Abstract:
  Tensors, or multidimensional arrays, are data structures that can naturally
represent visual data of multiple dimensions. Inherently able to efficiently
capture structured, latent semantic spaces and high-order interactions, tensors
have a long history of applications in a wide span of computer vision problems.
With the advent of the deep learning paradigm shift in computer vision, tensors
have become even more fundamental. Indeed, essential ingredients in modern deep
learning architectures, such as convolutions and attention mechanisms, can
readily be considered as tensor mappings. In effect, tensor methods are
increasingly finding significant applications in deep learning, including the
design of memory and compute efficient network architectures, improving
robustness to random noise and adversarial attacks, and aiding the theoretical
understanding of deep networks.
  This article provides an in-depth and practical review of tensors and tensor
methods in the context of representation learning and deep learning, with a
particular focus on visual data analysis and computer vision applications.
Concretely, besides fundamental work in tensor-based visual data analysis
methods, we focus on recent developments that have brought on a gradual
increase of tensor methods, especially in deep learning architectures, and
their implications in computer vision applications. To further enable the
newcomer to grasp such concepts quickly, we provide companion Python notebooks,
covering key aspects of the paper and implementing them, step-by-step with
TensorLy.


Abstract:
  We show that the nonlinear stochastic dynamics of a
measurement-feedback-based coherent Ising machine (MFB-CIM) in the presence of
quantum noise can be exploited to sample degenerate ground and low-energy spin
configurations of the Ising model. We formulate a general discrete-time
Gaussian-state model of the MFB-CIM which faithfully captures the nonlinear
dynamics present at and above system threshold. This model overcomes the
limitations of both mean-field models, which neglect quantum noise, and
continuous-time models, which assume long photon lifetimes. Numerical
simulations of our model show that when the MFB-CIM is operated in a
quantum-noise-dominated regime with short photon lifetimes (i.e., low cavity
finesse), homodyne monitoring of the system can efficiently produce samples of
low-energy Ising spin configurations, requiring many fewer roundtrips to sample
than suggested by established high-finesse, continuous-time models. We find
that sampling performance is robust to, or even improved by, turning off or
altogether reversing the sign of the parametric drive, but performance is
critically reduced in the absence of optical nonlinearity. For the class of
MAX-CUT problems with binary-signed edge weights, the number of roundtrips
sufficient to fully sample all spin configurations up to the first-excited
Ising energy, including all degeneracies, scales as $1.08^N$. At a problem size
of $N = 100$ with a few dozen (median of 20) such desired configurations per
instance, we have found median sufficient sampling times of $6\times10^6$
roundtrips; in an experimental implementation of an MFB-CIM with a 10 GHz
repetition rate, this corresponds to a wall-clock sampling time of 0.6 ms.


Abstract:
  We present an implementation of the $GW$ space-time approach that allows
cubic-scaling all-electron calculations with standard Gaussian basis sets
without exploiting any localization nor sparsity considerations. The
independent-electron susceptibility is constructed in a time representation
over a non-uniform distribution of real-space locations $\lbrace {\bf r}_k
\rbrace$ optimized within a separable resolution-of-the-identity framework to
reproduce standard Coulomb-fitting calculations with meV accuracy. The
compactness of the obtained $\lbrace {\bf r}_k \rbrace$ distribution leads to a
crossover with the standard Coulomb-fitting scheme for system sizes below a few
hundred electrons. The needed analytic continuation follows a recent approach
that requires the continuation of the screened Coulomb potential rather than
the much more structured self-energy. The present scheme is benchmarked over
large molecular sets and scaling properties are demonstrated on a family of
defected hexagonal boron-nitride flakes containing up to 6000 electrons.


Abstract:
  Theoretical models and experimental observations suggest that gamma-ray
bursts (GRB) and high-energy neutrino bursts travelling through the
interstellar space may reach the Earth at different speeds. We propose and
study in details the mechanism i), which always exists, where GRB are slowed
down due to the dispersion of light in the interstellar medium. In addition to
the standard media such as electrons and photons as CMB, we consider the medium
with invisible axions. The amount of GRB delays in different media are
calculated in details utilizing a novel technique in QFT by using the hitherto
known or estimated densities of particles in the space without introducing any
arbitrary parameter. Previously, the GRB delays have been interpreted as a sign
of Lorentz invariance violation by modifying the dispersion relation of Special
Relativity, which relates the energy, the momentum and the mass of a particle,
based on different mechanisms ii), such as a stringy spacetime foam, coming
from a quantum gravity effect and using an adjustable parameter. Obviously, all
the above-mentioned mechanisms i) and ii) are induced (seeming) Lorentz
invariance violations but not an intrinsic (genuine) one. The amount of GRB
delay due to the two aforementioned interpretations can be distinguished by
observing the time of arrival of light with different frequencies. Namely,
dispersion of light i) predicts that the higher energy GRB arrive the Earth
earlier, while in the other interpretations ii), they arrive later. We notice
that the needed amount for delay due to the dispersion of light shall have the
potential power to shed additional light on the microstructure of interstellar
media with respect to the densities of constituent particles and the origins of
their sources. Finally, we indicate the ways to detect the intrinsic Lorentz
invariance violation and to interpret them theoretically.


Abstract:
  Long-term deployment of a fleet of mobile robots requires reliable and secure
two-way communication channels between individual robots and remote human
operators for supervision and tasking. Existing open-source solutions to this
problem degrade in performance in challenging real-world situations such as
intermittent and low-bandwidth connectivity, do not provide security control
options, and can be computationally expensive on hardware-constrained mobile
robot platforms. In this paper, we present Robofleet, a lightweight open-source
system which provides inter-robot communication, remote monitoring, and remote
tasking for a fleet of ROS-enabled service-mobile robots that is designed with
the practical goals of resilience to network variance and security control in
mind.
  Robofleet supports multi-user, multi-robot communication via a central
server. This architecture deduplicates network traffic between robots,
significantly reducing overall network load when compared with native ROS
communication. This server also functions as a single entrypoint into the
system, enabling security control and user authentication. Individual robots
run the lightweight Robofleet client, which is responsible for exchanging
messages with the Robofleet server. It automatically adapts to adverse network
conditions through backpressure monitoring as well as topic-level priority
control, ensuring that safety-critical messages are successfully transmitted.
Finally, the system includes a web-based visualization tool that can be run on
any internet-connected, browser-enabled device to monitor and control the
fleet.
  We compare Robofleet to existing methods of robotic communication, and
demonstrate that it provides superior resilience to network variance while
maintaining performance that exceeds that of widely-used systems.


Abstract:
  Video frame interpolation can up-convert the frame rate and enhance the video
quality. In recent years, although the interpolation performance has achieved
great success, image blur usually occurs at the object boundaries owing to the
large motion. It has been a long-standing problem, and has not been addressed
yet. In this paper, we propose to reduce the image blur and get the clear shape
of objects by preserving the edges in the interpolated frames. To this end, the
proposed Edge-Aware Network (EA-Net) integrates the edge information into the
frame interpolation task. It follows an end-to-end architecture and can be
separated into two stages, \emph{i.e.}, edge-guided flow estimation and
edge-protected frame synthesis. Specifically, in the flow estimation stage,
three edge-aware mechanisms are developed to emphasize the frame edges in
estimating flow maps, so that the edge-maps are taken as the auxiliary
information to provide more guidance to boost the flow accuracy. In the frame
synthesis stage, the flow refinement module is designed to refine the flow map,
and the attention module is carried out to adaptively focus on the
bidirectional flow maps when synthesizing the intermediate frames. Furthermore,
the frame and edge discriminators are adopted to conduct the adversarial
training strategy, so as to enhance the reality and clarity of synthesized
frames. Experiments on three benchmarks, including Vimeo90k, UCF101 for
single-frame interpolation and Adobe240-fps for multi-frame interpolation, have
demonstrated the superiority of the proposed EA-Net for the video frame
interpolation task.


Abstract:
  With the increase in the availability of speech from varied domains, it is
imperative to use such out-of-domain data to improve existing speech systems.
Domain adaptation is a prominent pre-processing approach for this. We
investigate it for adapt microphone speech to the telephone domain.
Specifically, we explore CycleGAN-based unpaired translation of microphone data
to improve the x-vector/speaker embedding network for Telephony Speaker
Verification. We first demonstrate the efficacy of this on real challenging
data and then, to improve further, we modify the CycleGAN formulation to make
the adaptation task-specific. We modify CycleGAN's identity loss,
cycle-consistency loss, and adversarial loss to operate in the deep feature
space. Deep features of a signal are extracted from an auxiliary (speaker
embedding) network and, hence, preserves speaker identity. Our 3D
convolution-based Deep Feature Discriminators (DFD) show relative improvements
of 5-10% in terms of equal error rate. To dive deeper, we study a challenging
scenario of pooling (adapted) microphone and telephone data with data
augmentations and telephone codecs. Finally, we highlight the sensitivity of
CycleGAN hyper-parameters and introduce a parameter called probability of
adaptation.


Abstract:
  An implementation of a complex solver for the solution of the response
equations required to compute the complex response functions of damped response
theory is presented for the resolution-of-identity (RI) coupled-cluster singles
and approximate doubles CC2 method. The implementation uses a partitioned
formulation that avoids the storage of double excitation amplitudes to make it
applicable to large molecules. The solver is the keystone element for the
development of the damped coupled-cluster response formalism for linear and
nonlinear effects in resonant frequency regions at the RI-CC2 level of theory.
Illustrative results are reported for the one-photon absorption cross section
of C60, the electronic circular dichroism of $n$-helicenes ($n$ = 5, 6, 7), and
the $C_6$ dispersion coefficients of a set of selected organic molecules and
fullerenes.


Abstract:
  The Earth is constantly hit by energetic particles originating from galactic
sources. The flux of these particles is altered by the magnetized solar wind in
the heliosphere and the Earth's magnetic field. For this reason, the ability of
a particle to approach a spacecraft in LEO depends on its energy and the
position of the spacecraft within the Earth' magnetosphere. Moreover, there are
some areas (radiation belts) where the particles are trapped for a long time,
and therefore the flux of energetic particles is particularly high.
Occasionally, SEP contribute to the energetic particle flux too. DOSTEL is one
of the instruments aboard the \ac{ISS} that monitors the radiation field within
the European module Columbus. Because being installed inside the \ac{ISS},
particles produced by the interaction between the "primary" radiation and the
ISS materials are also measured. To describe the observations in such a complex
radiation field, we follow the method by Caballero-Lopez and Moraal (2012) in
order to compute the so-called yield function using precise measurements of the
proton and Helium energy spectra obtained by AMS and the systematic variation
of the DOSTEL measurements within the Earth's magnetosphere


Abstract:
  Residual networks (ResNets) employ skip connections in their networks --
reusing activations from previous layers -- to improve training convergence,
but these skip connections create challenges for hardware implementations of
ResNets. The hardware must either wait for skip connections to be processed
before processing more incoming data or buffer them elsewhere. Without skip
connections, ResNets would be more hardware-efficient. Thus, we present the
teacher-student learning method to gradually prune away all of a ResNet's skip
connections, constructing a network we call NonResNet. We show that when
implemented for FPGAs, NonResNet decreases ResNet's BRAM utilization by 9% and
LUT utilization by 3% and increases throughput by 5%.


Abstract:
  Directional excitation of guidance modes is central to many applications
ranging from light harvesting, optical information processing to quantum
optical technology. Of paramount interest, especially, the active control of
near-field directionality provides a new paradigm for the real-time on-chip
manipulation of light. Here we find that for a given dipolar source, its
near-field directionality can be toggled efficiently via tailoring the
polarization of surface waves that are excited, for example, via tuning the
chemical potential of graphene in a graphene-metasurface waveguide. This
finding enables a feasible scheme for the active near-field directionality.
Counterintuitively, we reveal that this scheme can transform a circular
electric/magnetic dipole into a Huygens dipole in the near-field coupling.
Moreover, for Janus dipoles, this scheme enables us to actively flip their
near-field coupling and non-coupling faces.


Abstract:
  Electron spins in silicon quantum dots are promising qubits due to their long
coherence times, scalable fabrication, and potential for all-electrical
control. However, charge noise in the host semiconductor presents a major
obstacle to achieving high-fidelity single- and two-qubit gates in these
devices. In this work, we measure the charge-noise spectrum of a Si/SiGe
singlet-triplet qubit over more than 13 decades in frequency using a
combination of methods, including dynamically-decoupled exchange oscillations
with up to 512 $\pi$ pulses during the qubit evolution. The charge noise is
colored across the entire frequency range of our measurements, although the
spectral exponent changes with frequency. Moreover, the charge-noise spectrum
inferred from conductance measurements of a proximal sensor quantum dot agrees
with that inferred from coherent oscillations of the singlet-triplet qubit,
suggesting that simple transport measurements can accurately characterize the
charge noise over a wide frequency range in Si/SiGe quantum dots.


Abstract:
  To get the energy spectrum distribution and cross-sections of emitted light
charged particles and explore the nuclear reaction, a experiment of 80.5 MeV/u
12C beam bombarding on C, W, Cu, Au, Pb targets has been carried out at
Institute of Modern Physics, Chinese Academy of Science. 30, 60 and 120 degree
relative to the incident beam have been detected using three sets of telescope
detectors. The results indicate that there is a tendency that heavier targets
have larger double differential cross-sections and the emitted fragments are
more likely to go forward. Besides, the decrease of cross-sections of fragments
producing with the increasing emitted angles may follow some kind of pattern.


Abstract:
  Using methods from nonstandard analysis, we define a nonstandard Minkowski
dimension which exists for all bounded sets and which has the property that
$\dim(A\times B) = \dim(A) + \dim(B)$.That is, our new dimension is
product-summable. To illustrate our theorem we generalize an example of
Falconer's to show that the standard upper Minkowski dimension, as well as the
Hausdorff dimension, are not product-summable. We also include a method for
creating sets of arbitrary rational dimension.


Abstract:
  In this paper, we have presented the big rip and pseudo rip cosmological
models in an extended theory of gravity. The matter field is considered to be
that of perfect fluid. The geometrical parameters are adjusted in such a manner
that it matches the prescriptions given by cosmological observations, to be
specific to the range of Hubble tension $(H_{0})$. The models favor phantom
behavior. The violation of strong energy conditions are shown in both the
models, as it has become essential in an extended gravity. The representative
values of the coupling parameter are significant on the evolution of the
universe.


Abstract:
  Charged particle optics, the description of particle trajectories in the
vicinity of some optical axis, describe the imaging properties of particle
optics devices. Here, we present a complete and compact description of charged
particle optics employing perturbative expansion of Hamiltonian mechanics. The
derived framework allows the straightforward computation of transversal and
longitudinal (chromatic) properties of static and dynamic optical devices with
straight and curved optical axes. It furthermore gives rise to geometric
integration schemes preserving the symplectic phase space structure and
pertaining Lagrange invariants, which may be employed to derive analytic
approximations of aberration coefficients and efficient numerical trajectory
solvers.


Abstract:
  We develop generalized approach to obtaining Edgeworth expansions for
$t$-statistics of an arbitrary order using computer algebra and combinatorial
algorithms. To incorporate various versions of mean-based statistics, we
introduce Adjusted Edgeworth expansions that allow polynomials in the terms to
depend on a sample size in a specific way and prove their validity. Provided
results up to 5th order include one and two-sample ordinary $t$-statistics with
biased and unbiased variance estimators, Welch $t$-test, and moderated
$t$-statistics based on empirical Bayes method, as well as general results for
any statistic with available moments of the sampling distribution. These
results are included in a software package that aims to reach a broad community
of researchers and serve to improve inference in a wide variety of analytical
procedures; practical considerations of using such expansions are discussed.


Abstract:
  The distribution and kinematics of the circumstellar medium (CSM) around a
supernova remnant (SNR) tell us useful information about the explosion of its
natal supernova (SN). Kepler's SNR, the remnant of SN1604, is widely regarded
to be of Type Ia origin. Its shock is moving through a dense, asymmetric CSM.
The presence of this dense gas suggests that its parent progenitor system
consisted of a white dwarf and an asymptotic giant branch (AGB) star. In this
paper, we analyze a new and long observation with the reflection grating
spectrometers (RGS) on board the XMM-Newton satellite, spatially resolving the
remnant emission in the cross-dispersion direction. We find that the CSM
component is blue-shifted with velocities in the general range 0-500 km/s. We
also derive information on the central bar structure and find that the
northwest half is blue-shifted, while the southeast half is red-shifted. Our
result is consistent with a picture proposed by previous studies, in which a
"runaway" AGB star moved to the north-northwest and toward us in the line of
sight, although it is acceptable for both single-degenerate and core-degenerate
scenarios for the progenitor system.


Abstract:
  Although deep models have greatly improved the accuracy and robustness of
image segmentation, obtaining segmentation results with highly accurate
boundaries and fine structures is still a challenging problem. In this paper,
we propose a simple yet powerful Boundary-Aware Segmentation Network (BASNet),
which comprises a predict-refine architecture and a hybrid loss, for highly
accurate image segmentation. The predict-refine architecture consists of a
densely supervised encoder-decoder network and a residual refinement module,
which are respectively used to predict and refine a segmentation probability
map. The hybrid loss is a combination of the binary cross entropy, structural
similarity and intersection-over-union losses, which guide the network to learn
three-level (ie, pixel-, patch- and map- level) hierarchy representations. We
evaluate our BASNet on two reverse tasks including salient object segmentation,
camouflaged object segmentation, showing that it achieves very competitive
performance with sharp segmentation boundaries. Importantly, BASNet runs at
over 70 fps on a single GPU which benefits many potential real applications.
Based on BASNet, we further developed two (close to) commercial applications:
AR COPY & PASTE, in which BASNet is integrated with augmented reality for
"COPYING" and "PASTING" real-world objects, and OBJECT CUT, which is a
web-based tool for automatic object background removal. Both applications have
already drawn huge amount of attention and have important real-world impacts.
The code and two applications will be publicly available at:
https://github.com/NathanUA/BASNet.


Abstract:
  The radiative transfer equation is a fundamental equation in transport theory
and applications, which is a 5-dimensional PDE in the stationary one-velocity
case, leading to great difficulties in numerical simulation. To tackle this
bottleneck, we first use the discrete ordinate technique to discretize the
scattering term, an integral with respect to the angular variables, resulting
in a semi-discrete hyperbolic system. Then, we make the spatial discretization
by means of the discontinuous Galerkin (DG) method combined with the sparse
grid method. The final linear system is solved by the block Gauss-Seidal
iteration method. The computational complexity and error analysis are developed
in detail, which show the new method is more efficient than the original
discrete ordinate DG method. A series of numerical results are performed to
validate the convergence behavior and effectiveness of the proposed method.


Abstract:
  Liquids realize a highly complex state of matter in which strong competing
kinetic and interaction effects come to life. As such, liquids are, generally,
more challenging to understand than either gases or solids. In weakly
interacting gases, the kinetic effects dominate. By contrast, low temperature
solids typically feature far smaller fluctuations about their ground state.
Notwithstanding their complexity, with the exception of quantum fluids (e.g.,
superfluid Helium) and supercooled liquids (including glasses), various aspects
of common liquid dynamics such as their dynamic viscosity are often assumed to
be given by rather simple, Arrhenius-type, activated forms with nearly constant
(i.e., temperature independent) energy barriers. In this work, we analyze
experimentally measured viscosities of numerous liquids far above their
equilibrium melting temperature to see how well this assumption fares. We find
marked deviations from simple activated dynamics. Even far above their
equilibrium melting temperatures, as the temperature drops, the viscosity of
these liquids increases more strongly than predicted by activated dynamics
dominated by a single uniform energy barrier. For metallic fluids, the scale of
the prefactor in the Arrhenius form for the viscosity is consistent with that
given by the product $nh$ with $n$ the number density and $h$ Planck's
constant. More generally, in all fluids (whether metallic or non-metallic),
$nh$ constitutes a lower bound on the viscosity. We find that a scaling of the
temperature axis (complementing that of the viscosity) leads to a partial
collapse of the temperature dependent viscosities of different fluids; such a
scaling allows for a functional dependence of the viscosity on temperature that
includes yet is far more general than activated Arrhenius form alone. We
speculate on relations between non-Arrhenius dynamics and thermodynamic
observables.


Abstract:
  How do active galactic nuclei with low optical luminosities produce powerful
radio emission? Recent studies of active galactic nuclei with moderate radio
and low optical luminosities (Fanaroff & Riley class I, FR I) searching for
broad nuclear emission lines in polarized light, as predicted by some active
galactic nucleus unification models, have found heterogeneous results. These
models typically consist of a central engine surrounded by a torus of discrete
dusty clouds. These clouds would absorb and scatter optical emission, blocking
broad nuclear emission lines, and reradiate in mid-infrared. Some scattered
broad-line emission may be observable, depending on geometry, which would be
polarized. We present a wide-band infrared spectroscopic analysis of 10 nearby
FR I radio galaxies to determine whether there is significant emission from a
dusty obscuring structure. We used Markov Chain Monte Carlo algorithms to
decompose Spitzer/IRS spectra of our sample. We constrained the wide-band
behavior of our models with photometry from the Two Micron All Sky Survey,
Spitzer/IRAC, Spitzer/MIPS, and Herschel/SPIRE. We find that one galaxy is best
fit by a clumpy torus and three others show some thermal mid-infrared
component. This suggests that in those three there is likely some obscuring
dust structure that is inconsistent with our torus models and there must be
some source of photons heating the dust. We conclude that 40% of our FR I radio
galaxies show evidence of obscuring dusty material, possibly some other form of
hidden broad-line nucleus, but only 10% favor the clumpy torus model
specifically.


Abstract:
  Here we report the performance of a discrete modulated CVQKD protocol based
on eight and sixteen state optimal APK constellations with pure loss quantum
channel transmission. Our constellations use the same amplitudes and state
probability as in an optimal four-state unidimensional constellation model and
applies amplitude/phase modulation on the quadratures of coherent states. We
compare the obtained results with the performance of conventional $m$-PSK and
$m$-APK constellations. The results show that the proposed constellations
applied to CVQKD protocol outperforms the conventional PSK and APK based
protocols in both secret key rate and link distance. In special, our protocol
is able to accomplish viable SKR's in low transmittance regime, $\tau<0.2$.


Abstract:
  We propose policy-gradient algorithms for solving the problem of control in a
risk-sensitive reinforcement learning (RL) context. The objective of our
algorithms is to maximize the distorted risk measure (DRM) of the cumulative
reward in an episodic Markov decision process (MDP). We derive a variant of the
policy gradient theorem that caters to the DRM objective. Using this theorem in
conjunction with a likelihood ratio (LR) based gradient estimation scheme, we
propose policy gradient algorithms for optimizing DRM in both on-policy and
off-policy RL settings. We derive non-asymptotic bounds that establish the
convergence of our algorithms to an approximate stationary point of the DRM
objective.


Abstract:
  The discovery of a large putative impact crater buried beneath Hiawatha
Glacier along the margin of the northwestern Greenland Ice Sheet has
reinvigorated interest into the nature of large impacts into thick ice masses.
This circular structure is relatively shallow and exhibits a small central
uplift, whereas a peak-ring morphology is expected. This discrepancy may be due
to long-term and ongoing subglacial erosion but may also be explained by a
relatively recent impact through the Greenland Ice Sheet, which is expected to
alter the final crater morphology. Here we model crater formation using
hydrocode simulations, varying pre-impact ice thickness and impactor
composition over crystalline target rock. We find that an ice-sheet thickness
of 1.5 or 2 km results in a crater morphology that is consistent with the
present morphology of this structure. Further, an ice sheet that thick
substantially inhibits ejection of rocky material, which might explain the
absence of rocky ejecta in most existing Greenland deep ice cores if the impact
occurred during the late Pleistocene. From the present morphology of the
putative Hiawatha impact crater alone, we cannot distinguish between an older
crater formed by a pre-Pleistocene impact into ice-free bedrock or a younger,
Pleistocene impact into locally thick ice, but based on our modeling we
conclude that latter scenario is possible.


Abstract:
  Domain adaptation (DA) approaches address domain shift and enable networks to
be applied to different scenarios. Although various image DA approaches have
been proposed in recent years, there is limited research towards video DA. This
is partly due to the complexity in adapting the different modalities of
features in videos, which includes the correlation features extracted as
long-term dependencies of pixels across spatiotemporal dimensions. The
correlation features are highly associated with action classes and proven their
effectiveness in accurate video feature extraction through the supervised
action recognition task. Yet correlation features of the same action would
differ across domains due to domain shift. Therefore we propose a novel
Adversarial Correlation Adaptation Network (ACAN) to align action videos by
aligning pixel correlations. ACAN aims to minimize the distribution of
correlation information, termed as Pixel Correlation Discrepancy (PCD).
Additionally, video DA research is also limited by the lack of cross-domain
video datasets with larger domain shifts. We, therefore, introduce a novel
HMDB-ARID dataset with a larger domain shift caused by a larger statistical
difference between domains. This dataset is built in an effort to leverage
current datasets for dark video classification. Empirical results demonstrate
the state-of-the-art performance of our proposed ACAN for both existing and the
new video DA datasets.


Abstract:
  Increasing scale and heterogeneity in data centers have led to the
development of federated clusters such as KubeFed, Hydra, and Pigeon, that
federate individual data center clusters. In our work, we introduce Megha, a
novel decentralized resource management framework for such federated clusters.
Megha employs flexible logical partitioning of clusters to distribute its
scheduling load, ensuring that the requirements of the workload are satisfied
with very low scheduling overheads. It uses a distributed global scheduler that
does not rely on a centralized data store but, instead, works with eventual
consistency, unlike other schedulers that use a tiered architecture or rely on
centralized databases. Our experiments with Megha show that it can schedule
tasks taking into account fairness and placement constraints with low resource
allocation times - in the order of tens of milliseconds.


Abstract:
  This paper investigates the reconfigurable reflecting surface (RIS)-aided
multiple-input-single-output (MISO) systems with imperfect channel state
information (CSI), where RIS-related channels are modeled by Rician fading.
Considering the overhead and complexity in practical systems, we employ the
low-complexity maximum ratio combining (MRC) beamforming at the base station
(BS), and configure the phase shifts of the RIS based on long-term statistical
CSI. Specifically, we first estimate the overall channel matrix based on the
linear minimum mean square error (LMMSE) estimator, and evaluate the
performance of MSE and normalized MSE (NMSE). Then, with the estimated channel,
we derive the closed-form expressions of the ergodic rate. The derived
expressions show that with Rician RIS-related channels, the rate can maintain
at a non-zero value when the transmit power is scaled down proportionally to
$1/M$ or $1/N^2$, where $M$ and $N$ are the number of antennas and reflecting
elements, respectively. However, if all the RIS-related channels are fully
Rayleigh, the transmit power of each user can only be scaled down
proportionally to $1/\sqrt{M}$ or $1/N$. Finally, numerical results verify the
promising benefits from the RIS to traditional MISO systems.


Abstract:
  Despite -- or maybe because of -- their astonishing capacity to fit data,
neural networks are believed to have difficulties extrapolating beyond training
data distribution. This work shows that, for extrapolations based on finite
transformation groups, a model's inability to extrapolate is unrelated to its
capacity. Rather, the shortcoming is inherited from a learning hypothesis:
Examples not explicitly observed with infinitely many training examples have
underspecified outcomes in the learner's model. In order to endow neural
networks with the ability to extrapolate over group transformations, we
introduce a learning framework counterfactually-guided by the learning
hypothesis that any group invariance to (known) transformation groups is
mandatory even without evidence, unless the learner deems it inconsistent with
the training data. Unlike existing invariance-driven methods for
(counterfactual) extrapolations, this framework allows extrapolations from a
single environment. Finally, we introduce sequence and image extrapolation
tasks that validate our framework and showcase the shortcomings of traditional
approaches.


Abstract:
  The active Brownian particle (ABP) model describes a swimmer, synthetic or
living, whose direction of swimming is a Brownian motion. The swimming is due
to a propulsion force, and the fluctuations are typically thermal in origin. We
present a 2D model where the fluctuations arise from nonthermal noise in a
propelling force acting at a single point, such as that due to a flagellum. We
take the overdamped limit and find several modifications to the traditional ABP
model. Since the fluctuating force causes a fluctuating torque, the diffusion
tensor describing the process has a coupling between translational and
rotational degrees of freedom. An anisotropic particle also exhibits a
noise-induced induced drift. We show that these effects have measurable
consequences for the long-time diffusivity of active particles, in particular
adding a contribution that is independent of where the force acts.


Abstract:
  Purpose: To determine whether evaporation, tangential flow, or a combination
of the two cause tear film breakup in a variety of instances; to estimate
related breakup parameters that cannot be measured in breakup during subject
trials; and to validate our procedure against previous work. Methods: Five
ordinary differential equation models for tear film thinning were designed that
model evaporation, osmosis, and various types of flow. Eight tear film breakup
instances of five healthy subjects that were identified in fluorescence images
in previous work were fit with these five models. The fitting procedure used a
nonlinear least squares optimization that minimized the difference of the
computed theoretical fluorescent intensity from the models and the experimental
fluorescent intensity from the images. The optimization was conducted over the
evaporation rate and up to three flow rate parameters. The smallest norm of the
difference was determined to correspond to the model that best explained the
tear film dynamics. Results: All of the breakup instances were best fit by
models with time-dependent flow. Our optimal parameter values and thinning rate
and fluid flow profiles compare well with previous partial differential
equation model results in most instances. Conclusion: Our fitting procedure
suggests that the combination of the Marangoni effect and evaporation cause
most of the breakup instances. Comparison with results from previous work
suggests that the simplified models can capture the essential tear film
dynamics in most cases, thereby validating this procedure as one that could be
used on many other instances.


Abstract:
  Reinforcement learning (RL) can be used to create a decision-making agent for
autonomous driving. However, previous approaches provide only black-box
solutions, which do not offer information on how confident the agent is about
its decisions. An estimate of both the aleatoric and epistemic uncertainty of
the agent's decisions is fundamental for real-world applications of autonomous
driving. Therefore, this paper introduces the Ensemble Quantile Networks (EQN)
method, which combines distributional RL with an ensemble approach, to obtain a
complete uncertainty estimate. The distribution over returns is estimated by
learning its quantile function implicitly, which gives the aleatoric
uncertainty, whereas an ensemble of agents is trained on bootstrapped data to
provide a Bayesian estimation of the epistemic uncertainty. A criterion for
classifying which decisions that have an unacceptable uncertainty is also
introduced. The results show that the EQN method can balance risk and time
efficiency in different occluded intersection scenarios, by considering the
estimated aleatoric uncertainty. Furthermore, it is shown that the trained
agent can use the epistemic uncertainty information to identify situations that
the agent has not been trained for and thereby avoid making unfounded,
potentially dangerous, decisions outside of the training distribution.


Abstract:
  Deep learning based image recognition systems have been widely deployed on
mobile devices in today's world. In recent studies, however, deep learning
models are shown vulnerable to adversarial examples. One variant of adversarial
examples, called adversarial patch, draws researchers' attention due to its
strong attack abilities. Though adversarial patches achieve high attack success
rates, they are easily being detected because of the visual inconsistency
between the patches and the original images. Besides, it usually requires a
large amount of data for adversarial patch generation in the literature, which
is computationally expensive and time-consuming. To tackle these challenges, we
propose an approach to generate inconspicuous adversarial patches with one
single image. In our approach, we first decide the patch locations basing on
the perceptual sensitivity of victim models, then produce adversarial patches
in a coarse-to-fine way by utilizing multiple-scale generators and
discriminators. The patches are encouraged to be consistent with the background
images with adversarial training while preserving strong attack abilities. Our
approach shows the strong attack abilities in white-box settings and the
excellent transferability in black-box settings through extensive experiments
on various models with different architectures and training methods. Compared
to other adversarial patches, our adversarial patches hold the most negligible
risks to be detected and can evade human observations, which is supported by
the illustrations of saliency maps and results of user evaluations. Lastly, we
show that our adversarial patches can be applied in the physical world.


Abstract:
  The high-order cumulants and factorial cumulants of conserved charges are
suggested to study the critical dynamics in heavy ion collisions. In this
paper, using parametric representation of the 3-dimensional Ising model, the
sign distribution on the phase diagram and temperature dependence of the
cumulants and factorial cumulants is studied and compared. In the vicinity of
the critical point, the cumulants and factorial cumulants can not be
distinguished. Far away from the critical point, sign changes occur in the
factorial cumulants comparing with the same order cumulants. The cause of these
sign changes is analysed. They may be used to measure the distance to the
critical point.


Abstract:
  In many choice problems, the interaction between several distinct variables
determines the payoff of each alternative. I propose and axiomatize a model of
a decision maker who recognizes that she may not accurately perceive the
correlation between these variables, and who takes this into account when
making her decision. She chooses as if she calculates each alternative's
expected outcome under multiple possible correlation structures, and then
evaluates it according to the worst expected outcome.


Abstract:
  Quantum error correction is an indispensable ingredient for scalable quantum
computing. In this Perspective we discuss a particular class of quantum codes
called low-density parity-check (LDPC) quantum codes. The codes we discuss are
alternatives to the surface code, which is the currently leading candidate to
implement quantum fault-tolerance. We introduce the zoo of LDPC quantum codes
and discuss their potential for making quantum computers robust against noise.
In particular, we explain recent advances in the theory of LDPC quantum codes
related to certain product constructions and discuss open problems in the
field.


Abstract:
  This paper introduces scattering transform for speech emotion recognition
(SER). Scattering transform generates feature representations which remain
stable to deformations and shifting in time and frequency without much loss of
information. In speech, the emotion cues are spread across time and localised
in frequency. The time and frequency invariance characteristic of scattering
coefficients provides a representation robust against emotion irrelevant
variations e.g., different speakers, language, gender etc. while preserving the
variations caused by emotion cues. Hence, such a representation captures the
emotion information more efficiently from speech. We perform experiments to
compare scattering coefficients with standard mel-frequency cepstral
coefficients (MFCCs) over different databases. It is observed that frequency
scattering performs better than time-domain scattering and MFCCs. We also
investigate layer-wise scattering coefficients to analyse the importance of
time shift and deformation stable scalogram and modulation spectrum
coefficients for SER. We observe that layer-wise coefficients taken
independently also perform better than MFCCs.


Abstract:
  Radiographs are used as the most important imaging tool for identifying spine
anomalies in clinical practice. The evaluation of spinal bone lesions, however,
is a challenging task for radiologists. This work aims at developing and
evaluating a deep learning-based framework, named VinDr-SpineXR, for the
classification and localization of abnormalities from spine X-rays. First, we
build a large dataset, comprising 10,468 spine X-ray images from 5,000 studies,
each of which is manually annotated by an experienced radiologist with bounding
boxes around abnormal findings in 13 categories. Using this dataset, we then
train a deep learning classifier to determine whether a spine scan is abnormal
and a detector to localize 7 crucial findings amongst the total 13. The
VinDr-SpineXR is evaluated on a test set of 2,078 images from 1,000 studies,
which is kept separate from the training set. It demonstrates an area under the
receiver operating characteristic curve (AUROC) of 88.61% (95% CI 87.19%,
90.02%) for the image-level classification task and a mean average precision
(mAP@0.5) of 33.56% for the lesion-level localization task. These results serve
as a proof of concept and set a baseline for future research in this direction.
To encourage advances, the dataset, codes, and trained deep learning models are
made publicly available.


Abstract:
  We report the results of combined analyses of X-ray and optical data of two
galaxy clusters, CL 0024$+$1654 and RX J0152.7$-$1357 at redshift $z = 0.395$
and $z = 0.830$, respectively, which offer a holistic physical description of
the two clusters. Our X-ray analysis yields temperature and density profile of
the gas in the intra-cluster medium (ICM). Using optical photometric and
spectroscopic data, complemented with mass distribution from gravitational
lensing study, we investigate any possible correlation between the physical
properties of the galaxy members, i.e., their color, morphology, and star
formation rate (SFR) with their environments. We quantify the properties of the
environment of each galaxy by galaxy number density, ICM temperature, and mass
density. Although our result shows that the two clusters exhibit a weaker
correlation compared to relaxed clusters, it still confirms the significant
effect of the ICM on the SFRs in the galaxies. The closer relation between
galaxy physical property and the condition of its immediate environment found
in this work indicates the locality of galaxy evolution, even within a larger
bound system such as cluster. Various physical mechanisms are suggested to
explain the relation between the properties of galaxies and their environment.


Abstract:
  Can we teach a robot to recognize and make predictions for activities that it
has never seen before? We tackle this problem by learning models for video from
text. This paper presents a hierarchical model that generalizes instructional
knowledge from large-scale text-corpora and transfers the knowledge to video.
Given a portion of an instructional video, our model recognizes and predicts
coherent and plausible actions multiple steps into the future, all in rich
natural language. To demonstrate the capabilities of our model, we introduce
the \emph{Tasty Videos Dataset V2}, a collection of 4022 recipes for zero-shot
learning, recognition and anticipation. Extensive experiments with various
evaluation metrics demonstrate the potential of our method for generalization,
given limited video data for training models.


Abstract:
  This is the second paper in a series in which we develop machine learning
(ML) moment closure models for the radiative transfer equation (RTE). In our
previous work \cite{huang2021gradient}, we proposed an approach to directly
learn the gradient of the unclosed high order moment, which performs much
better than learning the moment itself and the conventional $P_N$ closure.
However, the ML moment closure model in \cite{huang2021gradient} is not able to
guarantee hyperbolicity and long time stability. We propose in this paper a
method to enforce the global hyperbolicity of the ML closure model. The main
idea is to seek a symmetrizer (a symmetric positive definite matrix) for the
closure system, and derive constraints such that the system is globally
symmetrizable hyperbolic. It is shown that the new ML closure system inherits
the dissipativeness of the RTE and preserves the correct diffusion limit as the
Knunsden number goes to zero. Several benchmark tests including the Gaussian
source problem and the two-material problem show the good accuracy, long time
stability and generalizability of our globally hyperbolic ML closure model.


Abstract:
  We show that cascaded diffusion models are capable of generating high
fidelity images on the class-conditional ImageNet generation benchmark, without
any assistance from auxiliary image classifiers to boost sample quality. A
cascaded diffusion model comprises a pipeline of multiple diffusion models that
generate images of increasing resolution, beginning with a standard diffusion
model at the lowest resolution, followed by one or more super-resolution
diffusion models that successively upsample the image and add higher resolution
details. We find that the sample quality of a cascading pipeline relies
crucially on conditioning augmentation, our proposed method of data
augmentation of the lower resolution conditioning inputs to the
super-resolution models. Our experiments show that conditioning augmentation
prevents compounding error during sampling in a cascaded model, helping us to
train cascading pipelines achieving FID scores of 1.48 at 64x64, 3.52 at
128x128 and 4.88 at 256x256 resolutions, outperforming BigGAN-deep, and
classification accuracy scores of 63.02% (top-1) and 84.06% (top-5) at 256x256,
outperforming VQ-VAE-2.


Abstract:
  I examine global dynamics in a monetary model with overlapping generations of
finite-horizon agents and a binding lower bound on nominal interest rates. Debt
targeting rules exacerbate the possibility of self-fulfilling liquidity traps,
for agents expect austerity following deflationary slumps. Conversely, activist
but sustainable fiscal policy regimes - implementing intertemporally balanced
tax cuts and/or transfer increases in response to disinflationary trajectories
- are capable of escaping liquidity traps and embarking inflation into a
globally stable path that converges to the target. Should fiscal stimulus of
last resort be overly aggressive, however, spiral dynamics around the
liquidity-trap steady state exist, causing global indeterminacy.


Abstract:
  Multiple instances of Zika virus epidemic have been reported around the world
in the last two decades, turning the related illness into an international
concern. In this context the use of mathematical models for epidemics is of
great importance, since they are useful tools to study the underlying outbreak
numbers and allow one to test the effectiveness of different strategies used to
combat the associated diseases. This work deals with the development and
calibration of an epidemic model to describe the 2016 outbreak of Zika virus in
Brazil. A system of 8 differential equations with 8 parameters is employed to
model the evolution of the infection through two populations. Nominal values
for the model parameters are estimated from the literature. An inverse problem
is formulated and solved by comparing the system response to real data from the
outbreak. The calibrated results presents realistic parameters and returns
reasonable descriptions, with the curve shape similar to the outbreak evolution
and peak value close to the highest number of infected people during 2016.
Considerations about the lack of data for some initial conditions are also made
through an analysis over the response behavior according to their change in
value.


Abstract:
  Sparse manifold learning algorithms combine techniques in manifold learning
and sparse optimization to learn features that could be utilized for downstream
tasks. The standard setting of compressive sensing can not be immediately
applied to this setup. Due to the intrinsic geometric structure of data,
dictionary atoms might be redundant and do not satisfy the restricted isometry
property or coherence condition. In addition, manifold learning emphasizes
learning local geometry which is not reflected in a standard $\ell_1$
minimization problem. We propose weighted $\ell_0$ and weighted $\ell_1$
metrics that encourage representation via neighborhood atoms suited for
dictionary based manifold learning. Assuming that the data is generated from
Delaunay triangulation, we show the equivalence of weighted $\ell_1$ and
weighted $\ell_0$. We discuss an optimization program that learns the
dictionaries and sparse coefficients and demonstrate the utility of our
regularization on synthetic and real datasets.


Abstract:
  As the population grows, the need for a quality level of medical services
grows correspondingly, so does the demand for information technology in
medicine. The concept of "Smart Healthcare" offers many approaches aimed at
solving the acute problems faced by modern healthcare. In this paper, we review
the main problems of modern healthcare, analyze existing approaches and
technologies in the areas of digital twins, the Internet of Things and mobile
medicine, determine their effectiveness in solving the set problems, consider
the technologies that are used to monitor and treat patients and propose the
concept of the Smart Healthcare platform.


Abstract:
  Visible-Infrared person re-identification (VI-ReID) aims to match
cross-modality pedestrian images, breaking through the limitation of
single-modality person ReID in dark environment. In order to mitigate the
impact of large modality discrepancy, existing works manually design various
two-stream architectures to separately learn modality-specific and
modality-sharable representations. Such a manual design routine, however,
highly depends on massive experiments and empirical practice, which is time
consuming and labor intensive. In this paper, we systematically study the
manually designed architectures, and identify that appropriately separating
Batch Normalization (BN) layers is the key to bring a great boost towards
cross-modality matching. Based on this observation, the essential objective is
to find the optimal separation scheme for each BN layer. To this end, we
propose a novel method, named Cross-Modality Neural Architecture Search
(CM-NAS). It consists of a BN-oriented search space in which the standard
optimization can be fulfilled subject to the cross-modality task. Equipped with
the searched architecture, our method outperforms state-of-the-art counterparts
in both two benchmarks, improving the Rank-1/mAP by 6.70%/6.13% on SYSU-MM01
and by 12.17%/11.23% on RegDB. Code is released at
https://github.com/JDAI-CV/CM-NAS.


Abstract:
  Retinal degenerative diseases lead to the blindness of millions of people
around the world. In case of age-related macular degeneration (AMD), the
atrophy of retinal pigment epithelium (RPE) precedes neural dystrophy. But as
crucial as understanding both healthy and pathological RPE cell physiology is
for those diseases, no current technique allows subcellular in vivo or in vitro
live observation of this critical cell layer. To fill this gap, we propose
dynamic full-field OCT (D-FFOCT) as a candidate for live observation of in
vitro RPE phenotype. In this way, we monitored primary porcine and human stem
cell-derived RPE cells in stress model conditions by performing scratch assays.
In this study, we quantified wound healing parameters on the stressed RPE, and
observed different cell phenotypes, displayed by the D-FFOCT signal. In order
to decipher the subcellular contributions to these dynamic profiles, we
performed immunohistochemistry to identify which organelles generate the signal
and found mitochondria to be the main contributor to D-FFOCT contrast.
Altogether, D-FFOCT appears to be an innovative method to follow degenerative
disease evolution and could be an appreciated method in the future for live
patient diagnostics and to direct treatment choice.


Abstract:
  Novelty detection in discrete sequences is a challenging task, since
deviations from the process generating the normal data are often small or
intentionally hidden. Novelties can be detected by modeling normal sequences
and measuring the deviations of a new sequence from the model predictions.
However, in many applications data is generated by several distinct processes
so that models trained on all the data tend to over-generalize and novelties
remain undetected. We propose to approach this challenge through decomposition:
by clustering the data we break down the problem, obtaining simpler modeling
task in each cluster which can be modeled more accurately. However, this comes
at a trade-off, since the amount of training data per cluster is reduced. This
is a particular problem for discrete sequences where state-of-the-art models
are data-hungry. The success of this approach thus depends on the quality of
the clustering, i.e., whether the individual learning problems are sufficiently
simpler than the joint problem. While clustering discrete sequences
automatically is a challenging and domain-specific task, it is often easy for
human domain experts, given the right tools. In this paper, we adapt a
state-of-the-art visual analytics tool for discrete sequence clustering to
obtain informed clusters from domain experts and use LSTMs to model each
cluster individually. Our extensive empirical evaluation indicates that this
informed clustering outperforms automatic ones and that our approach
outperforms state-of-the-art novelty detection methods for discrete sequences
in three real-world application scenarios. In particular, decomposition
outperforms a global model despite less training data on each individual
cluster.


Abstract:
  In a graph G, a dissociation set is a subset of vertices which induces a
subgraph with vertex degree at most 1. Finding a dissociation set of maximum
cardinality in a graph is NP-hard even for bipartite graphs and is called the
maximum dissociation set problem. The complexity of maximum dissociation set
problem in various subclasses of graphs has been extensively studied in the
literature. In this paper, we study the maximum dissociation problem from
different perspectives and characterize the vertices belonging to all maximum
dissociation sets, and to no maximum dissociation set of a tree. We present a
linear time recognition algorithm which can determine whether a given vertex in
a tree is contained in all (or no) maximum dissociation sets of the tree. Thus
for a tree with n vertices, we can find all vertices belonging to all (or no)
maximum dissociation sets of the tree in O(n^2) time.


Abstract:
  Generating droplets from a continuous stream of fluid requires precise tuning
of a device to find optimized control parameter conditions. It is analytically
intractable to compute the necessary control parameter values of a
droplet-generating device that produces optimized droplets. Furthermore, as the
length scale of the fluid flow changes, the formation physics and optimized
conditions that induce flow decomposition into droplets also change. Hence, a
single proportional integral derivative controller is too inflexible to
optimize devices of different length scales or different control parameters,
while classification machine learning techniques take days to train and require
millions of droplet images. Therefore, the question is posed, can a single
method be created that universally optimizes multiple length-scale droplets
using only a few data points and is faster than previous approaches? In this
paper, a Bayesian optimization and computer vision feedback loop is designed to
quickly and reliably discover the control parameter values that generate
optimized droplets within different length-scale devices. This method is
demonstrated to converge on optimum parameter values using 60 images in only
2.3 hours, 30x faster than previous approaches. Model implementation is
demonstrated for two different length-scale devices: a milliscale inkjet device
and a micofluidics device.


Abstract:
  We investigate analytic solutions to the problem of finding driving
strategies which minimize total tractive energy consumption subject to
constraints that ensure safe separation for a fleet of trains travelling on the
same track in the same direction and minimize line occupancy time span for the
fleet. It is known that a fleet of trains can be safely separated if
intermediate signal times are prescribed by inequality constraints for each
train at each signal location. The optimal driving strategy for each train is
then uniquely defined by an optimal driving speed on each actively timed
segment. The problem of finding optimal intermediate times has been solved for
a fleet of two trains but for more than two trains the problem rapidly becomes
intractable as the number of trains and signals increases. The main difficulty
is in distinguishing between active and inactive constraints. This is
exacerbated by the fact that an inactive constraint with one set of prescribed
times may become active if the set of prescribed times changes. The curse of
dimensionality means it is not feasible to check every different combination of
designated active and inactive constraints and to optimize the corresponding
prescribed times. Nevertheless we can formulate a substantial optimization
problem with intermediate signal times prescribed by active equality
constraints for all trains in the fleet. We find necessary and sufficient
conditions on the optimal times and demonstrate viable solution algorithms to
find the associated optimal speed profiles for each train. Compared to the
schedule required for safe separation when each train uses the classic
single-train optimal strategy our method provides a substantial reduction in
the line occupancy time span with only a minimal increase in journey costs.


Abstract:
  Extracting detailed 3D information of objects from video data is an important
goal for holistic scene understanding. While recent methods have shown
impressive results when reconstructing meshes of objects from a single image,
results often remain ambiguous as part of the object is unobserved. Moreover,
existing image-based datasets for mesh reconstruction don't permit to study
models which integrate temporal information. To alleviate both concerns we
present SAIL-VOS 3D: a synthetic video dataset with frame-by-frame mesh
annotations which extends SAIL-VOS. We also develop first baselines for
reconstruction of 3D meshes from video data via temporal models. We demonstrate
efficacy of the proposed baseline on SAIL-VOS 3D and Pix3D, showing that
temporal information improves reconstruction quality. Resources and additional
information are available at http://sailvos.web.illinois.edu.


Abstract:
  We proposes a flexible person generation framework called Dressing in Order
(DiOr), which supports 2D pose transfer, virtual try-on, and several fashion
editing tasks. The key to DiOr is a novel recurrent generation pipeline to
sequentially put garments on a person, so that trying on the same garments in
different orders will result in different looks. Our system can produce
dressing effects not achievable by existing work, including different
interactions of garments (e.g., wearing a top tucked into the bottom or over
it), as well as layering of multiple garments of the same type (e.g., jacket
over shirt over t-shirt). DiOr explicitly encodes the shape and texture of each
garment, enabling these elements to be edited separately. Joint training on
pose transfer and inpainting helps with detail preservation and coherence of
generated garments. Extensive evaluations show that DiOr outperforms other
recent methods like ADGAN in terms of output quality, and handles a wide range
of editing functions for which there is no direct supervision.


Abstract:
  We present a unified framework for Petri nets and various variants, such as
pre-nets and Kock's whole-grain Petri nets. Our framework is based on a less
well-studied notion that we call $\Sigma$-nets, which allow finer control over
whether tokens are treated using the collective or individual token philosophy.
We describe three forms of execution semantics in which pre-nets generate
strict monoidal categories, $\Sigma$-nets (including whole-grain Petri nets)
generate symmetric strict monoidal categories, and Petri nets generate
commutative monoidal categories, all by left adjoint functors. We also
construct adjunctions relating these categories of nets to each other, in
particular showing that all kinds of net can be embedded in the unifying
category of $\Sigma$-nets, in a way that commutes coherently with their
execution semantics.


Abstract:
  The global formality of Dolgushev depends on the choice of a torsion-free
covariant derivative. We prove that the globalized formalities with respect to
two different covariant derivatives are homotopic. More explicitly, we derive
the statement by proving a more general homotopy equivalence between
$L_\infty$-morphisms that are twisted with gauge equivalent Maurer-Cartan
elements.


Abstract:
  Drive towards improved performance of machine learning models has led to the
creation of complex features representing a database of condensed matter
systems. The complex features, however, do not offer an intuitive explanation
on which physical attributes do improve the performance. The effect of the
database on the performance of the trained model is often neglected. In this
work we seek to understand in depth the effect that the choice of features and
the properties of the database have on a machine learning application. In our
experiments, we consider the complex phase space of carbon as a test case, for
which we use a set of simple, human understandable and cheaply computable
features for the aim of predicting the total energy of the crystal structure.
Our study shows that (i) the performance of the machine learning model varies
depending on the set of features and the database, (ii) is not transferable to
every structure in the phase space and (iii) depends on how well structures are
represented in the database.


Abstract:
  In modelling time series data coming from different sources, frequencies can
easily vary since some variable can be measured at higher frequencies, others,
at lower frequencies. Given data measured over spatial units and at varying
frequencies, we postulated a semiparametric spatiotemporal model. This
optimizes utilization of information from variables measured at higher
frequency by estimating its nonparametric effect on the response through the
backfitting algorithm in and additive modelling framework. Simulation studies
support the optimality of the model over simple generalized additive model with
aggregation of high frequency predictors to match the dependent variable
measured at lower frequency. With quarterly corn production and the dependent
variable, the model is fitted with predictors coming from remotely-sensed data
(vegetation and precipitation indices), predictive ability is better compared
to two benchmark generalized additive models.


Abstract:
  We present a continuous-wave, 810 nm laser with watt-level powers. Our system
is based on difference-frequency generation of 532 nm and 1550 nm fiber lasers
in a single pass through periodically poled lithium niobate (PPLN). We measure
the broadband spectral noise and residual intensity noise to be compatible with
off-resonant dipole trapping of ultracold atoms. Given the large bandwidth of
the fiber amplifiers, the output can be optimized for a range of wavelengths,
including the strontium clock-magic wavelength of 813 nm. Furthermore, with the
exploration of more appropriate nonlinear crystals, we believe there is a path
toward scaling this proof-of-principle design to many watts of power, and that
this approach could provide a robust, rack-mountable trapping-laser for future
use in strontium-based optical clocks.


Abstract:
  A Robotic Mobile Fulfillment System is a robotised parts-to-picker system
that is particularly well-suited for e-commerce warehousing. One distinguishing
feature of this type of warehouse is its high storage modularity. Numerous
robots are moving shelves simultaneously, and the shelves can be returned to
any open location after the picking operation is completed. This work focuses
on the real-time storage allocation problem to minimise the travel time of the
robots. An efficient -- but computationally costly -- Monte Carlo Tree Search
method is used offline to generate high-quality experience. This experience can
be learned by a neural network with a proper coordinates-based features
representation. The obtained neural network is used as an action predictor in
several new storage policies, either as-is or in rollout and supervised tree
search strategies. Resulting performance levels depend on the computing time
available at a decision step and are consistently better compared to real-time
decision rules from the literature.


Abstract:
  We present a comprehensive framework for egocentric interaction recognition
using markerless 3D annotations of two hands manipulating objects. To this end,
we propose a method to create a unified dataset for egocentric 3D interaction
recognition. Our method produces annotations of the 3D pose of two hands and
the 6D pose of the manipulated objects, along with their interaction labels for
each frame. Our dataset, called H2O (2 Hands and Objects), provides
synchronized multi-view RGB-D images, interaction labels, object classes,
ground-truth 3D poses for left & right hands, 6D object poses, ground-truth
camera poses, object meshes and scene point clouds. To the best of our
knowledge, this is the first benchmark that enables the study of first-person
actions with the use of the pose of both left and right hands manipulating
objects and presents an unprecedented level of detail for egocentric 3D
interaction recognition. We further propose the method to predict interaction
classes by estimating the 3D pose of two hands and the 6D pose of the
manipulated objects, jointly from RGB images. Our method models both inter- and
intra-dependencies between both hands and objects by learning the topology of a
graph convolutional network that predicts interactions. We show that our method
facilitated by this dataset establishes a strong baseline for joint hand-object
pose estimation and achieves state-of-the-art accuracy for first person
interaction recognition.


Abstract:
  In this paper we analyse the efficiency, precision, and accuracy of computing
elastic nucleon-nucleon (NN) scattering amplitudes with the wave-packet
continuum discretisation method (WPCD). This method provides approximate
scattering solutions at multiple scattering energies simultaneously. We
therefore utilise a graphics processing unit (GPU) to explore the benefits of
this inherent parallelism. From a theoretical perspective, the WPCD method
promises a speedup compared to a standard matrix-inversion method. We use the
chiral NNLO$_{\rm opt}$ interaction to demonstrate that WPCD enables efficient
computation of NN scattering amplitudes provided one can tolerate an averaged
method error of $~1-5$ mb in the total cross section. For scattering energies
$\gtrsim 40$ MeV, in the laboratory frame of reference, we find a much smaller
method error of $\lesssim 1$ mb. By increasing the number of wave-packets we
can further reduce the overall method error. However, the parallel leverage of
the WPCD method will be offset by the increased size of the resulting
discretisation mesh. In practice, the GPU implementation is only useful for
matrices that fit in the fast on-chip shared memory. Nevertheless, we find that
WPCD is a promising method for computationally efficient, statistical analyses
of EFT nuclear interactions, where we can utilise Bayesian inference methods to
incorporate relevant uncertainties.


Abstract:
  We study the holographic entanglement entropy at the Hawking-Page phase
transition point for the BTZ black hole in the gauge/gravity setup and derive a
first law of entanglement thermodynamics for the boundary field theory. We find
that when the Hawking-Page phase transition takes place, the Page curve will be
modified and then all the information of the black hole will come out at the
critical temperature. Furthermore, the influence of the first-order phase
transition of black holes on the Page curve is universal. These results mean
that black holes information does not have to escape by the radiation, and the
first-order phase transition of black holes can also cause information to
escape.


Abstract:
  State-of-the-art process discovery methods construct free-choice process
models from event logs. Consequently, the constructed models do not take into
account indirect dependencies between events. Whenever the input behaviour is
not free-choice, these methods fail to provide a precise model. In this paper,
we propose a novel approach for enhancing free-choice process models by adding
non-free-choice constructs discovered a-posteriori via region-based techniques.
This allows us to benefit from the performance of existing process discovery
methods and the accuracy of the employed fundamental synthesis techniques. We
prove that the proposed approach preserves fitness with respect to the event
log while improving the precision when indirect dependencies exist. The
approach has been implemented and tested on both synthetic and real-life
datasets. The results show its effectiveness in repairing models discovered
from event logs.


Abstract:
  Does having visual priors (e.g. the ability to detect objects) facilitate
learning to perform vision-based manipulation (e.g. picking up objects)? We
study this problem under the framework of transfer learning, where the model is
first trained on a passive vision task, and adapted to perform an active
manipulation task. We find that pre-training on vision tasks significantly
improves generalization and sample efficiency for learning to manipulate
objects. However, realizing these gains requires careful selection of which
parts of the model to transfer. Our key insight is that outputs of standard
vision models highly correlate with affordance maps commonly used in
manipulation. Therefore, we explore directly transferring model parameters from
vision networks to affordance prediction networks, and show that this can
result in successful zero-shot adaptation, where a robot can pick up certain
objects with zero robotic experience. With just a small amount of robotic
experience, we can further fine-tune the affordance model to achieve better
results. With just 10 minutes of suction experience or 1 hour of grasping
experience, our method achieves ~80% success rate at picking up novel objects.


Abstract:
  Properties of plasmonic materials are associated with surface plasmons - the
electromagnetic excitations coupled to coherent electron charge density
oscillations on a metal/dielectric interface. Although decay of such
oscillations cannot be avoided, there are prospects for controlling plasmon
damping dynamics. In spherical metal nanoparticles (MNPs) the basic properties
of Localized Surface Plasmons (LSPs) can be controlled with their radius. The
present paper handles the link between the size-dependent description of LSP
properties derived from the dispersion relation based on Maxwell's equations
and the quantum picture in which MNPs are treated as "quasi-particles". Such
picture, based on the reduced density-matrix of quantum open systems ruled by
the master equation in the Lindblad form, enables to distinguish between
damping processes of populations and coherences of multipolar plasmon
oscillatory states and to establish the intrinsic relations between the rates
of these processes, independently of the size of MNP. The impact of the
radiative and the nonradiative energy dissipation channels is discussed.


Abstract:
  The NMR measurements performed on a single orthorhombic crystal of
superconducting ferromagnet UCoGe (Y.Ihara et al, Phys. Rev. Lett. v.105,
206403 (2010)) demonstrate strongly anisotropic magnetic properties of this
material. The presented calculations allow to establish the dependence of
longitudinal spin-lattice relaxation rate from temperature and magnetic field.
The value 1/T_1T in field perpendicular to spontaneous magnetisation directed
along c-axis has maximum in vicinity of Curie temperature whereas it does not
reveal similar behaviour in field parallel to the direction of spontaneous
magnetisation. Also there was shown that the longitudinal spin-lattice
relaxation rate is strongly field dependent when the field directed in
b-crystallographic direction but field independent if magnetic field is
oriented along a-axis.


Abstract:
  Cubic boron nitride (cBN) is an ultra-wide bandgap, super-hard material with
potential for extreme-temperature and -pressure applications. A
proof-of-principle p-n junction using cBN was demonstrated almost three decades
ago. However, to date, there remain two unresolved challenges that prevent its
practical use in technologies: (i) it is difficult to produce high-quality cBN
films and (ii) it is difficult to controllably n- and p-dope its matrix. In
this theoretical work, we study the reasons for doping-limitations, which is an
acute issue in realizing cBN-based electronics. In particular, we find that
different unintentionally-present intrinsic and extrinsic defects act as
compensating defects and/or introduce trap states. In turn, the presence of
these defects and their complexes affect the incorporation, as well as the
electronic structure properties, of shallow dopants [silicon and beryllium],
which are introduced intentionally to n- and p-dope cBN. Our analysis of
doping-limitations provides a path towards finding solutions for controllably
n- and p-doping cBN.


Abstract:
  As is well known, pulsars are extremely stable rotators. However, although
slowly, they spin down thanks to brake mechanisms, which are in fact still
subject of intense investigation in the literature. Since pulsars are usually
modelled as highly magnetized neutron stars that emit beams of electromagnetic
radiation out of their magnetic poles, it is reasonable to consider that the
spindown has to do with a magnetic brake. Although an interesting and simple
idea, a pure magnetic brake is not able to adequately account for the spindown
rate. Thus, many alternative spindown mechanisms appear in the literature,
among them the pulsar wind model, where a wind of particles coming from the
pulsar itself can carry part of its rotational kinetic energy. Such a spindown
mechanism depends critically on three parameters, namely, the dipole magnetic
field $B$, the angle between the magnetic and rotation axes $(\phi)$, and the
density of primary particles $(\zeta)$ of the pulsar's magnetosphere.
Differently from a series of articles in this subject, we consider for the
first time in the literature a statistical modelling which includes a
combination of a dipole magnetic and wind brakes. As a result, we are able to
constrain the above referred parameters in particular for Crab and Vela
pulsars.


Abstract:
  Van der Waals materials such as thin films of transition-metal
dichalcogenides (TMDCs) manifest strongly bound exciton states in the visible
spectrum at ambient conditions that provide an ideal platform for
exciton-photon couplings. Utilizing semiconducting TMDCs in the form of
multilayer structure combined with metals can increase significantly the
light-matter interaction. In this way, the interaction between excitons and
surface-plasmon polaritons emerge as a platform for transferring the
electromagnetic energy at confined modal volumes. Here, we theoretically
investigate how moving electrons can be used as a probe of hybrid
exciton-plasmon polaritons of gold-WSe2 multilayers within the context of
electron energy-loss spectroscopy (EELS) and cathodoluminescence (CL)
spectroscopy. Interestingly, and in contrast to WSe2 slab waveguides where
quasi-propagating photonic modes interact with only exciton A, in gold-WSe2
multilayer, exciton A and exciton B can both strongly interact with surface
plasmon polaritons. Hence, we observe CL emission suppression at excitonic or
plasmonic peaks, which reveals the energy transfer between excitons and
plasmons in the form of nonradiating guided waves. Our work provides a
systematic study for deeper understanding of the effect of the configuration
and the thickness of layers on the photonic and plasmonic modes and hence on
the strength of the coupling between excitons and surface-plasmon polaritons.
Our findings pay the way for designing efficient photodetectors, sensitive
sensors, and light emitting devices based on metal/semiconducting hybrid
materials.


Abstract:
  For the paradigm of the quantum double-slit experiment (DSE), we apply
Ockham's Razor to interpret quantum observations and to evaluate terminology
often associated with wave-particle duality. One finds that the Correspondence
Principle (CP), combined with classical wave DSEs, e.g., Young [1804], is
sufficient to predict the observed quantum particle and wave phenomena. The
empirical approach of Ockham infers that observed individual quanta are whole
particles only; an individual quantum has no observed wavelike character. The
observed, so-called wave nature of quanta emerges only in the limit of large
numbers of particle observation events. That is, the "measurement problem" is
no problem at all; "particle" and "wave" derive from separate and different
aspects of the observations. Such artificial constructs as wave function
collapse are irrelevant to the observation of individual quanta, each of which
is a "particle" from emission to measurement. The histogram of detected events
is a collective property identical to a classical interference pattern in the
limit of large numbers, as the CP decrees. For a specific individual quantum,
the mystery of what happens in the region between emission and detection has so
far been inaccessible to direct observation in coincidence with detection of
the same quantum on the DSE screen and is therefore inessential to interpreting
the extant database. To analyze actual data and predict future DSEs, therefore,
the Correspondence Principle is sufficient. True scientific progress can only
follow from new, relevant experiments and not via mathematics or imagination.


Abstract:
  The Fermi-LAT DR1 and DR2 4FGL catalogues feature more than 5000 gamma-ray
sources of which about one fourth are not associated with already known
objects, and approximately one third are associated with blazars of uncertain
nature. We perform a three-category classification of the 4FGL DR1 and DR2
sources independently, using an ensemble of Artificial Neural Networks (ANNs)
to characterise them based on the likelihood of being a Pulsar (PSR), a BL Lac
type blazar (BLL) or a Flat Spectrum Radio Quasar (FSRQ). We identify candidate
PSR, BLL and FSRQ among the unassociated sources with approximate equipartition
among the three categories and select ten classification outliers as
potentially interesting for follow up studies.


Abstract:
  Let $\mathcal{A}$ be a connected cochain DG algebra such that its underlying
graded algebra $\mathcal{A}^{\#}$ is the graded skew polynomial algebra
$$k\langle x_1,x_2, x_3\rangle/\left(\begin{array}{ccc} x_1x_2+x_2x_1\\
x_2x_3+x_3x_2\\ x_3x_1+x_1x_3 \end {array}\right), |x_1|=|x_2|=|x_3|=1.$$ From
\cite{MWZ} or \cite{MWYZ}, one sees that the differential
$\partial_{\mathcal{A}}$ is determined by \begin{align*} \left(
\begin{array}{c} \partial_{\mathcal{A}}(x_1) \partial_{\mathcal{A}}(x_2)
\partial_{\mathcal{A}}(x_3) \end{array} \right)=M\left( \begin{array}{c} x_1^2
x_2^2 x_3^2 \end{array} \right), \end{align*} for some $M\in M_3(k)$. For the
case $1\le r(M)\le 3$, we compute $H(\mathcal{A})$ case by case. The
computational results in this paper give substantial support for \cite{MWZ},
where the various homological properties of such DG algebras are systematically
studied. We find some examples, which indicate that the cohomology graded
algebra of a Koszul Calabi-Yau DG algebra may be not left (right) Gorenstein.


Abstract:
  Given an integer $g \geq 0$ and a weight vector $w \in \mathbb{Q}^n \cap (0,
1]^n$ satisfying $2g - 2 + \sum w_i > 0$, let $\Delta_{g, w}$ denote the moduli
space of $n$-marked, $w$-stable tropical curves of genus $g$ and volume one. We
calculate the automorphism group $\mathrm{Aut}(\Delta_{g, w})$ for $g \geq 1$
and arbitrary $w$, and we calculate the group $\mathrm{Aut}(\Delta_{0, w})$
when $w$ is heavy/light. In both of these cases, we show that
$\mathrm{Aut}(\Delta_{g, w}) \cong \mathrm{Aut}(K_w)$, where $K_w$ is the
abstract simplicial complex on $\{1, \ldots, n\}$ whose faces are subsets with
$w$-weight at most $1$. We show that these groups are precisely the finite
direct products of symmetric groups. The space $\Delta_{g, w}$ may also be
identified with the dual complex of the divisor of singular curves in the
algebraic Hassett space $\overline{\mathcal{M}}_{g, w}$. Following the work of
Massarenti and Mella on the biregular automorphism group
$\mathrm{Aut}(\overline{\mathcal{M}}_{g, w})$, we show that
$\mathrm{Aut}(\Delta_{g, w})$ is naturally identified with the subgroup of
automorphisms which preserve the divisor of singular curves.


Abstract:
  We present the latest results of an ongoing multiplicity survey of exoplanet
hosts, which was initiated at the Astrophysical Institute and University
Observatory Jena, using data from the second data release of the ESA-Gaia
mission. In this study the multiplicity of 289 targets was investigated, all
located within a distance of about 500 pc from the Sun. In total, 41 binary,
and 5 hierarchical triple star systems with exoplanets were detected in the
course of this project, yielding a multiplicity rate of the exoplanet hosts of
about 16 %. A total of 61 companions (47 stars, a white dwarf, and 13 brown
dwarfs) were detected around the targets, whose equidistance and common proper
motion with the exoplanet hosts were proven with their precise Gaia DR2
astrometry, which also agrees with the gravitational stability of most of these
systems. The detected companions exhibit masses from about 0.016 up to 1.66
M$_\odot$ and projected separations in the range between about 52 and 9555 au.


Abstract:
  Popular wisdom amongst theoretical physicists says that spacetime is probably
not a fundamental entity of nature, but rather an emergent one. While most
arguments to support that view arise from very speculative ideas (usually
related to attempts to understand gravity at the quantum level), the argument
can also be made by only invoking standard physics. In this manuscript, I shall
argue that a novel theory of relativity -- which simply follows the three basic
principles that Einstein invoked as the building blocks of his theory of
general relativity -- might change the deal.


Abstract:
  The propagation of dust-ion-acoustic shock waves (DIASHWs) in a
three-component magnetized plasma having inertialess super-thermal electrons,
inertial warm positive ions and negative dust grains has been investigated. A
Burgers' equation is derived by employing the reductive perturbation method.
Under consideration of inertial warm positive ions and negative dust grains,
both positive and negative shock structures are numerically observed in the
presence of super-thermal electrons. The effects of oblique angle ($\delta$),
spectral index ($\kappa$), kinematic viscosity ($\eta$), number density and
charge state of the plasma species on the formation of the DIASHWs are
examined. It is found that the positive and negative shock wave potentials
increase with the oblique angle. It is also observed that the magnitude of the
amplitude of positive and negative shock waves is not affected by the variation
of the kinematic viscosity of plasma species but the steepness of the positive
and negative shock waves decreases with kinematic viscosity of plasma species.
The implications of our findings in space and laboratory plasmas are briefly
discussed.


Abstract:
  One of the most intriguing problems of light transport in solids is the
localization that has been observed in various disordered photonic
structures1-11. The light localization in defect-free icosahedral quasicrystals
has recently been predicted theoretically without experimental verification10.
Here we report on the fabrication of submicron-size dielectric icosahedral
quasicrystals and demonstrate the results of detailed studies of the photonic
properties of these structures. Here, we present the first direct experimental
observation of intrinsic light localization in defect-free quasicrystals. This
result was obtained in time-resolved measurements at different laser
wavelengths in the visible. We linked localization with the aperiodicity of the
icosahedral structure, which led to uncompensated scattering of light from an
individual structural element over the entire sphere, providing multiple
scattering inside the sample and, as a result, the intrinsic localization of
light.


Abstract:
  The numerical solution of partial differential equations by discretization
techniques is ubiquitous in computational physics. In this work we benchmark
this approach in the quantum realm by solving the heat equation for a square
plate subject to fixed temperatures at the edges and random heat sources and
sinks within the domain. The hybrid classical-quantum approach consists in the
solution on a quantum computer of the coupled linear system of equations that
result from the discretization step. Owing to the limitations in the number of
qubits and their connectivity, we use the Gauss-Seidel method to divide the
full system of linear equations into subsystems, which are solved iteratively
in block fashion. Each of the linear subsystems were solved using 2000Q and
Advantage quantum computers developed by D-Wave Systems Inc. By comparing
classical numerical and quantum solutions, we observe that the errors and chain
break fraction are, on average, greater on the 2000Q system. Unlike the
classical Gauss-Seidel method, the errors of the quantum solutions level off
after a few iterations of our algorithm. This is partly a result of the span of
the real number line available from the mapping of the chosen size of the set
of qubit states. We verified this by using techniques to progressively shrink
the range mapped by the set of qubit states at each iteration (increasing
floating-point accuracy). As a result, no leveling off is observed. However, an
increase in qubits does not translate to an overall lower error. This is
believed to be indicative of the increasing length of chains required for the
mapping to real numbers and the ensuing limitations of hardware.


Abstract:
  We establish a sufficient condition for a continuous map, acting on a compact
metric space, to have a Baire residual set of points exhibiting historic
behavior (also known as irregular points). This criterion applies, for
instance, to a minimal and non-uniquely ergodic map; to maps preserving two
distinct probability measures with full support; to non-trivial homoclinic
classes; to some non-uniformly expanding maps; and to partially hyperbolic
diffeomorphisms with two periodic points whose stable manifolds are dense,
including Ma\~n\'e and Shub examples of robustly transitive diffeomorphisms.
This way, our unifying approach recovers a collection of known deep theorems on
the genericity of the irregular set, for both additive and sub-additive
potentials, and also provides a number of new applications.


Abstract:
  Machine learning models are often deployed in different settings than they
were trained and validated on, posing a challenge to practitioners who wish to
predict how well the deployed model will perform on a target distribution. If
an unlabeled sample from the target distribution is available, along with a
labeled sample from a possibly different source distribution, standard
approaches such as importance weighting can be applied to estimate performance
on the target. However, importance weighting struggles when the source and
target distributions have non-overlapping support or are high-dimensional.
Taking inspiration from fields such as epidemiology and polling, we develop
Mandoline, a new evaluation framework that mitigates these issues. Our key
insight is that practitioners may have prior knowledge about the ways in which
the distribution shifts, which we can use to better guide the importance
weighting procedure. Specifically, users write simple "slicing functions" -
noisy, potentially correlated binary functions intended to capture possible
axes of distribution shift - to compute reweighted performance estimates. We
further describe a density ratio estimation framework for the slices and show
how its estimation error scales with slice quality and dataset size. Empirical
validation on NLP and vision tasks shows that \name can estimate performance on
the target distribution up to $3\times$ more accurately compared to standard
baselines.


Abstract:
  Blazars are a subclass of active galactic nuclei (AGN) that have a
relativistic jet with a small viewing angle towards the observer. Recent
results based on hadronic scenarios have motivated an ongoing discussion of how
a blazar can produce high energy neutrinos during a flaring state and which
scenario can successfully describe the observed gamma-ray behaviour. Markarian
421 is one of the closest and brightest objects in the extragalactic gamma-ray
sky and showed flaring activity over a 14-day period in March 2010. In this
work, we describe the performed analysis of \textit{Fermi}-LAT data from the
source focused on the MeV range (100 MeV - 1 GeV), and study the possibility of
a contribution coming from the $p\gamma$ interactions between protons and MeV
SSC target photons to fit the very high energy (VHE) gamma-ray emission. The
fit results were compared with two leptonic models (one-zone and two-zone)
using the Akaike Information Criteria (AIC) test, which evaluates
goodness-of-fit alongside the simplicity of the model. In all cases the
photohadronic model was favoured as a better fit description in comparison to
the one-zone leptonic model, and with respect to the two-zone model in the
majority of cases. Our results show the potential of a photohadronic
contribution to a lepto-hadronic origin of gamma-ray flux of blazars. Future
gamma-ray observations above tens of TeV and below 100 MeV in energy will be
crucial to test and discriminate between models.


Abstract:
  Nematode worms are one of most abundant metazoan groups on the earth,
occupying diverse ecological niches. Accurate recognition or identification of
nematodes are of great importance for pest control, soil ecology,
bio-geography, habitat conservation and against climate changes. Computer
vision and image processing have witnessed a few successes in species
recognition of nematodes; however, it is still in great demand. In this paper,
we identify two main bottlenecks: (1) the lack of a publicly available imaging
dataset for diverse species of nematodes (especially the species only found in
natural environment) which requires considerable human resources in field work
and experts in taxonomy, and (2) the lack of a standard benchmark of
state-of-the-art deep learning techniques on this dataset which demands the
discipline background in computer science. With these in mind, we propose an
image dataset consisting of diverse nematodes (both laboratory cultured and
naturally isolated), which, to our knowledge, is the first time in the
community. We further set up a species recognition benchmark by employing
state-of-the-art deep learning networks on this dataset. We discuss the
experimental results, compare the recognition accuracy of different networks,
and show the challenges of our dataset. We make our dataset publicly available
at: https://github.com/xuequanlu/I-Nema


Abstract:
  Deep learning typically requires large data sets and much compute power for
each new problem that is learned. Meta-learning can be used to learn a good
prior that facilitates quick learning, thereby relaxing these requirements so
that new tasks can be learned quicker; two popular approaches are MAML and the
meta-learner LSTM. In this work, we compare the two and formally show that the
meta-learner LSTM subsumes MAML. Combining this insight with recent empirical
findings, we construct a new algorithm (dubbed TURTLE) which is simpler than
the meta-learner LSTM yet more expressive than MAML. TURTLE outperforms both
techniques at few-shot sine wave regression and image classification on
miniImageNet and CUB without any additional hyperparameter tuning, at a
computational cost that is comparable with second-order MAML. The key to
TURTLE's success lies in the use of second-order gradients, which also
significantly increases the performance of the meta-learner LSTM by 1-6%
accuracy.


Abstract:
  Omnichannel retailing, a new form of distribution system, seamlessly
integrates the Internet and physical stores. This study considers the pricing
and fulfillment strategies of a retailer that has two sales channels: online
and one physical store. The retailer offers consumers three purchasing options:
delivery from the fulfillment center, buy online and pick up in-store (BOPS),
and purchasing at the store. Consumers choose one of these options to maximize
their utility, dividing them into several segments. Given the retailer can
induce consumers to the profitable segment by adjusting the online and store
prices, our analysis shows that it has three optimal strategies: (1) The
retailer excludes consumers far from the physical store from the market and
lets the others choose BOPS or purchasing at the store. (2) It lets consumers
far from the physical store choose delivery from the fulfillment center and the
others choose BOPS or purchasing at the store. (3) It lets all consumers choose
delivery from the fulfillment center. Finally, we present simple dynamic
simulations that considers how the retailer's optimal strategy changes as
consumers' subjective probability of believing the product is in stock
decreases. The results show that the retailer should offer BOPS in later
periods of the selling season to maximize its profit as the subjective
probability decreases.


Abstract:
  We prove convergence of renormalized correlations of primary fields, i. e.,
spins, disorders, fermions and energy densities, in the scaling limit of the
critical Ising model in arbitrary finitely connected domains, with fixed (plus
or minus) or free boundary conditions, or mixture thereof. We describe the
limits of correlations in terms of solutions of Riemann boundary value
problems, and prove their conformal covariance. Moreover, we prove fusion
rules, or operator product expansions, which describe asymptotics of the
scaling limits of the correlations as some of the points collide together. We
give explicit formulae for correlations in the case of simply-connected and
doubly-connected domains. Our presentation is self-contained, and the proofs
are simplified as compared to the previous work where particular cases are
treated.


Abstract:
  Question answering (QA) is an important use case on voice assistants. A
popular approach to QA is extractive reading comprehension (RC) which finds an
answer span in a text passage. However, extractive answers are often unnatural
in a conversational context which results in suboptimal user experience. In
this work, we investigate conversational answer generation for QA. We propose
AnswerBART, an end-to-end generative RC model which combines answer generation
from multiple passages with passage ranking and answerability. Moreover, a
hurdle in applying generative RC are hallucinations where the answer is
factually inconsistent with the passage text. We leverage recent work from
summarization to evaluate factuality. Experiments show that AnswerBART
significantly improves over previous best published results on MS MARCO 2.1
NLGEN by 2.5 ROUGE-L and NarrativeQA by 9.4 ROUGE-L.