factors. Several methods
are available to adjust for measured confounding, but the interpretation of
such analyses is challenging unless the causal question of interest is clearly
defined, and the estimator is aligned with the estimand. Additional
complications arise when patients in the ECA are eligible for the SAT at
multiple timepoints. In this paper, we use a case-study of a pivotal SAT of a
novel CAR-T therapy for heavily pre-treated patients with follicular lymphoma
to illustrate how a combination of the target trial and the ICH E9(R1) estimand
frameworks can be used to define the target estimand and avoid common
methodological pitfalls related to the design of the ECA and comparisons with
the SAT. We also propose an approach to address the challenge of how to define
an appropriate time zero for external controls who meet the SAT
inclusion/exclusion criteria at several timepoints. Use of the target trial and
estimand frameworks facilitates discussions amongst internal and external
stakeholders, as well as an early assessment of the adequacy of the available
RWD.


title:
Should I Get Involved? On the Privacy Perils of Mining Software
  Repositories for Research Participants

abstract:
  Mining Software Repositories (MSRs) is an evidence-based methodology that
cross-links data to uncover actionable information about software systems.
Empirical studies in software engineering often leverage MSR techniques as they
allow researchers to unveil issues and flaws in software development so as to
analyse the different factors contributing to them. Hence, counting on
fine-grained information about the repositories and sources being mined (e.g.,
server names, and contributors' identities) is essential for the
reproducibility and transparency of MSR studies. However, this can also
introduce threats to participants' privacy as their identities may be linked to
flawed/sub-optimal programming practices (e.g., code smells, improper
documentation), or vice-versa. Moreover, this can be extensible to close
collaborators and community members resulting "guilty by association". This
position paper aims to start a discussion about indirect participation in MSRs
investigations, the dichotomy of 'privacy vs. utility' regarding sharing
non-aggregated data, and its effects on privacy restrictions and ethical
considerations for participant involvement.


title:
Lattice calculation of the pion mass difference
  $M_{\pi^{+}}-M_{\pi^{0}}$ at order $\mathcal{O}(\alpha_{em})$

abstract:
  We present a lattice calculation of the charged/neutral pion mass difference
$M_{\pi^{+}}-M_{\pi^{0}}$ at order $\mathcal{O}(\alpha_{em})$ using the gauge
configurations produced by the Extended Twisted Mass Collaboration with
$N_{f}=2+1+1$ dynamical quark flavours at three values of the lattice spacing
($a \simeq 0.062, 0.082, 0.089~{\rm fm}$) and pion masses in the range $M_{\pi}
\simeq 250-500~{\rm MeV}$. We employ the RM123 method and expand the
path-integral around the isospin symmetric point at leading order in the
electromagnetic coupling $\alpha_{em}$. Making use of the recently proposed RTM
scheme, we evaluate the full $\mathcal{O}(\alpha_{em})$ contribution, with the
inclusion of the disconnected diagram. At the physical point, after performing
the continuum and infinite volume extrapolation, we obtain the value
$M_{\pi^{+}}-M_{\pi^{0}}= 4.622~(95)~{\rm MeV}$ which is in good agreement with
the experimental result $[ M_{\pi^{+}} - M_{\pi^{0}} ]^{exp.} = 4.5936(5)~{\rm
MeV}$.