Abstract:
  The aim of this note is to provoke discussion concerning arithmetic
properties of function $p_{d}(n)$ counting partitions of an positive integer
$n$ into $d$-th powers, where $d\geq 2$. Besides results concerning the
asymptotic behavior of $p_{d}(n)$ a little is known. In the first part of the
paper, we prove certain congruences involving functions counting various types
of partitions into $d$-th powers. The second part of the paper has experimental
nature and contains questions and conjectures concerning arithmetic behavior of
the sequence $(p_{d}(n))_{n\in\N}$. They based on our computations of
$p_{d}(n)$ for $n\leq 10^5$ in case of $d=2$, and $n\leq 10^{6}$ for $d=3, 4,
5$.


Abstract:
  We report the synthesis, crystal structure, and magnetic properties of two
new quantum antiferromagnets A3ReO5Cl2 (A = Sr and Ba). The crystal structure
is isostructural with the mineral pinalite Pb3WO5Cl2, in which the Re6+ ion is
square-pyramidally coordinated by five oxide atoms, and forms an anisotropic
triangular lattice (ATL) made of S = 1/2 spins. The magnetic interactions J and
J' in the ATL are estimated from magnetic susceptibilities to be 19.5 (44.9)
and 9.2 (19.3) K, respectively, with J'/J = 0.47 (0.43) for A = Ba (Sr). For
each compound, heat capacity at low temperatures shows a large T-linear
component with no signature of long-range magnetic order above 2 K, which
suggests a gapless spin liquid state of one-dimensional character of the J
chains in spite of the significantly large J' couplings. This is a consequence
of one-dimensionalization by geometrical frustration in the ATL magnet; a
similar phenomenon has been observed in two compounds with slightly smaller
J'/J values: Cs2CuCl4 (J'/J = 0.3) and the related compound Ca3ReO5Cl2 (0.32).
Our findings demonstrate that 5d mixed-anion compounds provide a unique
opportunity to explore novel quantum magnetism.


Abstract:
  As robots move from the laboratory into the real world, motion planning will
need to account for model uncertainty and risk. For robot motions involving
intermittent contact, planning for uncertainty in contact is especially
important, as failure to successfully make and maintain contact can be
catastrophic. Here, we model uncertainty in terrain geometry and friction
characteristics, and combine a risk-sensitive objective with chance constraints
to provide a trade-off between robustness to uncertainty and constraint
satisfaction with an arbitrarily high feasibility guarantee. We evaluate our
approach in two simple examples: a push-block system for benchmarking and a
single-legged hopper. We demonstrate that chance constraints alone produce
trajectories similar to those produced using strict complementarity
constraints; however, when equipped with a robust objective, we show the chance
constraints can mediate a trade-off between robustness to uncertainty and
strict constraint satisfaction. Thus, our study may represent an important step
towards reasoning about contact uncertainty in motion planning.


Abstract:
  Hovey introduced $A$-cordial labelings as a generalization of cordial and
harmonious labelings \cite{Hovey}. If $A$ is an Abelian group, then a labeling
$f \colon V (G) \rightarrow A$ of the vertices of some graph $G$ induces an
edge labeling on $G$; the edge $uv$ receives the label $f (u) + f (v)$. A graph
$G$ is $A$-cordial if there is a vertex-labeling such that (1) the vertex label
classes differ in size by at most one and (2) the induced edge label classes
differ in size by at most one.
  Patrias and Pechenik studied the larger class of finite abelian groups $A$
such that all path graphs are $A$-cordial. They posed a conjecture that all but
finitely many paths graphs are $A$-cordial for any Abelian group $A$. In this
paper we solve this conjecture. Moreover we show that all cycle graphs are
$A$-cordial for any Abelian group $A$ of odd order.


Abstract:
  Efficient and accurate object detection in video and image analysis is one of
the major beneficiaries of the advancement in computer vision systems with the
help of deep learning. With the aid of deep learning, more powerful tools
evolved, which are capable to learn high-level and deeper features and thus can
overcome the existing problems in traditional architectures of object detection
algorithms. The work in this thesis aims to achieve high accuracy in object
detection with good real-time performance.
  In the area of computer vision, a lot of research is going into the area of
detection and processing of visual information, by improving the existing
algorithms. The binarized neural network has shown high performance in various
vision tasks such as image classification, object detection, and semantic
segmentation. The Modified National Institute of Standards and Technology
database (MNIST), Canadian Institute for Advanced Research (CIFAR), and Street
View House Numbers (SVHN) datasets are used which is implemented using a
pre-trained convolutional neural network (CNN) that is 22 layers deep.
Supervised learning is used in the work, which classifies the particular
dataset with the proper structure of the model. In still images, to improve
accuracy, Googlenet is used. The final layer of the Googlenet is replaced with
the transfer learning to improve the accuracy of the Googlenet. At the same
time, the accuracy in moving images can be maintained by transfer learning
techniques. Hardware is the main backbone for any model to obtain faster
results with a large number of datasets. Here, Nvidia Jetson Nano is used which
is a graphics processing unit (GPU), that can handle a large number of
computations in the process of object detection. Results show that the accuracy
of objects detected by the transfer learning method is more when compared to
the existing methods.


Abstract:
  As weak lensing surveys are becoming deeper and cover larger areas,
information will be available on small angular scales down to the arcmin level.
To extract this extra information, accurate modelling of baryonic effects is
necessary. In this work, we adopt a baryonic correction model, which includes
gas both bound inside and ejected from dark matter (DM) haloes, a central
galaxy, and changes in the DM profile induced by baryons. We use this model to
incorporate baryons into a large suite of DM-only $N$-body simulations,
covering a grid of 75 cosmologies in the $\Omega_\mathrm{m}-\sigma_8$ parameter
space. We investigate how baryons affect Gaussian and non-Gaussian weak lensing
statistics and the cosmological parameter inferences from these statistics. Our
results show that marginalizing over baryonic parameters degrades the
constraints in $\Omega_\mathrm{m}-\sigma_8$ space by a factor of $2-4$ compared
to those with baryonic parameters fixed. We investigate the contribution of
each baryonic component to this degradation, and find that the distance to
which gas is ejected (from AGN feedback) has the largest impact due to its
degeneracy with cosmological parameters. External constraints on this
parameter, either from other datasets or from a better theoretical
understanding of AGN feedback, can significantly mitigate the impact of baryons
in an HSC-like survey.


Abstract:
  In this letter we study how fast the energy density of a quantum gas can
increase in time, when the inter-atomic interaction characterized by the
$s$-wave scattering length $a_\text{s}$ is increased from zero with arbitrary
time dependence. We show that, at short time, the energy density can at most
increase as $\sqrt{t}$, which can be achieved when the time dependence of
$a_\text{s}$ is also proportional to $\sqrt{t}$, and especially, a universal
maximum energy growth rate can be reached when $a_\text{s}$ varies as
$2\sqrt{\hbar t/(\pi m)}$. If $a_\text{s}$ varies faster or slower than
$\sqrt{t}$, it is respectively proximate to the quench process and the
adiabatic process, and both result in a slower energy growth rate. These
results are obtained by analyzing the short time dynamics of the short-range
behavior of the many-body wave function characterized by the contact, and are
also confirmed by numerical solving an example of interacting bosons with
time-dependent Bogoliubov theory. These results can also be verified
experimentally in ultracold atomic gases.


Abstract:
  In this note we continue our study of unidirectional solutions to
hydrodynamic Euler alignment systems with strongly singular communication
kernels $\phi(x):=|x|^{-(n+\alpha)}$ for $\alpha\in(0,2)$. Here, we consider
the critical case $\alpha=1$ and establish a couple of global existence results
of smooth solutions, together with a full description of their long time
dynamics. The first one is obtained via Schauder-type estimates under a null
initial entropy condition and the other is a small data result. In fact, using
Duhamel's approach we get that any solution is almost Lipschitz-continuous in
space. We extend the notion of weak solution for $\alpha\in[1,2)$ and prove the
existence of global Leray-Hopf solutions. Furthermore, we give an anisotropic
Onsager-type criteria for the validity of the natural energy law for weak
solutions of the system. Finally, we provide a series of quantitative estimates
that show how far the density of the limiting flock is from a uniform
distribution depending solely on the size of the initial entropy.


Abstract:
  A method for active learning of hyperspectral images (HSI) is proposed, which
combines deep learning with diffusion processes on graphs. A deep variational
autoencoder extracts smoothed, denoised features from a high-dimensional HSI,
which are then used to make labeling queries based on graph diffusion
processes. The proposed method combines the robust representations of deep
learning with the mathematical tractability of diffusion geometry, and leads to
strong performance on real HSI.


Abstract:
  Dynamic pricing schemes were introduced as an alternative to posted-price
mechanisms. In contrast to static models, the dynamic setting allows to update
the prices between buyer-arrivals based on the remaining sets of items and
buyers, and so it is capable of maximizing social welfare without the need for
a central coordinator. In this paper, we study the existence of optimal dynamic
pricing schemes in combinatorial markets. In particular, we concentrate on
multi-demand valuations, a natural extension of unit-demand valuations. The
proposed approach is based on computing an optimal dual solution of the maximum
social welfare problem with distinguished structural properties.
  Our contribution is twofold. By relying on an optimal dual solution, we show
the existence of optimal dynamic prices in unit-demand markets and in
multi-demand markets up to three buyers, thus giving new interpretations of
results of Cohen-Addad et al. and Berger et al. , respectively. Furthermore, we
provide an optimal dynamic pricing scheme for bi-demand valuations with an
arbitrary number of buyers. In all cases, our proofs also provide efficient
algorithms for determining the optimal dynamic prices.


Abstract:
  We propose effective scheme of deep learning method for high-order nonlinear
soliton equation and compare the activation function for high-order soliton
equation. The neural network approximates the solution of the equation under
the conditions of differential operator, initial condition and boundary
condition. We apply this method to high-order nonlinear soliton equation, and
verify its efficiency by solving the fourth-order Boussinesq equation and the
fifth-order Korteweg de Vries equation. The results show that deep learning
method can solve the high-order nonlinear soliton equation and reveal the
interaction between solitons.


Abstract:
  Circumplanetary discs can be linearly unstable to the growth of disc tilt in
the tidal potential of the star-planet system. We use three-dimensional
hydrodynamical simulations to characterize the disc conditions needed for
instability, together with its long term evolution. Tilt growth occurs for disc
aspect ratios, evaluated near the disc outer edge, of $H/r\gtrsim 0.05$, with a
weak dependence on viscosity in the wave-like regime of warp propagation. Lower
mass giant planets are more likely to have circumplanetary discs that satisfy
the conditions for instability. We show that the tilt instability can excite
the inclination to above the threshold where the circumplanetary disc becomes
unstable to Kozai--Lidov (KL) oscillations. Dissipation in the Kozai--Lidov
unstable regime caps further tilt growth, but the disc experiences large
oscillations in both inclination and eccentricity. Planetary accretion occurs
in episodic accretion events. We discuss implications of the joint tilt--KL
instability for the detectability of circumplanetary discs, for the obliquity
evolution of forming giant planets, and for the formation of satellite systems.


Abstract:
  Using fully-resolved simulations, we investigate the torque experienced by a
finite-length circular cylinder rotating steadily perpendicularly to its
symmetry axis. The aspect ratio $\chi$, i.e. the ratio of the length of the
cylinder to its diameter, is varied from 1 to 15. In the creeping-flow regime,
we employ the slender-body theory to derive the expression of the torque up to
order 4 with respect to the small parameter $1/\ln(2\chi)$. Numerical results
agree well with the corresponding predictions for $\chi\gtrsim3$. We introduce
an \textit{ad hoc} modification in the theoretical prediction to fit the
numerical results obtained with shorter cylinders, and a second modification to
account for the increase of the torque resulting from finite inertial effects.
In strongly inertial regimes, a prominent wake pattern made of two pairs of
counter-rotating vortices takes place. Nevertheless the flow remains stationary
and exhibits two distinct symmetries, one of which implies that the
contributions to the torque arising from the two cylinder ends are identical.
We build separate empirical formulas for the contributions of pressure and
viscous stress to the torque provided by the lateral surface and the cylinder
ends. We show that, in each contribution, the dominant scaling law may be
inferred from simple physical arguments. This approach eventually results in an
empirical formula for the rotation-induced torque valid throughout the range of
inertial regimes and aspect ratios considered in the simulations.


Abstract:
  Intent classification is an important task in natural language understanding
systems. Existing approaches have achieved perfect scores on the benchmark
datasets. However they are not suitable for deployment on low-resource devices
like mobiles, tablets, etc. due to their massive model size. Therefore, in this
paper, we present a novel light-weight architecture for intent classification
that can run efficiently on a device. We use character features to enrich the
word representation. Our experiments prove that our proposed model outperforms
existing approaches and achieves state-of-the-art results on benchmark
datasets. We also report that our model has tiny memory footprint of ~5 MB and
low inference time of ~2 milliseconds, which proves its efficiency in a
resource-constrained environment.


Abstract:
  In certain pulsar timing experiments, where observations are scheduled
approximately periodically (e.g. daily), timing models with significantly
different frequencies (including but not limited to glitch models with
different frequency increments) return near-equivalent timing residuals. The
average scheduling aperiodicity divided by the phase error due to
time-of-arrival uncertainties is a useful indicator of when the degeneracy is
important. Synthetic data are used to explore the effect of this degeneracy
systematically. It is found that phase-coherent tempo2 or temponest-based
approaches are biased sometimes toward reporting small glitch sizes regardless
of the true glitch size. Local estimates of the spin frequency alleviate this
bias. A hidden Markov model is free from bias towards small glitches and
announces explicitly the existence of multiple glitch solutions but sometimes
fails to recover the correct glitch size. Two glitches in the UTMOST public
data release are re-assessed, one in PSR J1709$-$4429 at MJD 58178 and the
other in PSR J1452$-$6036 at MJD 58600. The estimated fractional frequency jump
in PSR J1709$-$4429 is revised upward from $\Delta f/f = (54.6\pm 1.0) \times
10^{-9}$ to $\Delta f/f = (2432.2 \pm 0.1) \times 10^{-9}$ with the aid of
additional data from the Parkes radio telescope. We find that the available
UTMOST data for PSR J1452$-$6036 are consistent with $\Delta f/f = 270 \times
10^{-9} + N/(fT)$ with $N = 0,1,2$, where $T \approx 1\,\text{sidereal day}$ is
the observation scheduling period. Data from the Parkes radio telescope can be
included, and the $N = 0$ case is selected unambiguously with a combined
dataset.


Abstract:
  Contact tracing has been extensively studied from different perspectives in
recent years. However, there is no clear indication of why this intervention
has proven effective in some epidemics (SARS) and mostly ineffective in some
others (COVID-19). Here, we perform an exhaustive evaluation of random testing
and contact tracing on novel superspreading random networks to try to identify
which epidemics are more containable with such measures. We also explore the
suitability of positive rates as a proxy of the actual infection statuses of
the population. Moreover, we propose novel ideal strategies to explore the
potential limits of both testing and tracing strategies. Our study counsels
caution, both at assuming epidemic containment and at inferring the actual
epidemic progress, with current testing or tracing strategies. However, it also
brings a ray of light for the future, with the promise of the potential of
novel testing strategies that can achieve great effectiveness.


Abstract:
  Although ground robotic autonomy has gained widespread usage in structured
and controlled environments, autonomy in unknown and off-road terrain remains a
difficult problem. Extreme, off-road, and unstructured environments such as
undeveloped wilderness, caves, and rubble pose unique and challenging problems
for autonomous navigation. To tackle these problems we propose an approach for
assessing traversability and planning a safe, feasible, and fast trajectory in
real-time. Our approach, which we name STEP (Stochastic Traversability
Evaluation and Planning), relies on: 1) rapid uncertainty-aware mapping and
traversability evaluation, 2) tail risk assessment using the Conditional
Value-at-Risk (CVaR), and 3) efficient risk and constraint-aware kinodynamic
motion planning using sequential quadratic programming-based (SQP) model
predictive control (MPC). We analyze our method in simulation and validate its
efficacy on wheeled and legged robotic platforms exploring extreme terrains
including an abandoned subway and an underground lava tube.


Abstract:
  We present and characterize the classes of Grothendieck toposes having enough
supercompact objects or enough compact objects. In the process, we examine the
subcategories of supercompact objects and compact objects within such toposes
and classes of geometric morphism which interact well with these objects. We
also present canonical classes of sites generating such toposes.


Abstract:
  For a hereditary graph class $\mathcal{H}$, the $\mathcal{H}$-elimination
distance of a graph $G$ is the minimum number of rounds needed to reduce $G$ to
a member of $\mathcal{H}$ by removing one vertex from each connected component
in each round. The $\mathcal{H}$-treewidth of a graph $G$ is the minimum, taken
over all vertex sets $X$ for which each connected component of $G - X$ belongs
to $\mathcal{H}$, of the treewidth of the graph obtained from $G$ by replacing
the neighborhood of each component of $G-X$ by a clique and then removing $V(G)
\setminus X$. These parameterizations recently attracted interest because they
are simultaneously smaller than the graph-complexity measures treedepth and
treewidth, respectively, and the vertex-deletion distance to $\mathcal{H}$. For
the class $\mathcal{H}$ of bipartite graphs, we present non-uniform
fixed-parameter tractable algorithms for testing whether the
$\mathcal{H}$-elimination distance or $\mathcal{H}$-treewidth of a graph is at
most $k$. Along the way, we also provide such algorithms for all graph classes
$\mathcal{H}$ defined by a finite set of forbidden induced subgraphs.


Abstract:
  We adapt the direct approach to the semiclassical Bergman kernel asymptotics,
developed recently by A. Deleporte, J. Sj\"ostrand, and the first-named author
for real analytic exponential weights, to the smooth case. Similar to that
work, our approach avoids the use of the Kuranishi trick and it allows us to
construct the amplitude of the asymptotic Bergman projection by means of an
asymptotic inversion of an explicit Fourier integral operator.


Abstract:
  Deep learning has become the most powerful machine learning tool in the last
decade. However, how to efficiently train deep neural networks remains to be
thoroughly solved. The widely used minibatch stochastic gradient descent (SGD)
still needs to be accelerated. As a promising tool to better understand the
learning dynamic of minibatch SGD, the information bottleneck (IB) theory
claims that the optimization process consists of an initial fitting phase and
the following compression phase. Based on this principle, we further study
typicality sampling, an efficient data selection method, and propose a new
explanation of how it helps accelerate the training process of the deep
networks. We show that the fitting phase depicted in the IB theory will be
boosted with a high signal-to-noise ratio of gradient approximation if the
typicality sampling is appropriately adopted. Furthermore, this finding also
implies that the prior information of the training set is critical to the
optimization process and the better use of the most important data can help the
information flow through the bottleneck faster. Both theoretical analysis and
experimental results on synthetic and real-world datasets demonstrate our
conclusions.


Abstract:
  We propose a fully asynchronous networked aggregative game (Asy-NAG) where
each player minimizes a cost function that depends on its local action and the
aggregate of all players' actions. In sharp contrast to the existing NAGs, each
player in our Asy-NAG can compute an estimate of the aggregate action at any
wall-clock time by only using (possibly stale) information from nearby players
of a directed network. Such an asynchronous update does not require any
coordination among players. Moreover, we design a novel distributed algorithm
with an aggressive mechanism for each player to adaptively adjust the
optimization stepsize per update. Particularly, the slow players in terms of
updating their estimates smartly increase their stepsizes to catch up with the
fast ones. Then, we develop an augmented system approach to address the
asynchronicity and the information delays between players, and rigorously show
the convergence to a Nash equilibrium of the Asy-NAG via a perturbed coordinate
algorithm which is also of independent interest. Finally, we evaluate the
performance of the distributed algorithm through numerical simulations.


Abstract:
  An implicit and conservative numerical scheme is proposed for the isotropic
quantum Fokker-Planck equation describing the evolution of degenerate electrons
subject to elastic collisions with other electrons and ions. The electron-ion
and electron-electron collision operators are discretized using a discontinuous
Galerkin method, and the electron energy distribution is updated by an implicit
time integration method. The numerical scheme is designed to satisfy all
conservation laws exactly. Numerical tests and comparisons with other modeling
approaches are shown to demonstrate the accuracy and conservation properties of
the proposed method.


Abstract:
  Traditional laws of friction believe that the friction coefficient of two
specific solids takes constant value. However, molecular simulations revealed
that the friction coefficient of nanosized asperity depends strongly on contact
size and asperity radius. Since contacting surfaces are always rough consisting
of asperities varying dramatically in geometric size, a theoretical model is
developed to predict the friction behavior of fractal rough surfaces in this
work. The result of atomic-scale simulations of sphere-on-flat friction is
summarized into a uniform expression. Then, the size dependent feature of
friction at nanoscale is incorporated into the analysis of fractal rough
surfaces. The obtained results display the dependence of friction coefficient
on roughness, material properties and load. It is revealed that the friction
coefficient decreases with increasing contact area or external load. This model
gives a theoretical guideline for the prediction of friction coefficient and
the design of friction pairs.


Abstract:
  Building a benchmark dataset for hate speech detection presents various
challenges. Firstly, because hate speech is relatively rare, random sampling of
tweets to annotate is very inefficient in finding hate speech. To address this,
prior datasets often include only tweets matching known "hate words". However,
restricting data to a pre-defined vocabulary may exclude portions of the
real-world phenomenon we seek to model. A second challenge is that definitions
of hate speech tend to be highly varying and subjective. Annotators having
diverse prior notions of hate speech may not only disagree with one another but
also struggle to conform to specified labeling guidelines. Our key insight is
that the rarity and subjectivity of hate speech are akin to that of relevance
in information retrieval (IR). This connection suggests that well-established
methodologies for creating IR test collections can be usefully applied to
create better benchmark datasets for hate speech. To intelligently and
efficiently select which tweets to annotate, we apply standard IR techniques of
{\em pooling} and {\em active learning}. To improve both consistency and value
of annotations, we apply {\em task decomposition} and {\em annotator rationale}
techniques. We share a new benchmark dataset for hate speech detection on
Twitter that provides broader coverage of hate than prior datasets. We also
show a dramatic drop in accuracy of existing detection models when tested on
these broader forms of hate. Annotator rationales we collect not only justify
labeling decisions but also enable future work opportunities for
dual-supervision and/or explanation generation in modeling. Further details of
our approach can be found in the supplementary materials.


Abstract:
  The laser-driven generation of relativistic electron beams in plasma and
their acceleration to high energies with GV/m-gradients has been successfully
demonstrated. Now, it is time to focus on the application of laser-plasma
accelerated (LPA) beams. The "Accelerator Technology HElmholtz iNfrAstructure"
(ATHENA) of the Helmholtz Association fosters innovative particle accelerators
and high-power laser technology. As part of the ATHENAe pillar several
different applications driven by LPAs are to be developed, such as a compact
FEL, medical imaging and the first realization of LPA-beam injection into a
storage ring. The latter endeavour is conducted in close collaboration between
Deutsches Elektronen-Synchrotron (DESY), Karlsruhe Institute of Technology
(KIT) and Helmholtz Institute Jena (HIJ). In the cSTART project at KIT, a
compact storage ring optimized for short bunches and suitable to accept
LPA-based electron bunches is in preparation. In this conference contribution
we will introduce the 50 MeV LPA-based injector and give an overview about the
project goals. The key parameters of the plasma injector will be presented.
Finally, the current status of the project will be summarized.


Abstract:
  Perturbations are ubiquitous in metabolism. A central tool to understand and
control their influence on metabolic networks is sensitivity analysis, which
investigates how the network responds to external perturbations. We follow here
a structural approach: the analysis is based on the network stoichiometry only
and it does not require any quantitative knowledge of the reaction rates. We
consider perturbations of reaction rates and metabolite concentrations, at
equilibrium, and we investigate the responses in the network. For general
metabolic systems, this paper focuses on the sign of the responses, i.e.
whether a response is positive, negative or whether its sign depends on the
parameters of the system. In particular, we identify and describe the
subnetworks that are the main players in the sign description. These
subnetworks are associated to certain kernel vectors of the stoichiometric
matrix and are thus independent from the chosen kinetics.


Abstract:
  Learning high-level navigation behaviors has important implications: it
enables robots to build compact visual memory for repeating demonstrations and
to build sparse topological maps for planning in novel environments. Existing
approaches only learn discrete, short-horizon behaviors. These standalone
behaviors usually assume a discrete action space with simple robot dynamics,
thus they cannot capture the intricacy and complexity of real-world
trajectories. To this end, we propose Composable Behavior Embedding (CBE), a
continuous behavior representation for long-horizon visual navigation. CBE is
learned in an end-to-end fashion; it effectively captures path geometry and is
robust to unseen obstacles. We show that CBE can be used to performing
memory-efficient path following and topological mapping, saving more than an
order of magnitude of memory than behavior-less approaches.


Abstract:
  Though models with the radiative neutrino mass generation are
phenomenologically attractive, the complicated relationship between the flavour
structure of additional Yukawa matrices and the neutrino mass matrix sometimes
is a barrier to explore the models. We introduce a simple prescription to
analyze the relation in a class of models with the asymmetric Yukawa structure.
We then apply the treatment to the Zee-Babu model as a concrete example of the
class and discuss the phenomenological consequences of the model. The combined
studies among the neutrino physics, the lepton flavour violation, and the
search for the new particles at the collider experiments provide the anatomy of
the Zee-Babu model.


Abstract:
  Let $G$ be a finite simple graph with Laplacian polynomial
$\psi(G,\lambda)=\sum_{k=0}^n(-1)^{n-k}c_k\lambda^k$. In an earlier paper, the
coefficients $c_{n-4}$ and $c_{n-5}$ for tree with respect to some degree-based
graph invariants were computed. The aim of this paper is to continue this work
by giving an exact formula for the coefficients $c_{n-6}$. As a consequence of
this work, the Laplacian coefficients $c_{n-k}$ of a forest $F$, $1\leq k \leq
6$, are computed in terms of the number of closed walks in $F$ and its line
graph.


Abstract:
  A system of linear equations $L$ over $\mathbb{F}_q$ is common if the number
of monochromatic solutions to $L$ in any two-colouring of $\mathbb{F}_q^n$ is
asymptotically at least the expected number of monochromatic solutions in a
random two-colouring of $\mathbb{F}_q^n$. Motivated by existing results for
specific systems (such as Schur triples and arithmetic progressions), as well
as extensive research on common and Sidorenko graphs, the systematic study of
common systems of linear equations was recently initiated by Saad and Wolf.
  Building upon earlier work of Cameron, Cilleruelo and Serra, as well as Saad
and Wolf, common linear equations have recently been fully characterised by
Fox, Pham and Zhao, who asked about common \emph{systems} of equations. In this
paper we move towards a classification of common systems of two or more linear
equations. In particular we prove that any system containing an arithmetic
progression of length four is uncommon, confirming a conjecture of Saad and
Wolf. This follows from a more general result which allows us to deduce the
uncommonness of a general system from certain properties of one- or
two-equation subsystems.


Abstract:
  Let $(\xi_1, \eta_1)$, $(\xi_2, \eta_2),\ldots$ be independent identically
distributed $\mathbb{R}^2$-valued random vectors. We prove a strong law of
large numbers, a functional central limit theorem and a law of the iterated
logarithm for convergent perpetuities $\sum_{k\geq
0}b^{\xi_1+\ldots+\xi_k}\eta_{k+1}$ as $b\to 1-$. Under the standard actuarial
interpretation, these results correspond to the situation when the actuarial
market is close to the customer-friendly scenario of no risk.


Abstract:
  We prove that supports of a wide class of temperate distributions with
uniformly discrete support and spectrum on Euclidean spaces are finite unions
of translations of full-rank lattices. This result is a generalization of the
corresponding theorem for Fourier quasicrystals, and its proof uses the
technique of almost periodic distributions.


Abstract:
  Long-range correlation plays an important role in analyses of pionic
Bose-Einstein correlations (BECs). In many cases, such correlations are
phenomenologically introduced. In this investigation, we propose an analytic
form. By making use of the form, we analyze the OPAL BEC and the L3 BEC at
$Z^0$-pole and the CMS BEC at 0.9 and 7 TeV using our formulas and the
$\tau$-model. The parameters estimated by both approaches are found to be
consistent. Utilizing the Fourier transform in four-dimensional Euclidean
space, a number of pion-pair density distributions are also studied.


Abstract:
  In this paper, we study the convergence analysis for a robust stochastic
structure-preserving Lagrangian numerical scheme in computing effective
diffusivity of time-dependent chaotic flows, which are modeled by stochastic
differential equations (SDEs). Our numerical scheme is based on a splitting
method to solve the corresponding SDEs in which the deterministic subproblem is
discretized using structure-preserving schemes while the random subproblem is
discretized using the Euler-Maruyama scheme. We obtain a sharp and
uniform-in-time convergence analysis for the proposed numerical scheme that
allows us to accurately compute long-time solutions of the SDEs. As such, we
can compute the effective diffusivity for time-dependent flows. Finally, we
present numerical results to demonstrate the accuracy and efficiency of the
proposed method in computing effective diffusivity for the time-dependent
Arnold-Beltrami-Childress (ABC) flow and Kolmogorov flow in three-dimensional
space.


Abstract:
  Recently, there has been rapid and significant progress on image dehazing.
Many deep learning based methods have shown their superb performance in
handling homogeneous dehazing problems. However, we observe that even if a
carefully designed convolutional neural network (CNN) can perform well on
large-scaled dehazing benchmarks, the network usually fails on the
non-homogeneous dehazing datasets introduced by NTIRE challenges. The reasons
are mainly in two folds. Firstly, due to its non-homogeneous nature, the
non-uniformly distributed haze is harder to be removed than the homogeneous
haze. Secondly, the research challenge only provides limited data (there are
only 25 training pairs in NH-Haze 2021 dataset). Thus, learning the mapping
from the domain of hazy images to that of clear ones based on very limited data
is extremely hard. To this end, we propose a simple but effective approach for
non-homogeneous dehazing via ensemble learning. To be specific, we introduce a
two-branch neural network to separately deal with the aforementioned problems
and then map their distinct features by a learnable fusion tail. We show
extensive experimental results to illustrate the effectiveness of our proposed
method.


Abstract:
  We study permutations over the set of $\ell$-grams, that are feasible in the
sense that there is a sequence whose $\ell$-gram frequency has the same ranking
as the permutation. Codes, which are sets of feasible permutations, protect
information stored in DNA molecules using the rank-modulation scheme, and read
using the shotgun sequencing technique. We construct systematic codes with an
efficient encoding algorithm, and show that they are optimal in size. The
length of the DNA sequences that correspond to the codewords is shown to be
polynomial in the code parameters. Non-systematic with larger size are also
constructed.


Abstract:
  A promising channel for producing binary black hole mergers is the
Lidov-Kozai orbital resonance in hierarchical triple systems. While this
mechanism has been studied in isolation, the distribution of such mergers in
time and across star-forming environments is not well characterized. In this
work, we explore Lidov-Kozai-induced black hole mergers in open clusters,
combining semi-analytic and Monte Carlo methods to calculate merger rates and
delay times for eight different population models. We predict a merger rate
density of $\sim$1--10\,Gpc$^{-3}$\,yr$^{-1}$ for the Lidov-Kozai channel in
the local universe, and all models yield delay-time distributions in which a
significant fraction of binary black hole mergers (e.g., $\sim$20\%--50\% in
our baseline model) occur during the open cluster phase. Our findings suggest
that a substantial fraction of mergers from hierarchical triples occur within
star-forming regions in spiral galaxies.


Abstract:
  Automated diagnosis using deep neural networks in chest radiography can help
radiologists detect life-threatening diseases. However, existing methods only
provide predictions without accurate explanations, undermining the
trustworthiness of the diagnostic methods. Here, we present XProtoNet, a
globally and locally interpretable diagnosis framework for chest radiography.
XProtoNet learns representative patterns of each disease from X-ray images,
which are prototypes, and makes a diagnosis on a given X-ray image based on the
patterns. It predicts the area where a sign of the disease is likely to appear
and compares the features in the predicted area with the prototypes. It can
provide a global explanation, the prototype, and a local explanation, how the
prototype contributes to the prediction of a single image. Despite the
constraint for interpretability, XProtoNet achieves state-of-the-art
classification performance on the public NIH chest X-ray dataset.


Abstract:
  The paper deals with dynamics of expanding Lorenz maps, which appear in a
natural way as Poincar\`e maps in geometric models of well known Lorenz
attractor. We study connections between periodic points, completely invariant
sets and renormalizations. We show that in general, renormalization cannot be
fully characterized by a completely invariant set, however there are various
situations when such characterization is possible. This way we provide a better
insight into the structure of renormalizations in Lorenz maps, correcting some
gaps existing in the literature and completing to some extent the description
of possible dynamics in this important field of study.


Abstract:
  We propose a novel approach to few-shot action recognition, finding
temporally-corresponding frame tuples between the query and videos in the
support set. Distinct from previous few-shot works, we construct class
prototypes using the CrossTransformer attention mechanism to observe relevant
sub-sequences of all support videos, rather than using class averages or single
best matches. Video representations are formed from ordered tuples of varying
numbers of frames, which allows sub-sequences of actions at different speeds
and temporal offsets to be compared.
  Our proposed Temporal-Relational CrossTransformers (TRX) achieve
state-of-the-art results on few-shot splits of Kinetics, Something-Something V2
(SSv2), HMDB51 and UCF101. Importantly, our method outperforms prior work on
SSv2 by a wide margin (12%) due to the its ability to model temporal relations.
A detailed ablation showcases the importance of matching to multiple support
set videos and learning higher-order relational CrossTransformers.


Abstract:
  We present results on global very long baseline interferometry (VLBI)
observations at 327 MHz of eighteen compact steep-spectrum (CSS) and GHz-peaked
spectrum (GPS) radio sources from the 3C and the Peacock & Wall catalogues.
About 80 per cent of the sources have a 'double/triple' structure. The radio
emission at 327 MHz is dominated by steep-spectrum extended structures, while
compact regions become predominant at higher frequencies. As a consequence, we
could unambiguously detect the core region only in three sources, likely due to
self-absorption affecting its emission at this low frequency. Despite their low
surface brightness, lobes store the majority of the source energy budget, whose
correct estimate is a key ingredient in tackling the radio source evolution.
Low-frequency VLBI observations able to disentangle the lobe emission from that
of other regions are therefore the best way to infer the energetics of these
objects. Dynamical ages estimated from energy budget arguments provide values
between 2x10^3 and 5x10^4 yr, in agreement with the radiative ages estimated
from the fit of the integrated synchrotron spectrum, further supporting the
youth of these objects. A discrepancy between radiative and dynamical ages is
observed in a few sources where the integrated spectrum is dominated by
hotspots. In this case the radiative age likely represents the time spent by
the particles in these regions, rather than the source age.


Abstract:
  The design and application of an instrumented particle for the lagrangian
characterization of turbulent free surface flows is presented in this study.
This instrumented particle constitutes a local measurement device capable of
measuring both its instantaneous 3D translational acceleration and angular
velocity components, as well as recording them on an embarked removeable memory
card. A lithium ion polymer battery provides the instrumented particle with up
to 8 hours of autonomous operation. Entirely composed of commercial off the
shelf electronic components, it features accelerometer and gyroscope sensors
with a resolution of 16 bits for each individual axis, and maximum data
acquisition rates of 1 and 8 kHz, respectively, as well as several user
programmable dynamic ranges. Its ABS 3D printed body takes the form of a 36 mm
diameter hollow sphere, and has a total mass of (19.6 $\pm$ 0.5) g. Controlled
experiments, carried out to calibrate and validate its performance showed good
agreement when compared to reference techniques. In order to assess the
practicality of the instrumented particle, we apply it to the statistical
characterization of floater dynamics in experiments of surface wave turbulence.
In this feasibility study, we focused our attention on the distribution of
acceleration and angular velocity fluctuations as a function of the forcing
intensity. The IP's motion is also simultaneously registered by a 3D particle
tracking velocimetry (PTV) system, for the purposes of comparison. Beyond the
results particular to this study case, it constitutes a proof of both the
feasibility and potentiality of the IP as a tool for the experimental
characterization of particle dynamics in such flows.


Abstract:
  A realistic communication system model is critical in power system studies
emphasizing the cyber and physical intercoupling. In this paper, we provide
characteristics that could be used in modeling the underlying cyber network for
power grid models. A real utility communication network and a simplified
inter-substation connectivity model are studied, and their statistics could be
used to fulfill the requirements for different modeling resolutions.


Abstract:
  We present a novel large-context end-to-end automatic speech recognition
(E2E-ASR) model and its effective training method based on knowledge
distillation. Common E2E-ASR models have mainly focused on utterance-level
processing in which each utterance is independently transcribed. On the other
hand, large-context E2E-ASR models, which take into account long-range
sequential contexts beyond utterance boundaries, well handle a sequence of
utterances such as discourses and conversations. However, the transformer
architecture, which has recently achieved state-of-the-art ASR performance
among utterance-level ASR systems, has not yet been introduced into the
large-context ASR systems. We can expect that the transformer architecture can
be leveraged for effectively capturing not only input speech contexts but also
long-range sequential contexts beyond utterance boundaries. Therefore, this
paper proposes a hierarchical transformer-based large-context E2E-ASR model
that combines the transformer architecture with hierarchical encoder-decoder
based large-context modeling. In addition, in order to enable the proposed
model to use long-range sequential contexts, we also propose a large-context
knowledge distillation that distills the knowledge from a pre-trained
large-context language model in the training phase. We evaluate the
effectiveness of the proposed model and proposed training method on Japanese
discourse ASR tasks.


Abstract:
  Error-bounded lossy compression is becoming an indispensable technique for
the success of today's scientific projects with vast volumes of data produced
during the simulations or instrument data acquisitions. Not only can it
significantly reduce data size, but it also can control the compression errors
based on user-specified error bounds. Autoencoder (AE) models have been widely
used in image compression, but few AE-based compression approaches support
error-bounding features, which are highly required by scientific applications.
To address this issue, we explore using convolutional autoencoders to improve
error-bounded lossy compression for scientific data, with the following three
key contributions. (1) We provide an in-depth investigation of the
characteristics of various autoencoder models and develop an error-bounded
autoencoder-based framework in terms of the SZ model. (2) We optimize the
compression quality for main stages in our designed AE-based error-bounded
compression framework, fine-tuning the block sizes and latent sizes and also
optimizing the compression efficiency of latent vectors. (3) We evaluate our
proposed solution using five real-world scientific datasets and comparing them
with six other related works. Experiments show that our solution exhibits a
very competitive compression quality from among all the compressors in our
tests. In absolute terms, it can obtain a much better compression quality (100%
~ 800% improvement in compression ratio with the same data distortion) compared
with SZ2.1 and ZFP in cases with a high compression ratio.


Abstract:
  Recent millimeter and infrared observations have shown that gap and ring-like
structures are common in both dust thermal emission and scattered-light of
protoplanetary disks. We investigate the impact of the so-called Thermal Wave
Instability (TWI) on the millimeter and infrared scattered-light images of
disks. We perform 1+1D simulations of the TWI and confirm that the TWI operates
when the disk is optically thick enough for stellar light, i.e.,
small-grain-to-gas mass ratio of $\gtrsim0.0001$. The mid-plane temperature
varies as the waves propagate and hence gap and ring structures can be seen in
both millimeter and infrared emission. The millimeter substructures can be
observed even if the disk is fully optically thick since it is induced by the
temperature variation, while density-induced substructures would disappear in
the optically thick regime. The fractional separation between TWI-induced ring
and gap is $\Delta r/r \sim$ 0.2-0.4 at $\sim$ 10-50 au, which is comparable to
those found by ALMA. Due to the temperature variation, snow lines of volatile
species move radially and multiple snow lines are observed even for a single
species. The wave propagation velocity is as fast as $\sim$ 0.6 ${\rm
au~yr^{-1}}$, which can be potentially detected with a multi-epoch observation
with a time separation of a few years.


Abstract:
  Click-through rate (CTR) prediction is a critical problem in web search,
recommendation systems and online advertisement displaying. Learning good
feature interactions is essential to reflect user's preferences to items. Many
CTR prediction models based on deep learning have been proposed, but
researchers usually only pay attention to whether state-of-the-art performance
is achieved, and ignore whether the entire framework is reasonable. In this
work, we use the discrete choice model in economics to redefine the CTR
prediction problem, and propose a general neural network framework built on
self-attention mechanism. It is found that most existing CTR prediction models
align with our proposed general framework. We also examine the expressive power
and model complexity of our proposed framework, along with potential extensions
to some existing models. And finally we demonstrate and verify our insights
through some experimental results on public datasets.


Abstract:
  Numerical simulation is an essential tool for many applications involving
subsurface flow and transport, yet often suffers from computational challenges
due to the multi-physics nature, highly non-linear governing equations,
inherent parameter uncertainties, and the need for high spatial resolutions to
capture multi-scale heterogeneity. We developed CCSNet, a general-purpose
deep-learning modeling suite that can act as an alternative to conventional
numerical simulators for carbon capture and storage (CCS) problems where CO$_2$
is injected into saline aquifers in 2d-radial systems. CCSNet consists of a
sequence of deep learning models producing all the outputs that a numerical
simulator typically provides, including saturation distributions, pressure
buildup, dry-out, fluid densities, mass balance, solubility trapping, and sweep
efficiency. The results are 10$^3$ to 10$^4$ times faster than conventional
numerical simulators. As an application of CCSNet illustrating the value of its
high computational efficiency, we developed rigorous estimation techniques for
the sweep efficiency and solubility trapping.


Abstract:
  We present systematic and efficient solutions for both observability
enhancement and root-cause diagnosis of post-silicon System-on-Chips (SoCs)
validation with diverse usage scenarios. We model specification of interacting
flows in typical applications for message selection. Our method for message
selection optimizes flow specification coverage and trace buffer utilization.
We define the diagnosis problem as identifying buggy traces as outliers and
bug-free traces as inliers/normal behaviors, for which we use unsupervised
learning algorithms for outlier detection. Instead of direct application of
machine learning algorithms over trace data using the signals as raw features,
we use feature engineering to transform raw features into more sophisticated
features using domain specific operations. The engineered features are highly
relevant to the diagnosis task and are generic to be applied across any
hardware designs. We present debugging and root cause analysis of subtle
post-silicon bugs in industry-scale OpenSPARC T2 SoC. We achieve a trace buffer
utilization of 98.96\% with a flow specification coverage of 94.3\% (average).
Our diagnosis method was able to diagnose up to 66.7\% more bugs and took up to
847$\times$ less diagnosis time as compared to the manual debugging with a
diagnosis precision of 0.769.


Abstract:
  We present an open-source Python package, Orbits from Radial Velocity,
Absolute, and/or Relative Astrometry (orvara), to fit Keplerian orbits to any
combination of radial velocity, relative astrometry, and absolute astrometry
data from the Hipparcos-Gaia Catalog of Accelerations. By combining these three
data types, one can measure precise masses and sometimes orbital parameters
even when the observations cover a small fraction of an orbit. orvara achieves
its computational performance with an eccentric anomaly solver five to ten
times faster than commonly used approaches, low-level memory management to
avoid python overheads, and by analytically marginalizing out parallax,
barycenter proper motion, and the instrument-specific radial velocity zero
points. Through its integration with the Hipparcos and Gaia intermediate
astrometry package htof, orvara can properly account for the epoch astrometry
measurements of Hipparcos and the measurement times and scan angles of
individual Gaia epochs. We configure orvara with modifiable .ini configuration
files tailored to any specific stellar or planetary system. We demonstrate
orvara with a case study application to a recently discovered white dwarf/main
sequence (WD/MS) system, HD 159062. By adding absolute astrometry to literature
RV and relative astrometry data, our comprehensive MCMC analysis improves the
precision of HD 159062B's mass by more than an order of magnitude to
$0.6083^{+0.0083}_{-0.0073}\,M_\odot$. We also derive a low eccentricity and
large semimajor axis, establishing HD 159062AB as a system that did not
experience Roche lobe overflow.


Abstract:
  It has recently been established that cluster-like states -- states that are
in the same symmetry-protected topological phase as the cluster state --
provide a family of resource states that can be utilized for Measurement-Based
Quantum Computation. In this work, we ask whether it is possible to prepare
cluster-like states in finite time without breaking the symmetry protecting the
resource state. Such a symmetry-preserving protocol would benefit from
topological protection to errors in the preparation. We answer this question in
the positive by providing a Hamiltonian in one higher dimension whose
finite-time evolution is a unitary that acts trivially in the bulk, but pumps
the desired cluster state to the boundary. Examples are given for both the 1D
cluster state protected by a global symmetry, and various 2D cluster states
protected by subsystem symmetries. We show that even if unwanted symmetric
perturbations are present in the driving Hamiltonian, projective measurements
in the bulk along with post-selection is sufficient to recover a cluster-like
state. For a resource state of size $N$, failure to prepare the state is
negligible if the size of the perturbations are much smaller than $N^{-1/2}$.


Abstract:
  Detecting anomalies has been a fundamental approach in detecting potentially
fraudulent activities. Tasked with detection of illegal timber trade that
threatens ecosystems and economies and association with other illegal
activities, we formulate our problem as one of anomaly detection. Among other
challenges annotations are unavailable for our large-scale trade data with
heterogeneous features (categorical and continuous), that can assist in
building automated systems to detect fraudulent transactions. Modelling the
task as unsupervised anomaly detection, we propose a novel model Contrastive
Learning based Heterogeneous Anomaly Detector to address shortcomings of prior
models. Our model uses an asymmetric autoencoder that can effectively handle
large arity categorical variables, but avoids assumptions about structure of
data in low-dimensional latent space and is robust to changes to
hyper-parameters. The likelihood of data is approximated through an estimator
network, which is jointly trained with the autoencoder,using negative sampling.
Further the details and intuition for an effective negative sample generation
approach for heterogeneous data are outlined. We provide a qualitative study to
showcase the effectiveness of our model in detecting anomalies in timber trade.


Abstract:
  We often use perturbations to regularize neural models. For neural
encoder-decoders, previous studies applied the scheduled sampling (Bengio et
al., 2015) and adversarial perturbations (Sato et al., 2019) as perturbations
but these methods require considerable computational time. Thus, this study
addresses the question of whether these approaches are efficient enough for
training time. We compare several perturbations in sequence-to-sequence
problems with respect to computational time. Experimental results show that the
simple techniques such as word dropout (Gal and Ghahramani, 2016) and random
replacement of input tokens achieve comparable (or better) scores to the
recently proposed perturbations, even though these simple methods are faster.
Our code is publicly available at
https://github.com/takase/rethink_perturbations.


Abstract:
  A neutron decays into a proton, an electron, and an anti-neutrino through the
beta-decay process. The decay lifetime ($\sim$880 s) is an important parameter
in the weak interaction. For example, the neutron lifetime is a parameter used
to determine the |$V_{\rm ud}$| parameter of the CKM quark mixing matrix. The
lifetime is also one of the input parameters for the Big Bang Nucleosynthesis,
which predicts light element synthesis in the early universe. However,
experimental measurements of the neutron lifetime today are significantly
different (8.4 s or 4.0$\sigma$) depending on the methods. One is a bottle
method measuring surviving neutron in the neutron storage bottle. The other is
a beam method measuring neutron beam flux and neutron decay rate in the
detector. There is a discussion that the discrepancy comes from unconsidered
systematic error or undetectable decay mode, such as dark decay. A new type of
beam experiment is performed at the BL05 MLF J-PARC. This experiment measured
neutron flux and decay rate simultaneously with a time projection chamber using
a pulsed neutron beam. We will present the world situation of neutron lifetime
and the latest results at J-PARC.


Abstract:
  For two-dimensional percolation on a domain with the topology of a disc, we
introduce a nested-path operator (NP) and thus a continuous family of one-point
functions $W_k \equiv \langle \mathcal{R} \cdot k^\ell \rangle $, where $\ell$
is the number of independent nested closed paths surrounding the center, $k$ is
a path fugacity, and $\mathcal{R}$ projects on configurations having a cluster
connecting the center to the boundary. At criticality, we observe a power-law
scaling $W_k \sim L^{X_{\rm NP}}$, with $L$ the linear system size, and we
determine the exponent $X_{\rm NP}$ as a function of $k$. On the basis of our
numerical results, we conjecture an analytical formula, $X_{\rm NP} (k) =
\frac{3}{4}\phi^2 -\frac{5}{48}\phi^2/ (\phi^2-\frac{2}{3})$ where $k = 2
\cos(\pi \phi)$, which reproduces the exact results for $k=0,1$ and agrees with
the high-precision estimate of $X_{\rm NP}$ for other $k$ values. In addition,
we observe that $W_2(L)=1$ for site percolation on the triangular lattice with
any size $L$, and we prove this identity for all self-matching lattices.


Abstract:
  A key functionality of emerging connected autonomous systems such as smart
transportation systems, smart cities, and the industrial Internet-of-Things, is
the ability to process and learn from data collected at different physical
locations. This is increasingly attracting attention under the terms of
distributed learning and federated learning. However, in this setup data
transfer takes place over communication resources that are shared among many
users and tasks or subject to capacity constraints. This paper examines
algorithms for efficiently allocating resources to linear regression tasks by
exploiting the informativeness of the data. The algorithms developed enable
adaptive scheduling of learning tasks with reliable performance guarantees.


Abstract:
  Blazars emit a highly-variable non-thermal spectrum. It is usually assumed
that the same non-thermal electrons are responsible for the IR-optical-UV
emission (via synchrotron) and the gamma-ray emission (via inverse Compton).
Hence, the light curves in the two bands should be correlated. Orphan gamma-ray
flares (i.e., lacking a luminous low-frequency counterpart) challenge our
theoretical understanding of blazars. By means of large-scale two-dimensional
radiative particle-in-cell simulations, we show that orphan gamma-ray flares
may be a self-consistent by-product of particle energization in turbulent
magnetically-dominated pair plasmas. The energized particles produce the
gamma-ray flare by inverse Compton scattering an external radiation field,
while the synchrotron luminosity is heavily suppressed since the particles are
accelerated nearly along the direction of the local magnetic field. The ratio
of inverse Compton to synchrotron luminosity is sensitive to the initial
strength of turbulent fluctuations (a larger degree of turbulent fluctuations
weakens the anisotropy of the energized particles, thus increasing the
synchrotron luminosity). Our results show that the anisotropy of the
non-thermal particle population is key to modeling the blazar emission.


Abstract:
  We present a high fidelity snapshot spectroscopic radio imaging study of a
weak type I solar noise storm which took place during an otherwise
exceptionally quiet time. Using high fidelity images from the Murchison
Widefield Array, we track the observed morphology of the burst source for 70
minutes and identify multiple instances where its integrated flux density and
area are strongly anti-correlated with each other. The type I radio emission is
believed to arise due to electron beams energized during magnetic reconnection
activity. The observed anti-correlation is interpreted as evidence for presence
of MHD sausage wave modes in the magnetic loops and strands along which these
electron beams are propagating. Our observations suggest that the sites of
these small scale reconnections are distributed along the magnetic flux tube.
We hypothesise that small scale reconnections produces electron beams which
quickly get collisionally damped. Hence, the plasma emission produced by them
span only a narrow bandwidth and the features seen even a few MHz apart must
arise from independent electron beams.


Abstract:
  This study investigated how social interaction among robotic agents changes
dynamically depending on the individual belief of action intention. In a set of
simulation studies, we examine dyadic imitative interactions of robots using a
variational recurrent neural network model. The model is based on the free
energy principle such that a pair of interacting robots find themselves in a
loop, attempting to predict and infer each other's actions using active
inference. We examined how regulating the complexity term to minimize free
energy determines the dynamic characteristics of networks and interactions.
When one robot trained with tighter regulation and another trained with looser
regulation interact, the latter tends to lead the interaction by exerting
stronger action intention, while the former tends to follow by adapting to its
observations. The study confirms that the dyadic imitative interaction becomes
successful by achieving a high synchronization rate when a leader and a
follower are determined by developing action intentions with strong belief and
weak belief, respectively.


Abstract:
  We reveal the microscopic origin of electric polarization $\vec{P}$ induced
by noncollinear magnetic order. We show that in Mott insulators, such $\vec{P}$
is given by all possible combinations of position operators $\hat{\vec{r}}_{ij}
= (\vec{r}_{ij}^{\, 0},\vec{\boldsymbol{r}}_{ij}^{\phantom{0}})$ and transfer
integrals $\hat{t}_{ij} = (t_{ij}^{0},\boldsymbol{t}_{ij}^{\phantom{0}})$ in
the bonds, where $\vec{r}_{ij}^{\, 0}$ and $t_{ij}^{0}$ are spin-independent
contributions in the basis of Kramers doublet states, while
$\vec{\boldsymbol{r}}_{ij}^{\phantom{0}}$ and
$\boldsymbol{t}_{ij}^{\phantom{0}}$ stem solely from the spin-orbit
interaction. Among them, the combination $t_{ij}^{0}
\vec{\boldsymbol{r}}_{ij}^{\phantom{0}}$, which couples to the spin current,
remains finite in the centrosymmetric bonds, thus yielding finite $\vec{P}$ in
the case of noncollinear arrangement of spins. The form of the magnetoelectric
coupling, which is controlled by $\vec{\boldsymbol{r}}_{ij}^{\phantom{0}}$,
appears to be rich and is not limited to the phenomenological law $\vec{P} \sim
\boldsymbol{\epsilon}_{ij} \times [\boldsymbol{e}_{i} \times
\boldsymbol{e}_{j}]$ with $\boldsymbol{\epsilon}_{ij}$ being the bond vector
connecting the spins $\boldsymbol{e}_{i}$ and $\boldsymbol{e}_{j}$. Using
density-functional theory, we illustrate how the proposed mechanism work in the
spiral magnets CuCl$_2$, CuBr$_2$, CuO, and $\alpha$-Li$_2$IrO$_3$, providing
consistent explanation to available experimental data.


Abstract:
  Methods for constructing synthetic multidimensional electron hole equilibria
without using particle simulation are investigated. Previous approaches have
various limitations and approximations that make them unsuitable within the
context of expected velocity diffusion near the trapped-passing boundary. An
adjustable model of the distribution function is introduced that avoids
unphysical singularities there, and yet is sufficiently tractable analytically
to enable prescription of the potential spatial profiles. It is shown why
simple models of the charge density as being a function only of potential
cannot give solitary multidimensional electron holes, in contradiction of prior
suppositions. Fully self-consistent axisymmetric electron holes in the
drift-kinetic limit of electron motion (negligible gyro-radius) are constructed
and their properties relevant to observational interpretation and
finite-gyro-radius theory are discussed.


Abstract:
  In this paper we introduce the notions of phi-contractive parent-child
infinite iterated function system (pcIIFS) and orbital phi-contractive infinite
iterated function system (oIIFS) and we prove that the corresponding fractal
operator is weakly Picard. The corresponding notions of shift space, canonical
projection and their properties are also treated.


Abstract:
  Delays are ubiquitous in modern hybrid systems, which exhibit both continuous
and discrete dynamical behaviors. Induced by signal transmission, conversion,
the nature of plants, and so on, delays may appear either in the continuous
evolution of a hybrid system such that the evolution depends not only on the
present state but also on its execution history, or in the discrete switching
between its different control modes. In this paper we come up with a new model
of hybrid systems, called \emph{delay hybrid automata}, to capture the dynamics
of systems with the aforementioned two kinds of delays. Furthermore, based upon
this model we study the robust switching controller synthesis problem such that
the controlled delay system is able to satisfy the specified safety properties
regardless of perturbations. To the end, a novel method is proposed to
synthesize switching controllers based on the computation of differential
invariants for continuous evolution and backward reachable sets of discrete
jumps with delays. Finally, we implement a prototypical tool of our approach
and demonstrate it on some case studies.


Abstract:
  We study the molecular gas content of 24 star-forming galaxies at $z=3-4$,
with a median stellar mass of $10^{9.1}$ M$_{\odot}$, from the MUSE Hubble
Ultra Deep Field (HUDF) Survey. Selected by their Lyman-alpha-emission and
H-band magnitude, the galaxies show an average EW $\approx 20$ angstrom, below
the typical selection threshold for Lyman Alpha Emitters (EW $> 25$ angstrom),
and a rest-frame UV spectrum similar to Lyman Break Galaxies. We use rest-frame
optical spectroscopy from KMOS and MOSFIRE, and the UV features observed with
MUSE, to determine the systemic redshifts, which are offset from Lyman alpha by
346 km s$^{-1}$, with a 100 to 600 km s$^{-1}$ range. Stacking CO(4-3) and
[CI](1-0) (and higher-$J$ CO lines) from the ALMA Spectroscopic Survey of the
HUDF (ASPECS), we determine $3\sigma$ upper limits on the line luminosities of
$4.0\times10^{8}$ K km s$^{-1}$pc$^{2}$ and $5.6\times10^{8}$ K km
s$^{-1}$pc$^{2}$, respectively (for a 300 km s$^{-1}$ linewidth). Stacking the
1.2 mm and 3 mm dust continuum flux densities, we find a $3\sigma$ upper limits
of 9 $\mu$Jy and $1.2$ $\mu$Jy, respectively. The inferred gas fractions, under
the assumption of a 'Galactic' CO-to-H$_{2}$ conversion factor and gas-to-dust
ratio, are in tension with previously determined scaling relations. This
implies a substantially higher $\alpha_{\rm CO} \ge 10$ and $\delta_{\rm GDR}
\ge 1200$, consistent with the sub-solar metallicity estimated for these
galaxies ($12 + \log(O/H) \approx 7.8 \pm 0.2$). The low metallicity of $z \ge
3$ star-forming galaxies may thus make it very challenging to unveil their cold
gas through CO or dust emission, warranting further exploration of alternative
tracers, such as [CII].


Abstract:
  Multi-layer feedforward networks have been used to approximate a wide range
of nonlinear functions. An important and fundamental problem is to understand
the learnability of a network model through its statistical risk, or the
expected prediction error on future data. To the best of our knowledge, the
rate of convergence of neural networks shown by existing works is bounded by at
most the order of $n^{-1/4}$ for a sample size of $n$. In this paper, we show
that a class of variation-constrained neural networks, with arbitrary width,
can achieve near-parametric rate $n^{-1/2+\delta}$ for an arbitrarily small
positive constant $\delta$. It is equivalent to $n^{-1 +2\delta}$ under the
mean squared error. This rate is also observed by numerical experiments. The
result indicates that the neural function space needed for approximating smooth
functions may not be as large as what is often perceived. Our result also
provides insight to the phenomena that deep neural networks do not easily
suffer from overfitting when the number of neurons and learning parameters
rapidly grow with $n$ or even surpass $n$. We also discuss the rate of
convergence regarding other network parameters, including the input dimension,
network layer, and coefficient norm.


Abstract:
  This paper presents the components of a newly developed Malaysian SMEs -
Software Process Improvement model (MSME-SPI) that can assess SMEs soft-ware
development industry in managing and improving their software processes
capability. The MSME-SPI is developed in response to practitioner needs that
were highlighted in an empirical study with the Malaysian SME software
development industry. After the model development, there is a need for
independent feedback to show that the model meets its objectives. Consequently,
the validation phase is performed by involving a group of software process
improvement experts in examining the MSME-SPI model components. Besides, the
effectiveness of the MSME-SPI model is validated using an expert panel. Three
criteria were used to evaluate the effectiveness of the model namely:
usefulness, verifiability, and structure. The results show the model effective
to be used by SMEs with minor modifications. The validation phase contributes
towards a better understanding and use of the MSME-SPI model by the
practitioners in the field.


Abstract:
  Deep learning (DL) models for image-based malware detection have exhibited
their capability in producing high prediction accuracy. But model
interpretability is posing challenges to their widespread application in
security and safety-critical application domains. This paper aims for designing
an Interpretable Ensemble learning approach for image-based Malware Detection
(IEMD). We first propose a Selective Deep Ensemble Learning-based (SDEL)
detector and then design an Ensemble Deep Taylor Decomposition (EDTD) approach,
which can give the pixel-level explanation to SDEL detector outputs.
Furthermore, we develop formulas for calculating fidelity, robustness and
expressiveness on pixel-level heatmaps in order to assess the quality of EDTD
explanation. With EDTD explanation, we develop a novel Interpretable Dropout
approach (IDrop), which establishes IEMD by training SDEL detector. Experiment
results exhibit the better explanation of our EDTD than the previous
explanation methods for image-based malware detection. Besides, experiment
results indicate that IEMD achieves a higher detection accuracy up to 99.87%
while exhibiting interpretability with high quality of prediction results.
Moreover, experiment results indicate that IEMD interpretability increases with
the increasing detection accuracy during the construction of IEMD. This
consistency suggests that IDrop can mitigate the tradeoff between model
interpretability and detection accuracy.


Abstract:
  For robots to navigate and interact more richly with the world around them,
they will likely require a deeper understanding of the world in which they
operate. In robotics and related research fields, the study of understanding is
often referred to as semantics, which dictates what does the world "mean" to a
robot, and is strongly tied to the question of how to represent that meaning.
With humans and robots increasingly operating in the same world, the prospects
of human-robot interaction also bring semantics and ontology of natural
language into the picture. Driven by need, as well as by enablers like
increasing availability of training data and computational resources, semantics
is a rapidly growing research area in robotics. The field has received
significant attention in the research literature to date, but most reviews and
surveys have focused on particular aspects of the topic: the technical research
issues regarding its use in specific robotic topics like mapping or
segmentation, or its relevance to one particular application domain like
autonomous driving. A new treatment is therefore required, and is also timely
because so much relevant research has occurred since many of the key surveys
were published. This survey therefore provides an overarching snapshot of where
semantics in robotics stands today. We establish a taxonomy for semantics
research in or relevant to robotics, split into four broad categories of
activity, in which semantics are extracted, used, or both. Within these broad
categories we survey dozens of major topics including fundamentals from the
computer vision field and key robotics research areas utilizing semantics,
including mapping, navigation and interaction with the world. The survey also
covers key practical considerations, including enablers like increased data
availability and improved computational hardware, and major application areas
where...


Abstract:
  We introduce a differential extension of algebraic K-theory of an algebra
using Karoubi's Chern character. In doing so, we develop a necessary theory of
secondary transgression forms as well as a differential refinement of the
smooth Serre--Swan correspondence. Our construction subsumes the differential
K-theory of a smooth manifold when the algebra is complex-valued smooth
functions. Furthermore, our construction fits into a noncommutative
differential cohomology hexagon diagram.


Abstract:
  While the LCDM framework has been incredibly successful for modern cosmology,
it requires the admission of two mysterious substances as a part of the
paradigm, dark energy and dark matter. Although this framework adequately
explains most of the large-scale properties of the Universe (i.e., existence
and structure of the CMB, the large-scale structure of galaxies, the abundances
of light elements and the accelerating expansion), it has failed to make
significant predictions on smaller scale features such as the kinematics of
galaxies and their formation. In particular, the rotation curves of disk
galaxies (the original observational discovery of dark matter) are better
represented by non-Newtonian models of gravity that challenge our understanding
of motion in the low acceleration realm (much as general relativity provided an
extension of gravity into the high acceleration realm e.g., blackholes). The
tension between current cold dark matter scenarios and proposed new
formulations of gravity in the low energy regime suggests an upcoming paradigm
shift in cosmology. And, if history is a guide, observations will lead the way.


Abstract:
  As artificial intelligence (AI) systems are increasingly deployed, principles
for ethical AI are also proliferating. Certification offers a method to both
incentivize adoption of these principles and substantiate that they have been
implemented in practice. This paper draws from management literature on
certification and reviews current AI certification programs and proposals.
Successful programs rely on both emerging technical methods and specific design
considerations. In order to avoid two common failures of certification, program
designs should ensure that the symbol of the certification is substantially
implemented in practice and that the program achieves its stated goals. The
review indicates that the field currently focuses on self-certification and
third-party certification of systems, individuals, and organizations - to the
exclusion of process management certifications. Additionally, the paper
considers prospects for future AI certification programs. Ongoing changes in AI
technology suggest that AI certification regimes should be designed to
emphasize governance criteria of enduring value, such as ethics training for AI
developers, and to adjust technical criteria as the technology changes.
Overall, certification can play a valuable mix in the portfolio of AI
governance tools.


Abstract:
  We perform the analysis of the focusing nonlinear Schr\"odinger equation on
the half-line with time-dependent boundary conditions along the lines of the
nonlinear method of images with the help of B\"acklund transformations. The
difficulty arising from having such time-dependent boundary conditions at $x=0$
is overcome by changing the viewpoint of the method and fixing the B\"acklund
transformation at infinity as well as relating its value at $x=0$ to a
time-dependent reflection matrix. The interplay between the various aspects of
integrable boundary conditions is reviewed in detail to brush a picture of the
area. We find two possible classes of solutions. One is very similar to the
case of Robin boundary conditions whereby solitons are reflected at the
boundary, as a result of an effective interaction with their images on the
other half-line. The new regime of solutions supports the existence of one
soliton that is not reflected at the boundary but can be either absorbed or
emitted by it. We demonstrate that this is a unique feature of time-dependent
integrable boundary conditions.


Abstract:
  In this paper, we study the problem of exact community recovery in the
symmetric stochastic block model, where a graph of $n$ vertices is randomly
generated by partitioning the vertices into $K \ge 2$ equal-sized communities
and then connecting each pair of vertices with probability that depends on
their community memberships. Although the maximum-likelihood formulation of
this problem is discrete and non-convex, we propose to tackle it directly using
projected power iterations with an initialization that satisfies a partial
recovery condition. Such an initialization can be obtained by a host of
existing methods. We show that in the logarithmic degree regime of the
considered problem, the proposed method can exactly recover the underlying
communities at the information-theoretic limit. Moreover, with a qualified
initialization, it runs in $\mathcal{O}(n\log^2n/\log\log n)$ time, which is
competitive with existing state-of-the-art methods. We also present numerical
results of the proposed method to support and complement our theoretical
development.


Abstract:
  Large optimization problems with hard constraints arise in many settings, yet
classical solvers are often prohibitively slow, motivating the use of deep
networks as cheap "approximate solvers." Unfortunately, naive deep learning
approaches typically cannot enforce the hard constraints of such problems,
leading to infeasible solutions. In this work, we present Deep Constraint
Completion and Correction (DC3), an algorithm to address this challenge.
Specifically, this method enforces feasibility via a differentiable procedure,
which implicitly completes partial solutions to satisfy equality constraints
and unrolls gradient-based corrections to satisfy inequality constraints. We
demonstrate the effectiveness of DC3 in both synthetic optimization tasks and
the real-world setting of AC optimal power flow, where hard constraints encode
the physics of the electrical grid. In both cases, DC3 achieves near-optimal
objective values while preserving feasibility.


Abstract:
  I recount my personal experience interacting with Roman Jackiw in the 1980s,
when we both worked on Chern-Simons theories in three dimensions.


Abstract:
  The Minimum Linear Arrangement problem (MLA) consists of finding a mapping
$\pi$ from vertices of a graph to distinct integers that minimizes
$\sum_{\{u,v\}\in E}|\pi(u) - \pi(v)|$. In that setting, vertices are often
assumed to lie on a horizontal line and edges are drawn as semicircles above
said line. For trees, various algorithms are available to solve the problem in
polynomial time in $n=|V|$. There exist variants of the MLA in which the
arrangements are constrained. Iordanskii, and later Hochberg and Stallmann
(HS), put forward $O(n)$-time algorithms that solve the problem when
arrangements are constrained to be planar (also known as one-page book
embeddings). We also consider linear arrangements of rooted trees that are
constrained to be projective (planar embeddings where the root is not covered
by any edge). Gildea and Temperley (GT) sketched an algorithm for projective
arrangements which they claimed runs in $O(n)$ but did not provide any
justification of its cost. In contrast, Park and Levy claimed that GT's
algorithm runs in $O(n \log d_{max})$ where $d_{max}$ is the maximum degree but
did not provide sufficient detail. Here we correct an error in HS's algorithm
for the planar case, show its relationship with the projective case, and derive
simple algorithms for the projective and planar cases that run without a doubt
in $O(n)$ time.


Abstract:
  It is well known that a quantum circuit on $N$ qubits composed of Clifford
gates with the addition of $k$ non Clifford gates can be simulated on a
classical computer by an algorithm scaling as $\text{poly}(N)\exp(k)$[1]. We
show that, for a quantum circuit to simulate quantum chaotic behavior, it is
both necessary and sufficient that $k=O(N)$. This result implies the
impossibility of simulating quantum chaos on a classical computer.


Abstract:
  We present an algorithm for computing $\epsilon$-coresets for $(k,
\ell)$-median clustering of polygonal curves in $\mathbb{R}^d$ under the
Fr\'echet distance. This type of clustering is an adaption of Euclidean
$k$-median clustering: we are given a set of $n$ polygonal curves in
$\mathbb{R}^d$, each of complexity (number of vertices) at most $m$, and want
to compute $k$ median curves such that the sum of distances from the given
curves to their closest median curve is minimal. Additionally, we restrict the
complexity of the median curves to be at most $\ell$ each, to suppress
overfitting, a problem specific for sequential data. Our algorithm has running
time linear in $n$, sub-quartic in $m$ and quadratic in $\epsilon^{-1}$. With
high probability it returns $\epsilon$-coresets of size quadratic in
$\epsilon^{-1}$ and logarithmic in $n$ and $m$. We achieve this result by
applying the improved $\epsilon$-coreset framework by Langberg and Feldman to a
generalized $k$-median problem over an arbitrary metric space. Later we combine
this result with the recent result by Driemel et al. on the VC dimension of
metric balls under the Fr\'echet distance. Furthermore, our framework yields
$\epsilon$-coresets for any generalized $k$-median problem where the range
space induced by the open metric balls of the underlying space has bounded VC
dimension, which is of independent interest. Finally, we show that our
$\epsilon$-coresets can be used to improve the running time of an existing
approximation algorithm for $(1,\ell)$-median clustering.


Abstract:
  In this paper, we enable automated property verification of deliberative
components in robot control architectures. We focus on formalizing the
execution context of Behavior Trees (BTs) to provide a scalable, yet formally
grounded, methodology to enable runtime verification and prevent unexpected
robot behaviors. To this end, we consider a message-passing model that
accommodates both synchronous and asynchronous composition of parallel
components, in which BTs and other components execute and interact according to
the communication patterns commonly adopted in robotic software architectures.
We introduce a formal property specification language to encode requirements
and build runtime monitors. We performed a set of experiments, both on
simulations and on the real robot, demonstrating the feasibility of our
approach in a realistic application and its integration in a typical robot
software architecture. We also provide an OS-level virtualization environment
to reproduce the experiments in the simulated scenario.


Abstract:
  Aided by recent advances in Deep Learning, Image Caption Generation has seen
tremendous progress over the last few years. Most methods use transfer learning
to extract visual information, in the form of image features, with the help of
pre-trained Convolutional Neural Network models followed by transformation of
the visual information using a Caption Generator module to generate the output
sentences. Different methods have used different Convolutional Neural Network
Architectures and, to the best of our knowledge, there is no systematic study
which compares the relative efficacy of different Convolutional Neural Network
architectures for extracting the visual information. In this work, we have
evaluated 17 different Convolutional Neural Networks on two popular Image
Caption Generation frameworks: the first based on Neural Image Caption (NIC)
generation model and the second based on Soft-Attention framework. We observe
that model complexity of Convolutional Neural Network, as measured by number of
parameters, and the accuracy of the model on Object Recognition task does not
necessarily co-relate with its efficacy on feature extraction for Image Caption
Generation task.


Abstract:
  We present a computational study of the behaviour of a lipid-coated SonoVue
microbubble with initial radius $1 \, \mu \text{m} \leq R_0 \leq 2 \, \mu
\text{m}$, excited at frequencies (200-1500 kHz) significantly below the linear
resonance frequency and pressure amplitudes of up to 1500 kPa, an excitation
regime used in many focused ultrasound applications. The bubble dynamics are
simulated using the Rayleigh-Plesset equation and the Gilmore equation, in
conjunction with the Marmottant model for the lipid monolayer coating. Also, a
new continuously differentiable variant of the Marmottant model is introduced.
Below the onset of inertial cavitation, a linear regime is identified in which
the maximum pressure at the bubble wall is linearly proportional to the
excitation pressure amplitude and, likewise, the mechanical index. This linear
regime is bounded by the Blake pressure and, in line with recent in vitro
experiments, the onset of inertial cavitation is found to occur approximately
at an excitation pressure amplitude of 130-190 kPa, dependent on the initial
bubble size. In the nonlinear regime the maximum pressure at the bubble wall is
found to be readily predicted by the maximum bubble radius and both the
Rayleigh-Plesset and Gilmore equations are shown to predict the onset of sub-
and ultraharmonic frequencies of the acoustic emissions compared to in vitro
experiments. Neither the surface dilatational viscosity of the lipid monolayer
nor the compressibility of the liquid have a discernible influence on the
studied quantities, yet accounting for the lipid coating is critical for the
accurate prediction of the bubble behaviour. The Gilmore equation is shown to
be valid for the considered bubbles and excitation regime, and the
Rayleigh-Plesset equation also provides accurate qualitative predictions, even
though it is outside its range of validity for many of the considered cases.


Abstract:
  We consider the problem of private distributed matrix multiplication under
limited resources. Coded computation has been shown to be an effective solution
in distributed matrix multiplication, both providing privacy against the
workers and boosting the computation speed by efficiently mitigating
stragglers. In this work, we propose the use of recently-introduced bivariate
polynomial codes to further speed up private distributed matrix multiplication
by exploiting the partial work done by the stragglers rather than completely
ignoring them. We show that the proposed approach reduces the average
computation time of private distributed matrix multiplication compared to its
competitors in the literature while improving the upload communication cost and
the workers' storage efficiency.


Abstract:
  In this paper, a novel three-dimensional (3D) non-stationary geometry-based
stochastic model (GBSM) for the fifth generation (5G) and beyond 5G (B5G)
systems is proposed. The proposed B5G channel model (B5GCM) is designed to
capture various channel characteristics in (B)5G systems such as
space-time-frequency (STF) non-stationarity, spherical wavefront (SWF), high
delay resolution, time-variant velocities and directions of motion of the
transmitter, receiver, and scatterers, spatial consistency, etc. By combining
different channel properties into a general channel model framework, the
proposed B5GCM is able to be applied to multiple frequency bands and multiple
scenarios, including massive multiple-input multiple-output (MIMO),
vehicle-to-vehicle (V2V), high-speed train (HST), and millimeter wave-terahertz
(mmWave-THz) communication scenarios. Key statistics of the proposed B5GCM are
obtained and compared with those of standard 5G channel models and
corresponding measurement data, showing the generalization and usefulness of
the proposed model.


Abstract:
  Logit dynamics is a form of randomized game dynamics where players have a
bias towards strategic deviations that give a higher improvement in cost. It is
used extensively in practice. In congestion (or potential) games, the dynamics
converges to the so-called Gibbs distribution over the set of all strategy
profiles, when interpreted as a Markov chain. In general, logit dynamics might
converge slowly to the Gibbs distribution, but beyond that, not much is known
about their algorithmic aspects, nor that of the Gibbs distribution. In this
work, we are interested in the following two questions for congestion games: i)
Is there an efficient algorithm for sampling from the Gibbs distribution? ii)
If yes, do there also exist natural randomized dynamics that converges quickly
to the Gibbs distribution?
  We first study these questions in extension parallel congestion games, a
well-studied special case of symmetric network congestion games. As our main
result, we show that there is a simple variation on the logit dynamics (in
which we in addition are allowed to randomly interchange the strategies of two
players) that converges quickly to the Gibbs distribution in such games. This
answers both questions above affirmatively. We also address the first question
for the class of so-called capacitated $k$-uniform congestion games.
  To prove our results, we rely on the recent breakthrough work of Anari, Liu,
Oveis-Gharan and Vinzant (2019) concerning the approximate sampling of the base
of a matroid according to strongly log-concave probability distribution.


Abstract:
  The ability to recognize the position and order of the floor-level lines that
divide adjacent building floors can benefit many applications, for example,
urban augmented reality (AR). This work tackles the problem of locating
floor-level lines in street-view images, using a supervised deep learning
approach. Unfortunately, very little data is available for training such a
network $-$ current street-view datasets contain either semantic annotations
that lack geometric attributes, or rectified facades without perspective
priors. To address this issue, we first compile a new dataset and develop a new
data augmentation scheme to synthesize training samples by harassing (i) the
rich semantics of existing rectified facades and (ii) perspective priors of
buildings in diverse street views. Next, we design FloorLevel-Net, a multi-task
learning network that associates explicit features of building facades and
implicit floor-level lines, along with a height-attention mechanism to help
enforce a vertical ordering of floor-level lines. The generated segmentations
are then passed to a second-stage geometry post-processing to exploit
self-constrained geometric priors for plausible and consistent reconstruction
of floor-level lines. Quantitative and qualitative evaluations conducted on
assorted facades in existing datasets and street views from Google demonstrate
the effectiveness of our approach. Also, we present context-aware image overlay
results and show the potentials of our approach in enriching AR-related
applications.


Abstract:
  Natural language processing (NLP) has been applied to various fields
including text classification and sentiment analysis. In the shared task of
sentiment analysis of code-mixed tweets, which is a part of the SemEval-2020
competition~\cite{patwa2020sentimix}, we preprocess datasets by replacing emoji
and deleting uncommon characters and so on, and then fine-tune the
Bidirectional Encoder Representation from Transformers(BERT) to perform the
best. After exhausting top3 submissions, Our team MeisterMorxrc achieves an
averaged F1 score of 0.730 in this task, and and our codalab username is
MeisterMorxrc.


Abstract:
  In this paper, we illustrate how to fine-tune the entire Retrieval Augment
Generation (RAG) architecture in an end-to-end manner. We highlighted the main
engineering challenges that needed to be addressed to achieve this objective.
We also compare how end-to-end RAG architecture outperforms the original RAG
architecture for the task of question answering. We have open-sourced our
implementation in the HuggingFace Transformers library.


Abstract:
  We study solutions of the Bethe ansatz equations associated to the
orthosymplectic Lie superalgebras $\mathfrak{osp}_{2m+1|2n}$ and
$\mathfrak{osp}_{2m|2n}$. Given a solution, we define a reproduction procedure
and use it to construct a family of new solutions which we call a population.
To each population we associate a symmetric rational pseudo-differential
operator $\mathcal R$. Under some technical assumptions, we show that the
superkernel $W$ of $\mathcal R$ is a self-dual superspace of rational
functions, and the population is in a canonical bijection with the variety of
isotropic full superflags in $W$ and with the set of symmetric complete
factorizations of $\mathcal R$. In particular, our results apply to the case of
even Lie algebras of type D${}_m$ corresponding to
$\mathfrak{osp}_{2m|0}=\mathfrak{so}_{2m}$.


Abstract:
  Astounding results from Transformer models on natural language tasks have
intrigued the vision community to study their application to computer vision
problems. Among their salient benefits, Transformers enable modeling long
dependencies between input sequence elements and support parallel processing of
sequence as compared to recurrent networks e.g., Long short-term memory (LSTM).
Different from convolutional networks, Transformers require minimal inductive
biases for their design and are naturally suited as set-functions. Furthermore,
the straightforward design of Transformers allows processing multiple
modalities (e.g., images, videos, text and speech) using similar processing
blocks and demonstrates excellent scalability to very large capacity networks
and huge datasets. These strengths have led to exciting progress on a number of
vision tasks using Transformer networks. This survey aims to provide a
comprehensive overview of the Transformer models in the computer vision
discipline. We start with an introduction to fundamental concepts behind the
success of Transformers i.e., self-attention, large-scale pre-training, and
bidirectional encoding. We then cover extensive applications of transformers in
vision including popular recognition tasks (e.g., image classification, object
detection, action recognition, and segmentation), generative modeling,
multi-modal tasks (e.g., visual-question answering, visual reasoning, and
visual grounding), video processing (e.g., activity recognition, video
forecasting), low-level vision (e.g., image super-resolution, image
enhancement, and colorization) and 3D analysis (e.g., point cloud
classification and segmentation). We compare the respective advantages and
limitations of popular techniques both in terms of architectural design and
their experimental value. Finally, we provide an analysis on open research
directions and possible future works.


Abstract:
  Volcanic ash clouds often become multilayered and thin with distance from the
vent. We explore one mechanism for development of this layered structure. We
review data on the characteristics of turbulence layering in the free
atmosphere, as well as examples of observations of layered clouds both
near-vent and distally. We then explore and contrast the output of volcanic ash
transport and dispersal models with models that explicitly use the observed
layered structure of atmospheric turbulence. The results suggest that the
alternation of turbulent and quiescent atmospheric layers provides one
mechanism for development of multilayered ash clouds by modulating the manner
in which settling occurs.


Abstract:
  Differences in gait patterns of children with Duchenne muscular dystrophy
(DMD) and typically developing (TD) peers are visible to the eye, but
quantification of those differences outside of the gait laboratory has been
elusive. We measured vertical, mediolateral, and anteroposterior acceleration
using a waist-worn iPhone accelerometer during ambulation across a typical
range of velocities. Six TD and six DMD children from 3-15 years of age
underwent seven walking/running tasks, including five 25m walk/run tests at a
slow walk to running speeds, a 6-minute walk test (6MWT), and a
100-meter-run/walk (100MRW). We extracted temporospatial clinical gait features
(CFs) and applied multiple Artificial Intelligence (AI) tools to differentiate
between DMD and TD control children using extracted features and raw data.
Extracted CFs showed reduced step length and a greater mediolateral component
of total power (TP) consistent with shorter strides and Trendelenberg-like gait
commonly observed in DMD. AI methods using CFs and raw data varied
ineffectiveness at differentiating between DMD and TD controls at different
speeds, with an accuracy of some methods exceeding 91%. We demonstrate that by
using AI tools with accelerometer data from a consumer-level smartphone, we can
identify DMD gait disturbance in toddlers to early teens.