Abstract:
  We analyze existing measurements of [Fe/H] and [$\alpha$/Fe] for individual
red giant branch (RGB) stars in the Giant Stellar Stream (GSS) of M31 to
determine whether spatial abundance gradients are present. These measurements
were obtained from low- ($R \sim 3000$) and moderate- ($R \sim 6000$)
resolution Keck/DEIMOS spectroscopy using spectral synthesis techniques as part
of the Elemental Abundances in M31 survey. From a sample of 62 RGB stars
spanning the GSS at 17, 22, and 33 projected kpc, we measure a [Fe/H] gradient
of $-$0.018 $\pm$ 0.003 dex kpc$^{-1}$ and negligible [$\alpha$/Fe] gradient
with M31-centric radius. We investigate GSS abundance patterns in the outer
halo using additional [Fe/H] and [$\alpha$/Fe] measurements for 6 RGB stars
located along the stream at 45 and 58 projected kpc. These abundances provide
tentative evidence that the trends in [Fe/H] and [$\alpha$/Fe] beyond 40 kpc in
the GSS are consistent with those within 33 kpc. We also compare the GSS
abundances to 65 RGB stars located along the possibly related Southeast (SE)
shelf substructure at 12 and 18 projected kpc. The abundances of the GSS and SE
shelf are consistent, supporting a common origin hypothesis, although this
interpretation may be complicated by the presence of [Fe/H] gradients in the
GSS. We discuss the abundance patterns in the context of photometric studies
from the literature and explore implications for the properties of the GSS
progenitor, suggesting that the high $\langle$[$\alpha$/Fe]$\rangle$ of the GSS
(+0.40 $\pm$ 0.05 dex) favors a major merger scenario for its formation.


Abstract:
  In this paper, we present an approach to study the behavior of compliant
plates in granular media and optimize the performance of a robot that utilizes
this technique for mobility. From previous work and fundamental tests on thin
plate force generation inside granular media, we introduce an origami-inspired
mechanism with non-linear compliance in the joints that can be used in granular
propulsion. This concept utilizes one-sided joint limits to create an
asymmetric gait cycle that avoids more complicated alternatives often found in
other swimming/digging robots. To analyze its locomotion as well as its shape
and propulsive force, we utilize granular Resistive Force Theory (RFT) as a
starting point. Adding compliance to this theory enables us to predict the
time-based evolution of compliant plates when they are dragged and rotated. It
also permits more rational design of swimming robots where fin design variables
may be optimized against the characteristics of the granular medium. This is
done using a Python-based dynamic simulation library to model the deformation
of the plates and optimize aspects of the robot's gait. Finally, we prototype
and test robot with a gait optimized using the modelling techniques mentioned
above.


Abstract:
  CsV$_3$Sb$_5$ is a newly discovered Kagome superconductor that attracts great
interest due to its topological nontrivial band structure and the coexistence
of superconductivity and charge-density-wave (CDW) with many exotic properties.
Here, we report the detailed characterization of the CDW gap in high-quality
CsV$_3$Sb$_5$ single crystals using high-resolution angle-resolved
photoemission spectroscopy. We find that the CDW gap is strongly momentum
dependent. While gapped around the $M$ point, the electronic states remain
gapless around the $\Gamma$ point and along the $\Gamma$-$K$ direction. Such
momentum dependence indicates that the CDW is driven by the scattering of
electrons between neighboring $M$ points, where the band structure hosts
multiple saddle points and the density of state diverges near the Fermi level.
Our observations of the partially gapped Fermi surface and strongly
momentum-dependent CDW gap not only provide a foundation for uncovering the
mechanism of CDW in CsV$_3$Sb$_5$, but also shed light on the understanding of
how the CDW coexists with superconductivity in this topological Kagome
superconductor.


Abstract:
  The double descent curve is one of the most intriguing properties of deep
neural networks. It contrasts the classical bias-variance curve with the
behavior of modern neural networks, occurring where the number of samples nears
the number of parameters. In this work, we explore the connection between the
double descent phenomena and the number of samples in the deep neural network
setting. In particular, we propose a construction which augments the existing
dataset by artificially increasing the number of samples. This construction
empirically mitigates the double descent curve in this setting. We reproduce
existing work on deep double descent, and observe a smooth descent into the
overparameterized region for our construction. This occurs both with respect to
the model size, and with respect to the number epochs.


Abstract:
  To achieve high precision and accuracy for mass measurements of exotic nuclei
by Time-of-flight (TOF) methods: high-resolution beam-line magnetic-rigidity
time-of-flight (B$\rho$-TOF) and in-ring isochronous mass spectrometry (IMS), a
large-area electrostatic detector which possesses high position resolution and
good timing resolution at the same time is developed at the Rare-RI Ring in
RIBF, RIKEN Nishina Center, Japan. Besides TOF mass measurements, the detector
system will also be used for heavy ion beam trajectory monitoring or momentum
measurements for both beam-line and in-ring at the Rare-RI Ring. The position
and timing measurements of heavy ions are performed by detecting the secondary
electrons (SEs) emitted from a conversion foil during the passage of the ion.
The SEs are accelerated and bent with an angle of $90^{\circ}$ by electrostatic
fields onto a micro-channel-plate (MCP) electron multiplier which is coupled
with a position-sensitive delay-line anode. The dependence of the timing and
position resolution on applied high voltages of the detector potential plates
has been studied systematically via simulation and experimentally. An
isochronous condition of secondary electron transmission in the electrostatic
field of the detector is chosen to optimize the structure of the detector for
high performance. The best achieved timing resolution is less than 50 ps (in
$\sigma$) and position resolution $\sim$ 1 mm (in $\sigma$) for 2 dimensions,
respectively. The overall efficiency is $\sim$ 95$\%$ for heavy ion beam and
$\sim$ 75$\%$ for $\alpha$ particle from $^{241}$Am source.


Abstract:
  A theorem of A. and C. R\'enyi on periodic entire functions states that an
entire function $f(z) $ must be periodic if $ P(f(z)) $ is periodic, where $
P(z) $ is a non-constant polynomial. By extending this theorem, we can answer
some open questions related to the conjecture of C. C. Yang concerning
periodicity of entire functions. Moreover, we give more general forms for this
conjecture and we prove, in particular, that $ f(z) $ is periodic if either $
P(f(z)) f^{(k)}(z) $ or $ P(f(z))/f^{(k)}(z) $ is periodic, provided that $
f(z) $ has a finite Picard exceptional value. We also investigate the
periodicity of $ f(z) $ when $ f(z)^{n}+a_{1} f^{\prime}(z)+\cdots+a_{k}
f^{(k)}(z) $ is periodic. In all our results, the possibilities for the period
of $ f(z) $ are determined precisely.


Abstract:
  We study the time evolution of an incompressible fluid with axial symmetry
without swirl when the vorticity is sharply concentrated on $N$ annuli of radii
$\approx$ $r_0$ and thickness $\epsilon$. We prove that when $r_0= |\log
\epsilon|^\alpha, \,\, \alpha>2$, the vorticity field of the fluid converges as
$\epsilon \to 0$ to the point vortex model, at least for a small but positive
time. This result generalizes a previous paper that assumed a power law for the
relation between $r_0$ and $\epsilon$.


Abstract:
  We propose a method for detecting structural changes in a city using images
captured from vehicular mounted cameras over traversals at two different times.
We first generate 3D point clouds for each traversal from the images and
approximate GNSS/INS readings using Structure-from-Motion (SfM). A direct
comparison of the two point clouds for change detection is not ideal due to
inaccurate geo-location information and possible drifts in the SfM. To
circumvent this problem, we propose a deep learning-based non-rigid
registration on the point clouds which allows us to compare the point clouds
for structural change detection in the scene. Furthermore, we introduce a dual
thresholding check and post-processing step to enhance the robustness of our
method. We collect two datasets for the evaluation of our approach. Experiments
show that our method is able to detect scene changes effectively, even in the
presence of viewpoint and illumination differences.


Abstract:
  It can be argued that the ordinary description of the reversible quantum
process between two one-to-one correlated measurement outcomes is incomplete
because, by not specifying the direction of causality, it allows causal
structures that violate the time symmetry that is required of a reversible
process. This also means that it can be completed simply by time-symmetrizing
it, namely by requiring that the initial and final measurements evenly
contribute to the selection of their correlated pair of outcomes. This leaves
the description unaltered but shows that it is the quantum superposition of
unobservable time-symmetrized instances whose causal structure is completely
defined. Each instance consists of a causal loop: the final measurement that
changes backwards in time the input state of the unitary transformation that
leads to the state immediately before it. In former works, we have shown that
such loops exactly explain the quantum computational speedup and quantum
nonlocality. In this work we show that they lead to a completion of the
anthropic principle that allows a universe evolution with quantum speedup.


Abstract:
  The nonlinear coefficient {\gamma} of the step-index single-mode fiber (SMF)
is inversely proportional to the effective area of that fiber. An analytical
expression for the effective area of the hybrid HE11-mode of the SMF is derived
for the first time, using its exact electromagnetic field expressions. The
effective area is found in terms of generalized hypergeometric functions. A
simpler expression is also deduced under the weakly-guided fiber approximation,
and is found to be in agreement with established theory.


Abstract:
  Much work has been done in the area of the cluster weighted model (CWM),
which extends the finite mixture of regression model to include modelling of
the covariates. Although many types of distributions have been considered for
both the response and covariates, to our knowledge skewed distributions have
not yet been considered in this paradigm. Herein, a family of 24 novel CWMs are
considered which allows both the covariates and response variables to be
modelled using one of four skewed distributions, or the normal distribution.
Parameter estimation is performed using the expectation-maximization algorithm
and both simulated and real data are used for illustration.


Abstract:
  We applied the Koch snowflake fractal antenna in planning calibration of the
Five-hundred-meter Aperture Spherical radio Telescope (FAST), hypothesizing
second-order fractal primary reflectors can optimize the orientated sensitivity
of the telescope. Meanwhile, on the grounds of NASA Science Working Group
Report in 1984, we reexamine the strategy of Search for Extraterrestrial
Intelligence (SETI). A mathematical analysis of the radar equation will be
performed in the first section, aiming to make it convenient to design a
receiver system that can detect activities of an extraterrestrial civilization,
according to the observable region of the narrowband. Taking advantage of the
inherent potential of FAST, we simulate the theoretical detection of a
Kardashev Type I civilization by a snowflake-selected reflecting area.


Abstract:
  We consider the planar circular restricted three-body problem (PCRTBP), as a
model for the motion of a spacecraft relative to the Earth-Moon system. We
focus on the Lagrange equilibrium points $L_1$ and $L_2$. There are families of
Lyapunov periodic orbits around either $L_1$ or $L_2$, forming Lyapunov
manifolds. There also exist homoclinic orbits to the Lyapunov manifolds around
either $L_1$ or $L_2$, as well as heteroclinic orbits between the Lyapunov
manifold around $L_1$ and the one around $L_2$. The motion along the
homoclinic/heteroclinic orbits can be described via the scattering map, which
gives the future asymptotic of a homoclinic orbit as a function of the past
asymptotic. In contrast with the more customary Melnikov theory, we do not need
to assume that the asymptotic orbits have a special nature (periodic,
quasi-periodic, etc.).
  We add a non-conservative, time-dependent perturbation, as a model for a
thrust applied to the spacecraft for some duration of time, or for some other
effect, such as solar radiation pressure. We compute the first order
approximation of the perturbed scattering map, in terms of fast convergent
integrals of the perturbation along homoclinic/heteroclinic orbits of the
unperturbed system. As a possible application, this result can be used to
determine the trajectory of the spacecraft upon using the thrust.


Abstract:
  One of the serious threats related to climate change is an increase in the
number and severity of extreme weather events. A prominent example are
hurricanes, which result from rising coastal temperatures. Such extreme weather
events can cause extensive damages in infrastructure systems and, potentially,
destroy components in electricity transmission networks, which in turn can lead
to major blackouts. In our recent work, we study the risk of hurricane-induced
cascading failures in power systems of the U.S. East Coast using historical
wind field data sets. For this purpose, we model the destruction of overhead
transmission lines during hurricanes, where each failing line causes a
rerouting of power flow in the system. New power flows can overload additional
lines, which are then automatically deactivated and thereby cause another
rerouting of power flow and so on. Ultimately, a cascade of failures can unfold
that can black out large parts of the power system.


Abstract:
  Formal power series products appear in nonlinear control theory when systems
modeled by Chen-Fliess series are interconnected to form new systems. In fields
like adaptive control and learning systems, the coefficients of these formal
power series are estimated sequentially with real-time data. The main goal of
the present article is to prove the continuity and analyticity of such products
with respect to several natural (locally convex) topologies on spaces of
locally convergent formal power series in order to establish foundational
properties behind these technologies. In addition, it is shown that a
transformation group central to describing the output feedback connection is in
fact an analytic Lie group in this setting with certain regularity properties.


Abstract:
  Laser cooled ions trapped in a linear Paul trap are long-standing ideal
candidates for realizing quantum simulation, especially of many-body systems.
The properties that contribute to this also provide the opportunity to
demonstrate unexpected quantum phenomena in few-body systems. A pair of ions
interacting in such traps exchange vibrational quanta through the Coulomb
interaction. This linear interaction can be anharmonically modulated by an
elementary coupling to the internal two-level structure of one of the ions.
Driven by thermal energy in the passively coupled oscillators, which are
themselves coupled to the internal ground states of the ions, the nonlinear
interaction autonomously and unconditionally generates entanglement between the
mechanical modes of the ions. We examine this counter-intuitive thermally
induced entanglement for several experimentally feasible model systems, and
propose parameter regimes where state of the art trapped ion systems can
produce such phenomena. In addition, we demonstrate a multiqubit enhancement of
such thermally induced entanglement.


Abstract:
  Span-extraction reading comprehension models have made tremendous advances
enabled by the availability of large-scale, high-quality training datasets.
Despite such rapid progress and widespread application, extractive reading
comprehension datasets in languages other than English remain scarce, and
creating such a sufficient amount of training data for each language is costly
and even impossible. An alternative to creating large-scale high-quality
monolingual span-extraction training datasets is to develop multilingual
modeling approaches and systems which can transfer to the target language
without requiring training data in that language. In this paper, in order to
solve the scarce availability of extractive reading comprehension training data
in the target language, we propose a multilingual extractive reading
comprehension approach called XLRC by simultaneously modeling the existing
extractive reading comprehension training data in a multilingual environment
using self-adaptive attention and multilingual attention. Specifically, we
firstly construct multilingual parallel corpora by translating the existing
extractive reading comprehension datasets (i.e., CMRC 2018) from the target
language (i.e., Chinese) into different language families (i.e., English).
Secondly, to enhance the final target representation, we adopt self-adaptive
attention (SAA) to combine self-attention and inter-attention to extract the
semantic relations from each pair of the target and source languages.
Furthermore, we propose multilingual attention (MLA) to learn the rich
knowledge from various language families. Experimental results show that our
model outperforms the state-of-the-art baseline (i.e., RoBERTa_Large) on the
CMRC 2018 task, which demonstrate the effectiveness of our proposed
multi-lingual modeling approach and show the potentials in multilingual NLP
tasks.


Abstract:
  The commoditization of Malware-as-a-Service (MaaS) allows criminals to obtain
financial benefits at a low risk and with little technical background. One such
popular product in the underground economy is ransomware. In ransomware
attacks, data from infected systems is held hostage (encrypted) until a fee is
paid to the criminals. This modus operandi disrupts legitimate businesses,
which may become unavailable until the data is restored. A recent blackmailing
strategy adopted by criminals is to leak data online from the infected systems
if the ransom is not paid. Besides reputational damage, data leakage might
produce further economical losses due to fines imposed by data protection laws.
Thus, research on prevention and recovery measures to mitigate the impact of
such attacks is needed to adapt existing countermeasures to new strains.
  In this work, we perform an in-depth analysis of Avaddon, a ransomware
offered in the underground economy as an affiliate program business. This has
infected and leaked data from at least 23 organizations. Additionally, it runs
Distributed Denial-of-Service (DDoS) attacks against victims that do not pay
the ransom. We first provide an analysis of the criminal business model from
the underground economy. Then, we identify and describe its technical
capabilities. We provide empirical evidence of links between this variant and a
previous family, suggesting that the same group was behind the development and,
possibly, the operation of both campaigns.
  Finally, we describe a method to decrypt files encrypted with Avaddon in real
time. We implement and test the decryptor in a tool that can recover the
encrypted data from an infected system, thus mitigating the damage caused by
the ransomware. The tool is released open-source so it can be incorporated in
existing Antivirus engines.


Abstract:
  Whereas much of the success of the current generation of neural language
models has been driven by increasingly large training corpora, relatively
little research has been dedicated to analyzing these massive sources of
textual data. In this exploratory analysis, we delve deeper into the Common
Crawl, a colossal web corpus that is extensively used for training language
models. We find that it contains a significant amount of undesirable content,
including hate speech and sexually explicit content, even after filtering
procedures. We discuss the potential impacts of this content on language models
and conclude with future research directions and a more mindful approach to
corpus collection and analysis.


Abstract:
  In this work, we address the question of the adaptability of artificial
neural networks (NNs) used for impairments mitigation in optical transmission
systems. We demonstrate that by using well-developed techniques based on the
concept of transfer learning, we can efficaciously retrain NN-based equalizers
to adapt to the changes in the transmission system, using just a fraction (down
to 1%) of the initial training data or epochs. We evaluate the capability of
transfer learning to adapt the NN to changes in the launch power, modulation
format, symbol rate, or even fiber plants (different fiber types and lengths).
The numerical examples utilize the recently introduced NN equalizer consisting
of a convolutional layer coupled with bi-directional long-short term memory
(biLSTM) recurrent NN element. Our analysis focuses on long-haul coherent
optical transmission systems for two types of fibers: the standard single-mode
fiber (SSMF) and the TrueWave Classic (TWC) fiber. We underline the specific
peculiarities that occur when transferring the learning in coherent optical
communication systems and draw the limits for the transfer learning efficiency.
Our results demonstrate the effectiveness of transfer learning for the fast
adaptation of NN architectures to different transmission regimes and scenarios,
paving the way for engineering flexible and universal solutions for
nonlinearity mitigation.


Abstract:
  Model-predictive-control (MPC) offers an optimal control technique to
establish and ensure that the total operation cost of multi-energy systems
remains at a minimum while fulfilling all system constraints. However, this
method presumes an adequate model of the underlying system dynamics, which is
prone to modelling errors and is not necessarily adaptive. This has an
associated initial and ongoing project-specific engineering cost. In this
paper, we present an on- and off-policy multi-objective reinforcement learning
(RL) approach, that does not assume a model a priori, benchmarking this against
a linear MPC (LMPC - to reflect current practice, though non-linear MPC
performs better) - both derived from the general optimal control problem,
highlighting their differences and similarities. In a simple multi-energy
system (MES) configuration case study, we show that a twin delayed deep
deterministic policy gradient (TD3) RL agent offers potential to match and
outperform the perfect foresight LMPC benchmark (101.5%). This while the
realistic LMPC, i.e. imperfect predictions, only achieves 98%. While in a more
complex MES system configuration, the RL agent's performance is generally lower
(94.6%), yet still better than the realistic LMPC (88.9%). In both case
studies, the RL agents outperformed the realistic LMPC after a training period
of 2 years using quarterly interactions with the environment. We conclude that
reinforcement learning is a viable optimal control technique for multi-energy
systems given adequate constraint handling and pre-training, to avoid unsafe
interactions and long training periods, as is proposed in fundamental future
work.


Abstract:
  This study presents a large scale benchmarking on cloud based Speech-To-Text
systems: {Google Cloud Speech-To-Text}, {Microsoft Azure Cognitive Services},
{Amazon Transcribe}, {IBM Watson Speech to Text}. For each systems, 40158 clean
and noisy speech files about 101 hours are tested. Effect of background noise
on STT quality is also evaluated with 5 different Signal-to-noise ratios from
40dB to 0dB. Results showed that {Microsoft Azure} provided lowest
transcription error rate $9.09\%$ on clean speech, with high robustness to
noisy environment. {Google Cloud} and {Amazon Transcribe} gave similar
performance, but the latter is very limited for time-constraint usage. Though
{IBM Watson} could work correctly in quiet conditions, it is highly sensible to
noisy speech which could strongly limit its application in real life
situations.


Abstract:
  Metal nano-aerogels combine a large surface area, a high structural
stability, and a high catalytic activity towards a variety of chemical
reactions. The performance of such nanostructures is underpinned by the
atomic-level distribution of their constituents. Yet monitoring their
sub-nanoscale structure and composition to guide property optimization remains
extremely challenging. Here, we synthesized Pd nano-aerogels from a K2PdCl4
precursor and two different NaBH4 reductant concentrations in distilled water.
Atom probe tomography reveals that the aerogel is poly-crystalline and that
impurities (Na, K) are integrated from the solution into grain boundaries. Ab
initio calculations indicate that these impurities preferentially bound to the
Pd-metal surface and are ultimately found in grain boundaries forming as the
particles coalesce during synthesis, with Na atoms thermodynamically
equilibrating with the surrounding solution and K atoms remaining between
growing grains. If controlled, impurity integration, i.e. grain boundary
decoration, may offer opportunities for designing new nano-aerogels.


Abstract:
  To implement fair machine learning in a sustainable way, choosing the right
fairness objective is key. Since fairness is a concept of justice which comes
in various, sometimes conflicting definitions, this is not a trivial task
though. The most appropriate fairness definition for an artificial intelligence
(AI) system is a matter of ethical standards and legal requirements, and the
right choice depends on the particular use case and its context. In this
position paper, we propose to use a decision tree as means to explain and
justify the implemented kind of fairness to the end users. Such a structure
would first of all support AI practitioners in mapping ethical principles to
fairness definitions for a concrete application and therefore make the
selection a straightforward and transparent process. However, this approach
would also help document the reasoning behind the decision making. Due to the
general complexity of the topic of fairness in AI, we argue that specifying
"fairness" for a given use case is the best way forward to maintain confidence
in AI systems. In this case, this could be achieved by sharing the reasons and
principles expressed during the decision making process with the broader
audience.


Abstract:
  This paper introduces a multi-scale speech style modeling method for
end-to-end expressive speech synthesis. The proposed method employs a
multi-scale reference encoder to extract both the global-scale utterance-level
and the local-scale quasi-phoneme-level style features of the target speech,
which are then fed into the speech synthesis model as an extension to the input
phoneme sequence. During training time, the multi-scale style model could be
jointly trained with the speech synthesis model in an end-to-end fashion. By
applying the proposed method to style transfer task, experimental results
indicate that the controllability of the multi-scale speech style model and the
expressiveness of the synthesized speech are greatly improved. Moreover, by
assigning different reference speeches to extraction of style on each scale,
the flexibility of the proposed method is further revealed.


Abstract:
  We present a scenario where an axion-like field drives inflation until a
potential barrier, which keeps a waterfall field at the origin, disappears and
a waterfall transition occurs. Such a barrier separates the scale of inflation
from that of the waterfall transition. We find the observed spectrum of the
cosmic microwave background indicates that the decay constant of the inflaton
is well below the Planck scale, with the inflationary Hubble parameter spanning
a wide range. Further, our model involves dark matter candidates including the
inflaton itself. Also, for a complex waterfall field, we can determine
cosmologically the Peccei-Quinn scale associated with the strong CP problem.


Abstract:
  We study the role of average concurrence in entanglement swapping in quantum
networks. We begin with qubit pure states, and there is a very simple rule
governing the propagation of average concurrence in multiple swaps. We look at
examples of mixed qubit states, and find the relation for pure states gives an
upper bound on what is possible with mixed states. We then move on to qudits,
where we make use of the I-concurrence. Here the situation is not as simple as
for qubits, but in some cases relatively straightforward results can be
obtained.


Abstract:
  The multiplayer promise set disjointness is one of the most widely used
problems from communication complexity in applications. In this problem there
are $k$ players with subsets $S^1, \ldots, S^k$, each drawn from $\{1, 2,
\ldots, n\}$, and we are promised that either the sets are (1) pairwise
disjoint, or (2) there is a unique element $j$ occurring in all the sets, which
are otherwise pairwise disjoint. The total communication of solving this
problem with constant probability in the blackboard model is $\Omega(n/k)$.
  We observe for most applications, it instead suffices to look at what we call
the ``mostly'' set disjointness problem, which changes case (2) to say there is
a unique element $j$ occurring in at least half of the sets, and the sets are
otherwise disjoint. This change gives us a much simpler proof of an
$\Omega(n/k)$ randomized total communication lower bound, avoiding Hellinger
distance and Poincare inequalities. Using this we show several new results for
data streams:
  \begin{itemize} \item for $\ell_2$-Heavy Hitters, any $O(1)$-pass streaming
algorithm in the insertion-only model for detecting if an $\eps$-$\ell_2$-heavy
hitter exists requires $\min(\frac{1}{\eps^2}\log \frac{\eps^2n}{\delta},
\frac{1}{\eps}n^{1/2})$ bits of memory, which is optimal up to a $\log n$
factor. For deterministic algorithms and constant $\eps$, this gives an
$\Omega(n^{1/2})$ lower bound, improving the prior $\Omega(\log n)$ lower
bound. We also obtain lower bounds for Zipfian distributions. \item for
$\ell_p$-Estimation, $p > 2$, we show an $O(1)$-pass $\Omega(n^{1-2/p}
\log(1/\delta))$ bit lower bound for outputting an $O(1)$-approximation with
probability $1-\delta$, in the insertion-only model. This is optimal, and the
best previous lower bound was $\Omega(n^{1-2/p} + \log(1/\delta))$.
\end{itemize}


Abstract:
  This article is a lecture note on the potential theory of (possibly
non-reversible) Markov processes and on the connection of this theory with
quantitative analysis of the metastability of stochastic processes.


Abstract:
  Coupled oscillators are among the simplest composite quantum systems in which
the interplay of entanglement and interaction may be explored. We examine the
effects of coupling on fluctuations of the coordinates and momenta of the
oscillators in a single-excitation entangled state. We discover that coupling
acts as a mechanism for noise transfer between one pair of coordinate and
momentum and another. Through this noise transfer mechanism, the uncertainty
product is lowered, on average, relatively to its non-coupled level for one
pair of coordinate and momentum and it is enhanced for the other pair. This
novel mechanism may be explored in precision measurements in
entanglement-assisted sensing and metrology.


Abstract:
  The subject of this textbook is the analysis of Boolean functions. Roughly
speaking, this refers to studying Boolean functions $f : \{0,1\}^n \to \{0,1\}$
via their Fourier expansion and other analytic means. Boolean functions are
perhaps the most basic object of study in theoretical computer science, and
Fourier analysis has become an indispensable tool in the field. The topic has
also played a key role in several other areas of mathematics, from
combinatorics, random graph theory, and statistical physics, to Gaussian
geometry, metric/Banach spaces, and social choice theory.
  The intent of this book is both to develop the foundations of the field and
to give a wide (though far from exhaustive) overview of its applications. Each
chapter ends with a "highlight" showing the power of analysis of Boolean
functions in different subject areas: property testing, social choice,
cryptography, circuit complexity, learning theory, pseudorandomness, hardness
of approximation, concrete complexity, and random graph theory.
  The book can be used as a reference for working researchers or as the basis
of a one-semester graduate-level course. The author has twice taught such a
course at Carnegie Mellon University, attended mainly by graduate students in
computer science and mathematics but also by advanced undergraduates, postdocs,
and researchers in adjacent fields. In both years most of Chapters 1-5 and 7
were covered, along with parts of Chapters 6, 8, 9, and 11, and some additional
material on additive combinatorics. Nearly 500 exercises are provided at the
ends of the book's chapters.


Abstract:
  In this work, we address multi-modal information needs that contain text
questions and images by focusing on passage retrieval for outside-knowledge
visual question answering. This task requires access to outside knowledge,
which in our case we define to be a large unstructured passage collection. We
first conduct sparse retrieval with BM25 and study expanding the question with
object names and image captions. We verify that visual clues play an important
role and captions tend to be more informative than object names in sparse
retrieval. We then construct a dual-encoder dense retriever, with the query
encoder being LXMERT, a multi-modal pre-trained transformer. We further show
that dense retrieval significantly outperforms sparse retrieval that uses
object expansion. Moreover, dense retrieval matches the performance of sparse
retrieval that leverages human-generated captions.


Abstract:
  The advent of dispersion-engineered and highly nonlinear nanophotonics is
expected to open up an all-optical path towards the strong-interaction regime
of quantum optics by combining high transverse field confinement with
ultra-short-pulse operation. Obtaining a full understanding of photon dynamics
in such broadband devices, however, poses major challenges in the modeling and
simulation of multimode non-Gaussian quantum physics, highlighting the need for
sophisticated reduced models that facilitate efficient numerical study while
providing useful physical insight. In this manuscript, we review our recent
efforts in modeling broadband optical systems at varying levels of abstraction
and generality, ranging from multimode extensions of quantum input-output
theory for sync-pumped oscillators to the development of numerical methods
based on a field-theoretic description of nonlinear waveguides. We expect our
work not only to guide ongoing theoretical and experimental efforts towards
next-generation quantum devices but also to uncover essential physics of
broadband quantum photonics.


Abstract:
  In this proceeding, we study time evolution of a complex scalar field, in
symmetry broken phase, in presence of oscillating spacetime metric background.
We show that spacetime oscillations lead to parametric resonance of the field.
This generates excitations in the field for a wide range of frequency of
spacetime oscillations which ultimately lead to the formation of topological
vortices. The lowest frequency cut-off to induce this phenomena is set by
system size due to finite size effects.


Abstract:
  We study the general problem of Bayesian persuasion (optimal information
design) with continuous actions and continuous state space in arbitrary
dimensions. First, we show that with a finite signal space, the optimal
information design is always given by a partition. Second, we take the limit of
an infinite signal space and characterize the solution in terms of a
Monge-Kantorovich optimal transport problem with an endogenous information
transport cost. We use our novel approach to: 1. Derive necessary and
sufficient conditions for optimality based on Bregman divergences for
non-convex functions. 2. Compute exact bounds for the Hausdorff dimension of
the support of an optimal policy. 3. Derive a non-linear, second-order partial
differential equation whose solutions correspond to regular optimal policies.
We illustrate the power of our approach by providing explicit solutions to
several non-linear, multidimensional Bayesian persuasion problems.


Abstract:
  Light harvesting as the first step in photosynthesis is of prime importance
for life on earth. For a theoretical description of photochemical processes
during light harvesting, spectral densities are key quantities. They serve as
input functions for modeling the excitation energy transfer dynamics and
spectroscopic properties. Herein, a recently developed procedure is applied to
determine the spectral densities of the pigments in the minor antenna complex
CP29 of photosystem II which has recently gained attention because of its
active role in non-photochemical quenching processes in higher plants. To this
end, the density functional based tight binding (DFTB) method has been employed
to enable simulation of the ground state dynamics in a
quantum-mechanics/molecular mechanics (QM/MM) scheme for each chlorophyll
pigment. Subsequently, the time-dependent extension of the long-range corrected
DFTB approach has been used to obtain the excitation energy fluctuations along
the ground-state trajectories also in a QM/MM setting. From these results the
spectral densities have been determined and compared for different force fields
and to spectral densities from other light-harvesting complexes. In addition,
the excitation energy transfer in the CP29 complex has been studied using
ensemble-average wave packet dynamics.


Abstract:
  With the advent of generative adversarial networks, synthesizing images from
textual descriptions has recently become an active research area. It is a
flexible and intuitive way for conditional image generation with significant
progress in the last years regarding visual realism, diversity, and semantic
alignment. However, the field still faces several challenges that require
further research efforts such as enabling the generation of high-resolution
images with multiple objects, and developing suitable and reliable evaluation
metrics that correlate with human judgement. In this review, we contextualize
the state of the art of adversarial text-to-image synthesis models, their
development since their inception five years ago, and propose a taxonomy based
on the level of supervision. We critically examine current strategies to
evaluate text-to-image synthesis models, highlight shortcomings, and identify
new areas of research, ranging from the development of better datasets and
evaluation metrics to possible improvements in architectural design and model
training. This review complements previous surveys on generative adversarial
networks with a focus on text-to-image synthesis which we believe will help
researchers to further advance the field.


Abstract:
  The Antarctic Impulse Transient Antenna (\textsc{ANITA}) collaboration
\cite{Gorham:2016zah,Gorham:2018ydl,Gorham:2020zne} have reported observation
of two anomalous events with noninverted polarity. These events are proven to
be hard to explain in terms of conventional cosmic rays (CR). We propose that
these anomalous events represent the direct manifestation of the dark matter
(DM) annihilation events within the so-called axion quark nugget (AQN) DM
model, which was originally invented for completely different purpose to
explain the observed similarity between the dark and the visible components in
the Universe, i.e. $\Omega_{\rm DM}\sim \Omega_{\rm visible}$ without any
fitting parameters. We support this proposal by demonstrating that the
observations \cite{Gorham:2016zah,Gorham:2018ydl,Gorham:2020zne}, including the
frequency, intensity and time duration of the radio pulses nicely match the
emission features of the upward going AQN events. We list a number of features
of the AQN events which are very distinct from conventional CR air showers. The
observations (non-observation) of these features may substantiate (refute) our
proposal.


Abstract:
  Predicting the chemical properties of a molecule is of great importance in
many applications, including drug discovery and material design. Machine
learning based molecular property prediction holds the promise of enabling
accurate predictions at much less computationally complex cost when compared
to, for example, Density Functional Theory (DFT) calculations. Various
representation learning methods in a supervised setting, including the features
extracted using graph neural nets, have emerged for such tasks. However, the
vast chemical space and the limited availability of labels make supervised
learning challenging, calling for learning a general-purpose molecular
representation. Recently, pre-trained transformer-based language models on
large unlabeled corpus have produced state-of-the-art results in many
downstream natural language processing tasks. Inspired by this development, we
present molecular embeddings obtained by training an efficient transformer
encoder model, MoLFormer. This model employs a linear attention mechanism
coupled with highly parallelized training on SMILES sequences of 1.1 billion
unlabeled molecules from the PubChem and ZINC datasets. Experiments show that
the learned molecular representation outperforms supervised and unsupervised
graph neural net baselines on several regression and classification tasks from
10 benchmark datasets, while performing competitively on others. Further
analyses, specifically through the lens of attention, demonstrate that
MoLFormer indeed learns a molecule's local and global structural aspects. These
results provide encouraging evidence that large-scale molecular language models
can capture sufficient structural information to be able to predict diverse
molecular properties, including quantum-chemical properties


Abstract:
  In this paper, we study the fragmentation of a heavy quark into a jet near
threshold, meaning that final state jet carries most of the energy of the
fragmenting heavy quark. Using the heavy quark fragmentation function, we
simultaneously resum large logarithms of the jet radius $R$ and $1-z$, where
$z$ is the ratio of the jet energy to the initiating heavy quark energy. There
are numerically significant corrections to the leading order rate due to this
resummation. We also investigate the heavy quark fragmentation to a groomed
jet, using the soft drop grooming algorithm as an example. In order to do so,
we introduce a collinear-ultrasoft mode sensitive to the grooming region
determined by the algorithm's $z_{\mathrm{cut}}$ parameter. This allows us to
resum large logarithms of $z_{\mathrm{cut}}/(1-z)$, again leading to large
numerical corrections near the endpoint. A nice feature of the analysis of the
heavy quark fragmenting to a groomed jet is the heavy quark mass $m$ renders
the algorithm infrared finite, allowing a perturbative calculation. We analyze
this for $E_JR \sim m$ and $E_JR\gg m$, where $E_J$ is the jet energy. To do
the latter case, we introduce an ultracollinear-soft mode, allowing us to resum
large logarithms of $E_JR/m$. Finally, as an application we calculate the rate
for $e^+e^-$ collisions to produce a heavy quark jet in the endpoint region,
where we show that grooming effects have a sizable contribution near the
endpoint.


Abstract:
  Because of the fast advance rate and the improved personnel safety, tunnel
boring machines (TBMs) have been widely used in a variety of tunnel
construction projects. The dynamic modeling of TBM load parameters (including
torque, advance rate and thrust) plays an essential part in the design, safe
operation and fault prognostics of this complex engineering system. In this
paper, based on in-situ TBM operational data, we use the machine-learning (ML)
methods to build the real-time forecast models for TBM load parameters, which
can instantaneously provide the future values of the TBM load parameters as
long as the current data are collected. To decrease the model complexity and
improve the generalization, we also apply the least absolute shrinkage and
selection (Lasso) method to extract the essential features of the forecast
task. The experimental results show that the forecast models based on
deep-learning methods, {\it e.g.}, recurrent neural network and its variants,
outperform the ones based on the shallow-learning methods, {\it e.g.}, support
vector regression and random forest. Moreover, the Lasso-based feature
extraction significantly improves the performance of the resultant models.


Abstract:
  The scaled inverse of a nonzero element $a(x)\in \mathbb{Z}[x]/f(x)$, where
$f(x)$ is an irreducible polynomial over $\mathbb{Z}$, is the element $b(x)\in
\mathbb{Z}[x]/f(x)$ such that $a(x)b(x)=c \pmod{f(x)}$ for the smallest
possible positive integer scale $c$. In this paper, we investigate the scaled
inverse of $(x^i-x^j)$ modulo cyclotomic polynomial of the form $\Phi_{p^s}(x)$
or $\Phi_{p^s q^t}(x)$, where $p, q$ are primes with $p<q$ and $s, t$ are
positive integers. Our main results are that the coefficient size of the scaled
inverse of $(x^i-x^j)$ is bounded by $p-1$ with the scale $p$ modulo
$\Phi_{p^s}(x)$, and is bounded by $q-1$ with the scale not greater than $q$
modulo $\Phi_{p^s q^t}(x)$. Previously, the analogous result on cyclotomic
polynomials of the form $\Phi_{2^n}(x)$ gave rise to many lattice-based
cryptosystems, especially, zero-knowledge proofs. Our result provides more
flexible choice of cyclotomic polynomials in such cryptosystems. Along the way
of proving the theorems, we also prove several properties of
$\{x^k\}_{k\in\mathbb{Z}}$ in $\mathbb{Z}[x]/\Phi_{pq}(x)$ which might be of
independent interest.


Abstract:
  This work focuses on optimal controls of a class of stochastic SIS epidemic
models under regime switching. By assuming that a decision maker can either
influence the infectivity period or isolate infected individuals, our aim is to
minimize the expected discounted cost due to illness, medical treatment, and
the adverse effect on the society. In addition, a model with the incorporation
of vaccination is proposed. Numerical schemes are developed by approximating
the continuous-time dynamics using Markov chain approximation methods. It is
demonstrated that the approximation schemes converge to the optimal strategy as
the mesh size goes to zero. Numerical examples are provided to illustrate our
results.


Abstract:
  Non-reciprocal directional dichroism, also called the optical-diode effect,
is an appealing functional property inherent to the large class of
non-centrosymmetric magnets. However, the in-situ electric control of this
phenomenon is challenging as it requires a set of conditions to be fulfilled:
Special symmetries of the magnetic ground state, spin-excitations with
comparable magnetic- and electric-dipole activity and switchable electric
polarization. We demonstrate the isothermal electric switch between domains of
Ba$_2$CoGe$_2$O$_7$ possessing opposite magnetoelectric susceptibilities.
Combining THz spectroscopy and multiboson spin-wave analysis, we show that
unbalancing the population of antiferromagnetic domains generates the
non-reciprocal light absorption of spin excitations.


Abstract:
  To dynamically traverse challenging terrain, legged robots need to
continually perceive and reason about upcoming features, adjust the locations
and timings of future footfalls and leverage momentum strategically. We present
a pipeline that enables flexibly-parametrized trajectories for perceptive and
dynamic quadruped locomotion to be optimized in an online, receding-horizon
manner. The initial guess passed to the optimizer affects the computation
needed to achieve convergence and the quality of the solution. We consider two
methods for generating good guesses. The first is a heuristic initializer which
provides a simple guess and requires significant optimization but is
nonetheless suitable for adaptation to upcoming terrain. We demonstrate
experiments using the ANYmal C quadruped, with fully onboard sensing and
computation, to cross obstacles at moderate speeds using this technique. Our
second approach uses latent-mode trajectory regression (LMTR) to imitate expert
data - while avoiding invalid interpolations between distinct behaviors - such
that minimal optimization is needed. This enables high-speed motions that make
more expansive use of the robot's capabilities. We demonstrate it on flat
ground with the real robot and provide numerical trials that progress toward
deployment on terrain. These results illustrate a paradigm for advancing beyond
short-horizon dynamic reactions, toward the type of intuitive and adaptive
locomotion planning exhibited by animals and humans.


Abstract:
  This paper studies the application of the blended dynamics approach towards
distributed optimization problem where the global cost function is given by a
sum of local cost functions. The benefits include (i) individual cost function
need not be convex as long as the global cost function is strongly convex and
(ii) the convergence rate of the distributed algorithm is arbitrarily close to
the convergence rate of the centralized one. Two particular continuous-time
algorithms are presented using the proportional-integral-type couplings. One
has benefit of `initialization-free,' so that agents can join or leave the
network during the operation. The other one has the minimal amount of
communication information. After presenting a general theorem that can be used
for designing distributed algorithms, we particularly present a distributed
heavy-ball method and discuss its strength over other methods.


Abstract:
  Models in which the Higgs boson is a composite pseudo-Nambu-Goldstone boson
offer attractive solutions to the Higgs mass naturalness problem. We consider
three such models based on the minimal $SO(5) \rightarrow SO(4)$ symmetry
breaking pattern, and perform convergent global fits on the models under a
Bayesian framework in order to find the regions of their parameter spaces that
best fit a wide range of constraints, including recent Higgs measurements. We
use a novel technique to analyse the fine-tuning of the models, quantifying the
tuning as the Kullback-Leibler divergence from the prior to the posterior
probability on the parameter space. Each model is found to be able to satisfy
all constraints at the $3\sigma$ level simultaneously. As a by-product of the
fits, we analyse the collider phenomenology of our models in these viable
regions. In two of the three models, we find that the $g g \rightarrow H
\rightarrow \gamma \gamma$ cross section is less than ${\sim}90$% that
predicted by the SM, which is already in slight tension with experiment and
could potentially be ruled out in the future high-luminosity run of the LHC. In
addition, the lightest fermions $F$ arising from the new strong dynamics in
these models are seen in general to lie above ${\sim}1.1$ TeV, with the $F
\rightarrow tW^{+}$ and $F \rightarrow \bar{b}W^{+}$ decays offering
particularly promising channels for probing these models in future collider
searches.


Abstract:
  We present the ab initio results of the structural and magnetic properties of
the Pd host matrix implanted with Fe solute atoms at various concentrations. By
means of density functional theory we confirm that iron impurities are able to
initialize significant magnetization of the Pd atoms, when the impurity
consentation exceeds 3 at.%. Besides, we demonstrate that the imposed
magnetization depends on impurity positions in the host matrix, in particular,
there is a maximum of magnetization for a uniform distribution of the iron
impurity.


Abstract:
  Discourse relations among arguments reveal logical structures of a debate
conversation. However, no prior work has explicitly studied how the sequence of
discourse relations influence a claim's impact. This paper empirically shows
that the discourse relations between two arguments along the context path are
essential factors for identifying the persuasive power of an argument. We
further propose DisCOC to inject and fuse the sentence-level structural
discourse information with contextualized features derived from large-scale
language models. Experimental results and extensive analysis show that the
attention and gate mechanisms that explicitly model contexts and texts can
indeed help the argument impact classification task defined by Durmus et al.
(2019), and discourse structures among the context path of the claim to be
classified can further boost the performance.


Abstract:
  Let $D=(V,A)$ be a digraphs without isolated vertices. A vertex-degree based
invariant $I(D)$ related to a real function $\varphi$ of $D$ is defined as a
summation over all arcs, $I(D) = \frac{1}{2}\sum_{uv\in
A}{\varphi(d_u^+,d_v^-)}$, where $d_u^+$ (resp. $d_u^-$) denotes the out-degree
(resp. in-degree) of a vertex $u$. In this paper, we give the extremal values
and extremal digraphs of $I(D)$ over all digraphs with $n$ non-isolated
vertices. Applying these results, we obtain the extremal values of some
vertex-degree based topological indices of digraphs, such as the Randi\'{c}
index, the Zagreb index, the sum-connectivity index, the $GA$ index, the $ABC$
index and the harmonic index, and the corresponding extremal digraphs.


Abstract:
  Filamentary textures can take the form of braided, rope-like superstructures
in nonlinear media such as plasmas and superfluids. The formation of similar
superstructures in solids has been predicted, for example from flux lines in
superconductors. However, their observation has proved challenging. Here, we
use electron microscopy and numerical methods to reveal braided superstructures
of magnetic skyrmions in thin crystals of B20-type FeGe. Their discovery opens
the door to applications of rich topological landscapes of geometric braids in
magnetic solids.


Abstract:
  Radio frequency fingerprint identification (RFFI) is a promising device
authentication technique based on the transmitter hardware impairments. In this
paper, we propose a scalable and robust RFFI framework achieved by deep
learning powered radio frequency fingerprint (RFF) extractor. Specifically, we
leverage the deep metric learning to train an RFF extractor, which has
excellent generalization ability and can extract RFFs from previously unseen
devices. Any devices can be enrolled via the pre-trained RFF extractor and the
RFF database can be maintained efficiently for allowing devices to join and
leave. Wireless channel impacts the RFF extraction and is tackled by exploiting
channel independent feature and data augmentation. We carried out extensive
experimental evaluation involving 60 commercial off-the-shelf LoRa devices and
a USRP N210 software defined radio platform. The results have successfully
demonstrated that our framework can achieve excellent generalization abilities
for device classification and rogue device detection as well as effective
channel mitigation.


Abstract:
  A novel volume of fluid model (VoF) called explicit volume diffusion (EVD) is
developed for the simulation of interfacial flows, including those with
turbulence and primary spray atomisation. The EVD model is derived by volume
averaging the VoF equations over a physically-defined length scale. This
introduces unclosed sub-volume flux, sub-volume stress and volume averaged
surface tension force. Sub-volume fluctuations arise due to both turbulent
motions and other interface dynamics which can, in general, occur in both
laminar and turbulent flows. Both of these types of fluctuations are attenuated
by the volume averaging process. The sub-volume flux is closed by a gradient
diffusion model and involves an explicit volume diffusion coefficient that is
linked to the physical length scale. The sub-volume stress closure introduces
an explicit volume viscosity augmented by turbulent viscosity in turbulent
flows. The volume averaged surface tension force closure is based on fractal
properties of wrinkled sub-volume interfaces. These closures are evaluated
through a priori analysis of resolved flow simulations for a series of
two-dimensional laminar and three-dimensional turbulent interfacial shear
flows. Subsequently, full EVD simulations are validated for these shear flows
and for a laboratory airblast spray jet. Numerical convergence is demonstrated
by keeping the physical length scale constant while reducing the numerical grid
size so that numerical diffusion diminishes and becomes overwhelmed by the
explicit volume diffusion. A sensitivity analysis is also conducted for
variations in the physical length scale, which is compared to the boundary
layer thickness on the light fluid side of the interface.


Abstract:
  We present the system that we have developed for the identification and
verification of pills using images that are taken by the VeriMedi device. The
VeriMedi device is an Internet of Things device that takes pictures of a filled
pill vial from the bottom of the vial and uses the solution that is presented
in this research to identify the pills in the vials. The solution has two
serially connected deep learning solutions which do segmentation and
identification. The segmentation solution creates the masks for each pill in
the vial image by using the Mask R-CNN model, then segments and crops the pills
and blurs the background. After that, the segmented pill images are sent to the
identification solution where a Deep Metric Learning model that is trained with
Proxy Anchor Loss (PAL) function generates embedding vectors for each pill
image. The generated embedding vectors are fed into a one-layer fully connected
network that is trained with the exact solution to predict each single pill
image. Then, the aggregation/verification function aggregates the multiple
predictions coming from multiple single pill images and verifies the
correctness of the final prediction with respect to predefined rules. Besides,
we enhanced the PAL with a better proxy initialization that increased the
performance of the models and let the model learn the new classes of images
continually without retraining the model with the whole dataset. When the model
that is trained with initial classes is retrained only with new classes, the
accuracy of the model increases for both old and new classes. The
identification solution that we have presented in this research can also be
reused for other problem domains which require continual learning and/or
Fine-Grained Visual Categorization.


Abstract:
  The central molecular zone (CMZ) plays an essential role in regulating the
nuclear ecosystem of our Galaxy. To get an insight into the magnetic fields of
the CMZ, we employ the Gradient Technique (GT), which is rooted in the
anisotropy of magnetohydrodynamic turbulence. Our analysis is based on the data
of multiple wavelengths, including molecular emission lines, radio 1.4 GHz
continuum image, and Herschel 70 um image, as well as ionized [Ne II] and
Paschen-alpha emissions. The results are compared with the observations of
Planck 353 GHz and High-resolution Airborne Wideband Camera Plus (HWAC+) 53 um
polarized dust emissions. We map the wavelength-dependent magnetic field
orientation across the central molecular zone, including close-ups of the Radio
Arc and Sagittarius A West regions, on multi scales from ~0.1 pc to 10 pc. The
magnetic fields towards the central molecular zone traced by GT are globally
compatible with the polarization measurements, accounting for the contribution
from the galactic foreground and background. This correspondence suggests that
the magnetic field and turbulence are dynamically crucial in the galactic
center. We find that the magnetic fields associated with the Arched filaments
and the thermal components of the Radio Arc are in good agreement with the
HAWC+ polarization. Our measurement towards the non-thermal Radio Arc reveals
the poloidal magnetic field components in the galactic center. For Sagittarius
A West region, we find a great agreement between the GT measurement using [Ne
II] emission and HWAC+ 53 um observation. We use GT to predict the magnetic
fields associated with ionized Paschen-alpha gas down to scales of 0.1 pc.
These results demonstrate the potential power of GT in the high-resolution
mapping of magnetic fields and in decomposing contributions from different
velocity components and/or different gas phases.


Abstract:
  Despite recent advances in object detection using deep learning neural
networks, these neural networks still struggle to identify objects in art
images such as paintings and drawings. This challenge is known as the cross
depiction problem and it stems in part from the tendency of neural networks to
prioritize identification of an object's texture over its shape. In this paper
we propose and evaluate a process for training neural networks to localize
objects - specifically people - in art images. We generate a large dataset for
training and validation by modifying the images in the COCO dataset using AdaIn
style transfer. This dataset is used to fine-tune a Faster R-CNN object
detection network, which is then tested on the existing People-Art testing
dataset. The result is a significant improvement on the state of the art and a
new way forward for creating datasets to train neural networks to process art
images.


Abstract:
  3D object detection is vital for many robotics applications. For tasks where
a 2D perspective range image exists, we propose to learn a 3D representation
directly from this range image view. To this end, we designed a 2D
convolutional network architecture that carries the 3D spherical coordinates of
each pixel throughout the network. Its layers can consume any arbitrary
convolution kernel in place of the default inner product kernel and exploit the
underlying local geometry around each pixel. We outline four such kernels: a
dense kernel according to the bag-of-words paradigm, and three graph kernels
inspired by recent graph neural network advances: the Transformer, the
PointNet, and the Edge Convolution. We also explore cross-modality fusion with
the camera image, facilitated by operating in the perspective range image view.
Our method performs competitively on the Waymo Open Dataset and improves the
state-of-the-art AP for pedestrian detection from 69.7% to 75.5%. It is also
efficient in that our smallest model, which still outperforms the popular
PointPillars in quality, requires 180 times fewer FLOPS and model parameters


Abstract:
  In a previous paper we generalized the definition of a multilinear map to
arbitrary groups and introduced two multiparty key-exchange protocols using
nilpotent groups. In this paper we have a closer look at the protocols and will
address some incorrect cryptanalysis which have been proposed.


Abstract:
  In recent development of quantum technologies, a frequency conversion of
quantum signals has been studied widely. We investigate the optic-microwave
entanglement that is generated by applying an electro-optomechanical frequency
conversion scheme to one mode in an optical two-mode squeezed vacuum state. We
quantify entanglement of the converted two-mode Gaussian state, where surviving
entanglement of the state is analyzed with respect to the parameters of the
electro-optomechanical system. Furthermore, we show that there exists an upper
bound for the entanglement that survives after the conversion of highly
entangled optical states. Our study provides a theoretical platform for a
practical quantum illumination system.


Abstract:
  The 21 cm linear polarization due to Thomson scattering off free electrons
can probe the distribution of neutral hydrogen in the intergalactic medium
during the epoch of reionization, complementary to the 21 cm temperature
fluctuations. Previous study (Babich & Loeb 2005) estimated the strength of
polarization with a toy model and claimed that it can be detected with 1-month
observation of the Square Kilometre Array (SKA). Here we revisit this
investigation with account of nonlinear terms due to inhomogeneous
reionization, using seminumerical reionization simulations to provide the
realistic estimation of the 21 cm TE and EE angular power spectra ($C^{\rm
TE}_\ell$ and $C^{\rm EE}_\ell$). We find that (1) both power spectra are
enhanced on sub-bubble scales but suppressed on super-bubble scales, compared
with previous results; (2) $C^{\rm TE}_\ell$ displays a zero-crossing at
$\ell<100$, and its angular scale is sensitive to the scale-dependence of H I
bias on large scales; (3) the ratios of the power spectrum to its maximum value
during reionization at a given $\ell$, i.e. $C^{\rm TE}_\ell / C^{\rm
TE}_{\ell,{\rm max}} $ and $C^{\rm EE}_{\ell}/C^{\rm EE}_{\ell,{\rm max}}$,
show robust correlations with the global ionized fraction. However, measurement
of this signal will be very challenging not only because the overall strength
is weaker than the sensitivity of SKA, but also because of the polarized
foregrounds from diffuse synchrotron emission, and Faraday rotation which
modifies the observed polarization. Nevertheless, the 21 cm linear polarization
signal may still likely be detectable through other approaches, e.g. its
cross-correlation with other probes.


Abstract:
  Cloud-based enterprise search services (e.g., AWS Kendra) have been
entrancing big data owners by offering convenient and real-time search
solutions to them. However, the problem is that individuals and organizations
possessing confidential big data are hesitant to embrace such services due to
valid data privacy concerns. In addition, to offer an intelligent search, these
services access the user search history that further jeopardizes his/her
privacy. To overcome the privacy problem, the main idea of this research is to
separate the intelligence aspect of the search from its pattern matching
aspect. According to this idea, the search intelligence is provided by an
on-premises edge tier and the shared cloud tier only serves as an exhaustive
pattern matching search utility. We propose Smartness At Edge (SAED mechanism
that offers intelligence in the form of semantic and personalized search at the
edge tier while maintaining privacy of the search on the cloud tier. At the
edge tier, SAED uses a knowledge-based lexical database to expand the query and
cover its semantics. SAED personalizes the search via an RNN model that can
learn the user interest. A word embedding model is used to retrieve documents
based on their semantic relevance to the search query. SAED is generic and can
be plugged into existing enterprise search systems and enable them to offer
intelligent and privacy-preserving search without enforcing any change on them.
Evaluation results on two enterprise search systems under real settings and
verified by human users demonstrate that SAED can improve the relevancy of the
retrieved results by on average 24% for plain-text and 75% for encrypted
generic datasets.


Abstract:
  This paper considers autonomous flight formation of fixed-wing air vehicles.
Due to several issues such as limitations on communication bandwidth and low
observability requirements, an asynchronous control and communication approach
is designed in order to reduce the number of updates between the vehicles. With
the implementation of an event-triggered controller, it is possible to obtain a
more efficient communication scheme while also achieving the convergence
properties of the desired formation.


Abstract:
  This paper develops a Multiset Rewriting language with explicit time for the
specification and analysis of Time-Sensitive Distributed Systems (TSDS). Goals
are often specified using explicit time constraints. A good trace is an
infinite trace in which the goals are satisfied perpetually despite possible
interference from the environment. In our previous work (FORMATS 2016), we
discussed two desirable properties of TSDSes, realizability (there exists a
good trace) and survivability (where, in addition, all admissible traces are
good). Here we consider two additional properties, recoverability (all
compliant traces do not reach points-of-no-return) and reliability (the system
can always continue functioning using a good trace). Following (FORMATS 2016),
we focus on a class of systems called Progressing Timed Systems (PTS), where
intuitively only a finite number of actions can be carried out in a bounded
time period. We prove that for this class of systems the properties of
recoverability and reliability coincide and are PSPACE-complete. Moreover, if
we impose a bound on time (as in bounded model-checking), we show that for PTS
the reliability property is in the $\Pi_2^p$ class of the polynomial hierarchy,
a subclass of PSPACE. We also show that the bounded survivability is both
NP-hard and coNP-hard.


Abstract:
  The COnstrain Dark Energy with X-ray clusters (CODEX) sample contains the
largest flux limited sample of X-ray clusters at $0.35 < z < 0.65$. It was
selected from ROSAT data in the 10,000 square degrees of overlap with BOSS,
mapping a total number of 2770 high-z galaxy clusters. We present here the full
results of the CFHT CODEX program on cluster mass measurement, including a
reanalysis of CFHTLS Wide data, with 25 individual lensing-constrained cluster
masses. We employ $lensfit$ shape measurement and perform a conservative
colour-space selection and weighting of background galaxies. Using the
combination of shape noise and an analytic covariance for intrinsic variations
of cluster profiles at fixed mass due to large scale structure, miscentring,
and variations in concentration and ellipticity, we determine the likelihood of
the observed shear signal as a function of true mass for each cluster. We
combine 25 individual cluster mass likelihoods in a Bayesian hierarchical
scheme with the inclusion of optical and X-ray selection functions to derive
constraints on the slope $\alpha$, normalization $\beta$, and scatter
$\sigma_{\ln \lambda | \mu}$ of our richness-mass scaling relation model in
log-space: $\left<\ln \lambda | \mu \right> = \alpha \mu + \beta$, with $\mu =
\ln (M_{200c}/M_{\mathrm{piv}})$, and $M_{\mathrm{piv}} = 10^{14.81}
M_{\odot}$. We find a slope $\alpha = 0.49^{+0.20}_{-0.15}$, normalization $
\exp(\beta) = 84.0^{+9.2}_{-14.8}$ and $\sigma_{\ln \lambda | \mu} =
0.17^{+0.13}_{-0.09}$ using CFHT richness estimates. In comparison to other
weak lensing richness-mass relations, we find the normalization of the richness
statistically agreeing with the normalization of other scaling relations from a
broad redshift range ($0.0<z<0.65$) and with different cluster selection
(X-ray, Sunyaev-Zeldovich, and optical).


Abstract:
  Sequential fundraising in two sided online platforms enable peer to peer
lending by sequentially bringing potential contributors, each of whose
decisions impact other contributors in the market. However, understanding the
dynamics of sequential contributions in online platforms for peer lending has
been an open ended research question. The centralized investment mechanism in
these platforms makes it difficult to understand the implicit competition that
borrowers face from a single lender at any point in time. Matching markets are
a model of pairing agents where the preferences of agents from both sides in
terms of their preferred pairing for transactions can allow to decentralize the
market. We study investment designs in two sided platforms using matching
markets when the investors or lenders also face restrictions on the investments
based on borrower preferences. This situation creates an implicit competition
among the lenders in addition to the existing borrower competition, especially
when the lenders are uncertain about their standing in the market and thereby
the probability of their investments being accepted or the borrower loan
requests for projects reaching the reserve price. We devise a technique based
on sequential decision making that allows the lenders to adjust their choices
based on the dynamics of uncertainty from competition over time. We simulate
two sided market matchings in a sequential decision framework and show the
dynamics of the lender regret amassed compared to the optimal borrower-lender
matching and find that the lender regret depends on the initial preferences set
by the lenders which could affect their learning over decision making steps.


Abstract:
  In this short communication, I gave a generalization of measurement postulate
in quantum mechanics. It is regarding the case with partial measurement,
namely, measurement on only part of a wave function. Upon a partial
measurement, the wavefunction will either collapse in one of the eigenstate
covered by the measured partial wavefunction; or collapses out of the measured
part and shifts to the unmeasured part. An explanation in the WISE
(Wavefunction Is System Entity) interpretation is given.


Abstract:
  Three-dimensional (3D) point clouds are important data representations in
visualization applications. The rapidly growing utility and popularity of point
cloud processing strongly motivate a plethora of research activities on
large-scale point cloud processing and feature extraction. In this work, we
investigate point cloud resampling based on hypergraph signal processing
(HGSP). We develop a novel method to extract sharp object features and reduce
the data size of point cloud representation. By directly estimating hypergraph
spectrum based on hypergraph stationary processing, we design a spectral
kernel-based filter to capture high-dimensional interactions among point signal
nodes and to better preserve object surface outlines. Experimental results
validate the effectiveness of hypergraph in representing point clouds, and
demonstrate the robustness of the proposed algorithm under noise.


Abstract:
  The large quantities of dust that have been found in a number of high
redshift galaxies have led to suggestions that core-collapse supernovae (CCSNe)
are the main sources of their dust and have motivated the measurement of the
dust masses formed by local CCSNe. For Cassiopeia~A, an oxygen-rich remnant of
a Type~IIb CCSN, a dust mass of 0.6-1.1~M$_\odot$ has already been determined
by two different methods, namely (a) from its far-infrared spectral energy
distribution and (b) from analysis of the red-blue emission line asymmetries in
its integrated optical spectrum. We present a third, independent, method for
determining the mass of dust contained within Cas~A. This compares the relative
fluxes measured in similar apertures from [O~{\sc iii}] far-infrared and
visual-region emission lines, taking into account foreground dust extinction,
in order to determine internal dust optical depths, from which corresponding
dust masses can be obtained. Using this method we determine a dust mass within
Cas~A of at least 0.99$^{+0.10}_{-0.09}$~M$_\odot$.


Abstract:
  For continuous self-maps of compact metric spaces, we consider the notions of
generic and dense chaos introduced by Lasota and Snoha and their variations for
the distributional chaos, under the assumption of shadowing. We give some
equivalent properties to generic uniform (distributional) chaos in terms of
chains and prove their equivalence to generic (distributional) chaos under the
shadowing. Several examples illustrating the main results are also given.


Abstract:
  Robust and persistent localisation is essential for ensuring the safe
operation of autonomous vehicles. When operating in large and diverse urban
driving environments, autonomous vehicles are frequently exposed to situations
that violate the assumptions of algorithms, suffer from the failure of one or
more sensors, or other events that lead to a loss of localisation.
  This paper proposes the use of a behavior tree framework that can monitor the
performance of localisation health metrics and triggers intelligent responses
such as sensor switching and loss recovery. The algorithm presented selects the
best available sensor data at given time and location, and can perform a series
of actions to react to adverse situations. The behavior tree encapsulates the
system-level logic to give commands that make up the intelligent behaviors, so
that the localisation "actuators" (data association, optimisation, filters,
etc) can perform decoupled actions without needing context. Experimental
results to validate the algorithms are presented using the University of Sydney
Campus dataset which was taken weekly over an 18 month period. A video showing
the online localisation process can be found here: https://youtu.be/353uKqXLV5g


Abstract:
  DPLL algorithm for solving the Boolean satisfiability problem (SAT) can be
represented in the form of a procedure that, using heuristics $A$ and $B$,
select the variable $x$ from the input formula $\varphi$ and the value $b$ and
runs recursively on the formulas $\varphi[x := b]$ and $\varphi[x := 1 - b]$.
Exponential lower bounds on the running time of DPLL algorithms on
unsatisfiable formulas follow from the lower bounds for tree-like resolution
proofs. Lower bounds on satisfiable formulas are also known for some classes of
DPLL algorithms such as "myopic" and "drunken" algorithms.
  All lower bounds are made for the classes of DPLL algorithms that limit
heuristics access to the formula. In this paper we consider DPLL algorithms
with heuristics that have unlimited access to the formula but use small memory.
We show that for any pair of heuristics with small memory there exists a family
of satisfiable formulas $\Phi_n$ such that a DPLL algorithm that uses these
heuristics runs in exponential time on the formulas $\Phi_n$.


Abstract:
  This report describes the speaker diarization system developed by the ABSP
Laboratory team for the third DIHARD speech diarization challenge. Our primary
contribution is to develop acoustic domain identification (ADI) system for
speaker diarization. We investigate speaker embeddings based ADI system. We
apply a domain-dependent threshold for agglomerative hierarchical clustering.
Besides, we optimize the parameters for PCA-based dimensionality reduction in a
domain-dependent way. Our method of integrating domain-based processing schemes
in the baseline system of the challenge achieved a relative improvement of
$9.63\%$ and $10.64\%$ in DER for core and full conditions, respectively, for
Track 1 of the DIHARD III evaluation set.


Abstract:
  Though deep learning has shown successful performance in classifying the
label and severity stage of certain diseases, most of them give few
explanations on how to make predictions. Inspired by Koch's Postulates, the
foundation in evidence-based medicine (EBM) to identify the pathogen, we
propose to exploit the interpretability of deep learning application in medical
diagnosis. By determining and isolating the neuron activation patterns on which
diabetic retinopathy (DR) detector relies to make decisions, we demonstrate the
direct relation between the isolated neuron activation and lesions for a
pathological explanation. To be specific, we first define novel pathological
descriptors using activated neurons of the DR detector to encode both spatial
and appearance information of lesions. Then, to visualize the symptom encoded
in the descriptor, we propose Patho-GAN, a new network to synthesize medically
plausible retinal images. By manipulating these descriptors, we could even
arbitrarily control the position, quantity, and categories of generated
lesions. We also show that our synthesized images carry the symptoms directly
related to diabetic retinopathy diagnosis. Our generated images are both
qualitatively and quantitatively superior to the ones by previous methods.
Besides, compared to existing methods that take hours to generate an image, our
second level speed endows the potential to be an effective solution for data
augmentation.


Abstract:
  A new direct-detection-compatible signalling scheme is proposed for
fiber-optic communication over short distances. Controlled inter-symbol
interference is exploited to extract phase information, thereby achieving
spectral efficiencies about one bit less, per second per hertz, of those of a
coherent detector.


Abstract:
  Lung nodule malignancy prediction is an essential step in the early diagnosis
of lung cancer. Besides the difficulties commonly discussed, the challenges of
this task also come from the ambiguous labels provided by annotators, since
deep learning models may learn, even amplify, the bias embedded in them. In
this paper, we propose a multi-view "divide-and-rule" (MV-DAR) model to learn
from both reliable and ambiguous annotations for lung nodule malignancy
prediction. According to the consistency and reliability of their annotations,
we divide nodules into three sets: a consistent and reliable set (CR-Set), an
inconsistent set (IC-Set), and a low reliable set (LR-Set). The nodule in
IC-Set is annotated by multiple radiologists inconsistently, and the nodule in
LR-Set is annotated by only one radiologist. The proposed MV-DAR contains three
DAR submodels to characterize a lung nodule from three orthographic views. Each
DAR consists of a prediction network (Prd-Net), a counterfactual network
(CF-Net), and a low reliable network (LR-Net), learning on CR-Set, IC-Set, and
LR-Set, respectively. The image representation ability learned by CF-Net and
LR-Net is then transferred to Prd-Net by negative-attention module (NA-Module)
and consistent-attention module (CA-Module), aiming to boost the prediction
ability of Prd-Net. The MV-DAR model has been evaluated on the LIDC-IDRI
dataset and LUNGx dataset. Our results indicate not only the effectiveness of
the proposed MV-DAR model in learning from ambiguous labels but also its
superiority over present noisy label-learning models in lung nodule malignancy
prediction.


Abstract:
  This paper is devoted to study the thermal aspects of a photon gas within the
context of Planck-scale-modified dispersion relations. We study the spectrum of
radiation and the correction to the \textit{Stefan$-$Boltzmann} law in
different cases when the Lorentz symmetry is no longer preserved. Explicitly,
we examine two models within the context of CPT$-$even and CPT$-$odd sectors
respectively. To do so, three distinct scenarios of the Universe are
considered: the Cosmic Microwave Background (CMB), the electroweak epoch, and
the inflationary era. Moreover, the equations of state in these cases turn out
to display a dependence on Lorentz-breaking parameters. Finally, we also
provide for both theories the analyses of the Helmholtz free energy, the mean
energy, the entropy and the heat capacity.


Abstract:
  Developing machine learning models can be seen as a process similar to the
one established for traditional software development. A key difference between
the two lies in the strong dependency between the quality of a machine learning
model and the quality of the data used to train or perform evaluations. In this
work, we demonstrate how different aspects of data quality propagate through
various stages of machine learning development. By performing a joint analysis
of the impact of well-known data quality dimensions and the downstream machine
learning process, we show that different components of a typical MLOps pipeline
can be efficiently designed, providing both a technical and theoretical
perspective.


Abstract:
  In this paper, we treat an open problem related to the number of periodic
orbits of Hamiltonian diffeomorphisms on closed symplectic manifolds, so-called
generic Conley conjecture. Generic Conley conjecture states that generically
Hamiltonian diffeomorphisms have infinitely many simple contractible periodic
orbits. We prove generic Conley conjecture for very wide classes of symplectic
manifolds.


Abstract:
  There is a rapidly growing literature on dark patterns, user interface
designs -- typically related to shopping or privacy -- that researchers deem
problematic. Recent work has been predominantly descriptive, documenting and
categorizing objectionable user interfaces. These contributions have been
invaluable in highlighting specific designs for researchers and policymakers.
But the current literature lacks a conceptual foundation: What makes a user
interface a dark pattern? Why are certain designs problematic for users or
society?
  We review recent work on dark patterns and demonstrate that the literature
does not reflect a singular concern or consistent definition, but rather, a set
of thematically related considerations. Drawing from scholarship in psychology,
economics, ethics, philosophy, and law, we articulate a set of normative
perspectives for analyzing dark patterns and their effects on individuals and
society. We then show how future research on dark patterns can go beyond
subjective criticism of user interface designs and apply empirical methods
grounded in normative perspectives.


Abstract:
  An important class of structural models investigates the determinants of
skill formation and the optimal timing of interventions. To achieve point
identification of the parameters, researcher typically normalize the scale and
location of the unobserved skills. This paper shows that these seemingly
innocuous restrictions can severely impact the interpretation of the parameters
and counterfactual predictions. For example, simply changing the units of
measurements of observed variables might yield ineffective investment
strategies and misleading policy recommendations. To tackle these problems,
this paper provides a new identification analysis, which pools all restrictions
of the model, characterizes the identified set of all parameters without
normalizations, illustrates which features depend on these normalizations, and
introduces a new set of important policy-relevant parameters that are
identified under weak assumptions and yield robust conclusions. As a byproduct,
this paper also presents a general and formal definition of when restrictions
are truly normalizations.


Abstract:
  Exact solutions for quasineutral plasma acceleration of magnetized plasma in
the paraxial magnetic nozzle are obtained. It is shown that the non-monotonic
magnetic field with a local maximum of the magnetic field is a necessary
condition for the formation of the quasineutral accelerating potential
structure. A global nature of the accelerating potential that occurs as a
result of the constraint due to the regularity condition at the sonic point is
emphasized and properties of such solutions are discussed for the case of
general polytropic equation of state for electrons.


Abstract:
  Effective information disclosure in the context of databases with a large
conceptual schema is known to be a non-trivial problem. In particular the
formulation of ad-hoc queries is a major problem in such contexts. Existing
approaches for tackling this problem include graphical query interfaces, query
by navigation, and query by construction. In this article we propose the point
to point query mechanism that can be combined with the existing mechanism into
an unprecedented computer supported query formulation mechanism. In a point to
point query a path through the information structure is build. This path can
then be used to formulate more complex queries. A point to point query is
typically useful when users know some object types which are relevant for their
information need, but do not (yet) know how they are related in the conceptual
schema. Part of the point to point query mechanism is therefore the selection
of the most appropriate path between object types (points) in the conceptual
schema. This article both discusses some of the pragmatic issues involved in
the point to point query mechanism, and the theoretical issues involved in
finding the relevant paths between selected object types.


Abstract:
  Automatically detecting/segmenting object(s) that blend in with their
surroundings is difficult for current models. A major challenge is that the
intrinsic similarities between such foreground objects and background
surroundings make the features extracted by deep model indistinguishable. To
overcome this challenge, an ideal model should be able to seek valuable, extra
clues from the given scene and incorporate them into a joint learning framework
for representation co-enhancement. With this inspiration, we design a novel
Mutual Graph Learning (MGL) model, which generalizes the idea of conventional
mutual learning from regular grids to the graph domain. Specifically, MGL
decouples an image into two task-specific feature maps -- one for roughly
locating the target and the other for accurately capturing its boundary details
-- and fully exploits the mutual benefits by recurrently reasoning their
high-order relations through graphs. Importantly, in contrast to most mutual
learning approaches that use a shared function to model all between-task
interactions, MGL is equipped with typed functions for handling different
complementary relations to maximize information interactions. Experiments on
challenging datasets, including CHAMELEON, CAMO and COD10K, demonstrate the
effectiveness of our MGL with superior performance to existing state-of-the-art
methods.


Abstract:
  Let $\mu_1 \ge \ldots \ge \mu_n$ denote the eigenvalues of a graph $G$ with
$m$ edges and clique number $\omega(G)$. Nikiforov proved a spectral version of
Tur\'an's theorem that \[ \mu_1^2 \le \frac{2m(\omega - 1)}{\omega}, \] and
Bollob\'as and Nikiforov conjectured that for $G \not = K_n$ \[ \mu_1^2 +
\mu_2^2 \le \frac{2m(\omega - 1)}{\omega}. \] This paper proposes the
conjecture that for all graphs $(\mu_1^2 + \mu_2^2)$ in this inequality can be
replaced by the sum of the squares of the $\omega$ largest eigenvalues,
provided they are positive. We prove the conjecture for weakly perfect, Kneser,
Johnson and classes of strongly regular graphs. We also provide experimental
evidence and describe how the bound can be applied.


Abstract:
  A rotated odometer is an infinite interval exchange transformation (IET)
obtained as a composition of the von Neumann-Kakutani map and a finite IET of
intervals of equal length. In this paper, we consider rotated odometers for
which the finite IET is of intervals of length $2^{-N}$, for some $N \geq 1$.
We show that every such system is measurably isomorphic to a
$\mathbb{Z}$-action on a rooted tree, and that the unique minimal aperiodic
subsystem of this action is always measurably isomorphic to the action of the
adding machine. We discuss the applications of this work to the study of group
actions on binary trees.


Abstract:
  Near field acoustical signals from fireballs (ranges<200 km), when detected
by dense ground networks, may be used to estimate the orientation of the
trajectory of a fireball (Pujol et al., 2005) as well as fragmentation
locations (Kalenda et al., 2014; Edwards and Hildebrand, 2004). Distinguishing
ballistic arrivals (from the cylindrical shock of the fireball)from
fragmentation generated signals (quasi-spherical sources) remains a challenge,
but are obtainable through analysis of the acoustic path and the timing
observed at ground instruments. Here we describe an integrated computer code,
termed the Bolide Acoustic Modelling program or BAM, to estimate fireball
trajectories and energetics. We develop a new methodology for measuring energy
release from bolide fragmentation episodes solely from acoustic measurements
and incorporate this into BAM. We also explore the sensitivity of
seismo-acoustic fireball solutions and energy estimates to uncertainty in the
underlying atmospheric model. Applying BAM to the Stubenberg meteorite
producing fireball, we find the total fireball energy from ballistic arrivals
to be approximately $5 \times 10^{10}$J which compares favorably to the optical
estimate of $4.36 \times 10^{10}$J. The combined fragmentation energy of the
Stubenberg event from acoustic data was found to be $1.47^{+0.28}_{-0.12}
\times 10^{10}$J, roughly one third of the ballistic or optical total energy.
We also show that measuring fireball velocities from acoustic data alone is
very challenging but may be possible for slow, deeply penetrating fireballs
with shallow entry angles occurring over dense seismic/infrasound networks.


Abstract:
  Despite the increased computational resources, the simulation-based design
optimization (SBDO) procedure can be very expensive from a computational
viewpoint, especially if high-fidelity solvers are required. Multi-fidelity
metamodels have been successfully applied to reduce the computational cost of
the SBDO process. In this context, the paper presents the performance
assessment of an adaptive multi-fidelity metamodel based on a Gaussian process
regression (MF-GPR) for noisy data. The MF-GPR is developed to: (i) manage an
arbitrary number of fidelity levels, (ii) deal with objective function
evaluations affected by noise, and (iii) improve its fitting accuracy by
adaptive sampling. Multi-fidelity is achieved by bridging a low-fidelity
metamodel with metamodels of the error between successive fidelity levels. The
MF-GPR handles the numerical noise through regression. The adaptive sampling
method is based on the maximum prediction uncertainty and includes rules to
automatically select the fidelity to sample. The MF-GPR performance are
assessed on a set of five analytical benchmark problems affected by noisy
objective function evaluations. Since the noise introduces randomness in the
evaluation of the objective function, a statistical analysis approach is
adopted to assess the performance and the robustness of the MF-GPR. The paper
discusses the efficiency and effectiveness of the MF-GPR in globally
approximating the objective function and identifying the global minimum. One,
two, and three fidelity levels are used. The results of the statistical
analysis show that the use of three fidelity levels achieves a more accurate
global representation of the noise-free objective function compared to the use
of one or two fidelities.


Abstract:
  We investigate four finiteness conditions related to residual finiteness:
complete separability, strong subsemigroup separability, weak subsemigroup
separability and monogenic subsemigroup separability. For each of these
properties we examine under which conditions the property is preserved under
direct products. We also consider if any of the properties are inherited by the
factors in a direct product. We give necessary and sufficient conditions for
finite semigroups to preserve the properties of strong subsemigroup
separability and monogenic subsemigroup separability in a direct product.


Abstract:
  Pretraining has sparked groundswell of interest in deep learning workflows to
learn from limited data and improve generalization. While this is common for 2D
image classification tasks, its application to 3D medical imaging tasks like
chest CT interpretation is limited. We explore the idea of whether pretraining
a model on realistic videos could improve performance rather than training the
model from scratch, intended for tuberculosis type classification from chest CT
scans. To incorporate both spatial and temporal features, we develop a hybrid
convolutional neural network (CNN) and recurrent neural network (RNN) model,
where the features are extracted from each axial slice of the CT scan by a CNN,
these sequence of image features are input to a RNN for classification of the
CT scan. Our model termed as ViPTT-Net, was trained on over 1300 video clips
with labels of human activities, and then fine-tuned on chest CT scans with
labels of tuberculosis type. We find that pretraining the model on videos lead
to better representations and significantly improved model validation
performance from a kappa score of 0.17 to 0.35, especially for
under-represented class samples. Our best method achieved 2nd place in the
ImageCLEF 2021 Tuberculosis - TBT classification task with a kappa score of
0.20 on the final test set with only image information (without using clinical
meta-data). All codes and models are made available.


Abstract:
  We give a quantitative characterization of the pairs of weights $(w,v)$ for
which the dyadic version of the one-sided Hardy-Littlewood maximal operator
satisfies a restricted weak $(p,p)$ type inequality, for $1\leq p<\infty$. More
precisely, given any measurable set $E_0$ the estimate \[w(\{x\in \mathbb{R}^n:
M^{+,d}(\mathcal{X}_{E_0})(x)>t\})\leq
\frac{C[(w,v)]_{A_p^{+,d}(\mathcal{R})}^p}{t^p}v(E_0)\] holds if and only if
the pair $(w,v)$ belongs to $A_p^{+,d}(\mathcal{R})$, that is
\[\frac{|E|}{|Q|}\leq
[(w,v)]_{A_p^{+,d}(\mathcal{R})}\left(\frac{v(E)}{w(Q)}\right)^{1/p}\] for
every dyadic cube $Q$ and every measurable set $E\subset Q^+$. The proof
follows some ideas appearing in [Sheldy Ombrosi, \emph{Weak weighted
inequalities for a dyadic one-sided maximal function in {$\Bbb R^n$}}, Proc.
Amer. Math. Soc. \textbf{133} (2005), no.~6, 1769--1775].
  We also obtain a similar quantitative characterization for the non-dydadic
case in $\mathbb{R}^2$ by following the main ideas in [L.~Forzani, F.~J.
Mart\'{\i}n-Reyes, and S.~Ombrosi, \emph{Weighted inequalities for the
two-dimensional one-sided {H}ardy-{L}ittlewood maximal function}, Trans. Amer.
Math. Soc. \textbf{363} (2011), no.~4, 1699--1719].


Abstract:
  This work regards the order polytopes arising from the class of generalized
snake posets and their posets of meet-irreducible elements. Among generalized
snake posets of the same rank, we characterize those whose order polytopes have
minimal and maximal volume. We give a combinatorial characterization of the
circuits in these order polytopes and then conclude that every triangulation is
unimodular. For a generalized snake word, we count the number of flips for the
canonical triangulation of these order polytopes. We determine that the flip
graph of the order polytope of the poset whose lattice of filters comes from a
ladder is the Cayley graph of a symmetric group. Lastly, we introduce an
operation on triangulations called twists and prove that twists preserve
regular triangulations.


Abstract:
  Our research presents the case-study of a mobile phone based, voice-driven
platform - Mobile Vaani, established with a goal to empower poor and
marginalized communities to create their own local media. In this paper, we
derive a comprehensive theory of change for Mobile Vaani from data gathered
using the Most Significant Change technique. This paper contributes towards
formulating a theory of change for technology-driven community media platforms
which can be adapted to other ICTD interventions too.