Abstract:
  A predicate head is a verbal expression that plays a role as the structural
center of a sentence. Identifying predicate heads is critical to understanding
a sentence. It plays the leading role in organizing the relevant syntactic
elements in a sentence, including subject elements, adverbial elements, etc.
For some languages, such as English, word morphologies are valuable for
identifying predicate heads. However, Chinese offers no morphological
information to indicate words` grammatical roles. A Chinese sentence often
contains several verbal expressions; identifying the expression that plays the
role of the predicate head is not an easy task. Furthermore, Chinese sentences
are inattentive to structure and provide no delimitation between words.
Therefore, identifying Chinese predicate heads involves significant challenges.
In Chinese information extraction, little work has been performed in predicate
head recognition. No generally accepted evaluation dataset supports work in
this important area. This paper presents the first attempt to develop an
annotation guideline for Chinese predicate heads and their relevant syntactic
elements. This annotation guideline emphasizes the role of the predicate as the
structural center of a sentence. The design of relevant syntactic element
annotation also follows this principle. Many considerations are proposed to
achieve this goal, e.g., patterns of predicate heads, a flattened annotation
structure, and a simpler syntactic unit type. Based on the proposed annotation
guideline, more than 1,500 documents were manually annotated. The corpus will
be available online for public access. With this guideline and annotated
corpus, our goal is to broadly impact and advance the research in the area of
Chinese information extraction and to provide the research community with a
critical resource that has been lacking for a long time.


Abstract:
  Federated learning has emerged as a popular paradigm for collaboratively
training a model from data distributed among a set of clients. This learning
setting presents, among others, two unique challenges: how to protect privacy
of the clients' data during training, and how to ensure integrity of the
trained model. We propose a two-pronged solution that aims to address both
challenges under a single framework. First, we propose to create secure
enclaves using a trusted execution environment (TEE) within the server. Each
client can then encrypt their gradients and send them to verifiable enclaves.
The gradients are decrypted within the enclave without the fear of privacy
breaches. However, robustness check computations in a TEE are computationally
prohibitive. Hence, in the second step, we perform a novel gradient encoding
that enables TEEs to encode the gradients and then offloading Byzantine check
computations to accelerators such as GPUs. Our proposed approach provides
theoretical bounds on information leakage and offers a significant speed-up
over the baseline in empirical evaluation.


Abstract:
  We investigate the problem of exact cluster recovery using oracle queries.
Previous results show that clusters in Euclidean spaces that are convex and
separated with a margin can be reconstructed exactly using only $O(\log n)$
same-cluster queries, where $n$ is the number of input points. In this work, we
study this problem in the more challenging non-convex setting. We introduce a
structural characterization of clusters, called $(\beta,\gamma)$-convexity,
that can be applied to any finite set of points equipped with a metric (or even
a semimetric, as the triangle inequality is not needed). Using
$(\beta,\gamma)$-convexity, we can translate natural density properties of
clusters (which include, for instance, clusters that are strongly non-convex in
$\mathbb{R}^d$) into a graph-theoretic notion of convexity. By exploiting this
convexity notion, we design a deterministic algorithm that recovers
$(\beta,\gamma)$-convex clusters using $O(k^2 \log n + k^2
(6/\beta\gamma)^{dens(X)})$ same-cluster queries, where $k$ is the number of
clusters and $dens(X)$ is the density dimension of the semimetric. We show that
an exponential dependence on the density dimension is necessary, and we also
show that, if we are allowed to make $O(k^2 + k\log n)$ additional queries to a
"cluster separation" oracle, then we can recover clusters that have different
and arbitrary scales, even when the scale of each cluster is unknown.


Abstract:
  The blooming of fake news on social networks has devastating impacts on
society, economy, and public security. Although numerous studies are conducted
for the automatic detection of fake news, the majority tend to utilize deep
neural networks to learn event-specific features for superior detection
performance on specific datasets. However, the trained models heavily rely on
the training datasets and are infeasible to apply to upcoming events due to the
discrepancy between event distributions. Inspired by domain adaptation
theories, we propose an end-to-end adversarial adaptation network, dubbed as
MetaDetector, to transfer meta knowledge (event-shared features) between
different events. Specifically, MetaDetector pushes the feature extractor and
event discriminator to eliminate event-specific features and preserve required
event-shared features by adversarial training. Furthermore, the pseudo-event
discriminator is utilized to evaluate the importance of historical event posts
to obtain partial shared features that are discriminative for detecting fake
news. Under the coordinated optimization among the four submodules,
MetaDetector accurately transfers the meta-knowledge of historical events to
the upcoming event for fact checking. We conduct extensive experiments on two
large-scale datasets collected from Weibo and Twitter. The experimental results
demonstrate that MetaDetector outperforms the state-of-the-art methods,
especially when the distribution shift between events is significant.
Furthermore, we find that MetaDetector is able to learn the event-shared
features, and alleviate the negative transfer caused by the large distribution
shift between events.


Abstract:
  Ultra-hot Jupiters are tidally locked gas giants with dayside temperatures
high enough to dissociate hydrogen and other molecules. Their atmospheres are
vastly non-uniform in terms of chemistry, temperature and dynamics, and this
makes their high-resolution transmission spectra and cross-correlation signal
difficult to interpret. In this work, we use the SPARC/MITgcm global
circulation model to simulate the atmosphere of the ultra-hot Jupiter WASP-76b
under different conditions, such as atmospheric drag and the absence of TiO and
VO. We then employ a 3D Monte-Carlo radiative transfer code, HIRES-MCRT, to
self-consistently model high-resolution transmission spectra with iron (Fe I)
lines at different phases during the transit. To untangle the structure of the
resulting cross-correlation map, we decompose the limb of the planet into four
sectors, and we analyse each of their contributions separately. Our experiments
demonstrate that the cross-correlation signal of an ultra-hot Jupiter is
primarily driven by its temperature structure, rotation and dynamics, while
being less sensitive to the precise distribution of iron across the atmosphere.
We also show that the previously published iron signal of WASP-76b can be
reproduced by a model featuring iron condensation on the leading limb.
Alternatively, the signal may be explained by a substantial temperature
asymmetry between the trailing and leading limb, where iron condensation is not
strictly required to match the data. Finally, we compute the $K_{p}-V_{sys}$
maps of the simulated WASP-76b atmospheres, and we show that rotation and
dynamics can lead to multiple peaks that are displaced from zero in the
planetary rest frame.


Abstract:
  Single layer hexagonal boron nitride is produced on 2 inch Pt(111)/sapphire
wafers. The growth with borazine vapour deposition at process temperatures
between 1000 and 1300 K is in-situ investigated by photoelectron yield
measurements. The growth kinetics is slower at higher temperatures and follows
a tanh$^2$ law which better fits for higher temperatures. The crystal-quality
of h-BN/Pt(111) is inferred from scanning low energy electron diffraction (x-y
LEED). The data indicate a strong dependence of the epitaxy on the growth
temperature. The dominant structure is an aligned coincidence lattice with 10
h-BN on 9 Pt(1$\times$1) unit cells and follows the substrate twinning at the
millimeter scale.


Abstract:
  The goal of our present paper is to deliberate $*$-conformal $\eta$-Ricci
soliton within the framework of Kenmotsu manifolds. Here we have shown that a
Kenmotsu metric as a $*$-conformal $\eta$-Ricci soliton is Einstein metric if
the soliton vector field is contact. Further, we have evolved the
characterization of the Kenmotsu manifold or the nature of the potential vector
field when the manifold satisfies gradient almost $*$-conformal $\eta$-Ricci
soliton. Next, we have contrived $*$-conformal $\eta$-Ricci soliton admitting
$(\kappa,\mu)^\prime$-almost Kenmotsu manifold and proved that the manifold is
Ricci flat and is locally isometric to
$\mathbb{H}^{n+1}(-4)\times\mathbb{R}^n$. Finally we have constructed some
examples to illustrate the existence of $*$-conformal $\eta$-Ricci soliton,
gradient almost $*$-conformal $\eta$-Ricci soliton on Kenmotsu manifold and
$(\kappa,\mu)^\prime$-almost Kenmotsu manifolds.


Abstract:
  In most real-world large-scale online applications (e.g., e-commerce or
finance), customer acquisition is usually a multi-step conversion process of
audiences. For example, an impression->click->purchase process is usually
performed of audiences for e-commerce platforms. However, it is more difficult
to acquire customers in financial advertising (e.g., credit card advertising)
than in traditional advertising. On the one hand, the audience multi-step
conversion path is longer. On the other hand, the positive feedback is sparser
(class imbalance) step by step, and it is difficult to obtain the final
positive feedback due to the delayed feedback of activation. Multi-task
learning is a typical solution in this direction. While considerable multi-task
efforts have been made in this direction, a long-standing challenge is how to
explicitly model the long-path sequential dependence among audience multi-step
conversions for improving the end-to-end conversion. In this paper, we propose
an Adaptive Information Transfer Multi-task (AITM) framework, which models the
sequential dependence among audience multi-step conversions via the Adaptive
Information Transfer (AIT) module. The AIT module can adaptively learn what and
how much information to transfer for different conversion stages. Besides, by
combining the Behavioral Expectation Calibrator in the loss function, the AITM
framework can yield more accurate end-to-end conversion identification. The
proposed framework is deployed in Meituan app, which utilizes it to real-timely
show a banner to the audience with a high end-to-end conversion rate for
Meituan Co-Branded Credit Cards. Offline experimental results on both
industrial and public real-world datasets clearly demonstrate that the proposed
framework achieves significantly better performance compared with
state-of-the-art baselines.


Abstract:
  We prove a result on the fractional Sobolev regularity of composition of
paths of low fractional Sobolev regularity with functions of bounded variation.
The result relies on the notion of variability, proposed by us in the previous
article [43, arXiv:2003.11698]. Here we work under relaxed hypotheses,
formulated in terms of Sobolev norms, and we can allow discontinuous paths,
which is new. The result applies to typical realizations of certain Gaussian or
L\'evy processes, and we use it to show the existence of Stieltjes type
integrals involving compositions.


Abstract:
  The COVID-19 pandemic has spread globally for several months. Because its
transmissibility and high pathogenicity seriously threaten people's lives, it
is crucial to accurately and quickly detect COVID-19 infection. Many recent
studies have shown that deep learning (DL) based solutions can help detect
COVID-19 based on chest CT scans. However, most existing work focuses on 2D
datasets, which may result in low quality models as the real CT scans are 3D
images. Besides, the reported results span a broad spectrum on different
datasets with a relatively unfair comparison. In this paper, we first use three
state-of-the-art 3D models (ResNet3D101, DenseNet3D121, and MC3\_18) to
establish the baseline performance on the three publicly available chest CT
scan datasets. Then we propose a differentiable neural architecture search
(DNAS) framework to automatically search for the 3D DL models for 3D chest CT
scans classification with the Gumbel Softmax technique to improve the searching
efficiency. We further exploit the Class Activation Mapping (CAM) technique on
our models to provide the interpretability of the results. The experimental
results show that our automatically searched models (CovidNet3D) outperform the
baseline human-designed models on the three datasets with tens of times smaller
model size and higher accuracy. Furthermore, the results also verify that CAM
can be well applied in CovidNet3D for COVID-19 datasets to provide
interpretability for medical diagnosis.


Abstract:
  Object localization has a vital role in any object detector, and therefore,
has been the focus of attention by many researchers. In this article, a special
training approach is proposed for a light convolutional neural network (CNN) to
determine the region of interest (ROI) in an image while effectively reducing
the number of probable anchor boxes. Almost all CNN-based detectors utilize a
fixed input size image, which may yield poor performance when dealing with
various object sizes. In this paper, a different CNN structure is proposed
taking three different input sizes, to enhance the performance. In order to
demonstrate the effectiveness of the proposed method, two common data set are
used for training while tracking by localization application is considered to
demonstrate its final performance. The promising results indicate the
applicability of the presented structure and the training method in practice.


Abstract:
  In this paper, we investigate the random access problem for a
delay-constrained heterogeneous wireless network. As a first attempt to study
this new problem, we consider a network with two users who deliver
delay-constrained traffic to an access point (AP) via a common unreliable
collision wireless channel. We assume that one user (called user 1) adopts
ALOHA and we optimize the random access scheme of the other user (called user
2). The most intriguing part of this problem is that user 2 does not know the
information of user 1 but needs to maximize the system timely throughput. Such
a paradigm of collaboratively sharing spectrum is envisioned by DARPA to better
dynamically match the supply and demand in the future [1], [2]. We first
propose a Markov Decision Process (MDP) formulation to derive a modelbased
upper bound, which can quantify the performance gap of any designed schemes. We
then utilize reinforcement learning (RL) to design an R-learning-based [3]-[5]
random access scheme, called TSRA. We finally carry out extensive simulations
to show that TSRA achieves close-to-upper-bound performance and better
performance than the existing baseline DLMA [6], which is our counterpart
scheme for delay-unconstrained heterogeneous wireless network. All source code
is publicly available in https://github.com/DanzhouWu/TSRA.


Abstract:
  Cosmic rays (CRs) have critical impacts in the multiphase interstellar medium
(ISM), driving dynamical motions in low-density plasma and modifying the
ionization state, temperature, and chemical composition of higher-density
atomic and molecular gas. We present a study of CR propagation between the
ionized ISM and a neutral cloud. Using one-dimensional magnetohydrodynamic
particle-in-cell simulations which include ion-neutral drag to damp
Alfv$\acute{\text{e}}$n waves in the cloud, we self-consistently evolve the
kinetic physics of CRs and fluid dynamics of the multiphase gas. By introducing
the cloud in our periodic domain, our simulations break translational symmetry
and allow the emergence of spatial structure in the CR distribution function. A
negative spatial gradient forms across the fully-ionized ISM region while a
positive gradient forms across the neutral cloud. We connect our results with
CR hydrodynamics formulations by computing the wave-particle scattering rates
as predicted by quasilinear, fluid, and Fokker-Planck theory. For momenta where
the mean free path is short relative to the box size, we find excellent
agreement among all scattering rates. By exploring different cloud sizes and
ion-neutral collision rates, we show that our results are robust. Our work
provides a first-principles verification of CR hydrodynamics when particles
stream down their pressure gradient, and opens a pathway toward comprehensive
calibrations of transport coefficients from self-generated
Alfv$\acute{\text{e}}$n wave scattering with CRs.


Abstract:
  A word $w$ is said to be closed if it has a proper factor $x$ which occurs
exactly twice in $w$, as a prefix and as a suffix of $w$. Based on the concept
of Ziv-Lempel factorization, we define the closed $z$-factorization of finite
and infinite words. Then we find the closed $z$-factorization of the infinite
$m$-bonacci words for all $m \geq 2$. We also classify closed prefixes of the
infinite $m$-bonacci words.


Abstract:
  In the presence of certain symmetries, three-dimensional Dirac semimetals can
harbor not only surface Fermi arcs, but also surface Dirac cones. Motivated by
the experimental observation of rotation-symmetry-protected Dirac semimetal
states in iron-based superconductors, we investigate the potential intrinsic
topological phases in a $C_{4z}$-rotational invariant superconducting Dirac
semimetal with $s_{\pm}$-wave pairing. When the normal state harbors only
surface Fermi arcs on the side surfaces, we find that an interesting gapped
superconducting state with a quartet of Majorana cones on each side surface can
be realized, even though the first-order topology of its bulk is trivial. When
the normal state simultaneously harbors surface Fermi arcs and surface Dirac
cones, we find that a second-order time-reversal invariant topological
superconductor with helical Majorana hinge states can be realized. The criteria
for these two distinct topological phases have a simple geometric
interpretation in terms of three characteristic surfaces in momentum space. By
reducing the bulk material to a thin film normal to the axis of rotation
symmetry, we further find that a two-dimensional first-order time-reversal
invariant topological superconductor can be realized if the inversion symmetry
is broken by applying a gate voltage. Our work reveals that diverse topological
superconducting phases and types of Majorana modes can be realized in
superconducting Dirac semimetals.


Abstract:
  An example of an activation function $\sigma$ is given such that networks
with activations $\{\sigma, \lfloor\cdot\rfloor\}$, integer weights and a fixed
architecture depending on $d$ approximate continuous functions on $[0,1]^d$.
The range of integer weights required for $\varepsilon$-approximation of
H\"older continuous functions is derived, which leads to a convergence rate of
order $n^{\frac{-2\beta}{2\beta+d}}\log_2n$ for neural network regression
estimation of unknown $\beta$-H\"older continuous function with given $n$
samples.


Abstract:
  We consider the problem of principal component analysis from a data matrix
where the entries of each column have undergone some unknown permutation,
termed Unlabeled Principal Component Analysis (UPCA). Using algebraic geometry,
we establish that for generic enough data, and up to a permutation of the
coordinates of the ambient space, there is a unique subspace of minimal
dimension that explains the data. We show that a permutation-invariant system
of polynomial equations has finitely many solutions, with each solution
corresponding to a row permutation of the ground-truth data matrix. Allowing
for missing entries on top of permutations leads to the problem of unlabeled
matrix completion, for which we give theoretical results of similar flavor. We
also propose a two-stage algorithmic pipeline for UPCA suitable for the
practically relevant case where only a fraction of the data has been permuted.
Stage-I of this pipeline employs robust-PCA methods to estimate the
ground-truth column-space. Equipped with the column-space, stage-II applies
methods for linear regression without correspondences to restore the permuted
data. A computational study reveals encouraging findings, including the ability
of UPCA to handle face images from the Extended Yale-B database with
arbitrarily permuted patches of arbitrary size in $0.3$ seconds on a standard
desktop computer.


Abstract:
  In this paper, we study the geometry of $GT-$varieties $X_{d}$ with group a
finite cyclic group $\Gamma \subset \mathrm{GL}(n+1,\mathbb{K})$ of order $d$.
We prove that the homogeneous ideal $\mathrm{I}(X_{d})$ of $X_{d}$ is generated
by binomials of degree at most $3$ and we provide examples reaching this bound.
We give a combinatorial description of the canonical module of the homogeneous
coordinate ring of $X_{d}$ and we show that it is generated by monomial
invariants of $\Gamma$ of degree $d$ and $2d$. This allows us to characterize
the Castelnuovo-Mumford regularity of the homogeneous coordinate ring of $X_d$.
Finally, we compute the cohomology table of the normal bundle of the so called
$RL-$varieties. They are projections of the Veronese variety
$\nu_{d}(\mathbb{P}^{n}) \subset \mathbb{P}^{\binom{n+d}{n}-1}$ which naturally
arise from level $GT-$varieties.


Abstract:
  Conditioning a branching Brownian motion to have an atypically low maximum
leads to a suppression of the branching mechanism. In this note, we consider a
branching Brownian motion conditioned to have a maximum below $\sqrt{2}\alpha
t$ ($\alpha<1$). We study the precise effects of an early/late first branching
time and a low/high first branching location under this condition. We do so by
imposing additional constraints on the first branching time and location. We
obtain large deviation estimates, as well as the optimal first branching time
and location given the additional constraints.


Abstract:
  The correct modeling of $e^+e^-$ collision events at the International Linear
Collider (ILC), as well as the response of a collider detector like the Silicon
Detector (SiD), is crucial to evaluating the expected sensitivity to key
properties of the Higgs boson. In this document we describe the event
generation and detector simulation in use for the SiD Letters of Interest
submitted for the 2021 Snowmass community planning exercise.


Abstract:
  The occurrence of a planet transiting in front of its host star offers the
opportunity to observe the planet's atmosphere filtering starlight. The
fraction of occulted stellar flux is roughly proportional to the optically
thick area of the planet, the extent of which depends on the opacity of the
planet's gaseous envelope at the observed wavelengths. Chemical species, haze,
and clouds are now routinely detected in exoplanet atmospheres through rather
small features in transmission spectra, i.e., collections of planet-to-star
area ratios across multiple spectral bins and/or photometric bands.
Technological advances have led to a shrinking of the error bars down to a few
tens of parts per million (ppm) per spectral point for the brightest targets.
The upcoming James Webb Space Telescope (JWST) is anticipated to deliver
transmission spectra with precision down to 10 ppm. The increasing precision of
measurements requires a reassessment of the approximations hitherto adopted in
astrophysical models, including transit light curve models. Recently, it has
been shown that neglecting the planet's thermal emission can introduce
significant biases in the transit depth measured with the JWST/Mid-InfraRed
Instrument, integrated between 5 and 12 $\mu$m. In this paper, we take a step
forward by analyzing the effects of the approximation on transmission spectra
over the 0.6-12 $\mu$m wavelength range covered by various JWST instruments. We
present open source software to predict the spectral bias, showing that, if not
corrected, it may affect the inferred molecular abundances and thermal
structure of some exoplanet atmospheres.


Abstract:
  Alkaline-earth-like~(AEL) atoms with two valence electrons and a nonzero
nuclear spin can be excited to Rydberg state for quantum computing. Typical AEL
ground states possess no hyperfine splitting, but unfortunately a GHz-scale
splitting seems necessary for Rydberg excitation. Though strong magnetic fields
can induce a GHz-scale splitting, weak fields are desirable to avoid noise in
experiments. Here, we provide two solutions to this outstanding challenge with
realistic data of well-studied AEL isotopes. In the first theory, the two
nuclear spin qubit states $|0\rangle$ and $|1\rangle$ are excited to Rydberg
states $|r\rangle$ with detuning $\Delta$ and 0, respectively, where a
MHz-scale detuning $\Delta$ arises from a weak magnetic field on the order of
1~G. With a proper ratio between $\Delta$ and $\Omega$, the qubit state
$|1\rangle$ can be fully excited to the Rydberg state while $|0\rangle$ remains
there. In the second theory, we show that by choosing appropriate intermediate
states a two-photon Rydberg excitation can proceed with only one nuclear spin
qubit state. The second theory is applicable whatever the magnitude of the
magnetic field is. These theories bring a versatile means for quantum
computation by combining the broad applicability of Rydberg blockade and the
incomparable advantages of nuclear-spin quantum memory in two-electron neutral
atoms.


Abstract:
  In this paper, an improvement has been made on the variation after projection
(VAP) method, which is crucial in the calculations of high-spin states. It
turns out that, the form of the trial VAP wave function with spin $J$ can be
simplified by adopting just one projected state rather than previously adopting
all $(2J+1)$ angular momentum projected states for each selected reference
state, $|\Phi\rangle$. The present calculations show that such simplification
still minimizes the angular momentum projected energy up to a very good
approximation. In this simplified VAP scheme, one can obtain almost equivalent
VAP wave functions starting from different sets of the projected basis states.
This clearly shows that a nuclear state can not be identified with a single
intrinsic state, while in the traditional nuclear collective models, an
intrinsic state is usually assigned to a well deformed rotational band.


Abstract:
  Great research interests have been attracted to devise AI services that are
able to provide mental health support. However, the lack of corpora is a main
obstacle to this research, particularly in Chinese language. In this paper, we
propose PsyQA, a Chinese dataset of psychological health support in the form of
question and answer pair. PsyQA is crawled from a Chinese mental health service
platform, and contains 22K questions and 56K long and well-structured answers.
Based on the psychological counseling theories, we annotate a portion of answer
texts with typical strategies for providing support, and further present
in-depth analysis of both lexical features and strategy patterns in the
counseling answers. We also evaluate the performance of generating counseling
answers with the generative pretrained models. Results show that utilizing
strategies enhances the fluency and helpfulness of generated answers, but there
is still a large space for future research.


Abstract:
  The task of calculating similarities between strings held by different
organizations without revealing these strings is an increasingly important
problem in areas such as health informatics, national censuses, genomics, and
fraud detection. Most existing privacy-preserving string comparison functions
are either based on comparing sets of encoded character q-grams, allow only
exact matching of encrypted strings, or they are aimed at long genomic
sequences that have a small alphabet. The set-based privacy-preserving
similarity functions commonly used to compare name and address strings in the
context of privacy-preserving record linkage do not take the positions of
sub-strings into account. As a result, two very different strings can
potentially be considered as an exact match leading to wrongly linked records.
Existing set-based techniques also cannot identify the length of the longest
common sub-string across two strings. In this paper we propose a novel approach
for accurate and efficient privacy-preserving string matching based on suffix
trees that are encoded using chained hashing. We incorporate a hashing based
encoding technique upon the encoded suffixes to improve privacy against
frequency attacks such as those exploiting Benford's law. Our approach allows
various operations to be performed without the strings to be compared being
revealed: the length of the longest common sub-string, do two strings have the
same beginning, middle or end, and the longest common sub-string similarity
between two strings. These functions allow a more accurate comparison of, for
example, bank account, credit card, or telephone numbers, which cannot be
compared appropriately with existing privacy-preserving string matching
techniques. Our evaluation on several data sets with different types of strings
validates the privacy and accuracy of our proposed approach.


Abstract:
  We establish large sieve inequalities for power moduli in imaginary quadratic
number fields, extending earlier work of Baier and Bansal for the Gaussian
field.


Abstract:
  We have fit the far-ultraviolet (FUV) to mid-infrared (MIR) spectral energy
distributions (SEDs) for several nearby galaxies ($<$ 20 Mpc). Global, radial,
and local photometric measurements are explored to better understand how
SED-derived star formation histories (SFHs) and classic star formation rate
(SFR) tracers manifest at different scales. Surface brightness profiles and
radial SED fitting provide insight into stellar population gradients in stellar
discs and haloes. A double exponential SFH model is used in the SED fitting to
better understand the distributions of young vs. old populations throughout
these galaxies. Different regions of a galaxy often have undergone very
different SFHs, either in strength, rate, timing, or some combination of all
these factors. An analysis of individual stellar complexes within these
galaxies shows a relationship between the ages of stellar clusters and how
these clusters are distributed throughout the galaxy. These star formation
properties are presented alongside previously published HI observations to
provide a holistic picture of a small sample of nearby star-forming galaxies.
The results presented here show that there is a wide variety of star formation
gradients and average stellar age distributions that can manifest in a
$\Lambda$CDM universe.


Abstract:
  Convolutional neural networks, the state of the art for image segmentation,
have been successfully applied to histology images by many computational
researchers. However, the translatability of this technology to clinicians and
biological researchers is limited due to the complex and undeveloped user
interface of the code, as well as the extensive computer setup required. As an
extension of our previous work (arXiv:1812.07509), we have developed a tool for
segmentation of whole slide images (WSIs) with an easy to use graphical user
interface. Our tool runs a state-of-the-art convolutional neural network for
segmentation of WSIs in the cloud. Our plugin is built on the open source tool
HistomicsTK by Kitware Inc. (Clifton Park, NY), which provides remote data
management and viewing abilities for WSI datasets. The ability to access this
tool over the internet will facilitate widespread use by computational
non-experts. Users can easily upload slides to a server where our plugin is
installed and perform human in the loop segmentation analysis remotely. This
tool is open source, and has the ability to be adapted to segment of any
pathological structure. For a proof of concept, we have trained it to segment
glomeruli from renal tissue images, achieving an F-score > 0.97 on holdout
tissue slides.


Abstract:
  Distributed word representations are popularly used in many tasks in natural
language processing, adding that pre-trained word vectors on huge text corpus
achieved high performance in many different NLP tasks. This paper introduces
multiple high quality word vectors for the French language where two of them
are trained on huge crawled French data and the others are trained on an
already existing French corpus. We also evaluate the quality of our proposed
word vectors and the existing French word vectors on the French word analogy
task. In addition, we do the evaluation on multiple real NLP tasks that show
the important performance enhancement of the pre-trained word vectors compared
to the existing and random ones. Finally, we created a demo web application to
test and visualize the obtained word embeddings. The produced French word
embeddings are available to the public, along with the fine-tuning code on the
NLU tasks and the demo code.


Abstract:
  To connect structure, dynamics and function in systems with multibody
interactions, network scientists model random walks on hypergraphs and identify
communities that confine the walks for a long time. The two flow-based
community-detection methods Markov stability and the map equation identify such
communities based on different principles and search algorithms. But how
similar are the resulting communities? We explain both methods' machinery
applied to hypergraphs and compare them on synthetic and real-world hypergraphs
using various hyperedge-size biased random walks and time scales. We find that
the map equation is more sensitive to time-scale changes and that Markov
stability is more sensitive to hyperedge-size biases.


Abstract:
  We investigate accretion of dark matter onto a moving Schwarzschild black
hole. The dark matter is modeled by the collisionless Vlasov gas, assumed to be
in thermal equilibrium at infinity. We derive an exact stationary solution and
provide a compact formula for the mass accretion rate. In general, the mass
accretion rate is a nonmonotonic function of the black hole velocity. A
monotonic relation (the accretion rate proportional to the Lorentz factor
associated with the velocity of the black hole) is obtained for high asymptotic
temperatures of the gas. The derived accretion rates are relevant for the
growth of primordial black holes in the early Universe.


Abstract:
  I use a direct method to extract the longitudinal structure function in the
next-to-leading order approximation with respect to the number of active flavor
from the parametrization of parton distributions. The contribution of charm and
bottom quarks corresponding to the gluon distributions (i.e.,
$G_{n_{f}=3}(x,Q^{2})$ and $G_{n_{f}=5}(x,Q^{2})$) is considered. I compare the
obtained longitudinal structure function at $n_{f}=4$ with the H1 data
[Eur.Phys.J.C{\bf74}, 2814(2014) and Eur.Phys.J.C{\bf71}, 1579 (2011)] and the
result L.P.Kaptari et al.[ Phys.Rev.D{\bf 99}, 096019(2019)] which is based on
the Mellin transforms. These calculations compared with the results
  from CT18 [Phys.Rev.D103, 014013(2021)] parametrization model. The nonlinear
effects on the gluon distribution improve the behavior of the longitudinal
structure function in comparison with the H1 data and CT18 at low values of
$Q^{2}$.


Abstract:
  The transfer function of the baryon power spectrum from redshift $z\approx
1100$ to today has recently been, for the first time, determined from data by
Pardo and Spergel. We observe a remarkable coincidence between this function
and the transport function of a cold ideal Fermi gas at different redshifts.
Guided by this, we unveil an infinite set of critical temperatures of the
relativistic ideal Fermi gas which depend on a very finely quantized
long-distance cutoff. The sound horizon scale of Baryon Acoustic Oscillations
(BAO) seems set such a cutoff, which dials a critical temperature that is
subsequently reached during redshift. At the critical point the Fermi gas
becomes scale invariant and may condense to subsequently undergo gravitational
collapse, seeding small scale structure. We mention some profound implications
including the apparent quantization of Fermi momentum conjugate to the cutoff
and the corresponding "gapping" of temperature.


Abstract:
  This is a commentary on two papers (PNAS 117 (51) 32244-32250 (2020) and
arXiv:2011.06721), which observed a series of ordering transitions in a
strongly correlated two-dimensional electron system confined to a AlAs quantum
well. We summarize the main discoveries (electron nematicity, insulating
ferromagnetism, etc.) of them and speculate on $T-r_s$ phase diagram based on
theoretical considerations.


Abstract:
  The design, fabrication, and characterization of low loss ultra-compact bends
in high index (n = 3.1 at {\lambda} = 1550 nm) PECVD silicon rich silicon
nitride (SRN) is demonstrated and utilized to realize efficient, small
footprint thermo-optic phase shifter. Compact bends are structured into a
folded waveguide geometry to form a rectangular spiral within an area of 65 x
65 \mum2 possessing a total active waveguide length of 1.2 mm. The device
features a phase shifting efficiency of 8 mW/{\pi} and a 3 dB switching
bandwidth of 15 KHz. We propose SRN as a promising thermo-optic platform
combining the properties of silicon and silicon stoichiometric nitride


Abstract:
  Conformational isomers or conformers of molecules play a decisive role in
chemistry and biology. However, experimental methods to investigate chemical
reaction dynamics are typically not conformer-sensitive. Here, we report on a
gas-phase megaelectronvolt ultrafast electron diffraction investigation of
{\alpha}-phellandrene undergoing an electrocyclic ring-opening reaction. We
directly image the evolution of a specific set of {\alpha}-phellandrene
conformers into the product isomer predicted by the Woodward-Hoffmann rules in
real space and time. Our experimental results are in quantitative agreement
with nonadiabatic quantum molecular dynamics simulations, which provide
unprecedented detail of how conformation influences time scale and quantum
efficiency of photoinduced ring-opening reactions. Due to the prevalence of
large numbers of conformers in organic chemistry, our findings impact our
general understanding of reaction dynamics in chemistry and biology.


Abstract:
  We give a short introduction to discrete flat fronts in hyperbolic space and
prove that any discrete flat front in the mixed area sense admits a Weierstrass
representation.


Abstract:
  We simulate numerically the formation of spherically symmetric primordial
black holes (PBHs) seeded by different families of primordial curvature
perturbations profiles on a Friedman-Robertson-Walker (FRW) Universe filled by
radiation fluid. We have studied the dependency on the curvature profile of the
initial mass $M_{\rm BH,i}$ of the PBHs at the time of apparent horizon
formation $t_{AH}$, and the final mass $M_{\rm BH,f}$ after the accretion
process, using an excision technique, comparing $M_{\rm BH,i}$ to previous
analytical estimations obtained using compensated PBHs model approach. The
analytical estimations are in agreement with numerical results, except for
large values of the initial perturbation amplitude, when the compensated model
is less accurate. The masses $M_{\rm BH,f}$ and $M_{\rm BH,i}$ do not depend
only on the shape around the compaction function peak, but on the full profile
of the initial curvature perturbation. We also estimate the accretion effects,
and for those PBHs with masses relevant for the dark matter abundance, with a
final mass equal to the horizon crossing mass, we find $M_{\rm BH,f}\approx 3
M_{\rm BH,i} $.


Abstract:
  This paper deals with large-scale composite optimization problems having the
objective function formed as a sum of two terms, one has Lipschitz continuous
gradient along random subspaces and may be nonconvex and the second term is
simple, but possibly nonconvex and nonseparable. Under these settings we design
a stochastic coordinate proximal gradient method which takes into account the
nonseparable composite form of the objective function. This algorithm achieves
scalability by constructing at each iteration a local approximation model of
the nonseparable objective function along a random subspace with
user-determined dimension. We outline efficient techniques for selecting the
random subspace, yielding an implementation that has low cost per-iteration
while also achieving fast convergence rates. We present a probabilistic
worst-case complexity analysis for our stochastic coordinate proximal gradient
method in convex and nonconvex settings, in particular we prove
high-probability bounds on the number of iterations before a given optimality
is achieved. To the best of our knowledge, this work is the first proposing a
pure stochastic coordinate descent algorithm which is supported by global
efficiency estimates for general nonseparable composite optimization problems.
Preliminary numerical results also confirm the efficiency of our algorithm.


Abstract:
  The evolution of galaxies depends on their environments. In this work, active
galactic nucleus (AGN) activity in different environments has been studied. The
fractions of radio and optical AGN in four different environments have been
compared using samples of void, isolated, group member, and the brightest group
galaxies (BGGs). Galaxies in voids show significantly lower stellar ages,
concentrations, colours and surface mass densities, and they experience more
one-on-one interactions compared to the isolated galaxies and galaxies in
groups. In order to study pure environmental effects, the biases caused by the
stellar mass and galaxy type quantified by 4000$\AA$ break strength have been
removed. While the results confirm no dependence of the optical AGN activity on
environment in blue galaxies and with lower significance in green galaxies, a
higher fraction of optical AGN has been observed for the massive red galaxies
in voids compared to the galaxies in dense environments. This may be related to
the higher amount of one-on-one interaction observed in the void galaxies, or
it may reflect more fundamental differences in the host galaxies or
environments of the voids. The radio-mode AGN activity increases in dense
environment for red galaxies. No changes in the radio-loud AGN fraction have
been observed for the blue and green galaxies. This shows that the effect of
environment on AGN activity is not significant in the presence of cold gas in
galaxies. We also discuss whether the efficiency of gas accretion depends on
the properties of the host galaxy.


Abstract:
  While medical image segmentation is an important task for computer aided
diagnosis, the high expertise requirement for pixelwise manual annotations
makes it a challenging and time consuming task. Since conventional data
augmentations do not fully represent the underlying distribution of the
training set, the trained models have varying performance when tested on images
captured from different sources. Most prior work on image synthesis for data
augmentation ignore the interleaved geometric relationship between different
anatomical labels. We propose improvements over previous GAN-based medical
image synthesis methods by learning the relationship between different
anatomical labels. We use a weakly supervised segmentation method to obtain
pixel level semantic label map of images which is used learn the intrinsic
relationship of geometry and shape across semantic labels. Latent space
variable sampling results in diverse generated images from a base image and
improves robustness. We use the synthetic images from our method to train
networks for segmenting COVID-19 infected areas from lung CT images. The
proposed method outperforms state-of-the-art segmentation methods on a public
dataset. Ablation studies also demonstrate benefits of integrating geometry and
diversity.


Abstract:
  At the quantum many-body level, atom-light interfaces generally remain
challenging to solve for or understand in a non-perturbative fashion. Here, we
consider a waveguide quantum electrodynamics model, where two-level atoms
interact with and via propagating photons in a one-dimensional waveguide, and
specifically investigate the interplay of atomic position disorder, multiple
scattering of light, quantum nonlinear interactions and dissipation. We develop
qualitative arguments and present numerical evidence that such a system
exhibits a many-body localized~(MBL) phase, provided that atoms are less than
half excited. Interestingly, while MBL is usually formulated with respect to
closed systems, this system is intrinsically open. However, as dissipation
originates from transport of energy to the system boundaries and the subsequent
radiative loss, the lack of transport in the MBL phase makes the waveguide QED
system look essentially closed and makes applicable the notions of MBL.
Conversely, we show that if the system is initially in a delocalized phase due
to a large excitation density, rapid initial dissipation can leave the system
unable to efficiently transport energy at later times, resulting in a dynamical
transition to an MBL phase. These phenomena can be feasibly realized in
state-of-the-art experimental setups.


Abstract:
  We compare the accuracy, convergence rate and computational cost of
eigenerosion (EE) and phase-field (PF) methods. For purposes of comparison, we
specifically consider the standard test case of a center-crack panel loaded in
biaxial tension and assess the convergence of the energy error as the length
scale parameter and mesh size tend to zero simultaneously. The panel is
discretized by means of a regular mesh consisting of standard bilinear or Q1
elements. The exact stresses from the known analytical linear elastic solution
are applied to the boundary. All element integrals over the interior and the
boundary of the domain are evaluated exactly using the symbolic computation
program Mathematica. When the EE inelastic energy is enhanced by means of
Richardson extrapolation, EE is found to converge at twice the rate of PF and
to exhibit much better accuracy. In addition, EE affords a
one-order-of-magnitude computational speed-up over PF.


Abstract:
  Language models can generate harmful and biased outputs and exhibit
undesirable behavior according to a given cultural context. We propose a
Process for Adapting Language Models to Society (PALMS) with Values-Targeted
Datasets, an iterative process to significantly change model behavior by
crafting and fine-tuning on a dataset that reflects a predetermined set of
target values. We evaluate our process using three metrics: quantitative
metrics with human evaluations that score output adherence to a target value,
toxicity scoring on outputs; and qualitative metrics analyzing the most common
word associated with a given social category. Through each iteration, we add
additional training dataset examples based on observed shortcomings from
evaluations. PALMS performs significantly better on all metrics compared to
baseline and control models for a broad range of GPT-3 language model sizes
without compromising capability integrity. We find that the effectiveness of
PALMS increases with model size. We show that significantly adjusting language
model behavior is feasible with a small, hand-curated dataset.


Abstract:
  Superposition of two independent orthogonally polarized beams is a
conventional principle of creating a new light beam. Here, we intend to achieve
the inverse process, namely, extracting inherent polarization modes from a
single light beam. However, inherent polarization modes within a light beam are
always entangled so that a stable polarization is maintained during propagation
in free space. To overcome this limitation, we report an approach that breaks
the modulation symmetry of a light beam, thereby disentangling the inherent
polarization modes. Using polarization mode competition along with an optical
pen, polarization modes are extracted at will in the focal region of an
objective lens. This work demonstrates polarization mode extraction from a
light beam, which will not only provide an entirely new principle of
polarization modulation but also pave the way for multidimensional manipulation
of light fields, thereby facilitating extensive developments in optics.


Abstract:
  To perform their daily tasks, developers intensively make use of existing
resources by consulting open-source software (OSS) repositories. Such platforms
contain rich data sources, e.g., code snippets, documentation, and user
discussions, that can be useful for supporting development activities. Over the
last decades, several techniques and tools have been promoted to provide
developers with innovative features, aiming to bring in improvements in terms
of development effort, cost savings, and productivity. In the context of the EU
H2020 CROSSMINER project, a set of recommendation systems has been conceived to
assist software programmers in different phases of the development process. The
systems provide developers with various artifacts, such as third-party
libraries, documentation about how to use the APIs being adopted, or relevant
API function calls. To develop such recommendations, various technical choices
have been made to overcome issues related to several aspects including the lack
of baselines, limited data availability, decisions about the performance
measures, and evaluation approaches. This paper is an experience report to
present the knowledge pertinent to the set of recommendation systems developed
through the CROSSMINER project. We explain in detail the challenges we had to
deal with, together with the related lessons learned when developing and
evaluating these systems. Our aim is to provide the research community with
concrete takeaway messages that are expected to be useful for those who want to
develop or customize their own recommendation systems. The reported experiences
can facilitate interesting discussions and research work, which in the end
contribute to the advancement of recommendation systems applied to solve
different issues in Software Engineering.


Abstract:
  Urban analytics combines spatial analysis, statistics, computer science, and
urban planning to understand and shape city futures. While it promises better
policymaking insights, concerns exist around its epistemological scope and
impacts on privacy, ethics, and social control. This chapter reflects on the
history and trajectory of urban analytics as a scholarly and professional
discipline. In particular, it considers the direction in which this field is
going and whether it improves our collective and individual welfare. It first
introduces early theories, models, and deductive methods from which the field
originated before shifting toward induction. It then explores urban network
analytics that enrich traditional representations of spatial interaction and
structure. Next it discusses urban applications of spatiotemporal big data and
machine learning. Finally, it argues that privacy and ethical concerns are too
often ignored as ubiquitous monitoring and analytics can empower social
repression. It concludes with a call for a more critical urban analytics that
recognizes its epistemological limits, emphasizes human dignity, and learns
from and supports marginalized communities.


Abstract:
  Deep learning has led to unprecedented successes in solving some very
difficult problems in domains such as computer vision, natural language
processing, and general pattern recognition. These achievements are the
culmination of decades-long research into better training techniques and deeper
neural network models, as well as improvements in hardware platforms that are
used to train and execute the deep neural network models. Many
application-specific integrated circuit (ASIC) hardware accelerators for deep
learning have garnered interest in recent years due to their improved
performance and energy-efficiency over conventional CPU and GPU architectures.
However, these accelerators are constrained by fundamental bottlenecks due to
1) the slowdown in CMOS scaling, which has limited computational and
performance-per-watt capabilities of emerging electronic processors, and 2) the
use of metallic interconnects for data movement, which do not scale well and
are a major cause of bandwidth, latency, and energy inefficiencies in almost
every contemporary processor. Silicon photonics has emerged as a promising
CMOS-compatible alternative to realize a new generation of deep learning
accelerators that can use light for both communication and computation. This
article surveys the landscape of silicon photonics to accelerate deep learning,
with a coverage of developments across design abstractions in a bottom-up
manner, to convey both the capabilities and limitations of the silicon
photonics paradigm in the context of deep learning acceleration.


Abstract:
  With a key improvement, the auxiliary mass flow method is now able to compute
many badly-needed Feynman integrals encountered in cutting-edge collider
processes. We have successfully applied it to two-loop electroweak correction
to $e^+e^-\to HZ$, two-loop QCD corrections to $3j$, $W/Z/H+2j$, $t\bar{t}H$
and $4j$ production at hadron colliders, and three-loop QCD correction to
$t\bar{t}$ production at hadron colliders, all of which are crucial for
precision test in the following decade. Our results are important building
blocks and benchmarks for future study of these processes.


Abstract:
  The tiny modification of dispersion relation induced by Lorentz violation
(LV) is an essential issue in quantum gravity (QG) theories, which can be
magnified into significant effects when dealing with astrophysical observations
at high energy and long propagation distance. LV would lead to photon decay at
high energies, therefore, observations of high-energy photons could constrain
LV or even QG theories. The Large High Altitude Air Shower Observatory (LHAASO)
is the most sensitive gamma-array instrument currently operating above 100 TeV.
Recently, LHAASO reported the detection of 12 sources above 100 TeV with
maximum photon energy exceeding 1 PeV. According to these observations, the
most stringent restriction is achieved in this paper, i.e., limiting the LV
energy scale to $1.7\times10^{33} $ eV, which is over 139,000 times that of the
Planck energy and achieve an improvement of about 1.9 orders of magnitude over
previous limits.


Abstract:
  In this work, we propose an abstraction and refinement methodology for the
controller synthesis of discrete-time stochastic systems to enforce complex
logical properties expressed by deterministic finite automata (a.k.a. DFA). Our
proposed scheme is based on a notion of so-called
$(\epsilon,\delta)$-approximate probabilistic relations, allowing one to
quantify the similarity between stochastic systems modeled by discrete-time
stochastic games and their corresponding finite abstractions. Leveraging this
type of relations, the lower bound for the probability of satisfying the
desired specifications can be well ensured by refining controllers synthesized
over abstract systems to the original games. Moreover, we propose an
algorithmic procedure to construct such a relation for a particular class of
nonlinear stochastic systems with slope restrictions on the nonlinearity. The
proposed methods are demonstrated on a quadrotor example, and the results
indicate that the desired lower bound for the probability of satisfaction is
guaranteed.


Abstract:
  The climate is a non-equilibrium system undergoing the continuous action of
forcing and dissipation. Under the effect of a spatially inhomogeneous
absorption of solar energy, all the climate components dynamically respond by
redistributing energy until an approximate steady state is reached. In order to
improve the skill of climate models and correct their biases, it is essential
to investigate how such dynamical balance is reached. In general, the climate
system features multiple steady states for a given set of boundary conditions.
Here, we apply the Thermodynamic Diagnostic Tool (TheDiaTo) to investigate the
statistical properties of the five co-existing climates, ranging from a
snowball to an ice-free aquaplanet, obtained in MITgcm coupled simulations
under the same boundary conditions. The aim is to explore the multistability of
the climate by highlighting differences in competing steady states and their
characteristic signatures regarding the meridional transport of heat and water
mass, the Lorenz energy cycle and the material entropy production. Alternative
cloud parametrizations and descriptions of energy exchange are also used to
investigate how robust such signatures are and, at the same time, how the
statistical properties can be improved in the simulated climatic states. Thus
we show how the diagnostic tool can help in identifying strengths and
weaknesses of a model configuration.


Abstract:
  With the introduction of educational robotics (ER) and computational thinking
(CT) in classrooms, there is a rising need for operational models that help
ensure that CT skills are adequately developed. One such model is the Creative
Computational Problem Solving Model (CCPS) which can be employed to improve the
design of ER learning activities. Following the first validation with students,
the objective of the present study is to validate the model with teachers,
specifically considering how they may employ the model in their own practices.
The Utility, Usability and Acceptability framework was leveraged for the
evaluation through a survey analysis with 334 teachers. Teachers found the CCPS
model useful to foster transversal skills but could not recognise the impact of
specific intervention methods on CT-related cognitive processes. Similarly,
teachers perceived the model to be usable for activity design and intervention,
although felt unsure about how to use it to assess student learning and adapt
their teaching accordingly. Finally, the teachers accepted the model, as shown
by their intent to replicate the activity in their classrooms, but were less
willing to modify it or create their own activities, suggesting that they need
time to appropriate the model and underlying tenets.


Abstract:
  An oscillating spanwise pressure gradient is imposed numerically to control
the flow separation and reduce the drag of a turbulent flow in a channel with
square bars. The transverse flow produces a maximum drag reduction of 25\%, due
to lower pressure and skin-friction drag forces. The pressure drag reduction
reaches a maximum of 22\% and is due to a decrease of the positive high
pressure in front of the bars and an increase of the low negative pressure
behind the bars. The skin-friction drag reduction is caused by a lower
wall-shear stress along the cavity between the bars where the flow is fully
attached, while the wall-shear stress on the crest of the bars and in the
separated region behind the bars is unaffected. The spanwise laminar flow
obtained by neglecting the nonlinear terms involving the turbulent velocity
fluctuations is used to compute the power spent for oscillating the fluid along
the spanwise direction and an excellent agreement is found with the power spent
obtained by the averaged turbulent flow. A marginal or negative net power saved
is found by subtracting the power employed for controlling the flow from the
power saved thanks to the transverse flow. The control reduces the total drag
as the integral of the Reynolds stresses along the horizontal line connecting
the corners of two consecutive bars is decreased, which in turn impacts on the
pressure and wall-shear stress reductions.


Abstract:
  We aim to cluster financial assets in order to identify a small set of stocks
to approximate the level of diversification of the whole universe of stocks. We
develop a data-driven approach to clustering based on a correlation blockmodel
in which assets in the same cluster are highly correlated with each other and,
at the same time, have the same correlations with all other assets. We devise
an algorithm to detect the clusters, with theoretical analysis and practical
guidance. Finally, we conduct an empirical analysis to verify the performance
of the algorithm.


Abstract:
  In this paper, we study some controllability and observability properties for
a coupled system of time-discrete fourth- and second-order parabolic equations.
This system can be regarded as a simplification of the well-known stabilized
Kumamoto-Sivashinsky equation. Unlike the continuous case, we can prove only a
relaxed observability inequality which yields a $\phi(\triangle
t)$-controllability result. This result tells that we cannot reach exactly zero
but rather a small target whose size goes to 0 as the discretization parameter
$\triangle t$ goes to 0. The proof relies on a known Carleman estimate for
second-order time-discrete parabolic operators and a new Carleman estimate for
the time-discrete fourth-order equation.


Abstract:
  We investigate ytterbium gallium garnet Yb$_{3}$Ga$_{5}$O$_{12}$ in the
paramagnetic phase above the supposed magnetic transition at $T_{\lambda}
\approx 54$ mK. Our study combines susceptibility and specific heat
measurements with neutron scattering experiments and theoretical calculations.
Below 500 mK, the elastic neutron response is strongly peaked in the momentum
space. Along with that the inelastic spectrum develops flat excitation modes.
In magnetic field, the lowest energy branch follows a Zeeman shift in
accordance with the field-dependent specific heat data. An intermediate state
with spin canting away from the field direction is evidenced in small magnetic
fields. In the field of 2 T, the total magnetization almost saturates and the
measured excitation spectrum is well reproduced by the spin-wave calculations
taking into account solely the dipole-dipole interactions. The small positive
Curie-Weiss temperature derived from the susceptibility measurements is also
accounted for by the dipole spin model. Altogether, our results suggest that
Yb$_{3}$Ga$_{5}$O$_{12}$ is a quantum dipolar magnet.


Abstract:
  We begin with the theoretical study of spectral energy cascade due to the
propagation of high amplitude sound in the absence of thermal sources. To this
end, a first-principles-based system of governing equations, correct up to
second order in perturbation variables is derived. The exact energy corollary
of such second-order system of equations is then formulated and used to
elucidate the spectral energy dynamics of nonlinear acoustic waves. We then
extend this analysis to thermoacoustically unstable waves -- i.e. amplified as
a result of thermoacoustic instability. We drive such instability up until the
generation of shock waves. We further study the nonlinear wave propagation in
geometrically complex case of waves induced by the spark plasma between the
electrodes. This case adds the geometrical complexity of a curved,
three-dimensional shock, yielding vorticity production due to baroclinic
torque. Finally, detonation waves are simulated by using a low-order approach,
in a periodic setup subjected to high pressure inlet and exhaust of combustible
gaseous mixture. An order adaptive fully compressible and unstructured Navier
Stokes solver is currently under development to enable higher fidelity studies
of both the spark plasma and detonation wave problem in the future.


Abstract:
  The capability of the human to pay attention to both coarse and fine-grained
regions has been applied to computer vision tasks. Motivated by that, we
propose a collaborative learning framework in the complex domain for monaural
noise suppression. The proposed system consists of two principal modules,
namely spectral feature extraction module (FEM) and stacked glance-gaze modules
(GGMs). In FEM, the UNet-block is introduced after each convolution layer,
enabling the feature recalibration from multiple scales. In each GGM, we
decompose the multi-target optimization in the complex spectrum into two
sub-tasks. Specifically, the glance path aims to suppress the noise in the
magnitude domain to obtain a coarse estimation, and meanwhile, the gaze path
attempts to compensate for the lost spectral detail in the complex domain. The
two paths work collaboratively and facilitate spectral estimation from
complementary perspectives. Besides, by repeatedly unfolding the GGMs, the
intermediate result can be iteratively refined across stages and lead to the
ultimate estimation of the spectrum. The experiments are conducted on the
WSJ0-SI84, DNS-Challenge dataset, and Voicebank+Demand dataset. Results show
that the proposed approach achieves state-of-the-art performance over previous
advanced systems on the WSJ0-SI84 and DNS-Challenge dataset, and meanwhile,
competitive performance is achieved on the Voicebank+Demand corpus.


Abstract:
  The excess work performed in a heat-engine process with given finite
operation time \tau is bounded by the thermodynamic length, which measures the
distance during the relaxation along a path in the space of the thermodynamic
state. Unfortunately, the thermodynamic length, as a guidance for the heat
engine optimization, is beyond the experimental measurement. We propose to
measure the thermodynamic length \mathcal{L} through the extrapolation of
finite-time measurements
\mathcal{L}(\tau)=\int_{0}^{\tau}[P_{\mathrm{ex}}(t)]^{1/2}dt via the excess
power P_{\mathrm{ex}}(t). The current proposal allows to measure the
thermodynamic length for a single control parameter without requiring extra
effort to find the optimal control scheme. We illustrate the measurement
strategy via examples of the quantum harmonic oscillator with tuning frequency
and the classical ideal gas with changing volume.


Abstract:
  Chern-Simons topological quantum computer is a device that can be effectively
described by the Chern-Simons topological quantum field theory and used for
quantum computations. Quantum qudit gates of this quantum computer are
represented by sequences of quantum $\mathcal{R}$-matrices. Its dimension and
explicit form depend on the parameters of the Chern-Simons theory -- level $k$,
gauge group $SU(N)$, and representation, which is chosen to be symmetric
representation $[r]$. In this paper, we examine the universality of such a
quantum computer. We prove that for sufficiently large $k$ it is universal, and
the minimum allowed value of $k$ depends on the remaining parameters $r$ and
$N$.


Abstract:
  Online hate speech is an important issue that breaks the cohesiveness of
online social communities and even raises public safety concerns in our
societies. Motivated by this rising issue, researchers have developed many
traditional machine learning and deep learning methods to detect hate speech in
online social platforms automatically. However, most of these methods have only
considered single type textual feature, e.g., term frequency, or using word
embeddings. Such approaches neglect the other rich textual information that
could be utilized to improve hate speech detection. In this paper, we propose
DeepHate, a novel deep learning model that combines multi-faceted text
representations such as word embeddings, sentiments, and topical information,
to detect hate speech in online social platforms. We conduct extensive
experiments and evaluate DeepHate on three large publicly available real-world
datasets. Our experiment results show that DeepHate outperforms the
state-of-the-art baselines on the hate speech detection task. We also perform
case studies to provide insights into the salient features that best aid in
detecting hate speech in online social platforms.


Abstract:
  With the relaxation of the containment measurements around the globe,
monitoring the social distancing in crowded public places is of grate
importance to prevent a new massive wave of COVID-19 infections. Recent works
in that matter have limited themselves by detecting social distancing in
corridors up to small crowds by detecting each person individually considering
the full body in the image. In this work, we propose a new framework for
monitoring the social-distance using end-to-end Deep Learning, to detect crowds
violating the social-distance in wide areas where important occlusions may be
present. Our framework consists in the creation of a new ground truth based on
the ground truth density maps and the proposal of two different solutions, a
density-map-based and a segmentation-based, to detect the crowds violating the
social-distance constrain. We assess the results of both approaches by using
the generated ground truth from the PET2009 and CityStreet datasets. We show
that our framework performs well at providing the zones where people are not
following the social-distance even when heavily occluded or far away from one
camera.


Abstract:
  Let $p$ and $q$ be positive integers such that the $(p, q)$-leaper $L$ is
free. We prove that $L$ tours the board of size $4pq \times n$ for all
sufficiently large positive integers $n$. Combining this with the recently
established conjecture of Willcocks which states that $L$ tours the square
board of side $2(p + q)$, we conclude that furthermore $L$ tours all boards
both of whose sides are even and sufficiently large. This, in particular,
completely resolves the question of the Hamiltonicity of leaper graphs on
sufficiently large square boards.


Abstract:
  Given the abundance of applications of ranking in recent years, addressing
fairness concerns around automated ranking systems becomes necessary for
increasing the trust among end-users. Previous work on fair ranking has mostly
focused on application-specific fairness notions, often tailored to online
advertising, and it rarely considers learning as part of the process. In this
work, we show how to transfer numerous fairness notions from binary
classification to a learning to rank setting. Our formalism allows us to design
methods for incorporating fairness objectives with provable generalization
guarantees. An extensive experimental evaluation shows that our method can
improve ranking fairness substantially with no or only little loss of model
quality.


Abstract:
  Microprocessor design, debug, and validation research and development are
increasingly based on modeling and simulation at different abstraction layers.
Microarchitecture-level simulators have become the most commonly used tools for
performance evaluation, due to their high simulation throughput, compared to
lower levels of abstraction, but usually come at the cost of loss of hardware
accuracy. As a result, the implementation, speed, and accuracy of
microarchitectural simulators are becoming more and more crucial for
researchers and microprocessor architects. One of the most critical aspects of
a microarchitectural simulator is its ability to accurately express design
standards as various aspects of the microarchitecture change during design
refinement. On the other hand, modern microprocessor models rely on dedicated
hardware implementations, making the design space exploration a time-consuming
process that can be performed using a variety of methods, ranging from
high-level models to hardware prototyping. Therefore, the tradeoff between
simulation speed and accuracy, can be significantly varied, and an
application's performance measurements uncertain. In this paper, we present a
microarchitecture-level simulation modeling study, which enables as accurate as
possible performance modeling of a RISC-V out-of-order superscalar
microprocessor core. By diligently adjusting several important
microarchitectural parameters of the widely used gem5 simulator, we investigate
the challenges of accurate performance modeling on microarchitecture-level
simulation compared to accuracy and low simulation throughput of RTL simulation
of the target design. Further, we demonstrate the main sources of errors that
prevent high accuracy levels of the microarchitecture-level modeling.


Abstract:
  While many defences against adversarial examples have been proposed, finding
robust machine learning models is still an open problem. The most compelling
defence to date is adversarial training and consists of complementing the
training data set with adversarial examples. Yet adversarial training severely
impacts training time and depends on finding representative adversarial
samples. In this paper we propose to train models on output spaces with large
class separation in order to gain robustness without adversarial training. We
introduce a method to partition the output space into class prototypes with
large separation and train models to preserve it. Experimental results shows
that models trained with these prototypes -- which we call deep repulsive
prototypes -- gain robustness competitive with adversarial training, while also
preserving more accuracy on natural samples. Moreover, the models are more
resilient to large perturbation sizes. For example, we obtained over 50%
robustness for CIFAR-10, with 92% accuracy on natural samples and over 20%
robustness for CIFAR-100, with 71% accuracy on natural samples without
adversarial training. For both data sets, the models preserved robustness
against large perturbations better than adversarially trained models.


Abstract:
  Structured pruning methods are among the effective strategies for extracting
small resource-efficient convolutional neural networks from their dense
counterparts with minimal loss in accuracy. However, most existing methods
still suffer from one or more limitations, that include 1) the need for
training the dense model from scratch with pruning-related parameters embedded
in the architecture, 2) requiring model-specific hyperparameter settings, 3)
inability to include budget-related constraint in the training process, and 4)
instability under scenarios of extreme pruning. In this paper, we present
ChipNet, a deterministic pruning strategy that employs continuous Heaviside
function and a novel crispness loss to identify a highly sparse network out of
an existing dense network. Our choice of continuous Heaviside function is
inspired by the field of design optimization, where the material distribution
task is posed as a continuous optimization problem, but only discrete values (0
or 1) are practically feasible and expected as final outcomes. Our approach's
flexible design facilitates its use with different choices of budget
constraints while maintaining stability for very low target budgets.
Experimental results show that ChipNet outperforms state-of-the-art structured
pruning methods by remarkable margins of up to 16.1% in terms of accuracy.
Further, we show that the masks obtained with ChipNet are transferable across
datasets. For certain cases, it was observed that masks transferred from a
model trained on feature-rich teacher dataset provide better performance on the
student dataset than those obtained by directly pruning on the student data
itself.


Abstract:
  In the last years, a new generation of large-scale imaging surveys have
probed wide field regions around some nearby galaxies at unprecedented low
surface brightness regime (~28.0-29.0 mag arcsec^-2). This offers a chance of
discovering very faint dwarf satellites by means of visual inspection of these
public deep images. We report the first results of a systematic survey of faint
dwarf spheroidal galaxies in the vicinity of the bright late-type spiral NGC
253 galaxy by means of a visual inspection of the images taken by the Dark
Energy Survey. Three new dwarf galaxies have been discovered in the vicinity of
the brightest member of the Sculptor filament, the late-type spiral NGC 253.
Assuming they are companions of NGC 253, their total absolute V-magnitudes fall
in the -7 to -9 mag range, which is typical for dwarf satellites in the local
Universe. The central surface brightness tend to be extremely low for all the
discovered dwarfs and fall roughly in the range of 25-26 mag arcsec^-2 in
g-band. Using known data on distances and velocities of galaxies, we estimate
the total virial mass of the NGC 253 group to be 8 x 10^11 Mo, which gives the
virial radius R_200 = 186 kpc and the turn-around radius of 706 kpc. We also
discuss the possible existence of a spatially flattened and velocity-correlated
satellite system around NGC 253. This large-scale structure is orientated
almost edge-on to line of sight. The possible plane of satellites is only 31
kpc thick with the minor-to-major axis ratio of 0.14. Four out of five galaxies
with measured velocities follow a common velocity trend similar to those
observed in the planes of satellites around the Andromeda and Centaurus A
galaxies. However, the small number of galaxies with known velocities prevents
to reach a definitive conclusion about the formation scenario of the structure
and its possible relation to the surrounding cosmic web.


Abstract:
  The recent publication by Siraj & Loeb (2021; Nature Scientific Reports 11,
3803) attempts to revive the debate over whether the Chicxulub impactor was a
comet or an asteroid. They calculate that ~20% of long-period comets impacting
Earth will have first been disrupted by passage inside the Sun's Roche limit,
generating thousands of fragments, each the needed size of the Chicxulub
impactor. This would increase the impact rate of comets by a factor ~15, making
them as likely to hit the Earth as an asteroid. They also argue that a comet
would be a factor of 10 more likely to match the geochemical constraints, which
indicate the Chicxulub impactor was carbonaceous chondrite-like. These
conclusions are based on misinterpretations of the literature. Siraj & Loeb [1]
overestimate the number of fragments produced during tidal disruption of a
comet: tens of fragments are produced, not thousands. They also conflate
'carbonaceous chondrite' with specific types of carbonaceous chondrite, and
ignore the evidence of iridium, making comets seem more likely than asteroids
to match the Chicxulub impactor, when in fact they likely can be ruled out.
Rather than a comet, an asteroidal impactor similar to CM or CR carbonaceous
chondrites is strongly favored.


Abstract:
  Quantum computers are invaluable tools to explore the properties of complex
quantum systems. We show that dynamical localization of the quantum sawtooth
map, a highly sensitive quantum coherent phenomenon, can be simulated on
actual, small-scale quantum processors. Our results demonstrate that quantum
computing of dynamical localization may become a convenient tool for evaluating
advances in quantum hardware performances.


Abstract:
  We have calculated the thermal partition function for the thermodynamical
system of massless free bosonic higher spin fields (Fronsdal theory) by using
Feynman path integral formalism. However, we have established a connection
between the microscopic world (Quantum statistical) of the thermodynamical
system of massless free bosonic HS fields to the thermodynamics of the
macroscopic world its and also we have observed the duality between the
thermodynamical system of massless free HS fields on $d$-dimensional Minkowski
spacetime and thermodynamical system of Klein-Gordon scalar fields on
4-dimensional Minkowski spacetime at the thermal equilibrium condition. The
average energy of the canonical ensemble of the thermodynamical system of
massless free bosonic HS fields is not in agreement with the equipartition
theorem, hence it deviates from the classical behaviour of the system and it
shows that the true signature of the quantum thermodynamical system.


Abstract:
  Motive and electrical energy has played a crucial role in human civilization.
Since Ancient times, motive energy played a primary role in agricultural and
industrial production as well as transportation. At that time, motive energy
was provided by work of humans and draft animals. Later, work of water and wind
power was harnessed. During the 19$^{\text{th}}$ century, steam power became
the main source of motive energy in USA and Britain. Modern transportation and
industry depend on the work of heat engines that use fossil fuel. A brief
history of different sources of energy is presented in this work. The energy
consumptions in pre-industrial and industrial societies are calculated. The
lost opportunities for the Second Industrial Revolution (such as fast breeder
reactors and thermonuclear power stations) are discussed. The case that the
Solar Power will become the main source of energy by the second half of this
century is presented. It is calculated that the Solar Power has the potential
to bring about the new Industrial Revolution. Based on material and energy
resources available in the Solar System, it is demonstrated that the Solar
System Civilization supporting a population of 10 Quadrillion with a high
standard of living is possible.


Abstract:
  We review our recent work in the area of autonomous materials research,
highlighting the coupling of machine learning methods and models and more
problem-aware modeling. We review the general Bayesian framework for
closed-loop design employed by many autonomous materials platforms. We then
provide examples of our work on such platforms. We finally review our
approaches to extend current statistical and ML models to better reflect
problem-specific structure including the use of physics-based models and
incorporation of operational considerations into the decision-making procedure.


Abstract:
  In our recent work [8], we have studied the homogenization of the Poisson
equation in a class of non periodically perforated domains. In this paper, we
examine the case of the Stokes system. We consider a porous medium in which the
characteristic distance between two holes, denoted by $\epsilon$, is
proportional to the characteristic size of the holes. It is well known (see
[1],[17] and [19]) that, when the holes are periodically distributed in space,
the velocity converges to a limit given by the Darcy's law when the size of the
holes tends to zero. We generalize these results to the setting of [8]. The
non-periodic domains are defined as a local perturbation of a periodic
distribution of holes. We obtain classical results of the homogenization theory
in perforated domains (existence of correctors and regularity estimates uniform
in $\epsilon$) and we prove $H^2$--convergence estimates for particular force
fields.


Abstract:
  In this paper, we introduce a definition of phase response for a class of
multi-input multi-output (MIMO) linear time-invariant (LTI) systems whose
frequency responses are (semi-)sectorial at all frequencies. The newly defined
phase concept subsumes the well-known notions of positive real systems and
negative imaginary systems. We formulate a small phase theorem for feedback
stability, which complements the celebrated small gain theorem. The small phase
theorem lays the foundation of a phase theory of MIMO systems. We also discuss
time-domain interpretations of phase-bounded systems via both energy signal
analysis and power signal analysis. In addition, a sectored real lemma is
derived for the computation of MIMO phases, which serves as a natural
counterpart of the bounded real lemma.


Abstract:
  Empirical studies are fundamental in assessing the effectiveness of
implementations of branch-and-bound algorithms. The complexity of such
implementations makes empirical study difficult for a wide variety of reasons.
Various attempts have been made to develop and codify a set of standard
techniques for the assessment of optimization algorithms and their software
implementations; however, most previous work has been focused on classical
sequential algorithms. Since parallel computation has become increasingly
mainstream, it is necessary to re-examine and modernize these practices. In
this paper, we propose a framework for assessment based on the notion that
\emph{resource consumption} is at the heart of what we generally refer to as
the "effectiveness" of an implementation. The proposed framework carefully
distinguishes between an implementation's baseline efficiency, the efficacy
with which it utilizes a fixed allocation of resources, and its scalability, a
measure of how the efficiency changes as resources (typically additional
computing cores) are added or removed. Efficiency is typically applied to
sequential implementations, whereas scalability is applied to parallel
implementations. Efficiency and scalability are both important contributors in
determining the overall effectiveness of a given parallel implementation, but
the goal of improved efficiency is often at odds with the goal of improved
scalability. Within the proposed framework, we review the challenges to
effective evaluation and discuss the strengths and weaknesses of existing
methods of assessment.


Abstract:
  Learning based video compression attracts increasing attention in the past
few years. The previous hybrid coding approaches rely on pixel space operations
to reduce spatial and temporal redundancy, which may suffer from inaccurate
motion estimation or less effective motion compensation. In this work, we
propose a feature-space video coding network (FVC) by performing all major
operations (i.e., motion estimation, motion compression, motion compensation
and residual compression) in the feature space. Specifically, in the proposed
deformable compensation module, we first apply motion estimation in the feature
space to produce motion information (i.e., the offset maps), which will be
compressed by using the auto-encoder style network. Then we perform motion
compensation by using deformable convolution and generate the predicted
feature. After that, we compress the residual feature between the feature from
the current frame and the predicted feature from our deformable compensation
module. For better frame reconstruction, the reference features from multiple
previous reconstructed frames are also fused by using the non-local attention
mechanism in the multi-frame feature fusion module. Comprehensive experimental
results demonstrate that the proposed framework achieves the state-of-the-art
performance on four benchmark datasets including HEVC, UVG, VTL and MCL-JCV.


Abstract:
  For a given Jacobi-Jordan algebra $A$ and a vector space $V$ over a field
$k$, a non-abelian cohomological type object ${\mathcal H}^{2}_{A} \, (V, \,
A)$ is constructed: it classifies all Jacobi-Jordan algebras containing $A$ as
a subalgebra of codimension equal to ${\rm dim}_k (V)$. Any such algebra is
isomorphic to a so-called \emph{unified product} $A \, \natural \, V$.
Furthermore, we introduce the bicrossed (semi-direct, crossed, or skew crossed)
product $A \bowtie V$ associated to two Jacobi-Jordan algebras as a special
case of the unified product. Several examples and applications are provided:
the Galois group of the extension $A \subseteq A \bowtie V$ is described as a
subgroup of the semidirect product of groups ${\rm GL}_k (V) \rtimes {\rm
Hom}_k (V, \, A)$ and an Artin type theorem for Jacobi-Jordan algebra is
proven. The key tools for classifying supersolvable and flag Jacobi-Jordan
algebras are introduced.


Abstract:
  This manuscript presents a model of compliant contacts for time-critical
humanoid robot motion control. The proposed model considers the environment as
a continuum of spring-damper systems, which allows us to compute the equivalent
contact force and torque that the environment exerts on the contact surface. We
show that the proposed model extends the linear and rotational springs and
dampers - classically used to characterize soft terrains - to the case of large
contact surface orientations. The contact model is then used for the real-time
whole-body control of humanoid robots walking on visco-elastic environments.
The overall approach is validated by simulating walking motions of the iCub
humanoid robot. Furthermore, the paper compares the proposed whole-body control
strategy and state of the art approaches. In this respect, we investigate the
terrain compliance that makes the classical approaches assuming rigid contacts
fail. We finally analyze the robustness of the presented control design with
respect to non-parametric uncertainty in the contact-model.


Abstract:
  We find geometric conditions on a four-dimensional Hermitian manifold endowed
with a metric connection with totally skew-symmetric torsion under which the
complex structure is a harmonic map from the manifold into its twistor space
considered with a natural family of Riemannian metrics defined by means of the
metric and the given connection on the base manifold.


Abstract:
  Employing recent advances in response theory and nonequilibrium ensemble
reweighting, we study the dynamic and static correlations that give rise to an
electric field-dependent ionic conductivity in electrolyte solutions. We
consider solutions modeled with both implicit and explicit solvents, with
different dielectric properties, and at multiple concentrations. Implicit
solvent models at low concentrations and small dielectric constants exhibit
strongly field-dependent conductivities. We compared these results to the
Onsager-Wilson theory of the Wien effect, which provides a qualitatively
consistent prediction at low concentrations and high static dielectric
constants, but is inconsistent away from these regimes. The origin of the
discrepancy is found to be increased ion correlations under these conditions.
Explicit solvent effects act to suppress nonlinear responses, yielding a weakly
field-dependent conductivity over the range of physically realizable field
strengths. By decomposing the relevant time correlation functions, we find that
the insensitivity of the conductivity to the field results from the persistent
frictional forces on the ions from the solvent. Our findings illustrate the
utility of nonequilibrium response theory in rationalizing nonlinear transport
behavior.


Abstract:
  The creep motion of domain walls driven by external fields in magnetic thin
films is described by universal features related to the underlying depinning
transition. One key parameter in this description is the roughness exponent
characterizing the growth of fluctuations of the domain wall position with its
longitudinal length scale. The roughness amplitude, which gives information
about the scale of fluctuations, however, has received less attention. Albeit
their relevance, experimental reports of the roughness parameters, both
exponent and amplitude, are scarce. We report here experimental values of the
roughness parameters for different magnetic field intensities in the creep
regime at room temperature for a Pt/Co/Pt thin film. The mean value of the
roughness exponent is $\zeta = 0.74$, and we show that it can be rationalized
as an effective value in terms of the known universal values corresponding to
the depinning and thermal cases. In addition, it is shown that the roughness
amplitude presents a significant increase with decreasing field. These results
contribute to the description of domain wall motion in disordered thin magnetic
systems.


Abstract:
  Many Beyond-Standard Model physics signatures are enhanced in high-energy
neutrino interactions. To explore these signatures, ultra-large Cherenkov
detectors such as IceCube exploit event samples with charged current muon
neutrino interactions > 1 TeV. Most of these interactions occur below the
detector volume, and produce muons that enter the detector. However, the large
spacing between detectors leads to inefficiency for measuring muons with
energies below or near the critical energy of 400 GeV. In response, IceCube has
built a densely instrumented region within the larger detector. This provides
large samples of well-reconstructed interactions that are contained within the
densely instrumented region, extending up to energies of ~50 GeV. This leaves a
gap of relatively unexplored atmospheric-neutrino events with energies between
50 GeV and 1 TeV in the ultra-large detectors. In this paper we point out that
interesting Beyond Standard Model signatures may appear in this energy window,
and that early running of the DUNE far detectors can give insight into new
physics that may appear in this range.


Abstract:
  Quantum simulation represents the most promising quantum application to
demonstrate quantum advantage on near-term noisy intermediate-scale quantum
(NISQ) computers, yet available quantum simulation algorithms are prone to
errors and thus difficult to be realized. Herein, we propose a novel scheme to
utilize intrinsic gate errors of NISQ devices to enable controllable simulation
of open quantum system dynamics without ancillary qubits or explicit bath
engineering, thus turning unwanted quantum noises into useful quantum
resources. Specifically, we simulate energy transfer process in a
photosynthetic dimer system on IBM-Q cloud. By employing designed
decoherence-inducing gates, we show that quantum dissipative dynamics can be
simulated efficiently across coherent-to-incoherent regimes with results
comparable to those of the numerically-exact classical method. Moreover, we
demonstrate a calibration routine that enables consistent and predictive
simulations of open-quantum system dynamics in the intermediate coupling
regime. This work provides a new direction for quantum advantage in the NISQ
era.


Abstract:
  Blind Descent uses constrained but, guided approach to learn the weights. The
probability density function is non-zero in the infinite space of the dimension
(case in point: Gaussians and normal probability distribution functions). In
Blind Descent paper, some of the implicit ideas involving layer by layer
training and filter by filter training (with different batch sizes) were
proposed as probable greedy solutions. The results of similar experiments are
discussed. Octave (and proposed PyTorch variants) source code of the
experiments of this paper can be found at
https://github.com/PrasadNR/Attempted-Blind-Constrained-Descent-Experiments-ABCDE-
. This is compared against the ABCDE derivatives of the original PyTorch source
code of https://github.com/akshat57/Blind-Descent .


Abstract:
  The first full angular analysis of the $B^0 \to D^{*-} D_s^{*+}$ decay is
performed using 6 fb$^{-1}$ of $pp$ collision data collected with the LHCb
experiment at a centre-of-mass energy of 13 TeV. The $D_s^{*+} \to D_s^+
\gamma$ and $D^{*-} \to \bar{D}^0 \pi^-$ vector meson decays are used with the
subsequent $D_s^+ \to K^+ K^- \pi^+$ and $\bar{D}^0 \to K^+ \pi^-$ decays. All
helicity amplitudes and phases are measured, and the longitudinal polarisation
fraction is determined to be $f_{\rm L} = 0.578 \pm 0.010 \pm 0.011$ with
world-best precision, where the first uncertainty is statistical and the second
is systematic. The pattern of helicity amplitude magnitudes is found to align
with expectations from quark-helicity conservation in $B$ decays. The ratio of
branching fractions $[\mathcal{B}(B^0 \to D^{*-} D_s^{*+}) \times
\mathcal{B}(D_s^{*+} \to D_s^+ \gamma)]/\mathcal{B}(B^0 \to D^{*-} D_s^+)$ is
measured to be $2.045 \pm 0.022 \pm 0.071$ with world-best precision. In
addition, the first observation of the Cabibbo-suppressed $B_s^0 \to D^{*-}
D_s^+$ decay is made with a significance of seven standard deviations. The
branching fraction ratio $\mathcal{B}(B_s^0 \to D^{*-} D_s^+)/\mathcal{B}(B^0
\to D^{*-} D_s^+)$ is measured to be $0.049 \pm 0.006 \pm 0.003 \pm 0.002$,
where the third uncertainty is due to limited knowledge of the ratio of
fragmentation fractions.


Abstract:
  We present an architecture that is effective for continual learning in an
especially demanding setting, where task boundaries do not exist or are
unknown, and where classes have to be learned online (with each example
presented only once). To obtain good performance under these constraints, while
mitigating catastrophic forgetting, we exploit recent advances in contrastive,
self-supervised learning, allowing us to use a pre-trained, general purpose
image encoder whose weights can be frozen, which precludes forgetting. The
pre-trained encoder also greatly simplifies the downstream task of
classification, which we solve with an ensemble of very simple classifiers.
Collectively, the ensemble exhibits much better performance than any individual
classifier, an effect which is amplified through specialisation and competitive
selection. We assess the performance of the encoders-and-ensembles architecture
on standard continual learning benchmarks, where it outperforms prior
state-of-the-art by a large margin on the hardest problems, as well as in less
familiar settings where the data distribution changes gradually or the classes
are presented one at a time.


Abstract:
  Emotional support is a crucial ability for many conversation scenarios,
including social interactions, mental health support, and customer service
chats. Following reasonable procedures and using various support skills can
help to effectively provide support. However, due to the lack of a
well-designed task and corpora of effective emotional support conversations,
research on building emotional support into dialog systems remains untouched.
In this paper, we define the Emotional Support Conversation (ESC) task and
propose an ESC Framework, which is grounded on the Helping Skills Theory. We
construct an Emotion Support Conversation dataset (ESConv) with rich annotation
(especially support strategy) in a help-seeker and supporter mode. To ensure a
corpus of high-quality conversations that provide examples of effective
emotional support, we take extensive effort to design training tutorials for
supporters and several mechanisms for quality control during data collection.
Finally, we evaluate state-of-the-art dialog models with respect to the ability
to provide emotional support. Our results show the importance of support
strategies in providing effective emotional support and the utility of ESConv
in training more emotional support systems.