Abstract:
  We consider the Schr\"odinger equation on Riemannian symmetric spaces of
noncompact type. Previous studies in rank one included sharp-in-time pointwise
estimates for the Schr\"odinger kernel, dispersive properties, Strichartz
inequalities for a large family of admissible pairs, and global well-posedness
and scattering, both for small initial data. In this paper we establish
analogous results in the higher rank case. The kernel estimates, which is our
main result, are obtained by combining a subordination formula, an improved
Hadamard parametrix for the wave equation, and a barycentric decomposition
initially developed for the wave equation, which allows us to overcome a
well-known problem, namely the fact that the Plancherel density is not always a
differential symbol.


Abstract:
  Deep learning applications are drastically progressing in seismic processing
and interpretation tasks. However, the majority of approaches subsample data
volumes and restrict model sizes to minimise computational requirements.
Subsampling the data risks losing vital spatio-temporal information which could
aid training whilst restricting model sizes can impact model performance, or in
some extreme cases, renders more complicated tasks such as segmentation
impossible. This paper illustrates how to tackle the two main issues of
training of large neural networks: memory limitations and impracticably large
training times. Typically, training data is preloaded into memory prior to
training, a particular challenge for seismic applications where data is
typically four times larger than that used for standard image processing tasks
(float32 vs. uint8). Using a microseismic use case, we illustrate how over
750GB of data can be used to train a model by using a data generator approach
which only stores in memory the data required for that training batch.
Furthermore, efficient training over large models is illustrated through the
training of a 7-layer UNet with input data dimensions of 4096X4096. Through a
batch-splitting distributed training approach, training times are reduced by a
factor of four. The combination of data generators and distributed training
removes any necessity of data 1 subsampling or restriction of neural network
sizes, offering the opportunity of utilisation of larger networks,
higher-resolution input data or moving from 2D to 3D problem spaces.


Abstract:
  New ultra-hard rhombohedral B2N2 and BC2N - or hexagonal B6N6 and B3C6N3 -
are derived from 3R graphite based on crystal chemistry rationale schematizing
a mechanism for 2D => 3D transformation. Full unconstrained geometry
optimizations leading to ground state energy structures and energy derived
quantities as energy-volume equation of states (EOS) were based on computations
within the density functional theory (DFT) with generalized gradient
approximation (GGA) for exchange-correlation (XC) effects. The new binary and
ternary phases are characterized by tetrahedral stacking alike diamond,
visualized with charge density representations, and illustrating ion
characters. Atom averaged total energies are similar between cubic BN and
rh-B2N2 on one hand, and larger stabilization of rhombohedral BC2N versus cubic
and orthorhombic forms (in literature assessed from favored C-C and B-N
bonding), on the other hand. The electronic band structures are characteristic
of insulators with Egap ~ 5 eV. Both phases are characterized by large bulk and
shear moduli and very high hardness values i.e. HV(rh-B2N2) = 74 GPa and
HV(rh-BC2N) = 87 GPa.


Abstract:
  We consider a multi-view learning problem known as group independent
component analysis (group ICA), where the goal is to recover shared independent
sources from many views. The statistical modeling of this problem requires to
take noise into account. When the model includes additive noise on the
observations, the likelihood is intractable. By contrast, we propose Adaptive
multiView ICA (AVICA), a noisy ICA model where each view is a linear mixture of
shared independent sources with additive noise on the sources. In this setting,
the likelihood has a tractable expression, which enables either direct
optimization of the log-likelihood using a quasi-Newton method, or generalized
EM. Importantly, we consider that the noise levels are also parameters that are
learned from the data. This enables sources estimation with a closed-form
Minimum Mean Squared Error (MMSE) estimator which weights each view according
to its relative noise level. On synthetic data, AVICA yields better sources
estimates than other group ICA methods thanks to its explicit MMSE estimator.
On real magnetoencephalograpy (MEG) data, we provide evidence that the
decomposition is less sensitive to sampling noise and that the noise variance
estimates are biologically plausible. Lastly, on functional magnetic resonance
imaging (fMRI) data, AVICA exhibits best performance in transferring
information across views.


Abstract:
  The temperature in most parts of a protoplanetary disk is determined by
irradiation from the central star. Numerical experiments of Watanabe \& Lin
(2008) suggested that such disks, also called `passive disks', suffer from a
thermal instability. Here, we use analytical and numerical tools to elucidate
the nature of this instability. We find that it is related to the flaring of
the optical surface, the layer at which starlight is intercepted by the disk.
Whenever a disk annulus is perturbed thermally and acquires a larger scale
height, disk flaring becomes steeper in the inner part, and flatter in the
outer part. Starlight now shines more overhead for the inner part and so can
penetrate into deeper layers; conversely, it is absorbed more shallowly in the
outer part. These geometric changes allow the annulus to intercept more
starlight, and the perturbation grows. We call this the irradiation
instability. It requires only ingredients that are known to exist in realistic
disks, and operates best in parts that are both optically thick and
geometrically thin (inside 30AU, but can extend to further reaches when, e.g.,
dust settling is considered). An unstable disk develops travelling thermal
waves that reach order-unity in amplitude. In thermal radiation, such a disk
should appear as a series of bright rings interleaved with dark shadowed gaps,
while in scattered light it resembles a moving staircase. Depending on the gas
and dust responses, this instability could lead to a wide range of
consequences, such as {\w ALMA rings and gaps,} dust traps, vertical
circulation, vortices and turbulence.


Abstract:
  Much recent research is devoted to exploring tradeoffs between computational
accuracy and energy efficiency at different levels of the system stack.
Approximation at the floating point unit (FPU) allows saving energy by simply
reducing the number of computed floating point bits in return for accuracy
loss. Although, finding the most energy efficient approximation for various
applications with minimal effort is the main challenge. To address this issue,
we propose NEAT: a pin tool that helps users automatically explore the
accuracy-energy tradeoff space induced by various floating point
implementations. NEAT helps programmers explore the effects of simultaneously
using multiple floating point implementations to achieve the lowest energy
consumption for an accuracy constraint or vice versa. NEAT accepts one or more
user-defined floating point implementations and programmable placement rules
for where/when to apply them. NEAT then automatically replaces floating point
operations with different implementations based on the user-specified rules
during the runtime and explores the resulting tradeoff space to find the best
use of approximate floating point implementations for the precision tuning
throughout the program. We evaluate NEAT by enforcing combinations of 24/53
different floating point implementations with three sets of placement rules on
a wide range of benchmarks. We find that heuristic precision tuning at the
function level provides up to 22% and 48% energy savings at 1% and 10% accuracy
loss comparing to applying a single implementation for the whole application.
Also, NEAT is applicable to neural networks where it finds the optimal
precision level for each layer considering an accuracy target for the model.


Abstract:
  This paper deals with the time differential dual-phase-lag heat transfer
models aiming, at first, to identify the eventually restrictions that make them
thermodynamically consistent. At a first glance it can be observed that the
capability of a time differential dual-phase-lag model of heat conduction to
describe real phenomena depends on the properties of the differential operators
involved in the related constitutive equation. In fact, the constitutive
equation is viewed as an ordinary differential equation in terms of the heat
flux components (or in terms of the temperature gradient) and it results that,
for approximation orders greater than or equal to five, the corresponding
characteristic equation has at least a complex root having a positive real
part. That leads to a heat flux component (or temperature gradient) that grows
to infinity when the time tends to infinity and so there occur some
instabilities. Instead, when the approximation orders are lower than or equal
to four, this is not the case and there is the need to study the compatibility
with the Second Law of Thermodynamics. To this aim the related constitutive
equation is reformulated within the system of the fading memory theory, and
thus the heat flux vector is written in terms of the history of the temperature
gradient and on this basis the compatibility of the model with the
thermodynamical principles is analyzed.


Abstract:
  Black hole low mass X-ray binaries in their hard spectral state are found to
display two different correlations between the radio emission from the compact
jets and the X-ray emission from the inner accretion flow. Here, we present a
large data set of quasi-simultaneous radio and X-ray observations of the
recently discovered accreting black hole MAXI J1348-630 during its 2019/2020
outburst. Our results span almost six orders of magnitude in X-ray luminosity,
allowing us to probe the accretion-ejection coupling from the brightest to the
faintest phases of the outburst. We find that MAXI J1348-630 belongs to the
growing population of outliers at the highest observed luminosities.
Interestingly, MAXI J1348-630 deviates from the outlier track at $L_{\rm X}
\lesssim 7 \times 10^{35} (D / 2.2 \ {\rm kpc})^2$ erg s$^{-1}$ and ultimately
rejoins the standard track at $L_{\rm X} \simeq 10^{33} (D / 2.2 \ {\rm
kpc})^2$ erg s$^{-1}$, displaying a hybrid radio/X-ray correlation, observed
only in a handful of sources. However, for MAXI J1348-630 these transitions
happen at luminosities much lower than what observed for similar sources (at
least an order of magnitude). We discuss the behaviour of MAXI J1348-630 in
light of the currently proposed scenarios and we highlight the importance of
future deep monitorings of hybrid correlation sources, especially close to the
transitions and in the low luminosity regime.


Abstract:
  We present a geometric construction of a lattice that emulates the action of
a gauge field on a fermion. The construction consists of a square lattice made
of polymeric sites, where all clustered atoms are identical and represented by
potential wells or resonators supporting one bound state. The emulation covers
both abelian and non-abelian gauge fields. In the former case, Hofstadter's
butterfly is reproduced by means of a chain made of rotating dimers, subject to
periodic boundary conditions parallel to the chain. A rigorous map between this
model and Harper's Hamiltonian is derived. In the non-abelian case, band mixing
and wave confinement are obtained by interband coupling using SU(2) as an
internal group, \ie the effects are due to non-commutability of field
components. A colored model with SU(3) made of trimers is also studied, finding
thereby the appearance of flat bands in special configurations. This work
constitutes the first all-geometric emulation of the Peierls substitution, and
is valid for many types of waves.


Abstract:
  Distributed cloud networking builds on network functions virtualization (NFV)
and software defined networking (SDN) to enable the deployment of network
services in the form of elastic virtual network functions (VNFs) instantiated
over general purpose servers at distributed cloud locations. We address the
design of fast approximation algorithms for the NFV service distribution
problem (NSDP), whose goal is to determine the placement of VNFs, the routing
of service flows, and the associated allocation of cloud and network resources
that satisfy client demands with minimum cost. We show that in the case of
load-proportional costs, the resulting fractional NSDP can be formulated as a
multi-commodity-chain flow problem on a cloud augmented graph, and design a
queue-length based algorithm, named QNSD, that provides an O(\epsilon)
approximation in time O(1/\epsilon). We then address the case in which resource
costs are a function of the integer number of allocated resources and design a
variation of QNSD that effectively pushes for flow consolidation into a limited
number of active resources to minimize overall cloud network cost.


Abstract:
  We establish a Galois connection between sub-monads of an augmented monad and
sub-functors of the forgetful functor from its Eilenberg-Moore category. This
connection is given in terms of invariants and stabilizers defined through
universal properties. An explicit procedure for the computation of invariants
is given assuming the existence of suitable right adjoints. Additionally, in
the context of monoidal closed categories, a characterization of stabilizers is
made in terms of Tannakian reconstruction.


Abstract:
  This paper is concerned with the inverse elastic scattering problem for a
random potential in three dimensions. Interpreted as a distribution, the
potential is assumed to be a microlocally isotropic Gaussian random field whose
covariance operator is a classical pseudo-differential operator. Given the
potential, the direct scattering problem is shown to be well-posed in the
distribution sense by studying the equivalent Lippmann--Schwinger integral
equation. For the inverse scattering problem, we demonstrate that the
microlocal strength of the random potential can be uniquely determined with
probability one by a single realization of the high frequency limit of the
averaged compressional or shear backscattered far-field pattern of the
scattered wave. The analysis employs the integral operator theory, the Born
approximation in the high frequency regime, the microlocal analysis for the
Fourier integral operators, and the ergodicity of the wave field.


Abstract:
  Stateflow models are complex software models, often used as part of
safety-critical software solutions designed with Matlab Simulink. They
incorporate design principles that are typically very hard to verify formally.
In particular, the standard exhaustive formal verification techniques are
unlikely to scale well for the complex designs that are developed in industry.
Furthermore, the Stateflow language lacks a formal semantics, which
additionally hinders the formal analysis.
  To address these challenges, we lay here the foundations of a scalable
technique for provably correct formal analysis of Stateflow models, with
respect to invariant properties, based on bounded model checking (BMC) over
symbolic executions. The crux of our technique is: i) a representation of the
state space of Stateflow models as a symbolic transition system (STS) over the
symbolic configurations of the model, as the basis for BMC, and ii) application
of incremental BMC, to generate verification results after each unrolling of
the next-state relation of the transition system. To this end, we develop a
symbolic structural operational semantics (SSOS) for Stateflow, starting from
an existing structural operational semantics (SOS), and show the preservation
of invariant properties between the two. Next, we define bounded invariant
checking for STS over symbolic configurations as a satisfiability problem. We
develop an automated procedure for generating the initial and next-state
predicates of the STS, and propose an encoding scheme of the bounded invariant
checking problem as a set of constraints, ready for automated analysis with
standard, off-the-shelf satisfiability solvers. Finally, we present preliminary
performance results by applying our tool on an illustrative example.


Abstract:
  In this paper, we address the space-time video super-resolution, which aims
at generating a high-resolution (HR) slow-motion video from a low-resolution
(LR) and low frame rate (LFR) video sequence. A na\"ive method is to decompose
it into two sub-tasks: video frame interpolation (VFI) and video
super-resolution (VSR). Nevertheless, temporal interpolation and spatial
upscaling are intra-related in this problem. Two-stage approaches cannot fully
make use of this natural property. Besides, state-of-the-art VFI or VSR deep
networks usually have a large frame reconstruction module in order to obtain
high-quality photo-realistic video frames, which makes the two-stage approaches
have large models and thus be relatively time-consuming. To overcome the
issues, we present a one-stage space-time video super-resolution framework,
which can directly reconstruct an HR slow-motion video sequence from an input
LR and LFR video. Instead of reconstructing missing LR intermediate frames as
VFI models do, we temporally interpolate LR frame features of the missing LR
frames capturing local temporal contexts by a feature temporal interpolation
module. Extensive experiments on widely used benchmarks demonstrate that the
proposed framework not only achieves better qualitative and quantitative
performance on both clean and noisy LR frames but also is several times faster
than recent state-of-the-art two-stage networks. The source code is released in
https://github.com/Mukosame/Zooming-Slow-Mo-CVPR-2020 .


Abstract:
  Understanding the biomechanics of the heart in health and disease plays an
important role in the diagnosis and treatment of heart failure. The use of
computational biomechanical models for therapy assessment is paving the way for
personalized treatment, and relies on accurate constitutive equations mapping
strain to stress. Current state-of-the art constitutive equations account for
the nonlinear anisotropic stress-strain response of cardiac muscle using
hyperelasticity theory. While providing a solid foundation for understanding
the biomechanics of heart tissue, most current laws neglect viscoelastic
phenomena observed experimentally. Utilizing experimental data from human
myocardium and knowledge of the hierarchical structure of heart muscle, we
present a fractional nonlinear anisotropic viscoelastic constitutive model. The
model is shown to replicate biaxial stretch, triaxial cyclic shear and triaxial
stress relaxation experiments (mean error ~7.65%), showing improvements
compared to its hyperelastic (mean error ~25%) counterparts. Model sensitivity,
fidelity and parameter uniqueness are demonstrated. The model is also compared
to rate-dependent biaxial stretch as well as different modes of biaxial
stretch, illustrating extensibility of the model to a range of loading
phenomena.


Abstract:
  Robotic motion generation methods using machine learning have been studied in
recent years. Bilateral control-based imitation learning can imitate human
motions using force information. By means of this method, variable speed motion
generation that considers physical phenomena such as the inertial force and
friction can be achieved. However, the previous study only focused on a simple
reciprocating motion. To learn the complex relationship between the force and
speed more accurately, it is necessary to learn multiple actions using many
joints. In this paper, we propose a variable speed motion generation method for
multiple motions. We considered four types of neural network models for the
motion generation and determined the best model for multiple motions at
variable speeds. Subsequently, we used the best model to evaluate the
reproducibility of the task completion time for the input completion time
command. The results revealed that the proposed method could change the task
completion time according to the specified completion time command in multiple
motions.


Abstract:
  Higher-order singular value decomposition (HOSVD) is one of the most
efficient tensor decomposition techniques. It has the salient ability to
represent high_dimensional data and extract features. In more recent years, the
quaternion has proven to be a very suitable tool for color pixel representation
as it can well preserve cross-channel correlation of color channels. Motivated
by the advantages of the HOSVD and the quaternion tool, in this paper, we
generalize the HOSVD to the quaternion domain and define quaternion-based HOSVD
(QHOSVD). Due to the non-commutability of quaternion multiplication, QHOSVD is
not a trivial extension of the HOSVD. They have similar but different
calculation procedures. The defined QHOSVD can be widely used in various visual
data processing with color pixels. In this paper, we present two applications
of the defined QHOSVD in color image processing: multi_focus color image fusion
and color image denoising. The experimental results on the two applications
respectively demonstrate the competitive performance of the proposed methods
over some existing ones.


Abstract:
  Weak decays in superheavy nuclei with proton numbers Z = 118 - 120 and
neutron numbers N = 175 - 184 are studied within a microscopic formalism based
on deformed self-consistent Skyrme Hartree-Fock mean-field calculations with
pairing correlations. The half-lives of beta+ decay and electron capture are
compared with alpha-decay half-lives obtained from phenomenological formulas.
The sensitivity of the half-lives to the unknown Q-energies is studied by
comparing the results obtained from different approaches for the masses. It is
shown that alpha-decay is always dominant in this mass region. The competition
between alpha and beta+/EC decay modes is studied in seven alpha-decay chains
starting at different isotopes of Z=118, 119, and 120.


Abstract:
  With a better understanding of the loss surfaces for multilayer networks, we
can build more robust and accurate training procedures. Recently it was
discovered that independently trained SGD solutions can be connected along
one-dimensional paths of near-constant training loss. In this paper, we show
that there are mode-connecting simplicial complexes that form multi-dimensional
manifolds of low loss, connecting many independently trained models. Inspired
by this discovery, we show how to efficiently build simplicial complexes for
fast ensembling, outperforming independently trained deep ensembles in
accuracy, calibration, and robustness to dataset shift. Notably, our approach
only requires a few training epochs to discover a low-loss simplex, starting
from a pre-trained solution. Code is available at
https://github.com/g-benton/loss-surface-simplexes.


Abstract:
  Learning robust speaker embeddings is a crucial step in speaker diarization.
Deep neural networks can accurately capture speaker discriminative
characteristics and popular deep embeddings such as x-vectors are nowadays a
fundamental component of modern diarization systems. Recently, some
improvements over the standard TDNN architecture used for x-vectors have been
proposed. The ECAPA-TDNN model, for instance, has shown impressive performance
in the speaker verification domain, thanks to a carefully designed neural
model.
  In this work, we extend, for the first time, the use of the ECAPA-TDNN model
to speaker diarization. Moreover, we improved its robustness with a powerful
augmentation scheme that concatenates several contaminated versions of the same
signal within the same training batch. The ECAPA-TDNN model turned out to
provide robust speaker embeddings under both close-talking and distant-talking
conditions. Our results on the popular AMI meeting corpus show that our system
significantly outperforms recently proposed approaches.


Abstract:
  We propose a method for enantio-detection of chiral molecules based on a
cavity-molecule system, where the left- and right-handed molecules are coupled
with a cavity and two classical light fields to form cyclic three-level models.
Via the cavity-assisted three-photon processes based on the cyclic three-level
model, photons are generated continuously in the cavity even in the absence of
external driving to the cavity. However, the photonic fields generated from the
three-photon processes of left- and right-handed molecules differ with the
phase difference {\pi} according to the inherent properties of electric-dipole
transition moments of enantiomers. This provides a potential way to detect the
enantiomeric excess of chiral mixture by monitoring the output field of the
cavity.


Abstract:
  This article develops the multiple-input multiple-output (MIMO) technology
for weather radar sensing. There are ample advantages of MIMO that have been
highlighted that can improve the spatial resolution of the observations and
also the accuracy of the radar variables. These concepts have been introduced
here pertaining to weather radar observations with supporting simulations
demonstrating improvements to existing phased array technology. Already MIMO is
being used in a big way for hard target detection and tracking and also in the
automotive radar industry and it offers similar improvements for weather radar
observations. Some of the benefits are discussed here with a phased array
platform in mind which offers quadrant outputs.


Abstract:
  We demonstrate an efficient procedure for terahertz-wave radiation generation
by applying a nonlinear wavelength conversion approach using a mid-infrared
pump source. We used a 3.3 {\mu}m dual-chirped optical parametric amplification
source and a 1.5 {\mu}m fiber laser for a comparison of energy conversion
efficiencies. Nonlinear inorganic and organic crystals were used, and for the
periodically-poled lithium niobate crystal, the efficiencies were 1.3*10^-4 and
5.6*10^-12 for the 3.3 {\mu}m source and the 1.5 {\mu}m source, respectively.
We confirmed that nonlinear crystals could be pumped with tera-watt / cm^2
class by using an mid-infrared source, which reduces several undesirable
nonlinear optical effects.


Abstract:
  Measurement-based quantum computing (MBQC) promises natural compatibility
with quantum error correcting codes at the cost of a polynomial increase in
physical qubits. MBQC proposals have largely focused on photonic systems, where
2-qubit gates are difficult. Semiconductor spin qubits in quantum dots, on the
other hand, offer fast 2-qubit gates via the exchange interaction. In
exchange-based quantum computing, as with other solid-state qubits, leakage to
higher states is a serious problem that must be mitigated. Here, two hybrid
measurement-exchange schemes are proposed which quantify the benefits of MBQC
on quantum dot-based quantum computing. Measurement of double quantum dot
encoded qubits in the singlet-triplet basis, along with inter- and intra-qubit
exchange interaction, are used to perform one and two qubit operations. Both
schemes suppress individual qubit spin-state leakage errors, offer fast gate
times, and require only controllable exchange couplings, up to known phase and
Pauli corrections.


Abstract:
  Different from static images, videos contain additional temporal and spatial
information for better object detection. However, it is costly to obtain a
large number of videos with bounding box annotations that are required for
supervised deep learning. Although humans can easily learn to recognize new
objects by watching only a few video clips, deep learning usually suffers from
overfitting. This leads to an important question: how to effectively learn a
video object detector from only a few labeled video clips? In this paper, we
study the new problem of few-shot learning for video object detection. We first
define the few-shot setting and create a new benchmark dataset for few-shot
video object detection derived from the widely used ImageNet VID dataset. We
employ a transfer-learning framework to effectively train the video object
detector on a large number of base-class objects and a few video clips of
novel-class objects. By analyzing the results of two methods under this
framework (Joint and Freeze) on our designed weak and strong base datasets, we
reveal insufficiency and overfitting problems. A simple but effective method,
called Thaw, is naturally developed to trade off the two problems and validate
our analysis.
  Extensive experiments on our proposed benchmark datasets with different
scenarios demonstrate the effectiveness of our novel analysis in this new
few-shot video object detection problem.


Abstract:
  We conduct novel coverage probability analysis of downlink transmission in a
three-dimensional (3D) terahertz (THz) communication (THzCom) system. In this
system, we address the unique propagation properties in THz band, e.g.,
absorption loss, super-narrow directional beams, and high vulnerability towards
blockage, which are fundamentally different from those at lower frequencies.
Different from existing studies, we characterize the performance while
considering the effect of 3D directional antennas at both access points (APs)
and user equipments (UEs), and the joint impact of the blockage caused by the
user itself, moving humans, and wall blockers in a 3D environment. Under such
consideration, we develop a tractable analytical framework to derive a new
expression for the coverage probability by examining the regions where dominant
interferers (i.e., those can cause outage by themselves) can exist, and the
average number of interferers existing in these regions. Aided by numerical
results, we validate our analysis and reveal that ignoring the impact of the
vertical heights of THz devices in the analysis leads to a substantial
underestimation of the coverage probability. We also show that it is more
worthwhile to increase the antenna directivity at the APs than at the UEs, to
produce a more reliable THzCom system.


Abstract:
  We investigate Rindler's frame measurements. From its perspective, we found a
geometric/gravitational interpretation of speed of light, mass and uncertainty
principle. This can be interpreted as measurements of a black hole universal
clock. This lead to an emergence of a timeless state of gravity in a
mathematically consistent way. In other words, space my be a frozen time.


Abstract:
  The present work is devoted to an extension of the well-known Ehrling
inequalities, which quantitatively characterize compact embeddings of function
spaces, to more general operators. Firstly, a modified notion of continuity for
linear operators, named \emph{Ehrling continuity} and inspired by the classical
Ehrling inequality, is introduced, and then, a necessary and sufficient
condition for Ehrling continuity is provided via arguments based on general
topology. Secondly, general completely continuous operators between normed
spaces are characterized in terms of (generalized) Ehrling type inequalities.
To this end, the well-known local metrization of the weak topology (so to
speak, a \emph{very weak norm}) plays a crucial role. Thanks to these results,
a universal relation is observed among complete continuity, the very weak norm
and generalized Ehrling type inequality.


Abstract:
  We present order reduction results for linear time invariant descriptor
systems. Results are given for both forced and unforced systems as well methods
for constructing the reduced order systems. Our results establish a precise
connection between classical and new results on this topic, and lead to an
elementary construction of quasi-Weierstrass forms for a descriptor system.
Examples are given to illustrate the usefulness of our results.


Abstract:
  In the field of nuclear reactor physics, transient phenomena are usually
studied using deterministic or hybrids methods. These methods require many
approximations, such as: geometry, time and energy discretizations, material
homogenization and assumption of diffusion conditions, among others. In this
context, Monte Carlo simulations are specially adequate to study these
problems. Challenges presented when using Monte Carlo simulations in space-time
kinetics in fissile systems are the different time-scales involved in prompt
and delayed neutron emission, which implies that results obtained have a large
variance associated if an analog Monte Carlo simulation is utilized.
Furthermore, in both deterministic and Monte Carlo simulations delayed neutron
precursors are grouped in a $6$- or $8$- group structure, but nowadays there is
not a solid reason to keep this aggregation. In this work, and for the first
time, individual precursor data is implemented in a Monte Carlo simulation,
explicitly including the time dependence related to the $\beta$-delayed neutron
emission. This was accomplished by modifying the open source Monte Carlo code
OpenMC. In the modified code -- Time Dependent OpenMC or OpenMC(TD) -- time
dependency related to delayed neutron emission originated from $\beta$-decay
was addressed. Continuous energy neutron cross-sections data used comes from
JEFF-$3$.$1$.$1$ library. Individual precursor data was taken from
JEFF-$3$.$1$.$1$ (cumulative yields) and ENDF-B/VIII.$0$ (delayed neutron
emission probabilities and delayed neutron energy spectra). OpenMC(TD) was
tested in: i) a monoenergetic system; ii) an energy dependent unmoderated
system where the precursors were taken individually or in a group structure;
and finally iii) a light-water moderated energy dependent system, using
$6$-groups, $50$ and $40$ individual precursors.


Abstract:
  Let $X$ be an infinite linearly ordered set and let $Y$ be a nonempty subset
of $X$. We calculate the relative rank of the semigroup $OP(X,Y)$ of all
orientation-preserving transformations on $X$ with restricted range $Y$ modulo
the semigroup $O(X,Y)$ of all order-preserving transformations on $X$ with
restricted range $Y$. For $Y = X$, we characterize the relative generating sets
of minimal size.


Abstract:
  For an image query, unsupervised contrastive learning labels crops of the
same image as positives, and other image crops as negatives. Although
intuitive, such a native label assignment strategy cannot reveal the underlying
semantic similarity between a query and its positives and negatives, and
impairs performance, since some negatives are semantically similar to the query
or even share the same semantic class as the query. In this work, we first
prove that for contrastive learning, inaccurate label assignment heavily
impairs its generalization for semantic instance discrimination, while accurate
labels benefit its generalization. Inspired by this theory, we propose a novel
self-labeling refinement approach for contrastive learning. It improves the
label quality via two complementary modules: (i) self-labeling refinery (SLR)
to generate accurate labels and (ii) momentum mixup (MM) to enhance similarity
between query and its positive. SLR uses a positive of a query to estimate
semantic similarity between a query and its positive and negatives, and
combines estimated similarity with vanilla label assignment in contrastive
learning to iteratively generate more accurate and informative soft labels. We
theoretically show that our SLR can exactly recover the true semantic labels of
label-corrupted data, and supervises networks to achieve zero prediction error
on classification tasks. MM randomly combines queries and positives to increase
semantic similarity between the generated virtual queries and their positives
so as to improves label accuracy. Experimental results on CIFAR10, ImageNet,
VOC and COCO show the effectiveness of our method. PyTorch code and model will
be released online.


Abstract:
  The East Asian very-long-baseline interferometry (VLBI) Network (EAVN) is a
rapidly evolving international VLBI array that is currently promoted under
joint efforts among China, Japan, and Korea. EAVN aims at forming a joint VLBI
Network by combining a large number of radio telescopes distributed over East
Asian regions. After the combination of the Korean VLBI Network (KVN) and the
VLBI Exploration of Radio Astrometry (VERA) into KaVA, further expansion with
the joint array in East Asia is actively promoted. Here we report the first
imaging results (at 22 and 43 GHz) of bright radio sources obtained with KaVA
connected to Tianma 65-m and Nanshan 26-m Radio Telescopes in China. To test
the EAVN imaging performance for different sources, we observed four active
galactic nuclei (AGN) having different brightness and morphology. As a result,
we confirmed that Tianma 65-m Radio Telescope (TMRT) significantly enhances the
overall array sensitivity, a factor of 4 improvement in baseline sensitivity
and 2 in image dynamic range compared to the case of KaVA only. The addition of
Nanshan 26-m Radio Telescope (NSRT) further doubled the east-west angular
resolution. With the resulting high-dynamic-range, high-resolution images with
EAVN (KaVA+TMRT+NSRT), various fine-scale structures in our targets, such as
the counter-jet in M87, a kink-like morphology of the 3C273 jet and the weak
emission in other sources, are successfully detected. This demonstrates the
powerful capability of EAVN to study AGN jets and to achieve other science
goals in general. Ongoing expansion of EAVN will further enhance the angular
resolution, detection sensitivity and frequency coverage of the network.


Abstract:
  For an appropriate choice of a $\mathbb{Z}$-grading structure, we prove that
the wrapped Fukaya category of the symmetric square of a $(k+3)$-punctured
sphere, i.e. the Weinstein manifold given as the complement of $(k+3)$ generic
lines in $\mathbb{C}P^2$ is quasi-equivalent to the derived category of
coherent sheaves on a singular surface $\mathcal{Z}_{2,k}$ constructed as the
boundary of a toric Landau-Ginzburg model $(\mathcal{X}_{2,k},
\mathbf{w}_{2,k})$. We do this by first constructing a quasi-equivalence
between certain categorical resolutions of both sides and then localising. We
also provide a general homological mirror symmetry conjecture concerning all
the higher symmetric powers of punctured spheres. The corresponding toric
LG-models $(\mathcal{X}_{n,k},\mathbf{w}_{n,k})$ are constructed from the
combinatorics of curves on the punctured surface and are related to small toric
resolutions of the singularity $x_1\ldots x_{n+1}= v_1\ldots v_k$.


Abstract:
  Monolayers of transition metal dichalcogenides are ideal materials to control
both spin and valley degrees of freedom either electrically or optically.
Nevertheless, optical excitation mostly generates excitons species with
inherently short lifetime and spin/valley relaxation time. Here we demonstrate
a very efficient spin/valley optical pumping of resident electrons in n-doped
WSe2 and WS2 monolayers. We observe that, using a continuous wave laser and
appropriate doping and excitation densities, negative trion doublet lines
exhibit circular polarization of opposite sign and the photoluminescence
intensity of the triplet trion is more than four times larger with circular
excitation than with linear excitation. We interpret our results as a
consequence of a large dynamic polarization of resident electrons using
circular light.


Abstract:
  The nature of Wireless Sensor Networks (WSN) and the widespread of using WSN
introduce many security threats and attacks. An effective Intrusion Detection
System (IDS) should be used to detect attacks. Detecting such an attack is
challenging, especially the detection of Denial of Service (DoS) attacks.
Machine learning classification techniques have been used as an approach for
DoS detection. This paper conducted an experiment using Waikato Environment for
Knowledge Analysis (WEKA)to evaluate the efficiency of five machine learning
algorithms for detecting flooding, grayhole, blackhole, and scheduling at DoS
attacks in WSNs. The evaluation is based on a dataset, called WSN-DS. The
results showed that the random forest classifier outperforms the other
classifiers with an accuracy of 99.72%.


Abstract:
  This paper discusses capabilities that are essential to models applied in
policy analysis settings and the limitations of direct applications of
off-the-shelf machine learning methodologies to such settings. Traditional
econometric methodologies for building discrete choice models for policy
analysis involve combining data with modeling assumptions guided by
subject-matter considerations. Such considerations are typically most useful in
specifying the systematic component of random utility discrete choice models
but are typically of limited aid in determining the form of the random
component. We identify an area where machine learning paradigms can be
leveraged, namely in specifying and systematically selecting the best
specification of the random component of the utility equations. We review two
recent novel applications where mixed-integer optimization and cross-validation
are used to algorithmically select optimal specifications for the random
utility components of nested logit and logit mixture models subject to
interpretability constraints.


Abstract:
  The generation of non-Abelian geometric phases from a system of evanescently
coupled waveguides is extended towards the framework of nonorthogonal
coupled-mode theory. Here, we study an experimentally feasible tripod
arrangement of waveguides that contain dark states from which a nontrivial
U(2)-mixing can be obtained by means of an adiabatic parameter variation. We
investigate the influence of higher-order contributions beyond
nearest-neighbour coupling as well as self-coupling on the stability of a
U(3)-phase generated from an optical tetrapod setup. Our results indicate that,
despite the mode nonorthogonality, the symmetry of dark states protects the
geometric evolution of light from distortion.


Abstract:
  The canonical ensemble plays a crucial role in statistical mechanics in and
out of equilibrium. For example, the standard derivation of the fluctuation
theorem relies on the assumption that the initial state of the heat bath is the
canonical ensemble. On the other hand, the recent progress in the foundation of
statistical mechanics has revealed that a thermal equilibrium state is not
necessarily described by the canonical ensemble but can be a quantum pure state
or even a single energy eigenstate, as formulated by the eigenstate
thermalization hypothesis (ETH). Then, a question raised is how these two
pictures, the canonical ensemble and a single energy eigenstate as a thermal
equilibrium state, are compatible in the fluctuation theorem. In this paper, we
theoretically and numerically show that the fluctuation theorem holds in both
of the long and short-time regimes, even when the initial state of the bath is
a single energy eigenstate of a many-body system. Our proof of the fluctuation
theorem in the long-time regime is based on the ETH, while it was previously
shown in the short-time regime on the basis of the Lieb-Robinson bound and the
ETH [Phys. Rev. Lett. 119, 100601 (2017)]. The proofs for these time regimes
are theoretically independent and complementary, implying the fluctuation
theorem in the entire time domain. We also perform a systematic numerical
simulation of hard-core bosons by exact diagonalization and verify the
fluctuation theorem in both of the time regimes by focusing on the finite-size
scaling. Our results contribute to the understanding of the mechanism that the
fluctuation theorem emerges from unitary dynamics of quantum many-body systems,
and can be tested by experiments with, e.g., ultracold atoms.


Abstract:
  Reinforcement learning in complex environments may require supervision to
prevent the agent from attempting dangerous actions. As a result of supervisor
intervention, the executed action may differ from the action specified by the
policy. How does this affect learning? We present the Modified-Action Markov
Decision Process, an extension of the MDP model that allows actions to differ
from the policy. We analyze the asymptotic behaviours of common reinforcement
learning algorithms in this setting and show that they adapt in different ways:
some completely ignore modifications while others go to various lengths in
trying to avoid action modifications that decrease reward. By choosing the
right algorithm, developers can prevent their agents from learning to
circumvent interruptions or constraints, and better control agent responses to
other kinds of action modification, like self-damage.


Abstract:
  We present the long-term X-ray spectral and temporal analysis of a 'bare-type
AGN' Ark 120. We consider the observations from XMM-Newton, Suzaku, Swift, and
NuSTAR from 2003 to 2018. The spectral properties of this source are studied
using various phenomenological and physical models present in the literature.
We report (a) the variations of several physical parameters, such as the
temperature and optical depth of the electron cloud, the size of the Compton
cloud, and accretion rate for the last fifteen years. The spectral variations
are explained from the change in the accretion dynamics; (b) the X-ray time
delay between 0.2-2 keV and 3-10 keV light-curves exhibited zero-delay in 2003,
positive delay of 4.71 \pm 2.1 ks in 2013, and negative delay of 4.15 \pm 1.5
ks in 2014. The delays are explained considering Comptonization, reflection,
and light-crossing time; (c) the long term intrinsic luminosities obtained
using nthcomp, of the soft-excess and the primary continuum show a correlation
with a Pearson Correlation Coefficient of 0.922. This indicates that the
soft-excess and the primary continuum are originated from the same physical
process. From a physical model fitting, we infer that the soft excess for Ark
120 could be due to a small number of scatterings in the Compton cloud. Using
Monte-Carlo simulations, we show that indeed the spectra corresponding to fewer
scatterings could provide a steeper soft-excess power-law in the 0.2-3 keV
range. Simulated luminosities are found to be in agreement with the observed
values.


Abstract:
  We study the parameter dependence of complex geodesics with prescribed
boundary value and direction on bounded strongly linearly convex domains.As an
important application, we present a quantitative relationship between the
regularity of the pluricomplex Poisson kernel of such a domain, which is a
solution to a homogeneous complex Monge-Amp\`{e}re equation with boundary
singularity, and that of the boundary of the domain. Our results improve
considerably previous ones in this direction due to Chang-Hu-Lee and
Bracci-Patrizio.


Abstract:
  The LIGO-Virgo-Kagra collaboration (LVC) discovered recently GW190521, a
gravitational wave (GW) source associated with the merger between two black
holes (BHs) with mass $66$ M$_\odot$ and $>85$ M$_\odot$. GW190521 represents
the first BH binary (BBH) merger with a primary mass falling in the "upper
mass-gap" and the first leaving behind a $\sim 150$ M$_\odot$ remnant. So far,
the LVC reported the discovery of four further mergers having a total mass
$>100$ M$_\odot$, i.e. in the intermediate-mass black holes (IMBH) mass range.
Here, we discuss results from a series of 80 $N$-body simulations of young
massive clusters (YMCs) that implement relativistic corrections to follow
compact object mergers. We discover the development of a GW190521-like system
as the result of a 3rd-generation merger, and four IMBH-BH mergers with total
mass $~(300-350)$ M$_\odot$. We show that these IMBH-BH mergers are
low-frequency GW sources detectable with LISA and DECIGO out to redshift
$z=0.01-0.1$ and $z>100$, and we discuss how their detection could help
unravelling IMBH natal spins. For the GW190521 test case, we show that the
3rd-generation merger remnant has a spin and effective spin parameter that
matches the $90\%$ credible interval measured for GW190521 better than a
simpler double merger and comparably to a single merger. Due to GW recoil
kicks, we show that retaining the products of these mergers require birth-sites
with escape velocities $\gtrsim 50-100$ km s$^{-1}$, values typically attained
in galactic nuclei and massive clusters with steep density profiles.


Abstract:
  Label noise is frequently observed in real-world large-scale datasets. The
noise is introduced due to a variety of reasons; it is heterogeneous and
feature-dependent. Most existing approaches to handling noisy labels fall into
two categories: they either assume an ideal feature-independent noise, or
remain heuristic without theoretical guarantees. In this paper, we propose to
target a new family of feature-dependent label noise, which is much more
general than commonly used i.i.d. label noise and encompasses a broad spectrum
of noise patterns. Focusing on this general noise family, we propose a
progressive label correction algorithm that iteratively corrects labels and
refines the model. We provide theoretical guarantees showing that for a wide
variety of (unknown) noise patterns, a classifier trained with this strategy
converges to be consistent with the Bayes classifier. In experiments, our
method outperforms SOTA baselines and is robust to various noise types and
levels.


Abstract:
  A nearly autonomous management and control (NAMAC) system is designed to
furnish recommendations to operators for achieving particular goals based on
NAMAC's knowledge base. As a critical component in a NAMAC system, digital
twins (DTs) are used to extract information from the knowledge base to support
decision-making in reactor control and management during all modes of plant
operations. With the advancement of artificial intelligence and data-driven
methods, machine learning algorithms are used to build DTs of various functions
in the NAMAC system. To evaluate the uncertainty of DTs and its impacts on the
reactor digital instrumentation and control systems, uncertainty quantification
(UQ) and software risk analysis is needed. As a comprehensive overview of prior
research and a starting point for new investigations, this study selects and
reviews relevant UQ techniques and software hazard and software risk analysis
methods that may be suitable for DTs in the NAMAC system.


Abstract:
  In this note, we prove that the problem of computing the linear discrepancy
of a given matrix is $\Pi_2$-hard, even to approximate within $9/8 - \epsilon$
factor for any $\epsilon > 0$. This strengthens the NP-hardness result of Li
and Nikolov [ESA 2020] for the exact version of the problem, and answers a
question posed by them. Furthermore, since Li and Nikolov showed that the
problem is contained in $\Pi_2$, our result makes linear discrepancy another
natural problem that is $\Pi_2$-complete (to approximate).


Abstract:
  Chemical space is routinely explored by machine learning methods to discover
interesting molecules, before time-consuming experimental synthesizing is
attempted. However, these methods often rely on a graph representation,
ignoring 3D information necessary for determining the stability of the
molecules. We propose a reinforcement learning approach for generating
molecules in cartesian coordinates allowing for quantum chemical prediction of
the stability. To improve sample-efficiency we learn basic chemical rules from
imitation learning on the GDB-11 database to create an initial model applicable
for all stoichiometries. We then deploy multiple copies of the model
conditioned on a specific stoichiometry in a reinforcement learning setting.
The models correctly identify low energy molecules in the database and produce
novel isomers not found in the training set. Finally, we apply the model to
larger molecules to show how reinforcement learning further refines the
imitation learning model in domains far from the training data.


Abstract:
  We report the first Vivaldi arrays monolithically fabricated exclusively
using commercial, low-cost, 3D metal printing (direct metal laser sintering).
Furthermore, we developed one of the first dual-polarized Vivaldi arrays on a
triangular lattice, and compare it to a square lattice array. The triangular
lattice is attractive because it has a 15.5% larger cell size compared to the
square lattice and can be more naturally truncated into a wide range of
aperture shapes such as a rectangle, hexagon, or triangle. Both arrays operate
at 3-20 GHz and scan angles out to 60 degree from normal. The fabrication
process is significantly simplified compared to previously published Vivaldi
arrays since the antenna is ready for use directly after the standard printing
process is complete. This rapid manufacturing is further expedited by printing
the 'Sub-Miniature Push-on, Micro' (SMPM) connectors directly onto the
radiating elements, which simplifies assembly and reduces cost compared to
utilizing discrete RF connectors. The arrays have a modular design that allow
for combining multiple sub-arrays together for arbitrarily increasing the
aperture size. Simulations and measurement show that our arrays have similar
performance as previously published Vivaldi arrays, but with simpler
fabrication.


Abstract:
  We are not very good at measuring -- rigorously and quantitatively -- the
cyber security of systems. Our ability to measure cyber resilience is even
worse. And without measuring cyber resilience, we can neither improve it nor
trust its efficacy. It is difficult to know if we are improving or degrading
cyber resilience when we add another control, or a mix of controls, to harden
the system. The only way to know is to specifically measure cyber resilience
with and without a particular set of controls. What needs to be measured are
temporal patterns of recovery and adaptation, and not time-independent failure
probabilities. In this paper, we offer a set of criteria that would ensure
decision-maker confidence in the reliability of the methodology used in
obtaining a meaningful measurement.


Abstract:
  Recently, Gross, Mansour and Tucker introduced the partial-dual genus
polynomial of a ribbon graph as a generating function that enumerates the
partial duals of the ribbon graph by genus. It is analogous to the
extensively-studied polynomial in topological graph theory that enumerates by
genus all embeddings of a given graph. To investigate the partial-dual genus
polynomial one only needs to focus on bouquets, i.e. ribbon graphs with only
one vertex. In this paper, we shall further show that the partial-dual genus
polynomial of a bouquet essentially depends on the signed intersection graph of
the bouquet rather than on the bouquet itself. That is to say the bouquets with
the same signed intersection graph will have the same partial-dual genus
polynomial. We then prove that the partial-dual genus polynomial of a bouquet
contains non-zero constant term if and only if its signed intersection graph is
positive and bipartite. Finally we consider a conjecture posed by Gross,
Mansour and Tucker. that there is no orientable ribbon graph whose partial-dual
genus polynomial has only one non-constant term, we give a characterization of
non-empty bouquets whose partial-dual genus polynomials have only one term by
consider non-orientable case and orientable case separately.


Abstract:
  We obtain global, non-asymptotic convergence guarantees for independent
learning algorithms in competitive reinforcement learning settings with two
agents (i.e., zero-sum stochastic games). We consider an episodic setting where
in each episode, each player independently selects a policy and observes only
their own actions and rewards, along with the state. We show that if both
players run policy gradient methods in tandem, their policies will converge to
a min-max equilibrium of the game, as long as their learning rates follow a
two-timescale rule (which is necessary). To the best of our knowledge, this
constitutes the first finite-sample convergence result for independent policy
gradient methods in competitive RL; prior work has largely focused on
centralized, coordinated procedures for equilibrium computation.


Abstract:
  Recently, many enterprises are facing the difficulties brought out by the
limitation of warehouse land and the increase of loan cost. As a promising
approach to improve space utilization rate, puzzle-based storage systems
(PBSSs) are drawing more attention from logistics researchers. In previous
research about PBSS, concentration has been paid to single target item
problems. However, there are no consensus algorithms to solve load retrievals
route programming in PBSSs with multiple target items. In this paper, a
heuristic algorithm is proposed to solve load retrievals route programming in
PBSSs with multiple target items, multiple escorts and multiple IOs. In this
paper, new concepts about the proposed algorithm are defined, including target
IOs, target position of escorts, number of escorts required, et al. Then, the
decision procedures are designed according to these concepts. Based on Markov
decision process, the proposed algorithm makes the decision of the next action
by analyzing the current status, until all the target items arrive at the IOs.
The case study results have shown the effectiveness and efficiency of the
proposed heuristic algorithm.


Abstract:
  We discuss the propagation of surface waves in an isotropic half space
modelled with the linear Cosserat theory of isotropic elastic materials. To
this aim we use a method based on the algebraic analysis of the surface
impedance matrix and on the algebraic Riccati equation, and which is
independent of the common Stroh formalism. Due to this method, a new algorithm
which determines the amplitudes and the wave speed in the theory of isotropic
elastic Cosserat materials is described. Moreover, the method allows to prove
the existence and uniqueness of a subsonic solution of the secular equation, a
problem which remains unsolved in almost all generalised linear theories of
elastic materials. Since the results are suitable to be used for numerical
implementations, we propose two numerical algorithms which are viable for any
elastic material. Explicit numerical calculations are made for alumunium-epoxy
in the context of the Cosserat model. Since the novel form of the secular
equation for isotropic elastic material has not been explicitly derived
elsewhere, we establish it in this paper, too.


Abstract:
  Deep neural network (DNN) is a popular model implemented in many systems to
handle complex tasks such as image classification, object recognition, natural
language processing etc. Consequently DNN structural vulnerabilities become
part of the security vulnerabilities in those systems. In this paper we study
the root cause of DNN adversarial examples. We examine the DNN response surface
to understand its classification boundary. Our study reveals the structural
problem of DNN classification boundary that leads to the adversarial examples.
Existing attack algorithms can generate from a handful to a few hundred
adversarial examples given one clean image. We show there are infinitely many
adversarial images given one clean sample, all within a small neighborhood of
the clean sample. We then define DNN uncertainty regions and show
transferability of adversarial examples is not universal. We also argue that
generalization error, the large sample theoretical guarantee established for
DNN, cannot adequately capture the phenomenon of adversarial examples. We need
new theory to measure DNN robustness.


Abstract:
  A new solution for the Dutch national flag problem is proposed, requiring no
three-way comparisons, which gives quicksort a proper worst-case runtime of
$O(nk)$ for inputs with $k$ distinct elements. This is used together with other
known and novel techniques to construct a hybrid sort that is never
significantly slower than regular quicksort while speeding up drastically for
many input distributions.


Abstract:
  It is established that in the tensionless limit the chiral superstring
integrand is reduced to the chiral integrand of the ambitwistor string.


Abstract:
  Binarized Neural Networks (BNNs) have the potential to revolutionize the way
that deep learning is carried out in edge computing platforms. However, the
effectiveness of interpretability methods on these networks has not been
assessed.
  In this paper, we compare the performance of several widely used saliency
map-based interpretabilty techniques (Gradient, SmoothGrad and GradCAM), when
applied to Binarized or Full Precision Neural Networks (FPNNs). We found that
the basic Gradient method produces very similar-looking maps for both types of
network. However, SmoothGrad produces significantly noisier maps for BNNs.
GradCAM also produces saliency maps which differ between network types, with
some of the BNNs having seemingly nonsensical explanations. We comment on
possible reasons for these differences in explanations and present it as an
example of why interpretability techniques should be tested on a wider range of
network types.


Abstract:
  In this paper we establish the existence and multiplicity of nontrivial
solutions to the following problem \begin{align*} \begin{split}
(-\Delta)^{\frac{1}{2}}u+u+(\ln|\cdot|*|u|^2)&=f(u)+\mu|u|^{-\gamma-1}u,~\text{in}~\mathbb{R},
\end{split} \end{align*} where $\mu>0$, $(*)$ is the convolution operation
between two functions, $0<\gamma<1$, $f$ is a function with a certain type of
growth. We prove the existence of a nontrivial solution at a certain mountain
pass level and another ground state solution when the nonlinearity $f$ is of
exponential critical growth.


Abstract:
  We discuss the possible role of the Tietze extension theorem in providing a
rigorous topological base to the expanding space-time in cosmology. A simple
toy model has been introduced to show the analogy between the topological
extension from a circle $S$ to the whole space $M$ and the cosmic expansion
from a non-zero volume to the whole space-time in non-singular cosmological
models. A topological analogy to the cosmic scale factor function has been
suggested, the paper refers to the possible applications of the topological
extension in mathematical physics.


Abstract:
  Attention-based encoder-decoder framework is widely used in the scene text
recognition task. However, for the current state-of-the-art(SOTA) methods,
there is room for improvement in terms of the efficient usage of local visual
and global context information of the input text image, as well as the robust
correlation between the scene processing module(encoder) and the text
processing module(decoder). In this paper, we propose a Representation and
Correlation Enhanced Encoder-Decoder Framework(RCEED) to address these
deficiencies and break performance bottleneck. In the encoder module, local
visual feature, global context feature, and position information are aligned
and fused to generate a small-size comprehensive feature map. In the decoder
module, two methods are utilized to enhance the correlation between scene and
text feature space. 1) The decoder initialization is guided by the holistic
feature and global glimpse vector exported from the encoder. 2) The feature
enriched glimpse vector produced by the Multi-Head General Attention is used to
assist the RNN iteration and the character prediction at each time step.
Meanwhile, we also design a Layernorm-Dropout LSTM cell to improve model's
generalization towards changeable texts. Extensive experiments on the
benchmarks demonstrate the advantageous performance of RCEED in scene text
recognition tasks, especially the irregular ones.


Abstract:
  The presence of smart objects is increasingly widespread and their ecosystem,
also known as Internet of Things, is relevant in many different application
scenarios. The huge amount of temporally annotated data produced by these smart
devices demand for efficient techniques for transfer and storage of time series
data. Compression techniques play an important role toward this goal and,
despite the fact that standard compression methods could be used with some
benefit, there exist several ones that specifically address the case of time
series by exploiting their peculiarities to achieve a more effective
compression and a more accurate decompression in the case of lossy compression
techniques. This paper provides a state-of-the-art survey of the principal time
series compression techniques, proposing a taxonomy to classify them
considering their overall approach and their characteristics. Furthermore, we
analyze the performances of the selected algorithms by discussing and comparing
the experimental results that where provided in the original articles. The goal
of this paper is to provide a comprehensive and homogeneous reconstruction of
the state-of-the-art which is currently fragmented across many papers that use
different notations and where the proposed methods are not organized according
to a classification.


Abstract:
  Long-term neutrino-radiation hydrodynamics simulations in full general
relativity are performed for the collapse of rotating massive stars that are
evolved from He-stars with their initial mass of $20$ and $32M_\odot$. It is
shown that if the collapsing stellar core has sufficient angular momentum, the
rotationally-supported proto-neutron star (PNS) survives for seconds
accompanying the formation of a massive torus of mass larger than $1\,M_\odot$.
Subsequent mass accretion onto the central region produces a massive and
compact central object, and eventually enhances the neutrino luminosity beyond
$10^{53}$\,erg/s, resulting in a very delayed neutrino-driven explosion in
particular toward the polar direction. The kinetic energy of the explosion can
be appreciably higher than $10^{52}$ erg for a massive progenitor star and
compatible with that of energetic supernovae like broad-line type-Ic
supernovae. By the subsequent accretion, the massive PNS collapses eventually
into a rapidly spinning black hole, which could be a central engine for
gamma-ray bursts if a massive torus surrounds it.


Abstract:
  From tiny pacemaker chips to aircraft collision avoidance systems, the
state-of-the-art Cyber-Physical Systems (CPS) have increasingly started to rely
on Deep Neural Networks (DNNs). However, as concluded in various studies, DNNs
are highly susceptible to security threats, including adversarial attacks. In
this paper, we first discuss different vulnerabilities that can be exploited
for generating security attacks for neural network-based systems. We then
provide an overview of existing adversarial and fault-injection-based attacks
on DNNs. We also present a brief analysis to highlight different challenges in
the practical implementation of adversarial attacks. Finally, we also discuss
various prospective ways to develop robust DNN-based systems that are resilient
to adversarial and fault-injection attacks.


Abstract:
  This paper studies the robust satisfiability check and online control
synthesis problems for uncertain discrete-time systems subject to signal
temporal logic (STL) specifications. Different from existing techniques, this
work proposes an approach based on STL, reachability analysis, and temporal
logic trees. Firstly, a real-time version of STL semantics and a tube-based
temporal logic tree are proposed. We show that such a tree can be constructed
from every STL formula. Secondly, using the tube-based temporal logic tree, a
sufficient condition is obtained for the robust satisfiability check of the
uncertain system. When the underlying system is deterministic, a necessary and
sufficient condition for satisfiability is obtained. Thirdly, an online control
synthesis algorithm is designed. It is shown that when the STL formula is
robustly satisfiable and the initial state of the system belongs to the initial
root node of the tube-based temporal logic tree, it is guaranteed that the
trajectory generated by the controller satisfies the STL formula. The
effectiveness of the proposed approach is verified by an automated car
overtaking example.


Abstract:
  Recent exploration methods have proven to be a recipe for improving
sample-efficiency in deep reinforcement learning (RL). However, efficient
exploration in high-dimensional observation spaces still remains a challenge.
This paper presents Random Encoders for Efficient Exploration (RE3), an
exploration method that utilizes state entropy as an intrinsic reward. In order
to estimate state entropy in environments with high-dimensional observations,
we utilize a k-nearest neighbor entropy estimator in the low-dimensional
representation space of a convolutional encoder. In particular, we find that
the state entropy can be estimated in a stable and compute-efficient manner by
utilizing a randomly initialized encoder, which is fixed throughout training.
Our experiments show that RE3 significantly improves the sample-efficiency of
both model-free and model-based RL methods on locomotion and navigation tasks
from DeepMind Control Suite and MiniGrid benchmarks. We also show that RE3
allows learning diverse behaviors without extrinsic rewards, effectively
improving sample-efficiency in downstream tasks. Source code and videos are
available at https://sites.google.com/view/re3-rl.


Abstract:
  Human action recognition is an active research area in computer vision.
Although great process has been made, previous methods mostly recognize actions
based on depth data at only one scale, and thus they often neglect multi-scale
features that provide additional information action recognition in practical
application scenarios. In this paper, we present a novel framework focusing on
multi-scale motion information to recognize human actions from depth video
sequences. We propose a multi-scale feature map called Laplacian pyramid depth
motion images(LP-DMI). We employ depth motion images (DMI) as the templates to
generate the multi-scale static representation of actions. Then, we caculate
LP-DMI to enhance multi-scale dynamic information of motions and reduces
redundant static information in human bodies. We further extract the
multi-granularity descriptor called LP-DMI-HOG to provide more discriminative
features. Finally, we utilize extreme learning machine (ELM) for action
classification. The proposed method yeilds the recognition accuracy of 93.41%,
85.12%, 91.94% on public MSRAction3D dataset, UTD-MHAD and DHA dataset. Through
extensive experiments, we prove that our method outperforms state-of-the-art
benchmarks.


Abstract:
  Selective laser melting is receiving increasing interest as an additive
manufacturing technique. Residual stresses induced by the large temperature
gradients and inhomogeneous cooling process can favour the generation of
cracks. In this work, a crystal plasticity finite element model is developed to
simulate the formation of residual stresses and to understand the correlation
between plastic deformation, grain orientation and residual stresses in the
additive manufacturing process. The temperature profile and grain structure
from thermal-fluid flow and grain growth simulations are implemented into the
crystal plasticity model. An element elimination and reactivation method is
proposed to model the melting and solidification and to reinitialise state
variables, such as the plastic deformation, in the reactivated elements. The
accuracy of this method is judged against previous method based on the
stiffness degradation of liquid regions by comparing the plastic deformation as
a function of time induced by thermal stresses. The method is used to
investigate residual stresses parallel and perpendicular to the laser scan
direction, and the correlation with the maximum Schmid factor of the grains
along those directions. The magnitude of the residual stress can be predicted
as a function of the depth, grain orientation and position with respect to the
molten pool.


Abstract:
  The exceptional combination of strength and ductility in multi-component
alloys is often attributed to the interaction of dislocations with the various
solute atoms in the alloy. To study these effects on the mechanical properties
of such alloys there is a need to develop a modeling framework capable of
quantifying the effect of these solutes on the evolution of dislocation
networks. Large scale three-dimensional (3D) Discrete dislocation dynamics
(DDD) simulations can provide access to such studies but to date no relevant
approaches are available that aim for a complete representation of real alloys
with arbitrary chemical compositions. Here, we introduce a formulation of
dislocation interaction with substitutional solute atoms in fcc alloys in 3D
DDD simulations that accounts for solute strengthening induced by atomic misfit
as well as fluctuations in the cross-slip activation energy. Using this model,
we show that local fluctuations in the chemical composition of various
CrFeCoNi-based multi-principal element alloys (MPEA) lead to sluggish
dislocation motion, frequent cross-slip and alignment of dislocations with
solute aggregation features, explaining experimental observations related to
mechanical behavior and dislocation activity. It is also demonstrated, that
this behavior observed for certain MPEAs cannot be reproduced by assuming a
perfect solid solution. The developed method also provides a basis for further
investigations of dislocation plasticity in any real arbitrary fcc alloy with
substitutional solutes.


Abstract:
  The spacetime in the interior of a black hole can be described by an
homogeneous line element, for which the Einstein--Hilbert action reduces to a
one-dimensional mechanical model. We have shown in [SciPost Phys. 10, 022
(2021), [2010.07059]] that this model exhibits a symmetry under the
$(2+1)$-dimensional Poincar\'e group. Here we explain how this can be
understood as a broken infinite-dimensional BMS$_3$ symmetry. This is done by
reinterpreting the action for the model as a geometric action for BMS$_3$,
where the configuration space variables are elements of the algebra
$\mathfrak{bms}_3$ and the equations of motion transform as coadjoint vectors.
The Poincar\'e subgroup then arises as the stabilizer of the vacuum orbit. This
symmetry breaking is analogous to what happens with the Schwarzian action in
AdS$_2$ JT gravity, although in the present case there is no direct
interpretation in terms of boundary symmetries. This observation, together with
the fact that other lower-dimensional gravitational models (such as the BTZ
black hole) possess the same broken BMS$_3$ symmetries, provides yet another
illustration of the ubiquitous role played by this group.


Abstract:
  Embedding nonlinear dynamical systems into artificial neural networks is a
powerful new formalism for machine learning. By parameterizing ordinary
differential equations (ODEs) as neural network layers, these Neural ODEs are
memory-efficient to train, process time-series naturally and incorporate
knowledge of physical systems into deep learning models. However, the practical
applications of Neural ODEs are limited due to long inference times, because
the outputs of the embedded ODE layers are computed numerically with
differential equation solvers that can be computationally demanding. Here we
show that mathematical model order reduction methods can be used for
compressing and accelerating Neural ODEs by accurately simulating the
continuous nonlinear dynamics in low-dimensional subspaces. We implement our
novel compression method by developing Neural ODEs that integrate the necessary
subspace-projection and interpolation operations as layers of the neural
network. We validate our model reduction approach by comparing it to two
established acceleration methods from the literature in two classification
asks. In compressing convolutional and recurrent Neural ODE architectures, we
achieve the best balance between speed and accuracy when compared to the other
two acceleration methods. Based on our results, our integration of model order
reduction with Neural ODEs can facilitate efficient, dynamical system-driven
deep learning in resource-constrained applications.


Abstract:
  Superconducting crystals with lack of inversion symmetry can potentially host
unconventional pairing. However, till date, no direct conclusive experimental
evidence of such unconventional order parameters in non-centrosymmetric
superconductors has been reported. In this paper, through direct measurement of
the superconducting energy gap by scanning tunnelling spectroscopy, we report
the existence of both $s$-wave (singlet) and $p$-wave (triplet) pairing
symmetries in non-centrosymmetric Ru$_7$B$_3$. Our temperature and magnetic
field dependent studies also indicate that the relative amplitudes of the
singlet and triplet components of the order parameter change differently with
temperature.


Abstract:
  In this paper, we introduce a concept of norm-attainment in the projective
symmetric tensor product $\widehat{\otimes}_{\pi,s,N} X$ of a Banach space $X$,
which turns out to be naturally related to the classical norm-attainment of
$N$-homogeneous polynomials on $X$. Due to this relation, we can prove that
there exist symmetric tensors that do not attain their norms, which allows us
to study the problem of when the set of norm-attaining elements in
$\widehat{\otimes}_{\pi,s,N} X$ is dense. We show that the set of all
norm-attaining symmetric tensors is dense in $\widehat{\otimes}_{\pi,s,N} X$
for a large set of Banach spaces as $L_p$-spaces, isometric $L_1$-predual
spaces or Banach spaces with monotone Schauder basis, among others. Next, we
prove that if $X^*$ satisfies the Radon-Nikod\'ym and the approximation
property, then the set of all norm-attaining symmetric tensors in
$\widehat{\otimes}_{\pi,s,N} X^*$ is dense. From these techniques, we can
present new examples of Banach spaces $X$ and $Y$ such that the set of all
norm-attaining tensors in the projective tensor product $X
\widehat{\otimes}_\pi Y$ is dense, answering positively an open question from
the paper by S. Dantas, M. Jung, \'O. Rold\'an and A. Rueda Zoca.


Abstract:
  We give a simple unified proof for all existing rational hypergeometric
Ramanujan identities for $1/\pi$, and give a complete survey (without proof) of
several generalizations: rational hypergeometric identities for $1/\pi^c$,
Taylor expansions, upside-down formulas, and supercongruences.


Abstract:
  Large-scale Web-based services present opportunities for improving UI
policies based on observed user interactions. We address challenges of learning
such policies through model-free offline Reinforcement Learning (RL) with
off-policy training. Deployed in a production system for user authentication in
a major social network, it significantly improves long-term objectives. We
articulate practical challenges, compare several ML techniques, provide
insights on training and evaluation of RL models, and discuss generalizations.


Abstract:
  We introduce tools from discrete convexity theory and polyhedral geometry
into the theory of West's stack-sorting map $s$. Associated to each permutation
$\pi$ is a particular set $\mathcal V(\pi)$ of integer compositions that
appears in a formula for the fertility of $\pi$, which is defined to be
$|s^{-1}(\pi)|$. These compositions also feature prominently in more general
formulas involving families of colored binary plane trees called troupes and in
a formula that converts from free to classical cumulants in noncommutative
probability theory. We show that $\mathcal V(\pi)$ is a transversal discrete
polymatroid when it is nonempty. We define the fertilitope of $\pi$ to be the
convex hull of $\mathcal V(\pi)$, and we prove a surprisingly simple
characterization of fertilitopes as nestohedra arising from full binary plane
trees. Using known facts about nestohedra, we provide a procedure for
describing the structure of the fertilitope of $\pi$ directly from $\pi$ using
Bousquet-M\'elou's notion of the canonical tree of $\pi$. As a byproduct, we
obtain a new combinatorial cumulant conversion formula in terms of
generalizations of canonical trees that we call quasicanonical trees. We also
apply our results on fertilitopes to study combinatorial properties of the
stack-sorting map. In particular, we show that the set of fertility numbers has
density $1$, and we determine all infertility numbers of size at most $126$.
Finally, we reformulate the conjecture that $\sum_{\sigma\in
s^{-1}(\pi)}x^{\text{des}(\sigma)+1}$ is always real-rooted in terms of
nestohedra, and we propose natural ways in which this new version of the
conjecture could be extended.


Abstract:
  In this article, the form of basis set for solving helium Schr\"{o}dinger
equation is reinvestigated in perspective of geometry. With the help of theorem
proved by Gu $et~al.$, we construct a convenient variational basis set, which
emphasizes the geometric characteristics of trial wavefuncions. The main
advantage of this basis is that the angular part is complete for natural $L$
states with $L + 1$ terms and for unnatural $L$ states with $L$ terms, where
$L$ is the total angular quantum number. Compared with basis sets which contain
three Euler angles, this basis is very simple to use. More importantly, this
basis is quite easy to be generalized to more particle systems.


Abstract:
  It is well-known that when translating terms into graphical formalisms many
innessential details are abstracted away. In the case of $\lambda$-calculus and
proof-nets, these innessential details are captured by a notion of equivalence
on $\lambda$-terms known as $\simeq_\sigma$-equivalence in both the
intuitionistic (due to Regnier) and classical (due to Laurent) cases. The
purpose of this paper is to uncover a strong bisimulation behind
$\simeq_\sigma$-equivalence, as formulated by Laurent for Parigot's
$\lambda\mu$-calculus. This is achieved by introducing a relation $\simeq$,
defined over a revised presentation of $\lambda\mu$-calculus we dub $\Lambda
M$.
  More precisely, we first identify the reasons behind Laurent's
$\simeq_\sigma$-equivalence failing to be a strong bisimulation. Inspired by
Laurent's Polarized Proof-Nets, this leads us to distinguish multiplicative and
exponential reduction steps on terms. Second, we provide an enriched syntax
that allows to keep track of the renaming operation. These technical
ingredients are crucial to pave the way towards a strong bisimulation for the
classical case. We thus introduce a calculus $\Lambda M$ and a relation
$\simeq$ that we show to be a strong bisimulation with respect to reduction in
$\Lambda M$, i.e. two $\simeq$-equivalent terms have the exact same reduction
semantics, a result which fails for Regnier's $\simeq_\sigma$-equivalence in
$\lambda$-calculus as well as for Laurent's $\simeq_\sigma$-equivalence in
$\lambda\mu$. We also show that two $\simeq$-equivalent terms translate to
equivalent Polarized Proof-Nets. Although $\simeq$ is not strictly included in
Laurent's $\simeq_\sigma$, it can be seen as a restriction of it.


Abstract:
  Learning musical instruments using online instructional videos has become
increasingly prevalent. However, pre-recorded videos lack the instantaneous
feedback and personal tailoring that human tutors provide. In addition,
existing video navigations are not optimized for instrument learning, making
the learning experience encumbered. Guided by our formative interviews with
guitar players and prior literature, we designed Soloist, a mixed-initiative
learning framework that automatically generates customizable curriculums from
off-the-shelf guitar video lessons. Soloist takes raw videos as input and
leverages deep-learning based audio processing to extract musical information.
This back-end processing is used to provide an interactive visualization to
support effective video navigation and real-time feedback on the user's
performance, creating a guided learning experience. We demonstrate the
capabilities and specific use-cases of Soloist within the domain of learning
electric guitar solos using instructional YouTube videos. A remote user study,
conducted to gather feedback from guitar players, shows encouraging results as
the users unanimously preferred learning with Soloist over unconverted
instructional videos.


Abstract:
  We explore the nonunitary dynamics of $(2+1)$-dimensional free fermions and
show that the obtained steady state is critical regardless the strength of the
nonunitary evolution. Numerical results indicate that the entanglement entropy
has a logarithmic violation of the area-law and the mutual information between
two distant regions decays as a power-law function. In particular, we provide
an interpretation of these scaling behaviors in terms of a simple quasiparticle
pair picture. In addition, we study the dynamics of the correlation function
and demonstrate that this system has dynamical exponent $z=1$. We further
demonstrate the dynamics of the correlation function can be well captured by a
classical nonlinear master equation. Our method opens a door to a vast number
of nonunitary random dynamics in free fermions and can be generalized to any
dimensions.


Abstract:
  Many cultural traits characterizing intelligent behaviors are now thought to
be transmitted through statistical learning, motivating us to study its effects
on cultural evolution. We conduct a large-scale music data analysis and observe
that various statistical parameters of musical products approximately follow
the beta distribution and other conjugate distributions. We construct a simple
model of cultural evolution incorporating statistical learning and analytically
show that conjugate distributions emerge at equilibrium in the presence of
oblique transmission. The results demonstrate that the distribution of a
cultural trait within a population depends on the individual's model for
cultural production (the conjugate distribution law), and reveal interesting
possibilities for theoretical and experimental studies on cultural evolution
and social learning.


Abstract:
  We investigate the balance of power between stars and AGN across cosmic
history, based on the comparison between the infrared (IR) galaxy luminosity
function (LF) and the IR AGN LF. The former corresponds to emission from dust
heated by stars and AGN, whereas the latter includes emission from AGN-heated
dust only. We find that at all redshifts (at least up to z~2.5), the high
luminosity tails of the two LFs converge, indicating that the most
infrared-luminous galaxies are AGN-powered. Our results shed light to the
decades-old conundrum regarding the flatter high-luminosity slope seen in the
IR galaxy LF compared to that in the UV and optical. We attribute this
difference to the increasing fraction of AGN-dominated galaxies with increasing
total infrared luminosity (L_IR). We partition the L_IR-z parameter space into
a star-formation and an AGN-dominated region, finding that the most luminous
galaxies at all epochs lie in the AGN-dominated region. This sets a potential
`limit' to attainable star formation rates, casting doubt on the abundance of
`extreme starbursts': if AGN did not exist, L_IR>10^13 Lsun galaxies would be
significantly rarer than they currently are in our observable Universe. We also
find that AGN affect the average dust temperatures (T_dust) of galaxies and
hence the shape of the well-known L_IR-T_dust relation. We propose that the
reason why local ULIRGs are hotter than their high redshift counterparts is
because of a higher fraction of AGN-dominated galaxies amongst the former
group.


Abstract:
  The properties of a supersolid state (SS) in quasi-one-dimensional dipolar
Bose-Einstein condensate is studied, considering two possible mechanisms of
realization - due to repulsive three-body atomic interactions and quantum
fluctuations in the framework of the Lee-Huang-Yang (LHY) theory. The proposed
theoretical model, based on minimization of the energy functional, allows
evaluating the amplitude of the SS for an arbitrary set of parameters in the
governing Gross-Pitaevskii equation (GPE). To explore the dynamics of the SS
first, we numerically construct its ground state in different settings,
including periodic boundary conditions, box-like trap and parabolic potential,
then impose a perturbation. In oscillations of the perturbed supersolid we
observe the key manifestation of SS, namely the free flow of the superfluid
fraction through the crystalline component of the system. Two distinct
oscillation frequencies of the supersolid associated with the superfluid
fraction and crystalline components of the wave function are identified from
numerical simulations of the GPE.


Abstract:
  Arguably the most common and salient object in daily video communications is
the talking head, as encountered in social media, virtual classrooms,
teleconferences, news broadcasting, talk shows, etc. When communication
bandwidth is limited by network congestions or cost effectiveness, compression
artifacts in talking head videos are inevitable. The resulting video quality
degradation is highly visible and objectionable due to high acuity of human
visual system to faces. To solve this problem, we develop a multi-modality deep
convolutional neural network method for restoring face videos that are
aggressively compressed. The main innovation is a new DCNN architecture that
incorporates known priors of multiple modalities: the video-synchronized speech
signal and semantic elements of the compression code stream, including motion
vectors, code partition map and quantization parameters. These priors strongly
correlate with the latent video and hence they are able to enhance the
capability of deep learning to remove compression artifacts. Ample empirical
evidences are presented to validate the superior performance of the proposed
DCNN method on face videos over the existing state-of-the-art methods.


Abstract:
  In this paper we study the spontaneous development of symmetries in the early
layers of a Convolutional Neural Network (CNN) during learning on natural
images. Our architecture is built in such a way to mimic the early stages of
biological visual systems. In particular, it contains a pre-filtering step
$\ell^0$ defined in analogy with the Lateral Geniculate Nucleus (LGN).
Moreover, the first convolutional layer is equipped with lateral connections
defined as a propagation driven by a learned connectivity kernel, in analogy
with the horizontal connectivity of the primary visual cortex (V1). The layer
$\ell^0$ shows a rotational symmetric pattern well approximated by a Laplacian
of Gaussian (LoG), which is a well-known model of the receptive profiles of LGN
cells. The convolutional filters in the first layer can be approximated by
Gabor functions, in agreement with well-established models for the profiles of
simple cells in V1. We study the learned lateral connectivity kernel of this
layer, showing the emergence of orientation selectivity w.r.t. the learned
filters. We also examine the association fields induced by the learned kernel,
and show qualitative and quantitative comparisons with known group-based models
of V1 horizontal connectivity. These geometric properties arise spontaneously
during the training of the CNN architecture, analogously to the emergence of
symmetries in visual systems thanks to brain plasticity driven by external
stimuli.


Abstract:
  Phenomics is concerned with detailed description of all aspects of organisms,
from their physical foundations at genetic, molecular and cellular level, to
behavioural and psychological traits. Neuropsychiatric phenomics, endorsed by
NIMH, provides such broad perspective to understand mental disorders. It is
clear that learning sciences also need similar approach that will integrate
efforts to understand cognitive processes from the perspective of the brain
development, in temporal, spatial, psychological and social aspects. The brain
is a substrate shaped by genetic, epigenetic, cellular and environmental
factors including education, individual experiences and personal history,
culture, social milieu. Learning sciences should thus be based on the
foundation of neurocognitive phenomics. A brief review of selected aspects of
such approach is presented, outlining new research directions. Central,
peripheral and motor processes in the brain are linked to the inventory of the
learning styles.


Abstract:
  This article describes novel approaches to quickly estimate planar surfaces
from RGBD sensor data. The approach manipulates the standard algebraic fitting
equations into a form that allows many of the needed regression variables to be
computed directly from the camera calibration information. As such, much of the
computational burden required by a standard algebraic surface fit can be
pre-computed. This provides a significant time and resource savings, especially
when many surface fits are being performed which is often the case when RGBD
point-cloud data is being analyzed for normal estimation, curvature estimation,
polygonization or 3D segmentation applications. Using an integral image
implementation, the proposed approaches show a significant increase in
performance compared to the standard algebraic fitting approaches.


Abstract:
  Compressing Deep Neural Network (DNN) models to alleviate the storage and
computation requirements is essential for practical applications, especially
for resource limited devices. Although capable of reducing a reasonable amount
of model parameters, previous unstructured or structured weight pruning methods
can hardly truly accelerate inference, either due to the poor hardware
compatibility of the unstructured sparsity or due to the low sparse rate of the
structurally pruned network. Aiming at reducing both storage and computation,
as well as preserving the original task performance, we propose a generalized
weight unification framework at a hardware compatible micro-structured level to
achieve high amount of compression and acceleration. Weight coefficients of a
selected micro-structured block are unified to reduce the storage and
computation of the block without changing the neuron connections, which turns
to a micro-structured pruning special case when all unified coefficients are
set to zero, where neuron connections (hence storage and computation) are
completely removed. In addition, we developed an effective training framework
based on the alternating direction method of multipliers (ADMM), which converts
our complex constrained optimization into separately solvable subproblems.
Through iteratively optimizing the subproblems, the desired micro-structure can
be ensured with high compression ratio and low performance degradation. We
extensively evaluated our method using a variety of benchmark models and
datasets for different applications. Experimental results demonstrate
state-of-the-art performance.


Abstract:
  The asymptotic phase is a fundamental quantity for the analysis of
deterministic limit-cycle oscillators, and generalized definitions of the
asymptotic phase for stochastic oscillators have also been proposed. In this
article, we show that the asymptotic phase and also amplitude can be defined
for classical and semiclassical stochastic oscillators in a natural and unified
manner by using the eigenfunctions of the Koopman operator of the system. We
show that the proposed definition gives appropriate values of the phase and
amplitude for strongly stochastic limit-cycle oscillators, excitable systems
undergoing noise-induced oscillations, and also for quantum limit-cycle
oscillators in the semiclassical regime.


Abstract:
  The relationship between galaxy characteristics and the reionization of the
universe remains elusive, mainly due to the observational difficulty in
accessing the Lyman continuum (LyC) at these redshifts. It is thus important to
identify low-redshift LyC-leaking galaxies that can be used as laboratories to
investigate the physical processes that allow LyC photons to escape. The
weakness of the [S II] nebular emission lines relative to typical star-forming
galaxies has been proposed as a LyC predictor. In this paper, we show that the
[S II]-deficiency is an effective method to select LyC-leaking candidates using
data from the Low-redshift LyC Survey, which has detected flux below the Lyman
edge in 35 out of 66 star-forming galaxies with the Cosmic Origins Spectrograph
onboard the Hubble Space Telescope. We show that LyC leakers tend to be more [S
II]-deficient and that the fraction of their detections increases as [S
II]-deficiency becomes more prominent. Correlational studies suggest that [S
II]-deficiency complements other LyC diagnostics (such as strong Lyman-$\alpha$
emission and high [O III]/[O II]). Our results verify an additional technique
by which reionization-era galaxies could be studied.


Abstract:
  We propose a novel analytical model for anisotropic multi-layer cylindrical
structures containing graphene layers. The general structure is formed by an
aperiodic repetition of a three-layer sub-structure, where a graphene layer,
with an isotropic surface conductivity, has been sandwiched between two
adjacent magnetic materials. An external magnetic bias has been applied in the
axial direction. General matrix representation is obtained in our proposed
analytical model to find the dispersion relation. The relation will be used to
find the effective index of the structure and its other propagation parameters.
Two special exemplary structures have been introduced and studied to show the
richness of the proposed general structure regarding the related specific
plasmonic wave phenomena and effects. A series of simulations have been
conducted to demonstrate the noticeable wave-guiding properties of the
structure in the 10-40 THz band. A very good agreement between the analytical
and simulation results is observed. The proposed structure can be utilized to
design novel plasmonic devices such as absorbers, modulators, plasmonic sensors
and tunable antennas in the THz frequencies.


Abstract:
  In this paper, meant as a companion to arXiv:2006.04458, we consider a class
of non-integrable $2D$ Ising models in cylindrical domains, and we discuss two
key aspects of the multiscale construction of their scaling limit. In
particular, we provide a detailed derivation of the Grassmann representation of
the model, including a self-contained presentation of the exact solution of the
nearest neighbor model in the cylinder. Moreover, we prove precise asymptotic
estimates of the fermionic Green's function in the cylinder, required for the
multiscale analysis of the model. We also review the multiscale construction of
the effective potentials in the infinite volume limit, in a form suitable for
the generalization to finite cylinders. Compared to previous works, we
introduce a few important simplifications in the localization procedure and in
the iterative bounds on the kernels of the effective potentials, which are
crucial for the adaptation of the construction to domains with boundaries.