Abstract:
  Observed rotation curves in star-forming galaxies indicate a puzzling dearth
of dark matter in extended flat cores within haloes of mass $\geq\!
10^{12}M_\odot$ at $z\!\sim\! 2$. This is not reproduced by current
cosmological simulations, and supernova-driven outflows are not effective in
such massive haloes. We address a hybrid scenario where post-compaction merging
satellites heat up the dark-matter cusps by dynamical friction, allowing
AGN-driven outflows to generate cores. Using analytic and semi-analytic models
(SatGen), we estimate the dynamical-friction heating as a function of satellite
compactness for a cosmological sequence of mergers. Cosmological simulations
(VELA) demonstrate that satellites of initial virial masses
$>\!10^{11.3}M_\odot$, that undergo wet compactions, become sufficiently
compact for significant heating. Constituting a major fraction of the accretion
onto haloes $\geq\!10^{12}M_\odot$, these satellites heat-up the cusps in half
a virial time at $z\!\sim\! 2$. Using a model for outflow-driven core formation
(CuspCore), we demonstrate that the heated dark-matter cusps develop extended
cores in response to removal of half the gas mass, while the more compact
stellar systems remain intact. The mergers keep the dark matter hot, while the
gas supply, fresh and recycled, is sufficient for the AGN outflows. AGN indeed
become effective in haloes $\geq\!10^{12}M_\odot$, where the black-hole growth
is no longer suppressed by supernovae and its compaction-driven rapid growth is
maintained by a hot CGM. For simulations to reproduce the dynamical-friction
effects, they should resolve the compaction of the massive satellites and avoid
artificial tidal disruption. AGN feedback could be boosted by clumpy black-hole
accretion and clumpy response to AGN.


Abstract:
  We studied the dynamics of an object sliding down on a semi-sphere with
radius $R$. We consider the physical situation where the semi-sphere is free to
move over a horizontal surface. Also, we consider that all surfaces in contact
are friction-less. We analyze the values for the last contact angle
$\theta^\star$, corresponding to the angle when the object and the semi-sphere
detach one of each other, considering all possible scenarios with different
values of $m_A$ and $m_B$. We found that the last contact angle only depends on
the ratio between the masses, and it is independent of the acceleration of
gravity and semi-sphere radius. In addition, we found that the largest possible
value of $\theta^\star$ occurs for the case when the semi-sphere does not move.
On the opposite case, the minimum value of the angle occurs for $m_A \gg m_B$
occurring at the top of the semi-sphere.


Abstract:
  In a legal system, judgment consistency is regarded as one of the most
important manifestations of fairness. However, due to the complexity of factual
elements that impact sentencing in real-world scenarios, few works have been
done on quantitatively measuring judgment consistency towards real-world data.
In this paper, we propose an evaluation metric for judgment inconsistency,
Legal Inconsistency Coefficient (LInCo), which aims to evaluate inconsistency
between data groups divided by specific features (e.g., gender, region, race).
We propose to simulate judges from different groups with legal judgment
prediction (LJP) models and measure the judicial inconsistency with the
disagreement of the judgment results given by LJP models trained on different
groups. Experimental results on the synthetic data verify the effectiveness of
LInCo. We further employ LInCo to explore the inconsistency in real cases and
come to the following observations: (1) Both regional and gender inconsistency
exist in the legal system, but gender inconsistency is much less than regional
inconsistency; (2) The level of regional inconsistency varies little across
different time periods; (3) In general, judicial inconsistency is negatively
correlated with the severity of the criminal charges. Besides, we use LInCo to
evaluate the performance of several de-bias methods, such as adversarial
learning, and find that these mechanisms can effectively help LJP models to
avoid suffering from data bias.


Abstract:
  Multiplying matrices is among the most fundamental and compute-intensive
operations in machine learning. Consequently, there has been significant work
on efficiently approximating matrix multiplies. We introduce a learning-based
algorithm for this task that greatly outperforms existing methods. Experiments
using hundreds of matrices from diverse domains show that it often runs
$100\times$ faster than exact matrix products and $10\times$ faster than
current approximate methods. In the common case that one matrix is known ahead
of time, our method also has the interesting property that it requires zero
multiply-adds. These results suggest that a mixture of hashing, averaging, and
byte shuffling$-$the core operations of our method$-$could be a more promising
building block for machine learning than the sparsified, factorized, and/or
scalar quantized matrix products that have recently been the focus of
substantial research and hardware investment.


Abstract:
  The ability to automatically extract Knowledge Graphs (KG) from a given
collection of documents is a long-standing problem in Artificial Intelligence.
One way to assess this capability is through the task of slot filling. Given an
entity query in form of [Entity, Slot, ?], a system is asked to `fill' the slot
by generating or extracting the missing value from a relevant passage or
passages. This capability is crucial to create systems for automatic knowledge
base population, which is becoming in ever-increasing demand, especially in
enterprise applications. Recently, there has been a promising direction in
evaluating language models in the same way we would evaluate knowledge bases,
and the task of slot filling is the most suitable to this intent. The recent
advancements in the field try to solve this task in an end-to-end fashion using
retrieval-based language models. Models like Retrieval Augmented Generation
(RAG) show surprisingly good performance without involving complex information
extraction pipelines. However, the results achieved by these models on the two
slot filling tasks in the KILT benchmark are still not at the level required by
real-world information extraction systems. In this paper, we describe several
strategies we adopted to improve the retriever and the generator of RAG in
order to make it a better slot filler. Our KGI0 system (available at
https://github.com/IBM/retrieve-write-slot-filling) reached the top-1 position
on the KILT leaderboard on both T-REx and zsRE dataset with a large margin.


Abstract:
  Let $R$ be a commutative ring with non-zero identity. In this paper, we
introduce the concept of weakly $J$-ideals as a new generalization of
$J$-ideals. We call a proper ideal $I$ of a ring $R$ a weakly $J$-ideal if
whenever $a,b\in R$ with $0\neq ab\in I$ and $a\notin J(R)$, then $a\in I$.
Many of the basic properties and characterizations of this concept are studied.
We investigate weakly $J$-ideals under various contexts of constructions such
as direct products, localizations, homomorphic images. Moreover, a number of
examples and results on weakly $J$-ideals are discussed. Finally, the third
section is devoted to the characterizations of these constructions in an
amagamated ring along an ideal.


Abstract:
  We compute the Borel equivariant cohomology ring of the left $K$-action on a
homogeneous space $G/H$, where $G$ is a connected Lie group, $H$ and $K$ are
closed, connected subgroups and $2$ and the torsion primes of the Lie groups
are units of the coefficient ring. As a special case, this gives the singular
cohomology rings of biquotients $H \backslash G / K$.
  This depends on a version of the Eilenberg-Moore theorem developed in the
appendix, where a novel multiplicative structure on the two-sided bar
construction $\mathbf{B}(A',A,A'')$ is defined, valid when $A' \leftarrow A \to
A''$ is a pair of maps of homotopy Gerstenhaber algebras.


Abstract:
  Scaling relations are very useful tools for estimating unknown stellar
quantities. Within this framework, eclipsing binaries are ideal for this goal
because their mass and radius are known with a very good level of accuracy,
leading to improved constraints on the models. We aim to provide empirical
relations for the mass and radius as function of luminosity, metallicity, and
age. We investigate, in particular, the impact of metallicity and age on those
relations. We used a multi-dimensional fit approach based on the data from
DEBCat, an updated catalogue of eclipsing binary observations such as mass,
radius, luminosity, effective temperature, gravity, and metallicity. We used
the {PARAM web interface for the Bayesian estimation of stellar parameters,
along with} the stellar evolutionary code MESA to estimate the binary age,
assuming a coeval hypothesis for both members. We derived the mass and
radius-luminosity-metallicity-age relations using 56 stars, {with metallicity
and mass in the range -0.34<[Fe/H]<0.27 and 0.66<M/M{_\odot}<1.8}. With that,
the observed mass and radius are reproduced with an accuracy of 3.5% and 5.9%,
respectively, which is consistent with the other results in literature. We
conclude that including the age in such relations increases the quality of the
fit, particularly in terms of the mass, as compared to the radius. On the other
hand, as other authors have noted, we observed an higher dispersion on the mass
relation than in that of the radius. We propose that this is due to a stellar
age effect.


Abstract:
  Traditional normalization techniques (e.g., Batch Normalization and Instance
Normalization) generally and simplistically assume that training and test data
follow the same distribution. As distribution shifts are inevitable in
real-world applications, well-trained models with previous normalization
methods can perform badly in new environments. Can we develop new normalization
methods to improve generalization robustness under distribution shifts? In this
paper, we answer the question by proposing CrossNorm and SelfNorm. CrossNorm
exchanges channel-wise mean and variance between feature maps to enlarge
training distribution, while SelfNorm uses attention to recalibrate the
statistics to bridge gaps between training and test distributions. CrossNorm
and SelfNorm can complement each other, though exploring different directions
in statistics usage. Extensive experiments on different fields (vision and
language), tasks (classification and segmentation), settings (supervised and
semi-supervised), and distribution shift types (synthetic and natural) show the
effectiveness. Code is available at
https://github.com/amazon-research/crossnorm-selfnorm


Abstract:
  We use a combination of Hipparcos space mission data with the USNO dedicated
ground-based astrometric program URAT-Bright designed to complement and verify
Gaia results for the brightest stars in the south to estimate the small
perturbations of observed proper motions caused by exoplanets. One of the 1423
bright stars in the program, $\delta$ Pav, stands out with a small proper
motion difference between our long-term estimate and Gaia EDR3 value, which
corresponds to a projected velocity of $(-17,+13)$ m s$^{-1}$. This difference
is significant at a 0.994 confidence in the RA component, owing to the
proximity of the star and the impressive precision of proper motions. The
effect is confirmed by a comparison of long-term EDR3-Hipparcos and short-term
Gaia EDR3 proper motions at a smaller velocity, but with formally absolute
confidence. We surmise that the close Solar analog $\delta$ Pav harbors a
long-period exoplanet similar to Jupiter.


Abstract:
  This paper proposes a strategy to assess the robustness of different machine
learning models that involve natural language processing (NLP). The overall
approach relies upon a Search and Semantically Replace strategy that consists
of two steps: (1) Search, which identifies important parts in the text; (2)
Semantically Replace, which finds replacements for the important parts, and
constrains the replaced tokens with semantically similar words. We introduce
different types of Search and Semantically Replace methods designed
specifically for particular types of machine learning models. We also
investigate the effectiveness of this strategy and provide a general framework
to assess a variety of machine learning models. Finally, an empirical
comparison is provided of robustness performance among three different model
types, each with a different text representation.


Abstract:
  In this paper, we study gapsets and we focus on obtaining information on how
the maximum distance between to consecutive elements influences the behaviour
of the set. In particular, we prove that the cardinality of the set of gapsets
with genus $g$ such that the maximum distance between two consecutive elements
is $\k$ is equal to the cardinality of the set of gapsets with genus $g+1$ such
that the maximum distance between two consecutive elements is $\k+1$, when $2g
\leq 3\k$.


Abstract:
  Understanding the competition between superconductivity and other ordered
states (such as antiferromagnetic or charge-density-wave (CDW) state) is a
central issue in condensed matter physics. The recently discovered layered
kagome metal AV3Sb5 (A = K, Rb, and Cs) provides us a new playground to study
the interplay of superconductivity and CDW state by involving nontrivial
topology of band structures. Here, we conduct high-pressure electrical
transport and magnetic susceptibility measurements to study CsV3Sb5 with the
highest Tc of 2.7 K in AV3Sb5 family. While the CDW transition is monotonically
suppressed by pressure, superconductivity is enhanced with increasing pressure
up to P1~0.7 GPa, then an unexpected suppression on superconductivity happens
until pressure around 1.1 GPa, after that, Tc is enhanced with increasing
pressure again. The CDW is completely suppressed at a critical pressure P2~2
GPa together with a maximum Tc of about 8 K. In contrast to a common dome-like
behavior, the pressure-dependent Tc shows an unexpected double-peak behavior.
The unusual suppression of Tc at P1 is concomitant with the rapidly damping of
quantum oscillations, sudden enhancement of the residual resistivity and rapid
decrease of magnetoresistance. Our discoveries indicate an unusual competition
between superconductivity and CDW state in pressurized kagome lattice.


Abstract:
  The missing data issue is ubiquitous in health studies. Variable selection in
the presence of both missing covariates and outcomes is an important
statistical research topic but has been less studied. Existing literature
focuses on parametric regression techniques that provide direct parameter
estimates of the regression model. Flexible nonparametric machine learning
methods considerably mitigate the reliance on the parametric assumptions, but
do not provide as naturally defined variable importance measure as the
covariate effect native to parametric models. We investigate a general variable
selection approach when both the covariates and outcomes can be missing at
random and have general missing data patterns. This approach exploits the
flexibility of machine learning modeling techniques and bootstrap imputation,
which is amenable to nonparametric methods in which the covariate effects are
not directly available. We conduct expansive simulations investigating the
practical operating characteristics of the proposed variable selection
approach, when combined with four tree-based machine learning methods, XGBoost,
Random Forests, Bayesian Additive Regression Trees (BART) and Conditional
Random Forests, and two commonly used parametric methods, lasso and backward
stepwise selection. Numeric results suggest that when combined with bootstrap
imputation, XGBoost and BART have the overall best variable selection
performance with respect to the $F_1$ score and Type I error across various
settings. In general, there is no significant difference in the variable
selection performance due to imputation methods. We further demonstrate the
methods via a case study of risk factors for 3-year incidence of metabolic
syndrome with data from the Study of Women's Health Across the Nation.


Abstract:
  We explore coherent control of Penning and associative ionization in cold
collisions of metastable He$^*({2}^3\text{S})$ atoms via the quantum
interference between different states of the He$_2^*$ collision complex. By
tuning the preparation coefficients of the initial atomic spin states, we can
benefit from the quantum interference between molecular channels to maximize or
minimize the cross sections for Penning and associative ionization. In
particular, we find that we can enhance the ionization ratio by 30% in the cold
regime. This work is significant for the coherent control of chemical reactions
in the cold and ultracold regime.


Abstract:
  To make informed decisions in natural environments that change over time,
humans must update their beliefs as new observations are gathered. Studies
exploring human inference as a dynamical process that unfolds in time have
focused on situations in which the statistics of observations are
history-independent. Yet temporal structure is everywhere in nature, and yields
history-dependent observations. Do humans modify their inference processes
depending on the latent temporal statistics of their observations? We
investigate this question experimentally and theoretically using a change-point
inference task. We show that humans adapt their inference process to fine
aspects of the temporal structure in the statistics of stimuli. As such, humans
behave qualitatively in a Bayesian fashion, but, quantitatively, deviate away
from optimality. Perhaps more importantly, humans behave suboptimally in that
their responses are not deterministic, but variable. We show that this
variability itself is modulated by the temporal statistics of stimuli. To
elucidate the cognitive algorithm that yields this behavior, we investigate a
broad array of existing and new models that characterize different sources of
suboptimal deviations away from Bayesian inference. While models with 'output
noise' that corrupts the response-selection process are natural candidates,
human behavior is best described by sampling-based inference models, in which
the main ingredient is a compressed approximation of the posterior, represented
through a modest set of random samples and updated over time. This result comes
to complement a growing literature on sample-based representation and learning
in humans.


Abstract:
  Unemployment benefits in the US were extended by up to 73 weeks during the
Great Recession. Equilibrium labor market theory indicates that extensions of
benefit duration impact not only search decisions by job seekers but also job
vacancy creations by employers. Most of the literature focused on the former to
show partial equilibrium effect that increment of unemployment benefits
discourage job search and lead to a rise in unemployment. To study the total
effect of UI benefit extensions on unemployment, I follow border county
identification strategy, take advantage of quasi-differenced specification to
control for changes in future benefit policies, apply interactive fixed effects
model to deal with unobserved shocks so as to obtain unbiased and consistent
estimation. I find that benefit extensions have a statistically significant
positive effect on unemployment, which is consistent with the results of
prevailing literature.


Abstract:
  Human motion characteristics are used to monitor the progression of
neurological diseases and mood disorders. Since perceptions of emotions are
also interleaved with body posture and movements, emotion recognition from
human gait can be used to quantitatively monitor mood changes. Many existing
solutions often use shallow machine learning models with raw positional data or
manually extracted features to achieve this. However, gait is composed of many
highly expressive characteristics that can be used to identify human subjects,
and most solutions fail to address this, disregarding the subject's privacy.
This work introduces a novel deep neural network architecture to disentangle
human emotions and biometrics. In particular, we propose a cross-subject
transfer learning technique for training a multi-encoder autoencoder deep
neural network to learn disentangled latent representations of human motion
features. By disentangling subject biometrics from the gait data, we show that
the subject's privacy is preserved while the affect recognition performance
outperforms traditional methods. Furthermore, we exploit Guided Grad-CAM to
provide global explanations of the model's decision across gait cycles. We
evaluate the effectiveness of our method to existing methods at recognizing
emotions using both 3D temporal joint signals and manually extracted features.
We also show that this data can easily be exploited to expose a subject's
identity. Our method shows up to 7% improvement and highlights the joints with
the most significant influence across the average gait cycle.


Abstract:
  Standard Model (SM) of particle physics has achieved enormous success in
describing the interactions among the known fundamental constituents of nature,
yet it fails to describe phenomena for which there is very strong experimental
evidence, such as the existence of dark matter, and which point to the
existence of new physics not included in that model; beyond its existence,
experimental data, however, have not provided clear indications as to the
nature of that new physics. The effective field theory (EFT) approach, the
subject of this review, is designed for this type of situations; it provides a
consistent and unbiased framework within which to study new physics effects
whose existence is expected but whose detailed nature is known very
imperfectly. We will provide a description of this approach together with a
discussion of some of its basic theoretical aspects. We then consider
applications to high-energy phenomenology and conclude with a discussion of the
application of EFT techniques to the study of dark matter physics and it
possible interactions with the SM. In several of the applications we also
briefly discuss specific models that are ultraviolet complete and may realize
the effects described by the EFT.


Abstract:
  To study the heavy quark production processes, we use the transverse momentum
dependent (TMD, or unintegrated) gluon distribution function in a proton
obtained recently using the Kimber-Martin-Ryskin prescription from the
Bessel-inspired behavior of parton densities at small Bjorken $x$ values. We
obtained a good agreement of our results with the latest HERA experimental data
for reduced cross sections $\sigma^{c\overline{c}}_{\rm red}(x,Q^2)$ and
$\sigma^{b\overline{b}}_{\rm red}(x,Q^2)$, and also for deep inelastic
structure functions $F_2^c(x,Q^2)$ and $F_2^b(x,Q^2)$ in a wide range of $x$
and $Q^2$ values. Comparisons with the predictions based on the
Ciafaloni-Catani-Fiorani-Marchesini evolution equation and with the results of
conventional pQCD calculations performed at first three orders of perturbative
expansion are presented.


Abstract:
  Fast and automated inference of binary-lens, single-source (2L1S)
microlensing events with sampling-based Bayesian algorithms (e.g., Markov Chain
Monte Carlo; MCMC) is challenged on two fronts: high computational cost of
likelihood evaluations with microlensing simulation codes, and a pathological
parameter space where the negative-log-likelihood surface can contain a
multitude of local minima that are narrow and deep. Analysis of 2L1S events
usually involves grid searches over some parameters to locate approximate
solutions as a prerequisite to posterior sampling, an expensive process that
often requires human-in-the-loop domain expertise. As the next-generation,
space-based microlensing survey with the Roman Space Telescope is expected to
yield thousands of binary microlensing events, a new fast and automated method
is desirable. Here, we present a likelihood-free inference (LFI) approach named
amortized neural posterior estimation, where a neural density estimator (NDE)
learns a surrogate posterior $\hat{p}(\theta|x)$ as an observation-parametrized
conditional probability distribution, from pre-computed simulations over the
full prior space. Trained on 291,012 simulated Roman-like 2L1S simulations, the
NDE produces accurate and precise posteriors within seconds for any observation
within the prior support without requiring a domain expert in the loop, thus
allowing for real-time and automated inference. We show that the NDE also
captures expected posterior degeneracies. The NDE posterior could then be
refined into the exact posterior with a downstream MCMC sampler with minimal
burn-in steps.


Abstract:
  After disasters, distribution networks have to be restored by repair,
reconfiguration, and power dispatch. During the restoration process, changes
can occur in real time that deviate from the situations considered in
pre-designed planning strategies. That may result in the pre-designed plan to
become far from optimal or even unimplementable. This paper proposes a
centralized-distributed bi-level optimization method to solve the real-time
restoration planning problem. The first level determines integer variables
related to routing of the crews and the status of the switches using a genetic
algorithm (GA), while the second level determines the dispatch of
active/reactive power by using distributed model predictive control (DMPC). A
novel Aitken- DMPC solver is proposed to accelerate convergence and to make the
method suitable for real-time decision making. A case study based on the IEEE
123-bus system is considered, and the acceleration performance of the proposed
Aitken-DMPC solver is evaluated and compared with the standard DMPC method.


Abstract:
  We present extensive, well-sampled optical and ultraviolet photometry and
optical spectra of the Type Ia supernova (SN Ia) 2017hpa. The light curves
indicate that SN 2017hpa is a normal SN Ia with an absolute peak magnitude of
$M_{\rm max}^{B} \approx$ -19.12$\pm$0.11 mag and a post-peak decline rate \mb\
= 1.02$\pm$0.07 mag. According to the quasibolometric light curve, we derive a
peak luminosity of 1.25$\times$10$^{43}$ erg s$^{-1}$ and a $^{56}$Ni mass of
0.63$\pm$0.02 $M_{\odot}$. The spectral evolution of SN 2017hpa is similar to
that of normal SNe Ia, while it exhibits unusually rapid velocity evolution
resembling that of SN 1991bg-like SNe Ia or the high-velocity subclass of SNe
Ia, with a post-peak velocity gradient of $\sim$ 130$\pm$7 km s$^{-1}$
d$^{-1}$. Moreover, its early spectra ($t < -7.9$ d) show prominent
\CII~$\lambda$6580 absorption feature, which disappeared in near-maximum-light
spectra but reemerged at phases from $t \sim +8.7$ d to $t \sim +11.7$ d after
maximum light. This implies that some unburned carbon may mix deep into the
inner layer, and is supported by the low \CII~$\lambda$6580 to
\SiII~$\lambda$6355 velocity ratio ($\sim 0.81$) observed in SN 2017hpa. The
\OI~$\lambda$7774 line shows a velocity distribution like that of carbon. The
prominent carbon feature, low velocity seen in carbon and oxygen, and large
velocity gradient make SN 2017hpa stand out from other normal SNe Ia, and are
more consistent with predictions from a violent merger of two white dwarfs.
Detailed modelling is still needed to reveal the nature of SN 2017hpa.


Abstract:
  The law of a positive infinitely divisible process with no drift is
characterized by its L\'evy measure on the paths space. Based on recent results
of the two authors, it is shown that even for simple examples of such
processes, the knowledge of their L\'evy measures allows to obtain remarkable
distributional identities.


Abstract:
  Gravitational waves (GWs) at ultra-low frequencies (${\lesssim
100\,\mathrm{nHz}}$) are key to understanding the assembly and evolution of
astrophysical black hole (BH) binaries with masses $\sim
10^{6}-10^{9}\,M_\odot$ at low redshifts. These GWs also offer a unique window
into a wide variety of cosmological processes. Pulsar timing arrays (PTAs) are
beginning to measure this stochastic signal at $\sim 1-100\,\mathrm{nHz}$ and
the combination of data from several arrays is expected to confirm a detection
in the next few years. The dominant physical processes generating gravitational
radiation at $\mathrm{nHz}$ frequencies are still uncertain. PTA observations
alone are currently unable to distinguish a binary BH astrophysical foreground
from a cosmological background due to, say, a first order phase transition at a
temperature $\sim 1-100\,\mathrm{MeV}$ in a weakly-interacting dark sector.
This letter explores the extent to which incorporating integrated bounds on the
ultra-low frequency GW spectrum from any combination of cosmic microwave
background, big bang nucleosynethesis or astrometric observations can help to
break this degeneracy.


Abstract:
  Fluctuations of conserved charges are sensitive to the QCD phase transition
and a possible critical endpoint in the phase diagram at finite density. In
this work, we compute the baryon number fluctuations up to tenth order at
finite temperature and density. This is done in a QCD-assisted effective theory
that accurately captures the quantum- and in-medium effects of QCD at low
energies. A direct computation at finite density allows us to assess the
applicability of expansions around vanishing density. By using different
freeze-out scenarios in heavy-ion collisions, we translate these results into
baryon number fluctuations as a function of collision energy. We show that a
non-monotonic energy dependence of baryon number fluctuations can arise in the
non-critical crossover region of the phase diagram. Our results compare well
with recent experimental measurements of the kurtosis and the sixth-order
cumulant of the net-proton distribution from the STAR collaboration. They
indicate that the experimentally observed non-monotonic energy dependence of
fourth-order net-proton fluctuations is highly non-trivial. It could be an
experimental signature of an increasingly sharp chiral crossover and may
indicate a QCD critical point. The physics implications and necessary upgrades
of our analysis are discussed in detail.


Abstract:
  Contributions: This paper investigates the relations between undergraduate
software architecture students' self-confidence and their course expectations,
cognitive levels, preferred learning methods, and critical thinking.
Background: these students, often, lack self-confidence in their ability to use
their knowledge to design software architectures. Intended Outcomes:
Self-confidence is expected to be related to the students' course expectations,
cognitive levels, preferred learning methods, and critical thinking.
Application Design: We developed a questionnaire with open-ended questions to
assess the self-confidence levels and related factors, which was taken by
one-hundred ten students in two semesters. The students answers were coded and
analyzed afterward. Findings: We found that self-confidence is weakly
associated with the students' course expectations and critical thinking and
independent from their cognitive levels and preferred learning methods. The
results suggest that to improve the self-confidence of the students, the
instructors should ensure that the students' have "correct" course expectations
and work on improving the students' critical thinking capabilities.


Abstract:
  High-quality 4D reconstruction of human performance with complex interactions
to various objects is essential in real-world scenarios, which enables numerous
immersive VR/AR applications. However, recent advances still fail to provide
reliable performance reconstruction, suffering from challenging interaction
patterns and severe occlusions, especially for the monocular setting. To fill
this gap, in this paper, we propose RobustFusion, a robust volumetric
performance reconstruction system for human-object interaction scenarios using
only a single RGBD sensor, which combines various data-driven visual and
interaction cues to handle the complex interaction patterns and severe
occlusions. We propose a semantic-aware scene decoupling scheme to model the
occlusions explicitly, with a segmentation refinement and robust object
tracking to prevent disentanglement uncertainty and maintain temporal
consistency. We further introduce a robust performance capture scheme with the
aid of various data-driven cues, which not only enables re-initialization
ability, but also models the complex human-object interaction patterns in a
data-driven manner. To this end, we introduce a spatial relation prior to
prevent implausible intersections, as well as data-driven interaction cues to
maintain natural motions, especially for those regions under severe
human-object occlusions. We also adopt an adaptive fusion scheme for temporally
coherent human-object reconstruction with occlusion analysis and human parsing
cue. Extensive experiments demonstrate the effectiveness of our approach to
achieve high-quality 4D human performance reconstruction under complex
human-object interactions whilst still maintaining the lightweight monocular
setting.


Abstract:
  This graduate textbook on machine learning tells a story of how patterns in
data support predictions and consequential actions. Starting with the
foundations of decision making, we cover representation, optimization, and
generalization as the constituents of supervised learning. A chapter on
datasets as benchmarks examines their histories and scientific bases.
Self-contained introductions to causality, the practice of causal inference,
sequential decision making, and reinforcement learning equip the reader with
concepts and tools to reason about actions and their consequences. Throughout,
the text discusses historical context and societal impact. We invite readers
from all backgrounds; some experience with probability, calculus, and linear
algebra suffices.


Abstract:
  System-level test, or SLT, is an increasingly important process step in
today's integrated circuit testing flows. Broadly speaking, SLT aims at
executing functional workloads in operational modes. In this paper, we
consolidate available knowledge about what SLT is precisely and why it is used
despite its considerable costs and complexities. We discuss the types or
failures covered by SLT, and outline approaches to quality assessment, test
generation and root-cause diagnosis in the context of SLT. Observing that the
theoretical understanding for all these questions has not yet reached the level
of maturity of the more conventional structural and functional test methods, we
outline new and promising directions for methodical developments leveraging on
recent findings from software engineering.


Abstract:
  Novel Object Captioning is a zero-shot Image Captioning task requiring
describing objects not seen in the training captions, but for which information
is available from external object detectors. The key challenge is to select and
describe all salient detected novel objects in the input images. In this paper,
we focus on this challenge and propose the ECOL-R model (Encouraging Copying of
Object Labels with Reinforced Learning), a copy-augmented transformer model
that is encouraged to accurately describe the novel object labels. This is
achieved via a specialised reward function in the SCST reinforcement learning
framework (Rennie et al., 2017) that encourages novel object mentions while
maintaining the caption quality. We further restrict the SCST training to the
images where detected objects are mentioned in reference captions to train the
ECOL-R model. We additionally improve our copy mechanism via Abstract Labels,
which transfer knowledge from known to novel object types, and a Morphological
Selector, which determines the appropriate inflected forms of novel object
labels. The resulting model sets new state-of-the-art on the nocaps (Agrawal et
al., 2019) and held-out COCO (Hendricks et al., 2016) benchmarks.


Abstract:
  The Internet of Things (IoT) is becoming an indispensable part of everyday
life, enabling a variety of emerging services and applications. However, the
presence of rogue IoT devices has exposed the IoT to untold risks with severe
consequences. The first step in securing the IoT is detecting rogue IoT devices
and identifying legitimate ones. Conventional approaches use cryptographic
mechanisms to authenticate and verify legitimate devices' identities. However,
cryptographic protocols are not available in many systems. Meanwhile, these
methods are less effective when legitimate devices can be exploited or
encryption keys are disclosed. Therefore, non-cryptographic IoT device
identification and rogue device detection become efficient solutions to secure
existing systems and will provide additional protection to systems with
cryptographic protocols. Non-cryptographic approaches require more effort and
are not yet adequately investigated. In this paper, we provide a comprehensive
survey on machine learning technologies for the identification of IoT devices
along with the detection of compromised or falsified ones from the viewpoint of
passive surveillance agents or network operators. We classify the IoT device
identification and detection into four categories: device-specific pattern
recognition, Deep Learning enabled device identification, unsupervised device
identification, and abnormal device detection. Meanwhile, we discuss various
ML-related enabling technologies for this purpose. These enabling technologies
include learning algorithms, feature engineering on network traffic traces and
wireless signals, continual learning, and abnormality detection.


Abstract:
  Nonstationary signals are commonly analyzed and processed in the
time-frequency (T-F) domain that is obtained by the discrete Gabor transform
(DGT). The T-F representation obtained by DGT is spread due to windowing, which
may degrade the performance of T-F domain analysis and processing. To obtain a
well-localized T-F representation, sparsity-aware methods using $\ell_1$-norm
have been studied. However, they need to discretize a continuous parameter onto
a grid, which causes a model mismatch. In this paper, we propose a method of
estimating a sparse T-F representation using atomic norm. The atomic norm
enables sparse optimization without discretization of continuous parameters.
Numerical experiments show that the T-F representation obtained by the proposed
method is sparser than the conventional methods.


Abstract:
  This paper explores methods for constructing low multipole temperature and
polarisation likelihoods from maps of the cosmic microwave background
anisotropies that have complex noise properties and partial sky coverage. We
use Planck 2018 High Frequency Instrument (HFI) and updated SRoll2 temperature
and polarisation maps to test our methods. We present three likelihood
approximations based on quadratic cross spectrum estimators: (i) a variant of
the simulation-based likelihood (SimBaL) techniques used in the Planck legacy
papers to produce a low multipole EE likelihood; (ii) a semi-analytical
likelihood approximation (momento) based on the principle of maximum entropy;
(iii) a density-estimation `likelihood-free' scheme (DELFI). Approaches (ii)
and (iii) can be generalised to produce low multipole joint
temperature-polarisation (TTTEEE) likelihoods. We present extensive tests of
these methods on simulations with realistic correlated noise. We then analyse
the Planck data and confirm the robustness of our method and likelihoods on
multiple inter- and intra-frequency detector set combinations of SRoll2 maps.
The three likelihood techniques give consistent results and support a low value
of the optical depth to reoinization, tau, from the HFI. Our best estimate of
tau comes from combining the low multipole SRoll2 momento (TTTEEE) likelihood
with the CamSpec high multipole likelihood and is tau = 0.0627+0.0050-0.0058.
This is consistent with the SRoll2 team's determination of tau, though slightly
higher by 0.5 sigma, mainly because of our joint treatment of temperature and
polarisation.


Abstract:
  Drones are effective for reducing human activity and interactions by
performing tasks such as exploring and inspecting new environments, monitoring
resources and delivering packages. Drones need a controller to maintain
stability and to reach their goal. The most well-known drone controllers are
proportional-integral-derivative (PID) and proportional-derivative (PD)
controllers. However, the controller parameters need to be tuned and optimized.
In this paper, we introduce the use of two evolutionary algorithms,
biogeography-based optimization~(BBO) and particle swarm optimization (PSO),
for multi-objective optimization (MOO) to tune the parameters of the PD
controller of a drone. The combination of MOO, BBO, and PSO results in various
methods for optimization: vector evaluated BBO and PSO, denoted as VEBBO and
VEPSO; and non-dominated sorting BBO and PSO, denoted as NSBBO and NSPSO. The
multi-objective cost function is based on tracking errors for the four states
of the system. Two criteria for evaluating the Pareto fronts of the
optimization methods, normalized hypervolume and relative coverage, are used to
compare performance. Results show that NSBBO generally performs better than the
other methods.


Abstract:
  Deep neural networks (DNNs) used for brain-computer-interface (BCI)
classification are commonly expected to learn general features when trained
across a variety of contexts, such that these features could be fine-tuned to
specific contexts. While some success is found in such an approach, we suggest
that this interpretation is limited and an alternative would better leverage
the newly (publicly) available massive EEG datasets. We consider how to adapt
techniques and architectures used for language modelling (LM), that appear
capable of ingesting awesome amounts of data, towards the development of
encephalography modelling (EM) with DNNs in the same vein. We specifically
adapt an approach effectively used for automatic speech recognition, which
similarly (to LMs) uses a self-supervised training objective to learn
compressed representations of raw data signals. After adaptation to EEG, we
find that a single pre-trained model is capable of modelling completely novel
raw EEG sequences recorded with differing hardware, and different subjects
performing different tasks. Furthermore, both the internal representations of
this model and the entire architecture can be fine-tuned to a variety of
downstream BCI and EEG classification tasks, outperforming prior work in more
task-specific (sleep stage classification) self-supervision.


Abstract:
  We investigate the behavior of the Lyapunov spectrum of a linear
discrete-time system under the action of small perturbations in order to obtain
some verifiable conditions for stability and openness of the Lyapunov spectrum.
To this end we introduce the concepts of broken away solutions and splitted
systems. The main results obtained are a necessary condition for stability and
a sufficient condition for the openness of the Lyapunov spectrum, which is
given in terms of the system itself. Finally, examples of using the obtained
results are presented.


Abstract:
  We prove fixed point theorems in a space with a distance function that takes
values in a partially ordered monoid. On the one hand, such an approach allows
one to generalize some fixed point theorems in a broad class of spaces,
including metric and uniform spaces. On the other hand, compared to the
so-called cone metric spaces and $K$-metric spaces, we do not require that the
distance function range has a linear structure. We also consider several
applications of the obtained fixed point theorems. In particular, we consider
the questions of the existence of solutions of the Fredholm integral equation
in $L$-spaces.


Abstract:
  In this work, we have proposed augmented KRnets including both discrete and
continuous models. One difficulty in flow-based generative modeling is to
maintain the invertibility of the transport map, which is often a trade-off
between effectiveness and robustness. The exact invertibility has been achieved
in the real NVP using a specific pattern to exchange information between two
separated groups of dimensions. KRnet has been developed to enhance the
information exchange among data dimensions by incorporating the
Knothe-Rosenblatt rearrangement into the structure of the transport map. Due to
the maintenance of exact invertibility, a full nonlinear update of all data
dimensions needs three iterations in KRnet. To alleviate this issue, we will
add augmented dimensions that act as a channel for communications among the
data dimensions. In the augmented KRnet, a fully nonlinear update is achieved
in two iterations. We also show that the augmented KRnet can be reformulated as
the discretization of a neural ODE, where the exact invertibility is kept such
that the adjoint method can be formulated with respect to the discretized ODE
to obtain the exact gradient. Numerical experiments have been implemented to
demonstrate the effectiveness of our models.


Abstract:
  In this paper, we study 5d $\mathcal{N}=1$ $Sp(N)$ gauge theory with $N_f (
\leq 2N + 3 )$ flavors based on 5-brane web diagram with $O5$-plane. On the one
hand, we discuss Seiberg-Witten curve based on the dual graph of the 5-brane
web with $O5$-plane. On the other hand, we compute the Nekrasov partition
function based on the topological vertex formalism with $O5$-plane. Rewriting
it in terms of profile functions, we obtain the saddle point equation for the
profile function after taking thermodynamic limit. By introducing the
resolvent, we derive the Seiberg-Witten curve and its boundary conditions as
well as its relation to the prepotential in terms of the cycle integrals. They
coincide with those directly obtained from the dual graph of the 5-brane web
with $O5$-plane. This agreement gives further evidence for mirror symmetry
which relates Nekrasov partition function with Seiberg-Witten curve in the case
with orientifold plane and shed light on the non-toric Calabi-Yau 3-folds
including D-type singularities.


Abstract:
  In this paper, we study the problem of physical layer security in the uplink
of millimeter-wave massive multiple-input multiple-output (MIMO) networks and
propose a jamming detection and suppression method. The proposed method is
based on directional information of the received signals at the base station
antenna array. The proposed jamming detection method can accurately detect both
the existence and direction of the jammer using the received pilot signals in
the training phase. The obtained information is then exploited to develop a
channel estimator that excludes the jammer's angular subspace from received
training signals. The estimated channel information is then used for designing
a combiner at the base station that is able to effectively cancel out the
deliberate interference of the jammer. By numerical simulations, we evaluate
the performance of the proposed jamming detection method in terms of correct
detection probability and false alarm probability and show its effectiveness
when the jammer's power is substantially lower than the user's power. Also, our
results show that the proposed jamming suppression method can achieve a very
close spectral efficiency as the case of no jamming in the network


Abstract:
  In this study, using low-temperature scanning tunneling microscopy (STM), we
focus on understanding the native defects in pristine \textit{1T}-TiSe$_2$ at
the atomic scale. We probe how they perturb the charge density waves (CDWs) and
lead to local domain formation. These defects influence the correlation length
of CDWs. We establish a connection between suppression of CDWs, Ti
intercalation, and show how this supports the exciton condensation model of CDW
formation in \textit{1T}-TiSe$_2$.


Abstract:
  We developed recently [A. Fert\'e, et al., J. Phys. Chem. Lett. 11, 4359
(2020)] a method to compute single site double core hole (ssDCH or K$^{-2}$)
spectra. We refer to that method as NOTA+CIPSI. In the present paper this
method is applied to the O K$^{-2}$ spectrum of the CO$_2$ molecule, and we use
this as an example to discuss in detail its convergence properties. Using this
approach, a theoretical spectra in excellent agreement with the experimental
one is obtained. Thanks to a thorough interpretation of the shake-up states
responsible for the main satellite peaks and with the help of a comparison with
the O K$^{-2}$ spectrum of CO, we can highlight the clear signature of the two
non equivalent carbon oxygen bonds in the oxygen ssDCH CO$_2$ dication.


Abstract:
  Non-negative matrix factorization (NMF) is a powerful tool for dimensionality
reduction and clustering. Unfortunately, the interpretation of the clustering
results from NMF is difficult, especially for the high-dimensional biological
data without effective feature selection. In this paper, we first introduce a
row-sparse NMF with $\ell_{2,0}$-norm constraint (NMF_$\ell_{20}$), where the
basis matrix $W$ is constrained by the $\ell_{2,0}$-norm, such that $W$ has a
row-sparsity pattern with feature selection. It is a challenge to solve the
model, because the $\ell_{2,0}$-norm is non-convex and non-smooth. Fortunately,
we prove that the $\ell_{2,0}$-norm satisfies the Kurdyka-\L{ojasiewicz}
property. Based on the finding, we present a proximal alternating linearized
minimization algorithm and its monotone accelerated version to solve the
NMF_$\ell_{20}$ model. In addition, we also present a orthogonal NMF with
$\ell_{2,0}$-norm constraint (ONMF_$\ell_{20}$) to enhance the clustering
performance by using a non-negative orthogonal constraint. We propose an
efficient algorithm to solve ONMF_$\ell_{20}$ by transforming it into a series
of constrained and penalized matrix factorization problems. The results on
numerical and scRNA-seq datasets demonstrate the efficiency of our methods in
comparison with existing methods.


Abstract:
  We propose a program at B-factories of inclusive, multi-track displaced
vertex searches, which are expected to be low background and give excellent
sensitivity to non-minimal hidden sectors. Multi-particle hidden sectors often
include long-lived particles (LLPs) which result from approximate symmetries,
and we classify the possible decays of GeV-scale LLPs in an effective field
theory framework. Considering several LLP production modes, including dark
photons and dark Higgs bosons, we study the sensitivity of LLP searches with
different number of displaced vertices per event and track requirements per
displaced vertex, showing that inclusive searches can have sensitivity to a
large range of hidden sector models that are otherwise unconstrained by current
or planned searches.


Abstract:
  We present a derivation of the integral fluctuation theorem (IFT) for
isolated quantum systems based on some natural assumptions on transition
probabilities. Under these assumptions of "stiffness" and "smoothness" the IFT
immediately follows for microcanonical and pure quantum states. We numerically
check the IFT as well as the validity of our assumptions by analyzing two
exemplary systems. We have been informed by T. Sagawa et al. that he and his
co-workers found comparable numerical results and are preparing a corresponding
paper, which should be available on the same day as the present text. We
recommend reading their submission.


Abstract:
  Erbium-doped lithium niobate on insulator (Er:LNOI) is a promising platform
for photonic integrated circuits as it adds gain to the LNOI system and enables
on-chip lasers and amplifiers. A challenge for Er:LNOI laser is to increase its
output power while maintaining single-frequency and single (-transverse)-mode
operation. In this work, we demonstrate that single-frequency and single-mode
operation can be achieved even in a single multi-mode Er:LNOI microring by
introducing mode-dependent loss and gain competition. In a single microring
with a free spectral range of 192 GHz, we have achieved single-mode lasing with
an output power of 2.1 microwatt, a side-mode suppression of 35.5 dB, and a
linewidth of 1.27 MHz.


Abstract:
  In this paper, we study the response of large models from the BERT family to
incoherent inputs that should confuse any model that claims to understand
natural language. We define simple heuristics to construct such examples. Our
experiments show that state-of-the-art models consistently fail to recognize
them as ill-formed, and instead produce high confidence predictions on them. As
a consequence of this phenomenon, models trained on sentences with randomly
permuted word order perform close to state-of-the-art models. To alleviate
these issues, we show that if models are explicitly trained to recognize
invalid inputs, they can be robust to such attacks without a drop in
performance.


Abstract:
  Supergranules create a peak in the spatial spectrum of photospheric velocity
features. They have some properties of convection cells but their origin is
still being debated in the literature. The time-distance helioseismology
constitutes a method that is suitable for investigating the deep structure of
supergranules. Our aim is to construct the model of the flows in the average
supergranular cell using fully consistent time-distance inverse methodology. We
used the Multi-Channel Subtractive Optimally Localised Averaging inversion
method with regularisation of the cross-talk. We combined the difference and
the mean travel-time averaging geometries. We applied this methodology to
travel-time maps averaged over more than 10000 individual supergranular cells.
These cells were detected automatically in travel-time maps computed for 64
quiet days around the disc centre. The ensemble averaging method allows us to
significantly improve the signal-to-noise ratio and to obtain a clear picture
of the flows in the average supergranule. We found near-surface divergent
horizontal flows which quickly and monotonously weakened with depth; they
became particularly weak at the depth of about 7 Mm, where they even apparently
switched sign. To learn about the vertical component, we integrated the
continuity equation from the surface. The derived estimates of the vertical
flow depicted a sub-surface increase from about 5 m/s at the surface to about
35 m/s at the depth of about 3 Mm followed by a monotonous decrease to greater
depths. The vertical flow remained positive (an upflow) and became
indistinguishable from the background at the depth of about 15 Mm. We further
detected a systematic flow in the longitudinal direction. The course of this
systematic flow with depth agrees well with the model of the solar rotation in
the sub-surface layers.


Abstract:
  Statistical learning theory provides the foundation to applied machine
learning, and its various successful applications in computer vision, natural
language processing and other scientific domains. The theory, however, does not
take into account the unique challenges of performing statistical learning in
geospatial settings. For instance, it is well known that model errors cannot be
assumed to be independent and identically distributed in geospatial (a.k.a.
regionalized) variables due to spatial correlation; and trends caused by
geophysical processes lead to covariate shifts between the domain where the
model was trained and the domain where it will be applied, which in turn harm
the use of classical learning methodologies that rely on random samples of the
data. In this work, we introduce the geostatistical (transfer) learning
problem, and illustrate the challenges of learning from geospatial data by
assessing widely-used methods for estimating generalization error of learning
models, under covariate shift and spatial correlation. Experiments with
synthetic Gaussian process data as well as with real data from geophysical
surveys in New Zealand indicate that none of the methods are adequate for model
selection in a geospatial context. We provide general guidelines regarding the
choice of these methods in practice while new methods are being actively
researched.


Abstract:
  We show that the widely used relaxation time approximation to the
relativistic Boltzmann equation contains basic flaws, being incompatible with
microscopic and macroscopic conservation laws. We propose a new approximation
that fixes such fundamental issues and maintains the basic properties of the
linearized Boltzmann collision operator. We show how this correction affects
transport coefficients, such as the bulk viscosity and particle diffusion.


Abstract:
  Estimating the data density is one of the challenging problems in deep
learning. In this paper, we present a simple yet effective method for
estimating the data density using a deep neural network and the
Donsker-Varadhan variational lower bound on the KL divergence. We show that the
optimal critic function associated with the Donsker-Varadhan representation on
the KL divergence between the data and the uniform distribution can estimate
the data density. We also present the deep neural network-based modeling and
its stochastic learning. The experimental results and possible applications of
the proposed method demonstrate that it is competitive with the previous
methods and has a lot of possibilities in applied to various applications.


Abstract:
  A unified framework for the Chevalley and equitable presentation of
$U_q(sl_2)$ is introduced. It is given in terms of a system of Freidel-Maillet
type equations satisfied by a pair of quantum K-operators ${\cal K}^\pm$, whose
entries are expressed in terms of either Chevalley or equitable generators. The
Hopf algebra structure is reconsidered in light of this presentation, and
interwining relations for K-operators are obtained. A K-operator solving a
spectral parameter dependent Freidel-Maillet equation is also considered.
Specializations to $U_q(sl_2)$ admit a decomposition in terms of ${\cal
K}^\pm$. Explicit examples of K-matrices are constructed.


Abstract:
  Automatic medical image segmentation based on Computed Tomography (CT) has
been widely applied for computer-aided surgery as a prerequisite. With the
development of deep learning technologies, deep convolutional neural networks
(DCNNs) have shown robust performance in automated semantic segmentation of
medical images. However, semantic segmentation algorithms based on DCNNs still
meet the challenges of feature loss between encoder and decoder, multi-scale
object, restricted field of view of filters, and lack of medical image data.
This paper proposes a novel algorithm for automated vertebrae segmentation via
3D volumetric spine CT images. The proposed model is based on the structure of
encoder to decoder, using layer normalization to optimize mini-batch training
performance. To address the concern of the information loss between encoder and
decoder, we designed an Atrous Residual Path to pass more features from encoder
to decoder instead of an easy shortcut connection. The proposed model also
applied the attention module in the decoder part to extract features from
variant scales. The proposed model is evaluated on a publicly available dataset
by a variety of metrics. The experimental results show that our model achieves
competitive performance compared with other state-of-the-art medical semantic
segmentation methods.


Abstract:
  We introduce Robust Restless Bandits, a challenging generalization of
restless multi-arm bandits (RMAB). RMABs have been widely studied for
intervention planning with limited resources. However, most works make the
unrealistic assumption that the transition dynamics are known perfectly,
restricting the applicability of existing methods to real-world scenarios. To
make RMABs more useful in settings with uncertain dynamics: (i) We introduce
the Robust RMAB problem and develop solutions for a minimax regret objective
when transitions are given by interval uncertainties; (ii) We develop a double
oracle algorithm for solving Robust RMABs and demonstrate its effectiveness on
three experimental domains; (iii) To enable our double oracle approach, we
introduce RMABPPO, a novel deep reinforcement learning algorithm for solving
RMABs. RMABPPO hinges on learning an auxiliary "$\lambda$-network" that allows
each arm's learning to decouple, greatly reducing sample complexity required
for training; (iv) Under minimax regret, the adversary in the double oracle
approach is notoriously difficult to implement due to non-stationarity. To
address this, we formulate the adversary oracle as a multi-agent reinforcement
learning problem and solve it with a multi-agent extension of RMABPPO, which
may be of independent interest as the first known algorithm for this setting.
Code is available at https://github.com/killian-34/RobustRMAB.


Abstract:
  In this paper, we introduce a new framework for unsupervised deep homography
estimation. Our contributions are 3 folds. First, unlike previous methods that
regress 4 offsets for a homography, we propose a homography flow
representation, which can be estimated by a weighted sum of 8 pre-defined
homography flow bases. Second, considering a homography contains 8
Degree-of-Freedoms (DOFs) that is much less than the rank of the network
features, we propose a Low Rank Representation (LRR) block that reduces the
feature rank, so that features corresponding to the dominant motions are
retained while others are rejected. Last, we propose a Feature Identity Loss
(FIL) to enforce the learned image feature warp-equivariant, meaning that the
result should be identical if the order of warp operation and feature
extraction is swapped. With this constraint, the unsupervised optimization is
achieved more effectively and more stable features are learned. Extensive
experiments are conducted to demonstrate the effectiveness of all the newly
proposed components, and results show that our approach outperforms the
state-of-the-art on the homography benchmark datasets both qualitatively and
quantitatively. Code is available at
https://github.com/megvii-research/BasesHomo.


Abstract:
  Irregular cusp of an orthogonal modular variety is a cusp where the lattice
for Fourier expansion is strictly smaller than the lattice of translation.
Presence of such a cusp affects the study of pluricanonical forms on the
modular variety using modular forms. We study toroidal compactification over an
irregular cusp, and clarify there the cusp form criterion for the calculation
of Kodaira dimension. At the same time, we show that irregular cusps do not
arise frequently: besides the cases when the group is neat or contains -1, we
prove that the stable orthogonal groups of most (but not all) even lattices
have no irregular cusp.


Abstract:
  Collisions electrically charge grains which promotes growth by coagulation.
We present aggregation experiments with three large ensembles of basalt beads
($150\,\mu\mathrm{m} - 180\,\mu\mathrm{m})$, two of which are charged, while
one remains almost neutral as control system. In microgravity experiments, free
collisions within these samples are induced with moderate collision velocities
($0 - 0.2 \,\mathrm{m\,s}^{-1}$). In the control system, coagulation stops at
(sub-)mm size while the charged grains continue to grow. A maximum agglomerate
size of 5\,cm is reached, limited only by bead depletion in the free volume.
For the first time, charge-driven growth well into the centimeter range is
directly proven by experiments. In protoplanetary disks, this agglomerate size
is well beyond the critical size needed for hydrodynamic particle concentration
as, e.g., by the streaming instabilities.


Abstract:
  We offer an embedding of CPython that runs entirely in memory without
"touching" the disk. This in-memory embedding can load Python scripts directly
from memory instead these scripts having to be loaded from files on disk.
Malware that resides only in memory is harder to detect or mitigate against. We
intend for our work to be used by security researchers to rapidly develop and
deploy offensive techniques that is difficult for security products to analyze
given these instructions are in bytecode and only translated to machine-code by
the interpreter immediately prior to execution. Our work helps security
researchers and enterprise Red Teams who play offense. Red Teams want to
rapidly prototype malware for their periodic campaigns and do not want their
malware to be detected by the Incident Response (IR) teams prior to
accomplishing objectives. Red Teams also have difficulty running malware in
production from files on disk as modern enterprise security products emulate,
inspect, or quarantine such executables given these files have no reputation.
Our work also helps enterprise Hunt and IR teams by making them aware of the
viability of this type of attack. Our approach has been in use in production
for over a year and meets our customers' needs to quickly emulate
threat-actors' tasks, techniques, and procedures (TTPs).


Abstract:
  The tracking and timely resolution of service requests is one of the major
challenges in agile project management. Having an efficient solution to this
problem is a key requirement for Walmart to facilitate seamless collaboration
across its different business units. The Jira software is one of the popular
choices in industries for monitoring such service requests. A service request
once logged into the system by a reporter is referred to as a (Jira) ticket
which is assigned to an engineer for servicing. In this work, we explore how
the tickets which may arise in any of the Walmart stores and offices
distributed over several countries can be assigned to engineers efficiently.
Specifically, we will discuss how the introduction of a bot for automated
ticket assignment has helped in reducing the disparity in ticket assignment to
engineers by human managers and also decreased the average ticket resolution
time - thereby improving the experience for both the reporters and the
engineers. Additionally, the bot sends reminders and status updates over
different business communication platforms for timely tracking of tickets; it
can be suitably modified to provision for human intervention in case of special
needs by some teams. The current study conducted over data collected from
various teams within Walmart shows the efficacy of our bot.


Abstract:
  We rewrite the numerical ansatz of the Method of Auxiliary Sources (MAS),
typically used in computational electromagnetics, as a neural network, i.e. as
a composed function of linear and activation layers. MAS is a numerical method
for Partial Differential Equations (PDEs) that employs point sources, which are
also exact solutions of the considered PDE, as radial basis functions to match
a given boundary condition. In the framework of neural networks we rely on
optimization algorithms such as Adam to train MAS and find both its optimal
coefficients and positions of the central singularities of the sources. In this
work we also show that the MAS ansatz trained as a neural network can be used,
in the case of an unknown function with a central singularity, to detect the
position of such singularity.


Abstract:
  The process of learning a manipulation task depends strongly on the action
space used for exploration: posed in the incorrect action space, solving a task
with reinforcement learning can be drastically inefficient. Additionally,
similar tasks or instances of the same task family impose latent manifold
constraints on the most effective action space: the task family can be best
solved with actions in a manifold of the entire action space of the robot.
Combining these insights we present LASER, a method to learn latent action
spaces for efficient reinforcement learning. LASER factorizes the learning
problem into two sub-problems, namely action space learning and policy learning
in the new action space. It leverages data from similar manipulation task
instances, either from an offline expert or online during policy learning, and
learns from these trajectories a mapping from the original to a latent action
space. LASER is trained as a variational encoder-decoder model to map raw
actions into a disentangled latent action space while maintaining action
reconstruction and latent space dynamic consistency. We evaluate LASER on two
contact-rich robotic tasks in simulation, and analyze the benefit of policy
learning in the generated latent action space. We show improved sample
efficiency compared to the original action space from better alignment of the
action space to the task space, as we observe with visualizations of the
learned action space manifold. Additional details:
https://www.pair.toronto.edu/laser


Abstract:
  Cardiac imaging known as echocardiography is a non-invasive tool utilized to
produce data including images and videos, which cardiologists use to diagnose
cardiac abnormalities in general and myocardial infarction (MI) in particular.
Echocardiography machines can deliver abundant amounts of data that need to be
quickly analyzed by cardiologists to help them make a diagnosis and treat
cardiac conditions. However, the acquired data quality varies depending on the
acquisition conditions and the patient's responsiveness to the setup
instructions. These constraints are challenging to doctors especially when
patients are facing MI and their lives are at stake. In this paper, we propose
an innovative real-time end-to-end fully automated model based on convolutional
neural networks (CNN) to detect MI depending on regional wall motion
abnormalities (RWMA) of the left ventricle (LV) from videos produced by
echocardiography. Our model is implemented as a pipeline consisting of a 2D CNN
that performs data preprocessing by segmenting the LV chamber from the apical
four-chamber (A4C) view, followed by a 3D CNN that performs a binary
classification to detect if the segmented echocardiography shows signs of MI.
We trained both CNNs on a dataset composed of 165 echocardiography videos each
acquired from a distinct patient. The 2D CNN achieved an accuracy of 97.18% on
data segmentation while the 3D CNN achieved 90.9% of accuracy, 100% of
precision and 95% of recall on MI detection. Our results demonstrate that
creating a fully automated system for MI detection is feasible and propitious.


Abstract:
  For a semimartingale with jumps, we propose a new estimation method for
integrated volatility, i.e., the quadratic variation of the continuous
martingale part, based on the global jump filter proposed by Inatsugu and
Yoshida [8]. To decide whether each increment of the process has jumps, the
global jump filter adopts the upper $\alpha$-quantile of the absolute
increments as the threshold. This jump filter is called global since it uses
all the observations to classify one increment. We give a rate of convergence
and prove asymptotic mixed normality of the global realized volatility and its
variant "Winsorized global volatility". By simulation studies, we show that our
estimators outperform previous realized volatility estimators that use a few
adjacent increments to mitigate the effects of jumps.


Abstract:
  The singular value decomposition going with many problems in medical imaging,
non-destructive testing, geophysics, is of central importance. Unfortunately
the effective numerical determination of the singular functions in question is
a very ill-posed problem. The best known remedy to this problem goes back to
the work of D. Slepian, H.Landau and H. Pollak, Bell Labs 1960-1965. We show
that the master symmetries of the Korteweg-de Vries equation give a way to
extend the remarkable result of D. Slepian in connection with the Bessel
integral kernel and the existence of a differential operator that commutes with
the corresponding integral operator. The original results of the Bell Labs
group has already played an important role in the study of the limited angle
problem in X-ray tomography as well as in Random Matrix theory.


Abstract:
  We present d3p, a software package designed to help fielding runtime
efficient widely-applicable Bayesian inference under differential privacy
guarantees. d3p achieves general applicability to a wide range of probabilistic
modelling problems by implementing the differentially private variational
inference algorithm, allowing users to fit any parametric probabilistic model
with a differentiable density function. d3p adopts the probabilistic
programming paradigm as a powerful way for the user to flexibly define such
models. We demonstrate the use of our software on a hierarchical logistic
regression example, showing the expressiveness of the modelling approach as
well as the ease of running the parameter inference. We also perform an
empirical evaluation of the runtime of the private inference on a complex model
and find a $\sim$10 fold speed-up compared to an implementation using
TensorFlow Privacy.


Abstract:
  The structural connectome is often represented by fiber bundles generated
from various types of tractography. We propose a method of analyzing
connectomes by representing them as a Riemannian metric, thereby viewing them
as points in an infinite-dimensional manifold. After equipping this space with
a natural metric structure, the Ebin metric, we apply object-oriented
statistical analysis to define an atlas as the Fr\'echet mean of a population
of Riemannian metrics. We demonstrate connectome registration and atlas
formation using connectomes derived from diffusion tensors estimated from a
subset of subjects from the Human Connectome Project.


Abstract:
  Interest in physical therapy and individual exercises such as yoga/dance has
increased alongside the well-being trend. However, such exercises are hard to
follow without expert guidance (which is impossible to scale for personalized
feedback to every trainee remotely). Thus, automated pose correction systems
are required more than ever, and we introduce a new captioning dataset named
FixMyPose to address this need. We collect descriptions of correcting a
"current" pose to look like a "target" pose (in both English and Hindi). The
collected descriptions have interesting linguistic properties such as
egocentric relations to environment objects, analogous references, etc.,
requiring an understanding of spatial relations and commonsense knowledge about
postures. Further, to avoid ML biases, we maintain a balance across characters
with diverse demographics, who perform a variety of movements in several
interior environments (e.g., homes, offices). From our dataset, we introduce
the pose-correctional-captioning task and its reverse target-pose-retrieval
task. During the correctional-captioning task, models must generate
descriptions of how to move from the current to target pose image, whereas in
the retrieval task, models should select the correct target pose given the
initial pose and correctional description. We present strong cross-attention
baseline models (uni/multimodal, RL, multilingual) and also show that our
baselines are competitive with other models when evaluated on other
image-difference datasets. We also propose new task-specific metrics
(object-match, body-part-match, direction-match) and conduct human evaluation
for more reliable evaluation, and we demonstrate a large human-model
performance gap suggesting room for promising future work. To verify the
sim-to-real transfer of our FixMyPose dataset, we collect a set of real images
and show promising performance on these images.


Abstract:
  Industrial Internet of Things (IIoT) applications can benefit from leveraging
edge computing. For example, applications underpinned by deep neural networks
(DNN) models can be sliced and distributed across the IIoT device and the edge
of the network for improving the overall performance of inference and for
enhancing privacy of the input data, such as industrial product images.
However, low network performance between IIoT devices and the edge is often a
bottleneck. In this study, we develop ScissionLite, a holistic framework for
accelerating distributed DNN inference using the Transfer Layer (TL). The TL is
a traffic-aware layer inserted between the optimal slicing point of a DNN model
slice in order to decrease the outbound network traffic without a significant
accuracy drop. For the TL, we implement a new lightweight down/upsampling
network for performance-limited IIoT devices. In ScissionLite, we develop
ScissionTL, the Preprocessor, and the Offloader for end-to-end activities for
deploying DNN slices with the TL. They decide the optimal slicing point of the
DNN, prepare pre-trained DNN slices including the TL, and execute the DNN
slices on an IIoT device and the edge. Employing the TL for the sliced DNN
models has a negligible overhead. ScissionLite improves the inference latency
by up to 16 and 2.8 times when compared to execution on the local device and an
existing state-of-the-art model slicing approach respectively.


Abstract:
  The influence of implantation-induced point defects (PDs) on SiC oxidation is
investigated via molecular dynamics simulations. PDs generally increase the
oxidation rate of crystalline grains. Particularly, accelerations caused by Si
antisites and vacancies are comparable, and followed by Si interstitials, which
are higher than those by C antisites and C interstitials. However, in the grain
boundary (GB) region, defect contribution to oxidation is more complex, with C
antisites decelerating oxidation. The underlying reason is the formation of a
C-rich region along the oxygen diffusion pathway that blocks the access of O to
Si and thus reduces the oxidation rate, as compared to the oxidation along a GB
without defects.


Abstract:
  In this paper, we study the numerical stabilization of a 1D system of two
wave equations coupled by velocities with an internal, local control acting on
only one equation. In the theoretical part of this study, we distinguished two
cases. In the first one, the two waves assumed propagate at the same speed.
Under appropriate geometric conditions, we had proved that the energy decays
exponentially. While in the second case, when the waves propagate at different
speeds, under appropriate geometric conditions, we had proved that the energy
decays only at a polynomial rate. In this paper, we confirmed these two results
in a 1D numerical approximation. However, when the coupling region does not
intersect the damping region, the stabilization of the system is still
theoretically an open problem. But, here in both cases, we observed an
unpredicted behavior : the energy decays at an exponential rate when the
propagation speeds are the same or at a polynomial rate when they are
different.


Abstract:
  Recently, a generative variational autoencoder (VAE) has been proposed for
speech enhancement to model speech statistics. However, this approach only uses
clean speech in the training phase, making the estimation particularly
sensitive to noise presence, especially in low signal-to-noise ratios (SNRs).
To increase the robustness of the VAE, we propose to include noise information
in the training phase by using a noise-aware encoder trained on noisy-clean
speech pairs. We evaluate our approach on real recordings of different noisy
environments and acoustic conditions using two different noise datasets. We
show that our proposed noise-aware VAE outperforms the standard VAE in terms of
overall distortion without increasing the number of model parameters. At the
same time, we demonstrate that our model is capable of generalizing to unseen
noise conditions better than a supervised feedforward deep neural network
(DNN). Furthermore, we demonstrate the robustness of the model performance to a
reduction of the noisy-clean speech training data size.


Abstract:
  A long standing puzzle in the rheology of living cells is the origin of the
experimentally observed long time stress relaxation. The mechanics of the cell
is largely dictated by the cytoskeleton, which is a biopolymer network
consisting of transient crosslinkers, allowing for stress relaxation over time.
Moreover, these networks are internally stressed due to the presence of
molecular motors. In this work we propose a theoretical model that uses a
mode-dependent mobility to describe the stress relaxation of such prestressed
transient networks. Our theoretical predictions agree favorably with
experimental data of reconstituted cytoskeletal networks and may provide an
explanation for the slow stress relaxation observed in cells.


Abstract:
  The classic censored regression model (tobit model) has been widely used in
the economic literature. This model assumes normality for the error
distribution and is not recommended for cases where positive skewness is
present. Moreover, in regression analysis, it is well-known that a quantile
regression approach allows us to study the influences of the explanatory
variables on the dependent variable considering different quantiles. Therefore,
we propose in this paper a quantile tobit regression model based on
quantile-based log-symmetric distributions. The proposed methodology allows us
to model data with positive skewness (which is not suitable for the classic
tobit model), and to study the influence of the quantiles of interest, in
addition to accommodating heteroscedasticity. The model parameters are
estimated using the maximum likelihood method and an elaborate Monte Carlo
study is performed to evaluate the performance of the estimates. Finally, the
proposed methodology is illustrated using two female labor supply data sets.
The results show that the proposed log-symmetric quantile tobit model has a
better fit than the classic tobit model.


Abstract:
  We present the first study of cross-correlation between Cosmic Microwave
Background (CMB) gravitational lensing potential map measured by the $Planck$
satellite and $z\geq 0.8$ galaxies from the photometric redshift catalogues
from Herschel Extragalactic Legacy Project (HELP), divided into four sky
patches: NGP, Herschel Stripe-82 and two halves of SGP field, covering in total
$\sim 660$ deg$^{2}$ of the sky. Contrary to previous studies exploiting only
the common area between galaxy surveys and CMB lensing data, we improve the
cross-correlation measurements using the full available area of the CMB lensing
map. We estimate galaxy linear bias parameter, $b$, from joint analysis of
cross-power spectrum and galaxy auto-power spectrum using Maximum Likelihood
Estimation technique to obtain the value averaged over four fields as
$b=2.06_{-0.02}^{+0.02}$, ranging from $1.94_{-0.03}^{+0.04}$ for SGP Part-2 to
$3.03_{-0.09}^{+0.10}$ for NGP. We also estimate the amplitude of
cross-correlation and find the averaged value to be $A=0.52_{-0.08}^{+0.08}$
spanning from $0.34_{-0.19}^{+0.19}$ for NGP to $0.67_{-0.20}^{+0.21}$ for SGP
Part-1 respectively, significantly lower than expected value for the standard
cosmological model. We perform several tests on systematic errors that can
account for this discrepancy. We find that lower amplitude could be to some
extent explained by the lower value of median redshift of the catalogue,
however, we do not have any evidence that redshifts are systematically
overestimated.


Abstract:
  In the theory of local fields we have the well-known filtration of unit
groups. In this short paper we compute the first cohomology groups of unit
gorups for a finite Galois extension of local fields. We show that these
cohomology groups are closely related to the ramification indices.


Abstract:
  We introduce DeepGLEAM, a hybrid model for COVID-19 forecasting. DeepGLEAM
combines a mechanistic stochastic simulation model GLEAM with deep learning. It
uses deep learning to learn the correction terms from GLEAM, which leads to
improved performance. We further integrate various uncertainty quantification
methods to generate confidence intervals. We demonstrate DeepGLEAM on
real-world COVID-19 mortality forecasting tasks.


Abstract:
  In collaborative intelligence, an artificial intelligence (AI) model is
typically split between an edge device and the cloud. Feature tensors produced
by the edge sub-model are sent to the cloud via an imperfect communication
channel. At the cloud side, parts of the feature tensor may be missing due to
packet loss. In this paper we propose a method called Content-Adaptive Linear
Tensor Completion (CALTeC) to recover the missing feature data. The proposed
method is fast, data-adaptive, does not require pre-training, and produces
better results than existing methods for tensor data recovery in collaborative
intelligence.


Abstract:
  GADTs can be represented either as their Church encodings \`a la Atkey, or as
fixpoints \`a la Johann and Polonsky. While a GADT represented as its Church
encoding need not support a map function satisfying the functor laws, the
fixpoint representation of a GADT must support such a map function even to be
well-defined. The two representations of a GADT thus need not be the same in
general. This observation forces a choice of representation of data types in
languages supporting GADTs. In this paper we show that choosing whether to
represent data types as their Church encodings or as fixpoints determines
whether or not a language supporting GADTs can have parametric models. This
choice thus has important consequences for how we can program with, and reason
about, these advanced data types.


Abstract:
  Context. The North Ecliptic Pole (NEP) field provides a unique set of
panchromatic data, well suited for active galactic nuclei (AGN) studies.
Selection of AGN candidates is often based on mid-infrared (MIR) measurements.
Such method, despite its effectiveness, strongly reduces a catalog volume due
to the MIR detection condition. Modern machine learning techniques can solve
this problem by finding similar selection criteria using only optical and
near-infrared (NIR) data. Aims. Aims of this work were to create a reliable AGN
candidates catalog from the NEP field using a combination of optical SUBARU/HSC
and NIR AKARI/IRC data and, consequently, to develop an efficient alternative
for the MIR-based AKARI/IRC selection technique. Methods. A set of supervised
machine learning algorithms was tested in order to perform an efficient AGN
selection. Best of the models were formed into a majority voting scheme, which
used the most popular classification result to produce the final AGN catalog.
Additional analysis of catalog properties was performed in form of the spectral
energy distribution (SED) fitting via the CIGALE software. Results. The
obtained catalog of 465 AGN candidates (out of 33 119 objects) is characterized
by 73% purity and 64% completeness. This new classification shows consistency
with the MIR-based selection. Moreover, 76% of the obtained catalog can be
found only with the new method due to the lack of MIR detection for most of the
new AGN candidates. Training data, codes and final catalog are available via
the github repository. Final AGN candidates catalog will be also available via
the CDS service after publication.


Abstract:
  The note concerns the $\bar\partial$ problem on product domains in $\mathbb
C^2$. We show that there exists a bounded solution operator from $C^{k,
\alpha}$ into itself, $k\in \mathbb Z^+\cup \{0\}, 0<\alpha< 1$. The regularity
result is optimal in view of an example of Stein-Kerzman.


Abstract:
  It is shown numerically, in a chiral U(1) gauge Higgs theory in which the
left and right-handed fermion components have opposite U(1) charges, that the
spectrum of gauge and Higgs fields surrounding a static fermion contains both a
ground state and at least one stable excited state. To bypass the difficulties
associated with dynamical fermions in a lattice chiral gauge theory we consider
only static fermion sources in a quenched approximation, at fixed lattice
spacing and couplings, and with a lattice action along the lines suggested long
ago by Smit and Swift.


Abstract:
  We investigate the explicit implementation of quantum repeater protocols that
rely on three-qubit repetition codes using nitrogen-vacancy (NV) centers in
diamond as quantum memories. NV centers offer a two-qubit register,
corresponding to their electron and nuclear spins, which makes it possible to
perform deterministic two-qubit operations within one NV center. For quantum
repeater applications, we however need to do joint operations on two separate
NV centers. Here, we study two NV-based repeater structures that enable such
deterministic joint operations. One structure offers less consumption of
classical communication, hence is more resilient to decoherence effects,
whereas the other one relies on fewer numbers of physical resources and
operations. We assess and compare their performance for the task of secret key
generation under the influence of noise and decoherence with current and
near-term experimental parameters. We quantify the regimes of operation, where
one structure outperforms the other, and find the regions where encoded QRs
offer practical advantages over their non-encoded counterparts.


Abstract:
  Real-world videos contain many complex actions with inherent relationships
between action classes. In this work, we propose an attention-based
architecture that models these action relationships for the task of temporal
action localization in untrimmed videos. As opposed to previous works that
leverage video-level co-occurrence of actions, we distinguish the relationships
between actions that occur at the same time-step and actions that occur at
different time-steps (i.e. those which precede or follow each other). We define
these distinct relationships as action dependencies. We propose to improve
action localization performance by modeling these action dependencies in a
novel attention-based Multi-Label Action Dependency (MLAD)layer. The MLAD layer
consists of two branches: a Co-occurrence Dependency Branch and a Temporal
Dependency Branch to model co-occurrence action dependencies and temporal
action dependencies, respectively. We observe that existing metrics used for
multi-label classification do not explicitly measure how well action
dependencies are modeled, therefore, we propose novel metrics that consider
both co-occurrence and temporal dependencies between action classes. Through
empirical evaluation and extensive analysis, we show improved performance over
state-of-the-art methods on multi-label action localization
benchmarks(MultiTHUMOS and Charades) in terms of f-mAP and our proposed metric.


Abstract:
  As a basic building block, optical resonant cavities (ORCs) are widely used
in light manipulation; they can confine electromagnetic waves and improve the
interaction between light and matter, which also plays an important role in
cavity quantum electrodynamics, nonlinear optics and quantum optics. Especially
in recent years, the rise of metamaterials, artificial materials composed of
subwavelength unit cells, greatly enriches the design and function of ORCs.
Here, we review zero-index and hyperbolic metamaterials for constructing the
novel ORCs. Firstly, this paper introduces the classification and
implementation of zero-index and hyperbolic metamaterials. Secondly, the
distinctive properties of zero-index and hyperbolic cavities are summarized,
including the geometry-invariance, homogeneous/inhomogeneous field
distribution, and the topological protection (anomalous scaling law, size
independence, continuum of high-order modes, and dispersionless modes) for the
zero-index (hyperbolic) metacavities. Finally, the paper introduces some
typical applications of zero-index and hyperbolic metacavities, and prospects
the research of metacavities.


Abstract:
  Let $p(x)$ be an integer polynomial with $m\ge 2$ distinct roots
$\rho_1,\ldots,\rho_m$ whose multiplicities are
$\boldsymbol{\mu}=(\mu_1,\ldots,\mu_m)$. We define the D-plus discriminant of
$p(x)$ to be $D^+(p):= \prod_{1\le i<j\le m}(\rho_i-\rho_j)^{\mu_i+\mu_j}$. We
first prove a conjecture that $D^+(p)$ is a $\boldsymbol{\mu}$-symmetric
function of its roots $\rho_1,\ldots,\rho_m$. Our main result gives an explicit
formula for $D^+(p)$, as a rational function of its coefficients. Our proof is
ideal-theoretic, based on re-casting the classic Poisson resultant as the
"symbolic Poisson formula". The D-plus discriminant first arose in the
complexity analysis of a root clustering algorithm from Becker et al. (ISSAC
2016). The bit-complexity of this algorithm is proportional to a quantity
$\log(|D^+(p)|^{-1})$. As an application of our main result, we give an
explicit upper bound on this quantity in terms of the degree of $p$ and its
leading coefficient.


Abstract:
  We introduce Robin boundary conditions for biharmonic operators, which are a
model for elastically supported plates and are closely related to the study of
spaces of traces of Sobolev functions. We study the dependence of the operator,
its eigenvalues, and eigenfunctions on the Robin parameters. We show in
particular that when the parameters go to plus infinity the Robin problem
converges to other biharmonic problems, and obtain estimates on the rate of
divergence when the parameters go to minus infinity. We also analyse the
dependence of the operator on smooth perturbations of the domain, computing the
shape derivatives of the eigenvalues and giving a characterisation for critical
domains under volume and perimeter constraints. We include a number of open
problems arising in the context of our results.


Abstract:
  Let $h$ be the planar Gaussian free field and let $D_h$ be a supercritical
Liouville quantum gravity (LQG) metric associated with $h$. Such metrics arise
as subsequential scaling limits of supercritical Liouville first passage
percolation (Gwynne-Ding, 2020) and correspond to values of the matter central
charge $\mathbf{c}_{\mathrm M} \in (1,25)$. We show that a.s. the boundary of
each complementary connected component of a $D_h$-metric ball is a Jordan curve
and is compact and finite-dimensional with respect to $D_h$. This is in
contrast to the whole boundary of the $D_h$-metric ball, which is non-compact
and infinite-dimensional with respect to $D_h$ (Pfeffer, 2021). Using our
regularity results for boundary components of $D_h$-metric balls, we extend the
confluence of geodesics results of Gwynne-Miller (2019) to the case of
supercritical Liouville quantum gravity. These results show that two
$D_h$-geodesics with the same starting point and different target points
coincide for a non-trivial initial time interval.


Abstract:
  Under the validity of the positive mass theorem, the Yamabe flow on a smooth
compact Riemannian manifold of dimension $N \ge 3$ is known to exist for all
time $t$ and converges to a solution to the Yamabe problem as $t \to \infty$.
We prove that if a suitable perturbation, which may be smooth and arbitrarily
small, is imposed on the Yamabe flow on any given Riemannian manifold $M$ of
dimension $N \ge 5$, the resulting flow may blow up at multiple points on $M$
in the infinite time. Our proof is constructive, and indeed we construct such a
flow by using solutions of the Yamabe problem on the unit sphere $\mathbb{S}^N$
as blow-up profiles. We also examine the stability of the blow-up phenomena
under a negativity condition on the Ricci curvature at blow-up points.


Abstract:
  The recent and upcoming releases of the 3rd Generation Partnership Project's
5G New Radio specifications include features that are motivated by providing
connectivity services to a broad set of verticals, including the automotive,
rail, and air transport industries. Currently, several radio access network
features are being further enhanced or newly introduced in NR to improve 5G's
capability to provide fast, reliable, and non-limiting connectivity for
transport applications. In this article, we review the most important
characteristics and requirements of a wide range of services that are driven by
the desire to help the transport sector to become more sustainable,
economically viable, safe, and secure. These requirements will be supported by
the evolving and entirely new features of 5G NR systems, including accurate
positioning, reference signal design to enable multi-transmission and reception
points, service-specific scheduling configuration, and service quality
prediction.


Abstract:
  3D model generation from single 2D RGB images is a challenging and actively
researched computer vision task. Various techniques using conventional network
architectures have been proposed for the same. However, the body of research
work is limited and there are various issues like using inefficient 3D
representation formats, weak 3D model generation backbones, inability to
generate dense point clouds, dependence of post-processing for generation of
dense point clouds, and dependence on silhouettes in RGB images. In this paper,
a novel 2D RGB image to point cloud conversion technique is proposed, which
improves the state of art in the field due to its efficient, robust and simple
model by using the concept of parallelization in network architecture. It not
only uses the efficient and rich 3D representation of point clouds, but also
uses a novel and robust point cloud generation backbone in order to address the
prevalent issues. This involves using a single-encoder multiple-decoder deep
network architecture wherein each decoder generates certain fixed viewpoints.
This is followed by fusing all the viewpoints to generate a dense point cloud.
Various experiments are conducted on the technique and its performance is
compared with those of other state of the art techniques and impressive gains
in performance are demonstrated. Code is available at
https://github.com/mueedhafiz1982/


Abstract:
  A large number of processes in the mesoscopic world occur out of equilibrium,
where the time course of the system evolution becomes immensely important --
they being driven principally by dissipative effects. Non-equilibrium steady
states (NESS) represent a crucial category in such systems -- which are widely
observed in biological domains -- especially in chemical kinetics in cellular
processes, and molecular motors. In this study, we employ a model NESS
stochastic system which comprises of an colloidal microparticle, optically
trapped in a viscous fluid and externally driven by a temporally correlated
colored noise, and show that the work done on the system and the work
dissipated by it -- both follow the three Levy arcsine laws. These statistics
remain unchanged even in the presence of a perturbation generated by a
microbubble at close proximity to the trapped particle. We confirm our
experimental findings with theoretical simulations of the systems. Our work
provides an interesting insight into the NESS statistics of the meso-regime,
where stochastic fluctuations play a pivotal role.