Abstract:
  Preparation of nanoemulsions of triglyceride oils in water usually requires
high mechanical energy and sophisticated equipment. Recently, we showed that
alpha-to-beta (viz. gel-to-crystal) phase transition, observed with most lipid
substances (triglycerides, diglycerides, phospholipids, alkanes, etc.), may
cause spontaneous disintegration of micro-particles of these lipids, dispersed
in aqueous solutions of appropriate surfactants, into nanometer particles/drops
using a simple cooling/heating cycle of the lipid dispersion (Cholakova et al.
ACS Nano 14 (2020) 8594). In the current study we show that this "cold-burst
process" is observed also with natural oils of high practical interest, incl.
coconut oil, palm kernel oil and cocoa butter. Mean drop diameters of ca. 50 to
100 nm were achieved with some of the studied oils. From the results of
dedicated model experiments we conclude that intensive nano-fragmentation is
observed when the following requirements are met: (1) The three phase contact
angle at the air-water-solid lipid interface is below ca. 30 degrees; (2) The
equilibrium surface tension of the surfactant solution is below ca. 30 mN/m and
the dynamic surface tension decreases rapidly. (3) The surfactant solution
contains non-spherical surfactant micelles. e.g. ellipsoidal micelles or bigger
supramolecular aggregates; (4) The three phase contact angle measured at the
contact line (frozen oil-melted oil-surfactant solution) is also relatively
low. The mechanism(s) of the particle bursting process is revealed and, on this
basis, the role of all these factors is clarified and discussed. We explain all
main effects observed experimentally and define guiding principles for
optimization of the cold-burst process in various, practically relevant
lipid-surfactant systems.


Abstract:
  We establish a Kantorovich duality for the pseudometric $\mathcal{E}_\hbar$
introduced in [F. Golse, T. Paul, Arch. Rational Mech. Anal. 223 (2017),
57--94], obtained from the usual Monge-Kantorovich distance $d_{MK,2}$ between
classical densities by quantization of one of the two densities involved. We
show several type of inequalities comparing $d_{MK,2}$, $\mathcal{E}_\hbar$ and
$MK_\hbar$, a full quantum analogue of $d_{MK,2}$ introduced in [F. Golse, C.
Mouhot, T. Paul, Commun. Math. Phys. 343 (2016), 165--205], including an up to
$\hbar$ triangle inequality for $MK_\hbar$. Finally, we show that, when nice
optimal Kantorovich potentials exist for $\mathcal{E}_\hbar$, optimal couplings
induce classical/quantum optimal transports and the potentials are linked by a
semiquantum Legendre type transform.


Abstract:
  Federated learning (FL) is a promising privacy-preserving distributed machine
learning methodology that allows multiple clients (i.e., workers) to
collaboratively train statistical models without disclosing private training
data. Due to the characteristics of data remaining localized and the
uninspected on-device training process, there may exist Byzantine workers
launching data poisoning and model poisoning attacks, which would seriously
deteriorate model performance or prevent the model from convergence. Most of
the existing Byzantine-robust FL schemes are either ineffective against several
advanced poisoning attacks or need to centralize a public validation dataset,
which is intractable in FL. Moreover, to the best of our knowledge, none of the
existing Byzantine-robust distributed learning methods could well exert its
power in Non-Independent and Identically distributed (Non-IID) data among
clients. To address these issues, we propose FedCom, a novel Byzantine-robust
federated learning framework by incorporating the idea of commitment from
cryptography, which could achieve both data poisoning and model poisoning
tolerant FL under practical Non-IID data partitions. Specifically, in FedCom,
each client is first required to make a commitment to its local training data
distribution. Then, we identify poisoned datasets by comparing the Wasserstein
distance among commitments submitted by different clients. Furthermore, we
distinguish abnormal local model updates from benign ones by testing each local
model's behavior on its corresponding data commitment. We conduct an extensive
performance evaluation of FedCom. The results demonstrate its effectiveness and
superior performance compared to the state-of-the-art Byzantine-robust schemes
in defending against typical data poisoning and model poisoning attacks under
practical Non-IID data distributions.


Abstract:
  We apply on-shell amplitude techniques to the study of neutrino oscillations
in vacuum, focussing on processes involving W bosons. We start by determining
the 3-point amplitude involving one neutrino, one charged lepton and one W
boson, highlighting all the allowed kinematic structures. The result we obtain
contains terms generated at all orders in an expansion in the cutoff scale of
the theory, and we explicitly determine the lower dimensional operators behind
the generation of the different structures. We then use this amplitude to
construct the 4-point amplitude in which neutrinos are exchanged in the
s-channel, giving rise to oscillations. We also study in detail how flavor
enters in the amplitudes, and how the PMNS matrix emerges from the on-shell
perspective.


Abstract:
  By using the dry etching process of tantalum (Ta) film, we had obtained
transmon qubit with the best lifetime (T1) 503 us, suggesting that the dry
etching process can be adopted in the following multi-qubit fabrication with Ta
film. We also compared the relaxation and coherence times of transmons made
with different materials (Ta, Nb and Al) with the same design and fabrication
processes of Josephson junction, we found that samples prepared with Ta film
had the best performance, followed by those with Al film and Nb film. We
inferred that the reason for this difference was due to the different loss of
oxide materials located at the metal-air interface.


Abstract:
  Using logic gates is the traditional way of designing logic circuits.
However, most of the minimization algorithms concern a limited set of gates
(complete sets), like sum of products, exclusive-or sum of products, NAND
gates, NOR gates e.t.c.. In this paper, a method is proposed for minimizing
multi-output Boolean functions using any kind of two-input gates although it
can easily be extended to multi-input gates. The method is based on non-linear
mixed integer programming. The experimental results show that the method gives
the same or better results compared to other methods available in the
literature. However, other methods do not ensure that they produce the minimal
solution, while the main advantages of the proposed method are that it does
guarantee minimality and it can also handle Boolean functions for incompletely
specified functions. The method is general enough and can easily be extended to
more complicated design modules than just basic gates.


Abstract:
  Structured jets are recently invoked to explain the complex emission of gamma
ray bursts, such as GW 170817. Based on the accretion simulations, the jets are
expected to have a structure that is more complex than a simple top-hat. Also,
the structure of launching regions of blazar jets should influence their large
scale evolution. This is recently revealed by the interactions of jet
components in TXS 0506+056, where the jet is observed at a viewing angle close
to zero. Observational studies have also shown an anti-correlation between the
jet variability, measured e.g. by its minimum variability time scale, and the
Lorentz factor, that spans several orders of magnitude and covers both blazars
and GRBs samples. Motivated by those observational properties of black hole
sources, we investigate the accretion inflow and outflow properties, by means
of numerical GR MHD simulations. We perform axisymmetric calculations of the
structure and evolution of central engine, composed of magnetized torus around
Kerr black hole that is launching a non-uniform jet. We probe the jet
energetics at different points along the line of sight, and we measure the jet
time variability as localized in these specific regions. We quantify our
results by computing the minimum variability timescales and power density
spectra. We reproduce the MTS-$\Gamma$ correlation and we attribute it to the
black hole spin as the main driving parameter of the engine. We also find that
the PDS slope is not strongly affected by the black hole spin, while it differs
for various viewing angles.


Abstract:
  In dynamic and cramped industrial environments, achieving reliable Visual
Teach and Repeat (VT&R) with a single-camera is challenging. In this work, we
develop a robust method for non-synchronized multi-camera VT&R. Our
contribution are expected Camera Performance Models (CPM) which evaluate the
camera streams from the teach step to determine the most informative one for
localization during the repeat step. By actively selecting the most suitable
camera for localization, we are able to successfully complete missions when one
of the cameras is occluded, faces into feature poor locations or if the
environment has changed. Furthermore, we explore the specific challenges of
achieving VT&R on a dynamic quadruped robot, ANYmal. The camera does not follow
a linear path (due to the walking gait and holonomicity) such that precise
path-following cannot be achieved. Our experiments feature forward and backward
facing stereo cameras showing VT&R performance in cluttered indoor and outdoor
scenarios. We compared the trajectories the robot executed during the repeat
steps demonstrating typical tracking precision of less than 10cm on average.
With a view towards omni-directional localization, we show how the approach
generalizes to four cameras in simulation. Video: https://youtu.be/iAY0lyjAnqY


Abstract:
  Dynamic graphs are rife with higher-order interactions, such as co-authorship
relationships and protein-protein interactions in biological networks, that
naturally arise between more than two nodes at once. In spite of the ubiquitous
presence of such higher-order interactions, limited attention has been paid to
the higher-order counterpart of the popular pairwise link prediction problem.
Existing higher-order structure prediction methods are mostly based on
heuristic feature extraction procedures, which work well in practice but lack
theoretical guarantees. Such heuristics are primarily focused on predicting
links in a static snapshot of the graph. Moreover, these heuristic-based
methods fail to effectively utilize and benefit from the knowledge of latent
substructures already present within the higher-order structures. In this
paper, we overcome these obstacles by capturing higher-order interactions
succinctly as \textit{simplices}, model their neighborhood by face-vectors, and
develop a nonparametric kernel estimator for simplices that views the evolving
graph from the perspective of a time process (i.e., a sequence of graph
snapshots). Our method substantially outperforms several baseline higher-order
prediction methods. As a theoretical achievement, we prove the consistency and
asymptotic normality in terms of the Wasserstein distance of our estimator
using Stein's method.


Abstract:
  Recent works on sparse neural networks have demonstrated the possibility to
train a sparse subnetwork independently from scratch, to match the performance
of its corresponding dense network. However, identifying such sparse
subnetworks (winning tickets) either involves a costly iterative
train-prune-retrain process (e.g., Lottery Ticket Hypothesis) or an
over-extended training time (e.g., Dynamic Sparse Training). In this work, we
draw a unique connection between sparse neural network training and the deep
ensembling technique, yielding a novel ensemble learning framework called
FreeTickets. Instead of starting from a dense network, FreeTickets randomly
initializes a sparse subnetwork and then trains the subnetwork while
dynamically adjusting its sparse mask, resulting in many diverse sparse
subnetworks throughout the training process. FreeTickets is defined as the
ensemble of these sparse subnetworks freely obtained during this one-pass,
sparse-to-sparse training, which uses only a fraction of the computational
resources required by the vanilla dense training. Moreover, despite being an
ensemble of models, FreeTickets has even fewer parameters and training FLOPs
compared to a single dense model: this seemingly counter-intuitive outcome is
due to the high sparsity of each subnetwork. FreeTickets is observed to
demonstrate a significant all-round improvement compared to standard dense
baselines, in prediction accuracy, uncertainty estimation, robustness, and
efficiency. FreeTickets easily outperforms the naive deep ensemble with
ResNet50 on ImageNet using only a quarter of the training FLOPs required by the
latter. Our results provide insights into the strength of sparse neural
networks and suggest that the benefits of sparsity go way beyond the usually
expected inference efficiency.


Abstract:
  In part 1 of this paper some linear weighted generalized Fibonacci number
summation identities were derived using the fact that the Fibonacci number is
the residue of a rational function. In this part, using the same method, some
quadratic and cubic weighted generalized Fibonacci number summation identities
are derived, including some infinite series and generating functions. In
addition some quadratic and cubic weighted generalized Fibonacci number
summation identities with binomial coefficients, and some cubic and quartic
Fibonacci and Lucas number summation identities are derived.


Abstract:
  Language implementation frameworks, e.g., RPython and Truffle/Graal, are
practical tools for creating efficient virtual machines, including a
well-functioning just-in-time (JIT) compiler. It is demanding to support
multi-tier JIT compilation in such a framework for language developers. This
paper presents an idea to generate threaded code by reusing an existing
meta-tracing JIT compiler, as well as an interpreter design for it. Our
approach does not largely modify the RPython itself but constructs an effective
interpreter definition to enable threaded code generation in RPython. We expect
our system to be extended to support multi-level JIT compilation in the RPython
framework. We measured the potential performance of our threaded code
generation by simulating its behavior in PyPy. We confirmed that our approach
reduced code sizes by 80 \% and compilation times by 60 \% compared to PyPy's
JIT compiler on average, and ran about 7 \% faster than the interpreter-only
execution.


Abstract:
  The Q-Star structures, also called as gray holes with a radius of two times
greater than the corresponding Schwarzschild radius of same mass and from the
compact neutron star family with a very high density, have been obtained by
using a generalization of $q$-deformed fermions with two parameters $q$ and $p$
as the constituents of the star. Because the interaction between particles are
controlled by deformation parameters in $q,p$-fermions instead of a complicated
interaction Lagrangian, we consider the deformation parameters giving the
maximum Q-Star pressure between the interacting particles of stars. The cold
and hot Q-Stars in temperatures of $T=0$, $T=40\ \mathrm{MeV}$ and $T=60\
\mathrm{MeV}$ have been investigated from the numerical solutions of TOV
equations and it is obtained that the Q-Stars can reach up to $13$ solar mass
and $75$ Km radius. Moreover, it is found that the total mass and radius of the
star increase when the star gets cool down, and a much denser state is reached.


Abstract:
  Einstein's most famous equation -- $E=mc^2$ -- generated a short-circuit
between the concepts of mass and energy, which also affects other concepts like
matter, radiation, and vacuum. Physics currently has a mixture of classical,
relativistic, and quantum concepts of mass, which generates a great deal of
confusion and many problems. Clear definitions need to be established if one
wants to avoid ghost hunting. In particular, by abandoning the idea of mass and
focusing on time and energy, some interesting implications are emerging. It is
noted that the cutoff frequency of the quantum vacuum energy, consistent with
the observations of the cosmological constant, corresponds to that of the
Cosmic Microwave Background. This might be consistent with the hypothesis of a
rotating and expanding universe described by a Kerr-de Sitter metric with the
observed cosmological constant. To verify this hypothesis is crucial to prove
the rotation of the universe.


Abstract:
  In a quantum-noise limited system, weak-value amplification using
post-selection normally does not produce more sensitive measurements than
standard methods for ideal detectors: the increased weak value is compensated
by the reduced power due to the small post-selection probability. Here we
experimentally demonstrate recycled weak-value measurements using a pulsed
light source and optical switch to enable nearly deterministic weak-value
amplification of a mirror tilt. Using photon counting detectors, we demonstrate
a signal improvement by a factor of $4.4 \pm 0.2$ and a signal-to-noise ratio
improvement of $2.10 \pm 0.06$, compared to a single-pass weak-value
experiment, and also compared to a conventional direct measurement of the tilt.
The signal-to-noise ratio improvement could reach around 6 for the parameters
of this experiment, assuming lower loss elements.


Abstract:
  The massive spread of false information on social media has become a global
risk especially in a global pandemic situation like COVID-19. False information
detection has thus become a surging research topic in recent months.
NLP4IF-2021 shared task on fighting the COVID-19 infodemic has been organised
to strengthen the research in false information detection where the
participants are asked to predict seven different binary labels regarding false
information in a tweet. The shared task has been organised in three languages;
Arabic, Bulgarian and English. In this paper, we present our approach to tackle
the task objective using transformers. Overall, our approach achieves a 0.707
mean F1 score in Arabic, 0.578 mean F1 score in Bulgarian and 0.864 mean F1
score in English ranking 4th place in all the languages.


Abstract:
  We analyse three time integration schemes for unfitted methods in fluid
structure interaction. In Alghorithm 1 we propose a fully discrete monolithic
algorithm with P1 P1 stabilized finite elements for the fluid problem; for this
alghorithm we prove well-posedness, unconditional stability and convergence in
the case of linearized problem (see Propositions 2.4.2, 2.4.3 and Theorem
3.3.7, respectively). The analysis optimal convergence rates as expected from
the Euler scheme, and the supposed regularity of the solution to the continuous
problem. Moreover we introduce two algorithms that allow for a partitioning of
the coupled problem by exploiting an explicit-implicit treatment of the
transmission conditions. Algorithm 2 represents, essentially, a simplification
of Algorithm 1 since it simply treat the solid elastic forces in explicit form
using the displacement and velocities of the structure evaluated in the
previous time steps. Instead, Algorithm 3, is really a splitting algorithm that
involves the solution of two staggered problems. It splits the forces that
solid transfers to fluid in two contributions: the inertial contribution that
is treated in implicit form and the elastic contribution that is treated in
explicit form. We perform the stability analysis for both the schemes in
Theorems 4.3.1 and 4.3.3. Algorithm 2 results conditionally stable for all the
extrapolations considered, instead Algorithm 3 is unconditionally stable, for
extrapolations of order zero and one, and conditionally stable for the
extrapolation of order two. Since Algorithm 3 is the most promising, we perform
the convergence analysis in the linearized case (see Theorem 4.4.2) obtaining
results in line with those of the monolithic case, in particular the splitting
introduced preserves optimal conevegence rates.


Abstract:
  Traction adaptive motion planning and control has potential to improve an an
automated vehicle's ability to avoid accident in a critical situation. However,
such functionality require an accurate friction estimate for the road ahead of
the vehicle that is updated in real time. Current state of the art friction
estimation techniques include high accuracy local friction estimation in the
presence of tire slip, as well as rough classification of the road surface
ahead of the vehicle, based on forward looking camera. In this paper we show
that neither of these techniques in isolation yield satisfactory behavior when
deployed with traction adaptive motion planning and control functionality.
However, fusion of the two provides sufficient accuracy, availability and
foresight to yield near optimal behavior. To this end, we propose a fusion
method based on heteroscedastic gaussian process regression, and present
initial simulation based results.


Abstract:
  New and unique opportunities now exist to look for technosignatures (TS)
beyond traditional SETI radio searches, motivated by tremendous advances in
exoplanet science and observing capabilities in recent years. Space agencies,
both public and private, may be particularly interested in learning about the
community's views as to the optimal methods for future TS searches with current
or forthcoming technology. This report is an effort in that direction. We put
forward a set of possible mission concepts designed to search for TS, although
the data supplied by such missions would also benefit other areas of
astrophysics. We introduce a novel framework to analyze a broad diversity of TS
in a quantitative manner. This framework is based on the concept of ichnoscale,
which is a new parameter related to the scale of a TS cosmic footprint,
together with the number of potential targets where such TS can be searched
for, and whether or not it is continuous in time.


Abstract:
  Materials with strong $\chi^{(2)}$ optical nonlinearity, especially lithium
niobate, play a critical role in building optical parametric oscillators
(OPOs). However, chip-scale integration of low-loss $\chi^{(2)}$ materials
remains challenging and limits the threshold power of on-chip $\chi^{(2)}$ OPO.
Here we report the first on-chip lithium niobate optical parametric oscillator
at the telecom wavelengths using a quasi-phase matched, high-quality microring
resonator, whose threshold power ($\sim$30 $\mu$W) is 400 times lower than that
in previous $\chi^{(2)}$ integrated photonics platforms. An on-chip power
conversion efficiency of 11% is obtained at a pump power of 93 $\mu$W. The OPO
wavelength tuning is achieved by varying the pump frequency and chip
temperature. With the lowest power threshold among all on-chip OPOs
demonstrated so far, as well as advantages including high conversion
efficiency, flexibility in quasi-phase matching and device scalability, the
thin-film lithium niobate OPO opens new opportunities for chip-based tunable
classical and quantum light sources and provides an potential platform for
realizing photonic neural networks.


Abstract:
  In this paper, to improve wear resistance of components such as screws under
severe friction-wear, Fe-Cr-C-Mo-V-Ti-N hardfacing coatings were further
developed. The hardfacing coatings were acquired by shielded manual arc welding
(SMAW) method. The ferroalloys added into the coating flux of the hardfaced
electrode were jointly nitrided. The microstructure of the coatings was carried
out using X-ray diffraction(XRD), optical microscope(OM), field emission
scanning electron microscope (FESEM) and energy dispersive Xray spectrometry
(EDS). In addition, FactSage 7.0 software was employed to calculate the
equilibrium phase diagram of the hardfacings. The wear resistance was performed
on a pin-on-disc machine. The Fe-Cr-C- Mo-V-Ti-N hardfacings exhibited higher
wear resistance than cladding layer without nitrides.


Abstract:
  This demo paper presents the first system for playing the popular Angry Birds
game using a domain-independent planner. Our system models Angry Birds levels
using PDDL+, a planning language for mixed discrete/continuous domains. It uses
a domain-independent PDDL+ planner to generate plans and executes them. In this
demo paper, we present the system's PDDL+ model for this domain, identify key
design decisions that reduce the problem complexity, and compare the
performance of our system to model-specific methods for this domain. The
results show that our system's performance is on par with other domain-specific
systems for Angry Birds, suggesting the applicability of domain-independent
planning to this benchmark AI challenge.


Abstract:
  Federated learning (FL) is a collaborative machine learning paradigm, which
enables deep learning model training over a large volume of decentralized data
residing in mobile devices without accessing clients' private data. Driven by
the ever increasing demand for model training of mobile applications or
devices, a vast majority of FL tasks are implemented over wireless fading
channels. Due to the time-varying nature of wireless channels, however, random
delay occurs in both the uplink and downlink transmissions of FL. How to
analyze the overall time consumption of a wireless FL task, or more
specifically, a FL's delay distribution, becomes a challenging but important
open problem, especially for delay-sensitive model training. In this paper, we
present a unified framework to calculate the approximate delay distributions of
FL over arbitrary fading channels. Specifically, saddle point approximation,
extreme value theory (EVT), and large deviation theory (LDT) are jointly
exploited to find the approximate delay distribution along with its tail
distribution, which characterizes the quality-of-service of a wireless FL
system. Simulation results will demonstrate that our approximation method
achieves a small approximation error, which vanishes with the increase of
training accuracy.


Abstract:
  People with Alzheimer's disease and related dementias (ADRD) often show the
problem of repetitive questioning, which brings a great burden on persons with
ADRD (PwDs) and their caregivers. Conversational robots hold promise of coping
with this problem and hence alleviating the burdens on caregivers. In this
paper, we proposed a partially observable markov decision process (POMDP) model
for the PwD-robot interaction in the context of repetitive questioning, and
used Q-learning to learn an adaptive conversation strategy (i.e., rate of
follow-up question and difficulty of follow-up question) towards PwDs with
different cognitive capabilities and different engagement levels. The results
indicated that Q-learning was helpful for action selection for the robot. This
may be a useful step towards the application of conversational social robots to
cope with repetitive questioning in PwDs.


Abstract:
  In the paper we study what sets can be obtained as $\alpha$-limit sets of
backward trajectories in graph maps. We show that in the case of mixing maps,
all those $\alpha$-limit sets are $\omega$-limit sets and for all but finitely
many points $x$, we can obtain every $\omega$-limits set as the $\alpha$-limit
set of a backward trajectory starting in $x$. For zero entropy maps, every
$\alpha$-limit set of a backward trajectory is a minimal set. In the case of
maps with positive entropy, we obtain a partial characterization which is very
close to complete picture of the possible situations.


Abstract:
  Steering tokamak plasmas is commonly viewed as a way to avoid disruptions and
runaway electrons. Plasma steering sounds as safe as driving to work but will
be shown to more closely resemble driving at high speed through a dense fog on
an icy road. The long time required to terminate an ITER discharge compared to
time over which dangers can be foreseen is analogous to driving in a dense fog.
The difficulty of regaining plasma control if it is lost resembles driving on
an icy road. Disruptions and runaways are associated with three issues -- a
solution to one tends to complicate the solution to the other two: loss of
plasma position control, excessive heat deposition, and wall melting due to
runaway electrons. All three risks must be addressed for ITER to achieve its
mission and essentially eliminated before tokamak power plants can be deployed.


Abstract:
  Modern day computing increasingly relies on specialization to satiate growing
performance and efficiency requirements. A core challenge in designing such
specialized hardware architectures is how to perform mapping space search,
i.e., search for an optimal mapping from algorithm to hardware. Prior work
shows that choosing an inefficient mapping can lead to multiplicative-factor
efficiency overheads. Additionally, the search space is not only large but also
non-convex and non-smooth, precluding advanced search techniques. As a result,
previous works are forced to implement mapping space search using expert
choices or sub-optimal search heuristics.
  This work proposes Mind Mappings, a novel gradient-based search method for
algorithm-accelerator mapping space search. The key idea is to derive a smooth,
differentiable approximation to the otherwise non-smooth, non-convex search
space. With a smooth, differentiable approximation, we can leverage efficient
gradient-based search algorithms to find high-quality mappings. We extensively
compare Mind Mappings to black-box optimization schemes used in prior work.
When tasked to find mappings for two important workloads (CNN and MTTKRP), the
proposed search finds mappings that achieve an average $1.40\times$,
$1.76\times$, and $1.29\times$ (when run for a fixed number of steps) and
$3.16\times$, $4.19\times$, and $2.90\times$ (when run for a fixed amount of
time) better energy-delay product (EDP) relative to Simulated Annealing,
Genetic Algorithms and Reinforcement Learning, respectively. Meanwhile, Mind
Mappings returns mappings with only $5.32\times$ higher EDP than a possibly
unachievable theoretical lower-bound, indicating proximity to the global
optima.


Abstract:
  We demonstrate a passive down-conversion imaging system that converts
broadband ultraviolet light to narrow-band green light while preserving the
directionality of rays, and thus enabling direct down-conversion imaging. At
the same time our system has high transparency in the visible, enabling
superimposed visible and ultraviolet imaging. The frequency conversion is
performed by a subwavelength-thickness transparent downconverter based on
highly efficient CsPbBr3 nanocrystals incorporated into the focal plane of a
simple telescope or relay-lens geometry. The resulting imaging performance of
this down-conversion system approaches the diffraction limit. This
demonstration sets the stage for the incorporation of other high-efficiency
perovskite nanocrystal materials to enable passive multi-frequency conversion
imaging systems.


Abstract:
  In this work we give a complete picture of how to in a direct simple way
define the mass at null infinity in harmonic coordinates in three different
ways that we show satisfy the Bondi mass loss law. The first and second way
involve only the limit of metric (Trautman mass) respectively the null second
fundamental forms along asymptotically characteristic surfaces (asymptotic
Hawking mass) that only depend on the ADM mass. The last in an original way
involves construction of special characteristic coordinates at null infinity
(Bondi mass). The results here rely on asymptotics of the metric derived in
[24].


Abstract:
  The heaviest N=Z doubly-magic nucleus, $^{100}$Sn, and the neighboring nuclei
offer unique opportunities to investigate the properties of nuclear interaction
in extreme conditions. In particular, the Cd isotopes are expected to present
features similar to those found in the Sn isotopic chain, since they have only
two proton holes in the Z=50 shell. In this manuscript, the lifetime
measurements of low-lying states in the even-mass $^{102-108}$Cd is presented.
Thanks to the powerful detection capabilities of AGATA array and VAMOS++
spectrometer, the unusual employment of multi-nucleon transfer reactions
permitted to investigate the first 2$^+$ and 4$^+$ states in all these nuclei,
together with various deformed bands in $^{106}$Cd. The results were
interpreted in the context of new state-of-the-art beyond-mean-field
calculations, using the symmetry-conserving configuration-mixing approach.
Despite the similarities in the electromagnetic properties of the low-lying
states, there is a fundamental structural difference between the ground-state
bands in the Z=48 and Z=50 isotopes. The comparison between experimental and
theoretical results revealed a rotational character of the Cd nuclei, which
have prolate-deformed ground states with $\beta_2 \approx 0.2$. At this
deformation Z=48 becomes a closed-shell configuration, which is favored with
respect to the spherical one.


Abstract:
  In this paper, we consider a (nonlinear) transformation $\Phi$ of invertible
positive elements in $C^*$-algebras which preserves the norm of any of the
three fundamental means of positive elements; namely, $\|\Phi(A)\mm \Phi(B)\| =
\|A\mm B\|$, where $\mm$ stands for the arithmetic mean $A\nabla B=(A+B)/2$,
the geometric mean $A\#B=A^{1/2}(A^{-1/2}BA^{-1/2})^{1/2}A^{1/2}$, or the
harmonic mean $A!B=2(A^{-1} + B^{-1})^{-1}$. Assuming that $\Phi$ is surjective
and preserves either the norm of the arithmetic mean or the norm of the
geometric mean, we show that $\Phi$ extends to a Jordan $*$-isomorphism between
the underlying full algebras. If $\Phi$ is surjective and preserves the norm of
the harmonic mean, then we obtain the same conclusion in the special cases
where the underlying algebras are $AW^*$-algebras or commutative
$C^*$-algebras.
  In the commutative case, for a transformation $T: F(\mathrm{X})\subset
C_0(\mathrm{X})_+\rightarrow C_0(\mathrm{Y})_+$, we can relax the surjectivity
assumption and show that $T$ is a generalized composition operator if $T$
preserves the norm of the (arithmetic, geometric, harmonic, or in general any
power) mean of any finite collection of positive functions, provided that the
domain $F(\mathrm{X})$ contains sufficiently many elements to peak on compact
$G_\delta$ sets. When the image $T(F(\mathrm{X}))$ also contains sufficiently
many elements to peak on compact $G_\delta$ sets, $T$ extends to an algebra
$*$-isomorphism between the underlying full function algebras.


Abstract:
  The $k$-gonal models of random groups are defined as the quotients of free
groups on $n$ generators by cyclically reduced words of length $k$. As $k$
tends to infinity, this model approaches the Gromov density model. In this
paper we show that for any fixed $d_0 \in (0, 1)$, if positive $k$-gonal random
groups satisfy Property (T) with overwhelming probability for densities $d
>d_0$, then so do $nk$-gonal random groups, for any $n \in \mathbb{N}$. In
particular, this shows that for densities above 1/3, groups in $3k$-gonal
models satisfy Property (T) with probability 1 as $n$ approaches infinity.


Abstract:
  A data-centric approach with Natural Language Processing (NLP) to predict
personality types based on the MBTI (an introspective self-assessment
questionnaire that indicates different psychological preferences about how
people perceive the world and make decisions) through systematic enrichment of
text representation, based on the domain of the area, under the generation of
features based on three types of analysis: sentimental, grammatical and
aspects. The experimentation had a robust baseline of stacked models, with
premature optimization of hyperparameters through grid search, with gradual
feedback, for each of the four classifiers (dichotomies) of MBTI. The results
showed that attention to the data iteration loop focused on quality,
explanatory power and representativeness for the abstraction of more
relevant/important resources for the studied phenomenon made it possible to
improve the evaluation metrics results more quickly and less costly than
complex models such as the LSTM or state of the art ones as BERT, as well as
the importance of these results by comparisons made from various perspectives.
In addition, the study demonstrated a broad spectrum for the evolution and
deepening of the task and possible approaches for a greater extension of the
abstraction of personality types.


Abstract:
  We study the concomitant breaking of spatial translations and dilatations in
Ginzburg-Landau-like models, where the dynamics responsible for the symmetry
breaking is described by an effective Mexican hat potential for spatial
gradients. We show that there are fractonic modes with either subdimensional
propagation or no propagation altogether, namely, immobility. Such class of
effective field theories encompasses instances of helical superfluids and
meta-fluids, where fractons can be connected to an emergent symmetry under
higher moment charges, leading in turns to the trivialization of some elastic
coefficients. The introduction of a finite charge density alters the mobility
properties of fractons and leads to a competition between the chemical
potential and the superfluid velocity in determining the gap of the dilaton.
The mobility of fractons can also be altered at zero density upon considering
additional higher-derivative terms.


Abstract:
  Employing high-resolution EUV imaging observations from SDO/AIA, we analyse a
compressive plasma oscillation in a hot coronal loop triggered by a C-class
flare near one of its foot points as first studied by Kumar et al. We
investigate the oscillation properties in both the 131{\,}{\AA} and 94{\,}{\AA}
channels and find that what appears as a pure sloshing oscillation in the
131{\,}{\AA} channel actually transforms into a standing wave in the
94{\,}{\AA} channel at a later time. This is the first clear evidence of such
transformation confirming the results of a recent numerical study which
suggests that these two oscillations are not independent phenomena. We
introduce a new analytical expression to properly fit the sloshing phase of an
oscillation and extract the oscillation properties. For the AIA 131{\,}{\AA}
channel, the obtained oscillation period and damping time are 608$\pm$4{\,}s
and 431$\pm$20{\,}s, respectively during the sloshing phase. The corresponding
values for the AIA 94{\,}{\AA} channel are 617$\pm$3{\,}s and 828$\pm$50{\,}s.
During the standing phase that is observed only in the AIA 94{\,}{\AA} channel,
the oscillation period and damping time have increased to 791$\pm$5{\,}s and
1598$\pm$138{\,}s, respectively. The plasma temperature obtained from the DEM
analysis indicates substantial cooling of the plasma during the oscillation.
Considering this, we show that the observed oscillation properties and the
associated changes are compatible with damping due to thermal conduction. We
further demonstrate that the absence of a standing phase in the 131{\,}{\AA}
channel is a consequence of cooling plasma besides the faster decay of
oscillation in this channel.


Abstract:
  In this paper we consider traces at initial times for functions with mixed
time-space smoothness. Such results are often needed in the theory of evolution
equations. Our result extends and unifies many previous results. Our main
improvement is that we can allow general interpolation couples. The abstract
results are applied to regularity problems for fractional evolution equations
and stochastic evolution equation, where uniform trace estimates on the
half-line are shown.


Abstract:
  Differentially private (DP) machine learning allows us to train models on
private data while limiting data leakage. DP formalizes this data leakage
through a cryptographic game, where an adversary must predict if a model was
trained on a dataset D, or a dataset D' that differs in just one example.If
observing the training algorithm does not meaningfully increase the adversary's
odds of successfully guessing which dataset the model was trained on, then the
algorithm is said to be differentially private. Hence, the purpose of privacy
analysis is to upper bound the probability that any adversary could
successfully guess which dataset the model was trained on.In our paper, we
instantiate this hypothetical adversary in order to establish lower bounds on
the probability that this distinguishing game can be won. We use this adversary
to evaluate the importance of the adversary capabilities allowed in the privacy
analysis of DP training algorithms.For DP-SGD, the most common method for
training neural networks with differential privacy, our lower bounds are tight
and match the theoretical upper bound. This implies that in order to prove
better upper bounds, it will be necessary to make use of additional
assumptions. Fortunately, we find that our attacks are significantly weaker
when additional (realistic)restrictions are put in place on the adversary's
capabilities.Thus, in the practical setting common to many real-world
deployments, there is a gap between our lower bounds and the upper bounds
provided by the analysis: differential privacy is conservative and adversaries
may not be able to leak as much information as suggested by the theoretical
bound.


Abstract:
  A robot working in human-centric environments needs to know which kind of
objects exist in the scene, where they are, and how to grasp and manipulate
various objects in different situations to help humans in everyday tasks.
Therefore, object recognition and grasping are two key functionalities for such
robots. Most state-of-the-art tackles object recognition and grasping as two
separate problems while both use visual input. Furthermore, the knowledge of
the robot is fixed after the training phase. In such cases, if the robot faces
new object categories, it must retrain from scratch to incorporate new
information without catastrophic interference. To address this problem, we
propose a deep learning architecture with augmented memory capacities to handle
open-ended object recognition and grasping simultaneously. In particular, our
approach takes multi-views of an object as input and jointly estimates
pixel-wise grasp configuration as well as a deep scale- and rotation-invariant
representation as outputs. The obtained representation is then used for
open-ended object recognition through a meta-active learning technique. We
demonstrate the ability of our approach to grasp never-seen-before objects and
to rapidly learn new object categories using very few examples on-site in both
simulation and real-world settings.


Abstract:
  In this paper we construct distance-regular graphs admitting a transitive
action of the five sporadic simple groups discovered by E. Mathieu, the Mathieu
groups $M_{11}$, $M_{12}$, $M_{22}$, $M_{23}$ and $M_{24}$. From the code
spanned by the adjacency matrix of the strongly regular graph with parameters
(176,70,18,34) we obtain block designs having the full automorphism groups
isomorphic to the Higman-Sims finite simple group. Further, we discuss a
possibility of permutation decoding of the codes spanned by the adjacency
matrices of the graphs constructed and find small PD-sets for some of the
codes.


Abstract:
  In hot dense plasmas of intermediate or high-Z elements in the state of local
thermodynamic equilibrium, the number of electronic configurations contributing
to key macroscopic quantities such as the spectral opacity and equation of
state, can be enormous. In this work we present systematic methods for the
analysis of the number of relativistic electronic configurations in a plasma.
While the combinatoric number of configurations can be huge even for mid-Z
elements, the number of configurations which have non negligible population is
much lower and depends strongly and non-trivially on temperature and density.
We discuss two useful methods for the estimation of the number of populated
configurations: (i) using an exact calculation of the total combinatoric number
of configurations within superconfigurations in a converged
super-transition-array (STA) calculation, and (ii) by using an estimate for the
multidimensional width of the probability distribution for electronic
population over bound shells, which is binomial if electron exchange and
correlation effects are neglected. These methods are analyzed, and the
mechanism which leads to the huge number of populated configurations is
discussed in detail. Comprehensive average atom finite temperature density
functional theory (DFT) calculations are performed in a wide range of
temperature and density for several low, mid and high Z plasmas. The effects of
temperature and density on the number of populated configurations are discussed
and explained.


Abstract:
  We introduce a minimalist outbreak forecasting model that combines
data-driven parameter estimation with variational data assimilation. By
focusing on the fundamental components of nonlinear disease transmission and
representing data in a domain where model stochasticity simplifies into a
process with independent increments, we design an approach that only requires
four core parameters to be estimated. We illustrate this novel methodology on
COVID-19 forecasts. Results include case count and deaths predictions for the
US and all of its 50 states, the District of Columbia, and Puerto Rico. The
method is computationally efficient and is not disease- or location-specific.
It may therefore be applied to other outbreaks or other countries, provided
case counts and/or deaths data are available.


Abstract:
  Moir\'e lattices which consist of parallel but staggered periodic lattices
have been extensively explored due to their salient physical properties, such
as van Hove singularities[1, 2], commensurable incommensurable transitions[3],
non-Abelian gauge potentials[4], fractional quantum Hall effects[5-7], van der
Waals interfaces[8, 9] and unconventional superconductivity[10, 11]. However,
there are limited demonstrations of such concepts for classical wave systems.
Here, we realized gauge fields in one-dimensional Moir\'e phononic lattices
consisting of two superimposed periodic patterns which mismatched with each
other along one direction. Benefiting from gauge fields, we generated Landau
level flat bands near the Dirac cone and experimentally measured their spatial
localization in pressure-field distributions. Then, by mismatching lattices
along both directions, we constructed two-dimensional Moir\'e phononic lattices
with van der Waals interfaces. We found that acoustic waves efficiently
transported along van der Waals interfaces behaving as metallic networks. As
mismatched lattices are well-controllable, our study offers a novel path to
manipulate sound waves which are inaccessible in traditional periodic acoustic
systems, and can be easily extended to mechanics, optics, electromagnetics and
electronics.


Abstract:
  Stiffness variation and real-time position feedback are critical for any
robotic system but most importantly for active and wearable devices to interact
with the user and environment. Currently, for compact sizes, there is a lack of
solutions bringing high-fidelity feedback and maintaining design and functional
integrity. In this work, we propose a novel minimal clutch with integrated
stiffness variation and real-time position feedback whose performance surpasses
conventional jamming solutions. We introduce integrated design, modeling, and
verification of the clutch in detail. Preliminary experimental results show the
change in impedance force of the clutch is close to 24-fold at the maximum
force density of 15.64 N/cm2. We validated the clutch experimentally in (1)
enhancing the bending stiffness of a soft actuator to increase a soft
manipulator's gripping force by 73%; (2) enabling a soft cylindrical actuator
to execute omnidirectional movement; (3) providing real-time position feedback
for hand posture detection and impedance force for kinesthetic haptic feedback.
This manuscript presents the functional components with a focus on the
integrated design methodology, which will have an impact on the development of
soft robots and wearable devices.


Abstract:
  The light curve of KIC 8462852, a.k.a Boyajian's Star, undergoes deep dips
the origin of which remains unclear. A faint star $\approx$2\arcsec to the east
was discovered in Keck/NIRC2 imaging in Boyajian et al. (2016), but its status
as a binary, and possible contribution to the observed variability, was
unclear. Here, we use three epochs of Keck/NIRC2 imaging, spanning five years,
in JHK near-infrared bands to obtain 1-mas precision astrometry. We show that
the two objects exhibit common proper motion, measure a relative velocity of
$\mu=0.14\pm0.44$ mas yr$^{-1}$ ($\mu=0.30\pm0.93$ km s$^{-1}$) and conclude
that they are a binary pair at $880\pm10$ AU projected separation. There is
marginal detection of possible orbital motion, but our astrometry is
insufficient to characterize the orbit. We show that two other point sources
are not associated with KIC 8462852. We recommend that attempts to model KIC
8462852 A's light curve should revisit the possibility that the bound stellar
companion may play a role in causing the irregular brightness variations, for
example through disruption of the orbits of bodies around the primary due to
long-term orbital evolution of the binary orbit.


Abstract:
  Two-dimensional array-based datasets are pervasive in a variety of domains.
Current approaches for generative modeling have typically been limited to
conventional image datasets and performed in the pixel domain which do not
explicitly capture the correlation between pixels. Additionally, these
approaches do not extend to scientific and other applications where each
element value is continuous and is not limited to a fixed range. In this paper,
we propose a novel approach for generating two-dimensional datasets by moving
the computations to the space of representation bases and show its usefulness
for two different datasets, one from imaging and another from scientific
computing. The proposed approach is general and can be applied to any dataset,
representation basis, or generative model. We provide a comprehensive
performance comparison of various combinations of generative models and
representation basis spaces. We also propose a new evaluation metric which
captures the deficiency of generating images in pixel space.


Abstract:
  In the present work, we study the large scale matter power spectrum as well
as the observed galaxy power spectrum for non-canonical tachyon field dark
energy model considering the full general relativistic perturbation equations.
We form a set of coupled autonomous equations including both the background and
linearly perturbed quantities and obtain their solutions numerically with
proper set of initial conditions. We consider different scalar field potentials
for our study. Deviations from concordance $\Lambda$CDM model are studied for
different relevant quantities. Our study shows that non-canonical tachyon dark
energy model produces enhanced gravitational potentials, comoving density
contrast as well as linear growth factor for matter perturbations compared to
$\Lambda$CDM. It is also observed that for tachyon dark energy models, there is
suppression of power on large scales compared to both $\Lambda$CDM model as
well as previously studied canonical scalar field models.


Abstract:
  We present some problems related to the conjugacy classes of
$\mathsf{Aut}(\mathbb{N}^*)$.


Abstract:
  In the pseudospin-$\frac{1}{2}$ honeycomb Mott insulators with strong
spin-orbit coupling, there are two types of bond-dependent exchange
interactions, named Kitaev ($K$) and $\Gamma$, leading to strong frustration.
While the ground state of the Kitaev model is a quantum spin liquid with
fractionalized excitations, the ground state of the $\Gamma$ model remains
controversial. In particular, the phase diagram of the $K\Gamma$ model with
ferromagnetic $K$ and antiferromagnetic $\Gamma$ interactions has been
intensively studied because of its relevance to candidate materials such as
$\alpha$-RuCl$_{3}$. Numerical studies also included the effects of tuning the
bond strengths, i.e., $z$-bond strength different from the other bonds.
However, no clear consensus on the overall phase diagram has been reached yet.
Here we study the classical $K\Gamma$ model with the anisotropic bond strength
using Monte Carlo simulations to understand the emerging phases out of
competition between the two frustrated limits. We also address how the
anisotropic bond strength affects the phase diagram and strength of quantum
fluctuations. We found various large unit cell phases due to the competing
frustrations, and analyzed their intrinsic degeneracy based on the symmetry of
the Hamiltonian. Using the linear spin wave theory we showed that the
anisotropic bond strength enhances quantum fluctuations in the
$\Gamma$-dominant regime where a small reduced moment is observed. The
implications of our findings in relation to the quantum model are also
discussed.


Abstract:
  Many interesting applications of hyperbolic systems of equations are stiff,
and require the time step to satisfy restrictive stability conditions. One way
to avoid small time steps is to use implicit time integration. Implicit
integration is quite straightforward for first order schemes. High order
schemes instead need also to control spurious oscillations, which requires
limiting in space and time also in the implicit case. We propose a framework to
simplify considerably the application of high order non oscillatory schemes
through the introduction of a low order implicit predictor, which is used both
to set up the nonlinear weights of a standard high order space reconstruction,
and to achieve limiting in time. In this preliminary work, we concentrate on
the case of a third order scheme, based on DIRK integration in time and CWENO
reconstruction in space. The numerical tests involve linear and nonlinear
scalar conservation laws.


Abstract:
  Friedlander, Mac\^{e}do, and Pong recently introduced the projected polar
proximal point algorithm (P4A) for solving optimization problems by using the
closed perspective transforms of convex objectives. We analyse a generalization
(GP4A) which replaces the closed perspective transform with a more general
closed gauge. We decompose GP4A into the iterative application of two separate
operators, and analyse it as a splitting method. By showing that GP4A and its
under-relaxations exhibit global convergence whenever a fixed point exists, we
obtain convergence guarantees for P4A by letting the gauge specify to the
closed perspective transform for a convex function. We then provide
easy-to-verify sufficient conditions for the existence of fixed points for the
GP4A, using the Minkowski function representation of the gauge. Conveniently,
the approach reveals that global minimizers of the objective function for P4A
form an exposed face of the dilated fundamental set of the closed perspective
transform.


Abstract:
  Microservice and serverless computing systems open up massive versatility and
opportunity to distributed and datacenter-scale computing. In the meantime, the
deployments of modern datacenter resources are moving to disaggregated
architectures. With the flourishing growths from both sides, we believe this is
high time to write this vision paper to propose a potential research agenda to
achieve efficient deployments, management, and executions of next-generation
microservices on top of the emerging disaggregated datacenter architectures. In
particular, we envision a critical systems research direction of designing and
developing offloadable and migratable microservices on disaggregated
architectures. With this vision, we have surveyed the recent related work to
demonstrate the importance and necessity of researching it. We also outline the
fundamental challenges that distributed systems and datacenter-scale computing
research may encounter. We further propose a research roadmap to achieve our
envisioned objectives in a promising way. Within the roadmap, we identify
potential techniques and methods that can be leveraged.


Abstract:
  The cogirth, $g^\ast(M)$, of a matroid $M$ is the size of a smallest
cocircuit of $M$. Finding the cogirth of a graphic matroid can be done in
polynomial time, but Vardy showed in 1997 that it is NP-hard to find the
cogirth of a binary matroid. In this paper, we show that $g^\ast(M)\leq
\frac{1}{2}\vert E(M)\vert$ when $M$ is binary, unless $M$ simplifies to a
projective geometry. We also show that, when equality holds, $M$ simplifies to
a Bose-Burton geometry, that is, a matroid of the form $PG(r-1,2)-PG(k-1,2)$.
These results extend to matroids representable over arbitrary finite fields.


Abstract:
  Understanding an opponent agent helps in negotiating with it. Existing works
on understanding opponents focus on preference modeling (or estimating the
opponent's utility function). An important but largely unexplored direction is
recognizing an opponent's negotiation strategy, which captures the opponent's
tactics, e.g., to be tough at the beginning but to concede toward the deadline.
Recognizing complex, state-of-the-art, negotiation strategies is extremely
challenging, and simple heuristics may not be adequate for this purpose. We
propose a novel data-driven approach for recognizing an opponent's s
negotiation strategy. Our approach includes a data generation method for an
agent to generate domain-independent sequences by negotiating with a variety of
opponents across domains, a feature engineering method for representing
negotiation data as time series with time-step features and overall features,
and a hybrid (recurrent neural network-based) deep learning method for
recognizing an opponent's strategy from the time series of bids. We perform
extensive experiments, spanning four problem scenarios, to demonstrate the
effectiveness of our approach.


Abstract:
  In the paper, we proposed the Dantzig selector based on the $\ell_{1}-\alpha
\ell_{2}$~$(0< \alpha \leq1)$ minimization for the signal recovery. In the
Dantzig selector, the constraint $\|{\bf A}^{\top}({\bf b}-{\bf A}{\bf
x})\|_\infty \leq \eta$ for some small constant $\eta>0$ means the columns of
${\bf A}$ has very weakly correlated with the error vector ${\bf e}={\bf A}{\bf
x}-{\bf b}$. First, recovery guarantees based on the restricted isometry
property (RIP) are established for signals. Next, we propose the effective
algorithm to solve the proposed Dantzig selector. Last, we illustrate the
proposed model and algorithm by extensive numerical experiments for the
recovery of signals in the cases of Gaussian, impulsive and uniform noise. And
the performance of the proposed Dantzig selector is better than that of the
existing methods.


Abstract:
  Malaria microscopy, microscopic examination of stained blood slides to detect
parasite Plasmodium, is considered to be a gold-standard for detecting
life-threatening disease malaria. Detecting the plasmodium parasite requires a
skilled examiner and may take up to 10 to 15 minutes to completely go through
the whole slide. Due to a lack of skilled medical professionals in the
underdeveloped or resource deficient regions, many cases go misdiagnosed;
resulting in unavoidable complications and/or undue medication. We propose to
complement the medical professionals by creating a deep learning-based method
to automatically detect (localize) the plasmodium parasites in the photograph
of stained film. To handle the unbalanced nature of the dataset, we adopt a
two-stage approach. Where the first stage is trained to detect blood cells and
classify them into just healthy or infected. The second stage is trained to
classify each detected cell further into the life-cycle stage. To facilitate
the research in machine learning-based malaria microscopy, we introduce a new
large scale microscopic image malaria dataset. Thirty-eight thousand cells are
tagged from the 345 microscopic images of different Giemsa-stained slides of
blood samples. Extensive experimentation is performed using different CNN
backbones including VGG, DenseNet, and ResNet on this dataset. Our experiments
and analysis reveal that the two-stage approach works better than the one-stage
approach for malaria detection. To ensure the usability of our approach, we
have also developed a mobile app that will be used by local hospitals for
investigation and educational purposes. The dataset, its annotations, and
implementation codes will be released upon publication of the paper.


Abstract:
  There are many sources of data giving information about the number of
SARS-CoV-2 infections in the population, but all have major drawbacks,
including biases and delayed reporting. For example, the number of confirmed
cases largely underestimates the number of infections, deaths lag infections
substantially, while test positivity rates tend to greatly overestimate
prevalence. Representative random prevalence surveys, the only putatively
unbiased source, are sparse in time and space, and the results come with a big
delay. Reliable estimates of population prevalence are necessary for
understanding the spread of the virus and the effects of mitigation strategies.
We develop a simple Bayesian framework to estimate viral prevalence by
combining the main available data sources. It is based on a discrete-time SIR
model with time-varying reproductive parameter. Our model includes likelihood
components that incorporate data of deaths due to the virus, confirmed cases,
and the number of tests administered on each day. We anchor our inference with
data from random sample testing surveys in Indiana and Ohio. We use the results
from these two states to calibrate the model on positive test counts and
proceed to estimate the infection fatality rate and the number of new
infections on each day in each state in the USA. We estimate the extent to
which reported COVID cases have underestimated true infection counts, which was
large, especially in the first months of the pandemic. We explore the
implications of our results for progress towards herd immunity.


Abstract:
  This article presents a new primal-dual weak Galerkin method for second order
elliptic equations in non-divergence form. The new method is devised as a
constrained $L^p$-optimization problem with constraints that mimic the second
order elliptic equation by using the discrete weak Hessian locally on each
element. An equivalent min-max characterization is derived to show the
existence and uniqueness of the numerical solution. Optimal order error
estimates are established for the numerical solution under the discrete
$W^{2,p}$ norm, as well as the standard $W^{1,p}$ and $L^p$ norms. An
equivalent characterization of the optimization problem in term of a system of
fixed-point equations via the proximity operator is presented. An iterative
algorithm is designed based on the fixed-point equations to solve the
optimization problems. Implementation of the iterative algorithm is studied and
convergence of the iterative algorithm is established. Numerical experiments
for both smooth and non-smooth coefficients problems are presented to verify
the theoretical findings.


Abstract:
  Data in the energy domain grows at unprecedented rates and is usually
generated by heterogeneous energy systems. Despite the great potential that big
data-driven technologies can bring to the energy sector, general adoption is
still lagging. Several challenges related to controlled data exchange and data
integration are still not wholly achieved. As a result, fragmented applications
are developed against energy data silos, and data exchange is limited to few
applications. In this paper, we analyze the challenges and requirements related
to energy-related data applications. We also evaluate the use of Energy Data
Ecosystems (EDEs) as data-driven infrastructures to overcome the current
limitations of fragmented energy applications. EDEs are inspired by the
International Data Space (IDS) initiative launched in Germany at the end of
2014 with an overall objective to take both the development and use of the IDS
reference architecture model to a European/global level. The reference
architecture model consists of four architectures related to business,
security, data and service, and software aspects. This paper illustrates the
applicability of EDEs and IDS reference architecture in real-world scenarios
from the energy sector. The analyzed scenario is positioned in the context of
the EU-funded H2020 project PLATOON.


Abstract:
  Non-generating resources such as thermostatically controlled loads (TCLs) can
arbitrage energy prices and provide balancing reserves when aggregated due to
their thermal energy storage capacity. Based on a performed survey of Swedish
single- and two-family dwellings with electric heating, this paper quantifies
the potential of TCLs to provide reserves to the power system in Sweden. To
this end, dwellings with heat pumps and direct electric heaters are modeled as
thermal energy storage equivalents that can be included in a linear two-stage
problem formulation. We approach the operational flexibility of the TCLs by
modeling a risk-averse aggregator that controls decentralized TCLs and aims to
maximize its own profit. The results show a potential of 2 GW/0.1Hz averaged
over a year, and up to 6.4 GW/0.1Hz peak capacity. Based on a sensitivity
analysis we derive policy implications regarding market timing and activation
signal.


Abstract:
  In deformable registration, the geometric framework - large deformation
diffeomorphic metric mapping or LDDMM, in short - has inspired numerous
techniques for comparing, deforming, averaging and analyzing shapes or images.
Grounded in flows, which are akin to the equations of motion used in fluid
dynamics, LDDMM algorithms solve the flow equation in the space of plausible
deformations, i.e. diffeomorphisms. In this work, we make use of deep residual
neural networks to solve the non-stationary ODE (flow equation) based on a
Euler's discretization scheme. The central idea is to represent time-dependent
velocity fields as fully connected ReLU neural networks (building blocks) and
derive optimal weights by minimizing a regularized loss function. Computing
minimizing paths between deformations, thus between shapes, turns to find
optimal network parameters by back-propagating over the intermediate building
blocks. Geometrically, at each time step, ResNet-LDDMM searches for an optimal
partition of the space into multiple polytopes, and then computes optimal
velocity vectors as affine transformations on each of these polytopes. As a
result, different parts of the shape, even if they are close (such as two
fingers of a hand), can be made to belong to different polytopes, and therefore
be moved in different directions without costing too much energy. Importantly,
we show how diffeomorphic transformations, or more precisely bilipshitz
transformations, are predicted by our algorithm. We illustrate these ideas on
diverse registration problems of 3D shapes under complex topology-preserving
transformations. We thus provide essential foundations for more advanced shape
variability analysis under a novel joint geometric-neural networks
Riemannian-like framework, i.e. ResNet-LDDMM.


Abstract:
  Aims. The main purpose of this work is to provide a method to derive
tabulated observational constraints on the halo mass function (HMF) by studying
the magnification bias effect on high-redshift submillimeter galaxies. Under
the assumption of universality, we parametrize the HMF according to two
traditional models, namely the Sheth and Tormen (ST) and Tinker fits and assess
their performance in explaining the measured data within the {\Lambda} cold
dark matter ({\Lambda}CDM) model. We also study the potential influence of the
halo occupation distribution (HOD) parameters in this analysis and discuss two
important aspects regarding the HMF parametrization. Methods. We measure the
cross-correlation function between a foreground sample of GAMA galaxies with
redshifts in the range $0.2<z<0.8$ and a background sample of H-ATLAS galaxies
with redshifts in the range $1.2<z<4.0$ and carry out an MCMC algorithm to
check this observable against its mathematical prediction within the halo model
formalism. Results. If all HMF parameters are assumed to be positive, the ST
fit only seems to fully explain the measurements by forcing the mean number of
satellite galaxies in a halo to increase substantially from its prior mean
value. The Tinker fit, on the other hand, provides a robust description of the
data without relevant changes in the HOD parameters, but with some dependence
on the prior range of two of its parameters. When the normalization condition
for the HMF is dropped and we allow negative values of the $p_1$ parameter in
the ST fit, all the involved parameters are better determined, unlike the
previous models, thus deriving the most general HMF constraints. While all
cases are in agreement with the traditional fits within the uncertainties, the
last one hints at a slightly higher number of halos at intermediate and high
masses, raising the important point of the allowed parameter range.


Abstract:
  Small class-imbalanced datasets, common in many high-level semantic tasks
like discourse analysis, present a particular challenge to current
deep-learning architectures. In this work, we perform an extensive analysis on
sentence-level classification approaches for the News Discourse dataset, one of
the largest high-level semantic discourse datasets recently published. We show
that a multitask approach can improve 7% Micro F1-score upon current
state-of-the-art benchmarks, due in part to label corrections across tasks,
which improve performance for underrepresented classes. We also offer a
comparative review of additional techniques proposed to address resource-poor
problems in NLP, and show that none of these approaches can improve
classification accuracy in such a setting.


Abstract:
  Inspired by the Hardy-Littlewood maximal function, we propose a novel pooling
strategy which is called maxfun pooling. It is presented both as a viable
alternative to some of the most popular pooling functions, such as max pooling
and average pooling, and as a way of interpolating between these two
algorithms. We demonstrate the features of maxfun pooling with two
applications: first in the context of convolutional sparse coding, and then for
image classification.


Abstract:
  This study proposes a generalized coordinates based smoothed particle
hydrodynamics (GSPH) method with overset methods using a Total Lagrangian (TL)
formulation for large deformation and crack propagation problems. In the
proposed GSPH, the physical space is decomposed into multiple domains, each of
which is mapped to a local coordinate space (generalized space) to avoid
coordinate singularities as well as to flexibly change the spatial resolution.
The smoothed particle hydrodynamics (SPH) particles are then non-uniformly,
e.g., typically in the boundary-conforming way, distributed in the physical
space while they are defined uniformly in each generalized space similarly to
the normal SPH method, which are numerically related by a coordinate
transformation matrix. By solving a governing equation in each generalized
space, the shape and size of the SPH kernel can be spatially changed in the
physical space so that a spatial resolution is adaptively varied a priori
depending on the deformation characteristics, and thus, a low-cost calculation
with the less number of particles is achieved in complex shape structures.


Abstract:
  In this paper, we analyze the spectra of the preconditioned matrices arising
from discretized multi-dimensional Riesz spatial fractional diffusion
equations. The finite difference method is employed to approximate the
multi-dimensional Riesz fractional derivatives, which will generate symmetric
positive definite ill-conditioned multi-level Toeplitz matrices. The
preconditioned conjugate gradient method with a preconditioner based on the
sine transform is employed to solve the resulting linear system. Theoretically,
we prove that the spectra of the preconditioned matrices are uniformly bounded
in the open interval (1/2,3/2) and thus the preconditioned conjugate gradient
method converges linearly. The proposed method can be extended to multi-level
Toeplitz matrices generated by functions with zeros of fractional order. Our
theoretical results fill in a vacancy in the literature. Numerical examples are
presented to demonstrate our new theoretical results in the literature and show
the convergence performance of the proposed preconditioner that is better than
other existing preconditioners.


Abstract:
  Let $\alpha'$ and $\mu_i$ denote the matching number of a non-empty simple
graph $G$ with $n$ vertices and the $i$-th smallest eigenvalue of its Laplacian
matrix, respectively. In this paper, we prove a tight lower bound $$\alpha' \ge
\min\left\{\Big\lceil\frac{\mu_2}{\mu_n} (n -1)\Big\rceil,\ \
\Big\lceil\frac{1}{2}(n-1)\Big\rceil \right\}.$$ This bound strengthens the
result of Brouwer and Haemers who proved that if $n$ is even and $2\mu_2 \ge
\mu_n$, then $G$ has a perfect matching. A graph $G$ is factor-critical if for
every vertex $v\in V(G)$, $G-v$ has a perfect matching. We also prove an
analogue to the result of Brouwer and Haemers mentioned above by showing that
if $n$ is odd and $2\mu_2 \ge \mu_n$, then $G$ is factor-critical. We use the
separation inequality of Haemers to get a useful lemma, which is the key idea
in the proofs. This lemma is of its own interest and has other applications. In
particular, we prove similar results for the number of balloons, spanning even
subgraphs, as well as spanning trees with bounded degree.


Abstract:
  Two-dimensional (2D) materials are suitable hosts for the intercalation of
extrinsic guest ions such as Li+, Na+ and K+ as the interlayer coupling is
weak. This allows ion intercalation engineering of 2D materials, which may be a
key to advancing technological applications in energy storage, neuromorphic
electronics, and bioelectronics. However, ions that are extrinsic to the host
materials possess challenges in fabrication of devices as there are extra steps
of ion intercalation. This results in degradation of the long-term stability of
the intercalated atomically thin structures. Here, we introduce large-area
single-crystal ultra-thin layered MnO$_2$ via chemical vapor deposition,
spontaneously intercalated by potassium ions during the synthesis. We studied
the ultra-thin 2D K-MnO$_2$ in detail and showed that charge transport in these
crystals is dominated by motion of hydrated potassium ions in the interlayer
space. Moreover, K-MnO$_2$ crystals exhibit reversible layered-to-spinel phase
transition accompanied by an optical contrast change based on the electrical
and optical modulation of the potassium and the interlayer water concentration.
We used the electric field driven ionic motion in K-MnO$_2$ based devices to
demonstrate the memristive properties of two terminal devices. As a possible
application we showed that K-MnO$_2$ memristors display synapse-like behavior
such as short and long-term potentiation and depression as well as ionic
coupling effects.


Abstract:
  Extreme circumstances in which a local distribution system is electrically
isolated from the main power supply may not always be avoidable. Efforts must
be made to keep the lights on for such an isolated distribution system (IDS)
until reconnection to the main power source. In this paper, we propose a
strategy to enhance IDS survivability utilizing the coordination of two
flexible approaches, namely, separable energy storage systems (SMESSs), which
construct non-wires links for energy transmission between the IDS and the
external live power sources, and demand response (DR), which adjusts the
internal electrical demand of the IDS to provide effective operating stress
alleviation. Considering the uncertainty of renewable energy generation and
loads, a two-stage robust optimization (RO) model involving the joint
scheduling of these two approaches is constructed. The objective is to minimize
the fuel consumption and the decreased and nonserved demand under the
worst-case scenario to endow the IDS with extended survivability. Finally, test
is conducted and the results demonstrate the effectiveness of the proposed
method in enhancing the survivability of IDS.


Abstract:
  The Parker Solar Probe (PSP) mission presents a unique opportunity to study
the near-Sun solar wind closer than any previous spacecraft. During its fourth
and fifth solar encounters, PSP had the same orbital trajectory, meaning that
solar wind was measured at the same latitudes and radial distances. We identify
two streams measured at the same heliocentric distance ($\sim$0.13au) and
latitude ($\sim$-3.5$^{\circ}$) across these encounters to reduce spatial
evolution effects. By comparing the plasma of each stream, we confirm that they
are not dominated by variable transient events, despite PSP's proximity to the
heliospheric current sheet. Both streams are consistent with a previous slow
Alfv\'enic solar wind study once radial effects are considered, and appear to
originate at the Southern polar coronal hole boundary. We also show that the
switchback properties are not distinctly different between these two streams.
Low $\alpha$-particle abundance ($\sim$ 0.6 %) is observed in the encounter 5
stream, suggesting that some physical mechanism must act on coronal hole
boundary wind to cause $\alpha$-particle depletion. Possible explanations for
our observations are discussed, but it remains unclear whether the depletion
occurs during the release or the acceleration of the wind. Using a flux tube
argument, we note that an $\alpha$-particle abundance of $\sim$ 0.6 % in this
low velocity wind could correspond to an abundance of $\sim$ 0.9 % at 1 au.
Finally, as the two streams roughly correspond to the spatial extent of a
switchback patch, we suggest that patches are distinct features of coronal hole
wind.


Abstract:
  Fluorescence microscopy has enabled a dramatic development in modern biology
by visualizing biological organisms with micrometer scale resolution. However,
due to the diffraction limit, sub-micron/nanometer features are difficult to
resolve. While various super-resolution techniques are developed to achieve
nanometer-scale resolution, they often either require expensive optical setup
or specialized fluorophores. In recent years, deep learning has shown the
potentials to reduce the technical barrier and obtain super-resolution from
diffraction-limited images. For accurate results, conventional deep learning
techniques require thousands of images as a training dataset. Obtaining large
datasets from biological samples is not often feasible due to the
photobleaching of fluorophores, phototoxicity, and dynamic processes occurring
within the organism. Therefore, achieving deep learning-based super-resolution
using small datasets is challenging. We address this limitation with a new
convolutional neural network-based approach that is successfully trained with
small datasets and achieves super-resolution images. We captured 750 images in
total from 15 different field-of-views as the training dataset to demonstrate
the technique. In each FOV, a single target image is generated using the
super-resolution radial fluctuation method. As expected, this small dataset
failed to produce a usable model using traditional super-resolution
architecture. However, using the new approach, a network can be trained to
achieve super-resolution images from this small dataset. This deep learning
model can be applied to other biomedical imaging modalities such as MRI and
X-ray imaging, where obtaining large training datasets is challenging.


Abstract:
  We propose a new method for modeling the distribution function of high
dimensional extreme value distributions. The Pickands dependence function
models the relationship between the covariates in the tails, and we learn this
function using a neural network that is designed to satisfy its required
properties. Moreover, we present new methods for recovering the spectral
representation of extreme distributions and propose a generative model for
sampling from extreme copulas. Numerical examples are provided demonstrating
the efficacy and promise of our proposed methods.


Abstract:
  Non-Hermitian systems with parity-time ($\mathcal{PT}$) symmetry and
anti-$\mathcal{PT}$ symmetry give rise to exceptional points (EPs) with
intriguing properties related to, e.g., chiral transport and enhanced
sensitivity, due to the coalescence of eigenvectors. In this paper, we propose
a powerful and easily computable tool, based on the Hilbert-Schmidt speed
(HSS), which does not require the diagonalization of the evolved density
matrix, to detect exactly the EPs and hence the critical behavior of the
(anti-)$\mathcal{PT}\!-$symmetric systems, especially high-dimensional ones.
Our theoretical predictions, made without the need for modification of the
Hilbert space, which is performed by diagonalizing one of the observables, are
completely consistent with results extracted from recent experiments studying
the criticality in (anti-)$\mathcal{PT}\!-$symmetric systems. Nevertheless, not
modifying the Hilbert space of the non-Hermitian system, we find that the trace
distance, a measure of distinguishability of two arbitrary quantum states,
whose dynamics is known as a faithful witness of non-Markovianity in Hermitian
systems, may be non-contractive under the non-Hermitian evolution of the
system. Therefore, it lacks one of the most important characteristics which
must be met by any standard witness of non-Markovianity.


Abstract:
  Transformers recently are adapted from the community of natural language
processing as a promising substitute of convolution-based neural networks for
visual learning tasks. However, its supremacy degenerates given an insufficient
amount of training data (e.g., ImageNet). To make it into practical utility, we
propose a novel distillation-based method to train vision transformers. Unlike
previous works, where merely heavy convolution-based teachers are provided, we
introduce lightweight teachers with different architectural inductive biases
(e.g., convolution and involution) to co-advise the student transformer. The
key is that teachers with different inductive biases attain different knowledge
despite that they are trained on the same dataset, and such different knowledge
compounds and boosts the student's performance during distillation. Equipped
with this cross inductive bias distillation method, our vision transformers
(termed as CivT) outperform all previous transformers of the same architecture
on ImageNet.


Abstract:
  In this note, I find a new property of the congruence lattice, Con$L$, of an
SPS lattice $L$ (slim, planar, semimodular, where "slim" is the absence
of~$\mathsf M_3$ sublattices) with more than $2$ elements: \emph{there are at
least two dual atoms in Con$L$}. So the three-element chain cannot be
represented as the congruence lattice of an SPS lattice, supplementing a~result
of G. Cz\'edli.


Abstract:
  We investigate in a systematic way hypercontractivity property in Orlicz
spaces for Markov semi-groups related to homogeneous and non homogeneous
diffusions in $\mathbb{R}^n$. We provide an explicit construction of a family
of Orlicz functions for which we prove that the associated hypercontractivity
property is equivalent to a suitable functional inequality.
  Connections between our results and the Talagrand Conjecture on the
regularization effect of the Ornstein-Uhlenbeck semi-group in $\mathbb{L}_1$
are also discussed.


Abstract:
  Welcome to WeaSuL 2021, the First Workshop on Weakly Supervised Learning,
co-located with ICLR 2021. In this workshop, we want to advance theory, methods
and tools for allowing experts to express prior coded knowledge for automatic
data annotations that can be used to train arbitrary deep neural networks for
prediction. The ICLR 2021 Workshop on Weak Supervision aims at advancing
methods that help modern machine-learning methods to generalize from knowledge
provided by experts, in interaction with observable (unlabeled) data. In total,
15 papers were accepted. All the accepted contributions are listed in these
Proceedings.


Abstract:
  Anomalous diffusion, which shows a deviation of transport dynamics from the
framework of standard Brownian motion, is involved in the evolution of various
physical, chemical, biological, and economic systems. The study of such random
processes is of fundamental importance in unveiling the physical properties of
random walkers and complex systems. However, classical methods to characterize
anomalous diffusion are often disqualified for individual short trajectories,
leading to the launch of the Anomalous Diffusion (AnDi) Challenge. This
challenge aims at objectively assessing and comparing new approaches for single
trajectory characterization, with respect to three different aspects: the
inference of the anomalous diffusion exponent; the classification of the
diffusion model; and the segmentation of trajectories. In this article, to
address the inference and classification tasks in the challenge, we develop a
WaveNet-based deep neural network (WADNet) by combining a modified WaveNet
encoder with long short-term memory networks, without any prior knowledge of
anomalous diffusion. As the performance of our model has surpassed the current
1st places in the challenge leaderboard on both two tasks for all dimensions (6
subtasks), WADNet could be the part of state-of-the-art techniques to decode
the AnDi database. Our method presents a benchmark for future research, and
could accelerate the development of a versatile tool for the characterization
of anomalous diffusion.


Abstract:
  A nonlinear small-strain elastic theory is constructed from a systematic
expansion in Biot strains, truncated at quadratic order. The primary motivation
is the desire for a clean separation between stretching and bending energies
for shells, which appears to arise only from reduction of a bulk energy of this
type. An approximation of isotropic invariants, bypassing the solution of a
quartic equation or computation of tensor square roots, allows stretches,
rotations, stresses, and balance laws to be written in terms of derivatives of
position. Two-field formulations are also presented. Extensions to anisotropic
theories are briefly discussed.


Abstract:
  This article describes a technique to augment a typical RGBD sensor by
integrating depth estimates obtained via Structure-from-Motion (SfM) with
sensor depth measurements. Limitations in the RGBD depth sensing technology
prevent capturing depth measurements in four important contexts: (1) distant
surfaces (>5m), (2) dark surfaces, (3) brightly lit indoor scenes and (4)
sunlit outdoor scenes. SfM technology computes depth via multi-view
reconstruction from the RGB image sequence alone. As such, SfM depth estimates
do not suffer the same limitations and may be computed in all four of the
previously listed circumstances. This work describes a novel fusion of RGBD
depth data and SfM-estimated depths to generate an improved depth stream that
may be processed by one of many important downstream applications such as
robotic localization and mapping, as well as object recognition and tracking.


Abstract:
  A spatially homogeneous and locally rotationally symmetric Bianchi type-II
cosmological model under the influence of both shear and bulk viscosity has
been studied. Exact solutions are obtained with a barotropic equation of state
between thermodynamics pressure and the energy density of the fluid, and
considering the linear relationships amongst the energy density, the expansion
scalar and the shear scalar. Special cases with vanishing bulk viscosity
coefficients and with the perfect fluid in the absence of viscosity have also
been studied. The formal appearance of the solutions is the same for both the
viscous as well as the perfect fluids. The difference is only in choosing a
constant parameter which appears in the solutions. In the cases of either a
fluid with bulk viscosity alone or a perfect fluid, the barotropic equation of
state is no longer an additional assumption to be imposed; rather it follows
directly from the field equations.


Abstract:
  Inspired by the success of Deep Learning based approaches to English scene
text recognition, we pose and benchmark scene text recognition for three Indic
scripts - Devanagari, Telugu and Malayalam. Synthetic word images rendered from
Unicode fonts are used for training the recognition system. And the performance
is bench-marked on a new IIIT-ILST dataset comprising of hundreds of real scene
images containing text in the above mentioned scripts. We use a segmentation
free, hybrid but end-to-end trainable CNN-RNN deep neural network for
transcribing the word images to the corresponding texts. The cropped word
images need not be segmented into the sub-word units and the error is
calculated and backpropagated for the the given word image at once. The network
is trained using CTC loss, which is proven quite effective for
sequence-to-sequence transcription tasks. The CNN layers in the network learn
to extract robust feature representations from word images. The sequence of
features learnt by the convolutional block is transcribed to a sequence of
labels by the RNN+CTC block. The transcription is not bound by word length or a
lexicon and is ideal for Indian languages which are highly inflectional.
IIIT-ILST dataset, synthetic word images dataset and the script used to render
synthetic images are available at
http://cvit.iiit.ac.in/research/projects/cvit-projects/iiit-ilst


Abstract:
  Magnesium atoms fully embedded in helium nanodroplets are exposed to
two-color laser pulses in order to exemplarily study the contribution of a
dense and finite medium on multiphoton-triggered above-threshold ionization
(ATI). The angular-resolved photoelectron spectra show striking differences
with respect to results obtained on free atoms. Scattering of the Mg
photoelectrons, when traversing the helium environment, causes the angular
distribution to become almost isotropic. Furthermore, we observe a marked
increase in the ATI order, pointing out the impact of the helium environment on
the concerted electron emission process. Phase-of-the-phase spectroscopy,
however, reveals a marked loss in the $2\omega-\omega$ phase dependence of the
electron signal. Taking into account sideband formation on a quantitative
level, a Monte-Carlo simulation which includes laser-assisted electron
scattering can reproduce the experimental spectra and give insights into the
strong field-induced electron emission from disordered systems.


Abstract:
  We develop the theory of fractional gradient flows: an evolution aimed at the
minimization of a convex, l.s.c.~energy, with memory effects. This memory is
characterized by the fact that the negative of the (sub)gradient of the energy
equals the so-called Caputo derivative of the state. We introduce the notion of
energy solutions, for which we provide existence, uniqueness and certain
regularizing effects. We also consider Lipschitz perturbations of this energy.
For these problems we provide an a posteriori error estimate and show its
reliability. This estimate depends only on the problem data, and imposes no
constraints between consecutive time-steps. On the basis of this estimate we
provide an a priori error analysis that makes no assumptions on the smoothness
of the solution.


Abstract:
  Cosmogenic radio-nuclei are an important source of background for low-energy
neutrino experiments. In Borexino, cosmogenic $^{11}$C decays outnumber solar
$pep$ and CNO neutrino events by about ten to one. Highly efficient
identification of this background is mandatory for these neutrino analyses. We
present here the details of the most consolidated strategy, used throughout
Borexino solar neutrino measurements. It hinges upon finding the space-time
correlations between $^{11}$C decays, the preceding parent muons and the
accompanying neutrons. This article describes the working principles and
evaluates the performance of this Three-Fold Coincidence (TFC) technique in its
two current implementations: a hard-cut and a likelihood-based approach. Both
show stable performances throughout Borexino Phases II (2012-2016) and III
(2016-2020) data sets, with a $^{11}$C tagging efficiency of $\sim$90 % and
$\sim$63-66 % of the exposure surviving the tagging. We present also a novel
technique that targets specifically $^{11}$C produced in high-multiplicity
during major spallation events. Such $^{11}$C appear as a burst of events,
whose space-time correlation can be exploited. Burst identification can be
combined with the TFC to obtain about the same tagging efficiency of $\sim$90 %
but with a higher fraction of the exposure surviving, in the range of
$\sim$66-68 %.


Abstract:
  In Section 1, we revisit the partial differential equation (PDE) for the
probability generating function (PGF) of the time-nonhomogeneous BDI
(birth-and-death-with-immigration) process and derive a closed form solution.
To the best of our knowledge, this is a new mathematical result. We state this
result as Proposition 1. We state as Corollary 1 that the negative binomial
distribution of the time-homogeneous BDI process discussed in Part I extends to
the general time-nonhomogeneous case, provided that the ratio of the
immigration rate to the birth rate is a constant. In section 1.2, we take up
the heuristic approach discussed by Bartlett and Bailey (1964), and carry it
out to completion by arriving at the solution obtained above,.
  In Section 2, we present the results of our extensive simulation experiments
of the time-nonhomogeneous BD process that was analyzed in Part III-A and
confirm our analytic results. In Section 3, we undertake similar simulation
experiments for the BDI process that is analyzed in Section 1.
  As we discuss in Section 4, our stochastic model now seems more promising and
powerful than has been heretofore expected. In Appendix B, a closed form
solution for the M(t)/M(t)/infinity queue is obtained, as a special case of
this BDI process model.


Abstract:
  We show that a strong `spin'-orbit coupled one-dimensional (1D) hole gas is
achievable via applying a strong magnetic field to the original two-fold
degenerate (spin degeneracy) hole gas confined in a cylindrical Ge nanowire.
Both strong longitudinal and strong transverse magnetic fields are feasible to
achieve this goal. Based on quasi-degenerate perturbation calculations, we show
the induced low-energy subband dispersion of the hole gas can be written as
$E=\hbar^{2}k^{2}_{z}/(2m^{*}_{h})+\alpha\sigma^{z}k_{z}+g^{*}_{h}\mu_{B}B\sigma^{x}/2$,
a form exactly the same as that of the electron gas in the conduction band.
Here the Pauli matrices $\sigma^{z,x}$ represent a pseudo spin (or `spin' ),
because the real spin degree of freedom has been split off from the subband
dispersions by the strong magnetic field. Also, for a moderate nanowire radius
$R=10$ nm, the induced effective hole mass $m^{*}_{h}$ ($0.065\sim0.08~m_{e}$)
and the `spin'-orbit coupling $\alpha$ ($0.35\sim0.8$ eV~\AA) have a small
magnetic field dependence in the studied magnetic field interval $1<B<15$ T,
while the effective $g$-factor $g^{*}_{h}$ of the hole `spin' only has a small
magnetic field dependence in the large field region.


Abstract:
  Guessing Random Additive Noise Decoding (GRAND) is a recently proposed
approximate Maximum Likelihood (ML) decoding technique that can decode any
linear error-correcting block code. Ordered Reliability Bits GRAND (ORBGRAND)
is a powerful variant of GRAND, which outperforms the original GRAND technique
by generating error patterns in a specific order. Moreover, their simplicity at
the algorithm level renders GRAND family a desirable candidate for applications
that demand very high throughput. This work reports the first-ever hardware
architecture for ORBGRAND, which achieves an average throughput of up to $42.5$
Gbps for a code length of $128$ at an SNR of $10$ dB. Moreover, the proposed
hardware can be used to decode any code provided the length and rate
constraints. Compared to the state-of-the-art fast dynamic successive
cancellation flip decoder (Fast-DSCF) using a 5G polar $(128,105)$ code, the
proposed VLSI implementation has $49\times$ more average throughput while
maintaining similar decoding performance.


Abstract:
  One of the main aspects of switched affine systems that makes their
stabilizability study intricate is the existence of (generally) infinitely many
equilibrium points in the state space. Thus, prior to designing the switched
control, the user must specify one of these equilibrium points to be the goal
or reference. This can be a cumbersome task, especially if this goal is
partially given or only defined as a set of constraints. To tackle this issue,
in this paper we describe algorithms that can determine whether a given goal is
an equilibrium point of the system and also jointly search for equilibrium
points and design globally stabilizing switching functions.


Abstract:
  We consider the plurality consensus problem among $n$ agents. Initially, each
agent has one of $k$ different opinions. Agents choose random interaction
partners and revise their state according to a fixed transition function,
depending on their own state and the state of the interaction partners. The
goal is to reach a consensus configuration in which all agents agree on the
same opinion, and if there is initially a sufficiently large bias towards one
opinion, that opinion should prevail.
  We analyze a synchronized variant of the undecided state dynamics defined as
follows. The agents act in phases, consisting of a decision and a boosting
part. In the decision part, any agent that encounters an agent with a different
opinion becomes undecided. In the boosting part, undecided agents adopt the
first opinion they encounter.
  We consider this dynamics in the population model and the gossip model. For
the population model, our protocol reaches consensus (w.h.p.) in $O(\log^2 n)$
parallel time, providing the first polylogarithmic result for $k > 2$ (w.h.p.)
in this model. Without any assumption on the bias, fast consensus has only been
shown for $k = 2$ for the unsynchronized version of the undecided state
dynamics [Clementi et al., MFCS'18]. We show that the synchronized variant of
the undecided state dynamics reaches consensus (w.h.p.) in time $O(\log^2 n)$,
independently of the initial number, bias, or distribution of opinions. In both
models, we guarantee that if there is an initial bias of $\Omega(\sqrt{n \log
n})$, then (w.h.p.) that opinion wins.
  A simple extension of our protocol in the gossip model yields a dynamics that
does not depend on $n$ or $k$, is anonymous, and has (w.h.p.) runtime $O(\log^2
n)$. This solves an open problem formulated by Becchetti et al.~[Distributed
Computing,~2017].