Abstract:
  Programming digital devices and developing software is an important
professional qualification, which contributes to employment opportunities.
Despite this fact, there is a remarkable shortage in suitable human resources.
In this context, research studies focus on issues of programming didactic,
teaching models, programming paradigms, which are meant to enhance and optimize
programmers' skills. Recent development of brain imaging techniques such as
electroencephalography and the functional magnetic resonance imaging, have
provided additional opportunity for neuroscientists to explore the functional
organization of the human brain. With the use of these techniques, this
research is an approach to supporting learning in the field of learning and
teaching computer programming. On one hand, there is an attempt to connect
theoretical neurosciences with cognitive science; on the other hand, the
obtained research data will contribute to the identification of practices that
can be applied to formal and informal programming education.


Abstract:
  We prove a concentration phenomenon on the empirical eigenvalue distribution
(EED) of the principal submatrix in a random hermitian matrix whose
distribution is invariant under unitary conjugacy; for example, this class
includes GUE (Gaussian Unitary Ensemble) and Wishart matrices. More precisely,
if the EED of the whole matrix converges to some deterministic probability
measure $\mathfrak{m}$, then its fluctuation from the EED of the principal
submatrix, after a rescaling, concentrates at the Rayleigh measure (in general,
a Schwartz distribution) associated with $\mathfrak{m}$ by the Markov--Krein
correspondence. For the proof, we use the moment method with Weingarten
calculus and free probability. At some stage of calculations, the proof
requires a relation between the moments of the Rayleigh measure and free
cumulants of $\mathfrak{m}$. This formula is more or less known, but we provide
a different proof by observing a combinatorial structure of non-crossing
partitions.


Abstract:
  With the ability to transfer and process quantum information, large-scale
quantum networks will enable a suite of fundamentally new applications, from
quantum communications to distributed sensing, metrology, and computing. This
perspective reviews requirements for quantum network nodes and color centers in
diamond as suitable node candidates. We give a brief overview of
state-of-the-art quantum network experiments employing color centers in
diamond, and discuss future research directions, focusing in particular on the
control and coherence of qubits that distribute and store entangled states, and
on efficient spin-photon interfaces. We discuss a route towards large-scale
integrated devices combining color centers in diamond with other photonic
materials and give an outlook towards realistic future quantum network protocol
implementations and applications.


Abstract:
  Due to the large cross section of Ly$\alpha$ photons with hydrogen, Lyman
Alpha Emitters (LAEs) are sensitive to the presence of neutral hydrogen in the
intergalactic medium (IGM) during the Epoch of Reionization (EoR): the period
in the Universe's history where neutral hydrogen in the IGM is ionized. The
correlation of the ionized regions in the IGM with respect to the underlying
intrinsic LAEs has a pronounced effect on the number of observed LAEs and their
apparent clustering. As a result, observations of LAEs during the EoR can be
used as a probe of the EoR morphology. Here we build on previous works where we
parametrize the density-ionisation correlation during the EoR, and study how
the observed number density and angular correlation function (ACF) of LAEs
depends on this parametrization. Using Subaru measurements of the number
density of LAEs and their ACF at z = 6.6, we place constraints on the EoR
morphology. We find that measurements of LAEs at z = 6.6 alone cannot
distinguish between different density-ionization models at $68\%$ credibility.
However, adding information regarding the number density, and ACF, of LAEs at
$z = 6.6$ to 21cm power spectrum measurements using the hydrogen Epoch of
Reionization Array (HERA) at the midpoint of reionization can rule out
uncorrelated and outside-in reionization at $99\%$ credibility.


Abstract:
  The laser intensity dependence of the recoil energies from the Coulomb
explosion of small argon clusters has been investigated by resolving the
contributions of the individual charge states to the ion recoil energy spectra.
Between $10^{14}$ and $10^{15}$ W/cm$^2$ the high-energy tail of the ion energy
spectra changes its shape and develops into the well-known knee feature, which
results from the cluster size distribution, laser focal averaging, and
ionization saturation. Resolving the contributions of the different charge
states to the recoil energies, the experimental data reveal that the basic
assumption of an exploding homogeneously charged sphere cannot be maintained in
general. In fact, the energy spectra of the high-$q$ show distinct gaps in the
yields at low kinetic energies, which hints at more complex radial ion charge
distributions developing during the laser pulse impact.


Abstract:
  Quadrupeds are strong candidates for navigating challenging environments
because of their agile and dynamic designs. This paper presents a methodology
that extends the range of exploration for quadrupedal robots by creating an
end-to-end navigation framework that exploits walking and jumping modes. To
obtain a dynamic jumping maneuver while avoiding obstacles,
dynamically-feasible trajectories are optimized offline through
collocation-based optimization where safety constraints are imposed. Such
optimization schematic allows the robot to jump through window-shaped obstacles
by considering both obstacles in the air and on the ground. The resulted
jumping mode is utilized in an autonomous navigation pipeline that leverages a
search-based global planner and a local planner to enable the robot to reach
the goal location by walking. A state machine together with a decision making
strategy allows the system to switch behaviors between walking around obstacles
or jumping through them. The proposed framework is experimentally deployed and
validated on a quadrupedal robot, a Mini Cheetah, to enable the robot to
autonomously navigate through an environment while avoiding obstacles and
jumping over a maximum height of 13 cm to pass through a window-shaped opening
in order to reach its goal.


Abstract:
  In recent years, several systems have been developed to regulate the spread
of negativity and eliminate aggressive, offensive or abusive contents from the
online platforms. Nevertheless, a limited number of researches carried out to
identify positive, encouraging and supportive contents. In this work, our goal
is to identify whether a social media post/comment contains hope speech or not.
We propose three distinct models to identify hope speech in English, Tamil and
Malayalam language to serve this purpose. To attain this goal, we employed
various machine learning (support vector machine, logistic regression,
ensemble), deep learning (convolutional neural network + long short term
memory) and transformer (m-BERT, Indic-BERT, XLNet, XLM-Roberta) based methods.
Results indicate that XLM-Roberta outdoes all other techniques by gaining a
weighted $f_1$-score of $0.93$, $0.60$ and $0.85$ respectively for English,
Tamil and Malayalam language. Our team has achieved $1^{st}$, $2^{nd}$ and
$1^{st}$ rank in these three tasks respectively.


Abstract:
  Weak field gravitational wave solutions are investigated in Brans-Dicke (BD)
theory in the presence of a cosmological constant. In this setting the
background geometry is not flat but asymptotically de-Sitter. We investigate
the linearised field equations, and their gravitational wave solutions in a
certain gauge choice. We will show that this theory leads to massless scalar
waves as in original BD theory and in contrast to massive BD theory. The
effects of these waves on free particles and their polarization properties are
studied extensively and effects of the cosmological constant is analyzed in
these phenomena in detail. The energy flux of these waves are also discussed in
this background. By analyzing this flux, we obtain a critical distance where
the waves cannot propagate further, which extends Cosmic no Hair Conjecture
(CNC) to BD theory with a cosmological constant.


Abstract:
  This paper explores the impacts of decarbonisation of heat on demand and
subsequently on the generation capacity required to secure against system
adequacy standards. Gas demand is explored as a proxy variable for modelling
the electrification of heating demand in existing housing stock, with a focus
on impacts on timescales of capacity markets (up to four years ahead). The work
considers the systemic changes that electrification of heating could introduce,
including biases that could be introduced if legacy modelling approaches
continue to prevail. Covariates from gas and electrical regression models are
combined to form a novel, time-collapsed system model, with demand-weather
sensitivities determined using lasso-regularized linear regression. It is
shown, using a GB case study with one million domestic heat pump installations
per year, that the sensitivity of electrical system demand to temperature (and
subsequently sensitivities to cold/warm winter seasons) could increase by 50%
following four years of heat demand electrification. A central estimate of 1.75
kW additional peak demand per heat pump is estimated, with variability across
three published heat demand profiles leading to a range of more than 14 GW in
the most extreme cases. It is shown that the legacy approach of scaling
historic demand, as compared to the explicit modelling of heat, could lead to
over-procurement of 0.79 GW due to bias in estimates of additional capacity to
secure. Failure to address this issue could lead to {\pounds}100m overspend on
capacity over ten years.


Abstract:
  The theory of Frobenius groups with Frobenius complements of even order
largely reduces to tractable algebraic number theory. If we consider only
Frobenius complements with an upper bound $s$ on the number of distinct primes
dividing the order of their commutator subgroups, then the proportion of these
with odd order is less than $1/2^{s}$. A positive lower bound is also given.


Abstract:
  Using Gaia EDR3, we study the most spectacular and photogenic cluster of
Ptolemy. After deriving its membership, we identify in its colour-magnitude
diagram a star that definitively decided to straggle and dress in blue. Further
analysis with the FARCE telescope allows us to discover in its light curve the
secret of its rejuvenation, which we gladly share in this paper. This research
is an important contribution to attain the ultimate goal of astronomy as
professed by DJ Format.


Abstract:
  Implied volatility is at the very core of modern finance, notwithstanding
standard option pricing models continue to derive option prices starting from
the joint dynamics of the underlying asset price and the spot volatility. These
models often cause difficulties: no closed formulas for prices, demanding
calibration techniques, unclear maps between spot and implied volatility.
Inspired by the practice of using implied volatility as quoting system for
option prices, models for the joint dynamics of the underlying asset price and
the implied volatility have been proposed to replace standard option pricing
models. Starting from Carr and Sun (2014), we develop a framework based on the
Implied Remaining Variance where minimal conditions for absence of arbitrage
are identified, and smile bubbles are dealt with. The key concepts arising from
the new IRV framework are those of locally consistent dynamics and sandwiched
martingale. Within the new IRV framework, the results of Schweizer and Wissel
(2008b) are reformulated, while those of El Amrani, Jacquier and Martini (2021)
are independently derived.


Abstract:
  We propose new strategies to handle polygonal grids refinement based on
Convolutional Neural Networks (CNNs). We show that CNNs can be successfully
employed to identify correctly the "shape" of a polygonal element so as to
design suitable refinement criteria to be possibly employed within adaptive
refinement strategies. We propose two refinement strategies that exploit the
use of CNNs to classify elements' shape, at a low computational cost. We test
the proposed idea considering two families of finite element methods that
support arbitrarily shaped polygonal elements, namely Polygonal Discontinuous
Galerkin (PolyDG) methods and Virtual Element Methods (VEMs). We demonstrate
that the proposed algorithms can greatly improve the performance of the
discretization schemes both in terms of accuracy and quality of the underlying
grids. Moreover, since the training phase is performed off-line and is
independent of the differential model the overall computational costs are kept
low.


Abstract:
  We study the dynamic sensitivity of the quantum Rabi model, which exhibits
quantum criticality in the finite-component-system case. This dynamic
sensitivity can be detected by introducing an auxiliary two-level atom
far-off-resonantly coupled to the cavity field of the quantum Rabi model. We
find that when the quantum Rabi model goes through the critical point, the
auxiliary atom experiences a sudden decoherence, which can be characterised by
a sharp decay of the Loschmidt echo. Our scheme will provide a reliable way to
observe quantum phase transition in ultrastrongly coupled quantum systems.


Abstract:
  Software fault localization is one of the most expensive, tedious, and
time-consuming activities in program debugging. This activity becomes even much
more challenging in Software Product Line (SPL) systems due to the variability
of failures in SPL systems. These unexpected behaviors are caused by
variability faults which can only be exposed under some combinations of system
features. Although localizing bugs in non-configurable code has been
investigated in-depth, variability fault localization in SPL systems still
remains mostly unexplored. To approach this challenge, we propose a benchmark
for variability fault localization with a large set of 1,570 buggy versions of
six SPL systems and baseline variability fault localization performance
results. Our hope is to engage the community to propose new and better
approaches to the problem of variability fault localization in SPL systems.


Abstract:
  Machine understanding of user utterances in conversational systems is of
utmost importance for enabling engaging and meaningful conversations with
users. Entity Linking (EL) is one of the means of text understanding, with
proven efficacy for various downstream tasks in information retrieval. In this
paper, we study entity linking for conversational systems. To develop a better
understanding of what EL in a conversational setting entails, we analyze a
large number of dialogues from existing conversational datasets and annotate
references to concepts, named entities, and personal entities using
crowdsourcing. Based on the annotated dialogues, we identify the main
characteristics of conversational entity linking. Further, we report on the
performance of traditional EL systems on our Conversational Entity Linking
dataset, ConEL, and present an extension to these methods to better fit the
conversational setting. The resources released with this paper include
annotated datasets, detailed descriptions of crowdsourcing setups, as well as
the annotations produced by various EL systems. These new resources allow for
an investigation of how the role of entities in conversations is different from
that in documents or isolated short text utterances like queries and tweets,
and complement existing conversational datasets.


Abstract:
  Recent work (Xu et al., 2020) has suggested that numeral systems in different
languages are shaped by a functional need for efficient communication in an
information-theoretic sense. Here we take a learning-theoretic approach and
show how efficient communication emerges via reinforcement learning. In our
framework, two artificial agents play a Lewis signaling game where the goal is
to convey a numeral concept. The agents gradually learn to communicate using
reinforcement learning and the resulting numeral systems are shown to be
efficient in the information-theoretic framework of Regier et al. (2015);
Gibson et al. (2017). They are also shown to be similar to human numeral
systems of same type. Our results thus provide a mechanistic explanation via
reinforcement learning of the recent results in Xu et al. (2020) and can
potentially be generalized to other semantic domains.


Abstract:
  Learning an effective representation of 3D point clouds requires a good
metric to measure the discrepancy between two 3D point sets, which is
non-trivial due to their irregularity. Most of the previous works resort to
using the Chamfer discrepancy or Earth Mover's distance, but those metrics are
either ineffective in measuring the differences between point clouds or
computationally expensive. In this paper, we conduct a systematic study with
extensive experiments on distance metrics for 3D point clouds. From this study,
we propose to use sliced Wasserstein distance and its variants for learning
representations of 3D point clouds. In addition, we introduce a new algorithm
to estimate sliced Wasserstein distance that guarantees that the estimated
value is close enough to the true one. Experiments show that the sliced
Wasserstein distance and its variants allow the neural network to learn a more
efficient representation compared to the Chamfer discrepancy. We demonstrate
the efficiency of the sliced Wasserstein metric and its variants on several
tasks in 3D computer vision including training a point cloud autoencoder,
generative modeling, transfer learning, and point cloud registration.


Abstract:
  We present a streamlined, slightly modified version, in the two-variable
situation, of a beautiful, but not so well known, theory by B\"{o}gel, already
from the 1930s, on an alternative higher dimensional calculus of real
functions, a double calculus, which includes two-variable extensions of many
classical results from single variable calculus, such as Rolle's theorem,
Lagrange's mean value theorem, Cauchy's mean value theorem, Fermat's extremum
theorem, the first derivative test, and the first and second fundamental
theorems of calculus.


Abstract:
  The technology of dynamic map fusion among networked vehicles has been
developed to enlarge sensing ranges and improve sensing accuracies for
individual vehicles. This paper proposes a federated learning (FL) based
dynamic map fusion framework to achieve high map quality despite unknown
numbers of objects in fields of view (FoVs), various sensing and model
uncertainties, and missing data labels for online learning. The novelty of this
work is threefold: (1) developing a three-stage fusion scheme to predict the
number of objects effectively and to fuse multiple local maps with fidelity
scores; (2) developing an FL algorithm which fine-tunes feature models (i.e.,
representation learning networks for feature extraction) distributively by
aggregating model parameters; (3) developing a knowledge distillation method to
generate FL training labels when data labels are unavailable. The proposed
framework is implemented in the Car Learning to Act (CARLA) simulation
platform. Extensive experimental results are provided to verify the superior
performance and robustness of the developed map fusion and FL schemes.


Abstract:
  The isospin chemical potential region is known as the sign-problem free
region of quantum chromodynamics (QCD). In this paper, we introduce the isospin
chemical potential to the three-dimensional three-state Potts model to mimic
the dense QCD; e.g., the QCD effective model with heavy quarks at finite
density. We call it as QCD-like Potts model. The QCD-like Potts model does not
have the sign problem, but we can expect that it shares some properties with
QCD. Since we can obtain the non-approximated Potts spin configuration at
finite isospin chemical potential where the simple Metropolis algorithm can
work, we perform the persistent homology analysis towards exploring the dense
spatial structure of QCD. We show that the averaged birth-death ratio has the
same information with the Polyakov loop, but the maximum birth-death ratio has
additional information near the phase transition.


Abstract:
  The agricultural sector is still lagging behind from all other sectors in
terms of using the newest technologies. For production, the latest machines are
being introduced and adopted. However, pre-harvest and post-harvest processing
are still done by following traditional methodologies while tracing, storing,
and publishing agricultural data. As a result, farmers are not getting deserved
payment, consumers are not getting enough information before buying their
product, and intermediate person/processors are increasing retail prices. Using
blockchain, smart contracts, and IoT devices, we can fully automate the process
while establishing absolute trust among all these parties. In this research, we
explored the different aspects of using blockchain and smart contracts with the
integration of IoT devices in pre-harvesting and post-harvesting segments of
agriculture. We proposed a system that uses blockchain as the backbone while
IoT devices collect data from the field level, and smart contracts regulate the
interaction among all these contributing parties. The system implementation has
been shown in diagrams and with proper explanations. Gas costs of every
operation have also been attached for a better understanding of the costs. We
also analyzed the system in terms of challenges and advantages. The overall
impact of this research was to show the immutable, available, transparent, and
robustly secure characteristics of blockchain in the field of agriculture while
also emphasizing the vigorous mechanism that the collaboration of blockchain,
smart contract, and IoT presents.


Abstract:
  In multi-robot motion planning (MRMP) the aim is to plan the motion of
several robots operating in a common workspace, while avoiding collisions with
obstacles or with fellow robots. The main contribution of this paper is a
simple construction that serves as a lower bound for the computational cost of
a variety of prevalent MRMP problems. In particular we show that optimal
decoupling of multi-robot motion -- decoupling being a standard approach to
practically addressing MRMP -- is NP-hard. The basic problem for which we
present our construction is monotone MRMP, a restricted and natural MRMP
variant, where robots move one by one to their targets with no intermediate
stops. Observing the hardness of these restricted versions of MRMP is
significant as it guides the search for efficient solutions towards techniques
that can cope with intractable problems. Furthermore, our construction
highlights structural properties of difficult instances, such as the need of
robots to pass through many start and target positions of other robots. These
insights can lead to useful problem relaxations resulting in efficient
solutions and to suitable engineering of workspaces.


Abstract:
  Face Anti-spoofing (FAS) is a challenging problem due to complex serving
scenarios and diverse face presentation attack patterns. Especially when
captured images are low-resolution, blurry, and coming from different domains,
the performance of FAS will degrade significantly. The existing multi-modal FAS
datasets rarely pay attention to the cross-domain problems under deployment
scenarios, which is not conducive to the study of model performance. To solve
these problems, we explore the fine-grained differences between multi-modal
cameras and construct a cross-domain multi-modal FAS dataset under surveillance
scenarios called GREAT-FASD-S. Besides, we propose an Attention based Face
Anti-spoofing network with Feature Augment (AFA) to solve the FAS towards
low-quality face images. It consists of the depthwise separable attention
module (DAM) and the multi-modal based feature augment module (MFAM). Our model
can achieve state-of-the-art performance on the CASIA-SURF dataset and our
proposed GREAT-FASD-S dataset.


Abstract:
  Contrastive self-supervised learning has outperformed supervised pretraining
on many downstream tasks like segmentation and object detection. However,
current methods are still primarily applied to curated datasets like ImageNet.
In this paper, we first study how biases in the dataset affect existing
methods. Our results show that current contrastive approaches work surprisingly
well across: (i) object- versus scene-centric, (ii) uniform versus long-tailed
and (iii) general versus domain-specific datasets. Second, given the generality
of the approach, we try to realize further gains with minor modifications. We
show that learning additional invariances -- through the use of multi-scale
cropping, stronger augmentations and nearest neighbors -- improves the
representations. Finally, we observe that MoCo learns spatially structured
representations when trained with a multi-crop strategy. The representations
can be used for semantic segment retrieval and video instance segmentation
without finetuning. Moreover, the results are on par with specialized models.
We hope this work will serve as a useful study for other researchers. The code
and models are available at
https://github.com/wvangansbeke/Revisiting-Contrastive-SSL.


Abstract:
  Network regression models, where the outcome comprises the valued edge in a
network and the predictors are actor or dyad-level covariates, are used
extensively in the social and biological sciences. Valid inference relies on
accurately modeling the residual dependencies among the relations. Frequently
homogeneity assumptions are placed on the errors which are commonly incorrect
and ignore critical, natural clustering of the actors. In this work, we present
a novel regression modeling framework that models the errors as resulting from
a community-based dependence structure and exploits the subsequent
exchangeability properties of the error distribution to obtain parsimonious
standard errors for regression parameters.


Abstract:
  Major advances in public health have resulted from disease prevention.
However, prevention of a new infectious disease by vaccination or
pharmaceuticals is made difficult by the slow process of vaccine and drug
development. We propose an additional intervention that allows rapid control of
emerging infectious diseases, and can also be used to eradicate diseases that
rely almost exclusively on human-to-human transmission. The intervention is
based on (1) testing every individual for the disease, (2) repeatedly, and (3)
isolation of infected individuals. We show here that at a sufficient rate of
testing, the reproduction number is reduced below 1.0 and the epidemic will
rapidly collapse. The approach does not rely on strong or unrealistic
assumptions about test accuracy, isolation compliance, population structure or
epidemiological parameters, and its success can be monitored in real time by
following the test positivity rate. In addition to the compliance rate and
false negatives, the required rate of testing depends on the design of the
testing regime, with concurrent testing outperforming random sampling. Provided
that results are obtained rapidly, the test frequency required to suppress an
epidemic is monotonic and near-linear with respect to R0, the infectious
period, and the fraction of susceptible individuals. The testing regime is
effective against both early phase and established epidemics, and additive to
other interventions (e.g. contact tracing and social distancing). It is also
robust to failure: any rate of testing reduces the number of infections,
improving both public health and economic conditions. These conclusions are
based on rigorous analysis and simulations of appropriate epidemiological
models. A mass-produced, disposable test that could be used at home would be
ideal, due to the optimal performance of concurrent tests that return immediate
results.


Abstract:
  Online prediction for streaming time series data has practical use for many
real-world applications where downstream decisions depend on accurate forecasts
for the future. Deployment in dynamic environments requires models to adapt
quickly to changing data distributions without overfitting. We propose POLA
(Predicting Online by Learning rate Adaptation) to automatically regulate the
learning rate of recurrent neural network models to adapt to changing time
series patterns across time. POLA meta-learns the learning rate of the
stochastic gradient descent (SGD) algorithm by assimilating the prequential or
interleaved-test-then-train evaluation scheme for online prediction. We
evaluate POLA on two real-world datasets across three commonly-used recurrent
neural network models. POLA demonstrates overall comparable or better
predictive performance over other online prediction methods.


Abstract:
  Quantum systems composed of $N$ distinct particles in $\R^2$ with two-body
contact interactions of TMS type are shown to arise as limits - in the norm
resolvent sense - of Schr\"odinger operators with suitably rescaled potentials.


Abstract:
  We investigate the dynamics of cohesive particles in homogeneous isotropic
turbulence, based on one-way coupled simulations that include Stokes drag,
lubrication, cohesive and direct contact forces. We observe a transient
flocculation phase characterized by a growing average floc size, followed by a
statistically steady equilibrium phase. We analyze the temporal evolution of
floc size and shape due to aggregation, breakage, and deformation. Larger
turbulent shear and weaker cohesive forces yield elongated flocs that are
smaller in size. Flocculation proceeds most rapidly when the fluid and particle
time scales are balanced and a suitably defined Stokes number is \textit{O}(1).
During the transient stage, cohesive forces of intermediate strength produce
flocs of the largest size, as they are strong enough to cause aggregation, but
not so strong as to pull the floc into a compact shape. Small Stokes numbers
and weak turbulence delay the onset of the equilibrium stage. During
equilibrium, stronger cohesive forces yield flocs of larger size. The
equilibrium floc size distribution exhibits a preferred size that depends on
the cohesive number. We observe that flocs are generally elongated by turbulent
stresses before breakage. Flocs of size close to the Kolmogorov length scale
preferentially align themselves with the intermediate strain direction and the
vorticity vector. Flocs of smaller size tend to align themselves with the
extensional strain direction. More generally, flocs are aligned with the
strongest Lagrangian stretching direction. The Kolmogorov scale is seen to
limit floc growth. We propose a new flocculation model with a variable fractal
dimension that predicts the temporal evolution of the floc size and shape.


Abstract:
  In star-forming galaxies, the far-infrared (FIR) and radio-continuum
luminosities obey a tight empirical relation over a large range of
star-formation rates (SFR). To understand the physics, we examine
magneto-hydrodynamic galaxy simulations, which follow the genesis of cosmic ray
(CR) protons at supernovae and their advective and anisotropic diffusive
transport. We show that gravitational collapse of the proto-galaxy drives a
small-scale dynamo, which exponentially amplifies weak seed magnetic fields.
After saturation at small scales, they grow in scale to reach equipartition
with thermal and CR energy densities in Milky Way-mass galaxies. In small
galaxies, the magnetic energy saturates at the turbulent energy while it fails
to reach equipartition with thermal and CR energies. We solve for steady-state
spectra of CR protons, secondary electrons/positrons from hadronic CR-proton
interactions with the interstellar medium, and primary shock-accelerated
electrons at supernovae. The modeled radio-synchrotron emission is dominated by
primary electrons, irradiates the magnetised disc and bulge of our simulated
Milky Way-mass galaxy and weakly traces bubble-shaped magnetically-loaded
outflows. Our star-forming and star-bursting galaxies with saturated magnetic
fields match the global FIR-radio correlation (FRC) across four orders of
magnitude. Its intrinsic scatter arises due to (i) different magnetic
saturation levels that result from different seed magnetic fields, (ii)
different radio synchrotron luminosities for different specific SFRs at fixed
SFR and (iii) a varying radio intensity with galactic inclination. In agreement
with observations, several 100-pc-sized regions within star-forming galaxies
also obey the FRC, while the centres of starbursts exceed the FRC by a
substantial amount.


Abstract:
  Multimodal imaging has transformed neuroscience research. While it presents
unprecedented opportunities, it also imposes serious challenges. Particularly,
it is difficult to combine the merits of the interpretability attributed to a
simple association model with the flexibility achieved by a highly adaptive
nonlinear model. In this article, we propose an orthogonalized kernel debiased
machine learning approach, which is built upon the Neyman orthogonality and a
form of decomposition orthogonality, for multimodal data analysis. We target
the setting that naturally arises in almost all multimodal studies, where there
is a primary modality of interest, plus additional auxiliary modalities. We
establish the root-$N$-consistency and asymptotic normality of the estimated
primary parameter, the semi-parametric estimation efficiency, and the
asymptotic validity of the confidence band of the predicted primary modality
effect. Our proposal enjoys, to a good extent, both model interpretability and
model flexibility. It is also considerably different from the existing
statistical methods for multimodal data integration, as well as the
orthogonality-based methods for high-dimensional inferences. We demonstrate the
efficacy of our method through both simulations and an application to a
multimodal neuroimaging study of Alzheimer's disease.


Abstract:
  The aerial manipulator (AM) is a systematic operational robotic platform in
high standard on algorithm robustness. Directly deploying the algorithms to the
practical system will take numerous trial and error costs and even cause
destructive results. In this paper, a new modular simulation platform is
designed to evaluate aerial manipulation related algorithms before deploying.
In addition, to realize a fully autonomous aerial grasping, a series of
algorithm modules consisting a complete workflow are designed and integrated in
the simulation platform, including perception, planning and control modules.
This framework empowers the AM to autonomously grasp remote targets without
colliding with surrounding obstacles relying only on on-board sensors.
Benefiting from its modular design, this software architecture can be easily
extended with additional algorithms. Finally, several simulations are performed
to verify the effectiveness of the proposed system.


Abstract:
  Efficient machine learning implementations optimized for inference in
hardware have wide-ranging benefits, depending on the application, from lower
inference latency to higher data throughput and reduced energy consumption. Two
popular techniques for reducing computation in neural networks are pruning,
removing insignificant synapses, and quantization, reducing the precision of
the calculations. In this work, we explore the interplay between pruning and
quantization during the training of neural networks for ultra low latency
applications targeting high energy physics use cases. Techniques developed for
this study have potential applications across many other domains. We study
various configurations of pruning during quantization-aware training, which we
term quantization-aware pruning, and the effect of techniques like
regularization, batch normalization, and different pruning schemes on
performance, computational complexity, and information content metrics. We find
that quantization-aware pruning yields more computationally efficient models
than either pruning or quantization alone for our task. Further,
quantization-aware pruning typically performs similar to or better in terms of
computational efficiency compared to other neural architecture search
techniques like Bayesian optimization. Surprisingly, while networks with
different training configurations can have similar performance for the
benchmark application, the information content in the network can vary
significantly, affecting its generalizability.


Abstract:
  Part segmentations provide a rich and detailed part-level description of
objects, but their annotation requires an enormous amount of work. In this
paper, we introduce CGPart, a comprehensive part segmentation dataset that
provides detailed annotations on 3D CAD models, synthetic images, and real test
images. CGPart includes $21$ 3D CAD models covering $5$ vehicle categories,
each with detailed per-mesh part labeling. The average number of parts per
category is $24$, which is larger than any existing datasets for part
segmentation on vehicle objects. By varying the rendering parameters, we make
$168,000$ synthetic images from these CAD models, each with automatically
generated part segmentation ground-truth. We also annotate part segmentations
on $200$ real images for evaluation purposes. To illustrate the value of
CGPart, we apply it to image part segmentation through unsupervised domain
adaptation (UDA). We evaluate several baseline methods by adapting
top-performing UDA algorithms from related tasks to part segmentation.
Moreover, we introduce a new method called Geometric-Matching Guided domain
adaptation (GMG), which leverages the spatial object structure to guide the
knowledge transfer from the synthetic to the real images. Experimental results
demonstrate the advantage of our new algorithm and reveal insights for future
improvement. We will release our data and code.


Abstract:
  We describe Hilbert's spacefilling curve in several different ways: as an
automatic sequence of directions,as a regular and synchronized sequence of
coordinates of lattice points encountered, and as an automatic bitmap image.


Abstract:
  Multi-column dependencies in relational databases come associated with two
different computational tasks. The detection problem is to decide whether a
dependency of a certain type and size holds in a given database, the discovery
problem asks to enumerate all valid dependencies of that type. We settle the
complexity of both of these problems for unique column combinations (UCCs),
functional dependencies (FDs), and inclusion dependencies (INDs). We show that
the detection of UCCs and FDs is W[2]-complete when parameterized by the
solution size. The discovery of inclusion-wise minimal UCCs is proven to be
equivalent under parsimonious reductions to the transversal hypergraph problem
of enumerating the minimal hitting sets of a hypergraph. The discovery of FDs
is equivalent to the simultaneous enumeration of the hitting sets of multiple
input hypergraphs. We further identify the detection of INDs as one of the
first natural W[3]-complete problems. The discovery of maximal INDs is shown to
be equivalent to enumerating the maximal satisfying assignments of
antimonotone, 3-normalized Boolean formulas.


Abstract:
  This paper presents a new joint radar and communication technique based on
the classical stepped frequency radar waveform. The randomization in the
waveform, which is achieved by using permutations of the sequence of frequency
tones, is utilized for data transmission. A new signaling scheme is proposed in
which the mapping between incoming data and waveforms is performed based on an
efficient combinatorial transform called the Lehmer code. Considering the
optimum maximum likelihood (ML) detection, the union bound and the nearest
neighbour approximation on the communication block error probability is derived
for communication in an additive white Gaussian noise (AWGN) channel. The
results are further extended to incorporate the Rician fading channel model, of
which the Rayleigh fading channel model is presented as a special case.
Furthermore, an efficient communication receiver implementation is discussed
based on the Hungarian algorithm which achieves optimum performance with much
less operational complexity when compared to an exhaustive search. From the
radar perspective, two key analytical tools, namely, the ambiguity function
(AF) and the Fisher information matrix are derived. Furthermore, accurate
approximations to the Cramer-Rao lower bounds (CRLBs) on the delay and Doppler
estimation errors are derived based on which the range and velocity estimation
accuracy of the waveform is analysed. Numerical examples are used to highlight
the accuracy of the analysis and to illustrate the performance of the proposed
waveform.


Abstract:
  Given a monogenic function on the quaternionic algebra $\mathbb{H}$, the
Clifford algebra $\mathbb{R}_n$ or the octonionic algebra $\mathbb{O}$ we prove
that $|\nabla^m f|^\alpha$ is subharmonic for some $\alpha>0$ where $\nabla^m
f$ is the $m$-th order gradient of $f$. We find also the optimal value of
$\alpha$. This is generalization of a result of Calderon and Zygmund.


Abstract:
  An algorithm for the unbiased simulation of max-(or min-)id stochastic
processes is developed. Our algorithm only requires the simulation of finite
Poisson random measures and avoids the necessity of computing conditional
distributions of infinite (exponent)measures. Special emphasis is put on the
simulation of exchangeable max-(or min-)id sequences, in particular
exchangeable Sato-frailty sequences. Additionally, exact simulation schemes of
high-dimensional exchangeable exogenous shock models and exchangeable
max-stable sequences are sketched.


Abstract:
  In this paper, we investigate the simplest wormhole solution - the
Ellis-Bronnikov one - in the context of the Asymptotically Safe Gravity (ASG)
at the Planck scale. We work with three models, which employ Ricci scalar,
Kretschmann scalar, and squared Ricci tensor to improve the field equations by
turning the Newton constant into a running coupling constant. For all the
cases, we check the radial energy conditions of the wormhole solution and
compare them with those valid in General Relativity (GR). We verify that
asymptotic safety guarantees that the Ellis-Bronnikov wormhole can satisfy the
radial energy conditions at the throat radius, $r_0$, within an interval of
values of this latter. That is quite different from the result found in GR.
Following, we evaluate the effective radial state parameter, $\omega(r)$, at
$r_0$, showing that the quantum gravitational effects modify Einstein's field
equations in such a way that it is necessary a very exotic source of matter to
generate the wormhole spacetime -- phantom or quintessence-like. That occurs
within some ranges of throat radii, even though the energy conditions are or
not violated there. Finally, we find that, although at $r_0$ we have a
quintessence-like matter, on growing of $r$ we necessarily come across
phantom-like regions. We speculate if such a phantom fluid must always be
present in wormholes in the ASG context or even in more general quantum gravity
scenarios.


Abstract:
  Hydrogen, the smallest and most abundant element in nature, can be
efficiently incorporated within a solid and drastically modify its electronic
state - it has been known to induce novel magnetoelectric effects in complex
perovskites and modulate insulator-to-metal transition in a correlated Mott
oxide. Here we demonstrate that hydrogenation resolves an outstanding challenge
in chalcogenide classes of three-dimensional (3D) topological insulators and
magnets - the control of intrinsic bulk conduction that denies access to
quantum surface transport. With electrons donated by a reversible binding of H+
ions to Te(Se) chalcogens, carrier densities are easily changed by over 10^20
cm^-3, allowing tuning the Fermi level into the bulk bandgap to enter
surface/edge current channels. The hydrogen-tuned topological materials are
stable at room temperature and tunable disregarding bulk size, opening a
breadth of platforms for harnessing emergent topological states.


Abstract:
  In the present work, we propose new tensor Krylov subspace method for ill
posed linear tensor problems such as in color or video image restoration. Those
methods are based on the tensor-tensor discrete cosine transform that gives
fast tensor-tensor product computations. In particular, we will focus on the
tensor discrete cosine versions of GMRES, Golub-Kahan bidiagonalisation and
LSQR methods. The presented numerical tests show that the methods are very fast
and give good accuracies when solving some linear tensor ill-posed problems.


Abstract:
  We explain how logarithmic structures select natural principal components in
an intersection of schemes. These manifest in Chow homology and can be
understood using the geometry of strict transforms under logarithmic blowups.
Our motivation comes from Gromov--Witten theory. The toric contact cycles in
the moduli space of curves parameterize curves that admit a map to a fixed
toric variety with prescribed contact orders. We show that they are
intersections of virtual strict transforms of double ramification cycles in
blowups of the moduli space of curves. We supply a calculation scheme for the
virtual strict transforms, and deduce that toric contact cycles lie in the
tautological ring of the moduli space of curves. This is a higher dimensional
analogue of a result of Faber--Pandharipande. The operational Chow rings of
Artin fans play a basic role, and are shown to be isomorphic to rings of
piecewise polynomials on associated cone complexes. The ingredients in our
analysis are Fulton's blowup formula, Aluffi's formulas for Segre classes of
monomial schemes, piecewise polynomials, and degeneration methods. A model
calculation in toric intersection theory is treated without logarithmic methods
and may be read independently.


Abstract:
  We study stationary Stokes systems in divergence form with piecewise Dini
mean oscillation coefficients and data in a bounded domain containing a finite
number of subdomains with $C^{1,\rm{Dini}}$ boundaries. We prove that if $(u,
p)$ is a weak solution of the system, then $(Du, p)$ is bounded and piecewise
continuous. The corresponding results for stationary Navier-Stokes systems are
also established, from which the Lipschitz regularity of the stationary
$H^1$-weak solution in dimensions $d=2,3,4$ is obtained.


Abstract:
  Levitated nanodiamonds containing negatively charged nitrogen vacancy centers
(${\text{NV}}^{-}$) have been proposed as a platform to generate macroscopic
spatial superpositions. Requirements for this include having a long
${\text{NV}}^{-}$ spin coherence time, which necessitates formulating a
dynamical decoupling strategy in which the regular spin flips do not cancel the
growth of the superposition through the Stern-Gerlach effect in an
inhomogeneous magnetic field. Here, we propose a scheme to place a
$250$-nm-diameter diamond in a superposition with spatial separation of over
$250$ nm, while incorporating dynamical decoupling. We achieve this by letting
a diamond fall for $2.4$ m through a magnetic structure, including $1.13$ m in
an inhomogeneous region generated by magnetic teeth.


Abstract:
  Following the Unlimited Sampling strategy to alleviate the omnipresent
dynamic range barrier, we study the problem of recovering a bandlimited signal
from point-wise modulo samples, aiming to connect theoretical guarantees with
hardware implementation considerations. Our starting point is a class of
non-idealities that we observe in prototyping an unlimited sampling based
analog-to-digital converter. To address these non-idealities, we provide a new
Fourier domain recovery algorithm. Our approach is validated both in theory and
via extensive experiments on our prototype analog-to-digital converter,
providing the first demonstration of unlimited sampling for data arising from
real hardware, both for the current and previous approaches. Advantages of our
algorithm include that it is agnostic to the modulo threshold and it can handle
arbitrary folding times. We expect that the end-to-end realization studied in
this paper will pave the path for exploring the unlimited sampling methodology
in a number of real world applications.


Abstract:
  Predicting student performance is a fundamental task in Intelligent Tutoring
Systems (ITSs), by which we can learn about students' knowledge level and
provide personalized teaching strategies for them. Researchers have made plenty
of efforts on this task. They either leverage educational psychology methods to
predict students' scores according to the learned knowledge proficiency, or
make full use of Collaborative Filtering (CF) models to represent latent
factors of students and exercises. However, most of these methods either
neglect the exercise-specific characteristics (e.g., exercise materials), or
cannot fully explore the high-order interactions between students, exercises,
as well as knowledge concepts. To this end, we propose a Graph-based Exercise-
and Knowledge-Aware Learning Network for accurate student score prediction.
Specifically, we learn students' mastery of exercises and knowledge concepts
respectively to model the two-fold effects of exercises and knowledge concepts.
Then, to model the high-order interactions, we apply graph convolution
techniques in the prediction process. Extensive experiments on two real-world
datasets prove the effectiveness of our proposed Graph-EKLN.


Abstract:
  Growing up in an artificial intelligence-filled world, with Siri and Amazon
Alexa often within arm's - or speech's - reach, could have significant impact
on children. Conversational agents could influence how students
anthropomorphize computer systems or develop a theory of mind. Previous
research has explored how conversational agents are used and perceived by
children within and outside of learning contexts. This study investigates how
middle and high school students' perceptions of Alexa change through
programming their own conversational agents in week-long AI education
workshops. Specifically, we investigate the workshops' influence on student
perceptions of Alexa's intelligence, friendliness, aliveness, safeness,
trustworthiness, human-likeness, and feelings of closeness. We found that
students felt Alexa was more intelligent and felt closer to Alexa after the
workshops. We also found strong correlations between students' perceptions of
Alexa's friendliness and trustworthiness, and safeness and trustworthiness.
Finally, we explored how students tended to more frequently use computer
science-related diction and ideas after the workshops. Based on our findings,
we recommend designers carefully consider personification, transparency,
playfulness and utility when designing CAs for learning contexts.


Abstract:
  The discrepancy between the cost function used for training a speech
enhancement model and human auditory perception usually makes the quality of
enhanced speech unsatisfactory. Objective evaluation metrics which consider
human perception can hence serve as a bridge to reduce the gap. Our previously
proposed MetricGAN was designed to optimize objective metrics by connecting the
metric with a discriminator. Because only the scores of the target evaluation
functions are needed during training, the metrics can even be
non-differentiable. In this study, we propose a MetricGAN+ in which three
training techniques incorporating domain-knowledge of speech processing are
proposed. With these techniques, experimental results on the VoiceBank-DEMAND
dataset show that MetricGAN+ can increase PESQ score by 0.3 compared to the
previous MetricGAN and achieve state-of-the-art results (PESQ score = 3.15).


Abstract:
  In this work, we present a framework for product quality inspection based on
deep learning techniques. First, we categorize several deep learning models
that can be applied to product inspection systems. Also we explain entire steps
for building a deep learning-based inspection system in great detail. Second,
we address connection schemes that efficiently link the deep learning models to
the product inspection systems. Finally, we propose an effective method that
can maintain and enhance the deep learning models of the product inspection
system. It has good system maintenance and stability due to the proposed
methods. All the proposed methods are integrated in a unified framework and we
provide detailed explanations of each proposed method. In order to verify the
effectiveness of the proposed system, we compared and analyzed the performance
of methods in various test scenarios.


Abstract:
  A concept of drone launched short range rockets (DLSRR) is presented. A drone
or an aircraft rises DLSRR to a release altitude of up to 20 $km$. At the
release altitude, the drone or an aircraft is moving at a velocity of up to 700
$m/s$ and a steep angle of up to 68$^o$ to the horizontal. After DLSRRs are
released, their motors start firing. DLSRRs use slow burning motors to gain
altitude and velocity. At the apogee of their flight DLSRRs release projectiles
which fly to the target and strike it at high impact velocity. The projectiles
reach a target at ranges of up to 442 $km$ and impact velocities up to 1.88
$km/s$. We show that a rocket launched at high altitude and high initial
velocity does not need expensive thermal protection to survive ascent. Delivery
of munitions to target by DLSRRs should be much less expensive than delivery by
a conventional rocket. %% Even though delivery of munitions by bomber aircraft
is even less expensive, a bomber needs to fly close to the target, while a
DLSRR carrier releases the rockets from a distance of at least 200 $km$ from
the target. %% All parameters of DLSRRs, and their trajectories are calculated
based on theoretical (mechanical and thermodynamical) analysis and on several
MatLab programs.


Abstract:
  We investigate effects of non-zero Dirac and Majorana CP violating phases on
neutrino-antineutrino oscillations in a magnetic field of astrophysical
environments. It is shown that in the presence of strong magnetic fields and
dense matter, non-zero CP phases can induce new resonances in the oscillations
channels $\nu_e \leftrightarrow \bar{\nu}_e$, $\nu_e \leftrightarrow
\bar{\nu}_\mu$ and $\nu_e \leftrightarrow \bar{\nu}_{\tau}$. We also consider
all other possible oscillation channels with $\nu_\mu$ and $\nu_\tau$ in the
initial state. The resonances can potentially lead to significant phenomena in
neutrino oscillations accessible for observation in experiments. In particular,
we show that neutrino-antineutrino oscillations combined with Majorana-type CP
violation can affect the $\bar{\nu}_e$/$\nu_e$ ratio for neutrinos coming from
the supernovae explosion. This effect is more prominent for the normal neutrino
mass ordering. The detection of supernovae neutrino fluxes in the future
experiments, such as JUNO, DUNE and Hyper-Kamiokande, can give an insight into
the nature of CP violation and, consequently, provides a tool for
distinguishing the Dirac or Majorana nature of neutrinos.


Abstract:
  We show that $k$-point correlation measurements on output of a
non-interacting, multimode, random unitary allow to quantify the $k$-particle
coherence of $N\geq k$ identical (bosonic or fermionic) particles. For
separable many-particle input states, we establish an on average strictly
monotonic relationship between such $k$-particle coherences, the interference
contrast in the experimentally accessible counting statistics and the degree of
the particles' mutual distinguishability, as controlled by their internal
degrees of freedom. Non-separability on input can be unveiled by comparison of
correlation measurements of different orders.


Abstract:
  Deep learning is very data hungry, and supervised learning especially
requires massive labeled data to work well. Machine listening research often
suffers from limited labeled data problem, as human annotations are costly to
acquire, and annotations for audio are time consuming and less intuitive.
Besides, models learned from labeled dataset often embed biases specific to
that particular dataset. Therefore, unsupervised learning techniques become
popular approaches in solving machine listening problems. Particularly, a
self-supervised learning technique utilizing reconstructions of multiple
hand-crafted audio features has shown promising results when it is applied to
speech domain such as emotion recognition and automatic speech recognition
(ASR). In this paper, we apply self-supervised and multi-task learning methods
for pre-training music encoders, and explore various design choices including
encoder architectures, weighting mechanisms to combine losses from multiple
tasks, and worker selections of pretext tasks. We investigate how these design
choices interact with various downstream music classification tasks. We find
that using various music specific workers altogether with weighting mechanisms
to balance the losses during pre-training helps improve and generalize to the
downstream tasks.


Abstract:
  Pre-trained language models have recently advanced abstractive summarization.
These models are further fine-tuned on human-written references before summary
generation in test time. In this work, we propose the first application of
transductive learning to summarization. In this paradigm, a model can learn
from the test set's input before inference. To perform transduction, we propose
to utilize input document summarizing sentences to construct references for
learning in test time. These sentences are often compressed and fused to form
abstractive summaries and provide omitted details and additional context to the
reader. We show that our approach yields state-of-the-art results on CNN/DM and
NYT datasets. For instance, we achieve over 1 ROUGE-L point improvement on
CNN/DM. Further, we show the benefits of transduction from older to more recent
news. Finally, through human and automatic evaluation, we show that our
summaries become more abstractive and coherent.


Abstract:
  Context. Particles in protoplanetary disks go through a number of phases that
are dominated by collisions. In each of these events, grains exchange
electrical charge via triboelectric effects. This enhances the stability of
particle aggregates. Aim. Dielectric grains are easily charged by collisions.
Here, we investigate whether a charge is capable of inducing an aggregation of
particles and we consider how collision properties, such as ticking velocities
and collisional cross-sections, are altered. Methods. We explored aggregation
in microgravity experiments based on the observation of the motion of
submillimeter (submm) grains following many collisions. In the process, grains
attract each other, collide, stick, and ultimately form small aggregates.
Results. We observed a bottom-up formation of irregular aggregates from submm
grains. While some of the observed trajectories during the approach of grains
reflect the presence of a pure Coulomb potential, the motion is not always in
agreement with pure Kepler motion. Higher-order potentials of multipole charge
distributions stand as a plausible explanation for this behavior. An immediate
consequence of charging is that the particles continue to stick to each other
at velocities of $\sim 10 \, \rm cm/s,$ while surface forces of neutral grains
are only expected to allow sticking below $\sim 1 \, \rm mm/s$. No bouncing
collision was observed among hundreds of collisions in the given parameter
range. Applied to early phases of planet formation, the forming aggregates are
therefore the first steps in a new growth phase beyond the traditional bouncing
barrier in planet formation.


Abstract:
  We show that if $E/\mathbb{Q}$ is an elliptic curve with a rational
$p$-torsion for $p=2$ or $3$, then there is a congruence relation between
Ramanujan's tau function and $E$ modulo $p$. We make use of such congruences to
compute the Iwasawa invariants of $2$-adic and $3$-adic Mazur--Tate elements
attached to Ramanujan's tau function. We also investigate numerically the
Iwasawa invariants of the Mazur--Tate elements attached to an elliptic curve
with additive reduction at a fixed prime number.


Abstract:
  Bug localization is an important aspect of software maintenance because it
can locate modules that should be changed to fix a specific bug. Our previous
study showed that the accuracy of the information retrieval (IR)-based bug
localization technique improved when used in combination with code smell
information. Although this technique showed promise, the study showed limited
usefulness because of the small number of: 1) projects in the dataset, 2) types
of smell information, and 3) baseline bug localization techniques used for
assessment. This paper presents an extension of our previous experiments on
Bench4BL, the largest bug localization benchmark dataset available for bug
localization. In addition, we generalized the smell-aware bug localization
technique to allow different configurations of smell information, which were
combined with various bug localization techniques. Our results confirmed that
our technique can improve the performance of IR-based bug localization
techniques for the class level even when large datasets are processed.
Furthermore, because of the optimized configuration of the smell information,
our technique can enhance the performance of most state-of-the-art bug
localization techniques.


Abstract:
  Employing a combination of symmetry analysis, low-energy modeling, and ab
initio simulations, we predict the presence of magnetic-field-induced Weyl
points close to the Fermi level in CaKFe$_4$As$_4$. Depending on the relative
strengths of the magnetic field and of the spin-orbit coupling, the Weyl
fermions can carry a topological charge of $\pm1$ or $\pm2$, making
CaKFe$_4$As$_4$ a rare realization of a double-Weyl semimetal. We further
predict experimental manifestations of these Weyl points, both in bulk
properties, such as the anomalous Hall effect, and in surface properties, such
as the emergence of prominent Fermi arcs. Because CaKFe$_4$As$_4$ displays
unconventional fully-gapped superconductivity below 30 K, our findings open a
novel route to investigate the interplay between superconductivity and Weyl
fermions.


Abstract:
  Siamese deep-network trackers have received significant attention in recent
years due to their real-time speed and state-of-the-art performance. However,
Siamese trackers suffer from similar looking confusers, that are prevalent in
aerial imagery and create challenging conditions due to prolonged occlusions
where the tracker object re-appears under different pose and illumination. Our
work proposes SiamReID, a novel re-identification framework for Siamese
trackers, that incorporates confuser rejection during prolonged occlusions and
is well-suited for aerial tracking. The re-identification feature is trained
using both triplet loss and a class balanced loss. Our approach achieves
state-of-the-art performance in the UAVDT single object tracking benchmark.


Abstract:
  In the present work, we consider the industrial problem of estimating in
real-time the mold-steel heat flux in continuous casting mold. We approach this
problem by first considering the mold modeling problem (direct problem). Then,
we plant the heat flux estimation problem as the inverse problem of estimating
a Neumann boundary condition having as data pointwise temperature measurements
in the interior of the mold domain. We also consider the case of having a total
heat flux measurement together with the temperature measurements. We develop
two methodologies for solving this inverse problem. The first one is the
traditional Alifanov's regularization, the second one exploits the
parameterization of the heat flux. We develop the latter method to have an
offline-online decomposition with a computationally efficient online part to be
performed in real-time. In the last part of this work, we test these methods on
academic and industrial benchmarks. The results show that the parameterization
method outclasses Alifanov's regularization both in performance and
computational cost. Moreover, it proves to be robust with respect to the
measurements noise. Finally, the tests confirm that the computational cost is
suitable for real-time estimation of the heat flux.


Abstract:
  Spintronic heterostructures are considered to be the new generation THz
sources for their capability in producing high power and broadband THz
radiation. Here, we provide a brief review on the state-of-the-art in this
field. The optically excited bi- and tri-layer combinations of ferromagnetic
and nonmagnetic thin films have become increasingly popular. Towards optimizing
the THz conversion efficiency and broadband gapless spectrum from these THz
emitters, various control parameters need to be taken into consideration. The
inverse spin Hall effect in the heavy metal layer of the heterostructure is
primarily responsible for the generation of THz pulses. A few new results on
iron, platinum and tantalum based heterostructures have also been reported
here. It is observed that the Ta(2nm)/Fe(2nm)/Pt(2nm) tri-layer heterostructure
generates ~40(250)% stronger THz signal as compared to the counterpart
Fe(2nm)/Pt(2nm) (Fe(3nm)/Ta(2nm)) bi-layer heterostructure.


Abstract:
  Width-based planning methods deal with conjunctive goals by decomposing
problems into subproblems of low width. Algorithms like SIW thus fail when the
goal is not easily serializable in this way or when some of the subproblems
have a high width. In this work, we address these limitations by using a simple
but powerful language for expressing finer problem decompositions introduced
recently by Bonet and Geffner, called policy sketches. A policy sketch over a
set of Boolean and numerical features is a set of sketch rules that express how
the values of these features are supposed to change. Like general policies,
policy sketches are domain general, but unlike policies, the changes captured
by sketch rules do not need to be achieved in a single step. We show that many
planning domains that cannot be solved by SIW are provably solvable in low
polynomial time with the SIW_R algorithm, the version of SIW that employs
user-provided policy sketches. Policy sketches are thus shown to be a powerful
language for expressing domain-specific knowledge in a simple and compact way
and a convenient alternative to languages such as HTNs or temporal logics.
Furthermore, they make it easy to express general problem decompositions and
prove key properties of them like their width and complexity.


Abstract:
  In the majority of population synthesis calculations of close binary stars,
the common envelope (CE) phase is modeled using a standard prescription based
upon the conservation of energy, known as the alpha prescription. In this
prescription, the orbital separation of the secondary and giant core at the end
of the CE phase is taken to be the orbital separation when the envelope becomes
unbound. However, recent observations of planetary nebulae with binary cores
(BPNe), believed to be the immediate products of CE evolution, indicate orbital
periods that are significantly shorter than predicted by population synthesis
models using the alpha prescription. We argue that unbinding the envelope
provides a necessary, but not sufficient, condition to escape a merger during
CE evolution. The spiral-in of the secondary must also be halted. This requires
the additional dynamical constraint that the frictional torque on the secondary
be reduced to approximately zero. In this paper, we undertake a preliminary
examination of the effect of adding this dynamical constraint in population
synthesis calculations of BPNe. We assume that the frictional torque will be
sufficiently reduced when the secondary enters a region within the giant where
the mass-radius profile is flat. We crudely estimate the location of this
region as a function of the core mass based upon existing stellar models of AGB
stars between 1 and 7 solar masses. We calculate a theoretical orbital period
distribution of BPNe using a population synthesis code that incorporates this
dynamical constraint along with the alpha prescription.


Abstract:
  In machine learning, the term active learning regroups techniques that aim at
selecting the most useful data to label from a large pool of unlabelled
examples. While supervised deep learning techniques have shown to be
increasingly efficient on many applications, they require a huge number of
labelled examples to reach operational performances. Therefore, the labelling
effort linked to the creation of the datasets required is also increasing. When
working on defense-related remote sensing applications, labelling can be
challenging due to the large areas covered and often requires military experts
who are rare and whose time is primarily dedicated to operational needs.
Limiting the labelling effort is thus of utmost importance. This study aims at
reviewing the most relevant active learning techniques to be used for object
detection on very high resolution imagery and shows an example of the value of
such techniques on a relevant operational use case: aircraft detection.


Abstract:
  Although deep convolutional networks have achieved great performance in face
recognition tasks, the challenge of domain discrepancy still exists in real
world applications. Lack of domain coverage of training data (source domain)
makes the learned models degenerate in a testing scenario (target domain). In
face recognition tasks, classes in two domains are usually different, so
classical domain adaptation approaches, assuming there are shared classes in
domains, may not be reasonable solutions for this problem. In this paper,
self-supervised learning is adopted to learn a better embedding space where the
subjects in target domain are more distinguishable. The learning goal is
maximizing the similarity between the embeddings of each image and its mirror
in both domains. The experiments show its competitive results compared with
prior works. To know the reason why it can achieve such performance, we further
discuss how this approach affects the learning of embeddings.


Abstract:
  In this work we apply methods for describing 3D images to the problem of
encoding atomic environments in a way that is invariant to rotations,
translations, and permutations of the atoms and, crucially, can be decoded back
into the original environment modulo global orientation without the need for
training a model. From the point of view of decoding, the descriptor is
optimally complete and can be extended to arbitrary order, allowing for a
systematic convergence of the fidelity of the description. In experiments on
molecules ranging from 3 to 29 atoms in size, we demonstrate that positions can
be decoded with a 97% success rate and positions plus species with a 70% rate
of success, rising to 95% if a second fingerprint is used. In all cases,
consistent recovery is observed for molecules with 17 or fewer atoms.
Additionally, we evaluate the descriptor's performance in predicting the
energies and forces of bulk Ni, Cu, Li, Mo, Si and Ge by means of a neural
network model trained on DFT data. When comparing to six machine learning
interaction potential methods that use various descriptors and regression
schemes our descriptor is found be to competitive, in several cases
outperforming well established methods. The combined ability to both decode and
make property predictions from a representation that does not need to be
learned lays the foundations for a novel way of building generative models that
are tasked with solving the inverse problem of predicting atomic arrangements
that are statistically likely to have certain desired properties.


Abstract:
  In education and intervention programs, person's engagement has been
identified as a major factor in successful program completion. Automatic
measurement of person's engagement provides useful information for instructors
to meet program objectives and individualize program delivery. In this paper,
we present a novel approach for video-based engagement measurement in virtual
learning programs. We propose to use affect states, continuous values of
valence and arousal extracted from consecutive video frames, along with a new
latent affective feature vector and behavioral features for engagement
measurement. Deep learning-based temporal, and traditional
machine-learning-based non-temporal models are trained and validated on
frame-level, and video-level features, respectively. In addition to the
conventional centralized learning, we also implement the proposed method in a
decentralized federated learning setting and study the effect of model
personalization in engagement measurement. We evaluated the performance of the
proposed method on the only two publicly available video engagement measurement
datasets, DAiSEE and EmotiW, containing videos of students in online learning
programs. Our experiments show a state-of-the-art engagement level
classification accuracy of 63.3% and correctly classifying disengagement videos
in the DAiSEE dataset and a regression mean squared error of 0.0673 on the
EmotiW dataset. Our ablation study shows the effectiveness of incorporating
affect states in engagement measurement. We interpret the findings from the
experimental results based on psychology concepts in the field of engagement.


Abstract:
  This doctoral dissertation proposes a novel approach to enhance the
development of smart services for the Internet of Things (IoT) and smart
Cyber-Physical Systems (CPS). The proposed approach offers abstraction and
automation to the software engineering processes, as well as the Data Analytics
(DA) and Machine Learning (ML) practices. This is realized in an integrated and
seamless manner. We implement and validate the proposed approach by extending
an open source modeling tool, called ThingML. ThingML is a domain-specific
language and modeling tool with code generation for the IoT/CPS domain. Neither
ThingML nor any other IoT/CPS modeling tool supports DA/ML at the modeling
level. Therefore, as the primary contribution of the doctoral dissertation, we
add the necessary syntax and semantics concerning DA/ML methods and techniques
to the modeling language of ThingML. Moreover, we support the APIs of several
ML libraries and frameworks for the automated generation of the source code of
the target software in Python and Java. Our approach enables
platform-independent, as well as platform-specific models. Further, we assist
in carrying out semiautomated DA/ML tasks by offering Automated ML (AutoML), in
the background (in expert mode), and through model-checking constraints and
hints at design-time. Finally, we consider three use case scenarios from the
domains of network security, smart energy systems and energy exchange markets.


Abstract:
  Dynamical radiation pressure effects in cavity optomechanical systems give
rise to self-sustained oscillations or `phonon lasing' behavior, producing
stable oscillators up to GHz frequencies in nanoscale devices. Like in photonic
lasers, phonon lasing normally occurs in a single mechanical mode. We show here
that phase-locked, multimode phonon lasing can be established in a multimode
optomechanical system through Floquet dynamics induced by a temporally
modulated laser drive. We demonstrate this concept in a suitably engineered
silicon photonic nanocavity coupled to multiple GHz-frequency mechanical modes.
We find that the long-term frequency stability is significantly improved in the
multimode lasing state as a result of the phase locking. These results provide
a path towards highly stable ultra-compact oscillators, pulsed phonon lasing,
coherent waveform synthesis, and emergent many-mode phenomena in oscillator
arrays.


Abstract:
  Graph matching, also known as network alignment, refers to finding a
bijection between the vertex sets of two given graphs so as to maximally align
their edges. This fundamental computational problem arises frequently in
multiple fields such as computer vision and biology. Recently, there has been a
plethora of work studying efficient algorithms for graph matching under
probabilistic models. In this work, we propose a new algorithm for graph
matching: Our algorithm associates each vertex with a signature vector using a
multistage procedure and then matches a pair of vertices from the two graphs if
their signature vectors are close to each other. We show that, for two
Erd\H{o}s--R\'enyi graphs with edge correlation $1-\alpha$, our algorithm
recovers the underlying matching exactly with high probability when $\alpha \le
1 / (\log \log n)^C$, where $n$ is the number of vertices in each graph and $C$
denotes a positive universal constant. This improves the condition $\alpha \le
1 / (\log n)^C$ achieved in previous work.


Abstract:
  We studied the general advective accretion solutions around the Kerr black
hole (BH) with investigating two types of inflow gases at the outer accretion
boundary (AB). We classified these two types of gases as a \cm and a \hm inflow
gas at the outer AB on the basis of their temperatures and solutions. We found
that the \hm gas is more efficient for angular momentum transportation around
the outer AB than the \cm gas. The \hm gas can give global multiple
\cite[popular as shock solution][]{c89} or single sonic point solutions and the
\cm can give smooth global solution \cite[popular as ADAF][]{ny94} or two sonic
point solutions. These solutions also represented on a plane of energy and
angular momentum ($\be-L_0$) parameter space. Theoretically for the first time,
we explored the relation between the nature of accretion solutions with the
nature of initial accreting gas at the AB with detail computational and
possible physical analysis. We also found that the surface density of the flow
is highly affected with changing of the temperature at the AB, which can alter
the radiative emissivities of the flow. The flow variables of various advective
solutions also compared. On the basis of those results, we plotted some inner
disk structures around the BHs. Doing so, we conjectured about the
persistent/transient nature of spectral states, soft-excess and time scales of
variabilities around the black hole $X-$ ray binaries (BXBs) and active
galactic nuclei (AGNs).


Abstract:
  We find that the Schwinger Boson mean field theory(SBMFT) supplemented with
Gutzwiller projection provides an exceedingly accurate description for the
ground state of the spin-$\frac{1}{2}$ triangular lattice antiferromagnetic
Heisenberg model(spin-$\frac{1}{2}$ TLHAF). However, we find the SBMFT fails
even qualitatively in the description of the dynamical behavior of the system.
In particular, the SBMFT fails to predict the Goldstone mode in the magnetic
ordered phase. We show that the coherent peak in the two-spinon continuum in
the presence of spinon condensate should not be interpreted as a magnon mode.
The SBMFT also predicts incorrectly a gapless longitudinal spin fluctuation
mode in the magnetic ordered phase. We show that these failures are related to
the following facts: (1)Spinon condensation fails to provide a consistent
description of the order parameter manifold of the 120 degree ordered phase.
(2)There lacks in the SBMFT the coupling between the uncondensed spinon and the
spinon condensate, which breaks both the spin rotational and the translational
symmetry. (3)There lacks in the SBMFT the rigidity that is related to the no
double occupancy constraint on the spinon system. We show that such failures of
the SBMFT is neither restricted to the spin-$\frac{1}{2}$ TLHAF nor to the
magnetic ordered phase. We proposed a generalized SBMFT to resolve the first
two issues and a new formalism to address the third issue.


Abstract:
  When it comes to studying the impacts of decision making, the research has
been largely focused on examining the fairness of the decisions, the long-term
effects of the decision pipelines, and utility-based perspectives considering
both the decision-maker and the individuals. However, there has hardly been any
focus on precarity which is the term that encapsulates the instability in
people's lives. That is, a negative outcome can overspread to other decisions
and measures of well-being. Studying precarity necessitates a shift in focus -
from the point of view of the decision-maker to the perspective of the decision
subject. This centering of the subject is an important direction that unlocks
the importance of parting with aggregate measures to examine the long-term
effects of decision making. To address this issue, in this paper, we propose a
modeling framework that simulates the effects of compounded decision-making on
precarity over time. Through our simulations, we are able to show the
heterogeneity of precarity by the non-uniform ruinous aftereffects of negative
decisions on different income classes of the underlying population and how
policy interventions can help mitigate such effects.


Abstract:
  Freezing of ice has been largely reported from many aspects, especially its
complex pattern formation. Ice grown from liquid phase is usually
characteristic of lamellar morphology which plays a significant role in various
domains. However, tilted growth of ice via transition from coplanar to
non-coplanar growth in directional solidification has been paid little
attention in previous studies and there is misleading explanation of the
formation of tilted lamellar ice. Here, we in-situ investigated the variations
of tilting behavior of lamellar ice tip under different conditions within a
single ice crystal with manipulated orientation via unidirectional freezing of
aqueous solutions. It is found that tilted growth of ice tips is sensitive to
pulling velocity and solute type. These experimental results reveal intrinsic
tilted growth behavior of lamellar ice and enrich our understanding in pattern
formation of ice.


Abstract:
  Because of the recent applications to distributed storage systems,
researchers have introduced a new class of block codes, i.e., locally
recoverable (LRC) codes. LRC codes can recover information from erasure(s) by
accessing a small number of erasure-free code symbols and increasing the
efficiency of repair processes in large-scale distributed storage systems. In
this context, Tamo and Barg first gave a breakthrough by cleverly introducing a
good polynomial notion. Constructing good polynomials for locally recoverable
codes achieving Singleton-type bound (called optimal codes) is challenging and
has attracted significant attention in recent years. This article aims to
increase our knowledge on good polynomials for optimal LRC codes. Using tools
from algebraic function fields and Galois theory, we continue investigating
those polynomials and studying them by developing the Galois theoretical
approach initiated by Micheli in 2019. Specifically, we push further the study
of a crucial parameter $\mathcal G(f)$ (of a given polynomial $f$), which
measures how much a polynomial is "good" in the sense of LRC codes. We provide
some characterizations of polynomials with minimal Galois groups and prove some
properties of finite fields where polynomials exist with a specific size of
Galois groups. We also present some explicit shapes of polynomials with small
Galois groups. For some particular polynomials $f$, we give the exact formula
of $\mathcal G(f)$.


Abstract:
  Let $\Sigma$ be a closed subset of $\mathbb{R}^ {n+1}$ which is parabolic
Ahlfors-David regular and assume that $\Sigma$ satisfies a 2-sided corkscrew
condition. Assume, in addition, that $\Sigma$ is either time-forwards
Ahlfors-David regular, time-backwards Ahlfors-David regular, or parabolic
uniform rectifiable. We then first prove that
  $\Sigma$ satisfies a {\it weak synchronized two cube condition}. Based on
this we are able to revisit the argument in \cite{NS} and prove that $\Sigma$
contains {\it uniform big pieces of Lip(1,1/2) graphs}. When $\Sigma$ is
parabolic uniformly rectifiable the construction can be refined and in this
case we prove that $\Sigma$ contains {\it uniform big pieces of regular
parabolic Lip(1,1/2) graphs}. Similar results hold if $\Omega\subset\mathbb
R^{n+1}$ is a connected component of $\mathbb R^{n+1}\setminus\Sigma$ and in
this context we also give a parabolic counterpart of the main result in
\cite{AHMNT} by proving that if $\Omega$ is a one-sided parabolic chord arc
domain, and if $\Sigma$ is parabolic uniformly rectifiable, then $\Omega$ is in
fact a parabolic chord arc domain. Our results give a flexible parabolic
version of the classical (elliptic) result of G. David and D. Jerison
concerning the existence of uniform big pieces of Lipschitz graphs for sets
satisfying a two disc condition.


Abstract:
  I study a population model in which the reproduction rate lambda is inherited
with mutation, favoring fast reproducers in the short term, but conflicting
with a process that eliminates agglomerations of individuals. The model is a
variant of the triplet annihilation model introduced several decades ago [R.
Dickman, Phys. Rev. B~{\bf 40}, 7005 (1989)] in which organisms ("particles")
reproduce and diffuse on a lattice, subject to annihilation when (and only
when) occupying three consecutive sites. For diffusion rates below a certain
value, the population possesses two "survival strategies": (i) rare
reproduction (0 < lambda < lambda_{c,1}), in which a low density of diffusing
particles renders triplets exceedingly rare, and (ii) frequent reproduction
(lambda > lambda_{c,2}). For lambda between lambda_{c,1} and lambda_{c,2} there
is no active steady state. In the rare-reproduction regime, a mutating
$\lambda$ leads to stochastic boom-and-bust cycles in which the reproduction
rate fluctuates upward in certain regions, only to lead to extinction as the
local value of lambda becomes excessive. The global population can nevertheless
survive due to the presence of other regions, with reproduction rates that have
yet to drift upward.


Abstract:
  We perform a Koopman spectral analysis of elementary cellular automata (ECA).
By lifting the system dynamics using a one-hot representation of the system
state, we derive a matrix representation of the Koopman operator as a transpose
of the adjacency matrix of the state-transition network. The Koopman
eigenvalues are either zero or on the unit circle in the complex plane, and the
associated Koopman eigenfunctions can be explicitly constructed. From the
Koopman eigenvalues, we can judge the reversibility, determine the number of
connected components in the state-transition network, evaluate the periods of
asymptotic orbits, and derive the conserved quantities for each system. We
numerically calculate the Koopman eigenvalues of all rules of ECA on a
one-dimensional lattice of 13 cells with periodic boundary conditions. It is
shown that the spectral properties of the Koopman operator reflect Wolfram's
classification of ECA.


Abstract:
  We use quantum kinetic theory to calculate the thermoelectric transport
properties of the 2D single band Fermi-Hubbard model in the weak coupling
limit. For generic filling, we find that the high-temperature limiting
behaviors of the electrical ($\sim T$) and thermal ($\sim T^2$) resistivities
persist down to temperatures of order the hopping matrix element $T\sim t$,
almost an order of magnitude below the bandwidth. At half filling, perfect
nesting leads to anomalous low temperature scattering and nearly $T$-linear
electrical resistivity at all temperatures. We hypothesize that the $T$-linear
resistivity observed in recent cold atom experiments is continuously connected
to this weak coupling physics and suggest avenues for experimental
verification. We find a number of other novel thermoelectric results, such as a
low-temperature Wiedemann-Franz law with Lorenz coefficient $5\pi^2/36$.


Abstract:
  An innovative approach for the synthesis of inexpensive holographic smart
electromagnetic (EM) skins with advanced beamforming features is proposed. The
complex multiscale smart skin design is formulated within the Generalized Sheet
Transition Condition (GSTC) framework as a combination of a mask-constrained
isophoric inverse source problem and a micro-scale susceptibility dyadic
optimization. The solution strategy integrates a local search procedure based
on the iterative projection technique (IPT) and a System-by-Design (SbD)-based
optimization loop for the identification of optimal metasurface descriptors
matching the desired surface currents. The performance and the efficiency of
the proposed approach are assessed in a set of representative test cases
concerned with different smart skin apertures and target pattern masks.


Abstract:
  We report on the observation of bona fide stochastic resonance (SR) in a
nonGaussian active bath without any periodic forcing. Particles hopping in a
nanoscale double-well potential under the influence of correlated Poisson noise
display a series of equally-spaced peaks in the residence time distribution.
Maximal peaks are measured when the mean residence time matches a double
condition on the interval and correlation timescales of the noise,
demonstrating a new type of SR. The experimental findings agree with a simple
model that explains the emergence of SR without periodicity. Correlated
nonGaussian noise is common in living systems, suggesting that this type of SR
is widespread in this regime.


Abstract:
  We generalize Birkhoff's Theorem in the following fashion. We find necessary
and sufficient conditions for any spherically symmetric space-time to be static
in terms of the eigenvalues of the stress-energy tensor. In particular, we
generalize the Tolman-Oppenheimer-Volkoff equation and prove that Birkhoff's
theorem holds under the weaker hypothesis of no pressure (with respect to an
appropriate frame.) We provide equations that show how the coefficients of the
metric relate to the eigenvalues of the stress-energy tensor. These involve
integrals that are simple functions of those eigenvalues. We also determine
among all static spherically symmetric space-times those that are
asymptotically flat. A few examples are presented taking advantage of the
results. The calculations are done by viewing the space-times as warped
products and the computations are done using Cartan's moving frames approach.


Abstract:
  Deep Reinforcement Learning has emerged as an efficient dynamic obstacle
avoidance method in highly dynamic environments. It has the potential to
replace overly conservative or inefficient navigation approaches. However, the
integration of Deep Reinforcement Learning into existing navigation systems is
still an open frontier due to the myopic nature of
Deep-Reinforcement-Learning-based navigation, which hinders its widespread
integration into current navigation systems. In this paper, we propose the
concept of an intermediate planner to interconnect novel
Deep-Reinforcement-Learning-based obstacle avoidance with conventional global
planning methods using waypoint generation. Therefore, we integrate different
waypoint generators into existing navigation systems and compare the joint
system against traditional ones. We found an increased performance in terms of
safety, efficiency and path smoothness especially in highly dynamic
environments.


Abstract:
  Correlation matrices are used in many domains of neurosciences such as fMRI,
EEG, MEG. However, statistical analyses often rely on embeddings into a
Euclidean space or into Symmetric Positive Definite matrices which do not
provide intrinsic tools. The quotient-affine metric was recently introduced as
the quotient of the affine-invariant metric on SPD matrices by the action of
diagonal matrices. In this work, we provide most of the fundamental Riemannian
operations of the quotient-affine metric: the expression of the metric itself,
the geodesics with initial tangent vector, the Levi-Civita connection and the
curvature.


Abstract:
  We present a systematic study of holographic correlators in a vast array of
SCFTs with non-maximal superconformal symmetry. These theories include 4d
$\mathcal{N}=2$ SCFTs from D3-branes near F-theory singularities, 5d Seiberg
exceptional theories and 6d E-string theory, as well as 3d and 4d
phenomenological models with probe flavor branes. We consider current
multiplets and their generalizations with higher weights, dual to massless and
massive super gluons in the bulk. At leading order in the inverse central
charge expansion, connected four-point functions of these operators correspond
to tree-level gluon scattering amplitudes in AdS. We show that all such
tree-level four-point amplitudes in all these theories are fully fixed by
symmetries and consistency conditions and explicitly construct them. Our
results encode a wealth of SCFT data and exhibit various interesting emergent
structures. These include Parisi-Sourlas-like dimensional reductions, hidden
conformal symmetry and an AdS version of the color-kinematic duality.


Abstract:
  New MMT/Hectospec spectroscopy centered on the galaxy cluster A2626 and
covering a ${\sim} 1.8\,\text{deg}^2$ area out to $z \sim 0.46$ more than
doubles the number of galaxy redshifts in this region. The spectra confirm four
clusters previously identified photometrically. A2625, which was previously
thought to be a close neighbor of A2626, is in fact much more distant. The new
data show six substructures associated with A2626 and five more associated with
A2637. There is also a highly collimated collection of galaxies and galaxy
groups between A2626 and A2637 having at least three and probably four
substructures. At larger scales, the A2626--A2637 complex is not connected to
the Pegasus--Perseus filament.


Abstract:
  In order to safely deploy Deep Neural Networks (DNNs) within the perception
pipelines of real-time decision making systems, there is a need for safeguards
that can detect out-of-training-distribution (OoD) inputs both efficiently and
accurately. Building on recent work leveraging the local curvature of DNNs to
reason about epistemic uncertainty, we propose Sketching Curvature of OoD
Detection (SCOD), an architecture-agnostic framework for equipping any trained
DNN with a task-relevant epistemic uncertainty estimate. Offline, given a
trained model and its training data, SCOD employs tools from matrix sketching
to tractably compute a low-rank approximation of the Fisher information matrix,
which characterizes which directions in the weight space are most influential
on the predictions over the training data. Online, we estimate uncertainty by
measuring how much perturbations orthogonal to these directions can alter
predictions at a new test input. We apply SCOD to pre-trained networks of
varying architectures on several tasks, ranging from regression to
classification. We demonstrate that SCOD achieves comparable or better OoD
detection performance with lower computational burden relative to existing
baselines.


Abstract:
  For many of the 700 million illiterate people around the world, speech
recognition technology could provide a bridge to valuable information and
services. Yet, those most in need of this technology are often the most
underserved by it. In many countries, illiterate people tend to speak only
low-resource languages, for which the datasets necessary for speech technology
development are scarce. In this paper, we investigate the effectiveness of
unsupervised speech representation learning on noisy radio broadcasting
archives, which are abundant even in low-resource languages. We make three core
contributions. First, we release two datasets to the research community. The
first, West African Radio Corpus, contains 142 hours of audio in more than 10
languages with a labeled validation subset. The second, West African Virtual
Assistant Speech Recognition Corpus, consists of 10K labeled audio clips in
four languages. Next, we share West African wav2vec, a speech encoder trained
on the noisy radio corpus, and compare it with the baseline Facebook speech
encoder trained on six times more data of higher quality. We show that West
African wav2vec performs similarly to the baseline on a multilingual speech
recognition task, and significantly outperforms the baseline on a West African
language identification task. Finally, we share the first-ever speech
recognition models for Maninka, Pular and Susu, languages spoken by a combined
10 million people in over seven countries, including six where the majority of
the adult population is illiterate. Our contributions offer a path forward for
ethical AI research to serve the needs of those most disadvantaged by the
digital divide.


Abstract:
  Many-access channel (MnAC) model allows the number of users in the system and
the number of active users to scale as a function of the blocklength and as
such is suited for dynamic communication systems with massive number of users
such as the Internet of Things. Existing MnAC models assume a priori knowledge
of channel gains which is impractical since acquiring Channel State Information
(CSI) for massive number of users can overwhelm the available radio resources.
This paper incorporates Rayleigh fading effects to the MnAC model and derives
an upper bound on the symmetric message-length capacity of the Rayleigh-fading
Gaussian MnAC. Furthermore, a lower bound on the minimum number of channel uses
for discovering the active users is established. In addition, the performance
of Noisy Combinatorial Orthogonal Matching Pursuit (N-COMP) based group testing
(GT) is studied as a practical strategy for active device discovery.
Simulations show that, for a given SNR, as the number of users increase, the
required number of channel uses for N-COMP GT scales approximately the same way
as the lower bound on minimum user identification cost. Moreover, in the low
SNR regime, for sufficiently large population sizes, the number of channel uses
required by N-COMP GT was observed to be within a factor of two of the lower
bound when the expected number of active users scales sub-linearly with the
total population size.


Abstract:
  A long march of fifty years of successive theoretical progress and new
physics discovered using observations of gamma-ray bursts, has finally led to
the formulation of an efficient mechanism able to extract the rotational energy
of a Kerr black hole to power these most energetic astrophysical sources and
active galactic nuclei. We here present the salient features of this
long-sought mechanism, based on gravito-electrodynamics, and which represents
an authentic shift of paradigm of black holes as forever "alive" astrophysical
objects.