Abstract:
  3D object detection in point clouds is a challenging vision task that
benefits various applications for understanding the 3D visual world. Lots of
recent research focuses on how to exploit end-to-end trainable Hough voting for
generating object proposals. However, the current voting strategy can only
receive partial votes from the surfaces of potential objects together with
severe outlier votes from the cluttered backgrounds, which hampers full
utilization of the information from the input point clouds. Inspired by the
back-tracing strategy in the conventional Hough voting methods, in this work,
we introduce a new 3D object detection method, named as Back-tracing
Representative Points Network (BRNet), which generatively back-traces the
representative points from the vote centers and also revisits complementary
seed points around these generated points, so as to better capture the fine
local structural features surrounding the potential objects from the raw point
clouds. Therefore, this bottom-up and then top-down strategy in our BRNet
enforces mutual consistency between the predicted vote centers and the raw
surface points and thus achieves more reliable and flexible object localization
and class prediction results. Our BRNet is simple but effective, which
significantly outperforms the state-of-the-art methods on two large-scale point
cloud datasets, ScanNet V2 (+7.5% in terms of mAP@0.50) and SUN RGB-D (+4.7% in
terms of mAP@0.50), while it is still lightweight and efficient. Code will be
available at https://github.com/cheng052/BRNet.


Abstract:
  Scientists frequently generalize population level causal quantities such as
average treatment effect from a source population to a target population. When
the causal effects are heterogeneous, differences in subject characteristics
between the source and target populations may make such a generalization
difficult and unreliable. Reweighting or regression can be used to adjust for
such differences when generalizing. However, these methods typically suffer
from large variance if there is limited covariate distribution overlap between
the two populations. We propose a generalizability score to address this issue.
The score can be used as a yardstick to select target subpopulations for
generalization. A simplified version of the score avoids using any outcome
information and thus can prevent deliberate biases associated with inadvertent
access to such information. Both simulation studies and real data analysis
demonstrate convincing results for such selection.


Abstract:
  Counterfactual inference is a useful tool for comparing outcomes of
interventions on complex systems. It requires us to represent the system in
form of a structural causal model, complete with a causal diagram,
probabilistic assumptions on exogenous variables, and functional assignments.
Specifying such models can be extremely difficult in practice. The process
requires substantial domain expertise, and does not scale easily to large
systems, multiple systems, or novel system modifications. At the same time,
many application domains, such as molecular biology, are rich in structured
causal knowledge that is qualitative in nature. This manuscript proposes a
general approach for querying a causal biological knowledge graph, and
converting the qualitative result into a quantitative structural causal model
that can learn from data to answer the question. We demonstrate the
feasibility, accuracy and versatility of this approach using two case studies
in systems biology. The first demonstrates the appropriateness of the
underlying assumptions and the accuracy of the results. The second demonstrates
the versatility of the approach by querying a knowledge base for the molecular
determinants of a severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2)-induced cytokine storm, and performing counterfactual inference to
estimate the causal effect of medical countermeasures for severely ill
patients.


Abstract:
  In this work, we are dealing with some properties relating the zeros of a
polynomial and its Mahler measure. We provide estimates on the number of real
zeros of a polynomial, lower bounds on the distance between the zeros of a
polynomial and non-zeros located on the unit circle and a lower bound on the
number of zeros of a polynomial in the disk $\{\vert x-1\vert<1\}$.


Abstract:
  We design blackbox transfer-based targeted adversarial attacks for an
environment where the attacker's source model and the target blackbox model may
have disjoint label spaces and training datasets. This scenario significantly
differs from the "standard" blackbox setting, and warrants a unique approach to
the attacking process. Our methodology begins with the construction of a class
correspondence matrix between the whitebox and blackbox label sets. During the
online phase of the attack, we then leverage representations of highly related
proxy classes from the whitebox distribution to fool the blackbox model into
predicting the desired target class. Our attacks are evaluated in three complex
and challenging test environments where the source and target models have
varying degrees of conceptual overlap amongst their unique categories.
Ultimately, we find that it is indeed possible to construct targeted
transfer-based adversarial attacks between models that have non-overlapping
label spaces! We also analyze the sensitivity of attack success to properties
of the clean data. Finally, we show that our transfer attacks serve as powerful
adversarial priors when integrated with query-based methods, markedly boosting
query efficiency and adversarial success.


Abstract:
  The heavy traffic and related issues have always been concerns for modern
cities. With the help of deep learning and reinforcement learning, people have
proposed various policies to solve these traffic-related problems, such as
smart traffic signal control systems and taxi dispatching systems. People
usually validate these policies in a city simulator, since directly applying
them in the real city introduces real cost. However, these policies validated
in the city simulator may fail in the real city if the simulator is
significantly different from the real world. To tackle this problem, we need to
build a real-like traffic simulation system. Therefore, in this paper, we
propose to learn the human routing model, which is one of the most essential
part in the traffic simulator. This problem has two major challenges. First,
human routing decisions are determined by multiple factors, besides the common
time and distance factor. Second, current historical routes data usually covers
just a small portion of vehicles, due to privacy and device availability
issues. To address these problems, we propose a theory-guided residual network
model, where the theoretical part can emphasize the general principles for
human routing decisions (e.g., fastest route), and the residual part can
capture drivable condition preferences (e.g., local road or highway). Since the
theoretical part is composed of traditional shortest path algorithms that do
not need data to train, our residual network can learn human routing models
from limited data. We have conducted extensive experiments on multiple
real-world datasets to show the superior performance of our model, especially
with small data. Besides, we have also illustrated why our model is better at
recovering real routes through case studies.


Abstract:
  In the Ellis wormhole metrics we study characteristics of fluid dynamics and
the properties of linear sound waves. By implying the energy-momentum equation
and the continuity equation in the general relativistic manner we examine the
flow dynamics and solve the corresponding equations for a relatively simple
case - radial flow. To study the linear sound waves the equations governing the
mentioned physical system are linearized and solved and interesting
characteristic properties are found.


Abstract:
  Unit tangent bundles $UM$ of semi-Riemannian manifolds $M$ are shown to be
examples of dynamical Legendrian contact structures, which were defined in
recent work [25] of Sykes-Zelenko to generalize leaf spaces of 2-nondegenerate
CR manifolds. In doing so, Sykes-Zelenko extended the classification in
Porter-Zelenko [20] of regular, 2-nondegenerate CR structures to those that can
be recovered from their leaf space. The present paper treats dynamical
Legendrian contact structures associated with 2-nondegenerate CR structures
which were called "strongly regular" in Porter-Zelenko, named "L-contact
structures." Closely related to Lie-contact structures, L-contact manifolds
have homogeneous models given by isotropic Grassmannians of complex 2-planes
whose algebra of infinitesimal symmetries is one of $\mathfrak{so}(p+2,q+2)$ or
$\mathfrak{so}^*(2p+4)$ for $p\geq1$, $q\geq0$. Each 2-plane in the homogeneous
model is a split-quaternionic or quaternionic line, respectively, and more
general L-contact structures arise on contact manifolds with hypercomplex
structures, unit tangent bundles being a prime example. The Ricci curvature
tensor of $M$ is used to define the "Ricci-shifted" L-contact structure on
$UM$, whose Nijenhuis tensor vanishes when $M$ is conformally flat. In the
language of Sykes-Zelenko (for $M$ analytic), such $UM$ is the leaf space of a
2-nondegenerate CR manifold which is "recoverable" from $UM$, providing a new
source of examples of 2-nondegenerate CR structures.


Abstract:
  The matching literature often recommends market centralization under the
assumption that agents know their own preferences and that their preferences
are fixed. We find counterevidence to this assumption in a quasi-experiment. In
Germany's university admissions, a clearinghouse implements the early stages of
the Gale-Shapley algorithm in real time. We show that early offers made in this
decentralized phase, although not more desirable, are accepted more often than
later ones. These results, together with survey evidence and a theoretical
model, are consistent with students' costly learning about universities. We
propose a hybrid mechanism to combine the advantages of decentralization and
centralization.


Abstract:
  Dynamical studies of asteroid populations in retrograde orbits, that is with
orbital inclinations greater than 90 degrees, are interesting because the
origin of such orbits is still unexplained. Generally, the population of
retrograde asteroids includes mostly Centaurs and transneptunian objects
(TNOs). A special case is the near-Earth object (343158) 2009 HC82 from the
Apollo group. Another interesting object is the comet 333P/LINEAR, which for
several years was considered the second retrograde object approaching Earth.
Another comet in retrograde orbit, 161P Hartley/IRAS appears to be an object of
similar type. Thanks to the large amount of observational data for these two
comets, we tested various models of cometary non-gravitational forces applied
to their dynamics.
  The goal was to estimate which of non-gravitational perturbations could
affect the stability of retrograde bodies. In principle, we study the local
stability by measuring the divergence of nearby orbits.
  We numerically determined Lyapunov characteristic indicators (LCI) and the
associated Lyapunov times (LT). This time, our calculations were extended by
more advanced models of non-gravitational perturbations (i.e. Yarkovsky drift
and in selected cases cometary forces). This allowed us to estimate chaos in
the Lyapunov sense.
  We found that the Yarkovsky effect for obliquities of $\gamma=0^{\circ}$ and
$\gamma=180^{\circ}$ can change the LT substantially. In most cases, for the
prograde rotation, we received more stable solutions. Moreover, we confirmed
the role of retrograde resonances in this process. Additionally, the studied
cometary effects also significantly influence the long-term behaviour of the
selected comets. The LT can reach values from 100 to over 1000 years.
Conclusions. All of our results indicate that the use of models with
non-gravitational effects for retrograde bodies is clearly justified.


Abstract:
  Run-time domain shifts from training-phase domains are common in sensing
systems designed with deep learning. The shifts can be caused by sensor
characteristic variations and/or discrepancies between the design-phase model
and the actual model of the sensed physical process. To address these issues,
existing transfer learning techniques require substantial target-domain data
and thus incur high post-deployment overhead. This paper proposes to exploit
the first principle governing the domain shift to reduce the demand on
target-domain data. Specifically, our proposed approach called PhyAug uses the
first principle fitted with few labeled or unlabeled source/target-domain data
pairs to transform the existing source-domain training data into augmented data
for updating the deep neural networks. In two case studies of keyword spotting
and DeepSpeech2-based automatic speech recognition, with 5-second unlabeled
data collected from the target microphones, PhyAug recovers the recognition
accuracy losses due to microphone characteristic variations by 37% to 72%. In a
case study of seismic source localization with TDoA fngerprints, by exploiting
the frst principle of signal propagation in uneven media, PhyAug only requires
3% to 8% of labeled TDoA measurements required by the vanilla fingerprinting
approach in achieving the same localization accuracy.


Abstract:
  We propose Super Odometry, a high-precision multi-modal sensor fusion
framework, providing a simple but effective way to fuse multiple sensors such
as LiDAR, camera, and IMU sensors and achieve robust state estimation in
perceptually-degraded environments. Different from traditional sensor-fusion
methods, Super Odometry employs an IMU-centric data processing pipeline, which
combines the advantages of loosely coupled methods with tightly coupled methods
and recovers motion in a coarse-to-fine manner. The proposed framework is
composed of three parts: IMU odometry, visual-inertial odometry, and
laser-inertial odometry. The visual-inertial odometry and laser-inertial
odometry provide the pose prior to constrain the IMU bias and receive the
motion prediction from IMU odometry. To ensure high performance in real-time,
we apply a dynamic octree that only consumes 10 % of the running time compared
with a static KD-tree. The proposed system was deployed on drones and ground
robots, as part of Team Explorer's effort to the DARPA Subterranean Challenge
where the team won $1^{st}$ and $2^{nd}$ place in the Tunnel and Urban
Circuits, respectively.


Abstract:
  Visual Transformers (VTs) are emerging as an architectural paradigm
alternative to Convolutional networks (CNNs). Differently from CNNs, VTs can
capture global relations between image elements and they potentially have a
larger representation capacity. However, the lack of the typical convolutional
inductive bias makes these models more data-hungry than common CNNs. In fact,
some local properties of the visual domain which are embedded in the CNN
architectural design, in VTs should be learned from samples. In this paper, we
empirically analyse different VTs, comparing their robustness in a small
training-set regime, and we show that, despite having a comparable accuracy
when trained on ImageNet, their performance on smaller datasets can be largely
different. Moreover, we propose a self-supervised task which can extract
additional information from images with only a negligible computational
overhead. This task encourages the VTs to learn spatial relations within an
image and makes the VT training much more robust when training data are scarce.
Our task is used jointly with the standard (supervised) training and it does
not depend on specific architectural choices, thus it can be easily plugged in
the existing VTs. Using an extensive evaluation with different VTs and
datasets, we show that our method can improve (sometimes dramatically) the
final accuracy of the VTs. Our code is available at:
https://github.com/yhlleo/VTs-Drloc.


Abstract:
  Occlusion is very challenging in pedestrian detection. In this paper, we
propose a simple yet effective method named V2F-Net, which explicitly
decomposes occluded pedestrian detection into visible region detection and full
body estimation. V2F-Net consists of two sub-networks: Visible region Detection
Network (VDN) and Full body Estimation Network (FEN). VDN tries to localize
visible regions and FEN estimates full-body box on the basis of the visible
box. Moreover, to further improve the estimation of full body, we propose a
novel Embedding-based Part-aware Module (EPM). By supervising the visibility
for each part, the network is encouraged to extract features with essential
part information. We experimentally show the effectiveness of V2F-Net by
conducting several experiments on two challenging datasets. V2F-Net achieves
5.85% AP gains on CrowdHuman and 2.24% MR-2 improvements on CityPersons
compared to FPN baseline. Besides, the consistent gain on both one-stage and
two-stage detector validates the generalizability of our method.


Abstract:
  Flow polytopes are an important class of polytopes in combinatorics whose
lattice points and volumes have interesting properties and relations. The
Chan-Robbins-Yuen (CRY) polytope is a flow polytope with normalized volume
equal to the product of consecutive Catalan numbers. Zeilberger proved this by
evaluating the Morris constant term identity, but no combinatorial proof is
known. There is a refinement of this formula that splits the largest Catalan
number into Narayana numbers, which M\'esz\'aros gave an interpretation as the
volume of a collection of flow polytopes. We introduce a new refinement of the
Morris identity with combinatorial interpretations both in terms of lattice
points and volumes of flow polytopes. Our results generalize M\'esz\'aros's
construction and a recent flow polytope interpretation of the Morris identity
by Corteel-Kim-M\'esz\'aros. We prove the product formula of our refinement
following the strategy of the Baldoni-Vergne proof of the Morris identity.
Lastly, we study a symmetry of the Morris identity bijectively using the
Danilov-Karzanov-Koshevoy triangulation of flow polytopes and a bijection of
M\'esz\'aros-Morales-Striker.


Abstract:
  Using the recently mooted Galilean gauge theory we have constructed the model
for the Schroedinger field interacting wuth gravity which is also dynamical.
The dynamics of gravity is dictated by the Newtonian action in the
Newton-Cartan spacetime. The theory is highly constrained . An elaborate
analysis of the constraints of the theory have been performed. The symmetries
are explicitly verified and the uniqueness of the model has been established.
To the best of our knowledge both the model and its constraints structure are
unique in the literature


Abstract:
  Photo-realistic neural reconstruction and rendering of the human portrait are
critical for numerous VR/AR applications. Still, existing solutions inherently
rely on multi-view capture settings, and the one-shot solution to get rid of
the tedious multi-view synchronization and calibration remains extremely
challenging. In this paper, we propose MirrorNeRF - a one-shot neural portrait
free-viewpoint rendering approach using a catadioptric imaging system with
multiple sphere mirrors and a single high-resolution digital camera, which is
the first to combine neural radiance field with catadioptric imaging so as to
enable one-shot photo-realistic human portrait reconstruction and rendering, in
a low-cost and casual capture setting. More specifically, we propose a
light-weight catadioptric system design with a sphere mirror array to enable
diverse ray sampling in the continuous 3D space as well as an effective online
calibration for the camera and the mirror array. Our catadioptric imaging
system can be easily deployed with a low budget and the casual capture ability
for convenient daily usages. We introduce a novel neural warping radiance field
representation to learn a continuous displacement field that implicitly
compensates for the misalignment due to our flexible system setting. We further
propose a density regularization scheme to leverage the inherent geometry
information from the catadioptric data in a self-supervision manner, which not
only improves the training efficiency but also provides more effective density
supervision for higher rendering quality. Extensive experiments demonstrate the
effectiveness and robustness of our scheme to achieve one-shot photo-realistic
and high-quality appearance free-viewpoint rendering for human portrait scenes.


Abstract:
  In this work, we investigate non-Hermitian elastic waveguides with
periodically applied proportional feedback efforts, implemented through
piezoelectric sensors and actuators. Using onedimensional spectral models for
longitudinal motion, it is shown that dispersion diagrams of this family of
structures exhibit non-reciprocal imaginary frequency components, manifesting
as wave attenuation or amplification along opposite directions for all pass
bands. The effects of positive and negative proportional feedback, as well as
local and non-local actuation are investigated. Overall, switching the sign of
the feedback effort inverts the amplification direction, while increasing the
degree of non-locality produces splitting of the pass bands into multiple bands
with interchanging non-reciprocal behavior. Furthermore, skin modes localized
at the boundaries of finite domains are investigated and successfully predicted
by the winding number of the complex dispersion bands. These results contribute
to recent efforts in designing metamaterials with novel properties associated
with the physics of non-Hermitian systems, which may find fruitful
technological applications relying on vibration and noise control, wave
localization, filtering and multiplexing.


Abstract:
  We establish an identity for E f (Y) -E f (X), when X and Y both have matrix
variateskew-normal distributions and the function f fulfills some weak
conditions. Thecharacteristic function of matrix variate skew normal
distribution is then derived. Finally,we make use of it to derive some
necessary and sucient conditions for the comparisonof matrix variate
skew-normal distributions under six di erent orders, such as usualstochastic
order, convex order, increasing convex order, upper orthant order,directionally
convex order and supermodular order.


Abstract:
  The manifesto of the current article is to investigate the compact
anisotropic matter profiles in the context of one of the modified gravitational
theories, known as $f(\mathcal{R}, \mathcal{T})$ gravity, where $\mathcal{R}$
is a Ricci Scalar and $\mathcal{T}$ is the trace of the energy-momentum tensor.
To achieve the desired goal, we capitalized on the spherical symmetric
space-time and utilized the embedding class-1 solution via Karmarkar's
condition in modeling the matter profiles. To calculate the unidentified
constraints, Schwarzschild exterior solution along with experimental statistics
of three different stars LMC X-4, Cen X-3, and EXO 1785-248 are taken under
consideration. For the evaluation of the dynamical equations, a unique model
$f(\mathcal{R}, \mathcal{T})=\mathcal{R}+\beta \mathcal{R}^2+\lambda
\mathcal{T}$ has been considered, with $\beta$ and $\lambda$ being the real
constants. Different physical aspects have been exploited with the help of
modified dynamical equations. Conclusively, all the stars under observations
are realistic, stable, and are free from all singularities.


Abstract:
  With the progress in AI-based facial forgery (i.e., deepfake), people are
increasingly concerned about its abuse. Albeit effort has been made for
training classification (also known as deepfake detection) models to recognize
such forgeries, existing models suffer from poor generalization to unseen
forgery technologies and high sensitivity to changes in image/video quality. In
this paper, we advocate adversarial training for improving the generalization
ability to both unseen facial forgeries and unseen image/video qualities. We
believe training with samples that are adversarially crafted to attack the
classification models improves the generalization ability considerably.
Considering that AI-based face manipulation often leads to high-frequency
artifacts that can be easily spotted by models yet difficult to generalize, we
further propose a new adversarial training method that attempts to blur out
these specific artifacts, by introducing pixel-wise Gaussian blurring models.
With adversarial training, the classification models are forced to learn more
discriminative and generalizable features, and the effectiveness of our method
can be verified by plenty of empirical evidence. Our code will be made publicly
available.


Abstract:
  The surface brightness -- colour relation (SBCR) is a basic tool in
establishing precise and accurate distances within the Local Group. Detached
eclipsing binary stars with accurately determined radii and trigonometric
parallaxes allow for a calibration of the SBCRs with unprecedented accuracy. We
analysed four nearby eclipsing binary stars containing late F-type main
sequence components: AL Ari, AL Dor, FM Leo and BN Scl. We determined very
precise spectroscopic orbits and combined them with high precision ground- and
space-based photometry. We derived the astrophysical parameters of their
components with mean errors of 0.1% for mass and 0.4% for radius. We combined
those four systems with another 24 nearby eclipsing binaries with accurately
known radii from the literature for which $Gaia$ EDR3 parallaxes are available,
in order to derive the SBCRs. The resulting SBCRs cover stellar spectral types
from B9 V to G7 V. For calibrations we used Johnson optical $B$ and $V$, $Gaia$
$G_{\rm BP}$ and $G$ and 2MASS $JHK$ bands. The most precise relations are
calibrated using the infrared $K$ band and allow to predict angular diameters
of A-, F-, and G-type dwarf and subgiant stars with a precision of 1%.


Abstract:
  The Rubin Causal Model (RCM) is a framework that allows to define the causal
effect of an intervention as a contrast of potential outcomes. In recent years,
several methods have been developed under the RCM to estimate causal effects in
time series settings. None of these makes use of ARIMA models, which are
instead very common in the econometrics literature. In this paper, we propose a
novel approach, C-ARIMA, to define and estimate the causal effect of an
intervention in a time series setting under the RCM. We first formalize the
assumptions enabling the definition, the estimation and the attribution of the
effect to the intervention; we then check the validity of the proposed method
with an extensive simulation study, comparing its performance against a
standard intervention analysis approach. In the empirical application, we use
C-ARIMA to assess the causal effect of a permanent price reduction on
supermarket sales. The CausalArima R package provides an implementation of our
proposed approach.


Abstract:
  The renormalization group (RG) equation in the self-consistent local
potential approximation (SC-LPA) suggested earlier for the description of
continuous phase transitions in lattice models of the Landau-Ginzburg type has
been applied to the solution of the spin-1 Blume-Capel model on the simple
cubic lattice. The calculated transition temperatures of both continuous and
the first-order phase transitions (FOPTs) in zero external field have been
found to be in excellent agreement with the best available estimates. It has
been argued that the SC-LPA RG equation may give more accurate and complete
description of the FOPTs than those reported in alternative approaches. It has
been shown that the SC-LPA RG equation can be cast in the form of the
generalized Burgers' equation (GBE). In this formulation of the RG the FOPTs
have been shown to assume the form of the shock-wave solutions of GBE in the
inviscid limit. Universality of the RG flow in the vicinity of the fixed point
describing FOPTs has been discussed.


Abstract:
  In light of recently revised observational measurements of the radius and
spectroscopic parameters of the extremely old and metal-poor Gaia benchmark
star HD 140283 -- also known as the Methuselah star due to prior suggestions
that its age is in tension with the age of the Universe -- we present new, best
estimates for the star's mass and age from stellar modeling. These are derived
using 1D stellar evolutionary tracks computed with MESA and the most up-to-date
measurements from CHARA interferometry. Excluding modeling variance from the
uncertainties, we report a mass of $0.809 \pm 0.001 M_{\odot}$ and an age of
$12.01 \pm 0.05$ Gyr ($1 \sigma$). When dominant sources of modeling
uncertainty are taken into account, we report $0.81 \pm 0.05 M_{\odot}$ and $12
\pm 0.5$ Gyr, respectively. These results are consistent with recent
literature, and the best-fitting age is not in conflict with the currently
accepted age of the universe ($13.5$ Gyr; arXiv:1303.5089 [astro-ph.CO]).


Abstract:
  Direct Current (DC) power distribution has gained attention in the
Residential Nanogrids (RNGs) due to the substantial increase in the number of
roof-top Photovoltaic (PV) systems and internally DC appliances used in
buildings. Using DC distribution improves the efficiency of the RNGs compared
to AC distribution. This paper investigated the efficiency of a DC RNG for low
and high distribution voltage levels by exploring reasons for power losses. The
studied DC RNG consisted of various types of local loads, on-site PV
generation, and battery storage systems. The realistic load, PV profiles, and
converter efficiency curves were used to make the analysis more accurate. In
addition, three load profiles with low, medium, and high power consumptions
were considered to study the load impacts on the overall system efficiency.


Abstract:
  We propose a novel method that tackles the problem of unsupervised domain
adaptation for semantic segmentation by maximizing the cosine similarity
between the source and the target domain at the feature level. A segmentation
network mainly consists of two parts, a feature extractor and a classification
head. We expect that if we can make the two domains have small domain gap at
the feature level, they would also have small domain discrepancy at the
classification head. Our method computes a cosine similarity matrix between the
source feature map and the target feature map, then we maximize the elements
exceeding a threshold to guide the target features to have high similarity with
the most similar source feature. Moreover, we use a class-wise source feature
dictionary which stores the latest features of the source domain to prevent the
unmatching problem when computing the cosine similarity matrix and be able to
compare a target feature with various source features from various images.
Through extensive experiments, we verify that our method gains performance on
two unsupervised domain adaptation tasks (GTA5$\to$ Cityscaspes and
SYNTHIA$\to$ Cityscapes).


Abstract:
  The distributed dual ascent is an established algorithm to solve strongly
convex multi-agent optimization problems with separable cost functions, in the
presence of coupling constraints. In this paper, we study its asynchronous
counterpart. Specifically, we assume that each agent only relies on the
outdated information received from some neighbors. Differently from the
existing randomized and dual block-coordinate schemes, we show convergence
under heterogeneous delays, communication and update frequencies. Consequently,
our asynchronous dual ascent algorithm can be implemented without requiring any
coordination between the agents.


Abstract:
  The state of the art review broadly oversees the use of novel research
utilized in the creation of virtual environments applied in interactive art
experiences, with a specific focus on the application of procedural animation
in spatially augmented reality (SAR) exhibitions. These art exhibitions
frequently combine sensory displays that appeal, replace, and augment the
visual, auditory and touch or haptic senses. We analyze and break down
art-technology related innovations in the last three years, and thoroughly
identify the most recent and vibrant applications of interactive art
experiences in the review of numerous installation applications, studies, and
events. Display mediums such as virtual reality, augmented reality, mixed
reality, and robotics are overviewed in the context of art experiences such as
visual art museums, park or historic site tours, live concerts, and theatre. We
explore research and extrapolate how recent innovations can lead to different
applications that will be seen in the future.


Abstract:
  Human history has been shaped by armed conflicts. Rather than large-scale
interstate wars, low-intensity attacks have been more prevalent in the
post-World War era. These attacks are often carried out by non-state armed
groups (NAGs), which are supported by host states (HSs). We analyze the global
bipartite network of NAG-HS support and its evolution over the period of
1945-2010. We find striking parallels to ecological networks such as
mutualistic and parasitic forms of support, and a nested and modular network
architecture. The nestedness emerges from preferential behaviors: highly
connected players are more likely to both gain and lose connections.
Long-persisting major modules are identified, reflecting both regional and
trans-regional interests, which show significant turnover in their membership,
contrary to the transitory ones. Revealing this architecture further enables
the identification of actor's roles and provide insights for effective
intervention strategies.


Abstract:
  Advertising click fraud detection plays one of the vital roles in current
E-commerce websites as advertising is an essential component of its business
model. It aims at, given a set of corresponding features, e.g., demographic
information of users and statistical features of clicks, predicting whether a
click is fraudulent or not in the community. Recent efforts attempted to
incorporate attributed behavior sequence and heterogeneous network for
extracting complex features of users and achieved significant effects on click
fraud detection. In this paper, we propose a Multimodal and Contrastive
learning network for Click Fraud detection (MCCF). Specifically, motivated by
the observations on differences of demographic information, behavior sequences
and media relationship between fraudsters and genuine users on E-commerce
platform, MCCF jointly utilizes wide and deep features, behavior sequence and
heterogeneous network to distill click representations. Moreover, these three
modules are integrated by contrastive learning and collaboratively contribute
to the final predictions. With the real-world datasets containing 2.54 million
clicks on Alibaba platform, we investigate the effectiveness of MCCF. The
experimental results show that the proposed approach is able to improve AUC by
7.2% and F1-score by 15.6%, compared with the state-of-the-art methods.


Abstract:
  Conformal Ricci solitons are self similar solutions of the conformal Ricci
flow equation. This paper deals with the study of conformal Ricci solitons
within the framework of warped product manifolds which extends the notion of
usual Riemannian product manifolds. First, we prove that if a warped product
manifold admits conformal Ricci soliton then the base and the fiber also share
the same property. In the next section the characterization of conformal Ricci
solitons on warped product manifolds in terms of Killing and conformal vector
fields has been studied. Next, we prove that a warped product manifold
admitting conformal Ricci soliton with concurrent potential vector field is
Ricci flat. Finally, an application of conformal Ricci soliton on a class of
warped product spacetimes namely, generalized Robertson-Walker spacetimes has
been discussed.


Abstract:
  Visual dynamic complexity is a ubiquitous, hidden attribute of the visual
world that every dynamic vision system is faced with. However, it is implicit
and intractable which has never been quantitatively described due to the
difficulty in defending temporal features correlated to spatial image
complexity. To fill this vacancy, we propose a novel bio-robotic approach to
profile visual dynamic complexity which can be used as a new metric. Here we
apply a state-of-the-art brain-inspired motion detection neural network model
to explicitly profile such complexity associated with spatial-temporal
frequency (SF-TF) of visual scene. This model is for the first time implemented
in an autonomous micro-mobile robot which navigates freely in an arena with
visual walls displaying moving sine-wave grating or cluttered natural scene.
The neural dynamic response can make reasonable prediction on surrounding
complexity since it can be mapped monotonically to varying SF-TF of visual
scene. The experiments show this approach is flexible to different visual
scenes for profiling the dynamic complexity. We also use this metric as a
predictor to investigate the boundary of another collision detection visual
system performing in changing environment with increasing dynamic complexity.
This research demonstrates a new paradigm of using biologically plausible
visual processing scheme to estimate dynamic complexity of visual scene from
both spatial and temporal perspectives, which could be beneficial to predicting
input complexity when evaluating dynamic vision systems.


Abstract:
  We use photometric redshifts and statistical background subtraction to
measure stellar mass functions in galaxy group-mass
($4.5-8\times10^{13}~\mathrm{M}_\odot$) haloes at $1<z<1.5$. Groups are
selected from COSMOS and SXDF, based on X-ray imaging and sparse spectroscopy.
Stellar mass ($M_{\mathrm{stellar}}$) functions are computed for quiescent and
star-forming galaxies separately, based on their rest-frame $UVJ$ colours. From
these we compute the quiescent fraction and quiescent fraction excess (QFE)
relative to the field as a function of $M_{\mathrm{stellar}}$. QFE increases
with $M_{\mathrm{stellar}}$, similar to more massive clusters at $1<z<1.5$.
This contrasts with the apparent separability of $M_{\mathrm{stellar}}$ and
environmental factors on galaxy quiescent fractions at $z\sim 0$. We then
compare our results with higher mass clusters at $1<z<1.5$ and lower redshifts.
We find a strong QFE dependence on halo mass at fixed $M_{\mathrm{stellar}}$;
well fit by a logarithmic slope of $\mathrm{d}(\mathrm{QFE})/\mathrm{d}\log
(M_{\mathrm{halo}}) \sim 0.24 \pm 0.04$ for all $M_{\mathrm{stellar}}$ and
redshift bins. This dependence is in remarkably good qualitative agreement with
the hydrodynamic simulation BAHAMAS, but contradicts the observed dependence of
QFE on $M_{\mathrm{stellar}}$. We interpret the results using two toy models:
one where a time delay until rapid (instantaneous) quenching begins upon
accretion to the main progenitor ("no pre-processing") and one where it starts
upon first becoming a satellite ("pre-processing"). Delay times appear to be
halo mass dependent, with a significantly stronger dependence required without
pre-processing. We conclude that our results support models in which
environmental quenching begins in low-mass ($<10^{14}M_\odot$) haloes at $z>1$.


Abstract:
  We report an accurate method to determine DNA barcodes from the dwell time
measurement of protein tags (barcodes) along the DNA backbone using Brownian
dynamics simulation of a model DNA and use a recursive theoretical scheme which
improves the measurements to almost 100 % accuracy. The heavier protein tags
along the DNA backbone introduce a large speed variation in the chain that can
be understood using the idea of non-equilibrium tension propagation theory.
However, from an initial rough characterization of velocities into "fast"
(nucleotides) and "slow" (protein tags) domains, we introduce a physically
motivated interpolation scheme that enables us to determine the barcode
velocities rather accurately. Our theoretical analysis of the motion of the DNA
through a cylindrical nanopore opens up the possibility of its experimental
realization and carries over to multi-nanopore devices used for barcoding.


Abstract:
  This paper presents a few observations about pro-Kremlin propaganda between
2015 and early 2021 with a dataset from the East Stratcom Task Force (ESTF),
which is affiliated with the European Union (EU) but working independently from
it. Instead of focusing on misinformation and disinformation, the observations
are motivated by classical propaganda research and the ongoing transformation
of media systems. According to the tentative results, (i) the propaganda can be
assumed to target both domestic and foreign audiences. Of the countries and
regions discussed, (ii) Russia, Ukraine, the United States, and within Europe,
Germany, Poland, and the EU have been the most frequently discussed. Also other
conflict regions such as Syria have often appeared in the propaganda. In terms
of longitudinal trends, however, (iii) most of these discussions have decreased
in volume after the digital tsunami in 2016, although the conflict in Ukraine
seems to have again increased the intensity of pro-Kremlin propaganda. Finally,
(iv) the themes discussed align with state-centric war propaganda and conflict
zones, although also post-truth themes frequently appear; from conspiracy
theories via COVID-19 to fascism -- anything goes, as is typical to propaganda.


Abstract:
  We study Hamiltonicity in graphs obtained as the union of a deterministic
$n$-vertex graph $H$ with linear degrees and a $d$-dimensional random geometric
graph $G^d(n,r)$, for any $d\geq1$. We obtain an asymptotically optimal bound
on the minimum $r$ for which a.a.s. $H\cup G^d(n,r)$ is Hamiltonian. Our proof
provides a linear time algorithm to find a Hamilton cycle in such graphs.


Abstract:
  Background: Data errors are a common challenge in machine learning (ML)
projects and generally cause significant performance degradation in ML-enabled
software systems. To ensure early detection of erroneous data and avoid
training ML models using bad data, research and industrial practice suggest
incorporating a data validation process and tool in ML system development
process.
  Aim: The study investigates the adoption of a data validation process and
tool in industrial ML projects. The data validation process demands significant
engineering resources for tool development and maintenance. Thus, it is
important to identify the best practices for their adoption especially by
development teams that are in the early phases of deploying ML-enabled software
systems.
  Method: Action research was conducted at a large-software intensive
organization in telecommunications, specifically within the analytics R\&D
organization for an ML use case of classifying faults from returned hardware
telecommunication devices.
  Results: Based on the evaluation results and learning from our action
research, we identified three best practices, three benefits, and two barriers
to adopting the data validation process and tool in ML projects. We also
propose a data validation framework (DVF) for systematizing the adoption of a
data validation process.
  Conclusions: The results show that adopting a data validation process and
tool in ML projects is an effective approach of testing ML-enabled software
systems. It requires having an overview of the level of data (feature, dataset,
cross-dataset, data stream) at which certain data quality tests can be applied.


Abstract:
  Hydrogen intensity mapping is a new field in astronomy that promises to make
three-dimensional maps of the matter distribution of the Universe using the
redshifted $21\,\textrm{cm}$ line of neutral hydrogen gas (HI). Several ongoing
and upcoming radio interferometers, such as Tianlai, CHIME, HERA, HIRAX, etc.
are using this technique. These instruments are designed to map large swaths of
the sky by drift scanning over periods of many months. One of the challenges of
the observations is that the daytime data is contaminated by strong radio
signals from the Sun. In the case of Tianlai, this results in almost half of
the measured data being unusable. We try to address this issue by developing an
algorithm for solar contamination removal (AlgoSCR) from the radio data. The
algorithm is based on an eigenvalue analysis of the visibility matrix, and
hence is applicable only to interferometers. We apply AlgoSCR to simulated
visibilities, as well as real daytime data from the Tianlai dish array. The
algorithm can remove most of the solar contamination without seriously
affecting other sky signals and thus makes the data usable for certain
applications.


Abstract:
  Support Vector Machines (SVMs) are one of the most popular supervised
learning models to classify using a hyperplane in an Euclidean space. Similar
to SVMs, tropical SVMs classify data points using a tropical hyperplane under
the tropical metric with the max-plus algebra. In this paper, first we show
generalization error bounds of tropical SVMs over the tropical projective
space. While the generalization error bounds attained via VC dimensions in a
distribution-free manner still depend on the dimension, we also show
theoretically by extreme value statistics that the tropical SVMs for
classifying data points from two Gaussian distributions as well as empirical
data sets of different neuron types are fairly robust against the curse of
dimensionality. Extreme value statistics also underlie the anomalous scaling
behaviors of the tropical distance between random vectors with additional noise
dimensions. Finally, we define tropical SVMs over a function space with the
tropical metric and discuss the Gaussian function space as an example.


Abstract:
  We search for triangular numbers that are multiples of other triangular
numbers. It is found that for any positive non-square integer multiplier, there
is an infinity of multiples of triangular numbers that are triangular numbers
and recurrent relations are deduced theoretically. If the multiplier is a
squared integer, there is either one or no solution, depending on the
multiplier value.


Abstract:
  Due to the system scaling, transient errors caused by external noises, e.g.,
heat fluxes and particle strikes, have become a growing concern for the current
and upcoming extreme-scale high-performance-computing (HPC) systems. However,
since such errors are still quite rare as compared to no-fault cases, desirable
solutions call for low/no-overhead systems that do not compromise the
performance under no-fault conditions and also allow very fast fault recovery
to minimize downtime. In this paper, we present IterPro, a light-weight
compiler-assisted resilience technique to quickly and accurately recover
processes from transient-error-induced crashes. IterPro repairs the corrupted
process states on-the-fly upon occurrences of errors, enabling applications to
continue their executions instead of being terminated. IterPro also exploits
side effects introduced by induction variable based code optimization
techniques to improve its recovery capability. To this end, two new code
transformation passes are introduced to expose the side effects for resilience
purposes. We evaluated IterPro with 4 scientific workloads as well as the NPB
benchmarks suite. During their normal execution, IterPro incurs almost zero
runtime overhead and a small, fixed 27MB memory overhead. Meanwhile, IterPro
can recover on an average 83.55% of crash-causing errors within dozens of
milliseconds with negligible downtime. With such an effective recovery
mechanism, IterPro could tremendously mitigate the overheads and resource
requirements of the resilience subsystem in future extreme-scale systems.


Abstract:
  The framework of database repairs and consistent answers to queries is a
principled approach to managing inconsistent databases. We describe the first
system able to compute the consistent answers of general aggregation queries
with the COUNT(A), COUNT(*), SUM(A), MIN(A), and MAX(A) operators, and with or
without grouping constructs. Our system uses reductions to optimization
versions of Boolean satisfiability (SAT) and then leverages powerful SAT
solvers. We carry out an extensive set of experiments on both synthetic and
real-world data that demonstrate the usefulness and scalability of this
approach.


Abstract:
  We consider the Max-Cut problem. Let $G = (V,E)$ be a graph with adjacency
matrix $(a_{ij})_{i,j=1}^{n}$. Burer, Monteiro & Zhang proposed to find, for
$n$ angles $\left\{\theta_1, \theta_2, \dots, \theta_n\right\} \subset [0,
2\pi]$, minima of the energy $$ f(\theta_1, \dots, \theta_n) = \sum_{i,j=1}^{n}
a_{ij} \cos{(\theta_i - \theta_j)}$$ because configurations achieving a global
minimum leads to a partition of size 0.878 Max-Cut(G). This approach is known
to be computationally viable and leads to very good results in practice. We
prove that by replacing $\cos{(\theta_i - \theta_j)}$ with an explicit function
$g_{\varepsilon}(\theta_i - \theta_j)$ global minima of this new functional
lead to a $(1-\varepsilon)$Max-Cut(G). This suggests some interesting
algorithms that perform well. It also shows that the problem of finding
approximate global minima of energy functionals of this type is NP-hard in
general.


Abstract:
  Reducing the formation of cracks during growth of GaInP/GaInAs/Ge 3-junction
solar cells on Ge|Si virtual substrates has been attempted by thinning the
structure, namely the Ge bottom cell and the GaInAs middle cell. The
theoretical analysis performed using realistic device parameters indicates that
the GaInAs middle cell can be drastically thinned to 1000 nm while increasing
its In content to 8% with an efficiency loss in the 3-junction cell below 3%.
The experimental results show that the formation of macroscopic cracks is
prevented in thinned GaInAs/Ge 2-junction and GaInP/GaInAs/Ge 3-junction cells.
These prototype crack-free multijunction cells demonstrate the concept and were
used to rule out any possible component integration issue. The performance
metrics are limited by the high threading dislocation density over 2e7cm-2 in
the virtual substrates used, but an almost current matched, crack-free, thinned
3-junction solar cell is demonstrated, and the pathway towards solar cells with
higher voltages identified.


Abstract:
  Supertree methods are tree reconstruction techniques that combine several
smaller gene trees (possibly on different sets of species) to build a larger
species tree. The question of interest is whether the reconstructed supertree
converges to the true species tree as the number of gene trees increases (that
is, the consistency of supertree methods). In this paper, we are particularly
interested in the convergence rate of the maximum likelihood supertree.
Previous studies on the maximum likelihood supertree approach often formulate
the question of interest as a discrete problem and focus on reconstructing the
correct topology of the species tree. Aiming to reconstruct both the topology
and the branch lengths of the species tree, we propose an analytic approach for
analyzing the convergence of the maximum likelihood supertree method.
Specifically, we consider each tree as one point of a metric space and prove
that the distance between the maximum likelihood supertree and the species tree
converges to zero at a polynomial rate under some mild conditions. We further
verify these conditions for the popular exponential error model of gene trees.


Abstract:
  Semantic segmentation in autonomous driving predominantly focuses on learning
from large-scale data with a closed set of known classes without considering
unknown objects. Motivated by safety reasons, we address the video class
agnostic segmentation task, which considers unknown objects outside the closed
set of known classes in our training data. We propose a novel auxiliary
contrastive loss to learn the segmentation of known classes and unknown
objects. Unlike previous work in contrastive learning that samples the anchor,
positive and negative examples on an image level, our contrastive learning
method leverages pixel-wise semantic and temporal guidance. We conduct
experiments on Cityscapes-VPS by withholding four classes from training and
show an improvement gain for both known and unknown objects segmentation with
the auxiliary contrastive loss. We further release a large-scale synthetic
dataset for different autonomous driving scenarios that includes distinct and
rare unknown objects. We conduct experiments on the full synthetic dataset and
a reduced small-scale version, and show how contrastive learning is more
effective in small scale datasets. Our proposed models, dataset, and code will
be released at https://github.com/MSiam/video_class_agnostic_segmentation.


Abstract:
  Modern applications, such as social networking systems and e-commerce
platforms are centered around using large-scale storage systems for storing and
retrieving data. In the presence of concurrent accesses, these storage systems
trade off isolation for performance. The weaker the isolation level, the more
behaviors a storage system is allowed to exhibit and it is up to the developer
to ensure that their application can tolerate those behaviors. However, these
weak behaviors only occur rarely in practice, that too outside the control of
the application, making it difficult for developers to test the robustness of
their code against weak isolation levels.
  This paper presents MonkeyDB, a mock storage system for testing
storage-backed applications. MonkeyDB supports a Key-Value interface as well as
SQL queries under multiple isolation levels. It uses a logical specification of
the isolation level to compute, on a read operation, the set of all possible
return values. MonkeyDB then returns a value randomly from this set. We show
that MonkeyDB provides good coverage of weak behaviors, which is complete in
the limit. We test a variety of applications for assertions that fail only
under weak isolation. MonkeyDB is able to break each of those assertions in a
small number of attempts.


Abstract:
  The COVID-19 disease spreads swiftly, and nearly three months after the first
positive case was confirmed in China, Coronavirus started to spread all over
the United States. Some states and counties reported high number of positive
cases and deaths, while some reported lower COVID-19 related cases and
mortality. In this paper, the factors that could affect the risk of COVID-19
infection and mortality were analyzed in county level. An innovative method by
using K-means clustering and several classification models is utilized to
determine the most critical factors. Results showed that mean temperature,
percent of people below poverty, percent of adults with obesity, air pressure,
population density, wind speed, longitude, and percent of uninsured people were
the most significant attributes


Abstract:
  Gravitational-wave sources can serve as standard sirens to probe cosmology by
measuring their luminosity distance and redshift. Such standard sirens are also
useful to probe theories beyond general relativity with a modified
gravitational-wave propagation. Many of previous studies on the latter assume
multi-messenger observations so that the luminosity distance can be measured
with gravitational waves while the redshift is obtained by identifying sources'
host galaxies from electromagnetic counterparts. Given that gravitational-wave
events of binary neutron star coalescences with associated electromagnetic
counterpart detections are expected to be rather rare, it is important to
examine the possibility of using standard sirens with gravitational-wave
observations alone to probe gravity. In this paper, we achieve this by
extracting the redshift from the tidal measurement of binary neutron stars that
was originally proposed within the context of gravitational-wave cosmology
(another approach is to correlate "dark sirens" with galaxy catalogs that we do
not consider here). We consider not only observations with ground-based
detectors (e.g. Einstein Telescope) but also multi-band observations between
ground-based and space-based (e.g. DECIGO) interferometers. We find that such
multi-band observations with the tidal information can constrain a parametric
non-Einsteinian deviation in the luminosity distance (due to the modified
friction in the gravitational wave evolution) more stringently than the case
with electromagnetic counterparts by a factor of a few. We also map the
above-projected constraints on the parametric deviation to those on specific
theories and phenomenological models beyond general relativity to put the
former into context.


Abstract:
  Galaxy groups host the majority of matter and more than half of all the
galaxies in the Universe. Their hot ($10^7$ K), X-ray emitting intra-group
medium (IGrM) reveals emission lines typical of many elements synthesized by
stars and supernovae. Because their gravitational potentials are shallower than
those of rich galaxy clusters, groups are ideal targets for studying, through
X-ray observations, feedback effects, which leave important marks on their gas
and metal contents. Here, we review the history and present status of the
chemical abundances in the IGrM probed by X-ray spectroscopy. We discuss the
limitations of our current knowledge, in particular due to uncertainties in the
modeling of the Fe-L shell by plasma codes, and coverage of the volume beyond
the central region. We further summarize the constraints on the abundance
pattern at the group mass scale and the insight it provides to the history of
chemical enrichment. Parallel to the observational efforts, we review the
progress made by both cosmological hydrodynamical simulations and controlled
high-resolution 3D simulations to reproduce the radial distribution of metals
in the IGrM, the dependence on system mass from group to cluster scales, and
the role of AGN and SN feedback in producing the observed phenomenology.
Finally, we highlight future prospects in this field, where progress will be
driven both by a much richer sample of X-ray emitting groups identified with
eROSITA, and by a revolution in the study of X-ray spectra expected from
micro-calorimeters onboard XRISM and ATHENA.


Abstract:
  Vaccines are an important public health measure, but vaccine hesitancy and
refusal can create clusters of low vaccine coverage and reduce the
effectiveness of vaccination programs. Social media provides an opportunity to
estimate emerging risks to vaccine acceptance by including geographical
location and detailing vaccine-related concerns. Methods for classifying social
media posts, such as vaccine-related tweets, use language models (LMs) trained
on general domain text. However, challenges to measuring vaccine sentiment at
scale arise from the absence of tonal stress and gestural cues and may not
always have additional information about the user, e.g., past tweets or social
connections. Another challenge in LMs is the lack of commonsense knowledge that
are apparent in users metadata, i.e., emoticons, positive and negative words
etc. In this study, to classify vaccine sentiment tweets with limited
information, we present a novel end-to-end framework consisting of
interconnected components that use domain-specific LM trained on
vaccine-related tweets and models commonsense knowledge into a bidirectional
gated recurrent network (CK-BiGRU) with context-aware attention. We further
leverage syntactical, user metadata and sentiment information to capture the
sentiment of a tweet. We experimented using two popular vaccine-related Twitter
datasets and demonstrate that our proposed approach outperforms
state-of-the-art models in identifying pro-vaccine, anti-vaccine and neutral
tweets.


Abstract:
  Opacity is an information flow property that captures the notion of plausible
deniability in dynamic systems, that is whether an intruder can deduce that
"secret" behavior has occurred. In this paper we provide a general framework of
opacity to unify the many existing notions of opacity that exist for discrete
event systems. We use this framework to discuss language-based and state-based
notions of opacity over automata. We present several methods for language-based
opacity verification, and a general approach to transform state-based notions
into language-based ones. We demonstrate this approach for current-state and
initial-state opacity, unifying existing results. We then investigate the
notions of K-step opacity. We provide a language-based view of K-step opacity
encompassing two existing notions and two new ones. We then analyze the
corresponding language-based verification methods both formally and with
numerical examples. In each case, the proposed methods offer significant
reductions in runtime and space complexity.


Abstract:
  The numerical integration of an analytical function $f(x)$ using a finite set
of equidistant points can be performed by quadrature formulas like the
Newton-Cotes. Unlike Gaussian quadrature formulas however, higher-order
Newton-Cotes formulas are not stable, limiting the usable order of such
formulas. Existing work showed that by the use of orthogonal polynomials,
stable high-order quadrature formulas with equidistant points can be developed.
We improve upon such work by making use of (orthogonal) Gram polynomials and
deriving an iterative algorithm, together allowing us to reduce the
space-complexity of the original algorithm significantly.


Abstract:
  Predicting customer future purchases and lifetime value is a key metrics for
managing marketing campaigns and optimizing marketing spend. This task is
specifically challenging when the relationships between the customer and the
firm are of a noncontractual nature and therefore the future purchases need to
be predicted based mostly on historical purchases. This work compares two
approaches to predict customer future purchases, first using a
'buy-till-you-die' statistical model to predict customer behavior and later
using a neural network on the same dataset and comparing the results. This
comparison will lead to both quantitative and qualitative analysis of those two
methods as well as recommendation on how to proceed in different cases and
opportunities for future research.


Abstract:
  Human affects are complex paradox and an active research domain in affective
computing. Affects are traditionally determined through a self-report based
psychometric questionnaire or through facial expression recognition. However,
few state-of-the-arts pieces of research have shown the possibilities of
recognizing human affects from psychophysiological and neurological signals. In
this article, electroencephalogram (EEG) signals are used to recognize human
affects. The electroencephalogram (EEG) of 100 participants are collected where
they are given to watch one-minute video stimuli to induce different affective
states. The videos with emotional tags have a variety range of affects
including happy, sad, disgust, and peaceful. The experimental stimuli are
collected and analyzed intensively. The interrelationship between the EEG
signal frequencies and the ratings given by the participants are taken into
consideration for classifying affective states. Advanced feature extraction
techniques are applied along with the statistical features to prepare a fused
feature vector of affective state recognition. Factor analysis methods are also
applied to select discriminative features. Finally, several popular supervised
machine learning classifier is applied to recognize different affective states
from the discriminative feature vector. Based on the experiment, the designed
random forest classifier produces 89.06% accuracy in classifying four basic
affective states.


Abstract:
  Nonlinear light-matter interactions in structured materials are the source of
exciting properties and enable vanguard applications in photonics. However, the
magnitude of nonlinear effects is generally small, thus requiring high optical
intensities for their manifestation at the nanoscale. Here, we reveal a large
nonlinear response of monolayer hexagonal boron nitride (hBN) in the
mid-infrared phonon-polariton region, triggered by the strongly anharmonic
potential associated with atomic vibrations in this material. We present robust
first-principles theory predicting a threshold light field $\sim40\,$MV/m to
produce order-unity effects in Kerr nonlinearities and harmonic generation,
which are made possible by a combination of the long lifetimes exhibited by
optical phonons and the strongly asymmetric landscape of the configuration
energy in hBN. We further foresee polariton blockade at the few-quanta level in
nanometer-sized structures. In addition, by mixing static and optical fields,
the strong nonlinear response of monolayer hBN gives rise to substantial
frequency shifts of optical phonon modes, exceeding their spectral width for
in-plane DC fields that are attainable using lateral gating technology. We
therefore predict a practical scheme for electrical tunability of the
vibrational modes with potential interest in mid-infrared optoelectronics. The
strong nonlinear response, low damping, and robustness of hBN polaritons set
the stage for the development of applications in light modulation, sensing, and
metrology, while triggering the search for intense vibrational nonlinear
response in other ionic materials.


Abstract:
  The present paper is devoted to the description of local and 2-local
automorphisms on Cayley algebras over an arbitrary field $\mathbb{F}$. Given a
Cayley algebra $\mathcal{C}$ with norm $n$, let $O(\mathcal{C},n)$ be the
corresponding orthogonal group. We prove that the group of all local
automorphisms of $\mathcal{C}$ coincides with the group $\{\varphi\in
O(\mathcal{C},n)\mid \varphi(1)=1\}.$ Further we prove that the behavior of
2-local automorphisms depends on the Cayley algebra being split or division.
Every 2-local automorphism on the split Cayley algebra is an automorphism, i.e.
they form the exceptional Lie group $G_2(\mathbb{F})$ if
$\textrm{char}\mathbb{F}\neq 2,3$. On the other hand, on division Cayley
algebras over a field $\mathbb{F}$, the groups of 2-local automorphisms and
local automorphisms coincide, and they are isomorphic to the group
$\{\varphi\in O(\mathcal{C},n)\mid \varphi(1)=1\}.$


Abstract:
  At the fundamental level, quantum communication is ultimately limited by
noise. For instance, quantum signals cannot be amplified without the
introduction of noise in the amplified states. Furthermore, photon loss reduces
the signal-to-noise ratio, accentuating the effect of noise. Thus, most of the
efforts in quantum communications have been directed towards overcoming noise
to achieve longer communication distances, larger secret key rates, or to
operate in noisier environmental conditions. Here, we propose and
experimentally demonstrate a platform for quantum communication based on
ultrafast optical techniques. In particular, our scheme enables the
experimental realization of high-rates and quantum signal filtering approaching
a single spectro-temporal mode, resulting in a dramatic reduction in channel
noise. By experimentally realizing a 1-ps optically induced temporal gate, we
show that ultrafast time filtering can result in an improvement in noise
tolerance by a factor of up to 1200 compared to a 2-ns electronic filter
enabling daytime quantum key distribution or quantum communication in bright
fibers.


Abstract:
  Big data is gaining overwhelming attention since the last decade. Almost all
the fields of science and technology have experienced a considerable impact
from it. The cloud computing paradigm has been targeted for big data processing
and mining in a more efficient manner using the plethora of resources available
from computing nodes to efficient storage. Cloud data mining introduces the
concept of performing data mining and analytics of huge data in the cloud
availing the cloud resources. But can we do better? Yes, of course! The main
contribution of this chapter is the identification of four game-changing
technologies for the acceleration of computing and analysis of data mining
tasks in the cloud. Graphics Processing Units can be used to further accelerate
the mining or analytic process, which is called GPU accelerated analytics.
Further, Approximate Computing can also be introduced in big data analytics for
bringing efficacy in the process by reducing time and energy and hence
facilitating greenness in the entire computing process. Quantum Computing is a
paradigm that is gaining pace in recent times which can also facilitate
efficient and fast big data analytics in very little time. We have surveyed
these three technologies and established their importance in big data mining
with a holistic architecture by combining these three game-changers with the
perspective of big data. We have also talked about another future technology,
i.e., Neural Processing Units or Neural accelerators for researchers to explore
the possibilities. A brief explanation of big data and cloud data mining
concepts are also presented here.


Abstract:
  Ferromagnetic superconductor URhGe has orthorhombic structure and possesses
spontaneous magnetisation along the c-axis. Magnetic field directed along the
$b$-axis suppresses ferromagnetism in $c$-direction and leads to a metamagnetic
transition into polarised paramagnetic state in the $b$-direction. The theory
of these phenomena based on the specific magnetic anisotropy of this material
in $(b,c)$ plane is given. Line of the first order metamagnetic transition ends
at a critical point. The Van der Waals - type description of behaviour of
physical properties near this point is developed. The triplet superconducting
state destroyed by orbital effect is recreated in vicinity of the transition.
It is shown that the reentrance of superconductivity is caused by the sharp
increase of magnetic susceptibility in $b$ direction near the metamagnetic
transition. The specific behaviour of the upper critical field in direction of
spontaneous magnetisation in UCoGe and in UGe$_2$ related to the field
dependence of magnetic susceptibility is discussed.


Abstract:
  Let $p$ be a prime number, and let $k$ be an algebraically closed field of
characteristic $p$. We show that the tame fundamental group of a smooth affine
curve over $k$ is a projective profinite group. We prove that the fundamental
group of a smooth projective variety over $k$ is finitely presented. More
generally we prove that the tame fundamental group of a smooth quasi-projective
variety over $k$ which admits a good compactification is finitely presented.
  v2: references added. Thank you to all for the friendly and fruitful
comments.


Abstract:
  Community question answering and discussion platforms such as Reddit, Yahoo!
answers or Quora provide users the flexibility of asking open ended questions
to a large audience, and replies to such questions maybe useful both to the
user and the community on certain topics such as health, sports or finance.
Given the recent events around COVID-19, some of these platforms have attracted
2000+ questions from users about several aspects associated with the disease.
Given the impact of this disease on general public, in this work we investigate
ways to improve the ranking of user generated answers on COVID-19. We
specifically explore the utility of external technical sources of side
information (such as CDC guidelines or WHO FAQs) in improving answer ranking on
such platforms. We found that ranking user answers based on question-answer
similarity is not sufficient, and existing models cannot effectively exploit
external (side) information. In this work, we demonstrate the effectiveness of
different attention based neural models that can directly exploit side
information available in technical documents or verified forums (e.g., research
publications on COVID-19 or WHO website). Augmented with a temperature
mechanism, the attention based neural models can selectively determine the
relevance of side information for a given user question, while ranking answers.


Abstract:
  To celebrate Hans Frauenfelder's achievements, we examine energy(-like)
"landscapes" for complex living systems. Energy landscapes summarize all
possible dynamics of some physical systems. Energy(-like) landscapes can
explain some biomolecular processes, including gene expression and, as
Frauenfelder showed, protein folding. But energy-like landscapes and existing
frameworks like statistical mechanics seem impractical for describing many
living systems. Difficulties stem from living systems being high dimensional,
nonlinear, and governed by many, tightly coupled constituents that are noisy.
The predominant modeling approach is devising differential equations that are
tailored to each living system. This ad hoc approach faces the notorious
"parameter problem": models have numerous nonlinear, mathematical functions
with unknown parameter values, even for describing just a few intracellular
processes. One cannot measure many intracellular parameters or can only measure
them as snapshots in time. Another modeling approach uses cellular automata to
represent living systems as discrete dynamical systems with binary variables.
Quantitative (Hamiltonian-based) rules can dictate cellular automata (e.g.,
Cellular Potts Model). But numerous biological features, in current practice,
are qualitatively described rather than quantitatively (e.g., gene is (highly)
expressed or not (highly) expressed). Cellular automata governed by verbal
rules are useful representations for living systems and can mitigate the
parameter problem. However, they can yield complex dynamics that are difficult
to understand because much of the existing mathematical tools and theorems
apply to continuous but not discrete dynamical systems. Recent studies found
ways to overcome this challenge by discovering a predictive "landscape" that
yield low-dimensional representations of cellular automata dynamics. We review
these studies.


Abstract:
  The second phase of the APOGEE survey is providing near-infrared,
high-resolution, high signal-to-noise spectra of stars in the halo, disk, bar
and bulge of the Milky Way. The near-infrared spectral window is especially
important in the study of the Galactic bulge, where stars are obscured by the
dust and gas of the disk in its line-of-sight. We present a chemical
characterisation of the globular cluster NGC 6544 with high-resolution
spectroscopy. The characterisation of the cluster chemical fingerprint, given
its status of "interloper" towards the Galactic bulge and clear signatures of
tidal disruption in its core is crucial for future chemical tagging efforts.
Cluster members were selected from the DR16 of the APOGEE survey, using
chemo-dynamical criteria of individual stars. A sample of 23 members of the
cluster was selected. An analysis considering the intra-cluster abundance
variations, known anticorrelations is given. According to the RGB content of
the cluster, the iron content and $\alpha$-enhancement are [Fe/H] $= -1.44 \pm
0.04$ dex and [$\alpha$/Fe] $= 0.20 \pm 0.04$ dex, respectively. Cluster
members show a significant spread in [Fe/H] and [Al/Fe] that is larger than
expected based on measurement errors. An [Al/Fe] spread, signal of an Mg-Al
anticorrelation is observed and used to constraint the cluster mass budget,
along with C, N, Mg, Si, K, Ca, and Ce element variations are discussed. Across
all the analysed evolutionary stages (RGB and AGB), about $\sim2/3$ (14 out of
23) show distinct chemical patterns, possibly associated with second-generation
stars.


Abstract:
  The transition to a low-carbon economy is one of the ambitions of the
European Union for 2030. Biobased industries play an essential role in this
transition. However, there has been an on-going discussion about the actual
benefit of using biomass to produce biobased products, specifically the use of
agricultural materials (e.g., corn and sugarcane). This paper presents the
environmental impact assessment of 30% and 100% biobased PET (polyethylene
terephthalate) production using EU biomass supply chains (e.g., sugar beet,
wheat, and Miscanthus). An integral assessment between the life cycle
assessment methodology and the global sensitivity assessment is presented as an
early-stage support tool to propose and select supply chains that improve the
environmental performance of biobased PET production. From the results,
Miscanthus is the best option for the production of biobased PET: promoting EU
local supply chains, reducing greenhouse gas (GHG) emissions (process and
land-use change), and generating lower impacts in midpoint categories related
to resource depletion, ecosystem quality, and human health. This tool can help
improving the environmental performance of processes that could boost the shift
to a low-carbon economy.


Abstract:
  We demonstrate a low noise short wavelength infrared (SWIR) Sb based type II
superlattice (T2SL) avalanche photodiodes (APD). The SWIR GaSb/(AlAsSb/GaSb)
APD structure was designed based on impact ionization engineering and grown by
molecular beam epitaxy on GaSb substrate. At room temperature, the device
exhibits a 50 % cut-off wavelength of 1.74 micron. The device revealed to have
electron dominated avalanching mechanism with a gain value of 48 at room
temperature. The electron and hole impact ionization coefficients were
calculated and compared to give better prospect of the performance of the
device. Low excess noise, as characterized by the carrier ionization ratio of ~
0.07, has been achieved.


Abstract:
  Multilingual pretrained language models have demonstrated remarkable
zero-shot cross-lingual transfer capabilities. Such transfer emerges by
fine-tuning on a task of interest in one language and evaluating on a distinct
language, not seen during the fine-tuning. Despite promising results, we still
lack a proper understanding of the source of this transfer. Using a novel layer
ablation technique and analyses of the model's internal representations, we
show that multilingual BERT, a popular multilingual language model, can be
viewed as the stacking of two sub-networks: a multilingual encoder followed by
a task-specific language-agnostic predictor. While the encoder is crucial for
cross-lingual transfer and remains mostly unchanged during fine-tuning, the
task predictor has little importance on the transfer and can be reinitialized
during fine-tuning. We present extensive experiments with three distinct tasks,
seventeen typologically diverse languages and multiple domains to support our
hypothesis.


Abstract:
  We present a thermodynamics experiment suitable for first year undergraduate
students employing Stirling Engines to create a demonstration of energy
transformation and to measure the mechanical efficiency of such engines. Using
an inexpensive transparent chambered Stirling Engine, students can connect
concepts such as the theoretical pressure-volume diagram with the physical
movements of the engine's pistons and the resultant useful output work of a
spinning wheel. We found the majority of students successfully complete this
experiment obtaining results similar to when performed by the authors. In
addition to the core thermodynamics lesson, this experiment incorporates DC
circuits, oscilloscopes, and data analysis so it can be integrated into a wider
undergraduate physics course to combine the teaching of multiple subjects.


Abstract:
  Video super-resolution has recently become one of the most important
mobile-related problems due to the rise of video communication and streaming
services. While many solutions have been proposed for this task, the majority
of them are too computationally expensive to run on portable devices with
limited hardware resources. To address this problem, we introduce the first
Mobile AI challenge, where the target is to develop an end-to-end deep
learning-based video super-resolution solutions that can achieve a real-time
performance on mobile GPUs. The participants were provided with the REDS
dataset and trained their models to do an efficient 4X video upscaling. The
runtime of all models was evaluated on the OPPO Find X2 smartphone with the
Snapdragon 865 SoC capable of accelerating floating-point networks on its
Adreno GPU. The proposed solutions are fully compatible with any mobile GPU and
can upscale videos to HD resolution at up to 80 FPS while demonstrating high
fidelity results. A detailed description of all models developed in the
challenge is provided in this paper.


Abstract:
  We investigated magnetic textures in a Sc-doped hexaferrite film by means of
phase microscopy (PM) with a hole-free phase plate in a transmission electron
microscope. In a zero magnetic field, the stripe-shaped magnetic domains
coexist with magnetic bubbles. The magnetization in both magnetic domains was
oriented perpendicular to the film and the domain walls have an in-plane
magnetization. In the remnant state at 9.2 mT, several magnetic bubbles were
formed with the formation of stripe-shaped magnetic domains, and the
out-of-plane component in the stripe-shaped domains gradually appeared as the
film thickness increased. As the film thickness increases further, the magnetic
bubbles with clockwise or counter-clockwise spin helicities formed a triangular
lattice. These results in the remnant state suggest that the domain wall energy
in the magnetic bubble domains is lower in the thicker region.


Abstract:
  This paper addresses the use of data-driven evolving techniques applied to
fault prognostics. In such problems, accurate predictions of multiple steps
ahead are essential for the Remaining Useful Life (RUL) estimation of a given
asset. The fault prognostics' solutions must be able to model the typical
nonlinear behavior of the degradation processes of these assets, and be
adaptable to each unit's particularities. In this context, the Evolving Fuzzy
Systems (EFSs) are models capable of representing such behaviors, in addition
of being able to deal with non-stationary behavior, also present in these
problems. Moreover, a methodology to recursively track the model's estimation
error is presented as a way to quantify uncertainties that are propagated in
the long-term predictions. The well-established NASA's Li-ion batteries data
set is used to evaluate the models. The experiments indicate that generic EFSs
can take advantage of both historical and stream data to estimate the RUL and
its uncertainty.


Abstract:
  The mutual information is a measure of classical and quantum correlations of
great interest in quantum information. It is also relevant in quantum many-body
physics, by virtue of satisfying an area law for thermal states and bounding
all correlation functions. However, calculating it exactly or approximately is
often challenging in practice. Here, we consider alternative definitions based
on R\'enyi divergences. Their main advantage over their von Neumann counterpart
is that they can be expressed as a variational problem whose cost function can
be efficiently evaluated for families of states like matrix product operators
while preserving all desirable properties of a measure of correlations. In
particular, we show that they obey a thermal area law in great generality, and
that they upper bound all correlation functions. We also investigate their
behavior on certain tensor network states and on classical thermal
distributions.


Abstract:
  A dilute suspension of motile micro-organisms subjected to a strong ambient
flow, such as algae in the ocean, can be modelled as a population of
non-interacting, orientable active Brownian particles (ABPs). Using the
Smoluchowski equation (i.e. Fokker-Planck equation in space and orientation),
one can describe the non-trivial transport phenomena of ABPs such as taxes and
shear-induced migration. This work transforms the Smoluchowski equation into a
transport equation, in which the drifts and dispersions can be further
approximated as a function of the local flow field. The new model can be
applied to any global flow field due to its local nature, unlike previous
methods such as those utilising the generalised Taylor dispersion theory. The
transformation shows that the overall drift includes both the biased motility
of individual particles in the presence of taxis and the shear-induced
migration in the absence of taxis. In addition, it uncovers other new drifts
and dispersions caused by the interactions between the orientational dynamics
and the passive advection/diffusion of ABPs. Finally, the performance of this
model is assessed using examples of gyrotactic suspensions, where the proposed
model is demonstrated to be most accurate when the biased motility of particles
(i.e. taxis) is weak.


Abstract:
  With the rapid advancement of information and communication technologies,
many researchers have adopted alternative data sources from private data
vendors to study human movement dynamics in response to large-scale natural or
societal events. Big geosocial data such as georeferenced tweets are publicly
available and dynamically evolving as real-world events are happening, making
it more likely to capture the real-time sentiments and responses of
populations. However, precisely-geolocated geosocial data is scarce and biased
toward urban population centers. In this research, we developed a big geosocial
data analytical framework for extracting human movement dynamics in response to
large-scale events from publicly available georeferenced tweets. The framework
includes a two-stage data collection module that collects data in a more
targeted fashion in order to mitigate the data scarcity issue of georeferenced
tweets; in addition, a variable bandwidth kernel density estimation(VB-KDE)
approach was adopted to fuse georeference information at different spatial
scales, further augmenting the signals of human movement dynamics contained in
georeferenced tweets. To correct for the sampling bias of georeferenced tweets,
we adjusted the number of tweets for different spatial units (e.g., county,
state) by population. To demonstrate the performance of the proposed analytic
framework, we chose an astronomical event that occurred nationwide across the
United States, i.e., the 2017 Great American Eclipse, as an example event and
studied the human movement dynamics in response to this event. However, this
analytic framework can easily be applied to other types of large-scale events
such as hurricanes or earthquakes.


Abstract:
  The execution of similar units can be compared by their internal behaviors to
determine the causes of their potential performance issues. For instance, by
examining the internal behaviors of different fast or slow web requests more
closely and by clustering and comparing their internal executions, one can
determine what causes some requests to run slowly or behave in unexpected ways.
In this paper, we propose a method of extracting the internal behavior of web
requests as well as introduce a pipeline that detects performance issues in web
requests and provides insights into their root causes. First, low-level and
fine-grained information regarding each request is gathered by tracing both the
user space and the kernel space. Second, further information is extracted and
fed into an outlier detector. Finally, these outliers are then clustered by
their behavior, and each group is analyzed separately. Experiments revealed
that this pipeline is indeed able to detect slow web requests and provide
additional insights into their true root causes. Notably, we were able to
identify a real PHP cache contention using the proposed approach.


Abstract:
  The fundamental problem of stabilizing a general non-affine continuous-time
nonlinear system is investigated via piecewise affine linear models (PALMs) in
this paper. A novel integral sliding-mode parallel control (ISMPC) approach is
developed, where an uncertain piecewise affine system (PWA) is constructed to
model a non-affine continuous-time nonlinear system equivalently on a compact
region containing the origin. A piecewise integral sliding-mode parallel
controller is designed to globally stabilize the uncertain PWA and,
consequently, to semi-globally stabilize the original nonlinear system. The
proposed scheme enjoys two favorable features: i) some restrictions on the
system input channel are eliminated, thus the developed method is more relaxed
compared with the published approaches; and ii) it is convenient to be used to
deal with both matched and unmatched uncertainties of the system. Moreover, we
provide discussions about the universality analysis of the developed control
strategy for two kinds of typical nonlinear systems. Simulation results from
two numerical examples further demonstrate the performance of the developed
control approach.


Abstract:
  In this paper we report results of a numerical investigation of turbulent
natural gas combustion for jet in a coflow of lean combustion products in the
Delft-Jet-in-Hot-Coflow (DJHC) burner which emulates MILD (Moderate and Intense
Low Oxygen Dilution) combustion behavior. The focus is on assessing the
performance of the Eddy Dissipation Concept (EDC) model in combination with
two-equation turbulence models and chemical kinetic schemes for about 20
species (Correa mechanism and DRM19 mechanism) by comparing predictions with
experimental measurements. We study two different flame conditions
corresponding to two different oxygen levels (7.6% and 10.9% by mass) in the
hot coflow, and for two jet Reynolds number (Re=4100 and Re=8800). The mean
velocity and turbulent kinetic energy predicted by different turbulence models
are in good agreement with data without exhibiting large differences among the
model predictions. The realizable k-e model exhibits better performance in the
prediction of entrainment. The EDC combustion model predicts too early ignition
leading to a peak in the radial mean temperature profile at too low axial
distance. However the model correctly predicts the experimentally observed
decreasing trend of lift-off height with jet Reynolds number. A detailed
analysis of the mean reaction rate of the EDC model is made and as possible
cause for the deviations between model predictions and experiments a low
turbulent Reynolds number effect is identified. Using modified EDC model
constants prediction of too early ignition can be avoided. The results are
weakly sensitive to the sub-model for laminar viscosity and laminar diffusion
fluxes.


Abstract:
  The automorphism group $Aut(X,\mu)$ of a compact, complete metric space $X$
with a Radon measure $\mu$ is a subgroup of $\mathcal{U}(L^2(X,\mu))$-the
unitary group of operators on $L^2(X,\mu)$. The $Aut(X,\mu)$-action on the
generalized space $\mathcal{M}(X)$ is a proper action. Hence, there exists a
slice at each point of the generalized space $\mathcal{M}(X)$. Measure Groupoid
(virtual group) is subsequently employed to analyze the resulting dynamical
system as that of the ergodic action of the commutative algebra (a lattice)
$C(X)$ on the generalized space $\mathcal{M}(X)$ which is represented on a
commutative von Neumann algebra.


Abstract:
  Drying colloidal droplets have a wide range of applications from medical
diagnostics to coatings for industries. This paper explores the effects of the
substrate temperature (ranging from $25$ to $55 ^{\circ}$C) and various initial
concentrations ($\phi$) of $1$ to $20$ wt% of lysozyme in an aqueous solution
on its drying and final dried film state using bright-field optical microscopy.
The $\phi$ is divided into three regimes, ultra-concentrated ($20$ $<$ $\phi$
$\leq$ $17$ wt%), concentrated ($17$ $<$ $\phi$ $\leq$ $9$ wt%) and diluted
($9$ $<$ $\phi$ $\leq$ $1$ wt%). Increasing $\phi$ in these regimes finds that
this movement in the later non-linear region slows down as the front carries
and deposits protein molecules until the supply in solution is exhausted. In
the ultra-concentrated regime, the fluid front moves linearly throughout the
drying process. The deposition of protein onto the surface by the fluid front
creates the "coffee-ring" and increases with increasing $\phi$. A dimple is
observed in a central mound-structure, which grows with increasing $\phi$. As
the temperature increases the drying rate increases, reducing the time for
convective flow and the deposition of material at the fluid front.
Interestingly, at (T, $\phi$) = ($55 ^{\circ}$C, $20$ wt%), the droplet forms
the thickest film with suppressed ring formation. The dimple diminishes in the
ultra-concentrated regime, whereas it changes to an expanded spot in some
samples of the diluted and concentrated regimes with elevated temperatures.
Both initial concentration and substrate temperature lead to surface tension
and temperature gradients across the droplet, affecting the morphological
patterns. This study provides insights into protein-protein and
protein-substrate interactions driven by temperature and concentration for
biomedical and biosensing applications.


Abstract:
  The recently introduced anomaly-free twistor string in four dimensions is
shown to be defined not just in flat but also in curved twistor space. Further,
arguments are given that the classical limit of the corresponding string field
theory, if it exists, is related to general relativity, in particular to the
Isenberg and Yasskin construction using teleparallel gravity. For spacetimes of
Petrov type D with two shear-free null congruences the construction can be
simplified using two-dimensional twistor manifolds.


Abstract:
  We revisit here congruence relations for B\"uchi automata, which play a
central role in the automata-based verification. The size of the classical
congruence relation is in $3^{\mathcal{O}(n^2)}$, where $n$ is the number of
states of a given B\"uchi automaton $\mathcal{A}$. Here we present improved
congruence relations that can be exponentially coarser than the classical one.
We further give asymptotically optimal congruence relations of size
$2^{\mathcal{O}(n \log n)}$. Based on these optimal congruence relations, we
obtain an optimal translation from B\"uchi automata to a family of
deterministic finite automata (FDFW) that accepts the complementary language.
To the best of our knowledge, our construction is the first direct and optimal
translation from B\"uchi automata to FDFWs.


Abstract:
  COVID-19 has aided the spread of racism, as well as national insecurity,
distrust of immigrants, and general xenophobia, both of which may be linked to
the rise in anti-Asian hate crimes during the pandemic. Coronavirus Disease
2019(COVID19) is thought to have originated in late December 2019 in Wuhan,
China, and quickly spread across the world during the spring months of 2020.
Asian Americans recorded in increase in racially based hate crimes including
physical abuse and intimidation as COVID-19 spread throughout the United
States. This research study was conducted by high school students in the Bay
Area to compare the intention and characteristics of hate crimes against Asian
Americans to hate crimes against African Americans. According to studies of
both victim-related and most offender-related variables, hate crimes against
Asian Americans have been rapidly growing in the United States and vary from
those against African Americans. This leads to an investigation into the racial
disparity between Asian American offenders and those of other races. The nature
and characteristics of hate crimes against Asian Americans are compared to
those of hate crimes against African Americans in our research. According to
studies of all victim-related factors, hate crimes against Asian Americans are
similar to those against African Americans. Hate crimes against Asian
Americans, on the other hand, vary greatly from hate crimes against African
Americans in terms of the offender's ethnicity and all incident-related
variables.


Abstract:
  For a sum of squares domain of finite D'Angelo 1-type at the origin, we show
that the polynomial model obtained from the computation of the Catlin multitype
at the origin of such a domain is likewise a sum of squares domain. We also
prove, under the same finite type assumption, that the multitype is an
invariant of the ideal of holomorphic functions defining the domain. Both
results are proven using Martin Kolar's algorithm for the computation of the
multitype introduced in [13]. Given a sum of squares domain, we rewrite the
Kolar algorithm in terms of ideals of holomorphic functions and also introduce
an approach that explicitly constructs the homogeneous polynomial
transformations used in the algorithm.


Abstract:
  Image Captioning, or the automatic generation of descriptions for images, is
one of the core problems in Computer Vision and has seen considerable progress
using Deep Learning Techniques. We propose to use Inception-ResNet
Convolutional Neural Network as encoder to extract features from images,
Hierarchical Context based Word Embeddings for word representations and a Deep
Stacked Long Short Term Memory network as decoder, in addition to using Image
Data Augmentation to avoid over-fitting. For data Augmentation, we use
Horizontal and Vertical Flipping in addition to Perspective Transformations on
the images. We evaluate our proposed methods with two image captioning
frameworks- Encoder-Decoder and Soft Attention. Evaluation on widely used
metrics have shown that our approach leads to considerable improvement in model
performance.


Abstract:
  Over the last decade, the vector-apodizing phase plate (vAPP) coronagraph has
been developed from concept to on-sky application in many high-contrast imaging
systems on 8-m class telescopes. The vAPP is an geometric-phase patterned
coronagraph that is inherently broadband, and its manufacturing is enabled only
by direct-write technology for liquid-crystal patterns. The vAPP generates two
coronagraphic PSFs that cancel starlight on opposite sides of the point spread
function (PSF) and have opposite circular polarization states. The efficiency,
that is the amount of light in these PSFs, depends on the retardance offset
from half-wave of the liquid-crystal retarder. Using different liquid-crystal
recipes to tune the retardance, different vAPPs operate with high efficiencies
($>96\%$) in the visible and thermal infrared (0.55 $\mu$m to 5 $\mu$m). Since
2015, seven vAPPs have been installed in a total of six different instruments,
including Magellan/MagAO, Magellan/MagAO-X, Subaru/SCExAO, and LBT/LMIRcam.
Using two integral field spectrographs installed on the latter two instruments,
these vAPPs can provide low-resolution spectra (R$\sim$30) between 1 $\mu$m and
5 $\mu$m. We review the design process, development, commissioning, on-sky
performance, and first scientific results of all commissioned vAPPs. We report
on the lessons learned and conclude with perspectives for future developments
and applications.