Abstract:
  Self-supervised learning (especially contrastive learning) has attracted
great interest due to its huge potential in learning discriminative
representations in an unsupervised manner. Despite the acknowledged successes,
existing contrastive learning methods suffer from very low learning efficiency,
e.g., taking about ten times more training epochs than supervised learning for
comparable recognition accuracy. In this paper, we reveal two contradictory
phenomena in contrastive learning that we call under-clustering and
over-clustering problems, which are major obstacles to learning efficiency.
Under-clustering means that the model cannot efficiently learn to discover the
dissimilarity between inter-class samples when the negative sample pairs for
contrastive learning are insufficient to differentiate all the actual object
classes. Over-clustering implies that the model cannot efficiently learn
features from excessive negative sample pairs, forcing the model to
over-cluster samples of the same actual classes into different clusters. To
simultaneously overcome these two problems, we propose a novel self-supervised
learning framework using a truncated triplet loss. Precisely, we employ a
triplet loss tending to maximize the relative distance between the positive
pair and negative pairs to address the under-clustering problem; and we
construct the negative pair by selecting a negative sample deputy from all
negative samples to avoid the over-clustering problem, guaranteed by the
Bernoulli Distribution model. We extensively evaluate our framework in several
large-scale benchmarks (e.g., ImageNet, SYSU-30k, and COCO). The results
demonstrate our model's superiority (e.g., the learning efficiency) over the
latest state-of-the-art methods by a clear margin. Codes available at:
https://github.com/wanggrun/triplet .


Abstract:
  We present a novel construction of linear deformations for Lie algebras and
use it to prove the non-rigidity of several classes of Lie algebras in
different varieties. We consider the family of Lie algebras with an abelian
factor showing that, in general, they are not rigid even for the case of a
1-dimensional abelian factor. We also address the problem of k-rigidity for
k-step nilpotent Lie algebras. Using our construction and recent results in
[BCC] we prove the that the k-step free nilpotent Lie algebras are k-rigid but
not (k + 1)-rigid and that Heisenberg Lie algebras are 2-rigid but not 3-rigid.


Abstract:
  An interstellar communication network benefits from relay nodes placed in the
gravitational lenses of stars. The signal gains are of order $10^{9}$ with
optimal alignment, allowing for GBits connections at kW power levels with
meter-sized probes over parsec distances. If such a network exists, there might
be a node in our solar system: where is it? With some assumptions on the
network topology, candidate sky positions can be calculated. Apparent positions
are influenced by the parallax motion from the Earth's orbit around the Sun,
and the (slow) drifts caused by proper motions of nearby stars. With Gaia
astrometry, instantaneous positions can be determined with arcsec accuracy.
These potential node locations can be observed in targeted


Abstract:
  Since March 2020, companies nationwide have started work from home (WFH) due
to the rapid increase of confirmed COVID-19 cases in an attempt to help prevent
the coronavirus from spreading and rescue the economy from the pandemic. Many
organizations have conducted surveys to understand people's opinions towards
WFH. However, the findings are limited due to small sample size and the dynamic
topics over time. This study aims to understand the U.S. public opinions on
working from home during the COVID-19 pandemic. We conduct a large-scale social
media study using Twitter data to portrait different groups who have
positive/negative opinions about WFH. We perform an ordinary least squares
regression to investigate the relationship between the sentiment about WFH and
user characteristics including gender, age, ethnicity, median household income,
and population density. To better understand public opinion, we use latent
Dirichlet allocation to extract topics and discover how tweet contents relate
to people's attitudes. These findings provide evidence that sentiment about WFH
varies across user characteristics. Furthermore, the content analysis sheds
light on the nuanced differences in sentiment and reveals disparities relate to
WFH.


Abstract:
  This paper addresses the interesting problem of processing and analyzing data
in geographic information systems (GIS) to achieve a clear perspective on urban
sprawl. The term urban sprawl refers to overgrowth and expansion of low-density
areas with issues such as car dependency and segregation between residential
versus commercial use. Sprawl has impacts on the environment and public health.
In our work, spatiotemporal features related to real GIS data on urban sprawl
such as population growth and demographics are mined to discover knowledge for
decision support. We adapt data mining algorithms, Apriori for association rule
mining and J4.8 for decision tree classification to geospatial analysis,
deploying the ArcGIS tool for mapping. Knowledge discovered by mining this
spatiotemporal data is used to implement a prototype spatial decision support
system (SDSS). This SDSS predicts whether urban sprawl is likely to occur.
Further, it estimates the values of pertinent variables to understand how the
variables impact each other. The SDSS can help decision-makers identify
problems and create solutions for avoiding future sprawl occurrence and
conducting urban planning where sprawl already occurs, thus aiding sustainable
development. This work falls in the broad realm of geospatial intelligence and
sets the stage for designing a large scale SDSS to process big data in complex
environments, which constitutes part of our future work.


Abstract:
  With parallel and distributed computing (PDC) now wide-spread, modern
computing programs must incorporate PDC within the curriculum. ACM and IEEE
Computer Society's Computer Science curricular guidelines have recommended
exposure to PDC concepts since 2013. More recently, a variety of initiatives
have made PDC curricular content, lectures, and labs freely available for
undergraduate computer science programs. Despite these efforts, progress in
ensuring computer science students graduate with sufficient PDC exposure has
been uneven.
  This paper discusses the impact of ABET's revised criteria that have required
exposure to PDC to achieve accreditation for computer science programs since
2018. The authors reviewed 20 top ABET-accredited computer science programs and
analyzed how they covered the required PDC components in their curricula. Using
their own institutions as case studies, the authors examine in detail how three
different ABET-accredited computer science programs covered PDC using different
approaches, yet meeting the PDC requirements of these ABET criteria. The paper
also shows how ACM/IEEE Computer Society curricular guidelines for computer
engineering and software engineering programs, along with ABET accreditation
criteria, can cover PDC.


Abstract:
  It is well-known that plug-in statistical estimation of optimal transport
suffers from the curse of dimensionality. Despite recent efforts to improve the
rate of estimation with the smoothness of the problem, the computational
complexity of these recently proposed methods still degrades exponentially with
the dimension. In this paper, thanks to an infinite-dimensional sum-of-squares
representation, we derive a statistical estimator of smooth optimal transport
which achieves a precision $\varepsilon$ from $\tilde{O}(\varepsilon^{-2})$
independent and identically distributed samples from the distributions, for a
computational cost of $\tilde{O}(\varepsilon^{-4})$ when the smoothness
increases, hence yielding dimension-free statistical and computational rates,
with potentially exponentially dimension-dependent constants.


Abstract:
  As self-driving systems become better, simulating scenarios where the
autonomy stack may fail becomes more important. Traditionally, those scenarios
are generated for a few scenes with respect to the planning module that takes
ground-truth actor states as input. This does not scale and cannot identify all
possible autonomy failures, such as perception failures due to occlusion. In
this paper, we propose AdvSim, an adversarial framework to generate
safety-critical scenarios for any LiDAR-based autonomy system. Given an initial
traffic scenario, AdvSim modifies the actors' trajectories in a physically
plausible manner and updates the LiDAR sensor data to match the perturbed
world. Importantly, by simulating directly from sensor data, we obtain
adversarial scenarios that are safety-critical for the full autonomy stack. Our
experiments show that our approach is general and can identify thousands of
semantically meaningful safety-critical scenarios for a wide range of modern
self-driving systems. Furthermore, we show that the robustness and safety of
these systems can be further improved by training them with scenarios generated
by AdvSim.


Abstract:
  Transformer is a ubiquitous model for natural language processing and has
attracted wide attentions in computer vision. The attention maps are
indispensable for a transformer model to encode the dependencies among input
tokens. However, they are learned independently in each layer and sometimes
fail to capture precise patterns. In this paper, we propose a novel and generic
mechanism based on evolving attention to improve the performance of
transformers. On one hand, the attention maps in different layers share common
knowledge, thus the ones in preceding layers can instruct the attention in
succeeding layers through residual connections. On the other hand, low-level
and high-level attentions vary in the level of abstraction, so we adopt
convolutional layers to model the evolutionary process of attention maps. The
proposed evolving attention mechanism achieves significant performance
improvement over various state-of-the-art models for multiple tasks, including
image classification, natural language understanding and machine translation.


Abstract:
  We have run numerical simulations of Euclidean lattice quantum gravity for
metrics which are time-independent and spherically symmetric. The radial
variable is discretized as $r=hL_{Planck}$, with $h=0,1,...,N$ and $N$ up to
$10^5$. The Lagrangian is of the form $\sqrt{g}(R+\alpha R^2)$ (in units
$c=\hbar=G=1$) and the action is positive-definite, allowing the use of a
standard Metropolis algorithm with update probability $\exp(-\beta \Delta S)$.
By minimizing the $R+R^2$ action with respect to conformal modes, Bonanno and
Reuter have recently found analytical evidence of a non-trivial "rippled"
ground state resembling a kinetic condensate of QCD. Our simulations at low but
finite temperature ($T=\beta^{-1}$) also display strong localized oscillations
of the metric, whose total action $S$ remains $\ll \hbar$ thanks to the
indefinite sign of $R$. The average metric $\langle g_{rr} \rangle$ is
significantly different from flat space. The scaling properties of $S$ and
$\langle g_{rr} \rangle$ are investigated in dependence on $N$ and $\beta$.


Abstract:
  The flow of grains through narrow apertures posses an extraordinary
challenge: clogging. Strategies to alleviate the effect of clogging, such as
the use of external vibration, are always part of the design of machinery for
the handling of bulk materials. It has recently been shown that one way to
reduce clogging is to use a small fraction of small particles as an additive.
Besides, several works reported that self-repelling magnetic grains can flow
through narrow apertures with little clogging, which suggest these are
excellent candidates as "lubricating" additives for other granular materials.
In this work, we study the effect of adding self-repelling magnetic particles
to a sample of grains in two-dimensions. We find that, in contrast with
intuition, the added magnetic grains not necessarily aid the flow of the
original species.


Abstract:
  We present a novel theory and implementation for computing coupled electronic
and quantal nuclear subsystems on a single potential energy surface, moving
beyond the standard Born-Oppenheimer (BO) separation of nuclei and electrons.
We formulate an exact self-consistent nucleus-electron embedding potential from
the single product molecular wavefunction, and demonstrate that the fundamental
behavior of correlated nucleus-electron can be computed for mean-field
electrons that are responsive to a quantal anharmonic vibration of selected
nuclei in a discrete variable representation. Geometric gauge choices are
discussed and necessary for formulating energy invariant biorthogonal
electronic equations. Our method is further applied to characterize
vibrationally averaged molecular bonding properties of molecular energetics,
bond length, protonic and electron density. Moreover, post-Hartree-Fock
electron correlation can be conveniently computed on the basis of
nucleus-electron coupled molecular orbitals, as demonstrated to correlated
models of second-order M{\o}llet-Plesset perturbation and full configuration
interaction theories. Our approach not only accurately quantifies non-classical
nucleus-electron couplings for revising molecular bonding properties, but also
provides an alternative time-independent approach for deploying non-BO
molecular quantum chemistry.


Abstract:
  We characterize the extreme heartbeat star system MACHO 80.7443.1718 in the
LMC using TESS photometry and spectroscopic observations from the Magellan
Inamori Kyocera Echelle (MIKE) and SOAR Goodman spectographs. MACHO
80.7443.1718 was first identified as a heartbeat star system in the All-Sky
Automated Survey for SuperNovae (ASAS-SN) with $P_{\rm
orb}=32.836\pm0.008\,{\rm d}$. MACHO 80.7443.1718 is a young (${\sim}6$~Myr),
massive binary, composed of a B0 Iae supergiant with $M_1 \simeq 35 M_\odot$
and an O9.5V secondary with $M_2 \simeq 16 M_\odot$ on an eccentric
($e=0.51\pm0.03$) orbit. In addition to having the largest variability
amplitude amongst all known heartbeats stars, MACHO 80.7443.1718 is also one of
the most massive heartbeat stars yet discovered. The B[e] supergiant has Balmer
emission lines and permitted/forbidden metallic emission lines associated with
a circumstellar disk. The disk rapidly dissipates at periastron which could
indicate mass transfer to the secondary, but re-emerges immediately following
periastron passage. MACHO 80.7443.1718 also shows tidally excited oscillations
at the $N=25$ and $N=41$ orbital harmonics and has a rotational period of 4.4
d.


Abstract:
  Digitalization has led to radical changes in the distribution of goods across
various sectors. The tendency is to move from traditional buyer-seller markets
to subscription-based on-demand "smart" matching platforms enabled by pervasive
ICTs. The driving force behind this lies in the fact that assets, which were
scarce in the past, are readily abundant, approaching a regime of zero marginal
costs. This is also becoming a reality in electrified energy systems due to the
substantial growth of distributed renewable energy sources such as solar and
wind; the increasing number of small-scale storage units such as batteries and
heat pumps; and the availability of flexible loads that enable demand-side
management. In this context, this article proposes a system architecture based
on a logical (cyber) association of spatially distributed (physical) elements
as an approach to build a virtual microgrid operated as a software-defined
energy network (SDEN) that is enabled by packetized energy management. The
proposed cyber-physical system presumes that electrical energy is shared among
its members and that the energy sharing is enabled in the cyber domain by
handshakes inspired by resource allocation methods utilized in computer
networks, wireless communications, and peer-to-peer Internet applications
(e.g., BitTorrent). The proposal has twofold benefits: (i) reducing the
complexity of current market-based solutions by removing unnecessary and costly
mediations and (ii) guaranteeing energy access to all virtual microgrid members
according to their individual needs. This article concludes that the proposed
solution generally complies with the existing regulations but has highly
disruptive potential to organize a dominantly electrified energy system in the
mid- to long-term, being a technical counterpart to the recently developed
social-oriented microgrid proposals.


Abstract:
  The lightcone singularity at the origin is resolved by blowing up the
singular point to $CP^1$. The Lorentz group acts on the resolved lightcone and
has $CP^1$ as a special orbit. Using Wigner's method of associating unitary
irreducible representations of the Poincar\'e group to particle states, we find
that the special orbit gives rise to new vacuum states. These vacuum states are
labelled by the principal series representations of $SL(2,C)$. Some remarks are
included on the applications of these results to gauge theories and
asymptotically flat spacetimes.


Abstract:
  In this contribution we identify two scenarios for the evolutionary branch
cut universe. In the first scenario, the universe evolves continuously from the
negative complex cosmological time sector, prior to a primordial singularity,
to the positive one, circumventing continuously a branch cut, and no primordial
singularity occurs in the imaginary sector, only branch points. In the second
scenario, the branch cut and branch point disappear after the realisation of
the imaginary component of the complex time by means of a Wick rotation, which
is replaced by the thermal time. In the second scenario, the universe has its
origin in the Big Bang, but the model contemplates simultaneously a mirrored
parallel evolutionary universe going backwards in the cosmological thermal time
negative sector. A quantum formulation based on the WDW equation is sketched
and preliminary conclusions are drawn.


Abstract:
  In this work that continues the "NGICS - New Italian City Geological Study" a
project of the Department of Climatology and Geology of TS Corporation Srl, we
present the study relating to the Municipality of Montefiascone (VT). We
analyzed 25 years of astronomical, geological, meteorological and climatic
data, comparing them to verify the long-term trend of local variations in
temperatures, detections, solar radiation and geological events, with the
ultimate goal of understanding climate and geological changes a long term in
this geographical area. The analysis is performed using a statistical approach
and attention is used to minimize any effects caused by the error in case of
lack of data.


Abstract:
  In the last decade many research efforts have been focused on understanding
the rheology of disordered materials, and several theoretical predictions have
been put forward regarding their yielding behavior. Nevertheless, not many
experiments nor molecular dynamics simulations were dedicated to testing those
theoretical predictions. Here we use computer simulations to study the yielding
transition under two different loading schemes: standard simple shear dynamics,
and self-propelled, dense active systems. In the active systems a yielding
transition is observed as expected, when the self-propulsion is increased.
However, the range of self-propulsions in which a pure liquid regime exist
appears to vanish upon approaching the so-called "jamming point" at which
solidity of soft-sphere packings is lost. Such an "active yielding" transition
shares similarities with the generic yielding transition for shear flows. A
Herschel-Bulkley law is observed in both loading scenarios, with a clear
difference in the critical scaling exponents between the two, suggesting the
existent of different universality classes for the yielding transition under
different driving conditions. In addition, we present direct measurements of
length and time scales for both driving scenarios. A comparison with
theoretical predictions from recent literature reveals poor agreement with our
numerical results.


Abstract:
  Red lead and lead white are some of the most ancient and common pigments in
mural paintings. However, they tend to blacken with time due to their oxidation
to plattnerite (\b{eta}-PbO2). The possibility to induce the reconversion
reactions by CW laser heating is hereby discussed. A thermodynamic study by TGA
showed that direct cerussite or hydrocerussite formation from plattnerite are
not suitable reconversion routes, which was confirmed by laser irradiation
trials under CO2 and CO2/H2O fluxes. Minium (Pb3O4) and subsequent massicot
(\b{eta}-PbO) formation from plattnerite were achieved (confirmed by SEM-EDS,
XRD and micro-Raman) under Ar+, 810 nm diode and Nd:YAG lasers. The latter
appears to be the most suited for restauration purposes, given the broad minium
reconversion irradiance range. This is confirmed by successful trials on
macroscopic areas of naturally darkened red lead containing samples.


Abstract:
  In today's connected society, many people rely on mHealth and self-tracking
(ST) technology to help them adopt healthier habits with a focus on breaking
their sedentary lifestyle and staying fit. However, there is scarce evidence of
such technological interventions' effectiveness, and there are no standardized
methods to evaluate their impact on people's physical activity (PA) and health.
This work aims to help ST and HCI practitioners and researchers by empowering
them with systematic guidelines and a framework for designing and evaluating
technological interventions to facilitate health behavior change (HBC) and user
engagement (UE), focusing on increasing PA and decreasing sedentariness. To
this end, we conduct a literature review of 117 papers between 2008 and 2020,
which identifies the core ST HCI design methods and their efficacy, as well as
and the most comprehensive list to date of UE evaluation metrics for ST. Based
on the review's findings, we propose PAST SELF, a framework to guide the design
and evaluation of ST technology that has potential applications in industrial
and scientific settings. Finally, to facilitate researchers and practitioners,
we complement this paper with an open corpus and an adaptive exploration tool
for the PAST SELF data.


Abstract:
  Selective inference (post-selection inference) is a methodology that has
attracted much attention in recent years in the fields of statistics and
machine learning. Naive inference based on data that are also used for model
selection tends to show an overestimation, and so the selective inference
conditions the event that the model was selected. In this paper, we develop
selective inference in propensity score analysis with a semiparametric
approach, which has become a standard tool in causal inference. Specifically,
for the most basic causal inference model in which the causal effect can be
written as a linear sum of confounding variables, we conduct Lasso-type
variable selection by adding an $\ell_1$ penalty term to the loss function that
gives a semiparametric estimator. Confidence intervals are then given for the
coefficients of the selected confounding variables, conditional on the event of
variable selection, with asymptotic guarantees. An important property of this
method is that it does not require modeling of nonparametric regression
functions for the outcome variables, as is usually the case with semiparametric
propensity score analysis.


Abstract:
  Unmeasured covariates constitute one of the important problems in causal
inference. Even if there are some unmeasured covariates, some instrumental
variable methods such as a two-stage residual inclusion (2SRI) estimator, or a
limited-information maximum likelihood (LIML) estimator can obtain an unbiased
estimate for causal effects despite there being nonlinear outcomes such as
binary outcomes; however, it requires that we specify not only a correct
outcome model but also a correct treatment model. Therefore, detecting correct
models is an important process. In this paper, we propose two model selection
procedures: AIC-type and BIC-type, and confirm their properties. The proposed
model selection procedures are based on a LIML estimator. We prove that a
proposed BIC-type model selection procedure has model selection consistency,
and confirm their properties of the proposed model selection procedures through
simulation datasets.


Abstract:
  Row-column arrays have shown to be able to generate 3-D ultrafast ultrasound
images with an order of magnitude less independent electronic channels than
classic 2D matrix arrays. Unfortunately row-column array images suffer from
major imaging artefacts due to the high side lobes. This paper proposes a
row-column specific beamforming technique that exploits the incoherent nature
of certain row column array artefacts. The geometric mean of the data from each
row and column pair is taken prior to summation in beamforming, thus
drastically reducing incoherent imaging artefacts compared to traditional
coherent compounding. The effectiveness of this technique was demonstrated in
silico, and the results show an average fivefold reduction in side-lobe levels.
Significantly improved contrast was demonstrated with Tissue-to-noise ratio
increasing from $\sim$10dB to $\sim$30dB and Tissue Contrast Ratio increasing
from $\sim$21dB to $\sim$42dB when using the proposed new method compared to
Delay and Sum. These new techniques allowed for high quality 3D imaging whilst
maintaining high frame rate potential.


Abstract:
  Inertial particles advected by a background flow can show complex structures.
We consider inertial particles in a 2D Taylor-Green (TG) flow and characterize
particle dynamics as a function of the particle's Stokes number using dynamic
mode decomposition (DMD) method from particle image velocimetry (PIV)
like-data. We observe the formation of caustic structures and analyze them
using DMD to (a) determine the Stokes number of the particles, and (b) estimate
the particle Stokes number composition. Our analysis in this idealized flow
will provide useful insight to analyze inertial particles in more complex or
turbulent flows. We propose that the DMD technique can be used to perform a
similar analysis on an experimental system.


Abstract:
  With the emergence of personality computing as a new research field related
to artificial intelligence and personality psychology, we have witnessed an
unprecedented proliferation of personality-aware recommendation systems. Unlike
conventional recommendation systems, these new systems solve traditional
problems such as the cold start and data sparsity problems. This survey aims to
study and systematically classify personality-aware recommendation systems. To
the best of our knowledge, this survey is the first that focuses on
personality-aware recommendation systems. We explore the different design
choices of personality-aware recommendation systems, by comparing their
personality modeling methods, as well as their recommendation techniques.
Furthermore, we present the commonly used datasets and point out some of the
challenges of personality-aware recommendation systems.


Abstract:
  This paper aims to provide understandings for the effect of an
over-parameterized model, e.g. a deep neural network, memorizing
instance-dependent noisy labels. We first quantify the harms caused by
memorizing noisy instances, and show the disparate impacts of noisy labels for
sample instances with different representation frequencies. We then analyze how
several popular solutions for learning with noisy labels mitigate this harm at
the instance level. Our analysis reveals that existing approaches lead to
disparate treatments when handling noisy instances. While higher-frequency
instances often enjoy a high probability of an improvement by applying these
solutions, lower-frequency instances do not. Our analysis reveals new
understandings for when these approaches work, and provides theoretical
justifications for previously reported empirical observations. This observation
requires us to rethink the distribution of label noise across instances and
calls for different treatments for instances in different regimes.


Abstract:
  Almost all statistical and machine learning methods in analyzing brain
networks rely on distances and loss functions, which are mostly Euclidean or
matrix norms. The Euclidean or matrix distances may fail to capture underlying
subtle topological differences in brain networks. Further, Euclidean distances
are sensitive to outliers. A few extreme edge weights may severely affect the
distance. Thus it is necessary to use distances and loss functions that
recognize topology of data. In this review paper, we survey various topological
distance and loss functions from topological data analysis (TDA) and persistent
homology that can be used in brain network analysis more effectively. Although
there are many recent brain imaging studies that are based on TDA methods,
possibly due to the lack of method awareness, TDA has not taken as the
mainstream tool in brain imaging field yet. The main purpose of this paper is
provide the relevant technical survey of these powerful tools that are
immediately applicable to brain network data.


Abstract:
  A conjecture of Le says that the Deligne polytope $\Delta_d$ is generically
ordinary if $p\equiv 1\ (\!\!\bmod\ D(\Delta_d))$, where $D(\Delta_d)$ is a
combinatorial constant determined by $\Delta_d$. In this paper a counterexample
is given to show that the conjecture is not true in general.


Abstract:
  In event-based sensing, many sensors independently and asynchronously emit
events when there is a change in their input. Event-based sensing can present
significant improvements in power efficiency when compared to traditional
sampling, because (1) the output is a stream of events where the important
information lies in the timing of the events, and (2) the sensor can easily be
controlled to output information only when interesting activity occurs at the
input.
  Moreover, event-based sampling can often provide better resolution than
standard uniform sampling. Not only does this occur because individual
event-based sensors have higher temporal resolution, it also occurs because the
asynchrony of events allows for less redundant and more informative encoding.
We would like to explain how such curious results come about.
  To do so, we use ideal time encoding machines as a proxy for event-based
sensors. We explore time encoding of signals with low rank structure, and apply
the resulting theory to video. We then see how the asynchronous firing times of
the time encoding machines allow for better reconstruction than in the standard
sampling case, if we have a high spatial density of time encoding machines that
fire less frequently.


Abstract:
  Our research explores the potential of a humanoid robot for work in
unpredictable environments, but controlling a humanoid robot remains a very
difficult problem. In our previous work, we designed a prototype virtual
reality (VR) interface to allow an operator to command a humanoid robot.
However, while usable, the initial interface was not sufficient for commanding
the robot to perform the tasks; for example, in some cases, there was a lack of
precision available for robot control. The interface was overly cumbersome in
some areas as well. In this paper, we discuss numerous additions, inspired by
traditional interfaces and virtual reality video games, to our prior
implementation, providing additional ways to visualize and command a humanoid
robot to perform difficult tasks within a virtual world.


Abstract:
  We present a nonperturbative quasi-classical theory of graphene
photoconductivity. We consider the influence of low-frequency (microwave,
terahertz, mid-infrared) radiation on the static conductivity of a uniform
graphene layer and calculate its photoconductivity as a function of frequency,
polarization and strength of the external ac electric field, as well as on the
material properties (electron density, scattering time) and temperature. The
theory is valid at frequencies $\hbar\omega\lesssim 2E_F$ and at arbitrarily
strong ac electric fields. We compare our results with those of the third-order
perturbation theory and determine the applicability range of the perturbative
solutions.


Abstract:
  In spinor Bose-Einstein condensates, spin-changing collisions are a
remarkable proxy to coherently realize macroscopic many-body quantum states.
These processes have been, e.g., exploited to generate entanglement, to study
dynamical quantum phase transitions, and proposed for realizing nematic phases
in atomic condensates. In the same systems dressed by Raman beams, the coupling
between spin and momentum induces a spin dependence in the scattering processes
taking place in the gas. Here we show that, at weak couplings, such modulation
of the collisions leads to an effective Hamiltonian which is equivalent to the
one of an artificial spinor gas with spin-changing collisions that are tunable
with the Raman intensity. By exploiting this dressed-basis description, we
propose a robust protocol to coherently drive the spin-orbit coupled condensate
into the ferromagnetic stripe phase via crossing a quantum phase transition of
the effective low-energy model in an excited-state.


Abstract:
  Scientific communities naturally tend to organize around data ecosystems
created by the combination of their observational devices, their data
repositories, and the workflows essential to carry their research from
observation to discovery. However, these legacy data ecosystems are now
breaking down under the pressure of the exponential growth in the volume and
velocity of these workflows, which are further complicated by the need to
integrate the highly data intensive methods of the Artificial Intelligence
revolution. Enabling ground breaking science that makes full use of this new,
data saturated research environment will require distributed systems that
support dramatically improved resource sharing, workflow portability and
composability, and data ecosystem convergence.
  The Cybercosm vision presented in this white paper describes a radically
different approach to the architecture of distributed systems for
data-intensive science and its application workflows. As opposed to traditional
models that restrict interoperability by hiving off storage, networking, and
computing resources in separate technology silos, Cybercosm defines a minimally
sufficient hypervisor as a spanning layer for its data plane that virtualizes
and converges the local resources of the system's nodes in a fully
interoperable manner. By building on a common, universal interface into which
the problems that infect today's data-intensive workflows can be decomposed and
attacked, Cybercosm aims to support scalable, portable and composable workflows
that span and merge the distributed data ecosystems that characterize leading
edge research communities today.


Abstract:
  The refraction of space-time (ST) wave packets at planar interfaces between
non-dispersive, homogeneous, isotropic dielectrics exhibit fascinating
phenomena, even at normal incidence. Examples of such refractive phenomena
include group-velocity invariance across the interface, anomalous refraction,
and group-velocity inversion. Crucial differences emerge at oblique incidence
with respect to the results established at normal incidence. For example, the
group velocity of the refracted ST wave packet can be tuned simply by changing
the angle of incidence. In paper (III) of this sequence, we present
experimental verification of the refractive phenomena exhibited by ST wave
packets at oblique incidence that were predicted in paper (I). We also examine
a proposal for 'blind synchronization' whereby identical ST wave packets arrive
simultaneously at different receivers without \textit{a priori} knowledge of
their locations except that they are all located at the same depth beyond an
interface between two media. A first proof-of-principle experimental
demonstration of this effect is provided.


Abstract:
  In theories with higher time derivatives, the Hamiltonian analysis of
Ostrogradsky predicts an instability. However, this Hamiltonian treatment does
not correspond the way that these theories are treated in quantum field theory,
and the instability may be avoided in at least some cases. We present a very
simple model which illustrates these features.


Abstract:
  Conventional deep learning based methods for object detection require a large
amount of bounding box annotations for training, which is expensive to obtain
such high quality annotated data. Few-shot object detection, which learns to
adapt to novel classes with only a few annotated examples, is very challenging
since the fine-grained feature of novel object can be easily overlooked with
only a few data available. In this work, aiming to fully exploit features of
annotated novel object and capture fine-grained features of query object, we
propose Dense Relation Distillation with Context-aware Aggregation (DCNet) to
tackle the few-shot detection problem. Built on the meta-learning based
framework, Dense Relation Distillation module targets at fully exploiting
support features, where support features and query feature are densely matched,
covering all spatial locations in a feed-forward fashion. The abundant usage of
the guidance information endows model the capability to handle common
challenges such as appearance changes and occlusions. Moreover, to better
capture scale-aware features, Context-aware Aggregation module adaptively
harnesses features from different scales for a more comprehensive feature
representation. Extensive experiments illustrate that our proposed approach
achieves state-of-the-art results on PASCAL VOC and MS COCO datasets. Code will
be made available at https://github.com/hzhupku/DCNet.


Abstract:
  The Lawson criterion is a key concept in the pursuit of fusion energy,
relating the fuel density $n$, pulse duration $\tau$ or energy confinement time
$\tau_E$, and fuel temperature $T$ to the energy gain $Q$ of a fusion plasma.
The purpose of this paper is to explain and review the Lawson criterion and to
provide a compilation of achieved parameters for a broad range of historical
and contemporary fusion experiments. Although this paper focuses on the Lawson
criterion, it is only one of many equally important factors in assessing the
progress and ultimate likelihood of any fusion concept becoming a commercially
viable fusion-energy system. Only experimentally measured or inferred values of
$n$, $\tau$ or $\tau_E$, and $T$ that have been published in the peer-reviewed
literature are included in this paper, unless noted otherwise. For extracting
these parameters, we discuss methodologies that are necessarily specific to
different fusion approaches (including magnetic, inertial, and magneto-inertial
fusion). This paper is intended to serve as a reference for fusion researchers
and a tutorial for all others interested in fusion energy.


Abstract:
  Inaccurate slow streaming models of acoustically-induced fluid flow remain in
use due to the lack of a generalized alternative. The Multiscale Articulated
Differentials Method (MADaM; [see co-article]) solves this problem with a
complete, scale-sensitive spatiotemporal expansion. Applied to classic axial
Eckart streaming, the MADaM produces, in terms of a Burgers equation, a
long-sought transient solution able to accommodate frequencies and amplitudes
far beyond slow streaming models. Steady streaming is governed by a
corresponding Riccati equation that, when solved, produces simple expressions
explaining innate features of fast acoustic streaming.


Abstract:
  The influence of Ga incorporation into cubic In$_2$O$_3$ on the electronic
and vibrational properties is discussed for (In$_{1-x}$,Ga$_x$)$_2$O$_3$ alloy
films grown by molecular beam epitaxy. Using spectroscopic ellipsometry, a
linear dependence of the absorption onset on the Ga content $x$ is found with a
blueshift of up to 150 meV for $x = 0.1$. Consistently, the fundamental band
gap exhibits a blueshift as determined by hard X-ray photoelectron
spectroscopy. The dependence of the absorption onset and the effective electron
mass on the electron concentration is derived from the infrared dielectric
functions for a Sn doped alloy film. The influence of alloying on phonon modes
is analyzed on the basis of Raman spectroscopic measurements. The frequencies
of several phonon modes are identified as sensitive measures for the
spectroscopic determination of the Ga content.


Abstract:
  Simulating quantum many-body dynamics on classical computers is a challenging
problem due to the exponential growth of the Hilbert space. Artificial neural
networks have recently been introduced as a new tool to approximate
quantum-many body states. We benchmark the variational power of the restricted
Boltzmann machine quantum states and different shallow and deep neural
autoregressive quantum states to simulate global quench dynamics of a
non-integrable quantum Ising chain. We find that the number of parameters
required to represent the quantum state at a given accuracy increases
exponentially in time. The growth rate is only slightly affected by the network
architecture over a wide range of different design choices: shallow and deep
networks, small and large filter sizes, dilated and normal convolutions, with
and without shortcut connections.


Abstract:
  We characterize lower growth estimates for subsolutions in halfspaces of
fully nonlinear partial differential equations on the form $$ F(x,u,Du,D^2u) =
0 $$ in terms of solutions to ordinary differential equations built solely upon
a growth assumption on $F$. Using this characterization we derive several sharp
Phragmen-Lindel\"of-type theorems for certain classes of well known PDEs. The
equation need not be uniformly elliptic nor homogeneous and we obtain results
both in case the subsolution is bounded or unbounded. Among our results we
retrieve classical estimates in the halfspace for $p$-subharmonic functions and
extend those to more general equations; we prove sharp growth estimates, in
terms of $k$ and the asymptotic behaviour of $\int_{0}^{R} C(s) ds$, for
subsolutions of equations allowing for sublinear growth in the gradient of the
form $C(|x|)|Du|^k$ with $k\geq 1$; we establish a Phragmen-Lindel\"of theorem
for weak subsolutions of the variable exponent $p$-Laplace equation in
halfspaces, $1 < p(x) < \infty$, $p(x) \in C^1$, of which we conclude sharpness
by finding the "slowest growing" $p(x)$-harmonic function together with its
corresponding family of $p(x)$-exponents. The paper ends with a discussion of
our results from the point of view of a spatially dependent diffusion problem.


Abstract:
  Soft matter consists of meso-scale (nm~{\mu}m) structures that are formed by
weak interactions and reorganized under thermal fluctuations. The resulting
complex relaxation phenomena may be probed with microrheology, by observing the
movement of embedded probe particles. Because of the softness of the material,
however, perturbations to the probe that are inevitably added during
microrheology experiments prevent direct translation of those movements to
rheological properties. In this study, we conducted optical-trap-based
microrheology with significantly reduced mechanical perturbations; dual
feedback technology allowed us to apply well-determined optical-trapping forces
to a fluctuating embedded probe and precisely measure its response and
fluctuations with high spatiotemporal resolution. We demonstrate the improved
performance of this technique by studying an reconstituted network of actin
cytoskeletal filaments, observing their slow dynamics, homogeneous thermal
fluctuations as well as activated hopping between mesoscale microenvironments.


Abstract:
  Many extensions of the Standard Model predict the existence of new charged or
neutral gauge bosons, with a wide variety of phenomenological implications
depending on the model adopted. The search for such particles is extensively
carried through at the Large Hadron Collider (LHC), and it is therefore of
crucial importance to have for each proposed scenario quantitative predictions
that can be matched to experiments. In this work we focus on the implications
of one of these models, the TopFlavor Model, proposing a charged
$\text{W}^\prime$ boson that has preferential couplings to the third generation
fermions. We compare such predictions to the ones from the so called Sequential
Standard Model (SSM), that is used as benchmark, being one of the simplest and
most commonly considered models for searches at the LHC. We identify the
parameter space still open for searches at the LHC, and in particular we show
that the cross section for the processes $pp \to \text{W}^\prime \to \tau \nu$
and $pp \to \text{W}^\prime \to tb$ can be up to two orders of magnitude
smaller with respect to the SSM, depending on the free parameters of the model,
like the particle mass and its width. This study makes the case for further
searches at the LHC, and shows how a complete and systematic model independent
analysis of $\text{W}^\prime$ boson phenomenology at colliders is essential to
provide guidance for future searches.


Abstract:
  Aqueous proton transport plays a key role in acid-base neutralization, and
energy transport through biological membranes and hydrogen fuel cells.
Extensive experimental and theoretical studies have resulted in a highly
detailed elucidation of one of the underlying microscopic mechanisms for
aqueous excess proton transport, known as the von Grotthuss mechanism,
involving different hydrated proton configurations with associated high
fluxional structural dynamics. Hydroxide transport, with approximately two-fold
lower bulk diffusion rates than those of excess protons, has received much less
attention. We present femtosecond UV/IR pump-probe experiments and ab initio
molecular dynamics simulations of different proton transport pathways of the
bifunctional photoacid 7-hydroxyquinoline (7HQ) in water-methanol mixtures. For
7HQ solvent-dependent photoacidity, free energy-reactivity correlation
behaviour and QM/MM trajectories point to a dominant OH-/CH3O- transport
pathway, for all water-methanol mixing ratios investigated. This provides
conclusive evidence for the hydrolysis/methanolysis acid-base neutralization
pathway formulated by Manfred Eigen half a century ago.


Abstract:
  Existing spatial object recommendation algorithms generally treat objects
identically when ranking them. However, spatial objects often cover different
levels of spatial granularity and thereby are heterogeneous. For example, one
user may prefer to be recommended a region (say Manhattan), while another user
might prefer a venue (say a restaurant). Even for the same user, preferences
can change at different stages of data exploration. In this paper, we study how
to support top-k spatial object recommendations at varying levels of spatial
granularity, enabling spatial objects at varying granularity, such as a city,
suburb, or building, as a Point of Interest (POI). To solve this problem, we
propose the use of a POI tree, which captures spatial containment relationships
between POIs. We design a novel multi-task learning model called MPR (short for
Multi-level POI Recommendation), where each task aims to return the top-k POIs
at a certain spatial granularity level. Each task consists of two subtasks: (i)
attribute-based representation learning; (ii) interaction-based representation
learning. The first subtask learns the feature representations for both users
and POIs, capturing attributes directly from their profiles. The second subtask
incorporates user-POI interactions into the model. Additionally, MPR can
provide insights into why certain recommendations are being made to a user
based on three types of hints: user-aspect, POI-aspect, and interaction-aspect.
We empirically validate our approach using two real-life datasets, and show
promising performance improvements over several state-of-the-art methods.


Abstract:
  We show that all resonances in Dirichlet obstacle scattering (in $\mathbb{C}$
in odd dimensions and in the logarithmic cover of $\mathbb{C}\setminus\{0\}$ in
even dimensions) are generically simple in the class of obstacles with $C^k$
(and $C^\infty$) boundaries, $k \geq 2$.


Abstract:
  In this paper, we describe our participation in the TREC Health
Misinformation Track 2020. We submitted $11$ runs to the Total Recall Task and
13 runs to the Ad Hoc task. Our approach consists of 3 steps: (1) we create an
initial run with BM25 and RM3; (2) we estimate credibility and misinformation
scores for the documents in the initial run; (3) we merge the relevance,
credibility and misinformation scores to re-rank documents in the initial run.
To estimate credibility scores, we implement a classifier which exploits
features based on the content and the popularity of a document. To compute the
misinformation score, we apply a stance detection approach with a pretrained
Transformer language model. Finally, we use different approaches to merge
scores: weighted average, the distance among score vectors and rank fusion.


Abstract:
  Two-dimensional (2D) multiferroic materials with controllable magnetism have
promising prospects in miniaturized quantum device applications, such as
high-density data storage and spintronic devices. Here, using first-principles
calculations, we propose a coexistence of antiferromagnetism and
ferroelasticity in multiferroic $MnF_{4}$ monolayer. The $MnF_{4}$ monolayer is
found to be an intrinsic wide-gap semiconductor with large spin polarization ~3
$\mu_{B}$/Mn, in which the antiferromagnetic order originates from the
cooperation and competition of the direct exchange and super exchange.
$MnF_{4}$ monolayer is also characterized by strongly uniaxial magnetic
anisotropic behavior, that can be manipulated by the reversible ferroelastic
strain and carrier doping. Remarkably, the carrier doping not only leads to an
antiferromagnetic to ferromagnetic phase transformation, bult also could switch
the easy magnetization axis between the in-plane and out-of-plane directions.
In addition, the N\'eel temperature was evaluated to be about 140 K from the
Monte Carlo simulations based on the Heisenberg model. The combination of
antiferromagnetic and ferroelastic properties in $MnF_{4}$ monolayer provides a
promising platform for studying the magnetoelastic effects, and brings about
new concepts for next-generation nonvolatile memory and multi-stage storage.


Abstract:
  The equilibrium states of one-dimensional proton conductors in the systems
with hydrogen bonds are investigated. Our extended hard-core boson lattice
model includes short-range interactions between hydrogen ions, their transfer
along the hydrogen bonds with two-minima local anharmonic potential, as well as
their inter-bond hopping, and the modulating field is taken into account. The
exact diagonalization method for finite one-dimensional system with periodic
boundary conditions is used. The existence of various phases of the system at
$T = 0$, depending on the values of short-range interactions between particles
and the modulating field strength, is established by analyzing the character of
the obtained frequency dependence of one-particle spectral density; the phase
diagrams are built.


Abstract:
  Training Restricted Boltzmann Machines (RBMs) has been challenging for a long
time due to the difficulty of computing precisely the log-likelihood gradient.
Over the past decades, many works have proposed more or less successful
training recipes but without studying the crucial quantity of the problem: the
mixing time, i.e. the number of Monte Carlo iterations needed to sample new
configurations from a model. In this work, we show that this mixing time plays
a crucial role in the dynamics and stability of the trained model, and that
RBMs operate in two well-defined regimes, namely equilibrium and
out-of-equilibrium, depending on the interplay between this mixing time of the
model and the number of steps, $k$, used to approximate the gradient. We
further show empirically that this mixing time increases with the learning,
which often implies a transition from one regime to another as soon as $k$
becomes smaller than this time. In particular, we show that using the popular
$k$ (persistent) contrastive divergence approaches, with $k$ small, the
dynamics of the learned model are extremely slow and often dominated by strong
out-of-equilibrium effects. On the contrary, RBMs trained in equilibrium
display faster dynamics, and a smooth convergence to dataset-like
configurations during the sampling. Finally we discuss how to exploit in
practice both regimes depending on the task one aims to fulfill: (i) short $k$
can be used to generate convincing samples in short learning times, (ii) large
$k$ (or increasingly large) is needed to learn the correct equilibrium
distribution of the RBM. Finally, the existence of these two operational
regimes seems to be a general property of energy based models trained via
likelihood maximization.


Abstract:
  We investigate radial oscillations of pure neutron stars and hybrid stars,
employing equations of state of nuclear matter from Brueckner-Hartree-Fock
theory, and of quark matter from the Dyson-Schwinger quark model, performing a
Gibbs construction for the mixed phase in hybrid stars. We calculate the
eigenfrequencies and corresponding oscillation functions. Our results for the
zero points of the first-order radial oscillation frequencies give the maximum
mass of stable neutron stars, consistent with the common criterion
$dM/d\rho_c=0$. Possible observations of the radial oscillation frequencies
could help to learn more about the equation of state, predict the maximum mass
of neutron stars more precisely, and indicate the presence of quark matter.


Abstract:
  This paper is a scoping study to identify current methods used in handling
sparse data with contextual bandits in web settings. The area is highly current
and state of the art methods are identified. The years 2017-2020 are
investigated, and 19 method articles are identified, and two review articles.
Five categories of methods are described, making it easy to choose how to
address sparse data using contextual bandits with a method available for
modification in the specific setting of concern. In addition, each method has
multiple techniques to choose from for future evaluation. The problem areas are
also mentioned that each article covers. An overall updated understanding of
sparse data problems using contextual bandits in web settings is given. The
identified methods are policy evaluation (off-line and on-line) ,
hybrid-method, model representation (clusters and deep neural networks),
dimensionality reduction, and simulation.


Abstract:
  Latent heat thermal energy storage (LHTES) has been recommended as an
effective technology to the thermal management system of space exploration for
its excellent ability of storing thermal energy. However, it is well known that
the low thermal conductivity of phase change material (PCM) seriously weakens
the heat charging and discharging rates of LHTES system. In present study, the
electrohydrodynamic (EHD), which is a popular active heat transfer enhancement
technology, is introduced to enhance the PCM melting in a shell-tube LHTES
system under microgravity. In our numerical simulations, we mainly focus on the
combined effects of the electric Rayleigh number $T$ and the eccentricity
$\Gamma$ on the melting performance under microgravity. Based on the numerical
results, it is found that in contrast to the case without the electric field,
the presence of the electric field causes the heat transfer efficiency and
melting behavior of LHTES system to be enhanced significantly. In addition, our
results indicates that the EHD technique always shows a good performance in
accelerating the melting process even under microgravity, and specially, the
maximum time saving in some cases is more than $90\%$. Furthermore, we note
that although the concentric annulus is always the optimal configuration under
no-gravity condition, the optimal eccentric position of the internal tube
strongly depends on the electric Rayleigh number if the gravity effects are
taken into account.


Abstract:
  We report non-invasive detection of spin coherence in a collection of
Raman-driven cold atoms using dispersive Faraday rotation fluctuation
measurements, which opens up new possibilities of probing spin correlations in
quantum gases and other similar systems. We demonstrate five orders of
magnitude enhancement of the measured signal strength than the traditional spin
noise spectroscopy with thermal atoms in equilibrium. Our observations are in
good agreement with the comprehensive theoretical modeling of the driven atoms
at various temperatures. The extracted spin relaxation rate of cold rubidium
atoms with atom number density $\sim$10$^9/$cm$^3$ is of the order of
2$\pi\times$0.5 kHz at 150 $\mu$K, two orders of magnitude less than $\sim$
2$\pi\times$50 kHz of a thermal atomic vapor with atom number density
$\sim$10$^{12}/$cm$^3$ at 373 K.


Abstract:
  Most recent works on sentiment analysis have exploited the text modality.
However, millions of hours of video recordings posted on social media platforms
everyday hold vital unstructured information that can be exploited to more
effectively gauge public perception. Multimodal sentiment analysis offers an
innovative solution to computationally understand and harvest sentiments from
videos by contextually exploiting audio, visual and textual cues. In this
paper, we, firstly, present a first of its kind Persian multimodal dataset
comprising more than 800 utterances, as a benchmark resource for researchers to
evaluate multimodal sentiment analysis approaches in Persian language.
Secondly, we present a novel context-aware multimodal sentiment analysis
framework, that simultaneously exploits acoustic, visual and textual cues to
more accurately determine the expressed sentiment. We employ both
decision-level (late) and feature-level (early) fusion methods to integrate
affective cross-modal information. Experimental results demonstrate that the
contextual integration of multimodal features such as textual, acoustic and
visual features deliver better performance (91.39%) compared to unimodal
features (89.24%).


Abstract:
  Compressed sensing is a paradigm within signal processing that provides the
means for recovering structured signals from linear measurements in a highly
efficient manner. Originally devised for the recovery of sparse signals, it has
become clear that a similar methodology would also carry over to a wealth of
other classes of structured signals. In this work, we provide an overview over
the theory of compressed sensing for a particularly rich family of such
signals, namely those of hierarchically structured signals. Examples of such
signals are constituted by blocked vectors, with only few non-vanishing sparse
blocks. We present recovery algorithms based on efficient hierarchical
hard-thresholding. The algorithms are guaranteed to converge, in a stable
fashion both with respect to measurement noise as well as to model mismatches,
to the correct solution provided the measurement map acts isometrically
restricted to the signal class. We then provide a series of results
establishing the required condition for large classes of measurement ensembles.
Building upon this machinery, we sketch practical applications of this
framework in machine-type communications and quantum tomography.


Abstract:
  Several social factors impact how people respond to AI explanations used to
justify AI decisions affecting them personally. In this position paper, we
define a framework called the \textit{layers of explanation} (LEx), a lens
through which we can assess the appropriateness of different types of
explanations. The framework uses the notions of \textit{sensitivity} (emotional
responsiveness) of features and the level of \textit{stakes} (decision's
consequence) in a domain to determine whether different types of explanations
are \textit{appropriate} in a given context. We demonstrate how to use the
framework to assess the appropriateness of different types of explanations in
different domains.


Abstract:
  Conversations on social media (SM) are increasingly being used to investigate
social issues on the web, such as online harassment and rumor spread. For such
issues, a common thread of research uses adversarial reactions, e.g., replies
pointing out factual inaccuracies in rumors. Though adversarial reactions are
prevalent in online conversations, inferring those adverse views (or stance)
from the text in replies is difficult and requires complex natural language
processing (NLP) models. Moreover, conventional NLP models for stance mining
need labeled data for supervised learning. Getting labeled conversations can
itself be challenging as conversations can be on any topic, and topics change
over time. These challenges make learning the stance a difficult NLP problem.
  In this research, we first create a new stance dataset comprised of three
different topics by labeling both users' opinions on the topics (as in pro/con)
and users' stance while replying to others' posts (as in favor/oppose). As we
find limitations with supervised approaches, we propose a weakly-supervised
approach to predict the stance in Twitter replies. Our novel method allows
using a smaller number of hashtags to generate weak labels for Twitter replies.
Compared to supervised learning, our method improves the mean F1-macro by 8\%
on the hand-labeled dataset without using any hand-labeled examples in the
training set. We further show the applicability of our proposed method on COVID
19 related conversations on Twitter.


Abstract:
  The quantum Kibble-Zurek mechanism (QKZM) predicts universal dynamical
behavior in the vicinity of quantum phase transitions (QPTs). It is now well
understood for one-dimensional quantum matter. Higher-dimensional systems,
however, remain a challenge, complicated by fundamental differences of the
associated QPTs and their underlying conformal field theories. In this work, we
take the first steps towards exploring the QKZM in two dimensions. We study the
dynamical crossing of the QPT in the paradigmatic Ising model by a joint effort
of modern state-of-the-art numerical methods. As a central result, we quantify
universal QKZM behavior close to the QPT. However, upon traversing further into
the ferromagnetic regime, we observe deviations from the QKZM prediction. We
explain the observed behavior by proposing an {\it extended QKZM} taking into
account spectral information as well as phase ordering. Our work provides a
starting point towards the exploration of dynamical universality in
higher-dimensional quantum matter.


Abstract:
  These brief lecture notes cover the basics of neural networks and deep
learning as well as their applications in the quantum domain, for physicists
without prior knowledge. In the first part, we describe training using
backpropagation, image classification, convolutional networks and autoencoders.
The second part is about advanced techniques like reinforcement learning (for
discovering control strategies), recurrent neural networks (for analyzing time
traces), and Boltzmann machines (for learning probability distributions). In
the third lecture, we discuss first recent applications to quantum physics,
with an emphasis on quantum information processing machines. Finally, the
fourth lecture is devoted to the promise of using quantum effects to accelerate
machine learning.


Abstract:
  This study is motivated by the question of how singularity formation and
other forms of extreme behavior in nonlinear dissipative partial differential
equations are affected by stochastic excitations. To address this question we
consider the 1D fractional Burgers equation with additive colored noise as a
model problem. This system is interesting, because in the deterministic setting
it exhibits finite-time blow-up or a globally well-posed behavior depending on
the value of the fractional dissipation exponent. The problem is studied by
performing a series of accurate numerical computations combining
spectrally-accurate spatial discretization with a Monte-Carlo approach. First,
we carefully document the singularity formation in the deterministic system in
the supercritical regime where the blow-up time is shown to be a decreasing
function of the fractional dissipation exponent. Our main result for the
stochastic problem is that there is no evidence for the noise to regularize the
evolution by suppressing blow-up in the supercritical regime, or for the noise
to trigger blow-up in the subcritical regime. However, as the noise amplitude
becomes large, the blow-up times in the supercritical regime are shown to
exhibit an increasingly non-Gaussian behavior. Analogous observations are also
made for the maximum attained values of the enstrophy and the times when the
maxima occur in the subcritical regime.


Abstract:
  Pairs of photons entangled in their time-frequency degree of freedom are of
great interest in quantum optics research and applications, due to their
relative ease of generation and their high capacity for encoding information.
Here we analyze, both theoretically and experimentally, the behavior of
phase-insensitive spectrally-resolved interferences arising from two pairs of
time-frequency entangled photons. At its core, this is a multimode entanglement
swapping experiment, whereby a spectrally resolved joint measurement on the
idler photons from both pairs results in projecting the signal photons onto a
Bell state whose form depends on the measurement outcome. Our analysis is a
thorough exploration of what can be achieved using time-frequency entanglement
and spectrally-resolved Bell-state measurements.


Abstract:
  Public knowledge graphs such as DBpedia and Wikidata have been recognized as
interesting sources of background knowledge to build content-based recommender
systems. They can be used to add information about the items to be recommended
and links between those. While quite a few approaches for exploiting knowledge
graphs have been proposed, most of them aim at optimizing the recommendation
strategy while using a fixed knowledge graph. In this paper, we take a
different approach, i.e., we fix the recommendation strategy and observe
changes when using different underlying knowledge graphs. Particularly, we use
different language editions of DBpedia. We show that the usage of different
knowledge graphs does not only lead to differently biased recommender systems,
but also to recommender systems that differ in performance for particular
fields of recommendations.


Abstract:
  Neovascularisation is essential for tissue development and regeneration, in
addition to playing a key role in pathological settings such as ischemia and
tumour development. Experimental findings in the past two decades have led to
the identification of a new mechanism of neovascularisation, cluster-based
vasculogenesis, during which endothelial progenitor cells (EPCs) mobilised from
the bone marrow are capable of bridging distant vascular beds in a variety of
hypoxic settings in vivo. This process is characterised by the formation of EPC
clusters during its early stages and, while much progress has been made in
identifying various mechanisms underlying cluster formation, we are still far
from a comprehensive description of such spatio-temporal dynamics. In order to
achieve this, we propose a novel mathematical model of the early stages of
cluster-based vasculogenesis, comprising of a system of nonlocal partial
differential equations including key mechanisms such as endogenous chemotaxis,
matrix degradation, cell proliferation and cell-to-cell adhesion. We conduct a
linear stability analysis on the system, solve the equations numerically,
conduct a parametric analysis of the numerical solutions of the 1D problem to
investigate the role of underlying dynamics on the speed of cluster formation
and the size of clusters, and verify the key results of the parametric analysis
with simulations of the 2D problem. Our results, which qualitatively compare
with data from in vitro experiments, elucidate the complementary role played by
endogenous chemotaxis and matrix degradation in the formation of clusters, and
they indicate that previous approaches to the nonlocal modelling of
cell-to-cell adhesion, while they capture the aggregating effect of
cell-to-cell adhesion, are not sufficient to capture its stabilising effect on
clusters, and new continuum cell-adhesion modelling strategies are required.


Abstract:
  In this work the robustness of a feedback optimization scheme is discussed.
Previously known results in literature, on the convergence to local optima of
the optimization problem of interest, are extended to the case where the
sensitivities of the steady-state input-output map of the plant present bounded
uncertainties. The application of the scheme to a biological setting, with the
goal of maximizing the concentration of products of interest in a bioreactor,
under a continuous perfusion framework, is suggested and the potential of the
approach is exposed by means of a simple synthetic example. This extended
version contains also the Numerical data for example in "Robustness of a
feedback optimization scheme with application to bioprocess manufacturing"
report, for the sake of reproducibility of the results.


Abstract:
  Using Planck polarization data, we search for and constrain spatial
variations of the polarized dust foreground for cosmic microwave background
(CMB) observations, specifically in its spectral index, $\beta_d$. Failure to
account for such variations will cause errors in the foreground cleaning that
propagate into errors on cosmological parameter recovery from the cleaned CMB
map. It is unclear how robust prior studies of the Planck data which
constrained $\beta_d$ variations are due to challenges with noise modeling,
residual systematics, and priors. To clarify constraints on $\beta_d$ and its
variation, we employ two pixel space analyses of the polarized dust foreground
at $>3.7^{\circ}$ scales on $\approx 60\%$ of the sky at high Galactic
latitudes. A template fitting method, which measures $\beta_d$ over three
regions of $\approx 20\%$ of the sky, does not find significant deviations from
an uniform $\beta_d = 1.55$, consistent with prior Planck determinations. An
additional analysis in these regions, based on multifrequency fits to a dust
and CMB model per pixel, puts limits on $\sigma_{\beta_d}$, the Gaussian
spatial variation in $\beta_d$. At the highest latitudes, the data support
$\sigma_{\beta_d}$ up to $0.45$, $0.30$ at mid-latitudes, and $0.15$ at
low-latitudes. We also demonstrate that care must be taken when interpreting
the current Planck constraints, $\beta_d$ maps, and noise simulations. Due to
residual systematics and low dust signal to noise at high latitudes, forecasts
for ongoing and future missions should include the possibility of large values
of $\sigma_{\beta_d}$ as estimated in this paper, based on current polarization
data.


Abstract:
  Dynamic recommendation is essential for modern recommender systems to provide
real-time predictions based on sequential data. In real-world scenarios, the
popularity of items and interests of users change over time. Based on this
assumption, many previous works focus on interaction sequences and learn
evolutionary embeddings of users and items. However, we argue that
sequence-based models are not able to capture collaborative information among
users and items directly. Here we propose Dynamic Graph Collaborative Filtering
(DGCF), a novel framework leveraging dynamic graphs to capture collaborative
and sequential relations of both items and users at the same time. We propose
three update mechanisms: zero-order 'inheritance', first-order 'propagation',
and second-order 'aggregation', to represent the impact on a user or item when
a new interaction occurs. Based on them, we update related user and item
embeddings simultaneously when interactions occur in turn, and then use the
latest embeddings to make recommendations. Extensive experiments conducted on
three public datasets show that DGCF significantly outperforms the
state-of-the-art dynamic recommendation methods up to 30. Our approach achieves
higher performance when the dataset contains less action repetition, indicating
the effectiveness of integrating dynamic collaborative information.


Abstract:
  This paper proposes a novel active boundary loss for semantic segmentation.
It can progressively encourage the alignment between predicted boundaries and
ground-truth boundaries during end-to-end training, which is not explicitly
enforced in commonly used cross-entropy loss. Based on the predicted boundaries
detected from the segmentation results using current network parameters, we
formulate the boundary alignment problem as a differentiable direction vector
prediction problem to guide the movement of predicted boundaries in each
iteration. Our loss is model-agnostic and can be plugged into the training of
segmentation networks to improve the boundary details. Experimental results
show that training with the active boundary loss can effectively improve the
boundary F-score and mean Intersection-over-Union on challenging image and
video object segmentation datasets.


Abstract:
  Methyl-ammonium lead iodide perovskite crystallizes in different structures
depending on the temperature: orthorhombic, tetragonal and cubic. An important
point to be considered is the effect of the microscopic properties of the
different structures on the optical and photovoltaic properties. Using first
principles we obtain the absorption coefficients that will determine the
absorption of solar radiation. In order to analyze the contributions of the
different atoms to the absorption coefficients we split them into a
many-species expansion. Using a similar methodology we also split the
efficiencies as a many-species expansion. It allows the contribution of the
atomic species to be identified and quantified to the absorption coefficients
and to the solar cell efficiency of the different phases. Additionally the
effect of the cell thickness w is quantified.


Abstract:
  This paper addresses the construction and the stability of self-similar
solutions to the isentropic compressible Euler equations. These solutions model
a gas that implodes isotropically, ending in a singularity formation in finite
time. The existence of smooth solutions that vanish at infinity and do not have
vacuum regions was recently proved and, in this paper, we provide the first
construction of such smooth profiles, the first characterization of their
spectrum of radial perturbations as well as some endpoints of unstable
directions. Numerical simulations of the Euler equations provide evidence that
one of these endpoints is a shock formation that happens before the singularity
at the origin, showing that the implosion process is unstable.


Abstract:
  Visual change detection, aiming at segmentation of video frames into
foreground and background regions, is one of the elementary tasks in computer
vision and video analytics. The applications of change detection include
anomaly detection, object tracking, traffic monitoring, human machine
interaction, behavior analysis, action recognition, and visual surveillance.
Some of the challenges in change detection include background fluctuations,
illumination variation, weather changes, intermittent object motion, shadow,
fast/slow object motion, camera motion, heterogeneous object shapes and
real-time processing. Traditionally, this problem has been solved using
hand-crafted features and background modelling techniques. In recent years,
deep learning frameworks have been successfully adopted for robust change
detection. This article aims to provide an empirical review of the
state-of-the-art deep learning methods for change detection. More specifically,
we present a detailed analysis of the technical characteristics of different
model designs and experimental frameworks. We provide model design based
categorization of the existing approaches, including the 2D-CNN, 3D-CNN,
ConvLSTM, multi-scale features, residual connections, autoencoders and GAN
based methods. Moreover, an empirical analysis of the evaluation settings
adopted by the existing deep learning methods is presented. To the best of our
knowledge, this is a first attempt to comparatively analyze the different
evaluation frameworks used in the existing deep change detection methods.
Finally, we point out the research needs, future directions and draw our own
conclusions.


Abstract:
  General Relativity provides us with an extremely powerful tool to extract at
the same time astrophysical and cosmological information from the Stochastic
Gravitational Wave Backgrounds (SGWBs): the cross-correlation with other
cosmological tracers, since their anisotropies share a common origin and the
same perturbed geodesics. In this letter we explore the cross-correlation of
the cosmological and astrophysical SGWBs with Cosmic Microwave Background (CMB)
anisotropies, showing that future GW detectors, such as LISA or BBO, have the
ability to measure such cross-correlation signals. We also present, as a new
tool in this context, constrained realization maps of the SGWBs extracted from
the high-resolution CMB {\it Planck} maps. This technique allows, in the
low-noise regime, to faithfully reconstruct the expected SGWB map by starting
from CMB measurements.


Abstract:
  Principal Component Analysis (PCA) is the workhorse tool for dimensionality
reduction in this era of big data. While often overlooked, the purpose of PCA
is not only to reduce data dimensionality, but also to yield features that are
uncorrelated. Furthermore, the ever-increasing volume of data in the modern
world often requires storage of data samples across multiple machines, which
precludes the use of centralized PCA algorithms. This paper focuses on the dual
objective of PCA, namely, dimensionality reduction and decorrelation of
features, but in a distributed setting. This requires estimating the
eigenvectors of the data covariance matrix, as opposed to only estimating the
subspace spanned by the eigenvectors, when data is distributed across a network
of machines. Although a few distributed solutions to the PCA problem have been
proposed recently, convergence guarantees and/or communications overhead of
these solutions remain a concern. With an eye towards communications
efficiency, this paper introduces a feedforward neural network-based one
time-scale distributed PCA algorithm termed Distributed Sanger's Algorithm
(DSA) that estimates the eigenvectors of the data covariance matrix when data
is distributed across an undirected and arbitrarily connected network of
machines. Furthermore, the proposed algorithm is shown to converge linearly to
a neighborhood of the true solution. Numerical results are also provided to
demonstrate the efficacy of the proposed solution.


Abstract:
  In autonomous applications for mobility and transport, a high-rate and highly
accurate vehicle states estimation is achieved by fusing measurements of global
navigation satellite systems and inertial sensors. Since this kind of state
estimation suffers from poor parameterization, environment disturbances, or
even software and hardware failures, this paper introduces a novel scheme of
multi-sensor navigation system involving extended H$_\infty$ filter for
robustness enhancement of the navigation solution and zonotope for protection
level generation in combination with vehicle dynamic-model-aided fault
detection of the inertial sensor for reliable integrity monitoring. The
innovative scheme, applying extended H$_\infty$ filter and zonotope, is shown
as part of a tightly-coupled navigation system. Further, the consideration of
redundant information, e.g., vehicle dynamic model, for fault detection purpose
has long been investigated and is systematically described and discussed using
interval analysis theory in current publication. The robustness of the designed
approach is validated with real-world data in post-processing: decimeter
positioning accuracy is maintained, while the solution of conventional extended
Kalman filter diverges from ground truth; the difference is also significant
under inertial sensor faults. A real-time implementation of the designed
approach is promising and aimed in the future work.


Abstract:
  CP violation in charm decay was observed in the decays $D^0\rightarrow
P^{\pm}P^{\mp}$ of a $D^0$ meson to two pseudoscalars. When interpreted within
the SM, the results imply that the ratio of the relevant rescattering
amplitudes has a magnitude and phase that are both of $O(1)$. We discuss ways
to probe similar ratios in $D^0\rightarrow V^{\pm}P^{\mp}$ decays, where $V$ is
a vector that decays to two pseudoscalars, from the Dalitz-plot analysis of
time-integrated three-body decays. Compared to two-body decays, three-body
decays have the advantage that the complete system can be solved without the
need for time-dependent CP violation measurements or use of correlated
$D^0$--$\overline{D}^0$ production. We discuss the decays $D^0\rightarrow
\pi^+\pi^-\pi^0$ and $D^0\rightarrow K^+K^-\pi^0$ as examples by considering a
toy model of only two overlapping charged resonances, treating the underlying
pseudo two-body decays in full generality.


Abstract:
  We consider the horocyclic flow corresponding to a (topologically mixing)
Anosov flow or diffeomorphism, and establish the uniqueness of transverse
quasi-invariant measures with H\"older Jacobians. In the same setting, we give
a precise characterization of the equilibrium states of the hyperbolic system,
showing that existence of a family of Radon measures on the horocyclic
foliation such that any probability (invariant or not) having conditionals
given by this family, necessarily is the unique equilibrium state of the
system.


Abstract:
  Advancing the utility of social media data for research applications requires
methods for automatically detecting demographic information about social media
study populations, including users' age. The objective of this study was to
develop and evaluate a method that automatically identifies the exact age of
users based on self-reports in their tweets. Our end-to-end automatic natural
language processing (NLP) pipeline, ReportAGE, includes query patterns to
retrieve tweets that potentially mention an age, a classifier to distinguish
retrieved tweets that self-report the user's exact age ("age" tweets) and those
that do not ("no age" tweets), and rule-based extraction to identify the age.
To develop and evaluate ReportAGE, we manually annotated 11,000 tweets that
matched the query patterns. Based on 1000 tweets that were annotated by all
five annotators, inter-annotator agreement (Fleiss' kappa) was 0.80 for
distinguishing "age" and "no age" tweets, and 0.95 for identifying the exact
age among the "age" tweets on which the annotators agreed. A deep neural
network classifier, based on a RoBERTa-Large pretrained model, achieved the
highest F1-score of 0.914 (precision = 0.905, recall = 0.942) for the "age"
class. When the age extraction was evaluated using the classifier's
predictions, it achieved an F1-score of 0.855 (precision = 0.805, recall =
0.914) for the "age" class. When it was evaluated directly on the held-out test
set, it achieved an F1-score of 0.931 (precision = 0.873, recall = 0.998) for
the "age" class. We deployed ReportAGE on more than 1.2 billion tweets posted
by 245,927 users, and predicted ages for 132,637 (54%) of them. Scaling the
detection of exact age to this large number of users can advance the utility of
social media data for research applications that do not align with the
predefined age groupings of extant binary or multi-class classification
approaches.


Abstract:
  Measurement error is a pervasive issue which renders the results of an
analysis unreliable. The measurement error literature contains numerous
correction techniques, which can be broadly divided into those which aim to
produce exactly consistent estimators, and those which are only approximately
consistent. While consistency is a desirable property, it is typically attained
only under specific model assumptions. Two techniques, regression calibration
and simulation extrapolation, are used frequently in a wide variety of
parametric and semiparametric settings. However, in many settings these methods
are only approximately consistent. We generalize these corrections, relaxing
assumptions placed on replicate measurements. Under regularity conditions, the
estimators are shown to be asymptotically normal, with a sandwich estimator for
the asymptotic variance. Through simulation, we demonstrate the improved
performance of the modified estimators, over the standard techniques, when
these assumptions are violated. We motivate these corrections using the
Framingham Heart Study, and apply the generalized techniques to an analysis of
these data.


Abstract:
  The Wigner function is a quantum analogue of the classical joined
distribution of position and momentum. As such is should be a good tool to
study quantum-classical correspondence. In this paper, the classical limit of
the Wigner function is shown using the quantum harmonic oscillator as an
example. The Wigner function is found exactly for all states. The
semi-classical wavefunctions for highly excited states are used as the approach
to the classical limit. Therefore, one can found the classical limit of the
Wigner function for highly excited states and shown that it gives the classical
microcanonical ensemble.


Abstract:
  This paper presents a Tracking-Error Learning Control (TELC) algorithm for
precise mobile robot path tracking in off-road terrain. In traditional tracking
error-based control approaches, feedback and feedforward controllers are
designed based on the nominal model which cannot capture the uncertainties,
disturbances and changing working conditions so that they cannot ensure precise
path tracking performance in the outdoor environment. In TELC algorithm, the
feedforward control actions are updated by using the tracking error dynamics
and the plant-model mismatch problem is thus discarded. Therefore, the
feedforward controller gradually eliminates the feedback controller from the
control of the system once the mobile robot has been on-track. In addition to
the proof of the stability, it is proven that the cost functions do not have
local minima so that the coefficients in TELC algorithm guarantee that the
global minimum is reached. The experimental results show that the TELC
algorithm results in better path tracking performance than the traditional
tracking error-based control method. The mobile robot controlled by TELC
algorithm can track a target path precisely with less than $10$ cm error in
off-road terrain.


Abstract:
  Discovered in the context of card shuffling by Aldous, Diaconis and
Shahshahani, the cutoff phenomenon has since then been established in a variety
of Markov chains. However, proving cutoff remains a delicate affair, which
requires a detailed knowledge of the chain. Identifying the general mechanisms
underlying this phase transition -- without having to pinpoint its precise
location -- remains one of the most fundamental open problems in the area of
mixing times. In the present paper, we make a step in this direction by
establishing cutoff for Markov chains with non-negative curvature, under a
suitably refined product condition. The result applies, in particular, to
random walks on abelian Cayley expanders satisfying a mild degree condition,
hence in particular to \emph{almost all} abelian Cayley graphs. Our proof
relies on a quantitative \emph{entropic concentration principle}, which we
believe to lie behind all cutoff phenomena.


Abstract:
  We answer three questions posed by Bubeck and Linial on the limit densities
of subtrees in trees. We prove there exist positive $\varepsilon_1$ and
$\varepsilon_2$ such that every tree that is neither a path nor a star has
inducibility at most $1-\varepsilon_1$, where the inducibility of a tree $T$ is
defined as the maximum limit density of $T$, and that there are infinitely many
trees with inducibility at least $\varepsilon_2$. Finally, we construct a
universal sequence of trees; that is, a sequence in which the limit density of
any tree is positive.


Abstract:
  We present a knowledge-grounded dialog system developed for the ninth Dialog
System Technology Challenge (DSTC9) Track 1 - Beyond Domain APIs: Task-oriented
Conversational Modeling with Unstructured Knowledge Access. We leverage
transfer learning with existing language models to accomplish the tasks in this
challenge track. Specifically, we divided the task into four sub-tasks and
fine-tuned several Transformer models on each of the sub-tasks. We made
additional changes that yielded gains in both performance and efficiency,
including the combination of the model with traditional entity-matching
techniques, and the addition of a pointer network to the output layer of the
language model.


Abstract:
  The strong coupling constants are basic quantities that carry information of
the strong interactions among the baryon and meson multiplets as well as
information on the natures and internal structures of the involved hadrons.
These parameters enter to the transition matrix elements of various decays as
main inputs and they play key roles in analyses of the experimental data
including various hadrons. We determine the strong coupling constants among the
doubly heavy spin-$ 3/2 $ baryons, $\Xi^*_{QQ'} $ and $\Omega^*_{QQ'}$, and
light pseudoscalar mesons, $\pi$, $K$ and $\eta$, using the light-cone QCD. The
values obtained for the strong coupling constants under study may be used in
construction of the strong potentials among the doubly heavy spin-3/2 baryons
and light pseudoscalar mesons.


Abstract:
  We present new equilibrium solutions of stationary models of magnetized thick
disks (or tori) around Kerr black holes with synchronised scalar hair. The
models reported here largely extend our previous results based on constant
radial distributions of the specific angular momentum along the equatorial
plane. We introduce a new way to prescribe the distribution of the disk's
angular momentum based on a combination of two previous proposals and compute
the angular momentum distribution outside the equatorial plane by resorting to
the construction of von Zeipel cylinders. We find that the effect of the scalar
hair on the black hole spacetime can yield significant differences in the disk
morphology and properties compared to what is found if the spacetime is purely
Kerr. Some of the tori built within the most extreme, background hairy black
hole spacetime of our sample exhibit the appearance of two maxima in the
gravitational energy density which impacts the radial profile distributions of
the disk's thermodynamical quantities. The models reported in this paper can be
used as initial data for numerical evolutions with GRMHD codes to study their
stability properties. Moreover, they can be employed as illuminating sources to
build shadows of Kerr black holes with scalar hair which might help further
constrain the no-hair hypothesis as new observational data is collected.


Abstract:
  Consider a random Schr\"odinger-type operator of the form $H:=-H_X+V+\xi$
acting on a general graph $\mathscr G=(\mathscr V,\mathscr E)$, where $H_X$ is
the generator of a Markov process $X$ on $\mathscr G$, $V$ is a deterministic
potential with sufficient growth (so that $H$ has a purely discrete spectrum),
and $\xi$ is a random noise with at-most-exponential tails. We prove that $H$'s
eigenvalue point process is number rigid in the sense of Ghosh and Peres (Duke
Math. J. 166 (2017), no. 10, 1789--1858); that is, the number of eigenvalues in
any bounded domain $B\subset\mathbb C$ is determined by the configuration of
eigenvalues outside of $B$. Our general setting allows to treat cases where $X$
could be non-symmetric (hence $H$ is non-self-adjoint) and $\xi$ has long-range
dependence. Our strategy of proof consists of controlling the variance of the
trace of the semigroup $\mathrm e^{-t H}$ using the Feynman-Kac formula.


Abstract:
  Estimating the state of a deformable object is crucial for robotic
manipulation, yet accurate tracking is challenging when the object is
partially-occluded. To address this problem, we propose an occlusion-robust
RGBD sequence tracking framework based on Coherent Point Drift (CPD). To
mitigate the effects of occlusion, our method 1) Uses a combination of locally
linear embedding and constrained optimization to regularize the output of CPD,
thus enforcing topological consistency when occlusions create disconnected
pieces of the object; 2) Reasons about the free-space visible by an RGBD sensor
to better estimate the prior on point location and to detect tracking failures
during occlusion; and 3) Uses shape descriptors to find the most relevant
previous state of the object to use for tracking after a severe occlusion. Our
method does not rely on physics simulation or a physical model of the object,
which can be difficult to obtain in unstructured environments. Despite having
no physical model, our experiments demonstrate that our method achieves
improved accuracy in the presence of occlusion as compared to a physics-based
CPD method while maintaining adequate run-time.


Abstract:
  It has recently been demonstrated that black holes with spatially regular
horizons can support external scalar fields (scalar hairy configurations) which
are non-minimally coupled to the Gauss-Bonnet invariant of the curved
spacetime. The composed black-hole-scalar-field system is characterized by a
critical existence line $\alpha=\alpha(\mu r_{\text{H}})$ which, for a given
mass of the supported scalar field, marks the threshold for the onset of the
spontaneous scalarization phenomenon [here $\{\alpha,\mu,r_{\text{H}}\}$ are
respectively the dimensionless non-minimal coupling parameter of the field
theory, the proper mass of the scalar field, and the horizon radius of the
central supporting black hole]. In the present paper we use analytical
techniques in order to explore the physical and mathematical properties of the
marginally-stable composed black-hole-linearized-scalar-field configurations in
the eikonal regime $\mu r_{\text{H}}\gg1$ of large field masses. In particular,
we derive a remarkably compact analytical formula for the critical
existence-line $\alpha=\alpha(\mu r_{\text{H}})$ of the system which separates
bare Schwarzschild black-hole spacetimes from composed hairy (scalarized)
black-hole-field configurations.


Abstract:
  \emph{Uncertain Graph} (also known as \emph{Probabilistic Graph}) is a
generic model to represent many real\mbox{-}world networks from social to
biological. In recent times analysis and mining of uncertain graphs have drawn
significant attention from the researchers of the data management community.
Several noble problems have been introduced and efficient methodologies have
been developed to solve those problems. Hence, there is a need to summarize the
existing results on this topic in a self\mbox{-}organized way. In this paper,
we present a comprehensive survey on uncertain graph mining focusing on mainly
three aspects: (i) different problems studied, (ii) computational challenges
for solving those problems, and (iii) proposed methodologies. Finally, we list
out important future research directions.


Abstract:
  The visibility of targets determines performance and even success rate of
various applications, such as active slam, exploration, and target tracking.
Therefore, it is crucial to take the visibility of targets into explicit
account in trajectory planning. In this paper, we propose a general metric for
target visibility, considering observation distance and angle as well as
occlusion effect. We formulate this metric into a differentiable visibility
cost function, with which spatial trajectory and yaw can be jointly optimized.
Furthermore, this visibility-aware trajectory optimization handles dynamic
feasibility of position and yaw simultaneously. To validate that our method is
practical and generic, we integrate it into a customized quadrotor tracking
system. The experimental results show that our visibility-aware planner
performs more robustly and observes targets better. In order to benefit related
researches, we release our code to the public.


Abstract:
  Program SHELLSPEC is designed to calculate light curves, spectra, and images
of interacting binaries and extra-solar planets immersed in a moving gaseous or
dusty circumstellar matter. It solves simple radiative transfer along the line
of sight in 3D moving media. The Roche model and synthetic spectra from the
stellar atmosphere models such as TLUSTY from Ivan Hubeny can be used as a
boundary condition for the radiative transfer. The latest publicly available
version is Shellspec39. The code has been combined with other methods such as
Doppler tomography and interferometry and used to analyze spectroscopic,
photometric, and interferometric observations of binary stars and transiting
exoplanets. A few examples are briefly mentioned.


Abstract:
  In this article we study local constancy of the mod $p$ reduction of certain
$2$-dimensional crystalline representations of
$\mathrm{Gal}\left(\bar{\mathbb{Q}}_p/\mathbb{Q}_p\right)$ using the mod $p$
local Langlands correspondence. We prove local constancy in the weight space by
giving an explicit lower bound on the local constancy radius centered around
weights going up to $(p-1)^{2} +3$ and the slope fixed in $(0, \ p-1)$
satisfying certain constraints. We establish the lower bound by determining
explicitly the mod $p$ reductions at nearby weights and applying a local
constancy result of Berger.


Abstract:
  Performing reliable Bayesian inference on a big data scale is becoming a
keystone in the modern era of machine learning. A workhorse class of methods to
achieve this task are Markov chain Monte Carlo (MCMC) algorithms and their
design to handle distributed datasets has been the subject of many works.
However, existing methods are not completely either reliable or computationally
efficient. In this paper, we propose to fill this gap in the case where the
dataset is partitioned and stored on computing nodes within a cluster under a
master/slaves architecture. We derive a user-friendly centralised distributed
MCMC algorithm with provable scaling in high-dimensional settings. We
illustrate the relevance of the proposed methodology on both synthetic and real
data experiments.