Abstract:
  Periodic stacking of topologically trivial and non-trivial layers with
opposite symmetry of the valence and conduction bands induces topological
interface states that, in the strong coupling limit, hybridize both across the
topological and normal insulator layers. Using band structure engineering, such
superlattices can be effectively realized using the IV-VI lead tin
chalcogenides. This leads to emergent minibands with a tunable topology as
demonstrated both by theory and experiments. The topological minibands are
proven by magneto-optical spectroscopy, revealing Landau level transitions both
at the center and edges of the artificial superlattice mini Brillouin zone.
Their topological character is identified by the topological phase transitions
within the minibands observed as a function of temperature. The critical
temperature of this transition as well as the miniband gap and miniband width
can be precisely controlled by the layer thicknesses and compositions. This
witnesses the generation of a new fully tunable quasi-3D topological state that
provides a template for realization of magnetic Weyl semimetals and other
strongly interacting topological phases.


Abstract:
  Modern key-value stores, object stores, Internet proxy caches, as well as
Content Delivery Networks (CDN) often manage objects of diverse sizes, e.g.,
blobs, video files of different lengths, images with varying resolution, and
small documents. In such workloads, size-aware cache policies outperform
size-oblivious algorithms. Unfortunately, existing size-aware algorithms tend
to be overly complicated and computationally~expensive.
  Our work follows a more approachable pattern; we extend the prevalent
(size-oblivious) TinyLFU cache admission policy to handle variable sized items.
Implementing our approach inside two popular caching libraries only requires
minor changes. We show that our algorithms yield competitive or better
hit-ratios and byte hit-ratios compared to the state of the art size-aware
algorithms such as AdaptSize, LHD, LRB, and GDSF. Further, a runtime comparison
indicates that our implementation is faster by up to x3 compared to the best
alternative, i.e., it imposes much lower CPU overhead.


Abstract:
  In this paper, we consider the image captioning task from a new
sequence-to-sequence prediction perspective and propose CaPtion TransformeR
(CPTR) which takes the sequentialized raw images as the input to Transformer.
Compared to the "CNN+Transformer" design paradigm, our model can model global
context at every encoder layer from the beginning and is totally
convolution-free. Extensive experiments demonstrate the effectiveness of the
proposed model and we surpass the conventional "CNN+Transformer" methods on the
MSCOCO dataset. Besides, we provide detailed visualizations of the
self-attention between patches in the encoder and the "words-to-patches"
attention in the decoder thanks to the full Transformer architecture.


Abstract:
  Wikipedia abstract generation aims to distill a Wikipedia abstract from web
sources and has met significant success by adopting multi-document
summarization techniques. However, previous works generally view the abstract
as plain text, ignoring the fact that it is a description of a certain entity
and can be decomposed into different topics. In this paper, we propose a
two-stage model TWAG that guides the abstract generation with topical
information. First, we detect the topic of each input paragraph with a
classifier trained on existing Wikipedia articles to divide input documents
into different topics. Then, we predict the topic distribution of each abstract
sentence, and decode the sentence from topic-aware representations with a
Pointer-Generator network. We evaluate our model on the WikiCatSum dataset, and
the results show that \modelnames outperforms various existing baselines and is
capable of generating comprehensive abstracts. Our code and dataset can be
accessed at \url{https://github.com/THU-KEG/TWAG}


Abstract:
  When probabilistic classifiers are trained and calibrated, the so-called
grouping loss component of the calibration loss can easily be overlooked.
Grouping loss refers to the gap between observable information and information
actually exploited in the calibration exercise. We investigate the relation
between grouping loss and the concept of sufficiency, identifying
comonotonicity as a useful criterion for sufficiency. We revisit the probing
reduction approach of Langford & Zadrozny (2005) and find that it produces an
estimator of probabilistic classifiers that reduces grouping loss. Finally, we
discuss Brier curves as tools to support training and 'sufficient' calibration
of probabilistic classifiers.


Abstract:
  Data hiding is one widely used approach for protecting authentication and
ownership. Most multimedia content like images and videos are transmitted or
saved in the compressed form. This kind of lossy compression, such as JPEG, can
destroy the hidden data, which raises the need of robust data hiding. It is
still an open challenge to achieve the goal of data hiding that can be against
these compressions. Recently, deep learning has shown large success in data
hiding, while non-differentiability of JPEG makes it challenging to train a
deep pipeline for improving robustness against lossy compression. The existing
SOTA approaches replace the non-differentiable parts with differentiable
modules that perform similar operations. Multiple limitations exist: (a) large
engineering effort; (b) requiring a white-box knowledge of compression attacks;
(c) only works for simple compression like JPEG. In this work, we propose a
simple yet effective approach to address all the above limitations at once.
Beyond JPEG, our approach has been shown to improve robustness against various
image and video lossy compression algorithms.


Abstract:
  Short product lifecycles and a high variety of products force industrial
manufacturing processes to change frequently. Due to the manual approach of
many quality analysis techniques, they can significantly slow down adaption
processes of production systems or make production unprofitable. Therefore,
automating them can be a key technology for keeping pace with market demand of
the future. The methodology presented here aims at a meta-model supporting
automation for PFMEA. The method differentiates product requirements,
production steps and quality measures in such a way, that complex quality
requirements can be addressed in any instance of a factory using a common
meta-modeling language.


Abstract:
  Sarcasm is a sophisticated way of wrapping any immanent truth, mes-sage, or
even mockery within a hilarious manner. The advent of communications using
social networks has mass-produced new avenues of socialization. It can be
further said that humor, irony, sarcasm, and wit are the four chariots of being
socially funny in the modern days. In this paper, we manually extract the
sarcastic word distribution features of a benchmark pop culture sarcasm corpus,
containing sarcastic dialogues and monologues. We generate input sequences
formed of the weighted vectors from such words. We further propose an
amalgamation of four parallel deep long-short term networks (pLSTM), each with
distinctive activation classifier. These modules are primarily aimed at
successfully detecting sarcasm from the text corpus. Our proposed model for
detecting sarcasm peaks a training accuracy of 98.95% when trained with the
discussed dataset. Consecutively, it obtains the highest of 98.31% overall
validation accuracy on two handpicked Project Gutenberg English humor
literature among all the test cases. Our approach transcends previous
state-of-the-art works on several sarcasm corpora and results in a new gold
standard performance for sarcasm detection.


Abstract:
  Quantum corrections to holographic entanglement entropy require knowledge of
the bulk quantum state. In this paper, we derive a novel dual prescription for
the generalized entropy that allows us to interpret the leading quantum
corrections in a geometric way with minimal input from the bulk state. The
equivalence is proven using tools borrowed from convex optimization. The new
prescription does not involve bulk surfaces but instead uses a generalized
notion of a flow, which allows for possible sources or sinks in the bulk
geometry. In its discrete version, our prescription can alternatively be
interpreted in terms of a set of Planck-thickness bit threads, which can be
either classical or quantum. This interpretation uncovers an aspect of the
generalized entropy that admits a neat information-theoretic description,
namely, the fact that the quantum corrections can be cast in terms of
entanglement distillation of the bulk state. We also prove some general
properties of our prescription, including nesting and a quantum version of the
max multiflow theorem. These properties are used to verify that our proposal
respects known inequalities that a von Neumann entropy must satisfy, including
subadditivity and strong subadditivity, as well as to investigate the fate of
the holographic monogamy. Finally, using the Iyer-Wald formalism we show that
for cases with a local modular Hamiltonian there is always a canonical solution
to the program that exploits the property of bulk locality. Combining with
previous results by Swingle and Van Raamsdonk, we show that the consistency of
this special solution requires the semi-classical Einstein's equations to hold
for any consistent perturbative bulk quantum state.


Abstract:
  Assurance of asymptotic trajectory tracking in robotic manipulators with a
continuous control law in the presence of unmodeled dynamics or external
disturbance is a challenging problem. Recently, it is asserted that this aim is
achieved by designing a traditional model-free controller together with time
delay estimation (TDE) such that neither dynamical parameters nor conservative
assumptions on the external disturbance are required. In this note, the purpose
is to show that this claim is wrong. Additionally, modification of this method
for some special cases is presented.


Abstract:
  Summarization systems face the core challenge of identifying and selecting
important information. In this paper, we tackle the problem of content
selection in unsupervised extractive summarization of long, structured
documents. We introduce a wide range of heuristics that leverage cognitive
representations of content units and how these are retained or forgotten in
human memory. We find that properties of these representations of human memory
can be exploited to capture relevance of content units in scientific articles.
Experiments show that our proposed heuristics are effective at leveraging
cognitive structures and the organization of the document (i.e.\ sections of an
article), and automatic and human evaluations provide strong evidence that
these heuristics extract more summary-worthy content units.


Abstract:
  Chernoff approximations to strongly continuous one-parameter semigroups give
solutions to a wide class of differential equations. This paper studies the
rate of convergence of the Chernoff approximations. We provide simple natural
examples for which the convergence is arbitrary fast, is arbitrary slow, and
holds in the strong operator topology but does not hold in the norm operator
topology. We also prove a general theorem that gives an upper estimate for the
speed of decay of the norm of the residual term of the Chernoff approximations.
The result is applied to one-dimensional parabolic differential equations with
variable coefficients. The obtained estimates can be used for the numerical
solution of PDEs.


Abstract:
  Let $G$ be a transitive permutation group on a finite set $\Omega$ and recall
that a base for $G$ is a subset of $\Omega$ with trivial pointwise stabiliser.
The base size of $G$, denoted $b(G)$, is the minimal size of a base. If
$b(G)=2$ then we can study the Saxl graph $\Sigma(G)$ of $G$, which has vertex
set $\Omega$ and two vertices are adjacent if they form a base. This is a
vertex-transitive graph, which is conjectured to be connected with diameter at
most $2$ when $G$ is primitive. In this paper, we combine probabilistic and
computational methods to prove a strong form of this conjecture for all almost
simple primitive groups with soluble point stabilisers. In this setting, we
also establish best possible lower bounds on the clique and independence
numbers of $\Sigma(G)$ and we determine the groups with a unique regular
suborbit, which can be interpreted in terms of the valency of $\Sigma(G)$.


Abstract:
  This paper investigates bilateral control of teleoperators with closed
architecture and subjected to arbitrary bounded time-varying delay. A prominent
challenge for bilateral control of such teleoperators lies in the closed
architecture, especially in the context not involving interaction force/torque
measurement. This yields the long-standing situation that most bilateral
control rigorously developed in the literature is hard to be justified as
applied to teleoperators with closed architecture. With a new class of dynamic
feedback, we propose kinematic and adaptive dynamic controllers for
teleoperators with closed architecture, and we show that the proposed kinematic
and dynamic controllers are robust with respect to arbitrary bounded
time-varying delay. In addition, by exploiting the input-output properties of
an inverted form of the dynamics of robot manipulators with closed
architecture, we remove the assumption of uniform exponential stability of a
linear time-varying system due to the adaptation to the gains of the inner
controller in demonstrating stability of the presented adaptive dynamic
control. The application of the proposed approach is illustrated by the
experimental results using a Phantom Omni and a UR10 robot.


Abstract:
  In this paper, we explore open-domain sketch-to-photo translation, which aims
to synthesize a realistic photo from a freehand sketch with its class label,
even if the sketches of that class are missing in the training data. It is
challenging due to the lack of training supervision and the large geometric
distortion between the freehand sketch and photo domains. To synthesize the
absent freehand sketches from photos, we propose a framework that jointly
learns sketch-to-photo and photo-to-sketch generation. However, the generator
trained from fake sketches might lead to unsatisfying results when dealing with
sketches of missing classes, due to the domain gap between synthesized sketches
and real ones. To alleviate this issue, we further propose a simple yet
effective open-domain sampling and optimization strategy to "fool" the
generator into treating fake sketches as real ones. Our method takes advantage
of the learned sketch-to-photo and photo-to-sketch mapping of in-domain data
and generalizes it to the open-domain classes. We validate our method on the
Scribble and SketchyCOCO datasets. Compared with the recent competing methods,
our approach shows impressive results in synthesizing realistic color, texture,
and maintaining the geometric composition for various categories of open-domain
sketches. Our code is available at https://github.com/Mukosame/AODA


Abstract:
  Aluminum nitride (AlN) is an emerging material for integrated quantum
photonics with its excellent linear and nonlinear optical properties. In
particular, its second-order nonlinear susceptibility $\chi^{(2)}$ allows
single-photon generation. We have grown AlN thin films on silicon nitride via
reactive DC magnetron sputtering. The thin films have been characterized using
X-ray diffraction, optical reflectometry, atomic force microscopy, and scanning
electron microscopy. The crystalline properties of the thin films have been
improved by optimizing the nitrogen to argon ratio and the magnetron DC power
of the deposition process. X-ray diffraction measurements confirm the
fabrication of high-quality c-axis oriented thin films with a full width at
half maximum of the rocking curves of 3.9 deg. for 300-nm-thick films. Atomic
force microscopy measurements reveal a root mean square surface roughness below
1 nm. The AlN deposition on SiN allows us to fabricate hybrid photonic circuits
with a new approach that avoids the challenging patterning of AlN.


Abstract:
  An intuitive and straightforward modelling of many locally-interacting
quantum system requires a local description of the quantised electromagnetic
field. In a recent paper [Southall et al., J. Mod. Opt. 68, 647 (2021)], we
showed that such a description, which must overcome several no-go theorems, is
indeed possible. To better justify our approach, this paper presents a
systematic quantisation of one-dimensional electromagnetic fields in position
space. Starting from the assumption that its basic building blocks are local
bosonic particles with a clear direction of propagation--so-called bosons
localised in position (BLiPs)--we identify the relevant Schroedinger equation
and construct Lorentz-covariant electric and magnetic field observables. In
addition we show that our approach simplifies to the standard description of
quantum electrodynamics when restricted to a subspace of monochromatic photon
Fock states.


Abstract:
  Modern wide-field high-cadence surveys have revealed the significant
diversity of optical transient phenomena in their luminosity and timescale
distributions, which led to the discovery of some mysterious fast optical
transients (FOTs). These FOTs can usually rise and decline remarkably in a
timescale of a few days to weeks, which are obviously much rapider than
ordinary supernovae. SN 2019bkc/ATLAS19dqr is one of the fastest detected FOTs
so far and, meanwhile, it was found to be un-associated with a host galaxy.
These discoveries provide a good chance to explore the possible origins of
FOTs. So, we model the light curves of SN 2019bkc in details. It is found that
SN 2019bkc can be well explained by the thermal emission of an explosion ejecta
that is powered by a long-lasting central engine. The engine could be a
spinning-down millisecond magnetar or a fallback accretion onto a compact
object. Combining the engine property, the mass of the ejecta, and the
hostlessness of SN 2019bkc, we suggest that this FOT is likely to originate
from a merger of a white dwarf and a neutron star.


Abstract:
  Decentralised government levels are often entrusted with the management of
public works and required to ensure well-timed infrastructure delivery to their
communities. We investigate whether monitoring the activity of local procuring
authorities during the execution phase of the works they manage may expedite
the infrastructure delivery process. Focussing on an Italian regional law which
imposes monitoring by the regional government on "strategic" works carried out
by local buyers, we draw causal claims using a regression-discontinuity
approach, made unusual by the presence of multiple assignment variables.
Estimation is performed through discrete-time survival analysis techniques.
Results show that monitoring does expedite infrastructure delivery.


Abstract:
  Our motivation stems from current medical research aiming at personalized
treatment using a molecular-based approach. The goal is to develop a more
precise and targeted decision making process, relative to traditional
treatments based primarily on clinical diagnoses. A challenge we address is
evaluating treatment effects for individuals affected by Glioblastoma (GBM), a
brain cancer where targeted therapy is essential to improve patients'
prospects. Specifically, we consider the pathway associated to cytokine
TGF-beta, whose abnormal signalling activity has been found to be linked to the
progression of GBM and other tumors. We analyze treatment effects within a
causal framework represented by a Directed Acyclic Graph (DAG) model, whose
vertices are the variables belonging to the TGF-beta pathway. A major obstacle
in implementing the above program is represented by individual heterogeneity,
implying that patients will respond differently to the same therapy. We address
this issue through an infinite mixture of Gaussian DAG-models where both the
graphical structure as well as the allied model parameters are regarded as
uncertain. Our procedure determines a clustering structure of the units
reflecting the underlying heterogeneity, and produces subject-specific causal
effects through Bayesian model averaging across a variety of model features.
When applied to the GBM dataset, it reveals that regulation of TGF-beta
proteins produces heterogeneous effects, represented by clusters of patients
potentially benefiting from selective interventions.


Abstract:
  Galaxy lenses are frequently modeled as an elliptical mass distribution with
external shear and isothermal spheres to account for secondary and
line-of-sight galaxies. There is statistical evidence that some fraction of
observed quads are inconsistent with these assumptions, and require a
dipole-like contribution to the mass with respect to the light. Simplifying
assumptions about the shape of mass distributions can lead to the incorrect
recovery of parameters such as $H_0$. We create several tests of synthetic quad
populations with different deviations from an elliptical shape, then fit them
with an ellipse+shear model, and measure the recovered values of $H_0$.
Kinematic constraints are not included. We perform two types of fittings -- one
with a single point source and one with an array of sources emulating an
extended source. We carry out two model-free comparisons between our mock quads
and the observed population. One result of these comparisons is a statistical
inconsistency not yet mentioned in the literature: the image distance ratios
with respect to the lens center of observed quads appear to span a much wider
range than those of synthetic or simulated quads. Bearing this discrepancy in
mind, our mock populations can result in biases on $H_0$ $\sim10\%$.


Abstract:
  Transformers and variational autoencoders (VAE) have been extensively
employed for symbolic (e.g., MIDI) domain music generation. While the former
boast an impressive capability in modeling long sequences, the latter allow
users to willingly exert control over different parts (e.g., bars) of the music
to be generated. In this paper, we are interested in bringing the two together
to construct a single model that exhibits both strengths. The task is split
into two steps. First, we equip Transformer decoders with the ability to accept
segment-level, time-varying conditions during sequence generation.
Subsequently, we combine the developed and tested in-attention decoder with a
Transformer encoder, and train the resulting MuseMorphose model with the VAE
objective to achieve style transfer of long musical pieces, in which users can
specify musical attributes including rhythmic intensity and polyphony (i.e.,
harmonic fullness) they desire, down to the bar level. Experiments show that
MuseMorphose outperforms recurrent neural network (RNN) based baselines on
numerous widely-used metrics for style transfer tasks.


Abstract:
  Consider a two-person zero-sum search game between a hider and a searcher.
The hider hides among $n$ discrete locations, and the searcher successively
visits individual locations until finding the hider. Known to both players, a
search at location $i$ takes $t_i$ time units and detects the hider -- if
hidden there -- independently with probability $q_i$, for $i=1,\ldots,n$. The
hider aims to maximize the expected time until detection, while the searcher
aims to minimize it. We prove the existence of an optimal strategy for each
player. In particular, the hider's optimal mixed strategy hides in each
location with a nonzero probability, and the searcher's optimal mixed strategy
can be constructed with up to $n$ simple search sequences. We develop an
algorithm to compute an optimal strategy for each player, and compare the
optimal hiding strategy with the simple hiding strategy which gives the
searcher no location preference at the beginning of the search.


Abstract:
  We consider the generalized Langevin equation (GLE) in a harmonic potential
with power law decay memory. We study the anomalous diffusion of the particle's
displacement and velocity. By comparison with the free particle situation in
which the velocity was previously shown to be either diffusive or subdiffusive,
we find that, when trapped in a harmonic potential, the particle's displacement
may either be diffusive or superdiffusive. Under slightly stronger assumptions
on the memory kernel, namely, for kernels related to the broad class of
completely monotonic functions, we show that both the free particle and the
harmonically bounded GLE satisfy the equipartition of energy condition. This
generalizes previously known results for the GLE under particular kernel
instances such as the generalized Rouse kernel or (exactly) a power law
function.


Abstract:
  Recent works [PhysRevLett.125.170402 (2020) and PhysRevLett.125.043603
(2020)] verify that the perfect single-photon character is a global property of
resonance fluorescence including all the spectral components, and then the
absence of some components can spoil the single-photon character, which
considerably limits the application of filtering scheme to obtain single-photon
source (SPS) with subnatural linewidth. Here, we note that the linewidth of
fluorescent photon is not always limited by the intrinsic linewidth of the
emitter, and provide a scheme where the linewidth of the fluorescent single
photon can be much smaller than the natural linewidth of emitter by
manipulating external coherent fields. By simulating the response of the filter
or detection setup with a bandwidth approaching and even much smaller than the
natural linewidth of emitter, the realization of ultranarrow SPS can be
confirmed. Besides, we demonstrate that this scheme of ultranarrow SPS can be
implemented based on various real physical platforms.


Abstract:
  In this paper, we present a Modern Standard Arabic (MSA) Sentence difficulty
classifier, which predicts the difficulty of sentences for language learners
using either the CEFR proficiency levels or the binary classification as simple
or complex. We compare the use of sentence embeddings of different kinds
(fastText, mBERT , XLM-R and Arabic-BERT), as well as traditional language
features such as POS tags, dependency trees, readability scores and frequency
lists for language learners. Our best results have been achieved using
fined-tuned Arabic-BERT. The accuracy of our 3-way CEFR classification is F-1
of 0.80 and 0.75 for Arabic-Bert and XLM-R classification respectively and 0.71
Spearman correlation for regression. Our binary difficulty classifier reaches
F-1 0.94 and F-1 0.98 for sentence-pair semantic similarity classifier.


Abstract:
  For $n\geq 4$ (even), the function
$\varphi_{n\mathcal{L}}(z)=1+nz/(n+1)+z^n/(n+1)$ maps the unit disk
$\mathbb{D}$ onto a domain bounded by an epicycloid with $n-1$ cusps. In this
paper, the class $\mathcal{S}^*_{n\mathcal{L}} =
\mathcal{S}^*(\varphi_{n\mathcal{L}})$ is studied and various inclusion
relations are established with other subclasses of starlike functions. The
bounds on initial coefficients is also computed. Various radii problems are
also solved for the class $\mathcal{S}^*_{n\mathcal{L}}$.


Abstract:
  Motivated by the study of physical models associated with General Relativity,
we review some finite-dimensional, geometric and covariant formulations that
allow us to characterize in a simple manner the symmetries for classical field
theory by implementing an appropriate fibre-bundle structure, either at the
Lagrangian, the multisymplectic or the polysymplectic levels. In particular, we
are able to formulate Noether's theorems by means of the covariant momentum
maps and to systematically introduce a covariant Poisson-Hamiltonian framework.
Also, by focusing on the space plus time decomposition for a generic classical
field theory and its relation to these geometric formulations, we are able to
successfully recover the gauge content and the true local degrees of freedom
for the theory. In order to illustrate the relevance of these geometric
frameworks, we center our attention to the analysis of a model for
$3$-dimensional theory of General Relativity that involves an arbitrary
Immirzi-like parameter. At the Lagrangian level, we reproduce the field
equations of the system which for this model turn out to be equivalent to the
vanishing torsion condition and the Einstein equations. We also concentrate on
the analysis of the gauge symmetries of the system in order to obtain the
Lagrangian covariant momentum map associated with the theory and, consequently,
its corresponding Noether currents. Next, we aim our attention to describing
how the gauge symmetries of the model yield covariant canonical transformations
on the covariant multimomenta phase-space, thus giving rise to the existence of
a covariant momentum map. Besides, we analyze the physical system under
consideration within the De Donder-Weyl canonical theory implemented at the
polysymplectic level, thus establishing a relation from the covariant momentum
map to the conserved currents of the theory.


Abstract:
  Given the hard problem of consciousness (Chalmers, 1995) there are no brain
electrophysiological correlates of the subjective experience (the felt quality
of redness or the redness of red, the experience of dark and light, the quality
of depth in a visual field, the sound of a clarinet, the smell of mothball,
bodily sensations from pains to orgasms, mental images that are conjured up
internally, the felt quality of emotion, the experience of a stream of
conscious thought or the phenomenology of thought). However, there are brain
occipital and left temporal electrophysiological correlates of the subjective
experience (Pereira, 2015). Notwithstanding, as evoked signal, the change in
event-related brain potentials phase (frequency is the change in phase over
time) is instantaneous, that is, the frequency will transiently be infinite: a
transient peak in frequency (positive or negative), if any, is instantaneous in
electroencephalogram averaging or filtering that the event-related brain
potentials required and the underlying structure of the event-related brain
potentials in the frequency domain cannot be accounted, for example, by the
Wavelet Transform (WT) or the Fast Fourier Transform (FFT) analysis, because
they require that frequency is derived by convolution rather than by
differentiation. However, as I show in the current original research report,
one suitable method for analyse the instantaneous change in event-related brain
potentials phase and accounted for a transient peak in frequency (positive or
negative), if any, in the underlying structure of the event-related brain
potentials is the Empirical Mode Decomposition with post processing (Xie et
al., 2014) Ensemble Empirical Mode Decomposition (postEEMD) and Hilbert-Huang
Transform (HHT).


Abstract:
  Several encryption proposals for DNS have been presented since 2016, but
their adoption was not comprehensively studied yet. This research measured the
current adoption of DoH (DNS over HTTPS), DoT (DNS over TLS), and DoQ (DNS over
QUIC) for five months at the beginning of 2021 by three different organizations
with global coverage. By comparing the total values, amount of requests per
user, and the seasonality of the traffic, it was possible to obtain the current
adoption trends. Moreover, we actively scanned the Internet for still-unknown
working DoH servers and we compared them with a novel curated list of
well-known DoH servers. We conclude that despite growing in 2020, during the
first five months of 2021 there was statistically significant evidence that the
average amount of Internet traffic for DoH, DoT and DoQ remained stationary.
However, we found that the amount of, still unknown and ready to use, DoH
servers grew 4 times. These measurements suggest that even though the amount of
encrypted DNS is currently not growing, there may probably be more connections
soon to those unknown DoH servers for benign and malicious purposes.


Abstract:
  In this comment we discuss relative strengths and weaknesses of simplex and
Dirichlet Dempster-Shafer inference as applied to multi-resolution tests of
independence.


Abstract:
  Deep learning has become the leading approach to assisted target recognition.
While these methods typically require large amounts of labeled training data,
domain adaptation (DA) or transfer learning (TL) enables these algorithms to
transfer knowledge from a labelled (source) data set to an unlabelled but
related (target) data set of interest. DA enables networks to overcome the
distribution mismatch between the source and target that leads to poor
generalization in the target domain. DA techniques align these distributions by
minimizing a divergence measurement between source and target, making the
transfer of knowledge from source to target possible. While these algorithms
have advanced significantly in recent years, most do not explicitly leverage
global data manifold structure in aligning the source and target. We propose to
leverage global data structure by applying a topological data analysis (TDA)
technique called persistent homology to TL.
  In this paper, we examine the use of persistent homology in a domain
adversarial (DAd) convolutional neural network (CNN) architecture. The
experiments show that aligning persistence alone is insufficient for transfer,
but must be considered along with the lifetimes of the topological
singularities. In addition, we found that longer lifetimes indicate robust
discriminative features and more favorable structure in data. We found that
existing divergence minimization based approaches to DA improve the topological
structure, as indicated over a baseline without these regularization
techniques. We hope these experiments highlight how topological structure can
be leveraged to boost performance in TL tasks.


Abstract:
  Speaker diarization is the process of labeling different speakers in a speech
signal. Deep speaker embeddings are generally extracted from short speech
segments and clustered to determine the segments belong to same speaker
identity. The x-vector, which embeds segment-level speaker characteristics by
statistically pooling frame-level representations, is one of the most widely
used deep speaker embeddings in speaker diarization. Multi-scale aggregation,
which employs multi-scale representations from different layers, has recently
successfully been used in short duration speaker verification. In this paper,
we investigate a multi-scale aggregation approach in an x-vector embedding
framework for speaker diarization by exploiting multiple statistics pooling
layers from different frame-level layers. Thus, it is expected that x-vectors
with multi-scale aggregation have the potential to capture meaningful speaker
characteristics from short segments, effectively taking advantage of different
information at multiple layers. Experimental evaluation on the CALLHOME dataset
showed that our approach provides substantial improvement over the baseline
x-vectors.


Abstract:
  Microscopic studies on thin film superconductors play an important role for
probing non-equilibrium phase transitions and revealing dynamics at the
nanoscale. However, magnetic sensors with nanometer scale spatial and
picosecond temporal resolution are essential for exploring these. Here, we
present an all-optical, microwave-free method, that utilizes the negatively
charged nitrogen-vacancy (NV) center in diamond as a non-invasive quantum
sensor and enables the spatial detection of the Meissner state in a
superconducting thin film. We place an NV implanted diamond membrane on a
superconducting LSCO thin film. The strong B-field dependence of the NV
photoluminescence (PL) allows us to investigate the Meissner screening in LSCO
under an externally applied magnetic field in a non-resonant manner.


Abstract:
  Unsupervised video-based person re-identification (re-ID) methods extract
richer features from video tracklets than image-based ones. The
state-of-the-art methods utilize clustering to obtain pseudo-labels and train
the models iteratively. However, they underestimate the influence of two kinds
of frames in the tracklet: 1) noise frames caused by detection errors or heavy
occlusions exist in the tracklet, which may be allocated with unreliable labels
during clustering; 2) the tracklet also contains hard frames caused by pose
changes or partial occlusions, which are difficult to distinguish but
informative. This paper proposes a Noise and Hard frame Aware Clustering (NHAC)
method. NHAC consists of a graph trimming module and a node re-sampling module.
The graph trimming module obtains stable graphs by removing noise frame nodes
to improve the clustering accuracy. The node re-sampling module enhances the
training of hard frame nodes to learn rich tracklet information. Experiments
conducted on two video-based datasets demonstrate the effectiveness of the
proposed NHAC under the unsupervised re-ID setting.


Abstract:
  Diverse cognitive processes set different demands on locally segregated and
globally integrated brain activity. However, it remains unclear how resting
brains configure their functional organization to balance the demands on
network segregation and integration to best serve cognition. Here, we use an
eigenmode-based approach to identify hierarchical modules in functional brain
networks, and quantify the functional balance between network segregation and
integration. In a large sample of healthy young adults (n=991), we combine the
whole-brain resting state functional magnetic resonance imaging (fMRI) data
with a mean-filed model on the structural network derived from diffusion tensor
imaging and demonstrate that resting brain networks are on average close to a
balanced state. This state allows for a balanced time dwelling at segregated
and integrated configurations, and highly flexible switching between them.
Furthermore, we employ structural equation modelling to estimate general and
domain-specific cognitive phenotypes from nine tasks, and demonstrate that
network segregation, integration and their balance in resting brains predict
individual differences in diverse cognitive phenotypes. More specifically,
stronger integration is associated with better general cognitive ability,
stronger segregation fosters crystallized intelligence and processing speed,
and individual's tendency towards balance supports better memory. Our findings
provide a comprehensive and deep understanding of the brain's functioning
principles in supporting diverse functional demands and cognitive abilities,
and advance modern network neuroscience theories of human cognition.


Abstract:
  A reasonable amount of annotated data is required for fine-tuning pre-trained
language models (PLM) on downstream tasks. However, obtaining labeled examples
for different language varieties can be costly. In this paper, we investigate
the zero-shot performance on Dialectal Arabic (DA) when fine-tuning a PLM on
modern standard Arabic (MSA) data only -- identifying a significant performance
drop when evaluating such models on DA. To remedy such performance drop, we
propose self-training with unlabeled DA data and apply it in the context of
named entity recognition (NER), part-of-speech (POS) tagging, and sarcasm
detection (SRD) on several DA varieties. Our results demonstrate the
effectiveness of self-training with unlabeled DA data: improving zero-shot
MSA-to-DA transfer by as large as \texttildelow 10\% F$_1$ (NER), 2\% accuracy
(POS tagging), and 4.5\% F$_1$ (SRD). We conduct an ablation experiment and
show that the performance boost observed directly results from the unlabeled DA
examples used for self-training. Our work opens up opportunities for leveraging
the relatively abundant labeled MSA datasets to develop DA models for zero and
low-resource dialects. We also report new state-of-the-art performance on all
three tasks and open-source our fine-tuned models for the research community.


Abstract:
  A typical deep neural network (DNN) has a large number of trainable
parameters. Choosing a network with proper capacity is challenging and
generally a larger network with excessive capacity is trained. Pruning is an
established approach to reducing the number of parameters in a DNN. In this
paper, we propose a framework for pruning DNNs based on a population-based
global optimization method. This framework can use any pruning objective
function. As a case study, we propose a simple but efficient objective function
based on the concept of energy-based models. Our experiments on ResNets,
AlexNet, and SqueezeNet for the CIFAR-10 and CIFAR-100 datasets show a pruning
rate of more than $50\%$ of the trainable parameters with approximately $<5\%$
and $<1\%$ drop of Top-1 and Top-5 classification accuracy, respectively.


Abstract:
  The self-attention-based model, transformer, is recently becoming the leading
backbone in the field of computer vision. In spite of the impressive success
made by transformers in a variety of vision tasks, it still suffers from heavy
computation and intensive memory costs. To address this limitation, this paper
presents an Interpretability-Aware REDundancy REDuction framework (IA-RED$^2$).
We start by observing a large amount of redundant computation, mainly spent on
uncorrelated input patches, and then introduce an interpretable module to
dynamically and gracefully drop these redundant patches. This novel framework
is then extended to a hierarchical structure, where uncorrelated tokens at
different stages are gradually removed, resulting in a considerable shrinkage
of computational cost. We include extensive experiments on both image and video
tasks, where our method could deliver up to 1.4x speed-up for state-of-the-art
models like DeiT and TimeSformer, by only sacrificing less than 0.7% accuracy.
More importantly, contrary to other acceleration approaches, our method is
inherently interpretable with substantial visual evidence, making vision
transformer closer to a more human-understandable architecture while being
lighter. We demonstrate that the interpretability that naturally emerged in our
framework can outperform the raw attention learned by the original visual
transformer, as well as those generated by off-the-shelf interpretation
methods, with both qualitative and quantitative results. Project Page:
http://people.csail.mit.edu/bpan/ia-red/.


Abstract:
  This paper studies the problem of recovering the hidden vertex correspondence
between two edge-correlated random graphs. We focus on the Gaussian model where
the two graphs are complete graphs with correlated Gaussian weights and the
Erd\H{o}s-R\'enyi model where the two graphs are subsampled from a common
parent Erd\H{o}s-R\'enyi graph $\mathcal{G}(n,p)$. For dense graphs with
$p=n^{-o(1)}$, we prove that there exists a sharp threshold, above which one
can correctly match all but a vanishing fraction of vertices and below which
correctly matching any positive fraction is impossible, a phenomenon known as
the "all-or-nothing" phase transition. Even more strikingly, in the Gaussian
setting, above the threshold all vertices can be exactly matched with high
probability. In contrast, for sparse Erd\H{o}s-R\'enyi graphs with
$p=n^{-\Theta(1)}$, we show that the all-or-nothing phenomenon no longer holds
and we determine the thresholds up to a constant factor. Along the way, we also
derive the sharp threshold for exact recovery, sharpening the existing results
in Erd\H{o}s-R\'enyi graphs.
  The proof of the negative results builds upon a tight characterization of the
mutual information based on the truncated second-moment computation and an
"area theorem" that relates the mutual information to the integral of the
reconstruction error. The positive results follows from a tight analysis of the
maximum likelihood estimator that takes into account the cycle structure of the
induced permutation on the edges.


Abstract:
  Aircraft trajectory prediction requires the determination of the atmospheric
properties (pressure, temperature, and density) encountered by the aircraft
during its flight. This is accomplished by employing a tabulated prediction
published by a meteorological service, a static atmosphere model that does not
consider the atmosphere variation with time or horizontal position, such as the
International Civil Aviation Organization (ICAO) Standard Atmosphere (ISA), or
a variation to the later so it better conforms with the expected flight
conditions. This article proposes an easy-to-use quasi static model that
introduces temperature and pressure variations while respecting all the
hypotheses of the ISA model, resulting in more realistic trajectory predictions
as the obtained atmospheric properties bear a higher resemblance with the local
conditions encountered during the flight. The proposed model relies on two
parameters, the temperature and pressure offsets, and converges to the ISA
model when both are zero. Expressions to obtain the atmospheric properties are
established, and their dependencies with both parameters explained. The author
calls this model INSA (ICAO Non Standard Atmosphere) and releases its C++
implementation as open-source software.


Abstract:
  We extend our BDI (birth-death-immigration) process based stochastic model of
an infectious disease to time-nonhomogeneous cases. First, we discuss the
deterministic model, and derive the expected value of the infection process.
Then as an application we consider that a government issues a decree to its
citizens to curtail their activities that may incur further infections and show
how the public's tardy response may further increase infections and prolong the
epidemic much longer than one might think. We seek to solve a partial
differential equation for the probability generating function. We find,
however, that an exact solution is obtainable only for the BD process, i.e., no
arrivals of the infected from outside. The coefficient of variation for the
nonhomogeneous BD process is found to be well over unity. This result implies
that the variations among different sample paths will be as large as in the
negative binomial distribution with r<1, which was found in Part I for the
homogeneous BDI model. In the final section, we illustrate, using our running
example, how much information we can derive from the time dependent PMF
(probability mass function) P_k(t)=Pr[I(t)=k]. We present graphical plots of
the PMF at various t's, and cross-sections of this function at various k's. A
mesh plot of the function over the (k, t) plane summarizes the above numerous
plots. The results of this paper reinforce our earlier claim (see Abstract of
Part II) that it would be a futile effort to attempt to identify all possible
reasons why environments of similar situations differ so much in their epidemic
patterns. Mere "luck" plays a more significant role than most of us may
believe. We should be prepared for a worse possible scenario, which only a
stochastic model can provide with probabilistic qualification. An empirical
validation of the above results will be given in Part III-B.


Abstract:
  Chemical abundances studies of cataclysmic variables have revealed high
nitrogen to carbon ratios in a number of of cataclysmic variable white dwarfs
(based on ultraviolet emission and absorption lines), as well as possible
carbon deficiency in many secondaries (based on the absence of infrared CO
absorption lines). These indicate that the accreted material on the white dwarf
surface and the donor itself might be contaminated with CNO processed material.
To further understand the origin of this abundance anomaly, there is a need for
further chemical abundance study. In the present work, we carry out a far
ultraviolet spectral analysis of the extreme SU UMa dwarf nova TU Men and the U
Gem dwarf nova SS Aur using archival spectra. We derive the mass and
temperature of the WD using the recently available DR2 Gaia parallaxes. The
analysis of HST STIS spectra yields a WD mass $M_{\rm
wd}=0.77^{+0.16}_{-0.13}M_{\odot}$ with a temperature of $27,750 \pm 1000$~K
for TU Men, and a WD mass $M_{\rm wd} \sim 0.80 \pm 0.15$ with a temperature of
$\sim 30,000 \pm 1000$~K for SS Aur. However, the analysis of a FUSE spectrum
for SS Aur gives to a higher temperature $\sim 33,375 \pm 1875 $~K, yielding to
a higher WD mass $\sim 1 \pm 0.25 M_{\odot}$, which could be due to the effect
of a second hot emitting component present in the short wavelengths of FUSE.
Most importantly, based on the white dwarf far ultraviolet absorption lines, we
find that both systems have subsolar carbon and silicon abundances. For TU Men
we also find suprasolar nitrogen abundance, evidence of CNO processing.


Abstract:
  This study analyzes the impact of the COVID-19 pandemic on the subjective
well-being as measured through Twitter data indicators for Japan and Italy. It
turns out that, overall, the subjective well-being dropped by 11.7% for Italy
and 8.3% for Japan in the first nine months of 2020 compared to the last two
months of 2019 and even more compared to the historical mean of the indexes.
Through a data science approach we try to identify the possible causes of this
drop down by considering several explanatory variables including, climate and
air quality data, number of COVID-19 cases and deaths, Facebook Covid and flu
symptoms global survey, Google Trends data and coronavirus-related searches,
Google mobility data, policy intervention measures, economic variables and
their Google Trends proxies, as well as health and stress proxy variables based
on big data. We show that a simple static regression model is not able to
capture the complexity of well-being and therefore we propose a dynamic elastic
net approach to show how different group of factors may impact the well-being
in different periods, even over a short time length, and showing further
country-specific aspects. Finally, a structural equation modeling analysis
tries to address the causal relationships among the COVID-19 factors and
subjective well-being showing that, overall, prolonged mobility
restrictions,flu and Covid-like symptoms, economic uncertainty, social
distancing and news about the pandemic have negative effects on the subjective
well-being.


Abstract:
  In the celebrated paper by Jean Leray, published in JMPA journal in 1933, the
invading domains method was proposed to construct D-solutions for the
stationary Navier-Stokes flow around obstacle problem. In two dimensions,
whether Leray's D-solution achieves the prescribed limiting velocity at spatial
infinity became a major open problem since then. In this paper, we solve this
problem at small Reynolds numbers. The proof builds on a novel blow-down
argument which rescales the invading domains to the unit disc, and the ideas
developed in a recent paper [Korobkov-Pileckas-Russo2020], where the
nontriviality of Leray solutions in the general case was proved, and
[Korobkov-Ren-2021], where the uniqueness result for small Reynolds number was
established.


Abstract:
  The attention module, which is a crucial component in Transformer, cannot
scale efficiently to long sequences due to its quadratic complexity. Many works
focus on approximating the dot-then-exponentiate softmax function in the
original attention, leading to sub-quadratic or even linear-complexity
Transformer architectures. However, we show that these methods cannot be
applied to more powerful attention modules that go beyond the
dot-then-exponentiate style, e.g., Transformers with relative positional
encoding (RPE). Since in many state-of-the-art models, relative positional
encoding is used as default, designing efficient Transformers that can
incorporate RPE is appealing. In this paper, we propose a novel way to
accelerate attention calculation for Transformers with RPE on top of the
kernelized attention. Based upon the observation that relative positional
encoding forms a Toeplitz matrix, we mathematically show that kernelized
attention with RPE can be calculated efficiently using Fast Fourier Transform
(FFT). With FFT, our method achieves $\mathcal{O}(n\log n)$ time complexity.
Interestingly, we further demonstrate that properly using relative positional
encoding can mitigate the training instability problem of vanilla kernelized
attention. On a wide range of tasks, we empirically show that our models can be
trained from scratch without any optimization issues. The learned model
performs better than many efficient Transformer variants and is faster than
standard Transformer in the long-sequence regime.


Abstract:
  The purpose of this paper is to develop a shared control takeover strategy
for smooth and safety control transition from an automation driving system to
the human driver and to approve its positive impacts on drivers' behavior and
attitudes. A "human-in-the-loop" driving simulator experiment was conducted to
evaluate the impact of the proposed shared control takeover strategy under
different disengagement conditions. Results of thirty-two drivers showed shared
control takeover strategy could improve safety performance at the aggregated
level, especially at non-driving related disengagements. For more urgent
disengagements caused by another vehicle's sudden brake, a shared control
strategy enlarges individual differences. The primary reason is that some
drivers had higher self-reported mental workloads in response to the shared
control takeover strategy. Therefore, shared control between driver and
automation can involve driver's training to avoid mental overload when
developing takeover strategies.


Abstract:
  We propose a new architecture for artificial neural networks called
Householder-absolute neural layers, or Han-layers for short, that use
Householder reflectors as weight matrices and the absolute-value function for
activation. Han-layers, functioning as fully connected layers, are motivated by
recent results on neural-network variability and are designed to increase
activation ratio and reduce the chance of Collapse to Constants. Neural
networks constructed chiefly from Han-layers are called HanNets. By
construction, HanNets enjoy a theoretical guarantee that vanishing or exploding
gradient never occurs. We conduct several proof-of-concept experiments. Some
surprising results obtained on styled test problems suggest that, under certain
conditions, HanNets exhibit an unusual ability to produce nearly perfect
solutions unattainable by fully connected networks. Experiments on regression
datasets show that HanNets can significantly reduce the number of model
parameters while maintaining or improving the level of generalization accuracy.
In addition, by adding a few Han-layers into the pre-classification FC-layer of
a convolutional neural network, we are able to quickly improve a
state-of-the-art result on CIFAR10 dataset. These proof-of-concept results are
sufficient to necessitate further studies on HanNets to understand their
capacities and limits, and to exploit their potentials in real-world
applications.


Abstract:
  We introduce a new method for generating color images from sketches or edge
maps. Current methods either require some form of additional user-guidance or
are limited to the "paired" translation approach. We argue that segmentation
information could provide valuable guidance for sketch colorization. To this
end, we propose to leverage semantic image segmentation, as provided by a
general purpose panoptic segmentation network, to create an additional
adversarial loss function. Our loss function can be integrated to any baseline
GAN model. Our method is not limited to datasets that contain segmentation
labels, and it can be trained for "unpaired" translation tasks. We show the
effectiveness of our method on four different datasets spanning scene level
indoor, outdoor, and children book illustration images using qualitative,
quantitative and user study analysis. Our model improves its baseline up to 35
points on the FID metric. Our code and pretrained models can be found at
https://github.com/giddyyupp/AdvSegLoss.


Abstract:
  Cavity magnonics deals with the interaction of magnons - elementary
excitations in magnetic materials - and confined electromagnetic fields. We
introduce the basic physics and review the experimental and theoretical
progress of this young field that is gearing up for integration in future
quantum technologies. Much of its appeal is derived from the strong
magnon-photon coupling and the easily-reached nonlinear regime in microwave
cavities. The interaction of magnons with light as detected by Brillouin light
scattering is enhanced in magnetic optical resonators, which can be employed to
manipulate magnon distributions. The cavity photon-mediated coupling of a
magnon mode to a superconducting qubit enables measurements in the single
magnon limit.


Abstract:
  The influence of the orientation of gold nanorods in different assemblies has
been investigated using the Finite Difference Time Domain (FDTD) simulation
method. To understand the relative orientation, we vary the size and angle in
dimer geometries. Significant effects of plasmon coupling emerged in
longitudinal resonances having end-to-end configurations of gold nanorods. The
effect of orientational plasmon coupling in dimers gives rise to both bonding
and anti-bonding plasmon modes. Effects of various geometries like primary
monomer, dimer, trimer, and tetramer structures have been explored and compared
with their higher nanorod ensembles. The asymmetric spectral response in a 4 *
4 gold nanorods array indicates a Fano-like resonance. The variation of gap
distance in ordered arrays allowed modulation of the Fano resonance mode. The
plasmon modes' resonance wavelength and field enhancement have been tuned by
varying the gap distance, angular orientation, size irregularity between the
nanorods, and nanorod numbers in an array. The integrated nanostructures
studied here are not only significant for fundamental research but also
applications in plasmon-based devices.


Abstract:
  Let $E \subset \mathbb R^{n+1}$ be a parabolic uniformly rectifiable set. We
prove that every bounded solution $u$ to $$\partial_tu- \Delta u=0, \quad
\text{in} \quad \mathbb R^{n+1}\setminus E$$ satisfies a Carleson measure
estimate condition. An important technical novelty of our work is that we
develop a corona domain approximation scheme for $E$ in terms of regular
Lip(1/2,1) graph domains. This approximation scheme has an analogous elliptic
version which is an improvement of the known results in that setting.


Abstract:
  For robots to understand human instructions and perform meaningful tasks in
the near future, it is important to develop learned models that comprehend
referential language to identify common objects in real-world 3D scenes. In
this paper, we introduce a spatial-language model for a 3D visual grounding
problem. Specifically, given a reconstructed 3D scene in the form of point
clouds with 3D bounding boxes of potential object candidates, and a language
utterance referring to a target object in the scene, our model successfully
identifies the target object from a set of potential candidates. Specifically,
LanguageRefer uses a transformer-based architecture that combines spatial
embedding from bounding boxes with fine-tuned language embeddings from
DistilBert to predict the target object. We show that it performs competitively
on visio-linguistic datasets proposed by ReferIt3D. Further, we analyze its
spatial reasoning task performance decoupled from perception noise, the
accuracy of view-dependent utterances, and viewpoint annotations for potential
robotics applications.


Abstract:
  This paper examines the stability of the quantum random walk search
algorithm, when the walk coin is constructed by generalized Householder
reflection and additional phase shift, against inaccuracies in the phases used
to construct the coin. The optimization of the algorithm is done by numerical
methods - Monte Carlo, neural networks, and supervised machine learning. The
results of numerical simulations show that, with such a construction of the
Householder reflection, the algorithm is more stable to inaccuracies in the
specific values of these phases, as long as it is possible to control the phase
difference between the phase shift and the phase involved in the Householder
reflection. This paper explicitly shows as an example, how achieving a properly
designed phase difference would make quantum random walk search on a hypercube
more stable for coin register consisting of one, two, and three qubits.


Abstract:
  In this paper we consider the coalescing random walk model on general graphs
$G=(V,E)$. We set up a unified framework to study the leading order of decay
rate of $P_t$, the expectation of the fraction of occupied sites at time $t$,
particularly for the `Big Bang' regime where $t\ll
t_{\text{coal}}:=\mathbb{E}[\inf\{s:\text{There is only one particle at time
}s\}]$. Our results show that $P_t$ satisfies certain mean field behavior, if
the graphs satisfy certain transience-like conditions.
  We apply this framework to two families of graphs: (1) graphs given by
configuration model with degree $\ge 3$, and (2) finite and infinite
vertex-transitive graphs. In the first case, we show that for $1 \ll t \ll
|V|$, $P_t$ decays in the order of $t^{-1}$, and $(tP_t)^{-1}$ is approximately
the probability that two particles starting from the root of the corresponding
unimodular Galton-Watson tree never collide after one of them leaves the root,
which is also roughly $|V|/(2t_{\text{meet}})$, where $t_{\text{meet}}$ is the
mean meeting time of two independent walkers. By taking the local weak limit,
for the corresponding unimodular Galton-Watson tree we prove convergence of
$tP_t$ as $t\to\infty$. For the second family of graphs, if we take a sequence
of finite vertex-transitive graphs $G_n=(V_n, E_n)$, such that
$t_{\text{meet}}=O(|V_n|)$ and the inverse of the spectral gap $t_{\text{rel}}$
is $o(|V_n|)$, then for $t_{\text{rel}}\ll t\ll t_{\text{coal}}$, $(tP_t)^{-1}$
is approximately the probability that two random walks never meet before time
$t$, and also $|V|/(2t_{\text{meet}})$. In addition, we define a natural
uniform transience condition, and show that in the transitive setup it implies
the above for all $1\ll t\ll t_{\text{coal}}$. Such estimates of $tP_t$ are
also obtained for all infinite transient transitive unimodular graphs, in
particular, all transient transitive amenable graphs.


Abstract:
  Estimating and testing for differences in molecular phenotypes (e.g. gene
expression, chromatin accessibility, transcription factor binding) across
conditions is an important part of understanding the molecular basis of gene
regulation. These phenotypes are commonly measured using high-throughput
sequencing assays (e.g., RNA-seq, ATAC-seq, ChIP-seq), which provide
high-resolution count data that reflect how the phenotypes vary along the
genome. Multiple methods have been proposed to help exploit these
high-resolution measurements for differential expression analysis. However,
they ignore the count nature of the data, instead using normal approximations
that work well only for data with large sample sizes or high counts. Here we
develop count-based methods to address this problem. We model the data for each
sample using an inhomogeneous Poisson process with spatially structured
underlying intensity function, and then, building on multi-scale models for the
Poisson process, estimate and test for differences in the underlying intensity
function across samples (or groups of samples). Using both simulation and real
ATAC-seq data we show that our method outperforms previous normal-based
methods, especially in situations with small sample sizes or low counts.


Abstract:
  Coreference resolution is an important component in analyzing narrative text
from administrative data (e.g., clinical or police sources). However, existing
coreference models trained on general language corpora suffer from poor
transferability due to domain gaps, especially when they are applied to
gender-inclusive data with lesbian, gay, bisexual, and transgender (LGBT)
individuals. In this paper, we analyzed the challenges of coreference
resolution in an exemplary form of administrative text written in English:
violent death narratives from the USA's Centers for Disease Control's (CDC)
National Violent Death Reporting System. We developed a set of data
augmentation rules to improve model performance using a probabilistic data
programming framework. Experiments on narratives from an administrative
database, as well as existing gender-inclusive coreference datasets,
demonstrate the effectiveness of data augmentation in training coreference
models that can better handle text data about LGBT individuals.


Abstract:
  The Kerr nonlinear optical performance of silicon nanowire waveguides
integrated with 2D layered graphene oxide (GO) films is theoretically studied
and optimized based on experimentally measured linear and nonlinear optical
parameters of the GO films. The strong mode overlap between the silicon
nanowires and highly nonlinear GO films yields a significantly enhanced Kerr
nonlinearity for the hybrid waveguides. A detailed analysis for the influence
of waveguide geometry and GO film thickness on the propagation loss, nonlinear
parameter, and nonlinear figure of merit (FOM) is performed. The results show
that the effective nonlinear parameter and nonlinear FOM can be increased by up
to 52 and 79 times relative to bare silicon nanowires, respectively. Self-phase
modulation (SPM)-induced spectral broadening of optical pulses is used as a
benchmark to evaluate the nonlinear performance, examining the tradeoff between
enhancing Kerr nonlinearity and minimizing loss. By optimizing the device
parameters to balance this, a high spectral broadening factor of 27.8 can be
achieved, more than 6 times that achieved in previous experiments. Finally, the
influence of pulse chirp, material anisotropy, and the interplay between
saturable absorption and SPM is also discussed, together with the comparison
between the spectral broadening after going through GO-coated and
graphene-coated silicon waveguides. These results provide useful guidance for
optimizing the Kerr nonlinear optical performance of silicon waveguides
integrated with 2D layered GO films.


Abstract:
  This work proposes a detectability condition for linear time-varying systems
based on the exponential dichotomy spectrum. The condition guarantees the
existence of an observer, whose gain is determined only by the unstable modes
of the system. This allows for an observer design with low computational
complexity compared to classical estimation approaches. An extension of this
observer design to a class of nonlinear systems is proposed and local
convergence of the corresponding estimation error dynamics is proven. Numerical
results show the efficacy of the proposed observer design technique.


Abstract:
  We present a novel approach for high-order accurate numerical differentiation
on unstructured meshes of quadrilateral elements. To differentiate a given
function, an auxiliary function with greater smoothness properties is defined
which when differentiated provides the derivatives of the original function.
The method generalises traditional finite difference methods to meshes of
arbitrary topology in any number of dimensions for any order of derivative and
accuracy. We demonstrate the accuracy of the numerical scheme using dual
quadrilateral meshes and a refinement method based on subdivision surfaces. The
scheme is applied to the solution of a range of partial differential equations,
including both linear and nonlinear, and second and fourth order equations.


Abstract:
  Deep learning models (with neural networks) have been widely used in
challenging tasks such as computer-aided disease diagnosis based on medical
images. Recent studies have shown deep diagnostic models may not be robust in
the inference process and may pose severe security concerns in clinical
practice. Among all the factors that make the model not robust, the most
serious one is adversarial examples. The so-called "adversarial example" is a
well-designed perturbation that is not easily perceived by humans but results
in a false output of deep diagnostic models with high confidence. In this
paper, we evaluate the robustness of deep diagnostic models by adversarial
attack. Specifically, we have performed two types of adversarial attacks to
three deep diagnostic models in both single-label and multi-label
classification tasks, and found that these models are not reliable when
attacked by adversarial example. We have further explored how adversarial
examples attack the models, by analyzing their quantitative classification
results, intermediate features, discriminability of features and correlation of
estimated labels for both original/clean images and those adversarial ones. We
have also designed two new defense methods to handle adversarial examples in
deep diagnostic models, i.e., Multi-Perturbations Adversarial Training (MPAdvT)
and Misclassification-Aware Adversarial Training (MAAdvT). The experimental
results have shown that the use of defense methods can significantly improve
the robustness of deep diagnostic models against adversarial attacks.


Abstract:
  In \cite{Bahamonde:2019zea}, a spherically symmetric black hole (BH) was
derived from the quadratic form of $f(T)$. Here we derive the associated
energy, invariants of curvature, and torsion of this BH and demonstrate that
the higher-order contribution of torsion renders the singularity weaker
compared with the Schwarzschild BH of general relativity (GR). Moreover, we
calculate the thermodynamic quantities and reveal the effect of the
higher--order contribution on these quantities. Therefore, we derive a new
spherically symmetric BH from the cubic form of
$f(T)=T+\epsilon\Big[\frac{1}{2}\alpha T^2+\frac{1}{3}\beta T^3\Big]$, where
$\epsilon<<1$, $\alpha$, and $\beta$ are constants. The new BH is characterized
by the two constants $\alpha$ and $\beta$ in addition to $\epsilon$. At
$\epsilon=0$ we return to GR. We study the physics of these new BH solutions
via the same procedure that was applied for the quadratic BH. Moreover, we
demonstrate that the contribution of the higher-order torsion,
$\frac{1}{2}\alpha T^2+\frac{1}{3}\beta T^3$, may afford an interesting
physics.


Abstract:
  Let $a_1, \dots, a_n \in \mathbb{R}$ satisfy $\sum_i a_i^2 = 1$, and let
$\varepsilon_1, \ldots, \varepsilon_n$ be uniformly random $\pm 1$ signs and $X
= \sum_{i=1}^{n} a_i \varepsilon_i$. It is conjectured that $X = \sum_{i=1}^{n}
a_i \varepsilon_i$ has $\Pr[X \geq 1] \geq 7/64$. The best lower bound so far
is $1/20$, due to Oleszkiewicz. In this paper we improve this to $\Pr[X \geq 1]
\geq 6/64$.


Abstract:
  This article aims to study the existence of stable Bloch oscillations and
Landau-Zener tunneling in a non-Hermitian system when exposed to external
fields. We investigate a non-Hermitian $\cal{PT}$-symmetric diamond chain
network and its transport dynamics in two different situations, namely in a
flat band case and a non-flat band case. The considered system does not support
unbroken-$\cal{PT}$ phase or completely real eigenspectra in any of the
parametric regions in both the flat and non-flat band cases. In the flat band
case, up to a critical value of the gain-loss parameter, the bands are found to
be gapless or inseparable, and for other values the bands are isolated.
Considering the non-flat band case, all the bands are found to be complex
dispersive and are also isolated. In the case of completely broken $\cal{PT}$
phase, we look upon the possibility to have stable dynamics or Bloch
oscillations upon the application of external fields like synthetic electric
field. In particular, when the complex bands are isolated, we point out that
the Landau-Zener tunneling induced by the synthetic electric field can enable
Bloch oscillations. The amplitude of these Bloch oscillations is large and
persists for a long propagation distance which reveals that super Bloch
oscillations can be observed in the broken $\cal{PT}$ phase of the system. We
also report the amplified Bloch oscillations which pave the way towards
controlling transport phenomena in non-Hermitian systems.


Abstract:
  Emergent electromagnetic induction based on electrodynamics of noncollinear
spin states may enable dramatic miniaturization of inductor elements widely
used in electric circuits, yet many issues are to be solved toward application.
One such problem is how to increase working temperature. We report the large
emergent electromagnetic induction achieved around and above room temperature
based on short-period ($\leq$ 3 nm) spin-spiral states of a metallic helimagnet
${\rm YMn}_{6}{\rm Sn}_{6}$. The observed inductance value $L$ and its sign are
observed to vary to a large extent, depending not only on the spin helix
structure controlled by temperature and magnetic field but also on the current
density. The present finding on room-temperature operation and possible sign
control of $L$ may provide a new step toward realizing microscale quantum
inductors.


Abstract:
  Scattering immune propagation of light in topological photonic systems may
revolutionarize the design of integrated photonic circuits for information
processing and communications. In optics, various photonic topological circuits
have been developed, which were based on classical emulation of either quantum
spin Hall effect or quantum valley Hall effect. On the other hand, the
combination of both the valley and spin degrees of freedom can lead to a new
kind of topological transport phenomenon, dubbed quantum spin valley Hall
effect (QSVH), which can further expand the number of topologically protected
edge channels and would be useful for information multiplexing. However, it is
challenging to realize QSVH in most known material platforms, due to the
requirement of breaking both the (pseudo-)fermionic time-reversal (T) and
parity symmetries (P) individually, but leaving the combined symmetry S=TP
intact. Here, we propose an experimentally feasible platform to realize QSVH
for light, based on coupled ring resonators mediated by optical Kerr
nonlinearity. Thanks to the inherent flexibility of cross-mode modulation
(XMM), the coupling between the probe light can be engineered in a controllable
way such that spin-dependent staggered sublattice potential emerges in the
effective Hamiltonian. With delicate yet experimentally feasible pump
conditions, we show the existence of spin valley Hall induced topological edge
states. We further demonstrate that both degrees of freedom, i.e., spin and
valley, can be manipulated simultaneously in a reconfigurable manner to realize
spin-valley photonics, doubling the degrees of freedom for enhancing the
information capacity in optical communication systems.


Abstract:
  As quantum information processors grow in quantum bit (qubit) count and
functionality, the control and measurement system becomes a limiting factor to
large scale extensibility. To tackle this challenge and keep pace with rapidly
evolving classical control requirements, full control stack access is essential
to system level optimization. We design a modular FPGA (field-programmable gate
array) based system called QubiC to control and measure a superconducting
quantum processing unit. The system includes room temperature electronics
hardware, FPGA gateware, and engineering software. A prototype hardware module
is assembled from several commercial off-the-shelf evaluation boards and
in-house developed circuit boards. Gateware and software are designed to
implement basic qubit control and measurement protocols. System functionality
and performance are demonstrated by performing qubit chip characterization,
gate optimization, and randomized benchmarking sequences on a superconducting
quantum processor operating at the Advanced Quantum Testbed at Lawrence
Berkeley National Laboratory. The single-qubit and two-qubit process fidelities
are measured to be 0.9980$\pm$0.0001 and 0.948$\pm$0.004 by randomized
benchmarking. With fast circuit sequence loading capability, the QubiC performs
randomized compiling experiments efficiently and improves the feasibility of
executing more complex algorithms.


Abstract:
  Consider a PPT two-party protocol $\pi=(A,B)$ in which the parties get no
private inputs and obtain outputs $O^A,O^B\in \{0,1\}$, and let $V^A$ and $V^B$
denote the parties' individual views. Protocol $\pi$ has $\alpha$-agreement if
$Pr[O^A=O^B]=1/2+\alpha$. The leakage of $\pi$ is the amount of information a
party obtains about the event $\{O^A=O^B\}$; that is, the leakage $\epsilon$ is
the maximum, over $P\in\{A,B\}$, of the distance between $V^P|OA=OB$ and
$V^P|OA\neq OB$. Typically, this distance is measured in statistical distance,
or, in the computational setting, in computational indistinguishability. For
this choice, Wullschleger [TCC 09] showed that if $\alpha>>\epsilon$ then the
protocol can be transformed into an OT protocol.
  We consider measuring the protocol leakage by the log-ratio distance (which
was popularized by its use in the differential privacy framework). The
log-ratio distance between X,Y over domain \Omega is the minimal $\epsilon>0$
for which, for every $v\in\Omega$, $log(Pr[X=v]/Pr[Y=v])\in
[-\epsilon,\epsilon]$. In the computational setting, we use computational
indistinguishability from having log-ratio distance $\epsilon$. We show that a
protocol with (noticeable) accuracy $\alpha\in\Omega(\epsilon^2)$ can be
transformed into an OT protocol (note that this allows $\epsilon>>\alpha$). We
complete the picture, in this respect, showing that a protocol with $\alpha\in
o(\epsilon^2)$ does not necessarily imply OT. Our results hold for both the
information theoretic and the computational settings, and can be viewed as a
"fine grained" approach to "weak OT amplification".
  We then use the above result to fully characterize the complexity of
differentially private two-party computation for the XOR function, answering
the open question put by Goyal, Khurana, Mironov, Pandey, and Sahai [ICALP 16]
and Haitner, Nissim, Omri, Shaltiel, and Silbak [FOCS 18].


Abstract:
  Internet of Things (IoT) consists of a large number of devices connected
through a network, which exchange a high volume of data, thereby posing new
security, privacy, and trust issues. One way to address these issues is
ensuring data confidentiality using lightweight encryption algorithms for IoT
protocols. However, the design and implementation of such protocols is an
error-prone task; flaws in the implementation can lead to devastating security
vulnerabilities. Here we propose a new verification approach named
Encryption-BMC and Fuzzing (EBF), which combines Bounded Model Checking (BMC)
and Fuzzing techniques to check for security vulnerabilities that arise from
concurrent implementations of cyrptographic protocols, which include data race,
thread leak, arithmetic overflow, and memory safety. EBF models IoT protocols
as a client and server using POSIX threads, thereby simulating both entities'
communication. It also employs static and dynamic verification to cover the
system's state-space exhaustively. We evaluate EBF against three benchmarks.
First, we use the concurrency benchmark from SV-COMP and show that it
outperforms other state-of-the-art tools such as ESBMC, AFL, Lazy-CSeq, and
TSAN with respect to bug finding. Second, we evaluate an open-source
implementation called WolfMQTT. It is an MQTT client implementation that uses
the WolfSSL library. We show that \tool detects a data race bug, which other
approaches are unable to find. Third, to show the effectiveness of EBF, we
replicate some known vulnerabilities in OpenSSL and CyaSSL (lately WolfSSL)
libraries. EBF can detect the bugs in minimum time.


Abstract:
  There has been a growing interest in defining models of automata enriched
with time, such as finite automata extended with clocks (timed automata). In
this paper, we study deterministic timed finite state machines (TFSMs), i.e.,
finite state machines with a single clock, timed guards and timeouts which
transduce timed input words into timed output words. We solve the problem of
equivalence checking by defining a bisimulation from timed FSMs to untimed ones
and viceversa. Moreover, we apply these bisimulation relations to build the
intersection of two timed finite state machines by untiming them, intersecting
them and transforming back to the timed intersection.


Abstract:
  Given an elliptic curve $E$ over a global function field $K$, the Galois
action on the $n$-torsion points of $E$ gives rise to a mod-n Galois
representation $\rho_{E,n}$. For $K$ satisfying some mild conditions, we show
that the set of $E$ for which $\rho_{E,n}$ is as large as possible for all $n$,
has density $1$.


Abstract:
  Knowledge of the spatial organisation of economic activity within a city is
key to policy concerns. However, in developing cities with high levels of
informality, this information is often unavailable. Recent progress in machine
learning together with the availability of street imagery offers an affordable
and easily automated solution. Here we propose an algorithm that can detect
what we call 'visible firms' using street view imagery. Using Medell\'in,
Colombia as a case study, we illustrate how this approach can be used to
uncover previously unseen economic activity. Applying spatial analysis to our
dataset we detect a polycentric structure with five distinct clusters located
in both the established centre and peripheral areas. Comparing the density of
visible and registered firms, we find that informal activity concentrates in
poor but densely populated areas. Our findings highlight the large gap between
what is captured in official data and the reality on the ground.


Abstract:
  State-of-the-art object detection systems for autonomous driving achieve
promising results in clear weather conditions. However, such autonomous safety
critical systems also need to work in degrading weather conditions, such as
rain, fog and snow. Unfortunately, most approaches evaluate only on the KITTI
dataset, which consists only of clear weather scenes. In this paper we address
this issue and perform one of the most detailed evaluation on single and dual
modality architectures on data captured in real weather conditions. We analyse
the performance degradation of these architectures in degrading weather
conditions. We demonstrate that an object detection architecture performing
good in clear weather might not be able to handle degrading weather conditions.
We also perform ablation studies on the dual modality architectures and show
their limitations.


Abstract:
  The based loop space of the configuration space of $m$ points in
$\mathbb{R}^n$ can be viewed as the space of pure braids on $m$ strands in
$\mathbb{R}^{n+1}$. For $n>2$, Cohen and Gitler calculated its homology,
relating it to earlier work of Kohno on the case $n=2$ of the classical pure
braid group. We continue our study of spaces of pure braids in terms of
Kontsevich's CDGA of diagrams, the integration map for formality of
configuration spaces, and Chen's iterated integrals. In particular, we
construct a power series connection whose dual yields a Hopf algebra
isomorphism between the cohomology of braids and the bar construction on
diagrams. It further maps iterated Whitehead products in homotopy to trivalent
trees modulo the IHX relation. As an application, we establish a correspondence
between Milnor invariants of Brunnian spherical links and certain Chen
integrals. We finally show that a graphing map induces an injection of a
certain subspace of the homotopy of configuration spaces into the homotopy of
many spaces of $k$-dimensional string links in $\mathbb{R}^{n+k}$. We
conjecture that this graphing map is more generally injective on the rational
homotopy and rational homology of the $k$-fold loop space of configuration
space.


Abstract:
  High internal phase emulsions (HIPEs) are considered as an important
functional material and have been the focus of intense development effort, but
it has not been possible to alter their fundamental attributes at either the
microcosmic or macroscopic level, which severely limits their practical
applications in various areas. In this work, we report a general strategy for
creating complex HIPEs and we additionally show a route to forming HIPE films
at liquid interfaces. Double HIPEs and Janus HIPEs are both realized for the
first time. They feature complex microscopic patterns with a short-range
anisotropy, and exhibit non-Newtonian pseudoplastic flow behavior. By taking
advantage of their response to a high-pH subphase, interfacial films can be
successfully obtained, which are tunable in thickness and morphologies under
compression.


Abstract:
  We present the main sequence for all galaxies and star-forming galaxies for a
sample of 28,469 massive ($M_\star \ge 10^{11}$M$_\odot$) galaxies at cosmic
noon ($1.5 < z < 3.0$), uniformly selected from a 17.5 deg$^2$ area (0.33
Gpc$^3$ comoving volume at these redshifts). Our large sample allows for a
novel approach to investigating the galaxy main sequence that has not been
accessible to previous studies. We measure the main sequence in small mass bins
in the SFR-M$_{\star}$ plane without assuming a functional form for the main
sequence. With a large sample of galaxies in each mass bin, we isolate
star-forming galaxies by locating the transition between the star-forming and
green valley populations in the SFR-M$_{\star}$ plane. This approach eliminates
the need for arbitrarily defined fixed cutoffs when isolating the star-forming
galaxy population, which often biases measurements of the scatter around the
star-forming galaxy main sequence. We find that the main sequence for all
galaxies becomes increasingly flat towards present day at the high-mass end,
while the star-forming galaxy main sequence does not. We attribute this
difference to the increasing fraction of the collective green valley and
quiescent galaxy population from $z=3.0$ to $z=1.5$. Additionally, we measure
the total scatter around the star-forming galaxy main sequence and find that it
is $\sim0.5-1.0$ dex with little evolution as a function of mass or redshift.
We discuss the implications that these results have for pinpointing the
physical processes driving massive galaxy evolution.


Abstract:
  Let $a \in \mathbb{Z}_{>0}$ and $\epsilon, \epsilon_1, \epsilon_2, \epsilon_3
\in \{\pm 1\}$. We classify explicitly all singular moduli $x_1, x_2, x_3$
satisfying either $\epsilon_1 x_1^a + \epsilon_2 x_2^a + \epsilon_3 x_3^a \in
\mathbb{Q}$ or $(x_1 x_2 x_3)^{\epsilon a} \in \mathbb{Q}^{\times}$. In
particular, we show that all solutions in singular moduli $x_1, x_2, x_3$ to
the Fermat equations $x_1^a + x_2^a + x_3^a= 0$ and $x_1^a + x_2^a - x_3^a= 0$
satisfy $x_1 x_2 x_3 = 0$. Our proofs use a generalisation of a result of Faye
and Riffaut on the fields generated by sums and products of two singular
moduli, which we also establish.


Abstract:
  We investigate the Andreev reflection and Josephson effect in the $\alpha$-T3
lattice which falls between graphene ($\alpha = 0$) and the dice lattice
($\alpha = 1$) by adjusting the parameter $\alpha$. In the regime of specular
Andreev reflection, when the incident energy of electron is small, the
probability of Andreev reflection decreases as the parameter $\alpha$
increases. On the contrary, when the incident energy is large, the probability
of Andreev reflection increases as the parameter $\alpha$ increases.
Interestingly, when the incident energy approaches the superconducting
energy-gap function, the Andreev reflection with approximate all-angle perfect
transmission happens in the case of $\alpha = 1$. In the regime of Andreev
retro-reflection, when the parameter $\alpha$ increases, the probability of
Andreev reflection increases regardless of the value of incident energy. When
the incident energy approaches the superconducting energy-gap function, the
Andreev reflection with approximate all-angle perfect transmission happens
regardless of the value of $\alpha$. We also give the differential conductance
in these two regimes and find that the differential conductance increases as
the parameter $\alpha$ increases generally. In addition, the $\alpha$-T3
lattice-based Josephson current increases as $\alpha$ increases. When the
length of junction approaches zero, the critical Josephson currents in the
different values of $\alpha$ approach the same value.


Abstract:
  This paper addresses text recognition for domains with limited manual
annotations by a simple self-training strategy. Our approach should reduce
human annotation effort when target domain data is plentiful, such as when
transcribing a collection of single person's correspondence or a large
manuscript. We propose to train a seed system on large scale data from related
domains mixed with available annotated data from the target domain. The seed
system transcribes the unannotated data from the target domain which is then
used to train a better system. We study several confidence measures and
eventually decide to use the posterior probability of a transcription for data
selection. Additionally, we propose to augment the data using an aggressive
masking scheme. By self-training, we achieve up to 55 % reduction in character
error rate for handwritten data and up to 38 % on printed data. The masking
augmentation itself reduces the error rate by about 10 % and its effect is
better pronounced in case of difficult handwritten data.


Abstract:
  We consider the long time dynamics of nonlinear Schr\"odinger equations with
an external potential. More precisely, we look at Hartree type equations in
three or higher dimensions with small initial data. We prove an optimal decay
estimate, which is comparable to the decay of free solutions. Our proof relies
on good control on a high Sobolev norm of the solution to estimate the terms in
Duhamel's formula.


Abstract:
  We investigate the spatial distribution of spin orientation in magnetic
nanoparticles consisting of hard and soft magnetic layers. The nanoparticles
are synthesized in a core / shell spherical morphology where the magnetically
hard, high anisotropy layer is CoFe$_2$O$_4$ (CFO) while the lower anisotropy
material is Fe$_3$O$_4$ (FO). The nanoparticles have a mean diameter of
$\sim$9.2 - 9.6 nm and are synthesized as two variants: a conventional hard /
soft core / shell structure with a CFO core / FO shell (CFO@FO) and the
inverted structure FO core / CFO shell (FO@CFO). High resolution electron
microscopy confirms the coherent spinel structure across the core / shell
boundary in both variants while magnetometry indicates the nanoparticles are
superparamagnetic at 300 K and develop a considerable anisotropy at reduced
temperatures. Low temperature \textit{M vs. H} loops suggest a multi-step
reversal process. Temperature dependent small angle neutron scattering (SANS)
with full polarization analysis reveals a strong perpendicular plane alignment
of the spins near zero field, indicative of spin canting, but the perpendicular
alignment quickly disappears upon application of a weak field and little spin
ordering parallel to the field until the coercive field is reached. Above the
coercive field of the sample, spins orient predominantly along the field
direction. At both zero field and near saturation, the parallel magnetic SANS
peak coincides with the structural peak, indicating the magnetization is
uniform throughout the nanoparticle volume, while near the coercive field the
parallel scattering peak shifts to higher momentum transfer (Q), suggesting
that the coherent scattering volume is smaller and likely originates in the
softer Fe$_3$O$_4$ portion of the nanoparticle.


Abstract:
  We present the Open Predicate Query Language (OPQL); a method for
constructing a virtual KB (VKB) trained entirely from text. Large Knowledge
Bases (KBs) are indispensable for a wide-range of industry applications such as
question answering and recommendation. Typically, KBs encode world knowledge in
a structured, readily accessible form derived from laborious human annotation
efforts. Unfortunately, while they are extremely high precision, KBs are
inevitably highly incomplete and automated methods for enriching them are far
too inaccurate. Instead, OPQL constructs a VKB by encoding and indexing a set
of relation mentions in a way that naturally enables reasoning and can be
trained without any structured supervision. We demonstrate that OPQL
outperforms prior VKB methods on two different KB reasoning tasks and,
additionally, can be used as an external memory integrated into a language
model (OPQL-LM) leading to improvements on two open-domain question answering
tasks.


Abstract:
  We consider the classical Yang-Mills system coupled with a Dirac equation in
3+1 dimensions. Using that most of the nonlinear terms fulfill a null condition
we prove local well-posedness for data with minimal regularity assumptions.
This problem for smooth data was solved forty years ago by Y. Choquet-Bruhat
and D. Christodoulou. Our result generalizes a similar result for the
Yang-Mills equation by S. Selberg and A. Tesfahun.


Abstract:
  We present spectroscopic observations of superthin galaxies. Superthin
galaxies have the thinnest stellar disks among disk galaxies. A sample of 138
superthins was observed in visible light with the 3.5 m telescope at Apache
Point Observatory in New Mexico to obtain the rotation curves of the ionized
gas in the galaxies. The sample represents the largest survey of superthin
galaxies so far and provides a database to investigate the kinematic and
dynamic properties of this special type of extragalactic objects. Here we
present the rotation curves of our sample objects.


Abstract:
  Controlling the spin dynamics and spin lifetimes is one of the main
challenges in spintronics. To this end, the study of the spin diffusion in
two-dimensional electron gases (2DEGs) shows that when the Rashba and
Dresselhaus spin-orbit couplings (SOC) are balanced, a persistent spin helix
regime arises. There, a striped spin pattern shows a long lifetime, limited
only by the cubic Dresselhaus SOC, and its dynamics can be controlled by
in-plane drift fields. Here, we derive a spin diffusion equation for
non-degenerate two-subbands 2DEGs. We show that the intersubband scattering
rate, which is defined by the overlap of the subband densities, enters as a new
nob to control the spin dynamics, and can be controlled by electric fields,
being maximum for symmetric quantum wells. We find that for large intersubband
couplings the dynamics follow an effective diffusion matrix given by
approximately half of the subband-averaged matrices. This extra 1/2 factor
arises from Matthiessen's rule summing over the intra- and intersubband
scattering rates, and leads to a reduced diffusion constant and larger spin
lifetimes. We illustrate our findings with numerical solutions of the diffusion
equation with parameters extracted from realistic Schr\"odinger-Poisson
calculations.


Abstract:
  Distributed data analysis without revealing the individual data has recently
attracted significant attention in several applications. A collaborative data
analysis through sharing dimensionality reduced representations of data has
been proposed as a non-model sharing-type federated learning. This paper
analyzes the accuracy and privacy evaluations of this novel framework. In the
accuracy analysis, we provided sufficient conditions for the equivalence of the
collaborative data analysis and the centralized analysis with dimensionality
reduction. In the privacy analysis, we proved that collaborative users' private
datasets are protected with a double privacy layer against insider and external
attacking scenarios.


Abstract:
  There has recently been considerable interest in addressing the problem of
unifying distributed statistical analyses into a single coherent inference.
This problem naturally arises in a number of situations, including in big-data
settings, when working under privacy constraints, and in Bayesian model choice.
The majority of existing approaches have relied upon convenient approximations
of the distributed analyses. Although typically being computationally
efficient, and readily scaling with respect to the number of analyses being
unified, approximate approaches can have significant shortcomings -- the
quality of the inference can degrade rapidly with the number of analyses being
unified, and can be substantially biased even when unifying a small number of
analyses that do not concur. In contrast, the recent Fusion approach of Dai et
al. (2019) is a rejection sampling scheme which is readily parallelisable and
is exact (avoiding any form of approximation other than Monte Carlo error),
albeit limited in applicability to unifying a small number of low-dimensional
analyses. In this paper we introduce a practical Bayesian Fusion approach. We
extend the theory underpinning the Fusion methodology and, by embedding it
within a sequential Monte Carlo algorithm, we are able to recover the correct
target distribution. By means of extensive guidance on the implementation of
the approach, we demonstrate theoretically and empirically that Bayesian Fusion
is robust to increasing numbers of analyses, and coherently unifying analyses
which do not concur. This is achieved while being computationally competitive
with approximate schemes.


Abstract:
  The liquid argon time projection chamber (LArTPC) detector technology has an
excellent capability to measure properties of low-energy neutrinos produced by
the sun and supernovae and to look for exotic physics at very low energies. In
order to achieve those physics goals, it is crucial to identify and reconstruct
signals in the waveforms recorded on each TPC wire. In this paper, we report on
a novel algorithm based on a one-dimensional convolutional neural network (CNN)
to look for the region-of-interest (ROI) in raw waveforms. We test this
algorithm using data from the ArgoNeuT experiment in conjunction with an
improved noise mitigation procedure and a more realistic data-driven noise
model for simulated events. This deep-learning ROI finder shows promising
performance in extracting small signals and gives an efficiency approximately
twice that of the traditional algorithm in the low energy region of
$\sim$0.03-0.1 MeV. This method offers great potential to explore low-energy
physics using LArTPCs.