title:
Transference number in polymer electrolytes: mind the reference-frame
  gap

abstract:
  The transport coefficients, in particular the transference number, of
electrolyte solutions are important design parameters for electrochemical
energy storage devices. Recent observation of negative transference numbers in
PEO-LiTFSI under certain conditions has generated much discussion about its
molecular origins, by both experimental and theoretical means. However, one
overlooked factor in these efforts is the importance of the reference frame
(RF). This creates a non-negligible gap when comparing experiment and
simulation, because the fluxes in the experimental measurements of transport
coefficients and in the linear response theory used in the molecular dynamics
simulation are defined in different RFs. In this work, we show that by applying
a proper RF transformation, a much improved agreement between experimental and
simulation results can be achieved. Moreover, it is revealed that the anion
mass and the anion-anion correlation, rather than ion aggregates, play a
crucial role for the reported negative transference numbers.


title: Theory of proximity effect in $s+p$-wave superconductor junctions

abstract:
  We derive a boundary condition for the Nambu Keldysh Green's function in
diffusive normal metal / unconventional superconductor junctions applicable for
mixed parity pairing. Applying this theory to a 1d model of $s+p$-wave
superconductor, we calculate LDOS in DN and charge conductance of DN /
$s+p$-wave superconductor junctions. When the $s$-wave component of the pair
potential is dominant, LDOS has a gap like structure at zero energy and the
dominant pairing in DN is even-frequency spin-singlet $s$-wave. On the other
hand, when the $p$-wave component is dominant, the resulting LDOS has a zero
energy peak and the dominant pairing in DN is odd-frequency spin-triplet
$s$-wave. We show the robustness of the quantization of the conductance when
the magnitude of $p$-wave component of the pair potential is larger than that
of $s$-wave one. These results show the robustness of the anomalous proximity
effect specific to spin-triplet superconductor junctions.


title: SoK: SCT Auditing in Certificate Transparency

abstract:
  The Web public key infrastructure is essential to providing secure
communication on the Internet today, and certificate authorities play a crucial
role in this ecosystem by issuing certificates. These authorities may misissue
certificates or suffer misuse attacks, however, which has given rise to the
Certificate Transparency (CT) project. The goal of CT is to store all issued
certificates in public logs, which can then be checked for the presence of
potentially misissued certificates. Thus, the requirement that a given
certificate is indeed in one (or several) of these logs lies at the core of CT.
In its current deployment, however, most individual clients do not check that
the certificates they see are in logs, as requesting a proof of inclusion
directly reveals the certificate and thus creates the clear potential for a
violation of that client's privacy. In this paper, we explore the techniques
that have been proposed for privacy-preserving auditing of certificate
inclusion, focusing on their effectiveness, efficiency, and suitability in a
near-term deployment. In doing so, we also explore the parallels with related
problems involving browser clients. Guided by a set of constraints that we
develop, we ultimately observe several key limitations in many proposals,
ranging from their privacy provisions to the fact that they focus on the
interaction between a client and a log but leave open the question of how a
client could privately report any certificates that are missing.


title:
Symmetric mixed discontinuous Galerkin methods for linear
  viscoelasticity

abstract:
  We propose and rigorously analyse semi- and fully discrete discontinuous
Galerkin methods for an initial and boundary value problem describing inertial
viscoelasticity in terms of elastic and viscoelastic stress components, and
with mixed boundary conditions. The arbitrary-order spatial discretisation
imposes strongly the symmetry of the stress tensor, and it is combined with a
Newmark trapezoidal rule as time-advancing scheme. We establish stability and
convergence properties, and the theoretical findings are further confirmed via
illustrative numerical simulations in 2D and 3D.


title:
Estimating asymptotic phase and amplitude functions of limit-cycle
  oscillators from time series data

abstract:
  We propose a method for estimating the asymptotic phase and amplitude
functions of limit-cycle oscillators using observed time series data without
prior knowledge of their dynamical equations. The estimation is performed by
polynomial regression and can be solved as a convex optimization problem. The
validity of the proposed method is numerically illustrated by using
two-dimensional limit-cycle oscillators as examples. As an application, we
demonstrate data-driven fast entrainment with amplitude suppression using the
optimal periodic input derived from the estimated phase and amplitude
functions.


title: Tail-GAN: Nonparametric Scenario Generation for Tail Risk Estimation

abstract:
  The estimation of loss distributions for dynamic portfolios requires the
simulation of scenarios representing realistic joint dynamics of their
components, with particular importance devoted to the simulation of tail risk
scenarios. Commonly used parametric models have been successful in applications
involving a small number of assets, but may not be scalable to large or
heterogeneous portfolios involving multiple asset classes.
  We propose a novel data-driven approach for the simulation of realistic
multi-asset scenarios with a particular focus on the accurate estimation of
tail risk for a given class of static and dynamic portfolios selected by the
user. By exploiting the joint elicitability property of Value-at-Risk (VaR) and
Expected Shortfall (ES), we design a Generative Adversarial Network (GAN)
architecture capable of learning to simulate price scenarios that preserve tail
risk features for these benchmark trading strategies, leading to consistent
estimators for their Value-at-Risk and Expected Shortfall. We demonstrate the
accuracy and scalability of our method via extensive simulation experiments
using synthetic and market data. Our results show that, in contrast to other
data-driven scenario generators, our proposed scenario simulation method
correctly captures tail risk for both static and dynamic portfolios.


title:
$\beta$-DARTS: Beta-Decay Regularization for Differentiable Architecture
  Search

abstract:
  Neural Architecture Search~(NAS) has attracted increasingly more attention in
recent years because of its capability to design deep neural networks
automatically. Among them, differential NAS approaches such as DARTS, have
gained popularity for the search efficiency. However, they suffer from two main
issues, the weak robustness to the performance collapse and the poor
generalization ability of the searched architectures. To solve these two
problems, a simple-but-efficient regularization method, termed as Beta-Decay,
is proposed to regularize the DARTS-based NAS searching process. Specifically,
Beta-Decay regularization can impose constraints to keep the value and variance
of activated architecture parameters from too large. Furthermore, we provide
in-depth theoretical analysis on how it works and why it works. Experimental
results on NAS-Bench-201 show that our proposed method can help to stabilize
the searching process and makes the searched network more transferable across
different datasets. In addition, our search scheme shows an outstanding
property of being less dependent on training time and data. Comprehensive
experiments on a variety of search spaces and datasets validate the
effectiveness of the proposed method.


title: Correlation-Aware Deep Tracking

abstract:
  Robustness and discrimination power are two fundamental requirements in
visual object tracking. In most tracking paradigms, we find that the features
extracted by the popular Siamese-like networks cannot fully discriminatively
model the tracked targets and distractor objects, hindering them from
simultaneously meeting these two requirements. While most methods focus on
designing robust correlation operations, we propose a novel target-dependent
feature network inspired by the self-/cross-attention scheme. In contrast to
the Siamese-like feature extraction, our network deeply embeds cross-image
feature correlation in multiple layers of the feature network. By extensively
matching the features of the two images through multiple layers, it is able to
suppress non-target features, resulting in instance-varying feature extraction.
The output features of the search image can be directly used for predicting
target locations without extra correlation step. Moreover, our model can be
flexibly pre-trained on abundant unpaired images, leading to notably faster
convergence than the existing methods. Extensive experiments show our method
achieves the state-of-the-art results while running at real-time. Our feature
networks also can be applied to existing tracking pipelines seamlessly to raise
the tracking performance. Code will be available.


title: Joint Probability Estimation Using Tensor Decomposition and Dictionaries

abstract:
  In this work, we study non-parametric estimation of joint probabilities of a
given set of discrete and continuous random variables from their (empirically
estimated) 2D marginals, under the assumption that the joint probability could
be decomposed and approximated by a mixture of product densities/mass
functions. The problem of estimating the joint probability density function
(PDF) using semi-parametric techniques such as Gaussian Mixture Models (GMMs)
is widely studied. However such techniques yield poor results when the
underlying densities are mixtures of various other families of distributions
such as Laplacian or generalized Gaussian, uniform, Cauchy, etc. Further, GMMs
are not the best choice to estimate joint distributions which are hybrid in
nature, i.e., some random variables are discrete while others are continuous.
We present a novel approach for estimating the PDF using ideas from dictionary
representations in signal processing coupled with low rank tensor
decompositions. To the best our knowledge, this is the first work on estimating
joint PDFs employing dictionaries alongside tensor decompositions. We create a
dictionary of various families of distributions by inspecting the data, and use
it to approximate each decomposed factor of the product in the mixture. Our
approach can naturally handle hybrid $N$-dimensional distributions. We test our
approach on a variety of synthetic and real datasets to demonstrate its
effectiveness in terms of better classification rates and lower error rates,
when compared to state of the art estimators.


title: Translational Lung Imaging Analysis Through Disentangled Representations

abstract:
  The development of new treatments often requires clinical trials with
translational animal models using (pre)-clinical imaging to characterize
inter-species pathological processes. Deep Learning (DL) models are commonly
used to automate retrieving relevant information from the images. Nevertheless,
they typically suffer from low generability and explainability as a product of
their entangled design, resulting in a specific DL model per animal model.
Consequently, it is not possible to take advantage of the high capacity of DL
to discover statistical relationships from inter-species images.
  To alleviate this problem, in this work, we present a model capable of
extracting disentangled information from images of different animal models and
the mechanisms that generate the images. Our method is located at the
intersection between deep generative models, disentanglement and causal
representation learning. It is optimized from images of pathological lung
infected by Tuberculosis and is able: a) from an input slice, infer its
position in a volume, the animal model to which it belongs, the damage present
and even more, generate a mask covering the whole lung (similar overlap
measures to the nnU-Net), b) generate realistic lung images by setting the
above variables and c) generate counterfactual images, namely, healthy versions
of a damaged input slice.


title:
Size Exponents of Branched Polymers/ Extension of the Isaacson-Lubensky
  Formula and the Application to Lattice Trees

abstract:
  Branched polymers can be classified into two categories that obey the
different formulae: \begin{equation} \nu= \begin{cases}
\hspace{1mm}\displaystyle\frac{2(1+\nu_{0})}{d+2} & \hspace{3mm}\mbox{for
polymers
with}\hspace{2mm}\displaystyle\nu_{0}\ge\frac{1}{d+1}\hspace{10mm}\text{(I)}\\[3mm]
\hspace{5mm}2\nu_{0}& \hspace{3mm}\mbox{for polymers
with}\hspace{2mm}\displaystyle\nu_{0}\le\frac{1}{d+1}\hspace{10mm}\text{(II)}
\end{cases}\notag \end{equation} for the dilution limit in good solvents. The
category II covers the exceptional polymers having fully expanded
configurations. On the basis of these equalities, we discuss the size exponents
of the nested architectures and the lattice trees. In particular, we compare
our preceding result, $\nu_{d=2}=1/2$, for the $z$=2 polymer having
$\nu_{0}=1/4$ with the numerical result, $\nu_{d=2}\doteq 0.64115$, for the
lattice trees generated on the 2-dimensional lattice. Our conjecture is that
while both the conclusions in polymer physics and condensed matter physics are
correct, the discrepancy arises from the fact that the lattice trees are
constructed from less branched architectures than the branched polymers having
$\nu_{0} = 1/4$ in polymer physics. The present analysis suggests that the
2-dimensional lattice trees are the mixture of isomers having the mean ideal
size exponent of $\bar{\nu}_{0}\doteq0.32$.


title:
A Simple Hash-Based Early Exiting Approach For Language Understanding
  and Generation

abstract:
  Early exiting allows instances to exit at different layers according to the
estimation of difficulty. Previous works usually adopt heuristic metrics such
as the entropy of internal outputs to measure instance difficulty, which
suffers from generalization and threshold-tuning. In contrast, learning to
exit, or learning to predict instance difficulty is a more appealing way.
Though some effort has been devoted to employing such "learn-to-exit" modules,
it is still unknown whether and how well the instance difficulty can be
learned. As a response, we first conduct experiments on the learnability of
instance difficulty, which demonstrates that modern neural models perform
poorly on predicting instance difficulty. Based on this observation, we propose
a simple-yet-effective Hash-based Early Exiting approach (HashEE) that replaces
the learn-to-exit modules with hash functions to assign each token to a fixed
exiting layer. Different from previous methods, HashEE requires no internal
classifiers nor extra parameters, and therefore is more efficient. Experimental
results on classification, regression, and generation tasks demonstrate that
HashEE can achieve higher performance with fewer FLOPs and inference time
compared with previous state-of-the-art early exiting methods.


title: Constrained unsupervised anomaly segmentation

abstract:
  Current unsupervised anomaly localization approaches rely on generative
models to learn the distribution of normal images, which is later used to
identify potential anomalous regions derived from errors on the reconstructed
images. However, a main limitation of nearly all prior literature is the need
of employing anomalous images to set a class-specific threshold to locate the
anomalies. This limits their usability in realistic scenarios, where only
normal data is typically accessible. Despite this major drawback, only a
handful of works have addressed this limitation, by integrating supervision on
attention maps during training. In this work, we propose a novel formulation
that does not require accessing images with abnormalities to define the
threshold. Furthermore, and in contrast to very recent work, the proposed
constraint is formulated in a more principled manner, leveraging well-known
knowledge in constrained optimization. In particular, the equality constraint
on the attention maps in prior work is replaced by an inequality constraint,
which allows more flexibility. In addition, to address the limitations of
penalty-based functions we employ an extension of the popular log-barrier
methods to handle the constraint. Last, we propose an alternative
regularization term that maximizes the Shannon entropy of the attention maps,
reducing the amount of hyperparameters of the proposed model. Comprehensive
experiments on two publicly available datasets on brain lesion segmentation
demonstrate that the proposed approach substantially outperforms relevant
literature, establishing new state-of-the-art results for unsupervised lesion
segmentation, and without the need to access anomalous images.


title:
A Simpler Alternative: Minimizing Transition Systems Modulo Alternating
  Simulation Equivalence

abstract:
  This paper studies the reduction (abstraction) of finite-state transition
systems for control synthesis problems. We revisit the notion of alternating
simulation equivalence (ASE), a more relaxed condition than alternating
bisimulations, to relate systems and their abstractions. As with alternating
bisimulations, ASE preserves the property that the existence of a controller
for the abstraction is necessary and sufficient for a controller to exist for
the original system. Moreover, being a less stringent condition, ASE can reduce
systems further to produce smaller abstractions. We provide an algorithm that
produces minimal AS equivalent abstractions. The theoretical results are then
applied to obtain (un)schedulability certificates of periodic event-triggered
control systems sharing a communication channel. A numerical example
illustrates the results.


title:
Mean-field nature of synchronization stability in networks with multiple
  interaction layers

abstract:
  The interactions between the components of many real-world systems are best
modelled by networks with multiple layers. Different theories have been
proposed to explain how multilayered connections affect the linear stability of
synchronization in dynamical systems. However, the resulting equations are
computationally expensive, and therefore difficult, if not impossible, to solve
for large systems. To bridge this gap, we develop a mean-field theory of
synchronization for networks with multiple interaction layers. By assuming
quasi-identical layers, we obtain accurate assessments of synchronization
stability that are comparable with the exact results. In fact, the accuracy of
our theory remains high even for networks with very dissimilar layers, thus
posing a general question about the mean-field nature of synchronization
stability in multilayer networks. Moreover, the computational complexity of our
approach is only quadratic in the number of nodes, thereby allowing the study
of systems whose investigation was thus far precluded.


title:
Adaptive machine learning based surrogate modeling to accelerate
  PDE-constrained optimization in enhanced oil recovery

abstract:
  In this contribution, we develop an efficient surrogate modeling framework
for simulation-based optimization of enhanced oil recovery, where we
particularly focus on polymer flooding. The computational approach is based on
an adaptive training procedure of a neural network that directly approximates
an input-output map of the underlying PDE-constrained optimization problem. The
training process thereby focuses on the construction of an accurate surrogate
model solely related to the optimization path of an outer iterative
optimization loop. True evaluations of the objective function are used to
finally obtain certified results. Numerical experiments are given to evaluate
the accuracy and efficiency of the approach for a heterogeneous five-spot
benchmark problem.


title:
Cross-Modality Earth Mover's Distance for Visible Thermal Person
  Re-Identification

abstract:
  Visible thermal person re-identification (VT-ReID) suffers from the
inter-modality discrepancy and intra-identity variations. Distribution
alignment is a popular solution for VT-ReID, which, however, is usually
restricted to the influence of the intra-identity variations. In this paper, we
propose the Cross-Modality Earth Mover's Distance (CM-EMD) that can alleviate
the impact of the intra-identity variations during modality alignment. CM-EMD
selects an optimal transport strategy and assigns high weights to pairs that
have a smaller intra-identity variation. In this manner, the model will focus
on reducing the inter-modality discrepancy while paying less attention to
intra-identity variations, leading to a more effective modality alignment.
Moreover, we introduce two techniques to improve the advantage of CM-EMD.
First, the Cross-Modality Discrimination Learning (CM-DL) is designed to
overcome the discrimination degradation problem caused by modality alignment.
By reducing the ratio between intra-identity and inter-identity variances,
CM-DL leads the model to learn more discriminative representations. Second, we
construct the Multi-Granularity Structure (MGS), enabling us to align
modalities from both coarse- and fine-grained levels with the proposed CM-EMD.
Extensive experiments show the benefits of the proposed CM-EMD and its
auxiliary techniques (CM-DL and MGS). Our method achieves state-of-the-art
performance on two VT-ReID benchmarks.


title: On the structure of abelian Hopf algebras

abstract:
  We study the structure of the category of graded, connected,
countable-dimensional, commutative and cocommutative Hopf algebras over a
perfect field $k$ of characteristic $p$. Every $p$-torsion object in this
category is uniquely a direct sum of explicitly given indecomposables. This
gives rise to a similar classification of not necessarily $p$-torsion objects
that are either free as commutative algebras or cofree as cocommutative
coalgebras. We also completely classify those objects that are indecomposable
modulo $p$.


title:
Detection of Word Adversarial Examples in Text Classification: Benchmark
  and Baseline via Robust Density Estimation

abstract:
  Word-level adversarial attacks have shown success in NLP models, drastically
decreasing the performance of transformer-based models in recent years. As a
countermeasure, adversarial defense has been explored, but relatively few
efforts have been made to detect adversarial examples. However, detecting
adversarial examples may be crucial for automated tasks (e.g. review sentiment
analysis) that wish to amass information about a certain population and
additionally be a step towards a robust defense system. To this end, we release
a dataset for four popular attack methods on four datasets and four models to
encourage further research in this field. Along with it, we propose a
competitive baseline based on density estimation that has the highest AUC on 29
out of 30 dataset-attack-model combinations. Source code is available in
https://github.com/anoymous92874838/text-adv-detection.


title:
Enhanced weak force sensing based on atom-based coherent quantum noise
  cancellation in a hybrid cavity optomechanical system

abstract:
  We theoretically investigate the weak force-sensing based on coherent quantum
noise cancellation (CQNC) scheme in a hybrid cavity optomechanical system
containing a trapped ensemble of ultracold atoms and an optical parametric
amplifier (OPA). In our proposed system the back action noise can be completely
eliminated at all frequencies as well as through the proper choice of the OPA
parameters noise spectral density can be also reduced at lower frequencies.
This leads to the significant enhancement in the weak force sensing and also
surpasses the standard quantum limit (SQL) even for small input power at lower
detection frequency. Our study can be used for the realization of force sensor
based on hybrid cavity optomechanical systems and for coherent quantum control
in macroscopic systems.


title: 'Guided' Fractures in Graphene Mechanical Diode-like Structures

abstract:
  The concept of the diode is usually applied to electronic and thermal devices
but very rarely for mechanical ones. A recently proposed fracture rectification
effect in polymer-based structures with triangular voids defects has motivated
us to test these ideas at the nanoscale using graphene membranes. Using
fully-atomistic reactive molecular dynamics simulations we showed that robust
rectification-like effects exist. The fracture can be 'guided' to easier
propagate along one specific direction than its opposite. We also observed that
there is an optimal value for the spacing between each void for the
rectification effect.


title:
Experimental demonstration of Single-Level and Multi-Level-Cell
  RRAM-based In-Memory Computing with up to 16 parallel operations

abstract:
  Crossbar arrays of resistive memories (RRAM) hold the promise of enabling
In-Memory Computing (IMC), but essential challenges due to the impact of device
imperfection and device endurance have yet to be overcome. In this work, we
demonstrate experimentally an RRAM-based IMC logic concept with strong
resilience to RRAM variability, even after one million endurance cycles. Our
work relies on a generalization of the concept of in-memory Scouting Logic, and
we demonstrate it experimentally with up to 16 parallel devices (operands), a
new milestone for RRAM in-memory logic. Moreover, we combine IMC with
Multi-Level-Cell programming and demonstrate experimentally, for the first
time, an IMC RRAM-based MLC 2-bit adder.


title:
Quantity over Quality: Training an AV Motion Planner with Large Scale
  Commodity Vision Data

abstract:
  With the Autonomous Vehicle (AV) industry shifting towards Autonomy 2.0, the
performance of self-driving systems starts to rely heavily on large quantities
of expert driving demonstrations. However, collecting this demonstration data
typically involves expensive HD sensor suites (LiDAR + RADAR + cameras), which
quickly becomes financially infeasible at the scales required. This motivates
the use of commodity vision sensors for data collection, which are an order of
magnitude cheaper than the HD sensor suites, but offer lower fidelity. If it
were possible to leverage these for training an AV motion planner, observing
the `long tail' of driving events would become a financially viable strategy.
As our main contribution we show it is possible to train a high-performance
motion planner using commodity vision data which outperforms planners trained
on HD-sensor data for a fraction of the cost. We do this by comparing the
autonomy system performance when training on these two different sensor
configurations, and showing that we can compensate for the lower sensor
fidelity by means of increased quantity: a planner trained on 100h of commodity
vision data outperforms one with 25h of expensive HD data. We also share the
technical challenges we had to tackle to make this work. To the best of our
knowledge, we are the first to demonstrate that this is possible using
real-world data.


title:
Bridging the Source-to-target Gap for Cross-domain Person
  Re-Identification with Intermediate Domains

abstract:
  Cross-domain person re-identification (re-ID), such as unsupervised domain
adaptive (UDA) re-ID, aims to transfer the identity-discriminative knowledge
from the source to the target domain. Existing methods commonly consider the
source and target domains are isolated from each other, i.e., no intermediate
status is modeled between both domains. Directly transferring the knowledge
between two isolated domains can be very difficult, especially when the domain
gap is large. From a novel perspective, we assume these two domains are not
completely isolated, but can be connected through intermediate domains. Instead
of directly aligning the source and target domains against each other, we
propose to align the source and target domains against their intermediate
domains for a smooth knowledge transfer. To discover and utilize these
intermediate domains, we propose an Intermediate Domain Module (IDM) and a
Mirrors Generation Module (MGM). IDM has two functions: 1) it generates
multiple intermediate domains by mixing the hidden-layer features from source
and target domains and 2) it dynamically reduces the domain gap between the
source / target domain features and the intermediate domain features. While IDM
achieves good domain alignment, it introduces a side effect, i.e., the mix-up
operation may mix the identities into a new identity and lose the original
identities. To compensate this, MGM is introduced by mapping the features into
the IDM-generated intermediate domains without changing their original
identity. It allows to focus on minimizing domain variations to promote the
alignment between the source / target domain and intermediate domains, which
reinforces IDM into IDM++. We extensively evaluate our method under both the
UDA and domain generalization (DG) scenarios and observe that IDM++ yields
consistent performance improvement for cross-domain re-ID, achieving new state
of the art.


title:
Difficult for Thee, But Not for Me: Measuring the Difficulty and User
  Experience of Remediating Persistent IoT Malware

abstract:
  Consumer IoT devices may suffer malware attacks, and be recruited into
botnets or worse. There is evidence that generic advice to device owners to
address IoT malware can be successful, but this does not account for emerging
forms of persistent IoT malware. Less is known about persistent malware, which
resides on persistent storage, requiring targeted manual effort to remove it.
This paper presents a field study on the removal of persistent IoT malware by
consumers. We partnered with an ISP to contrast remediation times of 760
customers across three malware categories: Windows malware, non-persistent IoT
malware, and persistent IoT malware. We also contacted ISP customers identified
as having persistent IoT malware on their network-attached storage devices,
specifically QSnatch. We found that persistent IoT malware exhibits a mean
infection duration many times higher than Windows or Mirai malware; QSnatch has
a survival probability of 30% after 180 days, whereby most if not all other
observed malware types have been removed. For interviewed device users, QSnatch
infections lasted longer, so are apparently more difficult to get rid of, yet
participants did not report experiencing difficulty in following notification
instructions. We see two factors driving this paradoxical finding: First, most
users reported having high technical competency. Also, we found evidence of
planning behavior for these tasks and the need for multiple notifications. Our
findings demonstrate the critical nature of interventions from outside for
persistent malware, since automatic scan of an AV tool or a power cycle, like
we are used to for Windows malware and Mirai infections, will not solve
persistent IoT malware infections.


title: Anomaly Detection in Big Data

abstract:
  Anomaly is defined as a state of the system that do not conform to the normal
behavior. For example, the emission of neutrons in a nuclear reactor channel
above the specified threshold is an anomaly. Big data refers to the data set
that is \emph{high volume, streaming, heterogeneous, distributed} and often
\emph{sparse}. Big data is not uncommon these days. For example, as per
Internet live stats, the number of tweets posted per day has gone above 500
millions. Due to data explosion in data laden domains, traditional anomaly
detection techniques developed for small data sets scale poorly on large-scale
data sets. Therefore, we take an alternative approach to tackle anomaly
detection in big data. Essentially, there are two ways to scale anomaly
detection in big data. The first is based on the \emph{online} learning and the
second is based on the \emph{distributed} learning. Our aim in the thesis is to
tackle big data problems while detecting anomaly efficiently. To that end, we
first take \emph{streaming} issue of the big data and propose
Passive-Aggressive GMEAN (PAGMEAN) algorithms. Although, online learning
algorithm can scale well over large number of data points and dimensions, they
can not process data when it is distributed at multiple locations; which is
quite common these days. Therefore, we propose anomaly detection algorithm
which is inherently distributed using ADMM. Finally, we present a case study on
anomaly detection in nuclear power plant data.


title:
Isolated photon-hadron production in high energy $pp$ and $pA$
  collisions at RHIC and LHC

abstract:
  We compute the isolated photon production in association with a charged
hadron at mid rapidity in $pp$ and $pA$ based on the Color Glass Condensate
(CGC) framework of high energy QCD where, for the first time, we incorporate
the Sudakov effect of soft gluon emissions. Our results are based on the
leading order $q g \to q \gamma$ channel in the CGC framework and confronted
with the recent data from RHIC and LHC concerning the angular distributions and
out-of-plane transverse momentum distributions. We find that, while the CGC
computation alone results in too narrow distributions, with the help of the
Sudakov effect, we can get a satisfactory description of the data. With this as
a benchmark, we provide predictions for the magnitude of the nuclear effect
brought by the phenomena of gluon saturation in the CGC.


title:
Statistical visualisation for tidy and geospatial data in R via kernel
  smoothing methods in the eks package

abstract:
  Kernel smoothers are essential tools for data analysis due to their ability
to convey complex statistical information with concise graphical
visualisations. Their inclusion in the base distribution and in the many
user-contributed add-on packages of the \proglang{R} statistical analysis
environment caters well to the requirements for many practitioners. Though
there remain some important gaps for specialised data types, most notably for
tibbles (tidy data) within the tidyverse, and for simple features (geospatial
data) within the Geographical Information Systems (GIS) ecosystem. The proposed
\pkg{eks} package fills in these gaps. In addition to kernel density
estimation, which is the most widely implemented kernel smoother, this package
also caters for more complex data analysis situations, such as density-based
classification (supervised learning), mean shift clustering (unsupervised
learning), density derivative estimation, density ridge estimation, and
significance testing for density differences and for modal regions. We
illustrate with experimental data how to obtain and to interpret the
statistical graphical analyses for these kernel smoothing methods.


title: Relative distance matters for one-shot landmark detection

abstract:
  Contrastive learning based methods such as cascade comparing to detect (CC2D)
have shown great potential for one-shot medical landmark detection. However,
the important cue of relative distance between landmarks is ignored in CC2D. In
this paper, we upgrade CC2D to version II by incorporating a
simple-yet-effective relative distance bias in the training stage, which is
theoretically proved to encourage the encoder to project the relatively distant
landmarks to the embeddings with low similarities. As consequence, CC2Dv2 is
less possible to detect a wrong point far from the correct landmark.
Furthermore, we present an open-source, landmark-labeled dataset for the
measurement of biomechanical parameters of the lower extremity to alleviate the
burden of orthopedic surgeons. The effectiveness of CC2Dv2 is evaluated on the
public dataset from the ISBI 2015 Grand-Challenge of cephalometric radiographs
and our new dataset, which greatly outperforms the state-of-the-art one-shot
landmark detection approaches.


title: Spatio-temporal analysis of chromospheric heating in a plage region

abstract:
  Our knowledge of the heating mechanisms that are at work in the chromosphere
of plage regions remains highly unconstrained from observational studies. The
purpose of our study is to estimate the chromospheric heating terms from a
plage dataset, characterize their spatio-temporal distribution and set
constraints on the heating processes that are at work. We make use of NLTE
inversions to infer a model of the photosphere and chromosphere of a plage
dataset acquired with the Swedish 1-m Solar Telescope. We use this model
atmosphere to calculate the chromospheric radiative losses from H i, Ca ii and
Mg ii atoms. We approximate the chromospheric heating terms by the net
radiative losses predicted by the inverted model. In order to make the analysis
of time-series over a large field-of-view computationally tractable, we make
use of a neural network. In the lower chromosphere, the contribution from the
Ca ii lines is dominant and located in the surroundings of the photospheric
footpoints. In the upper chromosphere, the H i contribution is dominant.
Radiative losses in the upper chromosphere form an homogeneous patch that
covers the plage region. The net radiative losses can be split in a periodic
component with an average amplitude of ampQ = 7.6 kW m^{-2} and a static (or
very slowly evolving) component with a mean value of -26.1 kW m^{-2}. Our
interpretation is that in the lower chromosphere, the radiative losses are
tracing the sharp lower edge of the hot magnetic canopy, where the electric
current is expected to be large. In the upper chromosphere, both the magnetic
field and the distribution of net radiative losses are room-filling, whereas
the amplitude of the periodic component is largest. Our results suggest that
acoustic wave heating may be responsible for one third of the energy deposition
in the upper chromosphere, whereas other heating mechanisms must be responsible
for the rest.


title: Optical Properties of Yttrium Gallium Garnet

abstract:
  We report measurements of the reflection and transmission spectra of 2% doped
Thulium Yttrium Gallium Garnet (Tm:YGG) using variable-angle spectroscopic
ellipsometry(VASE) over a wavelength range from 210 to 1680 nm (0.73-5.9 eV).
The well-known Tm resonances are identified and separated from the aggregate
data, allowing us to calculate the previously unknown frequency dependence of
the complex refractive index of the host material. This information is
important for many applications of YGG in classical and quantum photonics,
including constructing optical cavities, laser-based applications, and quantum
information devices. A complete database of the obtained parameters is included
in the supplementary information.


title: Introduction to Toric Geometry

abstract:
  These notes are based on a series of lectures given by the author at the Max
Planck Institute for Mathematics in the Sciences in Leipzig. Addressed topics
include affine and projective toric varieties, abstract normal toric varieties
from fans, divisors on toric varieties and Cox's construction of a toric
variety as a GIT quotient. We emphasize the role of toric varieties in solving
systems of polynomial equations and provide many computational examples using
the Julia package Oscar.jl.


title: Square-free OM computation of global integral bases

abstract:
  For a prime $p$, the OM algorithm finds the $p$-adic factorization of an
irreducible polynomial $f\in\mathbb{Z}[x]$ in polynomial time. This may be
applied to construct $p$-integral bases in the number field $K$ defined by $f$.
In this paper, we adapt the OM techniques to work with a positive integer $N$
instead of $p$. As an application, we obtain an algorithm to compute global
integral bases in $K$, which does not require a previous factorization of the
discriminant of $f$.


title:
Matching resummed endpoint and continuum $\gamma$-ray spectra from
  dark-matter annihilation

abstract:
  For the minimal wino and Higgsino benchmark models we provide accurate energy
spectra of high-energy photons from TeV scale dark-matter annihilation
$\chi\chi\to \gamma+X$ by merging electroweak Sudakov resummation near maximal
energy with the electroweak parton-shower PPPC4DM, and accounting for the
Sommerfeld effect. Electroweak resummation significantly changes the shape of
the photon-energy spectrum in the wide range $E_\gamma \sim (0.6\ldots 1)\,
m_\chi$ and hence the form of the so-called "line-signal".


title:
Learning Set Functions Under the Optimal Subset Oracle via Equivariant
  Variational Inference

abstract:
  Learning set functions becomes increasingly more important in many
applications like product recommendation and compound selection in AI-aided
drug discovery. The majority of existing works study methodologies of set
function learning under the function value oracle, which, however, requires
expensive supervision signals. This renders it impractical for applications
with only weak supervisions under the Optimal Subset (OS) oracle, the study of
which is surprisingly overlooked. In this work, we present a principled yet
practical maximum likelihood learning framework, termed as EquiVSet, that
simultaneously meets the following desiderata of learning set functions under
the OS oracle: i) permutation invariance of the set mass function being
modeled; ii) permission of varying ground set; iii) fully differentiability;
iv) minimum prior; and v) scalability. The main components of our framework
involve: an energy-based treatment of the set mass function, DeepSet-style
architectures to handle permutation invariance, mean-field variational
inference, and its amortized variants. Although the framework is embarrassingly
simple, empirical studies on three real-world applications (including Amazon
product recommendation, set anomaly detection and compound selection for
virtual screening) demonstrate that EquiVSet outperforms the baselines by a
large margin.


title: Line Multiview Varieties

abstract:
  In this paper we present an algebraic study of line correspondences for
pinhole cameras, in contrast to the thoroughly studied point correspondences.
We define the line multiview variety as the Zariski closure of the image of the
map projecting lines in 3-space to tuples of image lines in 2-space. We prove
that in the case of generic camera matrices it is characterized by a natural
determinantal variety and we provide a complete description for any camera
arrangement. We investigate basic properties of this variety such as dimension,
smoothness and multidegree. Finally, we give experimental results for the
Euclidean distance degree and robustness under noise for triangulation of
lines.


title: Unfolding-Aided Bootstrapped Phase Retrieval in Optical Imaging

abstract:
  Phase retrieval in optical imaging refers to the recovery of a complex signal
from phaseless data acquired in the form of its diffraction patterns. These
patterns are acquired through a system with a coherent light source that
employs a diffractive optical element (DOE) to modulate the scene resulting in
coded diffraction patterns at the sensor. Recently, the hybrid approach of
model-driven network or deep unfolding has emerged as an effective alternative
because it allows for bounding the complexity of phase retrieval algorithms
while also retaining their efficacy. Additionally, such hybrid approaches have
shown promise in improving the design of DOEs that follow theoretical
uniqueness conditions. There are opportunities to exploit novel experimental
setups and resolve even more complex DOE phase retrieval applications. This
paper presents an overview of algorithms and applications of deep unfolding for
bootstrapped - regardless of near, middle, and far zones - phase retrieval.


title: Fail-Safe Generative Adversarial Imitation Learning

abstract:
  For flexible yet safe imitation learning (IL), we propose a modular approach
that uses a generative imitator policy with a safety layer, has an overall
explicit density/gradient, can therefore be end-to-end trained using generative
adversarial IL (GAIL), and comes with theoretical worst-case safety/robustness
guarantees. The safety layer's exact density comes from using a countable
non-injective gluing of piecewise differentiable injections and the
change-of-variables formula. The safe set (into which the safety layer maps) is
inferred by sampling actions and their potential future fail-safe fallback
continuations, together with Lipschitz continuity and convexity arguments. We
also provide theoretical bounds showing the advantage of using the safety layer
already during training (imitation error linear in the horizon) compared to
only using it at test time (quadratic error). In an experiment on challenging
real-world driver interaction data, we empirically demonstrate tractability,
safety and imitation performance of our approach.


title: Stable cohomology of congruence subgroups

abstract:
  We describe the $\mathbb{F}_p$-cohomology of the congruence subgroups
$SL_n(\mathbb{Z}, p^m)$ in degrees $* < p$, for all large enough $n$,
establishing a formula proposed by F. Calegari. Along the way, we also
establish a formula for the stable cohomology of $SL_n(\mathbb{Z}/p)$ with
certain twisted coefficients.


title: Observation of 2D Cherenkov radiation

abstract:
  For over 80 years of research, the conventional description of free-electron
radiation phenomena, such as Cherenkov radiation, has remained unchanged:
classical three-dimensional electromagnetic waves. Interestingly, in reduced
dimensionality, the properties of free-electron radiation are predicted to
fundamentally change. Here, we present the first observation of Cherenkov
surface waves, wherein free electrons emit narrow-bandwidth photonic
quasiparticles propagating in two-dimensions. The low dimensionality and narrow
bandwidth of the effect enable to identify quantized emission events through
electron energy loss spectroscopy. Our results support the recent theoretical
prediction that free electrons do not always emit classical light and can
instead become entangled with the photons they emit. The two-dimensional
Cherenkov interaction achieves quantum coupling strengths over two orders of
magnitude larger than ever reported, reaching the single-electron-single-photon
interaction regime for the first time with free electrons. Our findings pave
the way to previously unexplored phenomena in free-electron quantum optics,
facilitating bright, free-electron-based quantum emitters of heralded Fock
states.


title: Resilience in multiplex networks by addition of cross-repulsive links

abstract:
  A multiplex network of identical dynamical units becomes resilient against
parameter perturbation by adding selective linear diffusive cross-coupling
links. A parameter drift at any instant in one or multiple network nodes can
destroy synchrony, causing failure and even collapse in the network
performance. We introduced [PRE 95, 062204(2017)] a recovery strategy by
selective addition of cross-coupling links to save synchrony in the network
from the edge of failure due to parameter mismatch (small or large) in any
nodes. This concept is extended to 2-layered multiplex networks when the
emergent synchrony becomes resilient against a small or large parameter
drifting. In addition, the stability of the synchronous state is enhanced from
local stability to global stability of synchrony. By the addition of
cross-coupling, the network revives complete synchrony in all the nodes except
the perturbed nodes, which emerges into a type of generalized synchrony with
all the unperturbed nodes. The generalized synchrony is manifested simply by a
linear amplitude response in the state variable(s) of the perturbed node(s) by
a scaling factor proportional to the mismatch. A set of systematic rules has
been derived from the linear flow matrix of the dynamical system representing
the nodes dynamics that helps find the connectivity matrix of the
cross-coupling links. Lyapunov function stability condition is used to
determine the cross-coupling link strength that, in turn, establishes global
stability of synchrony of the multiplex network. We verify the efficacy of our
proposed coupling scheme with analytical results and numerical simulations of
two examples of multiplex networks. In the first example, we use non-local
connectivity in each layer, with nodal dynamics of the FitzHugh-Nagumo neuron
model.


title: Around the Spindle galaxy: the dark halo mass of NGC3115

abstract:
  We report observations of five dwarf galaxies in the vicinity of the luminous
S0 galaxy NGC 3115 performed with the Advanced Camera for Surveys on the Hubble
Space Telescope. Their distances determined via the Tip of the Red Giant Branch
are: 10.05 Mpc (UGCA 193), 9.95 Mpc (KKSG 17), 10.13 Mpc (2MASX-J0957-0915),
10.42 Mpc (2dFGRS-TGN218Z179) and 11.01 Mpc (KKSG 19). With their typical
distance error of about 0.75 Mpc all the five dwarfs are consistent to be true
satellites of the host galaxy NGC 3115 (10.2$\pm$0.2 Mpc). Using the DESI
Legacy Imaging Surveys we also found 5 new probable dwarf satellites of
NGC3115, as well as 4 new probable members of the neighboring group around NGC
3521 situated 3 Mpc away from the NGC 3115 group. Based on the radial
velocities and projected separations of 10 dwarf companions, we derived the
total (orbital) mass of NGC 3115 to be (4.89$\pm$1.48) $10^{12}$ $M_{\odot}$.
The ratio of the total mass-to-K-luminosity of NGC 3115 is (50$\pm$15)
$M_\odot/L_\odot$, which is typical for the early-type luminous galaxies.


title: Open Source Routers: A Survey

abstract:
  Variety, size and complexity of data types, services and applications in
Internet is continuously growing up. This increasing of complexity needs more
powerful and sophisticated equipment's. One group of these devices that has
essential role are routers. Some of vendors produce some elaborate and complex
products but the commercial solutions are too closed and inflexible. The term
"Open Source Routers" covers a lot of implementations of free software routers.
Open Source Routers are solutions to overcome commercial solutions with closed
platforms. In this article, we survey the existing implementations and a wide
array of past and state-of-the-art projects on open software routers followed
by a discussion of major challenges in this area.


title:
The $\mathbf{Z}_{2}$ topological invariants in 2D and 3D topological
  superconductors without time reversal symmetry

abstract:
  In theory of topological classification, the 2D topological superconductors
without time reversal symmetry are characterized by Chern numbers. However, in
reality, we find the Chern numbers can not reveal the whole properties of the
boundary states of the topological superconductors. We figure out some
particle-hole symmetry related $\mathbf{Z}_{2}$ invariants, which provide more
additional information of the topological superconductors than the Chern
numbers provide. With the $\mathbf{Z}_{2}$ invariant, we define weak and strong
topological superconductors in 2D systems. Moreover, we explain the causes of
mismatch between the Chern numbers and the numbers of boundary states in
topological superconductors, and claim that the robust Majorana zero modes are
characterized by the $\mathbf{Z}_{2}$ invariant rather than the Chern numbers.
We also extend the $\mathbf{Z}_{2}$ invariants to 3D non-time-reversal symmetry
superconductor systems including gapful and gapless situations.


title:
Capturing Shape Information with Multi-Scale Topological Loss Terms for
  3D Reconstruction

abstract:
  Reconstructing 3D objects from 2D images is both challenging for our brains
and machine learning algorithms. To support this spatial reasoning task,
contextual information about the overall shape of an object is critical.
However, such information is not captured by established loss terms (e.g. Dice
loss). We propose to complement geometrical shape information by including
multi-scale topological features, such as connected components, cycles, and
voids, in the reconstruction loss. Our method calculates topological features
from 3D volumetric data based on cubical complexes and uses an optimal
transport distance to guide the reconstruction process. This topology-aware
loss is fully differentiable, computationally efficient, and can be added to
any neural network. We demonstrate the utility of our loss by incorporating it
into SHAPR, a model for predicting the 3D cell shape of individual cells based
on 2D microscopy images. Using a hybrid loss that leverages both geometrical
and topological information of single objects to assess their shape, we find
that topological information substantially improves the quality of
reconstructions, thus highlighting its ability to extract more relevant
features from image datasets.


title: On Data Augmentation for Models Involving Reciprocal Gamma Functions

abstract:
  In this paper, we introduce a new and efficient data augmentation approach to
the posterior inference of the models with shape parameters when the reciprocal
gamma function appears in full conditional densities. Our approach is to
approximate full conditional densities of shape parameters by using Gauss's
multiplication formula and Stirling's formula for the gamma function, where the
approximation error can be made arbitrarily small. We use the techniques to
construct efficient Gibbs and Metropolis-Hastings algorithms for a variety of
models that involve the gamma distribution, Student's $t$-distribution, the
Dirichlet distribution, the negative binomial distribution, and the Wishart
distribution. The proposed sampling method is numerically demonstrated through
simulation studies.


title:
Deviations from the extended London model at high magnetic fields in
  YBa$_2$Cu$_3$O$_7$

abstract:
  We report on the evolution with magnetic field and temperature of the vortex
lattice (VL) in fully-oxygenated YBa2Cu3O7 as studied by time-of-flight small
angle neutron scattering. Using the HFM/EXED beamline, we have obtained data up
to 25.9 T - much higher than that available previously. Our VL structure
results indicate the progressive suppression by field of the superconductivity
along the crystallographic b (CuO chain) direction. The intensity of the
diffracted signal reveals the spatial variation of magnetization caused by the
VL (the "form factor"). Instead of a rapid fall-off with field, as seen in
superconductors with smaller upper critical fields, we find that the form
factor is almost constant with field above about 12 T. We speculate that this
is due to Pauli paramagnetic moments, which increase at high fields due to
alignment of the spins of quasiparticles in the vortex cores.


title: Exact performance of the five-qubit code with coherent errors

abstract:
  To well understand the behavior of quantum error correction codes (QECC) in
noise processes, we need to obtain explicit coding maps for QECC. Due to
extraordinary amount of computational labor that they entails, explicit coding
maps are a little known. Indeed this is even true for one of the most commonly
considered quantum codes-the five-qubit code, also known as the smallest
perfect code that permits corrections of generic single-qubit errors. With
direct but complicated computation, we obtain explicit process matrix of the
coding maps with a unital error channel for the five-qubit code. The process
matrix allows us to conduct exact analysis on the performance of the quantum
code. We prove that the code can correct a generic error in the sense that
under repeated concatenation of the coding map with itself, the code does not
make any assumption about the error model other than it being weak and thus can
remove the error(it can transform/take the error channel to the identity
channel if the error is sufficiently small.). We focus on the examination of
some coherent error models (non diagonal channels) studied in recent
literatures. We numerically derive a lower bound on threshold of the
convergence for the code. Furthermore, we analytically show how the code
affects the average gate infidelity and diamond distance of the error channels.
Explicit formulas of the two measurements for both pre-error channel and
post-error channel are derived, and we then analyze the logical error rates of
the aforesaid quantum code. Our findings tighten the upper bounds on diamond
distance of the noise channel after error corrections obtained in literature.


title: Reinforcement Learning in Possibly Nonstationary Environments

abstract:
  We consider reinforcement learning (RL) methods in offline nonstationary
environments. Many existing RL algorithms in the literature rely on the
stationarity assumption that requires the system transition and the reward
function to be constant over time. However, the stationarity assumption is
restrictive in practice and is likely to be violated in a number of
applications, including traffic signal control, robotics and mobile health. In
this paper, we develop a consistent procedure to test the nonstationarity of
the optimal policy based on pre-collected historical data, without additional
online data collection. Based on the proposed test, we further develop a
sequential change point detection method that can be naturally coupled with
existing state-of-the-art RL methods for policy optimisation in nonstationary
environments. The usefulness of our method is illustrated by theoretical
results, simulation studies, and a real data example from the 2018 Intern
Health Study. A Python implementation of the proposed procedure is available at
https://github.com/limengbinggz/CUSUM-RL


title:
Plutonium-241 as a possible isotope for neutrino mass measurement and
  capture

abstract:
  Tritium has been the isotope of choice for measurements of the neutrino mass
and planned detection of the relic neutrino background. The low mass of $^{3}$H
leads to large recoil energy of the nucleus. This has emerged as a limiting
factor for both measurements. We propose to use $^{241}$Pu as an alternative.
The recoil is 80x smaller and it has similar decay energy and lifetime as
$^{3}$H. We evaluate for the first time its soft-neutrino capture cross-section
and find $(\sigma v)_{\nu} = 1.52 \times 10^{-45}$. This is 40% of the capture
cross-section for tritium and makes $^{241}$Pu a promising alternative for
$^{3}$H.


title: Multiplicative maps on matrix algebras

abstract:
  In this paper, we use elementary method to give a classification of the
multiplicative maps on matrix algebra $M_{n}(\mF)$ over a field $\mF$ of
characteristic $0$. All the multiplicative maps are classified into three
classes: the trivial ones, the degenerate ones and the non-degenerate ones.


title: Cohen-Macaulay Weighted Oriented Edge Ideals and its Alexander Dual

abstract:
  The study of the edge ideal $I(D_{G})$ of a weighted oriented graph $D_{G}$
with underlying graph $G$ started in the context of Reed-Muller type codes. We
generalize a Cohen-Macaulay construction for $I(D_{G})$, which Villarreal gave
for edge ideals of simple graphs. We use this construction to classify all the
Cohen-Macaulay weighted oriented edge ideals, whose underlying graph is a
cycle. We show that the conjecture on Cohen-Macaulayness of $I(D_{G})$,
proposed by Pitones et al. (2019), holds for $I(D_{C_{n}})$, where $C_{n}$
denotes the cycle of length $n$. Miller generalized the concept of Alexander
dual ideals of square-free monomial ideals to arbitrary monomial ideals, and in
that direction, we study the Alexander dual of $I(D_{G})$ and its conditions to
be Cohen-Macaulay.


title: Optimal coordinates for Ricci-flat conifolds

abstract:
  We compute the indicial roots of the Lichnerowicz Laplacian on Ricci-flat
cones and give a detailed description of the corresponding radially homogeneous
tensor fields in its kernel. For a Ricci-flat conifold $(M,g)$ which may have
asymptotically conical as well as conically singular ends, we compute at each
end a lower bound for the order with which the metric converges to the tangent
cone. As a special subcase of our result, we show that any Ricci-flat ALE
manifold $(M^n,g)$ is of order $n$ and thereby close a small gap in a paper by
Cheeger and Tian.


title: Gaussian approximations for random vectors

abstract:
  We present several refinements on the fluctuations of sequences of random
vectors (with values in the Euclidean space $\mathbb{R}^d$) which converge
after normalization to a multidimensional Gaussian distribution. More precisely
we refine such results in two directions: first we give conditions under which
one can obtain bounds on the speed of convergence to the multidimensional
Gaussian distribution, and then we provide a setting in which one can obtain
precise moderate or large deviations (in particular we see at which scale the
Gaussian approximation for the tails ceases to hold and how the symmetry of the
Gaussian tails is then broken). These results extend some of our earlier works
obtained for real valued random variables, but they are not simple extensions,
as some new phenomena are observed that could not be visible in one dimension.
Even for very simple objects such as the symmetric random walk in
$\mathbb{Z}^d$, we observe a loss of symmetry that we can quantify for walks
conditioned to be far away from the origin. Also, unlike the one dimensional
case where the Kolmogorov distance is natural, in the multidimensional case
there is no more such a canonical distance. We choose to work with the
so-called convex distance, and as a consequence, the geometry of the Borel
measurable sets that we consider shall play an important role (also making the
proofs more complicated). We illustrate our results with some examples such as
correlated random walks, the characteristic polynomials of random unitary
matrices, or pattern countings in random graphs.


title: Pushdown Automata and Context-Free Grammars in Bisimulation Semantics

abstract:
  The Turing machine models an old-fashioned computer, that does not interact
with the user or with other computers, and only does batch processing.
Therefore, we came up with a Reactive Turing Machine that does not have these
shortcomings. In the Reactive Turing Machine, transitions have labels to give a
notion of interactivity. In the resulting process graph, we use bisimilarity
instead of language equivalence.
  Subsequently, we considered other classical theorems and notions from
automata theory and formal languages theory. In this paper, we consider the
classical theorem of the correspondence between pushdown automata and
context-free grammars. By changing the process operator of sequential
composition to a sequencing operator with intermediate acceptance, we get a
better correspondence in our setting. We find that the missing ingredient to
recover the full correspondence is the addition of a notion of state awareness.


title: Weakly Supervised Object Localization as Domain Adaption

abstract:
  Weakly supervised object localization (WSOL) focuses on localizing objects
only with the supervision of image-level classification masks. Most previous
WSOL methods follow the classification activation map (CAM) that localizes
objects based on the classification structure with the multi-instance learning
(MIL) mechanism. However, the MIL mechanism makes CAM only activate
discriminative object parts rather than the whole object, weakening its
performance for localizing objects. To avoid this problem, this work provides a
novel perspective that models WSOL as a domain adaption (DA) task, where the
score estimator trained on the source/image domain is tested on the
target/pixel domain to locate objects. Under this perspective, a DA-WSOL
pipeline is designed to better engage DA approaches into WSOL to enhance
localization performance. It utilizes a proposed target sampling strategy to
select different types of target samples. Based on these types of target
samples, domain adaption localization (DAL) loss is elaborated. It aligns the
feature distribution between the two domains by DA and makes the estimator
perceive target domain cues by Universum regularization. Experiments show that
our pipeline outperforms SOTA methods on multi benchmarks. Code are released at
\url{https://github.com/zh460045050/DA-WSOL_CVPR2022}.


title: Weyl modules for toroidal Lie algebras

abstract:
  In this paper we study Weyl modules for a toroidal Lie algebra $\CT$ with
arbitrary $n$ variables. Using the work of Rao \cite{1995}, we prove that the
level one global Weyl modules of $\CT$ are isomorphic to suitable submodules of
a Fock space representation of $\CT$ upto a twist. As an application, we
compute the graded character of the level one local Weyl module of $\CT$,
thereby generalising the work of Kodera \cite{ko}.


title: Detecting High-Quality GAN-Generated Face Images using Neural Networks

abstract:
  In the past decades, the excessive use of the last-generation GAN (Generative
Adversarial Networks) models in computer vision has enabled the creation of
artificial face images that are visually indistinguishable from genuine ones.
These images are particularly used in adversarial settings to create fake
social media accounts and other fake online profiles. Such malicious activities
can negatively impact the trustworthiness of users identities. On the other
hand, the recent development of GAN models may create high-quality face images
without evidence of spatial artifacts. Therefore, reassembling uniform color
channel correlations is a challenging research problem. To face these
challenges, we need to develop efficient tools able to differentiate between
fake and authentic face images. In this chapter, we propose a new strategy to
differentiate GAN-generated images from authentic images by leveraging spectral
band discrepancies, focusing on artificial face image synthesis. In particular,
we enable the digital preservation of face images using the Cross-band
co-occurrence matrix and spatial co-occurrence matrix. Then, we implement these
techniques and feed them to a Convolutional Neural Networks (CNN) architecture
to identify the real from artificial faces. Additionally, we show that the
performance boost is particularly significant and achieves more than 92% in
different post-processing environments. Finally, we provide several research
observations demonstrating that this strategy improves a comparable detection
method based only on intra-band spatial co-occurrences.


title:
Why Do Machine Learning Practitioners Still Use Manual Tuning? A
  Qualitative Study

abstract:
  Current advanced hyperparameter optimization (HPO) methods, such as Bayesian
optimization, have high sampling efficiency and facilitate replicability.
Nonetheless, machine learning (ML) practitioners (e.g., engineers, scientists)
mostly apply less advanced HPO methods, which can increase resource consumption
during HPO or lead to underoptimized ML models. Therefore, we suspect that
practitioners choose their HPO method to achieve different goals, such as
decrease practitioner effort and target audience compliance. To develop HPO
methods that align with such goals, the reasons why practitioners decide for
specific HPO methods must be unveiled and thoroughly understood. Because
qualitative research is most suitable to uncover such reasons and find
potential explanations for them, we conducted semi-structured interviews to
explain why practitioners choose different HPO methods. The interviews revealed
six principal practitioner goals (e.g., increasing model comprehension), and
eleven key factors that impact decisions for HPO methods (e.g., available
computing resources). We deepen the understanding about why practitioners
decide for different HPO methods and outline recommendations for improvements
of HPO methods by aligning them with practitioner goals.


title:
The Minkowski billiard-characterization of the EHZ-capacity of convex
  Lagrangian products

abstract:
  We rigorously state the connection between the minimal length of closed
$(K,T)$-Minkowski billiard trajectories and the EHZ-capacity of convex
Lagrangian products $K\times T\subset\mathbb{R}^n\times\mathbb{R}^n$. We prove
this connection--which was made explicit for the first time by Artstein-Avidan
and Ostrover under the assumption of smoothness and strict convexity of both
$K$ and $T$--in its full generality, i.e., without requiring any conditions on
the convex bodies $K$ and $T$. This prepares the computation of the
EHZ-capacity of convex Lagrangian products by using algorithms for determining
the minimal length of closed Minkowski billiards trajectories while one is
allowed to restrict oneself to the manageable class of convex polytopes--which,
obviously, are neither smooth nor strictly convex.


title: Quantum random walks in coupled photonic ring resonators

abstract:
  Quantum random walks use interference to obtain faster state space
exploration, which can be used for algorithmic purposes. Photonic technologies
provide a natural platform for many recent experimental demonstrations. Here we
analyze quantum random walks implemented by coherent light propagation in
series-coupled photonic ring resonators. We propose a family of graphs modeling
these devices and compare quantum and classical random walks on these
structures, calculating steady-state and time-dependent solutions. We obtain
conditions for quantum advantage in this setting and show how to recover
classical random walks by averaging over quantum phases. Preliminary device
feasibility tests are carried out via simulations and experimental results
using polymeric directional couplers.


title: Topological data analysis of truncated contagion maps

abstract:
  The investigation of dynamical processes on networks has been one focus for
the study of contagion processes. It has been demonstrated that contagions can
be used to obtain information about the embedding of nodes in a Euclidean
space. Specifically, one can use the activation times of threshold contagions
to construct contagion maps as a manifold-learning approach. One drawback of
contagion maps is their high computational cost. Here, we demonstrate that a
truncation of the threshold contagions may considerably speed up the
construction of contagion maps. Finally, we show that contagion maps may be
used to find an insightful low-dimensional embedding for single-cell
RNA-sequencing data in the form of cell-similarity networks and so reveal
biological manifolds. Overall, our work makes the use of contagion maps as
manifold-learning approaches on empirical network data more viable.


title: Asymptotic Optimality of Speed-Aware JSQ for Heterogeneous Systems

abstract:
  The Join-the-Shortest-Queue (JSQ) load-balancing scheme is known to minimise
the average delay of jobs in homogeneous systems consisting of identical
servers. However, it performs poorly in heterogeneous systems where servers
have different processing rates. Finding a delay optimal scheme remains an open
problem for heterogeneous systems. In this paper, we consider a speed-aware
version of the JSQ scheme for heterogeneous systems and show that it achieves
delay optimality in the fluid limit. One of the key issues in establishing this
optimality result for heterogeneous systems is to show that the sequence of
steady-state distributions indexed by the system size is tight in an
appropriately defined space. The usual technique for showing tightness by
coupling with a suitably defined dominant system does not work for
heterogeneous systems. To prove tightness, we devise a new technique that uses
the drift of exponential Lyapunov functions. Using the non-negativity of the
drift, we show that the stationary queue length distribution has an
exponentially decaying tail - a fact we use to prove tightness. Another
technical difficulty arises due to the complexity of the underlying state-space
and the separation of two time-scales in the fluid limit. Due to these factors,
the fluid-limit turns out to be a function of the invariant distribution of a
multi-dimensional Markov chain which is hard to characterise. By using some
properties of this invariant distribution and using the monotonicity of the
system, we show that the fluid limit is has a unique and globally attractive
fixed point.


title: A Remark on Evolution Equation of Stochastic Logical Dynamic Systems

abstract:
  Modelling is an essential procedure in analyzing and controlling a given
logical dynamic system (LDS). It has been proved that deterministic LDS can be
modeled as a linear-like system using algebraic state space representation.
However, due to the inherently non-linear, it is difficult to obtain the
algebraic expression of a stochastic LDS. This paper provides a unified
framework for transition analysis of LDSs with deterministic and stochastic
dynamics. First, modelling of LDS with deterministic dynamics is reviewed. Then
modeling of LDS with stochastic dynamics is considered, and non-equivalence
between subsystems and global system is proposed. Next, the reason for the
non-equivalence is provided. Finally, consistency condition is presented for
independent model and conditional independent model.


title:
Debiased Batch Normalization via Gaussian Process for Generalizable
  Person Re-Identification

abstract:
  Generalizable person re-identification aims to learn a model with only
several labeled source domains that can perform well on unseen domains. Without
access to the unseen domain, the feature statistics of the batch normalization
(BN) layer learned from a limited number of source domains is doubtlessly
biased for unseen domain. This would mislead the feature representation
learning for unseen domain and deteriorate the generalizaiton ability of the
model. In this paper, we propose a novel Debiased Batch Normalization via
Gaussian Process approach (GDNorm) for generalizable person re-identification,
which models the feature statistic estimation from BN layers as a dynamically
self-refining Gaussian process to alleviate the bias to unseen domain for
improving the generalization. Specifically, we establish a lightweight model
with multiple set of domain-specific BN layers to capture the discriminability
of individual source domain, and learn the corresponding parameters of the
domain-specific BN layers. These parameters of different source domains are
employed to deduce a Gaussian process. We randomly sample several paths from
this Gaussian process served as the BN estimations of potential new domains
outside of existing source domains, which can further optimize these learned
parameters from source domains, and estimate more accurate Gaussian process by
them in return, tending to real data distribution. Even without a large number
of source domains, GDNorm can still provide debiased BN estimation by using the
mean path of the Gaussian process, while maintaining low computational cost
during testing. Extensive experiments demonstrate that our GDNorm effectively
improves the generalization ability of the model on unseen domain.


title: Turbulence-resistant self-focusing vortex beams

abstract:
  We consider recently introduced self-focusing fields that carry orbital
angular momentum (OAM) [Opt. Lett. $\textbf{46}$, 2384$-$2387 (2021)] and in
particular, their propagation properties through a turbulent ocean. We show
that this type of field is especially robust against turbulence induced
degradation, when compared to a completely coherent beam. In moderately strong
oceanic turbulence, the self-focusing OAM beam features over five orders of
magnitude higher peak intensities at the receiver plane, an $\sim$80 $\%$
detection probability for the signal mode, as well as an energy transmission
efficiency in excess of 70 $\%$ over a link of $\sim$100 m.
Counter-intuitively, the focusing properties of such fields may be enhanced
with increasing turbulence, causing the mean squared waist to become smaller
with greater turbulence strength. Our results demonstrate that certain types of
partial coherence may be highly desirable for optical telecommunication
employing OAM.


title: Inspiralling compact objects with generic deformations

abstract:
  Self-gravitating bodies can have an arbitrarily complex shape, which implies
a much richer multipolar structure than that of a black hole in General
Relativity. With this motivation, we study the corrections to the dynamics of a
binary system due to generic, nonaxisymmetric mass quadrupole moments to
leading post-Newtonian (PN) order. Utilizing the method of osculating orbits
and a multiple scale analysis, we find analytic solutions to the precession and
orbital dynamics of a (generically eccentric) binary in terms of the
dimensionless modulus parameters $\epsilon_{m}$, corresponding to axial $m=1$
and polar $m=2$ corrections from oblateness/prolateness. The solutions to the
precession dynamics are exact for $0 \le \epsilon_{2} < 1$, and perturbative in
$\epsilon_{1} \ll 1$. We further compute the leading order corrections to the
gravitational wave amplitude and phase for a quasi-circular binary due to mass
quadrupole effects. Making use of the stationary phase approximation and
shifted uniform asymptotics (SUA), the corrections to the phase enter at
relative 2PN order, while the amplitude modulations enter at -0.5PN order with
a SUA amplitude correction at 5.5PN order, relative 2PN order to the leading
order SUA correction. By investigating the dephasing due to generic quadrupole
moments, we find that a phase difference $\gtrsim 0.1$~radians is achievable
for $\epsilon_{m} \gtrsim 10^{-3}$, which suggests that constraints with
current and future ground-based gravitational wave detectors are possible. Our
results can be implemented in parameter estimation studies to constrain generic
multipolar deformations of the Kerr geometry and of neutron stars.


title:
Ensembles of Vision Transformers as a New Paradigm for Automated
  Classification in Ecology

abstract:
  Monitoring biodiversity is paramount to manage and protect natural resources,
particularly in times of global change. Collecting images of organisms over
large temporal or spatial scales is a promising practice to monitor and study
biodiversity change of natural ecosystems, providing large amounts of data with
minimal interference with the environment. Deep learning models are currently
used to automate classification of organisms into taxonomic units. However,
imprecision in these classifiers introduce a measurement noise that is
difficult to control and can significantly hinder the analysis and
interpretation of data. In our study, we show that this limitation can be
overcome by ensembles of Data-efficient image Transformers (DeiTs), which
significantly outperform the previous state of the art (SOTA). We validate our
results on a large number of ecological imaging datasets of diverse origin, and
organisms of study ranging from plankton to insects, birds, dog breeds, animals
in the wild, and corals. On all the data sets we test, we achieve a new SOTA,
with a reduction of the error with respect to the previous SOTA ranging from
18.48% to 87.50%, depending on the data set, and often achieving performances
very close to perfect classification. The main reason why ensembles of DeiTs
perform better is not due to the single-model performance of DeiTs, but rather
to the fact that predictions by independent models have a smaller overlap, and
this maximizes the profit gained by ensembling. This positions DeiT ensembles
as the best candidate for image classification in biodiversity monitoring.


title: Floquet simulators for topological surface states in isolation

abstract:
  We propose dynamical protocols allowing for the engineered realization of
topological surface states in isolation. Our approach builds on the concept of
synthetic dimensions generated by driving systems with incommensurate
frequencies. As a concrete example, we consider 3d topological surface states
of a 4d quantum Hall insulator via a $(1+2_\mathrm{syn})$-dimensional protocol.
We present first principle analytical calculations demonstrating that no
supporting 4d bulk phase is required for a 3d topological surface phase. We
back the analytical approach by numerical simulations and present a detailed
blueprint for the realization of the synthetic surface phase with existing
quantum linear optical network device technology. We then discuss
generalizations, including a proposal for a quantum simulator of the
$(1+1_\mathrm{syn})$ dimensional surface of the common 3d topological
insulator.


title:
Distributed Matrix-Vector Multiplication with Sparsity and Privacy
  Guarantees

abstract:
  We consider the problem of designing a coding scheme that allows both
sparsity and privacy for distributed matrix-vector multiplication. Perfect
information-theoretic privacy requires encoding the input sparse matrices into
matrices distributed uniformly at random from the considered alphabet; thus
destroying the sparsity. Computing matrix-vector multiplication for sparse
matrices is known to be fast. Distributing the computation over the non-sparse
encoded matrices maintains privacy, but introduces artificial computing delays.
In this work, we relax the privacy constraint and show that a certain level of
sparsity can be maintained in the encoded matrices. We consider the
chief/worker setting while assuming the presence of two clusters of workers:
one is completely untrusted in which all workers collude to eavesdrop on the
input matrix and in which perfect privacy must be satisfied; in the partly
trusted cluster, only up to $z$ workers may collude and to which revealing
small amount of information about the input matrix is allowed. We design a
scheme that trades sparsity for privacy while achieving the desired
constraints. We use cyclic task assignments of the encoded matrices to tolerate
partial and full stragglers.


title:
Amending the Heston Stochastic Volatility Model to Forecast Local Motor
  Vehicle Crash Rates: A Case Study of Washington, D.C

abstract:
  Modelling crash rates in an urban area requires a swathe of data regarding
historical and prevailing traffic volumes and crash events and characteristics.
Provided that the traffic volume of urban networks is largely defined by
typical work and school commute patterns, crash rates can be determined with a
reasonable degree of accuracy. However, this process becomes more complicated
for an area that is frequently subject to peaks and troughs in traffic volume
and crash events owing to exogenous events (for example, extreme weather)
rather than typical commute patterns. One such area that is particularly
exposed to exogenous events is Washington, DC, which has seen a large rise in
crash events between 2009 and 2020. In this study, we adopt a forecasting model
that embeds heterogeneity and temporal instability in its estimates in order to
improve upon forecasting models currently used in transportation and road
safety research. Specifically, we introduce a stochastic volatility model that
aims to capture the nuances associated with crash rates in Washington, DC. We
determine that this model can outperform conventional forecasting models, but
it does not perform well in light of the unique travel patterns exhibited
throughout the COVID-19 pandemic. Nevertheless, its adaptability to the
idiosyncrasies of Washington, DC crash rates demonstrates its ability to
accurately simulate localised crash rates processes, which can be further
adapted in public policy contexts to form road safety targets.


title:
Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single
  Object Tracking in Point Clouds

abstract:
  3D single object tracking (3D SOT) in LiDAR point clouds plays a crucial role
in autonomous driving. Current approaches all follow the Siamese paradigm based
on appearance matching. However, LiDAR point clouds are usually textureless and
incomplete, which hinders effective appearance matching. Besides, previous
methods greatly overlook the critical motion clues among targets. In this work,
beyond 3D Siamese tracking, we introduce a motion-centric paradigm to handle 3D
SOT from a new perspective. Following this paradigm, we propose a matching-free
two-stage tracker M^2-Track. At the 1^st-stage, M^2-Track localizes the target
within successive frames via motion transformation. Then it refines the target
box through motion-assisted shape completion at the 2^nd-stage. Extensive
experiments confirm that M^2-Track significantly outperforms previous
state-of-the-arts on three large-scale datasets while running at 57FPS (~8%,
~17%, and ~22%) precision gains on KITTI, NuScenes, and Waymo Open Dataset
respectively). Further analysis verifies each component's effectiveness and
shows the motion-centric paradigm's promising potential when combined with
appearance matching.


title:
Do Perceived Gender Biases in Retrieval Results Affect Relevance
  Judgements?

abstract:
  This work investigates the effect of gender-stereotypical biases in the
content of retrieved results on the relevance judgement of users/annotators. In
particular, since relevance in information retrieval (IR) is a
multi-dimensional concept, we study whether the value and quality of the
retrieved documents for some bias-sensitive queries can be judged differently
when the content of the documents represents different genders. To this aim, we
conduct a set of experiments where the genders of the participants are known as
well as experiments where the participants genders are not specified. The set
of experiments comprise of retrieval tasks, where participants perform a rated
relevance judgement for different search query and search result document
compilations. The shown documents contain different gender indications and are
either relevant or non-relevant to the query. The results show the differences
between the average judged relevance scores among documents with various gender
contents. Our work initiates further research on the connection of the
perception of gender stereotypes in users with their judgements and effects on
IR systems, and aim to raise awareness about the possible biases in this
domain.


title:
A PDE-constrained optimization method for 3D-1D coupled problems with
  discontinuous solutions

abstract:
  A numerical method for coupled 3D-1D problems with discontinuous solutions at
the interfaces is derived and discussed. This extends a previous work on the
subject where only continuous solutions were considered. Thanks to properly
defined function spaces a well posed 3D-1D problem is obtained from the
original fully 3D problem and the solution is then found by a PDE-constrained
optimization reformulation. This is a domain decomposition strategy in which
unknown interface variables are introduced and a suitably defined cost
functional, expressing the error in fulfilling interface conditions, is
minimized constrained by the constitutive equations on the subdomains. The
resulting discrete problem is robust with respect to geometrical complexity
thanks to the use of independent discretizations on the various subdomains.
Meshes of different sizes can be used without affecting the conditioning of the
discrete linear system, and this is a peculiar aspect of the considered
formulation. An efficient resolution strategy is further proposed, based on the
use of a gradient based solver and yielding a method ready for parallel
implementation. A numerical experiment on a problem with known analytical
solution shows the accuracy of the method, and two examples on more complex
configurations are proposed to address the applicability of the approach to
practical problems.


title:
The Origins of Rydberg Atom Electrometer Transient Response and its
  Impact on Radio Frequency Pulse Sensing

abstract:
  Rydberg atoms have shown significant promise as the basis for highly
sensitive detectors of continuous radio-frequency (RF) E-fields. Here, we study
their time-dependent response to pulse-modulated RF E-fields at 19.4 GHz using
a cesium vapour cell at room temperature. We use density matrix simulations to
explain the time scales that shape the transient atomic response under
different laser conditions, finding them to be limited by dephasing mechanisms
including transit time broadening, Rydberg-Rydberg collisions, and ionization.
Using a matched filter, we demonstrate the detection of individual pulses with
durations from 10 $\mu$s down to 50 ns and amplitudes from 15000 $\mu$V
cm$^{-1}$ down to ~170 $\mu$V cm$^{-1}$, corresponding to a sensitivity of ~240
nV cm$^{-1}$ Hz$^{-1/2}$. Finally, we highlight the potential of a Rydberg
vapour cell as a receiver by detecting pulse trains from a rotating emitter on
a simulated passing aircraft.


title: Probing axion-like particles coupling to gluons at the LHC

abstract:
  Assuming ALPs couple to gluons only, they can be produced via the $p p \to a
j$ process and decay into two jets at the LHC. When the coupling parameter,
$C_{\tilde{G}}\, /\, f_a$, is small, the lifetime of ALPs can be long enough
leading to displaced final state jets. In this paper, we consider the signal
including both the prompt and long-lived cases of ALPs by employing a
specialized Delphes module to handle displaced jets. Relevant background
processes are generated and simulated at the detector level, and multivariate
analyses based on machine-learning are performed to discriminate signal and
background events and achieve the best sensitivities. Based on the data
accumulated for this study, we forecast the expected upper limits on
$C_{\tilde{G}}\, /\, f_a$ for ALP mass $m_a$ in the range 5$-$2300 GeV at 2-,
3- and 5-$\sigma$ significances at the High Luminosity-LHC with 14 TeV
center-of-mass energy and 3 ab$^{-1}$ integrated luminosity. Vast previously
unprobed regions in the parameter space spanned by $C_{\tilde{G}}\, /\, f_a$
and $m_a$ are probed and the best upper limits on $C_{\tilde{G}}\, /\, f_a$ at
2-$\sigma$ significance is found to be around $1.0 \times 10^{-2} \,\, {\rm
TeV^{-1}}$ for $m_a \sim 500$ GeV. The ALP mass is reconstructed from the
kinematics of final state jets and we find that it is measurable in this method
when $m_a$ is below about 1 TeV at the HL-LHC. The effects of systematic
uncertainties and validation of the EFT framework are also checked and
discussed.


title:
Modality-Adaptive Mixup and Invariant Decomposition for RGB-Infrared
  Person Re-Identification

abstract:
  RGB-infrared person re-identification is an emerging cross-modality
re-identification task, which is very challenging due to significant modality
discrepancy between RGB and infrared images. In this work, we propose a novel
modality-adaptive mixup and invariant decomposition (MID) approach for
RGB-infrared person re-identification towards learning modality-invariant and
discriminative representations. MID designs a modality-adaptive mixup scheme to
generate suitable mixed modality images between RGB and infrared images for
mitigating the inherent modality discrepancy at the pixel-level. It formulates
modality mixup procedure as Markov decision process, where an actor-critic
agent learns dynamical and local linear interpolation policy between different
regions of cross-modality images under a deep reinforcement learning framework.
Such policy guarantees modality-invariance in a more continuous latent space
and avoids manifold intrusion by the corrupted mixed modality samples.
Moreover, to further counter modality discrepancy and enforce invariant visual
semantics at the feature-level, MID employs modality-adaptive convolution
decomposition to disassemble a regular convolution layer into modality-specific
basis layers and a modality-shared coefficient layer. Extensive experimental
results on two challenging benchmarks demonstrate superior performance of MID
over state-of-the-art methods.


title:
Foliation divisorial contraction by the Sasaki-Ricci flow on Sasakian
  5-manifolds

abstract:
  Let (M,{\eta},{\xi},{\Phi},g) be a compact quasi-regular Sasakian 5-manifold
with finite cyclic quotient foliation singularities of type (1/r)(1,a). First,
we derive the foliation minimal model program by applying the resolution of
cyclic quotient foliation singularities. Secondly, based on the study of local
model of resolution of foliation singularities, we prove the foliation
canonical surgical contraction or the foliation extremal ray contraction under
the Sasaki-Ricci flow. As a consequence, we prove a Sasaki analogue of analytic
minimal model program with the Keahler-Ricci flow due to Song-Tian and
Song-Weinkove.


title:
Confinement Highlights the Different Electrical Transport Mechanisms
  Prevailing in Conducting Polymers

abstract:
  We study the differences in electrical charge transport dynamics of the
conductivity enhancement of poly(3,4-ethylenedioxythiophene) (PEDOT)
derivatives under geometrical confinement. The results of polymer blend
poly(3,4-ethylene dioxythiophene):poly(styrenesulfonate) and a polymer-monomer
blend, poly(3,4- ethylenedioxythiophene):tosylate, highlight the role of
dopants and processing conditions of these systems under confinement. The
prevailing transport length scales in confined geometry of characteristic
dimensions originate from varying disorder in these polymer systems. These
observable differences in two different PEDOTs introduced by molecular level
reorganization can be utilized to tune conducting polymer systems for efficient
electrical and thermoelectric properties. The electrical conductivity {\sigma}
of the polymer system, which is a function of the electronic structure at
molecular level and a connectivity parameter, has been probed in
cylindrical-alumina nanoscaffolds of various channel diameters, at different
frequencies {\omega} and temperatures T. The observations also emphasize the
role of disorder in these conducting polymer systems.


title: Machine learning model to project the impact of Ukraine crisis

abstract:
  Russia's attack on Ukraine on Thursday 24 February 2022 hitched financial
markets and the increased geopolitical crisis. In this paper, we select some
main economic indexes, such as Gold, Oil (WTI), NDAQ, and known currency which
are involved in this crisis and try to find the quantitative effect of this war
on them. To quantify the war effect, we use the correlation feature and the
relationships between these economic indices, create datasets, and compare the
results of forecasts with real data. To study war effects, we use Machine
Learning Linear Regression. We carry on empirical experiments and perform on
these economic indices datasets to evaluate and predict this war tolls and its
effects on main economics indexes.


title: Indefinite $q$-integrals from a method using $q$-Ricatti equations

abstract:
  Earlier work introduced a method for obtaining indefinite $q$-integrals of
$q$-special functions from the second-order linear $q$-difference equations
that define them. In this paper, we reformulate the method in terms of
$q$-Riccati equations, which are nonlinear and first order. We derive
$q$-integrals using fragments of these Riccati equations, and here only two
specific fragment types are examined in detail. The results presented here are
for $q$-Airy function, Ramanujan function, Jackson $q$-Bessel functions,
discrete $q$-Hermite polynomials, $q$-Laguerre polynomials, Stieltjes-Wigert
polynomial, little $q$-Legendre, and big $q$-Legendre polynomials.


title: Exact Price of Anarchy for Weighted Congestion Games with Two Players

abstract:
  This paper gives a complete analysis of worst-case equilibria for various
versions of weighted congestion games with two players and affine cost
functions. The results are exact price of anarchy bounds which are parametric
in the weights of the two players, and establish exactly how the primitives of
the game enter into the quality of equilibria. Interestingly, some of the
worst-cases are attained when the players' weights only differ slightly. Our
findings also show that sequential play improves the price of anarchy in all
cases, however, this effect vanishes with an increasing difference in the
players' weights. Methodologically, we obtain exact price of anarchy bounds by
a duality based proof mechanism, based on a compact linear programming
formulation that computes worst-case instances. This mechanism yields
duality-based optimality certificates which can eventually be turned into
purely algebraic proofs.


title: High Multiplicity Scheduling on Uniform Machines in FPT-Time

abstract:
  In high-multiplicity scheduling, jobs of the same size are encoded in an
efficient way, that is, for each size the number of jobs of that size is given
instead of a list of jobs. Similarly, machines are encoded. We consider
scheduling on uniform machines where a job of size $p_j$ takes time $p_j/s_i$
on a machine of speed $s_i$. Classical (NP-hard) objectives are Makespan
minimization ($C_{\max}$) and Santa Claus ($C_{\min}$). We show that both
objectives can be solved in time $\mathcal O( p_{\max}^{\mathcal O(d^2)}
\operatorname {poly} |I| )$ where $p_{\max}$ is the largest jobs size, $d$ the
number of different job sizes and $|I|$ the encoding length of the instance.
Our approach incorporates two structural theorems: The first allows us to
replace machines of large speed by multiple machines of smaller speed. The
second tells us that some fractional assignments can be used to reduce the
instance significantly. Using only the first theorem, we show some additional
results. For the problem Envy Minimization ($C_{\mathit{envy}}$), we propose an
$\mathcal O(s_{\max} \cdot p_{\max}^{\mathcal O(d^3)} \operatorname{poly} |I|)$
time algorithm (where $s_{\max}$ is the largest speed). For $C_{\max}$ and
$C_{\min}$ in the Restricted Assignment setting, we give an $\mathcal O( (d
p_{\max})^{\mathcal O(d^3)} \operatorname{poly} |I|)$ time algorithm. As far as
we know, those running times are better than the running times of the
algorithms known until today.


title:
Slow Magnetic Relaxation of a 12-Metallacrown-4 Complex with a
  Manganese(III)-Copper(II) Heterometallic Ring Motif

abstract:
  The heterobimetallic metallacrown (MC),
(TMA)$_2${Mn(OAc)$_2$[12-MC$_{Mn(III)Cu(II)N(shi)}$-4](CH$_3$OH)}$\cdot$2.90CH$_3$OH,
1, where TMA$^+$ is tetramethylammonium, $^-$OAc is acetate, and shi$^{3-}$ is
salicylhydroximate, consists of a Mn$^{II}$ ion captured in the central cavity
and alternating unambiguous and ordered manganese(III) and copper(II) sites
about the MC ring, a first for the archetypal MC structure design.
DC-magnetometry characterization and subsequent simulation with the Spin
Hamiltonian $$H =
-J_1(\mathbf{s}_1+\mathbf{s}_3)\cdot\mathbf{s}_5-J_2(\mathbf{s}_2+\mathbf{s}_4)\cdot\mathbf{s}_5-J_3\sum_{i=1}^{4}\mathbf{s}_i\cdot\mathbf{s}_{i+1}
+ d(s_{z,1}^2 + s_{z,3}^2) + \mu_B\sum_{j=1}^5 g_j\mathbf{s}_j\cdot\mathbf{B}$$
indicates an S = 5/2 ground state and a sizable axial zero-field splitting on
Mn$^{III}$. AC-susceptibility measurements reveal that 1 displays slow
magnetization relaxation akin to single-molecule magnet (SMM) behavior.