Abstract:
  Nematic polymer networks (NPNs) are nematic elastomers within which the
nematic director is enslaved to the elastic deformation. The elastic free
energy of a NPN sheet of thickness $h$ has both stretching and bending
components (the former scaling like h, the latter scaling like $h^3$). NPN
sheets bear a director field $\mathbf{m}$ imprinted in them (usually, uniformly
throughout their thickness); they can be activated by changing the nematic
order (e.g. by illumination or heating). This paper illustrates an attempt to
compute the bending energy of a NPN sheet and to show which role it can play in
determining the activated shape. Our approach is approximate: the activated
surface consists of flat sectors connected by ridges, where the unit normal
jumps and the bending energy is concentrated. By increasing the number of
ridges, we should get closer to the real situation, where the activated surface
is smooth and the bending energy is distributed on it. The method is applied to
a disk with imprinted a spiraling hedgehog. It is shown that upon activation
the disk, like a tiny hand, is able to grab a rigid lamina.


Abstract:
  We consider a general class of integro-differential evolution equations which
includes the governing equation of the generalized grey Brownian motion and the
time- and space-fractional heat equation. We present a general relation between
the parameters of the equation and the distribution of the underlying
stochastic processes, as well as discuss different classes of processes
providing stochastic solutions of these equations. For a subclass of evolution
equations, containing Saigo-Maeda generalized time-fractional operators, we
determine the parameters of the corresponding processes explicitly. Moreover,
we explain how self-similar stochastic solutions with stationary increments can
be obtained via linear fractional L\'evy motion for suitable
pseudo-differential operators in space.


Abstract:
  We classify the indecomposable objects in the monoidal center of Deligne's
interpolation category $Rep(S_t)$ by viewing $Rep(S_t)$ as a model-theoretic
limit in rank and characteristic. We further prove that the center of
$Rep(S_t)$ is semisimple if and only if $t$ is not a non-negative integer. In
addition, we identify the associated graded Grothendieck ring of this monoidal
center with that of the graded sum of the centers of representation categories
of finite symmetric groups with an induction product.


Abstract:
  Despite recent advances in its theoretical understanding, there still remains
a significant gap in the ability of existing PAC-Bayesian theories on
meta-learning to explain performance improvements in the few-shot learning
setting, where the number of training examples in the target tasks is severely
limited. This gap originates from an assumption in the existing theories which
supposes that the number of training examples in the observed tasks and the
number of training examples in the target tasks follow the same distribution,
an assumption that rarely holds in practice. By relaxing this assumption, we
develop two PAC-Bayesian bounds tailored for the few-shot learning setting and
show that two existing meta-learning algorithms (MAML and Reptile) can be
derived from our bounds, thereby bridging the gap between practice and
PAC-Bayesian theories. Furthermore, we derive a new computationally-efficient
PACMAML algorithm, and show it outperforms existing meta-learning algorithms on
several few-shot benchmark datasets.


Abstract:
  For any positive integer $t$, a \emph{$t$-broom} is a graph obtained from
$K_{1,t+1}$ by subdividing an edge once. In this paper, we show that, for
graphs $G$ without induced $t$-brooms, we have $\chi(G) = o(\omega(G)^{t+1})$,
where $\chi(G)$ and $\omega(G)$ are the chromatic number and clique number of
$G$, respectively. When $t=2$, this answers a question of Schiermeyer and
Randerath. Moreover, for $t=2$, we strengthen the bound on $\chi(G)$ to
$7.5\omega(G)^2$, confirming a conjecture of Sivaraman. For $t\geq 3$ and
\{$t$-broom, $K_{t,t}$\}-free graphs, we improve the bound to
$o(\omega^{t-1+\frac{2}{t+1}})$.


Abstract:
  The range mode problem is a fundamental problem and there is a lot of work
about it. There is also some work for the dynamic version of it and the
enumerating version of it, but there is no previous research about the dynamic
and enumerating version of it. We found an efficient algorithm for it.


Abstract:
  This paper studies the problem of decentralized continuum deformation
coordination of multi-agent systems aided by cooperative localization. We treat
agents as particles inside a triangular continuum (deformable body) in a2-D
motion space and let the continuum deformation coordination be defined by three
leaders located at vertices of a triangle, called the leading triangle. The
leaders desired trajectories are assigned as the solution of a constrained
optimal control problem such that safety requirements are satisfied in the
presence of disturbance and measurement noise. Followers distributed inside the
leading tri-angle acquire continuum deformation in a decentralized fashion by
integrating cooperative localization and local communication. Specifically,
cooperative localization estimates the global positions of all agents using
relative position measurements based primarily on proximity of agents.
Simulation results are presented for a network of ten agents.


Abstract:
  In robotic domains, learning and planning are complicated by continuous state
spaces, continuous action spaces, and long task horizons. In this work, we
address these challenges with Neuro-Symbolic Relational Transition Models
(NSRTs), a novel class of models that are data-efficient to learn, compatible
with powerful robotic planning methods, and generalizable over objects. NSRTs
have both symbolic and neural components, enabling a bilevel planning scheme
where symbolic AI planning in an outer loop guides continuous planning with
neural models in an inner loop. Experiments in four robotic planning domains
show that NSRTs can be learned after only tens or hundreds of training
episodes, and then used for fast planning in new tasks that require up to 60
actions and involve many more objects than were seen during training. Video:
https://tinyurl.com/chitnis-nsrts


Abstract:
  One knows that, the connected graded ring $P^{\otimes h}= \mathbb Z/2[t_1,
\ldots, t_h]= \{P_n^{\otimes h}\}_{n\geq 0},$ which is graded by the degree of
the homogeneous terms $P^{\otimes h}_n$ of degree $n$ in $h$ generators with
the degree of each $t_i$ being one, admits a left action of $\mathcal A$ as
well as a right action of the general linear group $GL_h.$ A central problem of
homotopy theory is to determine the structure of the space of
$GL_h$-coinvariants, $\mathbb Z/2\otimes_{GL_h}{\rm Ann}_{\overline{\mathcal
A}}[P^{\otimes h}_n]^{*}.$ Solving this problem is very difficult and still
open for $h\geq 4.$ In this Note, our intent is of studying the dimension of
$\mathbb Z/2\otimes_{GL_h}{\rm Ann}_{\overline{\mathcal A}}[P^{\otimes
h}_n]^{*}$ for the case $h = 4$ and the "generic" degrees $n$ of the form
$n_{k, r, s} = k(2^{s} - 1) + r.2^{s},$ where $k,\, r,\, s$ are positive
integers. Applying the results, we investigate the behaviour of the Singer
cohomological "transfer" of rank $4.$ Singer's transfer is a homomorphism from
a certain subquotient of the divided power algebra $\Gamma(a_1^{(1)}, \ldots,
a_h^{(1)})$ to mod-2 cohomology groups ${\rm Ext}_{\mathcal A}^{h, h+*}(\mathbb
Z/2, \mathbb Z/2)$ of the algebra $\mathcal A.$ This homomorphism is useful for
depicting the Ext groups. Additionally, in higher ranks, by using the results
on $\mathcal A$-generators for $P^{\otimes 5}$ and $P^{\otimes 6},$ we show in
Appendix that the transfer of rank 5 is an isomorphism in som certain degrees
of the form $n_{k, r, s}$, and that the transfer of rank 6 does not detect the
non-zero elements $h_2^{2}g_1 = h_4Ph_2\in {\rm Ext}_{\mathcal A}^{6, 6+n_{6,
10, 1}}(\mathbb Z/2, \mathbb Z/2)$, and $D_2\in {\rm Ext}_{\mathcal A}^{6,
6+n_{6, 10, 2}}(\mathbb Z/2, \mathbb Z/2).$ Besides, we also probe the behavior
of the Singer transfer of ranks 7 and 8 in internal degrees $\leq 15.$


Abstract:
  First examples of symmetric Hadamard matrices of orders 508 and 764 are
constructed. The method used is known as the propus construction. A conjecture
regarding this method is formally proposed but it appears implicitly in three
previous joint papers with N. A. Balonin et alias. The conjecture has been
verified for Hadamard matrices of order 4v with odd v<54. In this note the
conjecture is verified for v=55,57,59,61 and partially verified for v=63.


Abstract:
  Everyday conversations require understanding everyday events, which in turn,
requires understanding temporal commonsense concepts interwoven with those
events. Despite recent progress with massive pre-trained language models (LMs)
such as T5 and GPT-3, their capability of temporal reasoning in dialogs remains
largely under-explored. In this paper, we present the first study to
investigate pre-trained LMs for their temporal reasoning capabilities in
dialogs by introducing a new task and a crowd-sourced English challenge set,
TIMEDIAL. We formulate TIME-DIAL as a multiple-choice cloze task with over 1.1K
carefully curated dialogs. Empirical results demonstrate that even the best
performing models struggle on this task compared to humans, with 23 absolute
points of gap in accuracy. Furthermore, our analysis reveals that the models
fail to reason about dialog context correctly; instead, they rely on shallow
cues based on existing temporal patterns in context, motivating future research
for modeling temporal concepts in text and robust contextual reasoning about
them. The dataset is publicly available at:
https://github.com/google-research-datasets/timedial.


Abstract:
  We demonstrate that a separation of the velocity field in large and small
scales according to a streamwise Fourier decomposition identifies subspaces
with stable Lyapunov exponents and allows the dynamics to exhibit properties of
an inertial manifold, such as the synchronization of the small scales in
simulations sharing the same large scales or equivalently the decay of all
small scale flow states to the state uniquely determined from the large scale
flow. This behaviour occurs for deviations with streamwise wavelength smaller
than 130 wall units which was shown in earlier studies to correspond to the
streamwise spectral peak of the cross-flow velocity components of the top
Lyapunov vector of the turbulent flow.


Abstract:
  We probe the accuracy of linear ridge regression employing a 3-body local
density representation deriving from the atomic cluster expansion. We benchmark
the accuracy of this framework in the prediction of formation energies in
molecules, and forces-energies in solids. We find that such a simple regression
framework performs on par with state-of-the-art machine learning methods, which
are, in most cases, more complex and more computationally demanding.
Subsequently, we look for ways to sparsify the descriptor and further improve
the computational efficiency of the method. To this aim, we use both PCA and
LASSO regression for energy fitting on six single-elements datasets. Both
methods highlight the possibility to construct a descriptor that is 4 times
smaller than the original, with an equal or even improved accuracy.
Furthermore, we find that the reduced descriptors share a sizable fraction of
their features across the six independent datasets, hinting at the possibility
to design material-agnostic, optimally compressed, and accurate, descriptors.


Abstract:
  We present new stellar mass functions at $z\sim6$, $z\sim7$, $z\sim8$,
$z\sim9$ and, for the first time, $z\sim10$, constructed from $\sim800$
Lyman-Break galaxies previously identified over the XDF/UDF, parallels and the
five CANDELS fields. Our study is distinctive due to (1) the much deeper
($\sim200$ hour) wide-area Spitzer/IRAC imaging at $3.6\mu$m and $4.5\mu$m from
the GOODS Re-ionization Era wide Area Treasury from Spitzer (GREATS) program
and (2) consideration of $z\sim6-10$ sources over a $3\times$ larger area than
previous HST+Spitzer studies. The Spitzer/IRAC data enable $\ge2\sigma$
rest-frame optical detections for an unprecedented $50\%$ of galaxies down to a
stellar mass limit of $\sim10^{8}\mathcal{M}_\odot$ across all redshifts.
Schechter fits to our volume densities suggest a combined evolution in
characteristic mass $\mathcal{M}^*$ and normalization factor $\phi^*$ between
$z\sim6$ and $z\sim8$. The stellar mass density (SMD) increases by
$\sim1000\times$ in the $\sim500$ Myr between $z\sim10$ and $z\sim6$, with
indications of a steeper evolution between $z\sim10$ and $z\sim8$, similar to
the previously-reported trend of the star-formation rate density. Strikingly,
abundance matching to the Bolshoi-Planck simulation indicates halo mass
densities evolving at approximately the same rate as the SMD between $z\sim10$
and $z\sim4$. Our results show that the stellar-to-halo mass ratios, a proxy
for the star-formation efficiency, do not change significantly over the huge
stellar mass build-up occurred from $z\sim10$ to $z\sim6$, indicating that the
assembly of stellar mass closely mirrors the build-up in halo mass in the first
$\sim1$ Gyr of cosmic history. JWST is poised to extend these results into the
"first galaxy" epoch at $z\gtrsim10$.


Abstract:
  Co-prime sampling is a strategy for acquiring the signal below the Nyquist
rate. The prototype and extended co-prime samplers require two low rate
sub-samplers. One of the sub-samplers in the extended co-prime scheme is not
utilized for every alternate co-prime period. Therefore, this paper proposes a
time multiplexing strategy to utilize the vacant slots. It describes the
deviation from the existing theory. Closed-form expressions for weight
functions are provided. Acquisition of two signals is described with three
samplers as well as two samplers. A generalized structure is also proposed with
an extremely sparse co-prime sampling strategy.


Abstract:
  During plastic deformation of crystalline materials, point defects such as
vacancies and interstitials are generated by jogs on moving dislocations. A
detailed model for jog formation and transport during plastic deformation was
developed within the vector density-based continuum dislocation dynamics
framework (Lin and El-Azab, 2020; Xia and El-Azab, 2015). As a part of this
model, point defect generation associated with jog transport was formulated in
terms of the volume change due to the non-conservative motion of jogs. Balance
equations for the vacancies and interstitials including their rate of
generation due to jog transport were also formulated. A two-way coupling
between point defects and dislocation dynamics was then completed by including
the stress contributed by the eigen-strain of point defects. A jog drag stress
was further introduced into the mobility law of dislocations to account for the
energy dissipation during point defects generation. A number of test problems
and a fully coupled simulation of dislocation dynamics and point defect
generation and diffusion were performed. The results show that there is an
asymmetry of vacancy and interstitial generation due to the different formation
energies of the two types of defects. The results also show that a higher
hardening rate and a higher dislocation density are obtained when the point
defect generation mechanism is coupled to dislocation dynamics.


Abstract:
  As a first step towards a realistic phenomenological description of vector
and axial-vector mesons in nuclear matter, we calculate the spectral functions
of the $\rho$ and the $a_1$ meson in a chiral baryon-meson model as a
low-energy effective realization of QCD, taking into account the effects of
fluctuations from scalar mesons, nucleons, and vector mesons within the
Functional Renormalization Group (FRG) approach. The phase diagram of the
effective hadronic theory exhibits a nuclear liquid-gas phase transition as
well as a chiral phase transition at a higher baryon-chemical potential. The
in-medium $\rho$ and $a_1$ spectral functions are calculated by using the
previously introduced analytically-continued FRG (aFRG) method. Our results
show strong modifications of the spectral functions in particular near the
critical endpoints of both phase transitions which may well be of relevance for
electromagnetic rates in heavy-ion collisions or neutrino emissivities in
neutron-star merger events.


Abstract:
  Tomographic reconstruction, despite its revolutionary impact on a wide range
of applications, suffers from its ill-posed nature in that there is no unique
solution because of limited and noisy measurements. Traditional
optimization-based reconstruction relies on regularization to address this
issue; however, it faces its own challenge because the type of regularizer and
choice of regularization parameter are a critical but difficult decision.
Moreover, traditional reconstruction yields point estimates for the
reconstruction with no further indication of the quality of the solution. In
this work we address these challenges by exploring Gaussian processes (GPs).
Our proposed GP approach yields not only the reconstructed object through the
posterior mean but also a quantification of the solution uncertainties through
the posterior covariance. Furthermore, we explore the flexibility of the GP
framework to provide a robust model of the information across various length
scales in the object, as well as the complex noise in the measurements. We
illustrate the proposed approach on both synthetic and real tomography images
and show its unique capability of uncertainty quantification in the presence of
various types of noise, as well as reconstruction comparison with existing
methods.


Abstract:
  We investigated and demonstrated a 1.3 $\mu$m-band laser grown by
metalorganic vapour phase epitaxy (MOVPE) on a specially engineered metamorphic
parabolic graded In$_x$Ga$_{1-x}$As buffer and epitaxial structure on a GaAs
substrate. Bottom and upper cladding layers were built as a combination of
AlInGaAs and InGaP alloys in a superlattice sequence. This was implemented to
overcome (previously unreported) detrimental surface epitaxial dynamics and
instabilities: when single alloys are utilised to achieve thick layers on
metamorphic structures, surface instabilities induce defect generation. This
has represented a historically limiting factor for metamorphic lasers by MOVPE.
We describe a number of alternative strategies to achieve smooth surface
morphology to obtain efficient compressively strained In$_{0.4}$Ga$_{0.6}$As
quantum wells in the active layer. The resulting lasers exhibited low lasing
threshold with total slope efficiency of 0.34 W/A for a 500 $\mu$m long ridge
waveguide device. The emission wavelength is extended as far as 1360 nm.


Abstract:
  We initiate a study of the composition properties of interactive
differentially private mechanisms. An interactive differentially private
mechanism is an algorithm that allows an analyst to adaptively ask queries
about a sensitive dataset, with the property that an adversarial analyst's view
of the interaction is approximately the same regardless of whether or not any
individual's data is in the dataset. Previous studies of composition of
differential privacy have focused on non-interactive algorithms, but
interactive mechanisms are needed to capture many of the intended applications
of differential privacy and a number of the important differentially private
primitives.
  We focus on concurrent composition, where an adversary can arbitrarily
interleave its queries to several differentially private mechanisms, which may
be feasible when differentially private query systems are deployed in practice.
We prove that when the interactive mechanisms being composed are pure
differentially private, their concurrent composition achieves privacy
parameters (with respect to pure or approximate differential privacy) that
match the (optimal) composition theorem for noninteractive differential
privacy. We also prove a composition theorem for interactive mechanisms that
satisfy approximate differential privacy. That bound is weaker than even the
basic (suboptimal) composition theorem for noninteractive differential privacy,
and we leave closing the gap as a direction for future research, along with
understanding concurrent composition for other variants of differential
privacy.


Abstract:
  I begin this review by first defining what is meant by exoplanet
demographics, and then motivating why we would like as broad a picture of
exoplanet demographics as possible. I then outline the methodology and pitfalls
to measuring exoplanet demographics in practice. I next review the methods of
detecting exoplanets, focusing on the ability of these methods to detect wide
separation planets. For the purposes of this review, I define wide separation
as separations beyond the 'snow line' of the protoplanetary disk, which is at
roughly $\sim3$ au for a sunlike star. I note that this definition is somewhat
arbitrary, and the practical boundary depends on the host star mass, planet
mass and radius, and detection method. I review the approximate scaling
relations for the signal-to-noise ratio for the detectability of exoplanets as
a function of the relevant physical parameters, including the host star
properties. I provide abroad overview of what has already been learned from the
transit, radial velocity, direct imaging, and microlensing methods. I outline
the challenges to synthesizing the demographics using different methods and
discuss some preliminary first steps in this direction. Finally, I describe
future prospects for providing a nearly complete statistical census of
exoplanets.


Abstract:
  The narrow s-wave Feshbach resonance of a $^6$Li Fermi gas shows strong
three-body loss, which is proposed to be used to measure the minute change of a
magnetic field around the resonance. However, the eddy current will cause
ultracold atom experiencing a magnetic field delayed to the desired magnetic
field from the current of the magnetic coils. The elimination of the eddy
current effect will play a key role in any experiments that motivated to
measure the magnetic field to the precision of a part per million stability.
Here, we apply a method to correct the eddy current effect for precision
measurement of the magnetic field. We first record the three-body loss
influenced by the effect of induced eddy current, then use a certain model to
obtain the time constant of the actual magnetic field by fitting the atom loss.
This precisely determines the actual magnetic field according to the time
response of the three-body loss. After that, we implement the desired magnetic
field to the atoms so that we can analyze the three-body loss across the whole
narrow Feshbach resonance. The results show that the three-body recombination
is the dominated loss mechanism near the resonance. We expect this practical
method of correcting the eddy current error of the magnetic field can be
further applied to the future studies of quantum few- and many-body physics
near a narrow Feshbach resonance.


Abstract:
  This paper presents a novel approach for the automatic offline grasp pose
synthesis on known rigid objects for parallel jaw grippers. We use several
criteria such as gripper stroke, surface friction, and a collision check to
determine suitable 6D grasp poses on an object. In contrast to most available
approaches, we neither aim for the best grasp pose nor for as many grasp poses
as possible, but for a highly diverse set of grasps distributed all along the
object. In order to accomplish this objective, we employ a clustering algorithm
to the sampled set of grasps. This allows to simultaneously reduce the set of
grasp pose candidates and maintain a high variance in terms of position and
orientation between the individual grasps. We demonstrate that the grasps
generated by our method can be successfully used in real-world robotic grasping
applications.


Abstract:
  Integrating extracted knowledge from the Web to knowledge graphs (KGs) can
facilitate tasks like question answering. We study relation integration that
aims to align free-text relations in subject-relation-object extractions to
relations in a target KG. To address the challenge that free-text relations are
ambiguous, previous methods exploit neighbor entities and relations for
additional context. However, the predictions are made independently, which can
be mutually inconsistent. We propose a two-stage Collective Relation
Integration (CoRI) model, where the first stage independently makes candidate
predictions, and the second stage employs a collective model that accesses all
candidate predictions to make globally coherent predictions. We further improve
the collective model with augmented data from the portion of the target KG that
is otherwise unused. Experiment results on two datasets show that CoRI can
significantly outperform the baselines, improving AUC from .677 to .748 and
from .716 to .780, respectively.


Abstract:
  Researches have demonstrated that low bit-width (e.g., INT8) quantization can
be employed to accelerate the inference process. It makes the gradient
quantization very promising since the backward propagation requires
approximately twice more computation than forward one. Due to the variability
and uncertainty of gradient distribution, a lot of methods have been proposed
to attain training stability. However, most of them ignore the channel-wise
gradient distributions and the impact of gradients with different magnitudes,
resulting in the degradation of final accuracy. In this paper, we propose a
novel INT8 quantization training framework for convolutional neural network to
address the above issues. Specifically, we adopt Gradient Vectorized
Quantization to quantize the gradient, based on the observation that layer-wise
gradients contain multiple distributions along the channel dimension. Then,
Magnitude-aware Clipping Strategy is introduced by taking the magnitudes of
gradients into consideration when minimizing the quantization error, and we
present a theoretical derivation to solve the quantization parameters of
different distributions. Experimental results on broad range of computer vision
tasks, such as image classification, object detection and video classification,
demonstrate that the proposed Distribution Adaptive INT8 Quantization training
method has achieved almost lossless training accuracy for different backbones,
including ResNet, MobileNetV2, InceptionV3, VGG and AlexNet, which is superior
to the state-of-the-art techniques. Moreover, we further implement the INT8
kernel that can accelerate the training iteration more than 200% under the
latest Turing architecture, i.e., our method excels on both training accuracy
and speed.


Abstract:
  We show scalable and complete suppression of the recently reported
terahertz-pulse-induced switching between different resistance states of
antiferromagnetic CuMnAs thin films by ultrafast gating. The gating
functionality is achieved by an optically generated transiently conductive
parallel channel in the semiconducting substrate underneath the metallic layer.
The photocarrier lifetime determines the time scale of the suppression. As we
do not observe a direct impact of the optical pulse on the state of CuMnAs, all
observed effects are primarily mediated by the substrate. The sample region of
suppressed resistance switching is given by the optical spot size, thereby
making our scheme potentially applicable for transient low-power masking of
structured areas with feature sizes of ~100 nm and even smaller.


Abstract:
  This report presents research conducted on amplified self-healing
electrostatic (HASEL) actuators. HASEL actuators are comprised of a dielectric
fluid sealed between two inextensible layers with bonded, flexible electrodes
on its outer surface. When charge is applied to the electrodes, Coulomb force
compresses the fluid and causes the actuator to contract. In this work a
faster, more customizable and convenient way of creating a HASEL actuator is
presented, using a laser engraver to heat-seal and cut polypropylene sheets.
Using this technique, a hydraulically actuated soft lens is fabricated and
demonstrated.


Abstract:
  Deep Learning models have become potential candidates for auditory
neuroscience research, thanks to their recent successes on a variety of
auditory tasks. Yet, these models often lack interpretability to fully
understand the exact computations that have been performed. Here, we proposed a
parametrized neural network layer, that computes specific spectro-temporal
modulations based on Gabor kernels (Learnable STRFs) and that is fully
interpretable. We evaluated predictive capabilities of this layer on Speech
Activity Detection, Speaker Verification, Urban Sound Classification and Zebra
Finch Call Type Classification. We found out that models based on Learnable
STRFs are on par for all tasks with different toplines, and obtain the best
performance for Speech Activity Detection. As this layer is fully
interpretable, we used quantitative measures to describe the distribution of
the learned spectro-temporal modulations. The filters adapted to each task and
focused mostly on low temporal and spectral modulations. The analyses show that
the filters learned on human speech have similar spectro-temporal parameters as
the ones measured directly in the human auditory cortex. Finally, we observed
that the tasks organized in a meaningful way: the human vocalizations tasks
closer to each other and bird vocalizations far away from human vocalizations
and urban sounds tasks.


Abstract:
  We present detailed timing and spectral studies of the black hole candidate
MAXI J0637$-$430 during its 2019-2020 outburst using observations with the {\em
Neutron Star Interior Composition Explorer (NICER)} and the {\em Neil Gehrels
Swift Observatory}. We find that the source evolves through the
soft-intermediate, high-soft, hard-intermediate and low-hard states during the
outburst. No evidence of quasi-periodic oscillations is found in the power
density spectra of the source. Weak variability with fractional rms amplitude
$<5\%$ is found in the softer spectral states. In the hard-intermediate and
hard states, high variability with the fractional rms amplitude of $>20\%$ is
observed. The $0.7-10$ keV spectra with {\em NICER} are studied with a combined
disk-blackbody and nthcomp model along with the interstellar absorption. The
temperature of the disc is estimated to be $0.6$ keV in the rising phase and
decreased slowly to $0.1$ keV in the declining phase. The disc component was
not detectable or absent during the low hard state. From the state-transition
luminosity and the inner edge of the accretion flow, we estimate the mass of
the black hole to be in the range of 5$-$12 $M_{\odot}$, assuming the source
distance of $d<10$ kpc.


Abstract:
  Localization approach to $\mathcal N=2$ superconformal $SU(N) \times SU(N)$
quiver theory leads to a non-Gaussian two-matrix model representation for the
expectation value of BPS circular $SU(N)$ Wilson loop $\langle\mathcal
W\rangle$. We study the subleading $1/N^2$ term in the large $N$ expansion of
$\langle\mathcal W\rangle$ at weak and strong coupling. We concentrate on the
case of the symmetric quiver with equal gauge couplings which is equivalent to
the $\mathbb Z_{2}$ orbifold of the $SU(2N)$ $\mathcal N=4$ SYM theory. This
orbifold gauge theory should be dual to type IIB superstring in ${\rm
AdS}_5\times (S^{5}/\mathbb Z_{2})$. We present a string theory argument
suggesting that the $1/N^2$ term in $\langle\mathcal W\rangle$ in the orbifold
theory should have the same strong-coupling asymptotics $ \lambda^{3/2}$ as in
the $\mathcal N=4$ SYM case. We support this prediction by a numerical study of
the localization matrix model on the gauge theory side. We also find a relation
between the $1/N^2$ term in the Wilson loop expectation value and the
derivative of the free energy of the orbifold gauge theory on 4-sphere.


Abstract:
  As communication systems that employ millimeter wave (mmWave) frequency bands
must use large antenna arrays to overcome the severe propagation loss of mmWave
signals, hybrid beamforming has been considered as an integral component of
mmWave communications. Recently, intelligent reflecting surface (IRS) has been
proposed as an innovative technology that can significantly improve the
performance of mmWave communication systems through the use of low-cost passive
reflecting elements. In this paper, we study IRS-aided mmWave multiple-input
multiple-output (MIMO) systems with hybrid beamforming architectures. We first
exploit the sparse-scattering structure and large dimension of mmWave channels
to develop the joint design of IRS reflection matrix and hybrid beamformer for
narrowband MIMO systems. Then, we generalize the proposed joint design to
broadband MIMO systems with orthogonal frequency division multiplexing (OFDM)
modulation by leveraging the angular sparsity of frequency-selective mmWave
channels. Simulation results demonstrate that the proposed joint designs can
significantly enhance the spectral efficiency of the systems of interest and
achieve superior performance over the existing designs.


Abstract:
  We study a class of deterministic finite-horizon two-player nonzero-sum
differential games where players are endowed with different kinds of controls.
We assume that Player 1 uses piecewise-continuous controls, while Player 2 uses
impulse controls. For this class of games, we seek to derive conditions for the
existence of feedback Nash equilibrium strategies for the players. More
specifically, we provide a verification theorem for identifying such
equilibrium strategies, using the Hamilton-Jacobi-Bellman (HJB) equations for
Player 1 and the quasi-variational inequalities (QVIs) for Player 2. Further,
we show that the equilibrium number of interventions by Player 2 is upper
bounded. Furthermore, we specialize the obtained results to a scalar two-player
linear-quadratic differential game. In this game, Player 1's objective is to
drive the state variable towards a specific target value, and Player 2 has a
similar objective with a different target value. We provide, for the first
time, an analytical characterization of the feedback Nash equilibrium in a
linear-quadratic differential game with impulse control. We illustrate our
results using numerical experiments.


Abstract:
  Silicon (Si) donors in GaAs have been the topic of extensive studies since Si
is the most common and well understood n-type dopant in III-V semiconductor
devices and substrates. The indirect bandgap of AlAs compared to the direct one
of GaAs leads to interesting effects when introducing Si dopants. Here we
present a study of cross-sectional scanning tunneling microscopy (X-STM) and
density functional theory (DFT) calculations to study Si donors in AlAs at the
atomic scale. Based on their crystal symmetry and contrast strengths, we
identify Si donors up to four layers below the (110) surface of AlAs.
Interestingly, their short-range local density of states (LDOS) is very similar
to Si atoms in the (110) surface of GaAs. Additionally we show high-resolution
images of Si donors in all these layers. For empty state imaging, the
experimental and simulated STM images based on DFT show excellent agreement for
Si donor up to two layers below the surface.


Abstract:
  We derive several existence results concerning cycle types and, more
generally, the "mapping behavior" of complete mappings. Our focus is on
so-called first-order cyclotomic mappings, which are functions on a finite
field $\mathbb{F}_q$ that fix $0$ and restrict to the multiplication $x\mapsto
a_ix$ by a fixed element $a_i\in\mathbb{F}_q$ on each coset $C_i$ of a given
subgroup $C$ of $\mathbb{F}_q^{\ast}$. The gist of two of our main results is
that as long as $q$ is large enough relative to the index
$|\mathbb{F}_q^{\ast}:C|$, all cycle types of first-order cyclotomic
permutations with only long cycles on $\mathbb{F}_q^{\ast}$ can be achieved
through a complete mapping, as can all permutations of the cosets of $C$. Our
third main result provides new examples of complete mappings $f$ such that both
$f$ and its associated orthomorphism $f+\operatorname{id}$ permute the nonzero
field elements in one cycle.


Abstract:
  Motivated by recent transport experiments, we theoretically study the quantum
Hall effect in topological semimetal films. Owing to the confinement effect,
the bulk subbands originating from the chiral Landau levels establish energy
gaps that have quantized Hall conductance and can be observed in relatively
thick films. We find that the quantum Hall state is strongly anisotropic for
different confinement directions not only due to the presence of the surface
states but also because of the bulk chiral Landau levels. As a result, we
re-examine the quantum Hall effect from the surface Fermi arcs and chiral modes
in Weyl semimetals and give a more general view into this problem. Besides, we
also find that when a topological Dirac semimetal is confined in its rotational
symmetry axis, it hosts both quantum Hall and quantum spin Hall states, in
which the helical edge states are protected by the conservation of the spin-$z$
component.


Abstract:
  We investigate the phenomenology of a scalar top-philic dark matter candidate
when adding a dimension-five contact interaction term, as motivated by possible
underlying extensions of the Standard Model such as composite Higgs models. We
show that the presence of contact interactions can have a major impact on the
dark matter relic density as well as on its direct and indirect detection
prospects, while the collider phenomenology of the model is unaffected. This
underlines the complementarity of collider and cosmological constraints on dark
matter models.


Abstract:
  User interface design is a complex task that involves designers examining a
wide range of options. We present Spacewalker, a tool that allows designers to
rapidly search a large design space for an optimal web UI with integrated
support. Designers first annotate each attribute they want to explore in a
typical HTML page, using a simple markup extension we designed. Spacewalker
then parses the annotated HTML specification, and intelligently generates and
distributes various configurations of the web UI to crowd workers for
evaluation. We enhanced a genetic algorithm to accommodate crowd worker
responses from pairwise comparison of UI designs, which is crucial for
obtaining reliable feedback. Based on our experiments, Spacewalker allows
designers to effectively search a large design space of a UI, using the
language they are familiar with, and improve their design rapidly at a minimal
cost.


Abstract:
  Ultra-diffuse galaxies (UDGs) are very low-surface brightness galaxies with
large effective radii. Spectroscopic measurements of a few UDGs have revealed a
low dark matter content, based on the internal motion of stars or globular
clusters (GCs). This is in contrast to the large number of GCs found for these
systems, from which it would be expected to correspond to a large dark matter
halo mass. Here we present HST+ACS observations for the UDG MATLAS-2019 in the
NGC5846 group. Using the F606W and F814W filters, we trace the GC population
two magnitudes below the peak of the GC luminosity function (GCLF). Employing
Bayesian considerations, we identify 26+-6 GCs associated with the dwarf,
yielding a large specific frequency of S_N=58+-14. We use the turnover of the
GCLF to derive a distance of 21+-2 Mpc, which is consistent with the NGC5846
group of galaxies. Due to the superior image quality of the HST, we are able to
resolve the GCs and measure their sizes, which are consistent with the sizes of
GCs around Local Group galaxies. Using the linear relation between the total
mass of galaxies and of GCs, we derive a halo mass of 0.9(+-0.2) *10^11 M_solar
(M_solar/L_solar>1000). The high abundance of GCs, together with the small
uncertainties, make MATLAS-2019 one of the most extreme UDGs, which likely sets
an upper limit of the number of GCs for UDGs.


Abstract:
  Structured covariance matrix estimation in the presence of missing data is
addressed in this paper with emphasis on radar signal processing applications.
After a motivation of the study, the array model is specified and the problem
of computing the maximum likelihood estimate of a structured covariance matrix
is formulated. A general procedure to optimize the observed-data likelihood
function is developed resorting to the expectation-maximization algorithm. The
corresponding convergence properties are thoroughly established and the rate of
convergence is analyzed. The estimation technique is contextualized for two
practically relevant radar problems: beamforming and detection of the number of
sources. In the former case an adaptive beamformer leveraging the EM-based
estimator is presented; in the latter, detection techniques generalizing the
classic Akaike information criterion, minimum description length, and
Hannan-Quinn information criterion, are introduced. Numerical results are
finally presented to corroborate the theoretical study.


Abstract:
  When the characteristic of the underlying field is at least 5, we prove that
the adjustment matrix for blocks of $q$-Schur algebras of weights 3 and 4 is
the identity matrix. Moreover, we show that the decomposition numbers for
weight 3 blocks of $q$-Schur algebras are bounded above by one.


Abstract:
  Fairness is an important trait of open, free markets. Ethereum is a platform
meant to enable digital, decentralized markets. Though many researchers debate
the market's fairness, there are few discussions around the fairness of
automated markets, such as those hosted on Ethereum. In this paper, using pilot
studies, we consider unfair factors caused by adding the program. Because
CryptoKitties is one of the major blockchain-based games and has been in
operation for an extended period of time, we focus on its market to examine
fairness. As a result, we concluded that a gene determination algorithm in this
game has little randomness, and a significant advantage to gain profit is given
to players who know its bias over those who do not. We state incompleteness and
impact of the algorithm and other factors. Besides, we suppose countermeasures
to reduce CryptoKitties' unfairness as a market.


Abstract:
  We give a quasipolynomial-time algorithm for learning stochastic decision
trees that is optimally resilient to adversarial noise. Given an
$\eta$-corrupted set of uniform random samples labeled by a size-$s$ stochastic
decision tree, our algorithm runs in time
$n^{O(\log(s/\varepsilon)/\varepsilon^2)}$ and returns a hypothesis with error
within an additive $2\eta + \varepsilon$ of the Bayes optimal. An additive
$2\eta$ is the information-theoretic minimum.
  Previously no non-trivial algorithm with a guarantee of $O(\eta) +
\varepsilon$ was known, even for weaker noise models. Our algorithm is
furthermore proper, returning a hypothesis that is itself a decision tree;
previously no such algorithm was known even in the noiseless setting.


Abstract:
  Latent variable models have been successfully applied in lossless compression
with the bits-back coding algorithm. However, bits-back suffers from an
increase in the bitrate equal to the KL divergence between the approximate
posterior and the true posterior. In this paper, we show how to remove this gap
asymptotically by deriving bits-back coding algorithms from tighter variational
bounds. The key idea is to exploit extended space representations of Monte
Carlo estimators of the marginal likelihood. Naively applied, our schemes would
require more initial bits than the standard bits-back coder, but we show how to
drastically reduce this additional cost with couplings in the latent space.
When parallel architectures can be exploited, our coders can achieve better
rates than bits-back with little additional cost. We demonstrate improved
lossless compression rates in a variety of settings, especially in
out-of-distribution or sequential data compression.


Abstract:
  This paper develops a model based reinforcement learning (MBRL) framework for
learning online the value function of an infinite-horizon optimal control
problem while obeying safety constraints expressed as control barrier functions
(CBFs). Our approach is facilitated by the development of a novel class of
CBFs, termed Lyapunov-like CBFs (LCBFs), that retain the beneficial properties
of CBFs for developing minimally-invasive safe control policies while also
possessing desirable Lyapunov-like qualities such as positive
semi-definiteness. We show how these LCBFs can be used to augment a
learning-based control policy to guarantee safety and then leverage this
approach to develop a safe exploration framework in a MBRL setting. We
demonstrate that our developed approach can handle more general safety
constraints than comparative methods through a variety of numerical examples.


Abstract:
  Significant advances in video compression system have been made in the past
several decades to satisfy the nearly exponential growth of Internet-scale
video traffic. From the application perspective, we have identified three major
functional blocks including pre-processing, coding, and post-processing, that
have been continuously investigated to maximize the end-user quality of
experience (QoE) under a limited bit rate budget. Recently, artificial
intelligence (AI) powered techniques have shown great potential to further
increase the efficiency of the aforementioned functional blocks, both
individually and jointly. In this article, we review extensively recent
technical advances in video compression system, with an emphasis on deep neural
network (DNN)-based approaches; and then present three comprehensive case
studies. On pre-processing, we show a switchable texture-based video coding
example that leverages DNN-based scene understanding to extract semantic areas
for the improvement of subsequent video coder. On coding, we present an
end-to-end neural video coding framework that takes advantage of the stacked
DNNs to efficiently and compactly code input raw videos via fully data-driven
learning. On post-processing, we demonstrate two neural adaptive filters to
respectively facilitate the in-loop and post filtering for the enhancement of
compressed frames. Finally, a companion website hosting the contents developed
in this work can be accessed publicly at
https://purdueviper.github.io/dnn-coding/.


Abstract:
  We study the allocation of indivisible goods among groups of agents using
well-known fairness notions such as envy-freeness and proportionality. While
these notions cannot always be satisfied, we provide several bounds on the
optimal relaxations that can be guaranteed. For instance, our bounds imply that
when the number of groups is constant and the $n$ agents are divided into
groups arbitrarily, there exists an allocation that is envy-free up to
$\Theta(\sqrt{n})$ goods, and this bound is tight. Moreover, we show that while
such an allocation can be found efficiently, it is NP-hard to compute an
allocation that is envy-free up to $o(\sqrt{n})$ goods even when a fully
envy-free allocation exists. Our proofs make extensive use of tools from
discrepancy theory.


Abstract:
  Two-dimensional (2D) layered magnetic materials are generating a great amount
of interest for the next generation of electronic devices thanks to their
remarkable properties associated to spin dynamics. The recently discovered
layered VI$_3$ ferromagnetic phase belongs to this family, although a full
understanding of its properties is limited by an ill-defined crystallographic
structure. This is not any longer true. Here, we investigate the VI$_3$ crystal
structure upon cooling using both synchrotron X-ray and neutron powder
diffraction and provide structural models for the two structural transitions
occurring at 76 K and 32 K. Moreover, we confirm by magnetic measurements that
VI$_3$ becomes ferromagnetic at 50 K and discuss the difficulty of solving its
full magnetic structure by neutrons. We equally determined the magnetic
properties of our recently reported LiVI$_3$ phase, which is alike the
well-known CrI$_3$ ferromagnetic phase in terms of electronic and
crystallographic structures and found to our surprise an antiferromagnetic
behavior with a N\'eel temperature of 12 K. Such a finding provides extra clues
for a better understanding of magnetism in these low dimension compounds.
Finally, the easiness of preparing novel Li-based 2D magnetic materials by
chemical/electrochemical means opens wide the opportunity to design materials
with exotic properties.


Abstract:
  We prove new slope inequalities for relatively minimal fibred surfaces,
showing an influence of the relative irregularity, of the unitary rank and of
the Clifford index on the slope. The argument uses Xiao's method and a new
Clifford-type inequality for subcanonical systems on non-hyperelliptic curves.


Abstract:
  SU UMa stars are characterized by "superoutbursts" which are brighter at
maximum light and which last much longer than the more frequent "ordinary"
outbursts of these dwarf novae. Although there are now more than 1180 SU UMa
type dwarf novae catalogued, our knowledge on their superoutburst cycle length
Cso was hitherto limited to about 6$\%$ of the entire sample of known SU UMa
stars. Using public data bases we have determined new Cso values for a total of
206 additional SU UMa stars in the range 17 d $<$ Cso $<$ 4590 d (including
some ER UMa and WZ Sge type representants) within total time intervals between
2 and 57 years, and with an estimated uncertainty of $\pm$11$\%$. This way, we
are increasing our present knowledge of Cso values by a factor $\sim$3.8. Its
distribution is characterized by a broad maximum around Cso $\approx$ 270 days,
and slowly decreasing numbers till Cso $\approx$ 800 d. The domain Cso $>$ 450
d was unexplored until now; we add here 106 cases ($\sim$51$\%$ of our total
sample) in this range of long cycles, implying a better statistical basis for
future studies of their distribution. Our sample contains 16 known WZ Sge
stars, and we propose WZ Sge membership for 5 others hitherto classified as
ordinary SU UMa stars. Individual superoutburst timings deviate in average
about $\pm$7$\%$ of the cycle length from their overall linear ephemeris,
conrming a pronounced quasi-periodic repeatability of superoutbursts. All
relevant parameters are listed with their errors, and a table with individual
superoutburst epochs of our targets is given, enabling future researchers to
combine our results with other (past or future) observations.


Abstract:
  Upcoming measurements of the 21-cm line of neutral hydrogen will open a new
observational window into the early stages of structure growth, providing a
unique opportunity for probing large-scale cosmological signatures using the
small-scale signals from the first stars. In this paper we evaluate the
detection significance of compensated isocurvature perturbations (CIPs) from
observations of the 21-cm hydrogen-line during the cosmic-dawn era. CIPs are
modulations of the relative baryon and dark-matter density that leave the total
matter density unchanged. We find that, under different assumptions for
feedback and foregrounds, the ongoing HERA and upcoming SKA1-low experiments
will provide constraints on uncorrelated CIPs at the level of $\sigma(A_{\rm
CIP})= 10^{-3}-10^{-4}$, comparable to the sensitivity of upcoming CMB
experiments, and potentially exceeding the constraints from cosmic-variance
limited BAO surveys.


Abstract:
  We analyze the computational power of discrete-time recurrent neural networks
(NNs) with the saturated-linear activation function within the Chomsky
hierarchy. This model restricted to integer weights coincides with binary-state
NNs with the Heaviside activation function, which are equivalent to finite
automata (Chomsky level 3) recognizing regular languages (REG), while rational
weights make this model Turing-complete even for three analog-state units
(Chomsky level 0). For the intermediate model $\alpha$ANN of a binary-state NN
that is extended with $\alpha\geq 0$ extra analog-state neurons with rational
weights, we have established the analog neuron hierarchy 0ANNs $\subset$ 1ANNs
$\subset$ 2ANNs $\subseteq$ 3ANNs. The separation 1ANNs $\subsetneqq$ 2ANNs has
been witnessed by the non-regular deterministic context-free language (DCFL)
$L_\#=\{0^n1^n\mid n\geq 1\}$ which cannot be recognized by any 1ANN even with
real weights, while any DCFL (Chomsky level 2) is accepted by a 2ANN with
rational weights. In this paper, we strengthen this separation by showing that
any non-regular DCFL cannot be recognized by 1ANNs with real weights, which
means (DCFLs $\setminus$ REG) $\subset$ (2ANNs $\setminus$ 1ANNs), implying
1ANNs $\cap$ DCFLs = 0ANNs. For this purpose, we have shown that $L_\#$ is the
simplest non-regular DCFL by reducing $L_\#$ to any language in this class,
which is by itself an interesting achievement in computability theory.


Abstract:
  Low-cardinality activations permit an algorithm based on fetching the
inference values from pre-calculated lookup tables instead of calculating them
every time. This algorithm can have extensions, some of which offer abilities
beyond those of the currently used algorithms. It also allows for a simpler and
more effective CNN-specialized hardware.


Abstract:
  Before the launch of the Kepler Space Telescope, most studies of the rapidly
oscillating Ap (roAp) stars were conducted with ground-based photometric $B$
observations, supplemented with high-resolution time-resolved spectroscopy and
some space observations with the WIRE, MOST and BRITE satellites. These modes
of observation often only provided information on a single star at a time,
however, Kepler provided the opportunity to observe hundreds of thousands of
stars simultaneously. Over the duration of the primary 4-yr Kepler mission, and
its 4-yr reconfigured K2 mission, the telescope observed at least 14 new and
known roAp stars. This paper provides a summary the results of these
observations, including a first look at the entire data sets, and provides a
look forward to NASA's TESS mission.


Abstract:
  open Master Hearing Aid (openMHA) was developed and provided to the hearing
aid research community as an open-source software platform with the aim to
support sustainable and reproducible research towards improvement and new types
of assistive hearing systems not limited by proprietary software. The software
offers a flexible framework that allows the users to conduct hearing aid
research using tools and a number of signal processing plugins provided with
the software as well as the implementation of own methods. The openMHA software
is independent of a specific hardware and supports Linux, MacOS and Windows
operating systems as well as 32-bit and 64-bit ARM-based architectures such as
used in small portable integrated systems. www.openmha.org


Abstract:
  A method for recognizing the types of linear acoustic gravity waves (AGWs) in
the atmosphere from satellite measurements is proposed. It is shown that the
polarization relations between fluctuations of wave parameters (velocity,
density, temperature, and pressure) for freely propagating waves, as well as
evanescent wave modes, differ significantly, which makes it possible to
identify different types of atmospheric waves in experimental data. A
diagnostic diagram is proposed, with the help of which, from the phase shifts
of the observed parameters, it is possible to determine the type of wave, as
well as the direction of its movement relative to the vertical. Using phase
shifts between fluctuations of the velocity and thermodynamic parameters of the
atmosphere, not only the type of the wave, but also its spectral
characteristics can be determined. The verification of the proposed method for
identification of polar wave disturbances in measurements from the low-orbit
satellite Dynamics Explorer 2 was carried out. The verification showed that the
polarization relations of AGW in the thermosphere mainly correspond to the
gravity branch of acoustic-gravity waves freely propagating in the direction
from bottom to top. This conclusion is consistent with other results of AGW
observations in the atmosphere and ionosphere by ground-based and satellite
methods. No evanescent waves were observed on the considered orbits.


Abstract:
  Circumcenters play an important role in the design and analysis of
accelerating various iterative methods in optimization. In this work, we
propose Bregman (pseudo-)circumcenters associated with finite sets. We show the
existence and give explicit formulae for the unique backward and forward
Bregman pseudo-circumcenters of finite sets. Moreover, we use duality to
establish connections between backward and forward Bregman
(pseudo-)circumcenters. Various examples are presented to illustrate the
backward and forward Bregman (pseudo-)circumcenters of finite sets. Our general
framework for circumcenters paves the way for the development of accelerating
iterative methods by Bregman circumcenters.


Abstract:
  We present a slightly modified version of the well known "geometric Lorenz
attractor". It consists in a C1 open set O of vector fields in R3 having an
attracting region U containing: (1) a unique singular saddle point sigma; (2) a
unique attractor Lambda containing the singular point; (3) the maximal
invariant in U contains at most 2 chain recurrence classes, which are Lambda
and (at most) one hyperbolic horseshoe. The horseshoe and the singular
attractor have a collision along the union of 2 co-dimension 1 sub-manifolds
which divide O in 3 regions. By crossing this collision locus, the attractor
and the horseshoe may merge in a two-sided Lorenz attractor, or they may
exchange their nature: the Lorenz attractor expel the singular point sigma and
becomes a horseshoe and the horseshoe absorbs sigma becoming a Lorenz
attractor.
  By crossing this collision locus, the attractor and the horseshoe may merge
in a two-sided Lorenz attractor, or they may exchange their nature: the Lorenz
attractor expel the singular point sigma and becomes a horseshoe and the
horseshoe absorbs sigma becoming a Lorenz attractor.


Abstract:
  Monte Carlo Tree Search (MCTS) is a powerful approach to designing
game-playing bots or solving sequential decision problems. The method relies on
intelligent tree search that balances exploration and exploitation. MCTS
performs random sampling in the form of simulations and stores statistics of
actions to make more educated choices in each subsequent iteration. The method
has become a state-of-the-art technique for combinatorial games, however, in
more complex games (e.g. those with high branching factor or real-time ones),
as well as in various practical domains (e.g. transportation, scheduling or
security) an efficient MCTS application often requires its problem-dependent
modification or integration with other techniques. Such domain-specific
modifications and hybrid approaches are the main focus of this survey. The last
major MCTS survey has been published in 2012. Contributions that appeared since
its release are of particular interest for this review.


Abstract:
  This paper introduces an algorithm to perform optimal reorientation of a
spacecraft during a high speed flyby mission that maximizes the time a certain
target is kept within the field of view of scientific instruments. The method
directly handles the nonlinear dynamics of the spacecraft, sun exclusion
constraint, torque and momentum limits on the reaction wheels as well as
potential faults in these actuators. A sequential convex programming approach
was used to reformulate non-convex pointing objectives and other constraints in
terms of a series of novel convex cardinality minimization problems. These
subproblems were then efficiently solved even on limited hardware resources
using convex programming solvers implementing second-order conic constraints.
The proposed method was applied to a scenario that involved maximizing the
science time for the upcoming Comet Interceptor flyby mission developed by the
European Space Agency. Extensive simulation results demonstrate the capability
of the approach to generate viable trajectories even in the presence of
reaction wheel failures or prior dust particle impacts.


Abstract:
  We construct non-unique Leray-Hopf solutions for some dyadic models for
magnetohydrodynamics when the intermittency dimension $\delta$ is less than 1.
In contrast, uniqueness of Leray-Hopf solution is established in the case of
$\delta\geq 1$. Analogous results on uniqueness and non-uniqueness of
Leray-Hopf solution are also obtained for dyadic models of MHD with fractional
diffusion.


Abstract:
  In the past years there has been significant progress in Intelligent
Transport Systems in the domains of traffic management, driver assistance and
driver information. Within the German research project COmmunication Network
VEhicle Road Global Extension (CONVERGE), a system architecture for the
flexible interaction between different service providers and communication
network operators will be designed and implemented, called Car2X Systems
Network architecture. The Car2X Systems Network will establish a completely new
open communication-, services-, and organization architecture that reflects
state of the art communication technologies and IT security. Through
well-defined access methods, service providers like traffic control centers or
vehicle manufacturers can be integrated into the open and secure system
network. The ultimate goal is the decentralized and dynamic coupling of all
systems and actors across national and organizational borders. This paper is
focusing on the requirements and the concept of a service-oriented architecture
for publishing and finding services inside the system with intelligent
information distribution based on geographical and organizational conditions.


Abstract:
  We present a general method of constructing \textit{in-situ} pseodopotentials
from first principles, all-electron, full-potential electronic structure
calculations of a solid. The method is applied to bcc Na, at equilibrium
volume. The essential steps of the method involve (i) calculating an
all-electron Kohn-Sham eigenstate. (ii) Replacing the oscillating part of the
wavefunction (inside the muffin-tin spheres) of this state, with a smooth
function. (iii) Representing the smooth wavefunction in a Fourier series, and
(iv) inverting the Kohn-Sham equation, to extract the pseudopotential that
produces the state generated in steps (i)-(iii). It is shown that an
\textit{in-situ} pseudopotential can reproduce an all-electron, full-potential
eigenvalue up to the sixth significant digit. A comparison of the all-electron
theory, \textit{in-situ} pseudopotential theory and the standard nonlocal
pseudopotential theory demonstrates good agreement, e.g., in the energy
dispersion of the 3$s$ band state of bcc Na.


Abstract:
  Elaborating on works by Abouzaid and Mescher, we prove that for a Morse
function on a smooth compact manifold, its Morse cochain complex can be endowed
with an $\Omega B As$-algebra structure by counting moduli spaces of perturbed
Morse gradient trees. This rich structure descends to its already known
$A_\infty$-algebra structure. We then introduce the notion of $\Omega B
As$-morphism between two $\Omega B As$-algebras and prove that given two Morse
functions, one can construct an $\Omega B As$-morphism between their associated
$\Omega B As$-algebras by counting moduli spaces of two-colored perturbed Morse
gradient trees. This morphism induces a standard $A_\infty$-morphism between
the induced $A_\infty$-algebras. We work with integer coefficients, and provide
to this extent a detailed account on the sign conventions for $A_\infty$ (resp.
$\Omega B As$)-algebras and $A_\infty$ (resp. $\Omega B As$)-morphisms, using
polytopes (resp. moduli spaces) which explicitly realize the dg-operadic
objects encoding them. Our proofs also involve showing at the level of
polytopes that an $\Omega B As$-morphism between $\Omega B As$-algebras
naturally induces an $A_\infty$-morphism between $A_\infty$-algebras. This
paper comes in particular with a short survey on operads, $A_\infty$-algebras
and $A_\infty$-morphisms, the associahedra and the multiplihedra. All the
details on transversality, gluing maps, signs and orientations for the moduli
spaces defining the algebraic structures on the Morse cochains are thorougly
carried out. It moreover lays the basis for a second article in which we solve
the problem of finding a satisfactory homotopic notion of higher morphisms
between $A_\infty$-algebras and between $\Omega B As$-algebras, and show how
this higher algebra of $A_\infty$ and $\Omega B As$-algebras naturally arises
in the context of Morse theory.


Abstract:
  Quantum gravity is a challenge in physics, and the existence of graviton is
the prime question at present. We study the detectability of the quantum noise
induced by gravitons in this letter. The correlation of the quantum noise in
the squeezed state is calculated, but we are surprised to find that the result
is the same as those from Grishchuk et.al. and Parikh et.al. This implies that
they are essentially about one thing: the quantum noise from the gravitons. The
further discussion shows the quantum noise should be completed from the leading
order of the interaction between gravitation and the detector. The squeezed
factor is estimated, and with it, the spectrum of the quantum noise is found to
be of the form $\Omega_0 \sim \nu^{4+a_0 \beta+b_0}$. After comparing it to the
sensitivities of several gravitational wave detectors, we conclude that the
quantum noise is detectable in the future.


Abstract:
  We study the change of quantization for a class of global pseudodifferential
operators of infinite order in the setting of ultradifferentiable functions of
Beurling type. The composition of different quantizations as well as the
transpose of a quantization are also analysed, with applications to the Weyl
calculus. We also compare global $\omega$-hypoellipticity and global
$\omega$-regularity of these classes of pseudodifferential operators.


Abstract:
  We calculate the leading corrections to jet momentum broadening and
medium-induced branching that arise from the velocity of the moving medium at
first order in opacity. These results advance our knowledge of jet quenching
and demonstrate how it couples to collective flow of the quark-gluon plasma in
heavy-ion collisions and to the orbital motion of partons in cold nuclear
matter in deep inelastic scattering at the electron-ion collider. We also
compute the leading corrections to jet momentum broadening due to transverse
gradients of temperature and density. We find that these effects lead to both
anisotropic transverse momentum diffusion proportional to the medium velocity
and anisotropic medium-induced radiation emitted preferentially in the
direction of the flow. We isolate the relevant sub-eikonal corrections by
working with jets composed of scalar particles with arbitrary color factors
interacting with the medium by scalar QCD. Appropriate substitution of the
color factors and light-front wave functions allow us to immediately apply the
results to a range of processes including $q \rightarrow q g$ branching in real
QCD. The resulting general expressions can be directly coupled to hydrodynamic
simulations on an event-by-event basis to study the correlations between jet
quenching and the dynamics of various forms of nuclear matter.


Abstract:
  HERMES (High Energy Rapid Modular Ensemble of Satellites) Technological and
Scientific pathfinder is a space borne mission based on a LEO constellation of
nano-satellites. The 3U CubeSat buses host new miniaturized detectors to probe
the temporal emission of bright high-energy transients such as Gamma-Ray Bursts
(GRBs). Fast transient localization, in a field of view of several steradians
and with arcmin-level accuracy, is gained by comparing time delays among the
same event detection epochs occurred on at least 3 nano-satellites. With a
launch date in 2022, HERMES transient monitoring represents a keystone
capability to complement the next generation of gravitational wave experiments.
In this paper we will illustrate the HERMES payload design, highlighting the
technical solutions adopted to allow a wide-energy-band and sensitive X-ray and
gamma-ray detector to be accommodated in a CubeSat 1U volume together with its
complete control electronics and data handling system.


Abstract:
  We consider a time-like geodesics in the background of rotating
Einstein-Born-Infeld (EBI) black hole to examine the horizon and ergosphere
structure. The effective potential that governs the particle's motion in the
spacetime and the innermost stable circular orbits (ISCO) is also studied. A
qualitative analysis is conducted to find the redshifted ultrahigh
centre-of-mass (CM) energy as a result of a two-particle collision specifically
near the horizon. The recent Event Horizon Telescope (EHT) triggered a surge of
interest in strong gravitational lensing by black holes, which provide a new
tool comparing the black hole lensing in general relativity and alternate
gravity theories. Motivated by this, we also discussed both strong and
weak-field gravitational lensing in the space-time discretely for a uniform
plasma and a singular isothermal sphere. We calculated the light deflection
coefficients $\bar{a}$ and $\bar{b}$ in the strong field limits, and their
variance with the rotational parameter $a$ for different plasma frequency as
well as in vacuum. For EBI black holes, we found that plasma's presence
increases the photon sphere radius, the deflection angle, the deflection
coefficients $\bar{a}$, $\bar{b}$, the angular positions and the angular
separation between the relativistic images. It is also shown that with
increasing spin the impact of plasma on a strong gravitational lensing becomes
smaller as the spin parameter grows in the prograde orbit ($a>0$). For extreme
black holes, the strong gravitational effects in the homogenous plasma are
similar to those of in a vacuum. We investigate strong gravitational lensing
effects by supermassive black holes Sgr A* and M87*...


Abstract:
  Defeasible reasoning is the mode of reasoning where conclusions can be
overturned by taking into account new evidence. A commonly used method in
cognitive science and logic literature is to handcraft argumentation supporting
inference graphs. While humans find inference graphs very useful for reasoning,
constructing them at scale is difficult. In this paper, we automatically
generate such inference graphs through transfer learning from another NLP task
that shares the kind of reasoning that inference graphs support. Through
automated metrics and human evaluation, we find that our method generates
meaningful graphs for the defeasible inference task. Human accuracy on this
task improves by 20% by consulting the generated graphs. Our findings open up
exciting new research avenues for cases where machine reasoning can help human
reasoning. (A dataset of 230,000 influence graphs for each defeasible query is
located at: https://tinyurl.com/defeasiblegraphs.)


Abstract:
  An enumeration kernel as defined by Creignou et al. [Theory Comput. Syst.
2017] for a parameterized enumeration problem consists of an algorithm that
transforms each instance into one whose size is bounded by the parameter plus a
solution-lifting algorithm that efficiently enumerates all solutions from the
set of the solutions of the kernel. We propose to consider two new versions of
enumeration kernels by asking that the solutions of the original instance can
be enumerated in polynomial time or with polynomial delay from the kernel
solutions. Using the NP-hard Matching Cut problem parameterized by structural
parameters such as the vertex cover number or the cyclomatic number of the
input graph, we show that the new enumeration kernels present a useful notion
of data reduction for enumeration problems which allows to compactly represent
the set of feasible solutions.


Abstract:
  We establish generalizations of the Feldman-Moore theorem, the Glimm-Effros
dichotomy, and the Lusin-Novikov uniformization theorem from Polish spaces to
their quotients by Borel orbit equivalence relations.


Abstract:
  We analyse the performance of several iterative algorithms for the
quantisation of a probability measure $\mu$, based on the minimisation of a
Maximum Mean Discrepancy (MMD). Our analysis includes kernel herding, greedy
MMD minimisation and Sequential Bayesian Quadrature (SBQ). We show that the
finite-sample-size approximation error, measured by the MMD, decreases as $1/n$
for SBQ and also for kernel herding and greedy MMD minimisation when using a
suitable step-size sequence. The upper bound on the approximation error is
slightly better for SBQ, but the other methods are significantly faster, with a
computational cost that increases only linearly with the number of points
selected. This is illustrated by two numerical examples, with the target
measure $\mu$ being uniform (a space-filling design application) and with $\mu$
a Gaussian mixture.


Abstract:
  We curated WikiPII, an automatically labeled dataset composed of Wikipedia
biography pages, annotated for personal information extraction. Although
automatic annotation can lead to a high degree of label noise, it is an
inexpensive process and can generate large volumes of annotated documents. We
trained a BERT-based NER model with WikiPII and showed that with an adequately
large training dataset, the model can significantly decrease the cost of manual
information extraction, despite the high level of label noise. In a similar
approach, organizations can leverage text mining techniques to create
customized annotated datasets from their historical data without sharing the
raw data for human annotation. Also, we explore collaborative training of NER
models through federated learning when the annotation is noisy. Our results
suggest that depending on the level of trust to the ML operator and the volume
of the available data, distributed training can be an effective way of training
a personal information identifier in a privacy-preserved manner. Research
material is available at https://github.com/ratmcu/wikipiifed.


Abstract:
  Transport calculations for neutronic design require accurate nuclear data and
validated computational tools. In the Spallation Physics Group, at the European
Spallation Source, we perform shielding and neutron beam calculations to help
the deployment of the instrument suite for the current high brilliance (top)
moderator, as well for the design of the high intensity bottom moderator,
currently under study for the facility. This work includes providing the best
available nuclear data in addition to improving models and tools when
necessary. In this paper we present the status of these activities, which
include a set of thermal scattering kernels for moderator, reflector, and
structural materials, the development of new kernels for beryllium considering
crystallite size effects, nanodiamonds, liquid hydrogen and deuterium based on
path integral molecular dynamics, and the use of the software package NCrystal
to assist the development of nuclear data in the framework of the new HighNESS
project.


Abstract:
  Several Open Source Software (OSS) projects depend on the continuity of their
development communities to remain sustainable. Understanding how developers
become inactive or why they take breaks can help communities prevent
abandonment and incentivize developers to come back. In this paper, we propose
a novel method to identify developers' inactive periods by analyzing the
individual rhythm of contributions to the projects. Using this method, we
quantitatively analyze the inactivity of core developers in 18 OSS
organizations hosted on GitHub. We also survey core developers to receive their
feedback about the identified breaks and transitions. Our results show that our
method was effective for identifying developers' breaks. About 94% of the
surveyed core developers agreed with our state model of inactivity; 71% and 79%
of them acknowledged their breaks and state transition, respectively. We also
show that all core developers take breaks (at least once) and about a half of
them (~45%}) have completely disengaged from a project for at least one year.
We also analyzed the probability of transitions to/from inactivity and found
that developers who pause their activity have a ~35-55\% chance to return to an
active state; yet, if the break lasts for a year or longer, then the
probability of resuming activities drops to ~21-26%, with a ~54% chance of
complete disengagement. These results may support the creation of policies and
mechanisms to make OSS community managers aware of breaks and potential project
abandonment.


Abstract:
  In this paper, we investigate the use of a mass lumped fully explicit time
stepping scheme for the discretisation of the wave equation with underlying
material parameters that vary at arbitrarily fine scales. We combine the
leapfrog scheme for the temporal discretisation with the multiscale technique
known as Localized Orthogonal Decomposition for the spatial discretisation. To
speed up the method and to make it fully explicit, a special mass lumping
approach is introduced that relies on an appropriate interpolation operator.
This operator is also employed in the construction of the Localized Orthogonal
Decomposition and is a key feature of the approach. We prove that the method
converges with second order in the energy norm, with a leading constant that
does not depend on the scales at which the material parameters vary. We also
illustrate the performance of the mass lumped method in a set of numerical
experiments.


Abstract:
  The transformation between the metallic ($\beta$) and semi-conducting
($\alpha$) allotropes of tin is still not well understood. The phase transition
temperature stated in the literature, 286.2 K, seems to be inconsistent with
recent calorimetric measurements. In this paper, this intriguing aspect has
been explored in Sn and Sn-Cu (alloyed 0.5% Cu by weight) using temperature
resolved synchrotron x-ray diffraction measurements performed at the Indus-2
facility. Additionally, the $\alpha \rightleftharpoons \beta$ Sn transition has
been recorded using in-situ heating/cooling experiments in a scanning electron
microscope. Based on these measurements, a protocol has been suggested to
reduce the formation of $\alpha$-Sn in potentially susceptible systems. This
will be useful in experiments like TIN.TIN (The INdia-based TIN detector),
which proposes to employ ~100 - 1000 kg of superconducting tin-based detectors
to search for neutrinoless double beta decay in the isotope $^{124}$Sn.


Abstract:
  In this paper, we present a non-parametric structured latent variable model
for image generation, called NP-DRAW, which sequentially draws on a latent
canvas in a part-by-part fashion and then decodes the image from the canvas.
Our key contributions are as follows. 1) We propose a non-parametric prior
distribution over the appearance of image parts so that the latent variable
``what-to-draw'' per step becomes a categorical random variable. This improves
the expressiveness and greatly eases the learning compared to Gaussians used in
the literature. 2) We model the sequential dependency structure of parts via a
Transformer, which is more powerful and easier to train compared to RNNs used
in the literature. 3) We propose an effective heuristic parsing algorithm to
pre-train the prior. Experiments on MNIST, Omniglot, CIFAR-10, and CelebA show
that our method significantly outperforms previous structured image models like
DRAW and AIR and is competitive to other generic generative models. Moreover,
we show that our model's inherent compositionality and interpretability bring
significant benefits in the low-data learning regime and latent space editing.
Code is available at https://github.com/ZENGXH/NPDRAW.


Abstract:
  We study continuous finite element dicretizations for one dimensional
hyperbolic partial differential equations. The main contribution of the paper
is to provide a fully discrete spectral analysis, which is used to suggest
optimal values of the CFL number and of the stabilization parameters involved
in different types of stabilization operators. In particular, we analyze the
streamline-upwind Petrov-Galerkin (SUPG) stabilization technique, the
continuous interior penalty (CIP) stabilization method and the local projection
stabilization (LPS). Three different choices for the continuous finite element
space are compared: Bernstein polynomials, Lagrangian polynomials on equispaced
nodes, and Lagrangian polynomials on Gauss-Lobatto cubature nodes. For the last
choice, we only consider inexact quadrature based on the formulas corresponding
to the degrees of freedom of the element, which allows to obtain a fully
diagonal mass matrix. We also compare different time stepping strategies,
namely Runge-Kutta (RK), strong stability preserving RK (SSPRK) and deferred
correction time integration methods. The latter allows to alleviate the
computational cost as the mass matrix inversion is replaced by the high order
correction iterations. To understand the effects of these choices, both
time-continuous and fully discrete Fourier analysis are performed. These allow
to compare all the different combinations in terms of accuracy and stability,
as well as to provide suggestions for optimal values discretization parameters
involved. The results are thoroughly verified numerically both on linear and
non-linear problems, and error-CPU time curves are provided. Our final
conclusions suggest that cubature elements combined with SSPRK and CIP or LPS
stabilization are the most promising combinations.


Abstract:
  We consider the problem of computing the integral $$
  \int_{\mathcal{U}(d)} u_{i_1j_1}\cdots u_{i_nj_n} \bar{u}_{i'_1j'_1} \cdots
\bar{u}_{i'_{n'}j'_{n'}} dU, $$ where the integration takes place with respect
to the probability Haar measure on the unitary group $\mathcal{U}(d)$, and the
$u_{ij}$ denotes the $ij$-th entry of a unitary matrix $U$. We present a
unified approach connecting classical results, the explicit formula for the
integral given by B. Collins and P. Sniady and subsequent works of various
authors providing different points of view. Finally we are able to provide an
explicit formula for the $2n$-th moment of the trace of a unitary Haar random
matrix, generalizing a result of P. Diaconis.


Abstract:
  Advances in deep neural networks (DNNs) have shown tremendous promise in the
medical domain. However, the deep learning tools that are helping the domain,
can also be used against it. Given the prevalence of fraud in the healthcare
domain, it is important to consider the adversarial use of DNNs in manipulating
sensitive data that is crucial to patient healthcare. In this work, we present
the design and implementation of a DNN-based image translation attack on
biomedical imagery. More specifically, we propose Jekyll, a neural style
transfer framework that takes as input a biomedical image of a patient and
translates it to a new image that indicates an attacker-chosen disease
condition. The potential for fraudulent claims based on such generated 'fake'
medical images is significant, and we demonstrate successful attacks on both
X-rays and retinal fundus image modalities. We show that these attacks manage
to mislead both medical professionals and algorithmic detection schemes.
Lastly, we also investigate defensive measures based on machine learning to
detect images generated by Jekyll.


Abstract:
  In response to the ongoing COVID-19 pandemic, we present a robust deep
learning pipeline that is capable of identifying correct and incorrect
mask-wearing from real-time video streams. To accomplish this goal, we devised
two separate approaches and evaluated their performance and run-time
efficiency. The first approach leverages a pre-trained face detector in
combination with a mask-wearing image classifier trained on a large-scale
synthetic dataset. The second approach utilizes a state-of-the-art object
detection network to perform localization and classification of faces in one
shot, fine-tuned on a small set of labeled real-world images. The first
pipeline achieved a test accuracy of 99.97% on the synthetic dataset and
maintained 6 FPS running on video data. The second pipeline achieved a mAP(0.5)
of 89% on real-world images while sustaining 52 FPS on video data. We have
concluded that if a larger dataset with bounding-box labels can be curated,
this task is best suited using object detection architectures such as YOLO and
SSD due to their superior inference speed and satisfactory performance on key
evaluation metrics.


Abstract:
  Quantum cryptography is known for enabling functionalities that are
unattainable using classical information alone. Recently, Secure Software
Leasing (SSL) has emerged as one of these areas of interest. Given a target
circuit $C$ from a circuit class, SSL produces an encoding of $C$ that enables
a recipient to evaluate $C$, and also enables the originator of the software to
verify that the software has been returned -- meaning that the recipient has
relinquished the possibility of any further use of the software. Clearly, such
a functionality is unachievable using classical information alone, since it is
impossible to prevent a user from keeping a copy of the software. Recent
results have shown the achievability of SSL using quantum information for a
class of functions called compute-and-compare (these are a generalization of
the well-known point functions). These prior works, however all make use of
setup or computational assumptions. Here, we show that SSL is achievable for
compute-and-compare circuits without any assumptions.
  Our technique involves the study of quantum copy-protection, which is a
notion related to SSL, but where the encoding procedure inherently prevents a
would-be quantum software pirate from splitting a single copy of an encoding
for $C$ into two parts, each of which enables a user to evaluate $C$. We show
that point functions can be copy-protected without any assumptions, for a novel
security definition involving one honest and one malicious evaluator; this is
achieved by showing that from any quantum message authentication code, we can
derive such an honest-malicious copy-protection scheme. We then show that a
generic honest-malicious copy-protection scheme implies SSL; by prior work,
this yields SSL for compute-and-compare functions.


Abstract:
  Energy eigenstates in the random transverse field Edwards-Anderson (EA) model
and the random bond quantum Heisenberg XYZ model in a $d$-dimensional finite
cubic lattice are obtained for sufficiently weak interactions. The
Datta-Kennedy-Kirkwood-Thomas convergent perturbative expansion using the
contraction mapping theorem is developed for quantum spin systems with site-
and bond-dependent interactions. This expansion enables us to obtain energy
eigenstates in the random transverse field free spin model perturbed by
sufficiently weak longitudinal exchange interactions. This expansion is useful
also for the EA model perturbed by sufficiently weak transverse fields and
bond-dependent XY exchange interactions. In these models, their perturbations
split the two fold degenerate energy eigenvalues because of the ${\mathbb Z}_2$
symmetry in the unperturbed EA model. It is shown that the energy gap between
split energy eigenvalues is exponentially small in the system size. We provide
a sufficient condition on the perturbation for absence of level crossing
between arbitrary energy eigenstates.


Abstract:
  Background: Software is an important windows to offer a variety of complex
instrument control and data processing for nuclear magnetic resonance (NMR)
spectrometer. NMR software should allow researchers to flexibly implement
various functionality according to the requirement of applications. Scripting
system can offer an open environment for NMR users to write custom programs
with basic libraries. Emerging technologies, especially multivariate
statistical analysis and artificial intelligence, have been successfully
applied to NMR applications such as metabolomics and biomacromolecules.
Scripting system should support more complex NMR libraries, which will enable
the emerging technologies to be easily implemented in the scripting
environment. Result: Here, a novel NMR scripting system named "NMRPy" is
introduced. In the scripting system, both Java based NMR methods and original
CPython based libraries are supported. A module was built as a bridge to
integrate the runtime environment of Java and CPython. It works as an extension
in CPython environment, as well as interacts with Java part by Java Native
Interface. Leveraging the bridge, Java based instrument control and data
processing methods can be called as a CPython style. Compared with traditional
scripting system, NMRPy is easier for NMR researchers to develop complex
functionality with fast numerical computation, multivariate statistical
analysis, deep learning etc. Non-uniform sampling and protein structure
prediction methods based on deep learning can be conveniently integrated into
NMRPy. Conclusion: NMRPy offers a user-friendly environment to implement custom
functionality leveraging its powerful basic NMR and rich CPython libraries. NMR
applications with emerging technologies can be easily integrated. The scripting
system is free of charge and can be downloaded by visiting
http://www.spinstudioj.net/nmrpy.


Abstract:
  This paper is devoted to numerical simulations of the short-term behavior of
the spatial temperature distribution in a geothermal energy storage. Such
simulations are needed for the optimal control and management of residential
heating systems equipped with an underground thermal storage. We apply
numerical methods derived in our companion paper [8]. There we studied the
governing initial boundary value problem for a linear heat equation with
convection. Further, we perform extensive numerical experiments in which we
investigate properties of the spatio-temporal temperature distribution and of
its aggregated characteristics.


Abstract:
  In this paper we study the spectral gap of the path graph and illustrate an
interesting effect which has been described recently in the continuous setting.
More explicitly, in the large-volume limit and in the presence of a certain
external potential, it is shown that the spectral gap converges to zero
strictly faster than it does for the free Laplacian. The underlying mechanism
is a combination of the increase in volume and an effective degeneracy of the
ground state in the limiting regime.


Abstract:
  In this paper, we study the performance and generalizability of three
approaches for AD detection from speech on the recent ADReSSo challenge
dataset: 1) using conventional acoustic features 2) using novel pre-trained
acoustic embeddings 3) combining acoustic features and embeddings. We find that
while feature-based approaches have a higher precision, classification
approaches relying on the combination of embeddings and features prove to have
a higher, and more balanced performance across multiple metrics of performance.
Our best model, using such a combined approach, outperforms the acoustic
baseline in the challenge by 2.8\%.


Abstract:
  Recent works recognized lidars as an inherently streaming data source and
showed that the end-to-end latency of lidar perception models can be reduced
significantly by operating on wedge-shaped point cloud sectors rather then the
full point cloud. However, due to use of cartesian coordinate systems these
methods represent the sectors as rectangular regions, wasting memory and
compute. In this work we propose using a polar coordinate system and make two
key improvements on this design. First, we increase the spatial context by
using multi-scale padding from neighboring sectors: preceding sector from the
current scan and/or the following sector from the past scan. Second, we improve
the core polar convolutional architecture by introducing feature undistortion
and range stratified convolutions. Experimental results on the nuScenes dataset
show significant improvements over other streaming based methods. We also
achieve comparable results to existing non-streaming methods but with lower
latencies.


Abstract:
  Let $\mu_2(\Omega)$ be the first positive eigenvalue of the Neumann Laplacian
in a bounded domain $\Omega\subset\mathbb{R}^N$. It was proved by Szeg\H{o} for
$N=2$ and by Weinberger for $N \geq 2$ that among all equimeasurable domains
$\mu_2(\Omega)$ attains its global maximum if $\Omega$ is a ball. In the
present work, we develop the approach of Weinberger in two directions. Firstly,
we refine the Szeg\H{o}-Weinberger result for a class of domains of the form
$\Omega_{\text{out}}\setminus\overline{\Omega}_{\text{in}}$ which are either
centrally symmetric or symmetric of order $2$ (with respect to every coordinate
plane $(x_i,x_j)$) by showing that
$\mu_{2}(\Omega_{\text{out}}\setminus\overline{\Omega}_{\text{in}})\leq\mu_2(B_\beta\setminus\overline{B}_\alpha)$,
where $B_\alpha, B_\beta$ are balls centered at the origin such that
$B_\alpha\subset\Omega_{\text{in}}$ and
$|\Omega_{\text{out}}\setminus\overline{\Omega}_{\text{in}}|=|B_\beta\setminus\overline{B}_\alpha|$.
Secondly, we provide Szeg\H{o}-Weinberger type inequalities for higher
eigenvalues by imposing additional symmetry assumptions on the domain. Namely,
if $\Omega_{\text{out}}\setminus\overline{\Omega}_{\text{in}}$ is symmetric of
order $4$, then we prove
$\mu_{i}(\Omega_{\text{out}}\setminus\overline{\Omega}_{\text{in}})\leq\mu_i(B_\beta\setminus\overline{B}_\alpha)$
for $i=3,\dots,N+2$, where we also allow $\Omega_{\text{in}}$ and $B_\alpha$ to
be empty. If $N=2$ and the domain is symmetric of order $8$, then the latter
inequality persists for $i=5$. Counterexamples to the obtained inequalities for
domains outside of the considered symmetry classes are given. The existence and
properties of nonradial domains with required symmetries in higher dimensions
are discussed. As an auxiliary result, we obtain the non-radiality of the
eigenfunctions associated to $\mu_{N+2}(B_\beta\setminus\overline{B}_\alpha)$.


Abstract:
  Over the past year, remote speech intelligibility testing has become a
popular and necessary alternative to traditional in-person experiments due to
the need for physical distancing during the COVID-19 pandemic. A remote
framework was developed for conducting speech intelligibility tests with normal
hearing listeners. In this study, subjects used their personal computers to
complete sentence recognition tasks in anechoic and reverberant listening
environments. The results obtained using this remote framework were compared
with previously collected in-lab results, and showed higher levels of speech
intelligibility among remote study participants than subjects who completed the
test in the laboratory.


Abstract:
  A problem of identification of piecewise-constant unknown parameters of a
linear regression equation (LRE) is considered. Such parameters change their
values over the interval of the regressor finite (rather than persistent)
excitation. To solve it, the previously proposed I-DREM procedure and the
integral-based filtering method with the exponential forgetting and resetting
are improved: the integral of the filter equations is taken over the finite
time intervals, which belong to the finite excitation time range. This allows
one to obtain an exponentially bounded identification error over the excitation
interval, and, when the LRE parameters are constant outside such interval, to
provide exponential convergence of the identification error to zero. In
addition, the applied method of the regressor excitation normalization makes it
possible to obtain the same rate of convergence of the parameter error for the
regressors of various amplitudes. The stability and all the above-mentioned
properties are proved for the derived identification method. The results of
numerical experiments (including the case of the noise-contaminated regressor
and output measurements) fully support the obtained theoretical results.


Abstract:
  Image co-segmentation is an active computer vision task that aims to segment
the common objects from a set of images. Recently, researchers design various
learning-based algorithms to undertake the co-segmentation task. The main
difficulty in this task is how to effectively transfer information between
images to make conditional predictions. In this paper, we present CycleSegNet,
a novel framework for the co-segmentation task. Our network design has two key
components: a region correspondence module which is the basic operation for
exchanging information between local image regions, and a cycle refinement
module, which utilizes ConvLSTMs to progressively update image representations
and exchange information in a cycle and iterative manner. Extensive experiments
demonstrate that our proposed method significantly outperforms the
state-of-the-art methods on four popular benchmark datasets -- PASCAL VOC
dataset, MSRC dataset, Internet dataset, and iCoseg dataset, by 2.6%, 7.7%,
2.2%, and 2.9%, respectively.


Abstract:
  Here we derive some results on so called quantitative Runge approximation in
the case of the time-harmonic Maxwell equations. This provides a Runge
approximation having more explicit quantitative information. We additionally
derive some results on the conditional stability of the Cauchy problem for the
time-harmonic Maxwell equations.


Abstract:
  Classical spectrum analyzers are widely used but lack micro- or nanoscale
spatial resolution. On the other hand, quantum sensors, capable of working with
nanoscale precision, do not provide precise frequency resolution over a wide
range of frequencies. Using a single spin in diamond, we present a measurement
protocol for quantum probes which enables full signal reconstruction on a
nanoscale spatial resolution up to potentially 100 GHz. We achieve
$58\,\mathrm{nT/\sqrt{Hz}}$ amplitude and $0.095\,\mathrm{rad/\sqrt{Hz}}$ phase
sensitivity and a relative uncertainty of $10^{-12}$ for a 1.51 GHz signal
within 10 s of integration. This technique opens the way to quantum spectrum
analysis methods and yields many interesting potential applications.