Abstract:
  This paper presents an attempt to employ the mask language modeling approach
of BERT to pre-train a 12-layer Transformer model over 4,166 pieces of
polyphonic piano MIDI files for tackling a number of symbolic-domain
discriminative music understanding tasks. These include two note-level
classification tasks, i.e., melody extraction and velocity prediction, as well
as two sequence-level classification tasks, i.e., composer classification and
emotion classification. We find that, given a pre-trained Transformer, our
models outperform recurrent neural network based baselines with less than 10
epochs of fine-tuning. Ablation studies show that the pre-training remains
effective even if none of the MIDI data of the downstream tasks are seen at the
pre-training stage, and that freezing the self-attention layers of the
Transformer at the fine-tuning stage slightly degrades performance. All the
five datasets employed in this work are publicly available, as well as
checkpoints of our pre-trained and fine-tuned models. As such, our research can
be taken as a benchmark for symbolic-domain music understanding.


Abstract:
  In this paper, we characterize quasicrystalline interacting topological
phases of matter i.e., phases protected by some quasicrystalline structure. We
show that the elasticity theory of quasicrystals, which accounts for both
"phonon" and "phason" modes, admits non-trivial quantized topological terms
with far richer structure than their crystalline counterparts. We show that
these terms correspond to distinct phases of matter and also uncover
intrinsically quasicrystalline phases, which have no crystalline analogues. For
quasicrystals with internal $\mathrm{U}(1)$ symmetry, we discuss a number of
interpretations and physical implications of the topological terms, including
constraints on the mobility of dislocations in $d=2$ quasicrystals and a
quasicrystalline generalization of the Lieb-Schultz-Mattis-Oshikawa-Hastings
theorem. We then extend these ideas much further and address the complete
classification of quasicrystalline topological phases, including systems with
point-group symmetry as well as non-invertible phases. We hence obtain the
"Quasicrystalline Equivalence Principle," which generalizes the classification
of crystalline topological phases to the quasicrystalline setting.


Abstract:
  This paper is the third and final component of a three-part effort on
observers contracting a Riemannian distance between the state of the system and
its estimate. In Part I, we showed that such a contraction property holds if
the system dynamics and the Riemannian metric satisfy two key conditions: a
differential detectability property and a geodesic monotonicity property. With
the former condition being the focus of Part II, in this Part III, we study the
latter condition in relationship to the nullity of the second fundamental form
of the output function. We formulate sufficient and necessary conditions for it
to hold. We establish a link between it and the infinite gain margin property,
and we provide a systematic way for constructing a metric satisfying this
condition. Finally, we illustrate cases where both conditions hold.


Abstract:
  Quantum secret sharing (QSS) is an essential primitive for the future quantum
internet, which promises secure multiparty communication. However, developing a
large-scale QSS network is a huge challenge due to the channel loss and the
requirement of multiphoton interference or high-fidelity multipartite
entanglement distribution. Here, we propose a three-user QSS protocol without
monitoring signal disturbance, which is capable of ensuring the unconditional
security. The final key rate of our protocol can be demonstrated to break the
Pirandola-Laurenza-Ottaviani-Banchi bound of quantum channel and its simulated
transmission distance can approach over 600 km using current techniques. Our
results pave the way to realizing high-rate and large-scale QSS networks.


Abstract:
  We introduce a new binary detection technique, Binary INformation from Open
Clusters using SEDs (binocs), which we show is able to determine reliable
stellar multiplicity and masses over a much larger mass range than current
approaches. This new technique determines accurate component masses of binary
and single systems of the open clusters main sequence by comparing observed
magnitudes from multiple photometric filters to synthetic star spectral energy
distributions (SEDs) allowing systematically probing the binary population for
low mass stars in clusters for 8 well-studied open clusters. We provide new
deep, infrared photometric catalogs (1.2 - 8.0 microns) for the key open
clusters NGC 1960 (M36), NGC 2099 (M37), NGC 2420, and NGC2682 (M67), using
observation from NOAO/NEWFIRM and Spitzer}/IRAC. Using these deep
multi-wavelength catalogs, the binocs method is applied to these clusters to
determine accurate component masses for unresolved cluster binaries. We explore
binary fractions as a function of cluster age, Galactic location and
metallicity.


Abstract:
  In a celebrated paper of 1893, Hadamard established the maximal determinant
theorem, which establishes an upper bound on the determinant of a matrix with
complex entries of norm at most $1$. His paper concludes with the suggestion
that mathematicians study the maximum value of the determinant of an $n \times
n$ matrix with entries in $\{ \pm 1\}$. This is the Hadamard maximal
determinant problem.
  This survey provides complete proofs of the major results obtained thus far.
We focus equally on upper bounds for the determinant (achieved largely via the
study of the Gram matrices), and constructive lower bounds (achieved largely
via quadratic residues in finite fields and concepts from design theory). To
provide an impression of the historical development of the subject, we have
attempted to modernise many of the original proofs, while maintaining the
underlying ideas. Thus some of the proofs have the flavour of determinant
theory, and some appear in print in English for the first time.
  We survey constructions of matrices in order $n \equiv 3 \mod 4$, giving
asymptotic analysis which has not previously appeared in the literature. We
prove that there exists an infinite family of matrices achieving at least
$0.48$ of the maximal determinant bound. Previously the best known constant for
a result of this type was $0.34$.


Abstract:
  In this paper we study properties of regular solutions of quaternionic
Riccati equations. The obtained results we use for study of the asymptotic
behavior of solutions of two first-order linear quaternionic ordinary
differential equations.


Abstract:
  Machine learning models present a risk of adversarial attack when deployed in
production. Quantifying the contributing factors and uncertainties using
empirical measures could assist the industry with assessing the risk of
downloading and deploying common model types. This work proposes modifying the
traditional Drake Equation's formalism to estimate the number of potentially
successful adversarial attacks on a deployed model. The Drake Equation is
famously used for parameterizing uncertainties and it has been used in many
research fields outside of its original intentions to estimate the number of
radio-capable extra-terrestrial civilizations. While previous work has outlined
methods for discovering vulnerabilities in public model architectures, the
proposed equation seeks to provide a semi-quantitative benchmark for evaluating
and estimating the potential risk factors for adversarial attacks.


Abstract:
  The velocities of the quasiparticles that form Cooper pairs in a
superconductor are revealed by the upper critical magnetic field. Here we use
this property to assess superconductivity in magic-angle twisted bilayer
graphene (MATBG), which has been observed over a range of moir\'e band filling,
twist angle, and screening environment conditions. We find that for pairing
mechanisms that are unrelated to correlations within the MATBG flat bands,
minima in an average Fermi velocity $v_F^* \equiv k_B T_c \ell_c /\hbar $,
where $\ell_c$ is the magnetic length at the critical perpendicular magnetic
field, are always coincident with transition temperature maxima. Both extrema
occur near flat-band van Hove singularities. Since no such association is
present in MATBG experimental data, we conclude that electronic correlations
that yield a band-filling-dependent pairing glue must play a crucial role in
MATBG superconductivity.


Abstract:
  This paper deals with a version of the two-timing method which describes
various `slow' effects caused by externally imposed `fast' oscillations. Such
small oscillations are often called \emph{vibrations} and the research area can
be referred as \emph{vibrodynamics}. The governing equations represent a
generic system of first-order ODEs containing a prescribed oscillating velocity
u, given in a general form. Two basic small parameters stand in for the inverse
frequency and the ratio of two time-scales; they appear in equations as regular
perturbations. The proper connections between these parameters yield the
\emph{distinguished limits}, leading to the existence of closed systems of
asymptotic equations. The aim of this paper is twofold: (i) to clarify (or to
demystify) the choices of a slow variable, and (ii) to give a coherent
exposition which is accessible for practical users in applied mathematics,
sciences and engineering. We focus our study on the usually hidden aspects of
the two-timing method such as the \emph{uniqueness or multiplicity of
distinguished limits} and \emph{universal structures of averaged equations}.
The main result is the demonstration that there are two (and only two)
different distinguished limits. The explicit instruction for practically
solving ODEs for different classes of u is presented. The key roles of drift
velocity and the qualitatively new appearance of the linearized equations are
discussed. To illustrate the broadness of our approach, two examples from
mathematical biology are shown.


Abstract:
  In recent years, significant advances have been made in the design and
analysis of fully dynamic algorithms. However, these theoretical results have
received very little attention from the practical perspective. Few of the
algorithms are implemented and tested on real datasets, and their practical
potential is far from understood. Here, we present a quick reference guide to
recent engineering and theory results in the area of fully dynamic graph
algorithms.


Abstract:
  New spectrometric data on V Pup are combined with satellite photometry
(HIPPARCOS and recent TESS) to allow a revision of the absolute parameters with
increased precision. We find: $M_1$ = 14.0$\pm$0.5, $M_2$ = 7.3$\pm$0.3
(M$_\odot$); $R_{1}$ = 5.48$\pm$0.18, $R_2$ = 4.59$\pm$0.15 (R$_\odot$);
$T_{1}$ 26000$\pm 1000$, $T_2$ 24000 $\pm$1000 (K), age 5 $\pm$1 (Myr),
photometric distance 320 $\pm$10 (pc). The TESS photometry reveals
low-amplitude ($\sim$0.002 mag) variations of the $\beta$ Cep kind, consistent
with the deduced evolutionary condition and age of the optical primary. This
fact provides independent support to our understanding of the system as in a
process of Case A type interactive evolution that can be compared with $\mu^1$
Sco. The $\sim$10 M$_{\odot}$ amount of matter shed by the over-luminous
present secondary must have been mostly ejected from the system rather than
transferred, thus taking angular momentum out of the orbit and keeping the pair
in relative close proximity. New times of minima for V Pup have been studied
and the results compared with previous analyses. The implied variation of
period is consistent with the Case A evolutionary model, though we offer only a
tentative sketch of the original arrangement of this massive system. We are not
able to confirm the previously reported cyclical variations having a 5.47 yr
period with the new data, though a direct comparison between the HIPPARCOS and
TESS photometry points to the presence of third light from a star that is
cooler than those of the close binary, as mentioned in previous literature.


Abstract:
  We study the maximum matching problem in the random-order semi-streaming
setting. In this problem, the edges of an arbitrary $n$-vertex graph $G=(V, E)$
arrive in a stream one by one and in a random order. The goal is to have a
single pass over the stream, use $n \cdot poly(\log n)$ space, and output a
large matching of $G$.
  We prove that for an absolute constant $\epsilon_0 > 0$, one can find a $(2/3
+ \epsilon_0)$-approximate maximum matching of $G$ using $O(n \log n)$ space
with high probability. This breaks the natural boundary of $2/3$ for this
problem prevalent in the prior work and resolves an open problem of Bernstein
[ICALP'20] on whether a $(2/3 + \Omega(1))$-approximation is achievable.


Abstract:
  Using the recently constructed covariant Ito-Langevin dynamics, we develop a
covariant theory of non-equilibrium thermodynamics that is applicable to small
systems with multiplicative noises and with slow variables forming curved
manifolds. Assuming instantaneous detailed balance, we derive expressions for
work, heat, entropy production, and free energy both at ensemble level, as well
as at the level of individual dynamic trajectory. We also relate time-reversal
asymmetry to entropy production, and derive its consequences such as
fluctuation theorem and work relation. The theory is based on Ito-calculus, is
fully covariant under time-independent nonlinear transformation of variables,
and is applicable to systems strongly coupled to environments.


Abstract:
  Most of the existing deep learning based end-to-end image/video coding (DLEC)
architectures are designed for non-subsampled RGB color format. However, in
order to achieve a superior coding performance, many state-of-the-art
block-based compression standards such as High Efficiency Video Coding
(HEVC/H.265) and Versatile Video Coding (VVC/H.266) are designed primarily for
YUV 4:2:0 format, where U and V components are subsampled by considering the
human visual system. This paper investigates various DLEC designs to support
YUV 4:2:0 format by comparing their performance against the main profiles of
HEVC and VVC standards under a common evaluation framework. Moreover, a new
transform network architecture is proposed to improve the efficiency of coding
YUV 4:2:0 data. The experimental results on YUV 4:2:0 datasets show that the
proposed architecture significantly outperforms naive extensions of existing
architectures designed for RGB format and achieves about 10% average BD-rate
improvement over the intra-frame coding in HEVC.


Abstract:
  Dense circumstellar material (CSM) is thought to play an important role in
observed luminous optical transients: if such CSM is shocked, e.g. by ejecta
expelled from the progenitor during core-collapse, then radiation produced by
the shock-heated CSM can power bright UV/optical emission. If the initial CSM
has an `outer edge' where most of the mass is contained and at which the
optical depth is large, then shock breakout -- when photons are first able to
escape the shocked CSM -- occurs near this outer edge. The $\sim$thin shell of
shocked CSM subsequently expands, and in the ensuing cooling-envelope phase,
radiative and adiabatic losses compete to expend the CSM thermal energy. Here
we derive an analytic solution to the bolometric light-curve produced by such
shocked CSM. For the first time, we provide a solution to the cooling-envelope
phase that is applicable already starting from shock breakout. In particular,
we account for the planar CSM geometry that is relevant at early times and
impose physically-motivated initial conditions. We show that these effects can
dramatically impact the resulting light-curves, particularly if the CSM optical
depth is only marginally larger than $c/v_{\rm sh}$ (where $v_{\rm sh}$ is the
shock velocity). This has important implications for interpreting observed fast
optical transients, which have previously been modeled using either
computationally-expensive numerical simulations or more simplified models that
do not properly capture the early light-curve evolution.


Abstract:
  A failed core-collapse supernova from a non-rotating progenitor can eject
mass due to a weakening of gravity associated to neutrino emission by the
protoneutron star. This mechanism yields observable transients and sets an
upper limit to the mass of the black hole (BH) remnant. Previous global
simulations of this mechanism have included neutrino losses parametrically,
however, with direct implications for the ejecta mass and energy. Here we
evolve the inner supernova core with a spherically-symmetric,
general-relativistic neutrino radiation-hydrodynamic code until BH formation.
We then use the result in a Newtonian code that follows the response of the
outer layers of the star to the change in gravity and resolves the surface
pressure scale height. We find that the dense-matter equation of state (EOS)
can introduce a factor $\sim 2$ variation in gravitational mass lost to
neutrinos, with a stiff EOS matching previous parametric results, and a soft
EOS yielding lower ejecta masses and energies by a factor of several. This
difference is caused primarily by the longer time to BH formation in stiffer
EOSs. With a soft EOS, our red and yellow supergiant progenitors fail to unbind
mass if hydrogen recombination energy is not included. Using a linear ramp in
time for mass-energy lost to neutrinos (with suitable parameters) yields a
stellar response within $\sim 10\%$ of that obtained using the detailed history
of neutrino losses. Our results imply quantitative but not qualitative
modifications to previous predictions for shock breakout, plateau emission, and
final BH masses from these events.


Abstract:
  Natural-gradient descent (NGD) on structured parameter spaces (e.g., low-rank
covariances) is computationally challenging due to difficult Fisher-matrix
computations. We address this issue by using \emph{local-parameter coordinates}
to obtain a flexible and efficient NGD method that works well for a
wide-variety of structured parameterizations. We show four applications where
our method (1) generalizes the exponential natural evolutionary strategy, (2)
recovers existing Newton-like algorithms, (3) yields new structured
second-order algorithms via matrix groups, and (4) gives new algorithms to
learn covariances of Gaussian and Wishart-based distributions. We show results
on a range of problems from deep learning, variational inference, and evolution
strategies. Our work opens a new direction for scalable structured geometric
methods.


Abstract:
  Relation extraction is used to populate knowledge bases that are important to
many applications. Prior datasets used to train relation extraction models
either suffer from noisy labels due to distant supervision, are limited to
certain domains or are too small to train high-capacity models. This constrains
downstream applications of relation extraction. We therefore introduce: WebRED
(Web Relation Extraction Dataset), a strongly-supervised human annotated
dataset for extracting relationships from a variety of text found on the World
Wide Web, consisting of ~110K examples. We also describe the methods we used to
collect ~200M examples as pre-training data for this task. We show that
combining pre-training on a large weakly supervised dataset with fine-tuning on
a small strongly-supervised dataset leads to better relation extraction
performance. We provide baselines for this new dataset and present a case for
the importance of human annotation in improving the performance of relation
extraction from text found on the web.


Abstract:
  This paper reports methods and results in the DeeperForensics Challenge 2020
on real-world face forgery detection. The challenge employs the
DeeperForensics-1.0 dataset, one of the most extensive publicly available
real-world face forgery detection datasets, with 60,000 videos constituted by a
total of 17.6 million frames. The model evaluation is conducted online on a
high-quality hidden test set with multiple sources and diverse distortions. A
total of 115 participants registered for the competition, and 25 teams made
valid submissions. We will summarize the winning solutions and present some
discussions on potential research directions.


Abstract:
  Creating high performance implementations of deep learning primitives on CPUs
is a challenging task. Multiple considerations including multi-level cache
hierarchy, and wide SIMD units of CPU platforms influence the choice of program
transformations to apply for performance optimization. In this paper, we
present machine learning powered compiler techniques to optimize loop nests. We
take a two-pronged approach to code optimization: We first apply high level
optimizations to optimize the code to take optimal advantage of the cache
memories. Then, we perform low level, target-specific optimizations to
effectively vectorize the code to run well on the SIMD units of the machine.
For high level optimizations, we use polyhedral compilation techniques and deep
learning approaches. For low level optimization, we use a target specific code
generator that generates code using vector intrinsics and Reinforcement
Learning (RL) techniques to find the optimal parameters for the code generator.
We perform experimental evaluation of the developed techniques on various
matrix multiplications that occur in popular deep learning workloads. The
experimental results show that the compiler techniques presented in the paper
achieve 7.6X and 8.2X speed-ups over a baseline for sequential and parallel
runs respectively.


Abstract:
  For data given on a nonlinear space, like angles, symmetric positive
matrices, the sphere, or the hyperbolic space, there is often enough structure
to form a Riemannian manifold. We present the Julia package Manifolds.jl,
providing a fast and easy to use library of Riemannian manifolds and Lie
groups. We introduce a common interface, available in ManifoldsBase.jl, with
which new manifolds, applications, and algorithms can be implemented. We
demonstrate the utility of Manifolds.jl using B\'ezier splines, an optimization
task on manifolds, and a principal component analysis on nonlinear data. In a
benchmark, Manifolds.jl outperforms existing packages in Matlab or Python by
several orders of magnitude and is about twice as fast as a comparable package
implemented in C++.


Abstract:
  This paper presents computer simulations of Cu$_x$Zr$_{100-x}$ $(x=36,50,64)$
in the liquid and glass phases. The simulations are based on the
effective-medium theory (EMT) potentials. We find good invariance of both
structure and dynamics in reduced units along the isomorphs of the systems. The
state points studied involve a density variation of almost a factor of two and
temperatures going from 1500 K to above 4000 K for the liquids and from 500 K
to above 1500 K for the glasses. For comparison, results are presented also for
similar temperature variations along isochores, showing little invariance. In
general for a binary system the phase diagram has three axes: composition,
temperature and pressure (or density). When isomorphs are present, there are
effectively only two axes, and for a fixed composition just one. We conclude
that the liquid and glass parts of the thermodynamic phase diagram of this
metallic glass former at a fixed composition is effectively one-dimensional in
the sense that many physical properties are invariant along the same curves,
implying that in order to investigate the phase diagram, it is only necessary
to go across these curves.


Abstract:
  The ability of bacteria to colonize and grow on different surfaces is an
essential process for biofilm development and depends on complex biomechanical
interactions between the biofilm and the underlying substrate. Changes in the
physical properties of the underlying substrate are known to alter biofilm
expansion, but the mechanisms by which biofilms sense and respond to physical
features of their environment are still poorly understood. Here, we report the
use of synthetic polyacrylamide hydrogels with tunable stiffness and
controllable pore size to assess physical effects of the substrate on biofilm
development. Using time lapse microscopy to track the growth of expanding
Serratia marcescens colonies, we find that biofilm colony growth can increase
with increasing substrate stiffness on purely elastic substrates, unlike what
is found on traditional agar substrates. Using traction force microscopy, we
find that biofilms exert transient stresses correlated over length scales much
larger than a single bacterium. Our results are consistent with a model of
biofilm development in which the interplay between osmotic pressure arising
from the biofilm and the poroelastic response of the underlying substrate
controls biofilm growth and morphology.


Abstract:
  A theoretical investigation has been made on the propagation of ion-acoustic
(IA) shock waves (IASHWs) in a magnetized pair-ion plasma having inertial warm
positive and negative ions, and inertialess super-thermal electrons and
positrons. The well known Burgers' equation has been derived by employing the
reductive perturbation method. The plasma model supports both positive and
negative shock structures under consideration of super-thermal electrons and
positrons. It is found that the oblique angle ($\delta$) enhances the magnitude
of the amplitude of both positive and negative shock profiles. It is also
observed that the steepness of the shock profiles decreases with the kinematic
viscosity of the ion, and the height of the shock profile increases (decreases)
with the mass of the positive (negative) ion. The implications of the results
have been briefly discussed for space and laboratory plasmas.


Abstract:
  Highly heterogeneous, anisotropic coefficients, e.g. in the simulation of
carbon-fibre composite components, can lead to extremely challenging finite
element systems. Direct solvers for the resulting large and sparse linear
systems suffer from severe memory requirements and limited parallel
scalability, while iterative solvers in general lack robustness. Two-level
spectral domain decomposition methods can provide such robustness for symmetric
positive definite linear systems, by using coarse spaces based on independent
generalized eigenproblems in the subdomains. Rigorous condition number bounds
are independent of mesh size, number of subdomains, as well as coefficient
contrast. However, their parallel scalability is still limited by the fact that
(in order to guarantee robustness) the coarse problem is solved via a direct
method. In this paper, we introduce a multilevel variant in the context of
subspace correction methods and provide a general convergence theory for its
robust convergence for abstract, elliptic variational problems. Assumptions of
the theory are verified for conforming, as well as for discontinuous Galerkin
methods applied to a scalar diffusion problem. Numerical results illustrate the
performance of the method for two- and three-dimensional problems and for
various discretization schemes, in the context of scalar diffusion and linear
elasticity.


Abstract:
  Positioning systems and frameworks use various techniques to determine the
position of an object. Some of the existing solutions combine different sensory
data at the time of positioning in order to compute more accurate positions by
reducing the error introduced by the used individual positioning techniques. We
present OpenHPS, a generic hybrid positioning system implemented in TypeScript,
that can not only reduce the error during tracking by fusing different sensory
data based on different algorithms, but also also make use of combined tracking
techniques when calibrating or training the system. In addition to a detailed
discussion of the architecture, features and implementation of the extensible
open source OpenHPS framework, we illustrate the use of our solution in a
demonstrator application fusing different positioning techniques. While OpenHPS
offers a number of positioning techniques, future extensions might integrate
new positioning methods or algorithms and support additional levels of
abstraction including symbolic locations.


Abstract:
  We propose and demonstrate a new phase retrieval method for imaging through
random media. Although methods to recover the Fourier amplitude through random
distortions are well established, recovery of the Fourier phase has been a more
difficult problem and is still a very active research area. Here, we show that
by simply ensemble averaging shift-corrected images, the Fourier phase of an
object obscured by random distortions can be accurately retrieved up to the
diffraction limit. The method is simple, fast, does not have any optimization
parameters, and does not require prior knowledge or assumptions about the
sample. We demonstrate the feasibility and robustness of our method by
realizing all computational diffraction-limited imaging through atmospheric
turbulence as well as imaging through multiple scattering media.


Abstract:
  We study the high-energy phase diagram of a two-dimensional
spin-$\frac{1}{2}$ Heisenberg model on a square lattice in the presence of
disorder. The use of large-scale tensor network numerics allows us to compute
the bi-partite entanglement entropy for systems of up to $30\times7$ lattice
sites. We demonstrate the existence of a finite many-body localized phase for
large disorder strength $W$ for which the eigenstate thermalization hypothesis
is violated. Moreover, we show explicitly that the area law holds for excited
states in this phase and determine an estimate for the critical $W_{\rm{c}}$
where the transition to the ergodic phase occurs.


Abstract:
  Given a deterministic finite automaton and its implementation with at most
one single fault, that we can test on a set of inputs, we provide an algorithm
to find a test set that guarantees finding whether the fault exists.


Abstract:
  Gas electron multipliers (GEMs) with wire (WGEMs) or metal electrodes
(MGEMs), which don't use any plastic insulators between electrodes are created.
The chambers containing MGEMs (WGEMs) with pin-anodes are proposed as detectors
for searching of spin-dependent interactions between Dark Matter (DM) particles
and gases with nonzero-spin nuclei (H2, D2, 3He, 20Ne, CF4, CH4, etc.). In this
paper, we present a review of such chambers. For investigation of the gas
mixtures Ne+10%H2, H2 (D2) +3ppmTMAE, the chamber containing WGEM with
pin-anode detection system was constructed. In this paper we present the
results of an experimental study of these gaseous mixtures exited by an
{\alpha}-source. Mixture of Ar + 40 ppm C2H4 and mixture 50% Xe + 50%CF4 have
been investigated. The spatial distributions of photoelectron clouds produced
by primary scintillations on {\alpha}- and \b{eta}-particle tracks, as well as
the distributions of photoelectron clouds due to photons from avalanches at the
pin-anode, have been measured for the first time. In our experiments as another
filling of the chambers for search of low-mas WIMP (<10 GeV/c2), solar neutrino
and solar axions with spin-dependent interaction we propose to use the
mixtures: D2 +3ppmTMAE, 3He + 3%CH4, 20Ne + 10%H2, at pressure 10-17 bar. And
in our experiment with liquid gases is used the mixtures with 19F (LAr + CF4,
LXe + CF4) and mixture LCH4 + 40ppm TMAE. The time projection chamber (TPC)
with the mixture D2 + 3ppmTMAE filling allow to search of spin-dependent
interactions of solar axions and deuterium. As well as we present the detecting
systems for search of narrow pp-resonances (quarks) in accelerators
experiments.


Abstract:
  Domain adaptation aims to mitigate the domain gap when transferring knowledge
from an existing labeled domain to a new domain. However, existing
disentanglement-based methods do not fully consider separation between
domain-invariant and domain-specific features, which means the domain-invariant
features are not discriminative. The reconstructed features are also not
sufficiently used during training. In this paper, we propose a novel enhanced
separable disentanglement (ESD) model. We first employ a disentangler to
distill domain-invariant and domain-specific features. Then, we apply feature
separation enhancement processes to minimize contamination between
domain-invariant and domain-specific features. Finally, our model reconstructs
complete feature vectors, which are used for further disentanglement during the
training phase. Extensive experiments from three benchmark datasets outperform
state-of-the-art methods, especially on challenging cross-domain tasks.


Abstract:
  We first investigate the one-shot static entanglement cost to simulate a
bipartite quantum channel under the set of non-entangling channels. The lower
bound on the static entanglement cost is given by the generalized robustness of
the target channel as well as the robust-generating power of the channel, which
implies that the cost necessary to generate a bipartite quantum channel might
not be retrievable in general. Next, we conceive a set of quantum channels that
extends the set of non-entangling channels. We find out that the one-shot
static entanglement cost under the extended set of channels is given by the
channel's standard log-robustness. This gives the one-shot dynamic entanglement
cost under a set of free superchannels that do not generate dynamic
entanglement resource; the extended set is shown to be equivalent to the set of
free superchannels.


Abstract:
  While the front of a fluid shock is a few mean-free-paths thick, the front of
a collisionless shock can be orders of magnitude thinner. By bridging between a
collisional and a collisionless formalism, we assess the transition between
these two regimes. We consider non-relativistic, un-magnetized, planar shocks
in electron/ion plasmas. In addition, our treatment of the collisionless regime
is restricted to high Mach number electrostatic shocks. We find that the
transition can be parameterized by the upstream plasma parameter $\Lambda$
which measures the coupling of the upstream medium. For $\Lambda \lesssim
1.12$, the upstream is collisional, i.e. strongly coupled, and the strong shock
front is about $\mathcal{M}_1 \lambda_{\mathrm{mfp},1}$ thick, where
$\lambda_{\mathrm{mfp},1}$ and $\mathcal{M}_1 $ are the upstream mean-free-path
and Mach number respectively. A transition occurs for $\Lambda \sim 1.12$
beyond which the front is $\sim \mathcal{M}_1\lambda_{\mathrm{mfp},1}\ln
\Lambda/\Lambda$ thick for $\Lambda\gtrsim 1.12$. Considering $\Lambda$ can
reach billions in astrophysical settings, this allows to understand how the
front of a collisionless shock can be orders of magnitude smaller than the
mean-free-path, and how physics transitions continuously between these 2
extremes.


Abstract:
  We consider the complexity of deciding the winner of an election under the
Slater rule. In this setting we are given a tournament $T = (V, A)$, where the
vertices of V represent candidates and the direction of each arc indicates
which of the two endpoints is preferable for the majority of voters. The Slater
score of a vertex $v\in V$ is defined as the minimum number of arcs that need
to be reversed so that $T$ becomes acyclic and $v$ becomes the winner. We say
that $v$ is a Slater winner in $T$ if $v$ has minimum Slater score in $T$.
  Deciding if a vertex is a Slater winner in a tournament has long been known
to be NP-hard. However, the best known complexity upper bound for this problem
is the class $\Theta_2^p$, which corresponds to polynomial-time Turing machines
with parallel access to an NP oracle. In this paper we close this gap by
showing that the problem is $\Theta_2^p$-complete, and that this hardness
applies to instances constructible by aggregating the preferences of 7 voters.


Abstract:
  Detecting hidden convexity is one of the tools to address nonconvex
minimization problems. After giving a formal definition of hidden convexity, we
introduce the notion of conditional infimum, as it will prove instrumental in
detecting hidden convexity. We develop the theory of the conditional infimum,
and we establish a tower property, relevant for minimization problems. Thus
equipped, we provide a sufficient condition for hidden convexity in nonconvex
minimization problems. We illustrate our result on nonconvex quadratic
minimization problems. We conclude with perspectives for using the conditional
infimum in relation to the so-called S-procedure, to couplings and conjugacies,
and to lower bound convex programs.


Abstract:
  We consider sets of factors that can be avoided in square-free words on
two-generator free groups. The elements of the group are presented in terms of
0,1,2,3 such that 0 and 2 (resp.,1 and 3) are inverses of each other so that
02, 20, 13 and 31 do not occur in a reduced word. A Dean word is a reduced word
that does not contain occurrences of $uu$ for any nonempty $u$. Dean showed in
1965 that there exist infinite square-free reduced words. We show that if $w$
is a Dean word of length at least 59 then there are at most six reduced words
of length 3 avoided by $w$. We construct an infinite Dean word avoiding six
reduced words of length~3. We also construct infinite Dean words with low
critical exponent and avoiding fewer reduced words of length 3. Finally, we
show that the minimal frequency of a letter in a Dean word is $8/59$ and the
growth rate is close to 1.45818.


Abstract:
  A string $w$ is called a minimal absent word (MAW) for another string $T$ if
$w$ does not occur in $T$ but the proper substrings of $w$ occur in $T$. For
example, let $\Sigma = \{\mathtt{a, b, c}\}$ be the alphabet. Then, the set of
MAWs for string $w = \mathtt{abaab}$ is $\{\mathtt{aaa, aaba, bab, bb, c}\}$.
In this paper, we study combinatorial properties of MAWs in the sliding window
model, namely, how the set of MAWs changes when a sliding window of fixed
length $d$ is shifted over the input string $T$ of length $n$, where $1 \leq d
< n$. We present \emph{tight} upper and lower bounds on the maximum number of
changes in the set of MAWs for a sliding window over $T$, both in the cases of
general alphabets and binary alphabets. Our bounds improve on the previously
known best bounds [Crochemore et al., 2020].


Abstract:
  Financial literacy and financial education are important components of modern
life. The importance of financial literacy is increasing for financial
consumers because of the weakening of both government and employer-based
retirement systems. Unfortunately, empirical research shows that financial
consumers are not fully informed and are not able to make proper choices even
when appropriate information is available. More research is needed as to how
financial consumers obtain investment and financial planning information. A
primary data study was conducted to understand the differences between the
demographic categories of gender, age, education-level, and income-level with
the means of obtaining investment and financial planning information. In this
research study, which selected a population from the LinkedIn platform,
statistical differences between gender, age, education-level, and income-level
were confirmed. These differences helped to confirm prior research in this
field of study. Practical opportunities for commercial outreach to specific
populations became evident through this type of research. Providers of
investment and financial planning information can access their targeted
audience more effectively by understanding the demographic profile of the
audience, as well as the propensity of the demographic profile of the audience
to respond. As this type of research is relatively easy to construct and
administer, commercial outreach for providers of investment and financial
planning information can be conducted in a cost-efficient and effective manner.


Abstract:
  In this paper, we propose several models, which can realize synchronization
of complex networks in finite time effectively. The results apply to
heterogeneous dynamic networks, too. The mechanism of finite time convergence
is revealed. Different from many models, which assume the coupling matrix being
symmetric (or the connecting graph is undirected), here, the coupling matrix is
asymmetric (or the connecting graph is directed).


Abstract:
  Dynamical localization is the analog of Anderson localization in momentum
space, where the system's energy saturates and the single-particle
wave-functions are exponentially localized in momentum space. In the presence
of interactions, in the context of a periodically kicked Bose gas, it has been
argued that dynamical localization persists. Focusing on the Tonks (strongly
interacting) regime, we show that the many-body dynamically localized phase is
effectively thermal, a clear deviation from the breaking of ergodicity observed
in standard many-body localized systems. We relate the effective temperature to
the driving parameters, and thus quantitatively describe the loss of coherence
at large distances in this phase. Contrary to the non-interacting case, the
momentum distribution decays as a power-law at large momenta, characterized by
an effectively thermal Tan's contact. This is a rare example where driving and
many-body (dynamical) localization lead to an effectively ergodic state.


Abstract:
  Time discretizations of phase-field systems have been studied. For example, a
time discretization and an error estimate for a parabolic-parabolic phase-field
system have been studied by Colli--K. [Commun. Pure Appl. Anal. 18 (2019)].
Also, a time discretization and an error estimate for a simultaneous abstract
evolution equation applying parabolic-hyperbolic phase field systems and the
linearized equations of coupled sound and heat flow have been studied (see K.
[ESAIM Math. Model. Numer. Anal.54 (2020), Electron. J. Differential Equations
2020, Paper No. 96]). On the other hand, although existence, continuous
dependence estimates and behavior of solutions to nonlocal phase-field systems
with inertial terms have been studied by Grasselli--Petzeltov\'a--Schimperna
[Quart. Appl. Math. 65 (2007)], time discretizations of these systems seem to
be not studied yet. In this paper we focus on employing a time discretization
scheme for a nonlocal phase-field system with inertial term and establishing an
error estimate for the difference between continuous and discrete solutions.


Abstract:
  We describe a technique to measure the position of a dipolar scatterer based
on self-homodyne detection of the scattered light. The method can theoretically
reach the Heisenberg limit, at which information gained about the position is
constrained only by the back-action of the scattered light. The technique has
applications in the fields of levitated optomechanics and trapped ions and is
generally applicable to the position determination of confined light
scatterers.


Abstract:
  Braneworld scenarios consider our observable universe as a brane embedded in
a five-dimensional bulk. In this work, we consider thick braneworld systems in
the recently proposed dynamically equivalent scalar-tensor representation of
$f(R,T)$ gravity, where $R$ is the Ricci scalar and $T$ the trace of the
stress-energy tensor. In the general $f\left(R,T\right)$ case we consider two
different models: a brane model without matter fields where the geometry is
supported solely by the gravitational fields, and a second model where matter
is described by a scalar field with a potential. The particular cases for which
the function $f\left(R,T\right)$ is separable in the forms $F\left(R\right)+T$
and $R+G\left(T\right)$, which give rise to scalar-tensor representations with
a single auxiliary scalar field, are studied separately. The stability of the
gravitational sector is investigated and the models are shown to be stable
against small perturbations of the metric. Furthermore, we show that in the
$f\left(R,T\right)$ model in the presence of an extra matter field, the shape
of the graviton zero-mode develops internal structure under appropriate choices
of the parameters of the model.


Abstract:
  We propose a two-dimensional spectroscopic protocol for measuring the
time-dependent coherences between the stationary states of a system induced by
a time-dependent system-bath interaction. We also investigate the role of
temporally-correlated noise on coherence dephasing. This protocol enables
dynamical information about the system and its coupling to the environment to
be determined. Our results are based on the quantum-trajectory method, and are
obtained from both approximate, analytical and exact, numerical solutions of
the time-dependent Schroedinger equation. As an example, we show how this
protocol can be used to investigate exciton dynameds in conjugated polymers
induced by the coupling of their torsional modes with the environment.


Abstract:
  In the semileptonic decays of heavy mesons and baryons the lepton-mass
dependence factors out in the quadratic $\cos^2\theta$ coefficient of the
differential $\cos\theta$ distribution. We call the corresponding normalized
coefficient the convexity parameter. This observation opens the path to a test
of lepton universality in semileptonic heavy meson and baryon decays that is
independent of form-factor effects. By projecting out the quadratic rate
coefficient, dividing out the lepton-mass-dependent factor and restricting the
phase space integration to the $\tau$ lepton phase space, one can define
optimized partial rates which, in the Standard Model, are the same for all
three $(e,\mu,\tau)$ modes in a given semileptonic decay process. We discuss
how the identity is spoiled by New Physics effects. We discuss semileptonic
heavy meson decays such as $\bar{B}^0 \to D^{(\ast)+} \ell^- \bar\nu_\ell$ and
$B_c^- \to J/\psi (\eta_c)\ell^- \bar\nu_\ell$, and semileptonic heavy baryon
decays such as $\Lambda_b \to \Lambda_c \ell^- \bar\nu_\ell$ for each
$\ell=e,\mu,\tau$.


Abstract:
  Quantifying quantum states' complexity is a key problem in various subfields
of science, from quantum computing to black-hole physics. We prove a prominent
conjecture by Brown and Susskind about how random quantum circuits' complexity
increases. Consider constructing a unitary from Haar-random two-qubit quantum
gates. Implementing the unitary exactly requires a circuit of some minimal
number of gates - the unitary's exact circuit complexity. We prove that this
complexity grows linearly with the number of random gates, with unit
probability, until saturating after exponentially many random gates. Our proof
is surprisingly short, given the established difficulty of lower-bounding the
exact circuit complexity. Our strategy combines differential topology and
elementary algebraic geometry with an inductive construction of Clifford
circuits.


Abstract:
  Pebbles of millimeter sizes are abundant in protoplanetary discs around young
stars. Chondrules inside primitive meteorites - formed by melting of dust
aggregate pebbles or in impacts between planetesimals - have similar sizes. The
role of pebble accretion for terrestrial planet formation is nevertheless
unclear. Here we present a model where inwards-drifting pebbles feed the growth
of terrestrial planets. The masses and orbits of Venus, Earth, Theia (which
later collided with the Earth to form the Moon) and Mars are all consistent
with pebble accretion onto protoplanets that formed around Mars' orbit and
migrated to their final positions while growing. The isotopic compositions of
Earth and Mars are matched qualitatively by accretion of two generations of
pebbles, carrying distinct isotopic signatures. Finally, we show that the water
and carbon budget of Earth can be delivered by pebbles from the early
generation before the gas envelope became hot enough to vaporise volatiles.


Abstract:
  Existing neural information retrieval (IR) models have often been studied in
homogeneous and narrow settings, which has considerably limited insights into
their out-of-distribution (OOD) generalization capabilities. To address this,
and to facilitate researchers to broadly evaluate the effectiveness of their
models, we introduce Benchmarking-IR (BEIR), a robust and heterogeneous
evaluation benchmark for information retrieval. We leverage a careful selection
of 18 publicly available datasets from diverse text retrieval tasks and domains
and evaluate 10 state-of-the-art retrieval systems including lexical, sparse,
dense, late-interaction and re-ranking architectures on the BEIR benchmark. Our
results show BM25 is a robust baseline and re-ranking and
late-interaction-based models on average achieve the best zero-shot
performances, however, at high computational costs. In contrast, dense and
sparse-retrieval models are computationally more efficient but often
underperform other approaches, highlighting the considerable room for
improvement in their generalization capabilities. We hope this framework allows
us to better evaluate and understand existing retrieval systems, and
contributes to accelerating progress towards better robust and generalizable
systems in the future. BEIR is publicly available at
https://github.com/UKPLab/beir.


Abstract:
  This paper aims at solving mass precise peg-in-hole assembly. First, a
feature space and a response space are constructed according to the relative
pose and equivalent forces and moments. Then the contact states are segmented
in the feature space and the segmentation boundaries are mapped into the
response space. Further, a feature-based compliance control (FBCC) algorithm is
proposed based on boundary mapping. In the FBCC algorithm, a direction matrix
is designed to execute accurate adjustment and an integrator is applied to
eliminate the residual responses. Finally, the simulations and experiments
demonstrate the superiority, robustness, and generalization ability of the
FBCC.


Abstract:
  In a recent comment [Feller et. al, arXiv:2101.09186] on our Letter [Phys.
Rev. Lett. 122, 010403 (2019)], Feller et. al identified a mistake in our
mathematical expression of "strong independence" for states that satisfy
Spectrum Broadcast Structure. We concede that we wrote a mathematical condition
that is necessary but not sufficient. However, we used the original and correct
qualitative definition for "strong independence" throughout the paper and in
our proofs, therefore the proofs and statements, aside from the aforementioned
mathematical expression, remain correct.


Abstract:
  We measure the phase velocities of surface acoustic waves (SAWs) propagating
at different crystal orientations on (001)-cut GaAs substrates and their
temperature dependance. We design and fabricate sets of interdigital
transducers (IDTs) to induce 4 {\mu}m SAWs via the inverse piezoelectric (PZE)
effect between the PZE [110] direction (set as {\theta} = 0{\deg}) and the
non-PZE [100] direction ({\theta} = 45{\deg}) on GaAs. We also prepare ZnO film
sputtered GaAs substrates in order to launch SAWs efficiently by IDTs even in
the non-PZE direction. We quantify acoustic velocities between 1.4 and 300 K
from the resonant frequencies in the S11 parameter using a network analyzer. We
observe parabolic velocity-temperature trends at all {\theta}-values both on
GaAs and ZnO/GaAs substrates. Below 200 K, in ZnO/GaAs substrates slower SAW
modes appear around the [110] direction, which are unseen at RT.


Abstract:
  Optical power measurements are needed in practically all technologies based
on light. Here we report a general-purpose optical power detector based on the
photoacoustic effect. Optical power incident on the detector's black absorber
produces an acoustic signal, which is further converted into an electrical
signal using a silicon-cantilever pressure transducer. We demonstrate an
exceptionally large spectral coverage from ultraviolet to far infrared, with
the possibility for further extension to the terahertz region. The linear
dynamic range of the detector reaches 80 dB, ranging from a noise-equivalent
power of 6 nW to 600 mW.


Abstract:
  Open-source software (OSS) is a critical part of the software supply chain.
Recent social engineering attacks against OSS development teams have enabled
attackers to become code contributors and later inject malicious code or
vulnerabilities into the project with the goal of compromising dependent
software. The attackers have exploited interactions among development team
members and the social dynamics of team behavior to enable their attacks. We
introduce a security approach that leverages signatures and patterns of team
dynamics to predict the susceptibility of a software development team to social
engineering attacks that enable access to the OSS project code. The proposed
approach is programming language-, platform-, and vulnerability-agnostic
because it assesses the artifacts of OSS team interactions, rather than OSS
code.


Abstract:
  The balancing provided by hydropower reservoirs is essential in the
transition towards a decarbonised European energy system, but the resource
might be impacted by future climate change. In this work, we first analyse the
hydropower operation needed to balance a wind and solar dominated European
energy system, to signify whether and to what extent hydropower is required to
operate differently due to the decarbonisation of the energy system. Second, we
apply runoff data achieved with 10 dynamically downscaled climate models with
0.11 x 0.11 deg horizontal and daily resolution to project the future reservoir
inflow at three CO2 emissions scenarios: low (RCP2.6), mid (RCP4.5), and high
emissions (RCP8.5). We show that the decarbonised energy system increases the
ramp rates and seasonality of the hydropower operation. Despite large
interannual and intermodel variability, we found a significant change in annual
inflow due to climate change in 20 out of 22 European countries at the mid and
high emissions scenarios. The seasonal profile, as well as the frequency and
duration of droughts and floods, is also projected to be impacted.


Abstract:
  Spectral geometric methods have brought revolutionary changes to the field of
geometry processing -- however, when the data to be processed exhibits severe
partiality, such methods fail to generalize. As a result, there exists a big
performance gap between methods dealing with complete shapes, and methods that
address missing geometry. In this paper, we propose a possible way to fill this
gap. We introduce the first method to compute compositions of non-rigidly
deforming shapes, without requiring to solve first for a dense correspondence
between the given partial shapes. We do so by operating in a purely spectral
domain, where we define a union operation between short sequences of
eigenvalues. Working with eigenvalues allows to deal with unknown
correspondence, different sampling, and different discretization (point clouds
and meshes alike), making this operation especially robust and general. Our
approach is data-driven, and can generalize to isometric and non-isometric
deformations of the surface, as long as these stay within the same semantic
class (e.g., human bodies), as well as to partiality artifacts not seen at
training time.


Abstract:
  Recent developments in data acquisition technology allow us to collect 3D
texture meshes quickly. Those can help us understand and analyse the urban
environment, and as a consequence are useful for several applications like
spatial analysis and urban planning. Semantic segmentation of texture meshes
through deep learning methods can enhance this understanding, but it requires a
lot of labelled data. The contributions of this work are threefold: (1) a new
benchmark dataset of semantic urban meshes, (2) a novel semi-automatic
annotation framework, and (3) an annotation tool for 3D meshes. In particular,
our dataset covers about 4 km2 in Helsinki (Finland), with six classes, and we
estimate that we save about 600 hours of labelling work using our annotation
framework, which includes initial segmentation and interactive refinement. We
also compare the performance of several state-of-theart 3D semantic
segmentation methods on the new benchmark dataset. Other researchers can use
our results to train their networks: the dataset is publicly available, and the
annotation tool is released as open-source.


Abstract:
  Freshwater from rivers influences Indian summer monsoon rainfall and regional
tropical cyclones by shallowing the upper layer and warming the subsurface
ocean in the Bay of Bengal. Here, we use in situ and satellite data with
reanalysis products to showcase how river water can experience a significant
increase in salinity on subseasonal timescales. This involves the trapping and
homogenization of freshwater by a cyclonic eddy in the Bay. Specifically, in
October 2015, river water is shown to enter a particularly long-lived eddy
along with its attracting manifolds within a period of two weeks. The eddy
itself is quite unique in that it lasted for 16 months in the Bay where average
lifespans are of the order of 2-3 months. This low salinity water results in
the formation of a highly stratified surface layer. In fact, when freshest, the
eddy has the highest sea-level anomalies, spins fastest, and supports strong
lateral gradients in salinity. Subsequently, observations reveal progressive
homogenization of salinity and relaxation of sea-level anomalies and salinity
gradients within a month. In particular, salty water spirals in, and freshwater
is pulled out across the eddy boundary. Lagrangian experiments elucidate this
process, whereby horizontal chaotic mixing provides a mechanism for the rapid
increase in surface salinity on the order of timescale of a month. This pathway
is distinct from vertical mixing and likely to be important in the eddy-rich
Bay of Bengal.


Abstract:
  The hadron-quark/gluon duality formulated in terms of a topology change at a
density $n\gsim 2n_0$ $n_0\simeq 0.16$fm$^{-3}$ is found to describe the core
of massive compact stars in terms of quasiparticles of fractional baryon
charges, behaving neither like pure baryons nor like deconfined quarks. This
can be considered as the Cheshire-Cat mechanism~\cite{CC} for the hadron-quark
continuity arrived at bottom-up from skyrmions that is equivalent to the
"MIT-bag"-to-skyrmion continuity arrived at top-down from quarks/gluons. Hidden
symmetries, both local gauge and pseudo-conformal (or broken scale), emerge and
give rise both to the long-standing "effective $g_A^\ast\approx 1$" in nuclear
Gamow-Teller transitions at $\lsim n_0$ and to the pseudo-conformal sound
velocity $v_{pcs}^2/c^2\approx 1/3$ at $\gsim 3n_0$. It is suggested that what
has been referred to, since a long time, as "quenched $g_A$" in light nuclei
reflects what leads to the dilaton-limit $g_A^{\rm DL}=1$ at near the
(putative) infrared fixed point of scale invariance. These properties are
confronted with the recent observations in Gamow-Teller transitions and in
astrophysical observations.


Abstract:
  We demonstrate that cosmic string loops may provide a joint resolution of two
mysteries surrounding recently observed black holes. For a string tension in an
appropriate range, large radius string loops have the potential to provide the
nonlinearities in the early universe which seed supermassive black holes. The
more numerous smaller radius string loops can then seed intermediate mass black
holes, including those with a mass in the region between 65 and 135 solar
masses in which standard black hole formation scenarios predict no black holes
are able to form, but which have recently been detected by the LIGO/VIRGO
collaboration. We find that there could be as many as $10^6$ of intermediate
mass black holes per galaxy, providing a tantalizing target for gravitational
wave observatories to look for.


Abstract:
  Quantum and random walks were proven to be equivalent on finite graphs by
demonstrating how to construct a time-dependent random walk sharing the exact
same evolution of vertex probability of any given discrete-time coined quantum
walk. Such equivalence stipulated a deep connection between the processes that
is far stronger than simply considering quantum walks as quantum analogues of
random walks. This article expands on the connection between quantum and random
walks by demonstrating a procedure that constructs a time-dependent quantum
walk matching the evolution of vertex probability of any given random walk in a
unitary way. It is a trivial fact that a quantum walk measured at all time
steps of its evolution degrades to a random walk. More interestingly, the
method presented describes a quantum walk that matches a random walk without
measurement operations, such that the unitary evolution of the quantum walk
captures the probability evolution of the random walk. The construction
procedure is general, covering both homogeneous and non-homogeneous random
walks. For the homogeneous random walk case, the properties of unitary
evolution imply that the quantum walk described is time-dependent since
homogeneous quantum walks do not converge for arbitrary initial conditions


Abstract:
  Mutation testing is a well-established technique for assessing a test suite's
quality by injecting artificial faults into production code. In recent years,
mutation testing has been extended to machine learning (ML) systems, and deep
learning (DL) in particular; researchers have proposed approaches, tools, and
statistically sound heuristics to determine whether mutants in DL systems are
killed or not. However, as we will argue in this work, questions can be raised
to what extent currently used mutation testing techniques in DL are actually in
line with the classical interpretation of mutation testing. We observe that ML
model development resembles a test-driven development (TDD) process, in which a
training algorithm (`programmer') generates a model (program) that fits the
data points (test data) to labels (implicit assertions), up to a certain
threshold. However, considering proposed mutation testing techniques for ML
systems under this TDD metaphor, in current approaches, the distinction between
production and test code is blurry, and the realism of mutation operators can
be challenged. We also consider the fundamental hypotheses underlying classical
mutation testing: the competent programmer hypothesis and coupling effect
hypothesis. As we will illustrate, these hypotheses do not trivially translate
to ML system development, and more conscious and explicit scoping and concept
mapping will be needed to truly draw parallels. Based on our observations, we
propose several action points for better alignment of mutation testing
techniques for ML with paradigms and vocabularies of classical mutation
testing.


Abstract:
  Context. Many low-redshift active galactic nuclei harbor a supermassive black
hole accreting matter at low or medium rates. At such rates, the accretion flow
usually consists of a cold optically thick disk, plus a hot, low density,
collisionless corona. In the latter component, charged particles can be
accelerated to high energies by various mechanisms. Aims. We aim to
investigate, in detail, nonthermal processes in hot accretion flows onto
supermassive black holes, covering a wide range of accretion rates and
luminosities. Methods. We developed a model consisting of a thin
Shakura-Sunyaev disk plus an inner hot accretion flow or corona, modeled as a
radiatively inefficient accretion flow, where nonthermal processes take place.
We solved the transport equations for relativistic particles and estimated the
spectral energy distributions resulting from nonthermal interactions between
the various particle species and the fields in the source. Results. We covered
a variety of scenarios, from low accretion rates up to 10% of the Eddington
limit, and identified the relevant cooling mechanisms in each case. The
presence of hadrons in the hot flow is decisive for the spectral shape, giving
rise to secondary particles and gamma-ray cascades. We applied our model to the
source IC 4329A, confirming earlier results that showed evidence of nonthermal
particles in the corona.


Abstract:
  Graph-level representations are critical in various real-world applications,
such as predicting the properties of molecules. But in practice, precise graph
annotations are generally very expensive and time-consuming. To address this
issue, graph contrastive learning constructs instance discrimination task which
pulls together positive pairs (augmentation pairs of the same graph) and pushes
away negative pairs (augmentation pairs of different graphs) for unsupervised
representation learning. However, since for a query, its negatives are
uniformly sampled from all graphs, existing methods suffer from the critical
sampling bias issue, i.e., the negatives likely having the same semantic
structure with the query, leading to performance degradation. To mitigate this
sampling bias issue, in this paper, we propose a Prototypical Graph Contrastive
Learning (PGCL) approach. Specifically, PGCL models the underlying semantic
structure of the graph data via clustering semantically similar graphs into the
same group, and simultaneously encourages the clustering consistency for
different augmentations of the same graph. Then given a query, it performs
negative sampling via drawing the graphs from those clusters that differ from
the cluster of query, which ensures the semantic difference between query and
its negative samples. Moreover, for a query, PGCL further reweights its
negative samples based on the distance between their prototypes (cluster
centroids) and the query prototype such that those negatives having moderate
prototype distance enjoy relatively large weights. This reweighting strategy is
proved to be more effective than uniform sampling. Experimental results on
various graph benchmarks testify the advantages of our PGCL over
state-of-the-art methods.


Abstract:
  Let $F_1,\ldots,F_R$ be homogeneous polynomials of degree $d\ge 2$ with
integer coefficients in $n$ variables, and let $\mathbf{F}=(F_1,\ldots,F_R)$.
Suppose that $F_1,\ldots,F_R$ is a non-singular system and $n\ge
4^{d+2}d^2R^5$. We prove that there are infinitely many solutions to
$\mathbf{F}(\mathbf{x})=\mathbf{0}$ in prime coordinates if (i)
$\mathbf{F}(\mathbf{x})=\mathbf{0}$ has a non-singular solution over the
$p$-adic units $\U_p$ for all prime numbers $p$, and (ii)
$\mathbf{F}(\mathbf{x})=\mathbf{0}$ has a non-singular solution in the open
cube $(0,1)^n$.


Abstract:
  Millimeter wave (mmWave) is a practical solution to provide high data rate
for the vehicle-to-everything (V2X) communications. This enables the future
autonomous vehicles to exchange big data with the base stations (BSs) such as
the velocity and the location to improve the awareness of the advanced driving
assistance system (ADAS). In this context, we consider a single-cell multiuser
doubly-selective system wherein the BS simultaneously serves multiple vehicles.
To accomplish this requirement, the BS is implemented in hybrid architecture to
support multiple spatial streams while the vehicles have analog-only
structures. In this work, we develop a low-complexity hybrid precoding
algorithm wherein the design of the hybrid precoder at the BS and the analog
combiner at the vehicles require small training and feedback overhead. We
propose a two-stage hybrid precoding algorithm wherein the first stage designs
the analog beamformers as in single user scenario while the second stage
designs the multiuser digital precoder at the BS. In the second stage, we
derive closed-form digital precoders such as Maximum Ratio Transmission (MRT),
Zero-Forcing (ZF) and Minimum Mean Square Error (MMSE) as a first variant while
we propose iterative digital precoder as a second variant. The design of the
digital precoders for the two variants requires the limited feedback sent from
the vehicles to BS. We refer to the random vector quantization (RVQ) and the
beamsteering codebooks to quantize the feedbacks for variants I and II,
respectively, since the perfect feedback requires long overhead and large
training. We evaluate the rate loss incurred by the quantization of the digital
and analog codebooks against the perfect channel state information at the
transmitter (CSIT).


Abstract:
  We propose a new algorithm for computing the tensor rank decomposition or
canonical polyadic decomposition of higher-order tensors subject to a rank and
genericity constraint. Reformulating this as a system of polynomial equations
allows us to leverage recent numerical linear algebra tools from computational
algebraic geometry. We describe the complexity of our algorithm in terms of the
multigraded regularity of a multihomogeneous ideal. We prove effective bounds
for many formats and ranks and conjecture a general formula. These bounds are
of independent interest for overconstrained polynomial system solving. Our
experiments show that our algorithm can outperform state-of-the-art algebraic
algorithms by an order of magnitude in terms of accuracy, computation time, and
memory consumption.


Abstract:
  Legged robots have excellent terrestrial mobility for traversing diverse
environments and thus have the potential to be deployed in a wide variety of
scenarios. However, they are susceptible to falling and leg malfunction during
locomotion. Although the use of a large number of legs can overcome these
problems, it makes the body long and leads to many legs being constrained to
contact with the ground to support the long body, which impedes
maneuverability. To improve the locomotion maneuverability of such robots, the
present study focuses on dynamic instability, which induces rapid and large
movement changes, and uses a 12-legged robot with a flexible body axis. Our
previous work found that the straight walk of the robot becomes unstable
through Hopf bifurcation when the body axis flexibility is changed, which
induces body undulations. Furthermore, we developed a simple controller based
on the Hopf bifurcation and showed that the instability facilitates the turning
of the robot. In this study, we newly found that the straight walk becomes
unstable through pitchfork bifurcation when the body-axis flexibility is
changed in a way different from that in our previous work. In addition, the
pitchfork bifurcation induces a transition into a curved walk, whose curvature
can be controlled by the body-axis flexibility. We developed a simple
controller based on the pitchfork-bifurcation characteristics and demonstrated
that the robot can perform a turning maneuver superior to that with the
previous controller. This study provides a novel design principle for
maneuverable locomotion of many-legged robots using intrinsic dynamic
properties.


Abstract:
  The reliability of software that has a Deep Neural Network (DNN) as a
component is urgently important today given the increasing number of critical
applications being deployed with DNNs. The need for reliability raises a need
for rigorous testing of the safety and trustworthiness of these systems. In the
last few years, there have been a number of research efforts focused on testing
DNNs. However the test generation techniques proposed so far lack a check to
determine whether the test inputs they are generating are valid, and thus
invalid inputs are produced. To illustrate this situation, we explored three
recent DNN testing techniques. Using deep generative model based input
validation, we show that all the three techniques generate significant number
of invalid test inputs. We further analyzed the test coverage achieved by the
test inputs generated by the DNN testing techniques and showed how invalid test
inputs can falsely inflate test coverage metrics.
  To overcome the inclusion of invalid inputs in testing, we propose a
technique to incorporate the valid input space of the DNN model under test in
the test generation process. Our technique uses a deep generative model-based
algorithm to generate only valid inputs. Results of our empirical studies show
that our technique is effective in eliminating invalid tests and boosting the
number of valid test inputs generated.


Abstract:
  We prove that a matrix from the split orthogonal group over a polynomial ring
with coefficients in a small-dimensional ring can be reduced to a smaller
matrix by a bounded number of elementary orthogonal transformations. The bound
is given explicitly. This result is an effective version of the early
stabilisation of the orthogonal K1 functor proven by Suslin and Kopeiko. Since
the similar effective results for special linear and symplectic groups were
obtained by Vaserstein, the present paper closes the problem for split
classical groups.


Abstract:
  Biological agents are known to learn many different tasks over the course of
their lives, and to be able to revisit previous tasks and behaviors with little
to no loss in performance. In contrast, artificial agents are prone to
'catastrophic forgetting' whereby performance on previous tasks deteriorates
rapidly as new ones are acquired. This shortcoming has recently been addressed
using methods that encourage parameters to stay close to those used for
previous tasks. This can be done by (i) using specific parameter regularizers
that map out suitable destinations in parameter space, or (ii) guiding the
optimization journey by projecting gradients into subspaces that do not
interfere with previous tasks. However, these methods often exhibit subpar
performance in both feedforward and recurrent neural networks, with recurrent
networks being of interest to the study of neural dynamics supporting
biological continual learning. In this work, we propose Natural Continual
Learning (NCL), a new method that unifies weight regularization and projected
gradient descent. NCL uses Bayesian weight regularization to encourage good
performance on all tasks at convergence and combines this with gradient
projection using the prior precision, which prevents catastrophic forgetting
during optimization. Our method outperforms both standard weight regularization
techniques and projection based approaches when applied to continual learning
problems in feedforward and recurrent networks. Finally, the trained networks
evolve task-specific dynamics that are strongly preserved as new tasks are
learned, similar to experimental findings in biological circuits.


Abstract:
  The recently discovered electrical-induced switching of antiferromagnetic
(AF) materials that have spatial inversion asymmetry has enriched the field of
spintronics immensely and opened the door for the concept of antiferromagnetic
MRAM. CuMnAs is one promising AF material that exhibits such electrical
switching ability, and has been studied to switch using electrical pulses of
length millisecond down to picosecond, but with little focus on nanosecond
regime. We demonstrate here switching of CuMnAs/GaP using nanosecond pulses.
Our results showed that in the nanosecond regime low-energy switching, high
readout signal with highly reproducible behaviour down to a single pulse can be
achieved. Moreover, a comparison of the two switching methods of orthogonal
switching and polarity switching was done on same device showing two different
behaviours that can be exploited selectively for different future
memory/processing applications.


Abstract:
  Medical image diagnosis can be achieved by deep neural networks, provided
there is enough varied training data for each disease class. However, a
hitherto unknown disease class not encountered during training will inevitably
be misclassified, even if predicted with low probability. This problem is
especially important for medical image diagnosis, when an image of a hitherto
unknown disease is presented for diagnosis, especially when the images come
from the same image domain, such as dermoscopic skin images.
  Current out-of-distribution detection algorithms act unfairly when the
in-distribution classes are imbalanced, by favouring the most numerous disease
in the training sets. This could lead to false diagnoses for rare cases which
are often medically important. We developed a novel yet simple method to train
neural networks, which enables them to classify in-distribution dermoscopic
skin disease images and also detect novel diseases from dermoscopic images at
test time. We show that our BinaryHeads model not only does not hurt
classification balanced accuracy when the data is imbalanced, but also
consistently improves the balanced accuracy. We also introduce an important
method to investigate the effectiveness of out-of-distribution detection
methods based on presence of varying amounts of out-of-distribution data, which
may arise in real-world settings.


Abstract:
  Time series data have grown at an explosive rate in numerous domains and have
stimulated a surge of time series modeling research. A comprehensive comparison
of different time series models, for a considered data analytics task, provides
useful guidance on model selection for data analytics practitioners. Data
scarcity is a universal issue that occurs in a vast range of data analytics
problems, due to the high costs associated with collecting, generating, and
labeling data as well as some data quality issues such as missing data. In this
paper, we focus on the temporal classification/regression problem that attempts
to build a mathematical mapping from multivariate time series inputs to a
discrete class label or a real-valued response variable. For this specific
problem, we identify two types of scarce data: scarce data with small samples
and scarce data with sparsely and irregularly observed time series covariates.
Observing that all existing works are incapable of utilizing the sparse time
series inputs for proper modeling building, we propose a model called sparse
functional multilayer perceptron (SFMLP) for handling the sparsity in the time
series covariates. The effectiveness of the proposed SFMLP under each of the
two types of data scarcity, in comparison with the conventional deep sequential
learning models (e.g., Recurrent Neural Network, and Long Short-Term Memory),
is investigated through mathematical arguments and numerical experiments.


Abstract:
  The concurrency features of the Go language have proven versatile in the
development of a number of concurrency systems. However, correctness methods to
address challenges in Go concurrency debugging have not received much
attention. In this work, we present an automatic dynamic tracing mechanism that
efficiently captures and helps analyze the whole-program concurrency model.
Using an enhancement to the built-in tracer package of Go and a framework that
collects dynamic traces from application execution, we enable thorough
post-mortem analysis for concurrency debugging. Preliminary results about the
effectiveness and scalability (up to more than 2K goroutines) of our proposed
dynamic tracing for concurrent debugging are presented. We discuss the future
direction for exploiting dynamic tracing towards accelerating concurrent bug
exposure.


Abstract:
  Active, selective and stable catalysts are imperative for sustainable energy
conversion, and engineering materials with such properties are highly desired.
High-entropy alloys (HEAs) offer a vast compositional space for tuning such
properties. Too vast, however, to traverse without the proper tools. Here, we
report the use of Bayesian optimization on a model based on density functional
theory (DFT) to predict the most active compositions for the electrochemical
oxygen reduction reaction (ORR) with the least possible number of sampled
compositions for the two HEAs Ag-Ir-Pd-Pt-Ru and Ir-Pd-Pt-Rh-Ru. The discovered
optima are then scrutinized with DFT and subjected to experimental validation
where optimal catalytic activities are verified for Ag-Pd, Ir-Pt, and Pd-Ru
binary systems. This study offers insight into the number of experiments needed
for exploring the vast compositional space of multimetallic alloys which has
been determined to be on the order of 50 for ORR on these HEAs.


Abstract:
  In this paper we examine the conditions that an object $E$ with
$\text{ch}_0(E)=0$ has to satisfy in order for it to be asymptotically
(semi)stable with regard to Weak or Bridgeland stability conditions. This
notion turned out to be equivalent to sheaf Gieseker-Simpson (semi)stability or
a dual of it, depending on the curve considered.


Abstract:
  In this paper, we investigate the driving factors behind concatenation, a
simple but effective data augmentation method for low-resource neural machine
translation. Our experiments suggest that discourse context is unlikely the
cause for the improvement of about +1 BLEU across four language pairs. Instead,
we demonstrate that the improvement comes from three other factors unrelated to
discourse: context diversity, length diversity, and (to a lesser extent)
position shifting.


Abstract:
  As mobile traffic is dominated by content services (e.g., video), which
typically use recommendation systems, the paradigm of network-friendly
recommendations (NFR) has been proposed recently to boost the network
performance by promoting content that can be efficiently delivered (e.g.,
cached at the edge). NFR increase the network performance, however, at the cost
of being unfair towards certain contents when compared to the standard
recommendations. This unfairness is a side effect of NFR that has not been
studied in literature. Nevertheless, retaining fairness among contents is a key
operational requirement for content providers. This paper is the first to study
the fairness in NFR, and design fair-NFR. Specifically, we use a set of metrics
that capture different notions of fairness, and study the unfairness created by
existing NFR schemes. Our analysis reveals that NFR can be significantly
unfair. We identify an inherent trade-off between the network gains achieved by
NFR and the resulting unfairness, and derive bounds for this trade-off. We show
that existing NFR schemes frequently operate far from the bounds, i.e., there
is room for improvement. To this end, we formulate the design of Fair-NFR
(i.e., NFR with fairness guarantees compared to the baseline recommendations)
as a linear optimization problem. Our results show that the Fair-NFR can
achieve high network gains (similar to non-fair-NFR) with little unfairness.


Abstract:
  We explore induced mappings between character varieties by mappings between
surfaces. It is shown that these mappings are generally Poisson. We also
explicitly calculate the Poisson bi-vector in a new case.


Abstract:
  Over the years, families of forecasting models, such as the exponential
smoothing family and Autoregressive Integrated Moving Average, have expanded to
contain multiple possible forms and forecasting profiles. In this paper, we
question the need to consider such large families of models. We argue that
parsimoniously identifying suitable subsets of models will not decrease the
forecasting accuracy nor will it reduce the ability to estimate the forecast
uncertainty. We propose a framework that balances forecasting performance
versus computational cost, resulting in a set of reduced families of models and
empirically demonstrate this trade-offs. We translate computational benefits to
monetary cost savings and discuss the implications of our results in the
context of large retailers.


Abstract:
  It has been known that the Schwarzschild-de Sitter (Sch-dS) black hole may
not be in thermal equilibrium and also be found to be thermodynamically
unstable in the standard black hole thermodynamics. In the present work, we
investigate the possibility to realize the thermodynamical stability of the
Sch-dS black hole as an effective system by using the R\'{e}nyi statistics,
which includes the non-extensive nature of black holes. Our results indicate
that the non-extensivity allows the black hole to be thermodynamically stable
which gives rise to the lower bound on the non-extensive parameter. By
comparing the results to ones in the separated system approach, we find that
the effective temperature is always smaller than the black hole horizon
temperature and the thermodynamically stable black hole in effective approach
is always larger than one in separated approach at a certain temperature. There
exists only the zeroth-order phase transition from the the hot gas phase to the
black hole phase for the effective system while it is possible to have the
transition of both the zeroth order and the first order for the separated
system.


Abstract:
  The weakest known criterion for local rotational symmetry (LRS) in spacetimes
of Petrov type D is due to Goode and Wainwright (1986). Here it is shown, using
methods related to the Cartan-Karlhede procedure, to be equivalent to local
spatial rotation invariance of the Riemann tensor and its first derivatives.
Conformally flat spacetimes are similarly studied and it is shown that for
almost all cases the same criterion ensures LRS. Only for conformally flat
accelerated perfect fluids are three curvature derivatives required to ensure
LRS, showing that Ellis's original condition for that case is necessary as well
as sufficient.


Abstract:
  There is long research interest in electron-induced reactions of halogenated
molecules. It has been two decades since the cosmic-ray (CR) driven
electron-induced reaction (CRE) mechanism for the ozone hole formation was
proposed. The derived CRE equation with stratospheric equivalent chlorine level
and CR intensity as only two variables has well reproduced the observed data of
stratospheric O3 and temperatures over the past 40 years. The CRE predictions
of 11-year cyclic variations of the Antarctic O3 hole and associated
stratospheric cooling have also been well confirmed. Measured altitude profiles
of ozone and temperatures in Antarctic ozone holes provide convincing
fingerprints of the CRE mechanism. A quantitative estimate indicates that the
CRE-produced Cl atoms could completely deplete or even over-kill ozone in the
CR-peak polar stratospheric region, consistent with observed altitude profiles
of severest Antarctic ozone holes. After removing the natural CR effect, the
hidden recovery in the Antarctic O3 hole since around 1995 is clearly
discovered, while the recovery of O3 loss at mid-latitudes is being delayed by
>=10 years. These results have provided strong evidence of the CRE mechanism.
If the CR intensity keeps the current rising trend, the Antarctic O3 hole will
return to the 1980 level by around 2060, while the returning of the O3 layer at
mid-latitudes to the 1980 level will largely be delayed or will not even occur
by the end of this century. The results strongly indicate that the CRE
mechanism must be considered as a key factor in evaluating the O3 hole.


Abstract:
  We consider the existence of convolution of Roumieu type ultradistribution
with the kernel $e^{s(1+|x|^2)^{q/2}}, q\geq 1$, $s\in\RR\backslash\{0\}$.


Abstract:
  Checkerboard framings are an extension of checkerboard colorings for virtual
links. According to checkerboard framings, in 2017, Dye obtained an independent
invariant of virtual links: the cut point number. Checkerboard framings and cut
points can be used as a tool to extend other classical invariants to virtual
links. We prove that one of the conjectures in Dye's paper is correct.
Moreover, we analyze the connection and difference between checkerboard framing
obtained from virtual link diagram by adapting cut points and twisted link
diagram obtained from virtual link diagram by introducing bars. By adjusting
the normalized arrow polynomial of virtual links, we generalize it to twisted
links. And we show that it is an invariant for twisted link. Finally, we figure
out three characteristics of the normalized arrow polynomial of a checkerboard
colorable twisted link, which is a tool of detecting checkerboard colorability
of a twisted link. The latter two characteristics are the same as in the case
of checkerboard colorable virtual link diagram.


Abstract:
  Machine learning problems have an intrinsic geometric structure as central
objects including a neural network's weight space and the loss function
associated with a particular task can be viewed as encoding the intrinsic
geometry of a given machine learning problem. Therefore, geometric concepts can
be applied to analyze and understand theoretical properties of machine learning
strategies as well as to develop new algorithms. In this paper, we address
three seemingly unrelated open questions in machine learning by viewing them
through a unified framework grounded in differential geometry. Specifically, we
view the weight space of a neural network as a manifold endowed with a
Riemannian metric that encodes performance on specific tasks. By defining a
metric, we can construct geodesic, minimum length, paths in weight space that
represent sets of networks of equivalent or near equivalent functional
performance on a specific task. We, then, traverse geodesic paths while
identifying networks that satisfy a second objective. Inspired by the geometric
insight, we apply our geodesic framework to 3 major applications: (i) Network
sparsification (ii) Mitigating catastrophic forgetting by constructing networks
with high performance on a series of objectives and (iii) Finding high-accuracy
paths connecting distinct local optima of deep networks in the non-convex loss
landscape. Our results are obtained on a wide range of network architectures
(MLP, VGG11/16) trained on MNIST, CIFAR-10/100. Broadly, we introduce a
geometric framework that unifies a range of machine learning objectives and
that can be applied to multiple classes of neural network architectures.


Abstract:
  Smart Contracts (SCs) in Ethereum can automate tasks and provide different
functionalities to a user. Such automation is enabled by the `Turing-complete'
nature of the programming language (Solidity) in which SCs are written. This
also opens up different vulnerabilities and bugs in SCs that malicious actors
exploit to carry out malicious or illegal activities on the cryptocurrency
platform. In this work, we study the correlation between malicious activities
and the vulnerabilities present in SCs and find that some malicious activities
are correlated with certain types of vulnerabilities. We then develop and study
the feasibility of a scoring mechanism that corresponds to the severity of the
vulnerabilities present in SCs to determine if it is a relevant feature to
identify suspicious SCs. We analyze the utility of severity score towards
detection of suspicious SCs using unsupervised machine learning (ML) algorithms
across different temporal granularities and identify behavioral changes. In our
experiments with on-chain SCs, we were able to find a total of 1094 benign SCs
across different granularities which behave similar to malicious SCs, with the
inclusion of the smart contract vulnerability scores in the feature set.


Abstract:
  Face anti-spoofing (FAS) has lately attracted increasing attention due to its
vital role in securing face recognition systems from presentation attacks
(PAs). As more and more realistic PAs with novel types spring up, traditional
FAS methods based on handcrafted features become unreliable due to their
limited representation capacity. With the emergence of large-scale academic
datasets in the recent decade, deep learning based FAS achieves remarkable
performance and dominates this area. However, existing reviews in this field
mainly focus on the handcrafted features, which are outdated and uninspiring
for the progress of FAS community. In this paper, to stimulate future research,
we present the first comprehensive review of recent advances in deep learning
based FAS. It covers several novel and insightful components: 1) besides
supervision with binary label (e.g., '0' for bonafide vs. '1' for PAs), we also
investigate recent methods with pixel-wise supervision (e.g., pseudo depth
map); 2) in addition to traditional intra-dataset evaluation, we collect and
analyze the latest methods specially designed for domain generalization and
open-set FAS; and 3) besides commercial RGB camera, we summarize the deep
learning applications under multi-modal (e.g., depth and infrared) or
specialized (e.g., light field and flash) sensors. We conclude this survey by
emphasizing current open issues and highlighting potential prospects.


Abstract:
  Recent water line observations toward several low-mass protostars suggest low
water gas fractional abundances in the inner warm envelopes. Water destruction
by X-rays has been proposed to influence the water abundances in these regions,
but the detailed chemistry, including the nature of alternative oxygen
carriers, is not yet understood. In this study, we aim to understand the impact
of X-rays on the composition of low-mass protostellar envelopes, focusing
specifically on water and related oxygen bearing species. We compute the
chemical composition of two low-mass protostellar envelopes using a 1D
gas-grain chemical reaction network, under various X-ray field strengths.
According to our calculations, outside the water snowline, the water gas
abundance increases with $L_{\mathrm{X}}$. Inside the water snowline, water
maintains a high abundance of $\sim 10^{-4}$ for small $L_{\mathrm{X}}$, with
water and CO being the dominant oxygen carriers. For large $L_{\mathrm{X}}$,
the water gas abundances significantly decrease just inside the water snowline
(down to $\sim10^{-8}-10^{-7}$) and in the innermost regions ($\sim10^{-6}$).
For these cases, the O$_{2}$ and O gas abundances reach $\sim 10^{-4}$ within
the water snowline, and they become the dominant oxygen carriers. The HCO$^{+}$
and CH$_{3}$OH abundances, which have been used as tracers of the water
snowline, significantly increase/decrease within the water snowline,
respectively, as the X-ray fluxes become larger. The abundances of some other
dominant molecules, such as CO$_{2}$, OH, CH$_{4}$, HCN, and NH$_{3}$, are also
affected by strong X-ray fields, especially within their own snowlines. These
X-ray effects are larger in lower density envelope models. Future observations
of water and related molecules (using e.g., ALMA and ngVLA) will access the
regions around protostars where such X-ray induced chemistry is effective.


Abstract:
  This paper shows experimental results from the TCV tokamak that indicate
plasma-molecule interactions involving $D_2^+$ and possibly $D^-$ play an
important role as sinks of energy (through hydrogenic radiation as well as
dissociation) and particles during divertor detachment if low target
temperatures ($< 3$ eV) are achieved. Both molecular activated recombination
(MAR) and ion source reduction due to a power limitation effect are shown to be
important in reducing the ion target flux during a density ramp. In contrast,
the electron-ion recombination (EIR) ion sink is too small to play an important
role in reducing the ion target flux. MAR or EIR do not occur during $N_2$
seeding induced detachment as the target temperatures are not sufficiently low.
  The impact of $D_2^+$ is shown to be underestimated in present (vibrationally
unresolved) SOLPS-ITER simulations, which could result from an underestimated
$D_2 + D^+ \rightarrow D_2^+ + D$ rate. The converged SOLPS-ITER simulations
are post-processed with alternative reaction rates, resulting in considerable
contributions of $D_2^+$ to particle and power losses as well as dissociation
below the $D_2$ dissociation area. Those findings are in quantitative agreement
with the experimental results.


Abstract:
  The scattering equivalence of quantum field theories formulated with
light-front and instant-form kinematic subgroups is established using
non-perturbative methods. The difficulty with field theoretic formulations of
Dirac's forms of dynamics is that the free and interacting unitary
representations of the Poincar\'e group are defined on inequivalent
representations of the Hilbert space, which means that the concept of kinematic
transformations must be modified on the Hilbert space of the field theory. This
work addresses this problem by assuming the existence of a field theory with
the expected properties and constructs equivalent representations with instant
and front form kinematic subgroups. In this construction both the light-front
and instant-form formulations share the same vacuum and one-particle states.
The free field Fock space plays no role. There is no "quantization" of a
classical theory. The property that survives from the perturbative approach is
the notion of a kinematic subgroup, which means kinematic Poincar\'e
transformations can be trivially implemented by acting on suitable basis
vectors. This non-perturbative approach avoids dealing with issues that arise
in perturbative treatments where is it necessary to have a consistent treatment
of renormalization, rotational covariance, and the structure of the light-front
vacuum. While addressing these issues in a computational framework is important
for applications, this work may provide some insight into the nature of the
expected resolution and identifies the origin of some of differences between
the perturbative and non-perturbative approaches.


Abstract:
  Citizen Science aims to engage people in research activities on important
issues related to their well-being. Smart Cities aim to provide them with
services that improve the quality of their life. Both concepts have seen
significant growth in the last years, and can be further enhanced by combining
their purposes with IoT technologies that allow for dynamic and large-scale
communication and interaction. However, exciting and retaining the interest of
participants is a key factor for such initiatives. In this paper we suggest
that engagement in Citizen Science projects applied on Smart Cities
infrastructure can be enhanced through contextual and structural game elements
realized through augmented audio interactive mechanisms. Our inter-disciplinary
framework is described through the paradigm of a collaborative bird call
recognition game, in which users collect and submit audio data, which are then
classified and used for augmenting physical space with virtual soundscape maps.
We discuss the Playful Learning, Internet of Audio Things, and Bird Monitoring
principles that shaped the design of our paradigm, and analyze its potential
technical implementation.


Abstract:
  The Ewens sampling formula is a distribution related to the random partition
of a positive integer. In this study, we investigate the issue of non-existence
solutions in parameter estimation under the distribution. As a result, the
first and second moments matching estimators to the uniformly minimum variance
unbiased estimator are derived using the Ewens sampling formula in asymptotic
sense. A Monte Carlo simulation study is performed to evaluate the efficiency
of the resulting estimators.