Abstract:
  The problem of estimating the size of a population based on a subset of
individuals observed across multiple data sources is often referred to as
capture-recapture or multiple-systems estimation. This is fundamentally a
missing data problem, where the number of unobserved individuals represents the
missing data. As with any missing data problem, multiple-systems estimation
requires users to make an untestable identifying assumption in order to
estimate the population size from the observed data. Approaches to
multiple-systems estimation often do not emphasize the role of the identifying
assumption during model specification, which makes it difficult to decouple the
specification of the model for the observed data from the identifying
assumption. We present a re-framing of the multiple-systems estimation problem
that decouples the specification of the observed-data model from the
identifying assumptions, and discuss how log-linear models and the associated
no-highest-order interaction assumption fit into this framing. We present an
approach to computation in the Bayesian setting which takes advantage of
existing software and facilitates various sensitivity analyses. We demonstrate
our approach in a case study of estimating the number of civilian casualties in
the Kosovo war. Code used to produce this manuscript is available at
https://github.com/aleshing/revisiting-identifying-assumptions.


Abstract:
  To solve the hierarchy problem, the relaxion must remain trapped in the
correct minimum, even if the electroweak symmetry is restored after reheating.
In this scenario, the relaxion starts rolling again until the backreaction
potential, with its set of local minima, reappears. Depending on the time of
barrier reappearance, Hubble friction alone may be insufficient to retrap the
relaxion in a large portion of the parameter space. Thus, an additional source
of friction is required, which might be provided by coupling to a dark
photon.The dark photon experiences a tachyonic instability as the relaxion
rolls, which slows down the relaxion by backreacting to its motion, and
efficiently creates anisotropies in the dark photon energy-momentum tensor,
sourcing gravitational waves. We calculate the spectrum of the resulting
gravitational wave background from this new mechanism, and evaluate its
observability by current and future experiments. We further investigate the
possibility that the coherently oscillating relaxion constitutes dark matter
and present the corresponding constraints from gravitational waves.


Abstract:
  A regular left-order on finitely generated group $G$ is a total,
left-multiplication invariant order on $G$ whose corresponding positive cone is
the image of a regular language over the generating set of the group under the
evaluation map. We show that admitting regular left-orders is stable under
extensions and wreath products and give a classification of the groups all
whose left-orders are regular left-orders. In addition, we prove that solvable
Baumslag-Solitar groups $B(1,n)$ admits a regular left-order if and only if
$n\geq -1$. Finally, Hermiller and Sunic showed that no free product admits a
regular left-order, however we show that if $A$ and $B$ are groups with regular
left-orders, then $(A*B)\times \mathbb{Z}$ admits a regular left-order.


Abstract:
  Let $(X, D_{X})$ be an arbitrary pointed stable curve of topological type
$(g_{X}, n_{X})$ over an algebraically closed field of characteristic $p>0$. We
prove that the generalized Hasse-Witt invariants of prime-to-$p$ cyclic
admissible coverings of $(X, D_{X})$ attain maximum. As applications, we obtain
an anabelian formula for $(g_{X}, n_{X})$, and prove that the field structures
associated to inertia subgroups of marked points can be reconstructed
group-theoretically from open continuous homomorphisms of admissible
fundamental groups. Moreover, the formula for maximum generalized Hasse-Witt
invariants and the result concerning reconstructions of field structures play
important roles in the theory of moduli spaces of fundamental groups developed
by the author of the present paper.


Abstract:
  Deep learning (DL) based language models achieve high performance on various
benchmarks for Natural Language Inference (NLI). And at this time, symbolic
approaches to NLI are receiving less attention. Both approaches (symbolic and
DL) have their advantages and weaknesses. However, currently, no method
combines them in a system to solve the task of NLI. To merge symbolic and deep
learning methods, we propose an inference framework called NeuralLog, which
utilizes both a monotonicity-based logical inference engine and a neural
network language model for phrase alignment. Our framework models the NLI task
as a classic search problem and uses the beam search algorithm to search for
optimal inference paths. Experiments show that our joint logic and neural
inference system improves accuracy on the NLI task and can achieve state-of-art
accuracy on the SICK and MED datasets.


Abstract:
  We continue the study of AdS loop amplitudes in the spectral representation
and in position space. We compute the finite coupling 4-point function in
position space for the large-$N$ conformal Gross Neveu model on $AdS_3$. The
resummation of loop bubble diagrams gives a result proportional to a tree-level
contact diagram. We show that certain families of fermionic Witten diagrams can
be easily computed from their companion scalar diagrams. Thus, many of the
results and identities of [1] are extended to the case of external fermions. We
derive a spectral representation for ladder diagrams in AdS. Finally, we
compute various bulk 2-point correlators, extending the results of [1].


Abstract:
  Transparent Conductive Oxides (TCOs) are a class of materials that combine
high optical transparency with high electrical conductivity. This property
makes them uniquely appealing as transparent-conductive electrodes in solar
cells and interesting for optoelectronics and infrared-plasmonics applications.
One of the new challenges that researchers and engineers are facing is merging
optical and electrical control in a single device for developing
next-generation photovoltaic, opto-electronic devices and energyefficient
solid-state lighting. In this work, we investigated the possible variations in
the dielectric properties of aluminum-doped ZnO (AZO) upon gating, by means of
Spectroscopic Ellipsometry (SE). We investigated the electrical-bias-dependent
optical response of thin AZO films fabricated by magnetron sputtering, within a
parallel-plane capacitor configuration. We address the possibility to control
their optical and electric performances by applying bias, monitoring the effect
of charge injection/depletion in the AZO layer by means of in-operando SE vs
applied gate voltage.


Abstract:
  We describe a framework to assemble permanent-magnet cubes in 3D-printed
frames to construct dipole, quadrupole, and solenoid magnets, whose field, in
the absence of iron, can be calculated analytically in three spatial
dimensions. Rotating closely spaced dipoles and quadrupoles in opposite
directions allows us to adjust the integrated strength of a multipole.
Contributions of unwanted harmonics are calculated and found to be moderate. We
then combine multiple magnets to construct beam-line modules: chicane, triplet
cell, and solenoid focusing system.


Abstract:
  We study the evolution of qubits amplitudes in a one-dimensional chain
consisting of three equidistantly spaced noninteracting qubits embedded in an
open waveguide. The study is performed in the frame of single-excitation
subspace, where the only qubit in the chain is initially excited. We show that
the dynamics of qubits amplitudes crucially depend on the value of $kd$, where
$k$ is the wave vector, $d$ is a distance between neighbor qubits. If $kd$ is
equal to an integer multiple of $\pi$, then the qubits are excited to a
stationary level. In this case, it is the dark states which prevent qubits from
decaying to zero even though they do not contribute to the output spectrum of
photon emission. For other values of $kd$ the excitations of qubits exhibit the
damping oscillations which represent the vacuum Rabi oscillations in a
three-qubit system. In this case, the output spectrum of photon radiation is
determined by a subradiant state which has the lowest decay rate. We also
investigated the case with the frequency of a central qubit being different
from that of the edge qubits. In this case, the qibits decay rates can be
controlled by the frequency detuning between the central and the edge qubits.


Abstract:
  We demonstrate that the recent measurement of the anomalous magnetic moment
of the muon and dark matter can be simultaneously explained within the Minimal
Supersymmetric Standard Model. Dark matter is a mostly-bino state, with the
relic abundance obtained via co-annihilations with either the sleptons or wino.
The most interesting regions of parameter space will be tested by the next
generation of dark matter direct detection experiments.


Abstract:
  In [arxiv:2106.02560] we proposed a reduced density matrix functional theory
(RDMFT) for calculating energies of selected eigenstates of interacting
many-fermion systems. Here, we develop a solid foundation for this so-called
$\boldsymbol{w}$-RDMFT and present the details of various derivations. First,
we explain how a generalization of the Ritz variational principle to ensemble
states with fixed weights $\boldsymbol{w}$ in combination with the constrained
search would lead to a universal functional of the one-particle reduced density
matrix. To turn this into a viable functional theory, however, we also need to
implement an exact convex relaxation. This general procedure includes Valone's
pioneering work on ground state RDMFT as the special case
$\boldsymbol{w}=(1,0,\ldots)$. Then, we work out in a comprehensive manner a
methodology for deriving a compact description of the functional's domain. This
leads to a hierarchy of generalized exclusion principle constraints which we
illustrate in great detail. By anticipating their future pivotal role in
functional theories and to keep our work self-contained, several required
concepts from convex analysis are introduced and discussed.


Abstract:
  Fuzzing is a technique widely used in vulnerability detection. The process
usually involves writing effective fuzz driver programs, which, when done
manually, can be extremely labor intensive. Previous attempts at automation
leave much to be desired, in either degree of automation or quality of output.
In this paper, we propose IntelliGen, a framework that constructs valid fuzz
drivers automatically. First, IntelliGen determines a set of entry functions
and evaluates their respective chance of exhibiting a vulnerability. Then,
IntelliGen generates fuzz drivers for the entry functions through hierarchical
parameter replacement and type inference. We implemented IntelliGen and
evaluated its effectiveness on real-world programs selected from the Android
Open-Source Project, Google's fuzzer-test-suite and industrial collaborators.
IntelliGen covered on average 1.08X-2.03X more basic blocks and 1.36X-2.06X
more paths over state-of-the-art fuzz driver synthesizers FUDGE and FuzzGen.
IntelliGen performed on par with manually written drivers and found 10 more
bugs.


Abstract:
  A $k$-regular graph is called a divisible design graph (DDG for short) if its
vertex set can be partitioned into $m$ classes of size $n$, such that two
distinct vertices from the same class have exactly $\lambda_1$ common
neighbors, and two vertices from different classes have exactly $\lambda_2$
common neighbors. $4\times n$-lattice graph is the line graph of $K_{4,n}$.
This graph is a DDG with parameters $(4n,n+2,n-2,2,4,n)$. In the paper we
consider DDGs with these parameters. We prove that if $n$ is odd then such
graph can only be a $4\times n$-lattice graph. If $n$ is even we characterise
all DDGs with such parameters. Moreover, we characterise all DDGs with
parameters $(4n,3n-2,3n-6,2n-2,4,n)$ which are related to $4\times n$-lattice
graphs.


Abstract:
  This paper presents a novel and flexible multi-task multi-layer Bayesian
mapping framework with readily extendable attribute layers. The proposed
framework goes beyond modern metric-semantic maps to provide even richer
environmental information for robots in a single mapping formalism while
exploiting existing inter-layer correlations. It removes the need for a robot
to access and process information from many separate maps when performing a
complex task and benefits from the correlation between map layers, advancing
the way robots interact with their environments. To this end, we design a
multi-task deep neural network with attention mechanisms as our front-end to
provide multiple observations for multiple map layers simultaneously. Our
back-end runs a scalable closed-form Bayesian inference with only logarithmic
time complexity. We apply the framework to build a dense robotic map including
metric-semantic occupancy and traversability layers. Traversability ground
truth labels are automatically generated from exteroceptive sensory data in a
self-supervised manner. We present extensive experimental results on publicly
available data sets and data collected by a 3D bipedal robot platform on the
University of Michigan North Campus and show reliable mapping performance in
different environments. Finally, we also discuss how the current framework can
be extended to incorporate more information such as friction, signal strength,
temperature, and physical quantity concentration using Gaussian map layers. The
software for reproducing the presented results or running on customized data is
made publicly available.


Abstract:
  The level set method is a widely used tool for solving reachability and
invariance problems. However, some shortcomings, such as the difficulties of
handling dissipation function and constructing terminal conditions for solving
the Hamilton-Jacobi partial differential equation, limit the application of the
level set method in some problems with non-affine nonlinear systems and
irregular target sets. This paper proposes a method that can effectively avoid
the above tricky issues and thus has better generality. In the proposed method,
the reachable or invariant sets with different time horizons are characterized
by some non-zero sublevel sets of a value function. This value function is not
obtained by solving a viscosity solution of the partial differential equation
but by recursion and interpolation approximation. At the end of this paper,
some examples are taken to illustrate the accuracy and generality of the
proposed method.


Abstract:
  We prove that a special variety of quadratically constrained quadratic
programs, occurring frequently in conjunction with the design of wave systems
obeying causality and passivity (i.e. systems with bounded response),
universally exhibit strong duality. Directly, the problem of continuum
("grayscale" or "effective medium") device design for any (complex) quadratic
wave objective governed by independent quadratic constraints can be solved as a
convex program. The result guarantees that performance limits for many common
physical objectives can be made nearly "tight", and suggests far-reaching
implications for problems in optics, acoustics, and quantum mechanics.


Abstract:
  PQ-type adjacency polytopes $\nabla^{\rm PQ}_G$ are lattice polytopes arising
from finite graphs $G$. There is a connection between $\nabla^{\rm PQ}_G$ and
the engineering problem known as power-flow study, which models the balance of
electric power on a network of power generation. In particular, the normalized
volume of $\nabla^{\rm PQ}_G$ plays a central role. In the present paper, we
focus the case where $G$ is a join graph. In fact, formulas of the
$h^*$-polynomial and the normalized volume of $\nabla^{\rm PQ}_G$ of a join
graph $G$ are presented. Moreover, we give explicit formulas of the
$h^*$-polynomial and the normalized volume of $\nabla^{\rm PQ}_G$ when $G$ is a
complete multipartite graph or a wheel graph.


Abstract:
  In this paper, given a linear system of equations A x = b, we are finding
locations in the plane to place objects such that sending waves from the source
points and gathering them at the receiving points solves that linear system of
equations. The ultimate goal is to have a fast physical method for solving
linear systems. The issue discussed in this paper is to apply a fast and
accurate algorithm to find the optimal locations of the scattering objects. We
tackle this issue by using asymptotic expansions for the solution of the
underlyingpartial differential equation. This also yields a potentially faster
algorithm than the classical BEM for finding solutions to the Helmholtz
equation.


Abstract:
  In this paper, we provide a precise description of the compatibility
conditions for the initial data so that one can show the existence and
uniqueness of regular short-time solution to the Neumann initial-boundary
problem of a class of Landau-Lifshitz-Gilbert system with spin-polarized
transport, which is a strong nonlinear coupled parabolic system with non-local
energy.


Abstract:
  We prove F\"{o}llmer's pathwise It\^{o} formula for a Banach space-valued
c\`{a}dl\`{a}g path. We also relax the assumption on the sequence of partitions
along which we treat the quadratic variation of a path.


Abstract:
  We show that the global assumptions on the H-flux in the definition of
T-duality for principal torus bundles by Bunke, Rumpf, and Schick are not
required. That is, these global conditions are implied by the Poincar\'e bundle
condition. This is proved using a new and equivalent "Thom class" formulation
of T-duality for principal torus bundles. We then generalise the local
formulation of T-duality by Bunke, Schick, and Spitzweck to the torus case.


Abstract:
  let $\widetilde{\bf U}^\imath$ be a quasi-split universal $\imath$quantum
group associated to a quantum symmetric pair $(\widetilde{\bf U},
\widetilde{\bf U}^\imath)$ of Kac-Moody type with a diagram involution $\tau$.
We establish the Serre-Lusztig relations for $\widetilde{\bf U}^\imath$
associated to a simple root $i$ such that $i \neq \tau i$, complementary to the
Serre-Lusztig relations associated to $i=\tau i$ which we obtained earlier. A
conjecture on braid group symmetries on $\widetilde{\bf U}^\imath$ associated
to $i$ disjoint from $\tau i$ is formulated.


Abstract:
  We propose a novel distributed monetary system called Hearsay that tolerates
both Byzantine and rational behavior without the need for agents to reach
consensus on executed transactions. Recent work [5, 10, 15] has shown that
distributed monetary systems do not require consensus and can operate using a
broadcast primitive with weaker guarantees, such as reliable broadcast.
However, these protocols assume that some number of agents may be Byzantine and
the remaining agents are perfectly correct. For the application of a monetary
system in which the agents are real people with economic interests, the
assumption that agents are perfectly correct may be too strong. We expand upon
this line of thought by weakening the assumption of correctness and instead
adopting a fault tolerance model which allows up to $t < \frac{N}{3}$ agents to
be Byzantine and the remaining agents to be rational. A rational agent is one
which will deviate from the protocol if it is in their own best interest. Under
this fault tolerance model, Hearsay implements a monetary system in which all
rational agents achieve agreement on executed transactions. Moreover, Hearsay
requires only a single broadcast per transaction. In order to incentivize
rational agents to behave correctly in Hearsay, agents are rewarded with
transaction fees for participation in the protocol and punished for noticeable
deviations from the protocol. Additionally, Hearsay uses a novel broadcast
primitive called Rational Reliable Broadcast to ensure that agents can
broadcast messages under Hearsay's fault tolerance model. Rational Reliable
Broadcast achieves equivalent guarantees to Byzantine Reliable Broadcast [7]
but can tolerate the presence of rational agents. To show this, we prove that
following the Rational Reliable Broadcast protocol constitutes a Nash
equilibrium between rational agents and may therefore be of independent
interest.


Abstract:
  In this work we consider the online control of a known linear dynamic system
with adversarial disturbance and adversarial controller cost. The goal in
online control is to minimize the regret, defined as the difference between
cumulative cost over a period $T$ and the cumulative cost for the best policy
from a comparator class. For the setting we consider, we generalize the
previously proposed online Disturbance Response Controller (DRC) to the
adaptive gradient online Disturbance Response Controller. Using the modified
controller, we present novel regret guarantees that improves the established
regret guarantees for the same setting. We show that the proposed online
learning controller is able to achieve intermediate intermediate regret rates
between $\sqrt{T}$ and $\log{T}$ for intermediate convex conditions, while it
recovers the previously established regret results for general convex
controller cost and strongly convex controller cost.


Abstract:
  Quantum measurement is ultimately a physical process, resulting from an
interaction between the measured system and a measuring apparatus. Considering
the physical process of measurement within a thermodynamic context naturally
raises the following question: How can the work and heat be interpreted? In the
present paper we model the measurement process for an arbitrary discrete
observable as a measurement scheme. Here the system to be measured is first
unitarily coupled with an apparatus and subsequently the compound system is
objectified with respect to a pointer observable, thus producing definite
measurement outcomes. The work can therefore be interpreted as the change in
internal energy of the compound system due to the unitary coupling. By the
first law of thermodynamics, the heat is the subsequent change in internal
energy of this compound due to pointer objectification. We argue that the
apparatus serves as a stable record for the measurement outcomes only if the
pointer observable commutes with the Hamiltonian and show that such
commutativity implies that the uncertainty of heat will necessarily be
classical.


Abstract:
  Crack microgeometries pose a paramount influence on effective elastic
characteristics and sonic responses. Geophysical exploration based on seismic
methods are widely used to assess and understand the presence of fractures.
Numerical simulation as a promising way for this issue, still faces some
challenges. With the rapid development of computers and computational
techniques, discrete-based numerical approaches with desirable properties have
been increasingly developed, but have not yet extensively applied to seismic
response simulation for complex fractured media. For this purpose, we apply the
coupled LSM-DFN model (Liu and Fu, 2020b) to examining the validity in
emulating elastic wave propagation and scattering in naturally-fractured media.
By comparing to the theoretical values, the implement of the schema is
validated with input parameters optimization. Moreover, dynamic elastic moduli
from seismic responses are calculated and compared with static ones from
quasi-static loading of uniaxial compression tests. Numerical results are
consistent with the tendency of theoretical predictions and available
experimental data. It shows the potential for reproducing the seismic responses
in complex fractured media and quantitatively investigating the correlations
and differences between static and dynamic elastic moduli.


Abstract:
  Three-point correlators of spinning operators admit multiple tensor
structures compatible with conformal symmetry. For conserved currents in three
dimensions, we point out that helicity commutes with conformal transformations
and we use this to construct three-point structures which diagonalize helicity.
In this helicity basis, OPE data is found to be diagonal for mean-field
correlators of conserved currents and stress tensor. Furthermore, we use
Lorentzian inversion formula to obtain anomalous dimensions for conserved
currents at bulk tree-level order in holographic theories, which we compare
with corresponding flat-space gluon scattering amplitudes.


Abstract:
  The essence of the microgrid cyber-physical system (CPS) lies in the cyclical
conversion of information flow and energy flow. Most of the existing coupling
models are modeled with static networks and interface structures, in which the
closed-loop data flow characteristic is not fully considered. It is difficult
for these models to accurately describe spatiotemporal deduction processes,
such as microgrid CPS attack identification, risk propagation, safety
assessment, defense control, and cascading failure. To address this problem, a
modeling method for the coupling relations of microgrid CPS driven by hybrid
spatiotemporal events is proposed in the present work. First, according to the
topological correlation and coupling logic of the microgrid CPS, the cyclical
conversion mechanism of information flow and energy flow is analyzed, and a
microgrid CPS architecture with multi-agents as the core is constructed. Next,
the spatiotemporal evolution characteristic of the CPS is described by hybrid
automata, and the task coordination mechanism of the multi-agent CPS terminal
is designed. On this basis, a discrete-continuous correlation and terminal
structure characteristic representation method of the CPS based on
heterogeneous multi-groups are then proposed. Finally, four spatiotemporal
events, namely state perception, network communication, intelligent
decision-making, and action control, are defined. Considering the constraints
of the temporal conversion of information flow and energy flow, a microgrid CPS
coupling model is established, the effectiveness of which is verified by
simulating false data injection attack (FDIA) scenarios.


Abstract:
  Assessment and reporting of skills is a central feature of many digital
learning platforms. With students often using multiple platforms,
cross-platform assessment has emerged as a new challenge. While technologies
such as Learning Tools Interoperability (LTI) have enabled communication
between platforms, reconciling the different skill taxonomies they employ has
not been solved at scale. In this paper, we introduce and evaluate a
methodology for finding and linking equivalent skills between platforms by
utilizing problem content as well as the platform's clickstream data. We
propose six models to represent skills as continuous real-valued vectors and
leverage machine translation to map between skill spaces. The methods are
tested on three digital learning platforms: ASSISTments, Khan Academy, and
Cognitive Tutor. Our results demonstrate reasonable accuracy in skill
equivalency prediction from a fine-grained taxonomy to a coarse-grained one,
achieving an average recall@5 of 0.8 between the three platforms. Our skill
translation approach has implications for aiding in the tedious, manual process
of taxonomy to taxonomy mapping work, also called crosswalks, within the
tutoring as well as standardized testing worlds.


Abstract:
  The ability to transfer coherent quantum information between systems is a
fundamental component of quantum technologies and leads to coherent
correlations within the global quantum process. However correlation structures
in quantum channels are less studied than those in quantum states. Motivated by
recent techniques in randomized benchmarking, we develop a range of results for
efficient estimation of correlations within a bipartite quantum channel. We
introduce sub-unitarity measures that are invariant under local changes of
basis, generalize the unitarity of a channel, and allow for the analysis of
coherent information exchange within channels. Using these, we show that
unitarity is monogamous, and provide a novel information-disturbance relation.
We then define a notion of correlated unitarity that quantifies the
correlations within a given channel. Crucially, we show that this measure is
strictly bounded on the set of separable channels and therefore provides a
witness of non-separability. Finally, we describe how such measures for
effective noise channels can be efficiently estimated within different
randomized benchmarking protocols. We find that the correlated unitarity can be
estimated in a SPAM-robust manner for any separable quantum channel, and show
that a benchmarking/tomography protocol with mid-circuit resets can reliably
witness non-separability for sufficiently small reset errors. The tools we
develop provide information beyond that obtained via simultaneous randomized
benchmarking and so could find application in the analysis of cross-talk and
coherent errors in quantum devices.


Abstract:
  In this work, we want to learn to model the dynamics of similar yet distinct
groups of interacting objects. These groups follow some common physical laws
that exhibit specificities that are captured through some vectorial
description. We develop a model that allows us to do conditional generation
from any such group given its vectorial description. Unlike previous work on
learning dynamical systems that can only do trajectory completion and require a
part of the trajectory dynamics to be provided as input in generation time, we
do generation using only the conditioning vector with no access to generation
time's trajectories. We evaluate our model in the setting of modeling human
gait and, in particular pathological human gait.


Abstract:
  Although monitoring and covering are fundamental goals of a wireless sensor
network (WSN), the accidental death of sensors or the running out of their
energy would result in holes in the WSN. Such holes have the potential to
disrupt the primary functions of WSNs. This paper investigates the hole
detection and healing problems in hybrid WSNs with non-identical sensor sensing
ranges. In particular, we aim to propose centralized algorithms for detecting
holes in a given region and maximizing the area covered by a WSN in the
presence of environmental obstacles. To precisely identify the boundary of the
holes, we use an additively weighted Voronoi diagram and a polynomial-time
algorithm.Furthermore, since this problem is known to be computationally
difficult, we propose a centralized greedy 1/2-approximation algorithm to
maximize the area covered by sensors. Finally, we implement the algorithms and
run simulations to show that our approximation algorithm efficiently covers the
holes by moving the mobile sensors.


Abstract:
  Toward achieving robust and defensive neural networks, the robustness against
the weight parameters perturbations, i.e., sharpness, attracts attention in
recent years (Sun et al., 2020). However, sharpness is known to remain a
critical issue, "scale-sensitivity." In this paper, we propose a novel
sharpness measure, Minimum Sharpness. It is known that NNs have a specific
scale transformation that constitutes equivalent classes where functional
properties are completely identical, and at the same time, their sharpness
could change unlimitedly. We define our sharpness through a minimization
problem over the equivalent NNs being invariant to the scale transformation. We
also develop an efficient and exact technique to make the sharpness tractable,
which reduces the heavy computational costs involved with Hessian. In the
experiment, we observed that our sharpness has a valid correlation with the
generalization of NNs and runs with less computational cost than existing
sharpness measures.


Abstract:
  We ask the question: to what extent can recent large-scale language and image
generation models blend visual concepts? Given an arbitrary object, we identify
a relevant object and generate a single-sentence description of the blend of
the two using a language model. We then generate a visual depiction of the
blend using a text-based image generation model. Quantitative and qualitative
evaluations demonstrate the superiority of language models over classical
methods for conceptual blending, and of recent large-scale image generation
models over prior models for the visual depiction.


Abstract:
  This technical report presents a Systematic Literature Review (SLR) study
that focuses on identifying and classifying the recent research practices
pertaining to CPS development through MDE approaches. The study evaluates 140
research papers published during 2010-2018. Accordingly, a comprehensive
analysis of various MDE approaches used in the development life-cycle of CPS is
presented. Furthermore, the study identifies the research gaps and areas that
need more investigation. The contribution helps researchers and practitioners
to get an overall understanding of the research trends and existing challenges
for further research/development.


Abstract:
  Dark energy stars research is an issue of great interest since recent
astronomical observations with respect to measurements in distant supernovas,
cosmic microwave background and weak gravitational lensing confirm that the
universe is undergoing a phase of accelerated expansion and this cosmological
behavior is caused by the presence of a cosmic fluid which has a strong
negative pressure that allows to explain the expanding universe. In this paper,
we obtained new relativistic stellar configurations within the framework of
Einstein-Gauss-Bonnet (EGB) gravity considering negative anisotropic pressures
and the equation of state pr={\omega}\r{ho} where pr is the radial pressure,
{\omega} is the dark energy parameter, and \r{ho} is the dark energy density.
We have chosen a modified version of metric potential proposed by
Korkina-Orlyanskii (1991). For the new solutions we checked that the radial
pressure, metric coefficients, energy density and anisotropy are well defined
and are regular in the interior of the star and are dependent of the values of
the Gauss-Bonnet coupling constant. The solutions found can be used in the
development of dark energy stars models satisfying all physical acceptability
conditions, but the causality condition and strong energy condition cannot be
satisfied.


Abstract:
  Establishing and approaching the fundamental limit of orbital angular
momentum (OAM) multiplexing are necessary and increasingly urgent for current
multiple-input multiple-output research. In this work, we elaborate the
fundamental limit in terms of independent scattering channels (or degrees of
freedom of scattered fields) through angular-spectral analysis, in conjunction
with a rigorous Green function method. The scattering channel limit is
universal for arbitrary spatial mode multiplexing, which is launched by a
planar electromagnetic device, such as antenna, metasurface, etc, with a
predefined physical size. As a proof of concept, we demonstrate both
theoretically and experimentally the limit by a metasurface hologram that
transforms orthogonal OAM modes to plane-wave modes scattered at critically
separated angular-spectral regions. Particularly, a minimax optimization
algorithm is applied to suppress angular spectrum aliasing, achieving good
performances in both full-wave simulation and experimental measurement at
microwave frequencies. This work offers a theoretical upper bound and
corresponding approach route for engineering designs of OAM multiplexing.


Abstract:
  This paper considers the problem of the valuation for integer numbers of the
zeta function and of five other functions which are naturally associated to it.
A relatively elementary approach is exposed, which closely connects this still
partially open problem to five themes of parity: the notions of parity of a
function and of parity of the degree of a polynomial are here related to the
distinctions of parity concerning the natural argument of the six considered
functions as well as the integer numbers of which some inverse powers are
summed. The adopted method essentially aims at enabling the students in
mathematics to have an entry into this problem.


Abstract:
  Neural Ordinary Differential Equations (NODEs), a framework of
continuous-depth neural networks, have been widely applied, showing exceptional
efficacy in coping with some representative datasets. Recently, an augmented
framework has been successfully developed for conquering some limitations
emergent in application of the original framework. Here we propose a new class
of continuous-depth neural networks with delay, named as Neural Delay
Differential Equations (NDDEs), and, for computing the corresponding gradients,
we use the adjoint sensitivity method to obtain the delayed dynamics of the
adjoint. Since the differential equations with delays are usually seen as
dynamical systems of infinite dimension possessing more fruitful dynamics, the
NDDEs, compared to the NODEs, own a stronger capacity of nonlinear
representations. Indeed, we analytically validate that the NDDEs are of
universal approximators, and further articulate an extension of the NDDEs,
where the initial function of the NDDEs is supposed to satisfy ODEs. More
importantly, we use several illustrative examples to demonstrate the
outstanding capacities of the NDDEs and the NDDEs with ODEs' initial value.
Specifically, (1) we successfully model the delayed dynamics where the
trajectories in the lower-dimensional phase space could be mutually
intersected, while the traditional NODEs without any argumentation are not
directly applicable for such modeling, and (2) we achieve lower loss and higher
accuracy not only for the data produced synthetically by complex models but
also for the real-world image datasets, i.e., CIFAR10, MNIST, and SVHN. Our
results on the NDDEs reveal that appropriately articulating the elements of
dynamical systems into the network design is truly beneficial to promoting the
network performance.


Abstract:
  The difference in the density of states for up- and down-spin electrons in a
ferromagnet (F) results in spin-dependent scattering of electrons at a
ferromagnet / nonmagnetic (F/N) interface. In a F/N/F spin-valve, this causes a
current-independent difference in resistance ($\Delta R$) between antiparallel
(AP) and parallel (P) magnetization states. Giant magnetoresistance (GMR),
$\Delta R = R(AP) - R(P)$, is positive due to increased scattering of majority
and minority spin-electrons in the AP-state. If N is substituted for a
superconductor (S), there exists a competition between GMR and the
superconducting spin-valve effect: in the AP-state the net magnetic exchange
field acting on S is lowered and the superconductivity is reinforced meaning
$R(AP)$ decreases. For current-perpendicular-to-plane (CPP) spin-valves,
existing experimental studies show that GMR dominates ($\Delta R>0$) over the
superconducting spin valve effect ($\Delta R<0$) [J. Y. Gu et al., Phys. Rev. B
66, 140507(R) (2002)]. Here, however, we report a crossover from GMR ($\Delta R
> 0$) to the superconducting spin valve effect ($\Delta R < 0$) in CPP F/S/F
spin-valves as the superconductor thickness decreases below a critical value.


Abstract:
  We consider Markov Decision Processes (MDPs) in which every stationary policy
induces the same graph structure for the underlying Markov chain and further,
the graph has the following property: if we replace each recurrent class by a
node, then the resulting graph is acyclic. For such MDPs, we prove the
convergence of the stochastic dynamics associated with a version of optimistic
policy iteration (OPI), suggested in Tsitsiklis (2002), in which the values
associated with all the nodes visited during each iteration of the OPI are
updated.


Abstract:
  Forecast of football outcomes in terms of Home Win, Draw and Away Win relies
largely on ex ante probability elicitation of these events and ex post
verification of them via computation of probability scoring rules (Brier,
Ranked Probability, Logarithmic, Zero-One scores). Usually, appraisal of the
quality of forecasting procedures is restricted to reporting mean score values.
The purpose of this article is to propose additional tools of verification,
such as score decompositions into several components of special interest.
Graphical and numerical diagnoses of reliability and discrimination and kindred
statistical methods are presented using different techniques of binning (fixed
thresholds, quantiles, logistic and iso regression). These procedures are
illustrated on probability forecasts for the outcomes of the UEFA Champions
League (C1) at the end of the group stage based on typical Poisson regression
models with reasonably good results in terms of reliability as compared to
those obtained from bookmaker odds and whatever the technique used. Links with
research in machine learning and different areas of application (meteorology,
medicine) are discussed.


Abstract:
  In this work, we present an analysis tool to help golf beginners compare
their swing motion with experts' swing motion. The proposed application
synchronizes videos with different swing phase timings using the latent
features extracted by a neural network-based encoder and detects key frames
where discrepant motions occur. We visualize synchronized image frames and 3D
poses that help users recognize the difference and the key factors that can be
important for their swing skill improvement.


Abstract:
  Translation between natural language and source code can help software
development by enabling developers to comprehend, ideate, search, and write
computer programs in natural language. Despite growing interest from the
industry and the research community, this task is often difficult due to the
lack of large standard datasets suitable for training deep neural models,
standard noise removal methods, and evaluation benchmarks. This leaves
researchers to collect new small-scale datasets, resulting in inconsistencies
across published works. In this study, we present CoDesc -- a large parallel
dataset composed of 4.2 million Java methods and natural language descriptions.
With extensive analysis, we identify and remove prevailing noise patterns from
the dataset. We demonstrate the proficiency of CoDesc in two complementary
tasks for code-description pairs: code summarization and code search. We show
that the dataset helps improve code search by up to 22\% and achieves the new
state-of-the-art in code summarization. Furthermore, we show CoDesc's
effectiveness in pre-training--fine-tuning setup, opening possibilities in
building pretrained language models for Java. To facilitate future research, we
release the dataset, a data processing tool, and a benchmark at
\url{https://github.com/csebuetnlp/CoDesc}.


Abstract:
  The double copy is a well-established relationship between gravity and gauge
theories. It relates perturbative scattering amplitudes as well as classical
solutions, and recently there has been mounting evidence that it also applies
to non-perturbative information. In this paper, we consider the holonomy
properties of manifolds in gravity and prescribe a single copy of gravitational
holonomy that differs from the holonomy in gauge theory. We discuss specific
cases and give examples where the single copy holonomy group is reduced. Our
results may prove useful in extending the classical double copy. We also
clarify previous misconceptions in the literature regarding gravitational
Wilson lines and holonomy.


Abstract:
  The mass spectra of isovector $\Upsilon$, $\psi$, $\phi$, and $\omega$ meson
resonances are investigated, in the AdS/QCD and information entropy setups. The
differential configurational entropy is employed to obtain the mass spectra of
radial $S$-wave resonances, with higher excitation levels, in each one of these
meson families, whose respective first undisclosed states are discussed and
matched up to candidates in the Particle Data Group.


Abstract:
  Here we consider a one-dimensional $q$-state Potts model with an external
magnetic field and an anisotropic interaction that selects neighboring sites
that are in the spin state 1. The present model exhibits an unusual behavior in
the low-temperature region, where we observe an anomalous vigorous change in
the entropy for a given temperature. There is a steep behavior at a given
temperature in entropy as a function of temperature, quite similar to
first-order discontinuity, but there is no jump in the entropy. Similarly,
second derivative quantities like specific heat and magnetic susceptibility
also exhibit a strong acute peak rather similar to second-order phase
transition divergence, but once again there is no singularity at this point.
Correlation length also confirms this anomalous behavior at the same given
temperature, showing a strong and sharp peak which easily one may confuse with
a divergence. The temperature where occurs this anomalous feature we call
pseudo-critical temperature. We have analyzed physical quantities, like
correlation length, entropy, magnetization, specific heat, magnetic
susceptibility, and distant pair correlation functions. Furthermore, we analyze
the pseudo-critical exponent that satisfy a class of universality previously
identified in the literature for other one-dimensional models, these
pseudo-critical exponents are: for correlation length $\nu=1$, specific heat
$\alpha=3$ and magnetic susceptibility $\mu=3$.


Abstract:
  We show that the connectedness of the set of parameters for which the
over-rotation interval of a bimodal interval map is constant. In other words,
the over-rotation interval is a monotone function of a bimodal interval map.


Abstract:
  Motivated by applications in cognitive radio networks, we consider the
decentralized multi-player multi-armed bandit problem, without collision nor
sensing information. We propose Randomized Selfish KL-UCB, an algorithm with
very low computational complexity, inspired by the Selfish KL-UCB algorithm,
which has been abandoned as it provably performs sub-optimally in some cases.
We subject Randomized Selfish KL-UCB to extensive numerical experiments showing
that it far outperforms state-of-the-art algorithms in almost all environments,
sometimes by several orders of magnitude, and without the additional knowledge
required by state-of-the-art algorithms. We also emphasize the potential of
this algorithm for the more realistic dynamic setting, and support our claims
with further experiments. We believe that the low complexity and high
performance of Randomized Selfish KL-UCB makes it the most suitable for
implementation in practical systems amongst known algorithms.


Abstract:
  Chronometric dating is becoming increasingly important in areas such as the
Origin and evolution of Life on Earth and other planets, Origin and evolution
of the Earth and the Solar System... Electron Spin Resonance (ESR) dating is
based on exploiting effects of contamination by chemicals or ionizing
radiation, on ancient matter through its absorption spectrum and lineshape.
Interpreting absorption spectra as probability density functions (pdf), we use
the notion of Information Theory (IT) distance allowing us to position the
measured lineshape with respect to standard limiting pdf's (Lorentzian and
Gaussian). This paves the way to perform dating when several interaction
patterns between unpaired spins are present in geologic, planetary, meteorite
or asteroid matter namely classical-dipolar (for ancient times) and
quantum-exchange-coupled (for recent times). In addition, accurate bounds to
age are provided by IT from the evaluation of distances with respect to the
Lorentz and Gauss distributions. Dating arbitrary periods of
times~\cite{Anderson} and exploiting IT to introduce rigorous and accurate date
values might have interesting far reaching implications not only in Geophysics,
Geochronology~\cite{Bahain}, Planetary Science but also in Mineralogy,
Archaeology, Biology, Anthropology~\cite{Aitken},
Paleoanthropology~\cite{Taylor,Richter}...


Abstract:
  Let $p$ be a fixed odd prime. Let $E$ be an elliptic curve defined over a
number field $F$ with good supersingular reduction at all primes above $p$. We
study both the classical and plus/minus Selmer groups over the cyclotomic
$\mathbb{Z}_p$-extension of $F$. In particular, we give sufficient conditions
for these Selmer groups to not contain a non-trivial sub-module of finite
index. Furthermore, when $p$ splits completely in $F$, we calculate the Euler
characteristics of the plus/minus Selmer groups over the compositum of all
$\mathbb{Z}_p$-extensions of $F$ when they are defined.


Abstract:
  For a pair of bounded linear Hilbert space operators $A$ and $B$ one
considers the Lebesgue type decompositions of $B$ with respect to $A$ into an
almost dominated part and a singular part, analogous to the Lebesgue
decomposition for a pair of measures (in which case one speaks of an absolutely
continuous and a singular part). A complete parametrization of all Lebesgue
type decompositions will be given, and the uniqueness of such decompositions
will be characterized. In addition, it will be shown that the almost dominated
part of $B$ in a Lebesgue type decomposition has an abstract Radon-Nikodym
derivative with respect to the operator $A$.


Abstract:
  This paper presents a novel microwave photonic (MWP) radar scheme that is
capable of optically generating and processing broadband linear
frequency-modulated (LFM) microwave signals without using any radio-frequency
(RF) sources. In the transmitter, a broadband LFM microwave signal is generated
by controlling the period-one (P1) oscillation of an optically injected
semiconductor laser. After targets reflection, photonic de-chirping is
implemented based on a dual-drive Mach-Zehnder modulator (DMZM), which is
followed by a low-speed analog-to-digital converter (ADC) and digital signal
processer (DSP) to reconstruct target information. Without the limitations of
external RF sources, the proposed radar has an ultra-flexible tunability, and
the main operating parameters are adjustable, including central frequency,
bandwidth, frequency band, and temporal period. In the experiment, a fully
photonics-based Ku-band radar with a bandwidth of 4 GHz is established for
high-resolution detection and inverse synthetic aperture radar (ISAR) imaging.
Results show that a high range resolution reaching ~1.88 cm, and a
two-dimensional (2D) imaging resolution as high as ~1.88 cm x ~2.00 cm are
achieved with a sampling rate of 100 MSa/s in the receiver. The flexible
tunability of the radar is also experimentally investigated. The proposed radar
scheme features low cost, simple structure, and high reconfigurability, which,
hopefully, is to be used in future multifunction adaptive and miniaturized
radars.


Abstract:
  Comprehensive control of the domain wall nucleation process is crucial for
spin-based emerging technologies ranging from random-access and storage-class
memories over domain-wall logic concepts to nanomagnetic logic. In this work,
focused Ga+ ion-irradiation is investigated as an effective means to control
domain-wall nucleation in Ta/CoFeB/MgO nanostructures. We show that analogously
to He+ irradiation, it is not only possible to reduce the perpendicular
magnetic anisotropy but also to increase it significantly, enabling new,
bidirectional manipulation schemes. First, the irradiation effects are assessed
on film level, sketching an overview of the dose-dependent changes in the
magnetic energy landscape. Subsequent time-domain nucleation characteristics of
irradiated nanostructures reveal substantial increases in the anisotropy fields
but surprisingly small effects on the measured energy barriers, indicating
shrinking nucleation volumes. Spatial control of the domain wall nucleation
point is achieved by employing focused irradiation of pre-irradiated magnets,
with the diameter of the introduced circular defect controlling the coercivity.
Special attention is given to the nucleation mechanisms, changing from a
Stoner-Wohlfarth particle's coherent rotation to depinning from an anisotropy
gradient. Dynamic micromagnetic simulations and related measurements are used
in addition to model and analyze this depinning-dominated magnetization
reversal.


Abstract:
  The bulk-boundary correspondence in one dimension asserts that the physical
quantities defined in the bulk and at the edge are connected, as well
established in the argument for electric polarization. Recently, a spectral
bulk-boundary correspondence (SBBC), an extended version of the conventional
bulk-boundary correspondence to energy-dependent spectral functions, such as
Green's functions, has been proposed in chiral symmetric systems, in which the
chiral operator anticommutes with the Hamiltonian. In this study, we extend the
SBBC to a system with impurity scattering and dynamical self-energies,
regardless of the presence or absence of a gap in the energy spectrum.
Moreover, the SBBC is observed to hold even in a system without chiral
symmetry, which substantially generalizes its concept. The SBBC is demonstrated
with concrete models, such as superconducting nanowires and a
Su-Schrieffer-Heeger model. Its potential applications and certain remaining
issues are also discussed.


Abstract:
  6-14 micron Spitzer spectra obtained at 6 epochs between April 2005 and
October 2008 are used to determine temporal changes in dust features associated
with Sakurai's Object (V4334 Sgr), a low mass post-AGB star that has been
forming dust in an eruptive event since 1996. The obscured carbon-rich
photosphere is surrounded by a 40-milliarcsec torus and 32 arcsec PN. An
initially rapid mid-infrared flux decrease stalled after 21 April 2008.
Optically-thin emission due to nanometre-sized SiC grains reached a minimum in
October 2007, increased rapidly between 21-30 April 2008 and more slowly to
October 2008. 6.3-micron absorption due to PAHs increased throughout. 20
micron-sized SiC grains might have contributed to the 6-7 micron absorption
after May 2007. Mass estimates based on the optically-thick emission agree with
those in the absorption features if the large SiC grains formed before May 1999
and PAHs formed in April-June 1999. Estimated masses of PAH and large-SiC
grains in October 2008, were 3 x 10 -9 Msun and 10 -8 Msun, respectively. Some
of the submicron-sized silicates responsible for a weak 10 micron absorption
feature are probably located within the PN because the optical depth decreased
between October 2007 and October 2008. 6.9 micron absorption assigned to ~10
micron-sized crystalline melilite silicates increased between April 2005 and
October 2008. Abundance and spectroscopic constraints are satisfied if about
2.8 per cent cent of the submicron-sized silicates coagulated to form
melilites. This figure is similar to the abundance of melilite-bearing
calcium-aluminium-rich inclusions in chondritic meteorites.


Abstract:
  Lithium-ion battery manufacturing is a highly complicated process with
strongly coupled feature interdependencies, a feasible solution that can
analyse feature variables within manufacturing chain and achieve reliable
classification is thus urgently needed. This article proposes a random forest
(RF)-based classification framework, through using the out of bag (OOB)
predictions, Gini changes as well as predictive measure of association (PMOA),
for effectively quantifying the importance and correlations of battery
manufacturing features and their effects on the classification of electrode
properties. Battery manufacturing data containing three intermediate product
features from the mixing stage and one product parameter from the coating stage
are analysed by the designed RF framework to investigate their effects on both
the battery electrode active material mass load and porosity. Illustrative
results demonstrate that the proposed RF framework not only achieves the
reliable classification of electrode properties but also leads to the effective
quantification of both manufacturing feature importance and correlations. This
is the first time to design a systematic RF framework for simultaneously
quantifying battery production feature importance and correlations by three
various quantitative indicators including the unbiased feature importance (FI),
gain improvement FI and PMOA, paving a promising solution to reduce model
dimension and conduct efficient sensitivity analysis of battery manufacturing.


Abstract:
  Deep neural networks (DNNs) in the infinite width/channel limit have received
much attention recently, as they provide a clear analytical window to deep
learning via mappings to Gaussian Processes (GPs). Despite its theoretical
appeal, this viewpoint lacks a crucial ingredient of deep learning in finite
DNNs, laying at the heart of their success -- feature learning. Here we
consider DNNs trained with noisy gradient descent on a large training set and
derive a self consistent Gaussian Process theory accounting for strong
finite-DNN and feature learning effects. Applying this to a toy model of a
two-layer linear convolutional neural network (CNN) shows good agreement with
experiments. We further identify, both analytical and numerically, a sharp
transition between a feature learning regime and a lazy learning regime in this
model. Strong finite-DNN effects are also derived for a non-linear two-layer
fully connected network. Our self consistent theory provides a rich and
versatile analytical framework for studying feature learning and other non-lazy
effects in finite DNNs.


Abstract:
  This research seeks to measure the impact of people with technological
knowledge on regional digital economic activity and the implications of
prosperous cities' contagion effect on neighbouring ones. The focus of this
study is quantitative, cross-sectional, and its design is correlational-causal.
This study covers seven micro-regions of Minas Gerais in Brazil, organized in
89 municipalities, with 69% urban population and 31% rural. The data used
consisted of 4,361 observations obtained in the Brazilian government's public
repositories, organized into panel data, and analysed using partial least
squares, micro-regional spatial regression, and identification patterns with
machine learning. The confirmatory analysis of the regression test establishes
a significant impact between the CE's technological knowledge and the digital
economic activity AED through a predictive value of R2 = .749, \b{eta} = .867,
p = .000 (value t = 18,298). With high notoriety among the variables, public
and private university institutions (IUPP), professors with doctorates and
masters (DCNT), and information technology occupations (CBO). A geographic
concentration of companies that demand technology-based skills had effects by
slowing down the development of small municipalities, suggesting the
development of new government technology initiatives that support new business
models based on technological knowledge.


Abstract:
  This paper addresses the approximation of fractional harmonic maps. Besides a
unit-length constraint, one has to tackle the difficulty of nonlocality. We
establish weak compactness results for critical points of the fractional
Dirichlet energy on unit-length vector fields. We devise and analyze numerical
methods for the approximation of various partial differential equations related
to fractional harmonic maps. The compactness results imply the convergence of
numerical approximations. Numerical examples on spin chain dynamics and point
defects are presented to demonstrate the effectiveness of the proposed methods.


Abstract:
  We show the relation between three non trivial sectors of M2-brane theory
formulated in the LCG connected among them by canonical transformations. These
sectors correspond to the supermembrane theory formulated on a $M_9\times T^2$
on three different constant three-form backgrounds: M2-brane with constant
$C_{-}$, M2-brane with constant $C_{\pm}$ and M2-brane with a generic constant
$C_3$ denoted as CM2-brane. The first two exhibit a purely discrete
supersymmetric spectrum once the central charge condition, or equivalently, the
corresponding flux condition has been turned on. The CM2-brane is conjectured
to share this spectral property once that fluxes $C_{\pm}$ are turned on. As
shown in [1] they are duals to three inequivalent sectors of the D2-branes with
specific worldvolume and background RR and NSNS quantization conditions on each
case.


Abstract:
  With the rapid growth of blockchain, an increasing number of users have been
attracted and many implementations have been refreshed in different fields.
Especially in the cryptocurrency investment field, blockchain technology has
shown vigorous vitality. However, along with the rise of online business,
numerous fraudulent activities, e.g., money laundering, bribery, phishing, and
others, emerge as the main threat to trading security. Due to the openness of
Ethereum, researchers can easily access Ethereum transaction records and smart
contracts, which brings unprecedented opportunities for Ethereum scams
detection and analysis. This paper mainly focuses on the Ponzi scheme, a
typical fraud, which has caused large property damage to the users in Ethereum.
By verifying Ponzi contracts to maintain Ethereum's sustainable development, we
model Ponzi scheme identification and detection as a node classification task.
In this paper, we first collect target contracts' transactions to establish
transaction networks and propose a detecting model based on graph convolutional
network (GCN) to precisely distinguishPonzi contracts. Experiments on different
real-world Ethereum datasets demonstrate that our proposed model has promising
results compared with general machine learning methods to detect Ponzi schemes.


Abstract:
  Strain engineering of perovskite quantum dots (pQDs) enables widely-tunable
photonic device applications. However, manipulation at the single-emitter level
has never been attempted. Here, we present a tip-induced control approach
combined with tip-enhanced photoluminescence (TEPL) spectroscopy to engineer
strain, bandgap, and emission quantum yield of a single pQD. Single
CsPbBr$_{x}$I$_{3-x}$ pQDs are clearly resolved through hyperspectral TEPL
imaging with $\sim$10 nm spatial resolution. The plasmonic tip then directly
applies pressure to a single pQD to facilitate a bandgap shift up to $\sim$62
meV with Purcell-enhanced PL quantum yield as high as $\sim$10$^5$ for the
strain-induced pQD. Furthermore, by systematically modulating the tip-induced
compressive strain of a single pQD, we achieve dynamical bandgap engineering in
a reversible manner. In addition, we facilitate the quantum dot coupling for a
pQD ensemble with $\sim$0.8 GPa tip pressure at the nanoscale. Our approach
presents a new strategy to tune the nano-opto-electro-mechanical properties of
pQDs at the single-crystal level.


Abstract:
  Deep neural networks with batch normalization (BN-DNNs) are invariant to
weight rescaling due to their normalization operations. However, using weight
decay (WD) benefits these weight-scale-invariant networks, which is often
attributed to an increase of the effective learning rate when the weight norms
are decreased. In this paper, we demonstrate the insufficiency of the previous
explanation and investigate the implicit biases of stochastic gradient descent
(SGD) on BN-DNNs to provide a theoretical explanation for the efficacy of
weight decay. We identity two implicit biases of SGD on BN-DNNs: 1) the weight
norms in SGD training remain constant in the continuous-time domain and keep
increasing in the discrete-time domain; 2) SGD optimizes weight vectors in
fully-connected networks or convolution kernels in convolution neural networks
by updating components lying in the input feature span, while leaving those
components orthogonal to the input feature span unchanged. Thus, SGD without WD
accumulates weight noise orthogonal to the input feature span, and cannot
eliminate such noise. Our empirical studies corroborate the hypothesis that
weight decay suppresses weight noise that is left untouched by SGD.
Furthermore, we propose to use weight rescaling (WRS) instead of weight decay
to achieve the same regularization effect, while avoiding performance
degradation of WD on some momentum-based optimizers. Our empirical results on
image recognition show that regardless of optimization methods and network
architectures, training BN-DNNs using WRS achieves similar or better
performance compared with using WD. We also show that training with WRS
generalizes better compared to WD, on other computer vision tasks.


Abstract:
  We study stationary black holes in the presence of an external strong
magnetic field. In the case where the gravitational backreaction of the
magnetic field is taken into account, such an scenario is well described by the
Ernst-Wild solution to Einstein-Maxwell field equations, representing a
charged, stationary black hole immersed in a Melvin magnetic universe. This
solution, however, describes a physical situation only in the region close to
the black hole. This is due to the following two reasons: Firstly, Melvin
spacetime is not asymptotically locally flat; secondly, the non-static
Ernst-Wild solution is not even asymptotically Melvin due to the infinite
extension of its ergoregion. All this might seem to be an obstruction to
address an scenario like this; for instance, it seems to be an obstruction to
compute conserved charges as this usually requires a clear notion of
asymptotia. Here, we circumvent this obstruction by providing a method to
compute the conserved charges of such a black hole by restricting the analysis
to the near horizon region. We compute the Wald entropy, the mass, the electric
charge, and the angular momentum of stationary black holes in highly magnetized
environments from the horizon perspective, finding results in complete
agreement with other formalisms.


Abstract:
  In this thesis, we try to resolve the alleged problem of non-unitarity for
various anisotropic cosmological models. Using Wheeler-DeWitt formulation, we
quantized the anisotropic models with variable spatial curvature, namely
Bianchi II and Bianchi VI. We showed that Hamiltonian of respective models
admits self-adjoint extension, thus unitary evolution. We further extended the
unitary evolution for higher dimensional anisotropic cosmological models. We
also showed that unitarity of the model preserves the Noether symmetry but
loses the scale invariance. In later part of this thesis, we showed the
equivalence of Jordan and Einstein frames at the quantum level for the flat FRW
model. Obtained expressions for wave packet matched exactly in both the frames
indicating the equivalence of frames. We also showed that equivalence holds
true for various anisotropic quantum cosmological models, i.e., Bianchi I, V,
X, LRS Bianchi-I and Kantowski-Sachs models.


Abstract:
  In this work, we study music/video cross-modal recommendation, i.e.
recommending a music track for a video or vice versa. We rely on a
self-supervised learning paradigm to learn from a large amount of unlabelled
data. We rely on a self-supervised learning paradigm to learn from a large
amount of unlabelled data. More precisely, we jointly learn audio and video
embeddings by using their co-occurrence in music-video clips. In this work, we
build upon a recent video-music retrieval system (the VM-NET), which originally
relies on an audio representation obtained by a set of statistics computed over
handcrafted features. We demonstrate here that using audio representation
learning such as the audio embeddings provided by the pre-trained MuSimNet,
OpenL3, MusicCNN or by AudioSet, largely improves recommendations. We also
validate the use of the cross-modal triplet loss originally proposed in the
VM-NET compared to the binary cross-entropy loss commonly used in
self-supervised learning. We perform all our experiments using the Music Video
Dataset (MVD).


Abstract:
  We prove a conjecture of Zagier about the inverse of a $(K-1)\times (K-1)$
matrix $A=A_{K}$ using elementary methods. This formula allows one to express
the the product of single zeta values $\zeta(2r)\zeta(2K+1-2r)$, $1\leq r\leq
K-1$, in terms of the double zeta values $\zeta(2r,2K+1-2r)$, $1\leq r\leq K-1$
and $\zeta(2K+1)$.


Abstract:
  We propose a new Lagrange multiplier approach to construct positivity
preserving schemes for parabolic type equations. The new approach introduces a
space-time Lagrange multiplier to enforce the positivity with the
Karush-Kuhn-Tucker (KKT) conditions. We then use a predictor-corrector approach
to construct a class of positivity schemes: with a generic semi-implicit or
implicit scheme as the prediction step, and the correction step, which enforces
the positivity, can be implemented with negligible cost. We also present a
modification which allows us to construct schemes which, in addition to
positivity preserving, is also mass conserving. This new approach is not
restricted to any particular spatial discretization and can be combined with
various time discretization schemes. We establish stability results for our
first- and second-order schemes under a general setting, and present ample
numerical results to validate the new approach.


Abstract:
  We present a simple regulator-type framework designed specifically for
modelling formation of dwarf galaxies. We explore sensitivity of model
predictions for the stellar mass--halo mass and stellar mass--metallicity
relations to different modelling choices and parameter values. Despite its
simplicity, when coupled with realistic mass accretion histories of haloes from
simulations and reasonable choices for model parameter values, the framework
can reproduce a remarkably broad range of observed properties of dwarf galaxies
over seven orders of magnitude in stellar mass. In particular, we show that the
model can simultaneously match observational constraints on the stellar
mass-halo mass relation, as well as observed relations between stellar mass and
gas phase and stellar metallicities, gas mass, size, and star formation rate,
as well as general form and diversity of star formation histories (SFHs) of
observed dwarf galaxies. The model can thus be used to predict photometric
properties of dwarf galaxies hosted by dark matter haloes in $N$-body
simulations, such as colors, surface brightnesses, and mass-to-light ratios and
to forward model observations of dwarf galaxies. We present examples of such
modelling and show that colors and surface brightness distributions of model
galaxies are in good agreement with observed distributions for dwarfs in recent
observational surveys. We also show that in contrast with the common
assumption, the absolute magnitude-halo mass relation is generally predicted to
have a non-power law form in the dwarf regime, and that the fraction of haloes
that host detectable ultrafaint galaxies is sensitive to reionization redshift
(zrei) and is predicted to be consistent with observations for zrei<~9.


Abstract:
  This paper is devoted to a fractional generalization of the Dirichlet
distribution. The form of the multivariate distribution is derived assuming
that the $n$ partitions of the interval $[0,W_n]$ are independent and
identically distributed random variables following the generalized
Mittag-Leffler distribution. The expected value and variance of the
one-dimensional marginal are derived as well as the form of its probability
density function. A related generalized Dirichlet distribution is studied that
provides a reasonable approximation for some values of the parameters. The
relation between this distribution and other generalizations of the Dirichlet
distribution is discussed. Monte Carlo simulations of the one-dimensional
marginals for both distributions are presented.


Abstract:
  Can machine learning help us make better decisions about a changing planet?
In this paper, we illustrate and discuss the potential of a promising corner of
machine learning known as _reinforcement learning_ (RL) to help tackle the most
challenging conservation decision problems. RL is uniquely well suited to
conservation and global change challenges for three reasons: (1) RL explicitly
focuses on designing an agent who _interacts_ with an environment which is
dynamic and uncertain, (2) RL approaches do not require massive amounts of
data, (3) RL approaches would utilize rather than replace existing models,
simulations, and the knowledge they contain. We provide a conceptual and
technical introduction to RL and its relevance to ecological and conservation
challenges, including examples of a problem in setting fisheries quotas and in
managing ecological tipping points. Four appendices with annotated code provide
a tangible introduction to researchers looking to adopt, evaluate, or extend
these approaches.


Abstract:
  In a news recommender system, a reader's preferences change over time. Some
preferences drift quite abruptly (short-term preferences), while others change
over a longer period of time (long-term preferences). Although the existing
news recommender systems consider the reader's full history, they often ignore
the dynamics in the reader's behavior. Thus, they cannot meet the demand of the
news readers for their time-varying preferences. In addition, the
state-of-the-art news recommendation models are often focused on providing
accurate predictions, which can work well in traditional recommendation
scenarios. However, in a news recommender system, diversity is essential, not
only to keep news readers engaged, but also to play a key role in a democratic
society. In this PhD dissertation, our goal is to build a news recommender
system to address these two challenges. Our system should be able to: (i)
accommodate the dynamics in reader behavior; and (ii) consider both accuracy
and diversity in the design of the recommendation model. Our news recommender
system can also work for unprofiled, anonymous and short-term readers, by
leveraging the rich side information of the news items and by including the
implicit feedback in our model. We evaluate our model with multiple evaluation
measures (both accuracy and diversity-oriented metrics) to demonstrate the
effectiveness of our methods.


Abstract:
  The search of new superhard materials has received a strong impulse by
industrial demands for low-cost alternatives to diamond and $c$-BN, such as
metal borides. In this Letter we introduce a new family of superhard materials,
"fused borophenes", containing 2D boron layers which are interlinked to form a
3D network. These materials, identified through a high-throughput scan of
BxC1-x structures, exhibit Vicker's hardnesses comparable to those of the best
commercial metal borides. Due to their low formation enthalpies, fused
borophenes could be synthesized by high-temperature methods, starting from
appropriate precursors, or through quenching of high-pressure phases.


Abstract:
  Network slicing is emerging as a promising method to provide sought-after
versatility and flexibility to cope with ever-increasing demands. To realize
such potential advantages and to meet the challenging requirements of various
network slices in an on-demand fashion, we need to develop an agile and
distributed mechanism for resource provisioning to different network slices in
a heterogeneous multi-resource multi-domain mobile network environment. We
formulate inter-domain resource provisioning to network slices in such an
environment as an optimization problem which maximizes social welfare among
network slice tenants (so that maximizing tenants' satisfaction), while
minimizing operational expenditures for infrastructure service providers at the
same time. To solve the envisioned problem, we implement an iterative auction
game among network slice tenants, on one hand, and a plurality of price-taking
subnet service providers, on the other hand. We show that the proposed solution
method results in a distributed privacy-saving mechanism which converges to the
optimal solution of the described optimization problem. In addition to
providing analytical results to characterize the performance of the proposed
mechanism, we also employ numerical evaluations to validate the results,
demonstrate convergence of the presented algorithm, and show the enhanced
performance of the proposed approach (in terms of resource utilization,
fairness and operational costs) against the existing solutions.


Abstract:
  We investigate the effect of thermal fluctuations on the two-particle
spectral function for a disordered $s$-wave superconductor in two dimensions,
focusing on the evolution of the collective amplitude and phase modes. We find
three main effects of thermal fluctuations: (a) the phase mode is softened with
increasing temperature reflecting the decrease of superfluid stiffness; (b)
remarkably, the non-dispersive collective amplitude modes at finite energy near
${\bf q}=[0,0]$ and ${\bf q}=[\pi,\pi]$ survive even in presence of thermal
fluctuations in the disordered superconductor; and (c) the scattering of the
thermally excited fermionic quasiparticles leads to low energy incoherent
spectral weight that forms a strongly momentum-dependent background halo around
the phase and amplitude collective modes and broadens them. Due to momentum and
energy conservation constraints, this halo has a boundary which disperses
linearly at low momenta and shows a strong dip near the $[\pi,\pi]$ point in
the Brillouin zone.


Abstract:
  We analyze the gravitational-wave signal GW190521 under the hypothesis that
it was generated by the merger of two nonspinning black holes on hyperbolic
orbits. The best configuration matching the data corresponds to two black holes
of source frame masses of $81^{+62}_{-25}M_\odot$ and $52^{+32}_{-32}M_\odot$
undergoing two encounters and then merging into an intermediate-mass black
hole. Under the hyperbolic merger hypothesis, we find an increase of one unit
in the recovered signal-to-noise ratio and a 14 e-fold increase in the maximum
likelihood value compared to a quasi-circular merger with precessing spins. We
conclude that our results support the first gravitational-wave detection from
the dynamical capture of two stellar-mass black holes.


Abstract:
  We investigate the problem of when big mapping class groups are generated by
involutions. Restricting our attention to the class of self-similar surfaces,
which are surfaces with self-similar ends space, as defined by Mann and Rafi,
and with 0 or infinite genus, we show that, when the set of maximal ends is
infinite, then the mapping class groups of these surfaces are generated by
involutions, normally generated by a single involution, and uniformly perfect.
In fact, we derive this statement as a corollary of the corresponding statement
for the homeomorphism groups of these surfaces. On the other hand, among
self-similar surfaces with one maximal end, we produce infinitely many examples
in which their big mapping class groups are neither perfect nor generated by
torsion elements. These groups also do not have the automatic continuity
property.


Abstract:
  We have undertaken a systematic study of FRI and FRII radio galaxies with the
upgraded Giant Metrewave Radio Telescope (uGMRT) and MeerKAT. The main goal is
to explore whether the unprecedented few $\mu$Jy sensitivity reached in the
range 550-1712 MHz at the resolution of $\sim4^{\prime\prime}-7^{\prime\prime}$
reveals new features in the radio emission which might need us to revise our
current classification scheme for classical radio galaxies. In this paper we
present the results for the first set of four radio galaxies, i.e. 4C 12.02, 4C
12.03, CGCG 044-046 and CGCG 021-063. The sources have been selected from the
4C sample with well-defined criteria, and have been imaged with the uGMRT in
the range 550-850 MHz (band 4) and with the MeerKAT in the range 856-1712 MHz
(L-band). Full resolution images are presented for all sources in the sample,
together with MeerKAT in-band spectral images. Additionally, the uGMRT-MeerKAT
spectral image and MeerKAT L-band polarisation structure are provided for CGCG
044-046. Our images contain a wealth of morphological details, such as
filamentary structure in the emission from the lobes, radio emission beyond the
hot-spots in three sources, and misalignments. We briefly discuss the overall
properties of CGCG 044-046 in the light of the local environment as well, and
show possible restarted activity in 4C 12.03 which needs to be confirmed. We
conclude that at least for the sources presented here, the classical FRI/FRII
morphological classification still holds with the current improved imaging
capabilities, but the richness in details also suggests caution in the
systematic morphological classification carried out with automatic procedures
in surveys with poorer sensitivity and angular resolution.


Abstract:
  Despite many proposed algorithms to provide robustness to deep learning (DL)
models, DL models remain susceptible to adversarial attacks. We hypothesize
that the adversarial vulnerability of DL models stems from two factors. The
first factor is data sparsity which is that in the high dimensional data space,
there are large regions outside the support of the data distribution. The
second factor is the existence of many redundant parameters in the DL models.
Owing to these factors, different models are able to come up with different
decision boundaries with comparably high prediction accuracy. The appearance of
the decision boundaries in the space outside the support of the data
distribution does not affect the prediction accuracy of the model. However,
they make an important difference in the adversarial robustness of the model.
We propose that the ideal decision boundary should be as far as possible from
the support of the data distribution.\par In this paper, we develop a training
framework for DL models to learn such decision boundaries spanning the space
around the class distributions further from the data points themselves.
Semi-supervised learning was deployed to achieve this objective by leveraging
unlabeled data generated in the space outside the support of the data
distribution. We measure adversarial robustness of the models trained using
this training framework against well-known adversarial attacks We found that
our results, other regularization methods and adversarial training also support
our hypothesis of data sparcity. We show that the unlabeled data generated by
noise using our framework is almost as effective as unlabeled data, sourced
from existing data sets or generated by synthesis algorithms, on adversarial
robustness. Our code is available at
https://github.com/MahsaPaknezhad/AdversariallyRobustTraining.


Abstract:
  The environmental performance of shared micromobility services compared to
private alternatives has never been assessed using an integrated modal Life
Cycle Assessment (LCA) relying on field data. Such an LCA is conducted on three
shared micromobility services in Paris - bikes, second-generation e-scooters,
and e-mopeds - and their private alternatives. Global warming potential,
primary energy consumption, and the three endpoint damages are calculated.
Sensitivity analyses on vehicle lifespan, shipping, servicing distance, and
electricity mix are conducted. Electric micromobility ranks between active
modes and personal ICE modes. Its impacts are globally driven by vehicle
manufacturing. Ownership does not affect directly the environmental
performance: the vehicle lifetime mileage does. Assessing the sole carbon
footprint leads to biased environmental decision-making, as it is not
correlated to the three damages: multicriteria LCA is mandatory to preserve the
planet. Finally, a major change of paradigm is needed to eco-design modern
transportation policies.


Abstract:
  The missing mass refers to the probability of elements not observed in a
sample, and since the work of Good and Turing during WWII, has been studied
extensively in many areas including ecology, linguistic, networks and
information theory.
  This work determines what is the \emph{maximal variance of the missing mass},
for any sample and alphabet sizes. The result helps in understanding the
missing mass concentration properties.


Abstract:
  A linear argument must be consumed exactly once in the body of its function.
A linear type system can verify the correct usage of resources such as file
handles and manually managed memory. But this verification requires
bureaucracy. This paper presents linear constraints, a front-end feature for
linear typing that decreases the bureaucracy of working with linear types.
Linear constraints are implicit linear arguments that are to be filled in
automatically by the compiler. Linear constraints are presented as a qualified
type system,together with an inference algorithm which extends GHC's existing
constraint solver algorithm. Soundness of linear constraints is ensured by the
fact that they desugar into Linear Haskell.


Abstract:
  One remarkable feature of Weyl semimetals is the manifestation of their
topological nature in the form of the Fermi-arc surface states. In a recent
calculation by \cite{Johansson2018}, the current-induced spin polarization or
Edelstein effect has been predicted, within the semiclassical Boltzmann theory,
to be strongly amplified in a Weyl semimetal TaAs due to the existence of the
Fermi arcs. Motivated by this result, we calculate the Edelstein response of an
effective model for an inversion-symmetry-breaking Weyl semimetal in the
presence of an interface using linear response theory. The scatterings from
scalar impurities are included and the vertex corrections are computed within
the self-consistent ladder approximation. At chemical potentials close to the
Weyl points, we find the surface states have a much stronger response near the
interface than the bulk states by about one to two orders of magnitude. At
higher chemical potentials, the surface states' response near the interface
decreases to be about the same order of magnitude as the bulk states' response.
We attribute this phenomenon to the decoupling between the Fermi arc states and
bulk states at energies close to the Weyl points. The surface states which are
effectively dispersing like a one-dimensional chiral fermion become nearly
nondissipative. This leads to a large surface vertex correction and, hence, a
strong enhancement of the surface states' Edelstein response.


Abstract:
  Unsupervised domain adaptation (UDA) becomes more and more popular in
tackling real-world problems without ground truth of the target domain. Though
a mass of tedious annotation work is not needed, UDA unavoidably faces the
problem how to narrow the domain discrepancy to boost the transferring
performance. In this paper, we focus on UDA for semantic segmentation task.
Firstly, we propose a style-independent content feature extraction mechanism to
keep the style information of extracted features in the similar space, since
the style information plays a extremely slight role for semantic segmentation
compared with the content part. Secondly, to keep the balance of pseudo labels
on each category, we propose a category-guided threshold mechanism to choose
category-wise pseudo labels for self-supervised learning. The experiments are
conducted using GTA5 as the source domain, Cityscapes as the target domain. The
results show that our model outperforms the state-of-the-arts with a noticeable
gain on cross-domain adaptation tasks.


Abstract:
  Explicating implicit reasoning (i.e. warrants) in arguments is a
long-standing challenge for natural language understanding systems. While
recent approaches have focused on explicating warrants via crowdsourcing or
expert annotations, the quality of warrants has been questionable due to the
extreme complexity and subjectivity of the task. In this paper, we tackle the
complex task of warrant explication and devise various methodologies for
collecting warrants. We conduct an extensive study with trained experts to
evaluate the resulting warrants of each methodology and find that our
methodologies allow for high-quality warrants to be collected. We construct a
preliminary dataset of 6,000 warrants annotated over 600 arguments for 3
debatable topics. To facilitate research in related downstream tasks, we
release our guidelines and preliminary dataset.


Abstract:
  Based on a description of an amorphous solid as a collection of coupled
nanosize molecular clusters referred as basic blocks, we analyse the
statistical properties of its Hamiltonian. The information is then used to
derive the ensemble averaged density of the vibrational states (non-phonon)
which turns out to be a Gaussian in the bulk of the spectrum and an Airy
function in the low frequency regime. A comparison with experimental data for
five glasses confirms validity of our theoretical predictions.


Abstract:
  Schramm-Loewner evolution arises from driving the Loewner differential
equation with $\sqrt{\kappa}B$ where $\kappa > 0$ is a fixed parameter. In this
paper, we drive the Loewner differential equation with non-constant random
parameter, i.e. $d\xi(t) = \sqrt{\kappa_t}dB_t$. We show that in case
$\kappa_t$ is bounded below or above $8$, the construction still yields a
continuous trace. This is true in both cases either when driving the forward
equation or the backward equation by $\sqrt{\kappa_t}dB_t$. In the case of the
forward equation, we develop a new argument to show the result, without the
need of analysing the time-reversed equation.


Abstract:
  We consider the problem of learning latent features (aka embedding) for users
and items in a recommendation setting. Given only a user-item interaction
graph, the goal is to recommend items for each user. Traditional approaches
employ matrix factorization-based collaborative filtering methods. Recent
methods using graph convolutional networks (e.g., LightGCN) achieve
state-of-the-art performance. They learn both user and item embedding. One
major drawback of most existing methods is that they are not inductive; they do
not generalize for users and items unseen during training. Besides, existing
network models are quite complex, difficult to train and scale. Motivated by
LightGCN, we propose a graph convolutional network modeling approach for
collaborative filtering CF-GCN. We solely learn user embedding and derive item
embedding using light variant CF-LGCN-U performing neighborhood aggregation,
making it scalable due to reduced model complexity. CF-LGCN-U models naturally
possess the inductive capability for new items, and we propose a simple
solution to generalize for new users. We show how the proposed models are
related to LightGCN. As a by-product, we suggest a simple solution to make
LightGCN inductive. We perform comprehensive experiments on several benchmark
datasets and demonstrate the capabilities of the proposed approach.
Experimental results show that similar or better generalization performance is
achievable than the state of the art methods in both transductive and inductive
settings.


Abstract:
  Based on Lorentz invariance and Born reciprocity invariance, the canonical
quantization of Special Relativity (SR) has been shown to provide a unified
origin for the existence of Dirac's Hamiltonian and a self adjoint time
operator that circumvents Pauli's objection. As such, this approach restores to
Quantum Mechanics (QM) the treatment of space and time on an equivalent footing
as that of momentum and energy. Second quantization of the time operator field
follows step by step that of the Dirac Hamiltonian field. It introduces the
concept of time quanta, in a similar way to the energy quanta in Quantum Field
Theory (QFT). An early connection is found allready in Feshbach's unified
theory of nuclear reactions. Its possible relevance in current developments
such as Feshbach resonances in the fields of cold atom systems, of
Bose-Einstein condensates and in the problem of time in Quantum Gravity is
noted. .


Abstract:
  We reframe common tasks in jet physics in probabilistic terms, including jet
reconstruction, Monte Carlo tuning, matrix element - parton shower matching for
large jet multiplicity, and efficient event generation of jets in complex,
signal-like regions of phase space. We also introduce Ginkgo, a simplified,
generative model for jets, that facilitates research into these tasks with
techniques from statistics, machine learning, and combinatorial optimization.
We review some of the recent research in this direction that has been enabled
with Ginkgo. We show how probabilistic programming can be used to efficiently
sample the showering process, how a novel trellis algorithm can be used to
efficiently marginalize over the enormous number of clustering histories for
the same observed particles, and how dynamic programming, A* search, and
reinforcement learning can be used to find the maximum likelihood clustering in
this enormous search space. This work builds bridges with work in hierarchical
clustering, statistics, combinatorial optmization, and reinforcement learning.


Abstract:
  Voice Activity Detection (VAD) is not easy task when the input audio signal
is noisy, and it is even more complicated when the input is not even an audio
recording. This is the case with Silent Speech Interfaces (SSI) where we record
the movement of the articulatory organs during speech, and we aim to
reconstruct the speech signal from this recording. Our SSI system synthesizes
speech from ultrasonic videos of the tongue movement, and the quality of the
resulting speech signals are evaluated by metrics such as the mean squared
error loss function of the underlying neural network and the Mel-Cepstral
Distortion (MCD) of the reconstructed speech compared to the original. Here, we
first demonstrate that the amount of silence in the training data can have an
influence both on the MCD evaluation metric and on the performance of the
neural network model. Then, we train a convolutional neural network classifier
to separate silent and speech-containing ultrasound tongue images, using a
conventional VAD algorithm to create the training labels from the corresponding
speech signal. In the experiments our ultrasound-based speech/silence separator
achieved a classification accuracy of about 85\% and an AUC score around 86\%.


Abstract:
  We propose a classical emulation methodology to emulate quantum phenomena
arising from any non-classical quantum state using only a finite set of
coherent states or their statistical mixtures. This allows us to successfully
reproduce well-known quantum effects using resources that can be much more
feasibly generated in the laboratory. We present a simple procedure to
experimentally carry out quantum-state emulation with coherent states that also
applies to any general set of classical states that are easier to generate, and
demonstrate its capabilities in observing the Hong-Ou-Mandel effect, violating
Bell inequalities and witnessing quantum non-classicality.


Abstract:
  Robots may soon play a role in higher education by augmenting learning
environments and managing interactions between instructors and learners.
Little, however, is known about how the presence of robots in the learning
environment will influence academic integrity. This study therefore
investigates if and how college students cheat while engaged in a collaborative
sorting task with a robot. We employed a 2x2 factorial design to examine the
effects of cheating exposure (exposure to cheating or no exposure) and task
clarity (clear or vague rules) on college student cheating behaviors while
interacting with a robot. Our study finds that prior exposure to cheating on
the task significantly increases the likelihood of cheating. Yet, the tendency
to cheat was not impacted by the clarity of the task rules. These results
suggest that normative behavior by classmates may strongly influence the
decision to cheat while engaged in an instructional experience with a robot.


Abstract:
  Multiple small- to middle-scale cities, mostly located in northern China,
became epidemic hotspots during the second wave of the spread of COVID-19 in
early 2021. Despite qualitative discussions of potential social-economic
causes, it remains unclear how this pattern could be accounted for from a
quantitative approach. Through the development of an urban epidemic hazard
index (EpiRank), we came up with a mathematical explanation for this
phenomenon. The index is constructed from epidemic simulations on a multi-layer
transportation network model on top of local SEIR transmission dynamics, which
characterizes intra- and inter-city compartment population flow with a detailed
mathematical description. Essentially, we argue that these highlighted cities
possess greater epidemic hazards due to the combined effect of large regional
population and small inter-city transportation. The proposed index, dynamic and
applicable to different epidemic settings, could be a useful indicator for the
risk assessment and response planning of urban epidemic hazards in China; the
model framework is modularized and can be adapted for other nations without
much difficulty.


Abstract:
  Simulations, along with other similar applications like virtual worlds and
video games, require computational models of intelligence that generate
realistic and credible behavior for the participating synthetic characters.
Cognitive architectures, which are models of the fixed structure underlying
intelligent behavior in both natural and artificial systems, provide a
conceptually valid common basis, as evidenced by the current efforts towards a
standard model of the mind, to generate human-like intelligent behavior for
these synthetic characters. Sigma is a cognitive architecture and system that
strives to combine what has been learned from four decades of independent work
on symbolic cognitive architectures, probabilistic graphical models, and more
recently neural models, under its graphical architecture hypothesis. Sigma
leverages an extended form of factor graphs towards a uniform grand unification
of not only traditional cognitive capabilities but also key non-cognitive
aspects, creating unique opportunities for the construction of new kinds of
cognitive models that possess a Theory-of-Mind and that are perceptual,
autonomous, interactive, affective, and adaptive. In this paper, we will
introduce Sigma along with its diverse capabilities and then use three distinct
proof-of-concept Sigma models to highlight combinations of these capabilities:
(1) Distributional reinforcement learning models in; (2) A pair of adaptive and
interactive agent models that demonstrate rule-based, probabilistic, and social
reasoning; and (3) A knowledge-free exploration model in which an agent
leverages only architectural appraisal variables, namely attention and
curiosity, to locate an item while building up a map in a Unity environment.