Abstract:
  High-mass gamma-ray binaries consist of a presumptive pulsar in orbit with a
massive star. The intense outflows from the star can absorb radio emission from
the pulsar, making the detection of pulsation difficult. In this work, we
present the basic geometry and formulae that describe the absorption process of
a pulsar in binary with an O/B star and apply our model to two typical and
well-studied binaries: PSR~B1259-63/LS~2883 and LS 5039. We investigate the
influences of the equatorial disc of LS 2883 with different orientations on the
dispersion measure and free-free absorption of the radio pulsation from PSR
B1259-63. The observed data are consistent with the disc inserted on the
orbital plane with a relatively large inclination angle. For LS 5039, due to
its tight orbit, it was believed that the strong wind absorption makes
detecting radio emissions from the putative pulsar unlikely. However,
considering the wind interaction and orbital motion, a bow shock cavity and a
Coriolis shock would be formed, thereby allowing the pulsations to partially
avoid stellar outflow absorption. We investigate the dependence of the radio
optical depth on the observing frequencies, the orbital inclination angle, and
the wind parameters. We suppose that the presumptive pulsar in LS 5039 is
similar to PSR B1259-63 with pulsed emission extending to several tens of
gigahertz. In that case, there could be a transparent window for radio
pulsations when the pulsar is moving around the inferior conjunction. The
following deep monitoring of LS 5039 and other systems by radio telescopes at
high radio frequencies might reveal the nature of compact objects in the
future. Alternatively, even a null detection could still provide further
constraints on the properties of the putative pulsar and stellar outflows.


Abstract:
  With the rise in Internet of Things (IoT) devices, home network management
and security are becoming complex. There is an urgent requirement to make smart
home network management efficient. This work proposes an SDN-based architecture
to secure smart home networks through K-Nearest Neighbor (KNN) based device
classifications and malicious traffic detection. The efficiency is further
enhanced by offloading the computation-intensive KNN model to Field
Programmable Gate Arrays (FPGA), which offers parallel processing power of GPU
platforms at lower costs and higher efficiencies, and can be used to accelerate
time-sensitive tasks. The proposed parallelization and implementation of KNN on
FPGA are achieved by using the Vivado Design Suite from Xilinx and High-Level
Synthesis (HLS). When optimized with 10-fold cross-validation, the proposed
solution for KNN consistently exhibits the best performances on FPGA when
compared with four alternative KNN instances (i.e., 78% faster than the
parallel bubble sort-based implementation and 99\% faster than the other three
sorting algorithms). Moreover, with 36,225 training samples, the proposed KNN
solution classifies a test query with 95% accuracy in approximately 4
milliseconds on FPGA compared to 57 seconds on a CPU platform.


Abstract:
  Several Type Ia supernova analyses make use of non-simultaneous regressions
between observed supernova and host galaxy properties and supernova luminosity:
first the supernova magnitudes are corrected for their light curve shape and
color, and then they are separately corrected for their host galaxy masses.
This two-step regression methodology does not introduce any biases when there
are no correlations between the variables regressed in each correction step.
However, correlations between these covariates will bias estimates of the size
of the corrections, as well as estimates of the variance of the final
residuals. In this work, we analyze the general case of non-simultaneous
regression with correlated covariates to derive the functional forms of these
biases. We also simulate this effect on data from the literature to provide
corrections to remove these biases from the data sets studied. The biases
examined here can be entirely avoided by using simultaneous regression
techniques.


Abstract:
  Hashtag annotation for microblog posts has been recently formulated as a
sequence generation problem to handle emerging hashtags that are unseen in the
training set. The state-of-the-art method leverages conversations initiated by
posts to enrich contextual information for the short posts. However, it is
unrealistic to assume the existence of conversations before the hashtag
annotation itself. Therefore, we propose to leverage news articles published
before the microblog post to generate hashtags following a Retriever-Generator
framework. Extensive experiments on English Twitter datasets demonstrate
superior performance and significant advantages of leveraging news articles to
generate hashtags.


Abstract:
  End-to-end automatic speech recognition (ASR) can achieve promising
performance with large-scale training data. However, it is known that domain
mismatch between training and testing data often leads to a degradation of
recognition accuracy. In this work, we focus on the unsupervised domain
adaptation for ASR and propose CMatch, a Character-level distribution matching
method to perform fine-grained adaptation between each character in two
domains. First, to obtain labels for the features belonging to each character,
we achieve frame-level label assignment using the Connectionist Temporal
Classification (CTC) pseudo labels. Then, we match the character-level
distributions using Maximum Mean Discrepancy. We train our algorithm using the
self-training technique. Experiments on the Libri-Adapt dataset show that our
proposed approach achieves 14.39% and 16.50% relative Word Error Rate (WER)
reduction on both cross-device and cross-environment ASR. We also
comprehensively analyze the different strategies for frame-level label
assignment and Transformer adaptations.


Abstract:
  We present a complete set of transverse momentum dependent and collinear
parton densities including the photon over a wide phase space. The photon
density appears when evolving parton distributions with QED corrections. The
photons are produced by perturbative radiation using the Parton Branching
method. The QCD partons are constrained by a fit to HERA data. The lepton pair
production at high masses is described within the extracted TMD densities.


Abstract:
  Next-generation, high-brilliance x-ray photon sources call for new x-ray
optics. Here we demonstrate the feasibility of using monolithic diamond
channel-cut crystals as high-heat-load, beam-multiplexing, narrow-band,
mechanically-stable x-ray monochromators with high-power x-ray beams at
cutting-edge, high-repetition-rate x-ray free-electron laser (XFEL) facilities.
The diamond channel-cut crystals fabricated and characterized in these studies
are designed as two-bounce Bragg reflection monochromators directing 14.4-keV
or 12.4-keV x-rays within a 15-meV-bandwidth to $^{57}$Fe or $^{45}$Sc nuclear
resonant scattering experiments, respectively. The crystal design allows
out-of-band x-rays within a $\simeq 1$-eV XFEL bandwidth to be transmitted with
minimal losses to alternative simultaneous experiments. Only $\lesssim 2$\% of
the incident $\simeq 100$-W x-ray beam is absorbed in a 50-$\mu$m-thick first
diamond crystal reflector, ensuring that the monochromator crystal is highly
stable. Other x-ray optics applications of diamond channel-cut crystals are
anticipated.


Abstract:
  Maintaining computational load balance is important to the performant
behavior of codes which operate under a distributed computing model. This is
especially true for GPU architectures, which can suffer from memory
oversubscription if improperly load balanced. We present enhancements to
traditional load balancing approaches and explicitly target GPU architectures,
exploring the resulting performance. A key component of our enhancements is the
introduction of several GPU-amenable strategies for assessing compute work.
These strategies are implemented and benchmarked to find the most optimal data
collection methodology for in-situ assessment of GPU compute work. For the
fully kinetic particle-in-cell code WarpX, which supports MPI+CUDA parallelism,
we investigate the performance of the improved dynamic load balancing via a
strong scaling-based performance model and show that, for a laser-ion
acceleration test problem run with up to 6144 GPUs on Summit, the enhanced
dynamic load balancing achieves from 62%--74% (88% when running on 6 GPUs) of
the theoretically predicted maximum speedup; for the 96-GPU case, we find that
dynamic load balancing improves performance relative to baselines without load
balancing (3.8x speedup) and with static load balancing (1.2x speedup). Our
results provide important insights into dynamic load balancing and performance
assessment, and are particularly relevant in the context of distributed memory
applications ran on GPUs.


Abstract:
  In this paper, we propose a framework for environmental sound synthesis from
onomatopoeic words. As one way of expressing an environmental sound, we can use
an onomatopoeic word, which is a character sequence for phonetically imitating
a sound. An onomatopoeic word is effective for describing diverse sound
features. Therefore, using onomatopoeic words for environmental sound synthesis
will enable us to generate diverse environmental sounds. To generate diverse
sounds, we propose a method based on a sequence-to-sequence framework for
synthesizing environmental sounds from onomatopoeic words. We also propose a
method of environmental sound synthesis using onomatopoeic words and sound
event labels. The use of sound event labels in addition to onomatopoeic words
enables us to capture each sound event's feature depending on the input sound
event label. Our subjective experiments show that our proposed methods achieve
higher diversity and naturalness than conventional methods using sound event
labels.


Abstract:
  As ancient, gravitationally bound stellar populations, globular clusters are
abundant, vibrant laboratories characterized by high frequencies of dynamical
interactions coupled to complex stellar evolution. Using surface brightness and
velocity dispersion profiles from the literature, we fit $59$ Milky Way
globular clusters to dynamical models from the \texttt{CMC Cluster Catalog}.
Without doing any interpolation, and without any directed effort to fit any
particular cluster, $26$ globular clusters are well-matched by at least one of
our models. We discuss in particular the core-collapsed clusters NGC 6293, NGC
6397, NGC 6681, and NGC 6624, and the non-core-collapsed clusters NGC 288, NGC
4372, and NGC 5897. As NGC 6624 lacks well-fitting snapshots on the main
\texttt{CMC Cluster Catalog}, we run six additional models in order to refine
the fit. We calculate metrics for mass segregation, explore the production of
compact object sources such as millisecond pulsars, cataclysmic variables,
low-mass X-ray binaries, and stellar-mass black holes, finding reasonable
agreement with observations. Additionally, closely mimicking observational
cuts, we extract the binary fraction from our models, finding good agreement
except in the dense core regions of core-collapsed clusters. Accompanying this
paper are a number of \textsf{python} methods for examining the publicly
accessible \texttt{CMC Cluster Catalog}, as well as any other models generated
using \texttt{CMC}.


Abstract:
  The structure of $^{33}$Mg was investigated by means of two knockout
reactions, one-neutron removal from $^{34}$Mg and one-proton removal from
$^{34}$Al. Using comparative analysis of the population of observed excited
states in the residual $^{33}$Mg, the nature of these states can be deciphered.
In addition, the long-standing controversy about the parity of the $^{33}$Mg
ground state is resolved using momentum distribution analysis, showing a clear
signature for negative parity. Partial cross section measurements are compared
with the results of eikonal reaction theory combined with large-scale shell
model calculations of this complex nucleus located in the island of inversion,
where configuration mixing plays a major role.


Abstract:
  In the present paper we prove that every local and $2$-local derivation on
conservative algebras of $2$-dimensional algebras are derivations. Also, we
prove that every local and $2$-local automorphism on conservative algebras of
$2$-dimensional algebras are automorphisms.


Abstract:
  Consider a field $k$ of characteristic $0$, not necessarily algebraically
closed, and a fixed algebraic curve $f=0$ defined by a tame polynomial $f\in
k[x,y]$ with only quasi-homogeneous singularities. We prove that the space of
holomorphic foliations in the plane ${mathbb A}^2_\k$ having $f=0$ as a fixed
invariant curve is generated as $k[x,y]$-module by at most four elements, three
of them are the trivial foliations $fdx,fdy$ and $df$. Our proof is algorithmic
and constructs the fourth foliation explicitly. Using Serre's GAGA and
Quillen-Suslin theorem, we show that for a suitable field extension $K$ of $k$
such a module over $K[x,y]$ is actually generated by two elements, and
therefore, such curves are free divisors in the sense of K. Saito. After
performing Groebner basis for this module, we observe that in many well-known
examples, $K=k$.


Abstract:
  We present an experimental and theoretical study of the absorption and
emission spectra of Yb atoms in a solid Ne matrix at a resolution of 0.025 nm.
Five absorption bands were identified as due to transitions from the
$4f^{14}5d^06s^2\ ^1\!S_0$ ground state configuration to $4f^{14}5d^06s6p$ and
$4f^{13}5d^16s^2$ configurations. The two lowest energy bands were assigned to
outer-shell transitions to $6s6p\ ^3P_1$ and $^1P_1$ atomic states and
displayed the structure of a broad doublet and an asymmetric triplet,
respectively. The remaining three higher-frequency bands were assigned to
inner-shell transitions to distinct $J=1$ states arising from the
$4f^{13}5d^16s^2$ configuration and were highly structured with narrow
linewidths. A classical simulation was performed to identify the stability and
symmetry of possible trapping sites in the Ne crystal. It showed that the
overarching 1+2 structure of the high frequency bands could be predominantly
ascribed to crystal field splitting in the axial field of a 10-atom vacancy of
$C_{4v}$ symmetry. Their prominent substructures were shown to be
manifestations of phonon sidebands associated with the zero-phonon lines on
each crystal field state. Unprecedented for a metal-rare gas system, resolution
of individual phonon states on an allowed electronic transition was possible
under excitation spectroscopy which reflects the semi-quantum nature of solid
Ne. In contrast to the absorption spectra, emission spectra produced by
steady-state excitation into the $^1P_1$ absorption band consisted of simple,
unstructured fluorescence bands.


Abstract:
  A study of the gravitationally lensed blazar PKS 1830-211 was carried out
using multi waveband data collected by Fermi-LAT, Swift-XRT and Swift-UVOT
telescopes between MJD 58400 to MJD 58800 (9 Oct 2018 to 13 Nov 2019). Flaring
states were identified by analysing the gamma-ray light curve. Simultaneous
multi-waveband SED were obtained for those flaring periods. A cross-correlation
analysis of the multi-waveband data was carried out, which suggested a common
origin of the gamma-ray and X-ray emission. The broadband emission mechanism
was studied by modelling the SED using a leptonic model. Physical parameters of
the blazar were estimated from the broadband SED modelling. The blazar PKS
1830-211 is gravitationally lensed by at least two galaxies and has been
extensively studied in the literature because of this property. The
self-correlation of the gamma-ray light curve was studied to identify the
signature of lensing, but no conclusive evidence of correlation was found at
the expected time delay of 26 days.


Abstract:
  Reinforcement learning in large-scale environments is challenging due to the
many possible actions that can be taken in specific situations. We have
previously developed a means of constraining, and hence speeding up, the search
process through the use of motion primitives; motion primitives are sequences
of pre-specified actions taken across a state series. As a byproduct of this
work, we have found that if the motion primitives' motions and actions are
labeled, then the search can be sped up further. Since motion primitives may
initially lack such details, we propose a theoretically viewpoint-insensitive
and speed-insensitive means of automatically annotating the underlying motions
and actions. We do this through a differential-geometric, spatio-temporal
kinematics descriptor, which analyzes how the poses of entities in two motion
sequences change over time. We use this descriptor in conjunction with a
weighted-nearest-neighbor classifier to label the primitives using a limited
set of training examples.
  In our experiments, we achieve high motion and action annotation rates for
human-action-derived primitives with as few as one training sample. We also
demonstrate that reinforcement learning using accurately labeled trajectories
leads to high-performing policies more quickly than standard reinforcement
learning techniques. This is partly because motion primitives encode prior
domain knowledge and preempt the need to re-discover that knowledge during
training. It is also because agents can leverage the labels to systematically
ignore action classes that do not facilitate task objectives, thereby reducing
the action space.


Abstract:
  Analyzing temporal data (e.g., wearable device data) requires a decision
about how to combine information from the recent and distant past. In the
context of classifying sleep status from actigraphy, Webster's rescoring rules
offer one popular solution based on the long-term patterns in the output of a
moving-window model. Unfortunately, the question of how to optimize rescoring
rules for any given setting has remained unsolved. To address this problem and
expand the possible use cases of rescoring rules, we propose rephrasing these
rules in terms of epoch-specific features. Our features take two general forms:
(1) the time lag between now and the most recent [or closest upcoming] bout of
time spent in a given state, and (2) the length of the most recent [or closest
upcoming] bout of time spent in a given state. Given any initial moving window
model, these features can be defined recursively, allowing for straightforward
optimization of rescoring rules. Joint optimization of the moving window model
and the subsequent rescoring rules can also be implemented using gradient-based
optimization software, such as Tensorflow. Beyond binary classification
problems (e.g., sleep-wake), the same approach can be applied to summarize
long-term patterns for multi-state classification problems (e.g., sitting,
walking, or stair climbing). We find that optimized rescoring rules improve the
performance of sleep-wake classifiers, achieving accuracy comparable to that of
certain neural network architectures.


Abstract:
  This paper proposes to study the impact of image selective encryption on both
forensics and privacy preserving mechanisms. The proposed selective encryption
scheme works independently on each bitplane by encrypting the s most
significant bits of each pixel. We show that this mechanism can be used to
increase privacy by mitigating image recognition tasks. In order to guarantee a
trade-off between forensics analysis and privacy, the signal of interest used
for forensics purposes is extracted from the 8--s least significant bits of the
protected image. We show on the CASIA2 database that good tampering detection
capabilities can be achieved for s $\in$ {3,. .. , 5} with an accuracy above
80% using SRMQ1 features, while preventing class recognition tasks using CNN
with an accuracy smaller than 50%.


Abstract:
  Through in-depth analysis of ultra high frequency (UHF) stock price change
data, more reasonable discrete dynamic distribution models are constructed in
this paper. Firstly, we classify the price changes into several categories.
Then, temporal convolutional network (TCN) is utilized to predict the
conditional probability for each category. Furthermore, attention mechanism is
added into the TCN architecture to model the time-varying distribution for
stock price change data. Empirical research on constituent stocks of Chinese
Shenzhen Stock Exchange 100 Index (SZSE 100) found that the TCN framework model
and the TCN (attention) framework have a better overall performance than GARCH
family models and the long short-term memory (LSTM) framework model for the
description of the dynamic process of the UHF stock price change sequence. In
addition, the scale of the dataset reached nearly 10 million, to the best of
our knowledge, there has been no previous attempt to apply TCN to such a
large-scale UHF transaction price dataset in Chinese stock market.


Abstract:
  The $^9$C nucleus and related capture reaction,
${^8\mathrm{B}}(p,\gamma){^9\mathrm{C}}$, have been intensively studied with an
astrophysical interest. Due to the weakly-bound nature of $^9$C, its structure
is likely to be described as the three-body (${^7\mathrm{Be}}+p+p$). Its
continuum structure is also important to describe reaction processes of $^9$C,
with which the reaction rate of the ${^8\mathrm{B}}(p,\gamma){^9\mathrm{C}}$
process have been extracted indirectly. We perform three-body calculations on
$^9$C and discuss properties of its ground and low-lying states via breakup
reactions. We employ the three-body model of $^9$C using the Gaussian-expansion
method combined with the complex-scaling method. This model is implemented in
the four-body version of the continuum-discretized coupled-channels method, by
which breakup reactions of $^9$C are studied. The intrinsic spin of $^7$Be is
disregarded. By tuning a three-body interaction in the Hamiltonian of $^9$C, we
obtain the low-lying $2^+$ state with the resonant energy 0.781 MeV and the
decay width 0.137 MeV, which is consistent with the available experimental
information and a relatively high-lying second $2^+$ wider resonant state. Our
calculation predicts also sole $0^+$ and three $1^-$ resonant states. We
discuss the role of these resonances in the elastic breakup cross section of
$^9$C on $^{208}$Pb at 65 and 160 MeV/A. The low-lying 2$^+$ state is probed as
a sharp peak of the breakup cross section, while the 1$^-$ states enhance the
cross section around 3 MeV. Our calculations will further support the future
and ongoing experimental campaigns for extracting astrophysical information and
evaluating the two-proton removal cross-sections.


Abstract:
  We studied the weak-lined T Tauri star Hubble 4, a known long-period binary,
and its starspot phenomena. We used optical radial velocity (RV) data taken
over a span of 14 years (2004-2010, 2017-2019) at the McDonald Observatory 2.7m
Harlan J. Smith telescope and single epoch imaging from the HST/WFC3
instrument. The observed and apparent RV variations show contributions,
respectively, from the binary motion as well as from a large spot group on one
of the stars, presumed to be the primary. Fitting and removing the orbital
signal from the RVs, we found the lower bound on the lifetime of a previously
identified large spot group on the surface of the star to be at least 5.1
years. A $\sim5$ year lower limit is a long, but not unprecedented, duration
for a single spot group. The later epoch data indicate significant spot
evolution has occurred, placing an upper bound on the spot group lifetime at 12
years. We find that pre-main sequence evolutionary models for the age of Taurus
($\sim2$ Myr), combined with component mass estimates from the literature,
permit us to reproduce the HST relative photometry and the binary-induced
contribution to the apparent RV variations. The long-lived star spot we find on
Hubble 4 has significant implications for dynamo models in young stars, as it
adds evidence for long lifetimes of magnetic field topologies. There are also
significant implications for young star exoplanet searches as long-lived
coherent RV signals may be spot-induced and not the result of planetary motion.
(This paper includes data taken at The McDonald Observatory of The University
of Texas at Austin.)


Abstract:
  It is well-known that stochastic gradient noise (SGN) acts as implicit
regularization for deep learning and is essentially important for both
optimization and generalization of deep networks. Some works attempted to
artificially simulate SGN by injecting random noise to improve deep learning.
However, it turned out that the injected simple random noise cannot work as
well as SGN, which is anisotropic and parameter-dependent. For simulating SGN
at low computational costs and without changing the learning rate or batch
size, we propose the Positive-Negative Momentum (PNM) approach that is a
powerful alternative to conventional Momentum in classic optimizers. The
introduced PNM method maintains two approximate independent momentum terms.
Then, we can control the magnitude of SGN explicitly by adjusting the momentum
difference. We theoretically prove the convergence guarantee and the
generalization advantage of PNM over Stochastic Gradient Descent (SGD). By
incorporating PNM into the two conventional optimizers, SGD with Momentum and
Adam, our extensive experiments empirically verified the significant advantage
of the PNM-based variants over the corresponding conventional Momentum-based
optimizers.


Abstract:
  In this paper we show that ensemble Kalman inversion for linear inverse
problems can equivalently be formulated as a stochastic low-rank approximation
of Tikhonov regularization. This point of view allows us to introduce an
alternative sampling scheme based on the Nystr\"om method that improves
practical performance. Furthermore, we formulate an adaptive version of
ensemble Kalman inversion where the sample size is coupled with the
regularization parameter. We prove under standard assumptions that the proposed
scheme yields an order optimal regularization method if the discrepancy
principle is used as a stopping criterion.


Abstract:
  With the formation of next generation wireless communication, a growing
number of new applications like internet of things, autonomous car, and drone
is crowding the unlicensed spectrum. Licensed network such as the long-term
evolution (LTE) also comes to the unlicensed spectrum for better providing
high-capacity contents with low cost. However, LTE was not designed for sharing
spectrum with others. A cooperation center for these networks is costly because
they possess heterogeneous properties and everyone can enter and leave the
spectrum unrestrictedly, so the design will be challenging. Since it is
infeasible to incorporate potentially infinite scenarios with one unified
design, an alternative solution is to let each network learn its own
coexistence policy. Previous solutions only work on fixed scenarios. In this
work a reinforcement learning algorithm is presented to cope with the
coexistence between Wi-Fi and LTE agents in 5 GHz unlicensed spectrum. The
coexistence problem was modeled as a decentralized partially observable Markov
decision process (Dec-POMDP) and Bayesian approach was adopted for policy
learning with nonparametric prior to accommodate the uncertainty of policy for
different agents. A fairness measure was introduced in the reward function to
encourage fair sharing between agents. The reinforcement learning was turned
into an optimization problem by transforming the value function as likelihood
and variational inference for posterior approximation. Simulation results
demonstrate that this algorithm can reach high value with compact policy
representations, and stay computationally efficient when applying to agent set.


Abstract:
  Policy Space Response Oracles (PSRO) is a deep reinforcement learning
algorithm for two-player zero-sum games that has empirically found approximate
Nash equilibria in large games. Although PSRO is guaranteed to converge to a
Nash equilibrium, it may take an exponential number of iterations as the number
of infostates grows. We propose Extensive-Form Double Oracle (XDO), an
extensive-form double oracle algorithm that is guaranteed to converge to an
approximate Nash equilibrium linearly in the number of infostates. Unlike PSRO,
which mixes best responses at the root of the game, XDO mixes best responses at
every infostate. We also introduce Neural XDO (NXDO), where the best response
is learned through deep RL. In tabular experiments on Leduc poker, we find that
XDO achieves an approximate Nash equilibrium in a number of iterations 1-2
orders of magnitude smaller than PSRO. In experiments on a modified Leduc poker
game, we show that tabular XDO achieves over 11x lower exploitability than CFR
and over 82x lower exploitability than PSRO and XFP in the same amount of time.
We also show that NXDO beats PSRO and is competitive with NFSP on a large
no-limit poker game.


Abstract:
  The pressure strain correlation plays a critical role in the Reynolds stress
transport modelling. Accurate modelling of the pressure strain correlation
leads to proper prediction of turbulence stresses and subsequently the other
terms of engineering interest. However, classical pressure strain correlation
models are often unreliable for complex turbulent flows. Machine learning based
models have shown promise in turbulence modeling but their application has been
largely restricted to eddy viscosity based models. In this article, we outline
a rationale for the preferential application of machine learning and turbulence
data to develop models at the level of Reynolds stress modeling. As the
illustration We develop data driven models for the pressure strain correlation
for turbulent channel flow using neural networks. The input features of the
neural networks are chosen using physics based rationale. The networks are
trained with the high resolution DNS data of turbulent channel flow at
different friction Reynolds numbers. The testing of the models are performed
for unknown flow statistics at other friction Reynolds numbers and also for
turbulent plane Couette flows. Based on the results presented in this article,
the proposed machine learning framework exhibits considerable promise and may
be utilized for the development of accurate Reynolds stress models for flow
prediction.


Abstract:
  We introduce an Eulerian Perturbation Theory to study the clustering of
tracers for cosmologies in the presence of massive neutrinos. Our approach is
based on mapping recently-obtained Lagrangian Perturbation Theory results to
the Eulerian framework. We add Effective Field Theory counterterms,
IR-resummations and a biasing scheme to compute the one-loop redshift-space
power spectrum. To assess our predictions, we compare the power spectrum
multipoles against synthetic halo catalogues from the Quijote simulations,
finding excellent agreement on scales $k\lesssim 0.25 \,h \text{Mpc}^{-1}$. One
can obtain the same fitting accuracy using higher wave-numbers, but then the
theory fails to give a correct estimation of the linear bias parameter. We
further discuss the implications for the tree-level bispectrum. Finally,
calculating loop corrections is computationally costly, hence we derive an
accurate approximation wherein we retain only the main features of the kernels,
as produced by changes to the growth rate. As a result, we show how FFTLog
methods can be used to further accelerate the loop computations with these
reduced kernels.


Abstract:
  Combining the closed-world reasoning of answer set programming (ASP) with the
open-world reasoning of ontologies broadens the space of applications of
reasoners. Disjunctive hybrid MKNF knowledge bases succinctly extend ASP and in
some cases without increasing the complexity of reasoning tasks. However, in
many cases, solver development is lagging behind. As the result, the only known
method of solving disjunctive hybrid MKNF knowledge bases is based on
guess-and-verify, as formulated by Motik and Rosati in their original work. A
main obstacle is understanding how constraint propagation may be performed by a
solver, which, in the context of ASP, centers around the computation of
\textit{unfounded atoms}, the atoms that are false given a partial
interpretation. In this work, we build towards improving solvers for hybrid
MKNF knowledge bases with disjunctive rules: We formalize a notion of unfounded
sets for these knowledge bases, identify lower complexity bounds, and
demonstrate how we might integrate these developments into a solver. We discuss
challenges introduced by ontologies that are not present in the development of
solvers for disjunctive logic programs, which warrant some deviations from
traditional definitions of unfounded sets. We compare our work with prior
definitions of unfounded sets.


Abstract:
  It is a classical result that the category of finitely-generated free monoids
serves as a PROP for commutative bialgebras. Attaching permutations to fix the
order of multiplication, we construct an extension of this category that is
equivalent to the PROP for bialgebras.


Abstract:
  We study the $2k$-th moment of central values of the family of Dirichlet
$L$-functions to a fixed prime modulus. We establish sharp lower bounds for all
real $k \geq 0$ and sharp upper bounds for $k$ in the range $0 \leq k \leq 1$.


Abstract:
  We make use of the Parker Solar Probe (PSP) data to explore the nature of
solar wind turbulence focusing on the Alfv\'enic character and power spectra of
the fluctuations and their dependence on distance and context (i.e. large scale
solar wind properties), aiming to understand the role that different effects
such as source properties, solar wind expansion, stream interaction might play
in determining the turbulent state. We carry out a statistical survey of the
data from the first five orbits of PSP with a focus on how the fluctuation
properties at the large, MHD scales, vary with different solar wind streams and
distance from the Sun. A more in-depth analysis from several selected periods
is also presented. Our results show that as fluctuations are transported
outward by the solar wind, the magnetic field spectrum steepens while the shape
of the velocity spectrum remains unchanged. The steepening process is
controlled by the "age" of the turbulence, determined by the wind speed
together with the radial distance. Statistically, faster solar wind has higher
"Alfv\'enicity", with more dominant outward propagating wave component and more
balanced magnetic/kinetic energies. The outward wave dominance gradually
weakens with radial distance, while the excess of magnetic energy is found to
be stronger as we move closer toward the Sun. We show that the turbulence
properties can vary significantly stream to stream even if these streams are of
similar speed, indicating very different origins of these streams. Especially,
the slow wind that originates near the polar coronal holes has much lower
Alfv\'enicity compared with the slow wind that originates from the active
regions/pseudostreamers. We show that structures such as heliospheric current
sheets and velocity shears can play an important role in modifying the
properties of the turbulence.


Abstract:
  We describe an algorithm to compute the extremal eigenvalues and
corresponding eigenvectors of a symmetric matrix by solving a sequence of
Quadratic Binary Optimization problems. This algorithm is robust across many
different classes of symmetric matrices, can compute the eigenvector/eigenvalue
pair to essentially arbitrary precision, and with minor modifications can also
solve the generalized eigenvalue problem. Performance is analyzed on small
random matrices and selected larger matrices from practical applications.


Abstract:
  In this work, we report on the integrated sensorimotor control of the
Pisa/IIT SoftHand, an anthropomorphic soft robot hand designed around the
principle of adaptive synergies, with the BRL tactile fingertip (TacTip), a
soft biomimetic optical tactile sensor based on the human sense of touch. Our
focus is how a sense of touch can be used to control an anthropomorphic hand
with one degree of actuation, based on an integration that respects the hand's
mechanical functionality. We consider: (i) closed-loop tactile control to
establish a light contact on an unknown held object, based on the structural
similarity with an undeformed tactile image; and (ii) controlling the estimated
pose of an edge feature of a held object, using a convolutional neural network
approach developed for controlling other sensors in the TacTip family. Overall,
this gives a foundation to endow soft robotic hands with human-like touch, with
implications for autonomous grasping, manipulation, human-robot interaction and
prosthetics. Supplemental video: https://youtu.be/ndsxj659bkQ


Abstract:
  Recent researches show that deep learning model is susceptible to backdoor
attacks. Many defenses against backdoor attacks have been proposed. However,
existing defense works require high computational overhead or backdoor attack
information such as the trigger size, which is difficult to satisfy in
realistic scenarios. In this paper, a novel backdoor detection method based on
adversarial examples is proposed. The proposed method leverages intentional
adversarial perturbations to detect whether an image contains a trigger, which
can be applied in both the training stage and the inference stage (sanitize the
training set in training stage and detect the backdoor instances in inference
stage). Specifically, given an untrusted image, the adversarial perturbation is
added to the image intentionally. If the prediction of the model on the
perturbed image is consistent with that on the unperturbed image, the input
image will be considered as a backdoor instance. Compared with most existing
defense works, the proposed adversarial perturbation based method requires low
computational resources and maintains the visual quality of the images.
Experimental results show that, the backdoor detection rate of the proposed
defense method is 99.63%, 99.76% and 99.91% on Fashion-MNIST, CIFAR-10 and
GTSRB datasets, respectively. Besides, the proposed method maintains the visual
quality of the image as the l2 norm of the added perturbation are as low as
2.8715, 3.0513 and 2.4362 on Fashion-MNIST, CIFAR-10 and GTSRB datasets,
respectively. In addition, it is also demonstrated that the proposed method can
achieve high defense performance against backdoor attacks under different
attack settings (trigger transparency, trigger size and trigger pattern).
Compared with the existing defense work (STRIP), the proposed method has better
detection performance on all the three datasets, and is more efficient than
STRIP.


Abstract:
  Increasing volatilities within power transmission and distribution force
power grid operators to amplify their use of communication infrastructure to
monitor and control their grid. The resulting increase in communication creates
a larger attack surface for malicious actors. Indeed, cyber attacks on power
grids have already succeeded in causing temporary, large-scale blackouts in the
recent past. In this paper, we analyze the communication infrastructure of
power grids to derive resulting fundamental challenges of power grids with
respect to cybersecurity. Based on these challenges, we identify a broad set of
resulting attack vectors and attack scenarios that threaten the security of
power grids. To address these challenges, we propose to rely on a
defense-in-depth strategy, which encompasses measures for (i) device and
application security, (ii) network security, (iii) physical security, as well
as (iv) policies, procedures, and awareness. For each of these categories, we
distill and discuss a comprehensive set of state-of-the art approaches, and
identify further opportunities to strengthen cybersecurity in interconnected
power grids.


Abstract:
  The Track-1 of DSTC9 aims to effectively answer user requests or questions
during task-oriented dialogues, which are out of the scope of APIs/DB. By
leveraging external knowledge resources, relevant information can be retrieved
and encoded into the response generation for these out-of-API-coverage queries.
In this work, we have explored several advanced techniques to enhance the
utilization of external knowledge and boost the quality of response generation,
including schema guided knowledge decision, negatives enhanced knowledge
selection, and knowledge grounded response generation. To evaluate the
performance of our proposed method, comprehensive experiments have been carried
out on the publicly available dataset. Our approach was ranked as the best in
human evaluation of DSTC9 Track-1.


Abstract:
  This paper presents a numerical method for div-curl systems with normal
boundary conditions by using a finite element technique known as primal-dual
weak Galerkin (PDWG). The PDWG finite element scheme for the div-curl system
has two prominent features in that it offers not only an accurate and reliable
numerical solution to the div-curl system under the low $H^\alpha$-regularity
($\alpha>0$) assumption for the true solution, but also an effective
approximation of normal harmonic vector fields regardless the topology of the
domain. Results of seven numerical experiments are presented to demonstrate the
performance of the PDWG algorithm, including one example on the computation of
discrete normal harmonic vector fields.


Abstract:
  We introduce various colouring principles which generalise the so-called
"onto mapping principle" of Sierpinski to larger cardinals and general ideals.
We prove that these principles capture the notion of an Ulam matrix and allow
to characterise large cardinals, most notably weakly compact and ineffable
cardinals. We also develop the basic theory of these colouring principles,
connecting them to the classical negative square bracket partition relations,
proving pumping-up theorems, and deciding various instances of theirs.
  We also demonstrate that our principles provide a uniform way of obtaining
non-saturation results for ideals satisfying a property we call subnormality in
contexts where Ulam matrices might not be available.


Abstract:
  The accurate computational determination of chemical, materials, biological,
and atmospheric properties has critical impact on a wide range of health and
environmental problems, but is deeply limited by the computational scaling of
quantum-mechanical methods. The complexity of quantum-chemical studies arises
from the steep algebraic scaling of electron correlation methods, and the
exponential scaling in studying nuclear dynamics and molecular flexibility. To
date, efforts to apply quantum hardware to such quantum chemistry problems have
focused primarily on electron correlation. Here, we provide a framework which
allows for the solution of quantum chemical nuclear dynamics by mapping these
to quantum spin-lattice simulators. Using the example case of a short-strong
hydrogen bonded system, we construct the Hamiltonian for the nuclear degrees of
freedom on a single Born-Oppenheimer surface and show how it can be transformed
to a generalized Ising model Hamiltonian. We then demonstrate a method to
determine the local fields and spin-spin couplings needed to identically match
the molecular and spin-lattice Hamiltonians. We describe a protocol to
determine the on-site and inter-site coupling parameters of this Ising
Hamiltonian from the Born-Oppenheimer potential and nuclear kinetic energy
operator. Our approach represents a paradigm shift in the methods used to study
quantum nuclear dynamics, opening the possibility to solve both electronic
structure and nuclear dynamics problems using quantum computing systems.


Abstract:
  Eclipsing binaries (EBs) are unique benchmarks for stellar evolution. On the
one hand, detached EBs hosting at least one star with detectable solar-like
oscillations constitute ideal test objects to calibrate asteroseismic
measurements. On the other hand, the oscillations and surface activity of stars
that belong to EBs offer unique information about the evolution of binary
systems. This paper builds upon previous works dedicated to red giant stars
(RG) in EBs -- 20 known systems so far -- discovered by the NASA Kepler
mission. Here we report the discovery of 16 RGs in EBs also from the Kepler
data. This new sample includes three SB2-EBs with oscillations and six close
systems where the RG display a clear surface activity and complete oscillation
suppression. Based on dedicated high-resolution spectroscopic observations
(Apache Point Observatory, Observatoire de Haute Provence), we focus on three
main aspects. From the extended sample of 14 SB2-EBs, we first confirm that the
simple application of the asteroseismic scaling relations to RGs overestimates
masses and radii of RGs, by about 15% and 5%. This bias can be reduced by
employing either new asteroseismic reference values for RGs, or model-based
corrections of the asteroseismic parameters. Secondly, we confirm that close
binarity leads to a high level of photometric modulation (up to 10%), and a
suppression of solar-like oscillations. In particular, we show that it reduces
the lifetime of radial modes by a factor of up to 10. Thirdly, we use our 16
new systems to complement previous observational studies that aimed at
constraining tidal dissipation in interacting binaries. In particular, we
identify systems with circular orbits despite relatively young ages, which
suggests exploring complementary tidal dissipation mechanisms in the future.
Finally, we report the measurements of mass, radius, and age of three M-dwarf
companion stars.


Abstract:
  This proceeding broadly overviews the current landscape of heavy exotic
spectroscopy. Such work includes the composition of certain X, Y , and Z
states, and proceeds to discuss tetraquarks made exclusively of four quarks.


Abstract:
  Multimodal affect recognition constitutes an important aspect for enhancing
interpersonal relationships in human-computer interaction. However, relevant
data is hard to come by and notably costly to annotate, which poses a
challenging barrier to build robust multimodal affect recognition systems.
Models trained on these relatively small datasets tend to overfit and the
improvement gained by using complex state-of-the-art models is marginal
compared to simple baselines. Meanwhile, there are many different multimodal
affect recognition datasets, though each may be small. In this paper, we
propose to leverage these datasets using weakly-supervised multi-task learning
to improve the generalization performance on each of them. Specifically, we
explore three multimodal affect recognition tasks: 1) emotion recognition; 2)
sentiment analysis; and 3) sarcasm recognition. Our experimental results show
that multi-tasking can benefit all these tasks, achieving an improvement up to
2.9% accuracy and 3.3% F1-score. Furthermore, our method also helps to improve
the stability of model performance. In addition, our analysis suggests that
weak supervision can provide a comparable contribution to strong supervision if
the tasks are highly correlated.


Abstract:
  We study the decay of global energy for the wave equation with H\"older
continuous damping placed on the $C^{1,1}$-boundary of compact and non-compact
waveguides with star-shaped cross-sections. We show there is sharp
$t^{-1/2}$-decay when the damping is uniformly bounded from below on the
cylindrical wall of product cylinders where the Geometric Control Condition is
violated. On non-product cylinders, we also show that there is $t^{-1/3}$-decay
when the damping is uniformly bounded from below on the cylindrical wall.


Abstract:
  It has been argued that one role of social constructs, such as institutions,
trust and norms, is to coordinate the expectations of autonomous entities in
order to resolve collective action situations (such as collective risk
dilemmas) through the coordination of behaviour. While much work has addressed
the formal representation of these social constructs, in this paper we focus
specifically on the formal representation of, and associated reasoning with,
the expectations themselves. In particular, we investigate how explicit
reasoning about expectations can be used to encode both traditional game theory
solution concepts and social mechanisms for the social dilemma situation. We
use the Collective Action Simulation Platform (CASP) to model a collective risk
dilemma based on a flood plain scenario and show how using expectations in the
reasoning mechanisms of the agents making decisions supports the choice of
cooperative behaviour.


Abstract:
  Fast Radio Bursts (FRBs) are extremely energetic pulses of millisecond
duration and unknown origin. In order to understand the phenomenon that emits
these pulses, targeted and untargeted searches have been performed for
multi-wavelength counterparts, including the optical. The objective of this
work is to search for optical transients at the position of 8 well-localized
FRBs, after the arrival of the burst on different time-scales (typically at one
day, several months, and one year after FRB detection) in order to compare with
known transient optical light curves. We used the Las Cumbres Observatory
Global Telescope Network (LCOGT), which allows us to promptly take images owing
to its network of twenty-three telescopes working around the world. We used a
template subtraction technique on all the images we collected at different
epochs. We have divided the subtractions into two groups, in one group we use
the image of the last epoch as a template and in the other group we use the
image of the first epoch as a template. We have searched for bright optical
transients at the localizations of the FRBs (<1 arcsec) in the template
subtracted images. We have found no optical transients, so we have set limiting
magnitudes of optical counterparts. Typical limiting magnitudes in apparent
(absolute) magnitudes for our LCOGT data are ~22 (-19) mag in the r-band. We
have compared our limiting magnitudes with light curves of superluminous
supernovae (SLSNe), type Ia supernovae (SNe), supernovae associated with
gamma-ray bursts (GRB SNe), a kilonova, and tidal disruption events (TDEs). We
rule out that FRBs are associated with SLSN at a confidence of ~99.9%. We can
also rule out the brightest sub-types of type Ia SNe, GRB SNe and TDEs (under
some conditions) at similar confidence, though we cannot exclude scenarios
where FRBs are associated with the faintest sub-type of each of these transient
classes.


Abstract:
  To investigate the interaction between the nucleon $N$ and nucleon resonance
$N(1535)1/2^-$, the $\eta d$ threshold structure connected to the isoscalar
$S$-wave $N$-$N(1535)1/2^-$ system has been experimentally studied in the
$\gamma{d}${$\to$}$\pi^0\eta{d}$ reaction at incident photon energies ranging
from the reaction threshold to 1.15 GeV. A strong enhancement is observed near
the $\eta d$ threshold over the three-body phase-space contribution in the
$\eta d$ invariant-mass distribution. An analysis incorporating the known
isovector resonance $\mathcal{D}_{12}$ with a spin-parity of $2^+$ in the
$\pi^0d$ channel reveals the existence of a narrow isoscalar resonance-like
structure with $1^-$ in the $\eta d$ system. Using a Flatt\'e parametrization,
the mass is found to be $2.427_{-0.006}^{+0.013}$ GeV, close to the $\eta d$
threshold, and the width is $\left(0.029_{-0.029}^{+0.006}{\rm\
GeV}\right)+\left(0.00_{-0.00}^{+0.41}\right) p_\eta c$, where $p_\eta$ denotes
the $\eta$ momentum in the rest frame of the $\eta d$ system. The observed
structure would be attributed to a predicted isoscalar $1^-$ $\eta NN$ bound
state from $\eta NN$ and $\pi NN$ coupled-channel calculation, or an $\eta d$
virtual state owing to strong $\eta d$ attraction.


Abstract:
  Quantum Monte Carlo and quantum simulation are both important tools for
understanding quantum many-body systems. As a classical algorithm, quantum
Monte Carlo suffers from the sign problem, preventing its application to most
fermion systems and real time dynamics. In this paper, we introduce a novel
non-variational algorithm using quantum simulation as a subroutine to
accelerate quantum Monte Carlo by easing the sign problem. The quantum
subroutine can be implemented with shallow circuits and, by incorporating error
mitigation, can reduce the Monte Carlo variance by several orders of magnitude
even when the circuit noise is significant. As such, the proposed quantum
algorithm is applicable to near-term noisy quantum hardware.


Abstract:
  Some interactions between classical or quantum fields and matter are known to
be irreversible processes. Here we associate an entropy to the electromagnetic
field from well-known notions of statistical quantum mechanics, in particular
the notion of diagonal entropy. We base our work on the study of spontaneous
emission and light diffusion. We obtain a quantity which allows to quantify
irreversibility for a quantum and classical description of the electromagnetic
field, that we can study and interpret from a thermodynamical point of view.


Abstract:
  In this paper, we introduce the notion of a topological groupoid extension
and relate it to the already existing notion of a gerbe over a topological
stack. We further study the properties of a gerbe over a Serre, Hurewicz stack.


Abstract:
  This paper provides a new approach for offensive language and hate speech
detection on social media. Our approach incorporates an offensive lexicon
composed of implicit and explicit offensive and swearing expressions annotated
with binary classes: context-dependent and context-independent offensive. Due
to the severity of the hate speech and offensive comments in Brazil, and the
lack of research in Portuguese, Brazilian Portuguese is the language used to
validate the proposed method. Nevertheless, our proposal may be applied to any
other language or domain. Based on the obtained results, the proposed approach
showed high-performance overcoming the current baselines for European and
Brazilian Portuguese.


Abstract:
  In this project, we have used the computer vision algorithm SSD (Single Shot
detector) computer vision algorithm and trained this algorithm from the dataset
which consists of 139 Pictures. Images were labeled using Intel CVAT (Computer
Vision Annotation Tool)
  We trained this model for facial detection. We have deployed our trained
model and software in the Nvidia Jetson Nano Developer kit. Model code is
written in Pytorch's deep learning framework. The programming language used is
Python.


Abstract:
  A stochastic gravitational wave background is expected to emerge from the
superposition of numerous gravitational wave sources of both astrophysical and
cosmological origin. A number of cosmological models can have a parity
violation, resulting in the generation of circularly polarised gravitational
waves. We present a method to search for parity violation in the gravitational
wave data. We first apply this method to the most recent, third, LIGO-Virgo
observing run. We then investigate the constraining power of future A+
LIGO-Virgo detectors, including KAGRA to the network, for a gravitational wave
background generated by early universe cosmological turbulence.


Abstract:
  In IEEE 802.11bc, the broadcast mode on wireless local area networks (WLANs),
data rate control that is based on acknowledgement (ACK) mechanism similar to
the one in the current IEEE 802.11 WLANs is not applicable because ACK
mechanism is not implemented. This paper addresses this challenge by proposing
ACK-less data rate adaptation methods by capturing non-broadcast uplink frames
of STAs. In IEEE 802.11bc, an use case is assumed, where a part of STAs in the
broadcast recipients is also associated with non-broadcast APs, and such STAs
periodically transmit uplink frames including ACK frames. The proposed method
is based on the idea that by overhearing such uplink frames, the broadcast AP
surveys channel conditions at partial STAs, thereby setting appropriate data
rates for the STAs. Furthermore, in order to avoid reception failures in a
large portion of STAs, this paper proposes deep reinforcement learning
(DRL)-based data rate adaptation framework that uses a sim-to-real approach.
Therein, information of reception success/failure at broadcast recipient STAs,
that could not be notified to the broadcast AP in real deployments, are made
available by simulations beforehand, thereby forming data rate adaptation
strategies. Numerical results show that utilizing overheard uplink frames of
recipients makes it feasible to manage data rates in ACK-less broadcast WLANs,
and using the sim-to-real DRL framework can decrease reception failures.


Abstract:
  The objective of this paper is to present a meta-corpus of diplomatic
documents entitled Cartae Europae Medii Aevi or CEMA. It shows the logic and
limits of this meta-corpus, which contains 250,000 documents, by specifying
both its structure and its possible future extensions. The second part of the
paper is devoted to specific examples that will attempt to show the interest of
such a database. The third part examine the possibilities opened up by the
corpus in terms of historical semantics.


Abstract:
  The fundamental relationship of traffic flow is empirically estimated by
fitting a regression curve to a cloud of observations of traffic variables.
Such estimates, however, may suffer from the confounding/endogeneity bias due
to omitted variables such as driving behaviour and weather. To this end, this
paper adopts a causal approach to obtain an unbiased estimate of the
fundamental flow-density relationship using traffic detector data. In
particular, we apply a Bayesian non-parametric spline-based regression approach
with instrumental variables to adjust for the aforementioned confounding bias.
The proposed approach is benchmarked against standard curve-fitting methods in
estimating the flow-density relationship for three highway bottlenecks in the
United States. Our empirical results suggest that the saturated (or
hypercongested) regime of the estimated flow-density relationship using
correlational curve fitting methods may be severely biased, which in turn leads
to biased estimates of important traffic control inputs such as capacity and
capacity-drop. We emphasise that our causal approach is based on the physical
laws of vehicle movement in a traffic stream as opposed to a demand-supply
framework adopted in the economics literature. By doing so, we also aim to
conciliate the engineering and economics approaches to this empirical problem.
Our results, thus, have important implications both for traffic engineers and
transport economists.


Abstract:
  We theoretically investigate the threshold for the director reorientation
from the homeotropic state to the hybrid homeotropic-planar state and vice
versa in a cell filled with a flexoelectric nematic liquid crystal (NLC)
subjected to an electric field. The liquid crystal is doped by a CTAB-like
substance, a part of molecules of which dissociates into positive and negative
ions. The anchoring on one of the cell surfaces is assumed to be strong and
homeotropic, while the other surface can adsorb positive ions which play the
role of an orienting surfactant for NLC molecules on this surface. At certain
voltages, the orientational transitions in the bulk of the NLC are possible due
to the changing conditions for the director on the adsorbing surface. We
calculate respective threshold voltages as functions of anchoring parameters.
The existence of the critical values of these parameters, beyond which the
orientational transitions do not take place, is established.


Abstract:
  Exploring whether different patterns emerge across networks generated by
organizational structures, co-authorships and citations for characterizing and
evaluating cooperative relationships is particularly important for transferring
the research results into practice. This research-in-progress paper focuses on
using the structure of scientific collaborations and mapping knowledge transfer
to gain insight into the influence of collaborative research centres linked to
the German Research Foundation (DFG) funding. Within the German Centre for
Integrative Biodiversity Research (iDiv), the DFG sponsors research conducted
across all participating universities and institutes by more than hundred
research groups who bring their expertise to the manifold research fields of
biodiversity. Using iDiv research from 2013-2020, we build co-authorship
networks and identify the most cohesive communities in terms of collaboration
and compare them with groups presented on its website. Corresponding cited and
citing works are analysed by distributions to investigate disciplinary
collaboration. Our findings show that the number of publications and the
intensity of research collaboration have maintained a steady increase. Despite
the highly cohesive cooperation structure addressed by iDiv, the internal
scientific collaboration has not gained strong momentum compared with its
growing trends in international collaborations. The tendency towards covering
cross-disciplinary research foci is not evident.


Abstract:
  Conservative mechanism is a desirable property in decision-making problems
which balance the tradeoff between the exploration and exploitation. We propose
the novel \emph{conservative contextual combinatorial cascading bandit
($C^4$-bandit)}, a cascading online learning game which incorporates the
conservative mechanism. At each time step, the learning agent is given some
contexts and has to recommend a list of items but not worse than the base
strategy and then observes the reward by some stopping rules. We design the
$C^4$-UCB algorithm to solve the problem and prove its n-step upper regret
bound for two situations: known baseline reward and unknown baseline reward.
The regret in both situations can be decomposed into two terms: (a) the upper
bound for the general contextual combinatorial cascading bandit; and (b) a
constant term for the regret from the conservative mechanism. We also improve
the bound of the conservative contextual combinatorial bandit as a by-product.
Experiments on synthetic data demonstrate its advantages and validate our
theoretical analysis.


Abstract:
  Plasma emission (PE), i.e., electromagnetic radiation at the plasma frequency
and its second harmonic, is a general process occurring in both astrophysical
and laboratory plasmas. The prevailing theory presents a multi-stage process
attributed to the resonant coupling of beam-excited Langmuir waves with
ion-acoustic waves. Here we examine another possibility of the fundamental PE
induced by the resonant coupling of Z-mode and whistler (W) waves. Earlier
studies have been controversial in the plausibility and significance of such
process in plasmas. In this study we show that the matching condition of three
wave resonant interaction (Z+W ! O) can be satisfied over a wide regime of
parameters based on the magnetoionic theory, demonstrate the occurrence of such
process and further evaluate the rate of energy conversion from the pumped Z or
W mode to the fundamental O mode with particle-in-cell (PIC) simulations of
wave pumping. The study presents an alternative form of the fundamental PE,
which could possibly play a role in various astrophysical and laboratory
scenarios with both Z and W modes readily excited through the electron
cyclotron maser instability.


Abstract:
  One of the first and easy to use techniques for proving run time bounds for
evolutionary algorithms is the so-called method of fitness levels by Wegener.
It uses a partition of the search space into a sequence of levels which are
traversed by the algorithm in increasing order, possibly skipping levels. An
easy, but often strong upper bound for the run time can then be derived by
adding the reciprocals of the probabilities to leave the levels (or upper
bounds for these). Unfortunately, a similarly effective method for proving
lower bounds has not yet been established. The strongest such method, proposed
by Sudholt (2013), requires a careful choice of the viscosity parameters
$\gamma_{i,j}$, $0 \le i < j \le n$.
  In this paper we present two new variants of the method, one for upper and
one for lower bounds. Besides the level leaving probabilities, they only rely
on the probabilities that levels are visited at all. We show that these can be
computed or estimated without greater difficulties and apply our method to
reprove the following known results in an easy and natural way. (i) The precise
run time of the (1+1) EA on \textsc{LeadingOnes}. (ii) A lower bound for the
run time of the (1+1) EA on \textsc{OneMax}, tight apart from an $O(n)$ term.
(iii) A lower bound for the run time of the (1+1) EA on long $k$-paths. We also
prove a tighter lower bound for the run time of the (1+1) EA on jump functions
by showing that, regardless of the jump size, only with probability $O(2^{-n})$
the algorithm can avoid to jump over the valley of low fitness.


Abstract:
  Two signed graphs are called switching isomorphic to each other if one is
isomorphic to a switching of the other. The wheel $W_n$ is the join of the
cycle $C_n$ and a vertex. For $0 \leq p \leq n$, $\psi_{p}(n)$ is defined to be
the number of switching non-isomorphic signed $W_n$ with exactly $p$ negative
edges on $C_n$. The number of switching non-isomorphic signed $W_n$ is denoted
by $\psi(n)$. In this paper, we compute the values of $\psi_{p}(n)$ for
$p=0,1,2,3,4,n-4,n-3,n-2,n-1,n$ and of $\psi(n)$ for $n=4,5,...,10$. Our method
of obtaining $\psi_{p}(n)$ not only count the switching non-isomorphic signed
wheels but also generates them.


Abstract:
  Supervised learning techniques are at the center of many tasks in remote
sensing. Unfortunately, these methods, especially recent deep learning methods,
often require large amounts of labeled data for training. Even though
satellites acquire large amounts of data, labeling the data is often tedious,
expensive and requires expert knowledge. Hence, improved methods that require
fewer labeled samples are needed. We present MSMatch, the first semi-supervised
learning approach competitive with supervised methods on scene classification
on the EuroSAT and UC Merced Land Use benchmark datasets. We test both RGB and
multispectral images of EuroSAT and perform various ablation studies to
identify the critical parts of the model. The trained neural network achieves
state-of-the-art results on EuroSAT with an accuracy that is up to 19.76%
better than previous methods depending on the number of labeled training
examples. With just five labeled examples per class, we reach 94.53% and 95.86%
accuracy on the EuroSAT RGB and multispectral datasets, respectively. On the UC
Merced Land Use dataset, we outperform previous works by up to 5.59% and reach
90.71% with five labeled examples. Our results show that MSMatch is capable of
greatly reducing the requirements for labeled data. It translates well to
multispectral data and should enable various applications that are currently
infeasible due to a lack of labeled data. We provide the source code of MSMatch
online to enable easy reproduction and quick adoption.


Abstract:
  Anomaly detection is a task that recognizes whether an input sample is
included in the distribution of a target normal class or an anomaly class.
Conventional generative adversarial network (GAN)-based methods utilize an
entire image including foreground and background as an input. However, in these
methods, a useless region unrelated to the normal class (e.g., unrelated
background) is learned as normal class distribution, thereby leading to false
detection. To alleviate this problem, this paper proposes a novel two-stage
network consisting of an attention network and an anomaly detection GAN
(ADGAN). The attention network generates an attention map that can indicate the
region representing the normal class distribution. To generate an accurate
attention map, we propose the attention loss and the adversarial anomaly loss
based on synthetic anomaly samples generated from hard augmentation. By
applying the attention map to an image feature map, ADGAN learns the normal
class distribution from which the useless region is removed, and it is possible
to greatly reduce the problem difficulty of the anomaly detection task.
Additionally, the estimated attention map can be used for anomaly segmentation
because it can distinguish between normal and anomaly regions. As a result, the
proposed method outperforms the state-of-the-art anomaly detection and anomaly
segmentation methods for widely used datasets.


Abstract:
  We present a new, stable, mixed finite element (FE) method for linear
elastostatics of nearly incompressible solids. The method is the automatic
variationally stable FE (AVS-FE) method of Calo, Romkes and Valseth, in which
we consider a Petrov-Galerkin weak formulation where the stress and
displacement variables are in the space H(div)xH1, respectively. This allows us
to employ a fully conforming FE discretization for any elastic solid using
classical FE subspaces of H(div) and H1. Hence, the resulting FE approximation
yields both continuous stresses and displacements.
  To ensure stability of the method, we employ the philosophy of the
discontinuous Petrov-Galerkin (DPG) method of Demkowicz and Gopalakrishnan and
use optimal test spaces. Thus, the resulting FE discretization is stable even
as the Poisson ratio approaches 0.5, and the system of linear algebraic
equations is symmetric and positive definite. Our method also comes with a
built-in a posteriori error estimator as well as well as indicators which are
used to drive mesh adaptive refinements. We present several numerical
verifications of our method including comparisons to existing FE technologies.


Abstract:
  Methods for linking individuals across historical data sets, typically in
combination with AI based transcription models, are developing rapidly.
Probably the single most important identifier for linking is personal names.
However, personal names are prone to enumeration and transcription errors and
although modern linking methods are designed to handle such challenges these
sources of errors are critical and should be minimized. For this purpose,
improved transcription methods and large-scale databases are crucial
components. This paper describes and provides documentation for HANA, a newly
constructed large-scale database which consists of more than 1.1 million images
of handwritten word-groups. The database is a collection of personal names,
containing more than 105 thousand unique names with a total of more than 3.3
million examples. In addition, we present benchmark results for deep learning
models that automatically can transcribe the personal names from the scanned
documents. Focusing mainly on personal names, due to its vital role in linking,
we hope to foster more sophisticated, accurate, and robust models for
handwritten text recognition through making more challenging large-scale
databases publicly available. This paper describes the data source, the
collection process, and the image-processing procedures and methods that are
involved in extracting the handwritten personal names and handwritten text in
general from the forms.


Abstract:
  Feedback-controlled electric breakdown of graphene in air or vacuum is a
well-established way of fabricating tunnel junctions, nanogaps, and quantum
dots. We show that the method is equally applicable to encapsulated graphene
constrictions fabricated using hydrogen silsesquioxane. The silica-like layer
left by hydrogen silsesquioxane resist after electron-beam exposure remains
intact after electric breakdown of the graphene. We explore the conductance
switching behavior that is common in graphene nanostructures fabricated via
feedback-controlled breakdown, and show that it can be attributed to
atomic-scale fluctuations of graphene below the encapsulating layer. Our
findings open up new ways of fabricating encapsulated room-temperature
single-electron nanodevices and shed light on the underlying physical mechanism
of conductance switching in these graphene nanodevices.


Abstract:
  First-principles calculations of thermal transport in homogeneous materials
have reached remarkable predicting power. Modeling deterministically phonon
transport in nanostructures, however, poses novel challenges; notably, it
entails solving as many algebraic equations as the number of combinations of
wave vectors in the discretized Brillouen and polarizations. We show that,
within the relaxation time approximation of the Boltzmann transport equation
(BTE), this issue is resolved by interpolating the phonon distributions in the
vectorial phonon mean free paths (MFP) space. The coupling between structure
and mode-resolved heat transport is investigated in terms of angular-resolved
bulk thermal conductivity and phonon suppression function, the latter being
associated primarily to the material's geometry. Our method, termed the
anisotropic MFP-BTE (aMFP-BTE), allows for fast and accurate thermal
conductivity calculations in nanomaterials regardless of the number of phonon
branches and wave vectors. Furthermore, it naturally blends with
first-principles thermal transport calculations, therefore allowing for
multiscale, parameter-free simulations. We apply the aMFP-BTE to compute the
mode-resolved effective thermal conductivity of porous Si membranes, achieving
up to 50x speed with respect to the case with no interpolation. The proposed
approach unlocks the engineering of novel nanostructures, with applications to
thermoelectrics and heat management.


Abstract:
  With the emergence of Information and Communication Technologies (ICT) and
wireless embedded sensing devices into modern vehicles, Intelligent Transport
System (ITS) becomes a reality and an indispensable component of smart cities.
The purpose of ITS is to improve road safety and traffic efficiency as well as
offering infotainments services. In fact, warning drivers in the right time
about dangerous situations on the road and providing them with prior
information about traffic will undoubtedly leads to enhance driver's safety and
reduce traffic congestion. Technically speaking, ITS is based on
self-organizing wireless networks, known as vehicular ad-hoc networks (VANETs).
Mobile vehicles in VANET might play the role of stationary sensors in
infrastructure-based networks. They can detect, gather and disseminate
real-time data about traffic, driving conditions and potential hazards on
roads. In this respect, we review in this study, recent developments on the
design of VANET protocols and applications. We first introduce the architecture
of VANETs then we review their unique characteristics and applications.
Thereafter, we discuss the main research challenges and open issues to be
considered for designing efficient and a cost-effective VANET protocols and
applications.


Abstract:
  Despite the recent success of deep reinforcement learning (RL), domain
adaptation remains an open problem. Although the generalization ability of RL
agents is critical for the real-world applicability of Deep RL, zero-shot
policy transfer is still a challenging problem since even minor visual changes
could make the trained agent completely fail in the new task. To address this
issue, we propose a two-stage RL agent that first learns a latent unified state
representation (LUSR) which is consistent across multiple domains in the first
stage, and then do RL training in one source domain based on LUSR in the second
stage. The cross-domain consistency of LUSR allows the policy acquired from the
source domain to generalize to other target domains without extra training. We
first demonstrate our approach in variants of CarRacing games with customized
manipulations, and then verify it in CARLA, an autonomous driving simulator
with more complex and realistic visual observations. Our results show that this
approach can achieve state-of-the-art domain adaptation performance in related
RL tasks and outperforms prior approaches based on latent-representation based
RL and image-to-image translation.


Abstract:
  Most works in IRS-assisted systems have ignored the impact of the inevitable
residual hardware impairments (HWIs) at both the transceiver hardware and the
IRS while any relevant works have addressed only simple scenarios, e.g., with
single-antenna network nodes and/or without taking the randomness of phase
noise at the IRS into account. In this work, we aim at filling up this gap by
considering a general IRS-assisted multi-user (MU) multiple-input single-output
(MISO) system with imperfect CSI and correlated Rayleigh fading. In parallel,
we present a general computationally efficient methodology for IRS reflect
beamforming (RB) optimization. Specifically, we introduce an advantageous
channel estimation (CE) method for such systems accounting for the HWIs.
Moreover, we derive the uplink achievable spectral efficiency (SE) with
maximal-ratio combining (MRC) receiver, displaying three significant advantages
being: 1) its closed-form expression, 2) its dependence only on large-scale
statistics, and 3) its low training overhead. Notably, by exploiting the first
two benefits, we achieve to perform optimization with respect to the reflect
beamforming matrix (RBM) that can take place only at every several coherence
intervals, and thus, reduces significantly the computational cost compared to
other methods which require frequent phase optimization. Among the insightful
observations, we highlight that uncorrelated Rayleigh fading does not allow
optimization of the SE, which makes the application of an IRS ineffective.
Also, in the case that the phase drifts, describing the distortion of the
phases in the RBM, are uniformly distributed, the presence of an IRS provides
no advantage. The analytical results outperform previous works and are verified
by Monte-Carlo (MC) simulations.


Abstract:
  We present a comprehensive study of the quark sector of $2+1$ flavour QCD,
based on a self-consistent treatment of the coupled system of Schwinger-Dyson
equations for the quark propagator and the full quark-gluon vertex. The
individual form factors of the quark-gluon vertex are expressed in a special
tensor basis obtained from a set of gauge-invariant operators. The sole
external ingredient used as input to our equations is the Landau gauge gluon
propagator with $2+1$ dynamical quark flavours, obtained from studies with
Schwinger-Dyson equations, the functional renormalisation group approach, and
large volume lattice simulations. The appropriate renormalisation procedure
required in order to self-consistently accommodate external inputs stemming
from other functional approaches or the lattice is discussed in detail, and the
value of the gauge coupling is accurately determined at two vastly separated
renormalisation group scales.
  Our analysis establishes a clear hierarchy among the vertex form factors. We
identify only three dominant ones, in agreement with previous results. The
components of the quark propagator obtained from our approach are in excellent
agreement with the results from Schwinger-Dyson equations, the functional
renormalisation group, and lattice QCD simulation, a simple benchmark
observable being the chiral condensate in the chiral limit, which is computed
as $(245\,\textrm{MeV})^3$. The present approach has a wide range of
applications, including the self-consistent computation of bound-state
properties and finite temperature and density physics, which are briefly
discussed.


Abstract:
  A theoretical framework which unifies the conventional Mori-Zwanzig formalism
and the approximate Koopman learning is presented. In this framework, the
Mori-Zwanzig formalism, developed in statistical mechanics to tackle the hard
problem of construction of reduced-order dynamics for high-dimensional
dynamical systems, can be considered as a natural generalization of the Koopman
description of the dynamical system. We next show that similar to the
approximate Koopman learning methods, data-driven methods can be developed for
the Mori-Zwanzig formalism with Mori's linear projection operator. We developed
two algorithms to extract the key operators, the Markov and the memory kernel,
using time series of a reduced set of observables in a dynamical system. We
adopted the Lorenz `96 system as a test problem and solved for the operators,
which exhibit complex behaviors which are unlikely to be captured by
traditional modeling approaches, in Mori-Zwanzig analysis. The nontrivial
Generalized Fluctuation Dissipation relationship, which relates the memory
kernel with the two-time correlation statistics of the orthogonal dynamics, was
numerically verified as a validation of the solved operators. We present
numerical evidence that the Generalized Langevin Equation, a key construct in
the Mori-Zwanzig formalism, is more advantageous in predicting the evolution of
the reduced set of observables than the conventional approximate Koopman
operators.


Abstract:
  Gate-controlled supercurrent (GCS) in superconductor nanobridges has recently
attracted attention as a means to create superconducting field effect
transistors. Despite the clear advantage for applications with low power
consumption and high switching speeds, the microscopic mechanism of the field
effect is still under debate. In this work, we realize GCS for the first time
in an epitaxial superconductor, which is created as a shell on an InAs
nanowire. We show that the supercurrent in the epitaxial Al layer can be
switched to the normal state by applying $\simeq\pm$ 23$\,$V on a bottom gate
insulated from the nanowire by a crystalline hBN layer. Our extensive study on
the temperature and magnetic field dependencies of GCS suggests that hot
electron injection alone cannot explain our experimental findings.


Abstract:
  The growing energy and performance costs of deep learning have driven the
community to reduce the size of neural networks by selectively pruning
components. Similarly to their biological counterparts, sparse networks
generalize just as well, if not better than, the original dense networks.
Sparsity can reduce the memory footprint of regular networks to fit mobile
devices, as well as shorten training time for ever growing networks. In this
paper, we survey prior work on sparsity in deep learning and provide an
extensive tutorial of sparsification for both inference and training. We
describe approaches to remove and add elements of neural networks, different
training strategies to achieve model sparsity, and mechanisms to exploit
sparsity in practice. Our work distills ideas from more than 300 research
papers and provides guidance to practitioners who wish to utilize sparsity
today, as well as to researchers whose goal is to push the frontier forward. We
include the necessary background on mathematical methods in sparsification,
describe phenomena such as early structure adaptation, the intricate relations
between sparsity and the training process, and show techniques for achieving
acceleration on real hardware. We also define a metric of pruned parameter
efficiency that could serve as a baseline for comparison of different sparse
networks. We close by speculating on how sparsity can improve future workloads
and outline major open problems in the field.


Abstract:
  Semantic relationships, such as hyponym-hypernym, cause-effect,
meronym-holonym etc. between a pair of entities in a sentence are usually
reflected through syntactic patterns. Automatic extraction of such patterns
benefits several downstream tasks, including, entity extraction, ontology
building, and question answering. Unfortunately, automatic extraction of such
patterns has not yet received much attention from NLP and information retrieval
researchers. In this work, we propose an attention-based supervised deep
learning model, ASPER, which extracts syntactic patterns between entities
exhibiting a given semantic relation in the sentential context. We validate the
performance of ASPER on three distinct semantic relations -- hyponym-hypernym,
cause-effect, and meronym-holonym on six datasets. Experimental results show
that for all these semantic relations, ASPER can automatically identify a
collection of syntactic patterns reflecting the existence of such a relation
between a pair of entities in a sentence. In comparison to the existing
methodologies of syntactic pattern extraction, ASPER's performance is
substantially superior.


Abstract:
  Given a graph, the shortest-path problem requires finding a sequence of edges
with minimum cumulative length that connects a source vertex to a target
vertex. We consider a generalization of this classical problem in which the
position of each vertex in the graph is a continuous decision variable,
constrained to lie in a corresponding convex set. The length of an edge is then
defined as a convex function of the positions of the vertices it connects.
Problems of this form arise naturally in motion planning of autonomous
vehicles, robot navigation, and even optimal control of hybrid dynamical
systems. The price for such a wide applicability is the complexity of this
problem, which is easily seen to be NP-hard. Our main contribution is a strong
mixed-integer convex formulation based on perspective functions. This
formulation has a very tight convex relaxation and makes it possible to
efficiently find globally-optimal paths in large graphs and in high-dimensional
spaces.


Abstract:
  Deep Neural Networks (DNNs) are generated by sequentially performing linear
and non-linear processes. Using a combination of linear and non-linear
procedures is critical for generating a sufficiently deep feature space. The
majority of non-linear operators are derivations of activation functions or
pooling functions. Mathematical morphology is a branch of mathematics that
provides non-linear operators for a variety of image processing problems. We
investigate the utility of integrating these operations in an end-to-end deep
learning framework in this paper. DNNs are designed to acquire a realistic
representation for a particular job. Morphological operators give topological
descriptors that convey salient information about the shapes of objects
depicted in images. We propose a method based on meta-learning to incorporate
morphological operators into DNNs. The learned architecture demonstrates how
our novel morphological operations significantly increase DNN performance on
various tasks, including picture classification and edge detection.


Abstract:
  In this paper we study the behaviour of scalar flat K\"ahler ALE spaces and
their ADM mass under blow ups. In particular we prove that by blowing up
sufficiently many points at sufficiently big mutual distance one can produce
scalar flat metrics with arbitrarily large ADM mass. A general machinery for
producing scalar flat non Ricci flat ALE spaces of zero ADM mass is also
presented, using and integrating previous work by Rollin-Singer and
Hein-LeBrun.


Abstract:
  Masked language models have revolutionized natural language processing
systems in the past few years. A recently introduced generalization of masked
language models called warped language models are trained to be more robust to
the types of errors that appear in automatic or manual transcriptions of spoken
language by exposing the language model to the same types of errors during
training. In this work we propose a novel approach that takes advantage of the
robustness of warped language models to transcription noise for correcting
transcriptions of spoken language. We show that our proposed approach is able
to achieve up to 10% reduction in word error rates of both automatic and manual
transcriptions of spoken language.


Abstract:
  Inspired by the UNet architecture of semantic image segmentation, we propose
a lightweight UNet using depthwise separable convolutions (DSUNet) for
end-to-end learning of lane detection and path prediction (PP) in autonomous
driving. We also design and integrate a PP algorithm with convolutional neural
network (CNN) to form a simulation model (CNN-PP) that can be used to assess
CNN's performance qualitatively, quantitatively, and dynamically in a host
agent car driving along with other agents all in a real-time autonomous manner.
DSUNet is 5.16x lighter in model size and 1.61x faster in inference than UNet.
DSUNet-PP outperforms UNet-PP in mean average errors of predicted curvature and
lateral offset for path planning in dynamic simulation. DSUNet-PP outperforms a
modified UNet in lateral error, which is tested in a real car on real road.
These results show that DSUNet is efficient and effective for lane detection
and path prediction in autonomous driving.


Abstract:
  With the down-scaling of CMOS technology, the design complexity of very
large-scale integrated (VLSI) is increasing. Although the application of
machine learning (ML) techniques in electronic design automation (EDA) can
trace its history back to the 90s, the recent breakthrough of ML and the
increasing complexity of EDA tasks have aroused more interests in incorporating
ML to solve EDA tasks. In this paper, we present a comprehensive review of
existing ML for EDA studies, organized following the EDA hierarchy.


Abstract:
  We study quasi two-dimensional, monodisperse systems of active Brownian
particles (ABPs) for a range of activities, stiffnesses, and densities. We
develop a microscopic, analytical method for predicting the dense phase
structure formed after motility-induced phase separation (MIPS) has occurred,
including the dense cluster's area fraction, interparticle pressure, and
radius. Our predictions are in good agreement with our Brownian dynamics
simulations. We, then, derive a continuum model to investigate the relationship
between the predicted interparticle pressure, the swim pressure, and the
macroscopic pressure in the momentum equation. We find that formulating the
point-wise macroscopic pressure as the interparticle pressure and modeling the
particle activity through a spatially variant body force -- as opposed to a
volume-averaged swim pressure -- results in consistent predictions of pressure
in both the continuum model and the microscopic theory. This formulation of
pressure also results in nearly zero surface tension for the phase separated
domains, irrespective of activity, stiffness, and area fraction. Furthermore,
using Brownian dynamics simulations and our continuum model, we showed that
both the interface width and surface tension, are intrinsic characteristics of
the system. On the other hand, if we were to exclude the body force induced by
activity, we find that the resulting surface tension values are linearly
dependent on the size of the simulation, in contrast to the statistical
mechanical definition of surface tension.


Abstract:
  Delusive attacks aim to substantially deteriorate the test accuracy of the
learning model by slightly perturbing the features of correctly labeled
training examples. By formalizing this malicious attack as finding the
worst-case training data within a specific $\infty$-Wasserstein ball, we show
that minimizing adversarial risk on the perturbed data is equivalent to
optimizing an upper bound of natural risk on the original data. This implies
that adversarial training can serve as a principled defense against delusive
attacks. Thus, the test accuracy decreased by delusive attacks can be largely
recovered by adversarial training. To further understand the internal mechanism
of the defense, we disclose that adversarial training can resist the delusive
perturbations by preventing the learner from overly relying on non-robust
features in a natural setting. Finally, we complement our theoretical findings
with a set of experiments on popular benchmark datasets, which show that the
defense withstands six different practical attacks. Both theoretical and
empirical results vote for adversarial training when confronted with delusive
adversaries.


Abstract:
  Neurons exhibit complex geometry in their branched networks of neurites which
is essential to the function of individual neuron but also brings challenges to
transport a wide variety of essential materials throughout their neurite
networks for their survival and function. While numerical methods like
isogeometric analysis (IGA) have been used for modeling the material transport
process via solving partial differential equations (PDEs), they require long
computation time and huge computation resources to ensure accurate geometry
representation and solution, thus limit their biomedical application. Here we
present a graph neural network (GNN)-based deep learning model to learn the
IGA-based material transport simulation and provide fast material concentration
prediction within neurite networks of any topology. Given input boundary
conditions and geometry configurations, the well-trained model can predict the
dynamical concentration change during the transport process with an average
error less than 10% and 120~330 times faster compared to IGA simulations. The
effectiveness of the proposed model is demonstrated within several complex
neurite networks.


Abstract:
  We have performed an unbiased dense core survey toward the Orion A Giant
Molecular Cloud in the C$^{18}$O ($J$=1--0) emission line taken with the
Nobeyama Radio Observatory (NRO) 45-m telescope. The effective angular
resolution of the map is 26", which corresponds to $\sim$ 0.05 pc at a distance
of 414 pc. By using the Herschel-Planck H$_2$ column density map, we calculate
the C$^{18}$O fractional abundance and find that it is roughly constant over
the column density range of $\lesssim$ 5 $\times$ 10$^{22}$ cm$^{-3}$, although
a trend of C$^{18}$O depletion is determined toward higher column density.
Therefore, C$^{18}$O intensity can follow the cloud structure reasonably well.
The mean C$^{18}$O abundance in Orion A is estimated to be
5.7$\times$10$^{-7}$, which is about 3 times larger than the fiducial value. We
identified 746 C$^{18}$O cores with astrodendro and classified 709 cores as
starless cores. We compute the core masses by decomposing the Herschel-Planck
dust column density using the relative proportions of the C$^{18}$O integrated
intensities of line-of-sight components. Applying this procedure, we attempt to
remove the contribution of the background emission, i.e., the ambient gas
outside the cores. Then, we derived mass function for starless cores and found
that it resembles the stellar initial mass function (IMF). The CMF for starless
cores, $dN/dM$, is fitted with a power-law relation of $M^\alpha$ with a power
index of $\alpha = -$2.25$\pm$ 0.16 at the high-mass slope ($\gtrsim$ 0.44
$M_\odot$). We also found that the ratio of each core mass to the total mass
integrated along the line of sight is significantly large. Therefore, in the
previous studies, the core masses derived from the dust image are likely to be
overestimated at least by a factor of a few. Accordingly, such previous studies
may underestimate the star formation efficiency of individual cores.


Abstract:
  SDSS J134244.4+053056 is a tidal disruption event candidate with strong
temporal coronal line emitters and a long fading, mid-infrared dust echo. We
present detailed analyses of X-ray emission from a Swift/XRT observation in
2009 and the most recent XMM-Newton/pn observation in 2020. The two spectra can
be modeled with hard and soft components. While no significant variability is
detected in the hard component above 2 keV between these two observations, the
soft X-ray emission in 0.3-2 keV varies by a factor of $\sim5$. The luminosity
of this soft component fades from $\sim1.8\times10^{41}$ to
$\sim3.7\times10^{40}$ erg s$^{-1}$ from the observation in Swift to that of
XMM-Newton, which are 8 and 19 years after the outburst occurred, respectively.
The evolution of luminosity matches with the $t^{-5/3}$ decline law well; there
is a soft X-ray peak luminosity of 10$^{44}$ erg s$^{-1}$ at the time of the
optical flare. Furthermore, the spectra of the soft component harden slightly
in the decay phase, in which the photon index $\Gamma$ varies from
$4.8^{+1.2}_{-0.9}$ to $3.7\pm0.5$, although they are consistent with each
other if we consider the uncertainties. Additionally, by comparing the BH mass
estimate between the $M-\sigma$ correlation, the broad H$\alpha$ emission, and
the fundamental plane relation of BH accretion, we find that a value of
$\sim10^{5}$Msun is favored. If so, taking its X-ray spectral variation,
luminosity evolution, and further support from theory into account, we suggest
that SDSS J134244.4+053056 is a long-lived tidal disruption event candidate
lasting more than 18 years with an intermediate-mass black hole.


Abstract:
  We formulate a problem on diamagnetic levitation, which may be suitable for
specialized high-school or first-year students in physical sciences. We guide
the students, step-by-step, through the physics of diamagnetic levitation. The
calculations are simplified by assuming a ring-shaped geometry of the
diamagnetic object residing above a magnetic dipole. This problem was
originally intended for the International Physics Olympiad 2016 (IPHO 2016),
but was finally deemed surplus and therefore not set.


Abstract:
  Despite impressive success of machine learning algorithms in clinical natural
language processing (cNLP), rule-based approaches still have a prominent role.
In this paper, we introduce medspaCy, an extensible, open-source cNLP library
based on spaCy framework that allows flexible integration of rule-based and
machine learning-based algorithms adapted to clinical text. MedspaCy includes a
variety of components that meet common cNLP needs such as context analysis and
mapping to standard terminologies. By utilizing spaCy's clear and easy-to-use
conventions, medspaCy enables development of custom pipelines that integrate
easily with other spaCy-based modules. Our toolkit includes several core
components and facilitates rapid development of pipelines for clinical text.


Abstract:
  Computing the expectation of kernel functions is a ubiquitous task in machine
learning, with applications from classical support vector machines to
exploiting kernel embeddings of distributions in probabilistic modeling,
statistical inference, causal discovery, and deep learning. In all these
scenarios, we tend to resort to Monte Carlo estimates as expectations of
kernels are intractable in general. In this work, we characterize the
conditions under which we can compute expected kernels exactly and efficiently,
by leveraging recent advances in probabilistic circuit representations. We
first construct a circuit representation for kernels and propose an approach to
such tractable computation. We then demonstrate possible advancements for
kernel embedding frameworks by exploiting tractable expected kernels to derive
new algorithms for two challenging scenarios: 1) reasoning under missing data
with kernel support vector regressors; 2) devising a collapsed black-box
importance sampling scheme. Finally, we empirically evaluate both algorithms
and show that they outperform standard baselines on a variety of datasets.