Abstract:
  The conventionally independent power, water, and heating networks are
becoming more tightly connected, which motivates their joint optimal energy
scheduling to improve the overall efficiency of an integrated energy system.
However, such a joint optimization is known as a challenging problem with
complex network constraints and couplings of electric, hydraulic, and thermal
models that are nonlinear and nonconvex. We formulate an optimal
power-water-heat flow (OPWHF) problem and develop a computationally efficient
heuristic to solve it. The proposed heuristic decomposes OPWHF into
subproblems, which are iteratively solved via convex relaxation and
convex-concave procedure. Simulation results validate that the proposed
framework can improve operational flexibility and social welfare of the
integrated system, wherein the water and heating networks respond as virtual
energy storage to time-varying energy prices and solar photovoltaic generation.
Moreover, we perform sensitivity analysis to compare two modes of heating
network control: by flow rate and by temperature. Our results reveal that the
latter is more effective for heating networks with a wider space of pipeline
parameters.


Abstract:
  There are several proofs now for the stability of Toom's example of a
two-dimensional stable cellular automaton and its application to fault-tolerant
computation. Simon and Berman simplified and strengthened Toom's original
proof: the present report is a simplified exposition of their proof.


Abstract:
  We study the problem of recovering a collection of $n$ numbers from the
evaluation of $m$ power sums. This yields a system of polynomial equations,
which can be underconstrained ($m < n$), square ($m = n$), or overconstrained
($m > n$). Fibers and images of power sum maps are explored in all three
regimes, and in settings that range from complex and projective to real and
positive. This involves surprising deviations from the B\'ezout bound, and the
recovery of vectors from length measurements by $p$-norms.


Abstract:
  Malicious bots make up about a quarter of all traffic on the web, and degrade
the performance of personalization and recommendation algorithms that operate
on e-commerce sites. Positive-Unlabeled learning (PU learning) provides the
ability to train a binary classifier using only positive (P) and unlabeled (U)
instances. The unlabeled data comprises of both positive and negative classes.
It is possible to find labels for strict subsets of non-malicious actors, e.g.,
the assumption that only humans purchase during web sessions, or clear
CAPTCHAs. However, finding signals of malicious behavior is almost impossible
due to the ever-evolving and adversarial nature of bots. Such a set-up
naturally lends itself to PU learning. Unfortunately, standard PU learning
approaches assume that the labeled set of positives are a random sample of all
positives, this is unlikely to hold in practice. In this work, we propose two
modifications to PU learning that make it more robust to violations of the
selected-completely-at-random assumption, leading to a system that can filter
out malicious bots. In one public and one proprietary dataset, we show that
proposed approaches are better at identifying humans in web data than standard
PU learning methods.


Abstract:
  Optimization algorithms such as projected Newton's method, FISTA, mirror
descent and its variants enjoy near-optimal regret bounds and convergence
rates, but suffer from a computational bottleneck of computing "projections''
in potentially each iteration (e.g., $O(T^{1/2})$ regret of online mirror
descent). On the other hand, conditional gradient variants solve a linear
optimization in each iteration, but result in suboptimal rates (e.g.,
$O(T^{3/4})$ regret of online Frank-Wolfe). Motivated by this trade-off in
runtime v/s convergence rates, we consider iterative projections of close-by
points over widely-prevalent submodular base polytopes $B(f)$. We develop a
toolkit to speed up the computation of projections using both discrete and
continuous perspectives. We subsequently adapt the away-step Frank-Wolfe
algorithm to use this information and enable early termination. For the special
case of cardinality based submodular polytopes, we improve the runtime of
computing certain Bregman projections by a factor of $\Omega(n/\log(n))$. Our
theoretical results show orders of magnitude reduction in runtime in
preliminary computational experiments.


Abstract:
  Human Activity Recognition (HAR) is considered a valuable research topic in
the last few decades. Different types of machine learning models are used for
this purpose, and this is a part of analyzing human behavior through machines.
It is not a trivial task to analyze the data from wearable sensors for complex
and high dimensions. Nowadays, researchers mostly use smartphones or smart home
sensors to capture these data. In our paper, we analyze these data using
machine learning models to recognize human activities, which are now widely
used for many purposes such as physical and mental health monitoring. We apply
different machine learning models and compare performances. We use Logistic
Regression (LR) as the benchmark model for its simplicity and excellent
performance on a dataset, and to compare, we take Decision Tree (DT), Support
Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN).
Additionally, we select the best set of parameters for each model by grid
search. We use the HAR dataset from the UCI Machine Learning Repository as a
standard dataset to train and test the models. Throughout the analysis, we can
see that the Support Vector Machine performed (average accuracy 96.33%) far
better than the other methods. We also prove that the results are statistically
significant by employing statistical significance test methods.


Abstract:
  Numerical solutions to the equation for advection are determined using
different finite-difference approximations and physics-informed neural networks
(PINNs) under conditions that allow an analytical solution. Their accuracy is
examined by comparing them to the analytical solution. We used a machine
learning framework like PyTorch to implement PINNs. PINNs approach allows
training neural networks while respecting the PDEs as a strong constraint in
the optimization as apposed to making them part of the loss function. In
standard small-scale circulation simulations, it is shown that the conventional
approach incorporates a pseudo diffusive effect that is almost as large as the
effect of the turbulent diffusion model; hence the numerical solution is
rendered inconsistent with the PDEs. This oscillation causes inaccuracy and
computational uncertainty. Of all the schemes tested, only the PINNs
approximation accurately predicted the outcome. We assume that the PINNs
approach can transform the physics simulation area by allowing real-time
physics simulation and geometry optimization without costly and time-consuming
simulations on large supercomputers.


Abstract:
  Distributional regression is extended to Gaussian response vectors of
dimension greater than two by parameterizing the covariance matrix $\Sigma$ of
the response distribution using the entries of its Cholesky decomposition. The
more common variance-correlation parameterization limits such regressions to
bivariate responses -- higher dimensions require complicated constraints among
the correlations to ensure positive definite $\Sigma$ and a well-defined
probability density function. In contrast, Cholesky-based parameterizations
ensure positive definiteness for all distributional dimensions no matter what
values the parameters take, enabling estimation and regularization as for other
distributional regression models. In cases where components of the response
vector are assumed to be conditionally independent beyond a certain lag $r$,
model complexity can be further reduced by setting Cholesky parameters beyond
this lag to zero a priori. Cholesky-based multivariate Gaussian regression is
first illustrated and assessed on artificial data and subsequently applied to a
real-world 10-dimensional weather forecasting problem. There the regression is
used to obtain reliable joint probabilities of temperature across ten future
times, leveraging temporal correlations over the prediction period to obtain
more precise and meteorologically consistent probabilistic forecasts.


Abstract:
  We introduce and motivate generative modeling as a central task for machine
learning and provide a critical view of the algorithms which have been proposed
for solving this task. We overview how generative modeling can be defined
mathematically as trying to make an estimating distribution the same as an
unknown ground truth distribution. This can then be quantified in terms of the
value of a statistical divergence between the two distributions. We outline the
maximum likelihood approach and how it can be interpreted as minimizing
KL-divergence. We explore a number of approaches in the maximum likelihood
family, while discussing their limitations. Finally, we explore the alternative
adversarial approach which involves studying the differences between an
estimating distribution and a real data distribution. We discuss how this
approach can give rise to new divergences and methods that are necessary to
make adversarial learning successful. We also discuss new evaluation metrics
which are required by the adversarial approach.


Abstract:
  Complementary to the development of highly three-dimensional (3D) integrated
circuits in the continuation of Moore's law, there has been a growing interest
in new 3D deformation strategies to improve device performance. To continue
this search for new 3D deformation techniques, it is essential to explore
beforehand - using computational predictive methods - which strain tensor leads
to the desired properties. In this work, we study germanium (Ge) under an
isotropic 3D strain on the basis of first-principle methods. The transport and
optical properties are studied by a fully ab initio Boltzmann transport
equation and many-body Bethe-Salpeter equation (BSE) approach, respectively.
Our findings show that a direct band gap in Ge could be realized with only
0.34% triaxial tensile strain (negative pressure) and without the challenges
associated with Sn doping. At the same time a significant increase in
refractive index and carrier mobility - particularly for electrons - is
observed. These results demonstrate that there is a huge potential in exploring
the 3D deformation space for semiconductors - and potentially many other
materials - in order to optimize their properties.


Abstract:
  We describe a modulation-domain loss function for deep-learning-based speech
enhancement systems. Learnable spectro-temporal receptive fields (STRFs) were
adapted to optimize for a speaker identification task. The learned STRFs were
then used to calculate a weighted mean-squared error (MSE) in the modulation
domain for training a speech enhancement system. Experiments showed that adding
the modulation-domain MSE to the MSE in the spectro-temporal domain
substantially improved the objective prediction of speech quality and
intelligibility for real-time speech enhancement systems without incurring
additional computation during inference.


Abstract:
  The radio environment of the Moon at low frequencies, particularly in lunar
polar regions and the permanently shadowed regions (PSR) found there, is
relatively poorly explored and may contain some novel features. In addition,
these areas of the Moon, shielded from the natural and artificial emissions of
the Earth, and the natural radio emissions of the Sun and the other planets,
are near-ideal locations for radio astronomy observations in the last
unexplored region of the electromagnetic frequency spectrum. We are developing
a low-mass RadioLuna radio science precursor mission to deploy an
interferometric array on the lunar surface using Space Initiatives "Mote"
penetrometers. The current RadioLuna default mission would be an array of 10 -
12 penetrators deployed on the floor of Shackleton crater in the PSR 4 km below
the crater rim, where it would be shielded from all terrestial, solar and
planetary interference. At the present level of understanding of the low
frequency lunar radio environment there will be a tight coupling between
advances in technology and advances in science, and RadioLuna can be expected
to lead to improvements in both lunar radio science and lunar communication
techniques.


Abstract:
  We consider an optimal investment and risk control problem for an insurer
under the mean-variance (MV) criterion. By introducing a deterministic
auxiliary process defined forward in time, we formulate an alternative
time-consistent problem related to the original MV problem, and obtain the
optimal strategy and the value function to the new problem in closed-form. We
compare our formulation and optimal strategy to those under the precommitment
and game-theoretic framework. Numerical studies show that, when the financial
market is negatively correlated with the risk process, optimal investment may
involve short selling the risky asset and, if that happens, a less risk averse
insurer short sells more risky asset.


Abstract:
  Social norms characterize collective and acceptable group conducts in human
society. Furthermore, some social norms emerge from interactions of agents or
humans. To achieve agent autonomy and make norm satisfaction explainable, we
include emotions into the normative reasoning process, which evaluate whether
to comply or violate a norm. Specifically, before selecting an action to
execute, an agent observes the environment and infer the state and consequences
with its internal states after norm satisfaction or violation of a social norm.
Both norm satisfaction and violation provoke further emotions, and the
subsequent emotions affect norm enforcement. This paper investigates how
modeling emotions affect the emergence and robustness of social norms via
social simulation experiments. We find that an ability in agents to consider
emotional responses to the outcomes of norm satisfaction and violation (1)
promote norm compliance; and (2) improve societal welfare.


Abstract:
  Light field imaging introduced the capability to refocus an image after
capturing. Currently there are two popular methods for refocusing,
shift-and-sum and Fourier slice methods. Neither of these two methods can
refocus the light field in real-time without any pre-processing. In this paper
we introduce a machine learning based refocusing technique that is capable of
extracting 16 refocused images with refocusing parameters of
\alpha=0.125,0.250,0.375,...,2.0 in real-time. We have trained our network,
which is called RefNet, in two experiments. Once using the Fourier slice method
as the training -- i.e., "ground truth" -- data and another using the
shift-and-sum method as the training data. We showed that in both cases, not
only is the RefNet method at least 134x faster than previous approaches, but
also the color prediction of RefNet is superior to both Fourier slice and
shift-and-sum methods while having similar depth of field and focus distance
performance.


Abstract:
  The analysis of environmental mixtures is of growing importance in
environmental epidemiology, and one of the key goals in such analyses is to
identify exposures and their interactions that are associated with adverse
health outcomes. Typical approaches utilize flexible regression models combined
with variable selection to identify important exposures and estimate a
potentially nonlinear relationship with the outcome of interest. Despite this
surge in interest, no approaches to date can identify exposures and
interactions while controlling any form of error rates with respect to exposure
selection. We propose two novel approaches to estimating the health effects of
environmental mixtures that simultaneously 1) Estimate and provide valid
inference for the overall mixture effect, and 2) identify important exposures
and interactions while controlling the false discovery rate. We show that this
can lead to substantial power gains to detect weak effects of environmental
exposures. We apply our approaches to a study of persistent organic pollutants
and find that our approach is able to identify more interactions than existing
approaches.


Abstract:
  Digital investigations of stealthy attacks on Android devices pose particular
challenges to incident responders. Whereas consequential late detection demands
accurate and comprehensive forensic timelines to reconstruct all malicious
activities, reduced forensic footprints with minimal malware involvement, such
as when Living-Off-the-Land (LOtL) tactics are adopted, leave investigators
little evidence to work with. Volatile memory forensics can be an effective
approach since app execution of any form is always bound to leave a trail of
evidence in memory, even if perhaps ephemeral. Just-in-Time Memory Forensics
(JIT-MF) is a recently proposed technique that describes a framework to process
memory forensics on existing stock Android devices, without compromising their
security by requiring them to be rooted. Within this framework, JIT-MF drivers
are designed to promptly dump in-memory evidence related to app usage or
misuse. In this work, we primarily introduce a conceptualized presentation of
JIT-MF drivers. Subsequently, through a series of case studies involving the
hijacking of widely-used messaging apps, we show that when the target apps are
forensically enhanced with JIT-MF drivers, investigators can generate richer
forensic timelines to support their investigation, which are on average 26%
closer to ground truth.


Abstract:
  Here we develop an original approach to investigate the grand canonical
partition function of the multicomponent mixtures of Boltzmann particles with
hard-core interaction in finite and even small systems of the volumes above 20
fm$^3$. The derived expressions of the induced surface tension equation of
state are analyzed in details. It is shown that the metastable states, which
can emerge in the finite systems with realistic interaction, appear at very
high pressures at which the hadron resonance gas, most probably, is not
applicable at all. It is shown how and under what conditions the obtained
results for finite systems can be generalized to include into a formalism the
equation for curvature tension. The applicability range of the obtained
equations of induced surface and curvature tensions for finite systems is
discussed and their close relations to the equations of the morphological
thermodynamics are established. The hadron resonance gas model on the basis of
the obtained advanced equation of state is worked out. Also, this model is
applied to analyze the chemical freeze-out of hadrons and light nuclei with the
number of (anti-)baryons not exceeding 4, including the most problematic ratios
of hyper-triton and its antiparticle. Their multiplicities were measured by the
ALICE Collaboration in the central lead-lead collisions at the center-of-mass
energy $\sqrt{s_{\rm NN}} =$ 2.76 TeV.


Abstract:
  The Juno orbiter continues to collect data on Jupiter's gravity field with
unprecedented precision since 2016, recently reporting a non-hydrostatic
component in the tidal response of the planet. At the mid-mission perijove 17,
Juno registered a Love number $k_2=0.565\pm0.006$ that is $-4\pm1\%$
($1\sigma$) from the theoretical hydrostatic $k_2^{(hs)}=0.590$. Here we assess
whether the aforementioned departure of tides from hydrostatic equilibrium
represents the neglected gravitational contribution of dynamical tides. We
employ perturbation theory and simple tidal models to calculate a fractional
dynamical correction $\Delta k_2$ to the well-known hydrostatic $k_2$.
Exploiting the analytical simplicity of a toy uniform-density model, we show
how the Coriolis acceleration motivates the negative sign in the $\Delta k_2$
observed by Juno. By simplifying Jupiter's interior into a core-less,
fully-convective, and chemically-homogeneous body, we calculate $\Delta k_2$ in
a model following an $n=1$ polytrope equation of state. Our numerical results
for the $n=1$ polytrope qualitatively follow the behaviour of the
uniform-density model, mostly because the main component of the tidal flow is
similar in each case. Our results indicate that the gravitational effect of the
Io-induced dynamical tide leads to $\Delta k_2=-4\pm1\%$, in agreement with the
non-hydrostatic component reported by Juno. Consequently, our results suggest
that Juno obtained the first unambiguous detection of the gravitational effect
of dynamical tides in a gas giant planet. These results facilitate a future
interpretation of Juno tidal gravity data with the purpose of elucidating the
existence of a dilute core in Jupiter.


Abstract:
  Constraint-based environments such as configuration systems, recommender
systems, and scheduling systems support users in different decision making
scenarios. These environments exploit a knowledge base for determining
solutions of interest for the user. The development and maintenance of such
knowledge bases is an extremely time-consuming and error-prone task. Users
often specify constraints which do not reflect the real-world. For example,
redundant constraints are specified which often increase both, the effort for
calculating a solution and efforts related to knowledge base development and
maintenance. In this paper we present a new algorithm (CoreDiag) which can be
exploited for the determination of minimal cores (minimal non-redundant
constraint sets). The algorithm is especially useful for distributed knowledge
engineering scenarios where the degree of redundancy can become high. In order
to show the applicability of our approach, we present an empirical study
conducted with commercial configuration knowledge bases.


Abstract:
  Stellar-mass black holes can become embedded within the gaseous disks of
active galactic nuclei (AGNs). Afterwards, their interactions are mediated by
their gaseous surroundings. In this work, we study the evolution of
stellar-mass binary black holes (BBHs) embedded within AGN disks using a
combination of three-dimensional hydrodynamic simulations and analytic methods,
focusing on environments in which the AGN disk scale height $H$ is $\gtrsim$
the BBH sphere of influence. We model the local surroundings of the embedded
BBHs using a wind tunnel formalism and characterize different accretion regimes
based on the local properties of the disk, which range from wind-dominated to
quasi-spherical. We use our simulations to develop prescriptions for mass
accretion and drag for embedded BBHs. We use these prescriptions, along with
AGN disk models that can represent the Toomre-unstable outer regions of AGN
disks, to study the long-term evolution of the BBHs as they migrate through the
disk. We find that BBHs typically merge within $\lesssim 5-30\,{\rm Myr}$,
increasing their mass significantly in the process, allowing BBHs to enter (or
cross) the pair-instability supernova mass gap. The rate at which gas is
supplied to these BBHs often exceeds the Eddington limit, sometimes by several
orders of magnitude. We conclude that most embedded BBHs will merge before
migrating significantly in the disk. Depending on the conditions of the ambient
gas and the distance to the system, LISA can detect the transition between the
gas-dominated and gravitational wave dominated regime for inspiraling BBHs that
are formed sufficiently close to the AGN ($\lesssim$ 0.1 pc). We also discuss
possible electromagnetic signatures during and following the inspiral, finding
that it is generally unlikely but not inconceivable for the bolometric
luminosity of the BBH to exceed that of the host AGN.


Abstract:
  Multi-label classification tasks such as OCR and multi-object recognition are
a major focus of the growing machine learning as a service industry. While many
multi-label prediction APIs are available, it is challenging for users to
decide which API to use for their own data and budget, due to the heterogeneity
in those APIs' price and performance. Recent work shows how to select from
single-label prediction APIs. However the computation complexity of the
previous approach is exponential in the number of labels and hence is not
suitable for settings like OCR. In this work, we propose FrugalMCT, a
principled framework that adaptively selects the APIs to use for different data
in an online fashion while respecting user's budget. The API selection problem
is cast as an integer linear program, which we show has a special structure
that we leverage to develop an efficient online API selector with strong
performance guarantees. We conduct systematic experiments using ML APIs from
Google, Microsoft, Amazon, IBM, Tencent and other providers for tasks including
multi-label image classification, scene text recognition and named entity
recognition. Across diverse tasks, FrugalMCT can achieve over 90% cost
reduction while matching the accuracy of the best single API, or up to 8%
better accuracy while matching the best API's cost.


Abstract:
  Radio frequency (RF) wireless energy transfer (WET) is a key technology that
may allow seamlessly powering future massive low-energy Internet of Things
(IoT) networks. To enable efficient massive WET, channel state information
(CSI)-limited/free multi-antenna transmit schemes have been recently proposed
in the literature. The idea is to reduce/null the energy costs to be paid by
energy harvesting (EH) IoT nodes from participating in large-scale
time/power-consuming CSI training, but still enable some transmit spatial
gains. In this paper, we take another step forward by proposing a novel
CSI-free rotary antenna beamforming (RAB) WET scheme that outperforms all
state-of-the-art CSI-free schemes in a scenario where a power beacon (PB)
equipped with a uniform linear array (ULA) powers a large set of surrounding EH
IoT devices. RAB uses a properly designed CSI-free beamformer combined with a
continuous or periodic rotation of the ULA at the PB to provide average EH
gains that scale as $0.85\sqrt{M}$, where $M$ is the number of PB's antenna
elements. Moreover, a rotation-specific power control mechanism was proposed to
i) fairly optimize the WET process if devices' positioning information is
available, and/or ii) to avoid hazards to human health in terms of specific
absorption rate (SAR). We show that RAB performance even approaches quickly (or
surpasses, for scenarios with sufficiently large number of EH devices, or when
using the proposed power control) the performance of a traditional full-CSI
based transmit scheme, and it is also less sensitive to SAR constraints.
Finally, we discuss important practicalities related to RAB such as its
robustness against non line-of-sight conditions compared to other CSI-free WET
schemes, and its generalizability to scenarios where the PB uses other than a
ULA topology.


Abstract:
  Understanding inter-site mutual mode interaction in coupled physical systems
is essential to comprehend large compound systems as this local interaction
determines the successive multiple inter-site energy transfer efficiency. We
demonstrate that only the non-Hermitian coupling can correctly account for the
light transfer between two coupled optical cavities. We also reveal that the
non-Hermitian coupling effect becomes much crucial as the system dimension gets
smaller. Our results provide an important insight to deal with general
coupled-devices in the quantum regime.


Abstract:
  Galaxy clusters are filled with hot, diffuse X-ray emitting plasma, with a
stochastically tangled magnetic field whose energy is close to equipartition
with the energy of the turbulent motions \cite{zweibel1997, Vacca}. In the
cluster cores, the temperatures remain anomalously high compared to what might
be expected considering that the radiative cooling time is short relative to
the Hubble time \cite{cowie1977,fabian1994}. While feedback from the central
active galactic nuclei (AGN) \cite{fabian2012,birzan2012,churazov2000} is
believed to provide most of the heating, there has been a long debate as to
whether conduction of heat from the bulk to the core can help the core to reach
the observed temperatures \cite{narayan2001,ruszkowski2002,kunz2011}, given the
presence of tangled magnetic fields. Interestingly, evidence of very sharp
temperature gradients in structures like cold fronts implies a high degree of
suppression of thermal conduction \cite{markevitch2007}. To address the problem
of thermal conduction in a magnetized and turbulent plasma, we have created a
replica of such a system in a laser laboratory experiment. Our data show a
reduction of local heat transport by two orders of magnitude or more, leading
to strong temperature variations on small spatial scales, as is seen in cluster
plasmas \cite{markevitch2003}.


Abstract:
  Humans are increasingly interacting with machines through language, sometimes
in contexts where the user may not know they are talking to a machine (like
over the phone or a text chatbot). We aim to understand how system designers
and researchers might allow their systems to confirm its non-human identity. We
collect over 2,500 phrasings related to the intent of ``Are you a robot?". This
is paired with over 2,500 adversarially selected utterances where only
confirming the system is non-human would be insufficient or disfluent. We
compare classifiers to recognize the intent and discuss the precision/recall
and model complexity tradeoffs. Such classifiers could be integrated into
dialog systems to avoid undesired deception. We then explore how both a
generative research model (Blender) as well as two deployed systems (Amazon
Alexa, Google Assistant) handle this intent, finding that systems often fail to
confirm their non-human identity. Finally, we try to understand what a good
response to the intent would be, and conduct a user study to compare the
important aspects when responding to this intent.


Abstract:
  Unlike theoretical distributed learning (DL), DL over wireless edge networks
faces the inherent dynamics/uncertainty of wireless connections and edge nodes,
making DL less efficient or even inapplicable under the highly dynamic wireless
edge networks (e.g., using mmW interfaces). This article addresses these
problems by leveraging recent advances in coded computing and the deep dueling
neural network architecture. By introducing coded structures/redundancy, a
distributed learning task can be completed without waiting for straggling
nodes. Unlike conventional coded computing that only optimizes the code
structure, coded distributed learning over the wireless edge also requires to
optimize the selection/scheduling of wireless edge nodes with heterogeneous
connections, computing capability, and straggling effects. However, even
neglecting the aforementioned dynamics/uncertainty, the resulting joint
optimization of coding and scheduling to minimize the distributed learning time
turns out to be NP-hard. To tackle this and to account for the dynamics and
uncertainty of wireless connections and edge nodes, we reformulate the problem
as a Markov Decision Process and then design a novel deep reinforcement
learning algorithm that employs the deep dueling neural network architecture to
find the jointly optimal coding scheme and the best set of edge nodes for
different learning tasks without explicit information about the wireless
environment and edge nodes' straggling parameters. Simulations show that the
proposed framework reduces the average learning delay in wireless edge
computing up to 66% compared with other DL approaches. The jointly optimal
framework in this article is also applicable to any distributed learning scheme
with heterogeneous and uncertain computing nodes.


Abstract:
  Whereas low-temperature ferroelectrics have a well understood ordered spatial
dipole arrangement, the fate of these dipoles in paraelectric phases remains
poorly understood. Using density functional theory (DFT), we find that unlike
the case in conventional non-polar ABO$_3$ compounds illustrated here for cubic
BaZrO$_3$, the origin of the distribution of the B site off-centering in cubic
paraelectric such as BaTiO$_3$ is an intrinsic, energy stabilizing symmetry
breaking. Minimizing the internal energy E of a constrained cubic phase already
reveals the formation of a distribution of intrinsic local displacements that
(i) mimic the symmetries of the low temperature phases, while (ii) being the
precursors of what finite temperature DFT Molecular Dynamics finds as thermal
motifs. The implications of such symmetry breaking on the microscopic
structures and anomalous properties in these kinds of PE materials are
discussed.


Abstract:
  The latest advances in computer-assisted precision medicine are making it
feasible to move from population-wide models that are useful to discover
aggregate patterns that hold for group-based analysis to patient-specific
models that can drive patient-specific decisions with regard to treatment
choices, and predictions of outcomes of treatment. Body Composition is
recognized as an important driver and risk factor for a wide variety of
diseases, as well as a predictor of individual patient-specific clinical
outcomes to treatment choices or surgical interventions. 3D CT images are
routinely acquired in the oncological worklows and deliver accurate rendering
of internal anatomy and therefore can be used opportunistically to assess the
amount of skeletal muscle and adipose tissue compartments. Powerful tools of
artificial intelligence such as deep learning are making it feasible now to
segment the entire 3D image and generate accurate measurements of all internal
anatomy. These will enable the overcoming of the severe bottleneck that existed
previously, namely, the need for manual segmentation, which was prohibitive to
scale to the hundreds of 2D axial slices that made up a 3D volumetric image.
Automated tools such as presented here will now enable harvesting whole-body
measurements from 3D CT or MRI images, leading to a new era of discovery of the
drivers of various diseases based on individual tissue, organ volume, shape,
and functional status. These measurements were hitherto unavailable thereby
limiting the field to a very small and limited subset. These discoveries and
the potential to perform individual image segmentation with high speed and
accuracy are likely to lead to the incorporation of these 3D measures into
individual specific treatment planning models related to nutrition, aging,
chemotoxicity, surgery and survival after the onset of a major disease such as
cancer.


Abstract:
  We introduce a class of equivalences, which we call generalized semi-infinite
Hecke equivalences, between certain categories of representations of graded
associative algebras which appear in the setting of semi-infinite cohomology
for associative algebras and categories of representations of related algebras
of Hecke type which we call semi-infinite Hecke algebras. As an application we
obtain an equivalence between a category of representations of a non-twisted
affine Lie algebra $\widehat{\mathfrak g}$ of level $-2h^\vee-k$, where
$h^\vee$ is the dual Coxeter number of the underlying semisimple Lie algebra
$\mathfrak g$ and $k\in \mathbb{C}$, and the category of finitely generated
representations of the W-algebra associated to $\widehat{\mathfrak g}$ of level
$k$. When $k=-h^\vee$ this yields an equivalence between a category of
representations of $\widehat{\mathfrak g}$ of central charge $-h^\vee$ and the
category ${\rm Coh}({\rm Op}_{^LG}(D^\times))$ of coherent sheaves on the space
${\rm Op}_{^LG}(D^\times)$ of $^LG$-opers on the punctured disc $D^\times$,
where $^LG$ is the Langlands dual group to the algebraic group of adjoint type
with Lie algebra $\mathfrak g$. This can be regarded as a version of the local
geometric Langlands correspondence. The above mentioned equivalences generalize
to the case of affine Lie algebras the Skryabin equivalence between the
categories of generalized Gelfand-Graev representations of $\mathfrak g$ and
the categories of representations of the corresponding finitely generated
W-algebras, and Kostant's results on the classification of Whittaker modules
over $\mathfrak g$.


Abstract:
  Fish diseases in aquaculture constitute a significant hazard to nutriment
security. Identification of infected fishes in aquaculture remains challenging
to find out at the early stage due to the dearth of necessary infrastructure.
The identification of infected fish timely is an obligatory step to thwart from
spreading disease. In this work, we want to find out the salmon fish disease in
aquaculture, as salmon aquaculture is the fastest-growing food production
system globally, accounting for 70 percent (2.5 million tons) of the market. In
the alliance of flawless image processing and machine learning mechanism, we
identify the infected fishes caused by the various pathogen. This work divides
into two portions. In the rudimentary portion, image pre-processing and
segmentation have been applied to reduce noise and exaggerate the image,
respectively. In the second portion, we extract the involved features to
classify the diseases with the help of the Support Vector Machine (SVM)
algorithm of machine learning with a kernel function. The processed images of
the first portion have passed through this (SVM) model. Then we harmonize a
comprehensive experiment with the proposed combination of techniques on the
salmon fish image dataset used to examine the fish disease. We have conveyed
this work on a novel dataset compromising with and without image augmentation.
The results have bought a judgment of our applied SVM performs notably with
91.42 and 94.12 percent of accuracy, respectively, with and without
augmentation.


Abstract:
  We calculate the neutrino signal from Population III supermassive star
collapse using a neutrino transfer code originally developed for core collapse
supernovae and massive star collapse. Using this code, we are able to
investigate the supermassive star mass range thought to undergo neutrino
trapping ($\sim 10^4$ M$_\odot$), a mass range which has been neglected by
previous works because of the difficulty of neutrino transfer. For models in
this mass range, we observe a neutrino-sphere with a large radius and low
density compared to typical massive star neutrino-spheres. We calculate the
neutrino light-curve emitted from this neutrino-sphere. The resulting neutrino
luminosity is significantly lower than the results of a previous analytical
model. We briefly discuss the possibility of detecting a neutrino burst from a
supermassive star or the neutrino background from many supermassive stars and
conclude that the former is unlikely with current technology, unless the SMS
collapse is located as close as 1 Mpc, while the latter is also unlikely even
under very generous assumptions. However, the supermassive star neutrino
background is still of interest as it may serve as a source of noise in
proposed dark matter direct detection experiments.


Abstract:
  In the present work, a novel class of hybrid elements is proposed to
alleviate the locking anomaly in non-uniform rational B-spline (NURBS)-based
isogeometric analysis (IGA) using a two-field Hellinger-Reissner variational
principle. The proposed hybrid elements are derived by adopting the independent
interpolation schemes for displacement and stress field. The key highlight of
the present study is the choice and evaluation of higher-order terms for the
stress interpolation function to provide a locking-free solution. Furthermore,
the present study demonstrates the efficacy of the proposed elements with the
treatment of several two-dimensional linear-elastic benchmark problems
alongside the conventional single-field IGA, Lagrangian-based finite element
analysis (FEA), and hybrid FEA formulation. It is shown that the proposed class
of hybrid elements performs effectively for analyzing the nearly incompressible
problem domains that are severely affected by volumetric locking along with the
thin plate and shell problems where the shear and membrane locking is dominant.
A better coarse mesh accuracy of the proposed method in comparison with the
conventional formulation is demonstrated through various numerical examples.
Moreover, the formulation is not restricted to the locking-dominated problem
domains but can also be implemented to solve the problems of general form
without any special treatment. Thus, the proposed method is robust, most
efficient, and highly effective against different types of locking.


Abstract:
  21-cm radio signal has emerged as an important probe in investigating the
dark age of the Universe (recombination to reionization). In the current
analysis, we explore the combined effects of primordial black holes (PBH),
cooling off of the baryonic matter due to dark matter (DM) - baryon collisions
and interaction of dark matter - dark energy (DE) fluid on the 21-cm brightness
temperature. The variation of brightness temperature shows remarkable
dependence on DM mass ($m_{\chi}$) and the dark matter - baryon scattering
cross-section ($\sigma_0$). Bounds in $m_{\chi}$ - $\sigma_0$ parameter space
are obtained for different possible PBH masses and for different interacting
dark energy (IDE) models. These bounds are estimated based on the observed
excess ($-500^{+200}_{-500}$ mK) of 21-cm brightness temperature by EDGES
experiment. Eventually, bounds on PBH mass is also obtained for different
values of dark matter mass and for different IDE model coupling parameters. The
compatibility of the constraints of the IDE models, in the estimated bounds are
also addressed.


Abstract:
  The $N$-qubit Greenberger-Horne-Zeilinger (GHZ) states are the maximally
entangled states of $N$ qubits, which have had many important applications in
quantum information processing, such as quantum key distribution and quantum
secret sharing. Thus how to distinguish the GHZ states from other quantum
states becomes a significant problem. In this work, by presenting a family of
the generalized Clauser-Horne-Shimony-Holt (CHSH) inequality, we show that the
$N$-qubit GHZ states can be indeed identified by the maximal violations of the
generalized CHSH inequality under some specific measurement settings. The
generalized CHSH inequality is simple and contains only four correlation
functions for any $N$-qubit system, thus has the merit of facilitating
experimental verification. Furthermore, we present a quantum phenomenon of
robust violations of the generalized CHSH inequality, in which the maximal
violation of Bell's inequality can be robust under some specific noises adding
to the $N$-qubit GHZ states.


Abstract:
  We develop an effective version of the Chabauty--Kim method which gives
explicit upper bounds on the number of $S$-integral points on a hyperbolic
curve in terms of dimensions of certain Bloch--Kato Selmer groups. Using this,
we give a new "motivic" proof that the number of solutions to the $S$-unit
equation is bounded uniformly in terms of $\#S$.


Abstract:
  The tractability of certain CSPs for dense or sparse instances is known from
the 90s. Recently, the densification and the sparsification of CSPs were
formulated as computational tasks and the systematical study of their
computational complexity was initiated.
  We approach this problem by introducing the densification operator, i.e. the
closure operator that, given an instance of a CSP, outputs all constraints that
are satisfied by all of its solutions. According to the Galois theory of
closure operators, any such operator is related to a certain implicational
system (or, a functional dependency) $\Sigma$. We are specifically interested
in those classes of fixed-template CSPs, parameterized by constraint languages
$\Gamma$, for which the size of an implicational system $\Sigma$ is a
polynomial in the number of variables $n$. We show that in the Boolean case,
$\Sigma$ is of polynomial size if and only if $\Gamma$ is of bounded width. For
such languages, $\Sigma$ can be computed in log-space or in a logarithmic time
with a polynomial number of processors. Given an implicational system $\Sigma$,
the densification task is equivalent to the computation of the closure of input
constraints. The sparsification task is equivalent to the computation of the
minimal key. This leads to ${\mathcal O}({\rm poly}(n)\cdot N^2)$-algorithm for
the sparsification task where $N$ is the number of non-redundant
sparsifications of an original CSP.
  Finally, we give a complete classification of constraint languages over the
Boolean domain for which the densification problem is tractable.


Abstract:
  As the COVID-19 ravaging through the globe, accurate forecasts of the disease
spread is crucial for situational awareness, resource allocation, and public
health decision-making. Alternative to the traditional disease surveillance
data collected by the United States (US) Centers for Disease Control and
Prevention (CDC), big data from Internet such as online search volumes has been
previously shown to contain valuable information for tracking infectious
disease dynamics. In this study, we evaluate the feasibility of using Internet
search volume of relevant queries to track and predict COVID-19 pandemic. We
found strong association between COVID-19 death trend and the search volume of
symptom-related queries such as "loss of taste". Then, we further develop an
influenza-tracking model to predict future 4-week COVID-19 deaths on the US
national level, by combining search volume information with COVID-19 time
series information. Encouraged by the 20% error reduction on national level
comparing to the baseline time series model, we additionally build state-level
COVID-19 deaths models, leveraging the cross-state cross-resolution spatial
temporal framework that pools information from search volume and COVID-19
reports across states, regions and the nation. These variants of ARGOX are then
aggregated in a winner-takes-all ensemble fashion to produce the final
state-level 4-week forecasts. Numerical experiments demonstrate that our method
steadily outperforms time series baseline models, and achieves the
state-of-the-art performance among the publicly available benchmark models.
Overall, we show that disease dynamics and relevant public search behaviors
co-evolve during the COVID-19 pandemic, and capturing their dependencies while
leveraging historical cases/deaths as well as spatial-temporal cross-region
information will enable stable and accurate US national and state-level
forecasts.


Abstract:
  Exploiting the inner-shot and inter-shot dependencies is essential for
key-shot based video summarization. Current approaches mainly devote to
modeling the video as a frame sequence by recurrent neural networks. However,
one potential limitation of the sequence models is that they focus on capturing
local neighborhood dependencies while the high-order dependencies in long
distance are not fully exploited. In general, the frames in each shot record a
certain activity and vary smoothly over time, but the multi-hop relationships
occur frequently among shots. In this case, both the local and global
dependencies are important for understanding the video content. Motivated by
this point, we propose a Reconstructive Sequence-Graph Network (RSGN) to encode
the frames and shots as sequence and graph hierarchically, where the
frame-level dependencies are encoded by Long Short-Term Memory (LSTM), and the
shot-level dependencies are captured by the Graph Convolutional Network (GCN).
Then, the videos are summarized by exploiting both the local and global
dependencies among shots. Besides, a reconstructor is developed to reward the
summary generator, so that the generator can be optimized in an unsupervised
manner, which can avert the lack of annotated data in video summarization.
Furthermore, under the guidance of reconstruction loss, the predicted summary
can better preserve the main video content and shot-level dependencies.
Practically, the experimental results on three popular datasets i.e., SumMe,
TVsum and VTW) have demonstrated the superiority of our proposed approach to
the summarization task.


Abstract:
  Recently discovered photonic higher-order topological insulators enable
unprecedented flexibility in the robust localization of light in structures of
different dimensionality. While the potential of the two-dimensional systems is
currently under active investigation, only a few studies explore the physics of
the three-dimensional higher-order topological insulators. Here we propose a
three-dimensional structure with cubic symmetry exhibiting vanishing bulk
polarization but nonzero corner charge and hosting a zero-dimensional corner
state mediated by the long-range interactions. We trace the evolution of the
corner state with the next-nearest-neighbor coupling strength and prove the
topological origin of the corner mode calculating the associated topological
invariants. Our results thus reveal the potential of long-range couplings for
the formation of three-dimensional higher-order topological phases.


Abstract:
  World has been introduced with a new way of living by inventing this advanced
technology Internet of Things (IOT). By using this technology all the
real-world objects can be made to interconnect and communicate with each other
which can be done by diagnosing, sensing and networking over the internet to
fulfill some objectives. Due to advancements that have taken place in sensors,
wireless communication systems and cloud computing has brought steps to invent
this innovative technology. This paper deals with the elements, applications
and the challenges faced by Internet of Things. This paper mainly concentrates
on the current scenario of IoT


Abstract:
  We compare the two basic ordinal patterns, i.e., the original and amplitude
permutations, used to characterize vector structures. The original permutation
consists of the indexes of reorganized values in the original vector. By
contrast, the amplitude permutation comprises the positions of values in the
reordered vector, and it directly reflects the temporal structure. To
accurately convey the structural characteristics of vectors, we modify indexes
of equal values in permutations to be the same as, for example, the smallest or
largest indexes in each group of equalities. Overall, we clarify the
relationship between the original and amplitude permutations. And the results
have implications for time- and amplitude-symmetric vectors and will lead to
further theoretical and experimental studies.


Abstract:
  We analyse the high-quality Hertzsprung-Russell diagram (HRD) derived from
Gaia data release 2 for the Solar Neighbourhood. We start building an
almost-complete sample within 200 pc and for |b|>25 deg, so as to limit the
impact of known errors and artefacts in the Gaia catalog. Particular effort is
then put into improving the modelling of population of binaries, which produce
two marked features in the HRD: the sequence of near-equal mass binaries along
the lower main sequence, and the isolated group of hot subdwarfs. We describe a
new tool, BinaPSE, to follow the evolution of interacting binaries in a way
that improves the consistency with PARSEC evolutionary tracks for single stars.
BinaPSE is implemented into the TRILEGAL code for the generation of "partial
models" for both single and binary stellar populations, taking into account the
presence of resolved and unresolved binaries. We then fit the Gaia HRD via MCMC
methods that search for the star formation history (SFH) and initial binary
fraction (by mass) that maximise the likelihood. The main results are (i) the
binary fraction derived from the lower main sequence is close to 0.4, while
twice larger values are favoured when the upper part of the HRD is fitted; (ii)
present models predict the observed numbers of hot subdwarfs to within a factor
of 2; (iii) irrespective of the prescription for the binaries, the star
formation rate peaks at values 1.5e-4 Msun/yr at ages slightly above 2 Gyr, and
then decreases to 0.8e-4 Msun/yr at very old ages.


Abstract:
  Unsupervised representation learning of words from large multilingual corpora
is useful for downstream tasks such as word sense disambiguation, semantic text
similarity, and information retrieval. The representation precision of
log-bilinear fastText models is mostly due to their use of subword information.
In previous work, the optimization of fastText's subword sizes has not been
fully explored, and non-English fastText models were trained using subword
sizes optimized for English and German word analogy tasks. In our work, we find
the optimal subword sizes on the English, German, Czech, Italian, Spanish,
French, Hindi, Turkish, and Russian word analogy tasks. We then propose a
simple n-gram coverage model and we show that it predicts better-than-default
subword sizes on the Spanish, French, Hindi, Turkish, and Russian word analogy
tasks. We show that the optimization of fastText's subword sizes matters and
results in a 14% improvement on the Czech word analogy task. We also show that
expensive parameter optimization can be replaced by a simple n-gram coverage
model that consistently improves the accuracy of fastText models on the word
analogy tasks by up to 3% compared to the default subword sizes, and that it is
within 1% accuracy of the optimal subword sizes.


Abstract:
  Rotated object detection is a challenging issue of computer vision field.
Loss of spatial information and confusion of parametric order have been the
bottleneck for rotated detection accuracy. In this paper, we propose an
orientation-sensitive keypoint based rotated detector OSKDet. We adopt a set of
keypoints to characterize the target and predict the keypoint heatmap on ROI to
form a rotated target. By proposing the orientation-sensitive heatmap, OSKDet
could learn the shape and direction of rotated target implicitly and has
stronger modeling capabilities for target representation, which improves the
localization accuracy and acquires high quality detection results. To extract
highly effective features at border areas, we design a rotation-aware
deformable convolution module. Furthermore, we explore a new keypoint reorder
algorithm and feature fusion module based on the angle distribution to
eliminate the confusion of keypoint order. Experimental results on several
public benchmarks show the state-of-the-art performance of OSKDet.
Specifically, we achieve an AP of 77.81% on DOTA, 89.91% on HRSC2016, and
97.18% on UCAS-AOD, respectively.


Abstract:
  In large studies involving multi protocol Magnetic Resonance Imaging (MRI),
it can occur to miss one or more sub-modalities for a given patient owing to
poor quality (e.g. imaging artifacts), failed acquisitions, or hallway
interrupted imaging examinations. In some cases, certain protocols are
unavailable due to limited scan time or to retrospectively harmonise the
imaging protocols of two independent studies. Missing image modalities pose a
challenge to segmentation frameworks as complementary information contributed
by the missing scans is then lost. In this paper, we propose a novel model,
Multi-modal Gaussian Process Prior Variational Autoencoder (MGP-VAE), to impute
one or more missing sub-modalities for a patient scan. MGP-VAE can leverage the
Gaussian Process (GP) prior on the Variational Autoencoder (VAE) to utilize the
subjects/patients and sub-modalities correlations. Instead of designing one
network for each possible subset of present sub-modalities or using frameworks
to mix feature maps, missing data can be generated from a single model based on
all the available samples. We show the applicability of MGP-VAE on brain tumor
segmentation where either, two, or three of four sub-modalities may be missing.
Our experiments against competitive segmentation baselines with missing
sub-modality on BraTS'19 dataset indicate the effectiveness of the MGP-VAE
model for segmentation tasks.


Abstract:
  We compute the ghost spectral function in Yang-Mills theory by solving the
corresponding Dyson-Schwinger equation for a given input gluon spectral
function. The results encompass both scaling and decoupling solutions for the
gluon propagator input. The resulting ghost spectral function displays a
particle peak at vanishing momentum and a negative scattering spectrum, whose
infrared and ultraviolet tails are obtained analytically. The ghost dressing
function is computed in the entire complex plane, and its salient features are
identified and discussed.


Abstract:
  State estimation of multi-modal hybrid systems is an important problem with
many applications in the field robotics. However, incorporating discrete modes
in the estimation process is hampered by a potentially combinatorial growth in
computation. In this paper we present a novel incremental multi-hypothesis
smoother based on eliminating a hybrid factor graph into a multi-hypothesis
Bayes tree, which represents possible discrete state sequence hypotheses.
Following iSAM, we enable incremental inference by conditioning the past on the
future but we add to that the capability of maintaining multiple discrete mode
histories, exploiting the temporal structure of the problem to obtain a
simplified representation that unifies the multiple hypothesis tree with the
Bayes tree. In the results section we demonstrate the generality of the
algorithm with examples in three problem domains: lane change detection (1D),
aircraft maneuver detection (2D), and contact detection in legged robots (3D).


Abstract:
  The knowledge of the ages of stars hosting exoplanets allows us to obtain an
overview on the evolution of exoplanets and understand the mechanisms affecting
their life. The measurement of the ages of stars in the Galaxy is usually
affected by large uncertainties. An exception are the stellar clusters: for
their coeval members, born from the same molecular cloud, ages can be measured
with extreme accuracy. In this context, the project PATHOS is providing
candidate exoplanets orbiting members of stellar clusters and associations
through the analysis of high-precision light curves obtained with cutting-edge
tools. In this work, we exploited the data collected during the second year of
the TESS mission. We extracted, analysed, and modelled the light curves of
$\sim 90000$ stars in open clusters located in the northern ecliptic hemisphere
in order to find candidate exoplanets. We measured the frequencies of candidate
exoplanets in open clusters for different orbital periods and planetary radii,
taking into account the detection efficiency of our pipeline and the false
positive probabilities of our candidates. We analysed the Age--$R_{\rm P}$
distribution of candidate and confirmed exoplanets with periods $<100$ days and
well constrained ages. While no peculiar trends are observed for Jupiter-size
and (super-)Earth-size planets, we found that objects with $4\,R_{\rm Earth}
\lesssim R_{\rm P} \lesssim 13\,R_{\rm Earth}$ are concentrated at ages
$\lesssim 200$ Myr; different scenarios (atmospheric losses, migration, etc.)
are considered to explain the observed age-$R_{\rm P}$ distribution.


Abstract:
  In this work, we propose an automatic mesh generation algorithm, FlowMesher,
which can be used to generate unstructured meshes for mesh domains in any shape
with minimum (or even no) user intervention. The approach can generate
high-quality simplex meshes directly from scanned images in OBJ format in 2D
and 3D or just from a line drawing in 2-D. Mesh grading can be easily
controlled also. The FlowMesher is robust and easy to be implemented and is
useful for a variety of applications including surgical simulators.
  The core idea of the FlowMesher is that a mesh domain is considered as an
"airtight container" into which fluid particles are "injected" at one or
multiple selected interior points. The particles repel each other and occupy
the whole domain somewhat like blowing up a balloon. When the container is full
of fluid particles and the flow is stopped, a Delaunay triangulation algorithm
is employed to link the fluid particles together to generate an unstructured
mesh (which is then optimized using a combination of automated mesh smoothing
and element removal in 3D). The performance of the FlowMesher is demonstrated
by generating meshes for several 2D and 3D mesh domains including a scanned
image of a bone.


Abstract:
  We are interested in solving the congruences $f^3+g^3+1\equiv 0\pmod{fg}$ and
$f^4-4g^2+4\equiv 0\pmod{fg}$ in polynomials $f, g$ with rational coefficients.
Moreover, we present results of computations of all integer points on certain
one parametric curves of genus 1 and 3, related to cubic and quartic fields,
respectively.


Abstract:
  The earliest phases of star formation are characterised by intense mass
accretion from the circumstellar disk to the central star. One group of young
stellar objects, the FU Orionis-type stars exhibit accretion rate peaks
accompanied by bright eruptions. The occurrence of these outbursts might solve
the luminosity problem of protostars, play a key role in accumulating the final
star mass, and have a significant effect on the parameters of the envelope and
the disk. In the framework of the Structured Accretion Disks ERC project, we
are conducting a systematic investigation of these sources with millimeter
interferometry to examine whether they represent normal young stars in
exceptional times or they are unusual objects. Our results show that FU
Orionis-type stars can be similar to both Class I and Class II systems and may
be in a special evolutionary phase between the two classes with their
infall-driven episodic eruptions being the main driving force of the
transition.


Abstract:
  The cosmological constant if considered as a fundamental constant, provides
an information treatment for gravitation problems, both cosmological and of
black holes. The efficiency of that approach is shown via gedanken experiments
for the information behavior of the horizons for Schwarzschild-de Sitter and
Kerr-de Sitter metrics. A notion of entropy regarding any observer and in all
possible non-extreme black hole solutions is suggested, linked also to
Bekenstein bound. The suggested information approach forbids the existence of
naked singularities.


Abstract:
  This paper is an attempt to study the thermodynamics of the structure
formation in the large scale universe in the non local gravity using Boltzmann
statistics and the Tsallis statistics. The partition function is obtained in
both the approaches and the corresponding thermodynamics properties are
evaluated. The important thing about the paper is that we surprisingly get the
divergence free integrals and thus stress upon the fact that the nonlocal
gravity is the singularity free model of gravity.


Abstract:
  The main point of the paper is to take the explicit motivic Chabauty-Kim
method developed in papers of Dan-Cohen--Wewers and Dan-Cohen and the author
and make it work for non-rational curves. In particular, we calculate the
abstract form of an element of the Chabauty-Kim ideal for
$\mathbb{Z}[1/\ell]$-points on a punctured elliptic curve, and lay some
groundwork for certain kinds of higher genus curves. For this purpose, we
develop an "explicit Tannakian Chabauty-Kim method" using
$\mathbb{Q}_{p}$-Tannakian categories of Galois representations in place of
$\mathbb{Q}$-linear motives. In future work, we intend to use this method to
explicitly apply the Chabauty-Kim method to a curve of positive genus in a
situation where Quadratic Chabauty does not apply.


Abstract:
  Source code (Context) and its parsed abstract syntax tree (AST; Structure)
are two complementary representations of the same computer program.
Traditionally, designers of machine learning models have relied predominantly
either on Structure or Context. We propose a new model, which jointly learns on
Context and Structure of source code. In contrast to previous approaches, our
model uses only language-agnostic features, i.e., source code and features that
can be computed directly from the AST. Besides obtaining state-of-the-art on
monolingual code summarization on all five programming languages considered in
this work, we propose the first multilingual code summarization model. We show
that jointly training on non-parallel data from multiple programming languages
improves results on all individual languages, where the strongest gains are on
low-resource languages. Remarkably, multilingual training only from Context
does not lead to the same improvements, highlighting the benefits of combining
Structure and Context for representation learning on code.


Abstract:
  The purpose of this work is to introduce and characterize the Bounded
Acceleration Shortest Path (BASP) problem, a generalization of the Shortest
Path (SP) problem. This problem is associated to a graph: the nodes represent
positions of a mobile vehicle and the arcs are associated to pre-assigned
geometric paths that connect these positions. BASP consists in finding the
minimum-time path between two nodes. Differently from SP, we require that the
vehicle satisfy bounds on maximum and minimum acceleration and speed, that
depend on the vehicle position on the currently traveled arc. We prove that
BASP is NP-hard and define solution algorithm that achieves polynomial
time-complexity under some additional hypotheses on problem data.


Abstract:
  We studied the $e^+e^- \to h \gamma $ process at the International Linear
Collider (ILC) at $\sqrt{s}=250$ GeV, based on the full detector simulation of
the International Large Detector (ILD). This process is loop-induced in the
Standard Model (SM) and is sensitive to new physics which alters $h \gamma
\gamma$ or $h \gamma Z$ coupling. We performed the analysis by employing the
leading signal channels with $h \to b \bar{b}$ and $h \to WW^*$ and including
full SM background processes. The results are obtained for two scenarios of
beam polarisations each with an integrated luminosity of 900 fb$^{-1}$. We
found the expected significance of the SM signal is 0.40$\sigma$ for
$P(e^-,e^+)=(-0.8,+0.3)$ (the left-handed polarisation), and 0.06$\sigma$ for
$P(e^-,e^+)=(+0.8,-0.3)$ (the right-handed polarisation). The bounds on new
physics effects are reported as the 95% C.L. upper limit for the cross-section
of $e^+e^- \to h \gamma$: $\sigma_{h\gamma}^L <$ 1.8 fb and $\sigma_{h\gamma}^R
<$ 0.5 fb respectively for left- and right-handed polarisations. The
constraints on effective $h\gamma Z$ couplings are to be further studied.


Abstract:
  We present a finite-energy electroweak-monopole solution obtained by
considering non-linear extensions of the hypercharge sector of the Electroweak
Theory, based on logarithmic and exponential versions of electrodynamics. We
find constraints for a class of non-linear extensions and also work out an
estimate for the monopole mass in this scenario. We finally derive a lower
bound for the energy of the monopole and discuss the simpler case of a Dirac
magnetic charge.


Abstract:
  NASA Astrophysics Division funds development of cutting-edge technology to
enable its missions to achieve ambitious and groundbreaking science goals.
These technology development efforts are managed by the Physics of the Cosmos,
Cosmic Origins, and Exoplanet Exploration Programs. The NASA Strategic
Astrophysics Technology Program (SAT) was established in 2009 as a new
technology maturation program to fill the gap in the Technology Readiness Level
range from 3 to 6. Since program inception, 100 SAT grants have been openly
competed and awarded, along with dozens of direct-funded projects, leading to a
host of technologies advancing their Technology Readiness Levels and/or being
infused into space and suborbital missions and ground-based projects. We
present the portfolio distribution in terms of specific technology areas
addressed, including optics, detectors, coatings, corona graphs, star shades,
lasers, electronics, and cooling subsystems. We show an analysis of the rate of
Technology Readiness Level advances, infusion success stories, and other
benefits such as training the future astrophysics workforce, including students
and postdoctoral fellows hired by projects. Finally, we present the
Astrophysics Division current strategic technology maturation priorities for
investment, enabling a range of future strategic astrophysics missions.


Abstract:
  In this paper the seven-decade historical dataset of the Hungarian power
system is used to perform vulnerability assessment applying a complex network
approach.


Abstract:
  Each orthogonal group O(n) has a nontrivial GL(1)-extension, which we call
GPin(n). The identity component of GPin(n) is the more familiar GSpin(n), the
general Spin group. We prove that the restriction to GPin(n-1) of an
irreducible admissible representation of GPin(n) over a nonarchimedean local
field of characteristic zero is multiplicity free and also prove the analogous
theorem for GSpin(n). The case for GPin(n) is the analogue of the theorem for
O(n) proven by Aizenbud, Gourevitch, Rallis and Schiffmann, and the case for
GSpin(n) the one for SO(n) by Waldspurger.


Abstract:
  I first met Louis Nirenberg in person in 1972 when I became a Courant
Instructor. He was already a celebrated mathematician and a suave sophisticated
New Yorker, even though he was born in Hamilton, Canada and grew up in
Montreal. In this informal style paper I will describe some of his famous
papers, some of our joint work and other work he inspired. I will concentrate
on some of Louis' work inspired by geometric problems beginning around 1974,
especially the method of moving planes and implicit fully nonlinear elliptic
equations. I have also sprinkled throughout some comments on his character and
personality that I believe contributed to his great success.


Abstract:
  Phytoparasitic nematodes (or phytonematodes) are causing severe damage to
crops and generating large-scale economic losses worldwide. In soybean crops,
annual losses are estimated at 10.6% of world production. Besides, identifying
these species through microscopic analysis by an expert with taxonomy knowledge
is often laborious, time-consuming, and susceptible to failure. In this
perspective, robust and automatic approaches are necessary for identifying
phytonematodes capable of providing correct diagnoses for the classification of
species and subsidizing the taking of all control and prevention measures. This
work presents a new public data set called NemaDataset containing 3,063
microscopic images from five nematode species with the most significant damage
relevance for the soybean crop. Additionally, we propose a new Convolutional
Neural Network (CNN) model defined as NemaNet and a comparative assessment with
thirteen popular models of CNNs, all of them representing the state of the art
classification and recognition. The general average calculated for each model,
on a from-scratch training, the NemaNet model reached 96.99% accuracy, while
the best evaluation fold reached 98.03%. In training with transfer learning,
the average accuracy reached 98.88\%. The best evaluation fold reached 99.34%
and achieve an overall accuracy improvement over 6.83% and 4.1%, for
from-scratch and transfer learning training, respectively, when compared to
other popular models.


Abstract:
  The segmentation of emails into functional zones (also dubbed email zoning)
is a relevant preprocessing step for most NLP tasks that deal with emails.
However, despite the multilingual character of emails and their applications,
previous literature regarding email zoning corpora and systems was developed
essentially for English.
  In this paper, we analyse the existing email zoning corpora and propose a new
multilingual benchmark composed of 625 emails in Portuguese, Spanish and
French. Moreover, we introduce OKAPI, the first multilingual email segmentation
model based on a language agnostic sentence encoder. Besides generalizing well
for unseen languages, our model is competitive with current English benchmarks,
and reached new state-of-the-art performances for domain adaptation tasks in
English.


Abstract:
  Size distribution of sunspots provides key information about the generation
and emergence processes of the solar magnetic field. Previous studies on the
size distribution have primarily focused on either the whole group or
individual spot areas. In this paper, we investigate the organization of spot
areas within sunspot groups. In particular, we analyze the ratio, $\rm{R}$, of
the area of the biggest spot ($\rm{A_{big\_spot}}$) inside a group, to the
total area of that group ($\rm{A_{group}}$). We use sunspot observations from
Kislovodsk, Pulkovo and Debrecen observatories, together covering solar cycles
17 to 24. We find that at the time when the group area reaches its maximum, the
single biggest spot in a group typically occupies about 60% of the group area.
For half of all groups, $\rm R$ lies in the range between roughly 50% and 70%.
We also find R to change with the group area, $\rm{A_{group}}$, such that
$\rm{R}$ reaches a maximum of about 0.65 for groups with $\rm{A_{group}}\approx
200\mu$Hem and then remains at about 0.6 for lager groups. Our findings imply a
scale invariant emergence pattern, providing an observational constraint on the
emergence process. Furthermore, extrapolation of our results to larger sunspot
groups may have a bearing on the giant unresolved starspot features found in
Doppler images of highly active sun-like stars. Our results suggest that such
giant features are composed of multiple spots, with the largest spot occupying
roughly 55--75% of the total group area (i.e. of the area of the giant
starspots seen in Doppler images).


Abstract:
  After the appearance of the no-cloning theorem, approximate quantum cloning
machines (QCMs) have become one of the most well-studied subject in quantum
information theory. Among several measures to quantify the performance of a
QCM, single-qudit fidelity and global fidelity have been most widely used. In
this paper we compute the optimal global fidelity for phase-covariant cloning
machines via semi-definite programming optimization, thereby completing a
remaining gap in the previous results on QCMs. We also consider optimal
simulations of the cloning and transpose cloning map, both by a direct
optimization and by a composition of component-wise optimal QCMs. For the
cloning map the composition method is sub-optimal whereas for the transpose
cloning map the method is asymptotically optimal.


Abstract:
  We construct soliton solution of a variable coefficients nonlinear
Schr\"odinger equation in the presence of parity reflection - time reversal
$(\mathcal{PT})-$ symmetric Rosen-Morse potential using similarity
transformation technique. We transform the variable coefficients nonlinear
Schr\"odinger equation into the nonlinear Schr\"odinger equation with
$\mathcal{PT}-$symmetric potential with certain integrability conditions. We
investigate in-detail the features of the obtained soliton solutions with two
different forms of dispersion parameters. Further, we analyze the nonlinear
tunneling effect of soliton profiles by considering two different forms of
nonlinear barrier/well and dispersion barrier/well. Our results show that the
soliton can tunnel through nonlinear barrier/well and dispersion barrier/well
with enlarged and suppressed amplitudes depending on the sign of the height.
Our theoretical findings are experimentally realizable and might help to model
the optical devices.


Abstract:
  Grant-free non-orthogonal multiple access (GF-NOMA) is a potential technique
to support massive Ultra-Reliable and Low-Latency Communication (mURLLC)
service. However, the dynamic resource configuration in GF-NOMA systems is
challenging due to random traffics and collisions, that are unknown at the base
station (BS). Meanwhile, joint consideration of the latency and reliability
requirements makes the resource configuration of GF-NOMA for mURLLC more
complex. To address this problem, we develop a general learning framework for
signature-based GF-NOMA in mURLLC service taking into account the multiple
access signature collision, the UE detection, as well as the data decoding
procedures for the K-repetition GF and the Proactive GF schemes. The goal of
our learning framework is to maximize the long-term average number of
successfully served users (UEs) under the latency constraint. We first perform
a real-time repetition value configuration based on a double deep Q-Network
(DDQN) and then propose a Cooperative Multi-Agent learning technique based on
the DQN (CMA-DQN) to optimize the configuration of both the repetition values
and the contention-transmission unit (CTU) numbers. Our results show that the
number of successfully served UEs under the same latency constraint in our
proposed learning framework is up to ten times for the K-repetition scheme, and
two times for the Proactive scheme, more than that with fixed repetition values
and CTU numbers. In addition, the superior performance of CMA-DQN over the
conventional load estimation-based approach (LE-URC) demonstrates its
capability in dynamically configuring in long term. Importantly, our general
learning framework can be used to optimize the resource configuration problems
in all the signature-based GF-NOMA schemes.


Abstract:
  This paper outlines an approach for IEEE to take leadership for digital
privacy to align many existing IEEE Societies and efforts in the areas of
computer systems & applications security, organizational & global
architectures, policy-supporting legislation, originating new standards,
integrating compliance into technologies, and helping design decision-board
infrastructures for governance bodies. Much of the current emphasis on evolving
privacy technologies centers on big corporate enterprises and institutions,
causing the industry to support corporate assets protection mainly. Fostering
technology to empower individual privacy-enabling tools has lagged, and
personal privacy has diminished because corporate big data applications have
made sizable investments into exploiting private data. As one of the largest
individual-member-based organizations, IEEE is urged to develop a collaborative
approach for digital privacy with privacy-enabling technologies to benefit its
members. The recommendations outlined define a prospective course that could
result in future global individualized privacy capabilities which employ a
combination of synergistic technologies such as distributed ledgers,
differential privacy, homomorphic encryption, secure distributed multi-party
computation, zero-trust architectures, proof-of-origin of data, software, or
other techniques. Such an effort would involve community engagement and
outreach, academic peer-review events, the establishment of governance bodies,
coordination & expansion of existing standards, and the development of
publicly-accessible prototypes. Collaboration with other IEEE-sponsored efforts
for transactive energy systems, confidentiality and security of healthcare
records and devices, and other IEEE-funded projects will help magnify digital
privacy investments already in progress in these applications of emerging
technologies.


Abstract:
  Cardiovascular disease is a major threat to health and one of the primary
causes of death globally. The 12-lead ECG is a cheap and commonly accessible
tool to identify cardiac abnormalities. Early and accurate diagnosis will allow
early treatment and intervention to prevent severe complications of
cardiovascular disease. In the PhysioNet/Computing in Cardiology Challenge
2020, our objective is to develop an algorithm that automatically identifies 27
ECG abnormalities from 12-lead ECG recordings.


Abstract:
  With the rapid innovations in technology, wireless internet-connected devices
are more ubiquitous than ever and can be found in virtually every aspect of
both our personal and professional lives. In this paper, we propose a
comprehensive literature review that focuses on various network components that
create connectivity among different devices, specifically Wireless Sensor
Networks (WSNs), Radio-Frequency Identification (RFID) tags, Internet of Things
(IoT) devices, and how these devices helped usher in the 4th Industrial
Revolution, or Industry 4.0. This paper focuses on the protocols, architecture,
uses, security concerns, and solutions used in these network technologies, as
well as their differences and similarities.


Abstract:
  Being capable of sensing and behavioral adaptation in line with their
changing environments, cognitive cyber-physical systems (CCPSs) are the new
form of applications in future wireless networks. With the advancement of the
machine learning algorithms, the transmission scheme providing the best
performance can be utilized to sustain a reliable network of CCPS agents
equipped with self-decision mechanisms, where the interactions between each
agent are modeled in terms of service quality, security, and cost dimensions.
In this work, first, we provide network utility as a reliability metric, which
is a weighted sum of the individual utility values of the CCPS agents. The
individual utilities are calculated by mixing the quality of service (QoS),
security, and cost dimensions with the proportions determined by the
individualized user requirements. By changing the proportions, the CCPS network
can be tuned for different applications of next-generation wireless networks.
Then, we propose a secure transmission policy selection (STPS) mechanism that
maximizes the network utility by using the Markov-decision process (MDP). In
STPS, the CCPS network jointly selects the best performing physical layer
security policy and the parameters of the selected secure transmission policy
to adapt to the changing environmental effects. The proposed STPS is realized
by reinforcement learning (RL), considering its real-time decision mechanism
where agents can decide automatically the best utility providing policy in an
altering environment.


Abstract:
  Maximum bound principle (MBP) is an important property for a large class of
semilinear parabolic equations, in the sense that the time-dependent solution
of the equation with appropriate initial and boundary conditions and nonlinear
operator preserves for all time a uniform pointwise bound in absolute value. It
has been a challenging problem on how to design unconditionally MBP-preserving
high-order accurate time-stepping schemes for these equations. In this paper,
we combine the integrating factor Runge-Kutta (IFRK) method with the linear
stabilization technique to develop a stabilized IFRK (sIFRK) method, and
successfully derive sufficient conditions for the proposed method to preserve
MBP unconditionally in the discrete setting. We then elaborate some sIFRK
schemes with up to the third-order accuracy, which are proven to be
unconditionally MBP-preserving by verifying these conditions. In addition, it
is shown that many classic strong stability-preserving sIFRK schemes do not
satisfy these conditions except the first-order one. Extensive numerical
experiments are also carried out to demonstrate the performance of the proposed
method.


Abstract:
  Polygonal meshes are ubiquitous, but have only played a relatively minor role
in the deep learning revolution. State-of-the-art neural generative models for
3D shapes learn implicit functions and generate meshes via expensive
iso-surfacing. We overcome these challenges by employing a classical spatial
data structure from computer graphics, Binary Space Partitioning (BSP), to
facilitate 3D learning. The core operation of BSP involves recursive
subdivision of 3D space to obtain convex sets. By exploiting this property, we
devise BSP-Net, a network that learns to represent a 3D shape via convex
decomposition without supervision. The network is trained to reconstruct a
shape using a set of convexes obtained from a BSP-tree built over a set of
planes, where the planes and convexes are both defined by learned network
weights. BSP-Net directly outputs polygonal meshes from the inferred convexes.
The generated meshes are watertight, compact (i.e., low-poly), and well suited
to represent sharp geometry. We show that the reconstruction quality by BSP-Net
is competitive with those from state-of-the-art methods while using much fewer
primitives. We also explore variations to BSP-Net including using a more
generic decoder for reconstruction, more general primitives than planes, as
well as training a generative model with variational auto-encoders. Code is
available at https://github.com/czq142857/BSP-NET-original.


Abstract:
  Euler wrote several papers on Astronomy, most of them in Latin. This is a
commented translation of E304 'Considerationes de motu corporum coelestium'
(Considerations on the motion of celestial bodies). In this publication, Euler
essentially focuses on the solution of two particular motions of a three-body
problem consisting of Sun, Earth and Moon. The first motion, represents a
hypothetical situation of these three celestial bodies in perpetual alignment
in syzygy - the three-body problem on a straight line -, for which the
parameter that controls the distances among the planets was found to be given
by a quintic function. The conclusion was that if the Moon were four times more
distant from the Earth (either in conjunction or in opposition), a motion of
this kind would have been possible to exist, such that the Moon would appear
always connected to the Sun. The second motion considered by Euler was Moon
libration, when these planets are aligned in regular syzygy. Here, perhaps for
the first time, Euler introduces an archaic form of a Fourier sine series
expansion to describe the Moon's wagging motion. However, as Euler himself
recognizes, the calculations turned out very tedious, and led him to greatly
simplify his model in order to obtain some numerical values for the phenomenon.


Abstract:
  The ambitious energy target to achieve climate-neutrality in the European
Union (EU) energy system raises the feasibility question of using only
renewables across all energy sectors. As one of the EU's leading industrialized
countries, Germany has adopted several climate-action plans for the realistic
implementation and maximum utilization of renewable energies in its energy
system. The literature review shows a clear gap in comprehensive techniques
describing an open modeling approach for analyzing fully renewable and
sector-coupled energy systems. This paper outlines a method for analyzing the
100% renewable-based and sector-coupled energy system's feasibility in Germany.
Based on the open energy modeling framework, an hourly optimization tool
'OSeEM-DE' is developed to investigate the German energy system. The model
results show that a 100% renewable-based and sector-coupled system for
electricity and building heat is feasible in Germany. The investment capacities
and component costs depend on the parametric variations of the developed
scenarios. The annual investment costs vary between 17.6 and 26.6 bn Euro/yr
for volatile generators and between 23.7 and 28.8 bn Euro/yr for heat
generators. The model suggests an investment of a minimum of 2.7-3.9 bn Euro/yr
for electricity and heat storage. Comparison of OSeEM-DE results with recent
studies validates the percentage-wise energy mix composition and the calculated
Levelized Cost of Electricity (LCOE) values from the model. Sensitivity
analyses indicate that storage and grid expansion maximize the system's
flexibility and decrease the investment cost. The study concludes by showing
how the tool can analyze different energy systems in the EU context.


Abstract:
  We consider linear codes over a finite field $\mathbb{F}_q$, for odd $q$,
derived from determinantal varieties, obtained from symmetric matrices of
bounded ranks. A formula for the weight of a codeword is derived. Using this
formula, we have computed the minimum distance for the codes corresponding to
matrices upperbounded by any fixed, even rank. A conjecture is proposed for the
cases where the upper bound is odd. At the end of the article, tables for the
weights of these codes, for spaces of symmetric matrices up to order $5$, are
given.


Abstract:
  Elementary cellular automata (ECA) present iconic examples of complex
systems. Though described only by one-dimensional strings of binary cells
evolving according to nearest-neighbour update rules, certain ECA rules
manifest complex dynamics capable of universal computation. Yet, the
classification of precisely which rules exhibit complex behaviour remains a
significant challenge. Here we approach this question using tools from quantum
stochastic modelling, where quantum statistical memory -- the memory required
to model a stochastic process using a class of quantum machines -- can be used
to quantify the structure of a stochastic process. By viewing ECA rules as
transformations of stochastic patterns, we ask: Does an ECA generate structure
as quantified by the quantum statistical memory, and if so, how quickly? We
illustrate how the growth of this measure over time correctly distinguishes
simple ECA from complex counterparts. Moreover, it provides a more refined
means for quantitatively identifying complex ECAs -- providing a spectrum on
which we can rank the complexity of ECA by the rate in which they generate
structure.


Abstract:
  Conventional supervisory control theory assumes full synchronization between
the supervisor and the plant. This assumption is violated in a networked-based
communication setting due to the presence of delays, and this may result in
incorrect behavior of a supervisor obtained from conventional supervisory
control theory. This paper presents a technique to synthesize a networked
supervisor handling communication delays. For this purpose, first, a networked
supervisory control framework is provided, where the supervisor interacts with
the plant through control and observation channels, both of which introduce
delays. The control channel is FIFO, but the observation channel is assumed to
be non-FIFO so that the observation of events may not necessarily be received
by the supervisor in the same order as they occurred in the plant. It is
assumed that a global clock exists in the networked control system, and so the
communication delays are represented in terms of time. Based on the proposed
framework, a networked plant automaton is achieved, which models the behavior
of the plant under the effects of communication delays and disordered
observations. Based on the networked plant, the networked supervisor is
synthesized, which is guaranteed to be (timed networked) controllable,
nonblocking, time-lock free, (timed networked) maximally permissive, and
satisfies control requirements for the plant.


Abstract:
  We consider the problem of computing the k-means centers for a large
high-dimensional dataset in the context of edge-based machine learning, where
data sources offload machine learning computation to nearby edge servers.
k-Means computation is fundamental to many data analytics, and the capability
of computing provably accurate k-means centers by leveraging the computation
power of the edge servers, at a low communication and computation cost to the
data sources, will greatly improve the performance of these analytics. We
propose to let the data sources send small summaries, generated by joint
dimensionality reduction (DR) and cardinality reduction (CR), to support
approximate k-means computation at reduced complexity and communication cost.
By analyzing the complexity, the communication cost, and the approximation
error of k-means algorithms based on state-of-the-art DR/CR methods, we show
that: (i) it is possible to achieve a near-optimal approximation at a
near-linear complexity and a constant or logarithmic communication cost, (ii)
the order of applying DR and CR significantly affects the complexity and the
communication cost, and (iii) combining DR/CR methods with a properly
configured quantizer can further reduce the communication cost without
compromising the other performance metrics. Our findings are validated through
experiments based on real datasets.


Abstract:
  The current practice in land cover/land use change analysis relies heavily on
the individually classified maps of the multitemporal data set. Due to varying
acquisition conditions (e.g., illumination, sensors, seasonal differences), the
classification maps yielded are often inconsistent through time for robust
statistical analysis. 3D geometric features have been shown to be stable for
assessing differences across the temporal data set. Therefore, in this article
we investigate he use of a multitemporal orthophoto and digital surface model
derived from satellite data for spatiotemporal classification. Our approach
consists of two major steps: generating per-class probability distribution maps
using the random-forest classifier with limited training samples, and making
spatiotemporal inferences using an iterative 3D spatiotemporal filter operating
on per-class probability maps. Our experimental results demonstrate that the
proposed methods can consistently improve the individual classification results
by 2%-6% and thus can be an important postclassification refinement approach.


Abstract:
  In this work, we provide consistent compactifications of Einstein-Maxwell and
Einstein-Maxwell-Lovelock theories on direct product spacetimes of the form
$\mathcal{M}_D=\mathcal{M}_d\times\mathcal{K}^{p}$, where $\mathcal{K}^p$ is a
Euclidean internal manifold of constant curvature. For these compactifications
to take place, it is required the distribution of a precise flux of $p$-forms
over the internal manifold. The dynamic of the $p$-forms are demanded to be
controlled by two types of interaction. First, by specific couplings with the
curvature tensor and, second, by a suitable interaction with the
electromagnetic field of the $d$-dimensional brane, the latter being dictated
by a modification of the recently proposed theory of Quasitopological
Electromagnetism. The field equations of the corresponding compactified
theories, which are of second order, are solved and general homogenous charged
black p-branes are constructed. We explicitly provide homogenous
Reissner-Nordstr\"om black strings and black p-branes in Einstein-Maxwell
theory and homogenous charged Boulware-Deser black p-branes for quadratic and
cubic Maxwell-Lovelock gravities.


Abstract:
  Given a triangle, what is the equation of the line which bisects its area and
has a given slope? The set of all lines bisecting the area of a triangle has
been elegantly determined as a certain 'deltoid' envelope and this gives an
indirect method of solution. We find that vector algebra allows the equation to
be written down rather directly and neatly.


Abstract:
  In this paper, we report the first extended GeV $\gamma$-ray emission, at a
significant level of $\sim$ 8.13$\sigma$, from the region of the supernova
remnant (SNR) SNR G317.3-0.2 by analyzing $\sim$ 12.2 years of Fermi Large Area
Telescope (Fermi-LAT) Pass 8 data in the work. The best-fit position of the new
$\gamma$-ray source matches that of the 843 MHz radio energy band of SNR
G317.3-0.2, and there is no significant variability of the photon flux of the
corresponding light curve (LC) in the data for the 12.2 year period; therefore,
by excluding other known $\gamma$-ray sources or candidates within a 2$\sigma$
error radius from the best-fit position of SNR G317.3-0.2, we suggest that the
$\gamma$-ray source is likely to be a GeV counterpart of SNR G317.3-0.2.


Abstract:
  Existing classification-based face recognition methods have achieved
remarkable progress, introducing large margin into hypersphere manifold to
learn discriminative facial representations. However, the feature distribution
is ignored. Poor feature distribution will wipe out the performance improvement
brought about by margin scheme. Recent studies focus on the unbalanced
inter-class distribution and form a equidistributed feature representations by
penalizing the angle between identity and its nearest neighbor. But the problem
is more than that, we also found the anisotropy of intra-class distribution. In
this paper, we propose the `gradient-enhancing term' that concentrates on the
distribution characteristics within the class. This method, named IntraLoss,
explicitly performs gradient enhancement in the anisotropic region so that the
intra-class distribution continues to shrink, resulting in isotropic and more
compact intra-class distribution and further margin between identities. The
experimental results on LFW, YTF and CFP-FP show that our outperforms
state-of-the-art methods by gradient enhancement, demonstrating the superiority
of our method. In addition, our method has intuitive geometric interpretation
and can be easily combined with existing methods to solve the previously
ignored problems.


Abstract:
  Any modern network inference paradigm must incorporate multiple aspects of
network structure, including information that is often encoded both in vertices
and in edges. Methodology for handling vertex attributes has been developed for
a number of network models, but comparable techniques for edge-related
attributes remain largely unavailable. We address this gap in the literature by
extending the latent position random graph model to the line graph of a random
graph, which is formed by creating a vertex for each edge in the original
random graph, and connecting each pair of edges incident to a common vertex in
the original graph. We prove concentration inequalities for the spectrum of a
line graph, and then establish that although naive spectral decompositions can
fail to extract necessary signal for edge clustering, there exist
signal-preserving singular subspaces of the line graph that can be recovered
through a carefully-chosen projection. Moreover, we can consistently estimate
edge latent positions in a random line graph, even though such graphs are of a
random size, typically have high rank, and possess no spectral gap. Our results
also demonstrate that the line graph of a stochastic block model exhibits
underlying block structure, and we synthesize and test our methods in
simulations for cluster recovery and edge covariate inference in stochastic
block model graphs.


Abstract:
  We propose a multi-task, probabilistic approach to facilitate distantly
supervised relation extraction by bringing closer the representations of
sentences that contain the same Knowledge Base pairs. To achieve this, we bias
the latent space of sentences via a Variational Autoencoder (VAE) that is
trained jointly with a relation classifier. The latent code guides the pair
representations and influences sentence reconstruction. Experimental results on
two datasets created via distant supervision indicate that multi-task learning
results in performance benefits. Additional exploration of employing Knowledge
Base priors into the VAE reveals that the sentence space can be shifted towards
that of the Knowledge Base, offering interpretability and further improving
results.