title: ProFairRec: Provider Fairness-aware News Recommendation

abstract:
  News recommendation aims to help online news platform users find their
preferred news articles. Existing news recommendation methods usually learn
models from historical user behaviors on news. However, these behaviors are
usually biased on news providers. Models trained on biased user data may
capture and even amplify the biases on news providers, and are unfair for some
minority news providers. In this paper, we propose a provider fairness-aware
news recommendation framework (named ProFairRec), which can learn news
recommendation models fair for different news providers from biased user data.
The core idea of ProFairRec is to learn provider-fair news representations and
provider-fair user representations to achieve provider fairness. To learn
provider-fair representations from biased data, we employ provider-biased
representations to inherit provider bias from data. Provider-fair and -biased
news representations are learned from news content and provider IDs
respectively, which are further aggregated to build fair and biased user
representations based on user click history. All of these representations are
used in model training while only fair representations are used for user-news
matching to achieve fair news recommendation. Besides, we propose an
adversarial learning task on news provider discrimination to prevent
provider-fair news representation from encoding provider bias. We also propose
an orthogonal regularization on provider-fair and -biased representations to
better reduce provider bias in provider-fair representations. Moreover,
ProFairRec is a general framework and can be applied to different news
recommendation methods. Extensive experiments on a public dataset verify that
our ProFairRec approach can effectively improve the provider fairness of many
existing methods and meanwhile maintain their recommendation accuracy.


title: The Hausdorff and packing measure of some decimal expansions

abstract:
  We compute the exact Hausdorff and packing measure of sets of real numbers
whose decimal expansions in a given base are missing the digits beyond a given
threshold.


title: News Recommendation with Candidate-aware User Modeling

abstract:
  News recommendation aims to match news with personalized user interest.
Existing methods for news recommendation usually model user interest from
historical clicked news without the consideration of candidate news. However,
each user usually has multiple interests, and it is difficult for these methods
to accurately match a candidate news with a specific user interest. In this
paper, we present a candidate-aware user modeling method for personalized news
recommendation, which can incorporate candidate news into user modeling for
better matching between candidate news and user interest. We propose a
candidate-aware self-attention network that uses candidate news as clue to
model candidate-aware global user interest. In addition, we propose a
candidate-aware CNN network to incorporate candidate news into local behavior
context modeling and learn candidate-aware short-term user interest. Besides,
we use a candidate-aware attention network to aggregate previously clicked news
weighted by their relevance with candidate news to build candidate-aware user
representation. Experiments on real-world datasets show the effectiveness of
our method in improving news recommendation performance.


title: FUM: Fine-grained and Fast User Modeling for News Recommendation

abstract:
  User modeling is important for news recommendation. Existing methods usually
first encode user's clicked news into news embeddings independently and then
aggregate them into user embedding. However, the word-level interactions across
different clicked news from the same user, which contain rich detailed clues to
infer user interest, are ignored by these methods. In this paper, we propose a
fine-grained and fast user modeling framework (FUM) to model user interest from
fine-grained behavior interactions for news recommendation. The core idea of
FUM is to concatenate the clicked news into a long document and transform user
modeling into a document modeling task with both intra-news and inter-news
word-level interactions. Since vanilla transformer cannot efficiently handle
long document, we apply an efficient transformer named Fastformer to model
fine-grained behavior interactions. Extensive experiments on two real-world
datasets verify that FUM can effectively and efficiently model user interest
for news recommendation.


title: Lagrangian Descriptors and the Action Integral of Classical Mechanics

abstract:
  In this paper we bring together the method of Lagrangian descriptors and the
principle of least action, or more precisely, of stationary action, in both
deterministic and stochastic settings. In particular, we show how the action
can be used as a Lagrangian descriptor. This provides a direct connection
between Lagrangian descriptors and Hamiltonian mechanics, and we illustrate
this connection with benchmark examples.


title: On dually-CPT and strong-CPT posets

abstract:
  A poset is a containment of paths in a tree (CPT) if it admits a
representation by containment where each element of the poset is represented by
a path in a tree and two elements are comparable in the poset if and only if
the corresponding paths are related by the inclusion relation. Recently
Alc\'on, Gudi\~{n}o and Gutierrez introduced proper subclasses of CPT posets,
namely dually-CPT, and strongly-CPT. A poset $\mathbf{P}$ is dually-CPT, if and
only if $\mathbf{P}$ and its dual $\mathbf{P}^{d}$ both admit a CPT
representation. A poset $\mathbf{P}$ is strongly-CPT, if and only if
$\mathbf{P}$ and all the posets that share the same underlying comparability
graph admit a CPT representation. Where as the inclusion between Dually-CPT and
CPT was known to be strict. It was raised as an open question by Alc\'on,
Gudi\~{n}o and Gutierrez whether strongly-CPT was a strict subclass of
dually-CPT. We provide a proof that both classes actually coincide.


title:
Deep Non-rigid Structure-from-Motion: A Sequence-to-Sequence Translation
  Perspective

abstract:
  Directly regressing the non-rigid shape and camera pose from the individual
2D frame is ill-suited to the Non-Rigid Structure-from-Motion (NRSfM) problem.
This frame-by-frame 3D reconstruction pipeline overlooks the inherent
spatial-temporal nature of NRSfM, i.e., reconstructing the whole 3D sequence
from the input 2D sequence. In this paper, we propose to model deep NRSfM from
a sequence-to-sequence translation perspective, where the input 2D frame
sequence is taken as a whole to reconstruct the deforming 3D non-rigid shape
sequence. First, we apply a shape-motion predictor to estimate the initial
non-rigid shape and camera motion from a single frame. Then we propose a
context modeling module to model camera motions and complex non-rigid shapes.
To tackle the difficulty in enforcing the global structure constraint within
the deep framework, we propose to impose the union-of-subspace structure by
replacing the self-expressiveness layer with multi-head attention and delayed
regularizers, which enables end-to-end batch-wise training. Experimental
results across different datasets such as Human3.6M, CMU Mocap and InterHand
prove the superiority of our framework. The code will be made publicly
available


title:
Maximal density and the kappa values for the families $\{a,a+1,2a+1,n\}$
  and $\{a,a+1,2a+1,3a+1,n\}$

abstract:
  Let $M$ be a set of positive integers. We study the maximal density $\mu(M)$
of the sets of nonnegative integers $S$ whose elements do not differ by an
element in $M$. In 1973, Cantor and Gordon established a formula for $\mu(M)$
for $|M|\leq 2$. Since then, many researchers have worked upon the problem and
found several partial results in the case $|M|\geq 3$, including some results
in the case, $M$ is an infinite set. In this paper, we study the maximal
density problem for the families $M=\{a,a+1,2a+1,n\}$ and
$M=\{a,a+1,2a+1,3a+1,n\}$, where $a$ and $n$ are positive integers. In most of
the cases, we find bounds for the parameter \textit{kappa}, denoted by
$\kappa(M)$, which actually serves as a lower bound for $\mu(M)$. The parameter
$\kappa(M)$ has already got its importance due to its rich connection with the
problems such as the "lonely runner conjecture" in Diophantine approximations
and coloring parameters such as "circular coloring" and "fractional coloring"
in graph theory.


title:
Canonical Maps from Spaces of Higher Complex Structures to Hitchin
  Components

abstract:
  For $S$ a closed surface of genus $g\geq2$, we construct a canonical
diffeomorphism from the degree $3$ Fock-Thomas space $\mathcal{T}^3(S)$ of
higher complex structures to the $\text{SL}(3,\mathbb{R})$ Hitchin component.
Our construction is equivariant with respect to natural actions of the mapping
class group $\text{Mod}(S)$. For all $n \geq 3$, we show that the Fock-Thomas
space $\mathcal{T}^n(S)$ has a canonical vector bundle structure over
Teichm\"uller space. We then construct a $\text{Mod}(S)$-equivariant bundle
isomorphism from $\mathcal{T}^n(S)$ to a sub-bundle of the restriction of the
tangent bundle of the $\text{PSL}(n, \mathbb{R})$ Hitchin component to the
Fuchsian locus. As consequences, we prove that the higher degree moduli space
of complex structures is a bundle over the moduli space of Riemann surfaces and
that the action of $\text{Mod}(S)$ on $\mathcal{T}^n(S)$ is a proper action by
holomorphic automorphisms with respect to a canonical complex structure. The
core of our approach is a careful analysis of higher degree diffeomorphism
groups.


title: NeuRL: Closed-form Inverse Reinforcement Learning for Neural Decoding

abstract:
  Current neural decoding methods typically aim at explaining behavior based on
neural activity via supervised learning. However, since generally there is a
strong connection between learning of subjects and their expectations on
long-term rewards, we propose NeuRL, an inverse reinforcement learning approach
that (1) extracts an intrinsic reward function from collected trajectories of a
subject in closed form, (2) maps neural signals to this intrinsic reward to
account for long-term dependencies in the behavior and (3) predicts the
simulated behavior for unseen neural signals by extracting Q-values and the
corresponding Boltzmann policy based on the intrinsic reward values for these
unseen neural signals. We show that NeuRL leads to better generalization and
improved decoding performance compared to supervised approaches. We study the
behavior of rats in a response-preparation task and evaluate the performance of
NeuRL within simulated inhibition and per-trial behavior prediction. By
assigning clear functional roles to defined neuronal populations our approach
offers a new interpretation tool for complex neuronal data with testable
predictions. In per-trial behavior prediction, our approach furthermore
improves accuracy by up to 15% compared to traditional methods.


title:
A Comparative Analysis of Decision-Level Fusion for Multimodal Driver
  Behaviour Understanding

abstract:
  Visual recognition inside the vehicle cabin leads to safer driving and more
intuitive human-vehicle interaction but such systems face substantial obstacles
as they need to capture different granularities of driver behaviour while
dealing with highly limited body visibility and changing illumination.
Multimodal recognition mitigates a number of such issues: prediction outcomes
of different sensors complement each other due to different modality-specific
strengths and weaknesses. While several late fusion methods have been
considered in previously published frameworks, they constantly feature
different architecture backbones and building blocks making it very hard to
isolate the role of the chosen late fusion strategy itself. This paper presents
an empirical evaluation of different paradigms for decision-level late fusion
in video-based driver observation. We compare seven different mechanisms for
joining the results of single-modal classifiers which have been both popular,
(e.g. score averaging) and not yet considered (e.g. rank-level fusion) in the
context of driver observation evaluating them based on different criteria and
benchmark settings. This is the first systematic study of strategies for fusing
outcomes of multimodal predictors inside the vehicles, conducted with the goal
to provide guidance for fusion scheme selection.


title:
Reducing Model Jitter: Stable Re-training of Semantic Parsers in
  Production Environments

abstract:
  Retraining modern deep learning systems can lead to variations in model
performance even when trained using the same data and hyper-parameters by
simply using different random seeds. We call this phenomenon model jitter. This
issue is often exacerbated in production settings, where models are retrained
on noisy data. In this work we tackle the problem of stable retraining with a
focus on conversational semantic parsers. We first quantify the model jitter
problem by introducing the model agreement metric and showing the variation
with dataset noise and model sizes. We then demonstrate the effectiveness of
various jitter reduction techniques such as ensembling and distillation.
Lastly, we discuss practical trade-offs between such techniques and show that
co-distillation provides a sweet spot in terms of jitter reduction for semantic
parsing systems with only a modest increase in resource usage.


title: Iterative Adaptive Spectroscopy of Short Signals

abstract:
  We develop an iterative, adaptive frequency sensing protocol based on Ramsey
interferometry of a two-level system. Our scheme allows one to estimate unknown
frequencies with a high precision from short, finite signals. It avoids several
issues related to processing of decaying signals and reduces the experimental
overhead related to sampling. High precision is achieved by enhancing the
Ramsey sequence to prepare with high fidelity both the sensing and readout
state and by using an iterative procedure built to mitigate systematic errors
when estimating frequencies from Fourier transforms.


title: Pseudo-Goldstone Dark Matter Model with CP violation

abstract:
  We consider an explicitly CP-violating model with two Higgs doublets and one
complex singlet scalar. The singlet $S$ is charged under a global $\rm U(1)$
symmetry which is softly broken by a mass term $\mu^2 S^2+\hc$. Imaginary part
of $S$ is a stable dark matter candidate which at the tree level, in the limit
of zero momentum transfer, decouples from nucleons naturally satisfying all
existing direct detection limits on dark matter scattering cross-section. It is
explicitly shown that within this framework in the alignment limit CP-violation
is still present in contrast to a popular version of a 2-Higgs doublet model
with softly broken $Z_2$ symmetry. In this context, we investigate dark matter
implications of the model both with and without CP violation in the scalar
sector. In particular, dark matter relic abundance is calculated and the
possibility for its indirect detection is discussed.


title:
Sn/Ge substitution in
  ((C$_\textrm{n}$H$_{2\textrm{n}-1}$NH$_3$)$_2$PbI$_4$; n=3): An emerging 2D
  layered hybrid perovskites with enhanced optoelectronic properties$^\dag$

abstract:
  Two-dimensional (2D) perovskites show higher stability in comparison to their
three-dimensional (3D) counterparts. Therefore, 2D perovskites have invoked
remarkable attention in basic understanding of their physical properties and
optoelectronic applications. Here we present a low-dimensional naturally
self-assembled inorganic-organic (IO) hybrid systems based on primary cyclic
ammonium-based (C$_{\textrm{n}}$H$_{2\textrm{n}-1}$NH$_{3}$) semiconductor
series [viz. ((C$_{\textrm{n}}$H$_{2\textrm{n}-1}$ NH$_3$)$_2$PbI$_4$; n=3-6)].
However, the wide bandgap nature and presence of toxicity due to lead (Pb)
prohibit their applications. Therefore, in the present work, we study the role
of Ge/Sn substitution and Pb-vacancy (Pb-$\boxtimes$) to reduce concentration
of Pb and to enhance solar cell efficiency by the formation of mixed perovskite
structures. We have discussed the effect of spin-orbit coupling (SOC) using
state-of-the-art hybrid density functional theory (DFT). We find the mixed
conformers with Pb-$\boxtimes$ do not possess structural stability. Moreover,
they have indirect bandgap, which is not good for solar cell applications. Only
those conformers, which have favourable thermodynamics and structural
stability, are considered for further study of optical properties. Our results
infer that Sn substitution is more favorable than that of Ge in replacing Pb
and enhancing the efficiency. Exciton binding energies calculated using
Wannier-Mott approach for pristine and substituted conformers are larger than
lead halide perovskites, while the electron-phonon coupling is smaller in the
former. From computed spectroscopic limited maximum efficiency (SLME), these 2D
perovskites show enough promise as alternatives to conventional lead halide
perovskites.


title: Multi-wavelength temporal and spectral analysis of Blazar S5 1803+78

abstract:
  Blazars are a class of AGN, one of their jets is pointed towards the earth.
Here, we report about the multi-wavelength study for blazar S5 1803+78 between
MJD 58727 to MJD 59419. We analysed $\gamma$-ray data collected by Fermi-LAT,
X-ray data collected by Swift-XRT \& NuSTAR, optical photons detected by
Swift-UVOT \& TUBITAK observatory in Turkey. Three flaring states are
identified by analysing the $\gamma$-ray light curve. A day scale variability
is observed throughout the flares with the similar rise and decay times
suggesting a compact emission region located close to the central engine.
Cross-correlation studies are carried out between $\gamma$-ray, radio, and
X-ray bands, and no significant correlation is detected. The $\gamma$-ray and
optical emission are significantly correlated with zero time lag suggesting a
co-spatial origin of them. A significant positive correlation between the R-I
index and the V magnitude is observed. The broadband spectral energy
distributions (SEDs) modeling was performed for all the flaring episodes as
well as for one quiescent state for comparison. SEDs are best fitted with the
synchrotron-self Compton (SSC) model under a one-zone leptonic scenario. The
SED modeling shows that to explain the high flaring state strong Doppler
boosting is required.


title: Melting temperature prediction via first principles and deep learning

abstract:
  Melting is a high temperature process that requires extensive sampling of
configuration space, thus making melting temperature prediction computationally
very expensive and challenging. Over the past few years, I have built two
methods to address this challenge, one via direct density functional theory
(DFT) molecular dynamics (MD) simulations and the other via deep learning graph
neural networks. The DFT approach is based on statistical analysis of
small-size solid-liquid coexistence MD simulations. It eliminates the risk of
metastable superheated solid in the fast-heating method, while also
significantly reducing the computer cost relative to the traditional
large-scale coexistence method. Being both accurate and efficient (at the speed
of several days per material), it is considered as one of the best methods for
direct DFT melting temperature calculation. The deep learning method is based
on graph neural networks that effectively handles permutation invariance in
chemical formula, which drastically improves efficiency and reduces cost. At
the speed of milliseconds per material, the model is extremely fast, while
being moderately accurate, especially within the composition space expanded by
the dataset. I have implemented both methods into automated computer code
packages, making them publicly available and free to download. The DFT and deep
learning methods are highly complementary to each other, and hence they can be
potentially well integrated into a framework for melting temperature
prediction. I demonstrated examples of applying the methods to materials design
and discovery of high-melting-point materials.


title:
Is GitHub's Copilot as Bad As Humans at Introducing Vulnerabilities in
  Code?

abstract:
  Several advances in deep learning have been successfully applied to the
software development process. Of recent interest is the use of neural language
models to build tools that can assist in writing code. There is a growing body
of work to evaluate these tools and their underlying language models. We aim to
contribute to this line of research via a comparative empirical analysis of
these tools and language models from a security perspective. For the rest of
this paper, we use CGT (Code Generation Tool) to refer to language models as
well as other tools, such as Copilot, that are built with language models.
  The aim of this study is to compare the performance of one CGT, Copilot, with
the performance of human developers. Specifically, we investigate whether
Copilot is just as likely to introduce the same software vulnerabilities as
human developers.
  We will use the Big-Vul dataset proposed by Fan et al. - a dataset of
vulnerabilities introduced by human developers. For each entry in the dataset,
we will recreate the scenario before the bug was introduced and allow Copilot
to generate a completion. The completions are manually inspected by three
independent coders in order to be classified as 1. containing the same
vulnerability (introduced by the human), 2. containing a fix for the
vulnerability or 3. other. The "other" category is used as a catchall for
scenarios that are out of scope for this project.


title:
Noether currents in theories with higher derivatives and theories with
  differential field transformation in action (DFTA)

abstract:
  The article is devoted to the investigation of the Noether currents and
integrals of motion in the special subclass of theories with higher field
derivatives -- theories under differential field transformations in action
(DFTA). Under fairly general assertions, we derive a simple representation for
the integrals as sums of "old" integrals of motion, terms, that vanishes on the
"old" equations of motion and surface integrals. We show that for some
cosmological theories of that kind with high order derivatives the last
contribution can violate gauge invariance, specifically, diffeomorphisms. Then
we investigate ambiguity of the Noether procedure for higher derivative
theories and fix it in the way the problem resolves in quite general setting.
The obtained results are discussed for the several extensions of the General
Relativity, which are obtained by DFTA, in particular, for mimetic, disformal
and Regge-Teitelboim theories of gravity.


title:
Zeroth-Order Stochastic Coordinate Methods for Decentralized Non-convex
  Optimization

abstract:
  In this letter, we first propose a \underline{Z}eroth-\underline{O}rder
c\underline{O}ordinate \underline{M}ethod~(ZOOM) to solve the stochastic
optimization problem over a decentralized network with only zeroth-order~(ZO)
oracle feedback available. Moreover, we equip a simple mechanism "powerball" to
ZOOM and propose ZOOM-PB to accelerate the convergence of ZOOM. Compared with
the existing methods, we verify the proposed algorithms through two benchmark
examples in the literature, namely the black-box binary classification and the
generating adversarial examples from black-box DNNs in order to compare with
the existing state-of-the-art centralized and distributed ZO algorithms. The
numerical results demonstrate a faster convergence rate of the proposed
algorithms.


title: A Search for Heterocycles in GOTHAM Observations of TMC-1

abstract:
  We have conducted an extensive search for nitrogen-, oxygen- and
sulfur-bearing heterocycles toward Taurus Molecular Cloud 1 (TMC-1) using the
deep, broadband centimeter-wavelength spectral line survey of the region from
the GOTHAM large project on the Green Bank Telescope. Despite their ubiquity in
terrestrial chemistry, and the confirmed presence of a number of cyclic and
polycyclic hydrocarbon species in the source, we find no evidence for the
presence of any heterocyclic species. Here, we report the derived upper limits
on the column densities of these molecules obtained by Markov Chain Monte Carlo
(MCMC) analysis and compare this approach to traditional single-line upper
limit measurements. We further hypothesize why these molecules are absent in
our data, how they might form in interstellar space, and the nature of
observations that would be needed to secure their detection.


title:
Y-Z cut lithium niobate longitudinal piezoelectric resonant photoelastic
  modulator

abstract:
  The capability to modulate the intensity of an optical beam has scientific
and practical significance. In this work, we demonstrate Y-Z cut lithium
niobate acousto-optic modulators with record-high modulation efficiency,
requiring only $1.5~\text{W}/\text{cm}^2$ for 100% modulation at 7 MHz. These
modulators use a simple fabrication process; coating the top and bottom
surfaces of a thin lithium niobate wafer with transparent electrodes. The
fundamental shear acoustic mode of the wafer is excited through the transparent
electrodes by applying voltage with frequency corresponding to the resonant
frequency of this mode, confining an acoustic standing wave to the electrode
region. Polarization of light propagating through this region is modulated at
the applied frequency. Polarization modulation is converted to intensity
modulation by placing the modulator between polarizers. To showcase an
important application space for this modulator, we integrate it with a standard
image sensor and demonstrate 4 megapixel time-of-flight imaging


title:
CholecTriplet2021: A benchmark challenge for surgical action triplet
  recognition

abstract:
  Context-aware decision support in the operating room can foster surgical
safety and efficiency by leveraging real-time feedback from surgical workflow
analysis. Most existing works recognize surgical activities at a coarse-grained
level, such as phases, steps or events, leaving out fine-grained interaction
details about the surgical activity; yet those are needed for more helpful AI
assistance in the operating room. Recognizing surgical actions as triplets of
<instrument, verb, target> combination delivers comprehensive details about the
activities taking place in surgical videos. This paper presents
CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for
the recognition of surgical action triplets in laparoscopic videos. The
challenge granted private access to the large-scale CholecT50 dataset, which is
annotated with action triplet information. In this paper, we present the
challenge setup and assessment of the state-of-the-art deep learning methods
proposed by the participants during the challenge. A total of 4 baseline
methods from the challenge organizers and 19 new deep learning algorithms by
competing teams are presented to recognize surgical action triplets directly
from surgical videos, achieving mean average precision (mAP) ranging from 4.2%
to 38.1%. This study also analyzes the significance of the results obtained by
the presented approaches, performs a thorough methodological comparison between
them, in-depth result analysis, and proposes a novel ensemble method for
enhanced recognition. Our analysis shows that surgical workflow analysis is not
yet solved, and also highlights interesting directions for future research on
fine-grained surgical activity recognition which is of utmost importance for
the development of AI in surgery.


title:
Magnetically brightened dark electron-phonon bound states in a van der
  Waals antiferromagnet

abstract:
  In van der Waals (vdW) materials, strong coupling between different degrees
of freedom can hybridize elementary excitations into bound states with mixed
character. Correctly identifying the nature and composition of these bound
states is key to understanding their ground state properties and excitation
spectra. Here, we use ultrafast spectroscopy to reveal bound states of
d-orbitals and phonons in 2D vdW antiferromagnet NiPS3. These bound states
manifest themselves through equally spaced phonon replicas in frequency domain.
These states are optically dark above the N\'eel temperature and become
accessible with magnetic order. By launching this phonon and spectrally
tracking its amplitude, we establish the electronic origin of bound states as
localized d-d excitations. Our data directly yield electron-phonon coupling
strength which exceeds the highest known value in 2D systems. These results
demonstrate NiPS3 as a platform to study strong interactions between spins,
orbitals and lattice, and open pathways to coherent control of 2D magnets.


title: Breaking Character: Are Subwords Good Enough for MRLs After All?

abstract:
  Large pretrained language models (PLMs) typically tokenize the input string
into contiguous subwords before any pretraining or inference. However, previous
studies have claimed that this form of subword tokenization is inadequate for
processing morphologically-rich languages (MRLs). We revisit this hypothesis by
pretraining a BERT-style masked language model over character sequences instead
of word-pieces. We compare the resulting model, dubbed TavBERT, against
contemporary PLMs based on subwords for three highly complex and ambiguous MRLs
(Hebrew, Turkish, and Arabic), testing them on both morphological and semantic
tasks. Our results show, for all tested languages, that while TavBERT obtains
mild improvements on surface-level tasks \`a la POS tagging and full
morphological disambiguation, subword-based PLMs achieve significantly higher
performance on semantic tasks, such as named entity recognition and extractive
question answering. These results showcase and (re)confirm the potential of
subword tokenization as a reasonable modeling assumption for many languages,
including MRLs.


title: Minimum-Mass Limits for Streamlined Venus Atmospheric Probes

abstract:
  Small, expendable drop probes are an attractive method for making
measurements in the lower atmosphere of Venus, \,augmenting the capabilities of
orbiters or aerial platforms that must remain in the benign temperature region
above 50 km altitude. However, probe miniaturization is impeded by the need to
provide thermal and pressure protection for conventional payloads. This paper
determines the minimum mass limits for an insulated pressure vessel probe that
operates all the way to the Venusian surface. Scaling laws for the probe
performance and mass of major system components are explicitly derived using a
simple model that captures the relevant physics. Streamlining the probe is
found to be a highly effective strategy for lowering the system mass, but it
also reduces the time available for data collection and transmission.
Tradeoffs, guidelines and design charts are presented for an array of
miniaturized probes. Total system masses on the order of 5 kilograms are
plausible with streamlined probes if the desired science measurements can be
performed faster than a standard Venus descent timeline.


title:
Exact controllability to the trajectories of the one-phase Stefan
  problem

abstract:
  This paper deals with the exact controllability to the trajectories of the
one--phase Stefan problem in one spatial dimension. This is a free-boundary
problem that models solidification and melting processes. It is assumed that
the physical domain is filled by a medium whose state is liquid on the left and
solid, with constant temperature, on the right. In between we find a
free-boundary (the interface that separates the liquid from the solid). In the
liquid domain, a parabolic equation completed with initial and boundary
conditions must be satisfied by the temperature. On the interface, an
additional free-boundary requirement, called the {\it Stefan condition,} is
imposed. We prove the local exact controllability to the (smooth) trajectories.
To this purpose, we first reformulate the problem as the local null
controllability of a coupled PDE-ODE system with distributed controls. Then, a
new Carleman inequality for the adjoint of the linearized PDE-ODE system,
coupled on the boundary through nonlocal in space and memory terms, is
presented. This leads to the null controllability of an appropriate linear
system. Finally, a local result is obtained via local inversion, by using {\it
Liusternik-Graves' Theorem}. As a byproduct of our approach, we find that some
parabolic equations which contains memory terms localized on the boundary are
null-controllable.


title:
Nonlinear Optical Limiting with Hybrid Nanostructures of NiCo2O4 and
  Multiwall Carbon Nanotubes

abstract:
  Nonlinear optical (NLO) response in terms of reverse saturable absorption
(RSA) has been exploited extensively for optical limiting. Here, we
experimentally demonstrate that flower-like hybrid nanostructures of NiCo2O4
and Multiwall Carbon Nanotubes (NCO@MWCNT) exhibit a strong non-linearity in
their absorption, specifically an excited state absorption (ESA) induced RSA,
when exposed to nanosecond laser pulses. We obtain strong nonlinear absorption
coefficient (\b{eta}) and nonlinear refractive index (n2) in hybrid NCO@MWCNT,
the values that are 2-times and 2-orders of magnitude higher, respectively
compared to NCO or MWCNT alone. This offers straightforward application in
optical limiting with optical limiting (FOL) and optical onset (FON) threshold
ca. 2-10 times lower than benchmark NLO materials, e.g., graphene, family of
2-D transition metal dichalcogenides (TMDC) materials and recently established
NCO. Notably, for femtosecond pumping, NLO response of NCO@MWCNT is dominated
by saturable absorption (SA) with a week contribution from two photon
absorption (TPA), arising respectively from MWCNT and NCO. A remarkable sign
reversal along with a larger amplitude is obtained for n2 thanks to the charge
transfer from MWCNT to NCO.


title:
Beyond Cross-view Image Retrieval: Highly Accurate Vehicle Localization
  Using Satellite Image

abstract:
  This paper addresses the problem of vehicle-mounted camera localization by
matching a ground-level image with an overhead-view satellite map. Existing
methods often treat this problem as cross-view image retrieval, and use learned
deep features to match the ground-level query image to a partition (eg, a small
patch) of the satellite map. By these methods, the localization accuracy is
limited by the partitioning density of the satellite map (often in the order of
tens meters). Departing from the conventional wisdom of image retrieval, this
paper presents a novel solution that can achieve highly-accurate localization.
The key idea is to formulate the task as pose estimation and solve it by
neural-net based optimization. Specifically, we design a two-branch {CNN} to
extract robust features from the ground and satellite images, respectively. To
bridge the vast cross-view domain gap, we resort to a Geometry Projection
module that projects features from the satellite map to the ground-view, based
on a relative camera pose. Aiming to minimize the differences between the
projected features and the observed features, we employ a differentiable
Levenberg-Marquardt ({LM}) module to search for the optimal camera pose
iteratively. The entire pipeline is differentiable and runs end-to-end.
Extensive experiments on standard autonomous vehicle localization datasets have
confirmed the superiority of the proposed method. Notably, e.g., starting from
a coarse estimate of camera location within a wide region of 40m x 40m, with an
80% likelihood our method quickly reduces the lateral location error to be
within 5m on a new KITTI cross-view dataset.


title: Dynamic latent space relational event model

abstract:
  Dynamic relational processes, such as e-mail exchanges, bank loans and
scientific citations, are important examples of dynamic networks, in which the
relational events consistute time-stamped edges. There are contexts where the
network might be considered a reflection of underlying dynamics in some latent
space, whereby nodes are associated with dynamic locations and their relative
distances drive their interaction tendencies. As time passes nodes can change
their locations assuming new configurations, with different interaction
patterns. The aim of this paper is to define a dynamic latent space relational
event model. We then develop a computationally efficient method for inferring
the locations of the nodes. We make use of the Expectation Maximization
algorithm which embeds an extension of the universal Kalman filter. Kalman
filters are known for being effective tools in the context of tracking objects
in the space, with successful applications in fields such as geolocalization.
We extend its application to dynamic networks by filtering the signal from a
sequence of adjacency matrices and recovering the hidden movements. Besides the
latent space our formulation includes also more traditional fixed and random
effects, achieving a general model that can suit a large variety of
applications.


title:
Denoiser-based projections for 2-D super-resolution multi-reference
  alignment

abstract:
  We study the 2-D super-resolution multi-reference alignment (SR-MRA) problem:
estimating an image from its down-sampled, circularly-translated, and noisy
copies. The SR-MRA problem serves as a mathematical abstraction of the
structure determination problem for biological molecules. Since the SR-MRA
problem is ill-posed without prior knowledge, accurate image estimation relies
on designing priors that well-describe the statistics of the images of
interest. In this work, we build on recent advances in image processing, and
harness the power of denoisers as priors of images. In particular, we suggest
to use denoisers as projections, and design two computational frameworks to
estimate the image: projected expectation-maximization and projected method of
moments. We provide an efficient GPU implementation, and demonstrate the
effectiveness of these algorithms by extensive numerical experiments on a wide
range of parameters and images.


title: Pseudo-Geometric Strongly Regular Graphs with a Regular Point

abstract:
  We study pseudo-geometric strongly regular graphs whose second subconstituent
with respect to a vertex is a cover of a strongly regular graph or a complete
graph. By studying the structure of such graphs, we characterize all graphs
containing such a vertex, and use our characterization to find many new
strongly regular graphs. Thereby, we answer a question posed by Gardiner,
Godsil, Hensel, and Royle. We give an explicit construction for q new, pairwise
non-isomorphic graphs with the same parameters as the collinearity graph of
generalized quadrangles of order $(q,q)$ and a new non-geometric graph with the
same parameters as the collinearity graph of the Hermitian generalized
quadrangle of order $(q^2, q)$, for prime powers $q$. Using our
characterization, we computed 135478 new strongly regular graphs with
parameters (85,20,3,5) and 27 039 strongly regular graphs with parameters (156,
30, 4, 6).


title:
Towards Evaluation of Autonomously Generated Musical Compositions: A
  Comprehensive Survey

abstract:
  There are many applications that aim to create a complete model for an
autonomously generated composition; systems are able to generate muzak songs,
assist singers in transcribing songs or can imitate long-dead authors.
Subjective understanding of creativity or aesthetics differs not only within
preferences (popular authors or genres), but also differs on the basis of
experienced experience or socio-cultural environment. So, what do we want to
achieve with such an adaptation? What is the benefit of the resulting work for
the author, who can no longer evaluate this composition? And in what ways
should we evaluate such a composition at all?


title:
Existence of maximum likelihood estimates in exponential random graph
  models

abstract:
  We present a streamlined proof of the foundational result in the theory of
exponential random graph models (ERGMs) that the maximum likelihood estimate
exists if and only if the target statistic lies in the relative interior of the
convex hull of the set of realizable statistics. We also discuss how linear
dependence or "approximate linear dependence" of network statistics may lead to
degeneracy during model fitting.


title:
Iceberg Sensemaking: A Process Model for Critical Data Analysis and
  Visualization

abstract:
  We offer a new model of the sensemaking process for data science and visual
analytics. Whereas past sensemaking models have been built on theoretical
foundations in cognitivism and positivism, this model adopts interpretivist
foundations in order to reframe data sensemaking in humanistic terms. We
identify five key principles centered on the concept of schemas: Tacit and
Explicit Schemas, Schemas First and Always, Data as a Schematic Artifact,
Schematic Multiplicity, and Sensemaking Over Time. Our model uses the analogy
of an iceberg, where data is the visible tip of the schema underneath it. The
analysis process iteratively refines both the data and its schema in tandem. We
compare the roles of schemas in past sensemaking models and draw conceptual
distinctions based on a historical review of schemas in different philosophical
traditions. We validate the descriptive, predictive, and explanatory power of
our model through four analysis scenarios: uncovering data injustice,
investigating official data, teaching data wrangling, and producing data
mashups.


title:
Small cycle structure for words in conjugation invariant random
  permutations

abstract:
  We study the cycle structure of words in several random permutations. We
assume that the permutations are independent and that their distribution is
conjugation invariant, with a good control on their short cycles. If, after
successive cyclic simplifications, the word w still contains at least two
different letters, then we get a universal limiting joint law for small cycles
for the word in these permutations. These results can be seen as an extension
of our previous work [Kammoun and Ma\"ida, 2020] from the product of
permutations to any non-trivial word in the permutations and also as an
extension of the results of [Nica, 1994] from uniform permutations to general
conjugation invariant random permutations.


title:
Global Regularity for A Radiation Hydrodynamics Model with Viscosity and
  Thermal Conductivity

abstract:
  In this paper, we study the global wellposedness of a radiation hydrodynamics
model with viscosity and thermal conductivity. It is now well-understood that,
unlike the compressible Euler equations whose smooth solutions must blow up in
finite time no matter how small and how smooth the initial data is, the
dissipative structure of such a radiation hydrodynamics model can indeed
guarantee that its one-dimensional Cauchy problem admits a unique global smooth
solution provided that the initial data is sufficiently small, while for large
initial data, even if the heat conductivity is taken into account but the
viscosity effect is ignored, shock type singularities must appear infinite time
for smooth solutions of the Cauchy problem of one-dimensional radiation
hydrodynamics model with thermal conductivity and zero viscosity. Thus a
natural question is, if effects of both the viscosity and the thermal
conductivity are considered, does the one-dimensional radiation hydrodynamics
model with viscosity and thermal conductivity exist a unique global large
solution? We give an affirmative answer to this problem and show in this paper
that the initial-boundary value problem to the radiation hydrodynamics model in
an one-dimensional periodic box T \cong R/Z with viscosity and thermal
conductivity does exist a unique global smooth solution for any large initial
data. The main ingredient in our analysis is to introduce some delicate
estimates, especially an improved L^m([0, T], L^\infty(T))-estimate on the
absolute temperature for some m \in N and a pointwise estimate between the
absolute temperature, the specific volume, and the first-order spatial
derivative of the macro radiation flux, to deduce the desired positive lower
and upper bounds on the density and the absolute temperature.


title: Phase transitions above the upper critical dimension

abstract:
  These lecture notes provide an overview of the renormalization group (RG) as
a successful framework to understand critical phenomena above the upper
critical dimension $d_{\rm uc}$. After an introduction to the scaling picture
of continuous phase transitions, we discuss the apparent failure of the
Gaussian fixed point to capture scaling for Landau mean-field theory, which
should hold in the thermodynamic limit above $d_{\rm uc}$. We recount how
Fisher's dangerous-irrelevant-variable formalism applied to thermodynamic
functions partially repairs the situation but at the expense of hyperscaling
and finite-size scaling, both of which were, until recently, believed not to
apply above $d_{\rm uc}$. We recall limitations of various attempts to match
the RG with analytical and numerical results for Ising systems. We explain how
the extension of dangerous irrelevancy to the correlation sector is key to
marrying the above concepts into a comprehensive RG scaling picture that
renders hyperscaling and finite-size scaling valid in all dimensions. We
collect what we believe is the current status of the theory, including some new
insights and results. This paper is in grateful memory of Michael Fisher who
introduced many of the concepts discussed and who, half a century later,
contributed to their advancement.


title:
Rockafellian Relaxation in Optimization under Uncertainty:
  Asymptotically Exact Formulations

abstract:
  In practice, optimization models are often prone to unavoidable inaccuracies
due to lack of data and dubious assumptions. Traditionally, this placed special
emphasis on risk-based and robust formulations, and their focus on
"conservative" decisions. We develop, in contrast, an "optimistic" framework
based on Rockafellian relaxations in which optimization is conducted not only
over the original decision space but also jointly with a choice of model
perturbation. The framework enables us to address challenging problems with
ambiguous probability distributions from the areas of two-stage stochastic
optimization without relatively complete recourse, probability functions
lacking continuity properties, expectation constraints, and outlier analysis.
We are also able to circumvent the fundamental difficulty in stochastic
optimization that convergence of distributions fails to guarantee convergence
of expectations. The framework centers on the novel concepts of exact and
asymptotically exact Rockafellians, with interpretations of "negative"
regularization emerging in certain settings. We illustrate the role of
Phi-divergence, examine rates of convergence under changing distributions, and
explore extensions to first-order optimality conditions. The main development
is free of assumptions about convexity, smoothness, and even continuity of
objective functions.


title: Information-theoretic Online Memory Selection for Continual Learning

abstract:
  A challenging problem in task-free continual learning is the online selection
of a representative replay memory from data streams. In this work, we
investigate the online memory selection problem from an information-theoretic
perspective. To gather the most information, we propose the \textit{surprise}
and the \textit{learnability} criteria to pick informative points and to avoid
outliers. We present a Bayesian model to compute the criteria efficiently by
exploiting rank-one matrix structures. We demonstrate that these criteria
encourage selecting informative points in a greedy algorithm for online memory
selection. Furthermore, by identifying the importance of \textit{the timing to
update the memory}, we introduce a stochastic information-theoretic reservoir
sampler (InfoRS), which conducts sampling among selective points with high
information. Compared to reservoir sampling, InfoRS demonstrates improved
robustness against data imbalance. Finally, empirical performances over
continual learning benchmarks manifest its efficiency and efficacy.


title:
V$_2$C-based lithium batteries: The influence of magnetic phase and
  Hubbard interaction

abstract:
  MXenes are a family of two-dimensional materials that could be attractive for
use as electrodes in lithium batteries due to their high specific capacity. For
this purpose, it is necessary to evaluate magnitudes such as the lithium
adsorption energy and the magnitude of the open-circuit voltage for different
lithium concentrations. In this paper, we show through first principles
calculations that in a V$_2$C monolayer we must consider the high correlation
between the electrons belonging to vanadium to obtain correct results of these
quantities. We include this correlation employing the Hubbard coupling
parameter obtained by a linear response method. We found that the system is
antiferromagnetic and that the quantities studied depend on the magnetic phase
considered. Indirectly, experimental results could validate the theoretical
value of the theoretical Hubbard parameter.


title: Minimal Roman Dominating Functions: Extensions and Enumeration

abstract:
  Roman domination is one of the many variants of domination that keeps most of
the complexity features of the classical domination problem. We prove that
Roman domination behaves differently in two aspects: enumeration and extension.
We develop non-trivial enumeration algorithms for minimal Roman domination
functions with polynomial delay and polynomial space. Recall that the existence
of a similar enumeration result for minimal dominating sets is open for
decades. Our result is based on a polynomial-time algorithm for Extension Roman
Domination: Given a graph $G = (V,E)$ and a function $f:V\to\{0,1,2\}$, is
there a minimal Roman domination function $\Tilde{f}$ with $f\leq \Tilde{f}$?
Here, $\leq$ lifts $0< 1< 2$ pointwise; minimality is understood in this order.
Our enumeration algorithm is also analyzed from an input-sensitive viewpoint,
leading to a run-time estimate of $\Oh(\RomanUpperbound^n)$ for graphs of order
n; this is complemented by a lower bound example of
$\Omega(\RomanLowerbound^n)$.


title: Configuration and Collection Factors for Side-Channel Disassembly

abstract:
  Myriad uses, methodologies, and channels have been explored for side-channel
analysis. However, specific implementation considerations are often
unpublished. This paper explores select test configuration and collection
parameters, such as input voltage, shunt resistance, sample rate, and
microcontroller clock frequency, along with their impact on side-channel
analysis performance. The analysis use case considered is instruction
disassembly and classification using the microcontroller power side-channel. An
ATmega328P microcontroller and a subset of the AVR instruction set are used in
the experiments as the Device Under Test (DUT). A time-series convolutional
neural network (CNN) is used to evaluate classification performance at
clock-cycle fidelity. We conclude that configuration and collection parameters
have a meaningful impact on performance, especially where the
instruction-trace's signal to noise ratio (SNR) is impacted. Additionally, data
collection and analysis well above the Nyquist rate is required for
side-channel disassembly. We also found that 7V input voltage with 1 kiloohm
shunt and a sample rate of 250-500 MSa/s provided optimal performance in our
application, with diminishing returns or in some cases degradation at higher
levels.


title:
Risk-aware UAV-UGV Rendezvous with Chance-Constrained Markov Decision
  Process

abstract:
  We study a chance-constrained variant of the cooperative aerial-ground
vehicle routing problem, in which an Unmanned Aerial Vehicle (UAV) with limited
battery capacity and an Unmanned Ground Vehicle (UGV) that can also act as a
mobile recharging station need to jointly accomplish a mission such as
monitoring a set of points. Due to the limited battery capacity of the UAV, two
vehicles sometimes have to deviate from their task to rendezvous and recharge
the UAV\@. Unlike prior work that has focused on the deterministic case, we
address the challenge of stochastic energy consumption of the UAV\@. We are
interested in finding the optimal policy that decides when and where to
rendezvous such that the expected travel time of the UAV is minimized and the
probability of running out of charge is less than a user-defined tolerance. We
formulate this problem as a Chance Constrained Markov Decision Process (CCMDP).
To the best knowledge of the authors, this is the first CMDP-based formulation
for the UAV-UGV routing problems under power consumption uncertainty. We adopt
a Linear Programming (LP) based approach to solve the problem optimally. We
demonstrate the effectiveness of our formulation in the context of an
Intelligence Surveillance and Reconnaissance (ISR) mission.


title:
Analysis of Power-Oriented Fault Injection Attacks on Spiking Neural
  Networks

abstract:
  Spiking Neural Networks (SNN) are quickly gaining traction as a viable
alternative to Deep Neural Networks (DNN). In comparison to DNNs, SNNs are more
computationally powerful and provide superior energy efficiency. SNNs, while
exciting at first appearance, contain security-sensitive assets (e.g., neuron
threshold voltage) and vulnerabilities (e.g., sensitivity of classification
accuracy to neuron threshold voltage change) that adversaries can exploit. We
investigate global fault injection attacks by employing external power supplies
and laser-induced local power glitches to corrupt crucial training parameters
such as spike amplitude and neuron's membrane threshold potential on SNNs
developed using common analog neurons. We also evaluate the impact of
power-based attacks on individual SNN layers for 0% (i.e., no attack) to 100%
(i.e., whole layer under attack). We investigate the impact of the attacks on
digit classification tasks and find that in the worst-case scenario,
classification accuracy is reduced by 85.65%. We also propose defenses e.g., a
robust current driver design that is immune to power-oriented attacks, improved
circuit sizing of neuron components to reduce/recover the adversarial accuracy
degradation at the cost of negligible area and 25% power overhead. We also
present a dummy neuron-based voltage fault injection detection system with 1%
power and area overhead.


title: A review of knowledge graph application scenarios in cyber security

abstract:
  Facing the dynamic complex cyber environments, internal and external cyber
threat intelligence, and the increasing risk of cyber-attack, knowledge graphs
show great application potential in the cyber security area because of their
capabilities in knowledge aggregation, representation, management, and
reasoning. However, while most research has focused on how to develop a
complete knowledge graph, it remains unclear how to apply the knowledge graph
to solve industrial real challenges in cyber-attack and defense scenarios. In
this review, we provide a brief overview of the basic concepts, schema, and
construction approaches for the cyber security knowledge graph. To facilitate
future research on cyber security knowledge graphs, we also present a curated
collection of datasets and open-source libraries on the knowledge construction
and information extraction task. In the major part of this article, we conduct
a comparative review of the different works that elaborate on the recent
progress in the application scenarios of the cyber security knowledge graph.
Furthermore, a novel comprehensive classification framework is created to
describe the connected works from nine primary categories and eighteen
subcategories. Finally, we have a thorough outlook on several promising
research directions based on the discussion of existing research flaws.


title: Singlet extensions and W boson mass in the light of the CDF II result

abstract:
  Recently, the CDF collaboration has reported the precise measurement of the W
boson mass, $M_W = 80433.5\pm 9.4 \,$MeV, based on $8.8$ fb$^{-1}$ of
$\sqrt{s}=1.96$ TeV $p\bar{p}$ collision data from the CDF II detector at the
Fermilab Tevatron. This is about $7\sigma$ away from the Standard Model
prediction, $M_{W}^{\rm SM}=80357 \pm 6 \,$MeV. Such a large discrepancy may be
partially due to exotic particles that radiatively alter the relation between
the W and Z boson masses. In this Letter, we study singlet extensions of the
Standard Model focusing on the shift of the W boson mass. In the minimal
extension with a real singlet field, using the bounds from the electroweak
oblique parameters, B meson decays, LEP, and LHC, we find that the W boson mass
shift is at most a few MeV, and therefore it does not alleviate the tension
between the CDF II result and the SM prediction. We then examine how much
various bounds are relaxed when the singlet is allowed to decay invisibly and
find that the increase of the W boson mass does not exceed $5$ MeV due to the
bound from the Higgs signal strength. We also discuss phenomenological and
cosmological implications of the singlet extensions such as the muon $g-2$
anomaly, axion/hidden photon dark matter, and self-interacting dark radiation
as a possible alleviation of the Hubble tension.


title:
Image Reconstruction for MRI using Deep CNN Priors Trained without
  Groundtruth

abstract:
  We propose a new plug-and-play priors (PnP) based MR image reconstruction
method that systematically enforces data consistency while also exploiting
deep-learning priors. Our prior is specified through a convolutional neural
network (CNN) trained without any artifact-free ground truth to remove
undersampling artifacts from MR images. The results on reconstructing
free-breathing MRI data into ten respiratory phases show that the method can
form high-quality 4D images from severely undersampled measurements
corresponding to acquisitions of about 1 and 2 minutes in length. The results
also highlight the competitive performance of the method compared to several
popular alternatives, including the TGV regularization and traditional UNet3D.


title:
On the $(n+3)$-webs by rational curves induced by the forgetful maps on
  the moduli spaces $\mathcal M_{0,n+3}$

abstract:
  We discuss the curvilinear web $\boldsymbol{\mathcal W}_{0,n+3}$ on the
moduli space $\mathcal M_{0,n+3}$ of projective configurations of $n+3$ points
on $\mathbf P^1$ defined by the $n+3$ forgetful maps $\mathcal
M_{0,n+3}\rightarrow \mathcal M_{0,n+2}$. We recall classical results which
show that this web is linearizable when $n$ is odd, or is equivalent to a web
by conics when $n$ is even. We then turn to the abelian relations (ARs) of
these webs. After recalling the well-known case when $n=2$ (related to the
5-terms functional identity of the dilogarithm), we focus on the case of the
6-web $\boldsymbol{\mathcal W}_{{0,6}}$. We show that this web is isomorphic to
the web formed by the lines contained in Segre's cubic primal
$\boldsymbol{S}\subset \mathbf P^4$ and that a kind of `Abel's theorem' allows
to describe the ARs of $\boldsymbol{\mathcal W}_{{0,6}}$ by means of the
abelian 2-forms on the Fano surface $F_1(\boldsymbol{S})\subset G_1(\mathbf
P^4)$ of lines contained in $\boldsymbol{S}$. We deduce from this that
$\boldsymbol{\mathcal W}_{{0,6}}$ has maximal rank with all its ARs rational,
and that these span a space which is an irreducible $\mathfrak S_6$-module.
Then we take up an approach due to Damiano that we correct in the case when $n$
is odd: it leads to an abstract description of the space of ARs of
$\boldsymbol{\mathcal W}_{0,n+3}$ as a $\mathfrak S_{n+3}$-representation. In
particular, we obtain that this web has maximal rank for any $n\geq 2$.
Finally, we consider `Euler's abelian relation $\boldsymbol{\mathcal E}_n$', a
particular AR for $\boldsymbol{\mathcal W}_{0,n+3}$ constructed by Damiano from
a characteristic class on the grassmannian of 2-planes in $\mathbf R^{n+3}$ by
means of Gelfand-MacPherson theory of polylogarithmic forms. We give an
explicit conjectural formula for the components of $\boldsymbol{\mathcal E}_n$
that we prove to be correct for $n\leq 12$.


title:
Worst-case Performance of Greedy Policies in Bandits with Imperfect
  Context Observations

abstract:
  Contextual bandits are canonical models for sequential decision-making under
uncertainty in environments with time-varying components. In this setting, the
expected reward of each bandit arm consists of the inner product of an unknown
parameter and the context vector of that arm, perturbed with a random error.
The classical setting heavily relies on fully observed contexts, while study of
the richer model of imperfectly observed contextual bandits is immature. This
work considers Greedy reinforcement learning policies that take actions as if
the current estimates of the parameter and of the unobserved contexts coincide
with the corresponding true values. We establish that the non-asymptotic
worst-case regret grows logarithmically with the time horizon and the failure
probability, while it scales linearly with the number of arms. Numerical
analysis showcasing the above efficiency of Greedy policies is also provided.


title:
Revenue Management Under the Markov Chain Choice Model with Joint Price
  and Assortment Decisions

abstract:
  Finding the optimal product prices and product assortment are two fundamental
problems in revenue management. Usually, a seller needs to jointly determine
the prices and assortment while managing a network of resources with limited
capacity. However, there is not yet a tractable method to efficiently solve
such a problem. Existing papers studying static joint optimization of price and
assortment cannot incorporate resource constraints. Then we study the revenue
management problem with resource constraints and price bounds, where the prices
and the product assortments need to be jointly determined over time. We showed
that under the Markov chain (MC) choice model (which subsumes the multinomial
logit (MNL) model), we could reformulate the choice-based joint optimization
problem as a tractable convex conic optimization problem. We also proved that
an optimal solution with a constant price vector exists even with constraints
on resources. In addition, a solution with both constant assortment and price
vector can be optimal when there is no resource constraint.


title:
Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of
  Code-Mixed Clinical Texts

abstract:
  Despite the advances in digital healthcare systems offering curated
structured knowledge, much of the critical information still lies in large
volumes of unlabeled and unstructured clinical texts. These texts, which often
contain protected health information (PHI), are exposed to information
extraction tools for downstream applications, risking patient identification.
Existing works in de-identification rely on using large-scale annotated corpora
in English, which often are not suitable in real-world multilingual settings.
Pre-trained language models (LM) have shown great potential for cross-lingual
transfer in low-resource settings. In this work, we empirically show the
few-shot cross-lingual transfer property of LMs for named entity recognition
(NER) and apply it to solve a low-resource and real-world challenge of
code-mixed (Spanish-Catalan) clinical notes de-identification in the stroke
domain. We annotate a gold evaluation dataset to assess few-shot setting
performance where we only use a few hundred labeled examples for training. Our
model improves the zero-shot F1-score from 73.7% to 91.2% on the gold
evaluation set when adapting Multilingual BERT (mBERT) (Devlin et al., 2019)
from the MEDDOCAN (Marimon et al., 2019) corpus with our few-shot cross-lingual
target corpus. When generalized to an out-of-sample test set, the best model
achieves a human-evaluation F1-score of 97.2%.


title: Optimal Subsampling for Large Sample Ridge Regression

abstract:
  Subsampling is a popular approach to alleviating the computational burden for
analyzing massive datasets. Recent efforts have been devoted to various
statistical models without explicit regularization. In this paper, we develop
an efficient subsampling procedure for the large sample linear ridge
regression. In contrast to the ordinary least square estimator, the
introduction of the ridge penalty leads to a subtle trade-off between bias and
variance. We first investigate the asymptotic properties of the subsampling
estimator and then propose to minimize the asymptotic-mean-squared-error
criterion for optimality. The resulting subsampling probability involves both
ridge leverage score and L2 norm of the predictor. To further reduce the
computational cost for calculating the ridge leverage scores, we propose the
algorithm with efficient approximation. We show by synthetic and real datasets
that the algorithm is both statistically accurate and computationally efficient
compared with existing subsampling based methods.


title: Multimodal Machine Learning in Precision Health

abstract:
  As machine learning and artificial intelligence are more frequently being
leveraged to tackle problems in the health sector, there has been increased
interest in utilizing them in clinical decision-support. This has historically
been the case in single modal data such as electronic health record data.
Attempts to improve prediction and resemble the multimodal nature of clinical
expert decision-making this has been met in the computational field of machine
learning by a fusion of disparate data. This review was conducted to summarize
this field and identify topics ripe for future research. We conducted this
review in accordance with the PRISMA (Preferred Reporting Items for Systematic
reviews and Meta-Analyses) extension for Scoping Reviews to characterize
multi-modal data fusion in health. We used a combination of content analysis
and literature searches to establish search strings and databases of PubMed,
Google Scholar, and IEEEXplore from 2011 to 2021. A final set of 125 articles
were included in the analysis. The most common health areas utilizing
multi-modal methods were neurology and oncology. However, there exist a wide
breadth of current applications. The most common form of information fusion was
early fusion. Notably, there was an improvement in predictive performance
performing heterogeneous data fusion. Lacking from the papers were clear
clinical deployment strategies and pursuit of FDA-approved tools. These
findings provide a map of the current literature on multimodal data fusion as
applied to health diagnosis/prognosis problems. Multi-modal machine learning,
while more robust in its estimations over unimodal methods, has drawbacks in
its scalability and the time-consuming nature of information concatenation.


title: Measuring the False Sense of Security

abstract:
  Recently, several papers have demonstrated how widespread gradient masking is
amongst proposed adversarial defenses. Defenses that rely on this phenomenon
are considered failed, and can easily be broken. Despite this, there has been
little investigation into ways of measuring the phenomenon of gradient masking
and enabling comparisons of its extent amongst different networks. In this
work, we investigate gradient masking under the lens of its mensurability,
departing from the idea that it is a binary phenomenon. We propose and motivate
several metrics for it, performing extensive empirical tests on defenses
suspected of exhibiting different degrees of gradient masking. These are
computationally cheaper than strong attacks, enable comparisons between models,
and do not require the large time investment of tailor-made attacks for
specific models. Our results reveal metrics that are successful in measuring
the extent of gradient masking across different networks


title:
MedDistant19: A Challenging Benchmark for Distantly Supervised
  Biomedical Relation Extraction

abstract:
  Relation Extraction in the biomedical domain is challenging due to the lack
of labeled data and high annotation costs, needing domain experts. Distant
supervision is commonly used as a way to tackle the scarcity of annotated data
by automatically pairing knowledge graph relationships with raw texts.
Distantly Supervised Biomedical Relation Extraction (Bio-DSRE) models can
seemingly produce very accurate results in several benchmarks. However, given
the challenging nature of the task, we set out to investigate the validity of
such impressive results. We probed the datasets used by Amin et al. (2020) and
Hogan et al. (2021) and found a significant overlap between training and
evaluation relationships that, once resolved, reduced the accuracy of the
models by up to 71%. Furthermore, we noticed several inconsistencies with the
data construction process, such as creating negative samples and improper
handling of redundant relationships. We mitigate these issues and present
MedDistant19, a new benchmark dataset obtained by aligning the MEDLINE
abstracts with the widely used SNOMED Clinical Terms (SNOMED CT) knowledge
base. We experimented with several state-of-the-art models achieving an AUC of
55.4% and 49.8% at sentence- and bag-level, showing that there is still plenty
of room for improvement.


title:
A Fully Polynomial Time Approximation Scheme for Fixed-Horizon
  Constrained Stochastic Shortest Path Problem under Local Transitions

abstract:
  The fixed-horizon constrained stochastic shortest path problem (C-SSP) is a
formalism for planning in stochastic environments under certain operating
constraints. Chance-Constrained SSP (CC-SSP) is a variant that allows bounding
the probability of constraint violation, which is desired in many
safety-critical applications. This work considers an important variant of
(C)C-SSP under local transition, capturing a broad class of SSP problems where
state reachability exhibit a certain locality. Only a constant number of states
can share some subsequent states. (C)C-SSP under local transition is NP-Hard
even for a planning horizon of two. In this work, we propose a fully
polynomial-time approximation scheme for (C)C-SSP that computes (near) optimal
deterministic policies. Such an algorithm is the best approximation algorithm
attainable in theory


title: Dispersive determination of low energy $\pi K$ interactions

abstract:
  We summarize the application of dispersion relations for the determination of
low energy $\pi K$ interactions. In particular, we present our recent
dispersive analyses for the complete study of both $\pi K\to \pi K$ and $\pi
\pi \rightarrow K \bar K$ channels. First, we summarize our approach, we study
both channels by using several families of dispersion relations and present the
most relevant partial waves. Then we apply the dispersive formalism to the
threshold region, where we extract many different low-energy parameters.


title: Optimization of Asymmetric Quantum Otto Engine Cycles

abstract:
  We consider the optimization of the work output and fluctuations of a
finite-time quantum Otto heat engine cycle consisting of compression and
expansion work strokes of unequal duration. The asymmetry of the cycle is
characterized by a parameter $r_u$ giving the ratio of the times for the
compression and expansion work strokes. For such an asymmetric quantum Otto
engine cycle, with working substance chosen as a harmonic oscillator or a
two-level system, we find that the optimal values of $r_u$ maximising the work
output and the reliability (defined as the ratio of average work output to its
standard deviation) shows discontinuities as a function of the total time taken
for the cycle. Moreover we identify cycles of some specific duration where both
the work output and the reliability take their largest values for the same
value of the asymmetry parameter $r_u$.


title:
Temporal Knowledge Graph Reasoning with Low-rank and Model-agnostic
  Representations

abstract:
  Temporal knowledge graph completion (TKGC) has become a popular approach for
reasoning over the event and temporal knowledge graphs, targeting the
completion of knowledge with accurate but missing information. In this context,
tensor decomposition has successfully modeled interactions between entities and
relations. Their effectiveness in static knowledge graph completion motivates
us to introduce Time-LowFER, a family of parameter-efficient and time-aware
extensions of the low-rank tensor factorization model LowFER. Noting several
limitations in current approaches to represent time, we propose a cycle-aware
time-encoding scheme for time features, which is model-agnostic and offers a
more generalized representation of time. We implement our methods in a unified
temporal knowledge graph embedding framework, focusing on time-sensitive data
processing. The experiments show that our proposed methods perform on par or
better than the state-of-the-art semantic matching models on two benchmarks.


title:
Computing zeta functions of table algebra orders using the
  Bushnell-Reiner integral approach

abstract:
  We investigate the generalization of Solomon's zeta function for integral
group rings to integral adjacency algebras of association schemes and to orders
generated by the standard basis of an integral table algebra. As Solomon's
elementary method for computing this zeta function runs into computational
difficulties for ranks $3$ or more, a more efficient method is desired. We give
several examples to illustrate how the method proposed by Bushnell and Reiner
for zeta functions of orders can be applied to compute explicit zeta functions
for these orders.


title:
Driving black-box quantum thermal machines with optimal power/efficiency
  trade-offs using reinforcement learning

abstract:
  The optimal control of non-equilibrium open quantum systems is a challenging
task but has a key role in improving existing quantum information processing
technologies. We introduce a general model-free framework based on
Reinforcement Learning to identify out-of-equilibrium thermodynamic cycles that
are Pareto optimal trade-offs between power and efficiency for quantum heat
engines and refrigerators. The method does not require any knowledge of the
quantum thermal machine, nor of the system model, nor of the quantum state.
Instead, it only observes the heat fluxes, so it is both applicable to
simulations and experimental devices. We test our method identifying
Pareto-optimal trade-offs between power and efficiency in two systems: an
experimentally realistic refrigerator based on a superconducting qubit, where
we identify non-intuitive control sequences that reduce quantum friction and
outperform previous cycles proposed in literature; and a heat engine based on a
quantum harmonic oscillator, where we find cycles with an elaborate structure
that outperform the optimized Otto cycle.

categories: quant-ph cond-mat.mes-hall cs.LG physics.chem-ph


title:
3D-printed stand, timing interface, and coil localization tools for
  concurrent TMS-fMRI experiments

abstract:
  Concurrent TMS-fMRI involves administrating TMS while subjects are inside an
MRI scanner and allows the study of the effects of neurostimulation on
simultaneous brain activity. Despite its high promise, the technique has proven
challenging to implement for at least three reasons. First, it is difficult to
position and stabilize the TMS coil inside the MRI scanner in a way that
precisely targets a pre-specified brain region. Second, standard
task-presentation software suffers from imprecise timing, which can lead to TMS
causing large image artifacts. Third, it is difficult to verify the exact TMS
coil position during scanning. In this paper, we describe solutions to all
three of these challenges. First, we develop a 3D-printed TMS stand that is
fully adjustable and can reach most areas of the scalp. The stand is compatible
with various MR coils and features an adjustable mirror holder. Second, we
create an interface that can precisely time the TMS pulses with respect to the
fMRI image acquisition with a variance of under 1 ms. Third, we develop
software for precisely determining the TMS coil position inside the MRI scanner
and computing the location of maximal stimulation. All three tools are either
free or inexpensive. We provide detailed instructions for building and
implementing these tools to facilitate an efficient and reliable concurrent
TMS-fMRI setup.


title:
Macdonald formula, Ricci Curvature, and Concentration Locus for
  classical compact Lie groups

abstract:
  For Classical compact Lie groups, we use Macdonald's formula \cite{Ma} and
Ricci curvature for analyzing a "concentration locus", which is a tool to
detect where a sequence of metric, Borel measurable spaces concentrates its
measure.


title: DILEMMA: Self-Supervised Shape and Texture Learning with Transformers

abstract:
  There is a growing belief that deep neural networks with a shape bias may
exhibit better generalization capabilities than models with a texture bias,
because shape is a more reliable indicator of the object category. However, we
show experimentally that existing measures of shape bias are not stable
predictors of generalization and argue that shape discrimination should not
come at the expense of texture discrimination. Thus, we propose a pseudo-task
to explicitly boost both shape and texture discriminability in models trained
via self-supervised learning. For this purpose, we train a ViT to detect which
input token has been combined with an incorrect positional embedding. To retain
texture discrimination, the ViT is also trained as in MoCo with a
student-teacher architecture and a contrastive loss over an extra learnable
class token. We call our method DILEMMA, which stands for Detection of
Incorrect Location EMbeddings with MAsked inputs. We evaluate our method
through fine-tuning on several datasets and show that it outperforms MoCoV3 and
DINO. Moreover, we show that when downstream tasks are strongly reliant on
shape (such as in the YOGA-82 pose dataset), our pre-trained features yield a
significant gain over prior work. Code will be released upon publication.


title: Superradiant instability in slowly rotating Kerr-Newman black holes

abstract:
  We study the superradiant instability of slowly rotating Kerr-Newman (sKN)
black holes under a charged massive scalar perturbation. These black holes
resemble closer the Reissner-Nordstr\"{o}m black holes than the Kerr-Newman
black holes. We compute the negative absorption cross section of a massive
scalar for superradiant scattering in the low frequency limit. From the scalar
potential analysis, we find that the superradiant instability is not allowed in
the sKN black holes because the condition for a trapping well is not compatible
with the superradiance condition ($\omega<\omega_c$). However, the rate of
energy extraction might grow exponentially if the sKN black hole is placed
inside a reflecting cavity. Finally, we obtain the condition for the trapping
well to possess quasibound states in the sKN black holes by analyzing
asymptotic scalar potential and wave functions.


title: Fundamental polyhedra of projective elementary groups

abstract:
  For $O$ an imaginary quadratic ring, we compute a fundamental polyhedron of
$\text{PE}_2(O)$, the projective elementary subgroup of $\text{PSL}_2(O)$. This
allows for new, simplified proofs of theorems of Cohn, Nica, Fine, and Frohman.
Namely, we obtain a presentation for $\text{PE}_2(O)$, show that it has
infinite-index and is its own normalizer in $\text{PSL}_2(O)$, and split
$\text{PSL}_2(O)$ into a free product with amalgamation that has
$\text{PE}_2(O)$ as one of its factors.


title:
Large Itinerant Electron Exchange Coupling in the Magnetic Topological
  Insulator MnBi2Te4

abstract:
  Magnetism in topological materials creates phases exhibiting quantized
transport phenomena with applications in spintronics and quantum information.
The emergence of such phases relies on strong interaction between localized
spins and itinerant states comprising the topological bands, and the subsequent
formation of an exchange gap. However, this interaction has never been measured
in any intrinsic magnetic topological material. Using a multimodal approach,
this exchange interaction is measured in MnBi2Te4, the first realized intrinsic
magnetic topological insulator. Interrogating nonequilibrium spin dynamics,
itinerant bands are found to exhibit a strong exchange coupling to localized Mn
spins. Momentum-resolved ultrafast electron scattering and magneto-optic
measurements reveal that itinerant spins disorder via electron-phonon
scattering at picosecond timescales. Localized Mn spins, probed by resonant
X-ray scattering, disorder concurrently with itinerant spins, despite being
energetically decoupled from the initial excitation. Modeling the results using
atomistic simulations, the exchange coupling between localized and itinerant
spins is estimated to be >100 times larger than superexchange interactions.
This implies an exchange gap of >25 meV should occur in the topological surface
states. By directly quantifying local-itinerant exchange coupling, this work
validates the materials-by-design strategy of utilizing localized magnetic
order to create and manipulate magnetic topological phases, from static to
ultrafast timescales.


title: Differentially Private Fingerprinting for Location Trajectories

abstract:
  Location-based services have brought significant convenience to people in
their daily lives. Services like navigation, food delivery, and carpooling
frequently ask for location data from users. On the other side, researchers and
businesses are eager to acquire those data (that is collected by location-based
service providers) for various purposes. However, directly releasing those data
causes privacy concerns since location data contain users' sensitive
information, e.g., regular moving patterns and favorite spots. To solve this,
we propose a system that protects users' location data under differential
privacy and prevents unauthorized redistribution at the same time. Observing
high amount of noise introduced to achieve differential privacy, we implement a
novel post-processing scheme to regain data utility. In addition, we also
propose a novel fingerprinting scheme as a part of the post-processing (to
detect unauthorized redistribution of data). Our proposed fingerprinting scheme
considers correlations in location datasets and collusions among multiple
parties, which makes it hard for the attackers to infer the fingerprinting
codes and avoid accusation. Using the experiments on a real-life location
dataset, we show that our system achieves high fingerprint robustness against
state-of-the-art attacks. We also show the integrated fingerprinting scheme
increases data utility for differentially private datasets, which is beneficial
for data analyzers in data mining.


title: Fake news detection using parallel BERT deep neural networks

abstract:
  Fake news is a growing challenge for social networks and media. Detection of
fake news always has been a problem for many years, but after the evolution of
social networks and increasing speed of news dissemination in recent years has
been considered again. There are several approaches to solving this problem,
one of which is to detect fake news based on its text style using deep neural
networks. In recent years, one of the most used forms of deep neural networks
for natural language processing is transfer learning with transformers. BERT is
one of the most promising transformers who outperforms other models in many NLP
benchmarks. This article, we introduce MWPBert, which uses two parallel BERT
networks to perform veracity detection on full-text news articles. One of the
BERT networks encodes news headline, and another encodes news body. Since the
input length of the BERT network is limited and constant and the news body is
usually a long text, we cannot fed the whole news text into the BERT.
Therefore, using the MaxWorth algorithm, we selected the part of the news text
that is more valuable for fact-checking, and fed it into the BERT network.
Finally, we encode the output of the two BERT networks to an output network to
classify the news. The experiment results showed that the proposed model
outperformed previous models in terms of accuracy and other performance
measures.


title: Willingness to pay, surplus and Insurance policy under dual theory

abstract:
  In this paper, we aims to state some proprieties of willingness to pay (WTP)
for partial risk reduction and links with insurance within the dual theory of
decision. In the case of partial reduction, we get as Langlais (2005) that a
risk-averse decision maker (DM) can have a willingness to pay small than this
of a neutral one. By decomposition the WTP as Courbage and al (2008), we get
that a strong averse DM is willing to give more for a reduction of a high
probability portion rather than a low probability one. The main result is that
in the dual theory, reducing probability of risk and supply insurance can be
complementary if the surplus is increasing in risk reduction.


title: Edge Continual Learning for Dynamic Digital Twins over Wireless Networks

abstract:
  Digital twins (DTs) constitute a critical link between the real-world and the
metaverse. To guarantee a robust connection between these two worlds, DTs
should maintain accurate representations of the physical applications, while
preserving synchronization between real and digital entities. In this paper, a
novel edge continual learning framework is proposed to accurately model the
evolving affinity between a physical twin (PT) and its corresponding cyber twin
(CT) while maintaining their utmost synchronization. In particular, a CT is
simulated as a deep neural network (DNN) at the wireless network edge to model
an autonomous vehicle traversing an episodically dynamic environment. As the
vehicular PT updates its driving policy in each episode, the CT is required to
concurrently adapt its DNN model to the PT, which gives rise to a
de-synchronization gap. Considering the history-aware nature of DTs, the model
update process is posed a dual objective optimization problem whose goal is to
jointly minimize the loss function over all encountered episodes and the
corresponding de-synchronization time. As the de-synchronization time continues
to increase over sequential episodes, an elastic weight consolidation (EWC)
technique that regularizes the DT history is proposed to limit
de-synchronization time. Furthermore, to address the plasticity-stability
tradeoff accompanying the progressive growth of the EWC regularization terms, a
modified EWC method that considers fair execution between the historical
episodes of the DTs is adopted. Ultimately, the proposed framework achieves a
simultaneously accurate and synchronous CT model that is robust to catastrophic
forgetting. Simulation results show that the proposed solution can achieve an
accuracy of 90 % while guaranteeing a minimal desynchronization time.


title:
SOS! Self-supervised Learning Over Sets Of Handled Objects In Egocentric
  Action Recognition

abstract:
  Learning an egocentric action recognition model from video data is
challenging due to distractors (e.g., irrelevant objects) in the background.
Further integrating object information into an action model is hence
beneficial. Existing methods often leverage a generic object detector to
identify and represent the objects in the scene. However, several important
issues remain. Object class annotations of good quality for the target domain
(dataset) are still required for learning good object representation. Besides,
previous methods deeply couple the existing action models and need to retrain
them jointly with object representation, leading to costly and inflexible
integration. To overcome both limitations, we introduce Self-Supervised
Learning Over Sets (SOS), an approach to pre-train a generic Objects In Contact
(OIC) representation model from video object regions detected by an
off-the-shelf hand-object contact detector. Instead of augmenting object
regions individually as in conventional self-supervised learning, we view the
action process as a means of natural data transformations with unique
spatio-temporal continuity and exploit the inherent relationships among
per-video object sets. Extensive experiments on two datasets, EPIC-KITCHENS-100
and EGTEA, show that our OIC significantly boosts the performance of multiple
state-of-the-art video classification models.


title: Multi-Label Clinical Time-Series Generation via Conditional GAN

abstract:
  With wide applications of electronic health records (EHR), deep learning
methods have been adopted to analyze EHR data on various tasks such as
representation learning, clinical event prediction, and phenotyping. However,
due to privacy constraints, limited access to EHR becomes a bottleneck for deep
learning research. Recently, generative adversarial networks (GANs) have been
successful in generating EHR data. However, there are still challenges in
high-quality EHR generation, including generating time-series EHR and uncommon
diseases given imbalanced datasets. In this work, we propose a Multi-label
Time-series GAN (MTGAN) to generate EHR data and simultaneously improve the
quality of uncommon disease generation. The generator of MTGAN uses a gated
recurrent unit (GRU) with a smooth conditional matrix to generate sequences and
uncommon diseases. The critic gives scores using Wasserstein distance to
recognize real samples from synthetic samples by considering both data and
temporal features. We also propose a training strategy to calculate temporal
features for real data and stabilize GAN training. Furthermore, we design
multiple statistical metrics and prediction tasks to evaluate the generated
data. Experimental results demonstrate the quality of the synthetic data and
the effectiveness of MTGAN in generating realistic sequential EHR data,
especially for uncommon diseases.


title:
ShapePipe: a new shape measurement pipeline and weak-lensing application
  to UNIONS/CFIS data

abstract:
  UNIONS is an ongoing collaboration that will provide the largest deep
photometric survey of the Northern sky in four optical bands to date. As part
of this collaboration, CFIS is taking $r$-band data with an average seeing of
0.65 arcsec, which is complete to magnitude 24.5 and thus ideal for
weak-lensing studies. We perform the first weak-lensing analysis of CFIS
$r$-band data over an area spanning 1700 deg$^2$ of the sky. We create a
catalogue with measured shapes for 40 million galaxies, corresponding to an
effective density of 6.8 galaxies per square arcminute, and demonstrate a low
level of systematic biases. This work serves as the basis for further
cosmological studies using the full UNIONS survey of 4800 deg$^2$ when
completed. Here we present ShapePipe, a newly developed weak-lensing pipeline.
This pipeline makes use of state-of-the-art methods such as Ngmix for accurate
galaxy shape measurement. Shear calibration is performed with metacalibration.
We carry out extensive validation tests on the Point Spread Function (PSF), and
on the galaxy shapes. In addition, we create realistic image simulations to
validate the estimated shear. We quantify the PSF model accuracy and show that
the level of systematics is low as measured by the PSF residuals. Their effect
on the shear two-point correlation function is sub-dominant compared to the
cosmological contribution on angular scales <100 arcmin. The additive shear
bias is below 5x$10^{-4}$, and the residual multiplicative shear bias is at
most $10^{-3}$ as measured on image simulations. Using COSEBIs we show that
there are no significant B-modes present in second-order shear statistics. We
present convergence maps and see clear correlations of the E-mode with known
cluster positions. We measure the stacked tangential shear profile around
Planck clusters at a significance higher than $4\sigma$.


title:
DualPrompt: Complementary Prompting for Rehearsal-free Continual
  Learning

abstract:
  Continual learning aims to enable a single model to learn a sequence of tasks
without catastrophic forgetting. Top-performing methods usually require a
rehearsal buffer to store past pristine examples for experience replay, which,
however, limits their practical value due to privacy and memory constraints. In
this work, we present a simple yet effective framework, DualPrompt, which
learns a tiny set of parameters, called prompts, to properly instruct a
pre-trained model to learn tasks arriving sequentially without buffering past
examples. DualPrompt presents a novel approach to attach complementary prompts
to the pre-trained backbone, and then formulates the objective as learning
task-invariant and task-specific "instructions". With extensive experimental
validation, DualPrompt consistently sets state-of-the-art performance under the
challenging class-incremental setting. In particular, DualPrompt outperforms
recent advanced continual learning methods with relatively large buffer sizes.
We also introduce a more challenging benchmark, Split ImageNet-R, to help
generalize rehearsal-free continual learning research. Source code is available
at https://github.com/google-research/l2p.


title: Almost complex manifold with Betti number $b_i=0$ except $i=0, n/2, n$

abstract:
  This paper studies existence of $n=4k (k>1)$ dimensional simply-connected
closed almost complex manifold with Betti number $ b_i=0$ except $i=0, n/2, n$.
We characterize all the rational cohomology rings of such manifolds and show
they must have even Euler characteristic and even signature, which is to say
the middle Betti number $b_{n/2}$ must be even. Parallel to the author's
earlier work on realizing rational cohomology ring by smooth closed manifolds,
we state and prove Sullivan's rational surgery realization theorem for almost
complex manifold and demonstrate its application in our context. A prescribed
rational cohomology ring can be realized by a simply connected almost complex
manifold if and only if the ring structure supports the intersection form of a
closed manifold, and it holds Chern numbers that satisfy the signature equation
and the Riemann-Roch integrality relations, and the top Chern number equals the
Euler characteristic. According to Stong's characterization of $U$ and $SU$
cobordism, we explicitly compute the Riemann-Roch integrality relations among
Chern numbers in the case when only the middle and top Chern classes can be
nonzero. The necessary and sufficient conditions for realization are expressed
as a set of congruence relations among the signature and Euler characteristic,
we show that the lower bounds of the 2-adic order of the signature and the
Euler characteristic increase with respect to the dimension of the realizing
manifold.


title:
Emergent strongly coupled ultraviolet fixed point in four dimensions
  with 8 K\"ahler-Dirac fermions

abstract:
  The emergence of a strongly coupled ultraviolet fixed point in lattice models
that cross into the conformal window has long been hypothesized. The
4-dimensional SU(3) gauge system with 8 fundamental fermions is a good
candidate to study this phenomenon as it is expected to be very close to the
opening of the conformal window. I study the system using staggered lattice
fermions in the chiral limit. My numerical simulations employ improved lattice
actions that include heavy Pauli-Villars (PV) type bosons. This modification
does not affect the infrared dynamics but greatly reduces the ultraviolet
fluctuations, thus allowing the study of stronger renormalized couplings than
previously possible. I consider two different PV actions and find that both
show a continuous phase transition in the 8-flavor system.
  I investigate the critical behavior using finite size scaling of the
renormalized gradient flow coupling. The finite size scaling curve-collapse
analysis predicts a first order phase transition with discontinuity exponent
$\nu=1/4$ in the system without PV bosons. The scaling analysis with the PV
boson actions is not consistent with a first order phase transition. The
numerical data are well described by Berezinsky-Kosterlitz-Thouless (BKT)
scaling, though second order scaling cannot be excluded. BKT scaling would
imply that the 8-flavor system is the opening of the conformal window, an
exciting possibility that could be related to t'Hooft anomaly cancellation of
the system.


title:
On the pragmatism of using binary classifiers over data intensive neural
  network classifiers for detection of COVID-19 from voice

abstract:
  Lately, there has been a global effort by multiple research groups to detect
COVID-19 from voice. Different researchers use different kinds of information
from the voice signal to achieve this. Various types of phonated sounds and the
sound of cough and breath have all been used with varying degrees of success in
automated voice-based COVID-19 detection apps. In this paper, we show that
detecting COVID-19 from voice does not require custom-made non-standard
features or complicated neural network classifiers rather it can be
successfully done with just standard features and simple binary classifiers. In
fact, we show that the latter is not only more accurate and interpretable and
also more computationally efficient in that they can be run locally on small
devices. We demonstrate this from a human-curated dataset collected and
calibrated in clinical settings. On this dataset which comprises over 1000
speakers, a simple binary classifier is able to achieve 94% detection accuracy.


title:
Periodic and localized waves in parabolic-law media with third- and
  fourth-order dispersions

abstract:
  We study the propagation of femtosecond light pulses inside an optical fiber
medium exhibiting higher-order dispersion and cubic-quintic nonlinearities.
Pulse evolution in such system is governed by a higher-order nonlinear Schr%
\"{o}dinger equation incorporating second-, third-, and fourth-order
dispersions as well as cubic and quintic nonlinearities. Novel classes of
periodic wave solutions are identified for the first time by means of an
appropriate equation method. Results presented indicated the potentially rich
set of periodic waves in the system under the combined influence of
higher-order dispersive effects and cubic-quintic nonlinearity. Solitary waves
of both bright and dark types are also obtained as a limiting case for
appropriate periodic solutions. It is found that the velocity of these
structures is uniquely dependent on all orders of dispersion. Conditions on the
optical fiber parameters for the existence of these stable nonlinear wave-forms
are presented as well.