Abstract:
  The large volumes of structured data currently available, from Web tables to
open-data portals and enterprise data, open up new opportunities for progress
in answering many important scientific, societal, and business questions.
However, finding relevant data is difficult. While search engines have
addressed this problem for Web documents, there are many new challenges
involved in supporting the discovery of structured data. We demonstrate how the
Auctus dataset search engine addresses some of these challenges. We describe
the system architecture and how users can explore datasets through a rich set
of queries. We also present case studies which show how Auctus supports data
augmentation to improve machine learning models as well as to enrich analytics.


Abstract:
  In the era of big data, the utilization of credit-scoring models to determine
the credit risk of applicants accurately becomes a trend in the future. The
conventional machine learning on credit scoring data sets tends to have poor
classification for the minority class, which may bring huge commercial harm to
banks. In order to classify imbalanced data sets, we propose a new ensemble
algorithm, namely, Weighted-Hybrid-Sampling-Boost (WHSBoost). In data sampling,
we process the imbalanced data sets with weights by the Weighted-SMOTE method
and the Weighted-Under-Sampling method, and thus obtain a balanced training
sample data set with equal weight. In ensemble algorithm, each time we train
the base classifier, the balanced data set is given by the method above. In
order to verify the applicability and robustness of the WHSBoost algorithm, we
performed experiments on the simulation data sets, real benchmark data sets and
real credit scoring data sets, comparing WHSBoost with SMOTE, SMOTEBoost and
HSBoost based on SVM, BPNN, DT and KNN.


Abstract:
  In this article, we present a method to construct a positivity preserving
numerical scheme for both the jump-extended CEV process and jump-extended CIR
process, whose jumps are governed by a (compensated) spectrally positive
$\alpha$-stable process with $\alpha \in (1,2)$. The proposed scheme is
obtained by making the diffusion coefficient partially implicit and then
finding the appropriate adjustment factor. We show that the proposed scheme
converges and theoretically achieves a strong convergence rate of at least
$\frac{1}{2}\left(\frac{\alpha_-}{2} \wedge \frac{1}{\alpha}\right)$, where the
constant $\alpha_- < \alpha$ can be chosen arbitrarily close to $\alpha \in
(1,2)$. Finally, to support our result, we present some numerical simulations
which suggest that the optimal rate of convergence is $\frac{\alpha_-}{4}$.


Abstract:
  In this work we investigate a coupled system of degenerate and nonlinear
partial differential equations governing the transport of reactive solutes in
groundwater. We show that the system admits a unique weak solution provided the
nonlinear adsorption isotherm associated with the reaction process satisfies
certain physically reasonable structural conditions. We conclude, moreover,
that the solute concentrations stay non-negative if the source term is
componentwise non-negative and investigate numerically the finite speed of
propagation of compactly supported initial concentrations, in a two-component
test case.


Abstract:
  We theoretically clarify the functional form to be used in $t \to 0$
extrapolation in the small flow time expansion (SF$t$X) method for the
energy-momentum tensor (EMT), which facilitates lattice simulation of the EMT
based on the gradient flow. We argue that in the $t \to 0$ extrapolation
analysis, lattice data should be fitted by a power function in $g(\mu(t))$, the
flow time dependent running coupling, where the power is determined by the
perturbation order we consider. From actual lattice data, we confirm the
validity of the extrapolation function. Using the new extrapolation function,
we present updated lattice results for thermodynamics quantities in quenched
QCD; our results are consistent with the previous study [arXiv:1812.06444] but
we obtain smaller errors due to reduction of systematic errors.


Abstract:
  We address the Threshold Information Disclosure (TID) problem on Ethereum: An
arbitrary number of users commit to the scheduled disclosure of their
individual messages recorded on the Ethereum blockchain if and only if all such
messages are disclosed. Before a disclosure, only the original sender of each
message should know its contents. To accomplish this, we task a small council
with executing a distributed generation and threshold sharing of an asymmetric
key pair. The public key can be used to encrypt messages which only become
readable once the threshold-shared decryption key is reconstructed at a
predefined point in time and recorded on-chain. With blockchains like Ethereum,
it is possible to coordinate such procedures and attach economic stakes to the
actions of participating individuals. In this paper, we present ETHTID, an
Ethereum smart contract application to coordinate Threshold Information
Disclosure. We base our implementation on ETHDKG [1], a smart contract
application for distributed key generation and threshold sharing, and adapt it
to fit our differing use case as well as add functionality to oversee a
scheduled reconstruction of the decryption key. For our main cost saving
optimisation, we show that the security of the underlying cryptographic scheme
is maintained. We evaluate how the execution costs depend on the size of the
council and the threshold and show that the presented protocol is deployable on
Ethereum with a council of more than 200 members with gas savings of 20-40%
compared to ETHDKG.


Abstract:
  For fixed training data and network parameters in the other layers the L1
loss of a ReLU neural network as a function of the first layer's parameters is
a piece-wise affine function. We use the Deep ReLU Simplex algorithm to
iteratively minimize the loss monotonically on adjacent vertices and analyze
the trajectory of these vertex positions. We empirically observe that in a
neighbourhood around a local minimum, the iterations behave differently such
that conclusions on loss level and proximity of the local minimum can be made
before it has been found: Firstly the loss seems to decay exponentially slow at
iterated adjacent vertices such that the loss level at the local minimum can be
estimated from the loss levels of subsequently iterated vertices, and secondly
we observe a strong increase of the vertex density around local minima. This
could have far-reaching consequences for the design of new gradient-descent
algorithms that might improve convergence rate by exploiting these facts.


Abstract:
  Two recently published papers propose some very simple key distribution
schemes designed to enable two or more parties to establish a shared secret key
with the aid of a third party. Unfortunately, as we show, most of the schemes
are inherently insecure and all are incompletely specified - moreover, claims
that the schemes are inherently lightweight are shown to be highly misleading.
We also briefly critique a somewhat related very recent paper by the same
authors that uses similar techniques to achieve what are claimed to be secure
multiparty computations.


Abstract:
  We have examined the star formation history (SFH) of Andromeda VII (And VII),
the brightest and most massive dwarf spheroidal (dSph) satellite of the
Andromeda galaxy (M 31). Although M 31 is surrounded by several dSph companions
with old stellar populations and low metallicity, it has a metal-rich stellar
halo with an age of 6$-$8 Gyr. This indicates that any evolutionary association
between the stellar halo of M 31 and its dSph system is frail. Therefore, the
question is whether And VII (a high-metallicity dSph located $\sim$220 kpc from
M 31), can be associated with M 31's young, metal-rich halo. Here, we perform
the first reconstruction of the SFH of And VII employing long-period variable
(LPV) stars. As the most-evolved asymptotic giant branch (AGB) and red
supergiant (RSG) stars, the birth mass of LPVs can be determined by connecting
their near-infrared photometry to theoretical evolutionary tracks. We found 55
LPV candidates within two half-light radii, using multi-epoch imaging with the
Isaac Newton Telescope in the $i$ and $V$ bands. Based on their birth mass
function, the star-formation rate (SFR) of And VII was obtained as a function
of cosmic time. The main epoch of star formation occurred $\simeq 6.2$ Gyr ago
with a SFR of $0.006\pm0.002$ M$_\odot$ yr$^{-1}$. Over the past 6 Gyr, we find
slow star formation, which continued until 500 Myr ago with a SFR
$\sim0.0005\pm0.0002$ M$_\odot$ yr$^{-1}$. We determined And VII's stellar mass
$M=(13.3\pm5.3)\times10^6$ M$_\odot$ within a half-light radius
$r_{\frac{1}{2}}=3.8\pm0.3$ arcmin and metallicity $Z=0.0007$, and also derived
its distance modulus of $\mu=24.38$ mag.


Abstract:
  Knowledge graphs (KG) have become increasingly important to endow modern
recommender systems with the ability to generate traceable reasoning paths to
explain the recommendation process. However, prior research rarely considers
the faithfulness of the derived explanations to justify the decision making
process. To the best of our knowledge, this is the first work that models and
evaluates faithfully explainable recommendation under the framework of KG
reasoning. Specifically, we propose neural logic reasoning for explainable
recommendation (LOGER) by drawing on interpretable logical rules to guide the
path reasoning process for explanation generation. We experiment on three
large-scale datasets in the e-commerce domain, demonstrating the effectiveness
of our method in delivering high-quality recommendations as well as
ascertaining the faithfulness of the derived explanation.


Abstract:
  A high sensitivity, 7mm Very Long Baseline Array image of M\,87 is analyzed
in order to estimate the jet velocity within 0.65 mas of the point of origin.
The image captured a high signal to noise, double-ridged, counter-jet extending
$\sim 1$ mas from the nucleus. After defining conditions and requirements that
justify approximate time averaged bilateral symmetry, a continuous set of
Lorentz transformations are found that map the double-ridged counter-jet
intensity profile into the double-ridged jet intensity profile. The mapping is
realized by a uniformly accelerating flow with intrinsic velocity of $\sim
0.27$c at 0.4 mas (a de-projected distance of 0.38 lt-yrs) to $0.38$c at 0.65
mas (a de-projected distance of 0.61 lt-yrs) from the nucleus. Since the
velocity field is derived from the global surface brightness profile and does
not depend on the motion of enhanced features, it is most likely a bulk flow
velocity as opposed to a pattern velocity. This interpretation is corroborated
by the fact that the distribution of the apparent velocities of previously
identified individual features (from the literature) within 0.65 mas of the
nucleus are consistent with local hydrodynamic shocks being advected with the
local bulk flow velocity. The bulk flow velocity of the visible inner jet is a
constraint that can potentially break degeneracies between numerical
simulations that are designed to replicate both the annulus that was imaged by
the Event Horizon Telescope as well as the base of the inner jet.


Abstract:
  We consider k-essence, a scalar-tensor theory with first-order derivative
self-interactions that can screen local scales from scalar fifth forces, while
allowing for sizeable deviations from General Relativity on cosmological
scales. We construct fully nonlinear static stellar solutions that show the
presence of this screening mechanism, and we use them as initial data for
simulations of stellar oscillations and gravitational collapse in spherical
symmetry. We find that for k-essence theories of relevance for cosmology, the
screening mechanism works in the case of stellar oscillation and suppresses the
monopole scalar emission to undetectable levels. In collapsing stars, we find
that the Cauchy problem, although locally well posed, can lead to diverging
characteristic speeds for the scalar field. By introducing a ''fixing
equation'' in the spirit of J. Cayuso, N. Ortiz, and L. Lehner [Phys. Rev. D
96, 084043 (2017)], inspired in turn by dissipative relativistic hydrodynamics,
we manage to evolve collapsing neutron stars past the divergence of the
characteristic speeds. We show that, in these systems, the screening mechanism
is less efficient than for oscillating and static stars, because the collapsing
star must shed away all of its scalar hair before forming a black hole. For
k-essence theories of relevance for cosmology, the characteristic frequency of
the resulting scalar monopole signal is too low for terrestrial detectors, but
we conjecture that space-borne interferometers such as LISA might detect it if
a supernova explodes in the Galaxy.


Abstract:
  Dynamical mass estimates of simple systems such globular clusters (GCs) still
suffer from up to a factor of 2 uncertainty. This is primarily due to the
oversimplifications of standard dynamical models that often neglect the effects
of the long-term evolution of GCs. Here, we introduce a new approach to measure
the dynamical properties of GCs, based on the combination of a deep-learning
framework and the large amount of data from direct $N$-body simulations. Our
algorithm, $\texttt{$\pi$-DOC}$ ($\textit{Predicting Images for the Dynamics Of
stellar Clusters}$) is composed of two convolutional networks, trained to learn
the non-trivial transformation between an observed GC luminosity map and its
associated mass distribution, age, and distance. The training set is made of
V-band luminosity and mass maps constructed as mock observations from $N$-body
simulations. The tests on $\texttt{$\pi$-DOC}$ demonstrate that we can predict
the mass distribution with a mean error per pixel of 27%, and the age and
distance with an accuracy of 1.5 Gyr and 6 kpc, respectively. In turn, we
recover the shape of the mass-to-light profile and its global value with a mean
error of 12%, which implies that we efficiently trace mass segregation. A
preliminary comparison with observations indicates that our algorithm is able
to predict the dynamical properties of GCs within the limits of the training
set. These encouraging results demonstrate that our deep-learning framework and
its forward modelling approach can offer a rapid and adaptable tool competitive
with standard dynamical models.


Abstract:
  An idealized 1:2 scale demonstrator and a numerical parameter optimization
algorithm are proposed to closely reproduce the deformation shape and, thus,
spatial strain directions of a real aerodynamically loaded civil aircraft
spoiler using only four concentrated loads. Cost-efficient experimental studies
on demonstrators of increasing complexity are required to transfer knowledge
from coupons to full-scale structures and to build up confidence in novel
structural health monitoring (SHM) technologies. Especially for testing novel
sensor systems that depend on or are affected by mechanical strains, e.g.,
strain-based SHM methods, it is essential that the considered lab-scale
structures reflect the strain states of the real structure at operational
loading conditions. Finite element simulations with detailed models were
performed for static strength analysis and for comparison to experimental
measurements. The simulated and measured deformations and spatial strain
directions of the idealized demonstrator correlated well with the numerical
results of the real aircraft spoiler. Thus, using the developed idealized
demonstrator, strain-based SHM systems can be tested under conditions that
reflect operational aerodynamic pressure loads, while the test effort and costs
are significantly reduced. Furthermore, the presented loading optimization
algorithm can be easily adapted to mimic other pressure loads in plate-like
structures to reproduce specific structural conditions.


Abstract:
  An interesting proposal for detecting gravitational waves involves quantum
metrology of Bose-Einstein condensates (BECs). We consider a forced modulation
of the BEC trap, whose frequency matches that of an incoming continuous
gravitational wave. The trap modulation induces parametric resonance in the
BEC, which in turn enhances sensitivity of the BEC to gravitational waves. We
find that such a BEC detector could potentially be used to detect gravitational
waves across several orders of magnitude in frequency, with the sensitivity
depending on the speed of sound, size of the condensate, and frequency of the
phonons. We outline a possible BEC experiment and discuss the current
technological limitations. We also comment on the potential noise sources as
well as what is necessary for such a detector to become feasible.


Abstract:
  This paper details the modeling pipeline and validates the baseline analysis
choices of the DES Year 3 joint analysis of galaxy clustering and weak lensing
(a so-called "3$\times$2pt" analysis). These analysis choices include the
specific combination of cosmological probes, priors on cosmological and
systematics parameters, model parameterizations for systematic effects and
related approximations, and angular scales where the model assumptions are
validated. We run a large number of simulated likelihood analyses using
synthetic data vectors to test the robustness of our baseline analysis. We
demonstrate that the DES Year 3 modeling pipeline, including the calibrated
scale cuts, is sufficiently accurate relative to the constraining power of the
DES Year 3 analyses. Our systematics mitigation strategy accounts for
astrophysical systematics, such as galaxy bias, intrinsic alignments, source
and lens magnification, baryonic effects, and source clustering, as well as for
uncertainties in modeling the matter power spectrum, reduced shear, and
estimator effects. We further demonstrate excellent agreement between two
independently-developed modeling pipelines, and thus rule out any residual
uncertainties due to the numerical implementation.


Abstract:
  The Milky Way contains thousands of H II region candidates identified by
their characteristic mid-infrared morphology, but lacking detections of ionized
gas tracers such as radio continuum or radio recombination line emission. These
targets thus remain unconfirmed as H II regions. With only $\sim$2500 confirmed
H II regions in the Milky Way, Galactic surveys are deficient by several
thousand nebulae when compared to external galaxies with similar star formation
rates. Using sensitive 9 GHz radio continuum observations with the Karl G.
Jansky Very Large Array (VLA), we explore a sample of H II region candidates in
order to set observational limits on the actual total population of Galactic H
II regions. We target all infrared-identified "radio quiet" sources from the
WISE Catalog of Galactic H II regions between
$245^{\circ}\geq\ell\geq90^{\circ}$ with infrared diameters less than
80$^{\prime\prime}$. We detect radio continuum emission from 50% of the
targeted H II region candidates, providing strong evidence that most of the
radio quiet candidates are bona fide HII regions. We measure the peak and
integrated radio flux densities and compare the inferred Lyman continuum fluxes
using models of OB-stars. We conclude that stars of approximately spectral type
B2 and earlier are able to create H II regions with similar infrared and radio
continuum morphologies as the more luminous H II regions created by O-stars.
From our 50% detection rate of "radio quiet" sources, we set a lower limit of
$\sim$7000 for the H II region population of the Galaxy. Thus the vast majority
of the Milky Way's H II regions remain to be discovered.


Abstract:
  Grapevine winter pruning is a complex task, that requires skilled workers to
execute it correctly. The complexity of this task is also the reason why it is
time consuming. Considering that this operation takes about 80-120 hours/ha to
be completed, and therefore is even more crucial in large-size vineyards, an
automated system can help to speed up the process. To this end, this paper
presents a novel multidisciplinary approach that tackles this challenging task
by performing object segmentation on grapevine images, used to create a
representative model of the grapevine plants. Second, a set of potential
pruning points is generated from this plant representation. We will describe
(a) a methodology for data acquisition and annotation, (b) a neural network
fine-tuning for grapevine segmentation, (c) an image processing based method
for creating the representative model of grapevines, starting from the inferred
segmentation and (d) potential pruning points detection and localization, based
on the plant model which is a simplification of the grapevine structure. With
this approach, we are able to identify a significant set of potential pruning
points on the canes, that can be used, with further selection, to derive the
final set of the real pruning points.


Abstract:
  This paper is concerned with the large time behavior of the Cauchy problem
for Navier-Stokes/Allen-Cahn system describing the interface motion of
immiscible two-phase flow in 3-D. The existence and uniqueness of global
solutions and the stability of the phase separation state is proved under the
small initial perturbations. Moreover, the optimal time decay rates are
obtained for higher-order spatial derivatives of density, velocity and phase.
Our results implies that if the immiscible two-phase flow is initially located
near the phase separation state, then under small perturbation conditions, the
solution exists globally and decays algebraically to the complete separation
state of the two-phase flow, that is, there will be no interface fracture,
vacuum, shock wave, mass concentration at any time, and the interface thickness
tends to zero as the time $t\rightarrow+\infty$.


Abstract:
  We study orbital diamagnetism in $(2+1)$-dimensional Dirac electrons with a
short-range interaction at zero temperature. We introduce orbital magnetic
fields into spinless Dirac electrons on the $\pi$-flux square lattice, and
analyze them by using infinite density matrix renormalization group. It is
found that the diamagnetism remains intact when the short-range interaction $V$
is weak, while it is monotonically suppressed for larger interactions. Around
the quantum critical point (QCP) of a charge density wave phase transition, we
find a scaling behavior of the ground state energy density characteristic of
the chiral Ising universality class. This leads to universal orbital
diamagnetism in correlated Dirac electrons around the QCP, which may be
regarded as a quantum, magnetic analogue of critical Casimir effect which has
been widely studied for classical phase transitions.


Abstract:
  We review the current status and recent progress of microscopic many-body
approaches and phenomenological models, which are employed to construct the
equation of state of neutron stars. The equation of state is relevant for the
description of their structure and dynamical properties, and it rules also the
dynamics of core-collapse supernovae and binary neutron star mergers. We
describe neutron star matter assuming that the main degrees of freedom are
nucleons and hyperons, disregarding the appearance of quark matter. We compare
the theoretical predictions of the different equation-of-state models with the
currently available data coming from both terrestrial laboratory experiments
and recent astrophysical observations. We also analyse the importance of the
nuclear strong interaction and equation of state for the cooling properties of
neutron stars. We discuss the main open challenges in the description of the
equation of state, mainly focusing on the limits of the different many-body
techniques, the so-called "hyperon puzzle," and the dependence of the direct
URCA processes on the equation of state.


Abstract:
  In this work, we present a new, algorithm for multi-domain learning. Given a
pretrained architecture and a set of visual domains received sequentially, the
goal of multi-domain learning is to produce a single model performing a task in
all the domains together. Recent works showed how we can address this problem
by masking the internal weights of a given original conv-net through learned
binary variables. In this work, we provide a general formulation of binary mask
based models for multi-domain learning by affine transformations of the
original network parameters. Our formulation obtains significantly higher
levels of adaptation to new domains, achieving performances comparable to
domain-specific models while requiring slightly more than 1 bit per network
parameter per additional domain. Experiments on two popular benchmarks showcase
the power of our approach, achieving performances close to state-of-the-art
methods on the Visual Decathlon Challenge.


Abstract:
  There is a growing interest in the learning-to-learn paradigm, also known as
meta-learning, where models infer on new tasks using a few training examples.
Recently, meta-learning based methods have been widely used in few-shot
classification, regression, reinforcement learning, and domain adaptation. The
model-agnostic meta-learning (MAML) algorithm is a well-known algorithm that
obtains model parameter initialization at meta-training phase. In the meta-test
phase, this initialization is rapidly adapted to new tasks by using gradient
descent. However, meta-learning models are prone to overfitting since there are
insufficient training tasks resulting in over-parameterized models with poor
generalization performance for unseen tasks. In this paper, we propose a
Bayesian neural network based MAML algorithm, which we refer to as the B-SMALL
algorithm. The proposed framework incorporates a sparse variational loss term
alongside the loss function of MAML, which uses a sparsifying approximated KL
divergence as a regularizer. We demonstrate the performance of B-MAML using
classification and regression tasks, and highlight that training a sparsifying
BNN using MAML indeed improves the parameter footprint of the model while
performing at par or even outperforming the MAML approach. We also illustrate
applicability of our approach in distributed sensor networks, where sparsity
and meta-learning can be beneficial.


Abstract:
  Graph convolutional networks are becoming indispensable for deep learning
from graph-structured data. Most of the existing graph convolutional networks
share two big shortcomings. First, they are essentially low-pass filters, thus
the potentially useful middle and high frequency band of graph signals are
ignored. Second, the bandwidth of existing graph convolutional filters is
fixed. Parameters of a graph convolutional filter only transform the graph
inputs without changing the curvature of a graph convolutional filter function.
In reality, we are uncertain about whether we should retain or cut off the
frequency at a certain point unless we have expert domain knowledge. In this
paper, we propose Automatic Graph Convolutional Networks (AutoGCN) to capture
the full spectrum of graph signals and automatically update the bandwidth of
graph convolutional filters. While it is based on graph spectral theory, our
AutoGCN is also localized in space and has a spatial form. Experimental results
show that AutoGCN achieves significant improvement over baseline methods which
only work as low-pass filters.


Abstract:
  Star formation in galaxies is a hierarchical process with a wide range of
scales from smaller clusters to larger stellar complexes. Here, we present an
ultra-violet imaging study of the nearby flocculent spiral galaxy NGC 7793,
observed with the Ultra-Violet Imaging Telescope (UVIT). We find that the disk
scale-length estimated in Far-UV (2.64$\pm$0.16 kpc) is larger than that in
Near-UV (2.21$\pm$0.21 kpc) and optical (1.08 kpc), which supports the
inside-out growth scenario of the galaxy disk. The star-forming UV disk is also
found to be contained within the extent of H~I gas of column density greater
than $10^{21}$cm$^{-2}$. With the spatial resolution of UVIT (1 pixel $\sim$
6.8 pc), we identified 2046 young star-forming clumps in the galaxy with radii
between $\sim$ 12 - 70 pc, which matches well with the size of GMCs detected in
the galaxy. Around 61\% of the regions identified in our study have age younger
than 20 Myr, which points to a recent enhancement of star formation across the
galaxy. We also noticed that the youngest star-forming regions, with age $<$ 10
Myr, distinctly trace the flocculent arms of the galaxy. The estimated mass of
the clumps cover a range between $10^3 - 10 ^6 M_{\odot}$. We noticed a
gradient in the mass distribution of identified clumps along the spiral arms.
We have also studied the nuclear star cluster of the galaxy and found that the
stellar populations in the cluster outskirts are younger than the inner part.


Abstract:
  Identifying the intrinsic and extrinsic origins of magneto-transport in
spin-orbit coupled systems has long been a central theme in condensed matter
physics. However, it has been elusive owing to the lack of an appropriate
experimental tool. In this work, using terahertz time-domain spectroscopy, we
unambiguously disentangle the intrinsic and extrinsic contributions to the
anisotropic magnetoresistance (AMR) of a permalloy film. We find that the
scattering-independent intrinsic contribution to AMR is sizable and is as large
as the scattering-dependent extrinsic contribution to AMR. Moreover, the
portion of intrinsic contribution to total AMR increases with increasing
temperature due to the reduction of extrinsic contribution. Further
investigation reveals that the reduction of extrinsic contribution is caused by
the phonon/magnon-induced negative AMR. Our result will stimulate further
researches on other spin-orbit-interaction-induced phenomena for which
identifying the intrinsic and extrinsic contributions is important.


Abstract:
  The starting point to understanding cluster properties is the putative global
minimum and all the nearby local energy minima; however, locating them is
computationally expensive and challenging due to a combinatorial explosion
problem. The relative populations and spectroscopic properties of a molecule
that are a function of temperature can be approximately computed by employing
statistical thermodynamics. Here, we investigate temperature-entropy-driven
isomers distribution on Be$_6$B$_{11}^{-}$ fluxional cluster and the effect of
temperature on their infrared spectroscopy and relative populations. We
identify the vibration modes possessed by the cluster that significantly
contribute to the zero-point energy. A couple of steps are considered for
computing the temperature-dependent relative population: First, using a genetic
algorithm coupled to density functional theory, we performed an extensive and
systematic exploration of the potential/free energy surface of
Be$_6$B$_{11}^{-}$ cluster to locate the putative global minimum and elucidate
the low-energy structures. Second, the relative populations' temperature
effects are determined by considering the thermodynamic properties and
Boltzmann factors. The temperature-dependent relative populations show that the
entropies and temperature are essential for determining the global minimum. We
compute the temperature-dependent total infrared spectra employing the
Boltzmann factor weighted sums of each isomer's infrared spectrum and find that
at finite temperature, the total infrared spectrum is composed of an admixture
of infrared spectra that corresponds to the spectrum of the lowest energy
structure and its isomers located at higher energies. The methodology and
results describe the thermal effects in the relative population and the
infrared spectra.


Abstract:
  We provide a new five dimensional black string--like solution by means of the
embedding of a four dimensional regular black hole into a compact extra
dimension. We enunciate a list of constraints in order to that the five
dimensional black string--like solution to be regular, and, following these
constraints we construct our solution. Instead of the usual singularity, it is
formed a core whose topology corresponds to the product between the de--Sitter
core of the four dimensional Hayward solution and $S^1$. The horizon has
topology $S^2 \times S^1$. At infinity of the radial coordinate the regular
four dimensional geometry is asymptotically flat, {\it i.e}, at this place the
topology of the complete solution corresponds to the product between Minkowski
and $S^1$. At the induced four dimensional geometry we compute the correct
values of temperature and entropy.


Abstract:
  To reversify an arbitrary sequential algorithm $A$, we gently instrument $A$
with bookkeeping machinery. The result is a step-for-step reversible algorithm
that mimics $A$ step-for-step and stops exactly when $A$ does.
  Without loss of generality, we presume that algorithm $A$ is presented as an
abstract state machine that is behaviorally identical to $A$. The existence of
such representation has been proven theoretically, and the practicality of such
representation has been amply demonstrated.


Abstract:
  Quantum machine learning (QML) has been identified as one of the key fields
that could reap advantages from near-term quantum devices, next to optimization
and quantum chemistry. Research in this area has focused primarily on
variational quantum algorithms (VQAs), and several proposals to enhance
supervised, unsupervised and reinforcement learning (RL) algorithms with VQAs
have been put forward. Out of the three, RL is the least studied and it is
still an open question whether VQAs can be competitive with state-of-the-art
classical algorithms based on neural networks (NNs) even on simple benchmark
tasks. In this work, we introduce a training method for parametrized quantum
circuits (PQCs) to solve RL tasks for discrete and continuous state spaces
based on the deep Q-learning algorithm. To evaluate our model, we use it to
solve two benchmark environments from the OpenAI Gym, Frozen Lake and Cart
Pole. We provide insight into why the performance of a VQA-based Q-learning
algorithm crucially depends on the observables of the quantum model and show
how to choose suitable observables based on the RL task at hand. We compare the
performance of our model to that of a NN for agents that need similar time to
convergence, and find that our quantum model needs approximately one-third of
the parameters of the classical model to solve the Cart Pole environment in a
similar number of episodes on average. We also show how recent separation
results between classical and quantum agents for policy gradient RL can be
adapted to quantum Q-learning agents, which yields a quantum speed-up for
Q-learning. This work paves the way towards new ideas on how a quantum
advantage may be obtained for real-world problems in the future.


Abstract:
  In this work a mismatched section of a transmission line is examined for its
utility in a bandpass filtering application. A model to analyze the situation
is given. It was found that the transfer characteristics of such mismatched
transmission line resemble with that of a narrow bandpass filter with steeper
roll-off. It has been shown that the center frequency of the response depends
on the length of the mismatched section as well as the dielectric material of
the line. Further it has been shown that the center frequency can be tuned to a
desired value by adjusting the dielectric profile along the length of the line.
This was tested on a set of parallel wire transmission lines of three lengths
and liquid dielectrics. During the study it was noticed that a sensing
mechanism to detect variation in small volumes of the liquid dielectrics with
high sensitivity can be developed using mismatched transmission lines.


Abstract:
  Organ transplantation is often the last resort for treating end-stage
illness, but the probability of a successful transplantation depends greatly on
compatibility between donors and recipients. Current medical practice relies on
coarse rules for donor-recipient matching, but is short of domain knowledge
regarding the complex factors underlying organ compatibility. In this paper, we
formulate the problem of learning data-driven rules for organ matching using
observational data for organ allocations and transplant outcomes. This problem
departs from the standard supervised learning setup in that it involves
matching the two feature spaces (i.e., donors and recipients), and requires
estimating transplant outcomes under counterfactual matches not observed in the
data. To address these problems, we propose a model based on representation
learning to predict donor-recipient compatibility; our model learns
representations that cluster donor features, and applies donor-invariant
transformations to recipient features to predict outcomes for a given
donor-recipient feature instance. Experiments on semi-synthetic and real-world
datasets show that our model outperforms state-of-art allocation methods and
policies executed by human experts.


Abstract:
  One of the limiting factors in training data-driven, rare-event prediction
algorithms is the scarcity of the events of interest resulting in an extreme
imbalance in the data. There have been many methods introduced in the
literature for overcoming this issue; simple data manipulation through
undersampling and oversampling, utilizing cost-sensitive learning algorithms,
or by generating synthetic data points following the distribution of the
existing data. While synthetic data generation has recently received a great
deal of attention, there are real challenges involved in doing so for
high-dimensional data such as multivariate time series. In this study, we
explore the usefulness of the conditional generative adversarial network (CGAN)
as a means to perform data-informed oversampling in order to balance a large
dataset of multivariate time series. We utilize a flare forecasting benchmark
dataset, named SWAN-SF, and design two verification methods to both
quantitatively and qualitatively evaluate the similarity between the generated
minority and the ground-truth samples. We further assess the quality of the
generated samples by training a classical, supervised machine learning
algorithm on synthetic data, and testing the trained model on the unseen, real
data. The results show that the classifier trained on the data augmented with
the synthetic multivariate time series achieves a significant improvement
compared with the case where no augmentation is used. The popular flare
forecasting evaluation metrics, TSS and HSS, report 20-fold and 5-fold
improvements, respectively, indicating the remarkable statistical similarities,
and the usefulness of CGAN-based data generation for complicated tasks such as
flare forecasting.


Abstract:
  The shape of the anisotropic velocity distribution function, beyond the realm
of strict Maxwellians can play a significant role in determining the evolution
of the Weibel instability dictating the dynamics of self-generated magnetic
fields. For non-Maxwellian distribution functions, we show that the direction
of the maximum growth rate wavevector changes with shape. We investigate
different laser-plasma interaction model distributions which show that their
Weibel generated magnetic fields may require closer scrutiny beyond the second
moment (temperature) anisotropy ratio characterization.


Abstract:
  Correlated topological magnets are emerging as a new class of quantum
materials, exhibiting exotic interacting fermions and unconventional phase
transitions. Despite considerable interest, direct observation of the magnetic
manipulation of topological quasiparticles remains limited. Here we report a
correlated topological phase transition in a kagome spin-orbit semimetal,
examined by high-resolution photoemission spectroscopy. By modulating the
magnetic order, we observe a clear exchange gap collapse in our spectra,
associated with a large renormalization of a spin-orbit-gapped Weyl loop at the
Fermi level. This unexpected response suggests the collapse of opposite-spin
partner ferromagnetic Weyl loops into a paramagnetic Dirac loop. Taken together
with $ab$ $initio$ calculation, our results further indicate that
oppositely-charged Weyl points pair up and annihilate under collapse, and the
Fermi arc surface states are removed. Our findings suggest a novel topological
phase transition driven by magnetic interactions, guiding future exploration of
renormalized topology under correlated order parameters.


Abstract:
  CrSb is an attractive material for room-temperature antiferromagnetic
spintronic applications because of its high N\'{e}el temperature $\sim$700 K
and semi-metallic character. We study the magnetic properties of CrSb bilayers
on few-layer topological insulator thin films using \emph{ab initio} density
functional theory. We find that the intrinsic parts of the total anomalous Hall
conductivities of the thin films are non-zero, and approximately quantized. The
N\'{e}el temperature of CrSb bilayers on few-layer topological insulator thin
films is found to be approximately two times larger than that of an isolated
CrSb thin film. Due to the low Fermi level density of states of CrSb, Hall
quantization might be achievable by introducing disorder. CrSb bilayers on
topological insulator surfaces are therefore attractive candidates for
high-temperature quantum anomalous Hall effects.


Abstract:
  Clustering is grouping of data by the proximity of some properties. We report
on the possibility of increasing the efficiency of clustering of points in a
plane using artificial quantum neural networks after the replacement of the
two-level neurons called qubits represented by the spins S = 1/2 by the
three-level neurons called qutrits represented by the spins S = 1. The problem
has been solved by the slow adiabatic change of the Hamiltonian in time. The
methods for controlling a qutrit system using projection operators have been
developed and the numerical simulation has been performed. The Hamiltonians for
two well-known cluster-ing methods, one-hot encoding and k-means ++, have been
built. The first method has been used to partition a set of six points into
three or two clusters and the second method, to partition a set of nine points
into three clusters and seven points into four clusters. The simulation has
shown that the clustering problem can be ef-fectively solved on qutrits
represented by the spins S = 1. The advantages of clustering on qutrits over
that on qubits have been demonstrated. In particular, the number of qutrits
required to represent data points is smaller than the number of qubits by a
factor of log2 N / log3 N . Since, for qutrits, it is easier to partition the
data points into three clusters rather than two ones, the approximate
hierarchical procedure of data partition-ing into a larger number of clusters
is accelerated. At the exact data partition into more than three clusters, it
has been proposed to number the clusters by the numbers of states of the
corresponding multi-spin subsys-tems, instead of using the numbers of
individual spins. This reduces even more the number of qutrits (N log3 K
instead of NK ) required to implement the algorithm.


Abstract:
  We propose a globally convergent numerical method, called the
convexification, to numerically compute the viscosity solution to first-order
Hamilton-Jacobi equations through the vanishing viscosity process where the
viscosity parameter is a fixed small number. By convexification, we mean that
we employ a suitable Carleman weight function to convexify the cost functional
defined directly from the form of the Hamilton-Jacobi equation under
consideration. The strict convexity of this functional is rigorously proved
using a new Carleman estimate. We also prove that the unique minimizer of the
this strictly convex functional can be reached by the gradient descent method.
Moreover, we show that the minimizer well approximates the viscosity solution
of the Hamilton-Jacobi equation as the noise contained in the boundary data
tends to zero. Some interesting numerical illustrations are presented.


Abstract:
  Nanocellulose was extracted from short bast fibers, from hemp (Cannabis
Sativa L.) plants harvested at seed maturity, non-retted, mechanically
decorticated in a defibering apparatus giving non-aligned fibers. A chemical
pretreatment with NaOH and HCl allowed the removal of most of the
non-cellulosic components of the fibers. No bleaching was performed. The
chemically pretreated fibers were then refined in a beater and treated with a
cellulase enzyme, before the mechanical defibrillation in an ultrafine friction
grinder. The fibers were characterized by microscopy, infrared spectroscopy,
thermogravimetric analysis, and X-ray diffraction after each step of the
process, to understand the evolution of their morphology and composition. The
obtained nanocellulose suspension was composed of rod-like fibrils with widths
of 5-12 nm, stacks of nanofibrils with widths of 20-200 nm, and some larger
fibers. The crystallinity index was found to increase from 74% for the raw
fibers to 80% for the nanocellulose. The nanocellulose retained a yellowish
color indicating the presence of some residual lignin. Properties of nanopaper
prepared with the hemp nanocellulose were similar to those of nanopapers
prepared with wood pulp derived rod-like nanofibrils.


Abstract:
  We consider electrodiffusion of ions in fluids, described by the
Nernst-Planck-Navier-Stokes system, in three dimensional bounded domains, with
blocking (no-flux) boundary conditions for the ionic concentrations and Robin
boundary conditions for the electric potential, representing the presence of an
electrical double layer. We prove global existence of strong solutions for
arbitrary initial data in the case of two oppositely charged ionic species and
also in the case of more than two species when the diffusivities are equal and
the magnitudes of the valences are also equal. This result holds
unconditionally in the case where fluid flow is described by the Stokes
equations. In the case of Navier-Stokes coupling, the result holds
conditionally on Navier-Stokes regularity. We also consider a case of mixed
blocking-Dirichlet boundary conditions for the ionic concentrations and prove
global regularity for two oppositely charged species when the total
concentration of one species is sufficiently low.


Abstract:
  Previous cycle-consistency correspondence learning methods usually leverage
image patches for training. In this paper, we present a fully convolutional
method, which is simpler and more coherent to the inference process. While
directly applying fully convolutional training results in model collapse, we
study the underline reason behind this collapse phenomenon, indicating that the
absolute positions of pixels provide a shortcut to easily accomplish
cycle-consistence, which hinders the learning of meaningful visual
representations. To break this absolute position shortcut, we propose to apply
different crops for forward and backward frames, and adopt feature warping to
establish correspondence between two crops of a same frame. The former
technique enforces the corresponding pixels at forward and back tracks to have
different absolute positions, and the latter effectively blocks the shortcuts
going between forward and back tracks. In three label propagation benchmarks
for pose tracking, face landmark tracking and video object segmentation, our
method largely improves the results of vanilla fully convolutional
cycle-consistency method, achieving very competitive performance compared with
the self-supervised state-of-the-art approaches. Our trained model and code are
available at \url{https://github.com/Steve-Tod/STFC3}.


Abstract:
  This paper develops an algorithmic-based approach for proving inductive
properties of propositional sequent systems such as admissibility,
invertibility, cut-elimination, and identity expansion. Although undecidable in
general, these structural properties are crucial in proof theory because they
can reduce the proof-search effort and further be used as scaffolding for
obtaining other meta-results such as consistency. The algorithms -- which take
advantage of the rewriting logic meta-logical framework, and use rewrite- and
narrowing-based reasoning -- are explained in detail and illustrated with
examples throughout the paper. They have been fully mechanized in the
L-Framework, thus offering both a formal specification language and
off-the-shelf mechanization of the proof-search algorithms coming together with
semi-decision procedures for proving theorems and meta-theorems of the object
system. As illustrated with case studies in the paper, the L-Framework,
achieves a great degree of automation when used on several propositional
sequent systems, including single conclusion and multi-conclusion
intuitionistic logic, classical logic, classical linear logic and its dyadic
system, intuitionistic linear logic, and normal modal logics.


Abstract:
  For mobile robots to be effectively applied to real world unstructured
environments -- such as large scale farming -- they require the ability to
generate adaptive plans that account both for limited onboard resources, and
the presence of dynamic changes, including nearby moving individuals. This work
provides a real world empirical evaluation of our proposed hierarchical
framework for long-term autonomy of field robots, conducted on University of
Sydney's Swagbot agricultural robot platform. We demonstrate the ability of the
framework to navigate an unstructured and dynamic environment in an effective
manner, validating its use for long-term deployment in large scale farming, for
tasks such as autonomous weeding in the presence of moving individuals.


Abstract:
  We derive a new twist-3 partonic sum rule for the transverse spin of the
proton, which involves the well-know quark spin structure function
$g_T(x)=g_1(x)+g_2(x)$, the less-studied but known transverse gluon
polarization density $\Delta G_T(x)$, and quark and gluon canonical orbital
angular momentum densities associated with transverse polarization. This is the
counter part of the sum rule for the longitudinal spin of the proton derived by
Jaffe and Manohar previously. We relate the partonic canonical orbital angular
momentum densities to a new class of twist-3 generalized parton distribution
functions which are potentially measurable in deep-virtual exclusive processes.
We also discuss in detail an important technicality related to the transverse
polarization in the infinite momentum frame, i.e., separation of intrinsic
contributions from the extrinsic ones. We apply our finding to the
transverse-space distributions of partons, angular momentum, and magnetic
moment, respectively, in a transversely polarized proton.


Abstract:
  Molecular Communications (MC) is a bio-inspired communication technique that
uses molecules to transfer information among bio-nano devices. In this paper,
we focus on the detection problem for biological MC receivers employing ligand
receptors to infer the transmitted messages encoded into the concentration of
molecules, i.e., ligands. In practice, receptors are not ideally selective
against target ligands, and in physiological environments, they can interact
with multiple types of ligands at different reaction rates depending on their
binding affinity. This molecular cross-talk can cause a substantial
interference on MC. Here we consider a particular scenario, where there is
non-negligible concentration of interferer molecules in the channel, which have
similar receptor-binding characteristics with the information molecules, and
the receiver employs single type of receptors. We investigate the performance
of four different detection methods, which make use of different statistics of
the ligand-receptor binding reactions: instantaneous number of bound receptors,
unbound time durations of receptors, bound time durations of receptors, and
combination of unbound and bound time durations of receptors within a sampling
time interval. The performance of the introduced detection methods are
evaluated in terms of bit error probability for varying strength of molecular
interference, similarity between information and interferer molecules, number
of receptors, and received concentration difference between bit-0 and bit-1
transmissions. We propose synthetic receptor designs that can convert the
required receptor statistics to the concentration of intracellular molecules,
and chemical reaction networks that can chemically perform the computations
required for detection.


Abstract:
  We have analytically explored the Rayleigh-Sommerfeld scalar diffraction for
oblique incidence. We have explicitly derived the Fraunhofer diffraction
formulae for oblique incidence of plane scalar wave on various apertures, such
as single-slit, circular aperture, and diffraction grating. Such derivations in
the background of Rayleigh-Sommerfeld scalar diffraction theory would be
important for an undergraduate course on optics.


Abstract:
  High performance rack-scale offerings package disaggregated pools of compute,
memory and storage hardware in a single rack to run diverse workloads with
varying requirements, including applications that need low and predictable
latency. The intra-rack network is typically high speed Ethernet, which can
suffer from congestion leading to packet drops and may not satisfy the
stringent tail latency requirements for some workloads (including remote
memory/storage accesses). In this paper, we design a Predictable Low
Latency(PL2) network architecture for rack-scale systems with Ethernet as
interconnecting fabric. PL2 leverages programmable Ethernet switches to
carefully schedule packets such that they incur no loss with NIC and switch
queues maintained at small, near-zero levels. In our 100 Gbps rack-prototype,
PL2 keeps 99th-percentile memcached RPC latencies under 60us even when the RPCs
compete with extreme offered-loads of 400%, without losing traffic. Network
transfers for a machine learning training task complete 30% faster than a
receiver-driven scheme implementation modeled after Homa (222ms vs 321ms 99%ile
latency per iteration).


Abstract:
  Customer support via chat requires agents to resolve customer queries with
minimum wait time and maximum customer satisfaction. Given that the agents as
well as the customers can have varying levels of literacy, the overall quality
of responses provided by the agents tend to be poor if they are not predefined.
But using only static responses can lead to customer detraction as the
customers tend to feel that they are no longer interacting with a human. Hence,
it is vital to have variations of the static responses to reduce monotonicity
of the responses. However, maintaining a list of such variations can be
expensive. Given the conversation context and the agent response, we propose an
unsupervised frame-work to generate contextual paraphrases using autoregressive
models. We also propose an automated metric based on Semantic Similarity,
Textual Entailment, Expression Diversity and Fluency to evaluate the quality of
contextual paraphrases and demonstrate performance improvement with
Reinforcement Learning (RL) fine-tuning using the automated metric as the
reward function.


Abstract:
  We study the problem $\#\mathrm{EdgeSub}(\Phi)$ of counting $k$-edge
subgraphs satisfying a given graph property $\Phi$ in a large host graph $G$.
Building upon the breakthrough result of Curticapean, Dell and Marx (STOC 17),
we express the number of such subgraphs as a finite linear combination of graph
homomorphism counts and derive the complexity of computing this number by
studying its coefficients.
  Our approach relies on novel constructions of low-degree Cayley graph
expanders of $p$-groups, which might be of independent interest. The properties
of those expanders allow us to analyse the coefficients in the aforementioned
linear combinations over the field $\mathbb{F}_p$ which gives us significantly
more control over the cancellation behaviour of the coefficients. Our main
result is an exhaustive and fine-grained complexity classification of
$\#\mathrm{EdgeSub}(\Phi)$ for minor-closed properties $\Phi$, closing the
missing gap in previous work by Roth, Schmitt and Wellnitz (ICALP 21).
  Additionally, we observe that our methods also apply to modular counting.
Among others, we investigate the problems of modular counting of paths, cycles,
forests and matroid bases. In the course of our investigations we also provide
an exhaustive parameterized complexity classification for the problem of
counting graph homomorphisms modulo a prime $p$.


Abstract:
  Matter-wave interferometry with atoms propagating in a guiding potential is
expected to provide compact, scalable and precise inertial sensing. However, a
rotation sensing device based on the Sagnac effect with atoms guided in a ring
has not yet been implemented despite continuous efforts during the last two
decades. Here we discuss some intrinsic effects that limit the coherence in
such a device and propose a scheme that overcomes these limitations and enables
a multi-pass guiding Sagnac interferometer with a Bose-Einstein condensate
(BEC) on a chip in a ring potential. We analyze crucial dephasing effects:
potential roughness, phase diffusion due to atom-atom interactions and number
uncertainty, and phase fluctuations. Owing to the recent progress in achieving
high momentum beam splitting, creating smooth guides, and manipulating the
matter-wavepacket propagation, guided interferometry can be implemented within
the coherence time allowed by phase diffusion. Despite the lower particle flux
in a guided Sagnac ring and the miniaturization of the interferometer, the
estimated sensitivity, for reasonable and practical realizations of an atom
chip-based gyroscope, is comparable to that of free-space interferometers,
reaching 45 nrads^{-1}Hz^{-1/2}. A significant improvement over
state-of-the-art free-space gyrocope sensitivities can be envisioned by using
thermal atoms instead of a BEC, whereby the interferometer can be operated in a
continuous fashion with the coherence limited by the scattering rate of the
atoms with the background gas. Taking into account the sensitivity times length
of the interferometer as the figure of merit which takes into account
compactness, our configuration is expected to deliver a potential improvement
of 2-4 orders of magnitude over state-of-the-art free-space gyroscopes for a
BEC, and 4-6 orders of magnitude for thermal atoms.


Abstract:
  We introduce wave encoded acquisition and reconstruction techniques for
highly accelerated echo planar imaging (EPI) with reduced g-factor penalty and
image artifacts. Wave-EPI involves playing sinusoidal gradients during the EPI
readout while employing interslice shifts as in blipped-CAIPI acquisitions.
This spreads the aliasing in all spatial directions, thereby taking better
advantage of 3D coil sensitivity profiles. The amount of voxel spreading that
can be achieved by the wave gradients during the short EPI readout period is
constrained by the slew rate of the gradient coils and peripheral nerve
stimulation (PNS) monitor. We propose to use a half-cycle sinusoidal gradient
to increase the amount of voxel spreading that can be achieved while respecting
the slew and stimulation constraints. Extending wave-EPI to multi-shot
acquisition minimizes geometric distortion and voxel blurring at high in-plane
resolution, while structured low-rank regularization mitigates shot-to-shot
phase variations without additional navigators. We propose to use different
point spread functions (PSFs) for the k-space lines with positive and negative
polarities, which are calibrated with a FLEET-based reference scan and allow
for addressing gradient imperfections. Wave-EPI provided whole-brain
single-shot gradient echo (GE) and multi-shot spin echo (SE) EPI acquisitions
at high acceleration factors and was combined with g-Slider slab encoding to
boost the SNR level in 1mm isotropic diffusion imaging. Relative to
blipped-CAIPI, wave-EPI reduced average and maximum g-factors by up to 1.21-
and 1.37-fold, respectively. In conclusion, wave-EPI allows highly accelerated
single- and multi-shot EPI with reduced g-factor and artifacts and may
facilitate clinical and neuroscientific applications of EPI by improving the
spatial and temporal resolution in functional and diffusion imaging.


Abstract:
  I give an elementary proof that a quasi-symmetric design without repeated
blocks on $v$ points has at most $v\choose2$ blocks, with equality if and only
if it is a tight $4$-design.


Abstract:
  Resonant beam communications (RBCom) is capable of providing wide bandwidth
when using light as the carrier. Besides, the RBCom system possesses the
characteristics of mobility, high signal-to-noise ratio (SNR), and
multiplexing. Nevertheless, the channel of the RBCom system is distinct from
other light communication technologies due to the echo interference issue. In
this paper, we reveal the mechanism of the echo interference and propose the
method to eliminate the interference. Moreover, we present an exemplary design
based on frequency shifting and optical filtering, along with its mathematic
model and performance analysis. The numerical evaluation shows that the channel
capacity is greater than 15 bit/s/Hz.


Abstract:
  The simplest scheme for predicting real galaxy properties after performing a
dark matter simulation is to rank order the real systems by stellar mass and
the simulated systems by halo mass and then simply assume monotonicity - that
the more massive halos host the more massive galaxies. This has had some
success, but we study here if a better motivated and more accurate matching
scheme is easily constructed by looking carefully at how well one could predict
the simulated IllustrisTNG galaxy sample from its dark matter computations. We
find that using the dark matter rotation curve peak velocity, $v_{max}$, for
normal galaxies reduces the error of the prediction by 30% (18% for central
galaxies and 60% for satellite systems) - following expectations from the
physics of monolithic collapse. For massive systems with halo mass $>$
10$^{12.5}$ M$_{\odot}$ hierarchical merger driven formation is the better
model and dark matter halo mass remains the best single metric. Using a new
single variable that combines these effects, $\phi$ $=$
$v_{max}$/$v_{max,12.7}$ + M$_{peak}$/(10$^{12.7}$ M$_{\odot}$) allows further
improvement and reduces the error, as compared to ranking by dark matter mass
at $z=0$ by another 6% from $v_{max}$ ranking. Two parameter fits -- including
environmental effects produce only minimal further impact.


Abstract:
  Given a family of locally Lipschitz vector fields
$X(x)=(X_1(x),\dots,X_m(x))$ on $\mathbb{R}^n$, $m\leq n$, we study integral
functionals depending on $X$. Using the results in \cite{MPSC1}, we study the
convergence of minima, minimizers and momenta of those functionals. Moreover,
we apply these results to the periodic homogenization in Carnot groups and to
prove a $H$-compactness theorem for linear differential operators of the second
order depending on $X$.


Abstract:
  The inner representation of deep neural networks (DNNs) is indecipherable,
which makes it difficult to tune DNN models, control their training process,
and interpret their outputs. In this paper, we propose a novel approach to
investigate the inner representation of DNNs through topological data analysis
(TDA). Persistent homology (PH), one of the outstanding methods in TDA, was
employed for investigating the complexities of trained DNNs. We constructed
clique complexes on trained DNNs and calculated the one-dimensional PH of DNNs.
The PH reveals the combinational effects of multiple neurons in DNNs at
different resolutions, which is difficult to be captured without using PH.
Evaluations were conducted using fully connected networks (FCNs) and networks
combining FCNs and convolutional neural networks (CNNs) trained on the MNIST
and CIFAR-10 data sets. Evaluation results demonstrate that the PH of DNNs
reflects both the excess of neurons and problem difficulty, making PH one of
the prominent methods for investigating the inner representation of DNNs.


Abstract:
  Covariance matrix estimation techniques require high acquisition costs that
challenge the sampling systems' storing and transmission capabilities. For this
reason, various acquisition approaches have been developed to simultaneously
sense and compress the relevant information of the signal using random
projections. However, estimating the covariance matrix from the random
projections is an ill-posed problem that requires further information about the
data, such as sparsity, low rank, or stationary behavior. Furthermore, this
approach fails using high compression ratios. Therefore, this paper proposes an
algorithm based on the projected gradient method to recover a low-rank or
Toeplitz approximation of the covariance matrix. The proposed algorithm divides
the data into subsets projected onto different subspaces, assuming that each
subset contains an approximation of the signal statistics, improving the
inverse problem's condition. The error induced by this assumption is
analytically derived along with the convergence guarantees of the proposed
method. Extensive simulations show that the proposed algorithm can effectively
recover the covariance matrix of hyperspectral images with high compression
ratios (8-15% approx) in noisy scenarios. Additionally, simulations and
theoretical results show that filtering the gradient reduces the estimator's
error recovering up to twice the number of eigenvectors.


Abstract:
  Ly$\alpha$-emitting galaxies (LAEs) are easily detectable in the
high-redshift Universe and are potentially efficient tracers of large scale
structure at early epochs, as long as their observed properties do not strongly
depend on environment. We investigate the luminosity and equivalent width
functions of LAEs in the overdense field of a protocluster at redshift $z
\simeq 3.78$. Using a large sample of LAEs (many spectroscopically confirmed),
we find that the Ly$\alpha$ luminosity distribution is well-represented by a
Schechter (1976) function with $\log(L^{\ast}/{\rm erg s^{-1}}) =
43.26^{+0.20}_{-0.22}$ and $\log(\phi^{\ast}/{\rm
Mpc^{-3}})=-3.40^{+0.03}_{-0.04}$ with $\alpha=-1.5$. Fitting the equivalent
width distribution as an exponential, we find a scale factor of
$\omega=79^{+15}_{-15}$ Angstroms. We also measured the Ly$\alpha$ luminosity
and equivalent width functions using the subset of LAEs lying within the
densest cores of the protocluster, finding similar values for $L^*$ and
$\omega$. Hence, despite having a mean overdensity more than 2$\times$ that of
the general field, the shape of the Ly$\alpha$ luminosity function and
equivalent width distributions in the protocluster region are comparable to
those measured in the field LAE population by other studies at similar
redshift. While the observed Ly$\alpha$ luminosities and equivalent widths show
correlations with the UV continuum luminosity in this LAE sample, we find that
these are likely due to selection biases and are consistent with no intrinsic
correlations within the sample. This protocluster sample supports the strong
evolutionary trend observed in the Ly$\alpha$ escape fraction and suggest that
lower redshift LAEs can be on average significantly more dusty that their
counterparts at higher redshift.


Abstract:
  This paper proposes a generalization of Gaussian mixture models, where the
mixture weight is allowed to behave as an unknown function of time. This model
is capable of successfully capturing the features of the data, as demonstrated
by simulated and real datasets. It can be useful in studies such as clustering,
change-point and process control. In order to estimate the mixture weight
function, we propose two new Bayesian nonlinear dynamic approaches for
polynomial models, that can be extended to other problems involving polynomial
nonlinear dynamic models. One of the methods, called here component-wise
Metropolis-Hastings, apply the Metropolis-Hastings algorithm to each local
level component of the state equation. It is more general and can be used in
any situation where the observation and state equations are nonlinearly
connected. The other method tends to be faster, but is applied specifically to
binary data (using the probit link function). The performance of these methods
of estimation, in the context of the proposed dynamic Gaussian mixture model,
is evaluated through simulated datasets. Also, an application to an array
Comparative Genomic Hybridization (aCGH) dataset from glioblastoma cancer
illustrates our proposal, highlighting the ability of the method to detect
chromosome aberrations.


Abstract:
  Hyperdimensional computing (HDC) has emerged as a new light-weight learning
algorithm with smaller computation and energy requirements compared to
conventional techniques. In HDC, data points are represented by
high-dimensional vectors (hypervectors), which are mapped to high-dimensional
space (hyperspace). Typically, a large hypervector dimension ($\geq1000$) is
required to achieve accuracies comparable to conventional alternatives.
However, unnecessarily large hypervectors increase hardware and energy costs,
which can undermine their benefits. This paper presents a technique to minimize
the hypervector dimension while maintaining the accuracy and improving the
robustness of the classifier. To this end, we formulate the hypervector design
as a multi-objective optimization problem for the first time in the literature.
The proposed approach decreases the hypervector dimension by more than
$32\times$ while maintaining or increasing the accuracy achieved by
conventional HDC. Experiments on a commercial hardware platform show that the
proposed approach achieves more than one order of magnitude reduction in model
size, inference time, and energy consumption. We also demonstrate the trade-off
between accuracy and robustness to noise and provide Pareto front solutions as
a design parameter in our hypervector design.


Abstract:
  The conventional photon blockade for high-frequency mode is investigated in a
two-mode second-order nonlinear system embedded with a two-level atom. By
solving the master equation and calculating the zero-delay-time second-order
correlation function $g^{(2)}(0)$, we obtain that strong photon antibunching
can be achieved in this scheme. In particular, we find that by increasing the
linear coupling coefficient of the system, a perfect blockade region will be
formed near the zero second-order nonlinear coupling coefficient. Similarly, by
increasing the nonlinear coupling coefficient of the system, the perfect
blockade zone will appear. And this scheme is not sensitive to the reservoir
temperature, both of which make the current system easier to implement
experimentally.


Abstract:
  We develop an exact analytical approach to the optical response of a quantum
dot-microcavity system for arbitrary excitation strengths. The response is
determined in terms of the complex amplitudes of transitions between the rungs
of the Jaynes-Cummings ladder, explicitly isolating nonlinearities of different
orders. Increasing the pulse area of the excitation field, we demonstrate the
formation of a quantum Mollow quadruplet (QMQ), quantizing the semi-classical
Mollow triplet into a coherent superposition of a large number of transitions
between rungs of the ladder, with inner and outer doublets of the QMQ formed by
densely lying inner and outer quantum transitions between the split rungs.
Remarkably, a closed-form analytic approximation for the QMQ of any order of
nonlinearity is found in the high-field low-damping limit.


Abstract:
  There is a parameter ubiquitous throughout the deep learning world: learning
rate. There is likewise a ubiquitous question: what should that learning rate
be? The true answer to this question is often tedious and time consuming to
obtain, and a great deal of arcane knowledge has accumulated in recent years
over how to pick and modify learning rates to achieve optimal training
performance. Moreover, the long hours spent carefully crafting the perfect
learning rate can come to nothing the moment your network architecture,
optimizer, dataset, or initial conditions change ever so slightly. But it need
not be this way. We propose a new answer to the great learning rate question:
the Autonomous Learning Rate Controller. Find it at
https://github.com/fastestimator/ARC


Abstract:
  The Petty projection inequality for sets of finite perimeter is proved. Our
approach is based on Steiner symmetrization. Neither the affine Sobolev
inequality nor the functional Minkowski problem is used in our proof. Moreover,
for sets of finite perimeter, we prove the Petty projection inequality with
respect to Steiner symmetrization.


Abstract:
  This work introduces a second-order differential inclusion for unconstrained
convex optimization. In continuous level, solution existence in proper sense is
obtained and exponential decay of a novel Lyapunov function along with the
solution trajectory is derived as well. Then in discrete level, based on
numerical discretizations of the continuous differential inclusion, both an
inexact accelerated proximal point algorithm and an inexact accelerated
proximal gradient method are proposed, and some new convergence rates are
established via a discrete Lyapunov function.


Abstract:
  An edge stream is a common form of presentation of dynamic networks. It can
evolve with time, with new types of nodes or edges being continuously added.
Existing methods for anomaly detection rely on edge occurrence counts or
compare pattern snippets found in historical records. In this work, we propose
Isconna, which focuses on both the frequency and the pattern of edge records.
The burst detection component targets anomalies between individual timestamps,
while the pattern detection component highlights anomalies across segments of
timestamps. These two components together produce three intermediate scores,
which are aggregated into the final anomaly score. Isconna does not actively
explore or maintain pattern snippets; it instead measures the consecutive
presence and absence of edge records. Isconna is an online algorithm, it does
not keep the original information of edge records; only statistical values are
maintained in a few count-min sketches (CMS). Isconna's space complexity
$O(rc)$ is determined by two user-specific parameters, the size of CMSs. In
worst case, Isconna's time complexity can be up to $O(rc)$, but it can be
amortized in practice. Experiments show that Isconna outperforms five
state-of-the-art frequency- and/or pattern-based baselines on six real-world
datasets with up to 20 million edge records.


Abstract:
  Contrastive learning is a key technique of modern self-supervised learning.
The broader accessibility of earlier approaches is hindered by the need of
heavy computational resources (e.g., at least 8 GPUs or 32 TPU cores), which
accommodate for large-scale negative samples or momentum. The more recent
SimSiam approach addresses such key limitations via stop-gradient without
momentum encoders. In medical image analysis, multiple instances can be
achieved from the same patient or tissue. Inspired by these advances, we
propose a simple triplet representation learning (SimTriplet) approach on
pathological images. The contribution of the paper is three-fold: (1) The
proposed SimTriplet method takes advantage of the multi-view nature of medical
images beyond self-augmentation; (2) The method maximizes both intra-sample and
inter-sample similarities via triplets from positive pairs, without using
negative samples; and (3) The recent mix precision training is employed to
advance the training by only using a single GPU with 16GB memory. By learning
from 79,000 unlabeled pathological patch images, SimTriplet achieved 10.58%
better performance compared with supervised learning. It also achieved 2.13%
better performance compared with SimSiam. Our proposed SimTriplet can achieve
decent performance using only 1% labeled data. The code and data are available
at https://github.com/hrlblab/SimTriple.


Abstract:
  The Rabi Hamiltonian, describing the interaction between a two-level atomic
system and a single cavity mode of the electromagnetic field, is one of the
fundamental models in quantum optics. The model becomes exactly solvable by
considering an atom without permanent dipole moments, whose excitation energy
is quasi-resonant with the cavity photon energy, and by neglecting the non
resonant (counter-rotating) terms. In this case, after including the decay of
either the atom or the cavity mode to a continuum, one is able to derive the
well-known phenomenology of quasi-resonant transitions, including the
fluorescence triplets. In this work we consider the most general Rabi model,
incorporating the effects of permanent atomic electric dipole moments, and,
based on a perturbative analysis, we compare the intensities of emission lines
induced by rotating terms, counter-rotating terms and parity-symmetry-breaking
terms. The analysis reveals that the emission strength related to the existence
of permanent dipoles may surpass the one due to the counter-rotating
interaction terms, but is usually much weaker than the emission due to the
main, resonant coupling. This ratio can be modified in systems with a reduced
dimensionality or by engineering the energy spectral density of the continuum.


Abstract:
  As Field-programmable gate arrays (FPGAs) are widely adopted in clouds to
accelerate Deep Neural Networks (DNN), such virtualization environments have
posed many new security issues. This work investigates the integrity of DNN
FPGA accelerators in clouds. It proposes DeepStrike, a remotely-guided attack
based on power glitching fault injections targeting DNN execution. We
characterize the vulnerabilities of different DNN layers against fault
injections on FPGAs and leverage time-to-digital converter (TDC) sensors to
precisely control the timing of fault injections. Experimental results show
that our proposed attack can successfully disrupt the FPGA DSP kernel and
misclassify the target victim DNN application.


Abstract:
  We report on a compact and spectrally intense extreme-ultraviolet (XUV)
source, which is based on high-harmonic generation (HHG) driven by 395 nm
pulses. In order to minimize the XUV virtual source size and to maximize the
XUV flux, HHG is performed several Rayleigh lengths away from the driving laser
focal plane in a high-density gas jet. As a result, a high focused XUV
intensity of $5 \times 10^{13}$ W/cm$^2$ is achieved, using a beamline with a
length of only two meters and a modest driving laser pulse energy of 3 mJ. The
high XUV intensity is demonstrated by performing a nonlinear ionization
experiment in argon, using an XUV spectrum that is dominated by a single
harmonic at 22 eV. Ion charge states up to Ar$^{3+}$ are observed, which
requires the absorption of at least four XUV photons. The high XUV intensity
and the narrow bandwidth are ideally suited for a variety of applications
including photoelectron spectroscopy, the coherent control of resonant
transitions and the imaging of nanoscale structures.


Abstract:
  Let $(M,J,g,\omega)$ be a K\"ahler manifold. We prove a $W^{1,2}$ weak
Bott-Chern decomposition and a $W^{1,2}$ weak Dolbeault decomposition,
following the $L^2$ weak Kodaira decomposition on Riemannian manifolds.
Moreover, if the K\"ahler metric is complete and the sectional curvature is
bounded, the $W^{1,2}$ Bott-Chern decomposition is strictly related to the
space of $W^{1,2}$ Bott-Chern harmonic forms, i.e., $W^{1,2}$ smooth
differential forms which are in the kernel of an elliptic differential operator
of order $4$, called Bott-Chern Laplacian. We also generalize to the non
compact case the well known property that on compact K\"ahler manifolds the
kernel of the Dolbeault Laplacian and the kernel of the Bott-Chern Laplacian
coincide.


Abstract:
  The ensemble methods are meta-algorithms that combine several base machine
learning techniques to increase the effectiveness of the classification. Many
existing committees of classifiers use the classifier selection process to
determine the optimal set of base classifiers. In this article, we propose the
classifiers selection framework with relearning base classifiers. Additionally,
we use in the proposed framework the new generated feature, which can be
obtained after the relearning process. The proposed technique was compared with
state-of-the-art ensemble methods using three benchmark datasets and one
synthetic dataset. Four classification performance measures are used to
evaluate the proposed method.


Abstract:
  K(BEDT-TTF)2Cu(NCS)2 has been investigated by Raman scattering in both bulk
and nanoparticle compounds. Phonon modes from 20 to 1600 cm-1 have been
assigned. Focusing on the unexplored low frequency phonons, a plateau in
frequencies is observed in the bulk phonons between 50 and 100 K and assigned
to the signature of the bad metal phase. Nanoparticles of K(BEDT-TTF)2Cu(NCS)2
exhibit anomalies at 50 K associated to the crossover from a bad metal to a
Fermi liquid whose origins are discussed.


Abstract:
  We construct intrinsic Lipschitz graphs in Carnot groups with the property
that, at every point, there exist infinitely many different blow-up limits,
none of which is a homogeneous subgroup. This provides counterexamples to a
Rademacher theorem for intrinsic Lipschitz graphs.


Abstract:
  We consider the use of rational basis functions to compute the scattering and
inverse scattering transforms associated with the AKNS system. The proposed
numerical forward scattering transform computes the solution of the AKNS system
that is valid on the entire real axis and thereby computes a reflection
coefficient at a point by solving a single linear system. The proposed
numerical inverse scattering transform makes use of a novel improvement in the
rational function approach to the oscillatory Cauchy operator, enabling the
efficient solution of certain Riemann--Hilbert problems without contour
deformations. The latter development enables access to high-precision
computations and this is demonstrated on the inverse scattering transform for
the one-dimensional Schr\"odinger operator with a $\mathrm{sech}^2$ potential.


Abstract:
  Robotic grippers are widely used in industries for handling objects. This
paper presents the procedure of design and fabrication of a stepper motor
controlled robotic gripper to be used in industries for handling small objects.
The gripper has one degree of freedom for linear motion and one degree of
freedom for rotational motion. A linear actuator has been employed to give
translational motion to the gripper. Based on the design requirement data, a
kinematic analysis has been done to accurately predict the design parameters
useful in proper selection of motor and linear actuator.


Abstract:
  We investigate flux-grown Sm-deficient Sm$_x$B$_6$ ($x < 1$) by global and
local tools, including X-ray diffraction (XRD), electronic transport, and
scanning tunneling microscopy (STM) and spectroscopy (STS). All these tools
indicate a remarkable persistence of the SmB$_6$ local structure in the
flux-grown samples even for nominal Sm concentrations as low as $x=0.75$. As a
consequence, the overall electronic properties of Sm$_x$B$_6$, and particularly
the surface conductance at low temperature, is only affected locally by the
Sm-deficiency.


Abstract:
  The information of a quantum system acquired by a Maxwell demon can be used
for either work extraction or entanglement preparation. We study these two
tasks by using a thermal qubit, in which a demon obtains her information from
measurements on the environment of the qubit. The allowed entanglement, between
the qubit and an auxiliary system, is enhanced by the information. And, the
increment is find to be equivalent to the extractable work. The Maxwell demon
is called to be quantum by Beyer \textit{et al.} [Phys. Rev. Lett 123, 250606
(2019) ] if there is quantum steering from the environment to the qubit. In
this case, the postmeasured states of the qubit, after the measurements on its
environment, cannot be simulated by an objective local statistical ensemble. We
present a upper bound of extractable work, and equivalently of the allowed
entanglement, for unsteerable demons, considering two measurements inducing two
orthogonal changes of the Bloch vector of the qubit.


Abstract:
  Knowledge distillation is classically a procedure where a neural network is
trained on the output of another network along with the original targets in
order to transfer knowledge between the architectures. The special case of
self-distillation, where the network architectures are identical, has been
observed to improve generalization accuracy. In this paper, we consider an
iterative variant of self-distillation in a kernel regression setting, in which
successive steps incorporate both model outputs and the ground-truth targets.
This allows us to provide the first theoretical results on the importance of
using the weighted ground-truth targets in self-distillation. Our focus is on
fitting nonlinear functions to training data with a weighted mean square error
objective function suitable for distillation, subject to $\ell_2$
regularization of the model parameters. We show that any such function obtained
with self-distillation can be calculated directly as a function of the initial
fit, and that infinite distillation steps yields the same optimization problem
as the original with amplified regularization. Furthermore, we provide a closed
form solution for the optimal choice of weighting parameter at each step, and
show how to efficiently estimate this weighting parameter for deep learning and
significantly reduce the computational requirements compared to a grid search.


Abstract:
  We consider the problem of ON-OFF privacy in which a user is interested in
the latest message generated by one of n sources available at a server. The
user has the choice to turn privacy ON or OFF depending on whether he wants to
hide his interest at the time or not. The challenge of allowing the privacy to
be toggled between ON and OFF is that the user's online behavior is correlated
over time. Therefore, the user cannot simply ignore the privacy requirement
when privacy is OFF. We represent the user's correlated requests by an n-state
Markov chain. Our goal is to design ON-OFF privacy schemes with optimal
download rate that ensure privacy for past and future requests. We devise a
polynomial-time algorithm to construct an ON-OFF privacy scheme. Moreover, we
present an upper bound on the achievable rate. We show that the proposed scheme
is optimal and the upper bound is tight for some special families of Markov
chains. We also give an implicit characterization of the optimal achievable
rate as a linear programming (LP).


Abstract:
  We use a classical characterisation to prove that functions which are bounded
away from zero cannot be elements of reproducing kernel Hilbert spaces whose
reproducing kernels decays to zero in a suitable way. The result is used to
study Hilbert spaces on subsets of the real line induced by analytic
translation-invariant kernels which decay to zero at infinity.


Abstract:
  We introduce a new correlated percolation model on the $d$-dimensional
lattice $\mathbb{Z}^d$ called the random length worms model. Assume given a
probability distribution on the set of positive integers (the length
distribution) and $v \in (0,\infty)$ (the intensity parameter). From each site
of $\mathbb{Z}^d$ we start $\mathrm{POI}(v)$ independent simple random walks
with this length distribution. We investigate the connectivity properties of
the set $\mathcal{S}^v$ of sites visited by this cloud of random walks. It is
easy to show that if the second moment of the length distribution is finite
then $\mathcal{S}^v$ undergoes a percolation phase transition as $v$ varies.
Our main contribution is a sufficient condition on the length distribution
which guarantees that $\mathcal{S}^v$ percolates for all $v>0$ if $d \geq 5$.
E.g., if the probability mass function of the length distribution is $ m(\ell)=
c \cdot \ln(\ln(\ell))^{\varepsilon}/ (\ell^3 \ln(\ell)) 1[\ell \geq \ell_0] $
for some $\ell_0>e^e$ and $\varepsilon>0$ then $\mathcal{S}^v$ percolates for
all $v>0$. Note that the second moment of this length distribution is only
"barely" infinite. In order to put our result in the context of earlier results
about similar models (e.g., finitary random interlacements, loop percolation,
Poisson Boolean model, ellipses percolation, etc.), we define a natural family
of percolation models called the Poisson zoo and argue that the percolative
behaviour of the random length worms model is quite close to being "extremal"
in this family of models.


Abstract:
  We apply the renormalization group optimized perturbation theory (RGOPT) to
evaluate the quark contribution to the QCD pressure at finite temperatures and
baryonic densities, at next-to-leading order (NLO). Our results are compared to
NLO and state-of-the-art higher orders of standard perturbative QCD (pQCD) and
hard thermal loop perturbation theory (HTLpt). The RGOPT resummation provides a
nonperturbative approximation, exhibiting a drastically better remnant
renormalization scale dependence than pQCD, thanks to built-in renormalization
group invariance consistency. At NLO, upon simply adding to the RGOPT-resummed
quark contributions the purely perturbative NLO glue contribution, our results
show a remarkable agreement with ab initio lattice simulation data for
temperatures $0.25 \lesssim T \lesssim 1 \, {\rm GeV}$, with a remnant scale
dependence drastically reduced as compared to HTLpt.


Abstract:
  In this paper, a novel machine learning regression based single event
transient (SET) modeling method is proposed. The proposed method can obtain a
reasonable and accurate model without considering the complex physical
mechanism. We got plenty of SET current data of SMIC 130nm bulk CMOS by TCAD
simulation under different conditions (e.g. different LET and different drain
bias voltage). A multilayer feedfordward neural network is used to build the
SET pulse current model by learning the data from TCAD simulation. The proposed
model is validated with the simulation results from TCAD simulation. The
trained SET pulse current model is implemented as a Verilog-A current source in
the Cadence Spectre circuit simulator and an inverter with five fan-outs is
used to show the practicability and reasonableness of the proposed SET pulse
current model for circuit-level single-event effect (SEE) simulation.


Abstract:
  We construct examples of solutions to the incompressible porous media (IPM)
equation that must exhibit infinite in time growth of derivatives provided they
remain smooth. As an application, this allows us to obtain nonlinear
instability for a class of stratified steady states of IPM.


Abstract:
  In recent years, reference-based and supervised summarization evaluation
metrics have been widely explored. However, collecting human-annotated
references and ratings are costly and time-consuming. To avoid these
limitations, we propose a training-free and reference-free summarization
evaluation metric. Our metric consists of a centrality-weighted relevance score
and a self-referenced redundancy score. The relevance score is computed between
the pseudo reference built from the source document and the given summary,
where the pseudo reference content is weighted by the sentence centrality to
provide importance guidance. Besides an $F_1$-based relevance score, we also
design an $F_\beta$-based variant that pays more attention to the recall score.
As for the redundancy score of the summary, we compute a self-masked similarity
score with the summary itself to evaluate the redundant information in the
summary. Finally, we combine the relevance and redundancy scores to produce the
final evaluation score of the given summary. Extensive experiments show that
our methods can significantly outperform existing methods on both
multi-document and single-document summarization evaluation.


Abstract:
  Achieving fully tunable quantum confinement of excitons has been a
long-standing goal in optoelectronics and quantum photonics. We demonstrate
electrically controlled 1D quantum confinement of neutral excitons by means of
a lateral p-i-n junction in a monolayer transition metal dichalcogenide
semiconductor. Exciton trapping in the i-region occurs due to the dc Stark
effect induced by in-plane electric fields. Remarkably, we observe a new
confinement mechanism arising from the repulsive polaronic dressing of excitons
by electrons and holes in the surrounding regions. The overall confinement
potential leads to quantization of excitonic motion, which manifests in the
emergence of multiple spectrally narrow, voltage-dependent resonances in
reflectance and photoluminescence measurements. Additionally, the
photoluminescence from confined excitonic states exhibits high degree of linear
polarization, highlighting the 1D nature of quantum confinement. Electrically
tunable quantum confined excitons may provide a scalable platform for arrays of
identical single photon sources and constitute building blocks of strongly
correlated photonic many-body systems.


Abstract:
  Many objects commonly found in household and industrial environments are
represented by cylindrical and cubic shapes. Thus, it is available for robots
to manipulate them through the real-time detection of elliptic and rectangle
shape primitives formed by the circular and rectangle tops of these objects. We
devise a robust grasping system that enables a robot to manipulate cylindrical
and cubic objects in collaboration scenarios by the proposed perception
strategy including the detection of elliptic and rectangle shape primitives and
depth information. The proposed method of detecting ellipses and rectangles
incorporates a one-stage detection backbone and then, accommodates the proposed
adaptive multi-branch multi-scale net with a designed iterative feature pyramid
network, local inception net, and multi-receptive-filed feature fusion net to
generate object detection recommendations. In terms of manipulating objects
with different shapes, we propose the grasp synthetic to align the grasp pose
of the gripper with an object's pose based on the proposed detector and
registered depth information. The proposed robotic perception algorithm has
been integrated on a robot to demonstrate the ability to carry out human-robot
collaborative manipulations of cylindrical and cubic objects in real-time. We
show that the robotic manipulator, empowered by the proposed detector, performs
well in practical manipulation scenarios.(An experiment video is available in
YouTube, https://www.youtube.com/watch?v=Amcs8lwvNK8.)


Abstract:
  The software development industry has been evolving with new development
standards and service delivery models. Agile methodologies have reached their
completion with DevOps, thereby increasing the quality of the software and
creating greater speed in delivery. However, a gap regarding the formalization
of its adoption and implementation doubts became relevant. My hypothesis is
that, by systematizing the introduction of DevOps into the software development
process and defining the function of the members of the DevOps team members,
may well make it quicker to implement this process, thus reducing conflicts
between the teams. As part of the investigation of this hypothesis, the result
of the research will be applied in practical development environments i.e. in a
Technology Agency of the State of the Brazilian Government and also at the
Brazilian Company Neurotech in order to evaluate its effectiveness from metrics
appropriate for DevOps environments.


Abstract:
  ExploitDB is one of the important public websites, which contributes a large
number of vulnerabilities to official CVE database. Over 60\% of these
vulnerabilities have high- or critical-security risks. Unfortunately, over 73\%
of exploits appear publicly earlier than the corresponding CVEs, and about 40\%
of exploits do not even have CVEs. To assist in documenting CVEs for the
ExploitDB posts, we propose an open information method to extract 9 key
vulnerability aspects (vulnerable product/version/component, vulnerability
type, vendor, attacker type, root cause, attack vector and impact) from the
verbose and noisy ExploitDB posts. The extracted aspects from an ExploitDB post
are then composed into a CVE description according to the suggested CVE
description templates, which is must-provided information for requesting new
CVEs. Through the evaluation on 13,017 manually labeled sentences and the
statistically sampling of 3,456 extracted aspects, we confirm the high accuracy
of our extraction method. Compared with 27,230 reference CVE descriptions. Our
composed CVE descriptions achieve high ROUGH-L (0.38), a longest common
subsequence based metric for evaluating text summarization methods.


Abstract:
  The interface plays a critical role in the mechanical properties of
composites. In the present work, a novel phase-field based cohesive zone model
(CZM) is proposed for the cracking simulation. The competition and interaction
between the bulk and interfacial cracking are taken into consideration directly
in both displacement- and phase-field. A modified family of degradation
functions is utilized to describe traction-separation law in the CZM. Finite
element implementation of the present CZM was carried out with a completely
staggered algorithm. Several numerical examples, including a single bar tension
test, a double cantilever beam test, a three-point bending test, and a single
fiber-reinforced composite test, are carried out to validate the present model
by comparison with existing numerical and experimental results.The present
model shows its advantage on modeling interaction between bulk and interfacial
cracking. 1. Introduction