title: Optimizing Mixture of Experts using Dynamic Recompilations

abstract:
  The Mixture of Experts architecture allows for outrageously large neural
networks by scaling model parameter size independently from computational
demand (FLOPs). However, current DNN frameworks cannot effectively support the
dynamic data flow in Mixture of Experts, and implementations on top of these
frameworks need to use workarounds that introduce significant overheads. To
address the limitation of these frameworks, we present DynaMoE, a DNN library
that uses dynamic recompilations to optimize and adapt the use of computational
resources to the dynamic needs of Mixture of Experts models. Our evaluation
shows that DynaMoE achieves a 1.8x speedup and supports 2.3x larger model sizes
when compared to existing MoE systems, even when not using recompilations. We
then present further optimizations enabled by dynamic recompilations that yield
an additional 1.7x speedup while simultaneously reducing memory pressure and
improving model quality.


title: Estimation of prediction error with known covariate shift

abstract:
  In supervised learning, the estimation of prediction error on unlabeled test
data is an important task. Existing methods are usually built on the assumption
that the training and test data are sampled from the same distribution, which
is often violated in practice. As a result, traditional estimators like
cross-validation (CV) will be biased and this may result in poor model
selection. In this paper, we assume that we have a test dataset in which the
feature values are available but not the outcome labels, and focus on a
particular form of distributional shift called "covariate shift". We propose an
alternative method based on parametric bootstrap of the target of conditional
error. Empirically, our method outperforms CV for both simulation and real data
example across different modeling tasks.


title: Visual Commonsense in Pretrained Unimodal and Multimodal Models

abstract:
  Our commonsense knowledge about objects includes their typical visual
attributes; we know that bananas are typically yellow or green, and not purple.
Text and image corpora, being subject to reporting bias, represent this
world-knowledge to varying degrees of faithfulness. In this paper, we
investigate to what degree unimodal (language-only) and multimodal (image and
language) models capture a broad range of visually salient attributes. To that
end, we create the Visual Commonsense Tests (ViComTe) dataset covering 5
property types (color, shape, material, size, and visual co-occurrence) for
over 5000 subjects. We validate this dataset by showing that our grounded color
data correlates much better than ungrounded text-only data with crowdsourced
color judgments provided by Paik et al. (2021). We then use our dataset to
evaluate pretrained unimodal models and multimodal models. Our results indicate
that multimodal models better reconstruct attribute distributions, but are
still subject to reporting bias. Moreover, increasing model size does not
enhance performance, suggesting that the key to visual commonsense lies in the
data.


title:
Toward Data-Driven Digital Therapeutics Analytics: Literature Review and
  Research Directions

abstract:
  With the advent of Digital Therapeutics (DTx), the development of software as
a medical device (SaMD) for mobile and wearable devices has gained significant
attention in recent years. Existing DTx evaluations, such as randomized
clinical trials, mostly focus on verifying the effectiveness of DTx products.
To acquire a deeper understanding of DTx engagement and behavioral adherence,
beyond efficacy, a large amount of contextual and interaction data from mobile
and wearable devices during field deployment would be required for analysis. In
this work, the overall flow of the data-driven DTx analytics is reviewed to
help researchers and practitioners to explore DTx datasets, to investigate
contextual patterns associated with DTx usage, and to establish the (causal)
relationship of DTx engagement and behavioral adherence. This review of the key
components of data-driven analytics provides novel research directions in the
analysis of mobile sensor and interaction datasets, which helps to iteratively
improve the receptivity of existing DTx.


title: Stochastic Image Transmission with CoAP for Extreme Environments

abstract:
  Communication in extreme environments is an important research topic for
various use cases including environmental monitoring. A typical example is
underwater acoustic communication for 6G mobile networks. The major challenges
in such environments are extremely high-latency and high-error rate. They make
real-time image transmission difficult using existing communication protocols.
This is partly because frequent retransmission in noisy networks increases
latency and leads to serious deterioration of real-timeness. To address this
problem, this paper proposes a stochastic image transmission with Constrained
Application Protocol (CoAP) for extreme environments. The goal of the proposed
idea is to achieve approximate real-time image transmission without
retransmission using CoAP over UDP. To this end, an image is divided into
blocks, and value is assigned for each block based on the requirement. By the
stochastic transmission of blocks, the reception probability is guaranteed
without retransmission even when packets are lost in networks. We implemented
the proposed scheme using Raspberry Pi 4 to demonstrate the feasibility. The
performance of the proposed image transmission was confirmed from the
experimental results.


title:
SMLT: A Serverless Framework for Scalable and Adaptive Machine Learning
  Design and Training

abstract:
  In today's production machine learning (ML) systems, models are continuously
trained, improved, and deployed. ML design and training are becoming a
continuous workflow of various tasks that have dynamic resource demands.
Serverless computing is an emerging cloud paradigm that provides transparent
resource management and scaling for users and has the potential to
revolutionize the routine of ML design and training. However, hosting modern ML
workflows on existing serverless platforms has non-trivial challenges due to
their intrinsic design limitations such as stateless nature, limited
communication support across function instances, and limited function execution
duration. These limitations result in a lack of an overarching view and
adaptation mechanism for training dynamics and an amplification of existing
problems in ML workflows.
  To address the above challenges, we propose SMLT, an automated, scalable, and
adaptive serverless framework to enable efficient and user-centric ML design
and training. SMLT employs an automated and adaptive scheduling mechanism to
dynamically optimize the deployment and resource scaling for ML tasks during
training. SMLT further enables user-centric ML workflow execution by supporting
user-specified training deadlines and budget limits. In addition, by providing
an end-to-end design, SMLT solves the intrinsic problems in serverless
platforms such as the communication overhead, limited function execution
duration, need for repeated initialization, and also provides explicit fault
tolerance for ML training. SMLT is open-sourced and compatible with all major
ML frameworks. Our experimental evaluation with large, sophisticated modern ML
models demonstrate that SMLT outperforms the state-of-the-art VM based systems
and existing serverless ML training frameworks in both training speed (up to
8X) and monetary cost (up to 3X)


title:
Robustly Complete Finite-State Abstractions for Verification of
  Stochastic Systems

abstract:
  In this paper, we focus on discrete-time stochastic systems modelled by
nonlinear stochastic difference equations and propose robust abstractions for
verifying probabilistic linear temporal specifications. The current literature
focuses on developing sound abstraction techniques for stochastic dynamics
without perturbations. However, soundness thus far has only been shown for
preserving the satisfaction probability of certain types of temporal-logic
specification. We present constructive finite-state abstractions for verifying
probabilistic satisfaction of general {\omega}-regular linear-time properties
of more general nonlinear stochastic systems. Instead of imposing stability
assumptions, we analyze the probabilistic properties from the topological view
of metrizable space of probability measures. Such abstractions are both sound
and approximately complete. That is, given a concrete discrete-time stochastic
system and an arbitrarily small L1-perturbation of this system, there exists a
family of finite-state Markov chains whose set of satisfaction probabilities
contains that of the original system and meanwhile is contained by that of the
slightly perturbed system. A direct consequence is that, given a probabilistic
linear-time specification, initializing within the winning/losing region of the
abstraction system can guarantee a satisfaction/dissatisfaction for the
original system. We make an interesting observation that, unlike the
deterministic case, point-mass (Dirac) perturbations cannot fulfill the purpose
of robust completeness.


title:
The Effect of Multiple Imputation of Routine Pathology Variables on
  Laboratory Diagnosis of Hepatitis C Infection

abstract:
  Pathology tests are central to modern healthcare in terms of diagnosis and
patient management. Aggregated pathology results provide opportunities for
research into fundamental and applied questions in health and medicine, but
data analytic challenges appear since test profiles vary between medical
practitioners, resulting in missing data. In this study we provide an
analytical investigation of the laboratory diagnosis of Hepatitis C (HCV)
infection and focus on how to maximize the predictive value of routine
pathology data. We recommend using the Influx - Outflux measures to help
construct the imputation model when using multiple imputation.
  Data from 14,320 community-patients aged 15 - 100 years were accessed via ACT
Pathology (The Canberra Hospital, Australia). Influx and Outflux were
calculated to identify which variables were potentially powerful predictors of
missing values. Available Case analysis and Multiple Imputation were used to
accommodate missing values in the dataset. Logistic regression model and
stepwise selection method were used for analysing the imputed datasets. The
predictive power of all methods was compared.
  The predictive power of the models on multiply imputed data was similar to
the power of the models based on complete data. The advantage of multiply
imputed data was that it allowed for the inclusion of all the completed
variables in the logistic models, thus identifying a broader selection of test
results that could lead to the enhanced laboratory prediction of HCV.
  Multiple imputation is an important statistical resource allowing all
individuals in a study to contribute whatever data they have supplied to the
analysis. MI in combination with the values of Influx and Outflux identifies
potential predictors of HepC infection. Variables age, gender and alanine
aminotransferase have been shown to be strong laboratory predictors of HCV
infection.


title:
A generalization of Hardy's operator and an asymptotic Muntz-Szasz
  Theorem

abstract:
  The Hardy operator has all the monomial functions as eigenvectors. We study
bounded operators on L^2 that take monomial functions to multiples of other
monomials, with a shifted exponent. We prove that they all leave the space of
functions vanishing on [0,s] invariant. We prove an asymptotic Muntz-Szasz
theorem, characterizing the set of functions that are limits of linear
combinations of monomials with exponents between n and 2n.


title: Monomial Operators

abstract:
  We study monomial operators on $ L^2[0,1]$, that is bounded linear operators
that map each monomial $x^n$ to a multiple of $x^{p_n}$ for some $p_n$. We show
that they are all unitarily equivalent to weighted composition operators on a
Hardy space. We characterize what sequences $p_n$ can arise. In the case that
$p_n$ is a fixed translation of $n$, we give a criterion for boundedness of the
operator.


title:
DeeptDCS: Deep Learning-Based Estimation of Currents Induced During
  Transcranial Direct Current Stimulation

abstract:
  Objective: Transcranial direct current stimulation (tDCS) is a non-invasive
brain stimulation technique used to generate conduction currents in the head
and disrupt brain functions. To rapidly evaluate the tDCS-induced current
density in near real-time, this paper proposes a deep learning-based emulator,
named DeeptDCS. Methods: The emulator leverages Attention U-net taking the
volume conductor models (VCMs) of head tissues as inputs and outputting the
three-dimensional current density distribution across the entire head. The
electrode configurations are also incorporated into VCMs without increasing the
number of input channels; this enables the straightforward incorporation of the
non-parametric features of electrodes (e.g., thickness, shape, size, and
position) in the training and testing of the proposed emulator. Results:
Attention U-net outperforms standard U-net and its other three variants
(Residual U-net, Attention Residual U-net, and Multi-scale Residual U-net) in
terms of accuracy. The generalization ability of DeeptDCS to non-trained
electrode positions can be greatly enhanced through fine-tuning the model. The
computational time required by one emulation via DeeptDCS is a fraction of a
second. Conclusion: DeeptDCS is at least two orders of magnitudes faster than a
physics-based open-source simulator, while providing satisfactorily accurate
results. Significance: The high computational efficiency permits the use of
DeeptDCS in applications requiring its repetitive execution, such as
uncertainty quantification and optimization studies of tDCS.


title: DEAR: A Novel Deep Learning-based Approach for Automated Program Repair

abstract:
  The existing deep learning (DL)-based automated program repair (APR) models
are limited in fixing general software defects. % We present {\tool}, a
DL-based approach that supports fixing for the general bugs that require
dependent changes at once to one or multiple consecutive statements in one or
multiple hunks of code. % We first design a novel fault localization (FL)
technique for multi-hunk, multi-statement fixes that combines traditional
spectrum-based (SB) FL with deep learning and data-flow analysis. It takes the
buggy statements returned by the SBFL model, detects the buggy hunks to be
fixed at once, and expands a buggy statement $s$ in a hunk to include other
suspicious statements around $s$. We design a two-tier, tree-based LSTM model
that incorporates cycle training and uses a divide-and-conquer strategy to
learn proper code transformations for fixing multiple statements in the
suitable fixing context consisting of surrounding subtrees. We conducted
several experiments to evaluate {\tool} on three datasets: Defects4J (395
bugs), BigFix (+26k bugs), and CPatMiner (+44k bugs). On Defects4J dataset,
{\tool} outperforms the baselines from 42\%--683\% in terms of the number of
auto-fixed bugs with only the top-1 patches. On BigFix dataset, it fixes
31--145 more bugs than existing DL-based APR models with the top-1 patches. On
CPatMiner dataset, among 667 fixed bugs, there are 169 (25.3\%)
multi-hunk/multi-statement bugs. {\tool} fixes 71 and 164 more bugs, including
52 and 61 more multi-hunk/multi-statement bugs, than the state-of-the-art,
DL-based APR models.


title:
TOI-2046b, TOI-1181b and TOI-1516b, three new hot Jupiters from
  \textit{TESS}: planets orbiting a young star, a subgiant and a normal star

abstract:
  We present the confirmation and characterization of three hot Jupiters,
TOI-1181b, TOI-1516b, and TOI-2046b, discovered by the TESS space mission. The
reported hot Jupiters have orbital periods between 1.4 and 2.05 days. The
masses of the three planets are $1.18\pm0.14$ M$_{\mathrm{J}}$, $3.16\pm0.12$\,
M$_{\mathrm{J}}$, and 2.30 $\pm 0.28$ M$_{\mathrm{J}}$, for TOI-1181b,
TOI-1516b, and TOI-2046b, respectively. The stellar host of TOI-1181b is a F9IV
star, whereas TOI-1516b and TOI-2046b orbit F main sequence host stars. The
ages of the first two systems are in the range of 2-5 Gyrs. However, TOI-2046
is among the few youngest known planetary systems hosting a hot Jupiter, with
an age estimate of 100-400 Myrs. The main instruments used for the radial
velocity follow-up of these three planets are located at Ond\v{r}ejov,
Tautenburg and McDonald Observatory, and all three are mounted on 2-3 meter
aperture telescopes, demonstrating that mid-aperture telescope networks can
play a substantial role in the follow-up of gas giants discovered by
\textit{TESS} and in the future by \textit{PLATO}.


title:
Convergence analysis of the Newton-Schur method for the symmetric
  elliptic eigenvalue problem

abstract:
  In this paper, we consider the Newton-Schur method in Hilbert space and
obtain quadratic convergence. For the symmetric elliptic eigenvalue problem
discretized by the standard finite element method and non-overlapping domain
decomposition method, we use the Steklov-Poincar\'e operator to reduce the
eigenvalue problem on the domain $\Omega$ into the nonlinear eigenvalue
subproblem on $\Gamma$, which is the union of subdomain boundaries. We prove
that the convergence rate for the Newton-Schur method is $\epsilon_{N}\leq
CH^{2}(1+\ln(H/h))^{2}\epsilon^{2}$, where the constant $C$ is independent of
the fine mesh size $h$ and coarse mesh size $H$, and $\epsilon_{N}$ and
$\epsilon$ are errors after and before one iteration step respectively.
Numerical experiments confirm our theoretical analysis.


title: The Hardy-Weyl algebra

abstract:
  We study the algebra $\mathcal{A}$ generated by the Hardy operator $H$ and
the operator $M_x$ of multiplication by $x$ on $L^2[0,1]$. We call
$\mathcal{A}$ the Hardy-Weyl algebra. We show that its quotient by the compact
operators is isomorphic to the algebra of functions that are continuous on
$\Lambda$ and analytic on the interior of $\Lambda$ for a planar set $\Lambda$
= $[-1,0] \cup \bar{ \mathbb{D}(1,1)}$, which we call the lollipop. We find a
Toeplitz-like short exact sequence for the $C^*$-algebra generated by
$\mathcal{A}$. We study the operator $Z = H - M_x$, show that its point
spectrum is $(-1,0] \cup \mathbb{D}(1,1)$, and that the eigenvalues grow in
multiplicity as the points move to $0$ from the left.


title: Provably Confidential Language Modelling

abstract:
  Large language models are shown to memorize privacy information such as
social security numbers in training data. Given the sheer scale of the training
corpus, it is challenging to screen and filter these privacy data, either
manually or automatically. In this paper, we propose Confidentially Redacted
Training (CRT), a method to train language generation models while protecting
the confidential segments. We borrow ideas from differential privacy (which
solves a related but distinct problem) and show that our method is able to
provably prevent unintended memorization by randomizing parts of the training
process. Moreover, we show that redaction with an approximately correct
screening policy amplifies the confidentiality guarantee. We implement the
method for both LSTM and GPT language models. Our experimental results show
that the models trained by CRT obtain almost the same perplexity while
preserving strong confidentiality.


title: Depletion-Driven Morphological Control of Bundled Actin Networks

abstract:
  Actin is known to undergo a phase transition from a single-filament state to
a bundled state by the addition of polyethylene glycol (PEG) molecules in
sufficient concentration. While the depletion interaction experienced by these
biopolymers is well-known, the effect of changing the molecular weight of the
depletant is less well understood. Here, we experimentally identify a phase
transition in solutions of actin from networks of filaments to networks of
bundles by varying the molecular weight of PEG polymers, while holding the
concentration of these PEG polymers constant. We examine the states straddling
the phase transition in terms of micro and macroscale properties. We find that
the mesh size, bundle diameter, and intra-bundle spacing between filaments
across the line of criticality do not show significant differences, while the
relaxation time, storage modulus, and degree of bundling change between the two
states.


title:
Improved Limit on Tensor Currents in the Weak Interaction from $^8$Li
  $\beta$ Decay

abstract:
  The electroweak interaction in the Standard Model (SM) is described by a pure
vector-axial-vector structure, though any Lorentz-invariant component could
contribute. In this work, we present the most precise measurement of tensor
currents in the low-energy regime by examining the $\beta$-$\bar{\nu}$
correlation of trapped $^{8}$Li ions with the Beta-decay Paul Trap. We find
$a_{\beta\nu} = -0.3325 \pm 0.0013_{stat} \pm 0.0019_{syst}$ at $1\sigma$ for
the case of coupling to right-handed neutrinos $(C_T=-C_T')$, which is
consistent with the SM prediction.


title: Correlated Read Noise Reduction in Infrared Arrays Using Deep Learning

abstract:
  We present a new procedure rooted in deep learning to construct science
images from data cubes collected by astronomical instruments using HxRG
detectors in low-flux regimes. It improves on the drawbacks of the conventional
algorithms to construct 2D images from multiple readouts by using the readout
scheme of the detectors to reduce the impact of correlated readout noise. We
train a convolutional recurrent neural network on simulated astrophysical
scenes added to laboratory darks to estimate the flux on each pixel of science
images. This method achieves a reduction of the noise on constructed science
images when compared to standard flux-measurement schemes (correlated double
sampling, up-the-ramp sampling), which results in a reduction of the error on
the spectrum extracted from these science images. Over simulated data cubes
created in a low signal-to-noise ratio regime where this method could have the
largest impact, we find that the error on our constructed science images falls
faster than a $1/\sqrt{N}$ decay, and that the spectrum extracted from the
images has, averaged over a test set of three images, a standard error reduced
by a factor of 1.85 in comparison to the standard up-the-ramp pixel sampling
scheme. The code used in this project is publicly available on GitHub


title: Soft Metamaterials: Adaptation and Intelligence

abstract:
  We provide a perspective on soft metamaterials and biomatter with a special
emphasis on future research directions at the interface between material
intelligence and adaptation.


title:
Hazard Exposure Heterophily: A Latent Characteristic in Socio-Spatial
  Networks Influencing Community Resilience

abstract:
  We present a latent characteristic in socio-spatial networks, hazard-exposure
heterophily, to capture the extent to which populations with similar hazard
exposure could assist each other through social ties. Heterophily is the
tendency of unlike individuals to form social ties. Conversely, populations in
spatial areas with significant hazard exposure similarity, homophily, would
lack sufficient resourcefulness to aid each other to lessen the impact of
hazards. In the context of the Houston metropolitan area, we use Meta's Social
Connectedness data to construct a socio-spatial network in juxtaposition with
flood exposure data from National Flood Hazard Layer to analyze flood hazard
exposure of spatial areas. The results reveal the extent and spatial variation
of hazard-exposure heterophily in the study area. Notably, the results show
that lower-income areas have lower hazard-exposure heterophily possibly caused
by income segregation and the tendency of affordable housing development to be
located in flood zones. Less resourceful social ties due to high
hazard-exposure homophily may inhibit low-income areas from better coping with
hazard impacts and could contribute to their slower recovery. Overall, the
results underscore the significance of characterizing hazard-exposure
heterophily in socio-spatial networks to reveal community vulnerability and
resilience to hazards.


title: The College Application Problem

abstract:
  This paper considers the maximization of the expected maximum value of a
portfolio of random variables subject to a budget constraint. We refer to this
as the optimal college application problem. When each variable's cost, or each
college's application fee, is identical, we show that the optimal portfolios
are nested in the budget constraint, yielding an exact polynomial-time
algorithm. When colleges differ in their application fees, we show that the
problem is NP-complete. We provide four algorithms for this more general setup:
a branch-and-bound routine, a dynamic program that produces an exact solution
in pseudopolynomial time, a different dynamic program that yields a fully
polynomial-time approximation scheme, and a simulated-annealing heuristic.
Numerical experiments demonstrate the algorithms' accuracy and efficiency.


title:
Mathematical models and time-frequency heat maps for surface gravity
  waves generated by thin ships

abstract:
  Recent research suggests that studying the time-frequency response of ship
wave signals has potential to shed light on a range of applications, such as
inferring the dynamical and geometric properties of a moving vessel based on
the surface elevation data detected at a single point in space. We continue
this line of research here with a study of mathematical models for thin ships
using standard Wigley hulls and Wigley transom-stern hulls as examples.
Mathematical models of varying sophistication are considered. These include
basic minimal models which use applied pressure distributions as proxies for
the ship hull. The more complicated models are Michell's thin ship theory and
the Hogner model, both of which explicitly take into account the shape of the
hull. We outline a methodology for carefully choosing the form and parameter
values in the minimal models such that they reproduce the key features of the
more complicated models in the time-frequency domain. For example, we find that
a two-pressure model is capable of producing wave elevation signals that have a
similar time-frequency profile as that for Michell's thin ship theory applied
to the Wigley hull, including the crucially important features caused by
interference between waves created at the bow and stern of the ship. One of the
key tools in our analysis is the spectrogram, which is a heat-map visualisation
in the time-frequency domain. Our work here extends the existing knowledge on
the topic of spectrograms of ship waves. The theoretical results in this study
are supported by experimental data collected in a towing tank at the Australian
Maritime College using model versions of the standard Wigley hulls and Wigley
transom-stern hulls.


title:
UCL-Dehaze: Towards Real-world Image Dehazing via Unsupervised
  Contrastive Learning

abstract:
  While the wisdom of training an image dehazing model on synthetic hazy data
can alleviate the difficulty of collecting real-world hazy/clean image pairs,
it brings the well-known domain shift problem. From a different yet new
perspective, this paper explores contrastive learning with an adversarial
training effort to leverage unpaired real-world hazy and clean images, thus
bridging the gap between synthetic and real-world haze is avoided. We propose
an effective unsupervised contrastive learning paradigm for image dehazing,
dubbed UCL-Dehaze. Unpaired real-world clean and hazy images are easily
captured, and will serve as the important positive and negative samples
respectively when training our UCL-Dehaze network. To train the network more
effectively, we formulate a new self-contrastive perceptual loss function,
which encourages the restored images to approach the positive samples and keep
away from the negative samples in the embedding space. Besides the overall
network architecture of UCL-Dehaze, adversarial training is utilized to align
the distributions between the positive samples and the dehazed images. Compared
with recent image dehazing works, UCL-Dehaze does not require paired data
during training and utilizes unpaired positive/negative data to better enhance
the dehazing performance. We conduct comprehensive experiments to evaluate our
UCL-Dehaze and demonstrate its superiority over the state-of-the-arts, even
only 1,800 unpaired real-world images are used to train our network. Source
code has been available at https://github.com/yz-wang/UCL-Dehaze.


title: A smectic liquid crystal model in the periodic setting

abstract:
  We consider the asymptotic behavior as $\varepsilon $ goes to zero of the 2D
smectics model in the periodic setting given by \begin{equation*}
\mathcal{E}_{\varepsilon }( w) =\frac{1}{2}\int_{\mathbb{T}^{2}}\frac{1}{
\varepsilon }\left( \left\vert \partial_{1}\right\vert ^{-1}\left(
\partial_{2}w-\partial_{1}\frac{1}{2}w^{2}\right) \right) ^{2}+\varepsilon
\left( \partial_{1}w\right) ^{2}dx . \end{equation*} We show that the energy
$\mathcal{E}_\varepsilon(w)$ controls suitable $L^p$ and Besov norms of $w$ and
use this to demonstrate the existence of minimizers for
$\mathcal{E}_\varepsilon(w)$, which has not been proved for this smectics model
before, and compactness in $L^p$ for an energy-bounded sequence. We also prove
an asymptotic lower bound for $\mathcal{E}_\varepsilon(w)$ as $\varepsilon \to
0$ by means of an entropy argument.


title:
Wasserstein Adversarial Learning based Temporal Knowledge Graph
  Embedding

abstract:
  Research on knowledge graph embedding (KGE) has emerged as an active field in
which most existing KGE approaches mainly focus on static structural data and
ignore the influence of temporal variation involved in time-aware triples. In
order to deal with this issue, several temporal knowledge graph embedding
(TKGE) approaches have been proposed to integrate temporal and structural
information in recent years. However, these methods only employ a uniformly
random sampling to construct negative facts. As a consequence, the corrupted
samples are often too simplistic for training an effective model. In this
paper, we propose a new temporal knowledge graph embedding framework by
introducing adversarial learning to further refine the performance of
traditional TKGE models. In our framework, a generator is utilized to construct
high-quality plausible quadruples and a discriminator learns to obtain the
embeddings of entities and relations based on both positive and negative
samples. Meanwhile, we also apply a Gumbel-Softmax relaxation and the
Wasserstein distance to prevent vanishing gradient problems on discrete data;
an inherent flaw in traditional generative adversarial networks. Through
comprehensive experimentation on temporal datasets, the results indicate that
our proposed framework can attain significant improvements based on benchmark
models and also demonstrate the effectiveness and applicability of our
framework.


title: Joint Image Compression and Denoising via Latent-Space Scalability

abstract:
  When it comes to image compression in digital cameras, denoising is
traditionally performed prior to compression. However, there are applications
where image noise may be necessary to demonstrate the trustworthiness of the
image, such as court evidence and image forensics. This means that noise itself
needs to be coded, in addition to the clean image itself. In this paper, we
present a learnt image compression framework where image denoising and
compression are performed jointly. The latent space of the image codec is
organized in a scalable manner such that the clean image can be decoded from a
subset of the latent space at a lower rate, while the noisy image is decoded
from the full latent space at a higher rate. The proposed codec is compared
against established compression and denoising benchmarks, and the experiments
reveal considerable bitrate savings of up to 80% compared to cascade
compression and denoising.


title:
Machine Learning based Framework for Robust Price-Sensitivity Estimation
  with Application to Airline Pricing

abstract:
  We consider the problem of dynamic pricing of a product in the presence of
feature-dependent price sensitivity. Based on the Poisson semi-parametric
approach, we construct a flexible yet interpretable demand model where the
price related part is parametric while the remaining (nuisance) part of the
model is non-parametric and can be modeled via sophisticated ML techniques. The
estimation of price-sensitivity parameters of this model via direct one-stage
regression techniques may lead to biased estimates. We propose a two-stage
estimation methodology which makes the estimation of the price-sensitivity
parameters robust to biases in the nuisance parameters of the model. In the
first-stage we construct the estimators of observed purchases and price given
the feature vector using sophisticated ML estimators like deep neural networks.
Utilizing the estimators from the first-stage, in the second-stage we leverage
a Bayesian dynamic generalized linear model to estimate the price-sensitivity
parameters. We test the performance of the proposed estimation schemes on
simulated and real sales transaction data from Airline industry. Our numerical
studies demonstrate that the two-stage approach provides more accurate
estimates of price-sensitivity parameters as compared to direct one-stage
approach.


title:
fairlib: A Unified Framework for Assessing and Improving Classification
  Fairness

abstract:
  This paper presents fairlib, an open-source framework for assessing and
improving classification fairness. It provides a systematic framework for
quickly reproducing existing baseline models, developing new methods,
evaluating models with different metrics, and visualizing their results. Its
modularity and extensibility enable the framework to be used for diverse types
of inputs, including natural language, images, and audio. In detail, we
implement 14 debiasing methods, including pre-processing, at-training-time, and
post-processing approaches. The built-in metrics cover the most commonly used
fairness criterion and can be further generalized and customized for fairness
evaluation.


title:
Quantum dialogue without information leakage based on the entanglement
  swapping between any two Bell states and the shared secret Bell state

abstract:
  In order to avoid the risk of information leakage during the information
mutual transmission between two authorized participants, i.e., Alice and Bob, a
quantum dialogue protocol based on the entanglement swapping between any two
Bell states and the shared secret Bell state is proposed. The proposed protocol
integrates the ideas of block transmission, two-step transmission and unitary
operation encoding together using the Bell states as the information carriers.
Besides the entanglement swapping between any two Bell states, a shared secret
Bell state is also used to overcome the information leakage problem, which not
only makes Bob aware of the prepared initial state but also is used for Bob's
encoding and entanglement swapping. Security analysis shows that the proposed
protocol can resist the general active attacks from an outside eavesdropper
Eve. Moreover, the relation between the maximal amount of information Eve can
gain and the detection probability is derived.


title:
Exploring Entity Interactions for Few-Shot Relation Learning (Student
  Abstract)

abstract:
  Few-shot relation learning refers to infer facts for relations with a limited
number of observed triples. Existing metric-learning methods for this problem
mostly neglect entity interactions within and between triples. In this paper,
we explore this kind of fine-grained semantic meanings and propose our model
TransAM. Specifically, we serialize reference entities and query entities into
sequence and apply transformer structure with local-global attention to capture
both intra- and inter-triple entity interactions. Experiments on two public
benchmark datasets NELL-One and Wiki-One with 1-shot setting prove the
effectiveness of TransAM.


title:
A Nonlinear Car-following Controller Design Inspired By Human-driving
  Behaviors to Increase Comfort and Enhance Safety

abstract:
  This paper investigates the car-following problem and proposes a nonlinear
controller that considers driving comfort, safety concerns, steady-state
response and transient response. This controller is designed based on the
demands of lower cost, faster response, increased comfort, enhanced safety and
elevated extendability from the automotive industry. Design insights and
intuitions are provided in detail. Also, theoretical analysis are performed on
plant stability, string stability and tracking performance of the closed-loop
system. Conditions and guidelines are provided on the selection of control
parameters. Comprehensive simulations are conducted to demonstrate the efficacy
of the proposed controller in different driving scenarios.


title:
Surface states induced weak anti-localization effect in Bi0.85Sb0.15
  topological single crystal

abstract:
  We report, an experimental evidence of surface states (SS) driven
magneto-transport in a Bi0.85Sb0.15 single crystal. Detailed high field (up to
12T) and low temperature (down to 2K) magneto-transport measurements are been
carried out on the studied Bi0.85Sb0.15 single crystal. The phase, composition
and Raman modes are studied through X-ray diffraction, Energy dispersive X-ray,
and Raman spectroscopy. The obtained crystal shows non-saturating
magnetoresistance (4250%) at 2K and 12T, along with the existence of weak-anti
localization (WAL) effect at around zero magnetic field. Further, the
Hikami-Larkin-Nagaoka (HLN) analysis is performed to analyse the WAL effect.
The prefactor and phase coherence length are deduced at various temperatures,
which signified the presence of more than one conduction channel in the studied
Bi0.85Sb0.15 single crystal. The effect of quantum scattering, bulk
contribution from underneath the surface states and defects are been studied by
adding various field dependent quadratic, linear and constant terms to the SS
driven HLN equation. Various possible scattering mechanism are studied by
analysing the temperature dependence of the phase coherence length. Angle
dependent magneto-conductivity of the studied Bi0.85Sb0.15 single crystal
clearly confirmed the surface states dominated transport in present crystal.


title: Quantum secure dialogue with quantum encryption

abstract:
  How to solve the information leakage problem has become the research focus of
quantum dialogue. In this paper, in order to overcome the information leakage
problem in quantum dialogue, a novel approach for sharing the initial quantum
state privately between communicators, i.e., quantum encryption sharing, is
proposed by utilizing the idea of quantum encryption. The proposed protocol
uses EPR pairs as the private quantum key to encrypt and decrypt the traveling
photons, which can be repeatedly used after rotation. Due to quantum encryption
sharing, the public announcement on the state of the initial quantum state is
omitted, thus the information leakage problem is overcome. The
information-theoretical efficiency of the proposed protocol is nearly 100%,
much higher than previous information leakage resistant quantum dialogue
protocols. Moreover, the proposed protocol only needs single-photon
measurements and nearly uses single photons as quantum resource so that it is
convenient to implement in practice.


title: Approximating Choice Data by Discrete Choice Models

abstract:
  We obtain a necessary and sufficient condition under which parametric
random-coefficient discrete choice models can approximate the choice behavior
generated by nonparametric random utility models. The condition turns out to be
very simple and tractable. For the case under which the condition is not
satisfied (and hence, where some stochastic choice data are generated by a
random utility model that cannot be approximated), we provide algorithms to
measure the approximation errors. After applying our theoretical results and
the algorithm to real data, we found that the approximation errors can be large
in practice.


title: All You May Need for VQA are Image Captions

abstract:
  Visual Question Answering (VQA) has benefited from increasingly sophisticated
models, but has not enjoyed the same level of engagement in terms of data
creation. In this paper, we propose a method that automatically derives VQA
examples at volume, by leveraging the abundance of existing image-caption
annotations combined with neural models for textual question generation. We
show that the resulting data is of high-quality. VQA models trained on our data
improve state-of-the-art zero-shot accuracy by double digits and achieve a
level of robustness that lacks in the same model trained on human-annotated VQA
data.


title: A deep domain decomposition method based on Fourier features

abstract:
  In this paper we present a Fourier feature based deep domain decomposition
method (F-D3M) for partial differential equations (PDEs). Currently, deep
neural network based methods are actively developed for solving PDEs, but their
efficiency can degenerate for problems with high frequency modes. In this new
F-D3M strategy, overlapping domain decomposition is conducted for the spatial
domain, such that high frequency modes can be reduced to relatively low
frequency ones. In each local subdomain, multi Fourier feature networks
(MFFNets) are constructed, where efficient boundary and interface treatments
are applied for the corresponding loss functions. We present a general
mathematical framework of F-D3M, validate its accuracy and demonstrate its
efficiency with numerical experiments.


title: Holographic Schwinger effect and electric instability with anisotropy

abstract:
  According to the gauge-gravity duality, we systematically study the Schwinger
effect and electric instability with anisotropy in a top-down approach. The
anisotropic black brane and bubble (soliton) background in IIB supergravity are
employed and the dual theory in these backgrounds is expected to be anisotropic
theory at finite temperature and anisotropic theory with confinement
respectively. Then performing the potential analysis, we find due to the
anisotropy, the potential barrier behaves oppositely with parallel and
perpendicular electric fields, and this behavior agrees with the previous study
about the quark potential with anisotropy in this system. Afterwards, we
evaluate the pair production rate by solving the equation of motion for a
fundamental string numerically which reveals the consistent behavior with the
potential analysis. Furthermore, the probe D7-brane as flavor is introduced
into the bulk in order to investigate the electric instability. The vacuum
decay rate can be obtained by evaluating the imaginary part of the D7-brane
action which again agrees with our potential analysis. Solving the associated
constraint of gauge field strength on the flavor brane, we finally obtain the
V-A curve which displays that the behavior of the conductivity is distinct in
parallel and perpendicular direction which is in agreement with
phenomenologically holographic approaches in anisotropic fluid. Accordingly,
this work may be remarkable to study the electric features in strongly coupled
anisotropic system.


title:
P^3 Ranker: Mitigating the Gaps between Pre-training and Ranking
  Fine-tuning with Prompt-based Learning and Pre-finetuning

abstract:
  Compared to other language tasks, applying pre-trained language models (PLMs)
for search ranking often requires more nuances and training signals. In this
paper, we identify and study the two mismatches between pre-training and
ranking fine-tuning: the training schema gap regarding the differences in
training objectives and model architectures, and the task knowledge gap
considering the discrepancy between the knowledge needed in ranking and that
learned during pre-training. To mitigate these gaps, we propose Pre-trained,
Prompt-learned and Pre-finetuned Neural Ranker (P^3 Ranker). P^3 Ranker
leverages prompt-based learning to convert the ranking task into a pre-training
like schema and uses pre-finetuning to initialize the model on intermediate
supervised tasks. Experiments on MS MARCO and Robust04 show the superior
performances of P^3 Ranker in few-shot ranking. Analyses reveal that P^3 Ranker
is able to better accustom to the ranking task through prompt-based learning
and retrieve necessary ranking-oriented knowledge gleaned in pre-finetuning,
resulting in data-efficient PLM adaptation. Our code is available at
https://github.com/NEUIR/P3Ranker.


title:
Uncertainty estimation of pedestrian future trajectory using Bayesian
  approximation

abstract:
  Past research on pedestrian trajectory forecasting mainly focused on
deterministic predictions which provide only point estimates of future states.
These future estimates can help an autonomous vehicle plan its trajectory and
avoid collision. However, under dynamic traffic scenarios, planning based on
deterministic predictions is not trustworthy. Rather, estimating the
uncertainty associated with the predicted states with a certain level of
confidence can lead to robust path planning. Hence, the authors propose to
quantify this uncertainty during forecasting using stochastic approximation
which deterministic approaches fail to capture. The current method is simple
and applies Bayesian approximation during inference to standard neural network
architectures for estimating uncertainty. The authors compared the predictions
between the probabilistic neural network (NN) models with the standard
deterministic models. The results indicate that the mean predicted path of
probabilistic models was closer to the ground truth when compared with the
deterministic prediction. Further, the effect of stochastic dropout of weights
and long-term prediction on future state uncertainty has been studied. It was
found that the probabilistic models produced better performance metrics like
average displacement error (ADE) and final displacement error (FDE). Finally,
the study has been extended to multiple datasets providing a comprehensive
comparison for each model.


title:
The Mazur-Ulam property for a Banach space which satisfies a separation
  condition

abstract:
  We study $C$-rich spaces, lush spaces, and $C$-extremely regular spaces
concerning with the Mazur-Ulam property. We show that a uniform algebra and the
real part of a uniform algebra with the supremum norm are $C$-rich spaces,
hence lush spaces. We prove that a uniformly closed subalgebra of the algebra
of complex-valued continuous functions on a locally compact Hausdorff space
which vanish at infinity is $C$-extremely regular provided that it separates
the points of the underlying space and has no common zeros. In section 3 we
exhibit descriptions on the Choquet bounday, the \vSilov bounday, the strong
boundary points. We also recall the definition that a function space strongly
separates the points in the underlying space. We need to avoid the confusion
which appears because of the variety of names of these concepts; they sometimes
differs from authors to authors. After some preparation, we study the
Mazur-Ulam property in sections 4 through 6. We exhibit a sufficient condition
on a Banach space which has the Mazur-Ulam property and the complex Mazur-Ulam
property. In section 5 we consider a Banach space with a separation condition
$(*)$ (Definition 5.1). We prove that a real Banach space satisfying $(*)$ has
the Mazur-Ulam propety (Theorem 6.1), and a complex Banach space satisfying
$(*)$ has the complex Mazur-Ulam property (Theorem 6.3). Applying the results
in the previous sections we prove that an extremely $C$-regular complex linear
subspace has the complex Mazur-Ulam property (Corollary 6.4) in section 6. As a
consequence we prove that any closed subalgebra of the algebra of all
complex-valued continuous functions defined on a locally compact Hausdorff
space has the complex Mazur-Ulam property (Corollary 6.5).


title:
Improving Multi-Document Summarization through Referenced Flexible
  Extraction with Credit-Awareness

abstract:
  A notable challenge in Multi-Document Summarization (MDS) is the
extremely-long length of the input. In this paper, we present an
extract-then-abstract Transformer framework to overcome the problem.
Specifically, we leverage pre-trained language models to construct a
hierarchical extractor for salient sentence selection across documents and an
abstractor for rewriting the selected contents as summaries. However, learning
such a framework is challenging since the optimal contents for the abstractor
are generally unknown. Previous works typically create pseudo extraction oracle
to enable the supervised learning for both the extractor and the abstractor.
Nevertheless, we argue that the performance of such methods could be restricted
due to the insufficient information for prediction and inconsistent objectives
between training and testing. To this end, we propose a loss weighting
mechanism that makes the model aware of the unequal importance for the
sentences not in the pseudo extraction oracle, and leverage the fine-tuned
abstractor to generate summary references as auxiliary signals for learning the
extractor. Moreover, we propose a reinforcement learning method that can
efficiently apply to the extractor for harmonizing the optimization between
training and testing. Experiment results show that our framework substantially
outperforms strong baselines with comparable model sizes and achieves the best
results on the Multi-News, Multi-XScience, and WikiCatSum corpora.


title:
Correlation-induced magnetism in substrate-supported 2D metal-organic
  frameworks

abstract:
  Two-dimensional (2D) metal-organic frameworks (MOFs) in a kagome lattice can
exhibit strong electron-electron interactions, which can lead to tunable
quantum phases including many exotic magnetic phases. While technological
developments of 2D MOFs typically take advantage of substrates for growth,
support, and electrical contacts, investigations often ignore substrates and
their dramatic influence on electronic properties of MOFs. Here, we show how
substrates alter the correlated magnetic phases in kagome MOFs using systematic
density functional theory and mean-field Hubbard calculations. We demonstrate
that MOF-substrate coupling, MOF-substrate charge transfer, strain, and
external electric fields are key variables, activating and deactivating
magnetic phases in these materials. While we consider the example of
kagome-arranged 9,10-dicyanoanthracene molecules coordinated with copper atoms,
our findings should generalise to any kagome lattice. This work offers useful
predictions for tunable interaction-induced magnetism in surface-supported 2D
organic materials, opening the door to solid-state electronic and spintronic
technologies based on such systems.


title: Magnon Nernst Effect in Magnon Spin Hall Systems

abstract:
  Magnon spin Hall systems could hardly show experimentally observable particle
and thermal transport phenomena intrinsically due to the spin cancellation.
Here we demonstrated that the magnon spin Hall systems can exhibit magnon
Nernst effect and thermal Hall effect under external magnetic field by
considering two typical systems, i.e. the antiferromagnetically (AFM) coupled
bilayer honeycomb ferromagnets and monolayer collinear honeycomb
antiferromagnet. The both systems experience magnetic phase transitions from
AFM phase to a field-polarized phase via a spin-flop (SF) phase or directly. In
both systems, there exist magnon Nerst effect and also thermal Hall effect
under a longitudinal temperature gradient, which can be regarded as the
indicator of the magnetic phase transitions, with the Hall conductivity
dependence on magnetic field consistent with the order of magnetic phase
transition.


title:
Unsupervised Domain Adaptation Learning for Hierarchical Infant Pose
  Recognition with Synthetic Data

abstract:
  The Alberta Infant Motor Scale (AIMS) is a well-known assessment scheme that
evaluates the gross motor development of infants by recording the number of
specific poses achieved. With the aid of the image-based pose recognition
model, the AIMS evaluation procedure can be shortened and automated, providing
early diagnosis or indicator of potential developmental disorder. Due to
limited public infant-related datasets, many works use the SMIL-based method to
generate synthetic infant images for training. However, this domain mismatch
between real and synthetic training samples often leads to performance
degradation during inference. In this paper, we present a CNN-based model which
takes any infant image as input and predicts the coarse and fine-level pose
labels. The model consists of an image branch and a pose branch, which
respectively generates the coarse-level logits facilitated by the unsupervised
domain adaptation and the 3D keypoints using the HRNet with SMPLify
optimization. Then the outputs of these branches will be sent into the
hierarchical pose recognition module to estimate the fine-level pose labels. We
also collect and label a new AIMS dataset, which contains 750 real and 4000
synthetic infants images with AIMS pose labels. Our experimental results show
that the proposed method can significantly align the distribution of synthetic
and real-world datasets, thus achieving accurate performance on fine-grained
infant pose recognition.


title:
Crystal Twins: Self-supervised Learning for Crystalline Material
  Property Prediction

abstract:
  Machine learning (ML) models have been widely successful in the prediction of
material properties. However, large labeled datasets required for training
accurate ML models are elusive and computationally expensive to generate.
Recent advances in Self-Supervised Learning (SSL) frameworks capable of
training ML models on unlabeled data have mitigated this problem and
demonstrated superior performance in computer vision and natural language
processing tasks. Drawing inspiration from the developments in SSL, we
introduce Crystal Twins (CT): an SSL method for crystalline materials property
prediction. Using a large unlabeled dataset, we pre-train a Graph Neural
Network (GNN) by applying the redundancy reduction principle to the graph
latent embeddings of augmented instances obtained from the same crystalline
system. By sharing the pre-trained weights when fine-tuning the GNN for
regression tasks, we significantly improve the performance for 7 challenging
material property prediction benchmarks


title:
Results on bar-core partitions, core shifted Young diagrams, and doubled
  distinct cores

abstract:
  Simultaneous bar-cores, core shifted Young diagrams (or CSYDs), and doubled
distinct cores have been studied since Morris and Yaseen introduced the concept
of bar-cores. In this paper, our goal is to give a formula for the number of
these core partitions on $(s,t)$-cores and $(s,s+d,s+2d)$-cores for the
remaining cases that are not covered yet. In order to achieve this goal, we
observe a characterization of $\bar{s}$-core partitions to obtain
characterizations of doubled distinct $s$-core partitions and $s$-CSYDs. By
using them, we construct $NE$ lattice path interpretations of these core
partitions on $(s,t)$-cores. Also, we give free Motzkin path interpretations of
these core partitions on $(s,s+d,s+2d)$-cores.


title: Study of the $tH$ production at 14TeV and 100TeV $pp$ colliders

abstract:
  The flavor-changing neutral current interactions in the standard model are
suppressed seriously and such interactions can be used to search the new
physics beyond SM. The top quark and Higgs bosons are heavier than the other
particles in SM, we can expect the new physics plays a more important role in
their interactions. In this work, we study the flavor-changing neutral current
interactions between the top quark $\bar{t}q H$ through the production of the
single top associated with a Higgs boson on 14TeV and 100TeV $pp$ colliders. We
consider the leptonic decay channels of Higgs and study the signal. We find a
sensitive region $0.4\leq\Delta R\leq1.4$ for the leptons from Higgs bosons and
$\Delta R\geq1.8$ between the jets from top quark and leptons. We investigate
the detective abilities of the hadron colliders for the processes. Although
this process seems not comparable with $pp\to t\bar{t}$ with $t\to H q$ decay,
it is still attractive since this process can be used to distinguish the
$y_{tu}$ and $y_{tc}$.


title:
An Online Generalized Multiscale finite element method for heat and mass
  transfer problem with artificial ground freezing

abstract:
  In this paper, we present an Online Generalized Multiscale Finite Element
Method(Online GMsFEM) for heat and mass transfer problem in heterogeneous media
with artificial ground freezing pipes. The mathematical model of the process is
based on the classical Stefan model, which describes heat transfer with a phase
transition and takes into account filtration in a porous medium. The model is
described by a system of equations for temperature and pressure. For fine grid
solution, we use a finite element method using the fictitious domain method. To
derive a solution on the coarse grid, we use a model reduction procedure based
on Online GMsFEM. Online version of GMsFEM allows to us to take less number of
offline multiscale basis functions. In our approach, we use decoupled offline
basis functions constructed with snapshot space and based on spectral problems.
This is the standard approach of basis construction. To take into account
artificial ground freezing pipes, we compute an additional basis functions on
the offline stage. For the accurate approximation of phase change we add online
multiscale basis functions. We construct online basis that minimizes error by
values of local residuals. Online procedure is significantly improves the
accuracy of standard GMsFEM. We present numerical results in two-dimensional
domain with layered heterogeneity. To investigate accuracy of the method, we
present results with different number of offline and online basis functions.
The presented results show that Online GMsFEM can produce solution with high
accuracy and requires small computational resources.


title:
Virtual Analog Modeling of Distortion Circuits Using Neural Ordinary
  Differential Equations

abstract:
  Recent research in deep learning has shown that neural networks can learn
differential equations governing dynamical systems. In this paper, we adapt
this concept to Virtual Analog (VA) modeling to learn the ordinary differential
equations (ODEs) governing the first-order and the second-order diode clipper.
The proposed models achieve performance comparable to state-of-the-art
recurrent neural networks (RNNs) albeit using fewer parameters. We show that
this approach does not require oversampling and allows to increase the sampling
rate after the training has completed, which results in increased accuracy.
Using a sophisticated numerical solver allows to increase the accuracy at the
cost of slower processing. ODEs learned this way do not require closed forms
but are still physically interpretable.


title:
Go Back in Time: Generating Flashbacks in Stories with Event Temporal
  Prompts

abstract:
  Stories or narratives are comprised of a sequence of events. To compose
interesting stories, professional writers often leverage a creative writing
technique called flashback that inserts past events into current storylines as
we commonly observe in novels and plays. However, it is challenging for
machines to generate flashback as it requires a solid understanding of event
temporal order (e.g. "feeling hungry" before "eat," not vice versa), and the
creativity to arrange storylines so that earlier events do not always appear
first in narrative order. Two major issues in existing systems that exacerbate
the challenges: 1) temporal bias in pertaining and story datasets that leads to
monotonic event temporal orders; 2) lack of explicit guidance that helps
machines decide where to insert flashbacks. We propose to address these issues
using structured storylines to encode events and their pair-wise temporal
relations (before, after and vague) as temporal prompts that guide how stories
should unfold temporally. We leverage a Plan-and-Write framework enhanced by
reinforcement learning to generate storylines and stories end-to-end.
Evaluation results show that the proposed method can generate more interesting
stories with flashbacks while maintaining textual diversity, fluency, and
temporal coherence.


title: A Tool For Debugging Quantum Circuits

abstract:
  As the scale of quantum programs grows to match that of classical software,
the nascent field of quantum software engineering must mature and tools such as
debuggers will become increasingly important. However, developing a quantum
debugger is challenging due to the nature of a quantum computer; sneaking a
peek at the value of a quantum state will cause either partial or complete
collapse of the superposition and may destroy the necessary entanglement. As a
first step to developing a full quantum circuit debugger, we have designed and
implemented a quantum circuit debugging tool. The tool allows the user to
divide the circuit vertically or horizontally into smaller chunks known as
slices, and manage their simulation or execution for either interactive
debugging or automated testing. The tool also enables developers to track gates
within the overall circuit and each chunk to understand their behavior better.
Feedback on usefulness and usability from early users shows that using the tool
to slice and test their circuits has helped make the debugging process more
time-efficient for them.


title:
Resonant enhancement of particle emission from a parametrically driven
  condensate in a one-dimensional lattice

abstract:
  Motivated by recent experiments, we investigate particle emission from a
Bose-Einstein condensate in a one-dimensional lattice, where the interaction
strength is periodically modulated. The modulated interactions parametrically
excite a collective mode, leading to density oscillations. These collective
oscillations in turn drive particle emission. This multi-step process amplifies
the drive, producing larger particle jets. We find that the amplitude
dependence of the emission rate has a characteristic threshold behavior, as
seen in experiments.


title: Spatial-Temporal Meta-path Guided Explainable Crime Prediction

abstract:
  Exposure to crime and violence can harm individuals' quality of life and the
economic growth of communities. In light of the rapid development in machine
learning, there is a rise in the need to explore automated solutions to prevent
crimes. With the increasing availability of both fine-grained urban and public
service data, there is a recent surge in fusing such cross-domain information
to facilitate crime prediction. By capturing the information about social
structure, environment, and crime trends, existing machine learning predictive
models have explored the dynamic crime patterns from different views. However,
these approaches mostly convert such multi-source knowledge into implicit and
latent representations (e.g., learned embeddings of districts), making it still
a challenge to investigate the impacts of explicit factors for the occurrences
of crimes behind the scenes. In this paper, we present a Spatial-Temporal
Metapath guided Explainable Crime prediction (STMEC) framework to capture
dynamic patterns of crime behaviours and explicitly characterize how the
environmental and social factors mutually interact to produce the forecasts.
Extensive experiments show the superiority of STMEC compared with other
advanced spatiotemporal models, especially in predicting felonies (e.g.,
robberies and assaults with dangerous weapons).


title: Pik-Fix: Restoring and Colorizing Old Photo

abstract:
  Restoring and inpainting the visual memories that are present, but often
impaired, in old photos remains an intriguing but unsolved research topic.
Decades-old photos often suffer from severe and commingled degradation such as
cracks, defocus, and color-fading, which are difficult to treat individually
and harder to repair when they interact. Deep learning presents a plausible
avenue, but the lack of large-scale datasets of old photos makes addressing
this restoration task very challenging. Here we present a novel reference-based
end-to-end learning framework that is able to both repair and colorize old and
degraded pictures. Our proposed framework consists of three modules: a
restoration sub-network that conducts restoration from degradations, a
similarity sub-network that performs color histogram matching and color
transfer, and a colorization subnet that learns to predict the chroma elements
of images that have been conditioned on chromatic reference signals. The
overall system makes uses of color histogram priors from reference images,
which greatly reduces the need for large-scale training data. We have also
created a first-of-a-kind public dataset of real old photos that are paired
with ground truth "pristine" photos that have been that have been manually
restored by PhotoShop experts. We conducted extensive experiments on this
dataset and synthetic datasets, and found that our method significantly
outperforms previous state-of-the-art models using both qualitative comparisons
and quantitative measurements.


title: Self-Taught Metric Learning without Labels

abstract:
  We present a novel self-taught framework for unsupervised metric learning,
which alternates between predicting class-equivalence relations between data
through a moving average of an embedding model and learning the model with the
predicted relations as pseudo labels. At the heart of our framework lies an
algorithm that investigates contexts of data on the embedding space to predict
their class-equivalence relations as pseudo labels. The algorithm enables
efficient end-to-end training since it demands no off-the-shelf module for
pseudo labeling. Also, the class-equivalence relations provide rich supervisory
signals for learning an embedding space. On standard benchmarks for metric
learning, it clearly outperforms existing unsupervised learning methods and
sometimes even beats supervised learning models using the same backbone
network. It is also applied to semi-supervised metric learning as a way of
exploiting additional unlabeled data, and achieves the state of the art by
boosting performance of supervised learning substantially.


title:
ImAiR : Airwriting Recognition framework using Image Representation of
  IMU Signals

abstract:
  The problem of Airwriting Recognition is focused on identifying letters
written by movement of finger in free space. It is a type of gesture
recognition where the dictionary corresponds to letters in a specific language.
In particular, airwriting recognition using sensor data from wrist-worn devices
can be used as a medium of user input for applications in Human-Computer
Interaction (HCI). Recognition of in-air trajectories using such wrist-worn
devices is limited in literature and forms the basis of the current work. In
this paper, we propose an airwriting recognition framework by first encoding
the time-series data obtained from a wearable Inertial Measurement Unit (IMU)
on the wrist as images and then utilizing deep learning-based models for
identifying the written alphabets. The signals recorded from 3-axis
accelerometer and gyroscope in IMU are encoded as images using different
techniques such as Self Similarity Matrix (SSM), Gramian Angular Field (GAF)
and Markov Transition Field (MTF) to form two sets of 3-channel images. These
are then fed to two separate classification models and letter prediction is
made based on an average of the class conditional probabilities obtained from
the two models. Several standard model architectures for image classification
such as variants of ResNet, DenseNet, VGGNet, AlexNet and GoogleNet have been
utilized. Experiments performed on two publicly available datasets demonstrate
the efficacy of the proposed strategy. The code for our implementation will be
made available at https://github.com/ayushayt/ImAiR.


title: Three-dimensional Geospatial Interlinking with JedAI-spatial

abstract:
  Geospatial data constitutes a considerable part of (Semantic) Web data, but
so far, its sources are inadequately interlinked in the Linked Open Data cloud.
Geospatial Interlinking aims to cover this gap by associating geometries with
topological relations like those of the Dimensionally Extended 9-Intersection
Model. Due to its quadratic time complexity, various algorithms aim to carry
out Geospatial Interlinking efficiently. We present JedAI-spatial, a novel,
open-source system that organizes these algorithms according to three
dimensions: (i) Space Tiling, which determines the approach that reduces the
search space, (ii) Budget-awareness, which distinguishes interlinking
algorithms into batch and progressive ones, and (iii) Execution mode, which
discerns between serial algorithms, running on a single CPU-core, and parallel
ones, running on top of Apache Spark. We analytically describe JedAI-spatial's
architecture and capabilities and perform thorough experiments to provide
interesting insights about the relative performance of its algorithms.


title:
ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically
  Simulated Characters

abstract:
  The incredible feats of athleticism demonstrated by humans are made possible
in part by a vast repertoire of general-purpose motor skills, acquired through
years of practice and experience. These skills not only enable humans to
perform complex tasks, but also provide powerful priors for guiding their
behaviors when learning new tasks. This is in stark contrast to what is common
practice in physics-based character animation, where control policies are most
typically trained from scratch for each task. In this work, we present a
large-scale data-driven framework for learning versatile and reusable skill
embeddings for physically simulated characters. Our approach combines
techniques from adversarial imitation learning and unsupervised reinforcement
learning to develop skill embeddings that produce life-like behaviors, while
also providing an easy to control representation for use on new downstream
tasks. Our models can be trained using large datasets of unstructured motion
clips, without requiring any task-specific annotation or segmentation of the
motion data. By leveraging a massively parallel GPU-based simulator, we are
able to train skill embeddings using over a decade of simulated experiences,
enabling our model to learn a rich and versatile repertoire of skills. We show
that a single pre-trained model can be effectively applied to perform a diverse
set of new tasks. Our system also allows users to specify tasks through simple
reward functions, and the skill embedding then enables the character to
automatically synthesize complex and naturalistic strategies in order to
achieve the task objectives.


title: Cross-lingual Word Embeddings in Hyperbolic Space

abstract:
  Cross-lingual word embeddings can be applied to several natural language
processing applications across multiple languages. Unlike prior works that use
word embeddings based on the Euclidean space, this short paper presents a
simple and effective cross-lingual Word2Vec model that adapts to the Poincar\'e
ball model of hyperbolic space to learn unsupervised cross-lingual word
representations from a German-English parallel corpus. It has been shown that
hyperbolic embeddings can capture and preserve hierarchical relationships. We
evaluate the model on both hypernymy and analogy tasks. The proposed model
achieves comparable performance with the vanilla Word2Vec model on the
cross-lingual analogy task, the hypernymy task shows that the cross-lingual
Poincar\'e Word2Vec model can capture latent hierarchical structure from free
text across languages, which are absent from the Euclidean-based Word2Vec
representations. Our results show that by preserving the latent hierarchical
information, hyperbolic spaces can offer better representations for
cross-lingual embeddings.


title: Rings of invariants for three dimensional modular representations

abstract:
  Let $p>3$ be a prime number. We compute the rings of invariants of the
elementary abelian $p$-group $(\mathbb Z/p\mathbb Z)^r$ for $3$-dimensional
generic representations. Furthermore we show that these rings of invariants are
complete intersections.
  This proves a conjecture of Campbell, Shank and Wehlau in [CSW], which they
proved for $r=3$ and later Pierron and Shank proved it for $r=4$.


title:
Modeling Task Interactions in Document-Level Joint Entity and Relation
  Extraction

abstract:
  We target on the document-level relation extraction in an end-to-end setting,
where the model needs to jointly perform mention extraction, coreference
resolution (COREF) and relation extraction (RE) at once, and gets evaluated in
an entity-centric way. Especially, we address the two-way interaction between
COREF and RE that has not been the focus by previous work, and propose to
introduce explicit interaction namely Graph Compatibility (GC) that is
specifically designed to leverage task characteristics, bridging decisions of
two tasks for direct task interference. Our experiments are conducted on DocRED
and DWIE; in addition to GC, we implement and compare different multi-task
settings commonly adopted in previous work, including pipeline, shared
encoders, graph propagation, to examine the effectiveness of different
interactions. The result shows that GC achieves the best performance by up to
2.3/5.1 F1 improvement over the baseline.


title:
The Fredholm Navier-Stokes type equations for the de Rham complex over
  weighted H\"older spaces

abstract:
  We consider a family of initial problems for the Navier-Stokes type equations
generated by the de Rham complex in ${\mathbb R}^n \times [0,T]$, $n\geq 2$,
with a positive time $T$ over a scale weighted anisotropic H\"older spaces. As
the weights control the order of zero at the infinity with respect to the space
variables for vectors fields under the consideration, this actually leads to
initial problems over a compact manifold with the singular conic point at the
infinity. We prove that each problem from the family induces Fredholm open
injective mappings on elements of the scales. At the step $1$ of the complex we
may apply the results to the classical Navier-Stokes equations for
incompressible viscous fluid.


title: Constraints on the dark photon from parity-violation and the W-mass

abstract:
  We present an analysis of the experimental data for parity-violating electron
scattering (PVES) and atomic parity-violation, including the effects of a dark
photon. We derive the favored region of dark photon parameter space, which
provides a good description of the experimental data from the Qweak
Collaboration and the Jefferson Lab PVDIS Collaboration and simultaneously
relieves the tension between the neutron skin thickness determined in the
PREX-II experiment and nuclear-model predictions. In addition, we extract the
parameter region required to explain the latest W-boson mass anomaly. Our
results indicate that a heavy dark photon with mass above the Z boson mass is
favored, while other sources of new physics beyond the Standard Model in
addition to the dark photon would also be expected.


title:
A Scalable Algorithm for Shape Optimization with Geometric Constraints
  in Banach Spaces

abstract:
  This work develops an algorithm for PDE-constrained shape optimization based
on Lipschitz transformations. Building on previous work in this field, the
$p$-Laplace operator is utilized to approximate a descent method for Lipschitz
shapes. In particular, it is shown how geometric constraints are
algorithmically incorporated avoiding penalty terms by assigning them to the
subproblem of finding a suitable descent direction. A special focus is placed
on the scalability of the proposed methods for large scale parallel computers
via the application of multigrid solvers. The preservation of mesh quality
under large deformations, where shape singularities have to be smoothed or
generated within the optimization process, is also discussed. It is shown that
the interaction of hierarchically refined grids and shape optimization can be
realized by the choice of appropriate descent directions. The performance of
the proposed methods is demonstrated for energy dissipation minimization in
fluid dynamics applications.


title: Ionic Bonds Control Ferroelectric Behavior in Wurtzite Nitrides

abstract:
  Ferroelectricity enables key integrated technologies from non-volatile memory
to precision ultrasound. Wurtzite ferroelectric Al1-xScxN has recently
attracted attention because of its robust ferroelectricity and Si process
compatibility in addition to being the first known ferroelectric wurtzite.
However, the origin and control of ferroelectricity in wurtzite materials is
not yet fully understood. Here we show that the local bond ionicity, rather
than simply the change in tetrahedral distortion, is key to controlling the
macroscopic ferroelectric response, according to our coupled experimental and
computational results. Across the composition gradient in Sc < 0.35 range and
140-260 nm thickness in combinatorial thin films of Al1-xScxN, the pure
wurtzite phase exhibits a similar c/a ratio regardless of the Sc content, due
to elastic interaction with neighboring crystals. The coercive field and
spontaneous polarization significantly decrease with increasing Sc content
despite this invariant c/a ratio, due to the more ionic bonding nature of Sc-N
relative to the more covalent Al-N bonds, supported by DFT calculations. Based
on these insights, ionicity engineering is introduced as an approach to reduce
coercive field of Al1-xScxN for memory and other applications and to control
ferroelectric properties in other wurtzites.


title: Total positivity of copulas from a Markov kernel perspective

abstract:
  The underlying dependence structure between two random variables can be
described in manifold ways. This includes the examination of certain dependence
properties such as lower tail decreasingness (LTD), stochastic increasingness
(SI) or total positivity of order 2, the latter usually considered for a copula
(TP2) or (if existent) its density (d-TP2). In the present paper we investigate
total positivity of order 2 for a copula's Markov kernel (MK-TP2 for short), a
positive dependence property that is stronger than TP2 and SI, weaker than
d-TP2 but, unlike d-TP2, is not restricted to absolutely continuous copulas,
making it presumably the strongest dependence property defined for any copula
(including those with a singular part such as Marshall-Olkin copulas). We
examine the MK-TP2 property for different copula families, among them the class
of Archimedean copulas and the class of extreme value copulas. In particular we
show that, within the class of Archimedean copulas, the dependence properties
SI and MK-TP2 are equivalent.


title: Generalized Knowledge Distillation via Relationship Matching

abstract:
  The knowledge of a well-trained deep neural network (a.k.a. the "teacher") is
valuable for learning similar tasks. Knowledge distillation extracts knowledge
from the teacher and integrates it with the target model (a.k.a. the
"student"), which expands the student's knowledge and improves its learning
efficacy. Instead of enforcing the teacher to work on the same task as the
student, we borrow the knowledge from a teacher trained from a general label
space -- in this "Generalized Knowledge Distillation (GKD)", the classes of the
teacher and the student may be the same, completely different, or partially
overlapped. We claim that the comparison ability between instances acts as an
essential factor threading knowledge across tasks, and propose the RElationship
FacIlitated Local cLassifiEr Distillation (REFILLED) approach, which decouples
the GKD flow of the embedding and the top-layer classifier. In particular,
different from reconciling the instance-label confidence between models,
REFILLED requires the teacher to reweight the hard tuples pushed forward by the
student and then matches the similarity comparison levels between instances. An
embedding-induced classifier based on the teacher model supervises the
student's classification confidence and adaptively emphasizes the most related
supervision from the teacher. REFILLED demonstrates strong discriminative
ability when the classes of the teacher vary from the same to a fully
non-overlapped set w.r.t. the student. It also achieves state-of-the-art
performance on standard knowledge distillation, one-step incremental learning,
and few-shot learning tasks.


title: Identifying diverging-effective mass in MOSFET and $^3$He systems

abstract:
  Emerging devices such as a neuromorphic device and a qubit can use the Mott
transition phenomenon, but in particular, the diverging mechanism of the
phenomenon remains to be clarified. The diverging-effective mass near Mott
insulators was measured in strongly correlated Mott systems such as a fermion
$^3$He and a Si metal-oxide-semiconductor-field-effect transistor, and is
closely fitted by the effective mass obtained by the extension of the
Brinkman-Rice(BR) picture,
$m^*/m=1/[1-(U/U_c)^2]=1/(1-{\kappa}^2_{BR}{\rho}^4)$ when
${\kappa}^2_{BR}{\approx}1({\neq}$1), where $0<U/U_c={\kappa}_{BR}{\rho}^2<1$,
correlation strength is ${\kappa}_{BR}$, band-filling is ${\rho}$. Its
identification is a percolation of a constant mass in the Brinkman-Rice
picture. Over ${\kappa}_{BR}{\approx}0.96$ is evaluated.


title: CoCa: Contrastive Captioners are Image-Text Foundation Models

abstract:
  Exploring large-scale pretrained foundation models is of significant interest
in computer vision because these models can be quickly transferred to many
downstream tasks. This paper presents Contrastive Captioner (CoCa), a
minimalist design to pretrain an image-text encoder-decoder foundation model
jointly with contrastive loss and captioning loss, thereby subsuming model
capabilities from contrastive approaches like CLIP and generative methods like
SimVLM. In contrast to standard encoder-decoder transformers where all decoder
layers attend to encoder outputs, CoCa omits cross-attention in the first half
of decoder layers to encode unimodal text representations, and cascades the
remaining decoder layers which cross-attend to the image encoder for multimodal
image-text representations. We apply a contrastive loss between unimodal image
and text embeddings, in addition to a captioning loss on the multimodal decoder
outputs which predicts text tokens autoregressively. By sharing the same
computational graph, the two training objectives are computed efficiently with
minimal overhead. CoCa is pretrained end-to-end and from scratch on both
web-scale alt-text data and annotated images by treating all labels simply as
text, seamlessly unifying natural language supervision for representation
learning. Empirically, CoCa achieves state-of-the-art performance with
zero-shot transfer or minimal task-specific adaptation on a broad range of
downstream tasks, spanning visual recognition (ImageNet, Kinetics-400/600/700,
Moments-in-Time), crossmodal retrieval (MSCOCO, Flickr30K, MSR-VTT), multimodal
understanding (VQA, SNLI-VE, NLVR2), and image captioning (MSCOCO, NoCaps).
Notably on ImageNet classification, CoCa obtains 86.3% zero-shot top-1
accuracy, 90.6% with a frozen encoder and learned classification head, and new
state-of-the-art 91.0% top-1 accuracy on ImageNet with a finetuned encoder.


title: Quantization of Interacting Galilean Field theories

abstract:
  We present the quantum field description of Galilean electrodynamics
minimally coupled to massless Galilean fermion in (3 + 1) dimensions. At the
classical level, the Lagrangian is obtained as a null reduction of a
relativistic theory in one higher dimension. We use functional techniques to
develop the quantum field description of the theory. Quantum corrections to the
propagators and vertex are obtained upto first order and the theory is found to
be renormalizable to this order. The beta function of the theory is found to
grow linearly; the theory is not asymptotically free.


title:
Photon surfaces, shadows and accretion disks in gravity with minimally
  coupled scalar field

abstract:
  In this article, we conduct a sequential study of possible observable images
of black hole simulators described by two recently obtained rotating geometries
in Einstein gravity, minimally coupled to a scalar field. One of them,
"Kerr-like" (KL), can be seen as a legitimate alternative to the rotating
Fisher-Janis-Newman-Winicour (FJNW) solution, and the other (TSL) is a scalar
generalization of the Tomimatsu-Sato solution. Unlike the previous version of
the rotating FJNW, these solutions do indeed satisfy the system's equations of
motion. Our study includes both analytical and numerical calculations of
equatorial circular orbits, photon regions, gravitational shadows, and
radiation from thin accretion disks for various values of the object's angular
momentum and scalar charge. The TSL solution was found to simulate Kerr for all
valid parameter values with high accuracy. The maximum difference between the
deviations of shadows from a circle for the Kerr and TSL cases does not exceed
1% and fits into the experimental observational data M87*. However,
near-extreme objects show two times smaller peak values of the observed outflow
luminosity of the accretion disk than for the Kerr black hole. The KL solution
cannot be ruled out by the experimental data for small values of the scalar
charge either. As the scalar charge increases, the optical properties change
dramatically. The shadow can become multiply connected, strongly oblate, and
the photon region does not hide the singularity, so it should be classified as
a strong singularity.


title:
Scene Clustering Based Pseudo-labeling Strategy for Multi-modal Aerial
  View Object Classification

abstract:
  Multi-modal aerial view object classification (MAVOC) in Automatic target
recognition (ATR), although an important and challenging problem, has been
under studied. This paper firstly finds that fine-grained data, class imbalance
and various shooting conditions preclude the representational ability of
general image classification. Moreover, the MAVOC dataset has scene aggregation
characteristics. By exploiting these properties, we propose Scene Clustering
Based Pseudo-labeling Strategy (SCP-Label), a simple yet effective method to
employ in post-processing. The SCP-Label brings greater accuracy by assigning
the same label to objects within the same scene while also mitigating bias and
confusion with model ensembles. Its performance surpasses the official baseline
by a large margin of +20.57% Accuracy on Track 1 (SAR), and +31.86% Accuracy on
Track 2 (SAR+EO), demonstrating the potential of SCP-Label as post-processing.
Finally, we win the championship both on Track1 and Track2 in the CVPR 2022
Perception Beyond the Visible Spectrum (PBVS) Workshop MAVOC Challenge. Our
code is available at https://github.com/HowieChangchn/SCP-Label.


title: Second Order Path Variationals in Non-Stationary Online Learning

abstract:
  We consider the problem of universal dynamic regret minimization under
exp-concave and smooth losses. We show that appropriately designed Strongly
Adaptive algorithms achieve a dynamic regret of $\tilde O(d^2 n^{1/5} C_n^{2/5}
\vee d^2)$, where $n$ is the time horizon and $C_n$ a path variational based on
second order differences of the comparator sequence. Such a path variational
naturally encodes comparator sequences that are piecewise linear -- a powerful
family that tracks a variety of non-stationarity patterns in practice (Kim et
al, 2009). The aforementioned dynamic regret rate is shown to be optimal modulo
dimension dependencies and poly-logarithmic factors of $n$. Our proof
techniques rely on analysing the KKT conditions of the offline oracle and
requires several non-trivial generalizations of the ideas in Baby and Wang,
2021, where the latter work only leads to a slower dynamic regret rate of
$\tilde O(d^{2.5}n^{1/3}C_n^{2/3} \vee d^{2.5})$ for the current problem.


title:
Performance evaluations on the parallel CHAracteristic-Spectral-Mixed
  (CHASM) scheme

abstract:
  Performance evaluations on the deterministic algorithms for 6-D problems are
rarely found in literatures except some recent advances in the Vlasov and
Boltzmann community [Dimarco et al. (2018), Kormann et al. (2019)], due to the
extremely high complexity. Thus a detailed comparison among various techniques
shall be useful to the researchers in the related fields. We try to make a
thorough evaluation on a parallel CHAracteristic-Spectral-Mixed (CHASM) scheme
to support its usage. CHASM utilizes the cubic B-spline expansion in the
spatial space and spectral expansion in the momentum space, which many
potentially overcome the computational burden in solving classical and quantum
kinetic equations in 6-D phase space. Our purpose is three-pronged. First, we
would like show that by imposing some effective Hermite boundary conditions,
the local cubic spline can approximate to the global one as accurately as
possible. Second, we will illustrate the necessity of adopting the truncated
kernel method in calculating the pseudodifferential operator with a singular
symbol, since the widely used pseudo-spectral method [Ringhofer (1990)] might
fail to properly tackle the singularity. Finally, we make a comparison among
non-splitting Lawson schemes and Strang operator splitting. Our numerical
results demonstrate the advantage of the one-stage Lawson predictor-corrector
scheme over multi-stage ones as well as the splitting scheme in both accuracy
and stability.


title: Exploring the hadronic origin of LHAASO J1908+0621

abstract:
  Recent observations by the Large High Altitude Air Shower Observatory
(LHAASO) have paved the way for the observational detection of PeVatrons in the
Milky Way Galaxy, thus revolutionizing the field of $\gamma$-ray astrophysics.
In this paper, we study one such detected source, LHAASO J1908+0621, and
explore the origin of multi-TeV $\gamma$-ray emission from this source. A
middle-aged radio supernova remnant SNR G40.5-0.5 and a GeV pulsar PSR
J1907+0602 are co-spatial with LHAASO J1908+0621. Dense molecular clouds are
also found to be associated with SNR G40.5-0.5. We explain the multi-TeV
$\gamma$-ray emission observed from the direction of LHAASO J1908+0621, by the
hadronic interaction between accelerated protons that escaped from the SNR
shock front and cold protons present inside the dense molecular clouds, and the
leptonic emission from the pulsar wind nebula (PWN) associated with the pulsar
J1907+0602. Moreover, we explain lower energy $\gamma$-ray emission by
considering the radiative cooling of the electrons that escaped from SNR
G40.5-0.5. Finally, the combined lepto-hadronic scenario was used to explain
the multi-wavelength spectral energy distribution (SED) of LHAASO J1908+0621.
Although not yet significant, an ICECUBE hotspot of neutrino emission is
spatially associated with LHAASO J1908+0621, indicating a possible hadronic
contribution. In this paper, we show that if a hadronic component is present in
LHAASO J1908+0621, then the second generation ICECUBE observatory will detect
neutrino from this source.


title:
Zero-Episode Few-Shot Contrastive Predictive Coding: Solving
  intelligence tests without prior training

abstract:
  Video prediction models often combine three components: an encoder from pixel
space to a small latent space, a latent space prediction model, and a
generative model back to pixel space. However, the large and unpredictable
pixel space makes training such models difficult, requiring many training
examples. We argue that finding a predictive latent variable and using it to
evaluate the consistency of a future image enables data-efficient predictions
because it precludes the necessity of a generative model training. To
demonstrate it, we created sequence completion intelligence tests in which the
task is to identify a predictably changing feature in a sequence of images and
use this prediction to select the subsequent image. We show that a
one-dimensional Markov Contrastive Predictive Coding (M-CPC_1D) model solves
these tests efficiently, with only five examples. Finally, we demonstrate the
usefulness of M-CPC_1D in solving two tasks without prior training: anomaly
detection and stochastic movement video prediction.


title:
Continued Fractions and Probability Estimations in the Shor Algorithm --
  A Detailed and Self-Contained Treatise

abstract:
  The algorithm of Shor for prime factorization is a hybrid algorithm
consisting of a quantum part and a classical part. The main focus of the
classical part is a continued fraction analysis. The presentation of this is
often short, pointing to text books on number theory. In this contribution, we
present the relevant results and proofs from the theory of continued fractions
in detail (even in more detail than in text books) filling the gap to allow a
complete comprehension of the algorithm of Shor. Similarly, we provide a
detailed computation of the estimation of the probability that convergents will
provide the period required for determining a prime factor.


title: Freeness of type $B$ and conditional freeness for random matrices

abstract:
  The asymptotic freeness of independent unitarily invariant $N\times N$ random
matrices holds in expectation up to $O(N^{-2})$. An already known consequence
is the infinitesimal freeness in expectation. We put in evidence another
consequence for unitarily invariant random matrices: the almost sure asymptotic
freeness of type $B$. As byproducts, we recover the asymptotic cyclic
monotonicity, and we get the asymptotic conditional freeness. In particular,
the eigenvector empirical spectral distribution of the sum of two randomly
rotated random matrices converges towards the conditionally free convolution.
We also show new connections between infinitesimal freeness, freeness of type
$B$, conditional freeness, cyclic monotonicity and monotone independence.
Finally, we show rigorously that the BBP phase transition for an additive
rank-one perturbation of a GUE matrix is a consequence of the asymptotic
conditional freeness, and the arguments extend to the study of the outlier
eigenvalues of other unitarily invariant ensembles.


title: Probabilistic Symmetry for Improved Trajectory Forecasting

abstract:
  Trajectory prediction is a core AI problem with broad applications in
robotics and autonomous driving. While most existing works focus on
deterministic prediction, producing probabilistic forecasts to quantify
prediction uncertainty is critical for downstream decision-making tasks such as
risk assessment, motion planning, and safety guarantees. We introduce a new
metric, mean regional score (MRS), to evaluate the quality of probabilistic
trajectory forecasts. We propose a novel probabilistic trajectory prediction
model, Probabilistic Equivariant Continuous COnvolution (PECCO) and show that
leveraging symmetry, specifically rotation equivariance, can improve the
predictions' accuracy as well as coverage. On both vehicle and pedestrian
datasets, PECCO shows state-of-the-art prediction performance and improved
calibration compared to baselines.


title:
Some identities involving degenerate Stirling numbers associated with
  several degenerate polynomials and numbers

abstract:
  The aim of this paper is to investigate some properties, recurrence relations
and identities involving degenerate Stirling numbers of both kinds associated
with degenerate hyperharmonic numbers and also with degenerate Bernolli,
degenerate Euler, degenerate Bell and degenerate Fubini polynomials.


title:
Explain to Not Forget: Defending Against Catastrophic Forgetting with
  XAI

abstract:
  The ability to continuously process and retain new information like we do
naturally as humans is a feat that is highly sought after when training neural
networks. Unfortunately, the traditional optimization algorithms often require
large amounts of data available during training time and updates wrt. new data
are difficult after the training process has been completed. In fact, when new
data or tasks arise, previous progress may be lost as neural networks are prone
to catastrophic forgetting. Catastrophic forgetting describes the phenomenon
when a neural network completely forgets previous knowledge when given new
information. We propose a novel training algorithm called training by
explaining in which we leverage Layer-wise Relevance Propagation in order to
retain the information a neural network has already learned in previous tasks
when training on new data. The method is evaluated on a range of benchmark
datasets as well as more complex data. Our method not only successfully retains
the knowledge of old tasks within the neural networks but does so more
resource-efficiently than other state-of-the-art solutions.


title:
Explainable Anomaly Detection for Industrial Control System
  Cybersecurity

abstract:
  Industrial Control Systems (ICSs) are becoming more and more important in
managing the operation of many important systems in smart manufacturing, such
as power stations, water supply systems, and manufacturing sites. While massive
digital data can be a driving force for system performance, data security has
raised serious concerns. Anomaly detection, therefore, is essential for
preventing network security intrusions and system attacks. Many AI-based
anomaly detection methods have been proposed and achieved high detection
performance, however, are still a "black box" that is hard to be interpreted.
In this study, we suggest using Explainable Artificial Intelligence to enhance
the perspective and reliable results of an LSTM-based Autoencoder-OCSVM
learning model for anomaly detection in ICS. We demonstrate the performance of
our proposed method based on a well-known SCADA dataset.