title: Measurement of the H$^3\Delta_1$ Radiative Lifetime in ThO

abstract:
  The best limit on the electron electric dipole moment (eEDM) comes from the
ACME II experiment [Nature \textbf{562} (2018), 355-360] which probes physics
beyond the Standard Model at energy scales well above 1 TeV. ACME II measured
the eEDM by monitoring electron spin precession in a cold beam of the
metastable H$^3\Delta_1$ state of thorium monoxide (ThO) molecules, with an
observation time $\tau \approx 1$ ms for each molecule. We report here a new
measurement of the lifetime of the ThO (H$^3\Delta_1$) state, $\tau_H = 4.2\pm
0.5$ ms. Using an apparatus within which $\tau \approx \tau_H$ will enable a
substantial reduction in uncertainty of an eEDM measurement.


title: Few-shot Forgery Detection via Guided Adversarial Interpolation

abstract:
  Realistic visual media synthesis is becoming a critical societal issue with
the surge of face manipulation models; new forgery approaches emerge at an
unprecedented pace. Unfortunately, existing forgery detection methods suffer
significant performance drops when applied to novel forgery approaches. In this
work, we address the few-shot forgery detection problem by designing a
comprehensive benchmark based on coverage analysis among various forgery
approaches, and proposing Guided Adversarial Interpolation (GAI). Our key
insight is that there exist transferable distribution characteristics among
different forgery approaches with the majority and minority classes.
Specifically, we enhance the discriminative ability against novel forgery
approaches via adversarially interpolating the artifacts of the minority
samples to the majority samples under the guidance of a teacher network. Unlike
the standard re-balancing method which usually results in over-fitting to
minority classes, our method simultaneously takes account of the diversity of
majority information as well as the significance of minority information.
Extensive experiments demonstrate that our GAI achieves state-of-the-art
performances on the established few-shot forgery detection benchmark. Notably,
our method is also validated to be robust to choices of majority and minority
forgery approaches.


title:
Electrostatic focusing of cold and heavy molecules for the ACME electron
  EDM search

abstract:
  The current best upper limit for electron electric dipole moment (EDM),
$|d_e|<1.1\times10^{-29}\,$e$\cdot$cm ($90$% confidence), was set by the ACME
collaboration in 2018. The ACME experiment uses a spin-precession measurement
in a cold beam of ThO molecules to detect $d_e$. An improvement in statistical
uncertainty would be possible with more efficient use of molecules from the
cryogenic buffer gas beam source. Here, we demonstrate electrostatic focusing
of the ThO beam with a hexapole lens. This results in a factor of $16$
enhancement in the molecular flux detectable downstream, in a beamline similar
to that built for the next generation of ACME. We also demonstrate an upgraded
rotational cooling scheme that increases the ground state population by $3.5$
times compared to no cooling, consistent with expectations and a factor of
$1.4$ larger than previously in ACME. When combined with other demonstrated
improvements, we project over an order of magnitude improvement in statistical
sensitivity for the next generation ACME electron EDM search.


title:
Galapagos-2/Galfitm/GAMA -- multi-wavelength measurement of galaxy
  structure: separating the properties of spheroid and disk components in
  modern surveys

abstract:
  We present the capabilities of Galapagos--2 and Galfitm in the context of
fitting 2-component profiles to galaxies, on the way to providing complete
multi-band, multi-component fitting of large samples of galaxies in future
surveys. We release both the code and the fit results to 234,239 objects from
the DR3 of the Gama survey, a sample significantly deeper than previous works.
We use stringent tests on both simulated and real data, as well as comparison
to public catalogues to evaluate the advantages of using multi-band over
single-band data. We show that multi-band fitting using Galfitm provides
significant advantages when trying to decompose galaxies into their individual
constituents, as more data are being used, by effectively being able to use the
colour information buried in the individual exposures to its advantage. Using
simulated data, we find that multi-band fitting significantly reduces the
deviations from real parameter values, allows component sizes and S\'ersic
indices to be recovered more accurately, and, by design, constrains the
band-to-band variations of these parameters to more physical values. On both
simulated and real data, we confirm that the SEDs of the 2 main components can
be recovered to fainter magnitudes compared to using single-band fitting, which
tends to recover disks and bulges to - on average - have identical SEDs when
the galaxies become too faint, instead of the different SEDs they truly have.
By comparing our results to those provided by other fitting codes, we confirm
that they agree in general, but measurement errors can be significantly reduced
by using the multi-band tools developed by the MegaMorph project. We conclude
that the multi-band fitting employed by Galapagos-2 and Galfitm significantly
improves the accuracy of structural galaxy parameters and enables much larger
samples to be be used in a scientific analysis.


title:
Height of weighted recursive trees with sub-polynomially growing total
  weight

abstract:
  Weighted recursive trees are built by adding successively vertices with
predetermined weights to a tree: each new vertex is attached to a parent chosen
at random with probability proportional to its weight. In the case where the
total weight of the tree at step $n$ grows polynomially in $n$, we obtained in
(Pain-S\'enizergues 2022) an asymptotic expansion for the height of the tree,
which is similar to the behaviour of the maximum of branching random walks. In
this paper, we consider the case of a total weight growing sub-polynomially in
$n$ and obtain asymptotics for the height of the tree in several regimes. The
behaviour is similar to the one of a time-inhomogeneous branching random walk,
but in a regime which has not been studied so far.


title:
Learning Performance Graphs from Demonstrations via Task-Based
  Evaluations

abstract:
  In the learning from demonstration (LfD) paradigm, understanding and
evaluating the demonstrated behaviors plays a critical role in extracting
control policies for robots. Without this knowledge, a robot may infer
incorrect reward functions that lead to undesirable or unsafe control policies.
Recent work has proposed an LfD framework where a user provides a set of formal
task specifications to guide LfD, to address the challenge of reward shaping.
However, in this framework, specifications are manually ordered in a
performance graph (a partial order that specifies relative importance between
the specifications). The main contribution of this paper is an algorithm to
learn the performance graph directly from the user-provided demonstrations, and
show that the reward functions generated using the learned performance graph
generate similar policies to those from manually specified performance graphs.
We perform a user study that shows that priorities specified by users on
behaviors in a simulated highway driving domain match the automatically
inferred performance graph. This establishes that we can accurately evaluate
user demonstrations with respect to task specifications without expert
criteria.


title:
A Comparative Analysis of Machine Learning Models for Solar Flare
  Forecasting: Identifying High Performing Active Region Flare Indicators

abstract:
  Solar flares create adverse space weather impacting space and Earth-based
technologies. However, the difficulty of forecasting flares, and by extension
severe space weather, is accentuated by the lack of any unique flare trigger or
a single physical pathway. Studies indicate that multiple physical properties
contribute to active region flare potential, compounding the challenge. Recent
developments in Machine Learning (ML) have enabled analysis of higher
dimensional data leading to increasingly better flare forecasting techniques.
However, consensus on high-performing flare predictors remains elusive. In the
most comprehensive study till date, we conduct a comparative analysis of four
popular ML techniques (K-Nearest Neighbor, Logistic Regression, Random Forest
Classifier, and Support Vector Machine) by training these on magnetic
parameters obtained from the Helioseismic Magnetic Imager (HMI) onboard the
Solar Dynamics Observatory (SDO) during the entirety of solar cycle 24. We
demonstrate that Logistic Regression and Support Vector Machine algorithms
perform extremely well in forecasting active region flaring potential. The
logistic regression algorithm returns the highest true skill score of $0.967
\pm 0.018$, possibly the highest classification performance achieved with any
parametric study alone. From a comparative assessment, we establish that the
magnetic properties like total current helicity, total vertical current
density, total unsigned flux, R\_value, and total absolute twist are the
top-performing flare indicators. We also introduce and analyze two new
performance metrics, namely, Severe and Clear Space Weather indicators. Our
analysis constrains the most successful ML algorithms and identifies physical
parameters that contribute most to active region flare productivity.


title: ASVAAN: Semi-automatic side-channel analysis of Android NDK

abstract:
  Android is the most popular operating systems for smartphones and is also
well-known for its flexibility and security. However, although it is overall
considered very secure, there are still some vulnerabilities occasionally
discovered that allow getting user sensitive information bypassing security
controls and boundaries: among these, side-channel vulnerabilities are a
significant concern these days. Although there are several types of
side-channel vulnerabilities, ones focused on APIs still represent a great area
to explore, which, until now, has often been analysed manually. Only in the
latest years, there have been published some automatic solutions which focus on
performing automatic scanning of side-channel flaws in Android, created due to
the increasing codebase of the operating system; however, they present some
limitations.
  This paper introduces a new approach to discover Android NDK side-channel
leaks, which at the best of the author knowledge have never been investigated
through the usage of automatic or semi-automatic solutions. The approach
described in the work, allowed to identify more than 8 new side-channel leaks
in several Android NDK functions,which permitted to infer with great accuracy
application and websites launches on a victim device. The findings represents
the first discovered side-channel leaks in Android NDK functions, and were
responsibly disclosed to the Android Security Team of Google.


title: On the closure of Absolutely Norm attaining Operators

abstract:
  Let $H_1$ and $H_2$ be complex Hilbert spaces and $T:H_1\rightarrow H_2$ be a
bounded linear operator. We say $T$ to be norm attaining, if there exists $x\in
H_1$ with $\|x\|=1$ such that $\|Tx\|=\|T\|$. If for every closed subspace $M$
of $H_1$, the restriction $T|_{M}:M\rightarrow H_2$ is norm attaining then, $T$
is called absolutely norm attaining operator or $\mathcal{AN}$-operator. If we
replace the norm of the operator by the minimum modulus
$m(T)=\inf{\{\|Tx\|:x\in H_1,\; \|x\|=1}\}$, then $T$ is called the minimum
attaining and the absolutely minimum attaining operator (or
$\mathcal{AM}$-operator) respectively.
  In this article, we discuss about the operator norm closure of the
$\mathcal{AN}$-operators. We completely characterize operators in this closure
and study several important properties. We mainly give the spectral
characterization of the positive operators in this class and give the
representation when the operator is normal. Later we also study the analogous
properties for $\mathcal{AM}$-operators and prove that the closure of
$\mathcal{AM}$-operators is same as that of the closure of
$\mathcal{AN}$-operators. As a consequence, we prove similar results for
operators in the norm closure of $\mathcal{AM}$-operators.


title: Non-associative Frobenius algebras of type $G_2$ and $F_4$

abstract:
  Very recently, Maurice Chayet and Skip Garibaldi have introduced a class of
commutative non-associative algebras, for each simple linear algebraic group
over an arbitrary field (with some minor restriction on the characteristic).
  We give an explicit description of these algebras for groups of type $G_2$
and $F_4$ in terms of the octonion algebras and the Albert algebras,
respectively. As a byproduct, we determine all possible invariant commutative
algebra products on the representation with highest weight $2\omega_1$ for
$G_2$ and on the representation with highest weight $2\omega_4$ for $F_4$.
  It had already been observed by Chayet and Garibaldi that the automorphism
group for the algebras for type $F_4$ is equal to the group of type $F_4$
itself. Using our new description, we are able to show that the same result
holds for type $G_2$.


title: Decompositions of Augmented Bergman Complexes

abstract:
  We study the augmented Bergman complex of a closure operator on a finite set,
which interpolates between the order complex of proper flats and the
independence complex of the operator. In 2020, Braden, Huh, Matherne,
Proudfoot, and Wang showed that augmented Bergman complexes of matroids are
always gallery-connected, and recently Bullock, Kelley, Reiner, Ren, Shemy,
Shen, Sun, Tao, and Zhang strengthened "gallery-connected" to "shellable" by
providing two classes of shelling orders: "flag-to-basis" shellings and
"basis-to-flag" shellings.
  We show that augmented Bergman complexes of matroids are vertex decomposable,
a stronger property than shellable. We also prove that the augmented Bergman
complex of any closure operator is shellable if and only if lattice of flats
(that is, its non-augmented Bergman complex) is shellable. As a consequence, an
augmented Bergman complex is shellable if and only if it admits a flag-to-basis
shelling. Perhaps surprisingly, the same does not hold for basis-to-flag
shellings: we describe a closure operator whose augmented Bergman complex is
shellable, but has no shelling order with bases appearing first.


title: Generation of $SU(3)$ sum rule for charmed baryon decay

abstract:
  Flavor $SU(3)$ symmetry is a powerful tool to analyze charmed baryon decays.
In this work, we propose an approach to get $SU(3)$ sum rules for the singly
and doubly charmed baryon decays without writing the Wigner-Eckhart invariants
explicitly. The $SU(3)$ sum rules are computed routinely in several master
formulas. Hundreds of $SU(3)$ sum rules are found to serve as test of the
flavor symmetry in the charmed baryon decays.


title: How to design a network architecture using capacity planning

abstract:
  Building a network architecture must answer to organization needs, but also
to two major elements which are the need for dependability and performance. By
performance, we must understand the ability to meet an immediate need and the
ability to scale without reducing the performance of the whole as new elements
are added to the network infrastructure. This last point is covered by Capacity
Planning domain.


title:
Spatiotemporal Estimation of TROPOMI NO2 Column with Depthwise Partial
  Convolutional Neural Network

abstract:
  Satellite-derived measurements are negatively impacted by cloud cover and
surface reflectivity. These biases must be discarded and significantly increase
the amount of missing data within remote sensing images. This paper expands the
application of a partial convolutional neural network (PCNN) to incorporate
depthwise convolution layers, conferring temporal dimensionality to the
imputation process. The addition of a temporal dimension to the imputation
process adds a state of successive existence within the dataset which spatial
imputation cannot capture. The depthwise convolution process enables the PCNN
to independently convolve the data for each channel. The deep learning system
is trained with the Community Multiscale Air Quality model-simulated
tropospheric column density of Nitrogen Dioxide (TCDNO2) to impute TROPOspheric
Monitoring Instrument TCDNO2. The depthwise PCNN model achieves an index of
agreement of 0.82 and outperforms the default PCNN models, with and without
temporal dimensionality of data, and conventional data imputation methods such
as inverse distance weighting by 3-11% and 8-15% in the index of agreement and
correlation, respectively. The model demonstrates more consistency in the
reconstruction of TROPOspheric Monitoring Instrument tropospheric column
density of NO2 images. The model has also demonstrated the accurate imputation
of remote sensing images with over 95% of the data missing. PCNN enables the
accurate imputation of remote sensing data with large regions of missing data
and will benefit future researchers conducting data assimilation for numerical
models, emission studies, and human health impact analyses from air pollution.


title:
Revision of Faraday rotation measure constraint on primordial magnetic
  field based on results of IllustrisTNG simulation

abstract:
  Non-observation of Faraday rotation of polarized radio emission from
extragalactic sources can be used to constrain volume-filling intergalactic
magnetic field (IGMF) of cosmological origin. Previously derived Faraday
rotation constraints on IGMF have used analytic models that made a range of
simplifying assumptions about magnetic field evolution in the intergalactic
medium and did not consider the effect of baryonic feedback on large-scale
structures. We revise the Faraday rotation constraint on IGMF using a numerical
model of the intergalactic medium from IllustrisTNG cosmological simulation
that includes a sophisticated model of the baryonic feedback. We use the
IllustrisTNG model to calculate the rotation measure and compare the resulting
mean and median of the absolute value of the rotation measure with the data of
the NRAO VLA Sky Survey (NVSS). The numerical model of the intergalactic medium
includes the full magneto-hydrodynamic model of the compressed primordial
magnetic field as well as the model of the regions where the magnetic field is
not primordial, but it is rather produced by the process of the baryonic
feedback. Separating the two types of regions, we are able to assess the
primordial magnetic field influence on the Faraday rotation signal. We find
that correction of the model of compressed primordial field and account for the
fact that part of the intergalactic medium is occupied by magnetic fields
spread by the baryonic feedback process rather than by the primordial field
relaxed the Faraday rotation bound by a factor of $\simeq 3$, to $B_0<
1.8\times10^{-9}$ G for large correlation length IGMF.


title:
Leveraging Reaction-aware Substructures for Retrosynthesis and Reaction
  Prediction

abstract:
  Reaction outcome prediction and retrosynthesis analysis are two critical
tasks in organic chemistry. Recently, various machine learning approaches have
achieved promising results on both two tasks, the output molecules are usually
autoregressively decoded atom by atom with a generative model, utilizing text
generation or machine translation models in natural language processing. The
atom by atom decoding approach is not intuitive from a chemical perspective
because some substructures are relatively stable and remain unchanged during
the reaction. In this paper, we propose a substructure-level decoding model,
where the substructures are reaction-aware and can be automatically extracted
with a fully data-driven approach. We formulate the reaction outcome prediction
and retrosynthesis analysis as substructure-level sequence-to-sequence learning
tasks. Our approach achieves significant improvement on both two tasks over
previously reported models. For example, the top-1 accuracy for retrosynthesis
on the USPTO_full dataset is 51.6%, achieving a 5% absolute improvement over
the state-of-the-art models.


title: Genera from an algebraic index theorem for supermanifolds

abstract:
  We prove a super-version of Nest-Tsygan's algebraic index theorem. This work
is inspired by the appearance of the same cobordism invariants in three related
stories: index theory, trace methods in the deformation theory of algebras, and
partition functions in quantum field theory. We show that one can recover the
cobordism invariant appearing in supersymmetric quantum mechanics using trace
methods for the associated deformation quantization problem.


title:
Internet of Things Device Capabilities, Architectures, Protocols, and
  Smart Applications in Healthcare Domain: A Review

abstract:
  Nowadays, the Internet has spread to practically every country around the
world and is having unprecedented effects on people's lives. The Internet of
Things (IoT) is getting more popular and has a high level of interest in both
practitioners and academicians in the age of wireless communication due to its
diverse applications. The IoT is a technology that enables everyday things to
become savvier, everyday computation towards becoming intellectual, and
everyday communication to become a little more insightful. In this paper, the
most common and popular IoT device capabilities, architectures, and protocols
are demonstrated in brief to provide a clear overview of the IoT technology to
the researchers in this area. The common IoT device capabilities including
hardware (Raspberry Pi, Arduino, and ESP8266) and software (operating systems,
and built-in tools) platforms are described in detail. The widely used
architectures that have been recently evolved and used are the three-layer
architecture, SOA-based architecture, and middleware-based architecture. The
popular protocols for IoT are demonstrated which include CoAP, MQTT, XMPP,
AMQP, DDS, LoWPAN, BLE, and Zigbee that are frequently utilized to develop
smart IoT applications. Additionally, this research provides an in-depth
overview of the potential healthcare applications based on IoT technologies in
the context of addressing various healthcare concerns. Finally, this paper
summarizes state-of-the-art knowledge, highlights open issues and shortcomings,
and provides recommendations for further studies which would be quite
beneficial to anyone with a desire to work in this field and make breakthroughs
to get expertise in this area.


title: Controlling topological phases of matter with quantum light

abstract:
  Controlling the topological properties of quantum matter is a major goal of
condensed matter physics. A major effort in this direction has been devoted to
using classical light in the form of Floquet drives to manipulate and induce
states with non-trivial topology. A different route can be achieved with cavity
photons. Here we consider a prototypical model for topological phase
transition, the one-dimensional Su-Schrieffer-Heeger (SSH) model, coupled to a
single mode cavity. We show that quantum light can affect the topological
properties of the system, including the finite-length energy spectrum hosting
edge modes and the topological phase diagram. In particular we show that
depending on the lattice geometry and the strength of light-matter coupling one
can either turn a trivial phase into a topological one or viceversa using
quantum cavity fields. Furthermore, we compute the polariton spectrum of the
coupled electron-photon system, and we note that the lower polariton branch
disappears at the topological transition point. This phenomenon can be used to
probe the phase transition in the SSH model.

categories: cond-mat.mes-hall cond-mat.quant-gas cond-mat.str-el quant-ph


title:
An Algebraically Converging Stochastic Gradient Descent Algorithm for
  Global Optimization

abstract:
  We propose a new stochastic gradient descent algorithm for finding the global
optimizer of nonconvex optimization problems, referred to here as "AdaVar". A
key component in the algorithm is the adaptive tuning of the randomness based
on the value of the objective function. In the language of simulated annealing,
the temperature is state-dependent. With this, we can prove global convergence
with an algebraic rate both in probability and in the parameter space. This is
a major improvement over the classical rate from using a simpler control of the
noise term. The convergence proof is based on the actual discrete setup of the
algorithm. We also present several numerical examples demonstrating the
efficiency and robustness of the algorithm for global convergence.


title:
DeepZipper II: Searching for Lensed Supernovae in Dark Energy Survey
  Data with Deep Learning

abstract:
  Gravitationally lensed supernovae (LSNe) are important probes of cosmic
expansion, but they remain rare and difficult to find. Current cosmic surveys
likely contain and 5-10 LSNe in total while next-generation experiments are
expected to contain several hundreds to a few thousands of these systems. We
search for these systems in observed Dark Energy Survey (DES) 5-year SN fields
-- 10 3-sq. deg. regions of sky imaged in the $griz$ bands approximately every
six nights over five years. To perform the search, we utilize the DeepZipper
approach: a multi-branch deep learning architecture trained on image-level
simulations of LSNe that simultaneously learns spatial and temporal
relationships from time series of images. We find that our method obtains a LSN
recall of 61.13% and a false positive rate of 0.02% on the DES SN field data.
DeepZipper selected 2,245 candidates from a magnitude-limited ($m_i$ $<$ 22.5)
catalog of 3,459,186 systems. We employ human visual inspection to review
systems selected by the network and find three candidate LSNe in the DES SN
fields.


title:
$|V_{cb}|$, Lepton Flavour Universality and $SU(3)_F$ symmetry breaking
  in $B_s \to D_s^{(*)} \ell \nu_\ell$ decays through unitarity and lattice QCD

abstract:
  In addition to the well-known $B \to D^{(*)} \ell \nu_\ell$ decays,
semileptonic $B_s \to D_s^{(*)} \ell \nu_\ell$ processes offer the possibility
to determine the Cabibbo-Kobayashi-Maskawa (CKM) matrix element $\vert
V_{cb}\vert$. We implement the Dispersive Matrix (DM) approach to describe the
hadronic Form Factors (FFs) for the $B_s \to D_s^{(*)}$ transition in the whole
kinematical range, starting from recent Lattice QCD computations at large
values of the 4-momentum transfer. We extract $\vert V_{cb} \vert$ from the
experimental data, obtaining $\vert V_{cb} \vert \cdot 10^3 = (41.7 \pm 1.9)$
from $B_s \to D_s \ell \nu_\ell$ and $\vert V_{cb} \vert \cdot 10^3 = (40.7 \pm
2.4)$ from $B_s \to D_s^* \ell \nu_\ell$ decays. After averaging with the
values of $\vert V_{cb} \vert$ obtained from the $B \to D^{(*)}$ channels [1,2]
we get $\vert V_{cb} \vert \cdot 10^3 =(41.2 \pm 0.8)$, which is compatible
with the most recent inclusive estimate $\vert V_{cb} \vert_{\rm{incl}} \cdot
10^3 = 42.16 \pm 0.50$ [3] at the $1 \sigma$ level. In addition we test the
Lepton Flavour Universality (LFU) by computing the $\tau / \mu$ ratios of the
total decay rates, obtaining $R(D_s) = 0.298\,(5)$ and $R(D_s^*)= 0.250\,(6)$.
We also address the issue of the $SU(3)_F$ symmetry breaking by comparing the
hadronic FFs entering the semileptonic $B \to D^{(*)}$ and $B_s \to D_s^{(*)}$
channels.


title: Ensemble learning for portfolio valuation and risk management

abstract:
  We introduce an ensemble learning method for dynamic portfolio valuation and
risk management building on regression trees. We learn the dynamic value
process of a derivative portfolio from a finite sample of its cumulative cash
flow. The estimator is given in closed form. The method is fast and accurate,
and scales well with sample size and path space dimension. The method can also
be applied to Bermudan style options. Numerical experiments show good results
in moderate dimension problems.


title: Recommender May Not Favor Loyal Users

abstract:
  In academic research, recommender systems are often evaluated on benchmark
datasets, without much consideration about the global timeline. Hence, we are
unable to answer questions like: Do loyal users enjoy better recommendations
than non-loyal users? Loyalty can be defined by the time period a user has been
active in a recommender system, or by the number of historical interactions a
user has. In this paper, we offer a comprehensive analysis of recommendation
results along global timeline. We conduct experiments with five widely used
models, i.e., BPR, NeuMF, LightGCN, SASRec and TiSASRec, on four benchmark
datasets, i.e., MovieLens-25M, Yelp, Amazon-music, and Amazon-electronic. Our
experiment results give an answer "No" to the above question. Users with many
historical interactions suffer from relatively poorer recommendations. Users
who stay with the system for a short time period enjoy better recommendations.
Both findings are counter-intuitive. Interestingly, users who have recently
interacted with the system, with respect to the time point of the test
instance, enjoy better recommendations. The finding on recency applies to all
users, regardless of users' loyalty. Our study offers a different perspective
to understand recommender performance, and our findings could trigger a revisit
of recommender model design.


title: Dynamic Dialogue Policy Transformer for Continual Reinforcement Learning

abstract:
  Continual learning is one of the key components of human learning and a
necessary requirement of artificial intelligence. As dialogue can potentially
span infinitely many topics and tasks, a task-oriented dialogue system must
have the capability to continually learn, dynamically adapting to new
challenges while preserving the knowledge it already acquired. Despite the
importance, continual reinforcement learning of the dialogue policy has
remained largely unaddressed. The lack of a framework with training protocols,
baseline models and suitable metrics, has so far hindered research in this
direction. In this work we fill precisely this gap, enabling research in
dialogue policy optimisation to go from static to dynamic learning. We provide
a continual learning algorithm, baseline architectures and metrics for
assessing continual learning models. Moreover, we propose the dynamic dialogue
policy transformer (DDPT), a novel dynamic architecture that can integrate new
knowledge seamlessly, is capable of handling large state spaces and obtains
significant zero-shot performance when being exposed to unseen domains, without
any growth in network parameter size.


title:
A Machine Learning Approach to Determine the Semantic Versioning Type of
  npm Packages Releases

abstract:
  Semantic versioning policy is widely used to indicate the level of changes in
a package release. Unfortunately, there are many cases where developers do not
respect the semantic versioning policy, leading to the breakage of dependent
applications. To reduce such cases, we proposed using machine learning (ML)
techniques to effectively predict the new release type, i.e., patch, minor,
major, in order to properly determine the semantic versioning type. To perform
our prediction, we mined and used a number of features about a release, such as
the complexity of the changed code, change types, and development activities.
We then used four ML classifiers. To evaluate the performance of the proposed
ML classifiers, we conducted an empirical study on 31 JavaScript packages
containing a total of approximately 6,260 releases. We started by extracting 41
release level features from historical data of packages' source code and
repositories. Then, we used four machine learning classifiers, namely XGBoost,
Random Forest, Decision Tree, and Logistic Regression. We found that the
XGBoost classifiers performed the best, achieving median ROC AUC values of
0.78, 0.69, and 0.74 for major, minor, and patch releases, respectively. We
also found that features related to the change types in a release are the best
predictors group of features in determining the semantic versioning type.
Finally, we studied the generalizability of determining the semantic versioning
type by applying cross-package validation. Our results showed that the general
classifier achieved median ROC AUC values of 0.76, 0.69, and 0.75 for major,
minor, and patch releases.


title:
Room Temperature Superconductivity: the Roles of Theory and Materials
  Design

abstract:
  For half a century after the discovery of superconductivity, materials
exploration for better superconductors proceeded without knowledge of the
underlying mechanism. The 1957 BCS theory cleared that up: the superconducting
state occurs due to pairing of electrons over the Fermi surface. Over the
following half century higher critical temperature T$_c$ was achieved only
serendipitously as new materials were synthesized. Meanwhile the formal theory
of phonon-coupled superconductivity at the material-dependent level became
highly developed: given a known compound, its value of T$_c$, the
superconducting gap function, and several other properties of the
superconducting state became available independent of further experimental
input. More recently, density functional theory based computational materials
design has progressed to a predictive level -- new materials can be predicted
on the basis of various numerical algorithms. Taken together, these
capabilities enable theoretical prediction of new superconductors. Here the
process that resulted in three new highest temperature superconductors,
predicted numerically, confirmed experimentally -- SH$_3$, LaH$_{10}$, and
YH$_9$ -- is recounted. These hydrides have T$_c$ in the 200-280K range at
megabar pressures, and here the development will be chronicled. Current
activities and challenges are discussed, together with Regularities in
compressed hydrides that can guide further exploration.


title:
Experimental Validation of Time Reversal Multiple Access for UWB
  Wireless Communications Centered at the $273$ GHz Frequency

abstract:
  Ultra-high data rates with low-power consumption wireless communications and
low-complexity receivers is one of the key requirements for the next 6-th
Generation (6G) of communication networks. Sub-Terahertz (SubTHz) frequency
bands can support Ultra-WideBand (UWB) communications and can thus offer
unprecedented increase in the wireless network capacity, rendering those bands
and relevant research a strong candidate technology for 6G. However, in
contrast to millimeter-wave communications, the technological advances in
subTHz transceivers are not yet completely mature. In this paper, we focus on
the Time Reversal (TR) precoding scheme, which is a computationally simple and
robust technique capable of focusing UWB waveforms on both time and space, by
exploiting channel diversity when available. We design a novel experimental
setup, including a THz link inside a waveguide and operating at the carrier
frequency 273.6 GHz with 2 GHz transmission bandwidth, and deploy it to perform
multi-user communications with simple modulation. Our results demonstrate large
communication rates with only 3 mm space separation of the receiving antennas.


title: Distinct degrees and homogeneous sets

abstract:
  In this paper we investigate the extremal relationship between two
well-studied graph parameters: the order of the largest homogeneous set in a
graph $G$ and the maximal number of distinct degrees appearing in an induced
subgraph of $G$, denoted respectively by $\hom (G)$ and $f(G)$. Our main
theorem improves estimates due to several earlier researchers and shows that if
$G$ is an $n$-vertex graph with $\hom (G) \geq n^{1/2}$ then $f(G) \geq \big (
{n}/{\hom (G)} \big )^{1 - o(1)}$. The bound here is sharp up to the
$o(1)$-term, and asymptotically solves a conjecture of Narayanan and Tomon. In
particular, this implies that $\max \{ \hom (G), f(G) \} \geq n^{1/2 -o(1)}$
for any $n$-vertex graph $G$,which is also sharp. The above relationship
between $\hom (G)$ and $f(G)$ breaks down in the regime where $\hom (G) <
n^{1/2}$. Our second result provides a sharp bound for distinct degrees in
biased random graphs, i.e. on $f\big (G(n,p) \big )$. We believe that the
behaviour here determines the extremal relationship between $\hom (G)$ and
$f(G)$ in this second regime. Our approach to lower bounding $f(G)$ proceeds
via a translation into an (almost) equivalent probabilistic problem, and it can
be shown to be effective for arbitrary graphs. It may be of independent
interest.


title:
Sparse Interaction Neighborhood Selection for Markov Random Fields via
  Reversible Jump and Pseudoposteriors

abstract:
  We consider the problem of estimating the interacting neighborhood of a
Markov Random Field model with finite support and homogeneous pairwise
interactions based on relative positions of a two-dimensional lattice. Using a
Bayesian framework, we propose a Reversible Jump Monte Carlo Markov Chain
algorithm that jumps across subsets of a maximal range neighborhood, allowing
us to perform model selection based on a marginal pseudoposterior distribution
of models.


title: Posets and spaces of $k$-noncrossing RNA Structures

abstract:
  RNA molecules are single-stranded analogues of DNA that can fold into various
structures which influence their biological function within the cell. RNA
structures can be modelled combinatorially in terms of a certain type of graph
called an RNA diagram. In this paper we introduce a new poset of RNA diagrams
$\mathcal{B}^r_{f,k}$, $r\ge 0$, $k \ge 1$ and $f \ge 3$, which we call the
Penner-Waterman poset, and, using results from the theory of
multitriangulations, we show that this is a pure poset of rank
$k(2f-2k+1)+r-f-1$, whose geometric realization is the join of a simplicial
sphere of dimension $k(f-2k)-1$ and an $\left((f+1)(k-1)-1\right)$-simplex in
case $r=0$. As a corollary for the special case $k=1$, we obtain a result due
to Penner and Waterman concerning the topology of the space of RNA secondary
structures. These results could eventually lead to new ways to investigate
landscapes of RNA $k$-noncrossing structures.


title: The twist for Richardson varieties

abstract:
  We construct the twist automorphism of open Richardson varieties inside the
flag variety of a complex semisimple algebraic group. We show that the twist
map preserves totally positive parts, and prove a Chamber Ansatz formula for
it. Our twist map generalizes the twist maps previously constructed by
Berenstein-Fomin-Zelevinsky, Marsh-Scott, and Muller-Speyer. We use it to
explain the relationship between the two conjectural cluster structures for
Richardson varieties studied by Leclerc and by Ingermanson.


title: Building blocks of Cwebs in multiparton scattering amplitudes

abstract:
  The correlators of Wilson-line operators in non-abelian gauge theories are
known to exponentiate, and their logarithms can be organised in terms of the
collections of Feynman diagrams called Cwebs. The colour factors that appear in
the logarithm correspond to completely connected diagrams and are determined by
the web mixing matrices. In this article we introduce several new concepts: (a)
Normal ordering of the diagrams of a Cweb, (b) Fused-Webs (c) Basis and Family
of Cwebs. We use these ideas together with a Uniqueness theorem that we prove
to arrive at an understanding of the diagonal blocks, and several null matrices
that appear in the mixing matrices. We demonstrate using our formalism that,
once the basis Cwebs present upto order $\alpha_{s}^{n}$ are determined, the
number of exponentiated colour factors for several classes of Cwebs starting at
order $\alpha_{s}^{n+1}$ can be predicted. We further provide complete results
for the mixing matrices, to all orders in perturbation theory, for two special
classes of Cwebs using our framework.


title: $\mathbb{R}$-motivic $v_1$-periodic homotopy

abstract:
  We compute the $v_1$-periodic $\mathbb{R}$-motivic stable homotopy groups.
The main tool is the effective slice spectral sequence. Along the way, we also
analyze $\mathbb{C}$-motivic and $\eta$-periodic $v_1$-periodic homotopy from
the same perspective.


title: Studying a hot molecular core embedded in a photodissociation region

abstract:
  At the first Galactic quadrant, at l=33.134, b=-0.091, an extended
photodissociation region generated by an HII region complex lies. This region
is related to abundant molecular gas, and particularly, a hot molecular core,
known as G33.133-mm3, appears embedded in a molecular clump. Using data from
the James Clerk Maxwell Telescope with an angular resolution of about 15", we
studied the 13CO/C18O abundance ratio towards the mentioned molecular clump and
its relation with the ultraviolet radiation. At smaller spatial scales, using
data from the Atacama Large Millimeter Array (angular resolution about
0.7\arcsec), the hot molecular core G33.133-mm3, that has a size of about 2600
au, and is an appropriate site to form stars, was characterized. In particular,
some points about its chemistry are mentioned based on the emission of the
cyanide or nitrile radical (CN) and others more complex molecules, such as
CH3OH, CH3CN, CH3OCHO, and CH3CCH.


title: Mining Logical Event Schemas From Pre-Trained Language Models

abstract:
  We present NESL (the Neuro-Episodic Schema Learner), an event schema learning
system that combines large language models, FrameNet parsing, a powerful
logical representation of language, and a set of simple behavioral schemas
meant to bootstrap the learning process. In lieu of a pre-made corpus of
stories, our dataset is a continuous feed of "situation samples" from a
pre-trained language model, which are then parsed into FrameNet frames, mapped
into simple behavioral schemas, and combined and generalized into complex,
hierarchical schemas for a variety of everyday scenarios. We show that careful
sampling from the language model can help emphasize stereotypical properties of
situations and de-emphasize irrelevant details, and that the resulting schemas
specify situations more comprehensively than those learned by other systems.


title:
Matrix product operator algebras I: representations of weak Hopf
  algebras and projected entangled pair states

abstract:
  Matrix Product Operators (MPOs) are tensor networks representing operators
acting on 1D systems. They model a wide variety of situations, including
communication channels with memory effects, quantum cellular automata, mixed
states in 1D quantum systems, or holographic boundary models associated to 2D
quantum systems. A scenario where MPOs have proven particularly useful is to
represent algebras of non-trivial symmetries. Concretely, the boundary of both
symmetry protected and topologically ordered phases in 2D quantum systems
exhibit symmetries in the form of MPOs.
  In this paper, we develop a theory of MPOs as representations of algebraic
structures. We establish a dictionary between algebra and MPO properties which
allows to transfer results between both setups, covering the cases of
pre-bialgebras, weak bialgebras, and weak Hopf algebras. We define the notion
of pulling-through algebras, which abstracts the minimal requirements needed to
define topologically ordered 2D tensor networks from MPO algebras. We show, as
one of our main results, that any semisimple pivotal weak Hopf algebra is a
pulling-trough algebra. We demonstrate the power of this framework by showing
that they can be used to construct Kitaev's quantum double models for Hopf
algebras solely from an MPO representation of the Hopf algebra, in the exact
same way as MPO symmetries obtained from fusion categories can be used to
construct Levin-Wen string-net models, and to explain all their topological
features; it thus allows to describe both Kitaev and string-net models on the
same formal footing.

categories: quant-ph cond-mat.str-el math-ph math.MP


title:
Arch-Graph: Acyclic Architecture Relation Predictor for
  Task-Transferable Neural Architecture Search

abstract:
  Neural Architecture Search (NAS) aims to find efficient models for multiple
tasks. Beyond seeking solutions for a single task, there are surging interests
in transferring network design knowledge across multiple tasks. In this line of
research, effectively modeling task correlations is vital yet highly neglected.
Therefore, we propose \textbf{Arch-Graph}, a transferable NAS method that
predicts task-specific optimal architectures with respect to given task
embeddings. It leverages correlations across multiple tasks by using their
embeddings as a part of the predictor's input for fast adaptation. We also
formulate NAS as an architecture relation graph prediction problem, with the
relational graph constructed by treating candidate architectures as nodes and
their pairwise relations as edges. To enforce some basic properties such as
acyclicity in the relational graph, we add additional constraints to the
optimization process, converting NAS into the problem of finding a Maximal
Weighted Acyclic Subgraph (MWAS). Our algorithm then strives to eliminate
cycles and only establish edges in the graph if the rank results can be
trusted. Through MWAS, Arch-Graph can effectively rank candidate models for
each task with only a small budget to finetune the predictor. With extensive
experiments on TransNAS-Bench-101, we show Arch-Graph's transferability and
high sample efficiency across numerous tasks, beating many NAS methods designed
for both single-task and multi-task search. It is able to find top 0.16\% and
0.29\% architectures on average on two search spaces under the budget of only
50 models.


title:
Iso-doublet Vector Leptoquark solution to the Muon $g-2$, $R_{K, K^*}$,
  $R_{D,D^*}$, and $W$-mass Anomalies

abstract:
  We investigate the iso-doublet vector leptoquark $V_2$ as a solution to the
$B$ anomalies $R_{K, K^*}$ and $R_{D,D^*}$, as well as explaining the muon and
electron anomalous magnetic moments, and the very recent $W$-mass anomaly.


title: Rapid Spin Up and Spin Down of Flow Along Slopes

abstract:
  The near-bottom mixing that allows abyssal waters to upwell tilts isopycnals
and spins up flow over the flanks of mid-ocean ridges. Meso- and large-scale
currents along sloping topography are subjected to a delicate balance of Ekman
arrest and spin down. These two seemingly disparate oceanographic phenomena
share a common theory, which is based on a one-dimensional model of rotating,
stratified flow over a sloping, insulated boundary. This commonly used model,
however, lacks rapid adjustment of interior flows, limiting its ability to
capture the full physics of spin up and spin down of along-slope flow.
Motivated by two-dimensional dynamics, the present work extends the
one-dimensional model by constraining the vertically integrated cross-slope
transport and allowing for a barotropic cross-slope pressure gradient. This
produces a closed secondary circulation by forcing Ekman transport in the
bottom boundary layer to return in the interior. The extended model can thus
capture Ekman spin up and spin down physics: the interior return flow is turned
by the Coriolis acceleration, leading to rapid \linelabel{ll:slowdiff}rather
than slow diffusive adjustment of the along-slope flow. This
transport-constrained one-dimensional model accurately describes
two-dimensional mixing-generated spin up over an idealized ridge and provides a
unified framework for understanding the relative importance of Ekman arrest and
spin down of flow along a slope.


title:
Uncertainty-Aware Search Framework for Multi-Objective Bayesian
  Optimization

abstract:
  We consider the problem of multi-objective (MO) blackbox optimization using
expensive function evaluations, where the goal is to approximate the true
Pareto set of solutions while minimizing the number of function evaluations.
For example, in hardware design optimization, we need to find the designs that
trade-off performance, energy, and area overhead using expensive simulations.
We propose a novel uncertainty-aware search framework referred to as USeMO to
efficiently select the sequence of inputs for evaluation to solve this problem.
The selection method of USeMO consists of solving a cheap MO optimization
problem via surrogate models of the true functions to identify the most
promising candidates and picking the best candidate based on a measure of
uncertainty. We also provide theoretical analysis to characterize the efficacy
of our approach. Our experiments on several synthetic and six diverse
real-world benchmark problems show that USeMO consistently outperforms the
state-of-the-art algorithms.


title:
Light vectors coupled to anomalous currents with harmless Wess-Zumino
  terms

abstract:
  We reconsider the case of light vectors coupled to anomalous fermionic
currents, focussing on the interplay between UV and IR dynamics. Taking as a
general framework the gauging of the Standard Model accidental symmetries, we
show that it is possible to devise an anomaly-free UV completion with
mostly-chiral heavy fermions such that anomalous Wess-Zumino terms are
suppressed in the IR, thus relaxing would-be strong bounds from the
longitudinal emission of light vectors coupled to non-conserved currents. We
classify such scenarios and show that they will be extensively probed at the
high-luminosity phase of the LHC via the measurement of the $h \to Z \gamma$
rate and the direct search for non-decoupling charged leptons.


title:
Coupling Between Abyssal Boundary Layers and the Interior Ocean in the
  Absence of Along-Slope Variations

abstract:
  To close the overturning circulation, dense bottom water must upwell via
turbulent mixing. Recent studies have identified thin bottom boundary layers
(BLs) as locations of intense upwelling, yet it remains unclear how they
interact with and shape the large-scale circulation of the abyssal ocean. The
current understanding of this BL--interior coupling is shaped by 1D theory,
suggesting that variations in locally produced BL transport generate exchange
with the interior and thus a global circulation. Until now, however, this
picture has been based on a 1D theory that fails to capture the local evolution
in even highly idealized 2D geometries. The present work applies BL theory to
revised 1D dynamics, which more naturally generalizes to two and three
dimensions. The BL is assumed to be in quasi-equilibrium between the upwelling
of dense water and the convergence of downward buoyancy fluxes. The BL
transport, for which explicit formulae are presented, exerts an influence on
the interior by modifying the bottom boundary condition. In 1D, this BL
transport is independent of the interior evolution, but in 2D the BL and
interior are fully coupled. Once interior variables and the bottom slope are
allowed to vary in the horizontal, the resulting convergences and divergences
in the BL transport exchange mass with the interior. This framework allows for
the analysis of previously inaccessible problems such as the BL--interior
coupling in the presence of an exponential interior stratification, laying the
foundation for developing a full theory for the abyssal circulation.


title: Detection and Mitigation of Algorithmic Bias via Predictive Rate Parity

abstract:
  Recently, numerous studies have demonstrated the presence of bias in machine
learning powered decision-making systems. Although most definitions of
algorithmic bias have solid mathematical foundations, the corresponding bias
detection techniques often lack statistical rigor, especially for non-iid data.
We fill this gap in the literature by presenting a rigorous non-parametric
testing procedure for bias according to Predictive Rate Parity, a commonly
considered notion of algorithmic bias. We adapt traditional asymptotic results
for non-parametric estimators to test for bias in the presence of dependence
commonly seen in user-level data generated by technology industry applications
and illustrate how these approaches can be leveraged for mitigation. We further
propose modifications of this methodology to address bias measured through
marginal outcome disparities in classification settings and extend notions of
predictive rate parity to multi-objective models. Experimental results on real
data show the efficacy of the proposed detection and mitigation methods.


title: Maximum Entropy Baseline for Integrated Gradients

abstract:
  Integrated Gradients (IG), one of the most popular explainability methods
available, still remains ambiguous in the selection of baseline, which may
seriously impair the credibility of the explanations. This study proposes a new
uniform baseline, i.e., the Maximum Entropy Baseline, which is consistent with
the "uninformative" property of baselines defined in IG. In addition, we
propose an improved ablating evaluation approach incorporating the new
baseline, where the information conservativeness is maintained. We explain the
linear transformation invariance of IG baselines from an information
perspective. Finally, we assess the reliability of the explanations generated
by different explainability methods and different IG baselines through
extensive evaluation experiments.


title:
First-principles calculation of anomalous Hall and Nernst conductivity
  by local Berry phase

abstract:
  In this study, we implemented a finite-difference algorithm for computing
anomalous Hall and Nernst conductivity. Based on the expression to evaluate the
Berry curvature in an insulating system [J. Phys. Soc. Jpn. 74 1674(2005)], we
extended the methods to a metallic system. We calculated anomalous Hall
conductivity and Nernst conductivity in a two-dimensional ferromagnetic
material FeCl$_2$ and three-dimensional ferromagnetic transition metals bcc-Fe,
hcp-Co, and fcc-Ni. Our results are comparable to previously reported results
computed by Kubo-formula or Wannier representation. To evaluate anomalous
Nernst coefficients, the detailed Fermi-energy dependence of the anomalous Hall
conductivity is required. Nonetheless, previous methods based on Wannier
representation or Kubo-formula have numerical instability due to the
${\boldsymbol k}$-space Dirac monopole. The present method will open an
efficient thermoelectric material design based on the high-throughput
first-principles screening.


title: Memory effects displayed in the evolution of continuous variable system

abstract:
  We analyze non-Markovian memory effects displayed by the quantum Brownian
motion modelled as quantum harmonic oscillators coupled to a bath consisting of
harmonic oscillators. We study the time evolution of fidelity, Petz-R\'enyi
relative entropy and quantum entanglement for a family of 1-, 2- and 3-mode
Gaussian states.


title:
RL-CoSeg : A Novel Image Co-Segmentation Algorithm with Deep
  Reinforcement Learning

abstract:
  This paper proposes an automatic image co-segmentation algorithm based on
deep reinforcement learning (RL). Existing co-segmentation tasks mainly rely on
deep learning methods, and the obtained foreground edges are often rough. In
order to obtain more precise foreground edges, we use deep RL to solve this
problem and achieve the finer segmentation. To our best knowledge, this is the
first work to apply RL methods to co-segmentation. We define the problem as a
Markov Decision Process (MDP) and optimize it by RL with asynchronous advantage
actor-critic (A3C). The RL image co-segmentation network uses the correlation
between images to segment common and salient objects from a set of related
images. In order to achieve automatic segmentation, our RL-CoSeg method
eliminates user's hints. For the image co-segmentation problem, we propose a
collaborative RL algorithm based on the A3C model. We propose a Siamese RL
co-segmentation network structure to obtain the co-attention of images for
co-segmentation. We improve the self-attention for automatic RL algorithm to
obtain long-distance dependence and enlarge the receptive field. The image
feature information obtained by self-attention can be used to supplement the
deleted user's hints and help to obtain more accurate actions. Experimental
results have shown that our method can improve the performance effectively on
both coarse and fine initial segmentations, and it achieves the
state-of-the-art performance on Internet dataset, iCoseg dataset and MLMR-COS
dataset.


title: Generic Identifiability for REMIS: The Cointegrated Unit Root VAR

abstract:
  The "REtrieval from MIxed Sampling" (REMIS) approach based on blocking
developed in Anderson et al. (2016a) is concerned with retrieving an underlying
high frequency model from mixed frequency observations. In this paper we
investigate parameter-identifiability in the Johansen (1995) vector error
correction model for mixed frequency data. We prove that from the second
moments of the blocked process after taking differences at lag N (N is the slow
sampling rate), the parameters of the high frequency system are generically
identified. We treat the stock and the flow case as well as deterministic
terms.


title:
Explore More Guidance: A Task-aware Instruction Network for Sign
  Language Translation Enhanced with Data Augmentation

abstract:
  Sign language recognition and translation first uses a recognition module to
generate glosses from sign language videos and then employs a translation
module to translate glosses into spoken sentences. Most existing works focus on
the recognition step, while paying less attention to sign language translation.
In this work, we propose a task-aware instruction network, namely TIN-SLT, for
sign language translation, by introducing the instruction module and the
learning-based feature fuse strategy into a Transformer network. In this way,
the pre-trained model's language ability can be well explored and utilized to
further boost the translation performance. Moreover, by exploring the
representation space of sign language glosses and target spoken language, we
propose a multi-level data augmentation scheme to adjust the data distribution
of the training set. We conduct extensive experiments on two challenging
benchmark datasets, PHOENIX-2014-T and ASLG-PC12, on which our method
outperforms former best solutions by 1.65 and 1.42 in terms of BLEU-4. Our code
is published at https://github.com/yongcaoplus/TIN-SLT.


title:
3D Visual Tracking to Quantify Physical Contact Interactions in
  Human-to-Human Touch

abstract:
  Across a plethora of social situations, we touch others in natural and
intuitive ways to share thoughts and emotions, such as tapping to get one's
attention or caressing to soothe one's anxiety. A deeper understanding of these
human-to-human interactions will require, in part, the precise measurement of
skin-to-skin physical contact. Among prior efforts, each measurement approach
exhibits certain constraints, e.g., motion trackers do not capture the precise
shape of skin surfaces, while pressure sensors impede direct skin contact. In
contrast, this work develops an interference-free 3D visual tracking system
using a depth camera to measure the contact attributes between the bare hand of
a toucher and the forearm of a receiver. The toucher's hand is tracked as a
posed and positioned mesh by fitting a hand model to detected 3D joints,
whereas the forearm is extracted as a detailed 3D surface. Based on a contact
model of point clouds, the spatiotemporal contact changes are decomposed as six
high-resolution time-series attributes, i.e., contact area, indentation depth,
absolute velocity, and three orthogonal velocity components, together with
contact duration. To examine the system's capabilities and limitations, two
experiments were performed. First, to evaluate its ability to discern human
touches, one person delivered cued social messages, e.g., happiness, anger,
sympathy, to another using their preferred gestures. The results indicated that
messages, gestures, and the individual touchers, were readily discerned from
their contact attributes. Second, the measurement accuracy was validated
against independent devices, including an electromagnetic tracker, pressure
mat, and laser sensor. While validated here in the context of social
communication, this system is extendable to human touch interactions such as
maternal care of infants and massage therapy.


title:
Ground state preparation and energy estimation on early fault-tolerant
  quantum computers via quantum eigenvalue transformation of unitary matrices

abstract:
  Under suitable assumptions, the algorithms in [Lin, Tong, Quantum 2020] can
estimate the ground state energy and prepare the ground state of a quantum
Hamiltonian with near-optimal query complexities. However, this is based on a
block encoding input model of the Hamiltonian, whose implementation is known to
require a large resource overhead. We develop a tool called quantum eigenvalue
transformation of unitary matrices with real polynomials (QET-U), which uses a
controlled Hamiltonian evolution as the input model, a single ancilla qubit and
no multi-qubit control operations, and is thus suitable for early
fault-tolerant quantum devices. This leads to a simple quantum algorithm that
outperforms all previous algorithms with a comparable circuit structure for
estimating the ground state energy. For a class of quantum spin Hamiltonians,
we propose a new method that exploits certain anti-commutation relations and
further removes the need of implementing the controlled Hamiltonian evolution.
Coupled with Trotter based approximation of the Hamiltonian evolution, the
resulting algorithm can be very suitable for early fault-tolerant quantum
devices. We demonstrate the performance of the algorithm using IBM Qiskit for
the transverse field Ising model. If we are further allowed to use multi-qubit
Toffoli gates, we can then implement amplitude amplification and a new binary
amplitude estimation algorithm, which increases the circuit depth but decreases
the total query complexity. The resulting algorithm saturates the near-optimal
complexity for ground state preparation and energy estimating using a constant
number of ancilla qubits (no more than 3).


title:
Magnetization statics and dynamics in (Ir/Co/Pt)$_6$ multilayers with
  Dzyaloshinskii-Moriya interaction

abstract:
  Magnetic multilayers of (Ir/Co/Pt)$_6$ with interfacial Dzyaloshinskii-Moriya
interaction (IDMI) were deposited by magnetron sputtering with Co thickness
$d=1.8$ nm. Exploiting magneto-optical Kerr effect in longitudinal mode
microscopy, magnetic force microscopy, and vibrating sample magnetometry, the
magnetic field-driven evolution of domain structures and magnetization
hysteresis loops have been studied. The existence of weak stripe domains
structure was deduced -- tens micrometers size domains with in-plane "core"
magnetization modulated by hundred of nanometers domains with out-of-plane
magnetization. Micromagnetic simulations interpreted such magnetization
distribution. Quantitative evaluation of IDMI was carried out using Brillouin
light scattering (BLS) spectroscopy as the difference between Stokes and
anti-Stokes peak frequencies $\Delta f$. Due to the additive nature of IDMI,
the asymmetric combination of Ir and Pt covers led to large values of effective
IDMI energy density $D_\mathrm{eff}$. It was found that Stokes and anti-Stokes
frequencies as well as $\Delta f$, measured as a function of in-plane applied
magnetic field, show hysteresis. These results are explained under the
consideration of the influence of IDMI on the dynamics of the in-plane
magnetized "core" with weak stripe domains.


title: Localization Distillation for Object Detection

abstract:
  Previous knowledge distillation (KD) methods for object detection mostly
focus on feature imitation instead of mimicking the classification logits due
to its inefficiency in distilling the localization information. In this paper,
we investigate whether logit mimicking always lags behind feature imitation.
Towards this goal, we first present a novel localization distillation (LD)
method which can efficiently transfer the localization knowledge from the
teacher to the student. Second, we introduce the concept of valuable
localization region that can aid to selectively distill the classification and
localization knowledge for a certain region. Combining these two new
components, for the first time, we show that logit mimicking can outperform
feature imitation and the absence of localization distillation is a critical
reason for why logit mimicking underperforms for years. The thorough studies
exhibit the great potential of logit mimicking that can significantly alleviate
the localization ambiguity, learn robust feature representation, and ease the
training difficulty in the early stage. We also provide the theoretical
connection between the proposed LD and the classification KD, that they share
the equivalent optimization effect. Our distillation scheme is simple as well
as effective and can be easily applied to both dense horizontal object
detectors and rotated object detectors. Extensive experiments on the MS COCO,
PASCAL VOC, and DOTA benchmarks demonstrate that our method can achieve
considerable AP improvement without any sacrifice on the inference speed. Our
source code and pretrained models are publicly available at
https://github.com/HikariTJU/LD.


title:
Three-dimensional close-to-substrate trajectories of magnetic
  microparticles in dynamically changing magnetic field landscapes

abstract:
  The transport of magnetic nano- or microparticles in microfluidic devices
using artificially designed magnetic field landscapes (MFL) is promising for
the implementation of key functionalities in Lab-on-a-chip (LOC) systems. A
close-to-substrate transport is hereby instrumental to use changing
particle-substrate interactions upon analyte binding for analytics and
diagnostics. Here, we present an essential prerequisite for such an
application, namely the label-free quantitative experimental determination of
the three-dimensional trajectories of superparamagnetic particles (SPP)
transported by a dynamically changing MFL above a topographically flat
substrate. The evaluation of the SPP sharpness within defocused video-recorded
images, acquired by an optical bright-field microscope, was employed to obtain
a vertical z-coordinate. This method applied to a prototypical transport
scheme, using the static MFL of parallel-stripe domains superposed by a
particular magnetic field pulse sequence, revealed a hopping-like motion of the
magnetic particles, previously predicted by theory. Maximum vertical particle
jumps of several micrometers have been observed experimentally, corroborating
theoretical estimates for the particle-substrate distance. As our findings pave
the way towards precise quantification of particle-substrate separations in the
discussed transport system, they bear deep implications for future LOC
detection schemes using only optical microscopy.


title: "Smarter" NICs for faster molecular dynamics: a case study

abstract:
  This work evaluates the benefits of using a "smart" network interface card
(SmartNIC) as a compute accelerator for the example of the MiniMD molecular
dynamics proxy application. The accelerator is NVIDIA's BlueField-2 card, which
includes an 8-core Arm processor along with a small amount of DRAM and storage.
We test the networking and data movement performance of these cards compared to
a standard Intel server host using microbenchmarks and MiniMD. In MiniMD, we
identify two distinct classes of computation, namely core computation and
maintenance computation, which are executed in sequence. We restructure the
algorithm and code to weaken this dependence and increase task parallelism,
thereby making it possible to increase utilization of the BlueField-2
concurrently with the host. We evaluate our implementation on a cluster
consisting of 16 dual-socket Intel Broadwell host nodes with one BlueField-2
per host-node. Our results show that while the overall compute performance of
BlueField-2 is limited, using them with a modified MiniMD algorithm allows for
up to 20% speedup over the host CPU baseline with no loss in simulation
accuracy.


title: Bondi news in de Sitter space-time

abstract:
  Observations have shown that a model with a positive cosmological constant is
more appropriate to describe our universe. The aim of this manuscript is to
study the gravitational fields in de Sitter space-time. To achieve this goal,
de Sitter metric is written in Bondi Sachs form and the Einstein equations are
solved for this form of the metric in presence of $\Lambda$. On the other hand,
a null tetrad is defined for the de Sitter metric and its form in Bondi gauge
is obtained by using the general transformation. Then a new result for the
Bondi news in de Sitter background is obtained, using the coordinate
independent notion of Bondi news which was first presented by Penrose in 1963.


title: Quantified Reproducibility Assessment of NLP Results

abstract:
  This paper describes and tests a method for carrying out quantified
reproducibility assessment (QRA) that is based on concepts and definitions from
metrology. QRA produces a single score estimating the degree of reproducibility
of a given system and evaluation measure, on the basis of the scores from, and
differences between, different reproductions. We test QRA on 18 system and
evaluation measure combinations (involving diverse NLP tasks and types of
evaluation), for each of which we have the original results and one to seven
reproduction results. The proposed QRA method produces
degree-of-reproducibility scores that are comparable across multiple
reproductions not only of the same, but of different original studies. We find
that the proposed method facilitates insights into causes of variation between
reproductions, and allows conclusions to be drawn about what changes to system
and/or evaluation design might lead to improved reproducibility.


title:
Correlating $t\to cZ$ to the $W$ Mass and $B$ Physics with Vector-Like
  Quarks

abstract:
  The rare flavour changing top quark decay $t\to cZ$ is experimentally very
interesting due to the huge number of top quarks produced at the LHC.
Furthermore, in the Standard Model the corresponding rate is suppressed, such
that any observation of this process in the foreseeable future would prove the
presence of new physics. However, there are few (viable) models which can
generate a sizable branching ratio for $t\to cZ$ -- in fact vector-like quarks
seem to be the only realistic option. In this Letter, we investigate all three
representations (under the Standard Model gauge group) of vector-like quarks
($U$, $Q_1$ and $Q_7$) that can generate a sizable branching ratio for $t\to
cZ$ without violating other bounds. Nonetheless, these vector-like quarks still
give rise to loop-effects in $B$ physics and electroweak precision observables
which we calculate by matching the model on the Standard Model Effective Field
Theory (at one-loop). Taking into account the resulting constraints, we find
that Br($t\to cZ$) can be of the order of $10^{-6}$, $10^{-5}$ and $10^{-4}$
for $U$, $Q_1$ and $Q_7$, respectively. Note that these three vector-like
quarks are also exactly the ones which can lead to a sizable positive shift in
the prediction for $W$ mass, via the couplings to the top quark also needed for
a sizable Br($t\to cZ$), and therefore can reconcile theory with the recent
measurement by the CDF collaboration.


title:
Safety in Augmented Importance Sampling: Performance Bounds for Robust
  MPPI

abstract:
  This work explores the nature of augmented importance sampling in
safety-constrained model predictive control problems. When operating in a
constrained environment, sampling based model predictive control and motion
planning typically utilizes penalty functions or expensive optimization based
control barrier algorithms to maintain feasibility of forward sampling. In
contrast the presented algorithm utilizes discrete embedded barrier states in
augmented importance sampling to apply feedback with respect to a nominal state
when sampling. We will demonstrate that this approach of safety of discrete
embedded barrier states in augmented importance sampling is more sample
efficient by metric of collision free trajectories, is computationally feasible
to perform per sample, and results in better safety performance on a cluttered
navigation task with extreme un-modeled disturbances. In addition, we will
utilize the theoretical properties of augmented importance sampling and safety
control to derive a new bound on the free energy of the system.


title:
Non-integrability and chaos for natural Hamiltonian systems with a
  random potential

abstract:
  Consider the ensemble of Gaussian random potentials $\{V^L(q)\}_{L=1}^\infty$
on the $d$-dimensional torus where, essentially, $V^L(q)$ is a real-valued
trigonometric polynomial of degree $L$ whose coefficients are independent
standard normal variables. Our main result ensures that, with a probability
tending to 1 as $L\to\infty$, the dynamical system associated with the natural
Hamiltonian function defined by this random potential, $H^L:=\frac12|p|^2+
V^L(q)$, exhibits a number of chaotic regions which coexist with a
positive-volume set of invariant tori. In particular, these systems are
typically neither integrable with non-degenerate first integrals nor ergodic.
An analogous result for random natural Hamiltonian systems defined on the
cotangent bundle of an arbitrary compact Riemannian manifold is presented too.


title: New physics interpretation of $W$-boson mass anomaly

abstract:
  The CDF collaboration has recently reported an updated result on the
$W$-boson mass measurement, showing a $7\sigma$ deviation from the standard
model prediction. The discrepancy may indicate new contributions to the Fermi
coupling constant. We study simple extensions of the standard model by
introducing an extra scalar, fermion or vector field. It is found that the
tension implies the new physics existing in multi-TeV scales if the new
coupling to the electron and/or muon is of order unity.


title: Regularity results for a class of widely degenerate parabolic equations

abstract:
  Motivated by applications to gas filtration problems, we study the regularity
of weak solutions to the strongly degenerate parabolic PDE
$u_{t}-\mathrm{div}\left((\vert Du\vert-1)_{+}^{p-1}\frac{Du}{\vert
Du\vert}\right)=f$ in $\Omega_{T}=\Omega\times(0,T)$ ,where $\Omega$ is a
bounded domain in $\mathbb{R}^{n}$ for $n\geq2$, $p\geq2$ and
$\left(\,\cdot\,\right)_{+}$ stands for the positive part. Here, we establish
the higher differentiability and the higher integrability of the spatial
gradient of the weak solutions, as well as the existence of a weak time
derivative $u_{t}$, by initially assuming that $f\in
L^{\frac{p}{p-1}}\left(0,T;W^{1,\frac{p}{p-1}}\left(\Omega\right)\right)$.
Later, we also prove that it is possible to recover most of the previous
results by suitably weakening the assumption on the datum $f$. The main novelty
here is that the structure function of the above equation satisfies standard
ellipticity and growth conditions only outside the unit ball centered at the
origin. We would like to point out that some of the results obtained here can
be considered, on the one hand, as the parabolic counterpart of some elliptic
results established, for example, in [2, 4] and [5], and on the other hand as
the extension to a strongly degenerate setting of the known results for
parabolic equations.


title:
Machine learning predictions for local electronic properties of
  disordered correlated electron systems

abstract:
  We present a scalable machine learning (ML) model to predict local electronic
properties such as on-site electron number and double occupation for disordered
correlated electron systems. Our approach is based on the locality principle,
or the nearsightedness nature, of many-electron systems, which means local
electronic properties depend mainly on the immediate environment. A ML model is
developed to encode this complex dependence of local quantities on the
neighborhood. We demonstrate our approach using the square-lattice
Anderson-Hubbard model, which is a paradigmatic system for studying the
interplay between Mott transition and Anderson localization. We develop a
lattice descriptor based on group-theoretical method to represent the on-site
random potentials within a finite region. The resultant feature variables are
used as input to a multi-layer fully connected neural network, which is trained
from datasets of variational Monte Carlo (VMC) simulations on small systems. We
show that the ML predictions agree reasonably well with the VMC data. Our work
underscores the promising potential of ML methods for multi-scale modeling of
correlated electron systems.


title:
Automated Surface Texture Analysis via Discrete Cosine Transform and
  Discrete Wavelet Transform

abstract:
  Surface roughness and texture are critical to the functional performance of
engineering components. The ability to analyze roughness and texture
effectively and efficiently is much needed to ensure surface quality in many
surface generation processes, such as machining, surface mechanical treatment,
etc. Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are
two commonly used signal decomposition tools for surface roughness and texture
analysis. Both methods require selecting a threshold to decompose a given
surface into its three main components: form, waviness, and roughness. However,
although DWT and DCT are part of the ISO surface finish standards, there exists
no systematic guidance on how to compute these thresholds, and they are often
manually selected on case by case basis. This makes utilizing these methods for
studying surfaces dependent on the user's judgment and limits their automation
potential. Therefore, we present two automatic threshold selection algorithms
based on information theory and signal energy. We use machine learning to
validate the success of our algorithms both using simulated surfaces as well as
digital microscopy images of machined surfaces. Specifically, we generate
feature vectors for each surface area or profile and apply supervised
classification. Comparing our results with the heuristic threshold selection
approach shows good agreement with mean accuracies as high as 95\%. We also
compare our results with Gaussian filtering (GF) and show that while GF results
for areas can yield slightly higher accuracies, our results outperform GF for
surface profiles. We further show that our automatic threshold selection has
significant advantages in terms of computational time as evidenced by
decreasing the number of mode computations by an order of magnitude compared to
the heuristic thresholding for DCT.


title:
Efficient Construction of the BWT for Repetitive Text Using String
  Compression

abstract:
  We present a new semi-external algorithm that builds the Burrows-Wheeler
transform variant of Bauer et al. (a.k.a., BCR BWT) in linear expected time.
Our method uses compression techniques to reduce the computational costs when
the input is massive and repetitive. Concretely, we build on induced suffix
sorting (ISS) and resort to run-length and grammar compression to maintain our
intermediate results in compact form. Our compression format not only saves
space, but it also speeds up the required computations. Our experiments show
important savings in both space and computation time when the text is
repetitive. On average, we are 3.7x faster than the baseline compressed
approach, while maintaining a similar memory consumption. These results make
our method stand out as the only one (to our knowledge) that can build the BCR
BWT of a collection of 25 human genomes (75 GB) in about 7.3 hours, and using
only 27 GB of working memory.


title: Lossless plasmons in highly mismatched alloys

abstract:
  We explore the potential of highly mismatched alloys (HMAs) for realizing
lossless plasmonics. Systems with a plasmon frequency at which there are no
interband or intraband processes possible are called lossless, as there is no
2-particle loss channel for the plasmon. We find that the band splitting in
HMAs with a conduction band anticrossing guarantees a lossless frequency
window. When such a material is doped, producing plasmonic behavior, we study
the conditions required for the plasmon frequency to fall in the lossless
window, realizing lossless plasmons. Considering a generic class of HMAs with a
conduction band anticrossing, we find universal contours in their parameter
space within which lossless plasmons are possible for some doping range. Our
analysis shows that HMAs with heavier effective masses are most promising for
realizing a lossless plasmonic material.


title:
Chemical diversity of the atmospheres and interiors of sub-Neptunes -- A
  case study of GJ 436 b

abstract:
  The atmospheres of sub-Neptunes are expected to exhibit considerable chemical
diversity, beyond what is anticipated for gas-giant exoplanets. In the current
study, we construct self-consistent radiative transfer and equilibrium
chemistry models to explore this chemical diversity. We use GJ 436 b as a case
study to further study joint atmosphere-interior models. In particular, we
constrain the properties of the interior and atmosphere of the planet based on
the available Spitzer measurements. While it is possible to fit the emission
spectrum of GJ 436 b using a high-metallicity model, we demonstrate that such
an atmosphere is inconsistent with physically plausible interior structures. It
remains the case that no existing study can adequately fit the 4.5-micron
Spitzer secondary eclipse measurement, which is probably caused by chemical
disequilibrium. Finally, an information content analysis reveals that emission
and transmission spectra constrain the carbon-to-oxygen ratio and metallicity
at different wavelengths, but the former are less susceptible to flat spectra
stemming from highly metal-enriched atmospheres. With the recently-launched
JWST, we recommend that future analysis of emission and transmission spectra of
sub-Neptune planets are carried out self-consistently using both the
atmospheric and interior structure models.


title:
S-DABT: Schedule and Dependency-Aware Bug Triage in Open-Source Bug
  Tracking Systems

abstract:
  Fixing bugs in a timely manner lowers various potential costs in software
maintenance. However, manual bug fixing scheduling can be time-consuming,
cumbersome, and error-prone. In this paper, we propose the Schedule and
Dependency-aware Bug Triage (S-DABT), a bug triaging method that utilizes
integer programming and machine learning techniques to assign bugs to suitable
developers. Unlike prior works that largely focus on a single component of the
bug reports, our approach takes into account the textual data, bug fixing
costs, and bug dependencies. We further incorporate the schedule of developers
in our formulation to have a more comprehensive model for this multifaceted
problem. As a result, this complete formulation considers developers' schedules
and the blocking effects of the bugs while covering the most significant
aspects of the previously proposed methods. Our numerical study on four
open-source software systems, namely, EclipseJDT, LibreOffice, GCC, and
Mozilla, shows that taking into account the schedules of the developers
decreases the average bug fixing times. We find that S-DABT leads to a high
level of developer utilization through a fair distribution of the tasks among
the developers and efficient use of the free spots in their schedules. Via the
simulation of the issue tracking system, we also show how incorporating the
schedule in the model formulation reduces the bug fixing time, improves the
assignment accuracy, and utilizes the capability of each developer without much
comprising in the model run times. We find that S-DABT decreases the complexity
of the bug dependency graph by prioritizing blocking bugs and effectively
reduces the infeasible assignment ratio due to bug dependencies. Consequently,
we recommend considering developers' schedules while automating bug triage.


title: Complete nonlinear action for supersymmetric multiple D$0$-brane system

abstract:
  We present a complete nonlinear action for the dynamical system of nearly
coincident multiple D$0$-branes (mD$0$) which possesses, besides manifest
spacetime (target superspace) supersymmetry, also the worldline supersymmetry,
a counterpart of the local fermionic $\kappa$-symmetry of single D$0$-brane
(Dirichlet superparticle). The action contains an arbitrary non-vanishing
function ${\cal M}({\cal H})$ of the relative motion Hamiltonian ${\cal H}$.
The $D=10$ mD$0$ model with particular form of ${\cal M}({\cal H})$ can be
obtained by dimensional reduction from the action of eleven-dimensional
($D=11$) multiple M-wave (mM$0$) system.


title: Baire property of some function spaces

abstract:
  A compact space $X$ is called $\pi$-monolithic if whenever a surjective
continuous mapping $f:X\rightarrow K$ where $K$ is a compacta there exists a
compacta $T\subseteq X$ such that $f(T)=K$. A topological space $X$ is Baire if
the intersection of any sequence of open dense subsets of $X$ is dense in $X$.
Let $C_p(X,Y)$ denote the space of all continuous $Y$- valued functions
$C(X,Y)$ on a Tychonoff space $X$ with the topology of pointwise convergence.
In this paper we have proved that for a totally disconnected space $X$ the
space $C_p(X,\{0,1\})$ is Baire iff $C_p(X,K)$ is Baire whenever a
$\pi$-monolithic compact space $K$.
  For a Tychonoff space $X$ the space $C_p(X)$ is Baire iff $C_p(X,L)$ is Baire
whenever a Frechet space $L$.
  We construct a totally disconnected Tychonoff space $T$ such that $C_p(T,M)$
is Baire for a separable metric space $M$ iff M is a Peano continuum. Moreover,
the space $T$ such that $C_p(T,[0,1])$ is Baire but $Cp(T,\{0,1\})$ is not.


title:
Excesses in the low-mass Higgs-boson search and the W-boson mass
  measurement

abstract:
  The CDF collaboration recently reported a measurement of the $W$-bosos mass,
$M_W$, showing a large positive deviation from the Standard Model (SM)
prediction. The question arises whether extensions of the SM exist that can
accommodate such large values, and what further phenomenological consequences
arise from this. A different type of deviation from the SM has been observed
experimentally in the searches for light Higgs bosons. CMS has observed two
local $\sim 3\,\sigma$ excesses in the $\gamma\gamma$ and $\tau^+\tau^-$ final
states for a hypothetical Higgs-boson mass of $\sim 95$ GeV. These two excesses
are compatible with the corresponding ATLAS limits. A third excess was observed
in the Higgs-boson searches at LEP in the $b \bar b$ final state at the local
$\sim 2\,\sigma$ confidence level at about the same Higgs-boson mass. It was
shown recently that the three excesses can be described the the 2HDM extended
with a real singlet (N2HDM) of Yukawa type IV, while being in agreement with
all other theoretical and experimental constraints. We demonstrate that the
parameter space that accomodates the three excesses can also give a large
contribution to $M_W$ in agreement with the recent CDF measurement. We discuss
further phenomenological consequences of this scenario.


title:
Video Captioning: a comparative review of where we are and which could
  be the route

abstract:
  Video captioning is the process of describing the content of a sequence of
images capturing its semantic relationships and meanings. Dealing with this
task with a single image is arduous, not to mention how difficult it is for a
video (or images sequence). The amount and relevance of the applications of
video captioning are vast, mainly to deal with a significant amount of video
recordings in video surveillance, or assisting people visually impaired, to
mention a few. To analyze where the efforts of our community to solve the video
captioning task are, as well as what route could be better to follow, this
manuscript presents an extensive review of more than 105 papers for the period
of 2016 to 2021. As a result, the most-used datasets and metrics are
identified. Also, the main approaches used and the best ones. We compute a set
of rankings based on several performance metrics to obtain, according to its
performance, the best method with the best result on the video captioning task.
Finally, some insights are concluded about which could be the next steps or
opportunity areas to improve dealing with this complex task.


title: Evolution properties of the knot's defect

abstract:
  The defect of differential (cyclotomic) expansion for colored HOMFLY-PT
polynomials is conjectured to be invariant under any antiparallel evolution and
change linearly with the evolution in any parallel direction. In other words,
each ${\cal R}$-matrix can be substituted by an entire 2-strand braid in two
different ways: the defect remains intact when the braid is antiparallel and
changes by half of the added length when the braid is parallel.


title: A Compact Non-convex Ancient Curve Shortening Flow

abstract:
  An example of a compact, non-convex, embedded ancient solution for the curve
shortening flow, which is asymptotic to Yin-Yang curve, is constructed.


title:
Discovering material information using hierarchical Reformer model on
  financial regulatory filings

abstract:
  Most applications of machine learning for finance are related to forecasting
tasks for investment decisions. Instead, we aim to promote a better
understanding of financial markets with machine learning techniques. Leveraging
the tremendous progress in deep learning models for natural language
processing, we construct a hierarchical Reformer ([15]) model capable of
processing a large document level dataset, SEDAR, from canadian financial
regulatory filings. Using this model, we show that it is possible to predict
trade volume changes using regulatory filings. We adapt the pretraining task of
HiBERT ([36]) to obtain good sentence level representations using a large
unlabelled document dataset. Finetuning the model to successfully predict trade
volume changes indicates that the model captures a view from financial markets
and processing regulatory filings is beneficial. Analyzing the attention
patterns of our model reveals that it is able to detect some indications of
material information without explicit training, which is highly relevant for
investors and also for the market surveillance mandate of financial regulators.


title: Optical flow GNSS for navigation in the Indian subcontinent (NavIC)

abstract:
  This paper reveals about global navigation satellite system GNSS in the
indian subcontinent known as the navigation in the indian subcontinent(NavIC)
We have tried to model a new technique in GNSS known as the optical flow
tracking global navigation system (OF GNSS). This method using differential
equations is very accurate for very small distances on the surface of the earth
in the 1500km range of the Indian subcontinent satellite coverage. When we talk
of accuracy of the GPS system it should be very accurate on the surface of the
earth when used to show changes in coordinate of the moving body with respect
to the ground by the satellite which is situated on the earths orbit. Optical
flow is a method which uses movements with respect to x and y axis for
infinitesimal changes in its coordinates and then uses this algorithm to use it
in global positioning system to find accurate position of the body with respect
to the satellite coordinates with respect to ground positioning. The modern
method of differential frames is also very accurate as it involves
infinitesimal frames which are modelled together from the satellite to find
changes in the coordinates on the earths surface, so we have designed a new
algorithm in this paper on the Optical flow GNSS system which is an alternative
and can improve the study done in the design of these algorithms in this field
of applications.


title: The Odd Dark Matter Halos of Isolated Gas-Rich Ultra-Diffuse Galaxies

abstract:
  We analyze circular velocity profiles of seven ultra-diffuse galaxies (UDGs)
that are isolated and gas-rich. Assuming that the dark matter halos of these
UDGs have a modified Navarro-Frenk-White (NFW) density profile with a constant
density core, the inferred halo concentrations are systematically lower than
the cosmological median, even as low as $-0.6$ dex (about $5\sigma$ away) in
some cases. Alternatively, similar fits can be obtained with a density profile
that scales roughly as $1/r^2$ for radii larger than a few $\rm kpc$. Both
solutions require the radius where the halo circular velocity peaks ($R_{\rm
max}$) to be much larger than the median expectation. Surprisingly, we find an
overabundance of such large $R_{\rm max}$ halos in the IllustrisTNG dark
matter-only simulations compared to the Gaussian expectation. These halos form
late and have higher spins compared to median halos of similar masses. The
inner densities of the most extreme among these late-forming halos are higher
than their NFW counterparts, leading to a $\sim 1/r^2$ density profile.
However, the two well-resolved UDGs in our sample strongly prefer lower dark
matter densities in the center than the simulated ones. Comparing to
IllustrisTNG hydrodynamical simulations, we also find a tension in getting both
low enough circular velocities and high enough halo mass to accommodate the
measurements. Our results indicate that the gas-rich UDGs present a significant
challenge for galaxy formation models.


title:
Flood risk map from hydrological and mobility data: a case study in
  S\~ao Paulo (Brazil)

abstract:
  Cities increasingly face flood risk primarily due to extensive changes of the
natural land cover to built-up areas with impervious surfaces. In urban areas,
flood impacts come mainly from road interruption. This paper proposes an urban
flood risk map from hydrological and mobility data, considering the megacity of
S\~ao Paulo, Brazil, as a case study. We estimate the flood susceptibility
through the Height Above the Nearest Drainage algorithm; and the potential
impact through the exposure and vulnerability components. We aggregate all
variables into a regular grid and then classify the cells of each component
into three classes: Moderate, High, and Very High. All components, except the
flood susceptibility, have few cells in the Very High class. The flood
susceptibility component reflects the presence of watercourses, and it has a
strong influence on the location of those cells classified as Very High.


title:
Comparison Analysis of Traditional Machine Learning and Deep Learning
  Techniques for Data and Image Classification

abstract:
  The purpose of the study is to analyse and compare the most common machine
learning and deep learning techniques used for computer vision 2D object
classification tasks. Firstly, we will present the theoretical background of
the Bag of Visual words model and Deep Convolutional Neural Networks (DCNN).
Secondly, we will implement a Bag of Visual Words model, the VGG16 CNN
Architecture. Thirdly, we will present our custom and novice DCNN in which we
test the aforementioned implementations on a modified version of the Belgium
Traffic Sign dataset. Our results showcase the effects of hyperparameters on
traditional machine learning and the advantage in terms of accuracy of DCNNs
compared to classical machine learning methods. As our tests indicate, our
proposed solution can achieve similar - and in some cases better - results than
existing DCNNs architectures. Finally, the technical merit of this article lies
in the presented computationally simpler DCNN architecture, which we believe
can pave the way towards using more efficient architectures for basic tasks.


title: Snapshot hyperspectral imaging with quantum correlated photons

abstract:
  Hyperspectral imaging (HSI) has a wide range of applications from
environmental monitoring to biotechnology. Current snapshot HSI techniques all
require a trade-off between spatial and spectral resolution and are thus unable
to achieve high resolutions in both simultaneously. Additionally, the
techniques are resource inefficient with most of the photons lost through
spectral filtering. Here, we demonstrate a snapshot HSI technique utilizing the
strong spectro-temporal correlations inherent in entangled photons using a
modified quantum ghost spectroscopy system, where the target is directly imaged
with one photon and the spectral information gained through ghost spectroscopy
from the partner photon. As only a few rows of pixels near the edge of the
camera are used for the spectrometer, almost no spatial resolution is
sacrificed for spectral. Also since no spectral filtering is required, all
photons contribute to the HSI process making the technique much more resource
efficient.


title:
Turning Manual Concurrent Memory Reclamation into Automatic Reference
  Counting

abstract:
  Safe memory reclamation (SMR) schemes are an essential tool for lock-free
data structures and concurrent programming. However, manual SMR schemes are
notoriously difficult to apply correctly, and automatic schemes, such as
reference counting, have been argued for over a decade to be too slow for
practical purposes. A recent wave of work has disproved this long-held notion
and shown that reference counting can be as scalable as hazard pointers, one of
the most common manual techniques. Despite these tremendous improvements, there
remains a gap of up to 2x or more in performance between these schemes and
faster manual techniques such as epoch-based reclamation (EBR).
  In this work, we first advance these ideas and show that in many cases,
automatic reference counting can in fact be as fast as the fastest manual SMR
techniques. We generalize our previous Concurrent Deferred Reference Counting
(CDRC) algorithm to obtain a method for converting any standard manual SMR
technique into an automatic reference counting technique with a similar
performance profile. Our second contribution is extending this framework to
support weak pointers, which are reference-counted pointers that automatically
break pointer cycles by not contributing to the reference count, thus
addressing a common weakness in reference-counted garbage collection.
  Our experiments with a C++-library implementation show that our automatic
techniques perform in line with their manual counterparts, and that our weak
pointer implementation outperforms the best known atomic weak pointer library
by up to an order of magnitude on high thread counts. All together, we show
that the ease of use of automatic memory management can be achieved without
significant cost to practical performance or general applicability.