title: REPID: Regional Effect Plots with implicit Interaction Detection

abstract:
  Machine learning models can automatically learn complex relationships, such
as non-linear and interaction effects. Interpretable machine learning methods
such as partial dependence plots visualize marginal feature effects but may
lead to misleading interpretations when feature interactions are present.
Hence, employing additional methods that can detect and measure the strength of
interactions is paramount to better understand the inner workings of machine
learning models. We demonstrate several drawbacks of existing global
interaction detection approaches, characterize them theoretically, and evaluate
them empirically. Furthermore, we introduce regional effect plots with implicit
interaction detection, a novel framework to detect interactions between a
feature of interest and other features. The framework also quantifies the
strength of interactions and provides interpretable and distinct regions in
which feature effects can be interpreted more reliably, as they are less
confounded by interactions. We prove the theoretical eligibility of our method
and show its applicability on various simulation and real-world examples.


title: Enhancing Cross-lingual Prompting with Mask Token Augmentation

abstract:
  Prompting shows promising results in few-shot scenarios. However, its
strength for multilingual/cross-lingual problems has not been fully exploited.
Zhao and Sch\"utze (2021) made initial explorations in this direction by
presenting that cross-lingual prompting outperforms cross-lingual finetuning.
In this paper, we conduct empirical analysis on the effect of each component in
cross-lingual prompting and derive Universal Prompting across languages, which
helps alleviate the discrepancies between source-language training and
target-language inference. Based on this, we propose a mask token augmentation
framework to further improve the performance of prompt-based cross-lingual
transfer. Notably, for XNLI, our method achieves 46.54% with only 16 English
training examples per class, significantly better than 34.99% of finetuning.


title: Federated Graph Neural Networks: Overview, Techniques and Challenges

abstract:
  With its powerful capability to deal with graph data widely found in
practical applications, graph neural networks (GNNs) have received significant
research attention. However, as societies become increasingly concerned with
data privacy, GNNs face the need to adapt to this new normal. This has led to
the rapid development of federated graph neural networks (FedGNNs) research in
recent years. Although promising, this interdisciplinary field is highly
challenging for interested researchers to enter into. The lack of an insightful
survey on this topic only exacerbates this problem. In this paper, we bridge
this gap by offering a comprehensive survey of this emerging field. We propose
a unique 3-tiered taxonomy of the FedGNNs literature to provide a clear view
into how GNNs work in the context of Federated Learning (FL). It puts existing
works into perspective by analyzing how graph data manifest themselves in FL
settings, how GNN training is performed under different FL system architectures
and degrees of graph data overlap across data silo, and how GNN aggregation is
performed under various FL settings. Through discussions of the advantages and
limitations of existing works, we envision future research directions that can
help build more robust, dynamic, efficient, and interpretable FedGNNs.


title:
Optically-induced magnetization switching in NiCo2O4 thin films using
  ultrafast lasers

abstract:
  Recently, all-optical magnetization control has been garnering considerable
attention in realizing next-generation ultrafast magnetic information devices.
Here, employing a magneto-optical Kerr effect (MOKE) microscope, we observed
the laser-induced magnetization switching of ferrimagnetic oxide NiCo2O4 (NCO)
epitaxial thin films with perpendicular magnetic anisotropy, where the sample
was pumped at 1030-nm laser pulses, and magnetic domain images were acquired
via the MOKE microscope with a white light emitting diode. Laser pulses
irradiated an NCO thin film at various temperatures from 300 K to 400 K while
altering the parameters of pulse interval, fluence, and the number of pulses
with the absence of the external magnetic field. We observed accumulative
all-optical switching at 380 K and above. Our observation of oxide NCO thin
films facilitates the realization of chemically stable magnetization switching
using ultrafast lasers, and without applying a magnetic field.


title:
Accelerating Non-Negative and Bounded-Variable Linear Regression
  Algorithms with Safe Screening

abstract:
  Non-negative and bounded-variable linear regression problems arise in a
variety of applications in machine learning and signal processing. In this
paper, we propose a technique to accelerate existing solvers for these problems
by identifying saturated coordinates in the course of iterations. This is akin
to safe screening techniques previously proposed for sparsity-regularized
regression problems. The proposed strategy is provably safe as it provides
theoretical guarantees that the identified coordinates are indeed saturated in
the optimal solution. Experimental results on synthetic and real data show
compelling accelerations for both non-negative and bounded-variable problems.


title:
Review of the Fingerprint Liveness Detection (LivDet) competition
  series: from 2009 to 2021

abstract:
  Fingerprint authentication systems are highly vulnerable to artificial
reproductions of fingerprint, called fingerprint presentation attacks.
Detecting presentation attacks is not trivial because attackers refine their
replication techniques from year to year. The International Fingerprint
liveness Detection Competition (LivDet), an open and well-acknowledged meeting
point of academies and private companies that deal with the problem of
presentation attack detection, has the goal to assess the performance of
fingerprint presentation attack detection (FPAD) algorithms by using standard
experimental protocols and data sets. Each LivDet edition, held biannually
since 2009, is characterized by a different set of challenges against which
competitors must be dealt with. The continuous increase of competitors and the
noticeable decrease in error rates across competitions demonstrate a growing
interest in the topic. This paper reviews the LivDet editions from 2009 to 2021
and points out their evolution over the years.


title:
Learning Disentangled Behaviour Patterns for Wearable-based Human
  Activity Recognition

abstract:
  In wearable-based human activity recognition (HAR) research, one of the major
challenges is the large intra-class variability problem. The collected activity
signal is often, if not always, coupled with noises or bias caused by personal,
environmental, or other factors, making it difficult to learn effective
features for HAR tasks, especially when with inadequate data. To address this
issue, in this work, we proposed a Behaviour Pattern Disentanglement (BPD)
framework, which can disentangle the behavior patterns from the irrelevant
noises such as personal styles or environmental noises, etc. Based on a
disentanglement network, we designed several loss functions and used an
adversarial training strategy for optimization, which can disentangle activity
signals from the irrelevant noises with the least dependency (between them) in
the feature space. Our BPD framework is flexible, and it can be used on top of
existing deep learning (DL) approaches for feature refinement. Extensive
experiments were conducted on four public HAR datasets, and the promising
results of our proposed BPD scheme suggest its flexibility and effectiveness.
This is an open-source project, and the code can be found at
http://github.com/Jie-su/BPD


title: Exploring the Devil in Graph Spectral Domain for 3D Point Cloud Attacks

abstract:
  3D dynamic point clouds provide a discrete representation of real-world
objects or scenes in motion, which have been widely applied in immersive
telepresence, autonomous driving, surveillance, \textit{etc}. However, point
clouds acquired from sensors are usually perturbed by noise, which affects
downstream tasks such as surface reconstruction and analysis. Although many
efforts have been made for static point cloud denoising, few works address
dynamic point cloud denoising. In this paper, we propose a novel gradient-based
dynamic point cloud denoising method, exploiting the temporal correspondence
for the estimation of gradient fields -- also a fundamental problem in dynamic
point cloud processing and analysis. The gradient field is the gradient of the
log-probability function of the noisy point cloud, based on which we perform
gradient ascent so as to converge each point to the underlying clean surface.
We estimate the gradient of each surface patch by exploiting the temporal
correspondence, where the temporally corresponding patches are searched
leveraging on rigid motion in classical mechanics. In particular, we treat each
patch as a rigid object, which moves in the gradient field of an adjacent frame
via force until reaching a balanced state, i.e., when the sum of gradients over
the patch reaches 0. Since the gradient would be smaller when the point is
closer to the underlying surface, the balanced patch would fit the underlying
surface well, thus leading to the temporal correspondence. Finally, the
position of each point in the patch is updated along the direction of the
gradient averaged from corresponding patches in adjacent frames. Experimental
results demonstrate that the proposed model outperforms state-of-the-art
methods.


title:
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient
  Methods

abstract:
  Stochastic Gradient Descent-Ascent (SGDA) is one of the most prominent
algorithms for solving min-max optimization and variational inequalities
problems (VIP) appearing in various machine learning tasks. The success of the
method led to several advanced extensions of the classical SGDA, including
variants with arbitrary sampling, variance reduction, coordinate randomization,
and distributed variants with compression, which were extensively studied in
the literature, especially during the last few years. In this paper, we propose
a unified convergence analysis that covers a large variety of stochastic
gradient descent-ascent methods, which so far have required different
intuitions, have different applications and have been developed separately in
various communities. A key to our unified framework is a parametric assumption
on the stochastic estimates. Via our general theoretical framework, we either
recover the sharpest known rates for the known special cases or tighten them.
Moreover, to illustrate the flexibility of our approach we develop several new
variants of SGDA such as a new variance-reduced method (L-SVRGDA), new
distributed methods with compression (QSGDA, DIANA-SGDA, VR-DIANA-SGDA), and a
new method with coordinate randomization (SEGA-SGDA). Although variants of the
new methods are known for solving minimization problems, they were never
considered or analyzed for solving min-max problems and VIPs. We also
demonstrate the most important properties of the new methods through extensive
numerical experiments.


title: Multiple sampling and interpolation in Bergman spaces

abstract:
  We study multiple sampling and interpolation problems with unbounded
multiplicities in the weighted Bergman space, both in the hilbertian case p = 2
and the uniform case p = +$\infty$.


title:
Anisotropic magneto-resistance in MgO-based magnetic tunnel junctions
  induced by spin-orbit coupling

abstract:
  We performed a first-principles study of the tunneling anisotropic
magneto-resistance (TAMR) in Ag(Ir,Pt)$/$MgO$/$Fe junctions. Enhanced TAMR with
ideal and skewed fourfold angular dependence is found in-plane and out-of-plane
TAMR of the system, respectively, which shows simple barrier thickness
dependency with number around 10\% in some junctions. The complex angular
dependency of the interfacial resonant states due to the spin-orbit coupling
should be responsible to the complex and enhanced TAMR found in these
junctions.


title: Analysis of a blockchain protocol based on LDPC codes

abstract:
  In a blockchain Data Availability Attack (DAA), a malicious node publishes a
block header but withholds part of the block, which contains invalid
transactions. Honest full nodes, which can download and store the full
blockchain, are aware that some data are not available but they have no formal
way to prove it to light nodes, i.e., nodes that have limited resources and are
not able to access the whole blockchain data. A common solution to counter
these attacks exploits linear error correcting codes to encode the block
content. A recent protocol, called SPAR, employs coded Merkle trees and
low-density parity-check codes to counter DAAs. In this paper, we show that the
protocol is less secure than claimed, owing to a redefinition of the
adversarial success probability. As a consequence we show that, for some
realistic choices of the parameters, the total amount of data downloaded by
light nodes is larger than that obtainable with competitor solutions.


title:
Maximal Measure and Entropic Continuity of Lyapunov Exponents for $C^r$
  Surface Diffeomorphisms with Large Entropy

abstract:
  We prove a finite smooth version of the entropic continuity of Lyapunov
exponents of Buzzi-Crovisier-Sarig for $C^\infty$ surface diffeomorphisms [9].
As a consequence we show that any $C^r$, $r > 1$, smooth surface diffeomorphism
$f$ with $h\_{top}(f) > \frac{1}{r} \limsup\_n \frac{1}{n} \log^+ \|df^n\|$
admits a measure of maximal entropy.


title: High-Throughput Split-Tree Architecture for Nonbinary SCL Polar Decoder

abstract:
  Nonbinary polar codes defined over Galois field GF(q) have shown improved
error-correction performance than binary polar codes using
successive-cancellation list (SCL) decoding. However, nonbinary operations are
complex and a direct-mapped decoder results in a low throughput, representing
difficulties for practical adoptions. In this work, we develop, to the best of
our knowledge, the first hardware implementation for nonbinary SCL polar
decoding. We present a high-throughput decoder architecture using a split-tree
algorithm. The sub-trees are decoded in parallel by smaller sub-decoders with a
reconciliation stage to maintain constraints between sub-trees. A skimming
algorithm is proposed to reduce the reconciliation complexity for further
improved throughput. The split-tree nonbinary SCL (S-NBSCL) polar decoder is
prototyped using a 28nm CMOS technology for a (128,64) polar code over GF(256).
The decoder delivers 26.1 Mb/s throughput, 11.65 Mb/s/mm$^2$ area efficiency
and 28.8 nJ/b energy efficiency, outperforming the direct-mapped decoder by
10.3x, 4.4x and 2.7x, respectively, while achieving excellent error-correction
performance.


title: Convolutional Network Fabric Pruning With Label Noise

abstract:
  This paper presents an iterative pruning strategy for Convolutional Network
Fabrics (CNF) in presence of noisy training and testing data. With the
continuous increase in size of neural network models, various authors have
developed pruning approaches to build more compact network structures requiring
less resources, while preserving performance. As we show in this paper, because
of their intrinsic structure and function, Convolutional Network Fabrics are
ideal candidates for pruning. We present a series of pruning strategies that
can significantly reduce both the final network size and required training time
by pruning either entire convolutional filters or individual weights, so that
the grid remains visually understandable but that overall execution quality
stays within controllable boundaries. Our approach can be iteratively applied
during training so that the network complexity decreases rapidly, saving
computational time. The paper addresses both data-dependent and dataindependent
strategies, and also experimentally establishes the most efficient approaches
when training or testing data contain annotation errors.


title: Media Slant is Contagious

abstract:
  This paper analyzes the influence of partisan content from national cable TV
news on local reporting in U.S. newspapers. We provide a new
machine-learning-based measure of cable news slant, trained on a corpus of 40K
transcribed TV episodes from Fox News Channel (FNC), CNN, and MSNBC
(2005-2008). Applying the method to a corpus of 24M local newspaper articles,
we find that in response to an exogenous increase in local viewership of FNC
relative to CNN/MSNBC, local newspaper articles become more similar to FNC
transcripts (and vice versa). Consistent with newspapers responding to changes
in reader preferences, we see a shift in the framing of local news coverage
rather than just direct borrowing of cable news content. Further, cable news
slant polarizes local news content: right-leaning newspapers tend to adopt
right-wing FNC language, while left-leaning newspapers tend to become more
left-wing. Media slant is contagious.


title: Coboundaries and eigenvalues of finitary S-adic systems

abstract:
  An S-adic system is a symbolic dynamical system generated by iterating an
infinite sequence of substitutions or morphisms, called a directive sequence. A
finitary S-adic dynamical system is one where the directive sequence consists
of morphisms selected from a finite set. We study eigenvalues and coboundaries
for finitary recognizable S-adic dynamical systems. To do this we identify the
notions of straightness and essential words, and use them to define a
coboundary, which allows us to express necessary and sufficient conditions that
a complex number must satisfy in order to be a continuous or measurable
eigenvalue. We then apply our results to finitary directive sequences of
substitutions of constant length, and show how to create constant-length S-adic
shifts with non-trivial coboundaries. We show that in this case all continuous
eigenvalues are rational and we give a complete description of the rationals
that can be an eigenvalue, indicating how this leads to a Cobham-style result
for these systems.


title: Hyper-relationship Learning Network for Scene Graph Generation

abstract:
  Generating informative scene graphs from images requires integrating and
reasoning from various graph components, i.e., objects and relationships.
However, current scene graph generation (SGG) methods, including the unbiased
SGG methods, still struggle to predict informative relationships due to the
lack of 1) high-level inference such as transitive inference between
relationships and 2) efficient mechanisms that can incorporate all interactions
of graph components. To address the issues mentioned above, we devise a
hyper-relationship learning network, termed HLN, for SGG. Specifically, the
proposed HLN stems from hypergraphs and two graph attention networks (GATs) are
designed to infer relationships: 1) the object-relationship GAT or OR-GAT to
explore interactions between objects and relationships, and 2) the
hyper-relationship GAT or HR-GAT to integrate transitive inference of
hyper-relationships, i.e., the sequential relationships between three objects
for transitive reasoning. As a result, HLN significantly improves the
performance of scene graph generation by integrating and reasoning from object
interactions, relationship interactions, and transitive inference of
hyper-relationships. We evaluate HLN on the most popular SGG dataset, i.e., the
Visual Genome dataset, and the experimental results demonstrate its great
superiority over recent state-of-the-art methods. For example, the proposed HLN
improves the recall per relationship from 11.3\% to 13.1\%, and maintains the
recall per image from 19.8\% to 34.9\%. We will release the source code and
pretrained models on GitHub.


title: Global algebraic K-theory is Swan K-theory

abstract:
  We construct an equivalence between Hausmann's model of global stable
homotopy theory in terms of symmetric spectra on the one hand and spectral
Mackey functors in the sense of Barwick on a certain global effective Burnside
category on the other hand.
  Using this, we then give a new description of Schwede's global algebraic
$K$-theory by identifying the corresponding spectral Mackey functors with
certain categorically defined Mackey functors, making precise the slogan that
global algebraic $K$-theory is a coherent version of Swan $K$-theory.


title: SpeechPainter: Text-conditioned Speech Inpainting

abstract:
  We propose SpeechPainter, a model for filling in gaps of up to one second in
speech samples by leveraging an auxiliary textual input. We demonstrate that
the model performs speech inpainting with the appropriate content, while
maintaining speaker identity, prosody and recording environment conditions, and
generalizing to unseen speakers. Our approach significantly outperforms
baselines constructed using adaptive TTS, as judged by human raters in
side-by-side preference and MOS tests.


title: Asymptotic analysis of subwavelength halide perovskite resonators

abstract:
  Halide perovskites are promising materials with many significant applications
in photovoltaics and optoelectronics. In this paper, we use integral methods to
quantify the resonant properties of halide perovskite nano-particles. We prove
that, for arbitrarily small particles, the subwavelength resonant frequencies
can be expressed in terms of the eigenvalues of the Newtonian potential
associated with its shape. We also characterize the hybridized subwavelength
resonant frequencies of a dimer of two halide perovskite particles. Finally, we
examine the specific case of spherical resonators and demonstrate that our new
results are consistent with previous works.


title:
HiMA: A Fast and Scalable History-based Memory Access Engine for
  Differentiable Neural Computer

abstract:
  Memory-augmented neural networks (MANNs) provide better inference performance
in many tasks with the help of an external memory. The recently developed
differentiable neural computer (DNC) is a MANN that has been shown to
outperform in representing complicated data structures and learning long-term
dependencies. DNC's higher performance is derived from new history-based
attention mechanisms in addition to the previously used content-based attention
mechanisms. History-based mechanisms require a variety of new compute
primitives and state memories, which are not supported by existing neural
network (NN) or MANN accelerators. We present HiMA, a tiled, history-based
memory access engine with distributed memories in tiles. HiMA incorporates a
multi-mode network-on-chip (NoC) to reduce the communication latency and
improve scalability. An optimal submatrix-wise memory partition strategy is
applied to reduce the amount of NoC traffic; and a two-stage usage sort method
leverages distributed tiles to improve computation speed. To make HiMA
fundamentally scalable, we create a distributed version of DNC called DNC-D to
allow almost all memory operations to be applied to local memories with
trainable weighted summation to produce the global memory output. Two
approximation techniques, usage skimming and softmax approximation, are
proposed to further enhance hardware efficiency. HiMA prototypes are created in
RTL and synthesized in a 40nm technology. By simulations, HiMA running DNC and
DNC-D demonstrates 6.47x and 39.1x higher speed, 22.8x and 164.3x better area
efficiency, and 6.1x and 61.2x better energy efficiency over the
state-of-the-art MANN accelerator. Compared to an Nvidia 3080Ti GPU, HiMA
demonstrates speedup by up to 437x and 2,646x when running DNC and DNC-D,
respectively.


title:
Explaining reaction coordinates of alanine dipeptide isomerization
  obtained from deep neural networks using Explainable Artificial Intelligence
  (XAI)

abstract:
  A method for obtaining appropriate reaction coordinates is required to
identify transition states distinguishing product and reactant in complex
molecular systems. Recently, abundant research has been devoted to obtaining
reaction coordinates using artificial neural networks from deep learning
literature, where many collective variables are typically utilized in the input
layer. However, it is difficult to explain the details of which collective
variables contribute to the predicted reaction coordinates owing to the
complexity of the nonlinear functions in deep neural networks. To overcome this
limitation, we used Explainable Artificial Intelligence (XAI) methods of the
Local Interpretable Model-agnostic Explanation (LIME) and the game theory-based
framework known as Shapley Additive exPlanations (SHAP). We demonstrated that
XAI enables us to obtain the degree of contribution of each collective variable
to reaction coordinates that is determined by nonlinear regressions with deep
learning for the committor of the alanine dipeptide isomerization in vacuum. In
particular, both LIME and SHAP provide important features to the predicted
reaction coordinates, which are characterized by appropriate dihedral angles
consistent with those previously reported from the committor test analysis. The
present study offers an AI-aided framework to explain the appropriate reaction
coordinates, which acquires considerable significance when the number of
degrees of freedom increases.


title:
Exploiting deterministic algorithms to perform global sensitivity
  analysis for continuous-time Markov chain compartmental models with
  application to epidemiology

abstract:
  In this paper, we develop an approach of global sensitivity analysis for
compartmental models based on continuous-time Markov chains. We propose to
measure the sensitivity of quantities of interest by representing the Markov
chain as a deterministic function of the uncertain parameters and a random
variable with known distribution modeling intrinsic randomness. This
representation is exact and does not rely on meta-modeling. An application to a
SARS-CoV-2 epidemic model is included to illustrate the practical impact of our
approach.


title: Worldwide Gender Differences in Public Code Contributions

abstract:
  Gender imbalance is a well-known phenomenon observed throughout sciences
which is particularly severe in software development and Free/Open Source
Software communities. Little is know yet about the geography of this phenomenon
in particular when considering large scales for both its time and space
dimensions. We contribute to fill this gap with a longitudinal study of the
population of contributors to publicly available software source code. We
analyze the development history of 160 million software projects for a total of
2.2 billion commits contributed by 43 million distinct authors over a period of
50 years. We classify author names by gender using name frequencies and author
geographical locations using heuristics based on email addresses and time
zones. We study the evolution over time of contributions to public code by
gender and by world region. For the world overall, we confirm previous findings
about the low but steadily increasing ratio of contributions by female authors.
When breaking down by world regions we find that the long-term growth of female
participation is a worldwide phenomenon. We also observe a decrease in the
ratio of female participation during the COVID-19 pandemic, suggesting that
women's ability to contribute to public code has been more hindered than that
of men.


title:
Solutions monomiales minimales irr{\'e}ductibles dans
  $SL\_{2}(\mathbb{Z}/p^{n}\mathbb{Z})$

abstract:
  In this article we study the combinatorics of congruence subgroups of the
modular group. More precisely, we consider the notion of minimal monomial
solutions. These are the solutions of a matrix equation (also appearing in the
study of Coxeter friezes), modulo an integer $N$, whose components are
identical and minimal for this property. The objective here is to characterize
the solutions of this type verifying a property of irreducibility modulo a
prime power.


title: Saving Dense Retriever from Shortcut Dependency in Conversational Search

abstract:
  In conversational search (CS), it needs holistic understanding over
conversational inputs to retrieve relevant passages. In this paper, we
demonstrate the existence of a retrieval shortcut in CS, which causes models to
retrieve passages solely relying on partial history while disregarding the
latest question. With in-depth analysis, we first show naively trained dense
retrievers heavily exploit the shortcut and hence perform poorly when asked to
answer history-independent questions. To prevent models from solely relying on
the shortcut, we explore iterative hard negatives mined by pre-trained dense
retrievers. Experimental results show that training with the iterative hard
negatives effectively mitigates the dependency on the shortcut and makes
substantial improvement on recent CS benchmarks. Our retrievers achieve new
state-of-the-art results, outperforming the previous best models by 9.7 in
Recall@10 on QReCC and 12.4 in Recall@5 on TopiOCQA. Furthermore, in our
end-to-end QA experiments, FiD readers combined with our retrievers surpass the
previous state-of-the-art models by 3.7 and 1.0 EM scores on QReCC and
TopiOCQA, respectively.


title:
Towards Effective Multi-Task Interaction for Entity-Relation Extraction:
  A Unified Framework with Selection Recurrent Network

abstract:
  Entity-relation extraction aims to jointly solve named entity recognition
(NER) and relation extraction (RE). Recent approaches use either one-way
sequential information propagation in a pipeline manner or two-way implicit
interaction with a shared encoder. However, they still suffer from poor
information interaction due to the gap between the different task forms of NER
and RE, raising a controversial question whether RE is really beneficial to
NER. Motivated by this, we propose a novel and unified cascade framework that
combines the advantages of both sequential information propagation and implicit
interaction. Meanwhile, it eliminates the gap between the two tasks by
reformulating entity-relation extraction as unified span-extraction tasks.
Specifically, we propose a selection recurrent network as a shared encoder to
encode task-specific independent and shared representations and design two
sequential information propagation strategies to realize the sequential
information flow between NER and RE. Extensive experiments demonstrate that our
approaches can achieve state-of-the-art results on two common benchmarks, ACE05
and SciERC, and effectively model the multi-task interaction, which realizes
significant mutual benefits of NER and RE.


title: Adaptive Conformal Predictions for Time Series

abstract:
  Uncertainty quantification of predictive models is crucial in decision-making
problems. Conformal prediction is a general and theoretically sound answer.
However, it requires exchangeable data, excluding time series. While recent
works tackled this issue, we argue that Adaptive Conformal Inference (ACI,
Gibbs and Cand{\`e}s, 2021), developed for distribution-shift time series, is a
good procedure for time series with general dependency. We theoretically
analyse the impact of the learning rate on its efficiency in the exchangeable
and auto-regressive case. We propose a parameter-free method, AgACI, that
adaptively builds upon ACI based on online expert aggregation. We lead
extensive fair simulations against competing methods that advocate for ACI's
use in time series. We conduct a real case study: electricity price
forecasting. The proposed aggregation algorithm provides efficient prediction
intervals for day-ahead forecasting. All the code and data to reproduce the
experiments is made available.


title:
A Simple LP-Based Approximation Algorithm for the Matching Augmentation
  Problem

abstract:
  The Matching Augmentation Problem (MAP) has recently received significant
attention as an important step towards better approximation algorithms for
finding cheap $2$-edge connected subgraphs. This has culminated in a
$\frac{5}{3}$-approximation algorithm. However, the algorithm and its analysis
are fairly involved and do not compare against the problem's well-known LP
relaxation called the cut LP. In this paper, we propose a simple algorithm
that, guided by an optimal solution to the cut LP, first selects a DFS tree and
then finds a solution to MAP by computing an optimum augmentation of this tree.
Using properties of extreme point solutions, we show that our algorithm always
returns (in polynomial time) a better than $2$-approximation when compared to
the cut LP. We thereby also obtain an improved upper bound on the integrality
gap of this natural relaxation.


title:
Efficient Post-Processors for Improving Error-Correcting Performance of
  LDPC Codes

abstract:
  The error floor phenomenon, associated with iterative decoders, is one of the
most significant limitations to the applications of low-density parity-check
(LDPC) codes. A variety of techniques from code design to decoder
implementation have been proposed to address the error floor problem, among
which post-processors have shown to be both effective and
implementation-friendly. In this work, we take the inspiration from simulated
annealing to generalize the post-processor design using three methods:
quenching, extended heating, and focused heating, each of which targets a
different error structure. The resulting post-processor is demonstrated to
lower the error floors by two orders of magnitude for two structured code
examples, a (2209, 1978) array LDPC code, and a (1944, 1620) LDPC code used by
the IEEE 802.11n standard. The post-processor can be integrated to a
belief-propagation decoder with minimal overhead. The post-processor design is
equally applicable to other structured LDPC codes.


title: Disentangling Domain and Content

abstract:
  Many real-world datasets can be divided into groups according to certain
salient features (e.g. grouping images by subject, grouping text by font,
etc.). Often, machine learning tasks require that these features be represented
separately from those manifesting independently of the grouping. For example,
image translation entails changing the style of an image while preserving its
content. We formalize these two kinds of attributes as two complementary
generative factors called "domain" and "content", and address the problem of
disentangling them in a fully unsupervised way. To achieve this, we propose a
principled, generalizable probabilistic model inspired by the Variational
Autoencoder. Our model exhibits state-of-the-art performance on the composite
task of generating images by combining the domain of one input with the content
of another. Distinctively, it can perform this task in a few-shot, unsupervised
manner, without being provided with explicit labelling for either domain or
content. The disentangled representations are learned through the combination
of a group-wise encoder and a novel domain-confusion loss.


title:
CM congruence and trivial zeros of the Katz $p$-adic $L$-functions for
  CM fields

abstract:
  The aim of this paper is to investigate the trivial zeros of the Katz
$p$-adic $L$-functions by the CM congruence. We prove the existence of trivial
zeros of the Katz $p$-adic $L$-functions for general CM fields and establish a
first derivative formula of the cyclotomic $p$-adic $L$-functions at trivial
zeros under some Leopoldt hypothesis. The crucial ingredients in our proof are
a special case of $p$-adic Kronecker limit formula for CM fields and a leading
term formula of anticyclotomic $p$-adic $L$-functions at trivial zeros via the
explicit congruences between CM and non-CM Hida families of Hilbert cusp forms.


title: Local well-posedness for a class of singular Vlasov equations

abstract:
  In this article we study a singular Vlasov system on the torus where the
force field has the smoothness of a (fractional) derivative $D^{\alpha}$ of the
density, where $\alpha>0$. We prove local well-posedness in Sobolev spaces
without restriction on the data. This is in sharp contrast with the case
$\alpha=0$ which is ill-posed in Sobolev spaces for general data.


title:
Search for resonant pair production of Higgs bosons in the
  $b\bar{b}b\bar{b}$ final state using $pp$ collisions at $\sqrt{s}$ = 13 TeV
  with the ATLAS detector

abstract:
  A search for resonant Higgs boson pair production in the $b\bar{b}b\bar{b}$
final state is presented. The analysis uses 126-139 fb$^{-1}$ of $pp$ collision
data at $\sqrt{s}$ = 13 TeV collected with the ATLAS detector at the Large
Hadron Collider. The analysis is divided into two channels, targeting Higgs
boson decays which are reconstructed as pairs of small-radius jets or as
individual large-radius jets. Spin-0 and spin-2 benchmark signal models are
considered, both of which correspond to resonant $HH$ production via
gluon$-$gluon fusion. The data are consistent with Standard Model predictions.
Upper limits are set on the production cross-section times branching ratio to
Higgs boson pairs of a new resonance in the mass range from 251 GeV to 5 TeV.


title: Strong-field chiral imaging with twisted photoelectrons

abstract:
  Advances producing and measuring orbital angular momentum (OAM) in free
particles has led to substantial interest across fields. Here, we exploit
conserved helicity inherent in twisted photoelectrons from a chiral target to
propose a new ultrafast chiral imaging technique, dubbed photoelectron vortex
dichroism (PEVD). We theoretically demonstrate huge forward-backwards asymmetry
in OAM-resolved photoelectron emission, sensitive to molecular chirality, for
electrons ionized by strong linearly polarized fields averaged over the carrier
envelope phase. Using a chiral combination of hydrogenic states reveals the
fundamental mechanism for PEVD through OAM dependent asymmetry in the initial
state.


title:
Don't stop the training: continuously-updating self-supervised
  algorithms best account for auditory responses in the cortex

abstract:
  Over the last decade, numerous studies have shown that deep neural networks
exhibit sensory representations similar to those of the mammalian brain, in
that their activations linearly map onto cortical responses to the same sensory
inputs. However, it remains unknown whether these artificial networks also
learn like the brain. To address this issue, we analyze the brain responses of
two ferret auditory cortices recorded with functional UltraSound imaging (fUS),
while the animals were presented with 320 10\,s sounds. We compare these brain
responses to the activations of Wav2vec 2.0, a self-supervised neural network
pretrained with 960\,h of speech, and input with the same 320 sounds.
Critically, we evaluate Wav2vec 2.0 under two distinct modes: (i) "Pretrained",
where the same model is used for all sounds, and (ii) "Continuous Update",
where the weights of the pretrained model are modified with back-propagation
after every sound, presented in the same order as the ferrets. Our results show
that the Continuous-Update mode leads Wav2Vec 2.0 to generate activations that
are more similar to the brain than a Pretrained Wav2Vec 2.0 or than other
control models using different training modes. These results suggest that the
trial-by-trial modifications of self-supervised algorithms induced by
back-propagation aligns with the corresponding fluctuations of cortical
responses to sounds. Our finding thus provides empirical evidence of a common
learning mechanism between self-supervised models and the mammalian cortex
during sound processing.


title:
Beyond Natural Motion: Exploring Discontinuity for Video Frame
  Interpolation

abstract:
  Video interpolation is the task that synthesizes the intermediate frame given
two consecutive frames. Most of the previous studies have focused on
appropriate frame warping operations and refinement modules for the warped
frames. These studies have been conducted on natural videos having only
continuous motions. However, many practical videos contain a lot of
discontinuous motions, such as chat windows, watermarks, GUI elements, or
subtitles. We propose three techniques to expand the concept of transition
between two consecutive frames to address these issues. First is a new
architecture that can separate continuous and discontinuous motion areas. We
also propose a novel data augmentation strategy called figure-text mixing (FTM)
to make our model learn more general scenarios. Finally, we propose loss
functions to give supervisions of the discontinuous motion areas with the data
augmentation. We collected a special dataset consisting of some mobile games
and chatting videos. We show that our method significantly improves the
interpolation qualities of the videos on the special dataset. Moreover, our
model outperforms the state-of-the-art methods for natural video datasets
containing only continuous motions, such as DAVIS and UCF101.


title: Contextual Importance and Utility: aTheoretical Foundation

abstract:
  This paper provides new theory to support to the eXplainable AI (XAI) method
Contextual Importance and Utility (CIU). CIU arithmetic is based on the
concepts of Multi-Attribute Utility Theory, which gives CIU a solid theoretical
foundation. The novel concept of contextual influence is also defined, which
makes it possible to compare CIU directly with so-called additive feature
attribution (AFA) methods for model-agnostic outcome explanation. One key
takeaway is that the "influence" concept used by AFA methods is inadequate for
outcome explanation purposes even for simple models to explain. Experiments
with simple models show that explanations using contextual importance (CI) and
contextual utility (CU) produce explanations where influence-based methods
fail. It is also shown that CI and CU guarantees explanation faithfulness
towards the explained model.


title: Asymptotic dimension of intersection graphs

abstract:
  We show that intersection graphs of compact convex sets in R^n of bounded
aspect ratio have asymptotic dimension at most 2n+1. More generally, we show
this is the case for intersection graphs of systems of subsets of any metric
space of Assouad-Nagata dimension n that satisfy the following condition: For
each r,s>0 and every point p, the number of pairwise-disjoint elements of
diameter at least s in the system that are at distance at most r from p is
bounded by a function of r/s.


title: Smoothing finite-order bilipschitz homeomorphisms of 3-manifolds

abstract:
  We show that, for $\varepsilon=\dfrac{1}{4000}$, any action of a finite
cyclic group by $(1+\varepsilon)$-bilipschitz homeomorphisms on a closed
3-manifold is conjugated to a smooth action.


title:
An Automated FPGA-based Framework for Rapid Prototyping of Nonbinary
  LDPC Codes

abstract:
  Nonbinary LDPC codes have shown superior performance close to the Shannon
limit. Compared to binary LDPC codes of similar lengths, they can reach orders
of magnitudes lower error rate. However, multitude of design freedoms of
nonbinary LDPC codes complicates the practical code and decoder design process.
Fast simulations are critically important to evaluate the pros and cons. Rapid
prototyping on FPGA is attractive but takes significant design efforts due to
its high design complexity. We propose a high-throughput reconfigurable
hardware emulation architecture with decoder and peripheral co-design. The
architecture enables a library and script-based framework that automates the
construction of FPGA emulations. Code and decoder design parameters are
programmed either during run time or by script in design time. We demonstrate
the capability of the framework in evaluating practical code and decoder design
by experimenting with two popular nonbinary LDPC codes, regular (2, dc) codes
and quasi-cyclic codes: each emulation model can be auto-constructed within
hours and the decoder delivers excellent error-correcting performance on a
Xilinx Virtex-5 FPGA with throughput of up to hundreds of Mbps.


title: Roomsemble: Progressive web application for intuitive property search

abstract:
  A successful real estate search process involves locating a property that
meets a user's search criteria subject to an allocated budget and time
constraints. Many studies have investigated modeling housing prices over time.
However, little is known about how a user's tastes influence their real estate
search and purchase decisions. It is unknown what house a user would choose
taking into account an individual's personal tastes, behaviors, and
constraints, and, therefore, creating an algorithm that finds the perfect
match. In this paper, we investigate the first step in understanding a user's
tastes by building a system to capture personal preferences. We concentrated
our research on real estate photos, being inspired by house aesthetics, which
often motivates prospective buyers into considering a property as a candidate
for purchase. We designed a system that takes a user-provided photo
representing that person's personal taste and recommends properties similar to
the photo available on the market. The user can additionally filter the
recommendations by budget and location when conducting a property search. The
paper describes the application's overall layout including frontend design and
backend processes for locating a desired property. The proposed model, which
serves as the application's core, was tested with 25 users, and the study's
findings, as well as some key conclusions, are detailed in this paper.


title:
Multifrequency Array Calibration in Presence of Radio Frequency
  Interferences

abstract:
  Radio interferometers are phased arrays producing high-resolution images from
the covariance matrix of measurements. Calibration of such instruments is
necessary and is a critical task. This is how the estimation of instrumental
errors is usually done thanks to the knowledge of referenced celestial sources.
However, the use of high sensitive antennas in modern radio interferometers
(LOFAR, SKA) brings a new challenge in radio astronomy because there are more
sensitive to Radio Frequency Interferences (RFI). The presence of RFI during
the calibration process generally induces biases in state-of-the-art solutions.
The purpose of this paper is to propose an alternative to alleviate the effects
of RFI. For that, we first propose a model to take into account the presence of
RFI in the data across multiple frequency channels thanks to a low-rank
structured noise. We then achieve maximum likelihood estimation of the
calibration parameters with a Space Alternating Generalized
Expectation-Maximization (SAGE) algorithm for which we derive originally two
sets of complete data allowing close form expressions for the updates.
Numerical simulations show a significant gain in performance for RFI corrupted
data in comparison with some more classical methods.


title:
Some observations about Hankel determinants of the columns of Pascal
  triangle and related topics

abstract:
  This note collects some facts and conjectures about the Hankel determinants
and their generating functions of the columns of Hoggatt triangles which
apparently are related to combinatorial objects such as Young tableaux and
Narayana numbers.


title: Testing idealness in the filter oracle model

abstract:
  A filter oracle for a clutter consists of a finite set $V$ along with an
oracle which, given any set $X\subseteq V$, decides in unit time whether or not
$X$ contains a member of the clutter. Let $\mathfrak{A}_{2n}$ be an algorithm
that, given any clutter $\mathcal{C}$ over $2n$ elements via a filter oracle,
decides whether or not $\mathcal{C}$ is ideal. We prove that in the worst case,
$\mathfrak{A}_{2n}$ must make at least $2^n$ calls to the filter oracle. Our
proof uses the theory of cuboids.


title:
Choosing an algorithmic fairness metric for an online marketplace:
  Detecting and quantifying algorithmic bias on LinkedIn

abstract:
  In this paper, we derive an algorithmic fairness metric for the
recommendation algorithms that power LinkedIn from the fairness notion of equal
opportunity for equally qualified candidates. We borrow from the economic
literature on discrimination to arrive at a test for detecting algorithmic
discrimination, which we then use to audit two algorithms from LinkedIn with
respect to gender bias. Moreover, we introduce a framework for distinguishing
algorithmic bias from human bias, both of which can potentially exist on a
two-sided platform.


title: User-Oriented Robust Reinforcement Learning

abstract:
  Recently, improving the robustness of policies across different environments
attracts increasing attention in the reinforcement learning (RL) community.
Existing robust RL methods mostly aim to achieve the max-min robustness by
optimizing the policy's performance in the worst-case environment. However, in
practice, a user that uses an RL policy may have different preferences over its
performance across environments. Clearly, the aforementioned max-min robustness
is oftentimes too conservative to satisfy user preference. Therefore, in this
paper, we integrate user preference into policy learning in robust RL, and
propose a novel User-Oriented Robust RL (UOR-RL) framework. Specifically, we
define a new User-Oriented Robustness (UOR) metric for RL, which allocates
different weights to the environments according to user preference and
generalizes the max-min robustness metric. To optimize the UOR metric, we
develop two different UOR-RL training algorithms for the scenarios with or
without a priori known environment distribution, respectively. Theoretically,
we prove that our UOR-RL training algorithms converge to near-optimal policies
even with inaccurate or completely no knowledge about the environment
distribution. Furthermore, we carry out extensive experimental evaluations in 4
MuJoCo tasks. The experimental results demonstrate that UOR-RL is comparable to
the state-of-the-art baselines under the average and worst-case performance
metrics, and more importantly establishes new state-of-the-art performance
under the UOR metric.


title: Causality and time order -- relativistic and probabilistic aspects

abstract:
  We investigate temporal and causal threads in the fabric of contemporary
physical theories with an emphasis on empirical and operationalistic aspects.
Building on the axiomatization of general relativity proposed by J. Ehlers, F.
Pirani and A. Schild and the global space-time structure elaborated by R.
Penrose, S.W. Hawking, B. Carter and others, we argue that the current way of
doing relativistic physics presupposes treating time and causality as primitive
concepts, neither of them being `more primitive' than the other. The decision
regarding which concepts to assume as primitive and which statements to regard
as axioms depends on the choice of the angle at which we contemplate the whole.
This standard approach is based on the presupposition that the concept of a
point-like particle is a viable approximation. However, this assumption is not
supported by a realistic approach to doing physics and, in particular, by
quantum theory. We remove this assumption by analysing the recent works by M.
Eckstein and T. Miller. They consider the space $P(M)$ of probability measures
on space-time $M$ such that, for an element $\mu \in P(M)$, the number $\mu
(K)$ specifies the probability of the occurrence of some event associated with
the space-time region $K$ and the measure $\mu$. In this way, $M$ is not to be
regarded as a collection of space-time events, but rather as a support for
corresponding probability measures. As shown by Eckstein and Miller, the space
$P(M)$ inherits the causal order from the underlying space-time and facilitates
a rigorous notion of a `causal evolution of probability measures'. We look at
the deductive chains creating temporal and causal structures analysed in these
works, in order to highlight their operational (or quasi-operational) aspect.
This is impossible without taking into account the relative frequencies and
correlations observed in relevant experiments.


title:
Asymptotic behavior for delayed backward stochastic differential
  equations

abstract:
  This paper is devoted to study the asymptotic properties for the solution of
decoupled forward backward stochastic differential equations with delayed
generator. As an application, we establish a large deviation principe for
solution of the backward equation.


title:
XAI for Transformers: Better Explanations through Conservative
  Propagation

abstract:
  Transformers have become an important workhorse of machine learning, with
numerous applications. This necessitates the development of reliable methods
for increasing their transparency. Multiple interpretability methods, often
based on gradient information, have been proposed. We show that the gradient in
a Transformer reflects the function only locally, and thus fails to reliably
identify the contribution of input features to the prediction. We identify
Attention Heads and LayerNorm as main reasons for such unreliable explanations
and propose a more stable way for propagation through these layers. Our
proposal, which can be seen as a proper extension of the well-established LRP
method to Transformers, is shown both theoretically and empirically to overcome
the deficiency of a simple gradient-based approach, and achieves
state-of-the-art explanation performance on a broad range of Transformer models
and datasets.


title: ViNTER: Image Narrative Generation with Emotion-Arc-Aware Transformer

abstract:
  Image narrative generation is a task to create a story from an image with a
subjective viewpoint. Given the importance of the subjective feelings of
writers, readers, and characters in storytelling, an image narrative generation
method should consider human emotion. In this study, we propose a novel method
of image narrative generation called ViNTER (Visual Narrative Transformer with
Emotion arc Representation), which takes "emotion arc" as input to capture a
sequence of emotional changes. Since emotion arcs represent the trajectory of
emotional change, it is expected that we can include detailed information about
the emotional changes in the story to the model. We present experimental
results of both automatic and manual evaluations on the Image Narrative dataset
and demonstrate the effectiveness of the proposed approach.


title: The Any Light Particle Search experiment at DESY

abstract:
  The Any Light Particle Search (ALPS~II) is a light shining through a wall
(LSW) experiment searching for axion-like elementary particles in the sub-eV
mass range, which are motivated by astrophysics and cosmology and fulfill the
requirements for being dark matter. ALPS~II aims to measure an axion-to-photon
coupling of $2\times 10^{-11}\,\mathrm{GeV^{-1}}$, which is several orders of
magnitude better than that of previous LSW experiments and will thus
investigate a new parameter range. The increased performance is achieved by
enhancing the magnetic field interaction length to 2 $\times$ 106\,m and by
amplifying the signal in an optical cavity on each side of a light-tight
barrier. The expected signal is in the order of 1 photon per day, which will be
measured by photon detectors with very low dark count rates of $\mathcal
O(10^{-6}\,\mathrm{Hz})$. This article gives a technical overview on the
experiment design, previous and ongoing investigations and the current status
with focus on the single photon detection.


title:
Simplicial $q$-connectivity of directed graphs with applications to
  network analysis

abstract:
  Directed graphs are ubiquitous models for networks, and topological spaces
they generate, such as the directed flag complex, have become useful objects in
applied topology. The simplices are formed from directed cliques. We extend
Atkin's theory of $q$-connectivity to the case of directed simplices. This
results in a preorder where simplices are related by sequences of simplices
that share a $q$-face with respect to directions specified by chosen face maps.
We leverage the Alexandroff equivalence between preorders and topological
spaces to introduce a new class of topological spaces for directed graphs,
enabling to assign new homotopy types different from those of directed flag
complexes as seen by simplicial homology. We further introduce simplicial path
analysis enabled by the connectivity preorders. As an application we
characterise structural differences between various brain networks by computing
their longest simplicial paths.


title:
HAA4D: Few-Shot Human Atomic Action Recognition via 3D Spatio-Temporal
  Skeletal Alignment

abstract:
  Human actions involve complex pose variations and their 2D projections can be
highly ambiguous. Thus 3D spatio-temporal or 4D (i.e., 3D+T) human skeletons,
which are photometric and viewpoint invariant, are an excellent alternative to
2D+T skeletons/pixels to improve action recognition accuracy. This paper
proposes a new 4D dataset HAA4D which consists of more than 3,300 RGB videos in
300 human atomic action classes. HAA4D is clean, diverse, class-balanced where
each class is viewpoint-balanced with the use of 4D skeletons, in which as few
as one 4D skeleton per class is sufficient for training a deep recognition
model. Further, the choice of atomic actions makes annotation even easier,
because each video clip lasts for only a few seconds. All training and testing
3D skeletons in HAA4D are globally aligned, using a deep alignment model to the
same global space, making each skeleton face the negative z-direction. Such
alignment makes matching skeletons more stable by reducing intraclass
variations and thus with fewer training samples per class needed for action
recognition. Given the high diversity and skeletal alignment in HAA4D, we
construct the first baseline few-shot 4D human atomic action recognition
network without bells and whistles, which produces comparable or higher
performance than relevant state-of-the-art techniques relying on embedded space
encoding without explicit skeletal alignment, using the same small number of
training samples of unseen classes.


title: 2022 Roadmap for Materials for Quantum Technologies

abstract:
  Quantum technologies are poised to move the foundational principles of
quantum physics to the forefront of applications. This roadmap identifies some
of the key challenges and provides insights on materials innovations underlying
a range of exciting quantum technology frontiers. Over the past decades,
hardware platforms enabling different quantum technologies have reached varying
levels of maturity. This has allowed for first proof-of-principle
demonstrations of quantum supremacy, for example quantum computers surpassing
their classical counterparts, quantum communication with reliable security
guaranteed by laws of quantum mechanics, and quantum sensors uniting the
advantages of high sensitivity, high spatial resolution, and small footprints.
In all cases, however, advancing these technologies to the next level of
applications in relevant environments requires further development and
innovations in the underlying materials. From a wealth of hardware platforms,
we select representative and promising material systems in currently
investigated quantum technologies. These include both the inherent quantum bit
systems as well as materials playing supportive or enabling roles, and cover
trapped ions, neutral atom arrays, rare earth ion systems, donors in silicon,
color centers and defects in wide-band gap materials, two-dimensional materials
and superconducting materials for single-photon detectors. Advancing these
materials frontiers will require innovations from a diverse community of
scientific expertise, and hence this roadmap will be of interest to a broad
spectrum of disciplines.


title: Heavy Neutrinos at the FCC-hh in the $U(1)_{B-L}$ Model

abstract:
  We investigate the potential of the 100 TeV future circular collider (FCC-hh)
to probe heavy neutrinos. We concentrate in particular on heavy neutrino
production via a $U(1)_{B-L}$ $Z'$ gauge boson and contrast the resulting
limits with that mediated by Standard Model weak currents. We consider heavy
neutrino decays to semi-leptonic as well as fully leptonic final states,
particularly with muon flavour, and we show the importance of considering
searches both in prompt and displaced decays of the heavy neutrinos. For prompt
final states, semi-leptonic modes are more promising due to smaller background
and larger yields, and TeV-scale heavy neutrinos with active-sterile mixing
compatible with light neutrino mass generation in a seesaw scenario can be
probed for a 5 TeV $Z'$ and gauge coupling as low as $g_{B-L} = 10^{-2}$.
Displaced vertex searches can extend this range to heavy neutrino masses as low
as 10 GeV.


title:
Asymptotic probability of energy increasing solutions to the homogeneous
  Boltzmann equation

abstract:
  Weak solutions to the homogeneous Boltzmann equation with increasing energy
have been constructed by Lu and Wennberg. We consider an underlying microscopic
stochastic model with binary collision (Kac's model) and show that these
solutions are atypical. More precisely, we prove that the probability of
observing these paths is exponentially small in the number of particles and
compute the exponential rate. This result is obtained by improving the
established large deviation estimates in the canonical setting. Key ingredients
are the extension of Sanov's theorem to the microcanonical ensemble and large
deviations for the Kac's model in the microcanonical setting.


title: A construction of open descendant potentials in all genera

abstract:
  We present a construction of an open analogue of total descendant and total
ancestor potentials via an "open version" of Givental's action. Our
construction gives a genus expansion for an arbitrary solution to the open WDVV
equations satisfying a semisimplicity condition and admitting a unit. We show
that the open total descendant potentials we define satisfy the open
topological recursion relations in genus $0$ and $1$, the open string and open
dilaton equations.


title: Diameter of the commutation classes graph of a permutation

abstract:
  We define a statistic on the graph of commutation classes of a permutation of
the symmetric group which is used to show that these graphs are equipped with a
ranked poset structure, with a minimum and maximum. This characterization also
allows us to compute the diameter of the commutation graph for any permutation,
from which the results for the longest permutation and for fully commutative
permutations are recovered.


title:
Soliton resolution for equivariant self-dual Chern-Simons-Schr\"odinger
  equation in weighted Sobolev class

abstract:
  We consider the self-dual Chern-Simons-Schr\"odinger equation (CSS) under
equivariant symmetry, which is a $L^{2}$-critical equation. It is known that
(CSS) admits solitons and finite-time blow-up solutions. In this paper, we show
soliton resolution for any solutions with equivariant data in the weighted
Sobolev space $H^{1,1}$: every maximal solution decomposes into at most one
modulated soliton and a radiation. A striking fact is that the nonscattering
part must be a single modulated soliton. To our knowledge, this is the first
result on soliton resolution in a class of nonlinear Schr\"odinger equations
which are not known to be completely integrable. The key ingredient is the
defocusing nature of the equation in the exterior of a soliton profile. This is
a consequence of two distinctive features of (CSS): self-duality and non-local
nonlinearity.


title: Using Social Media Images for Building Function Classification

abstract:
  Urban land use on a building instance level is crucial geo-information for
many applications, yet difficult to obtain. An intuitive approach to close this
gap is predicting building functions from ground level imagery. Social media
image platforms contain billions of images, with a large variety of motifs
including but not limited to street perspectives. To cope with this issue this
study proposes a filtering pipeline to yield high quality, ground level imagery
from large social media image datasets. The pipeline ensures that all resulting
images have full and valid geotags with a compass direction to relate image
content and spatial objects from maps.
  We analyze our method on a culturally diverse social media dataset from
Flickr with more than 28 million images from 42 cities around the world. The
obtained dataset is then evaluated in a context of 3-classes building function
classification task. The three building classes that are considered in this
study are: commercial, residential, and other. Fine-tuned state-of-the-art
architectures yield F1-scores of up to 0.51 on the filtered images. Our
analysis shows that the performance is highly limited by the quality of the
labels obtained from OpenStreetMap, as the metrics increase by 0.2 if only
human validated labels are considered. Therefore, we consider these labels to
be weak and publish the resulting images from our pipeline together with the
buildings they are showing as a weakly labeled dataset.


title:
Optimization Conditions and Decomposable Algorithms for Convertible
  Nonconvex Optimization

abstract:
  This paper defines a convertible nonconvex function(CN function for short)
and a weak (strong) uniform (decomposable, exact) CN function, proves the
optimization conditions for their global solutions and proposes algorithms for
solving the unconstrained optimization problems with the decomposable CN
function. First, to illustrate the fact that some nonconvex functions,
nonsmooth or discontinuous, are actually weak uniform CN functions, examples
are given. The operational properties of the CN functions are proved, including
addition, subtraction, multiplication, division and compound operations.
Second, optimization conditions of the global optimal solution to unconstrained
optimization with a weak uniform CN function are proved. Based on the
unconstrained optimization problem with the decomposable CN function, a
decomposable algorithm is proposed by its augmented Lagrangian penalty function
and its convergence is proved. Numerical results show that an approximate
global optimal solution to unconstrained optimization with a CN function may be
obtained by the decomposable algorithms. The decomposable algorithm can
effectively reduce the scale in solving the unconstrained optimization problem
with the decomposable CN function. This paper provides a new idea for solving
unconstrained nonconvex optimization problems.


title: Exact Penalty Algorithm of Strong Convertible Nonconvex Optimization

abstract:
  This paper defines a strong convertible nonconvex(SCN) function for solving
the unconstrained optimization problems with the nonconvex or
nonsmooth(nondifferentiable) function. First, many examples of SCN function are
given, where the SCN functions are nonconvex or nonsmooth. Second, the
operational properties of the SCN functions are proved, including addition,
multiplication, compound operations and so on. Third, the SCN forms of some
special functions common in machine learning and engineering applications are
presented respectively where these SCN function optimization problems can be
transformed into minmax problems with a convex and concave objective function.
Fourth,a minmax optimization problem of SCN function and its penalty function
are defined. The optimization condition,exactness and stability of the minmax
optimization problem are proved. Finally, an algorithm of penalty function to
solve the minmax optimization problem and its convergence are given. This paper
provides an efficient technique for solving unconstrained nonconvex or
nonsmooth(nondifferentiable) optimization problems to avoid using
subdifferentiation or smoothing techniques.


title:
An algorithmic solution to the Blotto game using multi-marginal
  couplings

abstract:
  We describe an efficient algorithm to compute solutions for the general
two-player Blotto game on n battlefields with heterogeneous values. While
explicit constructions for such solutions have been limited to specific,
largely symmetric or homogeneous, setups, this algorithmic resolution covers
the most general situation to date: value-asymmetric game with asymmetric
budget. The proposed algorithm rests on recent theoretical advances regarding
Sinkhorn iterations for matrix and tensor scaling. An important case which had
been out of reach of previous attempts is that of heterogeneous but symmetric
battlefield values with asymmetric budget. In this case, the Blotto game is
constant-sum so optimal solutions exist, and our algorithm samples from an
\eps-optimal solution in time O(n^2 + \eps^{-4}), independently of budgets and
battlefield values. In the case of asymmetric values where optimal solutions
need not exist but Nash equilibria do, our algorithm samples from an \eps-Nash
equilibrium with similar complexity but where implicit constants depend on
various parameters of the game such as battlefield values.


title:
Layer-dependent Raman spectroscopy and electronic applications of
  wide-bandgap 2D semiconductor \b{eta}-ZrNCl

abstract:
  In recent years, two-dimensional (2D) layered semiconductors have received
much attention for their potential in next-generation electronics and
optoelectronics. Wide-bandgap 2D semiconductors are especially important in
blue and ultraviolet wavelength region, while there are very few 2D materials
in this region. Here, monolayer \b{eta}-type zirconium nitride chloride
(\b{eta}-ZrNCl) is isolated for the first time, which is an air-stable layered
material with a bandgap of ~3.0 eV in bulk. Systematical investigation of
layer-dependent Raman scattering of ZrNCl from monolayer, bilayer, to bulk
reveals a blue shift of its out-of-plane A1g peak at ~189 cm-1. Importantly,
this A1g peak is absent in monolayer, suggesting that it is a fingerprint to
quickly identify monolayer and for the thickness determination of 2D ZrNCl. The
back-gate field-effect transistor based on few-layer ZrNCl shows a high on/off
ratio of 108. These results suggest the potential of 2D \b{eta}-ZrNCl for
electronic applications.


title:
Kerker condition for enhancing emission rate and directivity of single
  emitter coupled to dielectric metasurfaces

abstract:
  Metasurfaces have the ability to control classical and non-classical states
of light to achieve controlled emission even at the level of single emitter.
Here, we unveil the Kerker condition induced emission rate enhancement with
strong directivity from a single emitter integrated within a dielectric
metasurface consists of silicon nano-disks. The simulation and analytical
calculations attest the Kerker condition with unidirectional light scattering
evolved by the constructive interference between electric dipole, toroidal
dipole, and the magnetic quadrupole. The results evince spatially-dependent
enhanced local density of optical states which reciprocates localized field
intensity. The rate enhancement of 400 times is achieved at zero phonon line of
nitrogen-vacancy center with superior emission directivity and collection
efficiency. The results have implications in on-demand single photon
generation, spin-photon interface, many-body interactions, and strong coupling.


title:
CTA Sensitivity on TeV scale Dark Matter Models with Complementary
  Limits from Direct Detection

abstract:
  With ever increasing pressure from collider physics and direct detection
experiments, particle physics models of TeV scale dark matter are gaining more
attention. In this work, we consider two realizations of the class of scalar
portal dark matter scenarios -- the inverse seesaw model and the inert doublet
model. Observations by the Cherenkov Telescope Array (CTA) of very-high-energy
$\gamma$ rays from dark matter annihilation in the context of these models are
simulated for the Draco and Sculptor dwarf spheroidal galaxies, and later
analyzed using ctools. We study the potential of CTA for the 5$\sigma$
detection of a dark matter annihilation signal. In the absence of a signal, we
also derive the 2$\sigma$ upper limits on the annihilation cross-section. We
compare our projected CTA sensitivity against the projected sensitivity of the
next generation of direct detection experiment, i.e. XENONnT. Although the
limits from CTA are significantly improved compared with the previous
generations of $\gamma$-ray experiments, they are still $\sim2$ orders of
magnitude above the thermal relic cross-section for the considered targets. In
the case of the inverse seesaw model, the constraint from the future direct
detection experiment XENONnT is much weaker than the CTA sensitivity, whereas
for the inert doublet model, XENONnT gives a bound an order of magnitude
stronger compared to the CTA limits.


title:
Recent Advances in Design of Electrocatalysts for High-Current-Density
  Water Splitting

abstract:
  Electrochemical water splitting technology for producing "green hydrogen" is
important for the global mission of carbon neutrality. Electrocatalysts with
decent performance at high current densities play a central role in the
industrial implementation of this technology. The field has advanced immensely
in recent years, as witnessed by many types of catalysts have been designed and
synthesized which work at industrially-relevant current densities (> 200 mA
cm-2). Note that the activity and stability of catalysts can be influenced by
their local reaction environment, which are closely related to the current
density. By discussing recent advances in this field, we summarize several key
aspects that affect the catalytic performance for high-current-density
electrocatalysis, including dimensionality of catalysts, surface chemistry,
electron transport path, morphology, and catalyst-electrolyte interplay. We
highlight the multiscale design strategy that considers these aspects
comprehensively for developing high-current-density catalysts. We also put
forward out perspectives on the future directions in this emerging field.


title: Classification results for nonsingular Bernoulli crossed products

abstract:
  We prove rigidity and classification results for type III factors given by
nonsingular Bernoulli actions of the free groups and more general free product
groups. This includes a large family of nonisomorphic Bernoulli crossed
products of type III$_1$ that cannot be distinguished by Connes
$\tau$-invariant. These are the first such classification results beyond the
well studied probability measure preserving case.


title:
Asymptotic characterisation of localised defect modes:
  Su-Schrieffer-Heeger and related models

abstract:
  This work establishes a general method for studying localised eigenmodes in
periodic media with defects. Our approach can be used to describe two broad
classes of perturbations to periodic differential problems, both compact and
non-compact: those caused by inserting a finite-sized piece of arbitrary
material and those caused by creating an interface between two different
periodic media. The results presented here characterise the existence of
localised eigenmodes in each case and, when they exist, determine their
eigenfrequencies and quantify the rate at which they decay away from the
defect. These results are obtained using both high-frequency homogenisation and
transfer matrix analysis, with good agreement between the two methods. We will
use problems based on the Su-Schrieffer-Heeger model (the canonical example of
a one-dimensional topological waveguide) to demonstrate the method.


title:
Novel methods for estimating the instantaneous and overall COVID-19 case
  fatality risk among care home residents in England

abstract:
  The COVID-19 pandemic has had high mortality rates in the elderly and frail
worldwide, particularly in care homes. This is driven by the difficulty of
isolating care homes from the wider community, the large population sizes
within care facilities (relative to typical households), and the age/frailty of
the residents. To quantify the mortality risk posed by disease, the case
fatality risk (CFR) is an important tool. This quantifies the proportion of
cases that result in death. Throughout the pandemic, CFR amongst care home
residents in England has been monitored closely. To estimate CFR, we apply both
novel and existing methods to data on deaths in care homes, collected by Public
Health England and the Care Quality Commission. We compare these different
methods, evaluating their relative strengths and weaknesses. Using these
methods, we estimate temporal trends in the instantaneous CFR (at both daily
and weekly resolutions) and the overall CFR across the whole of England, and
dis-aggregated at regional level. We also investigate how the CFR varies based
on age and on the type of care required, dis-aggregating by whether care homes
include nursing staff and by age of residents. This work has contributed to the
summary of measures used for monitoring the UK epidemic.


title:
Towards optical toroidal wavepacket through tightly focusing of
  cylindrical vector two dimensional spatiotemporal optical vortex

abstract:
  Spatiotemporal optical vortices (STOVs) carrying transverse orbital angular
momentum (OAM) are of rapidly growing interest for the field of optics due to
the new degree of freedom that can be exploited. In this paper, we propose
cylindrical vector two dimensional STOVs (2D-STOVs) containing two orthogonal
transverse OAMs in both x-t and y-t planes for the first time, and investigate
the tightly focusing of such fields using the Richards-Wolf vectorial
diffraction theory. Highly confined spatiotemporal wavepackets with
polarization structure akin to toroidal topology is generated, whose
spatiotemporal intensity distributions resemble the shape of Yo-Yo balls.
Highly focused radially polarized 2D-STOVs will produce wavepackets towards
transverse magnetic toroidal topology, while the focused azimuthally polarized
2D-STOVs will give rise to wavepackets towards transverse electric toroidal
topology. The presented method may pave a way to experimentally generate the
optical toroidal wavepackets in a controllable way, with potential applications
in electron acceleration, photonics, energy, transient light-matter
interaction, spectroscopy, quantum information processing, etc.


title: Treating Interference as Noise in Cell-Free Massive MIMO Networks

abstract:
  How to manage the interference introduced by the enormous wireless devices is
a crucial issue to address in the prospective sixth-generation (6G)
communications. The treating interference as noise (TIN) optimality conditions
are commonly used for interference management and thus attract significant
interest in existing wireless systems. Cell-free massive multiple-input
multiple-output (CF mMIMO) is a promising technology in 6G that exhibits high
system throughput and excellent interference management by exploiting a large
number of access points (APs) to serve the users collaboratively. In this
paper, we take the first step on studying TIN in CF mMIMO systems from a
stochastic geometry perspective by investigating the probability that the TIN
conditions hold with spatially distributed network nodes. We propose a novel
analytical framework for TIN in a CF mMIMO system with both Binomial Point
Process (BPP) and Poisson Point Process (PPP) approximations. We derive the
probability that the TIN conditions hold in close form using the PPP
approximation. Numerical results validate our derived expressions and
illustrate the impact of various system parameters on the probability that the
TIN conditions hold.


title:
Secure Beamforming Design for Rate-Splitting Multiple Access in
  Multi-antenna Broadcast Channel with Confidential Messages

abstract:
  As physical layer security evolves to multi-user systems, multi-user
interference (MUI) becomes an unavoidable issue. Recently, rate-splitting
multiple access (RSMA) emerges as a powerful non-orthogonal transmission
framework and interference management strategy with high spectral efficiency.
Unlike most works fully treating MUI as noise, we take all users' secrecy rate
requirements into consideration and propose an RSMA-based secure beamforming
approach to maximize the weighted sum-rate (WSR), where MUI is partially
decoded and partially treated as noise. User messages are split and encoded
into common and private streams. Each user not only decodes the common stream
and the intended private stream, but also tries to eavesdrop other users'
private streams. A successive convex approximation (SCA)-based approach is
proposed to maximize the instantaneous WSR under perfect channel state
information at the transmitter (CSIT). We then propose a joint weighted minimum
mean square error and SCA-based alternating optimization algorithm to maximize
the weighted ergodic sum-rate under imperfect CSIT. Numerical results
demonstrate RSMA achieves better WSR and is more robust to channel errors than
conventional multi-user linear precoding technique while ensuring all users'
security requirements. Besides, RSMA can satisfy all users' secrecy rate
requirements without introducing WSR loss thanks to its powerful interference
management capability.


title: The directed plump ordering

abstract:
  Based on Taylor's hereditarily directed plump ordinals, we define the
directed plump ordering on W-types in Martin-L\"of type theory. This ordering
is similar to the plump ordering but comes equipped with non-empty finite joins
in addition to the usual properties of the plump ordering.


title: Drive beam sources and longitudinal shaping techniques

abstract:
  Linear colliders are an attractive platform to explore high-precision physics
of newly discovered particles. The recent significant progress in advanced
accelerator technologies has motivated their applications to colliders which
has been discussed in the {\sc alegro} workshop. In this paper we discuss one
promising scheme, collinear wakefield acceleration. We especially discuss
available drive and witness beam sources based on L and S-band radiofrequency
technology, and also summarize available and forthcoming longitudinal shaping
techniques to improve the overall acceleration efficiency via the transformer
ratio.


title: Levi-Civita connections on quantum spheres

abstract:
  We introduce $q$-deformed connections on the quantum 2-sphere and 3-sphere,
satisfying a twisted Leibniz rule in analogy with $q$-deformed derivations. We
show that such connections always exist on projective modules. Furthermore, a
condition for metric compatibility is introduced, and an explicit formula is
given, parametrizing all metric connections on a free module. On the quantum
3-sphere, a q-deformed torsion freeness condition is introduced and we derive
explicit expressions for the Christoffel symbols of a Levi-Civita connection
for a general class of metrics. We also give metric connections on a class of
projective modules over the quantum 2-sphere. Finally, we outline a
generalization to any Hopf algebra with a (left) covariant calculus and
associated quantum tangent space.


title:
Taming numerical errors in simulations of continuous variable
  non-Gaussian state preparation

abstract:
  Numerical simulation of continuous variable quantum state preparation is a
necessary tool for optimization of existing quantum information processing
protocols. A powerful instrument for such simulation is the numerical
computation in the Fock state representation. It unavoidably uses an
approximation of the infinite-dimensional Fock space by finite complex vector
spaces implementable with classical digital computers. In this approximation we
analyze the accuracy of several currently available methods for computation of
the truncated coherent displacement operator. To overcome their limitations we
propose an alternative with improved accuracy based on the standard matrix
exponential. We then employ the method in analysis of non-Gaussian state
preparation scheme based on coherent displacement of a two mode squeezed vacuum
with subsequent photon counting measurement. We compare different detection
mechanisms, including avalanche photodiodes, their cascades, and photon number
resolving detectors in the context of engineering non-linearly squeezed cubic
states and construction of qubit-like superpositions between vacuum and single
photon states.


title: QCD Predictions for Event-Shape Distributions in Hadronic Higgs Decays

abstract:
  We study the six classical event-shape observables in hadronic Higgs decays
at next-to-leading order in QCD. To this end, we consider the decay of on-shell
Higgs bosons to three partons, taking into account both the Yukawa-induced
decay to $\mathrm{b}$-quark pairs and the loop-induced decay to two gluons via
an effective Higgs-gluon coupling. The results are discussed with a particular
focus on the discriminative power of event shapes regarding these two classes
of processes.


title: Dimension expanders via quiver representations

abstract:
  We relate the notion of dimension expanders to quiver representations and
their general subrepresentations, and use this relation to establish sharp
existence results.


title: Geometry of measures in random systems with complete connections

abstract:
  We study new relations between countable iterated function systems (IFS) with
overlaps, Smale endomorphisms and random systems with complete connections. We
prove that stationary measures for countable conformal IFS with overlaps and
placedependent probabilities, are exact dimensional; moreover we determine
their Hausdorff dimension. Next, we construct a family of fractals in the limit
set of a countable IFS with overlaps S, and study the dimension for certain
measures supported on these subfractals. In particular, we obtain families of
measures on these subfractals which are related to the geometry of the system.


title: Two-dimensional Functional Minerals for Sustainable Optics

abstract:
  Optical device is a key component in our lives and organic liquid crystals
are nowadays widely used to reduce human imprint. However, this technology
still suffers from relatively high costs, toxicity and other environmental
impacts, and cannot fully meet the demand of future sustainable society. Here
we describe an alternative approach to colour-tuneable optical devices, which
is based on sustainable inorganic liquid crystals derived from two-dimensional
mineral materials abundant in nature. The prototypical two-dimensional mineral
of vermiculite is massively produced by a green method, possessing
size-to-thickness ratios of >103, in-plane magnetisation of >10 emu g-1, and an
optical bandgap of >3 eV. These characteristics endow two-dimensional
vermiculite with sensitive magneto-birefringence response, which is several
orders of magnitude larger than organic counterparts, as well as capability of
broad-spectrum modulation. Our finding consequently permits the fabrication of
various chromic devices with low or even zero-energy consumption, which can be
used for sustainable optics.


title: Clouds in Gromov-Hausdorff Class: their completeness and centers

abstract:
  We consider the proper class of all metric spaces endowed with the
Gromov--Hausdorff distance. Its maximal subclasses, consisting of the spaces on
finite distance from each other, we call clouds. Multiplying all distances in a
metric space by the same positive real number, we obtain a similarity
transformation of the Gromov--Hausdorff class. In our previous work, we
observed that with such a transformation, some clouds can jump to others. To
characterize the phenomenon, we studied the stabilizers of the similarity
action. In this paper, we prove that every cloud with a nontrivial stabilizer
has a center, i.e., a metric space for which all similarities from the
stabilizer generate a new space at zero distance. Moreover, the center is
unique modulo zero distance. The proof is based on the cloud completeness
theorem.


title: Evolution Equations in Hilbert Spaces via the Lacunae Method

abstract:
  In this paper we consider evolution equations in the abstract Hilbert space
under the special conditions imposed on the operator at the right-hand side of
the equation. We establish the method that allows us to formulate the existence
and uniqueness theorem and find a solution in the form of a series on the root
vectors of the right-hand side. As an application we consider fractional
differential equations of various kinds. Such operators as the
Riemann-Liouville fractional differential operator, the Riesz potential, the
difference operator have been involved.


title:
A Ta-TaS2 monolithic catalyst with robust and metallic interface for
  superior hydrogen evolution

abstract:
  The use of highly active and robust catalysts is crucial for producing green
hydrogen by water electrolysis as we strive to achieve global carbon
neutrality. Noble metals like platinum are currently used in industry for the
hydrogen evolution reaction (HER), but suffer from scarcity, high price and
unsatisfied performance and stability at large current density, restricting
their large scale implementations. Here we report the synthesis of a new type
of monolithic catalyst (MC) consisting of a metal disulfide (e.g., TaS2)
catalyst vertically bonded to a conductive substrate of the same metal by
strong covalent bonds. These features give the MC a mechanically robust and
electrically near zero resistance interface, leading to an outstanding HER
performance including rapid charge transfer and excellent durability, together
with a low overpotential of 398 mV to achieve a current density of 2,000 mA
cm-2 as required by industry. The Ta TaS2 MC has a negligible performance decay
after 200 h operation at large current densities. In light of its unique
interface and the various choice of metal elements giving the same structure,
such monolithic materials may have broad uses besides catalysis.


title:
Low-rank tensor approximations for solving multi-marginal optimal
  transport problems

abstract:
  By adding entropic regularization, multi-marginal optimal transport problems
can be transformed into tensor scaling problems, which can be solved
numerically using the multi-marginal Sinkhorn algorithm. The main computational
bottleneck of this algorithm is the repeated evaluation of marginals. In
[Haasler et al., IEEE Trans. Inf. Theory, 67 (2021)], it has been suggested
that this evaluation can be accelerated when the application features an
underlying graphical model. In this work, we accelerate the computation further
by combining the tensor network dual of the graphical model with additional
low-rank approximations. For the color transfer of images, these added low rank
approximations save more than 96% of the computation time.


title:
Active-gel Theory for Multicellular Migration of Polar Cells in the
  Extra-cellular Matrix

abstract:
  We formulate an active-gel theory for multicellular migration in the
extra-cellular matrix (ECM). The cells are modeled as an active, polar solvent,
and the ECM as a viscoelastic solid. Our theory enables to analyze the dynamic
reciprocity between the migrating cells and their environment in terms of
distinct relative forces and alignment mechanisms. We analyze the linear
stability of polar cells migrating homogeneously in the ECM. Our theory
predicts that, as a consequence of cell-matrix alignment, contractile cells
migrate homogeneously for small wave vectors, while sufficiently extensile
cells migrate in domains. Homogeneous cell migration of both extensile and
contractile cells may be unstable for larger wave vectors, due to active forces
and the alignment of cells with their concentration gradient. These mechanisms
are stabilized by cellular alignment to the migration flow and matrix
stiffness. They are expected to be suppressed entirely for rigid matrices with
elastic moduli of order 10 kPa. Our theory should be useful in analyzing
multicellular migration and ECM patterning at the mesoscopic scale.


title: Unreasonable Effectiveness of Last Hidden Layer Activations

abstract:
  In standard Deep Neural Network (DNN) based classifiers, the general
convention is to omit the activation function in the last (output) layer and
directly apply the softmax function on the logits to get the probability scores
of each class. In this type of architectures, the loss value of the classifier
against any output class is directly proportional to the difference between the
final probability score and the label value of the associated class. Standard
White-box adversarial evasion attacks, whether targeted or untargeted, mainly
try to exploit the gradient of the model loss function to craft adversarial
samples and fool the model. In this study, we show both mathematically and
experimentally that using some widely known activation functions in the output
layer of the model with high temperature values has the effect of zeroing out
the gradients for both targeted and untargeted attack cases, preventing
attackers from exploiting the model's loss function to craft adversarial
samples. We've experimentally verified the efficacy of our approach on MNIST
(Digit), CIFAR10 datasets. Detailed experiments confirmed that our approach
substantially improves robustness against gradient-based targeted and
untargeted attack threats. And, we showed that the increased non-linearity at
the output layer has some additional benefits against some other attack methods
like Deepfool attack.


title:
The stable behavior of low thermal conductivity in 1T-sandwich structure
  with different components

abstract:
  Designing materials with low thermal conductivity (\k{appa}) is of demand for
thermal protection, heat insulation, thermoelectricity, etc. In this paper,
based on the start-of-art first-principles calculations, we propose a framework
of a 1T-sandwich structure for designing materials with low \k{appa}. The
1T-sandwich structure is the same as the well-known transition metal
dichalcogenide (TMD) but with light Carbon atoms in the middle plane. Using
different atoms to fill the outer positions, a few novel two-dimensional
materials are constructed as study cases, i.e., Mg2C, Janus MgBeC, Be2C, and
Mo2C. With a systematic and comparative study, the \k{appa} are calculated to
be 3.74, 8.26, 14.80, 5.13 W/mK, respectively. The consistent values indicate
the stable behavior of low \k{appa} in the 1T-sandwich structure, being
insensitive to the component. Our study would help design advanced functional
materials with reliable heat transfer performance for practical applications,
which reduces the influence of unavoidable impurities.


title: A strong Borel--Cantelli lemma for recurrence

abstract:
  Consider a mixing dynamical systems $([0,1], T, \mu)$, for instance a
piecewise expanding interval map with a Gibbs measure $\mu$. Given a
non-summable sequence $(m_k)$ of non-negative numbers, one may define $r_k (x)$
such that $\mu (B(x, r_k(x)) = m_k$. It is proved that for almost all $x$, the
number of $k \leq n$ such that $T^k (x) \in B_k (x)$ is approximately equal to
$m_1 + \ldots + m_n$. This is a sort of strong Borel--Cantelli lemma for
recurrence.
  A consequence is that \[
  \lim_{r \to 0} \frac{\log \tau_{B(x,r)} (x)}{- \log \mu (B (x,r))}
  = 1 \] for almost every $x$, where $\tau$ is the return time.


title:
KamLAND's search for correlated low-energy electron antineutrinos with
  astrophysical neutrinos from IceCube

abstract:
  We report the results of a search for MeV-scale astrophysical neutrinos in
KamLAND presented as an excess in the number of coincident neutrino
interactions associated with the publicly available high-energy neutrino
datasets from the IceCube Neutrino Observatory. We find no statistically
significant excess in the number of observed low-energy electron antineutrinos
in KamLAND, given a coincidence time window of +/-500s, +/-1,000 s, +/-3,600 s,
and +/-10,000 s around each of the IceCube neutrinos. We use this observation
to present limits from 1.8 MeV to 100 MeV on the electron antineutrino fluence,
assuming a mono-energetic flux. We then compare the results to several
astrophysical measurements performed by IceCube and place a limit at the 90%
confidence level on the electron antineutrino isotropic thermal luminosity from
the TXS 0506+056 blazar.


title: What can oracles teach us about the ultimate fate of life?

abstract:
  We settle two long-standing open problems about Conway's Life, a
two-dimensional cellular automaton. We solve the Generalized grandfather
problem: for all $n \geq 0$, there exists a configuration that has an $n$th
predecessor but not an $(n+1)$st one. We also solve (one interpretation of) the
Unique father problem: there exists a finite stable configuration that contains
a finite subpattern that has no predecessor patterns except itself. In
particular this gives the first example of an unsynthetizable still life. The
new key concept is that of a spatiotemporally periodic configuration (agar)
which has a unique chain of preimages; we show that this property is
semidecidable, and find examples of such agars using a SAT solver.
  Our results about the topological dynamics of Game of Life are as follows: it
never reaches its limit set; its dynamics on its limit set is chain-wandering,
in particular it is not topologically transitive and does not have dense
periodic points; and the spatial dynamics of its limit set is non-sofic, and
does not admit a sublinear gluing radius in the cardinal directions (in
particular it is not block-gluing). Our computability results are that Game of
Life's reachability problem, as well as the language of its limit set, are
PSPACE-hard.