Abstract:
  Within the framework of the Lindblad master equation, we propose a general
methodology to describe the effects of the environment on a system in dilute
gas phase. The phenomenological parameters characterizing the transitions
between rovibrational states of the system induced by collisions can be
extracted from experimental transition kinetic constants, relying on Energy Gap
fitting laws. As the availability of this kind of experimental data can be
limited, the present work relied on experimental line broadening coefficients,
however still using Energy Gap fitting laws. The 3 $\mu$m infrared spectral
range of acetylene was chosen to illustrate the proposed approach. The method
shows fair agreement with available experimental data while being
computationally inexpensive. The results are discussed in the context of state
laser quantum control.


Abstract:
  We consider the near-critical Erd\H{o}s-R\'{e}nyi random graph $G(n,p)$ and
provide a new probabilistic proof of the fact that, when $p$ is of the form
$p=p(n)=1/n+\lambda/n^{4/3}$ and $A$ is large,
\[\mathbb{P}(|\mathcal{C}_{\max}|>An^{2/3})\asymp
A^{-3/2}e^{-\frac{A^3}{8}+\frac{\lambda A^2}{2}-\frac{\lambda^2A}{2}}\] where
$\mathcal{C}_{\max}$ is the largest connected component of the graph. Our
result allows $A$ and $\lambda$ to depend on $n$. While this result is already
known, our proof relies only on conceptual and adaptable tools such as ballot
theorems, whereas the existing proof relies on a combinatorial formula specific
to Erd\H{o}s-R\'{e}nyi graphs, together with analytic estimates.


Abstract:
  For the general parametric regression models with covariates contaminated
with normal measurement errors, this paper proposes an accelerated version of
the classical simulation extrapolation algorithm to estimate the unknown
parameters in the regression function. By applying the conditional expectation
directly to the target function, the proposed algorithm successfully removes
the simulation step, by generating an estimation equation either for immediate
use or for extrapolating, thus significantly reducing the computational time.
Large sample properties of the resulting estimator, including the consistency
and the asymptotic normality, are thoroughly discussed. Potential wide
applications of the proposed estimation procedure are illustrated by examples,
simulation studies, as well as a real data analysis.


Abstract:
  Microstructural materials design is one of the most important applications of
inverse modeling in materials science. Generally speaking, there are two broad
modeling paradigms in scientific applications: forward and inverse. While the
forward modeling estimates the observations based on known parameters, the
inverse modeling attempts to infer the parameters given the observations.
Inverse problems are usually more critical as well as difficult in scientific
applications as they seek to explore the parameters that cannot be directly
observed. Inverse problems are used extensively in various scientific fields,
such as geophysics, healthcare and materials science. However, it is
challenging to solve inverse problems, because they usually need to learn a
one-to-many non-linear mapping, and also require significant computing time,
especially for high-dimensional parameter space. Further, inverse problems
become even more difficult to solve when the dimension of input (i.e.
observation) is much lower than that of output (i.e. parameters). In this work,
we propose a framework consisting of generative adversarial networks and
mixture density networks for inverse modeling, and it is evaluated on a
materials science dataset for microstructural materials design. Compared with
baseline methods, the results demonstrate that the proposed framework can
overcome the above-mentioned challenges and produce multiple promising
solutions in an efficient manner.


Abstract:
  Morales-Mendoza et al. present in 2013 a new class of discrete orthogonal
polynomials. They use these polynomials to design an unbiased FIR filter. In
their paper they make the statement that a representation of the polynomials
via hypergeometric functions is unknown. However Shakibaei Asli et al. found in
2017 a $_3F_2$ hypergeometric function representation. In this paper it is
shown that the "new" orthogonal polynomials belong to the class of the Hahn
polynomials. The transfer function of the unbiased FIR filter has been
determined. In the second part, an orthogonal unbiased FIR filter is designed
following the method of the orthogonal derivative described in the thesis of
the author. In the Appendix a table with a number of possible transformations
of the Hahn polynomials is given.


Abstract:
  Abductive reasoning starts from some observations and aims at finding the
most plausible explanation for these observations. To perform abduction, humans
often make use of temporal and causal inferences, and knowledge about how some
hypothetical situation can result in different outcomes. This work offers the
first study of how such knowledge impacts the Abductive NLI task -- which
consists in choosing the more likely explanation for given observations. We
train a specialized language model LMI that is tasked to generate what could
happen next from a hypothetical scenario that evolves from a given event. We
then propose a multi-task model MTL to solve the Abductive NLI task, which
predicts a plausible explanation by a) considering different possible events
emerging from candidate hypotheses -- events generated by LMI -- and b)
selecting the one that is most similar to the observed outcome. We show that
our MTL model improves over prior vanilla pre-trained LMs fine-tuned on
Abductive NLI. Our manual evaluation and analysis suggest that learning about
possible next events from different hypothetical scenarios supports abductive
inference.


Abstract:
  Fooling deep neural networks (DNNs) with the black-box optimization has
become a popular adversarial attack fashion, as the structural prior knowledge
of DNNs is always unknown. Nevertheless, recent black-box adversarial attacks
may struggle to balance their attack ability and visual quality of the
generated adversarial examples (AEs) in tackling high-resolution images. In
this paper, we propose an attention-guided black-box adversarial attack based
on the large-scale multiobjective evolutionary optimization, termed as LMOA. By
considering the spatial semantic information of images, we firstly take
advantage of the attention map to determine the perturbed pixels. Instead of
attacking the entire image, reducing the perturbed pixels with the attention
mechanism can help to avoid the notorious curse of dimensionality and thereby
improves the performance of attacking. Secondly, a large-scale multiobjective
evolutionary algorithm is employed to traverse the reduced pixels in the
salient region. Benefiting from its characteristics, the generated AEs have the
potential to fool target DNNs while being imperceptible by the human vision.
Extensive experimental results have verified the effectiveness of the proposed
LMOA on the ImageNet dataset. More importantly, it is more competitive to
generate high-resolution AEs with better visual quality compared with the
existing black-box adversarial attacks.


Abstract:
  Hysteretic magnetoresistance (MR) is often used as a signature of
ferromagnetism in conducting oxide thin films and heterostructures. Here,
magnetotransport is investigated in a non-magnetic uniformly La-doped SrSnO3
film grown using hybrid molecular beam epitaxy. A 12 nm La:SrSnO3/2 nm
SrSnO3/GdScO3 (110) film with insulating behavior exhibited a robust hysteresis
loop in the MR at T < 5 K accompanied by an anomaly at ~ +/- 3 T at T < 2.5 K.
Furthermore, MR with the field in-plane yielded a value exceeded 100% at 1.8 K.
Using detailed temperature-, angle- and magnetic field-dependent resistance
measurements, we illustrate the origin of hysteresis is not due to magnetism in
the film but rather is associated with the magnetocaloric effect of the GdScO3
substrate. Given GdScO3 and similar substrates are commonly used in complex
oxide research, this work highlights the importance of thermal coupling to
processes in the substrates which must be carefully accounted for in the data
interpretation for thin films and heterostructures utilizing these substrates.


Abstract:
  An alternative scheme is described to estimate the layer 1 LAXPC 20
background for faint sources where the source contribution to the 50-80 keV
count rate is less than 0.25 counts/sec (15 milli-crabs or $6 \times 10^{-11}$
ergs/s/cm$^2$). We consider 12 blank sky observations and based on their 50-80
keV count rate in 100 second time-bins, generate four template spectra which
are then used to estimate the background spectrum and lightcurve for a given
faint source observation. The variance of the estimated background subtracted
spectra for the 12 blank sky observations is taken as the energy dependent
systematic uncertainty which will dominate over the statistical one for
exposures longer than 5 ksecs. The estimated 100 second time bin background
lightcurve in the 4-20 keV band with a 3\% systematic error matches with the
blank sky ones. The 4-20 keV spectrum can be constrained for a source with flux
$\gtrapprox 1$ milli-crab. Fractional r.m.s variability of 10\% can be
determined for a $\sim 5$ milli-crab source lightcurve binned at 100 seconds.
To illustrate the scheme, the lightcurves, and spectra of three different blank
sky observations, three AGN sources (Mrk 0926, Mrk 110, NGC 4593), and LMC X-1
are shown.


Abstract:
  We propose that Hawking radiation-like phenomena may be observed in systems
that show butterfly effects. Suppose that a classical dynamical system has a
Lyapunov exponent $\lambda_L$, and is deterministic and non-thermal ($T=0$). We
argue that, if we quantize this system, the quantum fluctuations may imitate
thermal fluctuations with temperature $T \sim \hbar \lambda_L/2 \pi $ in a
semi-classical regime, and it may cause analogous Hawking radiation. We also
discuss that our proposal may provide an intuitive explanation of the existence
of the bound of chaos proposed by Maldacena, Shenker and Stanford.


Abstract:
  Determining the factors that have an influence on software systems
development and deployment project outcomes has been the focus of extensive and
ongoing research for more than 30 years. We provide here a survey of the
research literature that has addressed this topic in the period 1996-2006, with
a particular focus on empirical analyses. On the basis of this survey we
present a new classification framework that represents an abstracted and
synthesized view of the types of factors that have been asserted as influencing
project outcomes.


Abstract:
  National and local governments have implemented a large number of policies,
particularly non-pharmaceutical interventions, in response to the Covid-19
pandemic. Evaluating the effects of these policies, both on the number of
Covid-19 cases as well on other economic outcomes is a key ingredient for
policymakers to be able to determine which policies are most effective as well
as the relative costs and benefits of particular policies. In this paper, we
consider the relative merits of common identification strategies exploiting
variation in policy choices made across different locations by checking whether
the identification strategies are compatible with leading epidemic models in
the epidemiology literature. We argue that unconfoundedness type approaches are
likely to be more useful for evaluating policies than difference in differences
type approaches due to the highly nonlinear spread of cases during a pandemic.
For difference in differences, we further show that a version of this problem
continues to exist even when one is interested in understanding the effect of a
policy on other economic outcomes when those outcomes also depend on the number
of Covid-19 cases. We propose alternative approaches that are able to
circumvent these issues. We apply our proposed approach to study the effect of
state level shelter-in-place orders early in the pandemic.


Abstract:
  The impact of potential future measurements of beam charge asymmetries on the
current knowledge of Compton form factors is evaluated. Elaborating on the
results of a global neural network fit to deeply virtual Compton scattering
data, a Bayesian reweighting analysis quantifies the improved determination of
the real part of the Compton form factor $\mathcal{H}$. Such an improvement is
particularly relevant for the experimental determination of the proton
mechanical properties or for universality studies of generalized parton
distributions through analyses of virtual Compton scattering in both spacelike
and timelike regions.


Abstract:
  This paper identifies and addresses a serious design bias of existing salient
object detection (SOD) datasets, which unrealistically assume that each image
should contain at least one clear and uncluttered salient object. This design
bias has led to a saturation in performance for state-of-the-art SOD models
when evaluated on existing datasets. However, these models are still far from
satisfactory when applied to real-world scenes. Based on our analyses, we
propose a new high-quality dataset and update the previous saliency benchmark.
Specifically, our dataset, called Salient Objects in Clutter (SOC), includes
images with both salient and non-salient objects from several common object
categories. In addition to object category annotations, each salient image is
accompanied by attributes that reflect common challenges in real-world scenes,
which can help provide deeper insight into the SOD problem. Further, with a
given saliency encoder, e.g., the backbone network, existing saliency models
are designed to achieve mapping from the training image set to the training
ground-truth set. We, therefore, argue that improving the dataset can yield
higher performance gains than focusing only on the decoder design. With this in
mind, we investigate several dataset-enhancement strategies, including label
smoothing to implicitly emphasize salient boundaries, random image augmentation
to adapt saliency models to various scenarios, and self-supervised learning as
a regularization strategy to learn from small datasets. Our extensive results
demonstrate the effectiveness of these tricks. We also provide a comprehensive
benchmark for SOD, which can be found in our repository:
http://dpfan.net/SOCBenchmark.


Abstract:
  We derive an effective field theory for a type-II fracton starting from the
Haah code on the lattice. The effective topological theory is not given
exclusively in terms of an action; it must be supplemented with a condition
that selects physical states. Without the constraint, the action only describes
a type-I fracton. The constraint emerges from a condition that cube operators
multiply to the identity, and it cannot be consistently implemented in the
continuum theory at the operator level, but only in a weaker form, in terms of
matrix elements of physical states. Informed by these studies and starting from
the opposite end, i.e., the continuum, we discuss a Chern-Simons-like theory
that does not need a constraint or projector, and yet has no mobile
excitations. Whether this continuum theory admits a lattice counterpart remains
unanswered.


Abstract:
  As a wide variety of quantum computing platforms become available, methods
for assessing and comparing the performance of these devices are of increasing
interest and importance. Inspired by the success of single-qubit error rate
computations for tracking the progress of gate-based quantum computers, this
work proposes a Quantum Annealing Single-qubit Assessment (QASA) protocol for
quantifying the performance of individual qubits in quantum annealing
computers. The proposed protocol scales to large quantum annealers with
thousands of qubits and provides unique insights into the distribution of qubit
properties within a particular hardware device. The efficacy of the QASA
protocol is demonstrated by analyzing the properties of a D-Wave 2000Q system,
revealing unanticipated correlations in the qubit performance of that device. A
study repeating the QASA protocol at different annealing times highlights how
the method can be utilized to understand the impact of annealing parameters on
qubit performance. Overall, the proposed QASA protocol provides a useful tool
for assessing the performance of current and emerging quantum annealing
devices.


Abstract:
  Data-driven Reynolds-Averaged Navier-Stokes (RANS) turbulence closures are
increasing seen as a viable alternative to general-purpose RANS closures, when
LES reference data is available -- also in wind-energy. Parsimonious closures
with few, simple terms have advantages in terms of stability,
interpret-ability, and execution speed. However experience suggests that
closure model corrections need be made only in limited regions -- e.g.\ in the
near-wake of wind turbines and not in the majority of the flow. A parsimonious
model therefore must find a middle ground between precise corrections in the
wake, and zero corrections elsewhere. We attempt to resolve this impasse by
introducing a classifier to identify regions needing correction, and only fit
and apply our model correction there. We observe that such a classifier (which
must be computed only from RANS-available quantities) is straightforward to
construct, and accurate in operation. We further observe that the correction
models are significantly simpler (with fewer terms) when limited to the
identified regions than otherwise, but have similar or better accuracy. We
apply our framework to three flows consisting of multiple wind-turbines in
neutral conditions with interacting wakes.


Abstract:
  In this paper we present an analysis of the spin behavior of electrons
propagating through a laser field. We present an experimentally realizable
scenario in which spin-dependent effects of the interaction between the laser
and the electrons are dominant. The laser interaction strength and incident
electron velocity are in the nonrelativistic domain. This analysis may thus
lead to novel methods of creating and characterizing spin-polarized
nonrelativistic femtosecond electron pulses.


Abstract:
  We suggest a generalization of nonlinear $\sigma$-model for diffusive
superconducting systems to account for magnetoelectric effects due to
spin-orbit scattering. In the leading orders of spin-orbit strength and
gradient expansion it includes two additional terms responsible for the
spin-Hall effect and the spin-current swapping. First, assuming a
delta-correlated disorder we derive the new terms from the Keldysh path
integral representation of the generating functional. Then we argue
phenomenologically that they exhaust all invariants allowed in the effective
action to the leading order in the spin-orbit coupling (SOC). Finally, the
results are confirmed by a direct derivation of the saddle-point (Usadel)
equation from the quantum kinetic equations in the presence of randomly
distributed impurities with SOC. At this point we correct a recent derivation
of the Usadel equation that includes magneto-electric effects and does not
resort to the Born approximation.


Abstract:
  Graph Neural Networks (GNNs) perform learned message passing over an input
graph, but conventional wisdom says performing more than handful of steps makes
training difficult and does not yield improved performance. Here we show the
contrary. We train a deep GNN with up to 100 message passing steps and achieve
several state-of-the-art results on two challenging molecular property
prediction benchmarks, Open Catalyst 2020 IS2RE and QM9. Our approach depends
crucially on a novel but simple regularisation method, which we call ``Noisy
Nodes'', in which we corrupt the input graph with noise and add an auxiliary
node autoencoder loss if the task is graph property prediction. Our results
show this regularisation method allows the model to monotonically improve in
performance with increased message passing steps. Our work opens new
opportunities for reaping the benefits of deep neural networks in the space of
graph and other structured prediction problems.


Abstract:
  Quantum spin liquids are topological states of matter that arise in
frustrated quantum magnets at low temperatures. At low energies, such states
exhibit emergent gauge fields and fractionalized quasiparticles and can also
possess enhanced global symmetries compared to their parent microscopic
Hamiltonians. We study the consequences of this emergent gauge and symmetry
structure for the hydrodynamics of quantum spin liquids. Specifically, we
analyze two cases, the $U(1)$ spin liquid with a Fermi surface and the
$SU(4)$-symmetric "algebraic" spin liquid. We show that the emergent degrees of
freedom in the spin liquid phase lead to a variety of additional hydrodynamic
modes compared to the high-temperature paramagnetic phase. We identify a
hydrodynamic regime for the internal $U(1)$ gauge field common to both states,
characterized by slow diffusion of the internal transverse photon.


Abstract:
  Medical data sets are usually corrupted by noise and missing data. These
missing patterns are commonly assumed to be completely random, but in medical
scenarios, the reality is that these patterns occur in bursts due to sensors
that are off for some time or data collected in a misaligned uneven fashion,
among other causes. This paper proposes to model medical data records with
heterogeneous data types and bursty missing data using sequential variational
autoencoders (VAEs). In particular, we propose a new methodology, the Shi-VAE,
which extends the capabilities of VAEs to sequential streams of data with
missing observations. We compare our model against state-of-the-art solutions
in an intensive care unit database (ICU) and a dataset of passive human
monitoring. Furthermore, we find that standard error metrics such as RMSE are
not conclusive enough to assess temporal models and include in our analysis the
cross-correlation between the ground truth and the imputed signal. We show that
Shi-VAE achieves the best performance in terms of using both metrics, with
lower computational complexity than the GP-VAE model, which is the
state-of-the-art method for medical records.


Abstract:
  Deep generative models, such as Variational Autoencoders (VAEs), have been
employed widely in computational creativity research. However, such models
discourage out-of-distribution generation to avoid spurious sample generation,
limiting their creativity. Thus, incorporating research on human creativity
into generative deep learning techniques presents an opportunity to make their
outputs more compelling and human-like. As we see the emergence of generative
models directed to creativity research, a need for machine learning-based
surrogate metrics to characterize creative output from these models is
imperative. We propose group-based subset scanning to quantify, detect, and
characterize creative processes by detecting a subset of anomalous
node-activations in the hidden layers of generative models. Our experiments on
original, typically decoded, and "creatively decoded" (Das et al 2020) image
datasets reveal that the proposed subset scores distribution is more useful for
detecting creative processes in the activation space rather than the pixel
space. Further, we found that creative samples generate larger subsets of
anomalies than normal or non-creative samples across datasets. The node
activations highlighted during the creative decoding process are different from
those responsible for normal sample generation.


Abstract:
  Basing on some recently proposed methods for solving variational inequalities
with non-smooth operators, we propose an analogue of the Mirror Prox method for
the corresponding class of problems under the assumption of relative smoothness
and relative strong monotonicity of the operator.


Abstract:
  In this paper, we propose deep architectures for learning instance specific
abstain (reject option) binary classifiers. The proposed approach uses double
sigmoid loss function as described by Kulin Shah and Naresh Manwani in ("Online
Active Learning of Reject Option Classifiers", AAAI, 2020), as a performance
measure. We show that the double sigmoid loss is classification calibrated. We
also show that the excess risk of 0-d-1 loss is upper bounded by the excess
risk of double sigmoid loss. We derive the generalization error bounds for the
proposed architecture for reject option classifiers. To show the effectiveness
of the proposed approach, we experiment with several real world datasets. We
observe that the proposed approach not only performs comparable to the
state-of-the-art approaches, it is also robust against label noise. We also
provide visualizations to observe the important features learned by the network
corresponding to the abstaining decision.


Abstract:
  The Convolutional Neural Network (CNN) is a state-of-the-art architecture for
a wide range of deep learning problems, the quintessential example of which is
computer vision. CNNs principally employ the convolution operation, which can
be accelerated using the Fourier transform. In this paper, we present an
optical hardware accelerator that combines silicon photonics and free-space
optics, leveraging the use of the optical Fourier transform within several CNN
architectures. The hardware presented is a proof of concept, demonstrating that
this technology can be applied to artificial intelligence problems with a large
efficiency boost with respect to canonical methods.


Abstract:
  Anti-ThCr$_2$Si$_2$ type $RE_2$O$_2$Sb ($RE$ = rare earth) with Sb square net
has shown insulating conduction so far. Here we report the synthesis of
La$_2$O$_2$Sb epitaxial thin films for the first time by multilayer solid-phase
epitaxy. The valence state of Sb was about -2 evaluated from X-ray
photoemission spectroscopy measurement, and the indirect band gap of 0.17 eV
was observed. The La$_2$O$_2$Sb epitaxial thin film showed unexpectedly high
electrical conduction as a narrow gap semiconductor, whose resistivity at room
temperature was approximately ten-thousand-fold lower than that of
La$_2$O$_2$Sb bulk polycrystal, attributed to increased carrier mobility
probably due to suppressed Sb dimerization.


Abstract:
  The Beijing-Arizona Sky Survey (BASS) Data Release 3 (DR3) catalogue was
released in 2019, which contains the data from all BASS and the Mosaic z-band
Legacy Survey (MzLS) observations during 2015 January and 2019 March, about 200
million sources. We cross-match BASS DR3 with spectral databases from the Sloan
Digital Sky Survey (SDSS) and the Large Sky Area Multi-object Fiber
Spectroscopic Telescope (LAMOST) to obtain the spectroscopic classes of known
samples. Then, the samples are cross-matched with ALLWISE database. Based on
optical and infrared information of the samples, we use the XGBoost algorithm
to construct different classifiers, including binary classification and
multiclass classification. The accuracy of these classifiers with the best
input pattern is larger than 90.0 per cent. Finally, all selected sources in
the BASS DR3 catalogue are classified by these classifiers. The classification
label and probabilities for individual sources are assigned by different
classifiers. When the predicted results by binary classification are the same
as multiclass classification with optical and infrared information, the number
of star, galaxy and quasar candidates is separately 12 375 838 (P_S>0.95), 18
606 073 (P_G>0.95) and 798 928 (P_Q>0.95). For these sources without infrared
information, the predicted results can be as a reference. Those candidates may
be taken as input catalogue of LAMOST, DESI or other projects for follow up
observation. The classified result will be of great help and reference for
future research of the BASS DR3 sources.


Abstract:
  Visualization recommendation (VisRec) systems provide users with suggestions
for potentially interesting and useful next steps during exploratory data
analysis. These recommendations are typically organized into categories based
on their analytical actions, i.e., operations employed to transition from the
current exploration state to a recommended visualization. However, despite the
emergence of a plethora of VisRec systems in recent work, the utility of the
categories employed by these systems in analytical workflows has not been
systematically investigated. Our paper explores the efficacy of recommendation
categories by formalizing a taxonomy of common categories and developing a
system, Frontier, that implements these categories. Using Frontier, we evaluate
workflow strategies adopted by users and how categories influence those
strategies. Participants found recommendations that add attributes to enhance
the current visualization and recommendations that filter to sub-populations to
be comparatively most useful during data exploration. Our findings pave the way
for next-generation VisRec systems that are adaptive and personalized via
carefully chosen, effective recommendation categories.


Abstract:
  Cycle sets are algebraic structures introduced by Rump to study set theoretic
solutions to the Yang-Baxter equation. While studying cycle sets Rump also
introduced braces, which have since overtaken cycle sets as a tool for studying
solutions. This survey paper is primarily an introduction to cycle sets,
motivating their study and relating them to key results of brace theory and
Yang-Baxter theory. It is aimed at anyone from those already very familiar with
braces but less familiar with cycle sets, to those with only a basic level of
background in ring theory and group theory. We introduce cycle sets following
Rump's original results - giving more detailed, easy to follow versions of his
proofs - and then relate them back to left braces. We also go on to discuss
interesting constructions of cycle sets which do not necessarily correspond
directly to braces.


Abstract:
  Individuals infected with SARS-CoV-2, the virus that causes COVID-19, may
shed the virus in stool before developing symptoms, suggesting that
measurements of SARS-CoV-2 concentrations in wastewater could be a "leading
indicator" of COVID-19 prevalence. Multiple studies have corroborated the
leading indicator concept by showing that the correlation between wastewater
measurements and COVID-19 case counts is maximized when case counts are lagged.
However, the meaning of "leading indicator" will depend on the specific
application of wastewater-based epidemiology, and the correlation analysis is
not relevant for all applications. In fact, the quantification of a leading
indicator will depend on epidemiological, biological, and health systems
factors. Thus, there is no single "lead time" for wastewater-based COVID-19
monitoring. To illustrate this complexity, we enumerate three different
applications of wastewater-based epidemiology for COVID-19: a qualitative
"early warning" system; an independent, quantitative estimate of disease
prevalence; and a quantitative alert of bursts of disease incidence. The
leading indicator concept has different definitions and utility in each
application.


Abstract:
  Measuring sentence similarity is a key research area nowadays as it allows
machines to better understand human languages. In this paper, we proposed a
Cross-Attention Siamese Network (CATsNet) to carry out the task of learning the
semantic meanings of Chinese sentences and comparing the similarity between two
sentences. This novel model is capable of catching non-local features.
Additionally, we also tried to apply the long short-term memory (LSTM) network
in the model to improve its performance. The experiments were conducted on the
LCQMC dataset and the results showed that our model could achieve a higher
accuracy than previous work.


Abstract:
  The speech act of complaining is used by humans to communicate a negative
mismatch between reality and expectations as a reaction to an unfavorable
situation. Linguistic theory of pragmatics categorizes complaints into various
severity levels based on the face-threat that the complainer is willing to
undertake. This is particularly useful for understanding the intent of
complainers and how humans develop suitable apology strategies. In this paper,
we study the severity level of complaints for the first time in computational
linguistics. To facilitate this, we enrich a publicly available data set of
complaints with four severity categories and train different transformer-based
networks combined with linguistic information achieving 55.7 macro F1. We also
jointly model binary complaint classification and complaint severity in a
multi-task setting achieving new state-of-the-art results on binary complaint
detection reaching up to 88.2 macro F1. Finally, we present a qualitative
analysis of the behavior of our models in predicting complaint severity levels.


Abstract:
  In this article, we present our methodologies for SemEval-2021 Task-4:
Reading Comprehension of Abstract Meaning. Given a fill-in-the-blank-type
question and a corresponding context, the task is to predict the most suitable
word from a list of 5 options. There are three sub-tasks within this task:
Imperceptibility (subtask-I), Non-Specificity (subtask-II), and Intersection
(subtask-III). We use encoders of transformers-based models pre-trained on the
masked language modelling (MLM) task to build our Fill-in-the-blank (FitB)
models. Moreover, to model imperceptibility, we define certain linguistic
features, and to model non-specificity, we leverage information from hypernyms
and hyponyms provided by a lexical database. Specifically, for non-specificity,
we try out augmentation techniques, and other statistical techniques. We also
propose variants, namely Chunk Voting and Max Context, to take care of input
length restrictions for BERT, etc. Additionally, we perform a thorough ablation
study, and use Integrated Gradients to explain our predictions on a few
samples. Our best submissions achieve accuracies of 75.31% and 77.84%, on the
test sets for subtask-I and subtask-II, respectively. For subtask-III, we
achieve accuracies of 65.64% and 62.27%.


Abstract:
  This paper considers the problem of nonparametric quantile regression under
the assumption that the target conditional quantile function is a composition
of a sequence of low-dimensional functions. We study the nonparametric quantile
regression estimator using deep neural networks to approximate the target
conditional quantile function. For convenience, we shall refer to such an
estimator as a deep quantile regression (DQR) estimator. We show that the DQR
estimator achieves the nonparametric optimal convergence rate up to a
logarithmic factor determined by the intrinsic dimension of the underlying
compositional structure of the conditional quantile function, not the ambient
dimension of the predictor. Therefore, DQR is able to mitigate the curse of
dimensionality under the assumption that the conditional quantile function has
a compositional structure. To establish these results, we analyze the
approximation error of a composite function by neural networks and show that
the error rate only depends on the dimensions of the component functions. We
apply our general results to several important statistical models often used in
mitigating the curse of dimensionality, including the single index, the
additive, the projection pursuit, the univariate composite, and the generalized
hierarchical interaction models. We explicitly describe the prefactors in the
error bounds in terms of the dimensionality of the data and show that the
prefactors depends on the dimensionality linearly or quadratically in these
models. We also conduct extensive numerical experiments to evaluate the
effectiveness of DQR and demonstrate that it outperforms a kernel-based method
for nonparametric quantile regression.


Abstract:
  The mobile impurity in a Bose-Einstein condensate (BEC) is a paradigmatic
many-body problem. For weak interaction between the impurity and the BEC, the
impurity deforms the BEC only slightly and it is well described within the
Fr\"ohlich model and the Bogoliubov approximation. For strong local attraction
this standard approach, however, fails to balance the local attraction with the
weak repulsion between the BEC particles and predicts an instability where an
infinite number of bosons is attracted toward the impurity. Here we present a
solution of the Bose polaron problem beyond the Bogoliubov approximation which
includes the local repulsion between bosons and thereby stabilizes the Bose
polaron even near and beyond the scattering resonance. We show that the Bose
polaron energy remains bounded from below across the resonance and the size of
the polaron dressing cloud stays finite. Our results demonstrate how the
dressing cloud replaces the attractive impurity potential with an effective
many-body potential that excludes binding. We find that at resonance, including
the effects of boson repulsion, the polaron energy depends universally on the
effective range. Moreover, while the impurity contact is strongly peaked at
positive scattering length, it remains always finite. Our solution highlights
how Bose polarons are self-stabilized by repulsion, providing a mechanism to
understand quench dynamics and nonequilibrium time evolution at strong
coupling.


Abstract:
  Precision studies at electron-positron colliders with center-of-mass energies
in the charm-tau region and below have strongly contributed to our
understanding of light-meson interactions at low energies. We focus on the
processes involving two or three light mesons with invariant masses below
nucleon-antinucleon threshold. A prominent role is given to the interactions of
the nine lightest pseudoscalar mesons (pions, kaons, $\eta$, and $\eta'$) and
the two narrow neutral isoscalar vector mesons $\omega$ and $\phi$.
Experimental methods used to produce the mesons are reviewed as well as theory
tools to extract properties of the meson-meson interactions. Examples of recent
results from the DA$\Phi$NE, BEPCII, and VEPP-2000 colliders are presented. In
the outlook we briefly discuss prospects for further studies at future
super-charm-tau factories.


Abstract:
  Given a natural number $n\geq3$ and two points $a$ and $b$ in the unit disk
$\mathbb D$ in the complex plane, it is known that there exists a unique
elliptical disk having $a$ and $b$ as foci that can also be realized as the
intersection of a collection of convex cyclic $n$-gons whose vertices fill the
whole unit circle $\mathbb T$. What is less clear is how to find a convenient
formula or expression for such an elliptical disk. Our main results reveal how
orthogonal polynomials on the unit circle provide a useful tool for finding
such a formula for some values of $n$. The main idea is to realize the
elliptical disk as the numerical range of a matrix and the problem reduces to
finding the eigenvalues of that matrix.


Abstract:
  Polar codes can theoretically achieve very competitive Frame Error Rates. In
practice, their performance may depend on the chosen decoding procedure, as
well as other parameters of the communication system they are deployed upon. As
a consequence, designing efficient polar codes for a specific context can
quickly become challenging. In this paper, we introduce a methodology that
consists in training deep neural networks to predict the frame error rate of
polar codes based on their frozen bit construction sequence. We introduce an
algorithm based on Projected Gradient Descent that leverages the gradient of
the neural network function to generate promising frozen bit sequences. We
showcase on generated datasets the ability of the proposed methodology to
produce codes more efficient than those used to train the neural networks, even
when the latter are selected among the most efficient ones.


Abstract:
  We propose and study two DC (difference of convex functions) algorithms based
on exact penalty functions for solving nonsmooth DC optimization problems with
nonsmooth DC equality and inequality constraints. Both methods employ adaptive
penalty updating strategies to improve their performance. The first method is
based on exact penalty functions with individual penalty parameter for each
constraint (i.e. multidimensional penalty parameter) and utilizes a primal-dual
approach to penalty updates. The second method is based on the so-called
steering exact penalty methodology and relies on solving some auxiliary convex
subproblems to determine a suitable value of the penalty parameter. We present
a detailed convergence analysis of both methods and give several simple
numerical examples highlighting peculiarites of two different penalty updating
strategies studied in this paper.


Abstract:
  We call internal-wave billiard the dynamical system of a point particle that
moves freely inside a planar domain (the table) and is reflected by its
boundary according to this rule: reflections are standard Fresnel reflections
but with the pretense that the boundary at any collision point is either
horizontal or vertical (relative to a predetermined direction representing
gravity). These systems are point particle approximations for the motion of
internal gravity waves in closed containers, hence the name. For a class of
tables similar to rectangular trapezoids, but with the slanted leg replaced by
a general curve with downward concavity, we prove that the dynamics has only
three asymptotic regimes: (1) there exist a global attractor and a global
repellor, which are periodic and might coincide; (2) there exists a beam of
periodic trajectories, whose boundary (if any) comprises an attractor and a
repellor for all the other trajectories; (3) all trajectories are dense (that
is, the system is minimal). Furthermore, in the prominent case where the table
is an actual trapezoid, we study the sets in parameter space relative to the
three regimes. We prove in particular that the set for (1) has positive measure
(giving a rigorous proof of the existence of Arnol'd tongues for internal-wave
billiards), whereas the sets for (2) and (3) are non-empty but have measure
zero.


Abstract:
  Darwinian evolution is driven by random mutations, genetic recombination
(gene shuffling) and selection that favors genotypes with high fitness. For
systems where each genotype can be represented as a bitstring of length $L$, an
overview of possible evolutionary trajectories is provided by the $L$-cube
graph with nodes labeled by genotypes and edges directed toward the genotype
with higher fitness. Peaks (sinks in the graphs) are important since a
population can get stranded at a suboptimal peak. The fitness landscape is
defined by the fitness values of all genotypes in the system. Some notion of
curvature is necessary for a more complete analysis of the landscapes,
including the effect of recombination. The shape approach uses triangulations
(shapes) induced by fitness landscapes. The main topic for this work is the
interplay between peak patterns and shapes. Because of constraints on the
shapes for $L=3$ imposed by peaks, there are in total 25 possible combinations
of peak patterns and shapes. Similar constraints exist for higher $L$.
Specifically, we show that the constraints induced by the staircase
triangulation can be formulated as a condition of {\emph{universal positive
epistasis}}, an order relation on the fitness effects of arbitrary sets of
mutations that respects the inclusion relation between the corresponding
genetic backgrounds. We apply the concept to a large protein fitness landscape
for an immunoglobulin-binding protein expressed in Streptococcal bacteria.


Abstract:
  Given an integral hyperplane arrangement, Kamiya-Takemura-Terao (2008 & 2011)
introduced the notion of characteristic quasi-polynomial, which enumerates the
cardinality of the complement of the arrangement modulo a positive integer. The
most popular candidate for period of the characteristic quasi-polynomials is
the lcm period. In this paper, we initiate a study of period collapse in
characteristic quasi-polynomials stemming from the concept of period collapse
in the theory of Ehrhart quasi-polynomials. We say that period collapse occurs
in a characteristic quasi-polynomial when the minimum period is strictly less
than the lcm period. Our first main result is that in the non-central case,
with regard to period collapse anything is possible: period collapse occurs in
any dimension $\ge 1$, occurs for any value of the lcm period $\ge 2$, and the
minimum period when it is not the lcm period can be any proper divisor of the
lcm period. Our second main result states that in the central case, however, no
period collapse is possible in any dimension, that is, the lcm period is always
the minimum period.


Abstract:
  Is bias amplified when neural machine translation (NMT) models are optimized
for speed and evaluated on generic test sets using BLEU? We investigate
architectures and techniques commonly used to speed up decoding in
Transformer-based models, such as greedy search, quantization, average
attention networks (AANs) and shallow decoder models and show their effect on
gendered noun translation. We construct a new gender bias test set, SimpleGEN,
based on gendered noun phrases in which there is a single, unambiguous, correct
answer. While we find minimal overall BLEU degradation as we apply speed
optimizations, we observe that gendered noun translation performance degrades
at a much faster rate.


Abstract:
  This essay builds on the idea of grouping the polar curves of 2-variable
function germs into polar clusters. In the topological category, one obtains a
bijective correspondence between certain partitions of the polar quotients of
two topologically equivalent function germs. We explain how this bijective
correspondence may be refined in the Lipschitz category in terms of the
associated gradient canyons.


Abstract:
  Involuntary subject motion is the main source of artifacts in weight-bearing
cone-beam CT of the knee. To achieve image quality for clinical diagnosis, the
motion needs to be compensated. We propose to use inertial measurement units
(IMUs) attached to the leg for motion estimation. We perform a simulation study
using real motion recorded with an optical tracking system. Three IMU-based
correction approaches are evaluated, namely rigid motion correction, non-rigid
2D projection deformation and non-rigid 3D dynamic reconstruction. We present
an initialization process based on the system geometry. With an IMU noise
simulation, we investigate the applicability of the proposed methods in real
applications. All proposed IMU-based approaches correct motion at least as good
as a state-of-the-art marker-based approach. The structural similarity index
and the root mean squared error between motion-free and motion corrected
volumes are improved by 24-35% and 78-85%, respectively, compared with the
uncorrected case. The noise analysis shows that the noise levels of
commercially available IMUs need to be improved by a factor of $10^5$ which is
currently only achieved by specialized hardware not robust enough for the
application. The presented study confirms the feasibility of this novel
approach and defines improvements necessary for a real application.


Abstract:
  Metasurface lenses, namely metalenses, are ultrathin planar nanostructures
that are capable of manipulating the properties of incoming light and imparting
lens-like wavefront to the output. Although they have shown promising
potentials for the future miniaturization of optics, the chromatic aberration
inherited from their diffractive nature plagues them towards many practical
applications. Current solutions for creating achromatic metalenses usually
require searching through a large number of meta-atoms to find designs that
fulfill not only phase but phase dispersion requirements, which leads to
intensive design efforts. Besides, most designs are based on regular-shaped
antennas driven by the designers' intuition and experience, hence only cover a
limited design space. Here, we present an inverse design approach that
efficiently produces meta-atoms with unintuitive geometries required for
broadband achromatic metalenses. We restricted the generated shapes to hold
four-fold reflectional symmetry so that the resulting metalenses are
polarization insensitive. In addition, meta-atoms generated by our method
inheritably have round edges and corners, which make them
nanofabrication-friendly. Our experimental characterization shows that our
metalenses exhibit superior performance over a broad bandwidth of 465 nm in the
near-infrared regime. Our method offers a fast and efficient way of designing
high-performance achromatic metalenses and sheds new insights for unintuitive
design of other metaphotonic devices.


Abstract:
  Ketterer and Rajala showed an example of metric measure space, satisfying the
measure contraction property $MCP(0,3)$, that has different topological
dimensions at different regions of the space. In this article I propose a
refinement of that example, which satisfies the $CD(0,\infty)$ condition,
proving the non-constancy of topological dimension for CD spaces. This example
also shows that the weak curvature dimension bound, in the sense of
Lott-Sturm-Villani, is not sufficient to deduce any reasonable non-branching
condition. Moreover, it allows to answer to some open question proposed by
Schultz, about strict curvature dimension bounds and their stability with
respect to the measured Gromov Hausdorff convergence.


Abstract:
  Gravitational wave observation has the potential of probing ultralight
bosonic fields such as axion. Axion forms a cloud around a rotating black hole
(BH) by superradiant instability and should affect the gravitational waveform
from binary BHs. On the other hand, considering the cloud associated with a BH
in a binary system, tidal interaction depletes the cloud in some cases during
the inspiral phase. We made the exhaustive study of cloud depletion numerically
in a wide parameter range for equal mass binaries, assuming only the
quadrupolar tidal perturbation is at work. We found that clouds can avoid
disappearing due to the tidal effect only when $l=1$ mode is the fastest
growing mode and when the binary orbit is counter-rotating in the
non-relativistic parameter region.


Abstract:
  Federated learning (FL) has emerged as a promising master/slave learning
paradigm to alleviate systemic privacy risks and communication costs incurred
by cloud-centric machine learning methods. However, it is very challenging to
resist the single point of failure of the master aggregator and attacks from
malicious participants while guaranteeing model convergence speed and accuracy.
Recently, blockchain has been brought into FL systems transforming the paradigm
to a decentralized manner thus further improve the system security and learning
reliability. Unfortunately, the traditional consensus mechanism and
architecture of blockchain systems can hardly handle the large-scale FL task
due to the huge resource consumption, limited transaction throughput, and high
communication complexity. To address these issues, this paper proposes a
two-layer blockchaindriven FL framework, called as ChainsFL, which is composed
of multiple subchain networks (subchain layer) and a direct acyclic graph
(DAG)-based mainchain (mainchain layer). In ChainsFL, the subchain layer limits
the scale of each shard for a small range of information exchange, and the
mainchain layer allows each shard to share and validate the learning model in
parallel and asynchronously to improve the efficiency of cross-shard
validation. Furthermore, the FL procedure is customized to deeply integrate
with blockchain technology, and the modified DAG consensus mechanism is
proposed to mitigate the distortion caused by abnormal models. In order to
provide a proof-ofconcept implementation and evaluation, multiple subchains
base on Hyperledger Fabric are deployed as the subchain layer, and the
self-developed DAG-based mainchain is deployed as the mainchain layer. The
experimental results show that ChainsFL provides acceptable and sometimes
better training efficiency and stronger robustness compared with the typical
existing FL systems.


Abstract:
  Fifth-generation (5G) systems will extensively employ radio access network
(RAN) softwarization. This key innovation enables the instantiation of "virtual
cellular networks" running on different slices of the shared physical
infrastructure. In this paper, we propose the concept of Private Cellular
Connectivity as a Service (PCCaaS), where infrastructure providers deploy
covert network slices known only to a subset of users. We then present SteaLTE
as the first realization of a PCCaaS-enabling system for cellular networks. At
its core, SteaLTE utilizes wireless steganography to disguise data as noise to
adversarial receivers. Differently from previous work, however, it takes a
full-stack approach to steganography, contributing an LTE-compliant
steganographic protocol stack for PCCaaS-based communications, and packet
schedulers and operations to embed covert data streams on top of traditional
cellular traffic (primary traffic). SteaLTE balances undetectability and
performance by mimicking channel impairments so that covert data waveforms are
almost indistinguishable from noise. We evaluate the performance of SteaLTE on
an indoor LTE-compliant testbed under different traffic profiles, distance and
mobility patterns. We further test it on the outdoor PAWR POWDER platform over
long-range cellular links. Results show that in most experiments SteaLTE
imposes little loss of primary traffic throughput in presence of covert data
transmissions (< 6%), making it suitable for undetectable PCCaaS networking.


Abstract:
  Relaxor ferrolectrics are important in technological applications due to a
strong electromechanical response, energy storage capacity, electrocaloric
effect, and pyroelectric energy conversion properties. Current efforts to
discover and design new materials in this class generally rely on
substitutional doping of known ferroelectrics, as slight changes to local
compositional order can significantly affect the Curie temperature,
morphotropic phase boundary, and electromechanical responses. In this work, we
demonstrate that moving to the strong limit of compositional complexity in an
ABO3 perovskite allows stabilization of novel relaxor responses that do not
rely on a single narrow phase transition region. Entropy-assisted synthesis
approaches are used to create single crystal Ba(Ti0.2Sn0.2Zr0.2Hf0.2Nb0.2)O3
[Ba(5B)O] films. The high levels of configurational disorder present in this
system is found to influence dielectric relaxation, phase transitions,
nano-polar domain formation, and Curie temperature. Temperature-dependent
dielectric, Raman spectroscopy and second-harmonic generation measurements
reveal multiple phase transitions, a high Curie temperature of 570 K, and the
relaxor ferroelectric nature of Ba(5B)O films. The first principles theory
calculations are used to predict possible combinations of cations to quantify
the relative feasibility of formation of highly disordered single-phase
perovskite systems. The ability to stabilize single-phase perovskites with such
a large number of different cations on the B-sites offers new possibilities for
designing high-performance materials for piezoelectric, pyroelectric and
tunable dielectric applications.


Abstract:
  The advent of acoustic metamaterials opened up a new frontier in the control
of sound transmission. A key limitation, however, is that an acoustic
metamaterial based on a single local resonator in the unit cell produces a
restricted narrow-band attenuation peak; and when multiple local resonators are
used the emerging attenuation peaks -- while numerous -- are each still narrow
and separated by pass bands. Here, we present a new acoustic metamaterial
concept that yields a sound transmission loss through two antiresonances -- in
a single band gap -- that are fully coupled and hence provide a broadband
attenuation range; this is in addition to delivering an isolation intensity
that exceeds 90 decibels for both peaks. The underlying coupled resonance
mechanism is realized in the form of a single-panel, single-material pillared
plate structure with internal contiguous holes$-$a practical configuration that
lends itself to design adjustments and optimization for a frequency range of
interest, down to sub-kilohertz, and to mass fabrication.


Abstract:
  We introduce a one-dimensional lattice model whose hopping amplitudes are
modulated for equally spaced sites. Such mosaic lattice exhibits many
interesting topological and localization phenomena that do not exist in the
regular off-diagonal lattices. When the mosaic modulation is commensurate with
the underlying lattice, topologically nontrivial phases with zero- and
nonzero-energy edge modes are observed as we tune the modulation, where the
nontrivial regimes are characterized by quantized Berry phases. If the mosaic
lattice becomes incommensurate, Anderson localization will be induced purely by
the quasiperiodic off-diagonal modulations. The localized eigenstate is found
to be centered on two neighboring sites connected by the quasiperiodic hopping
terms. Furthermore, both the commensurate and incommensurate off-diagonal
mosaic lattices can host Chern insulators in their two-dimensional
generalizations. Our work provides a platform for exploring topological phases
and Anderson localization in low-dimensional systems.


Abstract:
  Three novel precipitation strengthened bcc alloys which exhibit a smooth
microstructural gradient with composition have been fabricated in bulk form by
induction casting. All three alloys are comprised of a mixture of disordered
A2-(Fe, Cr) and L2$_1$-ordered (Ni, Fe)$_{2}$AlTi type phases both as-cast and
after long-term annealing at 900 $^{\circ}$C. The ratio of disordered to
ordered phase, primary dendrite fraction, and overall microstructural
coarseness all decrease as Cr is replaced by Al and Ti. Differences in phase
composition are quantified through domain averaged principal component analysis
of energy dispersive spectroscopy data obtained during scanning transmission
electron microscopy. Bulk tensile testing reveals retained strengths of nearly
250 MPa up to 900 $^{\circ}$C for the alloys which contain a nanoscale
maze-like arrangement of ordered and disordered phases. One alloy, containing a
duplex microstructure with ductile dendritic regions and highly creep resistant
interdendritic regions, shows a promising balance between high temperature
ductility and strength. For this alloy, tension creep testing was carried out
at 700, 750, and 800 $^{\circ}$C for a broad range of loading conditions and
revealed upper bound creep rates which surpass similar ferritic superalloys and
rival those of several conventionally employed high temperature structural
alloys, including Inconel 617 and 718, at much lower density and raw material
cost.


Abstract:
  Entity linking (EL), the task of disambiguating mentions in text by linking
them to entities in a knowledge graph, is crucial for text understanding,
question answering or conversational systems. Entity linking on short text
(e.g., single sentence or question) poses particular challenges due to limited
context. While prior approaches use either heuristics or black-box neural
methods, here we propose LNN-EL, a neuro-symbolic approach that combines the
advantages of using interpretable rules based on first-order logic with the
performance of neural learning. Even though constrained to using rules, LNN-EL
performs competitively against SotA black-box neural approaches, with the added
benefits of extensibility and transferability. In particular, we show that we
can easily blend existing rule templates given by a human expert, with multiple
types of features (priors, BERT encodings, box embeddings, etc), and even
scores resulting from previous EL methods, thus improving on such methods. For
instance, on the LC-QuAD-1.0 dataset, we show more than $4$\% increase in F1
score over previous SotA. Finally, we show that the inductive bias offered by
using logic results in learned rules that transfer well across datasets, even
without fine tuning, while maintaining high accuracy.


Abstract:
  Mandarin-English code-switching (CS) is frequently used among East and
Southeast Asian people. However, the intra-sentence language switching of the
two very different languages makes recognizing CS speech challenging.
Meanwhile, the recent successful non-autoregressive (NAR) ASR models remove the
need for left-to-right beam decoding in autoregressive (AR) models and achieved
outstanding performance and fast inference speed, but it has not been applied
to Mandarin-English CS speech recognition. This paper takes advantage of the
Mask-CTC NAR ASR framework to tackle the CS speech recognition issue. We
further propose to change the Mandarin output target of the encoder to Pinyin
for faster encoder training and introduce the Pinyin-to-Mandarin decoder to
learn contextualized information. Moreover, we use word embedding label
smoothing to regularize the decoder with contextualized information and
projection matrix regularization to bridge that gap between the encoder and
decoder. We evaluate these methods on the SEAME corpus and achieved exciting
results.


Abstract:
  How and where proteins interface with one another can ultimately impact the
proteins' functions along with a range of other biological processes. As such,
precise computational methods for protein interface prediction (PIP) come
highly sought after as they could yield significant advances in drug discovery
and design as well as protein function analysis. However, the traditional
benchmark dataset for this task, Docking Benchmark 5 (DB5), contains only a
modest 230 complexes for training, validating, and testing different machine
learning algorithms. In this work, we expand on a dataset recently introduced
for this task, the Database of Interacting Protein Structures (DIPS), to
present DIPS-Plus, an enhanced, feature-rich dataset of 42,112 complexes for
geometric deep learning of protein interfaces. The previous version of DIPS
contains only the Cartesian coordinates and types of the atoms comprising a
given protein complex, whereas DIPS-Plus now includes a plethora of new
residue-level features including protrusion indices, half-sphere amino acid
compositions, and new profile hidden Markov model (HMM)-based sequence features
for each amino acid, giving researchers a large, well-curated feature bank for
training protein interface prediction methods. We demonstrate through rigorous
benchmarks that training an existing state-of-the-art (SOTA) model for PIP on
DIPS-Plus yields SOTA results, surpassing the performance of all other models
trained on residue-level and atom-level encodings of protein complexes to date.


Abstract:
  We present a population of 19 radio-luminous supernovae (SNe) with emission
reaching $L_{\nu}{\sim}10^{26}-10^{29}\,\rm{erg\,s^{-1}Hz^{-1}}$ in the first
epoch of the Very Large Array Sky Survey (VLASS) at $2-4$GHz. Our sample
includes one long Gamma-Ray Burst, SN 2017iuk/GRB171205A, and 18 core-collapse
SNe detected at $\approx (1-60)$years after explosion. No thermonuclear
explosion shows evidence for bright radio emission, and hydrogen-poor
progenitors dominate the sub-sample of core-collapse events with spectroscopic
classification at the time of explosion (79\%). We interpret these findings
into the context of the expected radio emission from the forward shock
interaction with the circumstellar medium (CSM). We conclude that these
observations require a departure from the single wind-like density profile
(i.e., $\rho_{\rm{CSM}}\propto r^{-2}$) that is expected around massive stars
and/or a departure from a spherical Newtonian shock. Viable alternatives
include the shock interaction with a detached, dense shell of CSM formed by a
large effective progenitor mass-loss rate $\dot M \sim (10^{-4}-10^{-1})$
M$_{\odot}$ yr$^{-1}$ (for an assumed wind velocity of
$1000\,\rm{km\,s^{-1}}$); emission from an off-axis relativistic jet entering
our line of sight; or the emergence of emission from a newly-born pulsar-wind
nebula. The relativistic SN 2012ap that is detected 5.7 and 8.5 years after
explosion with $L_{\nu}{\sim}10^{28}$ erg s$^{-1}$ Hz$^{-1}$ might constitute
the first detections of an off-axis jet+cocoon system in a massive star.
However, none of the VLASS-SNe with archival data points are consistent with
our model off-axis jet light curves. Future multi-wavelength observations will
distinguish among these scenarios.Our VLASS source catalogs, which were used to
perform the VLASS cross matching, are publicly available at
https://doi.org/10.5281/zenodo.4895112.


Abstract:
  In our earlier publication we have shown how to compute by iteration a
rational number ${u_{2,k}}$ in the two-term Machin-like formula for pi of kind
$$\frac{\pi}{4}=2^{k-1}\arctan\left(\frac{1}{u_{1,k}}\right)+\arctan\left(\frac{1}{u_{2,k}}\right),\qquad
k\in \mathbb{Z},\quad k\ge 1,$$ where ${u_{1,k}}$ can be chosen as an integer
${u_{1,k}} = \left\lfloor{{a_k}/\sqrt{2-a_{k-1}}}\right\rfloor$ with nested
radicals defined as ${a_k}=\sqrt{2+a_{k-1}}$ and $a_0 = 0$. In this work we
report an alternative method for determination of the integer $u_{1,k}$. This
approach is based on a simple iteration and does not require any irrational
(surd) numbers from the set $\left\{a_k\right\}$ in computation of the integer
$u_{1,k}$. Mathematica programs validating these results are presented.


Abstract:
  We report observations of nanosecond nanometer scale heterogeneous dynamics
in a free flowing colloidal jet revealed by ultrafast x-ray speckle visibility
spectroscopy. The nanosecond double-bunch mode of the Linac Coherent Light
Source free electron laser enabled the production of pairs of femtosecond
coherent hard x-ray pulses. By exploring the anisotropic summed speckle
visibility which relates to the correlation functions, we are able to evaluate
not only the average particle flow rate in a colloidal nanoparticle jet, but
also the heterogeneous flow field within. The reported methodology presented
here establishes the foundation for the study of nano- and atomic-scale
heterogeneous fluctuations in complex matter using x-ray free electron laser
sources.


Abstract:
  Deformable templates are essential to large-scale medical image registration,
segmentation, and population analysis. Current conventional and deep
network-based methods for template construction use only regularized
registration objectives and often yield templates with blurry and/or
anatomically implausible appearance, confounding downstream biomedical
interpretation. We reformulate deformable registration and conditional template
estimation as an adversarial game wherein we encourage realism in the moved
templates with a generative adversarial registration framework conditioned on
flexible image covariates. The resulting templates exhibit significant gain in
specificity to attributes such as age and disease, better fit underlying
group-wise spatiotemporal trends, and achieve improved sharpness and
centrality. These improvements enable more accurate population modeling with
diverse covariates for standardized downstream analyses and easier anatomical
delineation for structures of interest.


Abstract:
  Biological membranes undergo noticeable thermal fluctuations at physiological
temperatures. When two membranes approach each other, they hinder the out of
plane fluctuations of the other. This hindrance leads to an entropic repulsive
force between membranes which, in an interplay with attractive and repulsive
forces due to other sources, impacts a range of biological functions: cell
adhesion, membrane fusion, self-assembly, binding-unbinding transition among
others. In this work, we take cognizance of the fact that biological membranes
are not purely mechanical entities and, due to the phenomenon of
flexoelectricity, exhibit a coupling between deformation and electric
polarization. The ensuing coupled mechanics-electrostatics-statistical
mechanics problem is analytically intractable. We use a variational
perturbation method to analyze, in closed-form, the contribution of
flexoelectricity to the entropic force between two fluctuating membranes and
discuss its possible physical implications. We find that flexoelectricity leads
to a correction that switches from an enhanced attraction at close membrane
separations and an enhanced repulsion when the membranes are further apart.


Abstract:
  Reconfigurable intelligent surfaces (RISs) are one of the foremost
technological enablers of future wireless systems. They improve communication
and localization by providing a strong non-line-of-sight path to the receiver.
In this paper, we propose a pilot transmission method to enable the receiver to
separate signals arriving from different RISs and from the uncontrolled
multipath. This facilitates channel estimation and localization, as the channel
or its geometric parameters can be estimated for each path separately. Our
method is based on designing temporal phase profiles that are orthogonal across
RISs without affecting the RIS beamforming capabilities. We take into
consideration the limited resolution of the RIS phase shifters and show that in
the presence of this practical limitation, orthogonal phase profiles can be
designed based on Butson-type Hadamard matrices. For a localization scenario,
we show that with our proposed method the estimator can attain the theoretical
lower bound even with one-bit RIS phase resolution.


Abstract:
  This paper develops a planner that plans the action sequences and motion for
a dual-arm robot to lift up and flip heavy plates using crane pulley blocks.
The problem is motivated by the low payload of modern collaborative robots.
Instead of directly manipulating heavy plates that collaborative robots cannot
afford, the paper develops a planner for collaborative robots to operate crane
pulley blocks. The planner assumes a target plate is pre-attached to the crane
hook. It optimizes dual-arm action sequences and plans the robot's dual-arm
motion that pulls the rope of the crane pulley blocks to lift up the plate. The
crane pulley blocks reduce the payload that each robotic arm needs to bear.
When the plate is lifted up to a satisfying pose, the planner plans a pushing
motion for one of the robot arms to tumble over the plate while considering
force and moment constraints. The article presents the technical details of the
planner and several experiments and analysis carried out using a dual-arm robot
made by two Universal Robots UR3 arms. The influence of various parameters and
optimization goals are investigated and compared in depth. The results show
that the proposed planner is flexible and efficient.


Abstract:
  We design and validate new block propagation protocols for the peer-to-peer
(P2P) network of the Bitcoin blockchain. Despite its strong protection for
security and privacy, the current Bitcoin blockchain can only support a low
number of transactions per second (TPS). In this work, we redesign the current
Bitcoin's networking protocol to increase TPS without changing vital components
in its consensus-building protocol. In particular, we improve the compact-block
relaying protocol to enable the propagation of blocks containing a massive
number of transactions without inducing extra propagation latencies. Our
improvements consist of (i) replacing the existing store-and-forward
compact-block relaying scheme with a cut-through compact-block relaying scheme;
(ii) exploiting rateless erasure codes for P2P networks to increase
block-propagation efficiency. Since our protocols only need to rework the
current Bitcoin's networking protocol and does not modify the data structures
and crypto-functional components, they can be seamlessly incorporated into the
existing Bitcoin blockchain. To validate our designs, we perform analysis on
our protocols and implement a Bitcoin network simulator on NS3 to run different
block propagation protocols. The analysis and experimental results confirm that
our new block propagation protocols could increase the TPS of the Bitcoin
blockchain by 100x without compromising security and consensus-building.


Abstract:
  This study proposes the construction of a transmit signal for large-scale
antenna systems with cost-effective 1-bit digital-to-analog converters in the
downlink. Under quadrature-amplitude-modulation constellations, it is still an
open problem to overcome a severe error floor problem caused by its nature
property. To this end, we first present a feasibility condition which
guarantees that each user's noiseless signal is placed in the desired decision
region. For robustness to additive noise, we formulate an optimization problem,
we then transform the feasibility conditions to cascaded matrix form. We
propose a low-complexity algorithm to generate a 1-bit transmit signal based on
the proposed optimization problem formulated as a well-defined
mixed-integer-linear-programming. Numerical results validate the superiority of
the proposed method in terms of detection performance and computational
complexity.


Abstract:
  For a harmonically trapped system consisting of two bosons in one spatial
dimension with infinite contact repulsion (hard core bosons), we derive an
expression for the one-body density matrix $\rho_B$ in terms of centre of mass
and relative coordinates of the particles. The deviation from $\rho_F$, the
density matrix for the two fermions case, can be clearly identified. Moreover,
the obtained $\rho_B$ allows us to derive a closed form expression of the
corresponding momentum distribution $n_{B}(p)$. We show how the result deviates
from the noninteracting fermionic case, the deviation being associated to the
short range character of the interaction. Mathematically, our analytical
momentum distribution is expressed in terms of one and two variables confluent
hypergeometric functions. Our formula satisfies the correct normalization and
possesses the expected behavior at zero momentum. It also exhibits the high
momentum $1/p^4 $ tail with the appropriate Tan's coefficient. Numerical
results support our findings.


Abstract:
  Making predictions in a robust way is not easy for nonlinear systems. In this
work, a neural network computing framework, i.e., a spatiotemporal
convolutional network (STCN), was developed to efficiently and accurately
render a multistep-ahead prediction of a time series by employing a
spatial-temporal information (STI) transformation. The STCN combines the
advantages of both the temporal convolutional network (TCN) and the STI
equation, which maps the high-dimensional/spatial data to the future temporal
values of a target variable, thus naturally providing the prediction of the
target variable. From the observed variables, the STCN also infers the causal
factors of the target variable in the sense of Granger causality, which are in
turn selected as effective spatial information to improve the prediction
robustness. The STCN was successfully applied to both benchmark systems and
real-world datasets, all of which show superior and robust performance in
multistep-ahead prediction, even when the data were perturbed by noise. From
both theoretical and computational viewpoints, the STCN has great potential in
practical applications in artificial intelligence (AI) or machine learning
fields as a model-free method based only on the observed data, and also opens a
new way to explore the observed high-dimensional data in a dynamical manner for
machine learning.


Abstract:
  This paper proposes a hierarchical Bayesian model based on spatial concepts
that enables a robot to transfer the knowledge of places from experienced
environments to a new environment. The transfer of knowledge based on spatial
concepts is modeled as the calculation process of the posterior distribution
based on the observations obtained in each environment with the parameters of
spatial concepts generalized to environments as prior knowledge. We conducted
experiments to evaluate the generalization performance of spatial knowledge for
general places such as kitchens and the adaptive performance of spatial
knowledge for unique places such as `Emma's room' in a new environment. In the
experiments, the accuracies of the proposed method and conventional methods
were compared in the prediction task of location names from an image and a
position, and the prediction task of positions from a location name. The
experimental results demonstrated that the proposed method has a higher
prediction accuracy of location names and positions than the conventional
method owing to the transfer of knowledge.


Abstract:
  We study an analogue of Serre's modularity conjecture for projective
representations $\overline{\rho}: \operatorname{Gal}(\overline{K} / K)
\rightarrow \operatorname{PGL}_2(k)$, where $K$ is a totally real number field.
We prove new cases of this conjecture when $k = \mathbb{F}_5$ by using the
automorphy lifting theorems over CM fields established in previous work of the
authors.


Abstract:
  Distributed linearly separable computation, where a user asks some
distributed servers to compute a linearly separable function, was recently
formulated by the same authors and aims to alleviate the bottlenecks of
stragglers and communication cost in distributed computation. For this purpose,
the data center assigns a subset of input datasets to each server, and each
server computes some coded packets on the assigned datasets, which are then
sent to the user. The user should recover the task function from the answers of
a subset of servers, such the effect of stragglers could be tolerated. In this
paper, we formulate a novel secure framework for this distributed linearly
separable computation, where we aim to let the user only retrieve the desired
task function without obtaining any other information about the input datasets,
even if it receives the answers of all servers. In order to preserve the
security of the input datasets, some common randomness variable independent of
the datasets should be introduced into the transmission. We show that any
non-secure linear-coding based computing scheme for the original distributed
linearly separable computation problem, can be made secure without increasing
the communication cost. Then we focus on the case where the computation cost of
each server is minimum and aim to minimize the size of the randomness variable
introduced in the system while achieving the optimal communication cost. We
first propose an information theoretic converse bound on the randomness size.
We then propose secure computing schemes based on two well-known data
assignments, namely fractional repetition assignment and cyclic assignment. We
then propose a computing scheme with novel assignment, which strictly
outperforms the above two schemes. Some additional optimality results are also
obtained.


Abstract:
  We revisit the gradient descent dynamics of the spherical
Sherrington-Kirkpatrick ($p=2$) model with finite number of degrees of freedom.
For fully random initial conditions we confirm that the relaxation takes place
in three time regimes: a first algebraic one controlled by the decay of the
eigenvalue distribution of the random exchange interaction matrix at its edge
in the infinite size limit; a faster algebraic one determined by the
distribution of the gap between the two extreme eigenvalues; and a final
exponential one determined by the minimal gap sampled in the disorder average.
We also analyse the finite size effects on the relaxation from initial states
which are almost projected on the saddles of the potential energy landscape,
and we show that for deviations scaling as $N^{-\nu}$ from perfect alignment
the system escapes the initial configuration in a time-scale scaling as $\ln N$
after which the dynamics no longer "self-averages" with respect to the initial
conditions. We prove these statements with a combination of analytic and
numerical methods.


Abstract:
  The paper focuses on the kinematics control of a compliant serial manipulator
composed of a new type of dualtriangle elastic segments. Some useful
optimization techniques were applied to solve the geometric redundancy problem,
ensure the stability of the manipulator configurations with respect to the
external forces/torques applied to the endeffector. The efficiency of the
developed control algorisms is confirmed by simulation.


Abstract:
  Single domain generalization is a challenging case of model generalization,
where the models are trained on a single domain and tested on other unseen
domains. A promising solution is to learn cross-domain invariant
representations by expanding the coverage of the training domain. These methods
have limited generalization performance gains in practical applications due to
the lack of appropriate safety and effectiveness constraints. In this paper, we
propose a novel learning framework called progressive domain expansion network
(PDEN) for single domain generalization. The domain expansion subnetwork and
representation learning subnetwork in PDEN mutually benefit from each other by
joint learning. For the domain expansion subnetwork, multiple domains are
progressively generated in order to simulate various photometric and geometric
transforms in unseen domains. A series of strategies are introduced to
guarantee the safety and effectiveness of the expanded domains. For the domain
invariant representation learning subnetwork, contrastive learning is
introduced to learn the domain invariant representation in which each class is
well clustered so that a better decision boundary can be learned to improve
it's generalization. Extensive experiments on classification and segmentation
have shown that PDEN can achieve up to 15.28% improvement compared with the
state-of-the-art single-domain generalization methods.


Abstract:
  Many near-term quantum computing algorithms are conceived as variational
quantum algorithms, in which parameterized quantum circuits are optimized in a
hybrid quantum-classical setup. Examples are variational quantum eigensolvers,
quantum approximate optimization algorithms as well as various algorithms in
the context of quantum-assisted machine learning. A common bottleneck of any
such algorithm is constituted by the optimization of the variational
parameters. A popular set of optimization methods work on the estimate of the
gradient, obtained by means of circuit evaluations. We will refer to the way in
which one can combine these circuit evaluations as gradient rules. This work
provides a comprehensive picture of the family of gradient rules that vary
parameters of quantum gates individually. The most prominent known members of
this family are the parameter shift rule and the finite differences method. To
unite this family, we propose a generalized parameter shift rule that expresses
all members of the aforementioned family as special cases, and discuss how all
of these can be seen as providing access to a linear combination of exact
first- and second-order derivatives. We further prove that a parameter shift
rule with one non-shifted evaluation and only one shifted circuit evaluation
can not exist does not exist, and introduce a novel perspective for approaching
new gradient rules.


Abstract:
  The CW RF test of 1.3 GHz 9-cell cavity in liquid helium bath at 2 K is a
very important key point in the cavity procurement. Some problems can be found
through the test, according which to optimized and improve the process of
cavity. Recently, Medium temperature (mid-T) furnace bake of 1.3 GHz 9-cell
cavities have been carried out at IHEP. Through the proceed of mid-T bake, the
quality factor of cavity has been greatly improved. While the excitation of the
parasitic modes in the high Q cavities CW cold test has been encountered, which
implies an error source for the cavity gradient and quality factor
determination. In order to ensure the testing accuracy of superconducting
cavity, we have improved the testing system. Finally, the parasitic mode is
completely suppressed and the CW RF cold test of high Q cavity is guaranteed.


Abstract:
  We present THUNDR, a transformer-based deep neural network methodology to
reconstruct the 3d pose and shape of people, given monocular RGB images. Key to
our methodology is an intermediate 3d marker representation, where we aim to
combine the predictive power of model-free-output architectures and the
regularizing, anthropometrically-preserving properties of a statistical human
surface model like GHUM -- a recently introduced, expressive full body
statistical 3d human model, trained end-to-end. Our novel transformer-based
prediction pipeline can focus on image regions relevant to the task, supports
self-supervised regimes, and ensures that solutions are consistent with human
anthropometry. We show state-of-the-art results on Human3.6M and 3DPW, for both
the fully-supervised and the self-supervised models, for the task of inferring
3d human shape, joint positions, and global translation. Moreover, we observe
very solid 3d reconstruction performance for difficult human poses collected in
the wild.


Abstract:
  This paper is a review of a particular approach to the method of maximum
entropy as a general framework for inference. The discussion emphasizes the
pragmatic elements in the derivation. An epistemic notion of information is
defined in terms of its relation to the Bayesian beliefs of ideally rational
agents. The method of updating from a prior to a posterior probability
distribution is designed through an eliminative induction process. The
logarithmic relative entropy is singled out as the unique tool for updating
that (a) is of universal applicability; (b) that recognizes the value of prior
information; and (c) that recognizes the privileged role played by the notion
of independence in science. The resulting framework -- the ME method -- can
handle arbitrary priors and arbitrary constraints. It includes MaxEnt and
Bayes' rule as special cases and, therefore, it unifies entropic and Bayesian
methods into a single general inference scheme. The ME method goes beyond the
mere selection of a single posterior, but also addresses the question of how
much less probable other distributions might be, which provides a direct bridge
to the theories of fluctuations and large deviations.


Abstract:
  Sanctioned by its constitution, India is home to the world's most
comprehensive affirmative action program, where historically discriminated
groups are protected with vertical reservations implemented as "set asides,"
and other disadvantaged groups are protected with horizontal reservations
implemented as "minimum guarantees." A mechanism mandated by the Supreme Court
in 1995 suffers from important anomalies, triggering countless litigations in
India. Foretelling a recent reform correcting the flawed mechanism, we propose
the 2SMG mechanism that resolves all anomalies, and characterize it with
desiderata reflecting laws of India. Subsequently rediscovered with a high
court judgment and enforced in Gujarat, 2SMG is also endorsed by Saurav Yadav
v. State of UP (2020), in a Supreme Court ruling that rescinded the flawed
mechanism. While not explicitly enforced, 2SMG is indirectly enforced for an
important subclass of applications in India, because no other mechanism
satisfies the new mandates of the Supreme Court.


Abstract:
  In 1994, P.-G. Becker and W. Bergweiler listed all the differentially
algebraic solutions of three famous functional equations: the Schr{\"o}der's,
B{\"o}ttcher's and Abel's equations. The proof of this theorem combines various
domains of mathematics. This goes from the theory of iteration, which gave
birth to these equations, to the algebro-differential notion of coherent
families developed by M. Boshernitzan and L. A. Rubel. This survey is an
excursion into the history of these equations, in order to enlighten the
different pieces of mathematics they bring together and how these parts fit
into the result of P.-G. Becker and W. Bergweiler.


Abstract:
  Over-parameterization of neural networks benefits the optimization and
generalization yet brings cost in practice. Pruning is adopted as a
post-processing solution to this problem, which aims to remove unnecessary
parameters in a neural network with little performance compromised. It has been
broadly believed the resulted sparse neural network cannot be trained from
scratch to comparable accuracy. However, several recent works (e.g., [Frankle
and Carbin, 2019a]) challenge this belief by discovering random sparse networks
which can be trained to match the performance with their dense counterpart.
This new pruning paradigm later inspires more new methods of pruning at
initialization. In spite of the encouraging progress, how to coordinate these
new pruning fashions with the traditional pruning has not been explored yet.
This survey seeks to bridge the gap by proposing a general pruning framework so
that the emerging pruning paradigms can be accommodated well with the
traditional one. With it, we systematically reflect the major differences and
new insights brought by these new pruning fashions, with representative works
discussed at length. Finally, we summarize the open questions as worthy future
directions.


Abstract:
  Deuteration has been used as a tracer of the evolutionary phases of low- and
high-mass star formation. The APEX Telescope Large Area Survey (ATLASGAL)
provides an important repository for a detailed statistical study of massive
star-forming clumps in the inner Galactic disc at different evolutionary
phases. We study the amount of deuteration using NH2D in a representative
sample of high-mass clumps discovered by the ATLASGAL survey covering various
evolutionary phases of massive star formation. Unbiased spectral line surveys
at 3 mm were thus conducted towards ATLASGAL clumps between 85 and 93 GHz with
the Mopra telescope and from 84 to 115 GHz using the IRAM 30m telescope. A
subsample was followed up in the NH2D transition at 74 GHz with the IRAM 30m
telescope. We determined the deuterium fractionation from the column density
ratio of NH2D and NH3 and measured the NH2D excitation temperature for the
first time from the simultaneous modelling of the 74 and 110 GHz line using
MCWeeds. We find a large range of the NH2D to NH3 column density ratio up to
1.6+/-0.7 indicating a high degree of NH3 deuteration in a subsample of the
clumps. Our analysis yields a clear difference between NH3 and NH2D rotational
temperatures for a fraction of the sources. We therefore advocate observation
of the NH2D transitions at 74 and 110 GHz simultaneously to determine the NH2D
temperature directly. We determine a median ortho-to-para column density ratio
of 3.7+/-1.2. The high detection rate of NH2D confirms a high deuteration
previously found in massive star-forming clumps. Using the excitation
temperature of NH2D instead of NH3 is needed to avoid an overestimation of
deuteration. We measure a higher detection rate of NH2D in sources at early
evolutionary stages. The deuterium fractionation shows no correlation with
evolutionary tracers such as the NH3 (1,1) line width, or rotational
temperature.


Abstract:
  High dimensional data can contain multiple scales of variance. Analysis tools
that preferentially operate at one scale can be ineffective at capturing all
the information present in this cross-scale complexity. We propose a multiscale
joint characterization approach designed to exploit synergies between global
and local approaches to dimensionality reduction. We illustrate this approach
using Principal Components Analysis (PCA) to characterize global variance
structure and t-stochastic neighbor embedding (t-sne) to characterize local
variance structure. Using both synthetic images and real-world imaging
spectroscopy data, we show that joint characterization is capable of detecting
and isolating signals which are not evident from either PCA or t-sne alone.
Broadly, t-sne is effective at rendering a randomly oriented low-dimensional
map of local clusters, and PCA renders this map interpretable by providing
global, physically meaningful structure. This approach is illustrated using
imaging spectroscopy data, and may prove particularly useful for other
geospatial data given robust local variance structure due to spatial
autocorrelation and physical interpretability of global variance structure due
to spectral properties of Earth surface materials. However, the fundamental
premise could easily be extended to other high dimensional datasets, including
image time series and non-image data.


Abstract:
  The Hall effects on Casson fluid flow along a vertical plate has been
investigated numerically. The governing equations have been derived from
Navier-Stokes' equation and boundary layer approximation has been employed. By
using usual transformations, the obtained non-linear coupled partial
differential equations have been transformed into dimensionless governing
equations. These equations have been solved by applying the explicit finite
difference method. The MATLAB R2015a tool has been used for numerical
simulation. The stability and convergence criteria have been analyzed. The
effect of some important parameters on the primary velocity, secondary
velocity, temperature and concentration distributions as well as local shear
stress, Nusselt number and Sherwood number have been shown graphically.


Abstract:
  This paper investigates the optimal signal detection problem with a
particular interest in large-scale multiple-input multiple-output (MIMO)
systems. The problem is NP-hard and can be solved optimally by searching the
shortest path on the decision tree. Unfortunately, the existing optimal search
algorithms often involve prohibitively high complexities, which indicates that
they are infeasible in large-scale MIMO systems. To address this issue, we
propose a general heuristic search algorithm, namely, hyperaccelerated tree
search (HATS) algorithm. The proposed algorithm employs a deep neural network
(DNN) to estimate the optimal heuristic, and then use the estimated heuristic
to speed up the underlying memory-bounded search algorithm. This idea is
inspired by the fact that the underlying heuristic search algorithm reaches the
optimal efficiency with the optimal heuristic function. Simulation results show
that the proposed algorithm reaches almost the optimal bit error rate (BER)
performance in large-scale systems, while the memory size can be bounded. In
the meanwhile, it visits nearly the fewest tree nodes. This indicates that the
proposed algorithm reaches almost the optimal efficiency in practical
scenarios, and thereby it is applicable for large-scale systems. Besides, the
code for this paper is available at https://github.com/skypitcher/hats.


Abstract:
  We study the connection between the X-ray and UV properties of the broad
absorption line (BAL) wind in the highly X-ray variable quasar PG 2112+059 by
comparing Chandra-ACIS data with contemporaneous UV HST/STIS spectra in three
different epochs. We observe a correlation whereby an increase in the
equivalent-widths (EWs) of the BALs is accompanied by a redder UV spectrum. The
growth in the BALs EWs is also accompanied by a significant dimming in soft
X-ray emission (<2 keV), consistent with increased absorption. Variations in
the hard X-ray emission (>2 keV) are only accompanied by minor spectral
variations of the UV-BALs and do not show significant changes in the EW of
BALs. These trends suggest a wind-shield scenario where the outflow inclination
with respect to the line of sight is decreasing and/or the wind mass is
increasing. These changes elevate the covering fraction and/or column densities
of the BALs and are likely accompanied by a nearly contemporaneous increase in
the column density of the shield.


Abstract:
  Sub-nanosecond precision clock synchronization over the packet network has
been achieved by the White Rabbit protocol for a decade. However, few computer
systems utilize such a technique. We try to attract more interest in the clock
synchronization problem. We first introduce the basics of clock and
synchronization in the time and frequency discipline. Then we revisit several
related works, such as Google's Spanner, Huygens, FARMv2, DTP, and Sundial,
explain why these works could be improved. Finally, we briefly discuss an
independent time network approach towards low-cost and high-precision
synchronization.


Abstract:
  A chain of evolution algebras (CEA) is an uncountable family (depending on
time) of evolution algebras on the field of real numbers. The matrix of
structural constants of a CEA satisfies Kolmogorov-Chapman equation. In this
paper, we consider three CEAs of three-dimensional real evolution algebras.
These CEAs depend on several (non-zero) functions defined on the set of time.
For each chain we give full classification (up to isomorphism) of the algebras
depending on the time-parameter. We find concrete functions ensuring that the
corresponding CEA contains all possible three-dimensional evolution algebras.


Abstract:
  The mixing of N2 with H2 leads to very different plasmas from pure N2 and H2
plasma discharges. Numerous issues are therefore raised involving the processes
leading to ammonia (NH3) formation. The aim of this work is to better
characterize capacitively-coupled radiofrequency plasma discharges in N2 with
few percents of H2 (up to 5 pct), at low pressure (0.3 to 1 mbar) and low
coupled power (3 to 13 W). Both experimental measurements and numerical
simulations are performed. For clarity, we separated the results in two
complementary parts. The actual one (first part), presents the details on the
experimental measurements, while the second focuses on the simulation, a hybrid
model combining a 2D fluid module and a 0D kinetic module. Electron density is
measured by a resonant cavity method. It varies from 0.4 to 5e9 cm-3,
corresponding to ionization degrees from 2e-8 to 4e-7. Ammonia density is
quantified by combining IR absorption and mass spectrometry. It increases
linearly with the amount of H2 (up to 3e13 cm-3 at 5 pct H2). On the contrary,
it is constant with pressure, which suggests the dominance of surface processes
on the formation of ammonia. Positive ions are measured by mass spectrometry.
Nitrogen-bearing ions are hydrogenated by the injection of H2, N2H+ being the
major ion as soon as the amount of H2 is larger than 1 pct. The increase of
pressure leads to an increase of secondary ions formed by ion (or radical) -
neutral collisions (ex: N2H+, NH4+, H3+), while an increase of the coupled
power favours ions formed by direct ionization (ex: N2+, NH3+, H2+).