Abstract:
  The solution of linear systems of equations is a very frequent operation and
thus important in many fields. The complexity using classical methods increases
linearly with the size of equations. The HHL algorithm proposed by Harrow et
al. achieves exponential acceleration compared with the best classical
algorithm. However, it has a relatively high demand for qubit resources and the
solution $\left| x \right\rangle $ is in a normalized form. Assuming that the
eigenvalues of the coefficient matrix of the linear systems of equations can be
represented perfectly by finite binary number strings, three hybrid iterative
phase estimation algorithms (HIPEA) are designed based on the iterative phase
estimation algorithm in this paper. The complexity is transferred to the
measurement operation in an iterative way, and thus the demand of qubit
resources is reduced in our hybrid algorithms. Moreover, the solution is stored
in a classical register instead of a quantum register, so the exact
unnormalized solution can be obtained. The required qubit resources in the
three HIPEA algorithms are different. HIPEA-1 only needs one single ancillary
qubit. The number of ancillary qubits in HIPEA-2 is equal to the number of
nondegenerate eigenvalues of the coefficient matrix of linear systems of
equations. HIPEA-3 is designed with a flexible number of ancillary qubits. The
HIPEA algorithms proposed in this paper broadens the application range of
quantum computation in solving linear systems of equations by avoiding the
problem that quantum programs may not be used to solve linear systems of
equations due to the lack of qubit resources.


Abstract:
  In this work, we analyse the links between ghost penalty stabilisation and
aggregation-based discrete extension operators for the numerical approximation
of elliptic partial differential equations on unfitted meshes. We explore the
behavior of ghost penalty methods in the limit as the penalty parameter goes to
infinity, which returns a strong version of these methods. We observe that
these methods suffer locking in that limit. On the contrary, aggregated finite
element spaces are locking-free because they can be expressed as an extension
operator from well-posed to ill-posed degrees of freedom. Next, we propose
novel ghost penalty methods that penalise the distance between the solution and
its aggregation-based discrete extension. These methods are locking-free and
converge to aggregated finite element methods in the infinite penalty parameter
limit. We include an exhaustive set of numerical experiments in which we
compare weak (ghost penalty) and strong (aggregated finite elements) schemes in
terms of error quantities, condition numbers and sensitivity with respect to
penalty coefficients on different geometries, intersection locations and mesh
topologies.


Abstract:
  Edge & Fog computing have received considerable attention as promising
candidates for the evolution of robotic systems. In this letter, we propose
COTORRA, an Edge & Fog driven robotic testbed that combines context information
with robot sensor data to validate innovative concepts for robotic systems
prior to being applied in a production environment. In lab/university, we
established COTORRA as an easy applicable and modular testbed on top of
heterogeneous network infrastructure. COTORRA is open for pluggable robotic
applications. To verify its feasibility and assess its performance, we ran set
of experiments that show how autonomous navigation applications can achieve
target latencies bellow 15ms or perform an inter-domain (DLT) federation within
19 seconds.


Abstract:
  Contact tracing is the techno-choice of reference to address the COVID-19
pandemic. Many of the current approaches have severe privacy and security
issues and fail to offer a sustainable contact tracing infrastructure. We
address these issues introducing an innovative, privacy-preserving,
sustainable, and experimentally tested architecture that leverages batteryless
BLE beacons.


Abstract:
  Deep learning implementations on CPUs (Central Processing Units) are gaining
more traction. Enhanced AI capabilities on commodity x86 architectures are
commercially appealing due to the reuse of existing hardware and virtualization
ease. A notable work in this direction is the SLIDE system. SLIDE is a C++
implementation of a sparse hash table based back-propagation, which was shown
to be significantly faster than GPUs in training hundreds of million parameter
neural models. In this paper, we argue that SLIDE's current implementation is
sub-optimal and does not exploit several opportunities available in modern
CPUs. In particular, we show how SLIDE's computations allow for a unique
possibility of vectorization via AVX (Advanced Vector Extensions)-512.
Furthermore, we highlight opportunities for different kinds of memory
optimization and quantizations. Combining all of them, we obtain up to 7x
speedup in the computations on the same hardware. Our experiments are focused
on large (hundreds of millions of parameters) recommendation and NLP models.
Our work highlights several novel perspectives and opportunities for
implementing randomized algorithms for deep learning on modern CPUs. We provide
the code and benchmark scripts at https://github.com/RUSH-LAB/SLIDE


Abstract:
  The principal limitation in many areas of astronomy, especially for directly
imaging exoplanets, arises from instability in the point spread function (PSF)
delivered by the telescope and instrument. To understand the transfer function,
it is often necessary to infer a set of optical aberrations given only the
intensity distribution on the sensor - the problem of phase retrieval. This can
be important for post-processing of existing data, or for the design of optical
phase masks to engineer PSFs optimized to achieve high contrast, angular
resolution, or astrometric stability. By exploiting newly efficient and
flexible technology for automatic differentiation, which in recent years has
undergone rapid development driven by machine learning, we can perform both
phase retrieval and design in a way that is systematic, user-friendly, fast,
and effective. By using modern gradient descent techniques, this converges
efficiently and is easily extended to incorporate constraints and
regularization. We illustrate the wide-ranging potential for this approach
using our new package, Morphine. Challenging applications performed with this
code include precise phase retrieval for both discrete and continuous phase
distributions, even where information has been censored such as
heavily-saturated sensor data. We also show that the same algorithms can
optimize continuous or binary phase masks that are competitive with existing
best solutions for two example problems: an Apodizing Phase Plate (APP)
coronagraph for exoplanet direct imaging, and a diffractive pupil for
narrow-angle astrometry. The Morphine source code and examples are available
open-source, with a similar interface to the popular physical optics package
Poppy.


Abstract:
  Codebooks with small maximum cross-correlation amplitudes are used to
distinguish the signals from different users in CDMA communication systems. In
this paper, we first study the Jacobi sums over Galois rings of arbitrary
characteristics and completely determine their absolute values, which extends
the work in [34], where the Jacobi sums over Galois rings with characteristics
of a square of a prime number were discussed. Then, the deterministic
construction of codebooks based on the Jacobi sums over Galois rings of
arbitrary characteristics is presented, which produces asymptotically optimal
codebooks with respect to the Welch bound. In addition, the parameters of the
codebooks provided in this paper are new.


Abstract:
  We report a $\gamma$-ray enhancement event detected from Tycho's supernova
remnant (SNR), the outcome of a type Ia supernova explosion that occurred in
year 1572. The event lasted for 1.5 years and showed a factor of 3.6 flux
increase mainly in the energy range of 4--100 GeV. While several young SNRs
(including Tycho's SNR) were previously found to show peculiar X-ray structures
with flux variations in one- or several-year timescales, such an event at
$\gamma$-ray energies is for the first time seen. The hard $\gamma$-ray
emission and year-long timescale of the event necessitate a synchrotron
radiation process, although the required conditions are either ultra-high
energies for the electrons in the process, upto $\sim$10 PeV (well above the
cosmic-ray "knee" energy), or high inhomogeneity of the magnetic field in the
SNR. This event in Tycho's SNR is likely analogous to the $\gamma$-ray flares
observed in the Crab nebula, the comparably short timescales of them both
requiring a synchrotron process, and similar magnetohydrodynamic processes such
as magnetic reconnection would be at work as well in the SNR to accelerate
particles to ultra-relativistic energies. The event and its implications thus
reveal the more complicated side of the physical processes that can occur in
young SNRs.


Abstract:
  We prove that the only non-Archimedean strictly convex spaces are the zero
space and the one-dimensional linear space over $\, \mathbb{Z}/3\mathbb{Z}$,
with any of its trivial norms.


Abstract:
  Early identification of college dropouts can provide tremendous value for
improving student success and institutional effectiveness, and predictive
analytics are increasingly used for this purpose. However, ethical concerns
have emerged about whether including protected attributes in the prediction
models discriminates against underrepresented student groups and exacerbates
existing inequities. We examine this issue in the context of a large U.S.
research university with both residential and fully online degree-seeking
students. Based on comprehensive institutional records for this entire student
population across multiple years, we build machine learning models to predict
student dropout after one academic year of study, and compare the overall
performance and fairness of model predictions with or without four protected
attributes (gender, URM, first-generation student, and high financial need). We
find that including protected attributes does not impact the overall prediction
performance and it only marginally improves algorithmic fairness of
predictions. While these findings suggest that including protected attributes
is preferred, our analysis also offers guidance on how to evaluate the impact
in a local context, where institutional stakeholders seek to leverage
predictive analytics to support student success.


Abstract:
  We introduce the NCA and NCA-Markov dynamical maps for open quantum systems,
which go beyond weak-coupling master equations replacing the Born approximation
with a non-crossing approximation (NCA). We apply these NCA maps on a prototype
model of open quantum systems, the Ohmic spin-boson model at zero temperature,
showing that they can qualitatively reproduce its non-perturbative behaviour,
including its crossover from a coherent to an incoherent dynamics and its
localization quantum phase transition, which are entirely missed by
perturbative master equations. Remarkably, the time-local NCA-Markov map
qualitatively captures this physics entirely, opening up the possibility to
address strong-coupling effects at a numerical cost comparable to standard
Markovian master equations.


Abstract:
  Learning dynamics governed by differential equations is crucial for
predicting and controlling the systems in science and engineering. Neural
Ordinary Differential Equation (NODE), a deep learning model integrated with
differential equations, is popular in learning dynamics recently due to its
robustness to irregular samples and its flexibility to high-dimensional input.
However, the training of NODE is sensitive to the precision of the numerical
solver, which makes the convergence of NODE unstable, especially for
ill-conditioned dynamical systems. In this paper, to reduce the reliance on the
numerical solver, we propose to enhance the supervised signal in the training
of NODE. Specifically, we pre-train a neural differential operator (NDO) to
output an estimation of the derivatives to serve as an additional supervised
signal. The NDO is pre-trained on a class of basis functions and learns the
mapping between the trajectory samples of these functions to their derivatives.
To leverage both the trajectory signal and the estimated derivatives from NDO,
we propose an algorithm called NDO-NODE, in which the loss function contains
two terms: the fitness on the true trajectory samples and the fitness on the
estimated derivatives that are outputted by the pre-trained NDO. Experiments on
various kinds of dynamics show that our proposed NDO-NODE can consistently
improve the forecasting accuracy with one pre-trained NDO. Especially for the
stiff ODEs, we observe that NDO-NODE can capture the transitions in the
dynamics more accurately compared with other regularization methods.


Abstract:
  Semi-supervised methods of anomaly detection have seen substantial
advancement in recent years. Of particular interest are applications of such
methods to diverse, real-world anomaly detection problems where anomalous
variations can vary from the visually obvious to the very subtle. In this work,
we propose a novel fine-grained VAE-GAN architecture trained in a
semi-supervised manner in order to detect both visually distinct and subtle
anomalies. With the use of a residually connected dual-feature extractor, a
fine-grained discriminator and a perceptual loss function, we are able to
detect subtle, low inter-class (anomaly vs. normal) variant anomalies with
greater detection capability and smaller margins of deviation in AUC value
during inference compared to prior work whilst also remaining time-efficient
during inference. We achieve state of-the-art anomaly detection results when
compared extensively with prior semi-supervised approaches across a multitude
of anomaly detection benchmark tasks including trivial leave-one out tasks
(CIFAR-10 - AUPRCavg: 0.91; MNIST - AUPRCavg: 0.90) in addition to challenging
real-world anomaly detection tasks (plant leaf disease - AUC: 0.776; threat
item X-ray - AUC: 0.51), video frame-level anomaly detection (UCSDPed1 - AUC:
0.95) and high frequency texture with object anomalous defect detection (MVTEC
- AUCavg: 0.83).


Abstract:
  In this paper questions of the width in verbal subgroups of $A_n$ is
considered. Verbal width by set of squares of alternating group is found, it is
happened equals to 2. The criterion of squareness in $A_n$ is presented. The
necessary and sufficient conditions when an element of alternating group can be
presented as a squares of one element are also found by us. Some necessary
conditions to an element $g\in A_n$ being the square in $A_n$ are investigated.


Abstract:
  This paper introduces the Feasibility Governor (FG): an add-on unit that
enlarges the region of attraction of Model Predictive Control by manipulating
the reference to ensure that the underlying optimal control problem remains
feasible. The FG is developed for linear systems subject to polyhedral state
and input constraints. Offline computations using polyhedral projection
algorithms are used to construct the feasibility set. Online implementation
relies on the solution of a convex quadratic program that guarantees recursive
feasibility. The closed-loop system is shown to satisfy constraints, achieve
asymptotic stability, and exhibit zero-offset tracking.


Abstract:
  We present near-IR photometry and spectroscopy of 30 extremely luminous radio
and mid-IR selected galaxies. With bolometric luminosities exceeding
$\sim10^{13}$ $\rm{L_{\odot}}$ and redshifts ranging from $z = 0.880-2.853$, we
use VLT instruments X-shooter and ISAAC to investigate this unique population
of galaxies. Broad multi-component emission lines are detected in 18 galaxies
and we measure the near-IR lines $\rm{H\rm{\beta}}$,
$\text{[OIII]}\rm{\lambda}\rm{\lambda}4959,5007$ and $\rm{H\rm{\alpha}}$ in
six, 15 and 13 galaxies respectively, with 10 $\rm{Ly\alpha}$ and five CIV
lines additionally detected in the UVB arm. We use the broad
$\text{[OIII]}\rm{\lambda}5007$ emission lines as a proxy for the bolometric
AGN luminosity, and derive lower limits to supermassive black hole masses of
$10^{7.9}$-$10^{9.4}$ $\text{M}_{\odot}$ with expectations of corresponding
host masses of $10^{10.4}$-$10^{12.0}$ $\text{M}_{\odot}$. We measure
$\rm{\lambda}_{Edd}$ > 1 for eight of these sources at a $2\sigma$
significance. Near-IR photometry and SED fitting are used to compare stellar
masses directly. We detect both Balmer lines in five galaxies and use these to
infer a mean visual extinction of $A_{V}$ = 2.68 mag. Due to non-detections and
uncertainties in our $\rm{H\rm{\beta}}$ emission line measurements, we simulate
a broad $\rm{H\rm{\beta}}$ line of FWHM = 1480 $\rm{kms^{-1}}$ to estimate
extinction for all sources with measured $\rm{H\rm{\alpha}}$ emission. We then
use this to infer a mean $A_{V}=3.62$ mag, demonstrating the highly-obscured
nature of these galaxies, with the consequence of increasing our estimates of
black-hole masses by an 0.5 orders of magnitude in the most extreme and
obscured cases.


Abstract:
  The Kerr geometry admits the Carter symmetry, which ensures that the geodesic
equations are integrable. It is shown that gravitational waveforms associated
with extreme-mass-ratio inspirals involving a non-integrable compact object
display `glitch' phenomena, where the frequencies of gravitational waves
increase abruptly, when the orbit crosses certain spacetime regions known as
Birkhoff islands. The presence or absence of these features in data from
upcoming space-borne detectors will therefore allow not only for tests of
general relativity, but also of fundamental spacetime symmetries.


Abstract:
  The quantum vacuum shows an intensity-dependent nonlinear index of
refraction. Consequently, we may consider vacuum as a non-linear medium
enabling, at sufficiently high field strengths, a direct interaction of light
with light. In this context, we advocate an experiment to measure
light-by-light scattering by the means of self-focussing of an x-ray beam in
vacuum. Although hard to measure, we argue that for laser beams consisting of
highly energetic photons the beams' power distribution carries a distinctive
enough signature of self-focussing such that an observation at modern
free-electron laser facilities appears possible. Self-focussing also imposes a
limit on the most powerful x-ray beams that may propagate in vacuum being on
the order of $10$ PW for $10$ keV x-rays.


Abstract:
  Diffusive molecular communications (DiMC) have recently gained attention as a
candidate for nano- to micro- and macro-scale communications due to its
simplicity and energy efficiency. As signal propagation is solely enabled by
Brownian motion mechanics, DiMC faces severe inter-symbol interference (ISI),
which limits reliable and high data-rate communications. Herein, recent
literature on DiMC performance enhancement strategies is surveyed; key research
directions are identified. Signaling design and associated design constraints
are presented. Classical and novel transceiver designs are reviewed with an
emphasis on methods for ISI mitigation and performance-complexity tradeoffs.
Key parameter estimation strategies such as synchronization and channel
estimation are considered in conjunction with asynchronous and timing error
robust receiver methods. Finally, source and channel coding in the context of
DiMC is presented.


Abstract:
  The problem of Multi-Agent Path Finding (MAPF) calls for finding a set of
conflict-free paths for a fleet of agents operating in a given environment.
Arguably, the state-of-the-art approach to computing optimal solutions is
Conflict-Based Search (CBS). In this work we revisit the complexity analysis of
CBS to provide tighter bounds on the algorithm's run-time in the worst-case.
Our analysis paves the way to better pinpoint the parameters that govern (in
the worst case) the algorithm's computational complexity.
  Our analysis is based on two complementary approaches: In the first approach
we bound the run-time using the size of a Multi-valued Decision Diagram (MDD)
-- a layered graph which compactly contains all possible single-agent paths
between two given vertices for a specific path length.
  In the second approach we express the running time by a novel recurrence
relation which bounds the algorithm's complexity. We use generating
functions-based analysis in order to tightly bound the recurrence.
  Using these technique we provide several new upper-bounds on CBS's
complexity. The results allow us to improve the existing bound on the running
time of CBS for many cases. For example, on a set of common benchmarks we
improve the upper-bound by a factor of at least $2^{10^{7}}$.


Abstract:
  In this paper we give some results concerning Frechet differentiable mappings
between domains in normed spaces with controlled growth. The results are mainly
motivated by Pavlovic's equality for the Bloch semi-norm of continuously
differentiable mappings in the Bloch class on the unit ball of the Euclidean
space as well as the very recent Jocic's generalization of this result.


Abstract:
  An energetic muon beam is an attractive key to unlock new physics beyond the
Standard Model: the lepton flavor violation or the anomalous magnetic moment,
and also is a competitive candidate for the expected neutrino factory. Lots of
the muon scientific applications are limited by low flux cosmic-ray muons, low
energy muon sources or extremely expensive muon accelerators. An prompt
acceleration of the low-energy muon beam is found in the beam-driven plasma
wakefield up to $\mathrm{TV/m}$. The muon beam is accelerated from
$275\mathrm{MeV}$ to more than $10\mathrm{GeV}$ within $22.5\mathrm{ps}$.
Choosing the injection time of the muon beam in a proper range, the
longitudinal spatial distribution and the energy distribution of the
accelerated muon beam are compressed. The efficiency of the energy transfer
from the driven electron beam to the muon beam can reach $20\%$. The prompt
acceleration scheme is a promising avenue to bring the expected neutrino
factory and the muon collider into reality and to catch new physics beyond the
Standard Model.


Abstract:
  Using the generalized extreme value theory to characterize tail
distributions, we address liquidation, leverage, and optimal margins for
bitcoin long and short futures positions. The empirical analysis of perpetual
bitcoin futures on BitMEX shows that (1) daily forced liquidations to out-
standing futures are substantial at 3.51%, and 1.89% for long and short; (2)
investors got forced liquidation do trade aggressively with average leverage of
60X; and (3) exchanges should elevate current 1% margin requirement to 33% (3X
leverage) for long and 20% (5X leverage) for short to reduce the daily margin
call probability to 1%. Our results further suggest normality assumption on
return significantly underestimates optimal margins. Policy implications are
also discussed.


Abstract:
  The power management techniques to extend battery lifespan is becoming
increasingly important due to longer user applications' running time in mobile
devices. Even when users do not use any applications, battery lifespan
decreases continually. It occurs because of service daemons of mobile platform
and network-based data synchronization operations. In this paper, we propose a
new power management system that recognizes the idle time of the device to
reduce the battery consumption of mobile devices.


Abstract:
  Cybersecurity risk analysis plays an essential role in supporting
organizations make effective decision about how to manage and control
cybersecurity risk. Cybersecurity risk is a function of the interplay between
the defender, i.e., the organisation, and the attacker: decisions and actions
made by the defender second guess the decisions and actions taken by the
attacker and vice versa. Insight into this game between these two agents
provides a means for the defender to identify and make optimal decisions. To
date, the adversarial risk analysis framework has provided a
decision-analytical approach to solve such game problems in the presence of
uncertainty and uses Monte Carlo simulation to calculate and identify optimal
decisions. We propose an alternative framework to construct and solve a serial
of sequential Defend-Attack models, that incorporates the adversarial risk
analysis approach, but uses a new class of influence diagrams algorithm, called
hybrid Bayesian network inference, to identify optimal decision strategies.
Compared to Monte Carlo simulation the proposed hybrid Bayesian network
inference is more versatile because it provides an automated way to compute
hybrid Defend-Attack models and extends their use to involve mixtures of
continuous and discrete variables, of any kind. More importantly, the hybrid
Bayesian network approach is novel in that it supports dynamic decision making
whereby new real-time observations can update the Defend-Attack model in
practice. We also extend the Defend-Attack model to support cases involving
extra variables and longer decision sequence. Examples are presented,
illustrating how the proposed framework can be adjusted for more complicated
scenarios, including dynamic decision making.


Abstract:
  Observations indicate that a continuous supply of gas is needed to maintain
observed star formation rates in large, disky galaxies. To fuel star formation,
gas must reach the inner regions of such galaxies. Despite its crucial
importance for galaxy evolution, how and where gas joins galaxies is poorly
constrained observationally and is rarely explored in fully cosmological
simulations. To investigate gas accretion in the vicinity of galaxies, we
analyze the FIRE-2 cosmological zoom-in simulations for 4 Milky Way mass
galaxies (M_halo ~ 10E12 solar masses), focusing on simulations with cosmic ray
physics. We find that at z~0, gas approaches the disk with angular momentum
similar to the gaseous disk edge and low radial velocities, piling-up near the
edge and settling into full rotational support. Accreting gas moves
predominantly parallel to the disk with small but nonzero vertical velocity
components, and joins the disk largely in the outskirts as opposed to "raining"
down onto the disk. Once in the disk, gas trajectories are complex, being
dominated by spiral arm induced oscillations and feedback. However, time and
azimuthal averages show clear but slow net radial infall with transport speeds
of 1-3 km/s and net mass fluxes through the disk on the order of one solar mass
per year, comparable to the star formation rates of the galaxies and decreasing
towards galactic center as gas is sunk into star formation. These rates are
slightly higher in simulations without cosmic rays (1-7 km/s, ~4-5 solar masses
per year). We find overall consistency of our results with observational
constraints and discuss prospects of future observations of gas flows in and
around galaxies.


Abstract:
  In this paper we present a decoding algorithm for algebraic geometry codes
with error-correcting capacity beyond half the designed distance of the code.
This algorithm comes as a fusion of the Power Error Locating Pairs algorithm
for algebraic geometry codes and the technique used by Ehrhard in order to
correct these codes up to half the designed distance. The decoding radius of
this algorithm reaches that of Sudan algorithm, without any penalty given by
the genus of the curve.


Abstract:
  We define a probability measure preserving and r-discrete groupoid that is
associated to every invariant point process on a locally compact and second
countable group. This groupoid governs certain factor processes of the point
process, in particular the existence of Cayley factor graphs. With this method
we are able to show that point processes on amenable groups admit all (and only
admit) Cayley factor graphs of amenable groups, and that the Poisson point
process on groups with Kazhdan's Property (T) admits no Cayley factor graphs.
This gives examples of pmp countable Borel equivalence relations that cannot be
generated by any free action of a countable group.


Abstract:
  Let $G$ be a connected, complex reductive group. In this paper, we classify
$G\times G$-equivariant normal $\mathbb R$-test configurations of a polarized
$G$-compactification. Then for $\mathbb Q$-Fano $G$-compactifications, we
express the H-invariant of its equivariant normal $\mathbb R$-test
configurations in terms of the combinatory data. Based on \cite{Han-Li}, we
compute the semistable limit of a K-unstable Fano $G$-compactification. As an
application, we show that for the two K-unstable Fano $SO_4(\mathbb
C)$-compactifications, the corresponding semistable limits are indeed the limit
spaces of the normalized K\"ahler-Ricci flow.


Abstract:
  In this paper Modified gravity is studied over the linearized metric
perturbation in post-Minkowskian theory. This is a different aspect for two
body dynamics without taking usual self force originated from the radiative
backscattering of gravitational waves. The new multiplicative approach to
determine the background metric of curved space-time for two different massive
sources is computed in post-Newtonian theory. The theoretical result is checked
for galactic flat rotation curve and is very good agreement with solar
rotational speed.


Abstract:
  The LHC may produce light, weakly-interacting particles that decay to dark
matter, creating an intense and highly collimated beam of dark matter particles
in the far-forward direction. We investigate the prospects for detecting this
dark matter in two far-forward detectors proposed for a future Forward Physics
Facility: FASER$\nu$2, a 10-tonne emulsion detector, and FLArE, a 10- to
100-tonne LArTPC. We focus here on nuclear scattering, including elastic
scattering, resonant pion production, and deep inelastic scattering, and devise
cuts that efficiently remove the neutrino-induced background. In the
invisibly-decaying dark photon scenario, DM-nuclear scattering probes new
parameter space for dark matter masses 5 MeV $\lesssim m_{\chi} \lesssim$ 500
MeV. When combined with the DM-electron scattering studied previously,
FASER$\nu$2 and FLArE will be able to discover dark matter in a large swath of
the cosmologically-favored parameter space with MeV $\lesssim m_{\chi} \lesssim
$ GeV.


Abstract:
  We demonstrate an electro-optic wide-field method to enable fluorescence
lifetime microscopy (FLIM) with high throughput and single-molecule
sensitivity. Resonantly driven Pockels cells are used to efficiently gate
images at 39 MHz, allowing fluorescence lifetime to be captured on standard
camera sensors. Lifetime imaging of single molecules is enabled in wide-field
with exposure times of less than 100 milliseconds. This capability allows
combination of wide-field FLIM with single-molecule super-resolution
localization microscopy. Fast single-molecule dynamics such as FRET and
molecular binding events are captured from wide-field images without prior
spatial knowledge. A lifetime sensitivity of 1.9 times the photon shot-noise
limit is achieved, and high throughput is shown by acquiring wide-field FLIM
images with millisecond exposure and $>10^8$ photons per frame. Resonant
electro-optic FLIM allows lifetime contrast in any wide-field microscopy
method.


Abstract:
  Scaling of the behavior of a nanodevice means that the device function
(selectivity) is a unique smooth and monotonic function of a scaling parameter
that is an appropriate combination of the system's parameters. For the
uniformly charged cylindrical nanopore studied here these parameters are the
electrolyte concentration, $c$, voltage, $U$, the radius and the length of the
nanopore, $R$ and $H$, and the surface charge density on the nanopore's
surface, $\sigma$. Due to the non-linear dependence of selectivites on these
parameters, scaling can only be applied in certain limits. We show that the
Dukhin number, $\mathrm{Du}=|\sigma|/eRc\sim
|\sigma|\lambda_{\mathrm{D}}^{2}/eR$ ($\lambda_{\mathrm{D}}$ is the Debye
length), is an appropriate scaling parameter in the nanotube limit
($H\rightarrow\infty$). Decreasing the length of the nanopore, namely,
approaching the nanohole limit ($H\rightarrow 0$), an alternative scaling
parameter has been obtained that contains the pore length and is called the
modified Dukhin number: $\mathrm{mDu}\sim \mathrm{Du}\,
H/\lambda_{\mathrm{D}}\sim |\sigma|\lambda_{\mathrm{D}}H/eR$. We found that the
reason of non-linearity is that the double layers accumulating at the pore wall
in the radial dimension correlate with the double layers accumulating at the
entrances of the pore near the membrane on the two sides. Our modeling study
using the Local Equilibrium Monte Carlo method and the Poisson-Nernst-Planck
theory provides concentration, flux, and selectivity profiles, that show
whether the surface or the volume conduction dominates in a given region of the
nanopore for a given combination of the variables. We propose that the
inflection point of the scaling curve may be used to characterize the
transition point between the surface and volume conductions.


Abstract:
  We provide a new simple and transparent proof of the version of Kummer's test
given in [Tong, J. (1994). Amer. Math. Monthly. 101(5): 450--452]. Our proof is
based on an application of a Hardy--Littlewood Tauberian theorem.


Abstract:
  Transformer models are permutation equivariant. To supply the order and type
information of the input tokens, position and segment embeddings are usually
added to the input. Recent works proposed variations of positional encodings
with relative position encodings achieving better performance. Our analysis
shows that the gain actually comes from moving positional information to
attention layer from the input. Motivated by this, we introduce Decoupled
Positional Attention for Transformers (DIET), a simple yet effective mechanism
to encode position and segment information into the Transformer models. The
proposed method has faster training and inference time, while achieving
competitive performance on GLUE, XTREME and WMT benchmarks. We further
generalize our method to long-range transformers and show performance gain.


Abstract:
  We study possible superconducting states in transition metal dichalcogenide
(TMD) monolayers, assuming an on-site pairing potential that includes both
intra- and inter-orbital terms. We find that if the mirror symmetry with
respect to the system's plane is broken (e.g., by a substrate), this type of
pairing can give rise to unconventional superconductivity, including
time-reversal-invariant nodal and fully gapped topological phases. Using a
multi-orbital renormalization group procedure, we show how these phases may
result from the interplay between the local Coulomb repulsion, Hund's rule
coupling, and phonon-mediated attraction. In particular, for a range of
interaction parameters, the system transitions from a trivial phase to a nodal
phase and finally to a gapped topological phase upon increasing the strength of
the mirror symmetry breaking term.


Abstract:
  The reionization epoch concludes when ionizing photons reach every corner of
the Universe. Reionization has generally been assumed to be limited primarily
by the rate at which galaxies produce ionizing photons, but the recent
measurement of a surprisingly short ionizing photon mean free path of
$0.75^{+0.65}_{-0.45}$ proper Mpc at $z = 6$ by Becker et al. (2021) suggests
that absorption by residual neutral hydrogen in the otherwise ionized
intergalactic medium may play a much larger role than previously expected. Here
we show that consistency between this short mean free path and the coeval dark
pixel fraction in the Ly$\alpha$ forest requires a cumulative output of
$6.1^{+11}_{-2.4}$ ionizing photons per baryon by reionization's end. This
represents a dramatic increase in the ionizing photon budget over previous
estimates, greatly exacerbating the tension with measurements of the ionizing
output from galaxies at later times. Translating this constraint into the
instantaneous ionizing production from galaxies in our model, we find
$\log_{10}f_{\rm esc}\xi_{\rm ion}/\text{(erg/Hz)}^{-1} =25.02_{-0.21}^{+0.45}$
at $z\sim6$. Even with optimistic assumptions about the ionizing production
efficiency of early stellar populations, and assuming the galaxy luminosity
function extends to extremely faint sources ($M_{\text{UV}}\leq-11$), complete
reionization requires the escape fraction of ionizing photons to exceed $20\%$
across the galaxy population. This is far larger than observed in any galaxy
population at lower redshifts, requiring rapid evolution in galaxy properties
after the first billion years of cosmic time. This tension cannot be completely
relieved within existing observational constraints on the hydrogen neutral
fraction and mean free path.


Abstract:
  Social robots able to continually learn facial expressions could
progressively improve their emotion recognition capability towards people
interacting with them. Semi-supervised learning through ensemble predictions is
an efficient strategy to leverage the high exposure of unlabelled facial
expressions during human-robot interactions. Traditional ensemble-based
systems, however, are composed of several independent classifiers leading to a
high degree of redundancy, and unnecessary allocation of computational
resources. In this paper, we proposed an ensemble based on convolutional
networks where the early layers are strong low-level feature extractors, and
their representations shared with an ensemble of convolutional branches. This
results in a significant drop in redundancy of low-level features processing.
Training in a semi-supervised setting, we show that our approach is able to
continually learn facial expressions through ensemble predictions using
unlabelled samples from different data distributions.


Abstract:
  We study both averaging and maximal averaging problems for Product
$j$-varieties defined by $\Pi_j=\{x\in \mathbb F_q^d: \prod_{k=1}^d x_k=j\}$
for $j\in \mathbb F_q^*,$ where $\mathbb F_q^d$ denotes a $d$-dimensional
vector space over the finite field $\mathbb F_q$ with $q$ elements. We prove
the sharp $L^p\to L^r$ boundedness of averaging operators associated to Product
$j$-varieties. We also obtain the optimal $L^p$ estimate for a maximal
averaging operator related to a family of Product $j$-varieties
$\{\Pi_j\}_{j\in \mathbb F_q^*}.$


Abstract:
  Heavy-tailed random samples, as well as their sum or average, are encountered
in a number of signal processing applications in radar, communications,
finance, and natural sciences. Modeling such data through the Pareto
distribution is particularly attractive due to its simple analytical form, but
may lead to infinite variance and/or mean, which is not physically plausible:
in fact, samples are always bounded in practice, namely because of clipping
during the signal acquisition or deliberate censoring or trimming (truncation)
at the processing stage. Based on this motivation, the paper derives and
analyzes the distribution of the sum of right-censored Pareto Type-II
variables, which generalizes the conventional Pareto (Type-I) and Lomax
distributions. The distribution of the sum of truncated Pareto is also
obtained, and an analytical connection is drawn with the unbounded case. A
numerical analysis illustrates the findings, providing insights on several
aspects including the intimate mixture structure of the obtained expressions.


Abstract:
  The rapid growth of Decentralized Finance (DeFi) boosts the Ethereum
ecosystem. At the same time, attacks towards DeFi applications (apps) are
increasing. However, to the best of our knowledge, existing smart contract
vulnerability detection tools cannot be directly used to detect DeFi attacks.
That's because they lack the capability to recover and understand high-level
DeFi semantics, e.g., a user trades a token pair X and Y in a Decentralized
EXchange (DEX).
  In this work, we focus on the detection of two types of new attacks on DeFi
apps, including direct and indirect price manipulation attacks. The former one
means that an attacker directly manipulates the token price in DEX by
performing an unwanted trade in the same DEX by attacking the vulnerable DeFi
app. The latter one means that an attacker indirectly manipulates the token
price of the vulnerable DeFi app (e.g., a lending app). To this end, we propose
a platform-independent way to recover high-level DeFi semantics by first
constructing the cash flow tree from raw Ethereum transactions and then lifting
the low-level semantics to high-level ones, including token trade, liquidity
mining, and liquidity cancel. Finally, we detect price manipulation attacks
using the patterns expressed with the recovered DeFi semantics.
  We have implemented a prototype named \tool{} and applied it to more than 350
million transactions. It successfully detected 432 real-world attacks in the
wild. We confirm that they belong to four known security incidents and five
zero-day ones. We reported our findings. Two CVEs have been assigned. We
further performed an attack analysis to reveal the root cause of the
vulnerability, the attack footprint, and the impact of the attack. Our work
urges the need to secure the DeFi ecosystem.


Abstract:
  We present a method for the direct measurement of the Wigner characteristic
function of a thermalizing harmonic oscillator that is completely inaccessible
for control or measurement. The strategy employs a recently proposed
probe-measurement-based scheme [Phys. Rev. Lett. 122, 110406 (2019)] which
relies on the pulsed control of a two-level probe. We generalize this scheme to
the case of a nonunitary time evolution of the target harmonic oscillator,
describing its thermalization through contact to a finite-temperature
environment, given in the form of a Lindblad master equation. This
generalization is achieved using a superoperator formalism and yields
analytical expressions for the direct measurement of the characteristic
function, accounting for the decoherence during the measurement process.


Abstract:
  We discuss theoretically static and dynamical properties of $XY$ and
Heisenberg antiferromagnets on triangular lattice with random vacancies and
substitutional spins. It is shown that the distortion of $120^\circ$ magnetic
order produced by a single defect is described by electrostatic equations for a
field of an electrically neutral complex of six charges located around the
impurity. The first finite term in the multipole expansion of this field is the
octupole moment which decays as $1/r^3$ with the distance $r$. The linearity of
equations allows to describe analytically the distortion of the long-range
magnetic order at a small concentration $c$ of defects. We obtain analytically
renormalization of the elastic neutron scattering cross section and the magnon
spectrum $\epsilon_{\bf k}$ in the leading order in $c$. We find that the
scattering on impurities renormalizes weakly the bare spectrum $\epsilon_{\bf
k}\propto k$ at $k\gg\sqrt c$. However the renormalization is substantial of
the long-wavelength magnon spectrum at $k\ll\sqrt c$: $\epsilon_{\bf k}\propto
\sqrt{c /\ln(1/k)}$ at $k\to0$ and there is a parametrically large region in
which magnons with not too small momenta are overdamped and localized. This
strong modification of the long-wavelength spectrum leads to the stabilization
of the slightly distorted magnetic long-range order at $T<T_N\sim
S^2J/\ln(1/c)$ and to the considerable change in the density of states and in
the specific heat. The overdamped modes arise also in quasi-2D spin systems on
a stacked triangular lattice.


Abstract:
  The ability to independently deploy parts of a software system is one of the
cornerstones of modern software development, and allows for these parts to
evolve independently and at different speeds.
  A major challenge of such independent deployment, however, is to ensure that
despite their individual evolution, the interfaces between interacting parts
remain compatible. This is especially important for enterprise software
systems, which are often highly integrated and based on heterogenous IT
infrastructures.
  Although several approaches for interface evolution have been proposed, many
of these rely on the developer to adhere to certain rules, but provide little
guidance for doing so. In this paper, we present an approach for interface
evolution that is easy to use for developers, and also addresses typical
challenges of heterogenous enterprise software, especially legacy system
integration.


Abstract:
  Electrocatalysts for bifunctional oxygen reduction (ORR) and oxygen evolution
reaction (OER) are commonly studied under hydrodynamic conditions, rendering
the use of binders necessary to ensure the mechanical stability of the
electrode films. The presence of a binder, however, may influence the
properties of the materials under examination to an unknown extent. Herein, we
investigate the impact of Nafion on a highly active ORR/OER catalyst consisting
of MnFeNi oxide nanoparticles supported on multi-walled carbon nanotubes.
Electrochemical studies revealed that, in addition to enhancing the mechanical
stability and particle connectivity, Nafion poses a major impact on the ORR
selectivity, which correlates with a decrease in the valence state of Mn
according to X-ray absorption spectroscopy. These findings call for awareness
regarding the use of electrode additives, since in some cases the extent of
their impact on the properties of electrode films cannot be regarded as
negligible.


Abstract:
  Using the official data and aware of the uncertain source and insufficient
number of samples, we present a first and (for the moment) unique attempt to
study the first two months spread of COVID-19 in Madagascar. The approach has
been tested by predicting the number of contaminated persons for the next week
after fitting the inputs data collected within 7 or 15 days using standard
least $\chi^2$-fit method. Encouraged by this first test, we study
systematically during 67 days , 1-2 weeks new data and predict the contaminated
persons for the coming week. We find that the first month data are well
described by a linear or quadratic polynomial with an increase of about (4-5)
infected persons per day. Pursuing the analysis, one note that data until 46
days favour a cubic polynomial behaviour which signals an eventual near future
stronger growth as confirmed by the new data on the 48th day. We complete the
analysis until 67 days and find that the data until 77 days confirm the cubic
polynomial behaviour which is a remarkable feature of the pandemic spread in
Madagascar. We expect that these results will be useful for some new model
buildings. A comparison with some other SI-like models predictions is
done.These results may also be interpreted as the lowest values of the real
case due to the insufficient number of samples (12907 for 27 million habitants
on 05/06/20). The data analysis of the absolute number of cured persons until
67 days shows an approximate linear behaviour with about 3 cured persons per
day. However, the number of percentage number of cured persons decreases above
42-46 days indicating the limits of the hospital equipment and care to face the
2nd phase of the pandemic for the 67th first days. Some comments on the social,
economical and political impacts of COVID-19 and confinement for Madagascar
and, in general, for Worldwide are shortly discussed.


Abstract:
  It has been experimentally observed that the efficiency of distributed
training with stochastic gradient (SGD) depends decisively on the batch size
and -- in asynchronous implementations -- on the gradient staleness.
Especially, it has been observed that the speedup saturates beyond a certain
batch size and/or when the delays grow too large. We identify a data-dependent
parameter that explains the speedup saturation in both these settings. Our
comprehensive theoretical analysis, for strongly convex, convex and non-convex
settings, unifies and generalized prior work directions that often focused on
only one of these two aspects. In particular, our approach allows us to derive
improved speedup results under frequently considered sparsity assumptions. Our
insights give rise to theoretically based guidelines on how the learning rates
can be adjusted in practice. We show that our results are tight and illustrate
key findings in numerical experiments.


Abstract:
  Biomarkers play an important role in early detection and intervention in
Alzheimer's disease (AD). However, obtaining effective biomarkers for AD is
still a big challenge. In this work, we propose to use the worst transportation
cost as a univariate biomarker to index cortical morphometry for tracking AD
progression. The worst transportation (WT) aims to find the least economical
way to transport one measure to the other, which contrasts to the optimal
transportation (OT) that finds the most economical way between measures. To
compute the WT cost, we generalize the Brenier theorem for the OT map to the WT
map, and show that the WT map is the gradient of a concave function satisfying
the Monge-Ampere equation. We also develop an efficient algorithm to compute
the WT map based on computational geometry. We apply the algorithm to analyze
cortical shape difference between dementia due to AD and normal aging
individuals. The experimental results reveal the effectiveness of our proposed
method which yields better statistical performance than other competiting
methods including the OT.


Abstract:
  The origin semantics for transducers was proposed in 2014, and led to various
characterizations and decidability results that are in contrast with the
classical semantics. In this paper we add a further decidability result for
characterizing transducers that are close to one-way transducers in the origin
semantics. We show that it is decidable whether a non-deterministic two-way
word transducer can be resynchronized by a bounded, regular resynchronizer into
an origin-equivalent one-way transducer. The result is in contrast with the
usual semantics, where it is undecidable to know if a non-deterministic two-way
transducer is equivalent to some one-way transducer.


Abstract:
  The interplay of disorder and strong correlations in quantum many-body
systems remains an open question. That is despite much progress made in recent
years with ultracold atoms in optical lattices to better understand phenomena
such as many-body localization or the effect of disorder on Mott
metal-insulator transitions. Here, we utilize the numerical linked-cluster
expansion technique, extended to treat disordered quantum lattice models, and
study exact thermodynamic properties of the disordered Fermi-Hubbard model on
the square and cubic geometries. We consider box distributions for the disorder
in the onsite energy, the interaction strength, as well as the hopping
amplitude and explore how energy, double occupancy, entropy, heat capacity and
magnetic correlations of the system in the thermodynamic limit evolve as the
strength of disorder changes. We compare our findings with those obtained from
determinant quantum Monte Carlo simulations and discuss the relevance of our
results to experiments with cold fermionic atoms in optical lattices.


Abstract:
  The power-law distribution is ubiquitous and its mechanism seems to be
various. We find a general mechanism for the distribution. The distribution of
a geometrically growing system can be approximated by a log - completely
squared chi distribution with 1 degree of freedom (log-CS$\chi_1$), which
reaches asymptotically a power-law distribution, or by a log-normal
distribution, which has an infinite asymptotic slope, at the upper limit. For
the log-CS$\chi_1$, the asymptotic exponent of the power-law or the slope in a
log-log diagram seems to be related only to the variances of the system
parameters and their mutual correlation but independent of an initial
distribution of the system or any mean value of parameters. We can take the
log-CS$\chi_1$ as a unique approximation when the system should have a singular
initial distribution. The mechanism shows a comprehensiveness to be applicable
to wide practice. We derive a simple formula for the Zipf's exponent, which
will probably demand that the exponent should be near -1 rather than exactly
-1. We show that this approach can explain statistics of the COVID-19 pandemic.


Abstract:
  We prove that any one-relator group $G$ is the fundamental group of a compact
Sasakian manifold if and only if $G$ is either finite cyclic or isomorphic to
the fundamental group of a compact Riemann surface of genus g > 0 with at most
one orbifold point of order $n \geq 1$. We also classify all groups of
deficiency at least two that are also the fundamental group of some compact
Sasakian manifold.


Abstract:
  It is desirable, and often a necessity, for machine learning models to be
robust against adversarial attacks. This is particularly true for Bayesian
models, as they are well-suited for safety-critical applications, in which
adversarial attacks can have catastrophic outcomes. In this work, we take a
deeper look at the adversarial robustness of Bayesian Neural Networks (BNNs).
In particular, we consider whether the adversarial robustness of a BNN can be
increased by model choices, particularly the Lipschitz continuity induced by
the prior. Conducting in-depth analysis on the case of i.i.d., zero-mean
Gaussian priors and posteriors approximated via mean-field variational
inference, we find evidence that adversarial robustness is indeed sensitive to
the prior variance.


Abstract:
  Propagation Phase Contrast Synchrotron Microtomography (PPC-SR${\mu}$CT) is
the gold standard for non-invasive and non-destructive access to internal
structures of archaeological remains. In this analysis, the virtual specimen
needs to be segmented to separate different parts or materials, a process that
normally requires considerable human effort. In the Automated SEgmentation of
Microtomography Imaging (ASEMI) project, we developed a tool to automatically
segment these volumetric images, using manually segmented samples to tune and
train a machine learning model. For a set of four specimens of ancient Egyptian
animal mummies we achieve an overall accuracy of 94-98% when compared with
manually segmented slices, approaching the results of off-the-shelf commercial
software using deep learning (97-99%) at much lower complexity. A qualitative
analysis of the segmented output shows that our results are close in terms of
usability to those from deep learning, justifying the use of these techniques.


Abstract:
  We introduce differential primary decompositions for ideals in a commutative
ring. Ideal membership is characterized by differential conditions. The minimal
number of conditions needed is the arithmetic multiplicity. Minimal
differential primary decompositions are unique up to change of bases. Our
results generalize the construction of Noetherian operators for primary ideals
in the analytic theory of Ehrenpreis-Palamodov, and they offer a concise method
for representing affine schemes. The case of modules is also addressed. We
implemented an algorithm in Macaulay2 that computes the minimal decomposition
for an ideal in a polynomial ring.


Abstract:
  Axial light field resolution refers to the ability to distinguish features at
different depths by refocusing. The axial refocusing precision corresponds to
the minimum distance in the axial direction between two distinguishable
refocusing planes. High refocusing precision can be essential for some light
field applications like microscopy. In this paper, we propose a learning-based
method to extrapolate novel views from axial volumes of sheared epipolar plane
images (EPIs). As extended numerical aperture (NA) in classical imaging, the
extrapolated light field gives re-focused images with a shallower depth of
field (DOF), leading to more accurate refocusing results. Most importantly, the
proposed approach does not need accurate depth estimation. Experimental results
with both synthetic and real light fields show that the method not only works
well for light fields with small baselines as those captured by plenoptic
cameras (especially for the plenoptic 1.0 cameras), but also applies to light
fields with larger baselines.


Abstract:
  We determine the probability that a random polynomial of degree $n$ over
$\mathbb{Z}_p$ has exactly $r$ roots in $\mathbb{Q}_p$, and show that it is
given by a rational function of $p$ that is invariant under replacing $p$ by
$1/p$.


Abstract:
  We investigate the two-dimensional energy-momentum-tensor (EMT) distributions
of the nucleon on the light front, using the Abel transforms of the
three-dimensional EMT ones. We explicitly show that the main features of all
EMT distributions are kept intact in the course of the Abel transform. We also
examine the equivalence between the global and local conditions for the nucleon
stability in the three-dimensional Breit frame and in the two-dimensional
transverse plane on the light front. We also discuss the two-dimensional force
fields inside a nucleon on the light front.


Abstract:
  In recent years, single modality based gait recognition has been extensively
explored in the analysis of medical images or other sensory data, and it is
recognised that each of the established approaches has different strengths and
weaknesses. As an important motor symptom, gait disturbance is usually used for
diagnosis and evaluation of diseases; moreover, the use of multi-modality
analysis of the patient's walking pattern compensates for the one-sidedness of
single modality gait recognition methods that only learn gait changes in a
single measurement dimension. The fusion of multiple measurement resources has
demonstrated promising performance in the identification of gait patterns
associated with individual diseases. In this paper, as a useful tool, we
propose a novel hybrid model to learn the gait differences between three
neurodegenerative diseases, between patients with different severity levels of
Parkinson's disease and between healthy individuals and patients, by fusing and
aggregating data from multiple sensors. A spatial feature extractor (SFE) is
applied to generating representative features of images or signals. In order to
capture temporal information from the two modality data, a new correlative
memory neural network (CorrMNN) architecture is designed for extracting
temporal features. Afterwards, we embed a multi-switch discriminator to
associate the observations with individual state estimations. Compared with
several state-of-the-art techniques, our proposed framework shows more accurate
classification results.


Abstract:
  On-the-job learning consists in continuously learning while being used in
production, in an open environment, meaning that the system has to deal on its
own with situations and elements never seen before. The kind of systems that
seem to be especially adapted to on-the-job learning are dialogue systems,
since they can take advantage of their interactions with users to collect
feedback to adapt and improve their components over time. Some dialogue systems
performing on-the-job learning have been built and evaluated but no general
methodology has yet been defined. Thus in this paper, we propose a first
general methodology for evaluating on-the-job learning dialogue systems. We
also describe a task-oriented dialogue system which improves on-the-job its
natural language component through its user interactions. We finally evaluate
our system with the described methodology.


Abstract:
  Reconfigurable intelligent surface (RIS) assisted radio is considered as an
enabling technology with great potential for the sixth-generation (6G) wireless
communications standard. The achievable secrecy rate (ASR) is one of the most
fundamental metrics to evaluate the capability of facilitating secure
communication for RIS-assisted systems. However, the definition of ASR is based
on Shannon's information theory, which generally requires long codewords and
thus fails to quantify the secrecy of emerging delay-critical services.
Motivated by this, in this paper we investigate the problem of maximizing the
secrecy rate under a delay-limited quality-of-service (QoS) constraint, termed
as the effective secrecy rate (ESR), for an RIS-assisted multiple-input
single-output (MISO) wiretap channel subject to a transmit power constraint. We
propose an iterative method to find a stationary solution to the formulated
non-convex optimization problem using a block coordinate ascent method (BCAM),
where both the beamforming vector at the transmitter as well as the phase
shifts at the RIS are obtained in closed forms in each iteration. We also
present a convergence proof, an efficient implementation, and the associated
complexity analysis for the proposed method. Our numerical results demonstrate
that the proposed optimization algorithm converges significantly faster that an
existing solution. The simulation results also confirm that the secrecy rate
performance of the system with stringent delay requirements reduce
significantly compared to the system without any delay constraints, and that
this reduction can be significantly mitigated by an appropriately placed
large-size RIS.


Abstract:
  The fitting of physical models is often done only using a single target
observable. However, when multiple targets are considered, the fitting
procedure becomes cumbersome, there being no easy way to quantify the
robustness of the model for all different observables. Here, we illustrate that
one can jointly search for the best model for each desired observable through
multi-objective optimization. To do so we construct the Pareto front to study
if there exists a set of parameters of the model that can jointly describe
multiple, or all, observables. To alleviate the computational cost, the
predicted error for each targeted objective is approximated with a Gaussian
process model, as it is commonly done in the Bayesian optimization framework.
We applied this methodology to improve three different models used in the
simulation of stationary state $cis-trans$ photoisomerization of retinal in
rhodopsin. Optimization was done with respect to different experimental
measurements, including emission spectra, peak absorption frequencies for the
$cis$ and $trans$ conformers, and the energy storage.


Abstract:
  Using VLTI/GRAVITY and SINFONI data, we investigate the sub-pc gas and dust
structure around the nearby type 1 AGN hosted by NGC 3783. The K-band coverage
of GRAVITY uniquely allows a simultaneous analysis of the size and kinematics
of the broad line region (BLR), the size and structure of the near-IR continuum
emitting hot dust, and the size of the coronal line region (CLR). We find the
BLR probed through broad Br$\gamma$ emission is well described by a rotating,
thick disk with a radial distribution of clouds peaking in the inner region. In
our BLR model the physical mean radius of 16 light days is nearly twice the 10
day time lag that would be measured, which matches very well the 10 day time
lag that has been measured by reverberation mapping. We measure a hot dust FWHM
size of 0.74 mas (0.14 pc) and further reconstruct an image of the hot dust
which reveals a faint (5% of the total flux) offset cloud which we interpret as
an accreting cloud heated by the central AGN. Finally, we directly measure the
FWHM size of the nuclear CLR as traced by the [CaVIII] and narrow Br$\gamma$
line. We find a FWHM size of 2.2 mas (0.4 pc), fully in line with the
expectation of the CLR located between the BLR and narrow line region.
Combining all of these measurements together with larger scale near-IR integral
field unit and mid-IR interferometry data, we are able to comprehensively map
the structure and dynamics of gas and dust from 0.01--100 pc.


Abstract:
  In this paper, we employ the novel design of the metamaterial half-wave plate
by using the multiple layers of the metamaterials with some specific rotation
angles. The rotation angles are given by composite pulse control which is the
well-known quantum control technique. A big advantage of this method can
analytically calculate the rotation angles and can be easily extended to the
multiple layers. The more layers of the metamaterials can present more
bandwidth of half-wave plate. In this paper, we present 1, 3, 5, 7
configurations of layers of a metamaterial half-wave plate and we demonstrate
that our device increasingly enhance the bandwidth of performance.


Abstract:
  Explanation techniques are commonly evaluated using human-grounded methods,
limiting the possibilities for large-scale evaluations and rapid progress in
the development of new techniques. We propose a functionally-grounded
evaluation procedure for local model-agnostic explanation techniques. In our
approach, we generate ground truth for explanations when the black-box model is
Logistic Regression and Gaussian Naive Bayes and compare how similar each
explanation is to the extracted ground truth. In our empirical study,
explanations of Local Interpretable Model-agnostic Explanations (LIME), SHapley
Additive exPlanations (SHAP), and Local Permutation Importance (LPI) are
compared in terms of how similar they are to the extracted ground truth. In the
case of Logistic Regression, we find that the performance of the explanation
techniques is highly dependent on the normalization of the data. In contrast,
Local Permutation Importance outperforms the other techniques on Naive Bayes,
irrespective of normalization. We hope that this work lays the foundation for
further research into functionally-grounded evaluation methods for explanation
techniques.


Abstract:
  Active learning has been increasingly applied to screening functional
materials from existing materials databases with desired properties. However,
the number of known materials deposited in the popular materials databases such
as ICSD and Materials Project is extremely limited and consists of just a tiny
portion of the vast chemical design space. Herein we present an active
generative inverse design method that combines active learning with a deep
variational autoencoder neural network and a generative adversarial deep neural
network model to discover new materials with a target property in the whole
chemical design space. The application of this method has allowed us to
discover new thermodynamically stable materials with high band gap (SrYF$_5$)
and semiconductors with specified band gap ranges (SrClF$_3$, CaClF$_5$,
YCl$_3$, SrC$_2$F$_3$, AlSCl, As$_2$O$_3$), all of which are verified by the
first principle DFT calculations. Our experiments show that while active
learning itself may sample chemically infeasible candidates, these samples help
to train effective screening models for filtering out materials with desired
properties from the hypothetical materials created by the generative model. The
experiments show the effectiveness of our active generative inverse design
approach.


Abstract:
  We present the discovery of the fading radio transient FIRST
J153350.8+272729. The source had a maximum observed 5-GHz radio luminosity of
$8\times10^{39}$ erg s$^{-1}$ in 1986, but by 2019 had faded by a factor of
nearly 400. It is located 0.15 arcsec from the center of a galaxy (SDSS
J153350.89+272729) at 147 Mpc, which shows weak Type II Seyfert activity. We
show that a tidal disruption event (TDE) is the preferred scenario for FIRST
J153350.8+272729, although it could plausibly be interpreted as the afterglow
of a long-duration gamma-ray burst. This is only the second TDE candidate to be
first discovered at radio wavelengths. Its luminosity fills a gap between the
radio afterglows of sub-relativistic TDEs in the local universe, and
relativistic TDEs at high redshifts. The unusual properties of FIRST
J153350.8+272729 (ongoing nuclear activity in the host galaxy, high radio
luminosity) motivate more extensive TDE searches in untargeted radio surveys.


Abstract:
  We search some splitting (resp. finiteness) criteria by applying certain
splitting (resp. finiteness) property of the cohomological functors. We present
a connection from our approach to the realization problem of Nunke. This
equipped with several applications. For instance, we recover some results of
Jensen (and others) by applying simple methods. Additional applications include
a computation of some numerical invariants such as the projective dimension of
some injective modules and et cetera.


Abstract:
  We find the optimal investment strategy in a Black-Scholes market to minimize
the probability of so-called {\it lifetime exponential Parisian ruin}, that is,
the probability that wealth exhibits an excursion below zero of an
exponentially distributed time before the individual dies. We find that
leveraging the risky asset is worse for negative wealth when minimizing the
probability of lifetime exponential Parisian ruin than when minimizing the
probability of lifetime ruin. Moreover, when wealth is negative, the optimal
amount invested in the risky asset increases as the hazard rate of the
exponential ``excursion clock'' increases. In view of the heavy leveraging when
wealth is negative, we also compute the minimum probability of lifetime
exponential Parisian ruin under a constraint on investment. Finally, we derive
an asymptotic expansion of the minimum probability of lifetime exponential
Parisian ruin for small values of the hazard rate of the excursion clock. It is
interesting to find that, for small values of this hazard rate, the minimum
probability of lifetime exponential Parisian ruin is proportional to the
minimum occupation time studied in Bayraktar and Young, and the proportion
equals the hazard rate. To the best of our knowledge, our work is the first to
{\it control} the probability of Parisian ruin.


Abstract:
  Previous math word problem solvers following the encoder-decoder paradigm
fail to explicitly incorporate essential math symbolic constraints, leading to
unexplainable and unreasonable predictions. Herein, we propose Neural-Symbolic
Solver (NS-Solver) to explicitly and seamlessly incorporate different levels of
symbolic constraints by auxiliary tasks. Our NS-Solver consists of a problem
reader to encode problems, a programmer to generate symbolic equations, and a
symbolic executor to obtain answers. Along with target expression supervision,
our solver is also optimized via 4 new auxiliary objectives to enforce
different symbolic reasoning: a) self-supervised number prediction task
predicting both number quantity and number locations; b) commonsense constant
prediction task predicting what prior knowledge (e.g. how many legs a chicken
has) is required; c) program consistency checker computing the semantic loss
between predicted equation and target equation to ensure reasonable equation
mapping; d) duality exploiting task exploiting the quasi duality between
symbolic equation generation and problem's part-of-speech generation to enhance
the understanding ability of a solver. Besides, to provide a more realistic and
challenging benchmark for developing a universal and scalable solver, we also
construct a new large-scale MWP benchmark CM17K consisting of 4 kinds of MWPs
(arithmetic, one-unknown linear, one-unknown non-linear, equation set) with
more than 17K samples. Extensive experiments on Math23K and our CM17k
demonstrate the superiority of our NS-Solver compared to state-of-the-art
methods.


Abstract:
  Today, power waste is one of the most crucial problems which power stations
across the world are facing. One of the recent trends of the energy system is
the lean management technique. When lean management is indicated by the system,
customer value is increased and the waste process in industry or in a power
station is reduced. In this paper, first of all, we propose mathematical
modeling to reduce the cost of production, and then the simulation technique
applies to electricity transmission distribution systems. Furthermore, we
consider two criteria for comparison including the different costs of the
system and the rate of energy waste during the transmission. The primary
approach is to use both of the models in order to draw a comparison between
simulation results and mathematical models. Finally, the analysis of the test
results done by the CPLEX toolbox of MATLAB Software 2019 leads to a remarkable
decrease in the costs of energy demand in the electricity transmitting network
distribution system.


Abstract:
  In the Equal Maximum Flow Problem (EMFP), we aim for a maximum flow where we
require the same flow value on all edges in some given subsets of the edge set.
In this paper, we study the closely related Almost Equal Maximum Flow Problems
(AEMFP) where the flow values on edges of one homologous edge set differ at
most by the valuation of a so called deviation function~$\Delta$. We prove that
the integer almost equal maximum flow problem (integer AEMFP) is in general
$\mathcal{NP}$-complete, and show that even the problem of finding a fractional
maximum flow in the case of convex deviation functions is also
$\mathcal{NP}$-complete. This is in contrast to the EMFP, which is polynomial
time solvable in the fractional case. We provide inapproximability results for
the integral AEMFP. For the integer AEMFP we state a polynomial algorithm for
the constant deviation and concave case for a fixed number of homologous sets.


Abstract:
  Administering standardized examinations is a challenging task, especially for
those universities for which colleges affiliated to it are geographically
distributed over a wide area. Some of the challenges include maintaining
integrity and confidentiality of examination records, preventing mal-practices,
issuing unique identification numbers to a large student population and
managing assets required for the smooth conduct of examinations. These
challenges aggravate when colleges affiliated to universities demand academic
and administrative autonomy by demonstrating best practices consistently over a
long period.
  In this chapter, we describe a model for decentralized and autonomous
examination system to provide the necessary administrative support. The model
is based on two emerging technologies of Blockchain Technology and Internet of
Things (IoT). We adopt a software architecture approach to describe the model.
The prescriptive architecture consists of {\em architectural mappings} which
map functional and non-functional requirements to architectural elements of
blockchain technology and IoT. In architectural mappings, first, we identify
common use-cases in administering standardized examinations. Then we map these
use-cases to the core elements of blockchain, i.e. distributed ledgers,
cryptography, consensus protocols and smart-contracts and IoT. Such kind of
prescriptive architecture guide downstream software engineering processes of
implementation and testing


Abstract:
  A hot Markovian system can cool down faster than a colder one: this is known
as the Mpemba effect. Here, we show that a non-equilibrium driving via
stochastic reset can induce this phenomenon, when absent. Moreover, we derive
an optimal driving protocol simultaneously optimizing the appearance time of
the Mpemba effect, and the total energy dissipation into the environment,
revealing the existence of a Pareto front. Building upon previous experimental
results, our findings open up the avenue of possible experimental realizations
of optimal cooling protocols in Markovian systems.


Abstract:
  The study of the prophet inequality problem in the limited information regime
was initiated by Azar et al. [SODA'14] in the pursuit of prior-independent
posted-price mechanisms. As they show, $O(1)$-competitive policies are
achievable using only a single sample from the distribution of each agent. A
notable portion of their results relies on reducing the design of single-sample
prophet inequalities (SSPIs) to that of order-oblivious secretary (OOS)
policies. The above reduction comes at the cost of not fully utilizing the
available samples. However, to date, this is essentially the only method for
proving SSPIs for many combinatorial sets. Very recently, Rubinstein et al.
[ITCS'20] give a surprisingly simple algorithm which achieves the optimal
competitive ratio for the single-choice SSPI problem $-$ a result which is
unobtainable going through the reduction to secretary problems. Motivated by
this discrepancy, we study the competitiveness of simple SSPI policies
directly, without appealing to results from OOS literature. In this direction,
we first develop a framework for analyzing policies against a greedy-like
prophet solution. Using this framework, we obtain the first SSPI for general
(non-bipartite) matching environments, as well as improved competitive ratios
for transversal and truncated partition matroids. Second, motivated by the
observation that many OOS policies for matroids decompose the problem into
independent rank-$1$ instances, we provide a meta-theorem which applies to any
matroid satisfying this partition property. Leveraging the recent results by
Rubinstein et al., we obtain improved competitive guarantees (most by a factor
of $2$) for a number of matroids captured by the reduction of Azar et al.
Finally, we discuss applications of our SSPIs to the design of mechanisms for
multi-dimensional limited information settings with improved revenue and
welfare guarantees.


Abstract:
  Pion and kaon structural properties provide insights into the emergence of
mass within the Standard Model and attendant modulations by the Higgs boson.
Novel expressions of these effects, in impact parameter space and in mass and
pressure profiles, are exposed via $\pi$ and $K$ generalised parton
distributions, built using the overlap representation from light-front wave
functions constrained by one-dimensional valence distribution functions that
describe available data. Notably, e.g. $K$ pressure profiles are spatially more
compact than $\pi$ profiles and both achieve near-core pressures of similar
magnitude to that found in neutron stars.


Abstract:
  Epileptic seizure prediction has gained considerable interest in the
computational Epilepsy research community. This paper presents a Machine
Learning based method for epileptic seizure prediction which outperforms
state-of-the art methods. We compute a probability for a given epoch, of being
pre-ictal against interictal using the Random Forest classifier and introduce
new concepts to enhance the robustness of the algorithm to false alarms. We
assessed our method on 20 patients of the benchmark scalp EEG CHB-MIT dataset
for a seizure prediction horizon (SPH) of 5 minutes and a seizure occurrence
period (SOP) of 30 minutes. Our approach achieves a sensitivity of 82.07 % and
a low false positive rate (FPR) of 0.0799 /h. We also tested our approach on
intracranial EEG recordings.


Abstract:
  This study investigates the electronic states and physical quantities of an
organic charge-transfer complex HMTSF-TCNQ, which undergoes a
charge-density-wave (CDW) phase transition at temperature $T_c\simeq 30$ K. A
first-principles calculation is utilized to determine that the normal state is
a topological semimetal with open nodal lines. Besed on the first-principles
calculation, we develop a tight-binding model to investigate the electronic
state in detail. Below $T_c$, the CDW phase is examined in the tight-binding
scheme using the mean-field approximation. It is shown that the open nodal
lines are deformed into closed ones, and their shapes are sensitive to the
order parameter. Using this tight-binding model, we theoretically evaluate the
temperature dependencies of two physical quantities: the spin-lattice
relaxation time $T_1$ and the orbital magnetic susceptibility. In particular,
an anomalous plateau is obtained at low temperatures in the orbital
diamagnetism. We presume that this anomalous plateau originates owing to the
conflict between the interband diamagnetism, impurity scattering, and the nodal
line deformation. We also conduct an experiment to investigate the orbital
magnetism, and the results are in excellent quantitative agreement with the
theory.


Abstract:
  Dialog State Tracking (DST), an integral part of modern dialog systems, aims
to track user preferences and constraints (slots) in task-oriented dialogs. In
real-world settings with constantly changing services, DST systems must
generalize to new domains and unseen slot types. Existing methods for DST do
not generalize well to new slot names and many require known ontologies of slot
types and values for inference. We introduce a novel ontology-free framework
that supports natural language queries for unseen constraints and slots in
multi-domain task-oriented dialogs. Our approach is based on generative
question-answering using a conditional language model pre-trained on
substantive English sentences. Our model improves joint goal accuracy in
zero-shot domain adaptation settings by up to 9% (absolute) over the previous
state-of-the-art on the MultiWOZ 2.1 dataset.


Abstract:
  Face attribute editing aims to generate faces with one or multiple desired
face attributes manipulated while other details are preserved. Unlike prior
works such as GAN inversion, which has an expensive reverse mapping process, we
propose a simple feed-forward network to generate high-fidelity manipulated
faces. By simply employing some existing and easy-obtainable prior information,
our method can control, transfer, and edit diverse attributes of faces in the
wild. The proposed method can consequently be applied to various applications
such as face swapping, face relighting, and makeup transfer. In our method, we
decouple identity, expression, pose, and illumination using 3D priors; separate
texture and colors by using region-wise style codes. All the information is
embedded into adversarial learning by our identity-style normalization module.
Disentanglement losses are proposed to enhance the generator to extract
information independently from each attribute. Comprehensive quantitative and
qualitative evaluations have been conducted. In a single framework, our method
achieves the best or competitive scores on a variety of face applications.


Abstract:
  Consider the class of optimal partition problems with long range interactions
\[ \inf \left\{ \sum_{i=1}^k \lambda_1(\omega_i):\ (\omega_1,\ldots, \omega_k)
\in \mathcal{P}_r(\Omega) \right\}, \] where $\lambda_1(\cdot)$ denotes the
first Dirichlet eigenvalue, and $\mathcal{P}_r(\Omega)$ is the set of open
$k$-partitions of $\Omega$ whose elements are at distance at least $r$:
$\textrm{dist}(\omega_i,\omega_j)\geq r$ for every $i\neq j$. In this paper we
prove optimal uniform bounds (as $r\to 0^+$) in $\mathrm{Lip}$-norm for the
associated $L^2$-normalized eigenfunctions, connecting in particular the
nonlocal case $r>0$ with the local one $r \to 0^+$.
  The proof uses new pointwise estimates for eigenfunctions, a one-phase
Alt-Caffarelli-Friedman and the Caffarelli-Jerison-Kenig monotonicity formulas,
combined with elliptic and energy estimates. Our result extends to other
contexts, such as singularly perturbed harmonic maps with distance constraints.


Abstract:
  We consider a bounded domain $\Omega$ of $\mathbb{R}^N$, $N\ge3$, $h$ and $b$
continuous functions on $\Omega$. Let $\Gamma$ be a closed curve contained in
$\Omega$. We study existence of positive solutions $u \in
H^1_0\left(\Omega\right)$ to the perturbed Hardy-Sobolev equation: $$ -\Delta
u+h u+bu^{1+\delta}=\rho^{-\sigma}_\Gamma u^{2^*_\sigma-1} \qquad \textrm{ in }
\Omega, $$ where $2^*_\sigma:=\frac{2(N-\sigma)}{N-2}$ is the critical
Hardy-Sobolev exponent, $\sigma\in [0,2)$, $0< \delta<\frac{4}{N-2}$ and
$\rho_\Gamma$ is the distance function to $\Gamma$. We show that the existence
of minimizers does not depend on the local geometry of $\Gamma$ nor on the
potential $h$. For $N=3$, the existence of ground-state solution may depends on
the trace of the regular part of the Green function of $-\Delta+h$ and or on
$b$. This is due to the perturbative term of order ${1+\delta}$.


Abstract:
  In the sense of distributions, the derivative of the Heaviside unit step
function $H(t)$ is a generalized Dirac-$\delta$ distribution. If the velocity
$V(t)$ of a flat plate is impulsive, as $V(t)=H(t)$ (i.e., it is suddenly set
into motion with unit velocity at $t=0^+$), then its acceleration is
$V'(t)=\delta(t)$. The Dirac-$\delta$ distribution has no point values.
However, when the Dirac-$\delta$ is the forcing term of an ODE (in $t$), it
contributes to the solution. The recently published paper [Chaos Solitons
Fractals 117 (2018) 68] incorrectly treats the Dirac-$\delta$ function as being
identically 0. This Comment analyzes the source of this error, and provides
guidance on how to correct it (based on the established literature). The
mathematical error identified is in addition to some issues about rheological
models with fractional derivatives, which are also noted. That is to say,
whether or not an "Atangana--Baleanu fractional derivative" is used in [Chaos
Solitons Fractals 117 (2018) 68], the solution to Stokes' first problem
provided therein is not correct.


Abstract:
  Bin picking is a core problem in industrial environments and robotics, with
its main module as 6D pose estimation. However, industrial depth sensors have a
lack of accuracy when it comes to small objects. Therefore, we propose a
framework for pose estimation in highly cluttered scenes with small objects,
which mainly relies on RGB data and makes use of depth information only for
pose refinement. In this work, we compare synthetic data generation approaches
for object detection and pose estimation and introduce a pose filtering
algorithm that determines the most accurate estimated poses. We will make our


Abstract:
  As open source software (OSS) becomes increasingly mature and popular, there
are significant challenges with properly accounting for usability concerns for
the diverse end users. Participatory design, where multiple stakeholders
collaborate on iterating the design, can be an efficient way to address the
usability concerns for OSS projects. However, barriers such as a code-centric
mindset and insufficient tool support often prevent OSS teams from effectively
including end users in participatory design methods. This paper proposes
preliminary contributions to this problem through the user-centered exploration
of (1) a set of design guidelines that capture the needs of OSS participatory
design tools, (2) two personas that represent the characteristics of OSS
designers and end users, and (3) a low-fidelity prototype tool for end user
involvement in OSS projects. This work paves the road for future studies about
tool design that would eventually help improve OSS usability.


Abstract:
  Intent Recognition and Slot Identification are crucial components in spoken
language understanding (SLU) systems. In this paper, we present a novel
approach towards both these tasks in the context of low resourced and unwritten
languages. We present an acoustic based SLU system that converts speech to its
phonetic transcription using a universal phone recognition system. We build a
word-free natural language understanding module that does intent recognition
and slot identification from these phonetic transcription. Our proposed SLU
system performs competitively for resource rich scenarios and significantly
outperforms existing approaches as the amount of available data reduces. We
observe more than 10% improvement for intent classification in Tamil and more
than 5% improvement for intent classification in Sinhala. We also present a
novel approach towards unsupervised slot identification using normalized
attention scores. This approach can be used for unsupervised slot labelling,
data augmentation and to generate data for a new slot in a one-shot way with
only one speech recording


Abstract:
  Previous works on formally studying mobile robotic swarms consider necessary
and sufficient system hypotheses enabling to solve theoretical benchmark
problems (geometric pattern formation, gathering, scattering, etc.). We argue
that formal methods can also help in the early stage of mobile robotic swarms
protocol design, to obtain protocols that are correct-by-design, even for
problems arising from real-world use cases, not previously studied
theoretically. Our position is supported by a concrete case study. Starting
from a real-world case scenario, we jointly design the formal problem
specification, a family of protocols that are able to solve the problem, and
their corresponding proof of correctness, all expressed with the same formal
framework. The concrete framework we use for our development is the PACTOLE
library based on the COQ proof assistant.


Abstract:
  In recent years, transformer-based models have shown state-of-the-art results
for Natural Language Processing (NLP). In particular, the introduction of the
BERT language model brought with it breakthroughs in tasks such as question
answering and natural language inference, advancing applications that allow
humans to interact naturally with embedded devices. FPGA-based overlay
processors have been shown as effective solutions for edge image and video
processing applications, which mostly rely on low precision linear matrix
operations. In contrast, transformer-based NLP techniques employ a variety of
higher precision nonlinear operations with significantly higher frequency. We
present NPE, an FPGA-based overlay processor that can efficiently execute a
variety of NLP models. NPE offers software-like programmability to the end user
and, unlike FPGA designs that implement specialized accelerators for each
nonlinear function, can be upgraded for future NLP models without requiring
reconfiguration. We demonstrate that NPE can meet real-time conversational AI
latency targets for the BERT language model with $4\times$ lower power than
CPUs and $6\times$ lower power than GPUs. We also show NPE uses $3\times$ fewer
FPGA resources relative to comparable BERT network-specific accelerators in the
literature. NPE provides a cost-effective and power-efficient FPGA-based
solution for Natural Language Processing at the edge.


Abstract:
  A Heron triangle is a triangle whose side lengths and area are integers. Two
Heron triangles are amicable if the perimeter of one is the area of the other.
We show, using elementary techniques, that there is only one pair of amicable
Heron triangles.


Abstract:
  Today, the Internet of Things (IoT) is one of the emerging technologies that
enable the connection and transfer of information through communication
networks. The main idea of the IoT is the widespread presence of objects such
as mobile devices, sensors, and RFID. With the increase in traffic volume in
urban areas, the existing intelligent urban traffic management system based on
IoT can be vital. Therefore, this paper focused on security in urban traffic
based on using RFID. In our scheme, RFID tags chose as the purpose of this
article. We, in this paper, present a mutual authentication protocol that leads
to privacy based on hybrid cryptography. Also, an authentication process with
RFID tags is proposed that can be read at high speed. The protocol has
attempted to reduce the complexity of computing. At the same time, the proposed
method can withstand attacks such as spoofing of tag and reader, tag tracking,
and replay attack.


Abstract:
  Color-kinematics duality in the adjoint has proven key to the relationship
between gauge and gravity theory scattering amplitude predictions. In recent
work, we demonstrated that at four-point tree-level, a small number of
color-dual EFT building blocks could encode all higher-derivative single-trace
massless corrections to gauge and gravity theories compatible with adjoint
double-copy. One critical aspect was the trivialization of building
higher-derivative color-weights -- indeed, it is the mixing of kinematics with
non-adjoint-type color-weights (like the permutation-invariant d^4) which
permits description via adjoint double-copy. Here we find that such ideas
clarify the predictions of local five-point higher-dimensional operators as
well. We demonstrate how a single scalar building block can be combined with
color structures to build higher-derivative color factors that generate,
through double copy, the predictions of higher-derivative gauge-theory
operators. These may then be suitably mapped, through another double-copy, to
higher-derivative corrections in gravity.


Abstract:
  We consider semilinear elliptic second-order partial differential
inequalities of the form Lu +|u|q-1u < and = Lv +|v|q-1v (*) in the whole space
Rn, where n > and = 2, q > 0 and L is a linear elliptic second-order partial
differential operator in divergence form. We assume that the coefficients of
the operator L are defined, measurable and locally bounded in Rn, and that the
quadratic form associated with the operator L is symmetric and non-negative
definite. We obtain a Liouville comparison principle in terms of a capacity
associated with the operator L for solutions of (*), which are defined and
measurable in Rn and which belong locally to a Sobolev-type function space also
associated with the operator L.


Abstract:
  We count the ordered sum-free triplets of subsets in the group
$\mathbb{Z}/p\mathbb{Z}$, i.e., the triplets $(A,B,C)$ of sets $A,B,C \subset
\mathbb{Z}/p\mathbb{Z}$ for which the equation $a+b=c$ has no solution with
$a\in A$, $b \in B$ and $c \in C$. Our main theorem improves on a recent result
by Semchankau, Shabanov, and Shkredov using a different and simpler method. Our
proof relates previous results on the number of independent sets of regular
graphs by Kahn, Perarnau and Perkins, and Csikv\'ari to produce explicit
estimates on smaller order terms. We also obtain estimates for the number of
sum-free triplets of subsets in a general abelian group.


Abstract:
  In this paper, we are interested in the edge intersection graphs of paths of
a grid where each path has at most one bend, called B1-EPG graphs and first
introduced by Golumbic et al (2009). We also consider a proper subclass of
B1-EPG, the L-EPG graphs, which allows paths only in ``L'' shape. We show that
two superclasses of trees are B1-EPG (one of them being the cactus graphs). On
the other hand, we show that the block graphs are L-EPG and provide a linear
time algorithm to produce L-EPG representations of generalization of trees.
These proofs employed a new technique from previous results in the area based
on block-cutpoint trees of the respective graphs.


Abstract:
  In this paper, inspired by the successes of visionlanguage pre-trained models
and the benefits from training with adversarial attacks, we present a novel
transformerbased cross-modal fusion modeling by incorporating the both notions
for VQA challenge 2021. Specifically, the proposed model is on top of the
architecture of VinVL model [19], and the adversarial training strategy [4] is
applied to make the model robust and generalized. Moreover, two implementation
tricks are also used in our system to obtain better results. The experiments
demonstrate that the novel framework can achieve 76.72% on VQAv2 test-std set.


Abstract:
  We report on depinning of nearly-commensurate charge-density waves in 1T-TaS2
thin-films at room temperature. A combination of the differential
current-voltage measurements with the low-frequency noise spectroscopy provide
unambiguous means for detecting the depinning threshold field in quasi-2D
materials. The depinning process in 1T-TaS2 is not accompanied by an observable
abrupt increase in electric current - in striking contrast to depinning in the
conventional charge-density-wave materials with quasi-1D crystal structure. We
explained it by the fact that the current density from the charge-density waves
in the 1T-TaS2 devices is orders of magnitude smaller than the current density
of the free carriers available in the discommensuration network surrounding the
commensurate charge-density-wave islands. The depinning fields in 1T-TaS2
thin-film devices are several orders of magnitude larger than those in quasi-1D
van der Waals materials. Obtained results are important for the proposed
applications of the charge-density-wave devices in electronics.


Abstract:
  Abstractive summarization for long-document or multi-document remains
challenging for the Seq2Seq architecture, as Seq2Seq is not good at analyzing
long-distance relations in text. In this paper, we present BASS, a novel
framework for Boosting Abstractive Summarization based on a unified Semantic
graph, which aggregates co-referent phrases distributing across a long range of
context and conveys rich relations between phrases. Further, a graph-based
encoder-decoder model is proposed to improve both the document representation
and summary generation process by leveraging the graph structure. Specifically,
several graph augmentation methods are designed to encode both the explicit and
implicit relations in the text while the graph-propagation attention mechanism
is developed in the decoder to select salient content into the summary.
Empirical results show that the proposed architecture brings substantial
improvements for both long-document and multi-document summarization tasks.


Abstract:
  The excessive search for parking, known as cruising, generates pollution and
congestion. Cities are looking for approaches that will reduce the negative
impact associated with searching for parking. However, adequately measuring the
number of vehicles in search of parking is difficult and requires sensing
technologies. In this paper, we develop an approach that eliminates the need
for sensing technology by using parking meter payment transactions to estimate
parking occupancy and the number of cars searching for parking. The estimation
scheme is based on Particle Markov Chain Monte Carlo. We validate the
performance of the Particle Markov Chain Monte Carlo approach using data
simulated from a GI/GI/s queue. We show that the approach generates
asymptotically unbiased Bayesian estimates of the parking occupancy and
underlying model parameters such as arrival rates, average parking time, and
the payment compliance rate. Finally, we estimate parking occupancy and
cruising using parking meter data from SFpark, a large scale parking experiment
and subsequently, compare the Particle Markov Chain Monte Carlo parking
occupancy estimates against the ground truth data from the parking sensors. Our
approach is easily replicated and scalable given that it only requires using
data that cities already possess, namely historical parking payment
transactions.