Abstract:
  I consider higher-order terms of the Seeley expansion of the heat kernel,
which for smooth metrics are suppressed as inverse powers of the UV cutoff
$\Lambda$, and demonstrate how they result in an anomalous contribution to the
string effective action after doing uncertainties $\Lambda^{-2}\times
\Lambda^2$. For the Polyakov string these anomalies precisely reproduce at one
loop the result of KPZ-DDK obtained for the Liouville theory by the conformal
field theory technique. For the Nambu-Goto string I find a deviation from this
result which shows that the two string formulations may differ.


Abstract:
  Proposal of large-scale datasets has facilitated research on deep neural
models for news summarization. Deep learning can also be potentially useful for
spoken dialogue summarization, which can benefit a range of real-life scenarios
including customer service management and medication tracking. To this end, we
propose DialogSum, a large-scale labeled dialogue summarization dataset. We
conduct empirical analysis on DialogSum using state-of-the-art neural
summarizers. Experimental results show unique challenges in dialogue
summarization, such as spoken terms, special discourse structures, coreferences
and ellipsis, pragmatics and social common sense, which require specific
representation learning technologies to better deal with.


Abstract:
  In this work we analyse the stability of self gravitating spheres in the
context of gravitational cracking. Besides exploring the role played by the
anisotropy in the occurrence of cracking, we also study the effect of the
complexity factor recently introduced by L. Herrera in Phys. Rev. D 97, 044010
(2018). The models under study correspond to anisotropic solutions obtained in
the framework of the Gravitational Decoupling. The effect that the variation of
the decoupling parameter and the compactness of the source have on the
behaviour of the radial force is studied in detail.


Abstract:
  Let $R$ be a commutative ring with identity and $M$ be a unitary $R$-module.
By $J(R),$ we denote the Jacobson radical of $R$. The purpose of this paper is
to introduce the concept of weakly $J$-submodules generalizing $J$-submodules.
We call a proper submodule $N$ of $M$ a weakly $J$-submodule if whenever $0\neq
rm\in N$ for $r\in R$ and $m\in M$, then $r\in(J(R)M:M)$ or $m\in N.$ Various
properties and characterizations of weakly $J$-submodules are investigated
especially in the case of multiplication modules.


Abstract:
  The BFV-BRST Hamiltonian quantization method is presented for the theories
where the gauge parameters are restricted by differential equations. The
general formalism is exemplified by the Maxwell-like theory of symmetric tensor
field.


Abstract:
  Synchronization in neural system plays an important role in many brain
functions. Synchronization in the gamma frequency band (30Hz-100Hz) is involved
in a variety of cognitive phenomena; abnormalities of the gamma synchronization
are found in schizophrenia and autism spectrum disorder. Frequently, the
strength of synchronization is not very high and is intermittent even on short
time scales (a few cycles of oscillations). That is, the network exhibits
intervals of synchronization followed by intervals of desynchronization. Neural
circuits dynamics may show different distributions of desynchronization
durations even if the synchronization strength is fixed. In this study, we use
a conductance-based neural network exhibiting pyramidal-interneuron (PING)
gamma rhythm to study the temporal patterning of synchronized neural
oscillations. We found that changes in the synaptic strength (as well as
changes in the membrane kinetics) can alter the temporal patterning of
synchrony. Moreover, we found that the changes in the temporal pattern of
synchrony may be independent of the changes in the average synchrony strength.
Even though the temporal patterning may vary, there is a tendency for dynamics
with short (although potentially numerous) desynchronizations, similar to what
was observed in experimental studies of neural activity synchronization in the
brain. Recent studies suggested that the short desynchronizations dynamics may
facilitate the formation and the break-up of transient neural assemblies. Thus,
the results of this study suggest that changes of synaptic strength may alter
the temporal patterning of the gamma synchronization as to make the neural
networks more efficient in the formation of neural assemblies and the
facilitation of cognitive phenomena.


Abstract:
  We propose a pseudo-Goldstone boson dark matter (pGDM) particle in $SO(10)$
grand unified theory (GUT). Due to its Goldstone nature, this pGDM evades the
direct DM detection experiments which, otherwise, severely constrain the
parameter space of DM models. In $SO(10)$, the pGDM is embedded as a linear
combination of the Standard Model (SM) singlet scalars in ${\bf 16_H}$ and
${\bf 126_H}$ representations. We consider two scenarios for the intermediate
route of $SO(10)$ symmetry breaking (SB) to the SM: $SU(5) \times U(1)_X$ and
Pati-Salam the $SU(4)_c \times SU(2)_L \times SU(2)_R$ (4-2-2) gauge groups.
The vacuum expectation value of ${\bf 126_H}$, which triggers the breaking of
$U(1)_X$ and 4-2-2 symmetry in the two scenarios, respectively, determines the
pGDM lifetime whose astrophysical lower bound provides one of the most
stringent constraints. For the 4-2-2 route to $SO(10)$, the successful SM gauge
coupling unification requires the 4-2-2 breaking scale to be ${\cal O}
(10^{11})$ GeV, and most of the parameter space is excluded. For the $SU(5)
\times U(1)_X$ route, on the other hand, the $U(1)_X$ breaking scale can be
significantly higher, and a wide range of the parameter space is allowed.
Furthermore, the proton lifetime in the $SU(5)$ case is predicted to be $4.53
\times 10^{34}$ years, which lies well within the sensitivity reach of the
Hyper-Kamiokande experiment. We also examine the constraints on the model
parameter space from the Large Hadron Collider and the indirect DM search by
Fermi-LAT and MAGIC experiments.


Abstract:
  We derive simple relations which express 2D light front force distributions
in terms of 3D Breit frame pressure and shear force distributions.
Mathematically the relations correspond to invertible Abel transformation and
they establish one-to-one mathematical equivalence of 3D Breit frame force
distributions and 2D light front ones.
  Any knowledge (model calculation, experimental measurement, etc.) about
pressure and shear force distributions in Breit frame can be unambiguously
transformed into light front force distributions with the help of Abel
transformation. It is important that the transformation ensures 2D stability
conditions if the 3D stability conditions are satisfied.
  As an illustration of how the relations work, we calculated the light front
force distributions for a large nucleus as a liquid drop, and for large $N_c$
nucleon as a chiral soliton.


Abstract:
  In the standard Black-Scholes-Merton framework, dividends are represented as
a continuous dividend yield and the pricing of Vanilla options on a stock is
achieved through the well-known Black-Scholes formula. In reality however,
stocks pay a discrete fixed cash dividend at each dividend ex-date. This leads
to the so-called piecewise lognormal model, where the asset jumps from a fixed
known amount at each dividend date. There is however no exact closed-form
formula for the pricing of Vanilla options under this model. Approximations
must be used. While there exists many approximations taylored to this specific
problem in the litterature, this paper explores the use of existing well-known
basket option formulas for the pricing of European options on a single asset
with cash dividends in the piecewise lognormal model.


Abstract:
  Presidential speeches indicate the government's intentions and justifications
supported by a dedicated style and rhetoric oscillating between explanation and
controversy. Over a period of sixty years, can we observe stylistic variations
by the different French presidents of the Fifth Republic (1958-2018)? Based on
official transcripts of all their allocution, this paper illustrates the
stylistic evolution and presents the underlying main trends. This study shows
that de Gaulle's rhetoric is not mainly dedicated to his own person, or that
the two terms of J. Chirac are not fully similar. According to several overall
stylistic indicators, Macron's style does not appear as complex compared to his
predecessors (F. Hollande or N. Sarkozy) but a more careful analysis clearly
demonstrates his noticeable new style. Compared to the recent US presidents,
the French ones present some similarities (e.g., similar mean sentence length)
and dissimilarities (more I-words, less we-words). In this comparative
analysis, Macron's style is also clearly distinctive from both the US and
former French presidents. Opting for a more abstract discourse, less anchored
in space, using less numbers, E. Macron tends to use long sentences. These
various stylistic and rhetorical features could explain his being misunderstood
by the French people and his recurrent low approval ratings.


Abstract:
  Background: Cardiovascular diseases (CVDs) are among the leading causes of
death worldwide. Predictive scores providing personalised risk of developing
CVD are increasingly used in clinical practice. Most scores, however, utilise a
homogenous set of features and require the presence of a physician.
  Objective: The aim was to develop a new risk model (DiCAVA) using statistical
and machine learning techniques that could be applied in a remote setting. A
secondary goal was to identify new patient-centric variables that could be
incorporated into CVD risk assessments.
  Methods: Across 466,052 participants, Cox proportional hazards (CPH) and
DeepSurv models were trained using 608 variables derived from the UK Biobank to
investigate the 10-year risk of developing a CVD. Data-driven feature selection
reduced the number of features to 47, after which reduced models were trained.
Both models were compared to the Framingham score.
  Results: The reduced CPH model achieved a c-index of 0.7443, whereas DeepSurv
achieved a c-index of 0.7446. Both CPH and DeepSurv were superior in
determining the CVD risk compared to Framingham score. Minimal difference was
observed when cholesterol and blood pressure were excluded from the models
(CPH: 0.741, DeepSurv: 0.739). The models show very good calibration and
discrimination on the test data.
  Conclusion: We developed a cardiovascular risk model that has very good
predictive capacity and encompasses new variables. The score could be
incorporated into clinical practice and utilised in a remote setting, without
the need of including cholesterol. Future studies will focus on external
validation across heterogeneous samples.


Abstract:
  There is a growing set of observational data demonstrating that cosmic rays
exhibit small-scale anisotropies (5-30 deg) with amplitude deviations lying
between 0.01-0.1 percent that of the average cosmic ray flux. A broad range of
models have been proposed to explain these anisotropies ranging from
finite-scale magnetic field structures to dark matter annihilation. The
standard diffusion transport methods used in cosmic ray propagation do not
capture the transport physics in a medium with finite-scale or coherent
magnetic field structures. Here, we present a Monte Carlo transport method,
applying it to a series of finite-scale magnetic field structures to determine
the requirements of such fields in explaining the observed cosmic
ray,small-scale anisotropies.


Abstract:
  As a representative device exploiting both the solar energy and the radiative
cooling of deep-sky, the radiative temperature regulator (RTR) could switch
between heating and cooling modes self-adaptively at different temperatures.
However, the concept of RTR is challenging to be implemented due to the intense
parasitic absorption in phase-changing layers. Here, based on the theoretical
framework of energy conservation, we quantitatively reveal the intrinsic
relationships between solar heating and radiative cooling, especially
addressing the fundamental limiting factors, including the parasitic absorption
and the spectral emission selectivity, as well as the dynamic responses of the
phase-changing device under various operating conditions. The investigation
presents more insight into the underlying physics of RTRs and provides feasible
architectures for realizing such a kind of new functional device.


Abstract:
  We develop numerical methods for computing statistics of stochastic processes
on surfaces of general shape with drift-diffusion dynamics $d\mathbf{X}_t =
a(\mathbf{X}_t)dt + \mathbf{b}(\mathbf{X}_t)d\mathbf{W}_t$. We formulate
descriptions of Brownian motion and general drift-diffusion processes on
surfaces. We consider statistics of the form $u(\mathbf{x}) =
\mathbb{E}^{\mathbf{x}}\left[\int_0^\tau g(\mathbf{X}_t)dt \right] +
\mathbb{E}^{\mathbf{x}}\left[f(\mathbf{X}_\tau)\right]$ for a domain $\Omega$
and the exit stopping time $\tau = \inf_t \{t > 0 \; |\; \mathbf{X}_t \not\in
\Omega\}$, where $f,g$ are general smooth functions. For computing these
statistics, we develop high-order Generalized Moving Least Squares (GMLS)
solvers for associated surface PDE boundary-value problems based on
Backward-Kolmogorov equations. We focus particularly on the mean First Passage
Times (FPTs) given by the case $f = 0,\, g = 1$ where $u(\mathbf{x}) =
\mathbb{E}^{\mathbf{x}}\left[\tau\right]$. We perform studies for a variety of
shapes showing our methods converge with high-order accuracy both in capturing
the geometry and the surface PDE solutions. We then perform studies showing how
statistics are influenced by the surface geometry, drift dynamics, and
spatially dependent diffusivities.


Abstract:
  Starting from some results regarding the form of the Ricci scalar at a point
$P$ in a spacetime endowed with a minimum distance, we investigate how they
might be accommodated, specifically for the case of null separations, in a
as-simple-as-possible quantum structure for spacetime at $P$, and we try to
accomplish this in terms of potentially operationally-defined concepts. In so
doing, we provide a possible explicit form for the operator expressing the
Ricci scalar as a quantum observable, and give quantum-informational support,
thus regardless of or before field equations, to associating with a patch of
horizon an entropy proportional to its area.


Abstract:
  The importance of a node in a social network is identified through a set of
measures called centrality. Degree centrality, closeness centrality,
betweenness centrality and clustering coefficient are the most frequently used
metrics to compute node centrality. Their computational complexity in some
cases makes unfeasible, when not practically impossible, their computations.
For this reason we focused on two alternative measures, WERW-Kpath and Game of
Thieves, which are at the same time highly descriptive and computationally
affordable. Our experiments show that a strong correlation exists between
WERW-Kpath and Game of Thieves and the classical centrality measures. This may
suggest the possibility of using them as useful and more economic replacements
of the classical centrality measures.


Abstract:
  We present a new method to assess the properties of transiting planet
candidates by multicolor photometry. By analyzing multicolor transit/eclipse
light curves and apparent magnitudes of the target in parallel, this method
attempts to identify the nature of the system and provide a quantitative
constraint on the properties of unresolved companion(s). We demonstrate our
method by observing the six systems hosting candidate transiting planets,
identified by the K2 mission (EPIC 206036749, EPIC 206500801, EPIC 210513446,
EPIC 211800191, EPIC 220621087, and EPIC 220696233). Applying our analysis code
to the six targets, we find that EPIC 206036749, EPIC 210513446, and EPIC
211800191 are likely to be triple-star systems including eclipsing binaries,
and EPIC 220696233 is likely a planetary system, albeit further observations
are required to confirm the nature. Additionally, we confirm that the
systematic errors in the derived system parameters arising from adopting
specific isochrone models and observing instruments (passbands) are relatively
small. While this approach alone is not powerful enough to validate or refute
planet candidates, the technique allows us to constrain the properties of
resolved/unresolved companions, and prioritize the planet candidates for
further follow-up observations (e.g., radial-velocity measurements).


Abstract:
  We introduce a new topological quantum code, the three-dimensional subsystem
toric code (3D STC), which is a generalization of the stabilizer toric code.
The 3D STC can be realized by measuring geometrically-local parity checks of
weight at most three on the cubic lattice with open boundary conditions. We
prove that single-shot quantum error correction (QEC) is possible with the 3D
STC, i.e., one round of local parity-check measurements suffices to perform
reliable QEC even in the presence of measurement errors. We also propose an
efficient single-shot QEC strategy for the 3D STC and investigate its
performance. In particular, we numerically estimate the resulting storage
threshold against independent bit-flip, phase-flip and measurement errors to be
$p_\text{STC} \approx 1.045\%$. Such a high threshold together with local
parity-check measurements of small weight make the 3D STC particularly
appealing for realizing fault-tolerant quantum computing.


Abstract:
  We study the problem of designing systems in order to minimize cost while
meeting a given flexibility target. Flexibility is attained by enforcing a
joint chance constraint, which ensures that the system will exhibit feasible
operation with a given target probability level. Unfortunately, joint chance
constraints are complicated mathematical objects that often need to be
reformulated using mixed-integer programming (MIP) techniques. In this work, we
cast the design problem as a conflict resolution problem that seeks to minimize
cost while maximizing flexibility. We propose a purely continuous relaxation of
this problem that provides a significantly more scalable approach relative to
MIP methods and show that the formulation delivers solutions that closely
approximate the Pareto set of the original joint chance-constrained problem.


Abstract:
  In this work, we investigate the topological phase transitions in an
effective model for a topological thin film with high-frequency pumping. In
particular, our results show that the circularly polarized light can break the
time-reversal symmetry and induce the quantum anomalous Hall insulator (QAHI)
phase. Meanwhile, the bulk magnetic moment can also break the time-reversal
symmetry. Therefore, it shows rich phase diagram by tunning the intensity of
the light and the thickness of the thin film. Using the parameters fitted by
experimental data, we give the topological phase diagram of the Cr-doped
Bi$_{2}$Se$_{3}$ thin film, showing that by modulating the strength of the
polarized optical field in an experimentally accessible range, there are four
different phases: the normal insulator phase, the time-reversal-symmetry-broken
quantum spin Hall insulator phase, and two different QAHI phases with opposite
Chern numbers. Comparing with the non-doped Bi$_{2}$Se$_{3}$, it is found that
the interplay between the light and bulk magnetic moment separates the two
different QAHI phases with opposite Chern numbers. The results show that an
intrinsic magnetic topological insulator with high-frequency pumping is an
ideal platform for further exploring various topological phenomena with a
spontaneously broken time-reversal symmetry.


Abstract:
  Consistent inference of the electron density and temperature has been carried
out with multiple heterogeneous plasma diagnostic data sets at Wendelstein 7-X.
The predictive models of the interferometer, Thomson scattering and helium beam
emission spectroscopy systems have been developed in the Minerva framework and
combined to a single joint model. The electron density and temperature profiles
are modelled by Gaussian processes with their hyperparameters. The model
parameters such as the calibration factor of the Thomson scattering system and
the model predictive uncertainties are regarded as additional unknown
parameters. The joint posterior probability distribution of the electron
density and temperature profiles, hyperparameters of the Gaussian processes and
model parameters is explored by Markov chain Monte Carlo algorithms. The
posterior samples drawn from the joint posterior distribution are numerically
marginalised over the hyperparameters and model parameters to obtain the
marginal posterior distributions of the electron density and temperature
profiles. The inference of these profiles is performed with different
combinations of the interferometer and Thomson scattering data as well as
either the empirical electron density and temperature constraints at the
limiter/divertor positions introduced by \textit{virtual observations} or the
edge density and temperature from the helium beam emission data. Furthermore,
the addition of the X-ray imaging crystal spectrometers to the joint model for
the ion temperature profiles is demonstrated. All these profiles presented in
this work are inferred with the optimal hyperparameters and model parameters by
exploring the full joint posterior distribution which intrinsically embodies
Bayesian Occam's razor.


Abstract:
  In this paper, a deep collocation method (DCM) for thin plate bending
problems is proposed. This method takes advantage of computational graphs and
backpropagation algorithms involved in deep learning. Besides, the proposed DCM
is based on a feedforward deep neural network (DNN) and differs from most
previous applications of deep learning for mechanical problems. First, batches
of randomly distributed collocation points are initially generated inside the
domain and along the boundaries. A loss function is built with the aim that the
governing partial differential equations (PDEs) of Kirchhoff plate bending
problems, and the boundary/initial conditions are minimised at those
collocation points. A combination of optimizers is adopted in the
backpropagation process to minimize the loss function so as to obtain the
optimal hyperparameters. In Kirchhoff plate bending problems, the C1 continuity
requirement poses significant difficulties in traditional mesh-based methods.
This can be solved by the proposed DCM, which uses a deep neural network to
approximate the continuous transversal deflection, and is proved to be suitable
to the bending analysis of Kirchhoff plate of various geometries.


Abstract:
  Our recent work (Ayral et al., 2020 IEEE Computer Society Annual Symposium on
VLSI (ISVLSI)) showed the first implementation of the Quantum Divide and
Compute (QDC) method, which allows to break quantum circuits into smaller
fragments with fewer qubits and shallower depth. QDC can thus deal with the
limited number of qubits and short coherence times of noisy, intermediate-scale
quantum processors. This article investigates the impact of different noise
sources -- readout error, gate error and decoherence -- on the success
probability of the QDC procedure. We perform detailed noise modeling on the
Atos Quantum Learning Machine, allowing us to understand tradeoffs and
formulate recommendations about which hardware noise sources should be
preferentially optimized. We describe in detail the noise models we used to
reproduce experimental runs on IBM's Johannesburg processor. This work also
includes a detailed derivation of the equations used in the QDC procedure to
compute the output distribution of the original quantum circuit from the output
distribution of its fragments. Finally, we analyze the computational complexity
of the QDC method for the circuit under study via tensor-network
considerations, and elaborate on the relation the QDC method with
tensor-network simulation methods.


Abstract:
  Graph signal processing (GSP) is a key tool for satisfying the growing demand
for information processing over networks. However, the success of GSP in
downstream learning and inference tasks is heavily dependent on the prior
identification of the relational structures. Graphs are natural descriptors of
the relationships between entities of complex environments. The underlying
graph is not readily detectable in many cases and one has to infer the topology
from the observed signals. Firstly, we address the problem of graph signal
classification by proposing a novel framework for discriminative graph
learning. To learn discriminative graphs, we invoke the assumption that signals
belonging to each class are smooth with respect to the corresponding graph
while maintaining non-smoothness with respect to the graphs corresponding to
other classes. Secondly, we extend our work to tackle increasingly dynamic
environments and real-time topology inference. We develop a proximal gradient
(PG) method which can be adapted to situations where the data are acquired
on-the-fly. Beyond discrimination, this is the first work that addresses the
problem of dynamic graph learning from smooth signals where the sought network
alters slowly. The validation of the proposed frameworks is comprehensively
investigated using both synthetic and real data.


Abstract:
  The rapid growth in Mobile application users has made the researchers and
practitioners to think of intelligent tools that can help the users and
applications in delivering quality of services. Therefore, intelligent agent is
expected to become the tool for development of mobile outcome based education
(OBE) particularly in higher learning Institutions (HLI). In this context,
there is a lacking of OBE intelligent agent in assisting the academicians to
use in OBE management for mobile application system environment. This paper
presents the conceptual design and development of a mobile intelligent agent
based on mobile OBE called as i-MOBE. Since that, i-MOBE that we developed is
considered very important for academicians and students to facilitate them in
using for academic purpose in HLI particularly in helping them to monitor the
performance in teaching and learning (T&L). The system architecture will be
covering the conceptual design and its interaction as well as the system
configuration in helping the academicians to use the system in their T&L toward
effective and efficiency also can be applied in monitoring based on scenarios
such as test, assignment and projects and so on.


Abstract:
  The continuum-fitting and the iron-line methods are currently the two leading
techniques for measuring the spins of accreting black holes. In the past few
years, these two methods have been developed for testing fundamental physics.
In the present work, we employ state-of-the-art models to test black holes
through the continuum-fitting and the iron-line methods and we analyze three
NuSTAR observations of the black hole binary GRS 1716-249 during its outburst
in 2016-2017. In these three observations, the source was in a
hard-intermediate state and the spectra show both a strong thermal component
and prominent relativistic reflection features. Our analysis confirms the Kerr
nature of the black hole in GRS 1716-249 and provides quite stringent
constraints on possible deviations from the predictions of general relativity.


Abstract:
  This paper presents the first general (supervised) statistical learning
framework for point processes in general spaces. Our approach is based on the
combination of two new concepts, which we define in the paper: i) bivariate
innovations, which are measures of discrepancy/prediction-accuracy between two
point processes, and ii) point process cross-validation (CV), which we here
define through point process thinning. The general idea is to carry out the
fitting by predicting CV-generated validation sets using the corresponding
training sets; the prediction error, which we minimise, is measured by means of
bivariate innovations. Having established various theoretical properties of our
bivariate innovations, we study in detail the case where the CV procedure is
obtained through independent thinning and we apply our statistical learning
methodology to three typical spatial statistical settings, namely parametric
intensity estimation, non-parametric intensity estimation and Papangelou
conditional intensity fitting. Aside from deriving theoretical properties
related to these cases, in each of them we numerically show that our
statistical learning approach outperforms the state of the art in terms of mean
(integrated) squared error.


Abstract:
  Periodically poled thin-film lithium niobate (TFLN) waveguides have emerged
as a leading platform for highly efficient frequency conversion in the
near-infrared. However, the commonly used silica bottom-cladding results in
high absorption loss at wavelengths beyond 2.5 $\mu$m. In this work, we
demonstrate efficient frequency conversion in a TFLN-on-sapphire platform,
which features high transparency up to 4.5 $\mu$m. In particular, we report
generating mid-infrared light up to 3.66 $\mu$m via difference-frequency
generation of a fixed 1-$\mu$m source and a tunable telecom source, with
normalized efficiencies up to 200%/W-cm$^2$. These results show
TFLN-on-sapphire to be a promising platform for integrated nonlinear
nanophotonics in the mid-infrared.


Abstract:
  Neutrino-matter interactions play an important role in core-collapse
supernova (CCSN) explosions as they contribute to both lepton number and/or
four-momentum exchange between neutrinos and matter, and thus act as the agent
for neutrino-driven explosions. Due to the multiscale nature of neutrino
transport in CCSN simulations, an implicit treatment of neutrino-matter
interactions is desired, which requires solutions of coupled nonlinear systems
in each step of the time integration scheme. In this paper we design and
compare nonlinear iterative solvers for implicit systems with energy coupling
neutrino-matter interactions commonly used in CCSN simulations. Specifically,
we consider electron neutrinos and antineutrinos, which interact with static
matter configurations through the Bruenn~85 opacity set. The implicit systems
arise from the discretization of a non-relativistic two-moment model for
neutrino transport, which employs the discontinuous Galerkin (DG) method for
phase-space discretization and an implicit-explicit (IMEX) time integration
scheme. In the context of this DG-IMEX scheme, we propose two approaches to
formulate the nonlinear systems -- a coupled approach and a nested approach.
For each approach, the resulting systems are solved with Anderson-accelerated
fixed-point iteration and Newton's method. The performance of these four
iterative solvers has been compared on relaxation problems with various degree
of collisionality, as well as proto-neutron star deleptonization problems with
several matter profiles adopted from spherically symmetric CCSN simulations.
Numerical results suggest that the nested Anderson-accelerated fixed-point
solver is more efficient than other tested solvers for solving implicit
nonlinear systems with energy coupling neutrino-matter interactions.


Abstract:
  Let $F$ be a moduli space of lattice-polarized K3 surfaces. Suppose that one
has chosen a canonical effective ample divisor $R$ on a general K3 in $F$. We
call this divisor "recognizable" if its flat limit on Kulikov surfaces is well
defined. We prove that the normalization of the stable pair compactification
$\overline{F}^R$ for a recognizable divisor is a Looijenga semitoroidal
compactification.
  For polarized K3 surfaces $(X,L)$ of degree $2d$, we show that the sum of
rational curves in the linear system $|L|$ is a recognizable divisor, giving a
modular semitoroidal compactification of $F_{2d}$ for all $d$.


Abstract:
  This paper aims to assess the effects of industrial pollution on infant
mortality between the years 1850-1940 using full count decennial censuses. In
this period, US economy experienced a tremendous rise in industrial activity
with significant variation among different counties in absorbing manufacturing
industries. Since manufacturing industries are shown to be the main source of
pollution, we use the share of employment at the county level in this industry
to proxy for space-time variation in industrial pollution. Since male embryos
are more vulnerable to external stressors like pollution during prenatal
development, they will face higher likelihood of fetal death. Therefore, we
proxy infant mortality with different measures of gender ratio. We show that
the upswing in industrial pollution during late nineteenth century and early
twentieth century has led to an increase in infant mortality. The results are
consistent and robust across different scenarios, measures for our proxies, and
aggregation levels. We find that infants and more specifically male infants had
paid the price of pollution during upswing in industrial growth at the dawn of
the 20th century. Contemporary datasets are used to verify the validity of the
proxies. Some policy implications are discussed.


Abstract:
  Recent studies indicate that in many situations service times are affected by
the experienced queueing delay of the particular customer. This effect has been
detected in different areas, such as health care, call centers and
telecommunication networks. In this paper we present a methodology to analyze a
model having this property. The specific model is an M/M/c queue in which any
customer may be tagged at her arrival time if her queueing time will be above a
certain fixed threshold. All tagged customers are then served at a given rate
that may differ from the rate used for the non-tagged customers. We show how it
is possible to model the virtual queueing time of this queueing system by a
specific Markov chain. Then, solving the corresponding balance equations, we
give a recursive solution to compute the stationary distribution, which
involves a mixture of exponential terms. Using numerical experiments, we
demonstrate that the differences in service rates can have a crucial impact on
queueing time performance.


Abstract:
  E-commerce provides an efficient and effective way to exchange goods between
sellers and customers. E-commerce has been a popular method for doing business,
because of its simplicity of having commerce activity transparently available,
including customer voice and opinion about their own experience. Those
experiences can be a great benefit to understand customer experience
comprehensively, both for sellers and future customers. This paper applies to
e-commerces and customers in Indonesia. Many Indonesian customers expressed
their voice to open social network services such as Twitter and Facebook, where
a large proportion of data is in the form of conversational data. By
understanding customer behavior through open social network service, we can
have descriptions about the e-commerce services level in Indonesia. Thus, it is
related to the government's effort to improve the Indonesian digital economy
ecosystem. A method for finding core topics in large-scale internet
unstructured text data is needed, where the method should be fast but
sufficiently accurate. Processing large-scale data is not a straightforward
job, it often needs special skills of people and complex software and hardware
computer system. We propose a fast methodology of text mining methods based on
frequently appeared words and their word association to form network text
methodology. This method is adapted from Social Network Analysis by the model
relationships between words instead of actors.


Abstract:
  We report a simple and compact design of a dispersion compensated mode-locked
Yb:fiber oscillator based on a nonlinear amplifying loop mirror (NALM). The
fully polarization maintaining (PM) fiber integrated laser features a chirped
fiber Bragg grating (CFBG) for dispersion compensation and a fiber integrated
compact non-reciprocal phase bias device, which is alignment-free. The main
design parameters were determined by numerically simulating the pulse evolution
in the oscillator and by analyzing their impact on the laser performance.
Experimentally, we achieved an 88 fs compressed pulse duration with sub-fs
timing jitter at 54 MHz repetition rate and 51 mW of output power with 5.5 *
10-5 [20 Hz, 1 MHz] integrated relative intensity noise (RIN). Furthermore, we
demonstrate tight phase-locking of the laser's carrier-envelope offset
frequency (fceo) to a stable radio frequency (RF) reference and of one
frequency comb tooth to a stable optical reference at 291 THz.


Abstract:
  HotStuff is a state-of-the-art Byzantine fault-tolerant consensus protocol.
It can be pipelined to build large-scale blockchains. One of its variants
called LibraBFT is adopted in Facebook's Libra blockchain. Although it is well
known that pipelined HotStuff is secure against up to $1/3$ of Byzantine nodes,
its performance in terms of throughput and delay is still under-explored. In
this paper, we develop a multi-metric evaluation framework to quantitatively
analyze pipelined \mbox{HotStuff's performance} with respect to its chain
growth rate, chain quality, and latency. We then propose two attack strategies
and evaluate their effects on the performance of pipelined HotStuff. Our
analysis shows that the chain growth rate (resp, chain quality) of pipelined
HotStuff under our attacks can drop to as low as 4/9 (resp, 12/17) of that
without attacks when $1/3$ nodes are Byzantine. As another application, we use
our framework to evaluate certain engineering optimizations adopted by
LibraBFT. We find that these optimizations make the system more vulnerable to
our attacks than the original pipelined HotStuff. Finally, we provide two
countermeasures to thwart these attacks. We hope that our studies can shed
light on the rigorous understanding of the state-of-the-art pipelined HotStuff
protocol as well as its variants.


Abstract:
  We consider tensor product random fields $Y_d$, $d\in\mathbb{N}$, whose
covariance funtions are Gaussian kernels. The average case approximation
complexity $n^{Y_d}(\varepsilon)$ is defined as the minimal number of
evaluations of arbitrary linear functionals needed to approximate $Y_d$, with
relative $2$-average error not exceeding a given threshold
$\varepsilon\in(0,1)$. We investigate the growth of $n^{Y_d}(\varepsilon)$ for
arbitrary fixed $\varepsilon\in(0,1)$ and $d\to\infty$.
  Namely, we find criteria of boundedness for $n^{Y_d}(\varepsilon)$ on $d$ and
of tending $n^{Y_d}(\varepsilon)\to\infty$, $d\to\infty$, for any fixed
$\varepsilon\in(0,1)$. In the latter case we obtain necessary and sufficient
conditions for the following logarithmic asymptotics
  \begin{eqnarray*}
  \ln n^{Y_d}(\varepsilon)= a_d+q(\varepsilon)b_d+o(b_d),\quad d\to\infty,
  \end{eqnarray*}
  with any $\varepsilon\in(0,1)$. Here $q\colon (0,1)\to\mathbb{R}$ is a
non-decreasing function, $(a_d)_{d\in\mathbb{N}}$ is a sequence and
$(b_d)_{d\in\mathbb{N}}$ is a positive sequence such that $b_d\to\infty$,
$d\to\infty$.
  We show that only special quantiles of self-decomposable distribution
functions appear as functions $q$ in a given asymptotics. These general results
apply to $n^{Y_d}(\varepsilon)$ under particular assumptions on the length
scale parameters.


Abstract:
  In this work, we study the problem of user preference learning on the example
of parameter setting for a hearing aid (HA). We propose to use an agent that
interacts with a HA user, in order to collect the most informative data, and
learns user preferences for HA parameter settings, based on these data. We
model the HA system as two interacting sub-systems, one representing a user
with his/her preferences and another one representing an agent. In this system,
the user responses to HA settings, proposed by the agent. In our user model,
the responses are driven by a parametric user preference function. The agent
comprises the sequential mechanisms for user model inference and HA parameter
proposal generation. To infer the user model (preference function), Bayesian
approximate inference is used in the agent. Here we propose the normalized
weighted Kullback-Leibler (KL) divergence between true and agent-assigned
predictive user response distributions as a metric to assess the quality of
learned preferences. Moreover, our agent strategy for generating HA parameter
proposals is to generate HA settings, responses to which help resolving
uncertainty associated with prediction of the user responses the most. The
resulting data, consequently, allows for efficient user model learning. The
normalized weighted KL-divergence plays an important role here as well, since
it characterizes the informativeness of the data to be used for probing the
user. The efficiency of our approach is validated by numerical simulations.


Abstract:
  Empirical work often uses treatment assigned following geographic boundaries.
When the effects of treatment cross over borders, classical
difference-in-differences estimation produces biased estimates for the average
treatment effect. In this paper, I introduce a potential outcomes framework to
model spillover effects and decompose the estimate's bias in two parts: (1) the
control group no longer identifies the counterfactual trend because their
outcomes are affected by treatment and (2) changes in treated units' outcomes
reflect the effect of their own treatment status and the effect from the
treatment status of "close" units. I propose estimation strategies that can
remove both sources of bias and semi-parametrically estimate the spillover
effects themselves including in settings with staggered treatment timing. To
highlight the importance of spillover effects, I revisit analyses of three
place-based interventions.


Abstract:
  Exploiting short packets for communications is one of the key technologies
for realizing emerging application scenarios such as massive machine type
communications (mMTC) and ultra-reliable low-latency communications (uRLLC). In
this paper, we investigate short-packet communications to provide both
reliability and security guarantees simultaneously with an eavesdropper. In
particular, an outage probability considering both reliability and secrecy is
defined according to the characteristics of short-packet transmission, while
the effective throughput in the sense of outage is established as the
performance metric. Specifically, a general analytical framework is proposed to
approximate the outage probability and effective throughput. Furthermore,
closed-form expressions for these quantities are derived for the high
signal-to-noise ratio (SNR) regime. Both effective throughput obtained via a
general analytical framework and a high-SNR approximation are maximized under
an outage-probability constraint by searching for the optimal blocklength.
Numerical results verify the feasibility and accuracy of the proposed
analytical framework, and illustrate the influence of the main system
parameters on the blocklength and system performance under the
outage-probability constraint.


Abstract:
  We present the detections of shocked molecular hydrogen (H2) gas in near- and
mid-infrared and broad CO in millimeter from the mixed-morphology supernova
remnant (SNR) HB~3 (G132.7+1.3) using Palomar WIRC, the Spitzer GLIMPSE360 and
WISE surveys, and HHSMT. Our near-infrared narrow-band filter H2 2.12 micron
images of HB~3 show that both Spitzer IRAC and WISE 4.6 micron emission
originates from shocked H2 gas. The morphology of H2 exhibits thin filamentary
structures and a large scale of interaction sites between the HB~3 and nearby
molecular clouds. Half of HB~3, the southern and eastern shell of the SNR,
emits H2 in a shape of a "butterfly" or "W", indicating the interaction sites
between the SNR and dense molecular clouds. Interestingly, the H2 emitting
region in the southeast is also co-spatial to the interacting area between HB~3
and the H~II regions of the W3 complex, where we identified star-forming
activity.
  We further explore the interaction between HB~3 and dense molecular clouds
with detections of broad CO(3-2) and CO(2-1) molecular lines from the southern
and southeastern shells along the H2 emitting region. The widths of the broad
lines are 8-20 km/s; the detection of such broad lines is unambiguous, dynamic
evidence of the interactions between the SNR and clouds. The CO broad lines are
from two branches of the bright, southern H2 shell. We apply the Paris-Durham
shock model to the CO line profiles, which infer the shock velocities of 20 -
40 km/s, relatively low densities of 10^{3-4} cm^{-3} and strong (>200 micro
Gauss) magnetic fields.


Abstract:
  The recoil of atoms in arrays due to the emission or absorption of photons is
studied for sub-wavelength interatomic spacing. The atoms in the array interact
with each other through collective dipole-dipole interactions and with the
incident laser field in the low intensity limit. Shining uniform light on the
array gives rise to patterns of excitation and recoil in the array. These arise
due to the interference of different eigenmodes of excitation. The relation
between the recoil and the decay dynamics is studied when the array is in its
excitation eigenstates. The recoil experienced by a subradiant collective decay
is substantially larger than from independent atom decay. A method to calculate
the rate of recoil when steady state has been achieved with a constant influx
of photons is also described.


Abstract:
  Recent theoretical works on over-parameterized neural nets have focused on
two aspects: optimization and generalization. Many existing works that study
optimization and generalization together are based on neural tangent kernel and
require a very large width. In this work, we are interested in the following
question: for a binary classification problem with two-layer mildly
over-parameterized ReLU network, can we find a point with small test error in
polynomial time? We first show that the landscape of loss functions with
explicit regularization has the following property: all local minima and
certain other points which are only stationary in certain directions achieve
small test error. We then prove that for convolutional neural nets, there is an
algorithm which finds one of these points in polynomial time (in the input
dimension and the number of data points). In addition, we prove that for a
fully connected neural net, with an additional assumption on the data
distribution, there is a polynomial time algorithm.


Abstract:
  A new framework of thermodynamic modeling is proposed by introducing the
concept of differentiable programming, where all the thermodynamic observables
including both thermochemical quantities and phase equilibria can be
differentiated with respect to the underlying model parameters, thus allowing
the models learned by gradient-based optimization. It is shown that
thermodynamic modeling and deep learning can be seamlessly integrated and
unified within this framework. A preliminary successful application is
demonstrated for the Cu-Rh system. It is expected that thermodynamic modeling
in a deep learning style can increase prediction power of models, and provide
more effective guidance for design, synthesis and optimization of
multi-component materials with complex chemistry via learning various types of
data.


Abstract:
  Recent study reported that an aerosolised virus (COVID-19) can survive in the
air for a few hours. It is highly possible that people get infected with the
disease by breathing and contact with items contaminated by the aerosolised
virus. However, the aerosolised virus transmission and trajectories in various
meteorological environments remain unclear. This paper has investigated the
movement of aerosolised viruses from a high concentration source across a dense
urban area. The case study looks at the highly air polluted areas of London:
University College Hospital (UCH) and King Cross and St Pancras International
Station (KCSPI). We explored the spread and decay of COVID-19 released from the
hospital and railway stations with the prescribed meteorological conditions.
The study has three key findings: the primary result is that it is possible for
the virus to travel from meters up to hundred meters from the source location.
The secondary finding shows viruses released into the atmosphere from entry and
exit points at KCSPI remain trapped within a small radial distance of < 50m.
This strengthens the case for the use of face coverings to reduce the infection
rate. The final finding shows that there are different levels of risk at
various door locations for UCH, depending on which door is used there can be a
higher concentration of COVID-19. Although our results are based on London,
since the fundamental knowledge processes are the same, our study can be
further extended to other locations (especially the highly air polluted areas)
in the world.


Abstract:
  Despite the explosion of interest in healthcare AI research, the
reproducibility and benchmarking of those research works are often limited due
to the lack of standard benchmark datasets and diverse evaluation metrics. To
address this reproducibility challenge, we develop PyHealth, an open-source
Python toolbox for developing various predictive models on healthcare data.
  PyHealth consists of data preprocessing module, predictive modeling module,
and evaluation module. The target users of PyHealth are both computer science
researchers and healthcare data scientists. With PyHealth, they can conduct
complex machine learning pipelines on healthcare datasets with fewer than ten
lines of code. The data preprocessing module enables the transformation of
complex healthcare datasets such as longitudinal electronic health records,
medical images, continuous signals (e.g., electrocardiogram), and clinical
notes into machine learning friendly formats. The predictive modeling module
provides more than 30 machine learning models, including established ensemble
trees and deep neural network-based approaches, via a unified but extendable
API designed for both researchers and practitioners. The evaluation module
provides various evaluation strategies (e.g., cross-validation and
train-validation-test split) and predictive model metrics.
  With robustness and scalability in mind, best practices such as unit testing,
continuous integration, code coverage, and interactive examples are introduced
in the library's development. PyHealth can be installed through the Python
Package Index (PyPI) or https://github.com/yzhao062/PyHealth .


Abstract:
  We present results of a detailed investigation of the poorly studied X-ray
pulsar XTE J1858+034 based on the data obtained with the NuSTAR observatory
during the outburst of the source in 2019. The spectral analysis resulted in
the discovery of a cyclotron absorption feature in the source spectrum at ~48
keV both in the pulse phase averaged and resolved spectra. Accurate X-ray
localization of the source using the NuSTAR and Chandra observatories allowed
us to accurately determine the position of the X-ray source and identify the
optical companion of the pulsar. The analysis of the counterpart properties
suggested that the system is likely a symbiotic binary hosting an X-ray pulsar
and a late type companion star of K-M classes rather than Be X-ray binary as
previously suggested.


Abstract:
  Queuing systems with an unlimited number of devices with an incoming
nonstationary Poisson flow and a random flow controlled by a Markov chain are
investigated. The inexpediency of ap-proximation of the birth process by
Poisson flows in long-term forecasting is shown.


Abstract:
  We consider plain vanilla European options written on an underlying asset
that follows a continuous time semi-Markov multiplicative process. We derive a
formula and a renewal type equation for the martingale option price. In the
case in which intertrade times follow the Mittag-Leffler distribution, under
appropriate scaling, we prove that these option prices converge to the price of
an option written on geometric Brownian motion time-changed with the inverse
stable subordinator. For geometric Brownian motion time changed with an inverse
subordinator, in the more general case when the subordinator's Laplace exponent
is a special Bernstein function, we derive a time-fractional generalization of
the equation of Black and Scholes.


Abstract:
  Event extraction is challenging due to the complex structure of event records
and the semantic gap between text and event. Traditional methods usually
extract event records by decomposing the complex structure prediction task into
multiple subtasks. In this paper, we propose Text2Event, a
sequence-to-structure generation paradigm that can directly extract events from
the text in an end-to-end manner. Specifically, we design a
sequence-to-structure network for unified event extraction, a constrained
decoding algorithm for event knowledge injection during inference, and a
curriculum learning algorithm for efficient model learning. Experimental
results show that, by uniformly modeling all tasks in a single model and
universally predicting different labels, our method can achieve competitive
performance using only record-level annotations in both supervised learning and
transfer learning settings.


Abstract:
  This paper aims at introducing the formal foundations of the application of
reduced density-matrix theory and Green's function theory to the analysis of
molecular electronic transitions. For this sake, their mechanics, applied to
specific objects containing information related to the passage and the
interference between electronic states - the difference and the transition
density operators - are rigorously introduced in a self-contained way: After
reducing the corresponding N-body operators (where N is the number of electrons
of the system) using an operator partial-trace procedure, we derive the kernel
of the reduced one-body difference and transition density operators, as well as
the matrix representation of these operators in a finite-dimensional
one-particle-state basis. These derivations are done in first and second
quantization for the sake of completeness - the two formulations are
equivalently present in the literature -, and because second quantization is
extensively used in a second part of the paper. Natural orbitals are introduced
as appropriate bases for reducing the dimensionality of the problem and the
complexity of the analysis of the transition phenomenon: Natural-orbital
representation of density operators are often used as a tool to characterize
the nature of molecular electronic transitions, so we suggested with this
contribution to revisit their theoretical foundations in order to better
understand the origin and nature of these tools.


Abstract:
  In order to compute with $l$--adic sheaves or crystals on a line over
$\mathbb{F} _q$ a low-technology alternative to the traditional computation
with the Hecke operators on the automorphic side could be helpful. A program
which has evolved over the years in our discussions with M. Kontsevich centers
around the concept that, in the geometric case, there must exist certain
multiplication laws on the Galois--representation side that could be thought of
as precursors of the automorphic lifts: non-abelian Abel's theorems, and their
restrictions to diagonal, Clausen identities. To a varying extent, they can
determine the trace functions of $l$--adic sheaves or crystals with prescribed
ramification without directly appealing to the Hecke--eigen property on the
automorphic side.


Abstract:
  Neural Machine Translation (NMT) approaches employing monolingual data are
showing steady improvements in resource rich conditions. However, evaluations
using real-world low-resource languages still result in unsatisfactory
performance. This work proposes a novel zero-shot NMT modeling approach that
learns without the now-standard assumption of a pivot language sharing parallel
data with the zero-shot source and target languages. Our approach is based on
three stages: initialization from any pre-trained NMT model observing at least
the target language, augmentation of source sides leveraging target monolingual
data, and learning to optimize the initial model to the zero-shot pair, where
the latter two constitute a self-learning cycle. Empirical findings involving
four diverse (in terms of a language family, script and relatedness) zero-shot
pairs show the effectiveness of our approach with up to +5.93 BLEU improvement
against a supervised bilingual baseline. Compared to unsupervised NMT,
consistent improvements are observed even in a domain-mismatch setting,
attesting to the usability of our method.


Abstract:
  As users increasingly rely on cloud-based computing services, it is important
to ensure that uploaded speech data remains private. Existing solutions rely
either on server-side methods or focus on hiding speaker identity. While these
approaches reduce certain security concerns, they do not give users client-side
control over whether their biometric information is sent to the server. In this
paper, we formally define client-side privacy and discuss its three unique
technical challenges: (1) direct manipulation of raw data on client devices,
(2) adaptability with a broad range of server-side processing models, and (3)
low time and space complexity for compatibility with limited-bandwidth devices.
Solving these challenges requires new models that achieve high-fidelity
reconstruction, privacy preservation of sensitive personal attributes, and
efficiency during training and inference. As a step towards client-side privacy
for speech recognition, we investigate three techniques spanning signal
processing, disentangled representation learning, and adversarial training.
Through a series of gender and accent masking tasks, we observe that each
method has its unique strengths, but none manage to effectively balance the
trade-offs between performance, privacy, and complexity. These insights call
for more research in client-side privacy to ensure a safer deployment of
cloud-based speech processing services.


Abstract:
  We construct characteristic identities for the split (polarized) Casimir
operators of the simple Lie algebras in adjoint representation. By means of
these characteristic identities, for all simple Lie algebras we derive explicit
formulae for invariant projectors onto irreducible subrepresentations in
T^{\otimes 2} in the case when T is the adjoint representation. These
projectors and characteristic identities are considered from the viewpoint of
the universal description of the simple Lie algebras in terms of the Vogel
parameters.


Abstract:
  This paper investigates the phenomenon of support and resistance levels (SR
levels) in financial time series, which act as temporary price barriers that
reverses price trends. We develop a heuristic discovery algorithm for this
purpose, to discover and evaluate SR levels for intraday price series. Our
simple approach discovers SR levels which are able to reverse price trends
statistically significantly. Asset price entering SR levels with higher number
of price bounces before are more likely to bounce on such SR levels again. We
also show that the decay aspect of the discovered SR levels as decreasing
probability of price bounce over time. We conclude SR levels are features in
financial time series are not explained simply by AR(1) processes, stationary
or otherwise; and that they contribute to the temporary predictability and
stationarity of the investigated price series.


Abstract:
  Prediction is the making of statements, usually probabilistic, about future
events based on current information. Retrodiction is the making of statements
about past events based on current information. We present the foundations of
quantum retrodiction and highlight its intimate connection with the Bayesian
interpretation of probability. The close link with Bayesian methods enables us
to explore controversies and misunderstandings about retrodiction that have
appeared in the literature. To be clear, quantum retrodiction is universally
applicable and draws its validity directly from conventional predictive quantum
theory coupled with Bayes' theorem.


Abstract:
  We report a spin-free formulation of the multireference (MR) driven
similarity renormalization group (DSRG) by employing the ensemble normal
ordering of Mukherjee and Kutzelnigg [W. Kutzelnigg and D. Mukherjee, J. Chem.
Phys. 107, 432 (1997)]. This ensemble averages over all microstates for a given
total spin quantum number and, therefore, it is invariant with respect to SU(2)
transformations. As such, all equations may be reformulated in terms of
spin-free quantities and they closely resemble those of spin-adapted
closed-shell coupled cluster (CC) theory. The current implementation is used to
assess the accuracy of various truncated MR-DSRG methods (perturbation theory
up to third order and iterative methods with single and double excitations) in
computing the constants of thirty-three first-row diatomic molecules. The
accuracy trends for these first-row diatomics are consistent with our previous
benchmark on a small subset of closed-shell diatomic molecules. We then present
the first MR-DSRG application on transition-metal complexes by computing the
spin splittings of the [Fe(H$_2$O)$_6$]$^{2+}$ and [Fe(NH$_3$)$_6$]$^{2+}$
molecules. Focal point analysis (FPA) shows that third-order perturbative
corrections are essential to achieve reasonably converged energetics. A FPA
based on the linearized MR-DSRG theory with one- and two-body operators and up
to a quintuple-$\zeta$ basis set predicts the spin splittings of
[Fe(H$_2$O)$_6$]$^{2+}$ and [Fe(NH$_3$)$_6$]$^{2+}$ to be $-35.7$ and $-17.1$
kcal mol$^{-1}$, respectively, showing good agreement with results of local CC
theory with singles, doubles, and perturbative triples.


Abstract:
  We revisit two well-established verification techniques, $k$-induction and
bounded model checking (BMC), in the more general setting of fixed point theory
over complete lattices. Our main theoretical contribution is latticed
$k$-induction, which (i) generalizes classical $k$-induction for verifying
transition systems, (ii) generalizes Park induction for bounding fixed points
of monotonic maps on complete lattices, and (iii) extends from naturals $k$ to
transfinite ordinals $\kappa$, thus yielding $\kappa$-induction. The
lattice-theoretic understanding of $k$-induction and BMC enables us to apply
both techniques to the fully automatic verification of infinite-state
probabilistic programs. Our prototypical implementation manages to
automatically verify non-trivial specifications for probabilistic programs
taken from the literature that - using existing techniques - cannot be verified
without synthesizing a stronger inductive invariant first.


Abstract:
  Transferring human motion and appearance between videos of human actors
remains one of the key challenges in Computer Vision. Despite the advances from
recent image-to-image translation approaches, there are several transferring
contexts where most end-to-end learning-based retargeting methods still perform
poorly. Transferring human appearance from one actor to another is only ensured
when a strict setup has been complied, which is generally built considering
their training regime's specificities. In this work, we propose a shape-aware
approach based on a hybrid image-based rendering technique that exhibits
competitive visual retargeting quality compared to state-of-the-art neural
rendering approaches. The formulation leverages the user body shape into the
retargeting while considering physical constraints of the motion in 3D and the
2D image domain. We also present a new video retargeting benchmark dataset
composed of different videos with annotated human motions to evaluate the task
of synthesizing people's videos, which can be used as a common base to improve
tracking the progress in the field. The dataset and its evaluation protocols
are designed to evaluate retargeting methods in more general and challenging
conditions. Our method is validated in several experiments, comprising publicly
available videos of actors with different shapes, motion types, and camera
setups. The dataset and retargeting code are publicly available to the
community at: https://www.verlab.dcc.ufmg.br/retargeting-motion.


Abstract:
  We study the problem of distributed cooperative learning, where a group of
agents seeks to agree on a set of hypotheses that best describes a sequence of
private observations. In the scenario where the set of hypotheses is large, we
propose a belief update rule where agents share compressed (either sparse or
quantized) beliefs with an arbitrary positive compression rate. Our algorithm
leverages a unified communication rule that enables agents to access
wide-ranging compression operators as black-box modules. We prove the almost
sure asymptotic exponential convergence of beliefs around the set of optimal
hypotheses. Additionally, we show a non-asymptotic, explicit, and linear
concentration rate in probability of the beliefs on the optimal hypothesis set.
We provide numerical experiments to illustrate the communication benefits of
our method. The simulation results show that the number of transmitted bits can
be reduced to 5-10% of the non-compressed method in the studied scenarios.


Abstract:
  In the context of a geodesically complete Riemannian manifold $M$, we study
the self-adjointness of $\nabla^{\dagger}\nabla+V$ where $\nabla$ is a metric
covariant derivative (with formal adjoint $\nabla^{\dagger}$) on a Hermitian
vector bundle $\mathcal{V}$ over $M$, and $V$ is a locally square integrable
section of $\textrm{End }\mathcal{V}$ such that the (fiberwise) norm of the
"negative" part $V^{-}$ belongs to the local Kato class (or, more generally,
local contractive Dynkin class). Instead of the lower semiboundedness
hypothesis, we assume that there exists a number $\varepsilon \in [0,1]$ and a
positive function $q$ on $M$ satisfying certain growth conditions, such that
$\varepsilon \nabla^{\dagger}\nabla+V\geq -q$, the inequality being understood
in the quadratic form sense over $C_{c}^{\infty}(\mathcal{V})$. In the first
result, which pertains to the case $\epsilon \in [0,1)$, we use the elliptic
equation method. In the second result, which pertains to the case
$\varepsilon=1$, we use the hyperbolic equation method.


Abstract:
  In this paper, we conduct one of the very first studies for cross-corpora
performance evaluation in the spoken language identification (LID) problem.
Cross-corpora evaluation was not explored much in LID research, especially for
the Indian languages. We have selected three Indian spoken language corpora:
IIITH-ILSC, LDC South Asian, and IITKGP-MLILSC. For each of the corpus, LID
systems are trained on the state-of-the-art time-delay neural network (TDNN)
based architecture with MFCC features. We observe that the LID performance
degrades drastically for cross-corpora evaluation. For example, the system
trained on the IIITH-ILSC corpus shows an average EER of 11.80 % and 43.34 %
when evaluated with the same corpora and LDC South Asian corpora, respectively.
Our preliminary analysis shows the significant differences among these corpora
in terms of mismatch in the long-term average spectrum (LTAS) and
signal-to-noise ratio (SNR). Subsequently, we apply different feature level
compensation methods to reduce the cross-corpora acoustic mismatch. Our results
indicate that these feature normalization schemes can help to achieve promising
LID performance on cross-corpora experiments.


Abstract:
  The ability of bacteria to form biofilms hinders any conventional treatment
against chronic infections and has serious socio-economic implications. In this
sense, a nanocarrier capable of overcoming the barrier of the
mucopolysaccharide matrix of the biofilm and releasing its loaded-antibiotic
within would be desirable. Herein, a new nanosystem based on levofloxacin
(LEVO)-loaded mesoporous silica nanoparticles (MSNs) decorated with lectin
Concanavalin A (ConA) has been developed. The presence of ConA promotes its
internalization into the biofilm matrix, which increases the antimicrobial
efficacy of the antibiotic hosted within the mesopores. This nanodevice is
envisioned as a promising alternative to conventional infection treatments by
improving the antimicrobial efficacy and reducing side effects.


Abstract:
  This paper presents a review of ideas that interconnect Astrochemistry and
Galactic Dynamics. Since these two areas are vast and not recent, each one has
already been covered separately by several reviews. After a general historical
introduction, and a needed quick review of processes like the stellar
nucleosynthesis which gives the base to understand the interstellar formation
of simple chemical compounds (H2, CO, NH3 and H2O), we focus on a number of
topics which are at the crossing of the two areas, Dynamics and Astrochemistry.
Astrochemistry is a flourishing field which intends to study the presence and
formation of molecules as well as the influence of them into the structure,
evolution and dynamics of astronomical objects. The progress in the knowledge
on the existence of new complex molecules and of their process of formation
originates from the observational, experimental and theoretical areas which
compose the field. The interfacing areas include star formation, protoplanetary
disks, the role of the spiral arms and the chemical abundance gradients in the
galactic disk. It often happens that the physical conditions in some regions of
the ISM are only revealed by means of molecular observations. To organise a
classification of chemical evolution processes, we discuss about how
astrochemistry can act in three different contexts: i. the chemistry of the
early universe, including external galaxies, ii. star forming regions, and iii.
AGB stars and circumstellar envelopes. We mention that our research is
stimulated by plans for instruments and projects, such as the on-going LLAMA,
which consists in the construction of a 12m sub-mm radio telescope in the
Andes. Thus, modern and new facilities can play a key role in new discoveries
not only in astrochemistry but also in radio astronomy and related areas.
Furthermore, the research of the origin of life is also a stimulating
perspective.


Abstract:
  This article encloses some results on nonncommutative analogue of nonabelian
equations of Langmuir oscillations. One of the main contributions of this work
is to construct the Darbboux transformation for the solution of that equation
in noncommutative framework incorporating associated discrete Lax system.
Further the standard Darboux transformation on arbitrary eigenfunctions of the
Lax system are presented in quasideterminants for few index values. Moreover,
these computations include the derivation of noncommutative version of
nonabelian discrete nonlinear Schr$\ddot{o}$dinger which coincides with its
classical model under commutative limit. The end portion of this article
reveals the identity of noncommutative formalism incorporating a derivation of
an equation of motion which coincides with its existing commutative form in
background zero value of spectral parameter.


Abstract:
  The ubiquity of mobile phones makes mobile GUI understanding an important
task. Most previous works in this domain require human-created metadata of
screens (e.g. View Hierarchy) during inference, which unfortunately is often
not available or reliable enough for GUI understanding. Inspired by the
impressive success of Transformers in NLP tasks, targeting for purely
vision-based GUI understanding, we extend the concepts of Words/Sentence to
Pixel-Words/Screen-Sentence, and propose a mobile GUI understanding
architecture: Pixel-Words to Screen-Sentence (PW2SS). In analogy to the
individual Words, we define the Pixel-Words as atomic visual components (text
and graphic components), which are visually consistent and semantically clear
across screenshots of a large variety of design styles. The Pixel-Words
extracted from a screenshot are aggregated into Screen-Sentence with a Screen
Transformer proposed to model their relations. Since the Pixel-Words are
defined as atomic visual components, the ambiguity between their visual
appearance and semantics is dramatically reduced. We are able to make use of
metadata available in training data to auto-generate high-quality annotations
for Pixel-Words. A dataset, RICO-PW, of screenshots with Pixel-Words
annotations is built based on the public RICO dataset, which will be released
to help to address the lack of high-quality training data in this area. We
train a detector to extract Pixel-Words from screenshots on this dataset and
achieve metadata-free GUI understanding during inference. We conduct
experiments and show that Pixel-Words can be well extracted on RICO-PW and well
generalized to a new dataset, P2S-UI, collected by ourselves. The effectiveness
of PW2SS is further verified in the GUI understanding tasks including relation
prediction, clickability prediction, screen retrieval, and app type
classification.


Abstract:
  The study aims to design and develop a virtual structural design that
simulates the campus and its buildings of a community college in Bukidnon,
Philippines through Virtual Reality. With the immersion of technology, this
project represents the architectural design of the establishment with the use
of Virtual Reality Technology. The project uses a modified Iterative
Development Model which is a guide for the design and development of the 3D
Models and VR Application. TinkerCAD which is a web-based application has been
used to design buildings on the other hand Unity is used to develop the
structural designs of the buildings. The respondents of this study are the
Grade 12 Senior High students from the 4 schools which are geographically near
to the college. With this study, the researchers were able to showcase its VR
Application to the students and later evaluated using a System Usability Scale,
a 10 item questionnaire measuring usability with an overall average of 90% or
Point Score of 4.5 which is interpreted as excellent in a Likert table for
descriptive interpretation. With the use of the VR application potential
students of the college will be able to visualize and experience the present
structures of the college without being physically present in the area. In this
paper, the buildings and structures of NBCC were designed and developed through
a Virtual Reality Platform allowing students from different secondary schools
that are geographically near to the college to experience the feeling to be in
the school without being able to set a step in physically.


Abstract:
  Cells within living soft biological tissues seem to promote the maintenance
of a mechanical state within a defined range near a so-called set-point. This
mechanobiological process is often referred to as mechanical homeostasis.
During this process, cells intimately interact with the fibers of the
surrounding extracellular matrix (ECM). It remains poorly understood, however,
what individual cells actually regulate during these interactions, and how
these micromechanical regulations are translated to tissue level to lead to
what we macroscopically call mechanical homeostasis. Herein, we examine this
question by a combination of experiments, theoretical analysis and
computational modeling. We demonstrate that on short time scales (hours) -
during which deposition and degradation of ECM fibers can largely be neglected
- cells appear to regulate neither the stress / strain in the ECM nor their own
shape, but rather only the contractile forces that they exert on the
surrounding ECM.


Abstract:
  We present EuroCrops, a dataset based on self-declared field annotations for
training and evaluating methods for crop type classification and mapping,
together with its process of acquisition and harmonisation. By this, we aim to
enrich the research efforts and discussion for data-driven land cover
classification via Earth observation and remote sensing. Additionally, through
inclusion of self-declarations gathered in the scope of subsidy control from
all countries of the European Union (EU), this dataset highlights the
difficulties and pitfalls one comes across when operating on a transnational
level. We, therefore, also introduce a new taxonomy scheme, HCAT-ID, that
aspires to capture all the aspects of reference data originating from
administrative and agency databases. To address researchers from both the
remote sensing and the computer vision and machine learning communities, we
publish the dataset in different formats and processing levels.


Abstract:
  We use two non-Riemannian curvature tensors, the $\chi$-curvature and the
mean Berwald curvature to characterise a class of Finsler metrics admitting
first integrals.


Abstract:
  The development of modern technology extends human presence beyond cislunar
space and onto other planets, which presents an urgent need for high-capacity,
long-distance and interplanetary communication. Communication using photons as
carriers has a high channel capacity, but the optical diffraction limit in deep
space leads to inevitable huge geometric loss, setting an insurmountable
transmission distance for existing optical communication technologies. Here, we
propose and experimentally demonstrate a photon-inter-correlation optical
communication (PICOC) against an ultra-high channel loss. We treat light as a
stream of photons, and retrieve the additional information of internal
correlation and photon statistics globally from extremely weak pulse sequences.
We successfully manage to build high-fidelity communication channel with a loss
up to 160dB by separating a single-photon signal embedded in a noise ten times
higher. With only commercially available telescopes, PICOC allows establishment
of communication links from Mars to Earth communication using a milliwatt
laser, and from the edge of the solar system to Earth using a few watts laser.


Abstract:
  The crux of knowledge distillation is to effectively train a resource-limited
student model with the guide of a pre-trained larger teacher model. However,
when there is a large difference between the model complexities of teacher and
student (i.e., capacity gap), knowledge distillation loses its strength in
transferring knowledge from the teacher to the student, thus training a weaker
student. To mitigate the impact of the capacity gap, we introduce knowledge
distillation via intermediate heads. By extending the intermediate layers of
the teacher (at various depths) with classifier heads, we cheaply acquire a
cohort of heterogeneous pre-trained teachers. The intermediate classifier heads
can all together be efficiently learned while freezing the backbone of the
pre-trained teacher. The cohort of teachers (including the original teacher)
co-teach the student simultaneously. Our experiments on various teacher-student
pairs and datasets have demonstrated that the proposed approach outperforms the
canonical knowledge distillation approach and its extensions.


Abstract:
  We describe an efficient method for extracting the parts of $D$-dimensional
loop integrals that are needed to derive observables in classical general
relativity from scattering amplitudes. Our approach simplifies the soft-region
method of integration by judiciously combining terms before the final
integrations. We demonstrate the method by computing the required integrals for
black-hole scattering to the second Post-Minkowskian order in Einstein gravity
coupled to scalars. We also confirm recent results at the third
Post-Minkowskian order regarding universality and high-energy behavior of
gravitational interactions in maximal supergravity.


Abstract:
  Non-linear effects in the early Universe generate non-zero bispectra of the
cosmic microwave background (CMB) temperature and polarization, even in the
absence of primordial non-Gaussianity. In this paper, we compute the
contributions from isocurvature modes to the CMB bispectra using a modified
version of the second-order Boltzmann solver SONG. We investigate the ability
of current and future CMB experiments to constrain these modes with
observations of the bispectrum. Our results show that the enhancement due to
single isocurvature modes mixed with the adiabatic mode is negligible for the
parameter ranges currently allowed by the most recent Planck results. However,
we find that a large compensated isocurvature mode can produce a detectable
bispectrum when its correlation with the adiabatic mode is appreciable. The
non-observation of this contribution in searches for the lensing bispectrum
from Planck allows us to place a new constraint on the relative amplitude of
the correlated part of the compensated isocurvature mode of $f_{\rm
CIP}=1\pm100$. We compute forecasts for future observations by COrE, SO, CMB-S4
and an ideal experiment and conclude that a dedicated search for the bispectrum
from compensated modes could rule out a number of scenarios realised in the
curvaton model. In addition, the CMB-S4 experiment could detect the most
extreme of those scenarios ($f_{\rm CIP}=16.5$) at 2 to 3-$\sigma$
significance.


Abstract:
  Abstract: An induced-transmission filter (ITF) uses an ultrathin layer of
metal positioned at an electric-field node within a dielectric thin-film
bandpass filter to select one transmission band while suppressing other
transmission bands that would have been present without the metal layer. Here,
we introduce a switchable mid-infrared ITF where the metal film can be
"switched on and off", enabling the modulation of the filter response from
single-band to multiband. The switching is enabled by a deeply subwavelength
film of vanadium dioxide (VO2), which undergoes a reversible insulator-to-metal
phase transition. We designed and experimentally demonstrated an ITF that can
switch between two states: one broad passband across the long-wave infrared
(LWIR, 8 - 12 um) and one narrow passband at ~8.8 um. Our work generalizes the
ITF -- previously a niche type of bandpass filter -- into a new class of
tunable devices. Furthermore, our unique fabrication process -- which begins
with thin-film VO2 on a suspended membrane -- enables the integration of VO2
into any thin-film assembly that is compatible with physical vapor deposition
(PVD) processes, and is thus a new platform for realizing tunable thin-film
filters.


Abstract:
  Aims.We aim to locate the origin of a stealth coronal mass ejection (CME)
detected in situ by the MAG instrument on board Solar Orbiter, and make
connections between the CME observed at the Sun, and the interplanetary CME
(ICME) measured in situ. Methods. Remote sensing data are analysed using
advanced image processing techniques to identify the source region of the
stealth CME, and the global magnetic field at the time of the eruption is
examined using Potential Field Source Surface (PFSS) models. The observations
of the stealth CME at the Sun are compared with the magnetic field measured by
the Solar Orbiter spacecraft, and plasma properties measured by the Wind
spacecraft. Results. The source of the CME is found to be a quiet Sun cavity in
the northern hemisphere. We find that the stealth CME has a strong magnetic
field in situ, despite originating from a quiet Sun region with an extremely
weak magnetic field. Conclusions. The interaction of the ICME with its
surrounding environment is the likely cause of a higher magnetic field strength
measured in situ. Stealth CMEs require multi-wavelength and multi-viewpoint
observations in order to confidently locate the source region, however their
elusive signatures still pose many problems for space weather forecasting. The
findings have implications for Solar Orbiter observing sequences with
instruments such as EUI that are designed to capture stealth CMEs


Abstract:
  The development of the A64FX processor by Fujitsu has been a massive
innovation in vectorized processors and led to Fugaku: the current world's
fastest supercomputer. We use a variety of tools to analyze the behavior and
performance of several OpenMP applications with different compilers, and how
these applications scale on the different A64FX processors on clusters at Stony
Brook University and RIKEN.


Abstract:
  We present here the discovery of a new class of super-slow rotating asteroids
(P>1000 hours) in data extracted from the Asteroid Terrestrial-impact Last
Alert System (ATLAS) and Zwicky Transient Facility (ZTF) all-sky surveys. Of
the 39 rotation periods we report here, 32 have periods longer than any
previously reported unambiguous rotation periods currently in the Asteroid
Light Curve Database. In our sample, 7 objects have a rotation period > 4000
hours and the longest period we report here is 4812 hours (~200 days). We do
not observe any correlation between taxonomy, albedo, or orbital properties
with super-slow rotating status. The most plausible mechanism for the creation
of these very slow rotators is if their rotations were slowed by YORP
spin-down. Super-slow rotating asteroids may be common, with at least 0.4% of
the main-belt asteroid population with a size range between 2 and 20 km in
diameter rotating with periods longer than 1000 hours.


Abstract:
  We introduce a new sinc-type molecular beam epitaxy model which is derived
from a cosine-type energy functional. The landscape of the new functional is
remarkably similar to the classical MBE model with double well potential but
has the additional advantage that all its derivatives are uniformly bounded. We
consider first order IMEX and second order BDF2 discretization schemes. For
both cases we quantify explicit time step constraints for the energy
dissipation which is in good accord with the practical numerical simulations.
Furthermore we introduce a new theoretical framework and prove unconditional
uniform energy boundedness with no size restrictions on the time step. This is
the first unconditional (i.e. independent of the time step size) result for
semi-implicit methods applied to the phase field models without introducing any
artificial stabilization terms or fictitious variables.


Abstract:
  The next generation of bolometric experiments searching for rave events, in
particular for the neutrino-less double beta decay, needs fast,
high-sensitivity and easy-to-scale cryogenic light detectors. The CALDER
project (2014-2020) developed a new technology for light detection at cryogenic
temperature. In this paper we describe the achievements and the final prototype
of this project, consisting of a $5\times5$ cm$^2$, 650 $\mu$m thick silicon
substrate coupled to a single kinetic inductance detector made of a three-layer
aluminum-titanium-aluminum. The baseline energy resolution is
34$\pm$1(stat)$\pm$2(syst) eV RMS and the response time is 120 $\mu$s. These
features, along with the natural multiplexing capability of kinetic inductance
detectors, meet the requirements of future large-scale experiments.


Abstract:
  Knowledge retrieval and reasoning are two key stages in multi-hop question
answering (QA) at web scale. Existing approaches suffer from low confidence
when retrieving evidence facts to fill the knowledge gap and lack transparent
reasoning process. In this paper, we propose a new framework to exploit more
valid facts while obtaining explainability for multi-hop QA by dynamically
constructing a semantic graph and reasoning over it. We employ Abstract Meaning
Representation (AMR) as semantic graph representation. Our framework contains
three new ideas: (a) {\tt AMR-SG}, an AMR-based Semantic Graph, constructed by
candidate fact AMRs to uncover any hop relations among question, answer and
multiple facts. (b) A novel path-based fact analytics approach exploiting {\tt
AMR-SG} to extract active facts from a large fact pool to answer questions. (c)
A fact-level relation modeling leveraging graph convolution network (GCN) to
guide the reasoning process. Results on two scientific multi-hop QA datasets
show that we can surpass recent approaches including those using additional
knowledge graphs while maintaining high explainability on OpenBookQA and
achieve a new state-of-the-art result on ARC-Challenge in a computationally
practicable setting.


Abstract:
  Text style transfer has gained increasing attention from the research
community over the recent years. However, the proposed approaches vary in many
ways, which makes it hard to assess the individual contribution of the model
components. In style transfer, the most important component is the optimization
technique used to guide the learning in the absence of parallel training data.
In this work we empirically compare the dominant optimization paradigms which
provide supervision signals during training: backtranslation, adversarial
training and reinforcement learning. We find that backtranslation has
model-specific limitations, which inhibits training style transfer models.
Reinforcement learning shows the best performance gains, while adversarial
training, despite its popularity, does not offer an advantage over the latter
alternative. In this work we also experiment with Minimum Risk Training, a
popular technique in the machine translation community, which, to our
knowledge, has not been empirically evaluated in the task of style transfer. We
fill this research gap and empirically show its efficacy.


Abstract:
  We extend semi-supervised learning to the problem of domain adaptation to
learn significantly higher-accuracy models that train on one data distribution
and test on a different one. With the goal of generality, we introduce
AdaMatch, a method that unifies the tasks of unsupervised domain adaptation
(UDA), semi-supervised learning (SSL), and semi-supervised domain adaptation
(SSDA). In an extensive experimental study, we compare its behavior with
respective state-of-the-art techniques from SSL, SSDA, and UDA on vision
classification tasks. We find AdaMatch either matches or significantly exceeds
the state-of-the-art in each case using the same hyper-parameters regardless of
the dataset or task. For example, AdaMatch nearly doubles the accuracy compared
to that of the prior state-of-the-art on the UDA task for DomainNet and even
exceeds the accuracy of the prior state-of-the-art obtained with pre-training
by 6.4% when AdaMatch is trained completely from scratch. Furthermore, by
providing AdaMatch with just one labeled example per class from the target
domain (i.e., the SSDA setting), we increase the target accuracy by an
additional 6.1%, and with 5 labeled examples, by 13.6%.


Abstract:
  In complex, expensive optimization domains we often narrowly focus on finding
high performing solutions, instead of expanding our understanding of the domain
itself. But what if we could quickly understand the complex behaviors that can
emerge in said domains instead? We introduce surrogate-assisted phenotypic
niching, a quality diversity algorithm which allows to discover a large,
diverse set of behaviors by using computationally expensive phenotypic
features. In this work we discover the types of air flow in a 2D fluid dynamics
optimization problem. A fast GPU-based fluid dynamics solver is used in
conjunction with surrogate models to accurately predict fluid characteristics
from the shapes that produce the air flow. We show that these features can be
modeled in a data-driven way while sampling to improve performance, rather than
explicitly sampling to improve feature models. Our method can reduce the need
to run an infeasibly large set of simulations while still being able to design
a large diversity of air flows and the shapes that cause them. Discovering
diversity of behaviors helps engineers to better understand expensive domains
and their solutions.


Abstract:
  Density estimation is an important technique for characterizing distributions
given observations. Much existing research on density estimation has focused on
cases wherein the data lies in a Euclidean space. However, some kinds of data
are not well-modeled by supposing that their underlying geometry is Euclidean.
Instead, it can be useful to model such data as lying on a {\it manifold} with
some known structure. For instance, some kinds of data may be known to lie on
the surface of a sphere. We study the problem of estimating densities on
manifolds. We propose a method, inspired by the literature on "dequantization,"
which we interpret through the lens of a coordinate transformation of an
ambient Euclidean space and a smooth manifold of interest. Using methods from
normalizing flows, we apply this method to the dequantization of smooth
manifold structures in order to model densities on the sphere, tori, and the
orthogonal group.


Abstract:
  The mining and utilization of features directly affect the classification
performance of models used in the classification and recognition of
hyperspectral remote sensing images. Traditional models usually conduct feature
mining from a single perspective, with the features mined being limited and the
internal relationships between them being ignored. Consequently, useful
features are lost and classification results are unsatisfactory. To fully mine
and utilize image features, a new multi-scale feature-mining learning algorithm
(MGRNet) is proposed. The model uses principal component analysis to reduce the
dimensionality of the original hyperspectral image (HSI) to retain 99.99% of
its semantic information and extract dimensionality reduction features. Using a
multi-scale convolution algorithm, the input dimensionality reduction features
were mined to obtain shallow features, which then served as inputs into a
multi-scale graph convolution algorithm to construct the internal relationships
between eigenvalues at different scales. We then carried out cross fusion of
multi-scale information obtained by graph convolution, before inputting the new
information obtained into the residual network algorithm for deep feature
mining. Finally, a flexible maximum transfer function classifier was used to
predict the final features and complete the classification. Experiments on
three common hyperspectral datasets showed the MGRNet algorithm proposed in
this paper to be superior to traditional methods in recognition accuracy.


Abstract:
  Simulation of time dynamical physical problems has been a challenge for
classical computers due to their time-complexity. To demonstrate the dominance
of quantum computers over classical computers in this regime, here we simulate
a semi-empirical model where two neutral particles interact through
Lennard-Jones potential in a one-dimensional system. We implement the above
scenario on the IBM quantum experience platform using a 5-qubit real device. We
construct the Hamiltonian and then efficiently map it to quantum operators onto
quantum gates using the time-evolutionary unitary matrix obtained from the
Hamiltonian. We verify the results collected from the QASM-simulator and
compare it with that of the 5-qubit real chip ibmqx2.


Abstract:
  We characterize the Lie groups with finitely many connected components that
are $O(u)$-bilipschitz equivalent (almost quasiisometric in the sense that the
sublinear function $u$ replaces the additive bounds of quasiisometry) to the
real hyperbolic space, or to the complex hyperbolic plane. The
characterizations are expressed in terms of deformations of Lie algebras and in
terms of pinching of sectional curvature of left-invariant Riemannian metrics
in the real case. We also compare sublinear bilipschitz equivalence and coarse
equivalence, and prove that every coarse equivalence between the logarithmic
coarse structures of geodesic spaces is a $O(\log)$-bilipschitz equivalence.
The Lie groups characterized are exactly those whose logarithmic coarse
structure is equivalent to that of a real hyperbolic space or the complex
hyperbolic plane.


Abstract:
  Because of the pervasive usage of Neural Networks in human sensitive
applications, their interpretability is becoming an increasingly important
topic in machine learning. In this work we introduce a simple way to interpret
the output function of a neural network classifier that take as input
categorical variables. By exploiting a mapping between a neural network
classifier and a physical energy model, we show that in these cases each layer
of the network, and the logits layer in particular, can be expanded as a sum of
terms that account for the contribution to the classification of each input
pattern. For instance, at the first order, the expansion considers just the
linear relation between input features and output while at the second order
pairwise dependencies between input features are also accounted for. The
analysis of the contributions of each pattern, after an appropriate gauge
transformation, is presented in two cases where the effectiveness of the method
can be appreciated.


Abstract:
  Legged robots need to be capable of walking on diverse terrain conditions. In
this paper, we present a novel reinforcement learning framework for learning
locomotion on non-rigid dynamic terrains. Specifically, our framework can
generate quadruped locomotion on flat elastic terrain that consists of a matrix
of tiles moving up and down passively when pushed by the robot's feet. A
trained robot with 55cm base length can walk on terrain that can sink up to
5cm. We propose a set of observation and reward terms that enable this
locomotion; in which we found that it is crucial to include the end-effector
history and end-effector velocity terms into observation. We show the
effectiveness of our method by training the robot with various terrain
conditions.


Abstract:
  We demonstrate that black holes and stars in general relativity can be
destabilized by perturbations of non-minimally coupled vector fields. Focusing
on static and spherically symmetric backgrounds, our analysis shows that black
holes with sufficiently small mass and stars with sufficiently high densities
are subject to ghost or gradient-type instabilities. This holds for a large
class of vector-tensor theories whenever non-minimal couplings contribute to
linearized dynamics about a state with vanishing vector field and applies to
generalized Proca models that have sparked attention for their potential role
in cosmology and astrophysics. The stability criteria translate into bounds of
relevance for low-scale theories of dark energy and for ultra-light dark matter
scenarios.