Abstract:
  Reconstructing an indoor scene and generating a layout/floor plan in 3D or 2D
is a widely known problem. Quite a few algorithms have been proposed in the
literature recently. However, most existing methods either use RGB-D images,
thus requiring a depth camera, or depending on panoramic photos, assuming that
there is little to no occlusion in the rooms. In this work, we proposed GRIHA
(Generating Room Interior of a House using ARCore), a framework for generating
a layout using an RGB image captured using a simple mobile phone camera. We
take advantage of Simultaneous Localization and Mapping (SLAM) to assess the 3D
transformations required for layout generation. SLAM technology is built-in in
recent mobile libraries such as ARCore by Google. Hence, the proposed method is
fast and efficient. It gives the user freedom to generate layout by merely
taking a few conventional photos, rather than relying on specialized depth
hardware or occlusion-free panoramic images. We have compared GRIHA with other
existing methods and obtained superior results. Also, the system is tested on
multiple hardware platforms to test the dependency and efficiency.


Abstract:
  Hidden semi-Markov models generalise hidden Markov models by explicitly
modelling the time spent in a given state, the so-called dwell time, using some
distribution defined on the natural numbers. While the (shifted) Poisson and
negative binomial distribution provide natural choices for such distributions,
in practice, parametric distributions can lack the flexibility to adequately
model the dwell times. To overcome this problem, a penalised maximum likelihood
approach is proposed that allows for a flexible and data-driven estimation of
the dwell-time distributions without the need to make any distributional
assumption. This approach is suitable for direct modelling purposes or as an
exploratory tool to investigate the latent state dynamics. The feasibility and
potential of the suggested approach is illustrated by modelling muskox
movements in northeast Greenland using GPS tracking data. The proposed method
is implemented in the R-package PHSMM which is available on CRAN.


Abstract:
  Nondeterminism in neural network optimization produces uncertainty in
performance, making small improvements difficult to discern from run-to-run
variability. While uncertainty can be reduced by training multiple model
copies, doing so is time-consuming, costly, and harms reproducibility. In this
work, we establish an experimental protocol for understanding the effect of
optimization nondeterminism on model diversity, allowing us to isolate the
effects of a variety of sources of nondeterminism. Surprisingly, we find that
all sources of nondeterminism have similar effects on measures of model
diversity. To explain this intriguing fact, we identify the instability of
model training, taken as an end-to-end procedure, as the key determinant. We
show that even one-bit changes in initial parameters result in models
converging to vastly different values. Last, we propose two approaches for
reducing the effects of instability on run-to-run variability.


Abstract:
  It is prevalent to utilize external knowledge to help machine answer
questions that need background commonsense, which faces a problem that
unlimited knowledge will transmit noisy and misleading information. Towards the
issue of introducing related knowledge, we propose a semantic-driven
knowledge-aware QA framework, which controls the knowledge injection in a
coarse-to-careful fashion. We devise a tailoring strategy to filter extracted
knowledge under monitoring of the coarse semantic of question on the knowledge
extraction stage. And we develop a semantic-aware knowledge fetching module
that engages structural knowledge information and fuses proper knowledge
according to the careful semantic of questions in a hierarchical way.
Experiments demonstrate that the proposed approach promotes the performance on
the CommonsenseQA dataset comparing with strong baselines.


Abstract:
  System state estimation constitutes a key problem in several applications
involving multi-agent system architectures. This rests upon the estimation of
the state of each agent in the group, which is supposed to access only relative
measurements w.r.t. some neighbors state. Exploiting the standard least-squares
paradigm, the system state estimation task is faced in this work by deriving a
distributed Proximal Point-based iterative scheme. This solution entails the
emergence of interesting connections between the structural properties of the
stochastic matrices describing the system dynamics and the convergence behavior
toward the optimal estimate. A deep analysis of such relations is provided,
jointly with a further discussion on the penalty parameter that characterizes
the Proximal Point approach.


Abstract:
  Bohmian mechanics was designed to give rise to predictions identical to those
derived by standard quantum mechanics, while invoking a specific interpretation
of it - one which allows the classical notion of a particle to be maintained
alongside a guiding wave. For this, the Bohmian model makes use of a unique
quantum potential which governs the trajectory of the particle. In this work we
show that this interpretation of quantum theory naturally leads to the
derivation of interesting new phenomena. Specifically, we demonstrate how the
fundamental Casimir-Polder force, by which atoms are attracted to a surface,
may be temporarily suppressed by utilizing a specially designed quantum
potential. We show that when harnessing the quantum potential via a suitable
atomic wavepacket engineering, the absorption by the surface can be
dramatically reduced. This is proven both analytically and numerically.
Finally, an experimental scheme is proposed for achieving the required shape
for the atomic wavepacket. All these may enable new insights into Bohmian
mechanics as well as new applications to metrology and sensing.


Abstract:
  Segmentation of organs or lesions from medical images plays an essential role
in many clinical applications such as diagnosis and treatment planning. Though
Convolutional Neural Networks (CNN) have achieved the state-of-the-art
performance for automatic segmentation, they are often limited by the lack of
clinically acceptable accuracy and robustness in complex cases. Therefore,
interactive segmentation is a practical alternative to these methods. However,
traditional interactive segmentation methods require a large amount of user
interactions, and recently proposed CNN-based interactive segmentation methods
are limited by poor performance on previously unseen objects. To solve these
problems, we propose a novel deep learning-based interactive segmentation
method that not only has high efficiency due to only requiring clicks as user
inputs but also generalizes well to a range of previously unseen objects.
Specifically, we first encode user-provided interior margin points via our
proposed exponentialized geodesic distance that enables a CNN to achieve a good
initial segmentation result of both previously seen and unseen objects, then we
use a novel information fusion method that combines the initial segmentation
with only few additional user clicks to efficiently obtain a refined
segmentation. We validated our proposed framework through extensive experiments
on 2D and 3D medical image segmentation tasks with a wide range of previous
unseen objects that were not present in the training set. Experimental results
showed that our proposed framework 1) achieves accurate results with fewer user
interactions and less time compared with state-of-the-art interactive
frameworks and 2) generalizes well to previously unseen objects.


Abstract:
  The theory of discrete stochastic systems has been initiated by the work of
Shannon and von Neumann. While Shannon has considered memory-less communication
channels and their generalization by introducing states, von Neumann has
studied the synthesis of reliable systems from unreliable components. The
fundamental work of Rabin and Scott about deterministic finite-state automata
has led to two generalizations. First, the generalization of transition
functions to conditional distributions studied by Carlyle and Starke. This in
turn has led to a generalization of time-discrete Markov chains in which the
chains are governed by more than one transition probability matrix. Second, the
generalization of regular sets by introducing stochastic automata as described
by Rabin. Stochastic automata are well-investigated. This report provides a
short introduction to stochastic automata based on the valuable book of Claus.
This includes the basic topics of the theory of stochastic automata:
equivalence, minimization, reduction, covering, observability, and determinism.
Then stochastic versions of Mealy and Moore automata are studied and finally
stochastic language acceptors are considered as a generalization of
nondeterministic finite-state acceptors.


Abstract:
  We consider a 1D topological superconductor (TSC) constructed by coupling a
pair of Kitaev's Majorana chains with opposite spin configurations. Such a 1D
lattice model is known to be protected by a $T^2 = -1$ time-reversal symmetry.
Furthermore, we consider a modeled Rashba spin-orbit coupling on such a system
of $T^2=-1$ time-reversal symmetric TSC. The Rashba spin-orbit coupling
together with the chemical potential engineered the phase transitions of the
edge states in the system and consequently the number of Majorona's zero-energy
edge modes (MZM's) emerging at the edge of the coupled chains. Correspondingly,
the topological nature of the system is described by a phase diagram consisting
of three different phases. The three phases are characterized by a topological
winding number, $\mathcal{W}=1$, $2$ (with one and two MZM's: topological
phases) and $\mathcal{W}=0$ (devoid of any MZM: trivial insulating phase).


Abstract:
  In this letter, we provide fundamental insights into the dynamic
transcritical transition process using molecular dynamics simulations. A
transcritical region, which covers three different fluid states, was discovered
as a substitute for the traditional interface. The physical properties, such as
temperature and density, exhibited a highly non-linear distribution in the
transcritical region. Meanwhile, the surface tension was found to exist
throughout the transcritical region, and the magnitude was directly
proportional to $ - \rho \cdot {\nabla ^2}\rho $


Abstract:
  In this paper, we prove new upper bounds for sums of reciprocals of
fractional parts over general aligned boxes, thus extending a previous result
of the author concerning bounds for sums of reciprocals over symmetric boxes.
These new upper bounds depend solely on the volume of the boxes, and not on
their diameter. This generalisation relies a novel lattice-point counting
technique involving estimates for the higher successive minima of certain
naturally arising lattices.


Abstract:
  We use magneto-conductivity to study the magnetic proximity effect on surface
states of doped topological insulators. Our bilayers consist of a layer of
Fe$_7$Se$_8$, which is a metallic ferrimagnet and a layer of
Bi$_{0.8}$Sb$_{1.2}$Te$_{3}$ which is a highly hole-doped topological
insulator. Using transport measurements and a modified Hikami-Larkin-Nagaoka
model, we show that the ferromagnet shortens significantly the effective
coherence length of the surface states, suggesting that a gap is opened at the
Dirac point. We show that the magnetically induced gap persists on surface
states which are separated from the magnet by a topological insulator layer as
thick as 170 [nm]. Furthermore, the size of the gap is found to be proportional
to the magnetization that we extract from the anomalous Hall effect.
  Our results give information on the ties between carrier density, induced
magnetization and magnetically induced gap in topological insulator/ferromagnet
bilayers. This information is important both for a theoretical understanding of
magnetic interactions in topological insulators and for the practical
fabrication of such bilayers, which are the basis of various suggested
technologies, such as spintronic devices, far infra-red detectors etc.


Abstract:
  We study small field models of inflation, which, against previous
expectations, yield significant Gravitational Wave (GW) signal while
reproducing other measured observable quantities in the Cosmic Microwave
Background (CMB). We numerically study these, using previously published
analytic works as general guidelines. We first discuss the framework necessary
to understand model building, and some of its motivations. We review the
slow-roll paradigm, derive the slow-roll parameters, and discuss different
formulations thereof. We review the Lyth bound and its theoretical descendants,
we outline the small/large field taxonomy and their characterization in the
current nomenclature. We then present our models and the methods employed in
their building and examination. We employ MCMC simulations to evaluate model
likelihood and by process of marginalization extract the most probable
coefficients for these inflationary potentials. An additional method employed
is a multinomial fit, where we create a correspondence between coefficients and
observables. This allows the use of observable values directly to yield the
most likely coefficients. We compare the results of the two methods and
evaluate the level of tuning required for these models. We discuss an apparent
discrepancy between analytical approaches of evaluating Primordial Power
Spectrum (PPS) observables and the precise numerical results in our models. We
identify some of the sources of this discrepancy and remark on their meaning in
the age of precision cosmology. Finally, we present the results of our study,
for the most likely inflationary models with polynomial potentials of degree 5,
and 6. We demonstrate our ability to produce potentials that yield GW with a
tensor-to-scalar ratio r = 0.03. This is a realistic expectation of GW
detection sensitivity in the near future.


Abstract:
  E-textiles has received tremendous attention in recent years due to the
capability of integrating sensors into a garment to provide high precision
sensing of the human body. Besides sensing, a number of solutions for e-textile
garments have also integrated wireless interfaces allowing these sensing data
to be transmitted and also sensors that allow users to provide instructions
through touching. While this has provided a new level of sensing that can
result in unprecedented applications, there has been little attention placed on
on-body computing for e-textiles. Facilitating computing on e-textiles can
result in a new form of On-body Edge Computing, where sensor information are
processed very close to the body before being transmitted to an external device
or wireless access point. This form of computing can provide new security and
data privacy capabilities and at the same time provide opportunities for new
energy harvesting mechanisms to process the data through the garment. This
paper proposes this concept through embroidered Programmable Logic Array (PLA)
integrated into e-textiles. In the way that PLAs have programmable logic
circuits by interconnecting different AND, NOT and OR gates, we propose
e-textile based gates that are sewn into a garment and connected through
conductive thread stitching. Two designs are proposed and this includes Single
and Multi-Layered PLA. Experimental validations have been conducted at the
individual gates as well as the entire PLA circuits to determine the voltage
utilization as well as logic computing reliability. Our proposed approach can
usher in a new form of On-Body Edge Computing for e-textile garments for future
wearable technologies


Abstract:
  The layered vanadium antimonides AV3Sb5 (A = K, Rb, Cs) are a recently
discovered family of topological kagome metals with a rich phenomenology of
strongly correlated electronic phases including charge order and
superconductivity. Understanding how the singularities inherent to the kagome
electronic structure are linked to the observed many-body phases is a topic of
great interest and relevance. Here, we combine angle-resolved photoemission
spectroscopy and density functional theory to reveal multiple kagome-derived
van Hove singularities (vHs) coexisting near the Fermi level of CsV3Sb5 and
analyze their contribution to electronic symmetry breaking. Intriguingly, the
vHs in CsV3Sb5 have two distinct flavors - p-type and m-type - which originate
from their pure and mixed sublattice characters, respectively. This twofold vHs
is unique property of the kagome lattice, and its flavor critically determines
the pairing symmetry and ground states emerging in AV3Sb5 series. We establish
that, among the multiple vHs in CsV3Sb5, the m-type vHs of the dxz/dyz kagome
band and the p-type vHs of the dxy/dx2-y2 kagome band cross the Fermi level to
set the stage for electronic symmetry breaking. The former band exhibits
pronounced Fermi surface nesting, while the latter contributes via higher-order
vHs. Our work reveals the essential role of kagome-derived vHs for the
collective phenomena realized in the AV3Sb5 family, paving the way to a deeper
understanding of strongly correlated topological kagome systems.


Abstract:
  Quantum computing is quickly turning from a promise to a reality, witnessing
the launch of several cloud-based, general-purpose offerings, and IDEs.
Unfortunately, however, existing solutions typically implicitly assume intimate
knowledge about quantum computing concepts and operators. This paper introduces
Quantum Service-Oriented Computing (QSOC), including a model-driven methodology
to allow enterprise DevOps teams to compose, configure and operate enterprise
applications without intimate knowledge on the underlying quantum
infrastructure, advocating knowledge reuse, separation of concerns, resource
optimization, and mixed quantum- & conventional QSOC applications.


Abstract:
  Let $\mathcal{H}$ be a separable infinite-dimensional complex Hilbert space,
$\mathcal{B}(\mathcal{H})$ the algebra of bounded linear operators acting on
$\mathcal{H}$ and $\mathcal{J}$ a proper two-sided ideal of
$\mathcal{B}(\mathcal{H})$. Denote by $\mathcal{U}_\mathcal{J}(\mathcal{H})$
the group of all unitary operators of the form $I+\mathcal{J}$. Recall that an
operator $A \in \mathcal{B}(\mathcal{H})$ is diagonalizable if there exists a
unitary operator $U$ such that $UAU^*$ is diagonal with respect to some
orthonormal basis. A more restrictive notion of diagonalization can be
formulated with respect to a fixed orthonormal basis $\mathrm{e}=\{
e_n\}_{n\geq 1}$ and a proper operator ideal $\mathcal{J}$ as follows: $A \in
\mathcal{B}(\mathcal{H})$ is called restricted diagonalizable if there exists
$U\in \mathcal{U}_\mathcal{J}(\mathcal{H})$ such that $UAU^*$ is diagonal with
respect to $\mathrm{e}$. In this work we give necessary and sufficient
conditions for a diagonalizable operator to be restricted diagonalizable. Our
conditions become a characterization of those diagonalizable operators which
are restricted diagonalizable when the ideal is arithmetic mean closed. Then we
obtain results on the structure of the set of all restricted diagonalizable
operators. In this way we answer several open problems recently raised by
Belti\c{t}$\breve{\text{a}}$, Patnaik and Weiss.


Abstract:
  Query-based explanations for missing answers identify which operators of a
query are responsible for the failure to return a missing answer of interest.
This type of explanations has proven to be useful in a variety of contexts
including debugging of complex analytical queries. Such queries are frequent in
big data systems such as Apache Spark. We present a novel approach for
producing query-based explanations. Our approach is the first to support nested
data and to consider operators that modify the schema and structure of the data
(e.g., nesting and projections) as potential causes of missing answers. To
efficiently compute explanations, we propose a heuristic algorithm that applies
two novel techniques: (i) reasoning about multiple schema alternatives for a
query and (ii) re-validating at each step whether an intermediate result can
contribute to the missing answer. Using an implementation of our approach on
Spark, we demonstrate that it is the first to scale to large datasets and that
it often finds explanations that existing techniques fail to identify.


Abstract:
  We show that there is essentially a unique elliptic curve $E$ defined over a
cubic Galois extension $K$ of $\mathbb Q$ with a $K$-rational point of order 13
and such that $E$ is not defined over $\mathbb Q$.


Abstract:
  Kirchhoff's law of thermal radiation imposes a constraint on photon-based
energy harvesting processes since part of the incident energy flux is
inevitably emitted back to the source. By breaking the reciprocity of the
system, it is possible to overcome this restriction and improve the efficiency
of energy harvesting. Here, we design and analyze a semitransparent emitter
that fully absorbs normally incident energy from a given direction with zero
backward and unity forward emissivity. The nearly ideal performance with
wavelength-scale thickness is achieved due to the magneto-optical effect and
the guided-mode resonance engineered in the emitter structure. We derive the
general requirements for the nonreciprocal emitter using the temporal coupled
mode theory and the symmetry considerations. Finally, we provide a realistic
emitter design based on a photonic crystal slab consisting of a magnetic Weyl
semimetal and silicon.


Abstract:
  The purpose of this paper is to study the phenomenon of large intersections
in the framework of multiple recurrence for measure-preserving actions of
countable abelian groups. Among other things, we show: (1) If $G$ is a
countable abelian group and $\varphi, \psi : G \to G$ are homomorphisms such
that $\varphi(G)$, $\psi(G)$, and $(\psi - \varphi)(G)$ have finite index in
$G$, then for every ergodic measure-preserving system $(X, \mathcal{B}, \mu,
(T_g)_{g \in G})$, every set $A \in \mathcal{B}$, and every $\varepsilon > 0$,
the set $\{g \in G : \mu(A \cap T_{\varphi(g)}^{-1}A \cap T_{\psi(g)}^{-1}A) >
\mu(A)^3 - \varepsilon\}$ is syndetic. (2) If $G$ is a countable abelian group
and $r,s \in \mathbb{Z}$ are integers such that $rG$, $sG$, and $(r \pm s)G$
have finite index in $G$, then for every ergodic measure-preserving system $(X,
\mathcal{B}, \mu, (T_g)_{g \in G})$, every set $A \in \mathcal{B}$, and every
$\varepsilon > 0$, the set $\{g \in G : \mu(A \cap T_{rg}^{-1}A \cap
T_{sg}^{-1}A \cap T_{(r+s)g}^{-1}A) > \mu(A)^4 - \varepsilon\}$ is syndetic. In
particular, these extend and generalize results of Bergelson, Host, and Kra
concerning $\mathbb{Z}$-actions and of Bergelson, Tao, and Ziegler concerning
$\mathbb{F}_p^{\infty}$-actions. Using an ergodic version of the Furstenberg
correspondence principle, we obtain new combinatorial applications. We also
discuss numerous examples shedding light on the necessity of the various
hypotheses above. Our results lead to a number of interesting questions and
conjectures, formulated in the introduction and at the end of the paper.


Abstract:
  We give an algorithm for computing exact maximum flows on graphs with $m$
edges and integer capacities in the range $[1, U]$ in
$\widetilde{O}(m^{\frac{3}{2} - \frac{1}{328}} \log U)$ time. For sparse graphs
with polynomially bounded integer capacities, this is the first improvement
over the $\widetilde{O}(m^{1.5} \log U)$ time bound from [Goldberg-Rao JACM
`98].
  Our algorithm revolves around dynamically maintaining the augmenting
electrical flows at the core of the interior point method based algorithm from
[M\k{a}dry JACM `16]. This entails designing data structures that, in limited
settings, return edges with large electric energy in a graph undergoing
resistance updates.


Abstract:
  Using the Laplace derivative a Perron type integral, the Laplace integral, is
defined. Moreover, it is shown that this integral includes Perron integral and
to show that the inclusion is proper, an example of a function is constructed,
which is Laplace integrable but not Perron integrable. Properties of integrals
such as fundamental theorem of calculus, Hake's theorem, integration by parts,
convergence theorems, mean value theorems, the integral remainder form of
Taylor's theorem with an estimation of the remainder, are established. It turns
out that concerning the Alexiewicz's norm, the space of all Laplace integrable
functions is incomplete and contains the set of all polynomials densely.
Applications are shown to Poisson integral, a system of generalised ordinary
differential equations and higher-order generalised ordinary differential
equation.


Abstract:
  Recent multi-wavelength ALMA observations of the protoplanetary disk orbiting
around Elias 2-27 revealed a two armed spiral structure. The observed
morphology together with the young age of the star and the disk-to-star mass
ratio estimated from dust continuum emission make this system a perfect
laboratory to investigate the role of self-gravity in the early phases of star
formation. This is particularly interesting if we consider that gravitational
instabilities could be a fundamental first step for the formation of
planetesimals and planets. In this Letter, we model the rotation curve obtained
by CO data of Elias 2-27 with a theoretical rotation curve including both the
disk self-gravity and the star contribution to the gravitational potential. We
compare this model with a purely Keplerian one and with a simple power-law
function. We find that (especially for the $^{13}$CO isotopologue) the rotation
curve is better described by considering not only the star, but also the disk
self-gravity. We are thus able to obtain for the first time a dynamical
estimate of the disk mass of $0.08\pm0.04\,M_{\odot}$ and the star mass of
$0.46\pm0.03\,M_{\odot}$ (in the more general case), the latter being
comparable with previous estimates. From these values, we derive that the disk
is 17$\%$ of the star mass, meaning that it could be prone to gravitational
instabilities. This result would strongly support the hypothesis that the two
spiral arms are generated by gravitational instabilities.


Abstract:
  Dusty, neutral outflows and inflows are a common feature of nearby
star-forming galaxies. We characterize these flows in eight galaxies -- mostly
AGN -- selected for their widespread NaI D signatures from the Siding Spring
Southern Seyfert Spectroscopic Snapshot Survey (S7). This survey employs deep,
wide field-of-view integral field spectroscopy at moderate spectral resolution
(R=7000 at NaI D). We significantly expand the sample of sightlines in external
galaxies in which the spatially-resolved relationship has been studied between
cool, neutral gas properties -- N(NaI), Weq(NaI D) -- and dust -- E(B-V) from
both stars and gas. Our sample shows strong, significant correlations of total
Weq with E(B-V)_stars and g-i colour within individual galaxies; correlations
with E(B-V)_gas are present but weaker. Regressions yield slope variations from
galaxy to galaxy and intrinsic scatter ~1 Angstrom. The sample occupies regions
in the space of N(NaI) and Weq^abs vs. E(B-V)_gas that are consistent with
extrapolations from other studies to higher colour excess [E(B-V)_gas ~ 1]. For
perhaps the first time in external galaxies, we detect inverse P Cygni profiles
in the NaI D line, presumably due to inflowing gas. Via Doppler shifted NaI D
absorption and emission lines, we find ubiquitous flows that differ from
stellar rotation by >100 km/s or have |v,abs - v,em| > 100 km/s. Inflows and
outflows extend toward the edge of the detected stellar disk/FOV, together
subtend 10-40% of the projected disk, and have similar mean N(NaI) and Weq(NaI
D). Outflows are consistent with minor-axis or jet-driven flows, while inflows
tend toward the projected major axis. The inflows may result from
non-axisymmetric potentials, tidal motions, or halo infall.


Abstract:
  Objective measurement of perceptually motivated music attributes has
application in both target driven mixing and mastering methodologies and music
information retrieval. This work proposes a perceptual model of mix clarity
which decomposes a mixed input signal into transient, steady-state, and
residual components. Masking thresholds are calculated for each component and
their relative relationship is used to determine an overall masking score as
the model's output. Three variants of the model were tested against subjective
mix clarity scores gathered from a controlled listening test. The best
performing variant achieved a Spearman's rank correlation of rho = 0.8382
(p<0.01). Furthermore, the model output was analysed using an independent
dataset generated by progressively applying degradation effects to the test
stimuli. Analysis of the model suggested a close relationship between the
proposed model and the subjective mix clarity scores particularly when masking
was measured using linearly spaced analysis bands. Moreover, the presence of
noise-like residual signals was shown to have a negative effect on the
perceived mix clarity.


Abstract:
  The fundamental relationship of the atomic structure (represented by its
atomic property parameters, APPs) and its physical properties of a specific
inorganic substance can be realized in the bottom-up data-centric and the
top-down knowledge physics-centric ways. Nowadays these two approaches compete
and enhance one another qualitatively and quantitatively. We present our own
holistic method and implementation, based on the PAULING FILE peer-reviewed
inorganic substances database, the world largest materials database containing
under one shelter crystallographic structures, phase diagrams and large variety
of physical properties of single-phase inorganic substances. In addition we
present generated machine-learning data, as well as simulated DFT
physics-centered data, which are in close connection and comparison with the
PAULING FILE peer-reviewed reference data.


Abstract:
  The advent of noisy intermediate-scale quantum (NISQ) devices offers crucial
opportunities for the development of quantum algorithms. Here we evaluate the
noise tolerance of two quantum neural network (QNN) architectures on IBM's NISQ
devices, namely, dissipative QNN (DQNN) whose building-block perceptron is a
completely positive map, and the quantum approximate optimization algorithm
(QAOA). We compare these two approaches to learning an unknown unitary. While
both networks succeed in this learning task, we find that a DQNN learns an
unknown unitary more reliably than QAOA and is less susceptible to gate noise.


Abstract:
  We study the laser-induced torques in the antiferromagnet (AFM) Mn$_2$Au. We
find that even linearly polarized light may induce laser-induced torques in
Mn$_2$Au, i.e., the light does not have to be circularly polarized. The
laser-induced torques in Mn$_2$Au are comparable in magnitude to those in the
ferromagnets Fe, Co and FePt at optical frequencies. We also compute the
laser-induced torques at terahertz (THz) frequencies and compare them to the
spin-orbit torques (SOTs) excited by THz laser-pulses. We find the SOTs to be
dominant at THz frequencies for the laser-field strengths used in experiments.
Additionally, we show that the matrix elements of the spin-orbit interaction
(SOI) can be used to add SOI only during the Wannier interpolation, which we
call Wannier interpolation of SOI (WISOI). This technique allows us to perform
the Wannier interpolation conveniently for many magnetization directions from a
single set of Wannier functions.


Abstract:
  We analyze decoherence and objectivization processes in spin-spin models for
arbitrary spins. We first derive the most general analytic form of the
decoherence factor in the measurement limit, where the interaction Hamiltonian
dominates the rest. We then analyze thermal environments and derive exact,
analytic formulas for both the decoherence factor and the state fidelity of
post-interaction environment states. This allows to analyze the objectivization
process of the state of the central spin during the interaction. We do so
using, so called, Spectrum Broadcast Structures (SBS), which are specific
multipartite quantum states encoding a certain operation notion of objectivity.
We analyze analytically (for short times) and numerically how higher spin
influences the efficiency of decoherence and objectivization processes. As
expected, the higher the spin of the environment, the more efficient
decoherence and objectivization become. This work is a generalization of
previous studies, limited to spin-$1/2$ systems only, and we hope will be
useful in future objectivity experiments.


Abstract:
  Applicability of associated plasticity for particulate materials such as
soils does not yield satisfactory results when Coulomb's theory of shear
strength of soils is assumed, and the yield function derived accordingly is
used to define both the stress state and the direction of plastic flow. The
limitation mainly stems from the fact that Coulomb's theory (and its
derivatives) is a simplification that intentionally ignores deformation
characteristics that manifest from the particulate nature of such materials. It
is thus customary to apply a branch of plasticity called non-associated
plasticity for soils and similar materials. In the non-associated plasticity
framework, yield functions and plastic potential functions are different. The
former defines the mobilization of the stress state while the later defines the
direction of plastic flow. For soils, stress-dilatancy theories have become
central in the formulation of non-associated flow rules. In this paper, cyclic
stress-dilatancy relations are derived based on complementarity hypothesis of
stress-dilatancy conjugates. Both loading and unloading are explicitly
considered. Then, yield functions are derived based on the resulting
stress-dilatancy relations. In so doing, the resulting yield functions are
rendered with a quality to be used for the modelling of deformation behavior of
soils subjected to monotonic and cyclic loading conditions. The newly derived
yield functions are called Associated Cyclic Stress Dilatancy yield functions
for which the abbreviation ACStD is used. The theoretical framework is
established first for special cases of deformation modes-plane strain and
axisymmetric. The framework is generalized for considering Lode angle
dependency of the yield function and extending the Matusoka-Nakai criterion.


Abstract:
  Let $d \ge 3$ be an integer and let $P \in \mathbb{Z}[x]$ be a polynomial of
degree $d$ whose Galois group is $S_d$. Let $(a_n)$ be a linearly recuresive
sequence of integers which has $P$ as its characteristic polynomial. We prove,
under the generalized Riemann hypothesis, that the lower density of the set of
primes which divide at least one element of the sequence $(a_n)$ is positive.


Abstract:
  Symbol alphabets of n-particle amplitudes in N=4 super-Yang-Mills theory are
known to contain certain cluster variables of Gr(4,n) as well as certain
algebraic functions of cluster variables. In this paper we solve the C Z = 0
matrix equations associated to several cells of the totally non-negative
Grassmannian, combining methods of arXiv:2012.15812 for rational letters and
arXiv:2007.00646 for algebraic letters. We identify sets of parameterizations
of the top cell of Gr_+(5,9) for which the solutions produce all of (and only)
the cluster variable letters of the 2-loop nine-particle NMHV amplitude, and
identify plabic graphs from which all of its algebraic letters originate.


Abstract:
  In this paper we give a geometric condition which ensures that
$(q,p)$-Poincar\'e-Sobolev inequalities are implied from generalized
$(1,1)$-Poincar\'e inequalities related to $L^1$ norms in the context of
product spaces. The concept of eccentricity plays a central role in the paper.
We provide several $(1,1)$-Poincar\'e type inequalities adapted to different
geometries and then show that our selfimproving method can be applied to obtain
special interesting Poincar\'e-Sobolev estimates. Among other results, we prove
that for each rectangle $R$ of the form $R=I_1\times I_2 \subset
\mathbb{R}^{n}$ where $I_1\subset \mathbb{R}^{n_1}$ and $I_2\subset
\mathbb{R}^{n_2}$ are cubes with sides parallel to the coordinate axes, we have
that % \begin{equation*} \left( \frac{1}{w(R)}\int_{ R } |f
-f_{R}|^{p_{\delta,w}^*} \,wdx\right)^{\frac{1}{p_{\delta,w}^*}} \leq
c\,\delta^{\frac1p}(1-\delta)^{\frac1p}\,[w]_{A_{1,\mathfrak{R}}}^{\frac1p}\,
\Big(a_1(R)+a_2(R)\Big), \end{equation*} % where $\delta \in (0,1)$, $w \in
A_{1,\mathfrak{R}}$, $\frac{1}{p} -\frac{1}{ p_{\delta,w}^* }= \frac{\delta}{n}
\, \frac{1}{1+\log [w]_{A_{1,\mathfrak{R}}}}$ and $a_i(R)$ are bilinear analog
of the fractional Sobolev seminorms $[u]_{W^{\delta,p}(Q)}$ (See Theorem 2.18).
This is a biparameter weighted version of the celebrated fractional
Poincar\'e-Sobolev estimates with the gain
$\delta^{\frac1p}(1-\delta)^{\frac1p}$.


Abstract:
  We report growth, electronic structure and superconductivity of ultrathin
epitaxial CoSi2 films on Si(111). At low coverages, preferred islands with 2, 5
and 6 monolayers height develop, which agrees well with the surface energy
calculation. We observe clear quantum well states as a result of electronic
confinement and their dispersion agrees well with density functional theory
calculations, indicating weak correlation effect despite strong contributions
from Co 3d electrons. Ex-situ transport measurements show that
superconductivity persists down to at least 10 monolayers, with reduced Tc but
largely enhanced upper critical field. Our study opens up the opportunity to
study the interplay between quantum confinement, interfacial symmetry breaking
and superconductivity in an epitaxial silicide film, which is technologically
relevant in microelectronics.


Abstract:
  The auditory attention decoding (AAD) approach was proposed to determine the
identity of the attended talker in a multi-talker scenario by analyzing
electroencephalography (EEG) data. Although the linear model-based method has
been widely used in AAD, the linear assumption was considered oversimplified
and the decoding accuracy remained lower for shorter decoding windows.
Recently, nonlinear models based on deep neural networks (DNN) have been
proposed to solve this problem. However, these models did not fully utilize
both the spatial and temporal features of EEG, and the interpretability of DNN
models was rarely investigated. In this paper, we proposed novel convolutional
recurrent neural network (CRNN) based regression model and classification
model, and compared them with both the linear model and the state-of-the-art
DNN models. Results showed that, our proposed CRNN-based classification model
outperformed others for shorter decoding windows (around 90% for 2 s and 5 s).
Although worse than classification models, the decoding accuracy of the
proposed CRNN-based regression model was about 5% greater than other regression
models. The interpretability of DNN models was also investigated by visualizing
layers' weight.


Abstract:
  Geometric theories based on classical logic are conservative over their
intuitionistic counterparts for geometric implications. The latter result
(sometimes referred to as Barr's theorem) is squarely a consequence of
Gentzen's Hauptsatz. Prima facie though, cut elimination can result in
superexponentially longer proofs. In this paper it is shown that the
transformation of a classical proof of a geometric implication in a geometric
theory into an intuitionistic proof can be achieved in feasibly many steps.


Abstract:
  We extend the well-known formula for the Euler class of a real oriented
even-dimensional vector bundle in terms of the curvature of a metric connection
to the case of a general linear connection provided a metric is present. We
rewrite the classical Gauss-Bonnet theorem in dimension two in light of this
formula. We also discuss a potential application to a conjecture of Chern, and
make a brief digression to discuss $m$-quasi-Einstein manifolds.


Abstract:
  We study rainbow-free colourings of $k$-uniform hypergraphs; that is,
colourings that use $k$ colours but with the property that no hyperedge attains
all colours. We show that $p^*=(k-1)(\ln n)/n$ is the threshold function for
the existence of a rainbow-free colouring in a random $k$-uniform hypergraph.


Abstract:
  This paper investigates the distributed optimal output consensus problem of
second-order uncertain nonlinear multi-agent systems over weight-unbalanced
directed networks. Under the standard assumption that local cost functions are
strongly convex with globally Lipschitz gradients, a novel distributed dynamic
state feedback controller is developed such that the outputs of all the agents
reach the optimal solution to minimize the global cost function which is the
sum of all the local cost functions. The controller design is based on a
two-layer strategy, where a distributed optimal coordinator and a
reference-tracking controller are proposed to address the challenges arising
from unbalanced directed networks and uncertain nonlinear functions
respectively. A key feature of the proposed controller is that the nonlinear
functions containing the uncertainties and disturbances are not required to be
globally Lipschitz. Furthermore, by exploiting adaptive control technique, no
prior knowledge of the uncertainties or disturbances is required either. Two
simulation examples are finally provided to illustrate the effectiveness of the
proposed control scheme.


Abstract:
  In the present work, we report a comprehensive theoretical study of the $\log
ft$ values for the allowed and forbidden $\beta^-$ decay transitions of
$^{46}$K and $^{47}$K corresponding to recently available experimental data
from the GRIFFIN spectrometer at TRIUMF-ISAC [Phys. Rev. C {\bf 100}, 054327
(2019); Phys. Rev. C {\bf 102}, 054314 (2020)]. We perform the nuclear
shell-model calculation in $sdpf$-valence space, with the SDPF-MU interaction,
to calculate low-lying energy spectra of $^{46,47}$K and $^{46,47}$Ca. For
further investigation, we also calculate spectroscopic properties of
$^{46,47}$K, $^{46,47}$Ca and compare the results with the experimental data.
We suggest spin-parity of several levels, which were previously tentative, in
both the calcium isotopes. Based on the energy and $\log ft$ values, we
conclude that the state at 3.984 MeV in $^{46}$Ca could be either $3^{-}$ or
$2^{+}$ and a state at 4.432 MeV might be $3^{-}$. The spin-parity of the
states at 2.875, 3.951 and 4.453 MeV are predicted to be $1/2^-$, $3/2^-$ and
$1/2^-$ or $1/2^+$, respectively, in $^{47}$Ca. We also give the confirmation
to spin-parity of 2.850, 3.889 and 4.606 MeV states for $^{47}$Ca. Our results
of level schemes of $^{46,47}$K are in a reasonable agreement with the
experimental data, while the agreement of calculated $\log ft$ values for the
$\beta^-$ decay with the experimental data is limited.


Abstract:
  This paper presents a method for learning logical task specifications and
cost functions from demonstrations. Linear temporal logic (LTL) formulas are
widely used to express complex objectives and constraints for autonomous
systems. Yet, such specifications may be challenging to construct by hand.
Instead, we consider demonstrated task executions, whose temporal logic
structure and transition costs need to be inferred by an autonomous agent. We
employ a spectral learning approach to extract a weighted finite automaton
(WFA), approximating the unknown logic structure of the task. Thereafter, we
define a product between the WFA for high-level task guidance and a labeled
Markov decision process for low-level control. An inverse reinforcement
learning (IRL) problem is considered to learn a cost function by
backpropagating the loss between agent and expert behaviors through the
planning algorithm. Our proposed model, termed WFA-IRL, is capable of
generalizing the execution of the inferred task specification in a suite of
MiniGrid environments.


Abstract:
  This paper establishes bounds on the performance of empirical risk
minimization for large-dimensional linear regression. We generalize existing
results by allowing the data to be dependent and heavy-tailed. The analysis
covers both the cases of identically and heterogeneously distributed
observations. Our analysis is nonparametric in the sense that the relationship
between the regressand and the regressors is assumed to be unknown. The main
results of this paper indicate that the empirical risk minimizer achieves the
optimal performance (up to a logarithmic factor) in a dependent data setting.


Abstract:
  As more robots are implemented for contact-rich tasks, tactile sensors are in
increasing demand. For many circumstances, the contact is required to be
compliant, and soft sensors are in need. This paper introduces a novelly
designed soft sensor that can simultaneously estimate the contact force and
contact location. Inspired by humans' skin, which contains multi-layers of
receptors, the designed tactile sensor has a dual-layer structure. The first
layer is made of a conductive fabric that is responsible for sensing the
contact force. The second layer is composed of four small conductive rubbers
that can detect the contact location. Signals from the two layers are firstly
processed by Wheatstone bridges and amplifier circuits so that the measurement
noises are eliminated, and the sensitivity is improved. An Arduino chip is used
for processing the signal and analyzing the data. The contact force can be
obtained by a pre-trained model that maps from the voltage to force, and the
contact location is estimated by the voltage signal from the conductive rubbers
in the second layer. In addition, filtering methods are applied to eliminate
the estimation noise. Finally, experiments are provided to show the accuracy
and robustness of the sensor.


Abstract:
  Suppose two quantum circuit chips are located at different places, for which
we do not have any prior knowledge, and cannot see the internal structures
either. If we want to find out whether they have the same functions or not with
certainty, what should we do? In this paper, we show that this realistic
problem can be solved from the viewpoints of quantum nonlocality. Specifically,
we design an elegant protocol that examines underlying quantum nonlocality. We
prove that in the protocol the strongest nonlocality can be observed if and
only if two quantum circuits are equivalent to each other. We show that the
protocol also works approximately, where the distance between two quantum
circuits can be lower and upper bounded analytically by observed quantum
nonlocality. Furthermore, we also discuss the possibility to generalize the
protocol to multipartite cases, i.e., if we do equivalence checking for
multiple quantum circuits, we try to solve the problem in one go. Our work
introduces a nontrivial application of quantum nonlocality in quantum
engineering.


Abstract:
  Bayesian persuasion studies how an informed sender should partially disclose
information to influence the behavior of a self-interested receiver. Classical
models make the stringent assumption that the sender knows the receiver's
utility. This can be relaxed by considering an online learning framework in
which the sender repeatedly faces a receiver of an unknown, adversarially
selected type. We study, for the first time, an online Bayesian persuasion
setting with multiple receivers. We focus on the case with no externalities and
binary actions, as customary in offline models. Our goal is to design no-regret
algorithms for the sender with polynomial per-iteration running time. First, we
prove a negative result: for any $0 < \alpha \leq 1$, there is no
polynomial-time no-$\alpha$-regret algorithm when the sender's utility function
is supermodular or anonymous. Then, we focus on the case of submodular sender's
utility functions and we show that, in this case, it is possible to design a
polynomial-time no-$(1 - \frac{1}{e})$-regret algorithm. To do so, we introduce
a general online gradient descent scheme to handle online learning problems
with a finite number of possible loss functions. This requires the existence of
an approximate projection oracle. We show that, in our setting, there exists
one such projection oracle which can be implemented in polynomial time.


Abstract:
  We present newly-calibrated period-$\phi_{31}$-[Fe/H] relations for
fundamental mode RR Lyrae stars in the optical and, for the first time,
mid-infrared. This work's calibration dataset provides the largest and most
comprehensive span of parameter space to date with homogeneous metallicities
from $-3<\textrm{[Fe/H]}<0.4$ and accurate Fourier parameters derived from 1980
ASAS-SN ($V$-band) and 1083 WISE (NEOWISE extension, $W1$ and $W2$ bands) RR
Lyrae stars with well-sampled light curves. We compare our optical
period-$\phi_{31}$-[Fe/H] with those available in the literature and
demonstrate that our relation minimizes systematic trends in the lower and
higher metallicity range. Moreover, a direct comparison shows that our optical
photometric metallicities are consistent with both those from high-resolution
spectroscopy and globular clusters, supporting the good performance of our
relation. We found an intrinsic scatter in the photometric metallicities (0.41
dex in the $V$-band and 0.50 dex in the infrared) by utilizing large
calibration datasets covering a broad metallicity range. This scatter becomes
smaller when optical and infrared bands are used together (0.37 dex). Overall,
the relations derived in this work have many potential applications, including
large-area photometric surveys with JWST in the infrared and LSST in the
optical.


Abstract:
  Vortices are whirling disturbances commonly found in nature ranging from
tremendously small scales in Bose-Einstein condensates to cosmologically
colossal scales in spiral galaxies. An optical vortex, generally associated
with a spiral phase, can carry orbital angular momentum (OAM). The optical OAM
can either be in the longitudinal direction if the spiral phase twists in the
spatial domain or in the transverse direction if the phase rotates in the
spatiotemporal domain. In this article, we demonstrate the intersection of
spatiotemporal vortices and spatial vortices in a wave packet. As a result of
this intersection, the wave packet hosts a tilted OAM that provides an
additional degree of freedom to the applications that harness the OAM of
photons.


Abstract:
  With the rapid advances in IoT, edge and cloud computing solutions, it is
critical to educate and train students in computer science and engineering in
various aspects of IoT-edge-cloud (IoT-E-C) system architecture
implementations. We outline the design and development of an undergraduate
laboratory course that sets the goal of implementing various interfaces and
communication protocols to connect IoT, edge and cloud computing systems and
evaluating their performance. The lab setup is modular and based on open source
tools. In the IoT context, it consists of low-cost processing platforms with
various sensors and actuators. In the edge and cloud computing context, we
implement and deploy single board computers and Firebase cloud solutions,
respectively. The modular lab setup allows students to engineer and integrate
various communication protocol solutions, including MQTT, COAP and HTTP. In
addition to the system implementation, students can evaluate and benchmark the
performance of the entire system.


Abstract:
  We find the Hecke-Rogers type series representations of generating functions
of the Hurwitz class numbers which is very close to certain mock theta
functions. We also prove two combinatorial interpretation of Hurwitz class
numbers appeared on OEIS.


Abstract:
  The efficiency and accuracy of mapping are crucial in a large scene and
long-term AR applications. Multi-agent cooperative SLAM is the precondition of
multi-user AR interaction. The cooperation of multiple smart phones has the
potential to improve efficiency and robustness of task completion and can
complete tasks that a single agent cannot do. However, it depends on robust
communication, efficient location detection, robust mapping, and efficient
information sharing among agents. We propose a multi-intelligence collaborative
monocular visual-inertial SLAM deployed on multiple ios mobile devices with a
centralized architecture. Each agent can independently explore the environment,
run a visual-inertial odometry module online, and then send all the measurement
information to a central server with higher computing resources. The server
manages all the information received, detects overlapping areas, merges and
optimizes the map, and shares information with the agents when needed. We have
verified the performance of the system in public datasets and real
environments. The accuracy of mapping and fusion of the proposed system is
comparable to VINS-Mono which requires higher computing resources.


Abstract:
  The oscillation model, based on the wave equation on networks, can describe
user dynamics in online social networks. The fundamental equation of user
dynamics can be introduced into the oscillation model to explicitly describe
the causal relation of user dynamics yielded by certain specific network
structures. Moreover, by considering the sparseness of the link structure of
online social networks, a novel fundamental equation of different forms has
been devised. In this paper, we derive a closed-form solution of the new
fundamental equation. Also, we show that the closed-form solution of the new
fundamental equation can generate the general solution of the original wave
equation and investigate the characteristics of the derived general solution.


Abstract:
  A multitude of wireless devices for radio local area networks (RLAN) use
frequency bands that are shared with other radio services or applications.
European market surveillance activities on 5 GHz RLAN indicate a growing number
of non-compliant devices that do not fulfill all radio regulation requirements
and may cause interference. This paper describes a concept and method for
mitigating the use of non-compliant equipment based on a public key
infrastructure. If every RLAN device holds a security certificate, which is
revoked when non-compliance is found, then the connection establishment between
devices can be made dependent on compliance. The aim is that legitimate
equipment rejects connections to non-compliant devices, so that non-compliant
devices will become isolated.


Abstract:
  Under an applied traction, highly concentrated suspensions of solid particles
in fluids can turn from a state in which they flow to a state in which they
counteract the traction as an elastic solid: a shear-jammed state. Remarkably,
the suspension can turn back to the flowing state simply by inverting the
traction. A tensorial model is presented and tested in paradigmatic cases. We
show that, to reproduce the phenomenology of shear jamming in generic
geometries, it is necessary to link this effect to the elastic response
supported by the suspension microstructure rather than to a divergence of the
viscosity.


Abstract:
  The flow past a fixed finite-length circular cylinder, the axis of which
makes a nonzero angle with the incoming stream, is studied through
fully-resolved simulations, from creeping-flow conditions to strongly inertial
regimes. The investigation focuses on the way the body aspect ratio $\chi$
(defined as as the length-to-diameter ratio), the inclination angle $\theta$
with respect to the incoming flow and the Reynolds number $\text{Re}$ (based on
the cylinder diameter) affect the flow structure past the body and therefore
the hydrodynamic loads acting on it. The configuration $\theta=0^\circ$ (where
the cylinder is aligned with the flow) is considered first, from creeping-flow
conditions up to $\text{Re}=400$, with aspect ratios up to $20$ ($10$) for
$\text{Re}\leq10$ ($\text{Re}\geq10$). In the low-to-moderate Reynolds number
regime ($\text{Re}\lesssim5$), influence or the aspect ratio, inclination (from
$0^{\circ}$ to $30^{\circ}$), and inertial effects is examined by comparing
numerical results for the axial and transverse force components and the
spanwise torque with theoretical predictions based on the slender-body
approximation, possibly incorporating finite-Reynolds-number corrections.
Semiempirical models based on these predictions and incorporating finite-length
and inertial corrections extracted from the numerical data are derived. For
large enough Reynolds numbers ($\text{Re}\gtrsim10^2$), separation takes place
along the upstream part of the lateral surface of the cylinder, deeply
influencing the surface stress distribution. Numerical results are used to
build empirical models for the force components and the torque, valid for
moderately inclined cylinders ($|\theta|\lesssim30^\circ$) of arbitrary aspect
ratio up to $\text{Re}\approx300$ and matching those obtained at
low-to-moderate Reynolds number.


Abstract:
  Fine-grained image recognition is very challenging due to the difficulty of
capturing both semantic global features and discriminative local features.
Meanwhile, these two features are not easy to be integrated, which are even
conflicting when used simultaneously. In this paper, a retrieval-based
coarse-to-fine framework is proposed, where we re-rank the TopN classification
results by using the local region enhanced embedding features to improve the
Top1 accuracy (based on the observation that the correct category usually
resides in TopN results). To obtain the discriminative regions for
distinguishing the fine-grained images, we introduce a weakly-supervised method
to train a box generating branch with only image-level labels. In addition, to
learn more effective semantic global features, we design a multi-level loss
over an automatically constructed hierarchical category structure. Experimental
results show that our method achieves state-of-the-art performance on three
benchmarks: CUB-200-2011, Stanford Cars, and FGVC Aircraft. Also,
visualizations and analysis are provided for better understanding.


Abstract:
  The gradient descent-ascent (GDA) algorithm has been widely applied to solve
minimax optimization problems. In order to achieve convergent policy parameters
for minimax optimization, it is important that GDA generates convergent
variable sequences rather than convergent sequences of function values or
gradient norms. However, the variable convergence of GDA has been proved only
under convexity geometries, and there lacks understanding for general nonconvex
minimax optimization. This paper fills such a gap by studying the convergence
of a more general proximal-GDA for regularized nonconvex-strongly-concave
minimax optimization. Specifically, we show that proximal-GDA admits a novel
Lyapunov function, which monotonically decreases in the minimax optimization
process and drives the variable sequence to a critical point. By leveraging
this Lyapunov function and the K{\L} geometry that parameterizes the local
geometries of general nonconvex functions, we formally establish the variable
convergence of proximal-GDA to a critical point $x^*$, i.e., $x_t\to x^*,
y_t\to y^*(x^*)$. Furthermore, over the full spectrum of the
K{\L}-parameterized geometry, we show that proximal-GDA achieves different
types of convergence rates ranging from sublinear convergence up to finite-step
convergence, depending on the geometry associated with the K{\L} parameter.
This is the first theoretical result on the variable convergence for nonconvex
minimax optimization.


Abstract:
  After an artificial model background subtraction, the pixels have been
labelled as foreground and background. Previous approaches to secondary
processing the output for denoising usually use traditional methods such as the
Bayesian refinement method. In this paper, we focus on using a modified U-Net
model to approximate the result of the Bayesian refinement method and improve
the result. In our modified U-Net model, the result of background subtraction
from other models will be combined with the source image as input for learning
the statistical distribution. Thus, the losing information caused by the
background subtraction model can be restored from the source image. Moreover,
since the part of the input image is already the output of the other background
subtraction model, the feature extraction should be convenient, it only needs
to change the labels of the noise pixels. Compare with traditional methods,
using deep learning methods superiority in keeping details.


Abstract:
  In both flat and curved spacetimes, there are weak and strong versions of the
anti-Unruh/anti-Hawking effects, in which the KMS field temperature is
anti-correlated with the response of a detector and its inferred temperature.
We investigate for the first time the effects on the weak and strong
anti-Hawking effects for an Unruh-DeWitt detector orbiting a BTZ black hole in
the co-rotating frame. We find that rotation can significantly amplify the
strength of the weak anti-Hawking effect, whereas it can either amplify or
reduce the strength of the strong anti-Hawking effect depending on boundary
conditions. For the strong anti-Hawking effect, we find a non-monotonic
relationship between the angular momentum and detector temperature for each
boundary condition. In addition, we note that the weak anti-Hawking effect is
independent of a changing AdS length, while a longer AdS length increases the
temperature range of the strong anti-Hawking effect.


Abstract:
  In this work, we explore the constant-Q transform (CQT) for speech emotion
recognition (SER). The CQT-based time-frequency analysis provides variable
spectro-temporal resolution with higher frequency resolution at lower
frequencies. Since lower-frequency regions of speech signal contain more
emotion-related information than higher-frequency regions, the increased
low-frequency resolution of CQT makes it more promising for SER than standard
short-time Fourier transform (STFT). We present a comparative analysis of
short-term acoustic features based on STFT and CQT for SER with deep neural
network (DNN) as a back-end classifier. We optimize different parameters for
both features. The CQT-based features outperform the STFT-based spectral
features for SER experiments. Further experiments with cross-corpora evaluation
demonstrate that the CQT-based systems provide better generalization with
out-of-domain training data.


Abstract:
  Does a physical field exist independently of the interaction between the
field source and the test body used to measure it at a given point? What does
propagate between two physical bodies when they interact? These are the
fundamental questions we answer in the present work for electromagnetic and
gravitational interactions. We come to the conclusion that gravitational fields
as well as spacetime curvature cannot depend on the passive-gravitational to
inertial mass ratio of the test bodies, because this would lead to energy
conservation and causality violation except when this ratio is universally
exactly equal to one. For what concerns the hypothetical dependence of
electromagnetic fields on the charge to inertial-mass ratio of test bodies,
this would call for the propagation of an operationally ill defined quantity
(Coulomb . Meter$^2$ / Second$^2$) that would replace energy (Joule = Kilogram
. Meter$^2$ / Second$^2$) transfer in the interaction between two electrically
charged bodies. Hence electromagnetic fields cannot depend on the charge to
inertial-mass ratio of test bodies. A consequence of these results is also the
disqualification of Murat Ozer's unification theory of gravitation and
electromagnetism.


Abstract:
  A qualitative and quantitative extension of the chaotic models used to
generate self-similar traffic with long-range dependence (LRD) is presented by
means of the formulation of a model that considers the use of piecewise affine
one-dimensional maps. Based on the disaggregation of the temporal series
generated, a valid explanation of the behavior of the values of Hurst exponent
is proposed and the feasibility of their control from the parameters of the
proposed model is shown.


Abstract:
  We investigate the local differential privacy (LDP) guarantees of a
randomized privacy mechanism via its contraction properties. We first show that
LDP constraints can be equivalently cast in terms of the contraction
coefficient of the $E_\gamma$-divergence. We then use this equivalent formula
to express LDP guarantees of privacy mechanisms in terms of contraction
coefficients of arbitrary $f$-divergences. When combined with standard
estimation-theoretic tools (such as Le Cam's and Fano's converse methods), this
result allows us to study the trade-off between privacy and utility in several
testing and minimax and Bayesian estimation problems.


Abstract:
  In various provers and deductive verification tools, logical transformations
are used extensively in order to reduce a proof task into a number of simpler
tasks. Logical transformations are often part of the trusted base of such
tools. In this paper, we develop a framework to improve confidence in their
results. We follow a modular and skeptical approach: transformations are
instrumented independently of each other and produce certificates that are
checked by a third-party tool. Logical transformations are considered in a
higher-order logic, with type polymorphism and built-in theories such as
equality and integer arithmetic. We develop a language of proof certificates
for them and use it to implement the full chain of certificate generation and
certificate verification.


Abstract:
  We study the growth of linear matter density perturbations in a modified
gravity approach of scalar field couplings with metric and torsion. In the
equivalent scalar-tensor formulation, the matter fields in the Einstein frame
interact as usual with an effective dark energy component, whose dynamics is
presumably governed by a scalar field that sources a torsion mode. As a
consequence, the matter density ceases to be self-conserved, thereby making an
impact not only on the background cosmological evolution but also on the
perturbative spectrum of the local inhomogeneities. In order to estimate the
effect on the growth of the linear matter perturbations, with the least
possible alteration of the standard parametric form of the growth factor, we
resort to a suitable Taylor expansion of the corresponding exponent, known as
the growth index, about the value of the cosmic scale factor at the present
epoch. In particular, we obtain an appropriate fitting formula for the growth
index in terms of the coupling function and the matter density parameter. While
the overall parametric formulation of the growth factor is found to fit well
with the latest redshift-space-distortion (RSD) and the observational Hubble
(OH) data at low redshifts, the fitting formula enables us to constrain the
growth index to well within the concordant cosmological limits, thus ensuring
the viability of the formalism.


Abstract:
  Traditionally, promoted by the internet companies, continuous delivery is
more and more appealing to industries which develop systems with
safety-critical functions. Since safety-critical systems must meet regulatory
requirements and require specific safety assessment processes in addition to
the normal development steps, enabling continuous delivery of software in
safety-critical systems requires the automation of the safety assessment
process in the delivery pipeline. In this paper, we outline a continuous
delivery pipeline for realizing continuous safety assessment in
software-intensive safety-critical systems based on model-based safety
assessment methods.


Abstract:
  A domain in $\mathbb{R}^3$ that touches the $x_3$ axis at one point is found
with the following property. For any initial value in a $C^2$ class, the
axially symmetric Navier Stokes equations with Navier slip boundary condition
has a finite energy solution that stays bounded for any given time, i.e. no
finite time blow up of the fluid velocity occurs. The result seems to be the
first case where the Navier-Stokes regularity problem is solved beyond
dimension 2.


Abstract:
  With the development of computer-aided diagnosis (CAD) and image scanning
technology, Whole-slide Image (WSI) scanners are widely used in the field of
pathological diagnosis. Therefore, WSI analysis has become the key to modern
digital pathology. Since 2004, WSI has been used more and more in CAD. Since
machine vision methods are usually based on semi-automatic or fully automatic
computers, they are highly efficient and labor-saving. The combination of WSI
and CAD technologies for segmentation, classification, and detection helps
histopathologists obtain more stable and quantitative analysis results, save
labor costs and improve diagnosis objectivity. This paper reviews the methods
of WSI analysis based on machine learning. Firstly, the development status of
WSI and CAD methods are introduced. Secondly, we discuss publicly available WSI
datasets and evaluation metrics for segmentation, classification, and detection
tasks. Then, the latest development of machine learning in WSI segmentation,
classification, and detection are reviewed continuously. Finally, the existing
methods are studied, the applicabilities of the analysis methods are analyzed,
and the application prospects of the analysis methods in this field are
forecasted.


Abstract:
  We show that Riemann-Hurwitz-style translation formulas obtained by Kuz'min,
Kida, Iwasawa, Wingberg et alii for the lambda invariant attached to certain
Iwasawa moduli in cyclotomic Z{\ell}-extension of number fields are essentially
equivalent. More precisely, we prove that all these formulas,including those
stated in terms of representations, resul tidentically for purely algebraic
reasons from the arithmetic computation of a suitable Herbrand quotient which
it suffices to carry out in the cyclic case of prime degree {\ell}.


Abstract:
  New measurement and assessment techniques have been applied to the
radiochemical re-evaluation of the Trinity Event. Thirteen trinitite samples
were dissolved and analyzed using a combination of traditional decay counting
methods and the mass spectrometry techniques. The resulting data were assessed
using advanced simulation tools to afford a final yield determination of $24.8
\pm 2$ kilotons TNT equivalent, substantially higher than the previous DOE
released value of 21 kilotons. This article is intended to complement the work
of Susan Hanson and Warren Oldham, seen elsewhere in this issue.


Abstract:
  Code generation aims to automatically generate a piece of code given an input
natural language utterance. Currently, among dominant models, it is treated as
a sequence-to-tree task, where a decoder outputs a sequence of actions
corresponding to the pre-order traversal of an Abstract Syntax Tree. However,
such a decoder only exploits the preorder traversal based preceding actions,
which are insufficient to ensure correct action predictions. In this paper, we
first throughly analyze the context modeling difference between neural code
generation models with different traversals based decodings (preorder traversal
vs breadth-first traversal), and then propose to introduce a mutual learning
framework to jointly train these models. Under this framework, we continuously
enhance both two models via mutual distillation, which involves synchronous
executions of two one-to-one knowledge transfers at each training step. More
specifically, we alternately choose one model as the student and the other as
its teacher, and require the student to fit the training data and the action
prediction distributions of its teacher. By doing so, both models can fully
absorb the knowledge from each other and thus could be improved simultaneously.
Experimental results and in-depth analysis on several benchmark datasets
demonstrate the effectiveness of our approach. We release our code at
https://github.com/DeepLearnXMU/CGML.


Abstract:
  Integration of modern defence weapons into ship power systems poses a
challenge in terms of meeting the high ramp rate requirements of those loads.
It might be demanding for the generators to meet the ramp rates of these loads.
Failure to meet so, might lead to stability issues. This is addressed by
conglomeration of generators and energy storage elements to handle the required
power demand posed by loads. This paper proposes an energy management strategy
based on model predictive control that incorporates the uncertainty in the load
prediction. The proposed controller optimally coordinates the power split
between the generators and energy storage elements to guarantee that the power
demand is met taking into considerations the ramp rate limitations and the load
uncertainty. A low bandwidth model consisting of a single generator and a
single energy storage element is used to validate the results of the proposed
energy management strategy. The results demonstrate the robustness of the
controller under load prediction uncertainty and demonstrate the effect of load
uncertainty on battery capacity loss.


Abstract:
  Recently, a lot of effort has been dedicated to developing next-generation
optoelectronic devices based on two-dimensional materials, thanks to their
unique optical properties that are significantly different from those of their
bulk counterparts. In order to implement high-performance nanoscale optical
devices, an in-depth study of how linear and non-linear propagation occurs in
two-dimensional materials is required. Here, we focus on the theory behind the
propagation of electromagnetic waves in two-dimensional materials as well as
emerging applications in the fields of electronics, optics, sensors, which are
summarized and discussed in the paper.


Abstract:
  Non-Volatile Random Access Memory (NVRAM) is a novel type of hardware that
combines the benefits of traditional persistent memory (persistency of data
over hardware failures) and DRAM (fast random access). In this work, we
describe an algorithm that can be used to execute NVRAM programs and recover
the system after a hardware failure while taking the architecture of real-world
NVRAM systems into account. Moreover, the algorithm can be used to execute
NVRAM-destined programs on commodity persistent hardware, such as hard drives.
That allows us to test NVRAM algorithms using only cheap hardware, without
having access to the NVRAM. We report the usage of our algorithm to implement
and test NVRAM CAS algorithm.


Abstract:
  In this paper, we consider a future electricity market consisting of
aggregated energy prosumers, who are equipped with local wind power plants
(WPPs) to support (part of) their energy demands and can also trade energy with
day-ahead market (DAM) and energy balancing market (EBM). In addition, an
energy aggregator (EA) is established, who can provide the trading gateways
between prosumers and the markets. The EA is responsible for making pricing
strategies on the prosumers to influence their trading behaviours such that the
social benefit of the prosumer community is improved. Specifically, two price
packages are provided by the EA: wholesale price (WP) package and lump-sum (LS)
package, which can be flexibly selected by prosumers based on their own
preferences. Analytical energy-trading strategies will be derived for WP
prosumers and LS prosumers based on non-cooperative games and Nash resource
allocation strategies, respectively. In this work, a social cost optimization
problem will be formulated for the EA, where the detailed WP/LS selection plans
are unknown in advance. Consequently, a stochastic Stackelberg game between
prosumers and the EA is formulated, and a two-level stochastic convex
programming algorithm is proposed to minimize the expectation of the social
cost. The performance of the proposed algorithm is demonstrated with a
two-settlement based market model in the simulation.


Abstract:
  Hand tracking has become an integral feature of recent generations of
immersive virtual reality head-mounted displays. With the widespread adoption
of this feature, hardware engineers and software developers are faced with an
exciting array of opportunities and a number of challenges, mostly in relation
to the human user. In this article, I outline what I see as the main
possibilities for hand tracking to add value to immersive virtual reality as
well as some of the potential challenges in the context of the psychology and
neuroscience of the human user. It is hoped that this paper serves as a roadmap
for the development of best practices in the field for the development of
subsequent generations of hand tracking and virtual reality technologies.


Abstract:
  Let $(X,d,\mathfrak{m})$ be a metric measure space. The study of the
Wasserstein space $(\mathbb{P}_p(X),\mathbb{W}_p)$ associated to $X$ has proved
useful in describing several geometrical properties of $X.$ In this paper we
focus on the study of isometries of $\mathbb{P}_p(X)$ for $p \in (1,\infty)$
under the assumption that there is some characterization of optimal maps
between measures, the so called Good transport behaviour $GTB_p$. Our first
result states that the set of Dirac deltas is invariant under isometries of the
Wasserstein space. Additionally we obtain that the isometry groups of the base
Riemannian manifold $M$ coincides with the one of the Wasserstein space
$\mathbb{P}_p(M)$ under assumptions on the manifold; namely, for $p=2$ that the
sectional curvature is strictly positive and for general $p\in (1,\infty)$ that
$M$ is a Compact Rank One Symmetric Space.


Abstract:
  We report the discovery of a new high mass X-ray binary pulsar, XMMU
J050722.1-684758, possibly associated with the supernova remnant MCSNR
J0507-6847 in the Large Magellanic Cloud, using XMM-Newton X-ray observations.
Pulsations with a periodicity of 570 s are discovered from the Be X-ray binary
XMMU J050722.1-684758 confirming its nature as a HMXB pulsar. The HMXB is
located near the geometric centre of the supernova remnant MCSNR J0507-6847
(0.9 arcmin from the centre) which supports the XRB-SNR association. The
estimated age of the supernova remnant is 43-63 kyr which points to a middle
aged to old supernova remnant. The large diameter of the supernova remnant
combined with the lack of distinctive shell counterparts in optical and radio
indicates that the SNR is expanding into the tenous environment of the
superbubble N103. The estimated magnetic field strength of the neutron star is
$B\gtrsim10^{14}$ G assuming a spin equilibrium condition which is expected
from the estimated age of the parent remnant and assuming that the measured
mass-accretion rate remained constant throughout.


Abstract:
  We consider the problem of coded distributed computing where a large linear
computational job, such as a matrix multiplication, is divided into $k$ smaller
tasks, encoded using an $(n,k)$ linear code, and performed over $n$ distributed
nodes. The goal is to reduce the average execution time of the computational
job. We provide a connection between the problem of characterizing the average
execution time of a coded distributed computing system and the problem of
analyzing the error probability of codes of length $n$ used over erasure
channels. Accordingly, we present closed-form expressions for the execution
time using binary random linear codes and the best execution time any
linear-coded distributed computing system can achieve. It is also shown that
there exist \textit{good} binary linear codes that not only attain
(asymptotically) the best performance that any linear code (not necessarily
binary) can achieve but also are numerically stable against the inevitable
rounding errors in practice. We then develop a low-complexity algorithm for
decoding Reed-Muller (RM) codes over erasure channels. Our decoder only
involves additions, subtractions, {and inversion of relatively small matrices
of dimensions at most $\log n+1$}, and enables coded computation over
real-valued data. Extensive numerical analysis of the fundamental results as
well as RM- and polar-coded computing schemes demonstrate the excellence of the
RM-coded computation in achieving close-to-optimal performance while having a
low-complexity decoding and explicit construction. The proposed framework in
this paper enables efficient designs of distributed computing systems given the
rich literature in the channel coding theory.


Abstract:
  GuessWhat?! is a two-player visual dialog guessing game where player A asks a
sequence of yes/no questions (Questioner) and makes a final guess (Guesser)
about a target object in an image, based on answers from player B (Oracle).
Based on this dialog history between the Questioner and the Oracle, a Guesser
makes a final guess of the target object. Previous baseline Oracle model
encodes no visual information in the model, and it cannot fully understand
complex questions about color, shape, relationships and so on. Most existing
work for Guesser encode the dialog history as a whole and train the Guesser
models from scratch on the GuessWhat?! dataset. This is problematic since
language encoder tend to forget long-term history and the GuessWhat?! data is
sparse in terms of learning visual grounding of objects. Previous work for
Questioner introduces state tracking mechanism into the model, but it is
learned as a soft intermediates without any prior vision-linguistic insights.
To bridge these gaps, in this paper we propose Vilbert-based Oracle, Guesser
and Questioner, which are all built on top of pretrained vision-linguistic
model, Vilbert. We introduce two-way background/target fusion mechanism into
Vilbert-Oracle to account for both intra and inter-object questions. We propose
a unified framework for Vilbert-Guesser and Vilbert-Questioner, where
state-estimator is introduced to best utilize Vilbert's power on single-turn
referring expression comprehension. Experimental results show that our proposed
models outperform state-of-the-art models significantly by 7%, 10%, 12% for
Oracle, Guesser and End-to-End Questioner respectively.


Abstract:
  A classical density functional theory (cDFT) based on the PC-SAFT equation of
state is proposed for the calculation of adsorption equilibria of pure
substances and their mixtures in covalent organic frameworks (COFs). Adsorption
isotherms of methane, ethane, n-butane and nitrogen in the COFs TpPa-1 and
2,3-DhaTph are calculated and compared to results from grand canonical Monte
Carlo (GCMC) simulations. Mixture adsorption is investigated for the
methane/ethane and methane/n-butane binary systems. Excellent agreement between
PC-SAFT DFT and GCMC is obtained for all adsorption isotherms up to pressures
of 50 bar. The cDFT formalism accurately predicts the selective accumulation of
longer hydrocarbons for binary mixtures in the considered COFs. This
application shows substantial predictive power of PC-SAFT DFT solved in
three-dimensional geometries and the results suggest the method can in the
future also be applied for efficient optimization of force field parameters or
of structural properties of the porous material based on an analytical theory
as opposed to a stochastic simulation.


Abstract:
  Factorization-based gradient descent is a scalable and efficient algorithm
for solving low-rank matrix completion. Recent progress in structured
non-convex optimization has offered global convergence guarantees for gradient
descent under certain statistical assumptions on the low-rank matrix and the
sampling set. However, while the theory suggests gradient descent enjoys fast
linear convergence to a global solution of the problem, the universal nature of
the bounding technique prevents it from obtaining an accurate estimate of the
rate of convergence. In this paper, we perform a local analysis of the exact
linear convergence rate of gradient descent for factorization-based matrix
completion for symmetric matrices. Without any additional assumptions on the
underlying model, we identify the deterministic condition for local convergence
of gradient descent, which only depends on the solution matrix and the sampling
set. More crucially, our analysis provides a closed-form expression of the
asymptotic rate of convergence that matches exactly with the linear convergence
observed in practice. To the best of our knowledge, our result is the first one
that offers the exact rate of convergence of gradient descent for matrix
factorization in Euclidean space for matrix completion.


Abstract:
  The importance of digital identity as a foundation for digital public
services is considered. As the classical, centralised model digital identity
has proven to be subject to several limitations, self-sovereign identities are
proposed as replacement, especially in the context of e-government platforms
and direct participation to policymaking (e.g. through e-voting tools).


Abstract:
  The US Food and Drug Administration (FDA) has been actively promoting the use
of real-world data (RWD) in drug development. RWD can generate important
real-world evidence reflecting the real-world clinical environment where the
treatments are used. Meanwhile, artificial intelligence (AI), especially
machine- and deep-learning (ML/DL) methods, have been increasingly used across
many stages of the drug development process. Advancements in AI have also
provided new strategies to analyze large, multidimensional RWD. Thus, we
conducted a rapid review of articles from the past 20 years, to provide an
overview of the drug development studies that use both AI and RWD. We found
that the most popular applications were adverse event detection, trial
recruitment, and drug repurposing. Here, we also discuss current research gaps
and future opportunities.


Abstract:
  We present Aurora, an open-source package for particle transport, neutrals
and radiation modeling in magnetic confinement fusion plasmas. Aurora's modern
multi-language interface enables simulations of 1.5D impurity transport within
high-performance computing frameworks, particularly for the inference of
particle transport coefficients. A user-friendly Python library allows simple
interaction with atomic rates from the Atomic Data and Atomic Structure
database as well as other sources. This enables a range of radiation
predictions, both for power balance and spectroscopic analysis. We discuss here
the superstaging approximation for complex ions, as a way to group charge
states and reduce computational cost, demonstrating its wide applicability
within the Aurora forward model and beyond. Aurora also facilitates neutral
particle analysis, both from experimental spectroscopic data and other
simulation codes. Leveraging Aurora's capabilities to interface SOLPS-ITER
results, we demonstrate that charge exchange is unlikely to affect the total
radiated power from the ITER core during high performance operation. Finally,
we describe the ImpRad module in the OMFIT framework, developed to enable
experimental analysis and transport inferences on multiple devices using
Aurora.


Abstract:
  We consider the inference problem for high-dimensional linear models, when
covariates have an underlying spatial organization reflected in their
correlation. A typical example of such a setting is high-resolution imaging, in
which neighboring pixels are usually very similar. Accurate point and
confidence intervals estimation is not possible in this context with many more
covariates than samples, furthermore with high correlation between covariates.
This calls for a reformulation of the statistical inference problem, that takes
into account the underlying spatial structure: if covariates are locally
correlated, it is acceptable to detect them up to a given spatial uncertainty.
We thus propose to rely on the $\delta$-FWER, that is the probability of making
a false discovery at a distance greater than $\delta$ from any true positive.
With this target measure in mind, we study the properties of ensembled
clustered inference algorithms which combine three techniques: spatially
constrained clustering, statistical inference, and ensembling to aggregate
several clustered inference solutions. We show that ensembled clustered
inference algorithms control the $\delta$-FWER under standard assumptions for
$\delta$ equal to the largest cluster diameter. We complement the theoretical
analysis with empirical results, demonstrating accurate $\delta$-FWER control
and decent power achieved by such inference algorithms.


Abstract:
  Current black-box variational inference (BBVI) methods require the user to
make numerous design choices -- such as the selection of variational objective
and approximating family -- yet there is little principled guidance on how to
do so. We develop a conceptual framework and set of experimental tools to
understand the effects of these choices, which we leverage to propose best
practices for maximizing posterior approximation accuracy. Our approach is
based on studying the pre-asymptotic tail behavior of the density ratios
between the joint distribution and the variational approximation, then
exploiting insights and tools from the importance sampling literature. Our
framework and supporting experiments help to distinguish between the behavior
of BBVI methods for approximating low-dimensional versus
moderate-to-high-dimensional posteriors. In the latter case, we show that
mass-covering variational objectives are difficult to optimize and do not
improve accuracy, but flexible variational families can improve accuracy and
the effectiveness of importance sampling -- at the cost of additional
optimization challenges. Therefore, for moderate-to-high-dimensional posteriors
we recommend using the (mode-seeking) exclusive KL divergence since it is the
easiest to optimize, and improving the variational family or using model
parameter transformations to make the posterior and optimal variational
approximation more similar. On the other hand, in low-dimensional settings, we
show that heavy-tailed variational families and mass-covering divergences are
effective and can increase the chances that the approximation can be improved
by importance sampling.


Abstract:
  The notion of primitive chaos was proposed [J. Phys. Soc. Jpn. {\bf 79},
15002 (2010)] as a notion closely related to the fundamental problems of
physics itself such as determinism, causality, free will, predictability, and
irreversibility. In this letter, we introduce the notion of bird's-eye view
into the primitive chaos, and we find a new hierarchic structure of the
primitive chaos. This means that if we find a chaos in a real phenomenon or a
computer simulation, behind it, we can clearly realize the possibility of
tremendous varieties of chaos in the hierarchic structure unless we can see
them visually.


Abstract:
  Thanks to Atkinson (1938), we know the first two terms of the asymptotic
formula for the square mean integral value of the Riemann zeta function $\zeta$
on the critical line. Following both his work and the approach of Titchmarsh
(1986), we present an explicit version of the Atkinson formula, improving on a
recent bound by Simoni\v{c} (2020). Moreover, we extend the Atkinson formula to
the range $\Re(s)\in\left[\frac{1}{4},\frac{3}{4}\right]$, giving an explicit
bound for the square mean integral value of $\zeta$ and improving on a bound by
Helfgott and the authors (2019). We use mostly classical tools, such as the
approximate functional equation and the explicit convexity bounds of the zeta
function given by Backlund (1918).


Abstract:
  We prove hybrid subconvexity bounds twisted L-functions $L(s,f\times \chi)$
at the central point using a fourth moment estimate, including a new instance
of the Burgess subconvexity bound.


Abstract:
  The paper is devoted to model-theoretic properties of Kac-Moody groups with
the focus on elementary equivalence of Kac-Moody groups. We show that
elementary equivalence of (untwisted) affine Kac-Moody groups implies
coincidence of their generalized Cartan matrices and the elementary equivalence
of their ground fields. We also show that elementary equivalence of arbitrary
Kac-Moody groups over finite fields implies coincidence of these fields and an
isomorphism of their twin root data. The similar result is established for
Kac-Moody groups defined over infinite subfields of the algebraic closures of
finite fields.


Abstract:
  The Chernoff bound is an important inequality relation in probability theory.
The original version of the Chernoff bound is to give an exponential decreasing
bound on the tail distribution of sums of independent random variables. Recent
years, several works have been done by extending the original version of the
Chernoff bound to high-dimensional random objects, e.g., random matrices,
or/and to consider the relaxation that there is no requirement of independent
assumptions among random objects. In this work, we generalize the matrix
expander Chernoff bound studied by Garg et al. at work: A Matrix Expander
Chernoff Bound, to tensor expander Chernoff bounds. Our main tool is to develop
new tensor norm inequalities based on log-majorization techniques. These new
tensor norm inequalities are used to bound the expectation of Ky Fan norm of
the random tensor exponential function, then tensor expander Chernoff bounds
can be established. Compared with the matrix expander Chernoff bound, the
tensor expander Chernoff bounds proved at this work contributes following
aspects: (1) the random objects dimensions are increased from matrices
(two-dimensional data array) to tensors (multidimensional data array); (2) this
bound generalizes the identity map of the random objects summation to any
polynomial function of the random objects summation; (3) Ky Fan norm, instead
only the maximum or the minimum eigenvalues, for the function of the random
objects summation is considered; (4) we remove the restriction about the
summation of all mapped random objects is zero, which is required in the matrix
expander Chernoff bound derivation.


Abstract:
  In this paper, we introduce a new type of generalized neural network where
neurons and synapses maintain multiple states. We show that classical
gradient-based backpropagation in neural networks can be seen as a special case
of a two-state network where one state is used for activations and another for
gradients, with update rules derived from the chain rule. In our generalized
framework, networks have neither explicit notion of nor ever receive gradients.
The synapses and neurons are updated using a bidirectional Hebb-style update
rule parameterized by a shared low-dimensional "genome". We show that such
genomes can be meta-learned from scratch, using either conventional
optimization techniques, or evolutionary strategies, such as CMA-ES. Resulting
update rules generalize to unseen tasks and train faster than gradient descent
based optimizers for several standard computer vision and synthetic tasks.


Abstract:
  We present an efficient low-rank approximation algorithm for non-negative
tensors. The algorithm is derived from our two findings: First, we show that
rank-1 approximation for tensors can be viewed as a mean-field approximation by
treating each tensor as a probability distribution. Second, we theoretically
provide a sufficient condition for distribution parameters to reduce Tucker
ranks of tensors; interestingly, this sufficient condition can be achieved by
iterative application of the mean-field approximation. Since the mean-field
approximation is always given as a closed formula, our findings lead to a fast
low-rank approximation algorithm without using a gradient method. We
empirically demonstrate that our algorithm is faster than the existing
non-negative Tucker rank reduction methods and achieves competitive or better
approximation of given tensors.


Abstract:
  We construct Kahler-Einstein metrics with negative scalar curvature near an
isolated log canonical (non-log terminal) singularity. Such metrics are
complete near the singularity if the underlying space has complex dimension 2
or if the singularity is smoothable. In complex dimension 2, we show that any
complete Kahler-Einstein metric of negative scalar curvature near an isolated
log canonical (non-log terminal) singularity is smoothly asymptotically close
to one of the model metrics constructed by Kobayashi and Nakamura arising from
hyperbolic geometry.


Abstract:
  We introduce and characterize extremal 2-cuts for good fractal necklaces.
Using the characterization and the related topological properties of extremal
2-cuts, we prove that every good necklace has a unique necklace IFS in a
certain sense. Also, we prove that two good necklaces admit only rigid
homeomorphisms and thus the group of self-homeomorphisms of a good necklace is
countable. In addition, a certain weaker co-Hopfian property of good necklaces
is also obtained.


Abstract:
  Demand forecasting is a crucial component of demand management. While
shortening the forecasting horizon allows for more recent data and less
uncertainty, this frequently means lower data aggregation levels and a more
significant data sparsity. Sparse demand data usually results in lumpy or
intermittent demand patterns, which have sparse and irregular demand intervals.
Usual statistical and machine learning models fail to provide good forecasts in
such scenarios. Our research shows that competitive demand forecasts can be
obtained through two models: predicting the demand occurrence and estimating
the demand size. We analyze the usage of local and global machine learning
models for both cases and compare results against baseline methods. Finally, we
propose a novel evaluation criterion of lumpy and intermittent demand
forecasting models' performance. Our research shows that global classification
models are the best choice when predicting demand event occurrence. When
predicting demand sizes, we achieved the best results using Simple Exponential
Smoothing forecast. We tested our approach on real-world data consisting of 516
three-year-long time series corresponding to European automotive original
equipment manufacturers' daily demand.


Abstract:
  The strong force that binds atomic nuclei is governed by the rules of Quantum
Chromodynamics. Here we consider the suggestion the internal quark structure of
a nucleon will adjust self-consistently to the local mean scalar field in a
nuclear medium and that this may play a profound role in nuclear structure. We
show that one can derive an energy density functional based on this idea, which
successfully describes the properties of atomic nuclei across the periodic
table in terms of a small number of physically motivated parameters. Because
this approach amounts to a new paradigm for nuclear theory, it is vital to find
ways to test it experimentally and we review a number of the most promising
possibilities.