Abstract:
  Deep learning researchers and practitioners usually leverage GPUs to help
train their deep neural networks (DNNs) faster. However, choosing which GPU to
use is challenging both because (i) there are many options, and (ii) users
grapple with competing concerns: maximizing compute performance while
minimizing costs. In this work, we present a new practical technique to help
users make informed and cost-efficient GPU selections: make performance
predictions with the help of a GPU that the user already has. Our technique
exploits the observation that, because DNN training consists of repetitive
compute steps, predicting the execution time of a single iteration is usually
enough to characterize the performance of an entire training process. We make
predictions by scaling the execution time of each operation in a training
iteration from one GPU to another using either (i) wave scaling, a technique
based on a GPU's execution model, or (ii) pre-trained multilayer perceptrons.
We implement our technique into a Python library called Habitat and find that
it makes accurate iteration execution time predictions (with an average error
of 11.8%) on ResNet-50, Inception v3, the Transformer, GNMT, and DCGAN across
six different GPU architectures. Habitat supports PyTorch, is easy to use, and
is open source.


Abstract:
  Machine learning models fit complex algorithms to arbitrarily large datasets.
These algorithms are well-known to be high on performance and low on
interpretability. We use interactive visualization of slices of predictor space
to address the interpretability deficit; in effect opening up the black-box of
machine learning algorithms, for the purpose of interrogating, explaining,
validating and comparing model fits. Slices are specified directly through
interaction, or using various touring algorithms designed to visit
high-occupancy sections or regions where the model fits have interesting
properties. The methods presented here are implemented in the R package
\pkg{condvis2}.


Abstract:
  Bound states in superconductors are expected to exhibit a spatially resolved
electron-hole asymmetry which is the hallmark of their quantum nature. This
asymmetry manifests as oscillations at the Fermi wavelength, which is usually
tiny and thus washed out by thermal broadening or by scattering at defects.
Here we demonstrate theoretically and confirm experimentally that, when coupled
to magnetic impurities, bound states in a vortex core exhibit an emergent axial
electron-hole asymmetry on a much longer scale, set by the coherence length. We
study vortices in 2H-NbSe$_2$ and in 2H-NbSe$_{1.8}$S_{0.2}$ with magnetic
impurities, characterizing these with detailed Hubbard-corrected density
functional calculations. We find that the induced electron-hole imbalance
depends on the band character of the superconducting material. Our results open
interesting prospects for the study of coupled superconducting bound states.


Abstract:
  In this paper, we study the problem of evaluating the addition of elements to
a set. This problem is difficult, because it can, in the general case, not be
reduced to unconditional preferences between the choices. Therefore, we model
preferences based on the context of the decision. We discuss and compare two
different Siamese network architectures for this task: a twin network that
compares the two sets resulting after the addition, and a triplet network that
models the contribution of each candidate to the existing set. We evaluate the
two settings on a real-world task; learning human card preferences for deck
building in the collectible card game Magic: The Gathering. We show that the
triplet approach achieves a better result than the twin network and that both
outperform previous results on this task.


Abstract:
  We present a new ground-based visible transmission spectrum of the
high-gravity, hot Jupiter HAT-P-23b, obtained as part of the ACCESS project. We
derive the spectrum from five transits observed between 2016 and 2018, with
combined wavelength coverage between 5200 {\AA} - 9269 {\AA} in 200 {\AA} bins,
and with a median precision of 247 ppm per bin. HAT-P-23b's relatively high
surface gravity (g ~ 30 m/s^2), combined with updated stellar and planetary
parameters from Gaia DR2, gives a 5-scale-height signal of 384 ppm for a
hydrogen-dominated atmosphere. Bayesian models favor a clear atmosphere for the
planet with the tentative presence of TiO, after simultaneously modeling
stellar contamination, using spots parameter constraints from photometry. If
confirmed, HAT-P-23b would be the first example of a high-gravity gas giant
with a clear atmosphere observed in transmission at optical/NIR wavelengths;
therefore, we recommend expanding observations to the UV and IR to confirm our
results and further characterize this planet. This result demonstrates how
combining transmission spectroscopy of exoplanet atmospheres with long-term
photometric monitoring of the host stars can help disentangle the exoplanet and
stellar activity signals.


Abstract:
  A central question in the underdoped cuprates pertains to the nature of the
pseudogap ground state. A conventional metallic ground state of the pseudogap
region has been argued to host quantum oscillations upon destruction of the
superconducting order parameter by modest magnetic fields. Here we use low
applied measurement currents and millikelvin temperatures on ultra-pure single
crystals of underdoped YBa$_2$Cu$_3$O$_{6+x}$ to unearth an unconventional
quantum vortex matter ground state characterized by vanishing electrical
resistivity, magnetic hysteresis, and non-ohmic electrical transport
characteristics beyond the highest laboratory accessible static fields. A new
model of the pseudogap ground state is now required to explain quantum
oscillations that are hosted by the bulk quantum vortex matter state without
experiencing sizeable additional damping in the presence of a large maximum
superconducting gap; possibilities include a pair density wave.


Abstract:
  Two-dimensional electron gases (2DEGs) on the SrTiO3 (STO) surface or in
STO-based heterostructures have exhibited many intriguing phenomena, which are
strongly dependent on the 2DEG-carrier density. We report that the tunability
of the 2DEG-carrier density is significantly enhanced by adding a monolayer
LaTiO3 (LTO) onto the STO. Ultraviolet (UV) irradiation induced maximum carrier
density of the 2DEG in LTO/STO is increased by a factor of ~4 times, compared
to that of the bare STO. By oxygen gas exposure, it becomes 10 times smaller
than that of the bare STO. This enhanced tunability is attributed to the
drastic surface property change of a polar LTO layer by UV irradiation and O2
exposure. This indicates that the 2DEG controllability in LTO/STO is more
reliable than that on the bare STO driven by defects, such an oxygen vacancy.


Abstract:
  The rigidity propeties of higher rank diagonalizable actions is a major theme
in homogenous dynamics, with origins in work of Cassels and Swinnerton-Dyer in
the 1950s and Furstenberg. We survey both results and conjectures regarding
such actions, with emphasize on the applications of these results towards
understanding the distribution of integer points on varieties, quantum unique
ergodicity, and Diophantine approximations.


Abstract:
  We show that a signature of the quantum nature of gravity is the quantum
mechanical squeezing of the differential motion of two identical masses with
respect to their common mode. This is because the gravitational interaction
depends solely on the relative position of the two masses. In principle, this
squeezing is equivalent to quantum entanglement between the masses. In
practice, detecting the squeezing is more feasible than detecting the
entanglement. To that end, we propose an optical interferometric scheme to
falsify hypothetical models of gravity.


Abstract:
  As the COVID-19 epidemic began to worsen in the first months of 2020,
stringent lockdown policies were implemented in numerous cities throughout the
world to control human transmission and mitigate its spread. Although
transportation density reduction inside the city was felt subjectively, there
has thus far been no objective and quantitative study of its variation to
reflect the intracity population flows and their corresponding relationship
with lockdown policy stringency from the view of remote sensing images with the
high resolution under 1m. Accordingly, we here provide a quantitative
investigation of the transportation density reduction before and after lockdown
was implemented in six epicenter cities (Wuhan, Milan, Madrid, Paris, New York,
and London) around the world during the COVID-19 epidemic, which is
accomplished by extracting vehicles from the multi-temporal high-resolution
remote sensing images. A novel vehicle detection model combining unsupervised
vehicle candidate extraction and deep learning identification was specifically
proposed for the images with the resolution of 0.5m. Our results indicate that
transportation densities were reduced by an average of approximately 50% (and
as much as 75.96%) in these six cities following lockdown. The influences on
transportation density reduction rates are also highly correlated with policy
stringency, with an R^2 value exceeding 0.83. Even within a specific city, the
transportation density changes differed and tended to be distributed in
accordance with the city's land-use patterns. Considering that public
transportation was mostly reduced or even forbidden, our results indicate that
city lockdown policies are effective at limiting human transmission within
cities.


Abstract:
  In this paper, a diffuse-interface lattice Boltzmann method (DI-LBM) is
developed for fluid-particle interaction problems. In this method, the sharp
interface between the fluid and solid is replaced by a thin but nonzero
thickness transition region named diffuse interface, where the physical
variables varies continuously. In order to describe the diffuse interface, we
introduce a smooth function, which is similar to the order parameter in
phase-field model or the volume fraction of solid phase in the partially
saturated lattice Boltzmann method (PS-LBM). In addition, to depict the
fluid-particle interaction more accurately, a modified force term is also
proposed and included in the evolution equation of the DI-LBM. Some classical
problems are used to test the DI-LBM, and the results are in good agreement
with some available theoretical and numerical works. Finally, it is also found
that the DI-LBM is more efficient and accurate than the PS-LBM with the
superposition model.


Abstract:
  We present SoftDICE, which achieves state-of-the-art performance for
imitation learning. SoftDICE fixes several key problems in ValueDICE, an
off-policy distribution matching approach for sample-efficient imitation
learning. Specifically, the objective of ValueDICE contains logarithms and
exponentials of expectations, for which the mini-batch gradient estimate is
always biased. Second, ValueDICE regularizes the objective with replay buffer
samples when expert demonstrations are limited in number, which however changes
the original distribution matching problem. Third, the re-parametrization trick
used to derive the off-policy objective relies on an implicit assumption that
rarely holds in training. We leverage a novel formulation of distribution
matching and consider an entropy-regularized off-policy objective, which yields
a completely offline algorithm called SoftDICE. Our empirical results show that
SoftDICE recovers the expert policy with only one demonstration trajectory and
no further on-policy/off-policy samples. SoftDICE also stably outperforms
ValueDICE and other baselines in terms of sample efficiency on Mujoco benchmark
tasks.


Abstract:
  We develop a framework for comparing data manifolds, aimed, in particular,
towards the evaluation of deep generative models. We describe a novel tool,
Cross-Barcode(P,Q), that, given a pair of distributions in a high-dimensional
space, tracks multiscale topology spacial discrepancies between manifolds on
which the distributions are concentrated. Based on the Cross-Barcode, we
introduce the Manifold Topology Divergence score (MTop-Divergence) and apply it
to assess the performance of deep generative models in various domains: images,
3D-shapes, time-series, and on different datasets: MNIST, Fashion MNIST, SVHN,
CIFAR10, FFHQ, chest X-ray images, market stock data, ShapeNet. We demonstrate
that the MTop-Divergence accurately detects various degrees of mode-dropping,
intra-mode collapse, mode invention, and image disturbance. Our algorithm
scales well (essentially linearly) with the increase of the dimension of the
ambient high-dimensional space. It is one of the first TDA-based practical
methodologies that can be applied universally to datasets of different sizes
and dimensions, including the ones on which the most recent GANs in the visual
domain are trained. The proposed method is domain agnostic and does not rely on
pre-trained networks.


Abstract:
  With the standardization of 5G, commercial millimeter wave (mmWave)
communications has become a reality despite all the concerns about the
unfavorable propagation characteristics of these frequencies. Even though the
5G systems are still being rolled out, it is argued that their gigabits per
second rates may fall short in supporting many emerging applications, such as
3D gaming and extended reality. Such applications will require several hundreds
of gigabits per second to several terabits per second data rates with low
latency and high reliability, which are expected to be the design goals of the
next generation 6G communications systems. Given the potential of terahertz
(THz) communications systems to provide such data rates over short distances,
they are widely regarded to be the next frontier for the wireless
communications research. The primary goal of this chapter is to equip readers
with sufficient background about the mmWave and THz bands so that they are able
to both appreciate the necessity of using these bands for commercial
communications in the current wireless landscape and to reason the key design
considerations for the communications systems operating in these bands. Towards
this goal, this chapter provides a unified treatment of these bands with
particular emphasis on their propagation characteristics, channel models,
design and implementation considerations, and potential applications to 6G
wireless. A brief summary of the current standardization activities related to
the use of these bands for commercial communications applications is also
provided.


Abstract:
  Traditional supervised learning with deep neural networks requires a
tremendous amount of labelled data to converge to a good solution. For 3D
medical images, it is often impractical to build a large homogeneous annotated
dataset for a specific pathology. Self-supervised methods offer a new way to
learn a representation of the images in an unsupervised manner with a neural
network. In particular, contrastive learning has shown great promises by
(almost) matching the performance of fully-supervised CNN on vision tasks.
Nonetheless, this method does not take advantage of available meta-data, such
as participant's age, viewed as prior knowledge. Here, we propose to leverage
continuous proxy metadata, in the contrastive learning framework, by
introducing a new loss called y-Aware InfoNCE loss. Specifically, we improve
the positive sampling during pre-training by adding more positive examples with
similar proxy meta-data with the anchor, assuming they share similar
discriminative semantic features.With our method, a 3D CNN model pre-trained on
$10^4$ multi-site healthy brain MRI scans can extract relevant features for
three classification tasks: schizophrenia, bipolar diagnosis and Alzheimer's
detection. When fine-tuned, it also outperforms 3D CNN trained from scratch on
these tasks, as well as state-of-the-art self-supervised methods. Our code is
made publicly available here.


Abstract:
  The generative adversarial network (GAN) is a well-known model for learning
high-dimensional distributions, but the mechanism for its generalization
ability is not understood. In particular, GAN is vulnerable to the memorization
phenomenon, the eventual convergence to the empirical distribution. We consider
a simplified GAN model with the generator replaced by a density, and analyze
how the discriminator contributes to generalization. We show that with early
stopping, the generalization error measured by Wasserstein metric escapes from
the curse of dimensionality, despite that in the long term, memorization is
inevitable. In addition, we present a hardness of learning result for WGAN.


Abstract:
  Physical-layer based key generation schemes exploit the channel reciprocity
for secret key extraction, which can achieve information-theoretic secrecy
against eavesdroppers. Such methods, although practical, have been shown to be
vulnerable against man-in-the-middle (MitM) attacks, where an active adversary,
Mallory, can influence and infer part of the secret key generated between Alice
and Bob by injecting her own packet upon observing highly correlated
channel/RSS measurements from Alice and Bob. As all the channels remain stable
within the channel coherence time, Mallory's injected packets cause Alice and
Bob to measure similar RSS, which allows Mallory to successfully predict the
derived key bits. To defend against such a MitM attack, we propose to utilize a
reconfigurable antenna at one of the legitimate transceivers to proactively
randomize the channel state across different channel probing rounds. The
randomization of the antenna mode at every probing round breaks the temporal
correlation of the channels from the adversary to the legitimate devices, while
preserving the reciprocity of the channel between the latter. This prevents key
injection from the adversary without affecting Alice and Bob's ability to
measure common randomness. We theoretically analyze the security of the
protocol and conduct extensive simulations and real-world experiments to
evaluate its performance. Our results show that our approach eliminates the
advantage of an active MitM attack by driving down the probability of
successfully guessing bits of the secret key to a random guess.


Abstract:
  We have investigated tidal forces and geodesic deviation motion in the
4D-Einstein-Gauss-Bonnet spacetime. Our results show that tidal force and
geodesic deviation motion depend sharply on the sign of Gauss-Bonnet coupling
constant. Comparing with Schwarzschild spacetime, the strength of tidal force
becomes stronger for the negative Gauss-Bonnet coupling constant, but is weaker
for the positive one. Moreover, tidal force behaves like those in the
Schwarzschild spacetime as the coupling constant is negative, and like those in
Reissner-Nordstr\"{o}m black hole as the constant is positive. We also present
the change of geodesic deviation vector with Gauss-Bonnet coupling constant
under two kinds of initial conditions.


Abstract:
  Inspired by two basic mechanisms in animal visual systems, we introduce a
feature transform technique that imposes invariance properties in the training
of deep neural networks. The resulting algorithm requires less parameter
tuning, trains well with an initial learning rate 1.0, and easily generalizes
to different tasks. We enforce scale invariance with local statistics in the
data to align similar samples at diverse scales. To accelerate convergence, we
enforce a GL(n)-invariance property with global statistics extracted from a
batch such that the gradient descent solution should remain invariant under
basis change. Profiling analysis shows our proposed modifications takes 5% of
the computations of the underlying convolution layer. Tested on convolutional
networks and transformer networks, our proposed technique requires fewer
iterations to train, surpasses all baselines by a large margin, seamlessly
works on both small and large batch size training, and applies to different
computer vision and language tasks.


Abstract:
  In this paper, we study the issue of automatic singer identification (SID) in
popular music recordings, which aims to recognize who sang a given piece of
song. The main challenge for this investigation lies in the fact that a
singer's singing voice changes and intertwines with the signal of background
accompaniment in time domain. To handle this challenge, we propose the KNN-Net
for SID, which is a deep neural network model with the goal of learning local
timbre feature representation from the mixture of singer voice and background
music. Unlike other deep neural networks using the softmax layer as the output
layer, we instead utilize the KNN as a more interpretable layer to output
target singer labels. Moreover, attention mechanism is first introduced to
highlight crucial timbre features for SID. Experiments on the existing artist20
dataset show that the proposed approach outperforms the state-of-the-art method
by 4%. We also create singer32 and singer60 datasets consisting of Chinese pop
music to evaluate the reliability of the proposed method. The more extensive
experiments additionally indicate that our proposed model achieves a
significant performance improvement compared to the state-of-the-art methods.


Abstract:
  The physical scenario responsible for gamma-ray flaring activity and its
location for Flat Spectrum Radio Quasars is still debated. The study of the
statistical distribution of waiting-times between flares (the time intervals
between consecutive activity peaks) can give information on the distribution of
flaring times, and constrain the physical mechanism responsible for gamma-ray
emission. We adopt here a Scan-Statistic driven clustering method (iSRS) to
recognize flaring states within the FERMI-LAT data, and identify the time of
activity-peaks. Results: Flares waiting times can be described with a
poissonian process, consisting of a set of overlapping bursts of flares, with
an average burst duration of about 0.6 year, and average rate of 1.3/y . For
waiting times below 1d host-frame we found a statistically-relevant second
population, the fast-component, mainly from CTA 102 data. The period of
conspicuous detection of the fast component for CTA 102 coincides with the
crossing-time of the superluminal K1 feature with the C1 stationary feature in
radio reported in Jorstad et al. (2017); Casadio et al. (2019). Conclusions: To
reconcile the mechanism proposed in Jorstad et al. (2017); Casadio et al.
(2019) with the bursting-activity, we have to assume that plasma streams with a
typical length of about 2pc (in the stream reference-frame) reach the
recollimation-shock. Otherwise, the distribution of waiting-times can be
interpreted as originating from relativistic-plasma moving along the jet for a
deprojected length of about 30-50pc (assuming a Lorentz-factor=10), that
sporadically produces gamma-ray flares. In magnetic-reconnection scenario,
reconnection events or plasma injection to the reconnection-sites should be
intermittent. Individual plasmoids can be resolved in a few favourable cases
only (Christie et al., 2019); they could be responsible for the fast component.


Abstract:
  We study stochastic convex optimization with heavy-tailed data under the
constraint of differential privacy (DP). Most prior work on this problem is
restricted to the case where the loss function is Lipschitz. Instead, as
introduced by Wang, Xiao, Devadas, and Xu \cite{WangXDX20}, we study general
convex loss functions with the assumption that the distribution of gradients
has bounded $k$-th moments. We provide improved upper bounds on the excess
population risk under concentrated DP for convex and strongly convex loss
functions. Along the way, we derive new algorithms for private mean estimation
of heavy-tailed distributions, under both pure and concentrated DP. Finally, we
prove nearly-matching lower bounds for private stochastic convex optimization
with strongly convex losses and mean estimation, showing new separations
between pure and concentrated DP.


Abstract:
  There have been a number of corner detection methods proposed for event
cameras in the last years, since event-driven computer vision has become more
accessible. Current state-of-the-art have either unsatisfactory accuracy or
real-time performance when considered for practical use, for example when a
camera is randomly moved in an unconstrained environment. In this paper, we
present yet another method to perform corner detection, dubbed look-up
event-Harris (luvHarris), that employs the Harris algorithm for high accuracy
but manages an improved event throughput. Our method has two major
contributions, 1. a novel "threshold ordinal event-surface" that removes
certain tuning parameters and is well suited for Harris operations, and 2. an
implementation of the Harris algorithm such that the computational load per
event is minimised and computational heavy convolutions are performed only
"as-fast-as-possible", i.e. only as computational resources are available. The
result is a practical, real-time, and robust corner detector that runs more
than 2.6x the speed of current state-of-the-art; a necessity when using
high-resolution event-camera in real-time. We explain the considerations taken
for the approach, compare the algorithm to current state-of-the-art in terms of
computational performance and detection accuracy, and discuss the validity of
the proposed approach for event cameras.


Abstract:
  Metasurfaces are planar structures that can manipulate the amplitude, phase
and polarization (APP) of light at subwavelength scale. Although various
functionalities have been proposed based on metasurface, a most general optical
control, i.e., independent complex-amplitude (amplitude and phase) control of
arbitrary two orthogonal states of polarizations, has not yet been realized.
Such level of optical control can not only cover the various functionalities
realized previously, but also enable new functionalities that are not feasible
before. Here, we propose a single-layer dielectric metasurface to realize this
goal and experimentally demonstrate several advanced functionalities, such as
two independent full-color printing images under arbitrary elliptically
orthogonal polarizations and dual sets of printing-hologram integrations. Our
work opens the way for a wide range of applications in advanced image display,
information encoding, and polarization optics.


Abstract:
  The key challenge of unsupervised vehicle re-identification (Re-ID) is
learning discriminative features from unlabelled vehicle images. Numerous
methods using domain adaptation have achieved outstanding performance, but
those methods still need a labelled dataset as a source domain. This paper
addresses an unsupervised vehicle Re-ID method, which no need any types of a
labelled dataset, through a Self-supervised Metric Learning (SSML) based on a
feature dictionary. Our method initially extracts features from vehicle images
and stores them in a dictionary. Thereafter, based on the dictionary, the
proposed method conducts dictionary-based positive label mining (DPLM) to
search for positive labels. Pair-wise similarity, relative-rank consistency,
and adjacent feature distribution similarity are jointly considered to find
images that may belong to the same vehicle of a given probe image. The results
of DPLM are applied to dictionary-based triplet loss (DTL) to improve the
discriminativeness of learnt features and to refine the quality of the results
of DPLM progressively. The iterative process with DPLM and DTL boosts the
performance of unsupervised vehicle Re-ID. Experimental results demonstrate the
effectiveness of the proposed method by producing promising vehicle Re-ID
performance without a pre-labelled dataset. The source code for this paper is
publicly available on `https://github.com/andreYoo/VeRI_SSML_FD.git'.


Abstract:
  We report that the RS CVn-type star GT Mus (HR 4492, HD 101379 + HD 101380)
was the most active star in the X-ray sky in the last decade in terms of the
scale of recurrent energetic flares. We detected 11 flares from GT Mus in 8 yr
of observations with Monitor of All-sky X-ray Image (MAXI) from 2009 August to
2017 August. The detected flare peak luminosities were 1-4 $\times$ 10$^{33}$
erg s$^{-1}$ in the 2.0-20.0 keV band for its distance of 109.6 pc. Our timing
analysis showed long durations ($\tau_{\rm r} + \tau_{\rm d}$) of 2-6 days with
long decay times ($\tau_{\rm d}$) of 1-4 days. The released energies during the
decay phases of the flares in the 0.1-100 keV band ranged 1-11 $\times$
10$^{38}$ erg, which are at the upper end of the observed stellar flare. The
released energies during whole duration time ranged 2-13 $\times$ 10$^{38}$ erg
in the same band. We carried out X-ray follow-up observations for one of the 11
flares with Neutron star Interior Composition Explorer (NICER) on 2017 July 18
and found that the flare cooled quasi-statically. On the basis of a
quasi-static cooling model, the flare loop length is derived to be 4 $\times$
10$^{12}$ cm (or 60 R$_{\odot}$). The electron density is derived to be 1
$\times$ 10$^{10}$ cm$^{-3}$, which is consistent with the typical value of
solar and stellar flares (10$^{10-13}$ cm$^{-3}$). The ratio of the cooling
timescales between radiative cooling ($\tau_{\rm rad}$) and conductive cooling
($\tau_{\rm cond}$) is estimated to be $\tau_{\rm rad}$ $\sim$ 0.1$\tau_{\rm
cond}$ from the temperature; thus radiative cooling was dominant in this flare.


Abstract:
  Gluon dressing of the light quarks within hadrons is very strong and
extremely important in that it dynamically generates most of the observable
mass through the breaking of chiral symmetry. The quark and gluon parton
densities, $q(x)$ and $g(x)$, are necessarily interrelated since any gluon
emission and absorption process, especially dressing of a quark, contributes to
$g(x)$ and modifies $q(x)$. Guided by long-established results for the
parton-in-parton distributions from a strict 1-loop perturbative analysis of a
quark target, we extend the non-perturbative QCD approach based on the
Rainbow-Ladder truncation of the Dyson-Schwinger equations to describe the
interrelated valence $q_{\rm v}(x)$ and the dressing-gluon $g(x)$ for a hadron
at its intrinsic model scale. We employ the pion description from previous DSE
work that accounted for the gluon-in-quark effect and introduce a simple model
of the nucleon for exploratory purposes. We find typically \mbox{$\langle x
\rangle_g \sim 0.20$} for both pion and nucleon at the model scale, and the
valence quark helicity contributes 52\% of nucleon spin. We deduce both $q_{\rm
v}(x)$ and $g(x)$ from 30 calculated Mellin moments, and after adopting
existing data analysis results for $q_{\rm sea}(x)$, we find that NLO scale
evolution produces $g(x)$ in good agreement with existing data analysis results
for the pion at 1.3 GeV and the nucleon at 5 GeV$^2$. At the scale 2 GeV
typical of lattice-QCD calculations, we obtain \mbox{$\langle x \rangle_g^{\rm
N} = 0.42$} in good agreement with 0.38 from the average of recent lattice-QCD
calculations.


Abstract:
  We derive a multi-species BGK model with velocity-dependent collision
frequency for a non-reactive, multi-component gas mixture. The model is derived
by minimizing a weighted entropy under the constraint that the number of
particles of each species, total momentum, and total energy are conserved. We
prove that this minimization problem admits a unique solution for very general
collision frequencies. Moreover, we prove that the model satisfies an H-Theorem
and characterize the form of equilibrium.


Abstract:
  In this article, an analytical model is proposed for the study of
Transverse-electric (TE) surface plasmon polaritons (SPPs) in nonlinear
multi-layer graphene-based waveguides. Each graphene sheet has been located
between two different Kerr-type layers. As special cases of the general,
proposed structure, two new nonlinear graphene-based waveguides are introduced
and investigated in this paper. The obtained results show that the propagation
properties of these exemplary structures are adjustable via chemical potential
and nonlinear coefficients. A large value of the effective index, i.e. neff=82
is obtained for the chemical potential of 0.15 ev and the nonlinear ratio of
0.8 for the second structure at the frequency of 61 THz. The presented study
suggests a novel platform in graphene plasmonics, which can be used for the
design of innovative THz devices.


Abstract:
  The photoluminescence intermittency (blinking) of quantum dots is interesting
because it is an easily-measured quantum process whose transition statistics
cannot be explained by Fermi's Golden Rule. Commonly, the transition statistics
are power-law distributed, implying that quantum dots possess at least trivial
memories. By investigating the temporal correlations in the blinking data, we
demonstrate with high statistical confidence that quantum dot blinking data has
non-trivial memory, which we define to be statistical complexity greater than
one. We show that this memory cannot be discovered using the transition
distribution. We show by simulation that this memory does not arise from
standard data manipulations. Finally, we conclude that at least three physical
mechanisms can explain the measured non-trivial memory: 1) Storage of state
information in the chemical structure of a quantum dot; 2) The existence of
more than two intensity levels in a quantum dot; and 3) The overlap in the
intensity distributions of the quantum dot states, which arises from
fundamental photon statistics.


Abstract:
  The fifth generation (5G) mobile networks with enhanced connectivity and
positioning capabilities play an increasingly important role in the development
of automated vehicle-to-everything (V2X) and other advanced industrial Internet
of Things (IoT) systems. In this article, we address the prospects of 5G New
Radio (NR) sidelink based V2X networks and their applicability for increasing
the situational awareness, in terms of continuous tracking of moving connected
machines and vehicles, in industrial systems. For increased system flexibility
and fast deployments, we assume that the locations of the so-called anchor
nodes are unknown, and describe an extended Kalman filter-based joint
positioning and tracking framework in which the locations of both the anchor
nodes and the target nodes can be estimated simultaneously. We assess and
demonstrate the achievable 3D positioning and tracking performance in the
context of a realistic industrial warehouse facility, through extensive
ray-tracing based evaluations at the 26 GHz NR band. Our findings show that
when both angle-based and time-based measurements are utilized, reaching sub-1
meter accuracy is realistic and that the system is also relatively robust
against different node geometries. Finally, several research challenges towards
achieving robust, high-performance and cost-efficient positioning solutions are
outlined and discussed, identifying various potential directions for future
work.


Abstract:
  The spread of infectious epidemics is often accelerated by super-spreader
events. Understanding their effect is important, particularly in the context of
standard epidemiological models, which require estimates for parameters such as
$R_0$. In this letter, we show that the effective value of $R_0$ in
super-spreader situations is significantly large, of the order of hundreds,
suggesting a delta-function-like behavior during the event. Use of a well-mixed
room model supports these findings. They elucidate infection kinetic modeling
in enclosed environments, which differ from the standard SIR model, and provide
expressions for $R_0$ in terms of physical and operational parameters. The
overall impact of super-spreader events can be significant, depending on the
state of the epidemic and how the infections generated by the event
subsequently spread in the community.


Abstract:
  A natural class of conformally invariant ways for discovering the loops of a
conformal loop ensemble $\text{CLE}_4$ is given by a certain family of
$\text{SLE}_4^{\langle\mu\rangle}(-2)$ exploration processes for real $\mu$.
Such an exploration consists of one simple continuous path called the trunk of
the exploration that discovers $\text{CLE}_4$ loops along the way. The
parameter $\mu$ appears in the Loewner chain description of the path that
traces the trunk and all CLE loops encountered by the trunk in chronological
order. These explorations can also be interpreted in terms of level lines of a
Gaussian free field. It has been shown by Miller, Sheffield and Werner that the
trunk of such an exploration is an $\text{SLE}_4(\rho,-2-\rho)$ process for
some (unknown) value of $\rho \in (-2, 0)$. The main result of the present
paper is to establish the relation between $\mu$ and $\rho$, more specifically
to show that $\mu = -\pi\cot(\pi\rho/2)$. The crux of the paper is to show how
explorations of $\text{CLE}_4$ can be approximated by explorations of
$\text{CLE}_\kappa$ for $\kappa \uparrow 4$, which then makes it possible to
use recent results by Miller, Sheffield and Werner about the trunks of
$\text{CLE}_\kappa$ explorations for $\kappa < 4$.


Abstract:
  Privacy dashboards and transparency tools help users review and manage the
data collected about them online. Since 2016, Google has offered such a tool,
My Activity, which allows users to review and delete their activity data from
Google services. We conducted an online survey with $n = 153$ participants to
understand if Google's My Activity, as an example of a privacy transparency
tool, increases or decreases end-users' concerns and benefits regarding data
collection. While most participants were aware of Google's data collection, the
volume and detail was surprising, but after exposure to My Activity,
participants were significantly more likely to be both less concerned about
data collection and to view data collection more beneficially. Only $25\,\%$
indicated that they would change any settings in the My Activity service or
change any behaviors. This suggests that privacy transparency tools are quite
beneficial for online services as they garner trust with their users and
improve their perceptions without necessarily changing users' behaviors. At the
same time, though, it remains unclear if such transparency tools actually
improve end user privacy by sufficiently assisting or motivating users to
change or review data collection settings.


Abstract:
  We study the Hall conductivity of a two-dimensional electron gas under an
inhomogeneous magnetic field $B(x)$. First, we prove using the quantum kinetic
theory that an odd magnetic field can lead to a purely nonlinear Hall response.
Second, considering a real-space magnetic dipole consisting of a sign-changing
magnetic field and based on numerical semiclassical dynamics, we unveil a
parametric resonance involving the cyclotron ratio and a characteristic width
of $B(x)$, which can greatly enhance the Hall response. Different from previous
mechanisms that rely on the bulk Berry curvature dipole, here, the effect
largely stems from boundary states associated with the real-space magnetic
dipole. Our findings pave a new way to engineer current rectification and
higher harmonic generation in two-dimensional materials having or not crystal
inversion symmetry.


Abstract:
  Bacterial swarms display intriguing dynamical states like active turbulence.
Using a hydrodynamic model we now show that such dense active suspensions
manifest super-diffusion, via L\'evy walks, which masquerades as a crossover
from ballistic to diffusive scaling in measurements of
mean-squared-displacements, and is tied to the emergence of hitherto undetected
oscillatory streaks in the flow. Thus, while laying the theoretical framework
of an emergent advantageous strategy in the collective behaviour of
microorganisms, our study underlines the essential differences between active
and inertial turbulence.


Abstract:
  In this paper, a new family of rotationally symmetric planar graphs is
described based on an edge coalescence of planar chorded cycles. Their local
fractional metric dimension is established for those ones arisen from chorded
cycles of order up to six. Their asymptotic behaviour enables us to ensure the
existence of new families of rotationally symmetric planar graphs with either
constant or bounded local fractional dimension.


Abstract:
  We consider the problem of predicting the future path of a pedestrian using
its motion history and the motion history of the surrounding pedestrians,
called social information. Since the seminal paper on Social-LSTM,
deep-learning has become the main tool used to model the impact of social
interactions on a pedestrian's motion. The demonstration that these models can
learn social interactions relies on an ablative study of these models. The
models are compared with and without their social interactions module on two
standard metrics, the Average Displacement Error and Final Displacement Error.
Yet, these complex models were recently outperformed by a simple
constant-velocity approach. This questions if they actually allow to model
social interactions as well as the validity of the proof. In this paper, we
focus on the deep-learning models with a soft-attention mechanism for social
interaction modeling and study whether they use social information at
prediction time. We conduct two experiments across four state-of-the-art
approaches on the ETH and UCY datasets, which were also used in previous work.
First, the models are trained by replacing the social information with random
noise and compared to model trained with actual social information. Second, we
use a gating mechanism along with a $L_0$ penalty, allowing models to shut down
their inner components. The models consistently learn to prune their
soft-attention mechanism. For both experiments, neither the course of the
convergence nor the prediction performance were altered. This demonstrates that
the soft-attention mechanism and therefore the social information are ignored
by the models.


Abstract:
  Click-through rate (CTR) prediction plays a critical role in recommender
systems and online advertising. The data used in these applications are
multi-field categorical data, where each feature belongs to one field. Field
information is proved to be important and there are several works considering
fields in their models. In this paper, we proposed a novel approach to model
the field information effectively and efficiently. The proposed approach is a
direct improvement of FwFM, and is named as Field-matrixed Factorization
Machines (FmFM, or $FM^2$). We also proposed a new explanation of FM and FwFM
within the FmFM framework, and compared it with the FFM. Besides pruning the
cross terms, our model supports field-specific variable dimensions of embedding
vectors, which acts as soft pruning. We also proposed an efficient way to
minimize the dimension while keeping the model performance. The FmFM model can
also be optimized further by caching the intermediate vectors, and it only
takes thousands of floating-point operations (FLOPs) to make a prediction. Our
experiment results show that it can out-perform the FFM, which is more complex.
The FmFM model's performance is also comparable to DNN models which require
much more FLOPs in runtime.


Abstract:
  We prove that for every $n\geq 3$ the sharp upper bound for the dimension of
the symmetry groups of homogeneous, 2-nondegenerate, $(2n+1)$-dimensional CR
manifolds of hypersurface type with a $1$-dimensional Levi kernel is equal to
$n^2+7$. This supports Beloshapka's conjecture stating that hypersurface models
with a maximal finite dimensional group of symmetries for a given dimension of
the underlying manifold are Levi nondegenerate.


Abstract:
  In this work, we propose extreme compression techniques like binarization,
ternarization for Neural Decoders such as TurboAE. These methods reduce memory
and computation by a factor of 64 with a performance better than the quantized
(with 1-bit or 2-bits) Neural Decoders. However, because of the limited
representation capability of the Binary and Ternary networks, the performance
is not as good as the real-valued decoder. To fill this gap, we further propose
to ensemble 4 such weak performers to deploy in the edge to achieve a
performance similar to the real-valued network. These ensemble decoders give 16
and 64 times saving in memory and computation respectively and help to achieve
performance similar to real-valued TurboAE.


Abstract:
  Disordered solids respond to quasistatic shear with intermittent avalanches
of plastic activity, an example of the crackling noise observed in many
nonequilibrium critical systems. The temporal power spectrum of activity within
disordered solids consists of three distinct domains: a novel power-law rise
with frequency at low frequencies indicating anticorrelation, white-noise at
intermediate frequencies, and a power-law decay at high frequencies. As the
strain rate increases, the white-noise regime shrinks and ultimately disappears
as the finite strain rate restricts the maximum size of an avalanche. A new
strain-rate- and system-size-dependent scaling theory is derived for power
spectra in both the quasistatic and finite-strain-rate regimes. This theory is
validated using data from overdamped two- and three-dimensional molecular
dynamics simulations. We identify important exponents in the yielding
transition including the dynamic exponent $z$ which relates the size of an
avalanche to its duration, the fractal dimension of avalanches, and the
exponent characterizing the divergence in correlations with strain rate.
Results are related to temporal correlations within a single avalanche and
between multiple avalanches.


Abstract:
  Classifying hand-written digits and letters has taken a big leap with the
introduction of ConvNets. However, on very constrained hardware the time
necessary to train such models would be high. Our main contribution is twofold.
First, we extensively test an end-to-end vanilla neural network (MLP) approach
in pure numpy without any pre-processing or feature extraction done beforehand.
Second, we show that basic data mining operations can significantly improve the
performance of the models in terms of computational time, without sacrificing
much accuracy. We illustrate our claims on a simpler variant of the Extended
MNIST dataset, called Balanced EMNIST dataset. Our experiments show that,
without any data mining, we get increased generalization performance when using
more hidden layers and regularization techniques, the best model achieving
84.83% accuracy on a test dataset. Using dimensionality reduction done by PCA
we were able to increase that figure to 85.08% with only 10% of the original
feature space, reducing the memory size needed by 64%. Finally, adding methods
to remove possibly harmful training samples like deviation from the mean helped
us to still achieve over 84% test accuracy but with only 32.8% of the original
memory size for the training set. This compares favorably to the majority of
literature results obtained through similar architectures. Although this
approach gets outshined by state-of-the-art models, it does scale to some
(AlexNet, VGGNet) trained on 50% of the same dataset.


Abstract:
  In this work, we have systematically studied the role of point defects in the
recombination time of monolayer MoS$_2$ using time-dependent ab initio
non-adiabatic molecular dynamics simulations. Various types of point defects,
such as S vacancy, S interstitial, Mo vacancy and Mo interstitial have been
considered. We show that defects strongly accelerate the electron-hole
recombination, especially interstitial S atoms do that by 3 orders of magnitude
higher compared to pristine MoS$_2$. Mo defects (both vacancy and interstitial)
introduce a multitude of de-excitation pathways via various defect levels in
the energy gap. The results of this study provide some fundamental
understanding of photoinduced de-excitation dynamics in presence of defects in
highly technologically relevant 2D MoS$_2$.


Abstract:
  Let G be a simple non-compact linear connected Lie group and H be a closed
non-compact semisimple subgroup. We are interested in finding classes of
homogeneous spaces G/H admitting proper actions of discrete non virtually
abelian subgroups of G. We develop an algorithm for finding such homogeneous
spaces. As a testing example we obtain a list of all non-compact homogeneous
spaces G/H admitting proper action of a discrete and non virtually abelian
subgroup of G in the case when G has rank at most 8, and H is a maximal proper
semisimple subgroup.


Abstract:
  We compare spectra of the zonal harmonics of the large-scale magnetic field
of the Sun using observation results and solar dynamo models. The main solar
activity cycle as recorded in these tracers is a much more complicated
phenomenon than the eigen solution of solar dynamo equations with the growth
saturated by a back reaction of the dynamo-driven magnetic field on solar
hydrodynamics. The nominal 11(22)-year cycle as recorded in each mode has a
specific phase shift varying from cycle to cycle; the actual length of the
cycle varies from one cycle to another and from tracer to tracer. Both the
observation and the dynamo model show an exceptional role of the axisymmetric
$\ell_{5}$ mode. Its origin seems to be readily connected with the formation
and evolution of sunspots on the solar surface. The results of observations and
dynamo models show a good agreement for the low $\ell_{1}$ and $\ell_{3}$
modes. The results for these modes do not differ significantly for the
axisymmetric and nonaxisymmetric models. Our findings support the idea that the
sources of the solar dynamo arise as a result of both the distributed dynamo
processes in the bulk of the convection zone and the surface magnetic activity.


Abstract:
  The OPC UA protocol is an upcoming de-facto standard for building Industry
4.0 processes in Europe, and one of the few industrial protocols that promises
security features to prevent attackers from manipulating and damaging critical
infrastructures. Despite the importance of the protocol, challenges in the
adoption of OPC UA's security features by product vendors, libraries
implementing the standard, and end-users were not investigated so far.
  In this work, we systematically investigate 48 publicly available artifacts
consisting of products and libraries for OPC UA and show that 38 out of the 48
artifacts have one (or more) security issues. In particular, we show that 7 OPC
UA artifacts do not support the security features of the protocol at all. In
addition, 31 artifacts that partially feature OPC UA security rely on
incomplete libraries and come with misleading instructions. Consequently,
relying on those products and libraries will result in vulnerable
implementations of OPC UA security features. To verify our analysis, we design,
implement, and demonstrate attacks in which the attacker can steal credentials
exchanged between victims, eavesdrop on process information, manipulate the
physical process through sensor values and actuator commands, and prevent the
detection of anomalies.


Abstract:
  Flowing granular materials often abruptly arrest if not driven by sufficient
applied stresses. Such abrupt cessation of motion can be economically expensive
in industrial materials handling and processing, and is significantly
consequential in intermittent geophysical phenomena such as landslides and
earthquakes. Using discrete element simulations, we calculate a flow-arrest
state diagram for granular materials under the conditions of constant applied
pressure and shear stress, which are also most relevant in practice. Here the
material can dilate or compact, and flow or arrest, in response to the applied
stress. Our simulations highlight that under external stress, the intrinsic
response of granular materials is characterized by uniquely-defined
nonequilibrium steady states of flow or arrest, which are highly sensitive to
interparticle friction. While the flowing states can be equivalently
characterized by volume fraction, coordination number or internal stress ratio,
to characterize the states of shear arrest, one needs to also consider the
structural anisotropy in the contact network. We highlight the role of dilation
in the flow-arrest transition, and discuss our findings in the context of shear
jamming and discontinuous shear thickening.


Abstract:
  Social networks represent nowadays in many contexts the main source of
information transmission and the way opinions and actions are influenced. For
instance, generic advertisements are way less powerful than suggestions from
our contacts. However, this process hugely depends on the influence of people
who disseminate these suggestions. Therefore modern marketing often involves
paying some targeted users, or influencers, for advertising products or ideas.
Finding the set of nodes in a social network that lead to the highest
information spread -- the so-called Influence Maximization (IM) problem -- is
therefore a pressing question and as such it has recently attracted a great
research interest. In particular, several approaches based on Evolutionary
Algorithms (EAs) have been proposed, although they are known to scale poorly
with the graph size. In this paper, we tackle this limitation in two ways.
Firstly, we use approximate fitness functions to speed up the EA. Secondly, we
include into the EA various graph-aware mechanisms, such as smart
initialization, custom mutations and node filtering, to facilitate the EA
convergence. Our experiments show that the proposed modifications allow to
obtain a relevant runtime gain and also improve, in some cases, the spread
results.


Abstract:
  This contribution gives an extensive study on spectra of mixed graphs via its
Hermitian adjacency matrix of the second kind { ($N$-matrix for short)}
introduced by Mohar \cite{0001}. This matrix is indexed by the vertices of the
mixed graph, and the entry corresponding to an arc from $u$ to $v$ is equal to
the sixth root of unity $\omega=\frac{1+{\bf i}\sqrt{3}}{2}$ (and its symmetric
entry is $\bar{\omega}=\frac{1-{\bf i}\sqrt{3}}{2}$); the entry corresponding
to an undirected edge is equal to 1, and 0 otherwise. The main results of this
paper include the following: {equivalent} conditions for a mixed graph that
shares the same spectrum of its $N$-matrix with its underlying graph are given.
A sharp upper bound on the spectral radius is established and the corresponding
extremal mixed graphs are identified. Operations which are called two-way and
three-way switchings are discussed--they give rise to some cospectral mixed
graphs. We extract all the mixed graphs whose rank of its $N$-matrix is $2$
(resp. 3). Furthermore, we show that {if $M_G$ is a connected mixed graph with
rank $2,$ then $M_G$ is switching equivalent to each connected mixed graph to
which it is cospectral}. However, this does not hold for some connected mixed
graphs with rank $3$. We identify all mixed graphs whose eigenvalues of its
$N$-matrix lie in the range $(-\alpha,\, \alpha)$ for
$\alpha\in\left\{\sqrt{2},\,\sqrt{3},\,2\right\}$.


Abstract:
  In this paper, we describe the automorphism group of semidirect product of
two groups that fixes the non-normal subgroup of it. We have computed these
automorphisms for the non-abelian metacyclic $p$-group and non-abelian
$p$-groups $(p\ge 5)$ of order $p^{4}$, where $p$ is a prime.


Abstract:
  We consider the problem of coded computing, where a computational task is
performed in a distributed fashion in the presence of adversarial workers. We
propose techniques to break the adversarial toleration threshold barrier
previously known in coded computing. More specifically, we leverage
list-decoding techniques for folded Reed-Solomon codes and propose novel
algorithms to recover the correct codeword using side information. In the coded
computing setting, we show how the master node can perform certain carefully
designed extra computations to obtain the side information. The workload of
computing this side information is negligible compared to the computations done
by each worker. This side information is then utilized to prune the output of
the list decoder and uniquely recover the true outcome. We further propose
folded Lagrange coded computing (FLCC) to incorporate the developed techniques
into a specific coded computing setting. Our results show that FLCC outperforms
LCC by breaking the barrier on the number of adversaries that can be tolerated.
In particular, the corresponding threshold in FLCC is improved by a factor of
two asymptotically compared to that of LCC.


Abstract:
  Reconstruction of turbulent flow based on data assimilation methods is of
significant importance for improving the estimation of flow characteristics by
incorporating limited observations. Existing works mainly focus on using only
one observation data source, e.g., velocity, wall pressure, lift or drag force,
to reconstruct the flow. In practical applications observations are disparate
data sources that often vary in dimension and quality. Simultaneously
incorporating these disparate data is worth investigation to improve the flow
reconstruction. In this work, we investigate the disparate data assimilation
with ensemble methods to enhance the reconstruction of turbulent mean flows.
Specifically, a regularized ensemble Kalman method is employed to incorporate
the observation of velocity and different sources of wall quantities (e.g.,
wall shear stress, wall pressure distribution, lift and drag force). Three
numerical examples are used to demonstrate the capability of the proposed
framework for assimilating disparate observation data. The first two cases,
i.e., a one-dimensional planar channel flow and a two-dimensional transitional
flow over plate, are used to incorporate both the sparse velocity and wall
friction. In the third case of the flow over periodic hills, the wall pressure
distribution and the lift and drag force are regarded as observation in
addition to velocity, to recover the flow fields. The results demonstrate the
merits of incorporating various disparate data sources to improve the accuracy
of the flow-field estimation. The ensemble-based method can assimilate
disparate data non-intrusively and robustly without requiring significant
changes to the model simulation codes. The method demonstrated here opens up
possibilities for assimilating realistic experimental data, which are often
disparate.


Abstract:
  We define a new family of commuting operators $F_k$ in Khovanov-Rozansky link
homology, similar to the action of tautological classes in cohomology of
character varieties. We prove that $F_2$ satisfies ``hard Lefshetz property"
and hence exhibits the symmetry in Khovanov-Rozansky homology conjectured by
Dunfield, Gukov and Rasmussen.


Abstract:
  This paper provides details of the massless three-loop three-point integrals
calculation at the symmetric point. Our work aimed to extend known two-loop
results for such integrals to the three-loop level. Obtained results can find
their application in regularization-invariant symmetric point
momentum-subtraction (RI/SMOM) scheme QCD calculations of renormalization group
functions and various composite operator matrix elements. To calculate
integrals, we solve differential equations for auxiliary integrals by
transforming the system to the $\varepsilon$-form. Calculated integrals are
expressed through the basis of functions with uniform transcendental weight. We
provide expansion up to the transcendental weight six for the basis functions
in terms of harmonic polylogarithms with six-root of unity argument.


Abstract:
  We investigate a scenario inspired by natural supersymmetry, where neutrino
data is explained within a low-scale seesaw scenario. For this the Minimal
Supersymmetric Standard Model is extended by adding light right-handed
neutrinos and their superpartners, the R-sneutrinos. Moreover, we consider the
lightest electroweakinos to be higgsino-like.
  We demonstrate that a prospective international $e^+ e^-$ linear collider
with a center of mass energy of 1 TeV will be able to discover sleptons in
scenarios which can be difficult for current colliders. Moreover, we also show
that a measurement of the spectrum will be possible within 1-3 per-cent
accuracy.


Abstract:
  Sampling-based methods such as Rapidly-exploring Random Trees (RRTs) have
been widely used for generating motion paths for autonomous mobile systems. In
this work, we extend time-based RRTs with Control Barrier Functions (CBFs) to
generate, safe motion plans in dynamic environments with many pedestrians. Our
framework is based upon a human motion prediction model which is well suited
for indoor narrow environments. We demonstrate our approach on a high-fidelity
model of the Toyota Human Support Robot navigating in narrow corridors. We show
in three scenarios that our proposed online method can navigate safely in the
presence of moving agents with unknown dynamics.


Abstract:
  High Bandwidth Memory (HBM) provides massive aggregated memory bandwidth by
exposing multiple memory channels to the processing units. To achieve high
performance, an accelerator built on top of an FPGA configured with HBM (i.e.,
FPGA-HBM platform) needs to scale its performance according to the available
memory channels. In this paper, we propose an accelerator for BFS
(Breadth-First Search) algorithm, named as ScalaBFS, that builds multiple
processing elements to sufficiently exploit the high bandwidth of HBM to
improve efficiency. We implement the prototype system of ScalaBFS and conduct
BFS in both real-world and synthetic scale-free graphs on Xilinx Alveo U280
FPGA card real hardware. The experimental results show that ScalaBFS scales its
performance almost linearly according to the available memory pseudo channels
(PCs) from the HBM2 subsystem of U280. By fully using the 32 PCs and building
64 processing elements (PEs) on U280, ScalaBFS achieves a performance up to
19.7 GTEPS (Giga Traversed Edges Per Second). When conducting BFS in sparse
real-world graphs, ScalaBFS achieves equivalent GTEPS to Gunrock running on the
state-of-art Nvidia V100 GPU that features 64-PC HBM2 (twice memory bandwidth
than U280).


Abstract:
  Face is one of the predominant means of person recognition. In the process of
ageing, human face is prone to many factors such as time, attributes, weather
and other subject specific variations. The impact of these factors were not
well studied in the literature of face aging. In this paper, we propose a novel
holistic model in this regard viz., ``Face Age progression With Attribute
Manipulation (FAWAM)", i.e. generating face images at different ages while
simultaneously varying attributes and other subject specific characteristics.
We address the task in a bottom-up manner, as two submodules i.e. face age
progression and face attribute manipulation. For face aging, we use an
attribute-conscious face aging model with a pyramidal generative adversarial
network that can model age-specific facial changes while maintaining intrinsic
subject specific characteristics. For facial attribute manipulation, the age
processed facial image is manipulated with desired attributes while preserving
other details unchanged, leveraging an attribute generative adversarial network
architecture. We conduct extensive analysis in standard large scale datasets
and our model achieves significant performance both quantitatively and
qualitatively.


Abstract:
  In settings ranging from weather forecasts to political prognostications to
financial projections, probability estimates of future binary outcomes often
evolve over time. For example, the estimated likelihood of rain on a specific
day changes by the hour as new information becomes available. Given a
collection of such probability paths, we introduce a Bayesian framework --
which we call the Gaussian latent information martingale, or GLIM -- for
modeling the structure of dynamic predictions over time. Suppose, for example,
that the likelihood of rain in a week is 50 %, and consider two hypothetical
scenarios. In the first, one expects the forecast to be equally likely to
become either 25 % or 75 % tomorrow; in the second, one expects the forecast to
stay constant for the next several days. A time-sensitive decision-maker might
select a course of action immediately in the latter scenario, but may postpone
their decision in the former, knowing that new information is imminent. We
model these trajectories by assuming predictions update according to a latent
process of information flow, which is inferred from historical data. In
contrast to general methods for time series analysis, this approach preserves
important properties of probability paths such as the martingale structure and
appropriate amount of volatility and better quantifies future uncertainties
around probability paths. We show that GLIM outperforms three popular baseline
methods, producing better estimated posterior probability path distributions
measured by three different metrics. By elucidating the dynamic structure of
predictions over time, we hope to help individuals make more informed choices.


Abstract:
  We report different growth modes and corresponding magnetic properties of
thin EuSe films grown by molecular beam epitaxy on BaF2, Pb1-xEuxSe, GaAs, and
Bi2Se3 substrates. We show that EuSe growth predominantly in (001) orientation
on GaAs(111) and Bi2Se3, but along (111) crystallographic direction on BaF2
(111) and Pb1-xEuxSe (111). High-resolution transmission electron microscopy
measurements reveal an abrupt and highly crystalline interface for both (001)
and (111) EuSe films. In agreement with previous studies, ordered magnetic
phases include antiferromagnetic, ferrimagnetic, and ferromagnetic phases. In
contrast to previous studies, we found strong hysteresis for the
antiferromagnetic-ferrimagnetic transition. An ability to grow epitaxial films
of EuSe on Bi2Se3 and of Bi2Se3 on EuSe enables further investigation of
interfacial exchange interactions between various phases of an insulating
metamagnetic material and a topological insulator.


Abstract:
  We argue that the Standard Model quiver can be embedded into compact
Calabi-Yau geometries through orientifolded D3-branes at del Pezzo
singularities $\mathrm{dP}_n$ with $n\geq 5$ in a framework including moduli
stabilisation. To illustrate our approach, we explicitly construct a local
$\mathrm{dP}_{5}$ model via a combination of Higgsing and orientifolding. This
procedure reduces the original $\mathrm{dP}_{5}$ quiver gauge theory to the
Left-Right symmetric model with three families of quarks and leptons as well as
a Higgs sector to further break the symmetries to the Standard Model gauge
group. We embed this local model in a globally consistent Calabi-Yau flux
compactification with tadpole and Freed-Witten anomaly cancellations. The model
features closed string moduli stabilisation with a de Sitter minimum from
T-branes, supersymmetry broken by the K\"ahler moduli, and the MSSM as the low
energy spectrum. We further discuss phenomenological and cosmological
implications of this construction.


Abstract:
  We describe an analytical theory investigating the regime of validity of the
Fermi liquid theory in interacting, via the long-range Coulomb coupling,
two-dimensional Fermi systems comparing it with with the corresponding 3D
systems. We find that the 2D Fermi liquid theory and 2D quasiparticles are
robust up to high energies and temperatures of the order of Fermi energy above
the Fermi surface, very similar to the corresponding three-dimensional
situation. We calculate the phase diagram in the frequency-temperature space
separating the collisionless ballistic regime and the collision-dominated
hydrodynamic regime for 2D and 3D interacting electron systems. We also provide
the temperature corrections up to third order for the renormalized effective
mass, and comment on the validity of 2D Wiedemann-Franz law and 2D
Kadawoki-Woods relation.


Abstract:
  Deepfake is the manipulated video made with a generative deep learning
technique such as Generative Adversarial Networks (GANs) or Auto Encoder that
anyone can utilize. Recently, with the increase of Deepfake videos, some
classifiers consisting of the convolutional neural network that can distinguish
fake videos as well as deepfake datasets have been actively created. However,
the previous studies based on the CNN structure have the problem of not only
overfitting, but also considerable misjudging fake video as real ones. In this
paper, we propose a Vision Transformer model with distillation methodology for
detecting fake videos. We design that a CNN features and patch-based
positioning model learns to interact with all positions to find the artifact
region for solving false negative problem. Through comparative analysis on
Deepfake Detection (DFDC) Dataset, we verify that the proposed scheme with
patch embedding as input outperforms the state-of-the-art using the combined
CNN features. Without ensemble technique, our model obtains 0.978 of AUC and
91.9 of f1 score, while previous SOTA model yields 0.972 of AUC and 90.6 of f1
score on the same condition.


Abstract:
  The quantum ergotropy quantifies the maximal amount of work that can be
extracted from a quantum state without changing its entropy. Given that the
ergotropy can be expressed as the difference of quantum and classical relative
entropies of the quantum state with respect to the thermal state, we define the
classical ergotropy, which quantifies how much work can be extracted from
distributions that are inhomogeneous on the energy surfaces. A unified approach
to treat both quantum as well as classical scenarios is provided by geometric
quantum mechanics, for which we define the geometric relative entropy. The
analysis is concluded with an application of the conceptual insight to
conditional thermal states, and the correspondingly tightened maximum work
theorem.


Abstract:
  Climate change is a pressing issue that is currently affecting and will
affect every part of our lives. It's becoming incredibly vital we, as a
society, address the climate crisis as a universal effort, including those in
the Computer Vision (CV) community. In this work, we analyze the total cost of
CO2 emissions by breaking it into (1) the architecture creation cost and (2)
the life-time evaluation cost. We show that over time, these costs are
non-negligible and are having a direct impact on our future. Importantly, we
conduct an ethical analysis of how the CV-community is unintentionally
overlooking its own ethical AI principles by emitting this level of CO2. To
address these concerns, we propose adding "enforcement" as a pillar of ethical
AI and provide some recommendations for how architecture designers and broader
CV community can curb the climate crisis.


Abstract:
  In computational psycholinguistics, various language models have been
evaluated against human reading behavior (e.g., eye movement) to build
human-like computational models. However, most previous efforts have focused
almost exclusively on English, despite the recent trend towards linguistic
universal within the general community. In order to fill the gap, this paper
investigates whether the established results in computational psycholinguistics
can be generalized across languages. Specifically, we re-examine an established
generalization -- the lower perplexity a language model has, the more
human-like the language model is -- in Japanese with typologically different
structures from English. Our experiments demonstrate that this established
generalization exhibits a surprising lack of universality; namely, lower
perplexity is not always human-like. Moreover, this discrepancy between English
and Japanese is further explored from the perspective of (non-)uniform
information density. Overall, our results suggest that a cross-lingual
evaluation will be necessary to construct human-like computational models.


Abstract:
  Attacks on Industrial Control Systems (ICS) can lead to significant physical
damage. While offline safety and security assessments can provide insight into
vulnerable system components, they may not account for stealthy attacks
designed to evade anomaly detectors during long operational transients. In this
paper, we propose a predictive online monitoring approach to check the safety
of the system under potential stealthy attacks. Specifically, we adapt previous
results in reachability analysis for attack impact assessment to provide an
efficient algorithm for online safety monitoring for Linear Time-Invariant
(LTI) systems. The proposed approach relies on an offline computation of
symbolic reachable sets in terms of the estimated physical state of the system.
These sets are then instantiated online, and safety checks are performed by
leveraging ideas from ellipsoidal calculus. We illustrate and evaluate our
approach using the Tennessee-Eastman process. We also compare our approach with
the baseline monitoring approaches proposed in previous work and assess its
efficiency and scalability. Our evaluation results demonstrate that our
approach can predict in a timely manner if a false data injection attack will
be able to cause damage, while remaining undetected. Thus, our approach can be
used to provide operators with real-time early warnings about stealthy attacks.


Abstract:
  Adobe's Portable Document Format (PDF) is a popular way of distributing
view-only documents with a rich visual markup. This presents a challenge to NLP
practitioners who wish to use the information contained within PDF documents
for training models or data analysis, because annotating these documents is
difficult. In this paper, we present PDF Annotation with Labels and Structure
(PAWLS), a new annotation tool designed specifically for the PDF document
format. PAWLS is particularly suited for mixed-mode annotation and scenarios in
which annotators require extended context to annotate accurately. PAWLS
supports span-based textual annotation, N-ary relations and freeform,
non-textual bounding boxes, all of which can be exported in convenient formats
for training multi-modal machine learning models. A read-only PAWLS server is
available at https://pawls.apps.allenai.org/ and the source code is available
at https://github.com/allenai/pawls.


Abstract:
  The Hamilton-Waterloo problem is a problem of graph factorization. The
Hamilton-Waterloo problem HWP$(H;m,n;\alpha,\beta)$ asks for a
$2$-factorization of $H$ containing $\alpha$ $C_m$-factors and $\beta$
$C_n$-factors. In this paper, we almost completely solve the Hamilton-Waterloo
problem on wreath product graph $C_m \wr K_{16}$ with $C_{16}$-factors and
$C_m$-factors for an odd integer $m$.


Abstract:
  We perform a Bayesian analysis of the maximum mass $M_{\rm TOV}$ of neutron
stars with a quark core, incorporating the observational data from tidal
deformability of the GW170817 binary neutron star merger as detected by
LIGO/Virgo and the mass and radius of PSR J0030+0451 as detected by \nicer. The
analysis is performed under the assumption that the hadron-quark phase
transition is of first order, where the low-density hadronic matter described
in a unified manner by the soft QMF or the stiff DD2 equation of state (EOS)
transforms into a high-density phase of quark matter modeled by the generic
"Constant-sound-speed" (CSS) parameterization. The mass distribution measured
for the $2.14 \,{\rm M}_{\odot}$ pulsar, MSP J0740+6620, is used as the lower
limit on $M_{\rm TOV}$. We find the most probable values of the hybrid star
maximum mass are $M_{\rm TOV}=2.36^{+0.49}_{-0.26}\,{\rm M}_{\odot}$
($2.39^{+0.47}_{-0.28}\,{\rm M}_{\odot}$) for QMF (DD2), with an absolute upper
bound around $2.85\,{\rm M}_{\odot}$, to the $90\%$ posterior credible level.
Such results appear robust with respect to the uncertainties in the hadronic
EOS. We also discuss astrophysical implications of this result, especially on
the post-merger product of GW170817, short gamma-ray bursts, and other likely
binary neutron star mergers.


Abstract:
  Analogue Hamiltonian simulation is a promising application of quantum
computing in the NISQ era. Recent work has demonstrated the existence of
universal Hamiltonians -- simple spin lattice models that can simulate any
other quantum many body system. Until now proofs of universality have relied on
explicit constructions, which are shown to be able to replicate the physics of
some arbitrary target Hamiltonian. In this work, we derive necessary and
sufficient conditions for a family of Hamiltonians to be universal. The proof
relies on complexity theoretic concepts -- leveraging the ability to encode
quantum computation into the ground states of Hamiltonians. This offers a far
simpler route to proving universality, and provides insight into the
relationship between complexity and universality.


Abstract:
  Classical federated learning approaches incur significant performance
degradation in the presence of non-IID client data. A possible direction to
address this issue is forming clusters of clients with roughly IID data. Most
solutions following this direction are iterative and relatively slow, also
prone to convergence issues in discovering underlying cluster formations. We
introduce federated learning with taskonomy (FLT) that generalizes this
direction by learning the task-relatedness between clients for more efficient
federated aggregation of heterogeneous data. In a one-off process, the server
provides the clients with a pretrained (and fine-tunable) encoder to compress
their data into a latent representation, and transmit the signature of their
data back to the server. The server then learns the task-relatedness among
clients via manifold learning, and performs a generalization of federated
averaging. FLT can flexibly handle a generic client relatedness graph, when
there are no explicit clusters of clients, as well as efficiently decompose it
into (disjoint) clusters for clustered federated learning. We demonstrate that
FLT not only outperforms the existing state-of-the-art baselines in non-IID
scenarios but also offers improved fairness across clients.


Abstract:
  Chordal and factor-width decomposition methods for semidefinite programming
and polynomial optimization have recently enabled the analysis and control of
large-scale linear systems and medium-scale nonlinear systems. Chordal
decomposition exploits the sparsity of semidefinite matrices in a semidefinite
program (SDP), in order to formulate an equivalent SDP with smaller
semidefinite constraints that can be solved more efficiently. Factor-width
decompositions, instead, relax or strengthen SDPs with dense semidefinite
matrices into more tractable problems, trading feasibility or optimality for
lower computational complexity. This article reviews recent advances in
large-scale semidefinite and polynomial optimization enabled by these two types
of decomposition, highlighting connections and differences between them. We
also demonstrate that chordal and factor-width decompositions allow for
significant computational savings on a range of classical problems from control
theory, and on more recent problems from machine learning. Finally, we outline
possible directions for future research that have the potential to facilitate
the efficient optimization-based study of increasingly complex large-scale
dynamical systems.


Abstract:
  This paper examines the impact of coronavirus (COVID-19) on stock market
volatility (SMV) in Pakistan by controlling the effect of exchange rate,
interest rate and government/central bank interventions to combat the pandemic.
We used the vector autoregressive (VAR) model over a sample period ranging from
February 25, 2020 to December 7, 2020. We find that a shock to total daily
coronavirus cases in Pakistan lead to a significant increase in SMV. This
result is aligned with a vast literature on pandemics and investors uncertainty
and remains robust to several robustness checks applied in our analysis.


Abstract:
  A finite metric space is called here distance degree regular if its distance
degree sequence is the same for every vertex. A notion of designs in such
spaces is introduced that generalizes that of designs in $Q$-polynomial
distance-regular graphs. An approximation of their cumulative distribution
function, based on the notion of Christoffel function in approximation theory
is given. As an application we derive limit laws on the weight distributions of
binary orthogonal arrays of strength going to infinity. An analogous result for
combinatorial designs of strength going to infinity is given.


Abstract:
  Despite the intricacies involved in designing a computer as a teampartner, we
can observe patterns in team behavior which allow us to describe at a general
level how AI systems are to collaborate with humans. Whereas most work on
human-machine teaming has focused on physical agents (e.g. robotic systems),
our aim is to study how humans can collaborate with information agents. We
propose some appropriate team design patterns, and test them using our
Collaborative Intelligence Analysis (CIA) tool.


Abstract:
  The design of antibacterial-releasing coatings or wrapping materials with
controlled drug release capability is a promising strategy to minimise risks of
infection and medical device failure in vivo. Collagen fibres have been
employed as medical device building block, although they still fail to display
controlled release capability, competitive wet-state mechanical properties, and
retained triple helix organisation. We investigated this challenge by pursuing
a multiscale design approach integrating drug encapsulation, in-situ covalent
crosslinking and fibre spinning. By selecting ciprofloxacin (Cip) as a typical
antibacterial drug, wet spinning was selected as a triple helix-friendly route
towards Cip-encapsulated collagen fibres; whilst in situ crosslinking of
fibre-forming triple helices with 1,3 phenylenediacetic acid (Ph) was
hypothesised to yield Ph-Cip {\pi}-{\pi} stacking aromatic interactions and
enable controlled drug release. Higher tensile modulus and strength were
measured in Ph crosslinked fibres compared to state-of-the-art carbodiimide
crosslinked controls. Cip-encapsulated Ph-crosslinked fibres revealed decreased
elongation at break and significantly-enhanced drug retention in vitro with
respect to Cip-free variants and carbodiimide-crosslinked controls,
respectively. This multiscale manufacturing strategy provides new insight
aiming at wet spun collagen triple helices with nanoscale-regulated tensile
properties and drug release capability.


Abstract:
  Template-based discriminative trackers are currently the dominant tracking
methods due to their robustness and accuracy, and the Siamese-network-based
methods that depend on cross-correlation operation between features extracted
from template and search images show the state-of-the-art tracking performance.
However, general cross-correlation operation can only obtain relationship
between local patches in two feature maps. In this paper, we propose a novel
tracker network based on a powerful attention mechanism called Transformer
encoder-decoder architecture to gain global and rich contextual
interdependencies. In this new architecture, features of the template image is
processed by a self-attention module in the encoder part to learn strong
context information, which is then sent to the decoder part to compute
cross-attention with the search image features processed by another
self-attention module. In addition, we design the classification and regression
heads using the output of Transformer to localize target based on
shape-agnostic anchor. We extensively evaluate our tracker TrTr, on VOT2018,
VOT2019, OTB-100, UAV, NfS, TrackingNet, and LaSOT benchmarks and our method
performs favorably against state-of-the-art algorithms. Training code and
pretrained models are available at https://github.com/tongtybj/TrTr.


Abstract:
  Recent developments in Banach space theory provided unexpected examples of
unital Banach algebras that are isomorphic to Calkin algebras of Banach spaces,
however no example of a unital Banach algebra that cannot be realised as
a~Calkin algebra has been found so far. This naturally led to the question of
possible limitations of such assignments. In the present note we provide
examples of unital Banach algebras meeting the necessary density condition for
being the Calkin algebra of a separable Banach space that are not isomorphic to
Calkin algebras of such spaces, nonetheless. The examples may be found of the
form $C(X)$ for a compact space $X$, $\ell_1(G)$ for some torsion-free Abelian
group, and a~simple, unital AF $C^*$-algebra. Extensions to higher densities
are also presented.


Abstract:
  In this paper, we study a Target-Attacker-Defender (TAD) differential game
involving one attacker, one target and multiple defenders. We consider two
variations where (a) the attacker and the target have unlimited observation
range and the defenders are visibility constrained (b) only the attacker has
unlimited observation range and the remaining players are visibility
constrained. We model the players' interactions as a dynamic game with
asymmetric information. Here, the visibility constraints of the players induce
a visibility network which encapsulates the visibility information during the
evolution of the game. Based on this observation, we introduce network adapted
feedback or implementable strategies for visibility constrained players. Using
inverse game theory approach we obtain network adapted feedback Nash
equilibrium strategies. We introduce a consistency criterion for selecting a
subset (or refinement) of network adapted feedback Nash strategies, and provide
an optimization based approach for computing them. Finally, we illustrate our
results with numerical experiments.


Abstract:
  In order to better understand a complex theory like quantum mechanics, it is
sometimes useful to take a step back and create alternative theories, with more
intuitive foundations, and examine which features of quantum mechanics can be
reproduced by such a foil theory. A prominent example is Spekkens' toy theory,
which is based off a simple premise: "What if we took a common classical theory
and added the uncertainty principle as a postulate?" In other words, the theory
imposes an epistemic restriction on our knowledge about a physical system: only
half of the variables can ever be known to an observer. Like good science
fiction, from this simple principle a rich behaviour emerges, most notoriously
when we compose several systems. The toy theory emulates some aspects of
quantum non-locality, although crucially it is still a non-contextual model. In
this pedagogical review we consolidate different approaches to Spekkens' toy
theory, including the stabilizer formalism and the generalization to arbitrary
dimensions, completing them with new results where necessary. In particular, we
introduce a general characterization of measurements, superpositions and
entanglement in the toy theory.


Abstract:
  In this paper, we completely determine the irreducible characters of the four
families of Suzuki $p$-groups. This corrects some errors in the paper [8].


Abstract:
  Punctuation prediction for automatic speech recognition (ASR) output
transcripts plays a crucial role for improving the readability of the ASR
transcripts and for improving the performance of downstream natural language
processing applications. However, achieving good performance on punctuation
prediction often requires large amounts of labeled speech transcripts, which is
expensive and laborious. In this paper, we propose a Discriminative
Self-Training approach with weighted loss and discriminative label smoothing to
exploit unlabeled speech transcripts. Experimental results on the English
IWSLT2011 benchmark test set and an internal Chinese spoken language dataset
demonstrate that the proposed approach achieves significant improvement on
punctuation prediction accuracy over strong baselines including BERT, RoBERTa,
and ELECTRA models. The proposed Discriminative Self-Training approach
outperforms the vanilla self-training approach. We establish a new
state-of-the-art (SOTA) on the IWSLT2011 test set, outperforming the current
SOTA model by 1.3% absolute gain on F$_1$.


Abstract:
  Pattern formation in the classical and fractional Schnakenberg equations is
studied to understand the nonlocal effects of anomalous diffusion. Starting
with linear stability analysis, we find that if the activator and inhibitor
have the same diffusion power, the Turing instability space depends only on the
ratio of diffusion coefficients $\kappa_1/\kappa_2$. However, the smaller
diffusive powers might introduce larger unstable wave numbers with wider band,
implying that the patterns may be more chaotic in the fractional cases. We then
apply a weakly nonlinear analysis to predict the parameter regimes for spot,
stripe, and mixed patterns in the Turing space. Our numerical simulations
confirm the analytical results and demonstrate the differences of normal and
anomalous diffusion on pattern formation. We find that in the presence of
superdiffusion the patterns exhibit multiscale structures. The smaller the
diffusion powers, the larger the unstable wave numbers and the smaller the
pattern scales.


Abstract:
  Magnetic structures are known to possess magnon excitations confined to their
surfaces and interfaces, but these spatially localized modes are often not
resolved in spectroscopy experiments. We develop a theory to calculate the
confined magnon spectra and its associated spin scattering function, which is
the physical observable in neutron and electron scattering, and a proxy for
photon spectroscopy based on X-ray, Raman and THz sources. We show that extra
anisotropy at the surface or interface plays a key role in magnon confinement.
We obtain analytical expressions for the confinement length scale, and show
that it is qualitatively similar for ferromagnets and antiferromagnets in
dimension d>=2. For d=1 we find remarkable differences between ferromagnetic
and antiferromagnetic models. The theory indicates the presence of several
confined magnon resonances in addition to the usual magnons thought to explain
the excitations of magnetic nanostructures. Detecting these modes may elucidate
the impact of the interface on spin anisotropy and magnetic order.


Abstract:
  We propose a numerical method for approximating integro-differential
equations arising in age-of-infection epidemic models. The method is based on a
non-standard finite differences approximation of the integral term appearing in
the equation. The study of convergence properties and the analysis of the
qualitative behavior of the numerical solution show that it preserves all the
basic properties of the continuous model with no restrictive conditions on the
step-length $h$ of integration and that it recovers the continuous dynamic as
$h$ tends to zero.


Abstract:
  GAGG:Ce (Cerium-doped Gadolinium Aluminium Gallium Garnet) is a promising new
scintillator crystal. A wide array of interesting features, such as high light
output, fast decay times, almost non-existent intrinsic background and
robustness, make GAGG:Ce an interesting candidate as a component of new
space-based gamma-ray detectors. As a consequence of its novelty, literature on
GAGG:Ce is still lacking on points crucial to its applicability in space
missions. In particular, GAGG:Ce is characterized by unusually high and
long-lasting delayed luminescence. This afterglow emission can be stimulated by
the interactions between the scintillator and the particles of the near-Earth
radiation environment. By contributing to the noise, it will impact the
detector performance to some degree. In this manuscript we summarize the
results of an irradiation campaign of GAGG:Ce crystals with protons, conducted
in the framework of the HERMES-TP/SP (High Energy Rapid Modular Ensemble of
Satellites - Technological and Scientific Pathfinder) mission. A GAGG:Ce sample
was irradiated with 70 MeV protons, at doses equivalent to those expected in
equatorial and sun-synchronous Low-Earth orbits over orbital periods spanning 6
months to 10 years, time lapses representative of satellite lifetimes. We
introduce a new model of GAGG:Ce afterglow emission able to fully capture our
observations. Results are applied to the HERMES-TP/SP scenario, aiming at an
upper-bound estimate of the detector performance degradation due to the
afterglow emission expected from the interaction between the scintillator and
the near-Earth radiation environment.


Abstract:
  We study video crowd counting, which is to estimate the number of objects
(people in this paper) in all the frames of a video sequence. Previous work on
crowd counting is mostly on still images. There has been little work on how to
properly extract and take advantage of the spatial-temporal correlation between
neighboring frames in both short and long ranges to achieve high estimation
accuracy for a video sequence. In this work, we propose Monet, a novel and
highly accurate motion-guided non-local spatial-temporal network for video
crowd counting. Monet first takes people flow (motion information) as guidance
to coarsely segment the regions of pixels where a person may be. Given these
regions, Monet then uses a non-local spatial-temporal network to extract
spatial-temporally both short and long-range contextual information. The whole
network is finally trained end-to-end with a fused loss to generate a
high-quality density map. Noting the scarcity and low quality (in terms of
resolution and scene diversity) of the publicly available video crowd datasets,
we have collected and built a large-scale video crowd counting datasets,
VidCrowd, to contribute to the community. VidCrowd contains 9,000 frames of
high resolution (2560 x 1440), with 1,150,239 head annotations captured in
different scenes, crowd density and lighting in two cities. We have conducted
extensive experiments on the challenging VideoCrowd and two public video crowd
counting datasets: UCSD and Mall. Our approach achieves substantially better
performance in terms of MAE and MSE as compared with other state-of-the-art
approaches.


Abstract:
  In this paper, we set out to systematically design a minimal active RoT for
tiny low-end MCU-s. We begin with the following questions: (1) What functions
and hardware support are required to guarantee actions in the presence of
malware?, (2) How to implement this efficiently?, and (3) What security
benefits stem from such an active RoT architecture? We then design, implement,
formally verify, and evaluate GAROTA: Generalized Active Root-Of-Trust
Architecture. We believe that GAROTA is the first clean-slate design of an
active RoT for low-end MCU-s. We show how GAROTA guarantees that even a fully
software-compromised low-end MCU performs a desired action. We demonstrate its
practicality by implementing GAROTA in the context of three types of
applications where actions are triggered by: sensing hardware, network events
and timers. We also formally specify and verify GAROTA functionality and
properties.


Abstract:
  Warp drives in Einstein's general theory of relativity provide a unique
mechanism for manned interstellar travel. It is well-known that the classical
superluminal soliton spacetimes require negative energy densities, likely
sourced by quantum processes of the uncertainty principle. It has even been
claimed by few that negative energy densities are a requirement of superluminal
motion. However, recent studies suggest this may not be the case. A general
decomposition of the defining variables and the corresponding decomposition of
the Eulerian energy are studied. A geometrical interpretation of the Eulerian
energy is found, shedding new light on superluminal solitons generated by
realistic energy distributions. With this new interpretation, it becomes a
relatively simple matter to generate solitonic configurations, within a certain
subclass, that respect the positive energy constraint. Using this newfound
interpretation, a superluminal solitonic spacetime is presented that possesses
positive semi-definite energy. A modest numerical analysis is carried out on a
set of example configurations, finding total energy requirements four orders of
magnitude smaller than the solar mass. Extraordinarily, the example
configurations are generated by purely positive energy densities, a tremendous
improvement on the classical configurations. The geometrical interpretation of
the Eulerian energy thus opens new doors to generating realistic warp fields
for laboratory study and potential future manned interstellar travel.


Abstract:
  Trail camera imagery has increasingly gained popularity amongst biologists
for conservation and ecological research. Minimal human interference required
to operate camera traps allows capturing unbiased species activities. Several
studies - based on human and wildlife interactions, migratory patterns of
various species, risk of extinction in endangered populations - are limited by
the lack of rich data and the time-consuming nature of manually annotating
trail camera imagery. We introduce a challenging wildlife camera trap
classification dataset collected from two different locations in Southwestern
Florida, consisting of 104,495 images featuring visually similar species,
varying illumination conditions, skewed class distribution, and including
samples of endangered species, i.e. Florida panthers. Experimental evaluations
with ResNet-50 architecture indicate that this image classification-based
dataset can further push the advancements in wildlife statistical modeling. We
will make the dataset publicly available.


Abstract:
  In complex dynamical systems, the detection of coupling and its direction
from observed time series is a challenging task. We study coupling in coupled
Duffing oscillator systems in regular and chaotic dynamical regimes. By
observing the conditional mutual information (CMI) based on the Shannon
entropy, we successfully infer the direction of coupling for different system
regimes. Moreover, we show that in the weak coupling limit the values of CMI
can be used to infer the coupling parameters by computing the derivative of the
conditional mutual information with respect to the coupling strength, called
the information susceptibility. The complete numerical implementation is
available at https://repo.ijs.si/mbresar/duffing-cmi.