Abstract:
  Over the last few decades, deforestation and climate change have caused
increasing number of forest fires. In Southeast Asia, Indonesia has been the
most affected country by tropical peatland forest fires. These fires have a
significant impact on the climate resulting in extensive health, social and
economic issues. Existing forest fire prediction systems, such as the Canadian
Forest Fire Danger Rating System, are based on handcrafted features and require
installation and maintenance of expensive instruments on the ground, which can
be a challenge for developing countries such as Indonesia. We propose a novel,
cost-effective, machine-learning based approach that uses remote sensing data
to predict forest fires in Indonesia. Our prediction model achieves more than
0.81 area under the receiver operator characteristic (ROC) curve, performing
significantly better than the baseline approach which never exceeds 0.70 area
under ROC curve on the same tasks. Our model's performance remained above 0.81
area under ROC curve even when evaluated with reduced data. The results support
our claim that machine-learning based approaches can lead to reliable and
cost-effective forest fire prediction systems.


Abstract:
  Recent high pressure experiments discovered abnormal double-dome
superconductivities in the newly-synthesized kagome materials $A$V$_3$Sb$_5$
($A$ = K, Rb, Cs), which also host abundant emergent quantum phenomena such as
charge density wave (CDW), anomalous Hall effect, nontrivial topological
property, etc. In this work, by using first-principles electronic structure
calculations, we have studied the CDW state, superconductivity, and topological
property in CsV$_3$Sb$_5$ under pressures ($<$ 50 GPa). Based on the
electron-phonon coupling theory, our calculated superconducting $T_\text{c}$s
are consistent with the observed ones in the second superconducting dome at
high pressure, but are much higher than the measured values at low pressure.
The further calculations including the Hubbard U indicate that with modest
electron-electron correlation the magnetism on the V atoms exists at low
pressure and diminishes gradually at high pressure. We thus propose that the
experimentally observed superconductivity in CsV$_3$Sb$_5$ at ambient/low
pressures may still belong to the conventional Bardeen-Cooper-Schrieffer (BCS)
type but is partially suppressed by the V magnetism, while the
superconductivity under high pressure is fully conventional without invoking
the magnetism. We also predict that there are a second weak CDW state and
topological phase transitions in CsV$_3$Sb$_5$ under pressures. Our theoretical
assertion calls for future experimental examination.


Abstract:
  In the last decades, the interest to understand the connection between brain
and body has grown notably. For example, in psychoneuroimmunology many studies
associate stress, arising from many different sources and situations, to
changes in the immune system from the medical or immunological point of view as
well as from the biochemical one. In this paper we identify important
behaviours of this interplay between the immune system and stress from medical
studies and seek to represent them qualitatively in a paradigmatic, yet simple,
mathematical model. To that end we develop a differential equation model with
two equations for infection level and immune system, which integrates the
effects of stress as an additional parameter. We are able to reproduce a stable
healthy state for little stress, an oscillatory state between healthy and
infected states for high stress, and a "burn-out" or stable sick state for
extremely high stress. The mechanism between the different dynamics is
controlled by two saddle-node in cycle (SNIC) bifurcations. Furthermore, our
model is able to capture an induced infection upon dropping from moderate to
low stress, and it predicts increasing infection periods upon increasing before
eventually reaching a burn-out state.


Abstract:
  For any storage ring-based large-scale scientific facility, one of the most
important performance parameters is the dynamic aperture (DA), which measures
the motion stability of charged particles in a global manner. To date,
long-term tracking-based simulation is regarded as the most reliable method to
calculate DA. However, numerical tracking may become a significant issue,
especially when lots of candidate designs of a storage ring need to be
evaluated. In this paper, we present a novel machine learning-based method,
which can reduce the computation cost of DA tracking by approximately one order
of magnitude, while keeping sufficiently high evaluation accuracy. Moreover, we
demonstrate that this method is independent of concrete physical models of a
storage ring. This method has the potential to be applied to similar problems
of identifying irregular motions in other complex dynamical systems.


Abstract:
  The effect of asymmetric functionally graded material on the edge resonance
and the Fano resonance in semi-infinite FGM plates are reported in this work.
The edge resonance is weakened by the material perturbation and the complete
mode conversion is illustrated with incident $S_0$ mode. The Fano resonance
occurs on the reflected $A_0$ mode as a result of interference between the
resonance and direct scattering with incident $A_0$ mode. A hybrid analytical
model based on the collocation discretization and the modal decomposition of
the elastic field is developed to analyze the scattering properties of the
semi-infinite plates. The Fano line-shape is discussed in detail. The results
show that the Fano line shape is sensitive to the volume fraction, which is
beneficial for the quantitative guided wave application.


Abstract:
  Temperature-dependent angle-resolved photoemission spectroscopy (ARPES) was
carried out on single-crystalline EuB6 samples. By measuring ARPES spectra in
an extended Brillouin zone, a B 2p hole pocket centered at the X point is
clearly observed, thus proving the semimetallic nature of EuB6. Below the Curie
temperature TC, ARPES spectra show two B 2p bands of which separation is due to
an exchange interaction between local Eu 4f and itinerant B 2p electrons. The
exchange splitting becomes smaller as the temperature increases and disappears
well above TC. Additionally, a diffuse structure near the Fermi level survives
just above TC. Such behavior is well described by Monte Carlo simulations of a
Kondo lattice model, thus supporting the formation of magnetic polarons in
EuB6, which accounts for the resistivity upturn near above TC when lowering the
temperature.


Abstract:
  A Snyder model generated by the noncommutative coordinates and Lorentz
generators close a Lie algebra. The application of the Heisenberg double
construction is investigated for the Snyder coordinates and momenta generators.
It leads to the phase space of the Snyder model. Further, the extended Snyder
algebra is constructed by using the Lorentz algebra, in one dimension higher.
The dual pair of extended Snyder algebra and extended Snyder group is then
formulated. Two Heisenberg doubles are considered, one with the conjugate
tensorial momenta and another with the Lorentz matrices. Explicit formulae for
all Heisenberg doubles are given.


Abstract:
  In the field of network security, the concept of honeypots is well
established in research as well as in production. Honeypots are used to imitate
a legitimate target on the network and to raise an alert on any interaction.
This does not only help learning about a breach, but also allows researchers to
study the techniques of an attacker. With the rise of cloud computing,
container-based virtualization gained popularity for application deployment.
This paper investigates the possibilities of container-based honeypots and
introduces the concept of simulating container escapes as a deception
technique.


Abstract:
  Spontaneous parametric down conversion (PDC), in the perturbative limit, can
be considered as a probabilistic splitting of one input photon into two output
photons. Conversely, sum-frequency generation (SFG) implements the reverse
process of combining two input photons into one. Here we show that a
single-photon projective measurement in the temporal-mode basis of the output
photon of a two-photon SFG process effects a generalized measurement on the
input two-photon state. We describe the positive-operator-valued measure (POVM)
associated with such a measurement, and show that its elements are proportional
to the two-photon states produced by the time-reversed PDC process. Such a
detection acts as a joint measurement on two photons, and is thus an important
component of many quantum information processing protocols relying on photonic
entanglement. Using the retrodictive approach, we analyze the properties of the
two-photon POVM that are relevant for quantum protocols exploiting two-photon
states and measurements.


Abstract:
  Polar code lattices are formed from binary polar codes using Construction D.
In this paper, we propose a design technique for finite-dimension polar code
lattices. The dimension $n$ and target probability of decoding error are
parameters for this design. To select the rates of the Construction D component
codes, rather than using the capacity as in past work, we use the explicit
finite-length properties of the polar code. Under successive cancellation
decoding, density evolution allows choosing code rates that satisfy the equal
error probability rule. At an error-rate of $10^{-4}$, a dimension $n=128$
polar code lattice achieves a VNR of 2.5 dB, within 0.2 dB of the best-known
BCH code lattice, but with significantly lower decoding complexity.


Abstract:
  We present Pylot, a platform for autonomous vehicle (AV) research and
development, built with the goal to allow researchers to study the effects of
the latency and accuracy of their models and algorithms on the end-to-end
driving behavior of an AV. This is achieved through a modular structure enabled
by our high-performance dataflow system that represents AV software pipeline
components (object detectors, motion planners, etc.) as a dataflow graph of
operators which communicate on data streams using timestamped messages. Pylot
readily interfaces with popular AV simulators like CARLA, and is easily
deployable to real-world vehicles with minimal code changes.
  To reduce the burden of developing an entire pipeline for evaluating a single
component, Pylot provides several state-of-the-art reference implementations
for the various components of an AV pipeline. Using these reference
implementations, a Pylot-based AV pipeline is able to drive a real vehicle, and
attains a high score on the CARLA Autonomous Driving Challenge. We also present
several case studies enabled by Pylot, including evidence of a need for
context-dependent components, and per-component time allocation. Pylot is open
source, with the code available at https://github.com/erdos-project/pylot.


Abstract:
  We summarize the results of temporal and spectral analysis of the X-ray
pulsar 2S 1553-542 using the Nuclear Spectroscopic Telescope Array (NuSTAR) and
Swift during the outburst in January-February 2021. During the outburst, the
spin period of the neutron star was $P = 9.2822\pm 0.0001$ s based on NuSTAR
data. The temporal evolution of the spin period, pulse profile, and pulse
fraction is studied during the outburst. The spectra of the source are studied
for different days of the outburst and can be well described by a model
consisting of -- a black body emission or a power law. We have investigated the
inter-day evolution of different timing and spectral parameters during the
outburst. The energy dependence of the pulse profile was studied to investigate
the evolution of the individual peaks and emission geometry of the pulsar with
a different energy. The pulse profile of the source shows strong single peak
nature with a hump-like feature of relatively lower intensity and it evolves
significantly with different energy ranges. The evolution of the pulse profile
is studied during different phases of the outburst and the pulse fraction shows
a positive correlation with energy.


Abstract:
  Since the renaissance of deep learning (DL), facial expression recognition
(FER) has received a lot of interest, with continual improvement in the
performance. Hand-in-hand with performance, new challenges have come up. Modern
FER systems deal with face images captured under uncontrolled conditions (also
called in-the-wild scenario) including occlusions and pose variations. They
successfully handle such conditions using deep networks that come with various
components like transfer learning, attention mechanism and local-global context
extractor. However, these deep networks are highly complex with large number of
parameters, making them unfit to be deployed in real scenarios. Is it possible
to build a light-weight network that can still show significantly good
performance on FER under in-the-wild scenario? In this work, we methodically
build such a network and call it as Imponderous Net. We leverage on the
aforementioned components of deep networks for FER, and analyse, carefully
choose and fit them to arrive at Imponderous Net. Our Imponderous Net is a low
calorie net with only 1.45M parameters, which is almost 50x less than that of a
state-of-the-art (SOTA) architecture. Further, during inference, it can process
at the real time rate of 40 frames per second (fps) in an intel-i7 cpu. Though
it is low calorie, it is still power packed in its performance, overpowering
other light-weight architectures and even few high capacity architectures.
Specifically, Imponderous Net reports 87.09\%, 88.17\% and 62.06\% accuracies
on in-the-wild datasets RAFDB, FERPlus and AffectNet respectively. It also
exhibits superior robustness under occlusions and pose variations in comparison
to other light-weight architectures from the literature.


Abstract:
  We investigate the operation of dual-gate reconfigurable field-effect
transistor (RFET) in the programgate at drain (PGAD) and program-gate at source
(PGAS) configurations. To this end, dual-gate silicon nanowire (SiNW) FETs are
fabricated based on anisotropic wet chemical silicon etching and nickel
silicidation yielding silicide-SiNW Schottky junctions at source and drain.
Whereas in PGAD-configuration ambipolar operation is suppressed, switching is
deteriorated due to the injection through a Schottky-barrier. Operating the
RFET in PGAS configuration yields a switching behavior close to a conventional
MOSFET. This, howewer, needs to be traded off against strongly non-linear
output characteristics for small bias.


Abstract:
  We present a brief overview of some of the analytic perturbative techniques
for the computation of the Floquet Hamiltonian for a periodically driven, or
Floquet, quantum many-body system. The key technical points about each of the
methods discussed are presented in a pedagogical manner. They are followed by a
brief account of some chosen phenomena where these methods have provided useful
insights. We provide an extensive discussion of the Floquet-Magnus expansion,
the adiabatic-impulse approximation, and the Floquet perturbation theory. This
is followed by a relatively short discourse on the rotating wave approximation,
a Floquet-Magnus resummation technique and the Hamiltonian flow method. We also
provide a discussion of some open problems which may possibly be addressed
using these methods.


Abstract:
  We prove that, under some generic non-degeneracy assumptions, real analytic,
centrally symmetric plane domains are determined by their Dirichlet (resp.
Neumann) spectra. We prove that the conditions are open-dense for real analytic
convex domains. The proof is parallel to the proof that up/down symmetric
domains are spectrally determined. One step is to use a Maslov index
calculation to show that the second derivative of the defining function of a
centrally symmetric domain at the endpoints of a bouncing ball orbit is a
spectral invariant. This is also true for up/down symmetric domains, removing
an assumption from the proof in that case.


Abstract:
  We study random surfaces with a uniformly convex gradient interaction in the
presence of quenched disorder taking the form of a random independent external
field. Previous work on the model has focused on proving existence and
uniqueness of infinite-volume gradient Gibbs measures with a given tilt and on
studying the fluctuations of the surface and its discrete gradient.
  In this work we focus on the convergence of the thermodynamic limit,
establishing convergence of the finite-volume distributions with Dirichlet
boundary conditions to translation-covariant (gradient) Gibbs measures.
Specifically, it is shown that, when the law of the random field has finite
second moment and is symmetric, the distribution of the gradient of the surface
converges in dimensions $d\geq4$ while the distribution of the surface itself
converges in dimensions $d\geq 5$. Moreover, a power-law upper bound on the
rate of convergence in Wasserstein distance is obtained. The results partially
answer a question discussed by Cotar and K\"ulske


Abstract:
  How do inter-organizational networks emerge? Accounting for interdependence
among ties while studying tie formation is one of the key challenges in this
area of research. We address this challenge using an equilibrium framework
where firms' decisions to form links with other firms are modeled as a
strategic game. In this game, firms weigh the costs and benefits of
establishing a relationship with other firms and form ties if their net payoffs
are positive. We characterize the equilibrium networks as exponential random
graphs (ERGM), and we estimate the firms' payoffs using a Bayesian approach. To
demonstrate the usefulness of our approach, we apply the framework to a
co-investment network of venture capital firms in the medical device industry.
The equilibrium framework allows researchers to draw economic interpretation
from parameter estimates of the ERGM Model. We learn that firms rely on their
joint partners (transitivity) and prefer to form ties with firms similar to
themselves (homophily). These results hold after controlling for the
interdependence among ties. Another, critical advantage of a structural
approach is that it allows us to simulate the effects of economic shocks or
policy counterfactuals. We test two such policy shocks, namely, firm entry and
regulatory change. We show how new firms' entry or a regulatory shock of
minimum capital requirements increase the co-investment network's density and
clustering.


Abstract:
  In this paper we propose coupling conditions for a relaxation model for
vehicular traffic on networks. We present a matched asymptotic expansion
procedure to derive a LWR- network with well-known classical coupling
conditions from the relaxation network in the macroscopic limit. Similar to the
asymptotic limit of boundary value problems, we perform an asymptotic analysis
of the interface layers at the nodes and a matching procedure using
half-Riemann problems for the limit conservation law. Moreover, we present
numerical experiments comparing the relaxation network with the LWR network for
a broader range of coupling conditions.


Abstract:
  Nuclear spins of noble gases can maintain coherence for hours at ambient
conditions owing to their extraordinary isolation by the enclosing, complete
electronic shells. This isolation, however, impedes the ability to manipulate
and control them by optical means or by physical coupling to other spin gases.
Here we experimentally achieve strong coherent coupling between noble-gas spins
and the optically-accessible spins of alkali-metal vapor. Stochastic
spin-exchange collisions, underlying the coupling, accumulate to a coherent
periodic exchange of spin excitations between the two gases. We obtain a
coupling rate 10 times higher than the decay rate, observe the resultant
avoided crossing in the spectral response of the spins, and demonstrate the
external control over the coupling by magnetic fields. These results open a
route for efficient and rapid interfacing with noble-gas spins for applications
in quantum sensing and information.


Abstract:
  In this article, we propose and investigate ML group explainers in a general
game-theoretic setting with the focus on coalitional game values and games
based on the conditional and marginal expectation of an ML model. The
conditional game takes into account the joint distribution of the predictors,
while the marginal game depends on the structure of the model. The objective of
the article is to unify the two points of view under predictor dependencies and
to reduce the complexity of group explanations. To achieve this, we propose a
feature grouping technique that employs an information-theoretic measure of
dependence and design appropriate groups explainers. Furthermore, in the
context of coalitional game values with a two-step formulation, we introduce a
theoretical scheme that generates recursive coalitional game values under a
partition tree structure and investigate the properties of the corresponding
group explainers.


Abstract:
  With the growing popularity of cloud storage, removing duplicated data across
users is getting more critical for service providers to reduce costs. Recently,
Data resemblance detection is a novel technology to detect redundancy among
similarity. It extracts feature from each chunk content and treat chunks with
high similarity as candidates for removing redundancy. However, popular
resemblance methods such as "N-transform" and "Finesse" use only the chunk data
for feature extraction. A minor modification on the data chunk could seriously
deteriorate its capability for resemblance detection. In this paper, we
proposes a novel chunk-context aware resemblance detection algorithm, called
CARD, to mitigate this issue. CARD introduces a BP-Neural network-based
chunk-context aware model, and uses N-sub-chunk shingles-based initial feature
extraction strategy. It effectively integrates each data chunk content's
internal structure with the context information for feature extraction, the
impact of small changes in data chunks is significantly reduced. To evaluate
its performance, we implement a CARD prototype and conduct extensive
experiments using real-world data sets. The results show that CARD can detect
up to 75.03% more redundant data and accelerate the resemblance detection
operations by 5.6 to 17.8 times faster compared with the state-of-the-art
resemblance detection approaches.


Abstract:
  Artificial Intelligence techniques powered by deep neural nets have achieved
much success in several application domains, most significantly and notably in
the Computer Vision applications and Natural Language Processing tasks.
Surpassing human-level performance propelled the research in the applications
where different modalities amongst language, vision, sensory, text play an
important role in accurate predictions and identification. Several multimodal
fusion methods employing deep learning models are proposed in the literature.
Despite their outstanding performance, the complex, opaque and black-box nature
of the deep neural nets limits their social acceptance and usability. This has
given rise to the quest for model interpretability and explainability, more so
in the complex tasks involving multimodal AI methods. This paper extensively
reviews the present literature to present a comprehensive survey and commentary
on the explainability in multimodal deep neural nets, especially for the vision
and language tasks. Several topics on multimodal AI and its applications for
generic domains have been covered in this paper, including the significance,
datasets, fundamental building blocks of the methods and techniques,
challenges, applications, and future trends in this domain


Abstract:
  We present results from a resolved stellar population search for dwarf
satellite galaxies of six nearby (D $<5$ Mpc), sub-Milky-Way mass hosts using
deep ($m\sim27$ mag) optical imaging from the Large Binocular Telescope. We
perform image simulations to quantify our detection efficiency for dwarfs over
a large range in luminosity and size, and develop a fast catalog-based emulator
that includes a treatment of unresolved photometric blending. We discover no
new dwarf satellites, but we recover two previously known dwarfs (DDO 113 and
LV J1228+4358) with $M_{\text{V}}<-12$ that lie in our survey volume. We
preview a new theoretical framework to predict satellite luminosity functions
using analytic probability distribution functions and apply it to our sample,
finding that we predict one fewer classical dwarf and one more faint dwarf
($M_{\text{V}}\sim-7.5$) than we find in our observational sample (i.e., the
observational sample is slightly top-heavy). However, the overall number of
dwarfs in the observational sample (2) is in good agreement with the
theoretical expectations. Interestingly, DDO 113 shows signs of environmental
quenching and LV J1228+4358 is tidally disrupting, suggesting that low-mass
hosts may affect their satellites more severely than previously believed.


Abstract:
  A parsec-scale dusty torus is thought to be the cause of Active Galactic
Nuclei (AGN) dichotomy in the 1/2 types, narrow/broad emission lines. In a
previous work, on the basis of parsec-scale resolution infrared / optical dust
maps it was found that dust filaments, few parsecs wide, several hundred
parsecs long, were ubiquitous features crossing the centre of type 2 AGN, their
optical thickness being sufficient to fully obscure the optical nucleus. This
work presents the complementary view for type 1 and intermediate-type AGN. The
same type of narrow, collimated, dust filaments are equally found at the centre
of these AGN. The difference now resides in their location with respect to the
nucleus, next to it but not crossing it, as it is the case in type 2, and their
reduced optical thickness towards the centre, $A_V \lesssim 1.5 \rm{mag}$,
insufficient to obscure at UV nucleus wavelengths. It is concluded that large
scale, hundred pc to kpc long, dust filaments and lanes, reminiscent of those
seen in the Milky Way, are a common ingredient to the central parsec of
galaxies. Their optical thickness changes along their structure, in type 2
reaching optical depths high enough to obscure the nucleus in full. Their
location with respect to the nucleus and increasing gradient in optical depth
towards the centre could naturally lead to the canonical type 1/2 AGN
classification, making these filaments to play the role of the torus. Dust
filaments and lanes show equivalent morphologies in molecular gas. Available
gas kinematic indicates mass inflows at rates $ < ~ 1 M\odot~ yr^{-1}$.


Abstract:
  Capillaries are the smallest vessels in the body responsible for the delivery
of oxygen and nutrients to the surrounding cells. Various diseases have been
shown to alter the density of nutritive capillaries and the flow velocity of
erythrocytes. In previous studies, capillary density and flow velocity have
been assessed manually by trained specialists. Manual analysis of a standard
20-second long microvascular video takes on average 20 minutes and requires
extensive training. Several studies have reported that manual analysis hinders
the application of microvascular microscopy in a clinical setting. In this
paper, we present a fully automated state-of-the-art system, called
CapillaryNet, that can quantify skin nutritive capillary density and red blood
cell velocity from handheld microscopy videos. Moreover, CapillaryNet measures
several novel microvascular parameters that researchers were previously unable
to quantify, i.e. capillary hematocrit and Intra-capillary flow velocity
heterogeneity. Our system has been used to analyze skin microcirculation videos
from various patient groups (COVID-19, pancreatitis, and acute heart diseases).
Our proposed system excels from existing capillary detection systems as it
combines the speed of traditional computer vision algorithms and the accuracy
of convolutional neural networks.


Abstract:
  Directional tests to compare nested parametric models are developed in the
general context of covariance selection for Gaussian graphical models. The
exactness of the underlying saddlepoint approximation leads to exceptional
accuracy of the proposed approach. This is verified by simulation experiments
with high-dimensional parameters of interest, where the accuracy of standard
asymptotic approximations to the likelihood ratio test and some of its
higher-order modifications fails. The directional p-value isused to illustrate
the assessment of Markovian dependencies in a dataset from a veterinary trial
on cattle. A second example with microarray data shows how to select the graph
structure related to genetic anomalies due to acute lymphocytic leukemia.


Abstract:
  Two-wave with diffuse power (TWDP) is one of the most promising models for
description of small-scale fading effects in emerging wireless networks.
However, its current statistical characterization has several fundamental
issues. Primarily, conventional TWDP parameterization is not in accordance with
the model's underlying physical mechanisms. In addition, available TWDP
expressions for PDF, CDF, and MGF are given either in integral or approximate
forms, or as mathematically untractable closed-form expressions. Consequently,
the existing TWDP statistical characterization does not allow accurate
evaluation of system performance (such as error and outage probability) in all
fading conditions for most modulation and diversity techniques. In this paper,
the existing statistical characterization of the TWDP fading model is improved
by overcoming some of the noticed issues. In this regard, physically justified
TWDP parameterization is proposed and used for further calculations.
Additionally, exact infinite-series PDF and CDF are introduced. Based on these
expressions, the exact MGF of the SNR is derived in form suitable for
mathematical manipulations. The applicability of the proposed MGF for
derivation of the exact average symbol error probability (ASEP) is demonstrated
with the example of M-ary PSK modulation. Therefore, in this paper, M-ary PSK
ASEP is derived as an explicit expression for the first time in the literature.
The derived expression is further simplified for large SNR values in order to
obtain a closed-form asymptotic ASEP, which is shown to be applicable for SNR >
20 dB. All proposed expressions are verified by Monte Carlo simulation in a
variety of TWDP fading conditions.


Abstract:
  Model discovery aims at autonomously discovering differential equations
underlying a dataset. Approaches based on Physics Informed Neural Networks
(PINNs) have shown great promise, but a fully-differentiable model which
explicitly learns the equation has remained elusive. In this paper we propose
such an approach by integrating neural network-based surrogates with Sparse
Bayesian Learning (SBL). This combination yields a robust model discovery
algorithm, which we showcase on various datasets. We then identify a connection
with multitask learning, and build on it to construct a Physics Informed
Normalizing Flow (PINF). We present a proof-of-concept using a PINF to directly
learn a density model from single particle data. Our work expands PINNs to
various types of neural network architectures, and connects neural
network-based surrogates to the rich field of Bayesian parameter inference.


Abstract:
  Being a state-of-the-art network, Software Defined Networking (SDN) decouples
control and management planes from data plane of the forwarding devices by
implementing both the control and management planes at logically centralized
entity, called controller. This helps to make simple and easy both the network
control and management. Failure of links occurs frequently in a computer
network. To deal with the link failures, the existing approaches computes and
installs multiple paths for a flow at the switches in SDN without considering
the reliability value of the primary path. This incurs extra computation to
compute multiple paths, and both increased computation time and traffic to
install extra flow rules in the network. In this research work we propose a new
approach that calculates the link reliability and then installs the number of
multiple paths based on the reliability value of the primary path. More
specifically, if a primary path has higher reliability then a smaller number of
alternative paths should be installed. This shall decrease the path
computational time and flow rule installation load at controller. Resultantly
there shall be less flow rule entries in switch flow table which in turn will
avoid the overflow of the flow table. Through simulation results, our proposed
approach performs better as compared to the existing approach in term of
computational overhead at controller, end-to-end delay for packet deliver and
the traffic overhead for flow rule installation.


Abstract:
  We investigate four-body nuclear reactions in stellar environments
contributing to creation of light nuclei, exemplified by $^9$Be and $^{12}$C.
The originally assumed process is radiative capture, where nuclear clusters
combine into the excited final nucleus and photon emission populates the stable
nuclear ground states. Instead, we consider nuclear four-body recombination
reactions where a spectator nuclear particle replaces the photon. We first
develop the elaborate formalism for both, direct and sequential capture
processes, where the decaying three-body resonance is formed without and with
population of an intermediate two-body resonance, respectively. To facilitate
both calculations and practical applications we parameterize the involved cross
sections as done successfully in previous computations of reaction rates. We
consider the lowest-lying nuclear states with their dominant contributions at
low stellar temperatures. We calculate and compare reaction and production
rates for different processes. The direct reaction mechanism dominates by many
orders of magnitude at low temperature, where the sequential stepping stones
are energetically too expensive to use. At somewhat higher temperatures these
two different nuclear four-body mechanisms become comparable. Comparison to
radiative three-body capture reveals already formally, but also numerically,
that four-body nuclear recombination must dominate for sufficiently high
nuclear densities. Numerical values are given for all these rates as function
of temperature and density. The relative importance is exhibited.


Abstract:
  We experimentally demonstrate a polarization-entangled photon source at 810
nm using a type-II phase-matched PPKTP crystal pumped by a low-cost, broadband
laser diode with a central wavelength of 405 nm and a typical bandwidth of 0.53
nm. The PPKTP crystal is placed in a Sagnac-loop to realize the compact size
and high stability. The downconverted biphotons, the signal and the idler, have
typical bandwidths of 5.57 nm and 7.32 nm. We prepare two Bell states |Psi+>
and |Psi-> with the fidelities of 0.948+-0.004 and 0.963+-0.002. In
polarization correlation measurement, the visibilities are all higher than
96.2%, and in the Bell inequality test, the S value can achieve 2.78+-0.01. To
our knowledge, this experiment is the first to combine a multi-mode pump laser
with a Sagnac-loop configuration. This high-quality and low-cost entangled
photon source may have many practical applications in quantum information
processing.


Abstract:
  Impending catastrophic failure of granular earth slopes manifests distinct
kinematic patterns in space and time. While risk assessments of slope failure
hazards have routinely relied on the monitoring of ground motion, such
precursory failure patterns remain poorly understood. A key challenge is the
multiplicity of spatiotemporal scales and dynamical regimes. In particular,
there exist a precursory failure regime where two mesoscale mechanisms
coevolve, namely, the preferred transmission paths for force and damage.
Despite extensive studies, a formulation which can address their coevolution
not just in laboratory tests but also in large, uncontrolled field environments
has proved elusive. Here we address this problem by developing a slope
stability analytics framework which uses network flow theory and mesoscience to
model this coevolution and predict emergent kinematic clusters solely from
surface ground motion data. We test this framework on four data sets: one at
the laboratory scale using individual grain displacement data; three at the
field scale using line-of-sight displacement of a slope surface, from
ground-based radar in two mines and from space-borne radar for the 2017 Xinmo
landslide. The dynamics of the kinematic clusters deliver an early prediction
of the geometry, location and time of failure.


Abstract:
  The paper is concerned with identification of the key mechanisms controlling
deflagration-to-detonation transition in stellar medium. The issue of thermal
runaway triggered by positive feedback between the advancing flame and the
flame-driven precompression is discussed in the framework of a one-dimensional
flame-folding model. The paper is an extension of the authors' previous study
dealing with the non-stoichiometric fusion, $fuel \to products$, kinetics
(Phys.Rev.E, 103(2021)) over physically more relevant, $fuel+fuel \to
products$, kinetics. Despite this change the runaway effect endures. The
transition occurs prior to merging of the flame with the flame-supported
precursor shock, i.e. the pretransition flame does not reach the threshold of
Chapman-Jouguet deflagration.


Abstract:
  Synchronized movement of (both unicellular and multicellular) systems can be
observed almost everywhere. Understanding of how organisms are regulated to
synchronized behavior is one of the challenging issues in the field of
collective motion. It is hypothesized that one or a few agents in a group
regulate(s) the dynamics of the whole collective, known as leader(s). The
identification of the leader (influential) agent(s) is very crucial. This
article reviews different mathematical models that represent different types of
leadership. We focus on the improvement of the leader-follower classification
problem. It was found using a simulation model that the use of interaction
domain information significantly improves the leader-follower classification
ability using both linear schemes and information-theoretic schemes for
quantifying influence. This article also reviews different schemes that can be
used to identify the interaction domain using the motion data of agents.


Abstract:
  Quark-gluon plasma (QGP) droplet formation is re-considered with the addition
of three loop correction to the earlier loop factors in the mean field
potential. The correction of the three loop factor increases stability in the
droplet formations of QGP at different parametrization factors of the QGP fluid
and it is in better agreement in comparison to the lattice results of pressure,
energy density and other thermodynamic relations. This implies that the
contribution of the three loop enhances in showing the characteristic features
of the QGP fluid. It shows that increasing the loop increased the strength of
parametrization value which we defined earlier as a number parameter of fluid
dynamics. It indicates that the model with the loop correction boosts in
explaining about the formation of QGP droplet in the expansion of early
universe


Abstract:
  The distributions and abundances of molecules in protoplanetary disks are
powerful tracers of the physical and chemical disk structures. The abundance
ratios of HCN and its isomer HNC are known to be sensitive to gas temperature.
Their line ratios might therefore offer a unique opportunity to probe the
properties of the emitting gas. Using the 2D thermochemical code DALI, we ran a
set of disk models accounting for different stellar and disk properties, with
an updated chemical network for the nitrogen chemistry. These modeling results
were then compared with observations, including new ALMA observations of HNC
$J=3-2$ for the TW Hya disk and HNC $J=1-0$ for 29 disks in Lupus. Similar to
CN, HCN and HNC have brighter line emission in models with larger disk flaring
angles and higher UV fluxes. HNC and HCN are predicted to be abundant in the
warm surface layer and outer midplane region, which results in ring-shaped
emission patterns. However, the precise emitting regions and emission
morphology depend on the probed transition, as well as on other parameters such
as C and O abundances. The modeled HNC-to-HCN line intensity ratio increases
from $<0.1$ in the inner disk to up to 0.8 in the outer disk regions, which can
be explained by efficient HNC destruction at high temperatures. Disk-integrated
HNC line fluxes from current scarce observations and its radial distribution in
the TW Hya disk are broadly consistent with our model predictions. The
HNC-to-HCN flux ratio robustly increases with radius (decreasing temperature),
but its use as a chemical thermometer in disks is affected by other factors,
including UV flux and C and O abundances. High-spatial resolution ALMA disk
observations of HNC and HCN that can locate the emitting layers would have the
great potential to constrain both the disk thermal and UV radiation structures,
and also to verify our understanding of the nitrogen chemistry.


Abstract:
  We present a method to measure the spectral density of in-band optical
transmission impairments without coherent electrical reception and digital
signal processing at the receiver. We determine the method's accuracy by
numerical simulations and show experimentally its feasibility, including the
measure of in-band nonlinear distortions power densities.


Abstract:
  We present a detailed comparison of the rheological behaviour of sheared
sediment beds in a pressure-driven, straight channel configuration based on
data that was generated by means of fully coupled, grain-resolved direct
numerical simulations and experimental measurements reviously published by
Aussillous {\it et al.} (J. Fluid Mech., vol. 736, 2013, pp. 594-615). The
highly-resolved simulation data allows to compute the stress balance of the
suspension in the streamwise and vertical directions and the stress exchange
between the fluid and particle phase, which is information needed to infer the
rheology, but has so far been unreachable in experiments. Applying this
knowledge to the experimental and numerical data, we obtain the
statistically-stationary, depth-resolved profiles of the relevant rheological
quantities. The scaling behavior of rheological quantities such as the shear
and normal viscosities and the effective friction coefficient are examined and
compared to data coming from rheometry experiments and from widely-used
rheological correlations. We show that rheological properties that have
previously been inferred for annular Couette-type shear flows with neutrally
buoyant particles still hold for our setup of sediment transport in a
Poiseuille flow and in the dense regime we found good agreement with empirical
relationships derived therefrom. Subdividing the total stress into parts from
particle contact and hydrodynamics suggests a critical particle volume fraction
of 0.3 to separate the dense from the dilute regime. In the dilute regime,
i.e., the sediment transport layer, long-range hydrodynamic interactions are
screened by the porous media and the effective viscosity obeys the Einstein
relation.


Abstract:
  This is a motivating tutorial introduction to a semantic analysis of
programming languages using a graphical language as the representation of
terms, and graph rewriting as a representation of reduction rules. We show how
the graphical language automatically incorporates desirable features, such as
alpha-equivalence and how it can describe pure computation, imperative store,
and control features in a uniform framework. The graph semantics combines some
of the best features of structural operational semantics and abstract machines,
while offering powerful new methods for reasoning about contextual equivalence.
  All technical details are available in an extended technical report by Muroya
and the author and in Muroya's doctoral dissertation.


Abstract:
  The world is increasingly urbanizing and the building industry accounts for
more than 40% of energy consumption in the United States. To improve urban
sustainability, many cities adopt ambitious energy-saving strategies through
retrofitting existing buildings and constructing new communities. In this
situation, an accurate urban building energy model (UBEM) is the foundation to
support the design of energy-efficient communities. However, current UBEM are
limited in their abilities to capture the inter-building interdependency due to
their dynamic and non-linear characteristics. Those models either ignored or
oversimplified these building interdependencies, which can substantially affect
the accuracy of urban energy modeling. To fill the research gap, this study
proposes a novel data-driven UBEM synthesizing the solar-based building
interdependency and spatial-temporal graph convolutional network (ST-GCN)
algorithm. Especially, we took a university campus located in downtown Atlanta
as an example to predict the hourly energy consumption. Furthermore, we tested
the feasibility of the proposed model by comparing the performance of the
ST-GCN model with other common time-series machine learning models. The results
indicate that the ST-GCN model overall outperforms all others. In addition, the
physical knowledge embedded in the model is well interpreted. After discussion,
it is found that data-driven models integrated engineering or physical
knowledge can significantly improve the urban building energy simulation.


Abstract:
  Recently, free-space optical (FSO) communication systems have obtained a wide
range of applications for communications between unmanned aerial vehicles
(UAVs) because they offer several advantages with respect to the traditional RF
communication systems. To investigate the performance of UAV-based FSO systems
in terms of achievable data rates, we derive the exact closed-form expression
for the mean achievable rates of UAV-to-UAV transmission. Our results may serve
to investigate the interaction between different FSO system parameters and the
average data rates achieved by UAV-based optical wireless communications.


Abstract:
  Floods are the most common and among the most severe natural disasters in
many countries around the world. As global warming continues to exacerbate sea
level rise and extreme weather, governmental authorities and environmental
agencies are facing the pressing need of timely and accurate evaluations and
predictions of flood risks. Current flood forecasts are generally based on
historical measurements of environmental variables at monitoring stations. In
recent years, in addition to traditional data sources, large amounts of
information related to floods have been made available via social media.
Members of the public are constantly and promptly posting information and
updates on local environmental phenomena on social media platforms. Despite the
growing interest of scholars towards the usage of online data during natural
disasters, the majority of studies focus exclusively on social media as a
stand-alone data source, while its joint use with other type of information is
still unexplored. In this paper we propose to fill this gap by integrating
traditional historical information on floods with data extracted by Twitter and
Google Trends. Our methodology is based on vine copulas, that allow us to
capture the dependence structure among the marginals, which are modelled via
appropriate time series methods, in a very flexible way. We apply our
methodology to data related to three different coastal locations on the South
coast of the United Kingdom (UK). The results show that our approach, based on
the integration of social media data, outperforms traditional methods in terms
of evaluation and prediction of flood events.


Abstract:
  The effect of boundaries and how these can be used to influence the bulk
behaviour in geometrically frustrated systems are both long-standing puzzles,
often relegated to secondary role. Here we use numerical simulations and "proof
of concept" experiments to demonstrate that boundaries can be engineered to
control the bulk behavior in a colloidal artificial ice. We show that an
antiferromagnetic frontier forces the system to rapidly reach the ground state
(GS), as opposed to the commonly implemented open or periodic boundary
conditions. We also show that strategically placing defects at the corners
generates novel bistable states, or topological strings which result from
competing GS regions in the bulk. Our results could be generalized to other
frustrated micro and nanostructures where boundary conditions may be engineered
with lithographic techniques.


Abstract:
  With more than 4,300 confirmed exoplanets and counting, the next milestone in
exoplanet research is to determine which of these newly found worlds could
harbor life. Coronal Mass Ejections (CMEs), spawn by magnetically active,
superflare-triggering dwarf stars, pose a direct threat to the habitability of
terrestrial exoplanets as they can deprive them from their atmospheres. Here we
develop a readily implementable atmosphere sustainability constraint for
terrestrial exoplanets orbiting active dwarfs, relying on the magnetospheric
compression caused by CME impacts. Our constraint focuses on a systems
understanding of CMEs in our own heliosphere that, applying to a given
exoplanet, requires as key input the observed bolometric energy of flares
emitted by its host star. Application of our constraint to six famous
exoplanets, (Kepler-438b, Proxima-Centauri b, and Trappist-1d, -1e, -1f and
-1g), within or in the immediate proximity of their stellar host's habitable
zones, showed that only for Kepler-438b might atmospheric sustainability
against stellar CMEs be likely. This seems to align with some recent studies
that, however, may require far more demanding computational resources and
observational inputs. Our physically intuitive constraint can be readily and en
masse applied, as is or generalized, to large-scale exoplanet surveys to detect
planets that could be sieved for atmospheres and, perhaps, possible
biosignatures at higher priority by current and future instrumentation.


Abstract:
  Structural breaks have been commonly seen in applications. Specifically for
detection of change points in time, research gap still remains on the setting
in ultra high dimension, where the covariates may bear spurious correlations.
In this paper, we propose a two-stage approach to detect change points in ultra
high dimension, by firstly proposing the dynamic titled current correlation
screening method to reduce the input dimension, and then detecting possible
change points in the framework of group variable selection. Not only the
spurious correlation between ultra-high dimensional covariates is taken into
consideration in variable screening, but non-convex penalties are studied in
change point detection in the ultra high dimension. Asymptotic properties are
derived to guarantee the asymptotic consistency of the selection procedure, and
the numerical investigations show the promising performance of the proposed
approach.


Abstract:
  The involutory subalgebra K(E$_9$) of the affine Kac-Moody algebra E$_9$ was
recently shown to admit an infinite sequence of unfaithful representations of
ever increasing dimensions arXiv:2102.00870. We revisit these representations
and describe their associated ideals in more detail, with particular emphasis
on two chiral versions that can be constructed for each such representation.
For every such unfaithful representation we show that the action of K(E$_9$)
decomposes into a direct sum of two mutually commuting (`chiral' and
`anti-chiral') parabolic algebras with Levi subalgebra
$\mathfrak{so}(16)_+\,\oplus\,\mathfrak{so}(16)_-$. We also spell out the
consistency conditions for uplifting such representations to unfaithful
representations of K(E$_{10}$). From these results it is evident that the
holonomy groups so far discussed in the literature are mere shadows (in a
Platonic sense) of a much larger structure.


Abstract:
  We fill in a gap in the proof of the main theorem in our earlier paper [Ol].
At the same time, we prove a slightly stronger version of the theorem needed
for another paper.


Abstract:
  Recent theories and experiments have suggested hydrodynamic phonon transport
features in graphite at unusually high temperatures. Here, we report a
pico-second pump-probe thermal reflectance measurement of heat pulse
propagation in graphite. The measurement results reveal transient lattice
cooling near the adiabatic center of a 15 $\mu$m diameter ring-shape pump beam
at temperatures between 80 and 120 K. While such lattice cooling has not been
reported in recent diffraction measurements of second sound in graphite, the
observation here is consistent with both hydrodynamic phonon transport theory
and prior heat pulse measurements of second sound in bulk sodium fluoride.


Abstract:
  Cerebrospinal fluid (CSF) plays a pivotal role in normal functioning of
Brain. Intracranial compartments such as blood, brain and CSF are
incompressible in nature. Therefore, if a volume imbalance in one of the
aforenoted compartments is observed, the other reaches out to maintain net
change to zero. Whereas, CSF has higher compliance over long term. However, if
the CSF flow is obstructed in the ventricles, this compliance may get exhausted
early. Brain tumor on the other hand poses a similar challenge towards
destabilization of CSF flow by compressing any section of ventricles thereby
ensuing obstruction. To avoid invasive procedures to study effects of tumor on
CSF flow, numerical-based methods such as Finite element modeling (FEM) are
used which provide excellent description of underlying pathological
interaction. A 3D fluid-structure interaction (FSI) model is developed to study
the effect of tumor growth on the flow of cerebrospinal fluid in ventricle
system. The FSI model encapsulates all the physiological parameters which may
be necessary in analyzing intraventricular CSF flow behavior. Findings of the
model show that brain tumor affects CSF flow parameters by deforming the walls
of ventricles in this case accompanied by a mean rise of 74.23% in CSF flow
velocity and considerable deformation on the walls of ventricles.


Abstract:
  Extreme mass-ratio inspirals~(EMRIs) detectable by the Laser Inteferometric
Space Antenna~(LISA) are unique probes of astrophysics and fundamental physics.
Parameter estimation for these sources is challenging, especially because the
waveforms are long, complicated, known only numerically, and slow to compute in
the most relevant regime, where the dynamics is relativistic. We perform a
time-consuming Fisher-matrix error analysis of the EMRI parameters using
fully-relativistic numerical waveforms to leading order in an adiabatic
expansion on a Kerr background, taking into account the motion of the LISA
constellation, higher harmonics, and also including the leading correction from
the spin of the secondary in the post-adiabatic approximation. We pay
particular attention to the convergence of the numerical derivatives in the
Fisher matrix and to the numerical stability of the covariance matrix, which
for some systems requires computing the numerical waveforms with approximately
$90$-digit precision. Our analysis confirms previous results (obtained with
approximated but much more computationally efficient waveforms) for the
measurement errors on the binary's parameters. We also show that the inclusion
of higher harmonics improves the errors on the luminosity distance and on the
orbital angular momentum angles by one order and two orders of magnitude,
respectively, which might be useful to identify the environments where EMRIs
live. We particularly focus on the measurability of the spin of the secondary,
confirming that it cannot be measured with sufficient accuracy. However, due to
correlations, its inclusion in the waveform model can deteriorate the accuracy
on the measurements of other parameters by orders of magnitude, unless a
physically-motivated prior on the secondary spin is imposed.


Abstract:
  Coronavirus disease 2019 (CoViD-19) is an infectious disease caused by severe
acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Among many symptoms,
cough, fever and tiredness are the most common. People over 60 years old and
with associated comorbidities are most likely to develop a worsening health
condition. This paper proposes a non-integer order model to describe the
dynamics of CoViD-19 in a standard population. The model incorporates the
reinfection rate in the individuals recovered from the disease. Numerical
simulations are performed for different values of the order of the fractional
derivative and of reinfection rate. The results are discussed from a biological
point of view.


Abstract:
  The actual mechanism(s) powering galactic outflows in active galactic nuclei
(AGN) is still a matter of debate. At least two physical models have been
considered in the literature: wind shocks and radiation pressure on dust. Here
we provide a first quantitative comparison of the AGN radiative feedback
scenario with observations of galactic outflows. We directly compare our
radiation pressure-driven shell models with the observational data from the
most recent compilation of molecular outflows on galactic scales. We show that
the observed dynamics and energetics of galactic outflows can be reproduced by
AGN radiative feedback, with the inclusion of radiation trapping and/or
luminosity evolution. The predicted scalings of the outflow energetics with AGN
luminosity can also quantitatively account for the observational scaling
relations. Furthermore, sources with both ultra-fast and molecular outflow
detections are found to be located in the `forbidden' region of the
$N_\mathrm{H} - \lambda$ plane. Overall, an encouraging agreement is obtained
over a wide range of AGN and host galaxy parameters. We discuss our results in
the context of recent observational findings and numerical simulations. In
conclusion, AGN radiative feedback is a promising mechanism for driving
galactic outflows that should be considered, alongside wind feedback, in the
interpretation of future observational data.


Abstract:
  Stars form by accreting material from their surrounding disks. There is a
consensus that matter flowing through the disk is channelled onto the stellar
surface by the stellar magnetic field. This is thought to be strong enough to
truncate the disk close to the so-called corotation radius where the disk
rotates at the same rate as the star. Spectro-interferometric studies in young
stellar objects show that Hydrogen is mostly emitted in a region of a few
milliarcseconds across, usually located within the dust sublimation radius. Its
origin is still a matter of debate and it can be interpreted as coming from the
stellar magnetosphere, a rotating wind or a disk. In the case of
intermediate-mass Herbig AeBe stars, the fact that the Br gamma emission is
spatially resolved rules out that most of the emission comes from the
magnetosphere. This is due to the weak magnetic fields (some tenths of G)
detected in these sources, resulting in very compact magnetospheres. In the
case of T Tauri sources, their larger magnetospheres should make them easier to
resolve. However, the small angular size of the magnetosphere (a few tenths of
milliarcseconds), along with the presence of winds emitting in Hydrogen make
the observations interpretation challenging. Here, we present direct evidence
of magnetospheric accretion by spatially resolving the inner disk of the 60 pc
T Tauri star TW Hydrae through optical long baseline interferometry. We find
that the hydrogen near-infrared emission comes from a region approximately 3.5
stellar radii (R*) across. This region is within the continuum dusty disk
emitting region (Rcont = 7 R*) and smaller than the corotation radius which is
twice as big. This indicates that the hydrogen emission originates at the
accretion columns, as expected in magnetospheric accretion models, rather than
in a wind emitted at much larger distance (>1au).


Abstract:
  We prove that if a compact set E in complex Euclidean space is contained in
an arc J, then there is a choice of J whose polynomial hull is the union of J
and the polynomial hull of E. This strengthens an earlier result of the author.
We also correct an inaccuracy in the statement, and fill a gap in the proof, of
that earlier result.


Abstract:
  We propose an unsupervised solution to the Authorship Verification task that
utilizes pre-trained deep language models to compute a new metric called
DV-Distance. The proposed metric is a measure of the difference between the two
authors comparing against pre-trained language models. Our design addresses the
problem of non-comparability in authorship verification, frequently encountered
in small or cross-domain corpora. To the best of our knowledge, this paper is
the first one to introduce a method designed with non-comparability in mind
from the ground up, rather than indirectly. It is also one of the first to use
Deep Language Models in this setting. The approach is intuitive, and it is easy
to understand and interpret through visualization. Experiments on four datasets
show our methods matching or surpassing current state-of-the-art and strong
baselines in most tasks.


Abstract:
  We prove explicit lower bounds for the smallest singular value and upper
bounds for the condition number of rectangular, multivariate Vandermonde
matrices with scattered nodes on the complex unit circle. Analogously to the
Shannon-Nyquist criterion, the nodes are assumed to be separated by a constant
divided by the used polynomial degree. If this constant grows linearly with the
spatial dimension, the condition number is uniformly bounded. If it grows only
logarithmically with the spatial dimension, the condition number grows slightly
stronger than exponentially with the spatial dimension. Both results are quasi
optimal and improve over all previously known results of such type.


Abstract:
  Ship detection in remote sensing images plays a crucial role in various
applications and has drawn increasing attention in recent years. However,
existing arbitrary-oriented ship detection methods are generally developed on a
set of predefined rotated anchor boxes. These predefined boxes not only lead to
inaccurate angle predictions but also introduce extra hyper-parameters and high
computational cost. Moreover, the prior knowledge of ship size has not been
fully exploited by existing methods, which hinders the improvement of their
detection accuracy. Aiming at solving the above issues, in this paper, we
propose a center-head point extraction based detector (named CHPDet) to achieve
arbitrary-oriented ship detection in remote sensing images. Our CHPDet
formulates arbitrary-oriented ships as rotated boxes with head points which are
used to determine the direction. And rotated Gaussian kernel is used to map the
annotations into target heatmaps. Keypoint estimation is performed to find the
center of ships. Then, the size and head point of the ships are regressed. The
orientation-invariant model (OIM) is also used to produce orientation-invariant
feature maps. Finally, we use the target size as prior to finetune the results.
Moreover, we introduce a new dataset for multi-class arbitrary-oriented ship
detection in remote sensing images at a fixed ground sample distance (GSD)
which is named FGSD2021. Experimental results on FGSD2021 and two other widely
used data sets, i.e., HRSC2016, and UCAS-AOD demonstrate that our CHPDet
achieves state-of-the-art performance and can well distinguish between bow and
stern. Code and FGSD2021 dataset are available at
https://github.com/zf020114/CHPDet.


Abstract:
  The $H^m$-conforming virtual elements of any degree $k$ on any shape of
polytope in $\mathbb R^n$ with $m, n\geq1$ and $k\geq m$ are recursively
constructed by gluing conforming virtual elements on faces in a universal way.
For the lowest degree case $k=m$, the set of degrees of freedom only involves
function values and derivatives up to order $m-1$ at the vertices of the
polytope. The inverse inequality and several norm equivalences for the
$H^m$-conforming virtual elements are rigorously proved. The $H^m$-conforming
virtual elements are then applied to discretize a polyharmonic equation with a
lower order term. With the help of the interpolation error estimate and norm
equivalences, the optimal error estimates are derived for the $H^m$-conforming
virtual element method.


Abstract:
  Imitation learning and instruction-following are two common approaches to
communicate a user's intent to a learning agent. However, as the complexity of
tasks grows, it could be beneficial to use both demonstrations and language to
communicate with an agent. In this work, we propose a novel setting where an
agent is given both a demonstration and a description, and must combine
information from both the modalities. Specifically, given a demonstration for a
task (the source task), and a natural language description of the differences
between the demonstrated task and a related but different task (the target
task), our goal is to train an agent to complete the target task in a zero-shot
setting, that is, without any demonstrations for the target task. To this end,
we introduce Language-Aided Reward and Value Adaptation (LARVA) which, given a
source demonstration and a linguistic description of how the target task
differs, learns to output a reward / value function that accurately describes
the target task. Our experiments show that on a diverse set of adaptations, our
approach is able to complete more than 95% of target tasks when using
template-based descriptions, and more than 70% when using free-form natural
language.


Abstract:
  We propose a novel distance-based regularization method for deep metric
learning called Multi-level Distance Regularization (MDR). MDR explicitly
disturbs a learning procedure by regularizing pairwise distances between
embedding vectors into multiple levels that represents a degree of similarity
between a pair. In the training stage, the model is trained with both MDR and
an existing loss function of deep metric learning, simultaneously; the two
losses interfere with the objective of each other, and it makes the learning
process difficult. Moreover, MDR prevents some examples from being ignored or
overly influenced in the learning process. These allow the parameters of the
embedding network to be settle on a local optima with better generalization.
Without bells and whistles, MDR with simple Triplet loss achieves
the-state-of-the-art performance in various benchmark datasets: CUB-200-2011,
Cars-196, Stanford Online Products, and In-Shop Clothes Retrieval. We
extensively perform ablation studies on its behaviors to show the effectiveness
of MDR. By easily adopting our MDR, the previous approaches can be improved in
performance and generalization ability.


Abstract:
  Implicitly filtered large-eddy simulation (LES) is by nature numerically
under-resolved. With the sole exception of Fourier-spectral methods, discrete
numerical derivative operators cannot accurately represent the dynamics of all
of the represented scales. Since the resolution scale in an LES usually lies in
the inertial range, these poorly represented scales are dynamically significant
and errors in their dynamics can affect all resolved scales. This Letter is
focused on characterizing the effects of numerical dispersion error by studying
the energy cascade in LES of convecting homogeneous isotropic turbulence.
Numerical energy and transfer spectra reveal that energy is not transferred at
the appropriate rate to wavemodes where significant dispersion error is
present. This leads to a deficiency of energy in highly dispersive modes and an
accompanying pile up of energy in the well resolved modes, since dissipation by
the subgrid model is diminished. An asymptotic analysis indicates that
dispersion error causes a phase decoherence between triad interacting
wavemodes, leading to a reduction in the mean energy transfer rate for these
scales. These findings are relevant to a wide range of LES, since turbulence
commonly convects through the grid in practical simulations. Further, these
results indicate that the resolved scales should be defined to not include the
dispersive modes.


Abstract:
  Brazil is the 5th largest country in the world, despite of having a ``High
Human Development'' it is the 9th most unequal country. The existing Brazilian
micro pension programme is one of the safety nets for poor people. To become
eligible for this benefit, each person must have an income that is less than a
quarter of the Brazilian minimum monthly wage and be either over 65 or
considered disabled. That minimum income corresponds to approximately $2$
dollars per day. This paper analyses quantitatively some aspects of this
programme in the Public Pension System of Brazil. We look for the impact of
some particular economic variables on the number of people receiving the
benefit, and seek if that impact significantly differs among the 27 Brazilian
Federal Units. We search for heterogeneity. We perform regression and spatial
cluster analysis for detection of geographical grouping. We use a database that
includes the entire population that receives the benefit. Afterwards, we
calculate the amount that the system spends with the beneficiaries, estimate
values \textit{per capita} and the weight of each UF, searching for
heterogeneity reflected on the amount spent \textit{per capita}. In this latter
calculation we use a more comprehensive database, by individual, that includes
all people that started receiving a benefit under the programme in the period
from 2nd of January 2018 to 6th of April 2018. We compute the expected
discounted benefit and confirm a high heterogeneity among UF's as well as
gender. We propose achieving a more equitable system by introducing `age
adjusting factors' to change the benefit age.


Abstract:
  By constructing the configuration of D3-branes with D(-1)-branes as
D-instantons, we study the three-dimensional Yang-Mills Chern-Simons theory in
holography. Due to the presence of the D-instantons, the D7-branes with
discrepant embedding functions are able to be introduced in order to include
the fundamental fermions (as flavors) and the Chern-Simons term (at very low
energy) in the dual theory. The vacuum structure at zero temperature is studied
in the soliton background and it illustrates the topological phase transition
in the presence of instantons. Moreover, since the confinement/deconfinement
phase transition could be holographically identified as the Hawking-Page
transition in the bulk, we accordingly calculate the critical temperature of
the deconfinement phase transition by collecting the bulk onshell action as the
thermodynamical free energy. On the other hand, we evaluate the difference of
the entanglement entropy in slab configuration by using the RT formula since
the confinement may also be characterized by the entanglement entropy.
Altogether we find the behavior of the critical temperature is in qualitative
agreement with the behavior of the critical length determined by the
entanglement entropy which implies the entanglement entropy could indeed be a
character of the confinement in our setup and the D3-D(-1) system would be a
remarkable approach to study the three-dimensional gauge theory.


Abstract:
  Which samples should be labelled in a large data set is one of the most
important problems for trainingof deep learning. So far, a variety of active
sample selection strategies related to deep learning havebeen proposed in many
literatures. We defined them as Active Deep Learning (ADL) only if
theirpredictor is deep model, where the basic learner is called as predictor
and the labeling schemes iscalled selector. In this survey, three fundamental
factors in selector designation were summarized. Wecategory ADL into
model-driven ADL and data-driven ADL, by whether its selector is model-drivenor
data-driven. The different characteristics of the two major type of ADL were
addressed in indetail respectively. Furthermore, different sub-classes of
data-driven and model-driven ADL are alsosummarized and discussed emphatically.
The advantages and disadvantages between data-driven ADLand model-driven ADL
are thoroughly analyzed. We pointed out that, with the development of
deeplearning, the selector in ADL also is experiencing the stage from
model-driven to data-driven. Finally,we make discussion on ADL about its
uncertainty, explanatory, foundations of cognitive science etc.and survey on
the trend of ADL from model-driven to data-driven.


Abstract:
  In this paper, we employ Machine Learning algorithms on multi-mission
observations for the classification of accretion states of outbursting Black
hole X-ray binaries for the first time. Archival data from RXTE, Swift, MAXI
and AstroSat observatories are used to generate the hardness intensity diagrams
(HIDs) for outbursts of the sources XTE J1859+226 (1999 outburst), GX 339-4
(2002, 2004, 2007 and 2010 outbursts), IGR J17091-3624 (2016 outburst), and
MAXI J1535-571 (2017 outburst). Based on variation of X-ray flux, hardness
ratios, presence of various types of Quasi-periodic Oscillations (QPOs), photon
indices and disk temperature, we apply clustering algorithms like K-Means
clustering and Hierarchical clustering to classify the accretion states
(clusters) of each outburst. As multiple parameters are involved in the
classification process, we show that clustering algorithms club together the
observations of similar characteristics more efficiently than the `standard'
method of classification. We also infer that K-Means clustering provides more
reliable results than Hierarchical clustering. We demonstrate the importance of
the classification based on machine learning by comparing it with results from
`standard' classification.


Abstract:
  We consider the possible existence of gravitationally bound stringlike
objects in the framework of the generalized hybrid metric-Palatini gravity
theory, in which the gravitational action is represented by an arbitrary
function of the Ricci and of the Palatini scalars, respectively. The theory
admits an equivalent scalar-tensor representation in terms of two independent
scalar fields. Assuming cylindrical symmetry, and the boost invariance of the
metric, we obtain the gravitational field equations that describe cosmic
stringlike structures in the theory. The physical and geometrical properties of
the cosmic strings are determined by the two scalar fields, as well by an
effective field potential, functionally dependent on both scalar fields. The
field equations can be exactly solved for a vanishing, and a constant
potential, respectively, with the corresponding string tension taking both
negative and positive values. Furthermore, for more general classes of
potentials, having an additive and a multiplicative algebraic structure in the
two scalar fields, the gravitational field equations are solved numerically.
For each potential we investigate the effects of the variations of the
potential parameters and of the boundary conditions on the structure of the
cosmic string. In this way, we obtain a large class of stable stringlike
astrophysical configurations, whose basic parameters (string tension and
radius) depend essentially on the effective field potential, and on the
boundary conditions.


Abstract:
  We consider the problem of partitioning a spectrum band into M channels of
equal bandwidth, and then further assigning these M channels into P licensed
channels and M-P unlicensed channels. Licensed channels can be accessed both
for licensed and opportunistic use following a tiered structure which has a
higher priority for licensed use. Unlicensed channels can be accessed only for
opportunistic use. We address the following question in this paper. Given a
market setup, what values of M and P maximize the net spectrum utilization of
the spectrum band? While this problem is of fundamental nature, it is highly
relevant practically, e.g., in the context of partitioning the recently
proposed Citizens Broadband Radio Service band. If M is too high or too low, it
may decrease spectrum utilization due to limited channel capacity or due to
wastage of channel capacity, respectively. If P is too high (low), it will not
incentivize the wireless operators who are primarily interested in unlicensed
channels (licensed channels) to join the market. These tradeoffs are captured
in our optimization problem which manifests itself as a two-stage Stackelberg
game. We design an algorithm to solve the Stackelberg game and hence find the
optimal M and P. The algorithm design also involves an efficient Monte Carlo
integrator to evaluate the expected value of the involved random variables like
spectrum utilization and operators' revenue. We also benchmark our algorithms
using numerical simulations.


Abstract:
  Software developers are social creatures: they communicate, collaborate, and
promote their work in a variety of channels. Twitter, GitHub, Stack Overflow,
and other platforms offer developers opportunities to network and exchange
ideas. Researchers analyze content on these sites to learn about trends and
topics in software engineering. However, insight mined from the text of Stack
Overflow questions or GitHub issues is highly focused on detailed and technical
aspects of software development. In this paper, we present a relatively new
online community for software developers called DEV. On DEV users write
long-form posts about their experiences, preferences, and working life in
software, zooming out from specific issues and files to reflect on broader
topics. About 50,000 users have posted over 140,000 articles related to
software development. In this work, we describe the content of posts on DEV
using a topic model, showing that developers discuss a rich variety and mixture
of social and technical aspects of software development. We show that
developers use DEV to promote themselves and their work: 83% link their
profiles to their GitHub profiles and 56% to their Twitter profiles. 14% of
users pin specific GitHub repos in their profiles. We argue that DEV is
emerging as an important hub for software developers, and a valuable source of
insight for researchers to complement data from platforms like GitHub and Stack
Overflow.


Abstract:
  First-generation long-distance quantum repeater networks require quantum
memories capable of interfacing with telecom photons to perform
quantum-interference-mediated entanglement generation operations. The ability
to demonstrate these interconnections using real-life fiber connections in a
long-distance setting is paramount to realize a scalable quantum internet. Here
we address these significant challenges by observing Hong-Ou-Mandel (HOM)
interference between indistinguishable telecom photons produced in two
independent room temperature quantum memories, separated by a distance of 158
km. We obtained interference visibilities after long-distance propagation of
$\rm \boldsymbol{V=(38\pm2)\%}$ for single-photon level experimental inputs.
This first-of-its-kind quantum network prototype connecting quantum
laboratories in Stony Brook University and Brookhaven National Laboratory is
envisioned to evolve into a large-scale memory-assisted entanglement
distribution quantum network, the basis for inter-city quantum communication.


Abstract:
  As environments involving both robots and humans become increasingly common,
so does the need to account for people during planning. To plan effectively,
robots must be able to respond to and sometimes influence what humans do. This
requires a human model which predicts future human actions. A simple model may
assume the human will continue what they did previously; a more complex one
might predict that the human will act optimally, disregarding the robot;
whereas an even more complex one might capture the robot's ability to influence
the human. These models make different trade-offs between computational time
and performance of the resulting robot plan. Using only one model of the human
either wastes computational resources or is unable to handle critical
situations. In this work, we give the robot access to a suite of human models
and enable it to assess the performance-computation trade-off online. By
estimating how an alternate model could improve human prediction and how that
may translate to performance gain, the robot can dynamically switch human
models whenever the additional computation is justified. Our experiments in a
driving simulator showcase how the robot can achieve performance comparable to
always using the best human model, but with greatly reduced computation.


Abstract:
  Continual learning has become increasingly important as it enables NLP models
to constantly learn and gain knowledge over time. Previous continual learning
methods are mainly designed to preserve knowledge from previous tasks, without
much emphasis on how to well generalize models to new tasks. In this work, we
propose an information disentanglement based regularization method for
continual learning on text classification. Our proposed method first
disentangles text hidden spaces into representations that are generic to all
tasks and representations specific to each individual task, and further
regularizes these representations differently to better constrain the knowledge
required to generalize. We also introduce two simple auxiliary tasks: next
sentence prediction and task-id prediction, for learning better generic and
specific representation spaces. Experiments conducted on large-scale benchmarks
demonstrate the effectiveness of our method in continual text classification
tasks with various sequences and lengths over state-of-the-art baselines. We
have publicly released our code at https://github.com/GT-SALT/IDBR.


Abstract:
  Partially Observable Monte-Carlo Planning (POMCP) is a powerful online
algorithm able to generate approximate policies for large Partially Observable
Markov Decision Processes. The online nature of this method supports
scalability by avoiding complete policy representation. The lack of an explicit
representation however hinders policy interpretability and makes policy
verification very complex. In this work, we propose two contributions. The
first is a method for identifying unexpected actions selected by POMCP with
respect to expert prior knowledge of the task. The second is a shielding
approach that prevents POMCP from selecting unexpected actions. The first
method is based on Satisfiability Modulo Theory (SMT). It inspects traces
(i.e., sequences of belief-action-observation triplets) generated by POMCP to
compute the parameters of logical formulas about policy properties defined by
the expert. The second contribution is a module that uses online the logical
formulas to identify anomalous actions selected by POMCP and substitutes those
actions with actions that satisfy the logical formulas fulfilling expert
knowledge. We evaluate our approach on Tiger, a standard benchmark for POMDPs,
and a real-world problem related to velocity regulation in mobile robot
navigation. Results show that the shielded POMCP outperforms the standard POMCP
in a case study in which a wrong parameter of POMCP makes it select wrong
actions from time to time. Moreover, we show that the approach keeps good
performance also if the parameters of the logical formula are optimized using
trajectories containing some wrong actions.


Abstract:
  In this paper we study the electromagnetic scattering, absorption, and
performance bounds for short time modulated pulses that impinge on a
time-varying lossy layer that is sandwiched between vacuum and a perfect
electric conductor. The electric characteristics of the layer, namely, the
conductivity, permittivity, and permeability are assumed to change abruptly or
gradually in time. We demonstrate numerically that a time-varying absorbing
layer that undergoes temporal switching of its permittivity and conductance can
absorb the power of a modulated, ultra-wideband, as well as a
quasi-monochromatic, pulsed wave beyond what is dictated by the time invariant
Rozanov bound when integrating over the whole frequency spectrum. We suggest
and simulate a practical metamaterial realization that is constructed as a
three-dimensional array of resistor loaded dipole. By switching only the
dipole's load resistance, desired effective media properties are obtained.
Furthermore, we show that Rozanov's bound can be bypassed with abrupt and a
more practical gradual, soft, switching thus overcoming some possible causality
issue in abrupt switching.


Abstract:
  A novel algorithm named Perturbed Prox-Preconditioned SPIDER (3P-SPIDER) is
introduced. It is a stochastic variancereduced proximal-gradient type algorithm
built on Stochastic Path Integral Differential EstimatoR (SPIDER), an algorithm
known to achieve near-optimal first-order oracle inequality for nonconvex and
nonsmooth optimization. Compared to the vanilla prox-SPIDER, 3P-SPIDER uses
preconditioned gradient estimators. Preconditioning can either be applied
"explicitly" to a gradient estimator or be introduced "implicitly" as in
applications to the EM algorithm. 3P-SPIDER also assumes that the
preconditioned gradients may (possibly) be not known in closed analytical form
and therefore must be approximated which adds an additional degree of
perturbation. Studying the convergence in expectation, we show that 3P-SPIDER
achieves a near-optimal oracle inequality O(n^(1/2) /epsilon) where n is the
number of observations and epsilon the target precision even when the gradient
is estimated by Monte Carlo methods. We illustrate the algorithm on an
application to the minimization of a penalized empirical loss.


Abstract:
  Detection in large-scale scenes is a challenging problem due to small objects
and extreme scale variation. It is essential to focus on the image regions of
small objects. In this paper, we propose a novel Adaptive Zoom (AdaZoom)
network as a selective magnifier with flexible shape and focal length to
adaptively zoom the focus regions for object detection. Based on policy
gradient, we construct a reinforcement learning framework for focus region
generation, with the reward formulated by object distributions. The scales and
aspect ratios of the generated regions are adaptive to the scales and
distribution of objects inside. We apply variable magnification according to
the scale of the region for adaptive multi-scale detection. We further propose
collaborative training to complementarily promote the performance of AdaZoom
and the detection network. To validate the effectiveness, we conduct extensive
experiments on VisDrone2019, UAVDT, and DOTA datasets. The experiments show
AdaZoom brings a consistent and significant improvement over different
detection networks, achieving state-of-the-art performance on these datasets,
especially outperforming the existing methods by AP of 4.64% on Vis-Drone2019.


Abstract:
  We consider the structure of the point-line incidence matrix of the
projective space $\mathrm{PG}(3,q)$ connected with orbits of points and lines
under the stabilizer group of the twisted cubic. Structures of submatrices with
incidences between a union of line orbits and an orbit of points are
investigated. For the unions consisting of two or three line orbits, the
original submatrices are split into new ones, in which the incidences are also
considered. For each submatrix (apart from the ones corresponding to a special
type of lines), the numbers of lines through every point and of points lying on
every line are obtained. This corresponds to the numbers of ones in columns and
rows of the submatrices.


Abstract:
  We address the problem of weakly-supervised semantic segmentation (WSSS)
using bounding box annotations. Although object bounding boxes are good
indicators to segment corresponding objects, they do not specify object
boundaries, making it hard to train convolutional neural networks (CNNs) for
semantic segmentation. We find that background regions are perceptually
consistent in part within an image, and this can be leveraged to discriminate
foreground and background regions inside object bounding boxes. To implement
this idea, we propose a novel pooling method, dubbed background-aware pooling
(BAP), that focuses more on aggregating foreground features inside the bounding
boxes using attention maps. This allows to extract high-quality pseudo
segmentation labels to train CNNs for semantic segmentation, but the labels
still contain noise especially at object boundaries. To address this problem,
we also introduce a noise-aware loss (NAL) that makes the networks less
susceptible to incorrect labels. Experimental results demonstrate that learning
with our pseudo labels already outperforms state-of-the-art weakly- and
semi-supervised methods on the PASCAL VOC 2012 dataset, and the NAL further
boosts the performance.


Abstract:
  This paper introduces a family of valid inequalities, that we term
consistency cuts, to be applied to a Dantzig-Wolfe reformulation (or
decomposition) with linking variables. We prove that these cuts ensure an
integer solution to the corresponding Dantzig-Wolfe relaxation when certain
criteria to the structure of the decomposition are met. We implement the cuts
and use them to solve a commonly used test set of 200 instances of the temporal
knapsack problem. We assess the performance with and without the cuts and
compare further to CPLEX and other solution methods that have historically been
used to solve the test set. By separating consistency cuts we show that we can
obtain optimal integer solutions much faster than the other methods and even
solve the remaining unsolved problems in the test set. We also perform a second
test on instances from the MIPLIB 2017 online library of mixed-integer
programs, showing the potential of the cuts on a wider range of problems.


Abstract:
  We study homogenization of a boundary obstacle problem on $ C^{1,\alpha} $
domain $D$ for some elliptic equations with uniformly elliptic coefficient
matrices $\gamma$. For any $ \epsilon\in\mathbb{R}_+$, $\partial D=\Gamma \cup
\Sigma$, $\Gamma \cap \Sigma=\emptyset $ and $ S_{\epsilon}\subset \Sigma $
with suitable assumptions,\ we prove that as $\epsilon$ tends to zero, the
energy minimizer $ u^{\epsilon} $ of $ \int_{D} |\gamma\nabla u|^{2} dx $,
subject to $ u\geq \varphi $ on $ S_{\varepsilon} $, up to a subsequence,
converges weakly in $ H^{1}(D) $ to $ \widetilde{u} $ which minimizes the
energy functional $\int_{D}|\gamma\nabla u|^{2}+\int_{\Sigma}
(u-\varphi)^{2}_{-}\mu(x) dS_{x}$, where $\mu(x)$ depends on the structure of
$S_{\epsilon}$ and $ \varphi $ is any given function in
$C^{\infty}(\overline{D})$.


Abstract:
  To improve the system performance towards the Shannon limit, advanced radio
resource management mechanisms play a fundamental role. In particular,
scheduling should receive much attention, because it allocates radio resources
among different users in terms of their channel conditions and QoS
requirements. The difficulties of scheduling algorithms are the tradeoffs need
to be made among multiple objectives, such as throughput, fairness and packet
drop rate. We propose a smart scheduling scheme based on deep reinforcement
learning (DRL). We not only verify the performance gain achieved, but also
provide implementation-friend designs, i.e., a scalable neural network design
for the agent and a virtual environment training framework. With the scalable
neural network design, the DRL agent can easily handle the cases when the
number of active users is time-varying without the need to redesign and retrain
the DRL agent. Training the DRL agent in a virtual environment offline first
and using it as the initial version in the practical usage helps to prevent the
system from suffering from performance and robustness degradation due to the
time-consuming training. Through both simulations and field tests, we show that
the DRL-based smart scheduling outperforms the conventional scheduling method
and can be adopted in practical systems.


Abstract:
  First principles investigations of the high temperature superconducting
system Ba$_2$CuO$_{3+\delta}$, recently discovered at $\delta\approx0.2$ at
$T_c=70$ K, are applied to demonstrate the effects of oxygen ordering on the
electronic and magnetic properties. The observed `highly over-doped'
superconducting phase displays stretched Cu-planar oxygen O$_{\rm P}$ distances
and anomalously shortened Cu-apical O$_{\rm A}$ separations compared with other
cuprates. The stoichiometric system $\delta=0$, with its strongly
one-dimensional (1D) Cu-O$_{\rm P}$ chain structure, when nonmagnetic shows 1D
Fermi surfaces that lead, within density functional theory, to
antiferromagnetic Cu-O$_{\rm P}$ chains (a spin-Peierls instability).
Accounting for 1D fluctuations and small interchain coupling according to the
theory of Schulz indicates this system, like Sr$_2$CuO$_3$, is near the 1D
Luttinger-liquid quantum critical phase. The unusual Cu-O bond lengths per se
have limited effects on other properties for $\delta$=0. We find that a
`doubled bilayer' structure of alternating Cu-O$_{\rm P}$ chains and wide rung
Cu$_3$O$_4$ ladders is the energetically preferred one of three possibilities
where the additional oxygen ions bridge Cu-O$_{\rm P}$ chains in the
superconducting phase $\delta=1/4$. Nominal formal valences of the three Cu
sites are discussed. The six-fold (octahedral) site is the most highly
oxidized, accepting somewhat more holes in the $d_{z^2}$ orbital than in the
$d_{x^2-y^2}$ orbital. The implication is that two-band physics is involved in
the pairing mechanism and the superconducting carriers. The Fermi surfaces of
this metallic bilayer structure show both 1D and 2D strong (incipient) nesting
instabilities, possibly accounting for the lack of clean single-phase samples
based on this structure and suggesting importance for the pairing mechanism.


Abstract:
  In this paper, we investigate the problem of coordination between economic
dispatch (ED) and demand response (DR) in multi-energy systems (MESs), aiming
to improve the economic utility and reduce the waste of energy in MESs. Since
multiple energy sources are coupled through energy hubs (EHs), the
supply-demand constraints are nonconvex. To deal with this issue, we propose a
linearization method to transform the coordination problem to a convex social
welfare optimization one. Then a decentralized algorithm based on parallel
Alternating Direction Method of Multipliers (ADMM) and dynamic average tracking
protocol is developed, where each agent could only make decisions based on
information from their neighbors. Moreover, by using variational inequality and
Lyapunov-based techniques, we show that our algorithm could always converge to
the global optimal solution. Finally, a case study on the modified IEEE 14-bus
network verifies the feasibility and effectiveness of our algorithm.


Abstract:
  In this paper, we propose a fully automatic system for generating comic books
from videos without any human intervention. Given an input video along with its
subtitles, our approach first extracts informative keyframes by analyzing the
subtitles, and stylizes keyframes into comic-style images. Then, we propose a
novel automatic multi-page layout framework, which can allocate the images
across multiple pages and synthesize visually interesting layouts based on the
rich semantics of the images (e.g., importance and inter-image relation).
Finally, as opposed to using the same type of balloon as in previous works, we
propose an emotion-aware balloon generation method to create different types of
word balloons by analyzing the emotion of subtitles and audios. Our method is
able to vary balloon shapes and word sizes in balloons in response to different
emotions, leading to more enriched reading experience. Once the balloons are
generated, they are placed adjacent to their corresponding speakers via speaker
detection. Our results show that our method, without requiring any user inputs,
can generate high-quality comic pages with visually rich layouts and balloons.
Our user studies also demonstrate that users prefer our generated results over
those by state-of-the-art comic generation systems.


Abstract:
  We introduce a state-of-the-art audio-visual on-screen sound separation
system which is capable of learning to separate sounds and associate them with
on-screen objects by looking at in-the-wild videos. We identify limitations of
previous work on audio-visual on-screen sound separation, including the
simplicity and coarse resolution of spatio-temporal attention, and poor
convergence of the audio separation model. Our proposed model addresses these
issues using cross-modal and self-attention modules that capture audio-visual
dependencies at a finer resolution over time, and by unsupervised pre-training
of audio separation model. These improvements allow the model to generalize to
a much wider set of unseen videos. We also show a robust way to further improve
the generalization capability of our models by calibrating the probabilities of
our audio-visual on-screen classifier, using only a small amount of in-domain
videos labeled for their on-screen presence. For evaluation and semi-supervised
training, we collected human annotations of on-screen audio from a large
database of in-the-wild videos (YFCC100m). Our results show marked improvements
in on-screen separation performance, in more general conditions than previous
methods.


Abstract:
  We show that if a sequence $M_n$ of closed aspherical $d$-dimensional
Riemannian manifolds with Ricci curvature uniformly bounded below and diameter
uniformly bounded above collapses, then for all large enough $n$, the
fundamental groups $\pi_1(M_n)$ have non-trivial finitely generated abelian
normal subgroups. In particular, the groups $\pi_1(M_n)$ cannot be
non-elementary hyperbolic.