Abstract:
  We describe a $p$-dimensional conformal defect of a free Dirac fermion on a
$d$-dimensional flat space as boundary conditions on a conformally equivalent
space $\mathbb{H}^{p+1} \times \mathbb{S}^{d-p-1}$. We classify allowed
boundary conditions and find that the Dirichlet type of boundary conditions
always exists while the Neumann type of boundary condition exists only for a
two-codimensional defect. For the two-codimensional defect, a double trace
deformation triggers a renormalization group flow from the Neumann boundary
condition to the Dirichlet boundary condition, and the free energy at UV fixed
point is always larger than that at IR fixed point. This provides us with
further support of a conjectured $C$-theorem in DCFT.


Abstract:
  Consider the random Cayley graph of a finite group $G$ with respect to $k$
generators chosen uniformly at random, with $1 \ll \log k \ll \log |G|$; denote
it $G_k$. A conjecture of Aldous and Diaconis (1985) asserts, for $k \gg \log
|G|$, that the random walk on this graph exhibits cutoff. Further, the cutoff
time should be a function only of $k$ and $|G|$, to sub-leading order. This was
verified for all Abelian groups in the '90s. We extend the conjecture to $1 \ll
k \lesssim \log |G|$. We establish cutoff for all Abelian groups under the
condition $k - d(G) \gg 1$, where $d(G)$ is the minimal size of a generating
subset of $G$, which is almost optimal. The cutoff time is described
(abstractly) in terms of the entropy of random walk on $\mathbb Z^k$. This
abstract definition allows us to deduce that the cutoff time can be written as
a function only of $k$ and $|G|$ when $d(G) \ll \log |G|$ and $k - d(G) \asymp
k \gg 1$; this is not the case when $d(G) \asymp \log |G| \asymp k$. For
certain regimes of $k$, we find the limit profile of the convergence to
equilibrium. Wilson (1997) conjectured that $\mathbb Z_2^d$ gives rise to the
slowest mixing time for $G_k$ amongst all groups of size at most $2^d$. We give
a partial answer, verifying the conjecture for nilpotent groups. This is
obtained via a comparison result of independent interest between the mixing
times of nilpotent $G$ and a corresponding Abelian group $\overline G$, namely
the direct sum of the Abelian quotients in the lower central series of $G$. We
use this to refine a celebrated result of Alon and Roichman (1994): we show for
nilpotent $G$ that $G_k$ is an expander provided $k - d(\overline G) \gtrsim
\log |G|$. As another consequence, we establish cutoff for nilpotent groups
with relatively small commutators, including high-dimensional special groups,
such as Heisenberg groups.


Abstract:
  Underwater image restoration attracts significant attention due to its
importance in unveiling the underwater world. This paper elaborates on a novel
method that achieves state-of-the-art results for underwater image restoration
based on the unsupervised image-to-image translation framework. We design our
method by leveraging from contrastive learning and generative adversarial
networks to maximize mutual information between raw and restored images.
Additionally, we release a large-scale real underwater image dataset to support
both paired and unpaired training modules. Extensive experiments with
comparisons to recent approaches further demonstrate the superiority of our
proposed method.


Abstract:
  Distilling data into compact and interpretable analytic equations is one of
the goals of science. Instead, contemporary supervised machine learning methods
mostly produce unstructured and dense maps from input to output. Particularly
in deep learning, this property is owed to the generic nature of simple
standard link functions. To learn equations rather than maps, standard
non-linearities can be replaced with structured building blocks of atomic
functions. However, without strong priors on sparsity and structure,
representational complexity and numerical conditioning limit this direct
approach. To scale to realistic settings in science and engineering, we propose
an informed equation learning system. It provides a way to incorporate expert
knowledge about what are permitted or prohibited equation components, as well
as a domain-dependent structured sparsity prior. Our system then utilizes a
robust method to learn equations with atomic functions exhibiting
singularities, as e.g. logarithm and division. We demonstrate several
artificial and real-world experiments from the engineering domain, in which our
system learns interpretable models of high predictive power.


Abstract:
  The random substitutional solid solution between the antiferromagnetic (AFM)
full-Heusler alloy Ru$_2$MnSn and the ferromagnetic (FM) full-Heusler alloy
Ru$_2$FeSn provides a rare opportunity to study FM-AFM phase competition in a
near-lattice-matched, cubic system, with full solubility. At intermediate $x$
in Ru$_2$Mn$_{1-x}$Fe$_x$Sn this system displays suppressed magnetic ordering
temperatures, spatially coexisting FM and AFM order, and strong coercivity
enhancement, despite rigorous chemical homogeneity. Here, we construct the most
detailed temperature- and $x$-dependent understanding of the magnetic phase
competition and coexistence in this system to date, combining
wide-temperature-range neutron diffraction and small-angle neutron scattering
with magnetometry and specific heat measurements on thoroughly characterized
polycrystals. A complete magnetic phase diagram is generated, showing FM-AFM
coexistence between $x \approx 0.30$ and $x \approx 0.70$. Important new
insight is gained from the extracted length scales for magnetic phase
coexistence (25-100 nm), the relative magnetic volume fractions and ordering
temperatures, in addition to remarkable $x$-dependent trends in magnetic and
electronic contributions to specific heat. An unusual feature in the magnetic
phase diagram (an intermediate FM phase) is also shown to arise from an
extrinsic effect related to a minor Ru-rich secondary phase. The established
magnetic phase diagram is then discussed with the aid of phenomenological
modeling, clarifying the nature of the mesoscale phase coexistence with respect
to the understanding of disordered Heisenberg models.


Abstract:
  Deep learning sequence models have been successfully applied to the task of
morphological inflection. The results of the SIGMORPHON shared tasks in the
past several years indicate that such models can perform well, but only if the
training data cover a good amount of different lemmata, or if the lemmata that
are inflected at test time have also been seen in training, as has indeed been
largely the case in these tasks. Surprisingly, standard models such as the
Transformer almost completely fail at generalizing inflection patterns when
asked to inflect previously unseen lemmata -- i.e. under "wug test"-like
circumstances. While established data augmentation techniques can be employed
to alleviate this shortcoming by introducing a copying bias through
hallucinating synthetic new word forms using the alphabet in the language at
hand, we show that, to be more effective, the hallucination process needs to
pay attention to substrings of syllable-like length rather than individual
characters or stems. We report a significant performance improvement with our
substring-based hallucination model over previous data hallucination methods
when training and test data do not overlap in their lemmata.


Abstract:
  In this paper, we consider fractional Hartree (fHE) and fractional cubic
nonlinear Schr\"odinger (fNLS) equations on $\mathbb R^d$. We establish norm
inflation (stronger phenomena than mere ill-posedness) for fHE and fNLS in
Fourier-Lebesgue and modulation spaces with negative regularity. (In
particular, these spaces include Sobolev spaces.) We further show that this can
be even worse by exhibiting norm inflation with infinite loss of regularity. To
establish later phenomena, we employ Fourier analytic approach and introduce
new resonant sets corresponding to the fractional dispersion
$(-\Delta)^{\alpha/2}$.
  In particular, we obtain norm inflation above scaling critical regularity in
some of these spaces when dispersion index $\alpha$ is large enough. It turns
out that our approach could treat both equations (fHE) and (fNLS) in a unified
manner. The method also should be applicable to wider classes of nonlinear
equations with Hartree-type nonlinearity.


Abstract:
  Calibration of neural networks is a topical problem that is becoming
increasingly important for real-world use of neural networks. The problem is
especially noticeable when using modern neural networks, for which there is
significant difference between the model confidence and the confidence it
should have. Various strategies have been successfully proposed, yet there is
more space for improvements. We propose a novel approach that introduces a
differentiable metric for expected calibration error and successfully uses it
as an objective for meta-learning, achieving competitive results with
state-of-the-art approaches. Our approach presents a new direction of using
meta-learning to directly optimize model calibration, which we believe will
inspire further work in this promising and new direction.


Abstract:
  In the current control design of safety-critical autonomous systems, formal
verification techniques are typically applied after the controller is designed
to evaluate whether the required properties (e.g., safety) are satisfied.
However, due to the increasing system complexity and the fundamental hardness
of designing a controller with formal guarantees, such an open-loop process of
design-then-verify often results in many iterations and fails to provide the
necessary guarantees. In this paper, we propose a correct-by-construction
control learning framework that integrates the verification into the control
design process in a closed-loop manner, i.e., design-while-verify.
Specifically, we leverage the verification results (computed reachable set of
the system state) to construct feedback metrics for control learning, which
measure how likely the current design of control parameters can meet the
required reach-avoid property for safety and goal-reaching. We formulate an
optimization problem based on such metrics for tuning the controller
parameters, and develop an approximated gradient descent algorithm with a
difference method to solve the optimization problem and learn the controller.
The learned controller is formally guaranteed to meet the required reach-avoid
property. By treating verifiability as a first-class objective and effectively
leveraging the verification results during the control learning process, our
approach can significantly improve the chance of finding a control design with
formal property guarantees. This is demonstrated via a set of experiments on
both linear and non-linear systems that use model-based or neural network based
controllers.


Abstract:
  In a topological description of elementary matter proposed by
Bilson-Thompson, the leptons and quarks of a single generation, together with
the electroweak gauge bosons, are represented as elements of the framed braid
group of three ribbons. By identifying these braids with emergent topological
excitations of ribbon networks, it has been possible to encode this braid model
into the framework of quantum geometry provided by loop quantum gravity. In the
case of trivalent networks, it has not been possible to generate particle
interactions, because the braids correspond to noiseless subsystems, meaning
they commute with the evolution algebra generated by the local Pachner moves.
In the case of tetravalent networks, interactions are only possible when the
model's original simplicity, in which interactions take place via the
composition of braids, is sacrificed. We demonstrate that it possible to
preserve both the original classification of fermions, as well as their
interaction via the braid product, if we embed the braid in a trivalent scheme,
and supplement the local Pachner moves, with a non-local and graph changing
1-handle attachment. Moreover, we use Kauffman-Lins recoupling theory to obtain
invariants of braided networks that distinguish topological configurations
associated to particles in the Bilson-Thompson model.


Abstract:
  Motivated by applications in reliable and secure communication, we address
the problem of tiling (or partitioning) a finite constellation in
$\mathbb{Z}_{2^L}^n$ by subsets, in the case that the constellation does not
possess an abelian group structure. The property that we do require is that the
constellation is generated by a linear code through an injective mapping. The
intrinsic relation between the code and the constellation provides a sufficient
condition for a tiling to exist. We also present a necessary condition.
Inspired by a result in group theory, we discuss results on tiling for the
particular case when the finer constellation is an abelian group as well.


Abstract:
  With an easy application of maximum principle, we establish a Schwarz-type
lemma for squeezing function on finitely connected planar domains that directly
yields the explicit formula for squeezing function on doubly connected domains
obtained by Ng, Tang and Tsai.


Abstract:
  In this paper, we present deep learning frameworks for audio-visual scene
classification (SC) and indicate how individual visual and audio features as
well as their combination affect SC performance. Our extensive experiments,
which are conducted on DCASE (IEEE AASP Challenge on Detection and
Classification of Acoustic Scenes and Events) Task 1B development dataset,
achieve the best classification accuracy of 82.2%, 91.1%, and 93.9% with audio
input only, visual input only, and both audio-visual input, respectively. The
highest classification accuracy of 93.9%, obtained from an ensemble of
audio-based and visual-based frameworks, shows an improvement of 16.5% compared
with DCASE baseline.


Abstract:
  Opinion-evolution and spread processes on networks (e.g., infectious disease
spread, opinion formation in social networks) are not only high dimensional but
also volatile and multiscale in nature. In this study, we explore whether
snapshot data from these processes can admit terse representations.
Specifically, using three case studies, we explore whether the data are
compressible in the Laplacian-eigenvector basis, in the sense that each
snapshot can be approximated well using a (possibly different) small set of
basis vectors. The first case study is concerned with a linear consensus model
that is subject to a stochastic input at an unknown location; both empirical
and formal analyses are used to characterize compressibility. Second,
compressibility of state snapshots for a stochastic voter model is assessed via
an empirical study. Finally, compressibility is studied for state-level daily
COVID-19 positivity-rate data. The three case studies indicate that state
snapshots from opinion-evolution and spread processes allow terse
representations, which nevertheless capture their rich propagative dynamics.


Abstract:
  In this paper, we explore an electromechanical metastructure consisting of a
periodic array of piezoelectric bimorphs with resistive-inductive loads for
simultaneous harvesting and attenuation of traveling wave energy. We develop
fully coupled analytical models, i.e., an electroelastic transfer matrix
method, and exploit both locally-resonant and Bragg band gaps to achieve a
multifunctional metastructure which is capable for maximum energy conversion
and vibration mitigation in a broadband fashion. Our analytical and numerical
results show that the proposed metastructure can achieve energy harvesting
efficiency up to 95% at the local resonance frequency of 3.18 kHz, while
reaching about 51% at 5.8 kHz near the upper limit of the Bragg band gap. The
broadband vibration mitigation performance based on 50% power attenuation is
predicted as 1.8 kHz and 1.1 kHz in the vicinity of the band gaps. The
theoretical frameworks and the applicability of the proposed metastructure are
validated using a full-scale experimental setup.


Abstract:
  All hydrodynamical simulations of turbulent astrophysical phenomena require
sub-grid scale models to properly treat energy dissipation and metal mixing. We
present the first implementation and application of an anisotropic eddy
viscosity and metal mixing model in Lagrangian astrophysical simulations,
including a dynamic procedure for the model parameter. We compare these two
models directly to the common Smagorinsky and dynamic variant. Using the
mesh-free finite mass method as an example, we show that the anisotropic model
is best able to reproduce the proper Kolmogorov inertial range scaling in
homogeneous, isotropic turbulence. Additionally, we provide a method to
calibrate the metal mixing rate that ensures numerical convergence. In our
first application to cosmological simulations, we find that all models strongly
impact the early evolution of galaxies leading to differences in enrichment and
thermodynamic histories. The anisotropic model has the strongest impact, with
little difference between the dynamic and the constant-coefficient variant. We
also find that the metal distribution functions in the circumgalactic gas are
significantly tighter at all redshifts, with the anisotropic model providing
the tightest distributions. This is contrary to a recent study that found metal
mixing to be relatively unimportant on cosmological scales. In all of our
experiments the constant-coefficient Smagorinsky and anisotropic models rivaled
their dynamic counterparts, suggesting that the computationally inexpensive
constant-coefficient models are viable alternatives in cosmological contexts.


Abstract:
  In the context of forthcoming galaxy surveys, to ensure unbiased constraints
on cosmology and gravity when using non-linear structure information,
percent-level accuracy is required when modelling the power spectrum. This
calls for frameworks that can accurately capture the relevant physical effects,
while allowing for deviations from $\Lambda$CDM. Massive neutrino and baryonic
physics are two of the most relevant such effects. We present an integration of
the halo model reaction frameworks for massive neutrinos and
beyond-$\Lambda$CDM cosmologies. The integrated halo model reaction, combined
with a pseudo power spectrum modelled by HMCode2020 is then compared against
$N$-body simulations that include both massive neutrinos and an $f(R)$
modification to gravity. We find that the framework is 4% accurate down to at
least $k\approx 3 \, h/{\rm Mpc}$ for a modification to gravity of $|f_{\rm
R0}|\leq 10^{-5}$ and for the total neutrino mass $M_\nu \equiv \sum m_\nu \leq
0.15$ eV. We also find that the framework is 4% consistent with EuclidEmulator2
as well as the Bacco emulator for \B{most of the considered} $\nu w$CDM
cosmologies down to at least $k \approx 3 \, h$/Mpc. Finally, we compare
against hydrodynamical simulations employing HMCode2020's baryonic feedback
modelling on top of the halo model reaction. For $\nu \Lambda$CDM cosmologies
we find 2% accuracy for $M_\nu \leq 0.48$eV down to at least $k\approx 5h$/Mpc.
Similar accuracy is found when comparing to $\nu w$CDM hydrodynamical
simulations with $M_\nu = 0.06$eV. This offers the first non-linear,
theoretically general means of accurately including massive neutrinos for
beyond-$\Lambda$CDM cosmologies, and further suggests that baryonic, massive
neutrino and dark energy physics can be reliably modelled independently.


Abstract:
  We address the problem of teaching a deep reinforcement learning (RL) agent
to follow instructions in multi-task environments. Instructions are expressed
in a well-known formal language -- linear temporal logic (LTL) -- and can
specify a diversity of complex, temporally extended behaviours, including
conditionals and alternative realizations. Our proposed learning approach
exploits the compositional syntax and the semantics of LTL, enabling our RL
agent to learn task-conditioned policies that generalize to new instructions,
not observed during training. To reduce the overhead of learning LTL semantics,
we introduce an environment-agnostic LTL pretraining scheme which improves
sample-efficiency in downstream environments. Experiments on discrete and
continuous domains target combinatorial task sets of up to $\sim10^{39}$ unique
tasks and demonstrate the strength of our approach in learning to solve
(unseen) tasks, given LTL instructions.


Abstract:
  We develop the formalism to analyze cluster synchronization for dynamical
elements coupled via hypergraphs beyond pairwise interactions. We introduce the
notion of edge partitions and show how node and edge partitions allow us to
identify admissible cluster synchronization states and simplify their linear
stability calculations. The analysis in terms of node and edge partitions
provides a principled way to track dynamics on hypergraphs, and the projected
Laplacian matrices based on each edge cluster are essential to block
diagonalizing the Jacobian in order to reduce the dimensionality of the
stability analysis. Our work enables detailed analysis of patterns of
synchronization beyond full synchronization and beyond dyadic interactions.


Abstract:
  Over the years, static taint analysis emerged as the analysis of choice to
detect some of the most common web application vulnerabilities, such as SQL
injection (SQLi) and cross-site scripting (XSS)~\cite{OWASP}. Furthermore, from
an implementation perspective, the IFDS dataflow framework stood out as one of
the most successful vehicles to implement static taint analysis for real-world
Java applications. While existing approaches scale reasonably to medium-size
applications (e.g. up to one hour analysis time for less than 100K lines of
code), our experience suggests that no existing solution can scale to very
large industrial code bases (e.g. more than 1M lines of code). In this paper,
we present our novel IFDS-based solution to perform fast and precise static
taint analysis of very large industrial Java web applications. Similar to
state-of-the-art approaches to taint analysis, our IFDS-based taint analysis
uses \textit{access paths} to abstract objects and fields in a program.
However, contrary to existing approaches, our analysis is demand-driven, which
restricts the amount of code to be analyzed, and does not rely on a
computationally expensive alias analysis, thereby significantly improving
scalability.


Abstract:
  We present a new practical framework based on deep reinforcement learning and
decision-time planning for real-world vehicle repositioning on ride-hailing (a
type of mobility-on-demand, MoD) platforms. Our approach learns the
spatiotemporal state-value function using a batch training algorithm with deep
value networks. The optimal repositioning action is generated on-demand through
value-based policy search, which combines planning and bootstrapping with the
value networks. For the large-fleet problems, we develop several algorithmic
features that we incorporate into our framework and that we demonstrate to
induce coordination among the algorithmically-guided vehicles. We benchmark our
algorithm with baselines in a ride-hailing simulation environment to
demonstrate its superiority in improving income efficiency meausred by
income-per-hour. We have also designed and run a real-world experiment program
with regular drivers on a major ride-hailing platform. We have observed
significantly positive results on key metrics comparing our method with
experienced drivers who performed idle-time repositioning based on their own
expertise.


Abstract:
  Our present study involves the strange stars model in the framework of
$f(R,T)$ theory of gravitation. We have taken a linear function of the Ricci
scalar $R$ and the trace $T$ of the stress-energy tensor $T_{\mu \nu}$ for the
expression of $f(R,T)$, i.e., $f(R,T)=R+ 2 \gamma T $ to obtain the proposed
model, where $\gamma$ is a coupling constant. Moreover, to solve the
hydrostatic equilibrium equations, we consider a linear equation of state
between the radial pressure $p_r$ and matter density $\rho$ as $p_r=\alpha
\rho-\beta$, where $\alpha$ and $\beta$ are some positive constants, Both
$\alpha,\,\beta$ depend on coupling constant $\gamma$ which have been also
depicted in this paper. By employing the Krori-Barua {\em ansatz} already
reported in the literature [J. Phys. A, Math. Gen. 8:508, 1975] we have found
the solutions of the field equations in $f (R, T )$ gravity. The effect of
coupling constant $\gamma$ have been studied on the model parameters like
density, pressures, anisotropic factor, compactness, surface redshift, etc.
both numerically and graphically. A suitable range for $\gamma$ is also
obtained. The physical acceptability and stability of the stellar system have
been tested by different physical tests, e.g., the causality condition, Herrera
cracking concept, relativistic adiabatic index, energy conditions, etc. One can
regain the solutions in Einstein gravity when $\gamma\rightarrow 0$


Abstract:
  This short note describes a Bloom filter variant that takes advantage of
modern SIMD instructions to increase speed by 30%-450%. This filter, the split
block Bloom filter, is used by Apache Impala, Apache Kudu, Apache Parquet, and
Apache Arrow.


Abstract:
  Given a suitable solution $V(t,x)$ to the Korteweg--de Vries equation on the
real line, we prove global well-posedness for initial data $u(0,x) \in V(0,x) +
H^{-1}(\mathbb{R})$.
  Our conditions on $V$ do include regularity but do not impose any assumptions
on spatial asymptotics. We show that periodic profiles $V(0,x) \in
H^5(\mathbb{R}/\mathbb{Z})$ satisfy our hypotheses. In particular, we can treat
localized perturbations of the much-studied periodic traveling wave solutions
(cnoidal waves) of KdV. In our companion paper we show that smooth step-like
initial data also satisfy our hypotheses.
  We employ the method of commuting flows introduced by Killip and Vi\c{s}an;
in the special case $V\equiv 0$, we recover their sharp $H^{-1}(\mathbb{R})$
result.


Abstract:
  We present measurements of differential cross sections and analyzing powers
for the elastic 2H(~d; d)d scattering process. The data were obtained using a
130 MeV polarized deuteron beam. Cross sections and spin observables of the
elastic scattering process were measured at the AGOR facility at KVI using two
independent setups, namely BINA and BBS. The data harvest at setups are in
excellent agreement with each other and allowed us to carry out a thorough
systematic analysis to provide the most accurate data in elastic
deuteron-deuteron scattering at intermediate energies. The results can be used
to confront upcoming state-of-the-art calculations in the four-nucleon
scattering domain, and will, thereby, provide further insights in the dynamics
of three- and four-nucleon forces in few-nucleon systems.


Abstract:
  Bitcoin has emerged as a revolutionary payment system with its decentralized
ledger concept however it has significant problems such as high transaction
fees and long confirmation times. Lightning Network (LN), which was introduced
much later, solves most of these problems with an innovative concept called
off-chain payments. With this advancement, Bitcoin has become an attractive
venue to perform micro-payments which can also be adopted in many IoT
applications (e.g. toll payments). Nevertheless, it is not feasible to host LN
and Bitcoin on IoT devices due to the storage, memory, and processing
requirements. Therefore, in this paper, we propose an efficient and secure
protocol that enables an IoT device to use LN through an untrusted gateway
node. The gateway hosts LN and Bitcoin nodes and can open & close LN channels,
send LN payments on behalf of the IoT device. This delegation approach is
powered by a (2,2)-threshold scheme that requires the IoT device and the LN
gateway to jointly perform all LN operations which in turn secures both
parties' funds. Specifically, we propose to thresholdize LN's Bitcoin public
and private keys as well as its commitment points. With these and several other
protocol level changes, IoT device is protected against revoked state
broadcast, collusion, and ransom attacks. We implemented the proposed protocol
by changing LN's source code and thoroughly evaluated its performance using a
Raspberry Pi. Our evaluation results show that computational and communication
delays associated with the protocol are negligible. To the best of our
knowledge, this is the first work that implemented threshold cryptography in
LN.


Abstract:
  Classic information extraction techniques consist in building questions and
answers about the facts. Indeed, it is still a challenge to subjective
information extraction systems to identify opinions and feelings in context. In
sentiment-based NLP tasks, there are few resources to information extraction,
above all offensive or hateful opinions in context. To fill this important gap,
this short paper provides a new cross-lingual and contextual offensive lexicon,
which consists of explicit and implicit offensive and swearing expressions of
opinion, which were annotated in two different classes: context dependent and
context-independent offensive. In addition, we provide markers to identify hate
speech. Annotation approach was evaluated at the expression-level and achieves
high human inter-annotator agreement. The provided offensive lexicon is
available in Portuguese and English languages.


Abstract:
  Reconfigurable intelligent surfaces (RISs) have attracted wide interest from
industry and academia since they can shape the wireless environment into a
desirable form with a low cost. In practice, RISs have three types of
implementations: 1) reflective, where signals can be reflected to the users on
the same side of the base station (BS), 2) transmissive, where signals can
penetrate the RIS to serve the users on the opposite side of the BS, and 3)
hybrid, where the RISs have a dual function of reflection and transmission.
However, existing works focus on the reflective type RISs, and the other two
types of RISs are not well investigated. In this letter, a downlink multi-user
RIS-assisted communication network is considered, where the RIS can be one of
these types. We derive the system sum-rate, and discuss which type can yield
the best performance under a specific user distribution. Numerical results
verify our analysis.


Abstract:
  A broadcast on a nontrivial connected graph G with vertex set V is a function
f from V to {0,1,...,diam(G)} such that f(v) is at most the eccentricity of v
for all v in V. The weight of f is the sum of the function values taken over V.
A vertex u hears f from v if f(v) is positive and d(u,v) is at most f(v). A
broadcast f is dominating if every vertex of G hears f. The upper broadcast
number of G is {\Gamma}_{b}(G), which is the maximum weight of a minimal
dominating broadcast on G. A broadcast f is boundary independent if, for any
vertex w that hears f from vertices v_{1},...,v_{k}, where k is at least 2, the
distance d(w,v_{i}) equals f(v_{i}) for each i. The maximum weight of a
boundary independent broadcast is the boundary independence broadcast number
{\alpha}_{bn}(G). We compare {\alpha}_{bn} to {\Gamma}_{b}, showing that
neither is an upper bound for the other. We show that the differences
{\Gamma}_{b}-{\alpha}_{bn} and {\alpha}_{bn}-{\Gamma}_{b} are unbounded, the
ratio {\alpha}_{bn}/{\Gamma}_{b} is bounded for all graphs, and
{\Gamma}_{b}/{\alpha}_{bn} is bounded for bipartite graphs but unbounded in
general.


Abstract:
  The spectrum efficiency (SE) and security of the secondary users (SUs) in the
cognitive radio networks (CRNs) have become two main issues due to the
limitation interference to the primary users (PUs) and the shared spectrum with
the PUs. Intelligent reflecting surface (IRS) has been recently proposed as a
revolutionary technique which can help to enhance the SE and physical layer
security of wireless communications. This paper investigates the application of
IRS in an underlay CRN, where a multi-antenna cognitive base station (CBS)
utilizes spectrum assigned to the PU to communicate with a SU via IRS in the
presence of multiple coordinated eavesdroppers (Eves). To achieve the trade-off
between the secrecy rate (SR) and energy consumption, we investigate the
secrecy energy efficiency (SEE) maximization problem by jointly designing the
transmit beamforming at the CBS and the reflect beamforming at the IRS. To
solve the non-convex problem with coupled variables, we propose an iterative
alternating optimization algorithm to solve the sub-problems alternately, by
utilizing an iterative penalty function based algorithm for sub-problem 1 and
the difference of two-convex functions method for sub-problem 2. Furthermore,
we provide a second-order-cone-programming (SOCP) approximation approach to
reduce the computational complexity. Finally, the simulation results
demonstrate that IRS can help significantly improve the SE and enhance the
physical layer security in the CRNs. Moreover, the effectiveness and
superiority of our proposed algorithm in achieving the trade-off between the SR
and energy consumption are verified.


Abstract:
  Most 3D shape completion approaches rely heavily on partial-complete shape
pairs and learn in a fully supervised manner. Despite their impressive
performances on in-domain data, when generalizing to partial shapes in other
forms or real-world partial scans, they often obtain unsatisfactory results due
to domain gaps. In contrast to previous fully supervised approaches, in this
paper we present ShapeInversion, which introduces Generative Adversarial
Network (GAN) inversion to shape completion for the first time. ShapeInversion
uses a GAN pre-trained on complete shapes by searching for a latent code that
gives a complete shape that best reconstructs the given partial input. In this
way, ShapeInversion no longer needs paired training data, and is capable of
incorporating the rich prior captured in a well-trained generative model. On
the ShapeNet benchmark, the proposed ShapeInversion outperforms the SOTA
unsupervised method, and is comparable with supervised methods that are learned
using paired data. It also demonstrates remarkable generalization ability,
giving robust results for real-world scans and partial inputs of various forms
and incompleteness levels. Importantly, ShapeInversion naturally enables a
series of additional abilities thanks to the involvement of a pre-trained GAN,
such as producing multiple valid complete shapes for an ambiguous partial
input, as well as shape manipulation and interpolation.


Abstract:
  Self-supervised learning provides a promising path towards eliminating the
need for costly label information in representation learning on graphs.
However, to achieve state-of-the-art performance, methods often need large
numbers of negative examples and rely on complex augmentations. This can be
prohibitively expensive, especially for large graphs. To address these
challenges, we introduce Bootstrapped Graph Latents (BGRL) - a graph
representation learning method that learns by predicting alternative
augmentations of the input. BGRL uses only simple augmentations and alleviates
the need for contrasting with negative examples, and is thus scalable by
design. BGRL outperforms or matches prior methods on several established
benchmarks, while achieving a 2-10x reduction in memory costs. Furthermore, we
show that BGRL can be scaled up to extremely large graphs with hundreds of
millions of nodes in the semi-supervised regime - achieving state-of-the-art
performance and improving over supervised baselines where representations are
shaped only through label information. In particular, our solution centered on
BGRL constituted one of the winning entries to the Open Graph Benchmark - Large
Scale Challenge at KDD Cup 2021, on a graph orders of magnitudes larger than
all previously available benchmarks, thus demonstrating the scalability and
effectiveness of our approach.


Abstract:
  Majorana bosons, that is, tight bosonic analogs of the Majorana fermionic
quasiparticles of condensed matter physics, are forbidden for gapped free
bosonic matter within a standard Hamiltonian scenario. We show how the
interplay between dynamical metastability and nontrivial bulk topology makes
their emergence possible in noninteracting bosonic chains undergoing Markovian
dissipation. This leads to a distinctive form of topological metastability,
whereby a conserved Majorana boson localized on one edge is paired, in general,
with a symmetry generator localized on the opposite edge. We argue that
Majorana bosons are robust against disorder and identifiable by signatures in
the zero-frequency steady-state power spectrum. Our results suggest that
symmetry-protected topological phases for free bosons may arise in transient
metastable regimes, which persist over practical timescales.


Abstract:
  Multivariate point processes are widely applied to model event-type data such
as natural disasters, online message exchanges, financial transactions or
neuronal spike trains. One very popular point process model in which the
probability of occurrences of new events depend on the past of the process is
the Hawkes process. In this work we consider the nonlinear Hawkes process,
which notably models excitation and inhibition phenomena between dimensions of
the process. In a nonparametric Bayesian estimation framework, we obtain
concentration rates of the posterior distribution on the parameters, under mild
assumptions on the prior distribution and the model. These results also lead to
convergence rates of Bayesian estimators. Another object of interest in
event-data modelling is to recover the graph of interaction - or Granger
connectivity graph - of the phenomenon. We provide consistency guarantees on
Bayesian methods for estimating this quantity; in particular, we prove that the
posterior distribution is consistent on the graph adjacency matrix of the
process, as well as a Bayesian estimator based on an adequate loss function.


Abstract:
  Causal structure discovery from observational data is fundamental to the
causal understanding of autonomous systems such as medical decision support
systems, advertising campaigns and self-driving cars. This is essential to
solve well-known causal decision making and prediction problems associated with
those real-world applications. Recently, recursive causal discovery algorithms
have gained particular attention among the research community due to their
ability to provide good results by using Conditional Independent (CI) tests in
smaller sub-problems. However, each of such algorithms needs a refinement
function to remove undesired causal relations of the discovered graphs.
Notably, with the increase of the problem size, the computation cost (i.e., the
number of CI-tests) of the refinement function makes an algorithm expensive to
deploy in practice. This paper proposes a generic causal structure refinement
strategy that can locate the undesired relations with a small number of
CI-tests, thus speeding up the algorithm for large and complex problems. We
theoretically prove the correctness of our algorithm. We then empirically
evaluate its performance against the state-of-the-art algorithms in terms of
solution quality and completion time in synthetic and real datasets.


Abstract:
  Nonreciprocal radiation refers to electromagnetic wave radiation in which a
structure provides different responses under the change of the direction of the
incident field. Modern wireless telecommunication systems demand versatile
apparatuses which are capable of full-duplex nonreciprocal wave processing and
amplification, especially in the reflective state. To realize such a unique,
extraordinary and versatile functionality, we propose an architecture in which
a chain of series cascaded radiating patches are integrated with nonreciprocal
phase shifters, providing an original and efficient apparatus for full-duplex
reflective beamsteering. Such an ultrathin reflective metasurface can provide
directive and diverse radiation beams, large wave amplification, steerable
beams by simply changing the bias of the gradient active nonmagnetic
nonreciprocal phase shifters, and is immune to undesired time harmonics. Having
accomplished all these functionalities in the reflective state, the metasurface
represents a conspicuous apparatus for efficient, controllable and programmable
wave engineering.


Abstract:
  Electric-field noise due to surfaces disturbs the motion of nearby trapped
ions, compromising the fidelity of gate operations that are the basis for
quantum computing algorithms. We present a method that predicts the effect of
dielectric materials on the ion's motion. Such dielectrics are integral
components of ion traps. Quantitative agreement is found between a model with
no free parameters and measurements of a trapped ion in proximity to dielectric
mirrors. We expect that this approach can be used to optimize the design of
ion-trap-based quantum computers and network nodes.


Abstract:
  General Santos City, as the tuna capital of the Philippines, relies with the
presence of tricycles in moving people and goods. Considered as a
highly-urbanized city, General Santos City serves as vital link of the entire
SOCKSARGEN region's economic activities. With the current thrust of the city in
providing a sustainable transport service, several options were identified to
adopt in the entire city, that includes cleaner and better transport mode.
Electric tricycle is an after sought alternative that offers better choice in
terms of identified factors of sustainable transport: reliability, safety,
comfort, environment, affordability, and facility. A literature review was
conducted to provide a comparison of cost and emission between a motorized
tricycle and an e-tricycle. The study identified the existing tricycle industry
of the city and reviewed the modal share with the city's travel pattern. The
survey revealed a number of hazards were with the current motorized tricycle
that needs to address for the welfare of the passengers and drivers. The study
favors the shift to adopting E-tricycle. The model derived from binary
logistics regression provided a 72.72% model accuracy. Based from the results
and findings, electric tricycle can be an alternative mode of public transport
in the city that highly support sustainable option that provides local populace
to improve their quality of life through mobility and economic activity.
Further recommendation to local policy makers in the transport sector of the
city include the clustering of barangays for better traffic management and
franchise regulation, the inclusion of transport-related infrastructure related
to tricycle service with their investment planning and programming, the roll
out and implementation of tricycle code of the city, and the piloting activity
of introducing e-tricycle in the city.


Abstract:
  The observation of superconductivity in infinite layer nickelate
(Nd,Sr)NiO$_{2}$ thin films has led to rapid theoretical and experimental
investigations of these copper-oxide-analogue systems [1-15]. Superconductivity
has also been found in (Pr,Sr)NiO$_{2}$ [16,17], but not previously in
(La,Sr)NiO$_{2}$ [2], raising a fundamental question whether superconductivity
is associated with the presence of rare-earth moments [18,19]. Here we show
that with significant materials optimization, substantial portions of the
La$_{1-x}$Sr$_{x}$NiO$_{2}$ phase diagram can enter the regime of coherent
low-temperature transport ($x$ = 0.14 - 0.20), with subsequent superconducting
transitions and a maximum onset of ~ 9 K at $x$ = 0.20. Additionally, we
observe the unexpected indication of a superconducting ground state in undoped
LaNiO$_{2}$, which likely reflects the self-doped nature of the electronic
structure. Combining the results of (La/Pr/Nd)$_{1-x}$Sr$_{x}$NiO$_{2}$ reveals
a generalized superconducting dome, characterized by systematic shifts in the
unit cell volume and in the relative electron-hole populations across the
lanthanides.


Abstract:
  Stochastic local search (SLS) is a successful paradigm for solving the
satisfiability problem of propositional logic. A recent development in this
area involves solving not the original instance, but a modified, yet logically
equivalent one. Empirically, this technique was found to be promising as it
improves the performance of state-of-the-art SLS solvers.
  Currently, there is only a shallow understanding of how this modification
technique affects the runtimes of SLS solvers. Thus, we model this modification
process and conduct an empirical analysis of the hardness of logically
equivalent formulas. Our results are twofold. First, if the modification
process is treated as a random process, a lognormal distribution perfectly
characterizes the hardness; implying that the hardness is long-tailed. This
means that the modification technique can be further improved by implementing
an additional restart mechanism. Thus, as a second contribution, we
theoretically prove that all algorithms exhibiting this long-tail property can
be further improved by restarts. Consequently, all SAT solvers employing this
modification technique can be enhanced.


Abstract:
  The prediction of surrounding traffic participants behavior is a crucial and
challenging task for driver assistance and autonomous driving systems. Today's
approaches mainly focus on modeling dynamic aspects of the traffic situation
and try to predict traffic participants behavior based on this. In this article
we take a first step towards extending this common practice by calculating
location-specific a-priori lane change probabilities. The idea behind this is
straight forward: The driving behavior of humans may vary in exactly the same
traffic situation depending on the respective location. E.g. drivers may ask
themselves: Should I pass the truck in front of me immediately or should I wait
until reaching the less curvy part of my route lying only a few kilometers
ahead? Although, such information is far away from allowing behavior prediction
on its own, it is obvious that today's approaches will greatly benefit when
incorporating such location-specific a-priori probabilities into their
predictions. For example, our investigations show that highway interchanges
tend to enhance driver's motivation to perform lane changes, whereas curves
seem to have lane change-dampening effects. Nevertheless, the investigation of
all considered local conditions shows that superposition of various effects can
lead to unexpected probabilities at some locations. We thus suggest dynamically
constructing and maintaining a lane change probability map based on customer
fleet data in order to support onboard prediction systems with additional
information. For deriving reliable lane change probabilities a broad customer
fleet is the key to success.


Abstract:
  A hybrid surface integral equation partial differential equation (SIE-PDE)
formulation without the boundary condition requirement is proposed to solve the
transverse magnetic (TM) electromagnetic problems. In the proposed formulation,
the computational domain is decomposed into two overlapping domains: the SIE
and PDE domains. In the SIE domain, complex structures with piecewise
homogeneous media, e.g., highly conductive media, are included. An equivalent
model for those structures is constructed by replacing them with the background
medium and introducing a surface equivalent electric current density on an
enclosed boundary to represent their electromagnetic effects. The remaining
computational domain and homogeneous background medium replaced domain consist
of the PDE domain, in which inhomogeneous or non-isotropic media are included.
Through combining the surface equivalent electric current density and the
inhomogeneous Helmholtz equation, a hybrid SIE-PDE formulation is derived. It
requires no boundary conditions, and is mathematically equivalent to the
original physical model. Through careful construction of basis functions to
expand electric fields and the equivalent current density, the discretized
formulation is made compatible with the SIE and PDE domain interface. The
accuracy and efficiency are validated through two numerical examples. Results
show that the proposed SIE-PDE formulation can obtain accurate results, and
significant performance improvements in terms of CPU time and memory
consumption compared with the FEM are achieved.


Abstract:
  Time-delay interferometry (TDI) is a processing step essential for the
scientific exploitation of LISA, as it reduces the otherwise overwhelming laser
noise in the interferometric measurements. The fundamental idea is to define
new laser-noise-free observables by combining appropriately time-shifted
measurements. First- and second-generation TDI combinations cancel laser noise
under the assumption that the LISA armlengths are constant or evolve linearly
with time, respectively. We recently extended TDI by solving for the
laser-noise-free combinations implicitly, writing the likelihood of the data
directly in terms of the basic measurements, and using a discretized
representation of the delays that can accommodate any time dependence of the
armlengths. We named the resulting formalism "TDI-infinity'' [PRD 103, 082001
(2021)].
  According to Tinto, Dhurandhar, and Joshi [arXiv/2105.02054], our
matrix-based approach is invalidated by the simplified start-up conditions
assumed for the design matrix that connects the time series of laser-noise
fluctuations to the time series of interferometric measurements along the LISA
arms. Here we respond that, if those boundary conditions are indeed
unrealistic, they do not invalidate the algorithm, since one can simply
truncate the design matrix to exclude "incomplete'' measurements, or set them
to zero. Our formalism then proceeds unmodified, except that the length of the
laser-noise canceling time series is reduced by the number of excluded
measurements. Tinto and colleagues further claim that the matrix formulation is
merely a finite representation of the polynomial ring of delay operators
introduced by Dhurandar, Nayak, and Vinet to formalize TDI. We show that this
is only true if all interferometric delays are exact multiples of the sampling
interval, which will not be possible in practical contexts such as LISA.


Abstract:
  In this article, we investigate global norm of potential vector field in
Ricci soliton. In particular, we have deduced certain conditions so that the
potential vector field has finite global norm in expanding Ricci soliton. We
have also showed that if the potential vector field has finite global norm in
complete non-compact Ricci soliton having finite volume, then the scalar
curvature becomes constant.


Abstract:
  Transfer learning is critical for efficient information transfer across
multiple related learning problems. A simple, yet effective transfer learning
approach utilizes deep neural networks trained on a large-scale task for
feature extraction. Such representations are then used to learn related
downstream tasks. In this paper, we investigate transfer learning capacity of
audio representations obtained from neural networks trained on a large-scale
sound event detection dataset. We build and evaluate these representations
across a wide range of other audio tasks, via a simple linear classifier
transfer mechanism. We show that such simple linear transfer is already
powerful enough to achieve high performance on the downstream tasks. We also
provide insights into the attributes of sound event representations that enable
such efficient information transfer.


Abstract:
  The prohibitive cost and low fidelity of experimental data in industry scale
thermofluid systems limit the usefulness of pure data-driven machine learning
methods. Physics-informed neural networks (PINN) strive to overcome this by
embedding the physics equations in the construction of the neural network loss
function. In the present paper, the mixed-variable PINN methodology is applied
to develop steady-state and transient surrogate models of incompressible
laminar flow with heat transfer through a 2D internal domain with obstructions.
Automatic spatial and temporal differentiation is applied to the partial
differential equations for mass, momentum and energy conservation, and the
residuals are included in the loss function, together with the boundary and
initial values. Good agreement is obtained between the PINN and CFD results for
both the steady-state and transient cases, but normalization of the PDEs proves
to be crucial. Although this proves the ability of the PINN approach to solve
multiple physics-based PDEs on a single domain, the PINN takes significantly
longer to solve than the traditional finite volume numerical methods utilized
in commercial CFD software.


Abstract:
  The concept of synthetic dimensions in photonics has attracted rapidly
growing interest in the past few years. Among a variety of photonic systems,
the ring resonator system under dynamic modulation has been investigated in
depth both in theory and experiment, and has proven to be a powerful way to
build synthetic frequency dimensions. In this tutorial, we start with a
pedagogical introduction to the theoretical approaches in describing the
dynamically modulated ring resonator system, and then review experimental
methods in building such a system. Moreover, we discuss important physical
phenomena in synthetic dimensions, including nontrivial topological physics.
Our tutorial provides a pathway towards studying the dynamically modulated ring
resonator system, understanding synthetic dimensions in photonics, and
discusses future prospects for both fundamental research and practical
applications using synthetic dimensions.


Abstract:
  We compute the $\Lambda$ and $\overline{\Lambda}$ global polarizations in
semi-central heavy-ion collisions using the core-corona model where the source
of $\Lambda$'s and $\overline{\Lambda}$'s is taken as consisting of a
high-density core and a less dense corona. We show that the overall properties
of the polarization excitation functions can be linked to the relative
abundance of $\Lambda$s coming from the core versus those coming from the
corona. For low collision energies, the former are more abundant whereas for
higher energies the latter become more abundant. The main consequence of this
reversing of the relative abundance is that both polarizations peak at
collision energies $\sqrt{s_{NN}}\lesssim 10$ GeV. The exact positions and
heights of these peaks depend not only on this reversal of relative abundances,
but also on the centrality class, which is directly related to the QGP volume
and lifetime, as well as on the relative abundances of $\Lambda$s and
$\overline{\Lambda}$s in the core and corona regions. The intrinsic
polarizations are computed from a field theoretical approach that links the
alignment of the strange quark spin with the thermal vorticity and modeling the
QGP volume and lifetime using a Bjorken expansion scenario. We predict that the
$\Lambda$ and $\overline{\Lambda}$ global polarizations should peak at the
energy range accessible to NICA and HADES.


Abstract:
  Machine learning applications to symbolic mathematics are becoming
increasingly popular, yet there lacks a centralized source of real-world
symbolic expressions to be used as training data. In contrast, the field of
natural language processing leverages resources like Wikipedia that provide
enormous amounts of real-world textual data. Adopting the philosophy of
"mathematics as language," we bridge this gap by introducing a pipeline for
distilling mathematical expressions embedded in Wikipedia into symbolic
encodings to be used in downstream machine learning tasks. We demonstrate that
a $\textit{mathematical}$ $\textit{language}$ $\textit{model}$ trained on this
"corpus" of expressions can be used as a prior to improve the performance of
neural-guided search for the task of symbolic regression.


Abstract:
  In this paper, we extend the correspondence between Bayesian estimation and
optimal smoothing in a Reproducing Kernel Hilbert Space (RKHS) adding a convexe
constraints on the solution. Through a sequence of approximating Hilbertian
spaces and a discretized model, we prove that the Maximum A Posteriori (MAP) of
the posterior distribution is exactly the optimal constrained smoothing
function in the RKHS. This paper can be read as a generalization of the paper
[7] of Kimeldorf-Wahba where it is proved that the optimal smoothing solution
is the mean of the posterior distribution.


Abstract:
  We present a determination of the perturbative QCD (pQCD) coupling using the
V+A channel ALEPH $\tau$-decay data. The determination involves the
double-pinched Borel-Laplace Sum Rules and Finite Energy Sum Rules. The
theoretical basis is the Operator Product Expansion (OPE) of the V+A channel
Adler function in which the higher order terms of the leading-twist part
originate from a model based on the known structure of the leading renormalons
of this quantity. The applied evaluation methods are contour-improved
perturbation theory (CIPT), fixed-order perturbation theory (FOPT), and
Principal Value of the Borel resummation (PV). All the methods involve
truncations in the order of the coupling. In contrast to the truncated CIPT
method, the truncated FOPT and PV methods account correctly for the suppression
of various renormalon contributions of the Adler function in the mentioned sum
rules. The extracted value of the ${\overline {\rm MS}}$ coupling is
$\alpha_s(m_{\tau}^2) = 0.3116 \pm 0.0073$ [$\alpha_s(M_Z^2)=0.1176 \pm
0.0010$] for the average of the FOPT and PV methods, which we regard as our
main result. On the other hand, if we include in the average also the CIPT
method, the resulting values are significantly higher, $\alpha_s(m_{\tau}^2) =
0.3194 \pm 0.0167$ [$\alpha_s(M_Z^2)=0.1186 \pm 0.0021$].


Abstract:
  In the past few years, there has been much work on incorporating fairness
requirements into algorithmic rankers, with contributions coming from the data
management, algorithms, information retrieval, and recommender systems
communities. In this survey we give a systematic overview of this work,
offering a broad perspective that connects formalizations and algorithmic
approaches across subfields. An important contribution of our work is in
developing a common narrative around the value frameworks that motivate
specific fairness-enhancing interventions in ranking. This allows us to unify
the presentation of mitigation objectives and of algorithmic techniques to help
meet those objectives or identify trade-offs.


Abstract:
  In this work, we present bilayer flat-band Hamiltonians, in which all bulk
states are localized and specified by extensive local integrals of motion
(LIOMs). The present systems are bilayer extension of Creutz ladder, which is
studied previously. In order to construct models, we employ building blocks,
cube operators, which are linear combinations of fermions defined in each cube
of the bilayer lattice. There are eight cubic operators, and the Hamiltonians
are composed of the number operators of them, the LIOMs. A suitable arrangement
of locations of the cube operators is needed to have exact projective
Hamiltonians. The projective Hamiltonians belong to a topological
classification class, BDI class. With the open boundary condition, the
constructed Hamiltonians have gapless edge modes, which commute with each other
as well as the Hamiltonian. This result comes from a symmetry analogous to the
one-dimensional chiral symmetry of the BDI class. These results indicate that
the projective Hamiltonians describe a kind of symmetry protected topological
phase matter. Careful investigation of topological indexes, such as Berry
phase, string operator, is given. We also show that by using the gapless edge
modes, a generalized Sachdev-Ye-Kitaev (SYK) model is constructed.


Abstract:
  Traditional crowd counting approaches usually use Gaussian assumption to
generate pseudo density ground truth, which suffers from problems like
inaccurate estimation of the Gaussian kernel sizes. In this paper, we propose a
new measure-based counting approach to regress the predicted density maps to
the scattered point-annotated ground truth directly. First, crowd counting is
formulated as a measure matching problem. Second, we derive a semi-balanced
form of Sinkhorn divergence, based on which a Sinkhorn counting loss is
designed for measure matching. Third, we propose a self-supervised mechanism by
devising a Sinkhorn scale consistency loss to resist scale changes. Finally, an
efficient optimization method is provided to minimize the overall loss
function. Extensive experiments on four challenging crowd counting datasets
namely ShanghaiTech, UCF-QNRF, JHU++, and NWPU have validated the proposed
method.


Abstract:
  Group control of connected and autonomous vehicles on automated highways is
challenging for the advanced driver assistance systems (ADAS) and the automated
driving systems (ADS). This paper investigates the differential game-based
approach to autonomous convoy control with the aim of deployment on automated
highways. Under the non-cooperative differential games, the coupled vehicles
make their decisions independently while their states are interdependent. The
receding horizon Nash equilibrium of the linear-quadratic differential game
provides the convoy a distributed state-feedback control strategy. This
approach suffers a fundamental issue that neither a Nash equilibrium's
existence nor the uniqueness is guaranteed. We convert the individual
dynamics-based differential game to a relative dynamics-based optimal control
problem that carries all the features of the differential game. The existence
of a unique Nash control under the differential game corresponds to a unique
solution to the optimal control problem. The latter is shown, as well as the
asymptotic stability of the closed-loop system. Simulations illustrate the
effectiveness of the presented convey control scheme and how it well suits
automated highway driving scenarios.


Abstract:
  Autonomous driving is regarded as one of the most promising remedies to
shield human beings from severe crashes. To this end, 3D object detection
serves as the core basis of such perception system especially for the sake of
path planning, motion prediction, collision avoidance, etc. Generally, stereo
or monocular images with corresponding 3D point clouds are already standard
layout for 3D object detection, out of which point clouds are increasingly
prevalent with accurate depth information being provided. Despite existing
efforts, 3D object detection on point clouds is still in its infancy due to
high sparseness and irregularity of point clouds by nature, misalignment view
between camera view and LiDAR bird's eye of view for modality synergies,
occlusions and scale variations at long distances, etc. Recently, profound
progress has been made in 3D object detection, with a large body of literature
being investigated to address this vision task. As such, we present a
comprehensive review of the latest progress in this field covering all the main
topics including sensors, fundamentals, and the recent state-of-the-art
detection methods with their pros and cons. Furthermore, we introduce metrics
and provide quantitative comparisons on popular public datasets. The avenues
for future work are going to be judiciously identified after an in-deep
analysis of the surveyed works. Finally, we conclude this paper.


Abstract:
  Wax is what you put on a surfboard to avoid slipping. It is an essential tool
to go surfing... We introduce WAX-ML a research-oriented Python library
providing tools to design powerful machine learning algorithms and feedback
loops working on streaming data. It strives to complement JAX with tools
dedicated to time series. WAX-ML makes JAX-based programs easy to use for
end-users working with pandas and xarray for data manipulation. It provides a
simple mechanism for implementing feedback loops, allows the implementation of
online learning and reinforcement learning algorithms with functions, and makes
them easy to integrate by end-users working with the object-oriented
reinforcement learning framework from the Gym library. It is released with an
Apache open-source license on GitHub at https://github.com/eserie/wax-ml.


Abstract:
  We implement a model for the two-point statistics of biased tracers that
combines dark matter dynamics from $N$-body simulations with an analytic
Lagrangian bias expansion. Using Aemulus, a suite of $N$-body simulations built
for emulation of cosmological observables, we emulate the cosmology dependence
of these nonlinear spectra from redshifts $z = 0$ to $z=2$. We quantify the
accuracy of our emulation procedure, which is sub-per cent at $k=1\, h {\rm
Mpc}^{-1}$ for the redshifts probed by upcoming surveys and improves at higher
redshifts. We demonstrate its ability to describe the statistics of complex
tracer samples, including those with assembly bias and baryonic effects,
reliably fitting the clustering and lensing statistics of such samples at
redshift $z\simeq 0.4$ to scales of $k_{\rm max} \approx 0.6\,
h\mathrm{Mpc}^{-1}$. We show that the emulator can be used for unbiased
cosmological parameter inference in simulated joint clustering and
galaxy--galaxy lensing analyses with data drawn from an independent $N$-body
simulation. These results indicate that our emulator is a promising tool that
can be readily applied to the analysis of current and upcoming datasets from
galaxy surveys.


Abstract:
  One class of competitive candidates for dark matter is ultralight bosons. If
they exist, these bosons may form long-lived bosonic clouds surrounding
rotating black holes via superradiant instabilities, acting as sources of
gravity and affecting the propagation of gravitational waves around the host
black hole. During extreme-mass-ratio inspirals, the bosonic clouds will
survive the inspiral phase and can affect the quasinormal-mode frequencies of
the perturbed black-hole-bosonic-cloud system. In this work, we compute the
shifts of gravitational quasinormal-mode frequencies of a rotating black hole
due to the presence of a surrounding bosonic cloud. We then perform a mock
analysis on simulated Laser Interferometer Space Antenna observational data
containing injected ringdown signals from supermassive black holes with and
without a bosonic cloud. We find that with less than an hour of observational
data of the ringdown phase of nearby supermassive black holes such as
Sagittarius A* and M32, we can rule out or confirm the existence of
cloud-forming ultralight bosons of mass $ \sim 10^{-17} \rm eV$.


Abstract:
  Image Super Resolution (SR) finds applications in areas where images need to
be closely inspected by the observer to extract enhanced information. One such
focused application is an offline forensic analysis of surveillance feeds. Due
to the limitations of camera hardware, camera pose, limited bandwidth, varying
illumination conditions, and occlusions, the quality of the surveillance feed
is significantly degraded at times, thereby compromising monitoring of
behavior, activities, and other sporadic information in the scene. For the
proposed research work, we have inspected the effectiveness of four
conventional yet effective SR algorithms and three deep learning-based SR
algorithms to seek the finest method that executes well in a surveillance
environment with limited training data op-tions. These algorithms generate an
enhanced resolution output image from a sin-gle low-resolution (LR) input
image. For performance analysis, a subset of 220 images from six surveillance
datasets has been used, consisting of individuals with varying distances from
the camera, changing illumination conditions, and complex backgrounds. The
performance of these algorithms has been evaluated and compared using both
qualitative and quantitative metrics. These SR algo-rithms have also been
compared based on face detection accuracy. By analyzing and comparing the
performance of all the algorithms, a Convolutional Neural Network (CNN) based
SR technique using an external dictionary proved to be best by achieving robust
face detection accuracy and scoring optimal quantitative metric results under
different surveillance conditions. This is because the CNN layers progressively
learn more complex features using an external dictionary.


Abstract:
  Nas \'ultimas d\'ecadas houve forte mudan\c{c}a no perfil das
publica\c{c}\~oes em an\'alise econ\^omica do direito e nos m\'etodos
emp\'iricos mais utilizados. Por\'em, nos pr\'oximos anos a mudan\c{c}a pode
ser maior e mais r\'apida, exigindo adapta\c{c}\~ao dos pesquisadores da
\'area. Neste cap\'itulo analiso algumas tend\^encias recentes de mudan\c{c}a e
oportunidades futuras que se avizinham a partir avan\c{c}os nas bases de dados,
estat\'istica, computa\c{c}\~ao e no arcabou\c{c}o regulat\'orio dos pa\'ises.
Avan\c{c}o a hip\'otese de que expans\~ao de objetos e m\'etodos favorecer\'a
equipes de pesquisa maiores e interdisciplinares e apresento evid\^encias
circunstanciais a partir de dados bibliom\'etricos de que isso j\'a vem
acontecendo no Journal of Law and Economics.


Abstract:
  Research in artificial intelligence (AI) for social good presupposes some
definition of social good, but potential definitions have been seldom suggested
and never agreed upon. The normative question of what AI for social good
research should be "for" is not thoughtfully elaborated, or is frequently
addressed with a utilitarian outlook that prioritizes the needs of the majority
over those who have been historically marginalized, brushing aside realities of
injustice and inequity. We argue that AI for social good ought to be assessed
by the communities that the AI system will impact, using as a guide the
capabilities approach, a framework to measure the ability of different policies
to improve human welfare equity. Furthermore, we lay out how AI research has
the potential to catalyze social progress by expanding and equalizing
capabilities. We show how the capabilities approach aligns with a participatory
approach for the design and implementation of AI for social good research in a
framework we introduce called PACT, in which community members affected should
be brought in as partners and their input prioritized throughout the project.
We conclude by providing an incomplete set of guiding questions for carrying
out such participatory AI research in a way that elicits and respects a
community's own definition of social good.


Abstract:
  In this paper, we focus on the relationship between the fifth Painlev\'{e}
equation and a Jacobi weight perturbed with random singularities,
\begin{equation*} w(z)=\left(1-z^2\right)^{\alpha}{\rm
e}^{-\frac{t}{z^2-k^2}},~~~z,k\in[-1,1],~\alpha,t>0. \end{equation*} By using
the ladder operator approach, we obtain that an auxiliary quantity $R_n(t)$,
which is closely related to the recurrence coefficients of monic polynomials
orthogonal with $w(z)$, satisfies a particular Painlev\'{e} V equation.


Abstract:
  Fluid simulations are often performed using the incompressible Navier-Stokes
equations (INSE), leading to sparse linear systems which are difficult to solve
efficiently in parallel. Recently, kinetic methods based on the
adaptive-central-moment multiple-relaxation-time (ACM-MRT) model have
demonstrated impressive capabilities to simulate both laminar and turbulent
flows, with quality matching or surpassing that of state-of-the-art INSE
solvers. Furthermore, due to its local formulation, this method presents the
opportunity for highly scalable implementations on parallel systems such as
GPUs. However, an efficient ACM-MRT-based kinetic solver needs to overcome a
number of computational challenges, especially when dealing with complex solids
inside the fluid domain. In this paper, we present multiple novel GPU
optimization techniques to efficiently implement high-quality ACM-MRT-based
kinetic fluid simulations in domains containing complex solids. Our techniques
include a new communication-efficient data layout, a load-balanced
immersed-boundary method, a multi-kernel launch method using a simplified
formulation of ACM-MRT calculations to enable greater parallelism, and the
integration of these techniques into a parametric cost model to enable
automated parameter search to achieve optimal execution performance. We also
extended our method to multi-GPU systems to enable large-scale simulations. To
demonstrate the state-of-the-art performance and high visual quality of our
solver, we present extensive experimental results and comparisons to other
solvers.


Abstract:
  The information bottleneck (IB) principle has been suggested as a way to
analyze deep neural networks. The learning dynamics are studied by inspecting
the mutual information (MI) between the hidden layers and the input and output.
Notably, separate fitting and compression phases during training have been
reported. This led to some controversy including claims that the observations
are not reproducible and strongly dependent on the type of activation function
used as well as on the way the MI is estimated. Our study confirms that
different ways of binning when computing the MI lead to qualitatively different
results, either supporting or refusing IB conjectures. To resolve the
controversy, we study the IB principle in settings where MI is non-trivial and
can be computed exactly. We monitor the dynamics of quantized neural networks,
that is, we discretize the whole deep learning system so that no approximation
is required when computing the MI. This allows us to quantify the information
flow without measurement errors. In this setting, we observed a fitting phase
for all layers and a compression phase for the output layer in all experiments;
the compression in the hidden layers was dependent on the type of activation
function. Our study shows that the initial IB results were not artifacts of
binning when computing the MI. However, the critical claim that the compression
phase may not be observed for some networks also holds true.


Abstract:
  Modelling the process that a listener actuates in deriving the words intended
by a speaker requires setting a hypothesis on how lexical items are stored in
memory. This work aims at developing a system that imitates humans when
identifying words in running speech and, in this way, provide a framework to
better understand human speech processing. We build a speech recognizer for
Italian based on the principles of Stevens' model of Lexical Access in which
words are stored as hierarchical arrangements of distinctive features (Stevens,
K. N. (2002). "Toward a model for lexical access based on acoustic landmarks
and distinctive features," J. Acoust. Soc. Am., 111(4):1872-1891). Over the
past few decades, the Speech Communication Group at the Massachusetts Institute
of Technology (MIT) developed a speech recognition system for English based on
this approach. Italian will be the first language beyond English to be
explored; the extension to another language provides the opportunity to test
the hypothesis that words are represented in memory as a set of
hierarchically-arranged distinctive features, and reveal which of the
underlying mechanisms may have a language-independent nature. This paper also
introduces a new Lexical Access corpus, the LaMIT database, created and labeled
specifically for this work, that will be provided freely to the speech research
community. Future developments will test the hypothesis that specific acoustic
discontinuities - called landmarks - that serve as cues to features, are
language independent, while other cues may be language-dependent, with powerful
implications for understanding how the human brain recognizes speech.


Abstract:
  The management of the energy consumption and thermal dissipation of
multi-core heterogeneous platforms is becoming increasingly important as it can
have direct impact on the platform performance. This paper discusses an
approach that enables fast exploration and validation of heterogeneous system
on chips (SoCs) platform configurations with respect to their thermal
dissipation. Such platforms can be configured to find the optimal trade-off
between performance and power consumption. This directly reflects in the head
dissipation of the platform, which when increases over a given threshold will
actually decrease the performance of the platform. Therefore, it is important
to be able to quickly probe and explore different configurations and identify
the most suitable one. However, this task is hindered by the large space of
possible configurations of such platforms and by the time required to benchmark
each configurations. As such, we propose an approach in which we construct a
model of the thermal dissipation of a given platform using a system
identification methods and then we use this model to explore and validate
different configurations. The approach allows us to decrease the exploration
time with several orders of magnitude. We exemplify the approach on an
Odroid-XU4 board featuring an Exynos 5422 SoC.


Abstract:
  Based on the Kharzeev-McLerran-Warringa (KMW) model that estimates strong
electromagnetic (EM) fields generated in relativistic heavy-ion collisions, we
generalize the formulas of EM fields in the vacuum by incorporating the
longitudinal position dependence, the generalized charge distributions and
retardation correction. We further generalize the formulas of EM fields in the
pure quark-gluon plasma (QGP) medium by incorporating a constant Ohm electric
conductivity and also during the realistic early-time stages QGP evolution by
using a time-dependent electric conductivity. Using the extended KMW model, we
observe a slower time evolution and a more reasonable impact parameter $b$
dependence of the magnetic field strength than those from the original KMW
model in the vacuum. The inclusion of medium effects by using the lattice data
helps to further prolong the time evolution of magnetic field, such that the
magnetic field strength during the realistic QGP evolution at thermal
freeze-out time can meet the $1\sigma$ bound constrained from experimentally
measured difference in global polarizations of $\Lambda$ and $\bar{\Lambda}$
hyperons in Au+Au collisions at top RHIC energy. These generalized formulations
in the extended KMW model will be potentially useful for many EM fields
relevant studies in heavy-ion collisions, especially at lower colliding
energies and for various species of colliding nuclei.


Abstract:
  Fetal motion is unpredictable and rapid on the scale of conventional MR scan
times. Therefore, dynamic fetal MRI, which aims at capturing fetal motion and
dynamics of fetal function, is limited to fast imaging techniques with
compromises in image quality and resolution. Super-resolution for dynamic fetal
MRI is still a challenge, especially when multi-oriented stacks of image slices
for oversampling are not available and high temporal resolution for recording
the dynamics of the fetus or placenta is desired. Further, fetal motion makes
it difficult to acquire high-resolution images for supervised learning methods.
To address this problem, in this work, we propose STRESS (Spatio-Temporal
Resolution Enhancement with Simulated Scans), a self-supervised
super-resolution framework for dynamic fetal MRI with interleaved slice
acquisitions. Our proposed method simulates an interleaved slice acquisition
along the high-resolution axis on the originally acquired data to generate
pairs of low- and high-resolution images. Then, it trains a super-resolution
network by exploiting both spatial and temporal correlations in the MR time
series, which is used to enhance the resolution of the original data.
Evaluations on both simulated and in utero data show that our proposed method
outperforms other self-supervised super-resolution methods and improves image
quality, which is beneficial to other downstream tasks and evaluations.


Abstract:
  The detection of a wide range of substructures such as rings, cavities and
spirals has become a common outcome of high spatial resolution imaging of
protoplanetary disks, both in the near-infrared scattered light and in the
thermal millimetre continuum emission. The most frequent interpretation of
their origin is the presence of planetary-mass companions perturbing the gas
and dust distribution in the disk (perturbers), but so far the only bona-fide
detection has been the two giant planets around PDS 70. Here, we collect a
sample of 15 protoplanetary disks showing substructures in SPHERE scattered
light images and present a homogeneous derivation of planet detection limits in
these systems. We also estimate the mass of these perturbers through a Hill
radius prescription and a comparison to ALMA data. Assuming that one single
planet carves each substructure in scattered light, we find that more massive
perturbers are needed to create gaps within cavities than rings, and that we
might be close to a detection in the cavities of RX J1604, RX J1615, Sz Cha, HD
135344B and HD 34282. We reach typical mass limits in these cavities of 3-10
Mjup. For planets in the gaps between rings, we find that the detection limits
of SPHERE are about an order of magnitude away in mass, and that the gaps of
PDS 66 and HD 97048 seem to be the most promising structures for planet
searches. The proposed presence of massive planets causing spiral features in
HD 135344B and HD 36112 are also within SPHERE's reach assuming hot-start
models.These results suggest that current detection limits are able to detect
hot-start planets in cavities, under the assumption that they are formed by a
single perturber located at the centre of the cavity. More realistic planet
mass constraints would help to clarify whether this is actually the case, which
might point to perturbers not being the only way of creating substructures.


Abstract:
  Causal graphs (CGs) are compact representations of the knowledge of the data
generating processes behind the data distributions. When a CG is available,
e.g., from the domain knowledge, we can infer the conditional independence (CI)
relations that should hold in the data distribution. However, it is not
straightforward how to incorporate this knowledge into predictive modeling. In
this work, we propose a model-agnostic data augmentation method that allows us
to exploit the prior knowledge of the CI encoded in a CG for supervised machine
learning. We theoretically justify the proposed method by providing an excess
risk bound indicating that the proposed method suppresses overfitting by
reducing the apparent complexity of the predictor hypothesis class. Using
real-world data with CGs provided by domain experts, we experimentally show
that the proposed method is effective in improving the prediction accuracy,
especially in the small-data regime.


Abstract:
  With the upcoming Run 3 of the LHC, the FASERv and SND@LHC detectors will
start a new era of neutrino physics using the far-forward high-energy neutrino
beam produced in collisions at ATLAS. This emerging LHC neutrino physics
program requires reliable estimates of the LHC's forward neutrino fluxes and
their uncertainties. In this paper we provide a new fast-neutrino flux
simulation, implemented as a RIVET module, to address this issue. We present
the expected energy distributions going through the FASERv and SND@LHC
detectors based on various commonly used event generators, analyze the origin
of those neutrinos, and present the expected neutrino event rates.


Abstract:
  A large proportion of transiting planetary systems appear to possess only a
single planet as opposed to multiple transiting planets. This excess of singles
is indicative of significant mutual inclinations existing within a large number
of planetary systems, but the origin of these misalignments is unclear.
Moreover, recent observational characterization reveals that mutual
inclinations tend to increase with proximity to the host star. These trends are
both consistent with the dynamical influence of a strong quadrupolar potential
arising from the host star during its early phase of rapid rotation, coupled
with a non-zero stellar obliquity. Here, we simulate a population of planetary
systems subject to the secular perturbation arising from a tilted, oblate host
star as it contracts and spins down subsequent to planet formation. We
demonstrate that this mechanism can reproduce the general increase in
planet-planet mutual inclinations with proximity to the host star, and
delineate a parameter space wherein the host star can drive dynamical
instabilities. We suggest that approximately 5-10\% of low-mass Kepler systems
are susceptible to this instability mechanism, suggesting that a significant
number of single-transiting planets may truly be intrinsically single. We also
report a novel connection between instability and stellar obliquity reduction
and make predictions that can be tested within upcoming TESS observations.


Abstract:
  We present general results on fluctuations and spatial correlations of the
empirical density and current of Markovian diffusion in an equilibrium or
non-equilibrium steady state on all time scales. We unravel a deep connection
between the kinematics of empirical current and "dual-reversal" symmetry. We
highlight the essential role of coarse-graining in space -- the fluctuations of
empirical density and current defined in a point are insensitive to
non-conservative forces, and are proven to diverge on all time scales in
dimensions higher than one, implying non-Gaussian statistics. A spatial
coarse-graining is required to uncover salient features of currents due to
non-conservative forces. We apply the results to minimal two-dimensional
examples of irreversible diffusion. Our findings provide a deeper understanding
of time-averaged observables and may allow for a more reliable analysis of
single-molecule and particle-tracking experiments.


Abstract:
  Feature selection (FS) is an important research topic in machine learning.
Usually, FS is modelled as a+ bi-objective optimization problem whose
objectives are: 1) classification accuracy; 2) number of features. One of the
main issues in real-world applications is missing data. Databases with missing
data are likely to be unreliable. Thus, FS performed on a data set missing some
data is also unreliable. In order to directly control this issue plaguing the
field, we propose in this study a novel modelling of FS: we include reliability
as the third objective of the problem. In order to address the modified
problem, we propose the application of the non-dominated sorting genetic
algorithm-III (NSGA-III). We selected six incomplete data sets from the
University of California Irvine (UCI) machine learning repository. We used the
mean imputation method to deal with the missing data. In the experiments,
k-nearest neighbors (K-NN) is used as the classifier to evaluate the feature
subsets. Experimental results show that the proposed three-objective model
coupled with NSGA-III efficiently addresses the FS problem for the six data
sets included in this study.


Abstract:
  We propose a notion of minimal free resolutions for differential modules, and
we prove existence and uniqueness results for such resolutions. We also take
the first steps toward studying the structure of minimal free resolutions of
differential modules. Our main result in this direction explains a sense in
which the minimal free resolution of a differential module is a deformation of
the minimal free resolution of its homology; this leads to structural results
that mirror classical theorems about minimal free resolutions of modules.


Abstract:
  We consider a generalisation of the Basilica group to all odd primes: the
$p$-Basilica groups acting on the $p$-adic tree. We show that the $p$-Basilica
groups have the $p$-congruence subgroup property but not the congruence
subgroup property nor the weak congruence subgroup property. This provides the
first examples of weakly branch groups with such properties. In addition, the
$p$-Basilica groups give the first examples of weakly branch, but not branch,
groups which are super strongly fractal. We compute the orders of the
congruence quotients of these groups, which enable us to determine the
Hausdorff dimensions of the $p$-Basilica groups. Lastly, we show that the
$p$-Basilica groups do not possess maximal subgroups of infinite index and that
they have infinitely many non-normal maximal subgroups.


Abstract:
  U($N$) supersymmetric Yang-Mills theory naturally appears as the low-energy
effective theory of a system of $N$ D-branes and open strings between them.
Transverse spatial directions emerge from scalar fields, which are $N\times N$
matrices with color indices; roughly speaking, the eigenvalues are the
locations of D-branes. In the past, it was argued that this simple 'emergent
space' picture cannot be used in the context of gauge/gravity duality, because
the ground-state wave function delocalizes at large $N$, leading to a conflict
with the locality in the bulk geometry. In this paper we show that this
conventional wisdom is not correct: the ground-state wave function does not
delocalize, and there is no conflict with the locality of the bulk geometry.
This conclusion is obtained by clarifying the meaning of the 'diagonalization
of a matrix' in Yang-Mills theory, which is not as obvious as one might think.
This observation opens up the prospect of characterizing the bulk geometry via
the color degrees of freedom in Yang-Mills theory, all the way down to the
center of the bulk.


Abstract:
  We contribute to the theoretical understanding of randomized search
heuristics for dynamic problems. We consider the classical vertex coloring
problem on graphs and investigate the dynamic setting where edges are added to
the current graph. We then analyze the expected time for randomized search
heuristics to recompute high quality solutions. The (1+1)~Evolutionary
Algorithm and RLS operate in a setting where the number of colors is bounded
and we are minimizing the number of conflicts. Iterated local search algorithms
use an unbounded color palette and aim to use the smallest colors and,
consequently, the smallest number of colors.
  We identify classes of bipartite graphs where reoptimization is as hard as or
even harder than optimization from scratch, i.e., starting with a random
initialization. Even adding a single edge can lead to hard symmetry problems.
However, graph classes that are hard for one algorithm turn out to be easy for
others. In most cases our bounds show that reoptimization is faster than
optimizing from scratch. We further show that tailoring mutation operators to
parts of the graph where changes have occurred can significantly reduce the
expected reoptimization time. In most settings the expected reoptimization time
for such tailored algorithms is linear in the number of added edges. However,
tailored algorithms cannot prevent exponential times in settings where the
original algorithm is inefficient.


Abstract:
  Spoken language understanding (SLU) topic has seen a lot of progress these
last three years, with the emergence of end-to-end neural approaches. Spoken
language understanding refers to natural language processing tasks related to
semantic extraction from speech signal, like named entity recognition from
speech or slot filling task in a context of human-machine dialogue.
Classically, SLU tasks were processed through a cascade approach that consists
in applying, firstly, an automatic speech recognition process, followed by a
natural language processing module applied to the automatic transcriptions.
These three last years, end-to-end neural approaches, based on deep neural
networks, have been proposed in order to directly extract the semantics from
speech signal, by using a single neural model. More recent works on
self-supervised training with unlabeled data open new perspectives in term of
performance for automatic speech recognition and natural language processing.
In this paper, we present a brief overview of the recent advances on the French
MEDIA benchmark dataset for SLU, with or without the use of additional data. We
also present our last results that significantly outperform the current
state-of-the-art with a Concept Error Rate (CER) of 11.2%, instead of 13.6% for
the last state-of-the-art system presented this year.


Abstract:
  Completely positive factorization (CPF) is a critical task with applications
in many fields. This paper proposes a novel method for the CPF. Based on the
idea of exterior point iteration, an optimization model is given, which aims to
orthogonally transform a symmetric lower rank factor to be nonnegative. The
optimization problem can be solved via a modified nonlinear conjugate gradient
method iteratively. The iteration points locate on the exterior of the
orthonormal manifold and the closed set whose transformed matrices are
nonnegative before convergence generally. Convergence analysis is given for the
local or global optimum of the objective function, together with the iteration
algorithm. Some potential issues that may affect the CPF are explored
numerically. The exterior point method performs much better than other
algorithms, not only in the efficiency of computational cost or accuracy, but
also in the ability to address the CPF in some hard cases.


Abstract:
  Dirac notation is the most common way to describe quantum states and
operations on states. It is very convenient and allows for quick visual
distinction between vectors, scalars and operators. For quantum processes that
involve interactions of multiple systems an even better visualisation has been
proposed by Coecke and Kissinger, in the form of a diagrammatic formalism
[CK2017]. Their notation expresses formulas in the form of diagrams, somewhat
similar to Feynman diagrams, and is more general than the circuit notation for
quantum computing.
  This document consists of two parts. (1) We give a brief summary of the
diagrammatic notation of quantum processes, tailored to readers who already
know quantum physics and are not interested in general process theory. For this
audience our summary is less daunting than the encyclopaedic book by Coecke and
Kissinger [CK2017], and on the other hand more accessible than the
ultra-compact introduction of [KTW2017]. We deviate a somewhat from
[CK2017,KTW2017] in that we do not assume basis states to equal their own
complex conjugate; this means that we do not use symmetric notation for basis
states, and it leads us to explicitly show arrows on wires where they are
usually omitted.
  (2) We extend the work of Kissinger, Tull and Westerbaan [KTW2017] which
gives a diagrammatic security proof for BB84 and 6-state Quantum Key
Distribution. Their proof is based on a sequence of diagrammatic manipulations
that works when the bases used in the protocol are mutually unbiased. We extend
this result to 8-state encoding, which has been proposed as a tool in quantum
key recycling protocols [SdV2017,LS2018], and which does not have mutually
unbiased bases.


Abstract:
  Variations in the grain size distribution are to be expected in the
interstellar medium (ISM) due to grain growth and destruction. In this work, we
present a dust collision model to be implemented inside a magnetohydrodynamical
(MHD) code that takes into account grain growth and shattering of charged dust
grains of a given composition (silicate or graphite). We integrate this model
in the MHD code Athena, and builds on a previous implementation of the dynamics
of charged dust grains in the same code. To demonstrate the performance of this
coagulation model, we study the variations in the grain size distribution of a
single-sized population of dust with radius 0.05 $\mu$m inside several dust
filaments formed during a 2D MHD simulation. We also consider a realistic dust
distribution with sizes ranging from 50 \AA~to 0.25 $\mu$m and analyze both the
variations in the size distribution for graphite and silicates, as well as of
the far ultraviolet extinction curve. From the obtained results, we conclude
that the methodology here presented, based on the MHD evolution of the equation
of motion for a charged particle, is optimal for studying the coagulation of
charged dust grains in a diffuse regime such as a molecular cloud envelope.
Observationally, these variations in the dust size distribution are translated
into variations in the far ultraviolet extinction curve, and they are mainly
caused by small graphite dust grains.


Abstract:
  Eigenstate thermalization in quantum many-body systems implies that
eigenstates at high energy are similar to random vectors. Identifying systems
where at least some eigenstates are non-thermal is an outstanding question. In
this work we show that interacting quantum models that have a nullspace -- a
degenerate subspace of eigenstates at zero energy (zero modes), which
corresponds to infinite temperature, provide a route to non-thermal
eigenstates. We analytically show the existence of a zero mode which can be
represented as a matrix product state for a certain class of local
Hamiltonians. In the more general case we use a subspace disentangling
algorithm to generate an orthogonal basis of zero modes characterized by
increasing entanglement entropy. We show evidence for an area-law entanglement
scaling of the least entangled zero mode in the broad parameter regime, leading
to a conjecture that all local Hamiltonians with the nullspace feature zero
modes with area-law entanglement scaling, and as such, break the strong
thermalization hypothesis. Finally, we find zero-modes in constrained models
and propose setup for observing their experimental signatures.