Abstract:
  With the increasing importance of the Mittag-Leffler function in the physical
applications, these days many researchers are studying various generalizations
and extensions of the Mittag-Leffler function. In this paper efforts are made
to define bicomplex extension of the Mittag-Leffler function and also its
analyticity and region of convergence are discussed. Various properties of the
bicomplex Mittag-Leffler function including integral representation, recurrence
relations, duplication formula and differential relations are established.


Abstract:
  Understanding the scene around the ego-vehicle is key to assisted and
autonomous driving. Nowadays, this is mostly conducted using cameras and laser
scanners, despite their reduced performances in adverse weather conditions.
Automotive radars are low-cost active sensors that measure properties of
surrounding objects, including their relative speed, and have the key advantage
of not being impacted by rain, snow or fog. However, they are seldom used for
scene understanding due to the size and complexity of radar raw data and the
lack of annotated datasets. Fortunately, recent open-sourced datasets have
opened up research on classification, object detection and semantic
segmentation with raw radar signals using end-to-end trainable models. In this
work, we propose several novel architectures, and their associated losses,
which analyse multiple "views" of the range-angle-Doppler radar tensor to
segment it semantically. Experiments conducted on the recent CARRADA dataset
demonstrate that our best model outperforms alternative models, derived either
from the semantic segmentation of natural images or from radar scene
understanding, while requiring significantly fewer parameters. Both our code
and trained models are available at https://github.com/valeoai/MVRSS.


Abstract:
  Even-parity interorbital spin-triplet pairing emerges as an intriguing
candidate in multiorbital superconductors with significant Hund's and
spin-orbit coupling. Within such a state, the pairing is dominated by the
intraband pseudospin-singlet component via the spin-orbit coupling,
distinguishing it from a pure spin triplet and motivating the name, shadowed
triplet. With atomic spin-orbit coupling, it was shown that this pairing
exhibits an anisotropic $s$-wave character, while higher angular momentum
pairing symmetries such as $d$- or $g$-wave have been proposed based on
phenomenological analyses in candidate systems. A natural question is then
whether higher angular momentum pairings may arise with this form of
spin-triplet pairing. Here, we examine the interplay between spin-orbit
coupling and the electronic dispersions in correlated metals and demonstrate
how they can be realized. We apply this idea to Sr$_{2}$RuO$_{4}$ and determine
the competition among different pairing states as multiple spin-orbit coupling
parameters are tuned. The presence of both $d$- and $g$-wave pairings,
including a $d+ig$ state, are found when momentum-dependent spin-orbit coupling
with $d$-wave character is increased. Implications of the theory and future
directions are also discussed.


Abstract:
  Personalization and active learning are key aspects to successful learning.
These aspects are important to address in intelligent educational applications,
as they help systems to adapt and close the gap between students with varying
abilities, which becomes increasingly important in the context of online and
distance learning. We run a comparative head-to-head study of learning outcomes
for two popular online learning platforms: Platform A, which follows a
traditional model delivering content over a series of lecture videos and
multiple-choice quizzes, and Platform B, which creates a personalized learning
environment and provides problem-solving exercises and personalized feedback.
We report on the results of our study using pre- and post-assessment quizzes
with participants taking courses on an introductory data science topic on two
platforms. We observe a statistically significant increase in the learning
outcomes on Platform B, highlighting the impact of well-designed and
well-engineered technology supporting active learning and problem-based
learning in online education. Moreover, the results of the self-assessment
questionnaire, where participants reported on perceived learning gains, suggest
that participants using Platform B improve their metacognition.


Abstract:
  Central Pattern Generators (CPGs) models have been long used to investigate
both the neural mechanisms that underlie animal locomotion as well as a tool
for robotic research. In this work we propose a spiking CPG neural network and
its implementation on neuromorphic hardware as a means to control a simulated
lamprey model. To construct our CPG model, we employ the naturally emerging
dynamical systems that arise through the use of recurrent neural populations in
the Neural Engineering Framework (NEF). We define the mathematical formulation
behind our model, which consists of a system of coupled abstract oscillators
modulated by high-level signals, capable of producing a variety of output
gaits. We show that with this mathematical formulation of the Central Pattern
Generator model, the model can be turned into a Spiking Neural Network (SNN)
that can be easily simulated with Nengo, an SNN simulator. The spiking CPG
model is then used to produce the swimming gaits of a simulated lamprey robot
model in various scenarios. We show that by modifying the input to the network,
which can be provided by sensory information, the robot can be controlled
dynamically in direction and pace. The proposed methodology can be generalized
to other types of CPGs suitable for both engineering applications and
scientific research. We test our system on two neuromorphic platforms,
SpiNNaker and Loihi. Finally, we show that this category of spiking algorithms
shows a promising potential to exploit the theoretical advantages of
neuromorphic hardware in terms of energy efficiency and computational speed.


Abstract:
  In fruit tree growth, pruning is an important management practice for
preventing overcrowding, improving canopy access to light and promoting
regrowth. Due to the slow nature of agriculture, decisions in pruning are
typically made using tradition or rules of thumb rather than data-driven
analysis. Many existing algorithmic, simulation-based approaches rely on
high-fidelity digital captures or purely computer-generated fruit trees, and
are unable to provide specific results on an orchard scale. We present a
framework for suggesting pruning strategies on LiDAR-scanned commercial fruit
trees using a scoring function with a focus on improving light distribution
throughout the canopy. A scoring function to assess the quality of the tree
shape based on its light availability and size was developed for comparative
analysis between trees, and was validated against yield characteristics,
demonstrating a reasonable correlation against fruit count with an $R^2$ score
of 0.615 for avocado and 0.506 for mango. A tool was implemented for simulating
pruning by algorithmically estimating which parts of a tree point cloud would
be removed given specific cut points using structural analysis of the tree,
validated experimentally with an average F1 score of 0.78 across 144
experiments. Finally, new pruning locations were suggested and we used the
previous two stages to estimate the improvement of the tree given these
suggestions. The light distribution was improved by up to 25.15\%,
demonstrating a 16\% improvement over commercial pruning on a real tree, and
certain cut points were discovered which improved light distribution with a
smaller negative impact on tree volume. The final results suggest value in the
framework as a decision making tool for commercial growers, or as a starting
point for automated pruning since the entire process can be performed with
little human intervention.


Abstract:
  Convolutional neural networks (CNNs) have become a powerful technique to
decode EEG and have become the benchmark for motor imagery EEG
Brain-Computer-Interface (BCI) decoding. However, it is still challenging to
train CNNs on multiple subjects' EEG without decreasing individual performance.
This is known as the negative transfer problem, i.e. learning from dissimilar
distributions causes CNNs to misrepresent each of them instead of learning a
richer representation. As a result, CNNs cannot directly use multiple subjects'
EEG to enhance model performance directly. To address this problem, we extend
deep transfer learning techniques to the EEG multi-subject training case. We
propose a multi-branch deep transfer network, the Separate-Common-Separate
Network (SCSN) based on splitting the network's feature extractors for
individual subjects. We also explore the possibility of applying Maximum-mean
discrepancy (MMD) to the SCSN (SCSN-MMD) to better align distributions of
features from individual feature extractors. The proposed network is evaluated
on the BCI Competition IV 2a dataset (BCICIV2a dataset) and our online recorded
dataset. Results show that the proposed SCSN (81.8%, 53.2%) and SCSN-MMD
(81.8%, 54.8%) outperformed the benchmark CNN (73.4%, 48.8%) on both datasets
using multiple subjects. Our proposed networks show the potential to utilise
larger multi-subject datasets to train an EEG decoder without being influenced
by negative transfer.


Abstract:
  It is well known and has been shown that the gain performance of Gaseous
Electron Multipliers (GEM) depends on the size of the holes. With an optical
scanner it is possible to measure the dimensions of the holes, and to predict
the performance of GEMs. However, the gain prediction of GEMs that are
manufactured with a double mask etching technique is not straightforward. With
the hole size information alone, it is not possible to make precise prediction
of the gain. We show that the alignment of the photo-masks between the two
sides of the GEM foils plays a crucial role. A misalignment of a few microns
can lower the gain substantially. The study is performed by using the Helsinki
high definition optical scanner for quality control of GEM foils, and this will
show its true potential.


Abstract:
  In the fields of co-simulation and component-based modelling, designers
import models as building blocks to create a composite model that provides more
complex functionalities. Modelling tools perform instantaneous cycle detection
(ICD) on the composite models having feedback loops to reject the models if the
loops are mathematically unsound and to improve simulation performance. In this
case, the analysis relies heavily on the availability of dependency information
from the imported models. However, the cycle detection problem becomes harder
when the model's input to output dependencies are mode-dependent, i.e. changes
for certain events generated internally or externally as inputs. The number of
possible modes created by composing such models increases significantly and
unknown factors such as environmental inputs make the offline (statical) ICD a
difficult task. In this paper, an online ICD method is introduced to address
this issue for the models used in cyber-physical systems. The method utilises
an oracle as a central source of information that can answer whether the
individual models can make mode transition without creating instantaneous
cycles. The oracle utilises three types of data-structures created offline that
are adaptively chosen during online (runtime) depending on the frequency as
well as the number of models that make mode transitions. During the analysis,
the models used online are stalled from running, resulting in the discrepancy
with the physical system. The objective is to detect an absence of the
instantaneous cycle while minimising the stall time of the model simulation
that is induced from the analysis. The benchmark results show that our method
is an adequate alternative to the offline analysis methods and significantly
reduces the analysis time.


Abstract:
  Spiking neural networks (SNNs) offer an inherent ability to process
spatial-temporal data, or in other words, realworld sensory data, but suffer
from the difficulty of training high accuracy models. A major thread of
research on SNNs is on converting a pre-trained convolutional neural network
(CNN) to an SNN of the same structure. State-of-the-art conversion methods are
approaching the accuracy limit, i.e., the near-zero accuracy loss of SNN
against the original CNN. However, we note that this is made possible only when
significantly more energy is consumed to process an input. In this paper, we
argue that this trend of "energy for accuracy" is not necessary -- a little
energy can go a long way to achieve the near-zero accuracy loss. Specifically,
we propose a novel CNN-to-SNN conversion method that is able to use a
reasonably short spike train (e.g., 256 timesteps for CIFAR10 images) to
achieve the near-zero accuracy loss. The new conversion method, named as
explicit current control (ECC), contains three techniques (current
normalisation, thresholding for residual elimination, and consistency
maintenance for batch-normalisation), in order to explicitly control the
currents flowing through the SNN when processing inputs. We implement ECC into
a tool nicknamed SpKeras, which can conveniently import Keras CNN models and
convert them into SNNs. We conduct an extensive set of experiments with the
tool -- working with VGG16 and various datasets such as CIFAR10 and CIFAR100 --
and compare with state-of-the-art conversion methods. Results show that ECC is
a promising method that can optimise over energy consumption and accuracy loss
simultaneously.


Abstract:
  The Kepler-1647 is a binary system with two Sun-type stars (approximately
1.22 and 0.97 Solar mass). It has the most massive circumbinary planet (1.52
Jupiter mass) with the longest orbital period (1,107.6 days) detected by the
Kepler probe and is located within the habitable zone (HZ) of the system. In
this work, we investigated the ability to form and house an Earth-sized planet
within its HZ. First, we computed the limits of its HZ and performed numerical
stability tests within that region. We found that HZ has three sub-regions that
show stability, one internal, one co-orbital, and external to the host planet
Kepler-1647b. Within the limits of these three regions, we performed numerical
simulations of planetary formation. In the regions inner and outer to the
planet, we used two different density profiles to explore different conditions
of formation. In the co-orbital region, we used eight different values of total
disc mass. We showed that many resonances are located within regions causing
much of the disc material to be ejected before a planet is formed. Thus, the
system might have two asteroid belts with Kirkwood gaps, similar to the Solar
System\'s main belt of asteroids. The co-orbital region proved to be extremely
sensitive, not allowing the planet formation, but showing that this binary
system has the capacity to have Trojan bodies. Finally, we looked for regions
of stability for an Earth-sized moon. We found that there is stability for a
moon with this mass up to 0.4 Hill\'s radius from the host planet.


Abstract:
  Distribution matchers for finite alphabets are shown to have informational
divergences that grow logarithmically with the block length, generalizing a
basic result for binary strings.


Abstract:
  An important issue in the field of academic measurement is how to evaluate
academic influence scientifically and comprehensively, which can help
government and research organizations better allocate academic resources and
recruit researchers. It is generally accepted that using weighted citations to
measure academic influence is more reasonable than treating all citations
equally. Given the limitations of the existing c-index, the first index in
bibliometric literature that measures output based on the quantity and quality
of received citations, we propose the x-index, which assigns weight to each
citation according to its distance. By defining collaboration distance and
citation distance, we first analyze the properties of the collaboration network
and citation distance, then perform theoretical and empirical analyses on
c-index to reveal its shortcomings, finally, we suggest the x-index and conduct
experiments and analysis on the x-index. Experimental results demonstrate that
compared with the c-index, h-index, and g-index, the x-index shows a stronger
discriminatory power.


Abstract:
  The resonant magnetic perturbation (RMP) field has been utilized to suppress
the edge localized mode (ELM) in tokamak plasmas. The pedestal top during the
RMP ELM suppression is characterized by the increased broadband fluctuation,
i.e. plasma turbulence. The origin of the increased fluctuation and its
characteristics were not fully understood. Here, we show that the increased
fluctuation involves the weak but significant nonlinear coupling in a narrow
region at the pedestal top and the spatial structure of turbulence becomes less
predictable around that region as expected for the stochastic fields around
magnetic islands. We analyzed the bicoherence and the rescaled Jensen-Shannon
complexity of the electron temperature fluctuation measured in KSTAR experiment
to identify the nonlinear coupling and the change of statistical turbulence
characteristics, respectively. Our analyses suggest that the nonlinear coupling
through a partially stochastic island layer at the pedestal top contributes to
the fluctuation increase during the RMP ELM suppression.


Abstract:
  We review the most recent RANSAC-like hypothesize-and-verify robust
estimators. The best performing ones are combined to create a state-of-the-art
version of the Universal Sample Consensus (USAC) algorithm. A recent objective
is to implement a modular and optimized framework, making future RANSAC modules
easy to be included. The proposed method, USACv20, is tested on eight publicly
available real-world datasets, estimating homographies, fundamental and
essential matrices. On average, USACv20 leads to the most geometrically
accurate models and it is the fastest in comparison to the state-of-the-art
robust estimators. All reported properties improved performance of original
USAC algorithm significantly. The pipeline will be made available after
publication.


Abstract:
  We introduce a new definition of speculative bubbles in discrete-time models
based on the discounted stock price losing mass at some finite drop-down under
an equivalent martingale measure. We provide equivalent probabilistic
characterisations of this definition and give examples of discrete-time
martingales that are speculative bubbles and those that are not. In the
Markovian case, we provide sufficient analytic conditions for the presence of
speculative bubbles. We also show that the existence of speculative bubbles is
directly linked to the existence of a non-trivial solution to a linear Volterra
integral equation of the second kind involving the Markov kernel. Finally, we
show that our definition of speculative bubbles in discrete time is consistent
with the strict local martingale definition of speculative bubbles in
continuous time in the sense that a properly discretised strict local
martingale in continuous time is a speculative bubble in discrete time.


Abstract:
  In the era of big data, deep learning has become an increasingly popular
topic. It has outstanding achievements in the fields of image recognition,
object detection, and natural language processing et al. The first priority of
deep learning is exploiting valuable information from a large amount of data,
which will inevitably induce privacy issues that are worthy of attention.
Presently, several privacy-preserving deep learning methods have been proposed,
but most of them suffer from a non-negligible degradation of either efficiency
or accuracy. Negative database (\textit{NDB}) is a new type of data
representation which can protect data privacy by storing and utilizing the
complementary form of original data. In this paper, we propose a
privacy-preserving deep learning method named NegDL based on \textit{NDB}.
Specifically, private data are first converted to \textit{NDB} as the input of
deep learning models by a generation algorithm called \textit{QK}-hidden
algorithm, and then the sketches of \textit{NDB} are extracted for training and
inference. We demonstrate that the computational complexity of NegDL is the
same as the original deep learning model without privacy protection.
Experimental results on Breast Cancer, MNIST, and CIFAR-10 benchmark datasets
demonstrate that the accuracy of NegDL could be comparable to the original deep
learning model in most cases, and it performs better than the method based on
differential privacy.


Abstract:
  Nowadays ransomware has become a new profitable form of attack. This type of
malware acts as a form of extortion which encrypts the files in a victim's
computer and forces the victim to pay the ransom to have the data recovered.
Even companies and tech savvy people must use extensive resources to maintain
backups for recovery or else they will lose valuable data, not mentioning
average users. Unfortunately, not any recovery tool can effectively defend
various types of ransomware. To address this challenge, we propose a novel
ransomware defense mechanism that can be easily deployed in modern Windows
system to recover the data and mitigate a ransomware attack. The uniqueness of
our approach is to fight the virus like a virus. We leverage Alternative Data
Streams which are sometimes used by malicious applications, to develop a data
protection method that misleads the ransomware to attack only file 'shells'
instead of the actual file content. We evaluated different file encrypting
ransomware and demonstrate usability, efficiency and effectiveness of our
approach.


Abstract:
  Spoken dialogue systems such as Siri and Alexa provide great convenience to
people's everyday life. However, current spoken language understanding (SLU)
pipelines largely depend on automatic speech recognition (ASR) modules, which
require a large amount of language-specific training data. In this paper, we
propose a Transformer-based SLU system that works directly on phones. This
acoustic-based SLU system consists of only two blocks and does not require the
presence of ASR module. The first block is a universal phone recognition
system, and the second block is a Transformer-based language model for phones.
We verify the effectiveness of the system on an intent classification dataset
in Mandarin Chinese.


Abstract:
  This paper presents a formulation of Snapshot Positioning as a mixed-integer
least-squares problem. In snapshot positioning one estimates a position from
code-phase and possibly Doppler observations of a Global Navigation Satellite
Systems (GNSS) without knowing the time of departure (timestamp) of the codes.
Solving the problem allows a receiver to determine a fix from short
radio-frequency snapshots missing the time-stamp information embedded in the
GNSS data stream. This is used to reduced the time to first fix in some
receivers, and it is used in certain wildlife trackers. This paper presents two
new formulations of the problem and an algorithm that solves the resulting
mixed-integer least-squares problems. We also show that the new formulations
can produce fixes even with huge initial errors, much larger than permitted in
Van Diggelen's widely-cited coarse-time navigation method.


Abstract:
  Intelligent and adaptive online education systems aim to make high-quality
education available for a diverse range of students. However, existing systems
usually depend on a pool of hand-made questions, limiting how fine-grained and
open-ended they can be in adapting to individual students. We explore targeted
question generation as a controllable sequence generation task. We first show
how to fine-tune pre-trained language models for deep knowledge tracing
(LM-KT). This model accurately predicts the probability of a student answering
a question correctly, and generalizes to questions not seen in training. We
then use LM-KT to specify the objective and data for training a model to
generate questions conditioned on the student and target difficulty. Our
results show we succeed at generating novel, well-calibrated language
translation questions for second language learners from a real online education
platform.


Abstract:
  Exciton-polaritons are quasi-particles arising from the strong coupling
regime between excitons and photons. In planar microcavitites, phenomena such
as superfluidity or Bose-Einstein condensation can be observed. Those systems
have demonstrated to be very efficient in the hydrodynamic generation of
topological excitations, such as vortex-antivortex pairs or dark solitons.
However, the lifetime and motion of those excitations were limited by the
driven dissipative nature of the system. In this thesis, we present a rich
variety of results about the generation and control of such topological
excitations. Taking advantage of the optical bistability present in our system,
we were able to greatly enhanced the propagation length of vortices and
solitons generated in the wake of a structural defect, revealing in the mean
time an unexpected binding mechanism of the solitons which propagate parallel.
This behaviour was recovered in a specifically designed experiment, where we
artificially imprint dark soliton pairs on demand on a polariton superfluid.
The adaptability of our technique allowed for a detailed study of this
phenomenon, that we directly connected to the driven-dissipative nature of our
system. Finally, confined dark solitons were generated within guided intensity
channels on a static polariton fluid. The absence of flow lead to the
development of transverse snake instabilities of which we studied the
interesting properties.


Abstract:
  In Machine Learning, the $\mathsf{SHAP}$-score is a version of the Shapley
value that is used to explain the result of a learned model on a specific
entity by assigning a score to every feature. While in general computing
Shapley values is an intractable problem, we prove a strong positive result
stating that the $\mathsf{SHAP}$-score can be computed in polynomial time over
deterministic and decomposable Boolean circuits. Such circuits are studied in
the field of Knowledge Compilation and generalize a wide range of Boolean
circuits and binary decision diagrams classes, including binary decision trees
and Ordered Binary Decision Diagrams (OBDDs).
  We also establish the computational limits of the SHAP-score by observing
that computing it over a class of Boolean models is always polynomially as hard
as the model counting problem for that class. This implies that both
determinism and decomposability are essential properties for the circuits that
we consider. It also implies that computing $\mathsf{SHAP}$-scores is
intractable as well over the class of propositional formulas in DNF. Based on
this negative result, we look for the existence of fully-polynomial randomized
approximation schemes (FPRAS) for computing $\mathsf{SHAP}$-scores over such
class. In contrast to the model counting problem for DNF formulas, which admits
an FPRAS, we prove that no such FPRAS exists for the computation of
$\mathsf{SHAP}$-scores. Surprisingly, this negative result holds even for the
class of monotone formulas in DNF. These techniques can be further extended to
prove another strong negative result: Under widely believed complexity
assumptions, there is no polynomial-time algorithm that checks, given a
monotone DNF formula $\varphi$ and features $x,y$, whether the
$\mathsf{SHAP}$-score of $x$ in $\varphi$ is smaller than the
$\mathsf{SHAP}$-score of $y$ in $\varphi$.


Abstract:
  Reactions on the proton-rich nuclides drive the nucleosynthesis in
Core-Collapse Supernovae (CCSNe) and in X-ray bursts (XRBs). CCSNe eject the
nucleosynthesis products to the interstellar medium and hence are a potential
inventory of p-nuclei, whereas in XRBs nucleosynthesis powers the light curves.
In both astrophysical sites the Ni-Cu cycle, which features a competition
between $^{59}$Cu(p,$\alpha$)$^{56}$Ni and $^{59}$Cu(p,$\gamma$)$^{60}$Zn,
could potentially halt the production of heavier elements. Here, we report the
first direct measurement of $^{59}$Cu(p,$\alpha$)$^{56}$Ni using a
re-accelerated $^{59}$Cu beam and cryogenic solid hydrogen target. Our results
show that the reaction proceeds predominantly to the ground state of $^{56}$Ni
and the experimental rate has been found to be lower than Hauser-Feshbach-based
statistical predictions. New results hint that the $\nu p$-process could
operate at higher temperatures than previously inferred and therefore remains a
viable site for synthesizing the heavier elements.


Abstract:
  Rendering objects invisible to impinging acoustic waves (cloaking) and
creating acoustic illusions (holography) has been attempted using active and
passive approaches. While passive methods are applicable only to narrow
frequency bands, active approaches attempt to respond dynamically, interfering
with broadband incident or scattered wavefields by emitting secondary waves.
Without prior knowledge of the primary wavefield, the signals for the secondary
sources need to be estimated and adapted in real-time. This has thus far
impeded active cloaking and holography for broadband wavefields. We present
experimental results of active acoustic cloaking and holography without prior
knowledge of the wavefield so that objects remain invisible and illusions
intact even for broadband moving sources. This opens novel research directions
and facilitates practical applications including architectural acoustics, and
stealth.


Abstract:
  Pretrained text encoders, such as BERT, have been applied increasingly in
various natural language processing (NLP) tasks, and have recently demonstrated
significant performance gains. However, recent studies have demonstrated the
existence of social bias in these pretrained NLP models. Although prior works
have made progress on word-level debiasing, improved sentence-level fairness of
pretrained encoders still lacks exploration. In this paper, we proposed the
first neural debiasing method for a pretrained sentence encoder, which
transforms the pretrained encoder outputs into debiased representations via a
fair filter (FairFil) network. To learn the FairFil, we introduce a contrastive
learning framework that not only minimizes the correlation between filtered
embeddings and bias words but also preserves rich semantic information of the
original sentences. On real-world datasets, our FairFil effectively reduces the
bias degree of pretrained text encoders, while continuously showing desirable
performance on downstream tasks. Moreover, our post-hoc method does not require
any retraining of the text encoders, further enlarging FairFil's application
space.


Abstract:
  Medical toxicology is the clinical specialty that treats the toxic effects of
substances, be it an overdose, a medication error, or a scorpion sting. The
volume of toxicological knowledge and research has, as with other medical
specialties, outstripped the ability of the individual clinician to entirely
master and stay current with it. The application of machine learning techniques
to medical toxicology is challenging because initial treatment decisions are
often based on a few pieces of textual data and rely heavily on prior
knowledge. ML techniques often do not represent knowledge in a way that is
transparent for the physician, raising barriers to usability. Rule-based
systems and decision tree learning are more transparent approaches, but often
generalize poorly and require expert curation to implement and maintain. Here,
we construct a probabilistic logic network to represent a portion of the
knowledge base of a medical toxicologist. Our approach transparently mimics the
knowledge representation and clinical decision-making of practicing clinicians.
The software, dubbed Tak, performs comparably to humans on straightforward
cases and intermediate difficulty cases, but is outperformed by humans on
challenging clinical cases. Tak outperforms a decision tree classifier at all
levels of difficulty. Probabilistic logic provides one form of explainable
artificial intelligence that may be more acceptable for use in healthcare, if
it can achieve acceptable levels of performance.


Abstract:
  Dimensionality reduction (DR) techniques help analysts to understand patterns
in high-dimensional spaces. These techniques, often represented by scatter
plots, are employed in diverse science domains and facilitate similarity
analysis among clusters and data samples. For datasets containing many
granularities or when analysis follows the information visualization mantra,
hierarchical DR techniques are the most suitable approach since they present
major structures beforehand and details on demand. However, current
hierarchical DR techniques are not fully capable of addressing literature
problems because they do not preserve the projection mental map across
hierarchical levels or are not suitable for most data types. This work presents
HUMAP, a novel hierarchical dimensionality reduction technique designed to be
flexible on preserving local and global structures and preserve the mental map
throughout hierarchical exploration. We provide empirical evidence of our
technique's superiority compared with current hierarchical approaches and show
two case studies to demonstrate its strengths.


Abstract:
  In this article, we introduce the notion of nonlocal diffusion as a means to
describe the change, and possible polarization, that occurs in some public
sentiment.


Abstract:
  This Conceptual Design Report describes LUXE (Laser Und XFEL Experiment), an
experimental campaign that aims to combine the high-quality and high-energy
electron beam of the European XFEL with a powerful laser to explore the
uncharted terrain of quantum electrodynamics characterised by both high energy
and high intensity. We will reach this hitherto inaccessible regime of quantum
physics by analysing high-energy electron-photon and photon-photon interactions
in the extreme environment provided by an intense laser focus. The physics
background and its relevance are presented in the science case which in turn
leads to, and justifies, the ensuing plan for all aspects of the experiment:
Our choice of experimental parameters allows (i) effective field strengths to
be probed at and beyond the Schwinger limit and (ii) a precision to be achieved
that permits a detailed comparison of the measured data with calculations. In
addition, the high photon flux predicted will enable a sensitive search for new
physics beyond the Standard Model. The initial phase of the experiment will
employ an existing 40 TW laser, whereas the second phase will utilise an
upgraded laser power of 350 TW. All expectations regarding the performance of
the experimental set-up as well as the expected physics results are based on
detailed numerical simulations throughout.


Abstract:
  We combine Newton's variational method with ideas from eigenvector
continuation to construct a fast & accurate emulator for two-body scattering
observables. The emulator will facilitate the application of rigorous
statistical methods for interactions that depend smoothly on a set of free
parameters. Our approach begins with a trial $K$ or $T$ matrix constructed from
a small number of exact solutions to the Lippmann--Schwinger equation.
Subsequent emulation only requires operations on small matrices. We provide
several applications to short-range potentials with and without the Coulomb
interaction and partial-wave coupling. It is shown that the emulator can
accurately extrapolate far from the support of the training data. When used to
emulate the neutron-proton cross section with a modern chiral interaction as a
function of 26 free parameters, it reproduces the exact calculation with
negligible error and provides an over 300x improvement in CPU time.


Abstract:
  We introduce the UPG package for highly efficient Bayesian inference in
probit, logit, multinomial logit and binomial logit models. UPG offers a
convenient estimation framework for balanced and imbalanced data settings where
sampling efficiency is ensured through Markov chain Monte Carlo boosting
methods. All sampling algorithms are implemented in C++, allowing for rapid
parameter estimation. In addition, UPG provides several methods for fast
production of output tables and summary plots that are easily accessible to a
broad range of users.


Abstract:
  Sommerfeld-enhanced dark matter (DM) annihilation through $s$-wave has been
widely considered as a consistent explanation for both the observed cosmic-ray
(CR) positron excess and the DM thermal relic density. However, as the $s$-wave
Sommerfeld-enhanced annihilation cross section increases monotonically with
decreasing DM velocity, severe constraints appear from the data of gamma rays
from dwarf spheroidal satellite galaxies (dSphs) and the cosmic microwave
background (CMB), as the relevant typical DM velocities are even lower than
that in the Galactic halo. In this work, we consider Sommerfeld-enhanced
$p$-wave DM annihilation where the DM annihilation cross section can be
enhanced at certain velocities but eventually be highly suppressed when the DM
velocities become extremely low. We calculate the velocity-dependent
astrophysics factors ($J$-factors) for the Sommerfeld-enhanced $p$-wave DM
annihilation for fifteen nearby dSphs. Taking the channel of DM annihilating
into $4\mu$ through two light mediators as an example, we show that there are
parameter regions where this mechanism can account for the CR positron excess
and the DM relic density, while being compatible with the constraints from
dSphs gamma rays measured by Fermi-LAT and that from the CMB measured by
PLANCK.


Abstract:
  Conventional wireless techniques are becoming inadequate for beyond
fifth-generation (5G) networks due to latency and bandwidth considerations. To
improve the error performance and throughput of wireless communication systems,
we propose physical layer network coding (PNC) in an intelligent reflecting
surface (IRS)-assisted environment. We consider an IRS-aided butterfly network,
where we propose an algorithm for obtaining the optimal IRS phases. Also,
analytic expressions for the bit error rate (BER) are derived. The numerical
results demonstrate that the proposed scheme significantly improves the BER
performance. For instance, the BER at the relay in the presence of a 32-element
IRS is three orders of magnitudes less than that without an IRS.


Abstract:
  The Quantum Fisher Information matrix (QFIM) is a central metric in promising
algorithms, such as Quantum Natural Gradient Descent and Variational Quantum
Imaginary Time Evolution. Computing the full QFIM for a model with $d$
parameters, however, is computationally expensive and generally requires
$\mathcal{O}(d^2)$ function evaluations. To remedy these increasing costs in
high-dimensional parameter spaces, we propose using simultaneous perturbation
stochastic approximation techniques to approximate the QFIM at a constant cost.
We present the resulting algorithm and successfully apply it to prepare
Hamiltonian ground states and train Variational Quantum Boltzmann Machines.


Abstract:
  Low-temperature plasma (LTP) processing is a green and flexible food
processing technology with only known limitation of low energy efficiency.
Three established fundamental mechanisms of plasma food processing includes: i)
physical damage to membranes and internal cellular components due to
bombardment of energetic ions: ii) radicals and charged species led chemical
reactions: iii) and radiation-induced modifications. In this investigation, we
present a new mechanism of generation and extraction of energetic $N_2$
neutrals from glow discharge plasmas for efficient food processing. For
nitrogen dc glow discharge plasma we demonstrate by experiment selective
generation and extraction of energetic $N_2$ neutrals and $N_2^+$ ions for
treatment of different food samples. Using numerical simulations, the plasma
processing parameters are defined for selective extraction of $N_2$ or $N_2^+$
species with specific energies. Treatment of native finger millet flour or ragi
flour (Eleusine coracana) with selectively extracted energetic $N_2$ neutrals,
with an estimated average energy of 34.20 eV, resulted in improvement of
shelf-life and functional properties. Additionally, this processing technique
achieved high energy efficiency of ~ 46%. This method of food engineering has
advantages of control over active species generation, high power efficiency,
reduced processing cost, and chemical-free. Therefore, it opens up wide
possibilities in plasma food processing for global food security and food
safety.


Abstract:
  Fluid-filled soft visuotactile sensors such as the Soft-bubbles alleviate key
challenges for robust manipulation, as they enable reliable grasps along with
the ability to obtain high-resolution sensory feedback on contact geometry and
forces. Although they are simple in construction, their utility has been
limited due to size constraints introduced by enclosed custom IR/depth imaging
sensors to directly measure surface deformations. Towards mitigating this
limitation, we investigate the application of state-of-the-art monocular depth
estimation to infer dense internal (tactile) depth maps directly from the
internal single small IR imaging sensor. Through real-world experiments, we
show that deep networks typically used for long-range depth estimation (1-100m)
can be effectively trained for precise predictions at a much shorter range
(1-100mm) inside a mostly textureless deformable fluid-filled sensor. We
propose a simple supervised learning process to train an object-agnostic
network requiring less than 10 random poses in contact for less than 10 seconds
for a small set of diverse objects (mug, wine glass, box, and fingers in our
experiments). We show that our approach is sample-efficient, accurate, and
generalizes across different objects and sensor configurations unseen at
training time. Finally, we discuss the implications of our approach for the
design of soft visuotactile sensors and grippers.


Abstract:
  A natural notion of higher order rectifiability is introduced for subsets of
Heisenberg groups $\mathbb{H}^n$ in terms of covering a set almost everywhere
by a countable union of $(\mathbf{C}_H^{1,\alpha},\mathbb{H})$-regular
surfaces, for some $0 < \alpha \leq 1$. We prove that a sufficient condition
for $C^{1,\alpha}$-rectifiability of low-codimensional subsets in Heisenberg
groups is the almost everywhere existence of suitable approximate tangent
paraboloids.


Abstract:
  In complex oxides, the electrons under the influence of competing energetics
are the cornerstone of coexistence (or phase-separation) of two or more
electronic/magnetic phases in same structural configuration. Probing of growth
and evolution of such phase-coexistence state is crucial to determine the
correct mechanism of related phase-transition. Here, we demonstrate the
combination of terahertz (THz) time-domain spectroscopy and DC transport as a
novel strategy to probe the electronic phase-coexistence. This is demonstrated
in disorder controlled phase-separated rare-earth nickelate thin films which
exhibit metal-insulator transition in dc conductivity at around 180 K but lack
this transition in terahertz (THz) dynamics conductivity down to low
temperature. Such pronounced disparity exploits two extreme attributes: i)
enormous sensitivity of THz radiation to a spatial range of its
wavelength-compatible electronic inhomogeneities and ii) insensitivity to a
range beyond the size of its wavelength. This feature is generic in nature
(sans a photo-induced effect), depends solely on the size of
insulating/metallic clusters and formulates a methodology with unique
sensitivity to investigate electronic phase-coexistence and phase transition of
any material system.


Abstract:
  Deep learning has shown impressive performance on challenging perceptual
tasks and has been widely used in software to provide intelligent services.
However, researchers found deep neural networks vulnerable to adversarial
examples. Since then, many methods are proposed to defend against adversaries
in inputs, but they are either attack-dependent or shown to be ineffective with
new attacks. And most of existing techniques have complicated structures or
mechanisms that cause prohibitively high overhead or latency, impractical to
apply on real software.
  We propose DAFAR, a feedback framework that allows deep learning models to
detect/purify adversarial examples in high effectiveness and universality, with
low area and time overhead. DAFAR has a simple structure, containing a victim
model, a plug-in feedback network, and a detector. The key idea is to import
the high-level features from the victim model's feature extraction layers into
the feedback network to reconstruct the input. This data stream forms a
feedback autoencoder. For strong attacks, it transforms the imperceptible
attack on the victim model into the obvious reconstruction-error attack on the
feedback autoencoder directly, which is much easier to detect; for weak
attacks, the reformation process destroys the structure of adversarial
examples. Experiments are conducted on MNIST and CIFAR-10 data-sets, showing
that DAFAR is effective against popular and arguably most advanced attacks
without losing performance on legitimate samples, with high effectiveness and
universality across attack methods and parameters.


Abstract:
  Applying simultaneously the methodology of Non-Equilibrium Thermodynamics
with Internal Variables (NET-IV) and the framework of General Equation for the
Non-Equilibrium Reversible-Irreversible Coupling (GENERIC), we demonstrate
that, in heat conduction theories, entropy current multipliers can be
interpreted as relaxed state variables. Fourier's law and its various
extensions -- the Maxwell-Cattaneo-Vernotte, Guyer-Krumhansl, Jeffreys type,
Ginzburg-Landau (Allen-Cahn) type and ballistic-diffusive -- heat conduction
equations are derived in both formulations. Along these lines, a comparison of
NET-IV and GENERIC is also performed. Our results may pave the way for
microscopic/multiscale understanding of beyond-Fourier heat conduction, and
open new ways for numerical simulations of heat-conduction problems.


Abstract:
  Indoor panorama typically consists of human-made structures parallel or
perpendicular to gravity. We leverage this phenomenon to approximate the scene
in a 360-degree image with (H)orizontal-planes and (V)ertical-planes. To this
end, we propose an effective divide-and-conquer strategy that divides pixels
based on their plane orientation estimation; then, the succeeding instance
segmentation module conquers the task of planes clustering more easily in each
plane orientation group. Besides, parameters of V-planes depend on camera yaw
rotation, but translation-invariant CNNs are less aware of the yaw change. We
thus propose a yaw-invariant V-planar reparameterization for CNNs to learn. We
create a benchmark for indoor panorama planar reconstruction by extending
existing 360 depth datasets with ground truth H\&V-planes (referred to as
PanoH&V dataset) and adopt state-of-the-art planar reconstruction methods to
predict H\&V-planes as our baselines. Our method outperforms the baselines by a
large margin on the proposed dataset.


Abstract:
  This work presents a novel technique that integrates the methodologies of
machine learning and system identification to solve multiclass problems. Such
an approach allows to extract and select sets of representative features with
reduced dimensionality, as well as predicts categorical outputs. The efficiency
of the method was tested by running case studies investigated in machine
learning, obtaining better absolute results when compared with classical
classification algorithms.


Abstract:
  Cloud computing facilitates the access of applications and data from any
location by a distributed storage system. Erasure codes offer better data
replication technique with reduced storage costs for more reliability. This
paper considers the erasure-coded data center with multiple servers in a
wireless network where each is equipped with a base-station. The cause of
latency in the file retrieval process is mainly due to queuing delays at each
server. This work puts forth a stochastic optimization framework for obtaining
the optimal scheduling policy that maximizes users' quality of service (QoS)
while adhering to the latency requirements. We further show that the problem
has non-linear functions of expectations in objective and constraints and is
impossible to solve with traditional SGD like algorithms. We propose a new
algorithm that addresses compositional structure in the problem. Further, we
show that the proposed algorithm achieves a faster convergence rate than the
best-known results. Finally, we test the efficacy of the proposed method in a
simulated environment.


Abstract:
  A weighted simplicial complex is a simplicial complex with values (called
weights) on the vertices. In this paper, we consider weighted simplicial
complexes with $\mathbb{R}^2$-valued weights. We study the weighted homology
and the weighted analytic torsion for such weighted simplicial complexes.


Abstract:
  We study the Cauchy problem for the quasi-geostrophic equations with the
critical dissipation in the two dimensional half space under the homogeneous
Dirichlet boundary condition. We show the global existence, the uniqueness and
the analyticity of solutions, and the real analyticity up to the boundary is
obtained. We will show one of natural ways to estimate the nonlinear term for
functions satisfying the Dirichlet boundary condition.


Abstract:
  The main purpose of this paper is to give a combinatorial realization of
Kirillov-Reshetikhin (KR simply) crystals $B^{r, s}$ for type
$\text{E}_n^{(1)}$ with a minuscule node $r$ and $s \ge 1$. To do this, we
describe explicitly the crystal of the quantum nilpotent subalgebra associated
with the translation by the negative of the $r$-th fundamental weight. Then the
crystal can be extended as an affine crystal, in which a certain subcrystal
characterized by the $\varepsilon_r^*$-statistic is isomorphic to $B^{r,s}$ as
an affine crystal, where $\varepsilon_r^*$ is also realized precisely in terms
of triple and quadruple paths.


Abstract:
  The well known constant rank constraint qualification [Math. Program. Study
21:110--126, 1984] introduced by Janin for nonlinear programming has been
recently extended to a conic context by exploiting the eigenvector structure of
the problem. In this paper we propose a more general and geometric approach for
defining a new extension of this condition to the conic context. The main
advantage of our approach is that we are able to recast the strong second-order
properties of the constant rank condition in a conic context. In particular, we
obtain a second-order necessary optimality condition that is stronger than the
classical one obtained under Robinson's constraint qualification, in the sense
that it holds for every Lagrange multiplier, even though our condition is
independent of Robinson's condition.


Abstract:
  We study the topological susceptibility and the fourth cumulant of the QCD
vacuum in the presence of a uniform, background magnetic field in two-and-three
flavor QCD finding novel, model-independent sum rules relating the shift in the
topological susceptibility due to the background field to the shift in the
quark condensates, and the shift in the fourth cumulant to the shifts in the
quark condensates and susceptibilities


Abstract:
  In this paper novel simulation methods are provided for the generalised
inverse Gaussian (GIG) L\'{e}vy process. Such processes are intractable for
simulation except in certain special edge cases, since the L\'{e}vy density
associated with the GIG process is expressed as an integral involving certain
Bessel Functions, known as the Jaeger integral in diffusive transport
applications. We here show for the first time how to solve the problem
indirectly, using generalised shot-noise methods to simulate the underlying
point processes and constructing an auxiliary variables approach that avoids
any direct calculation of the integrals involved. The resulting augmented
bivariate process is still intractable and so we propose a novel thinning
method based on upper bounds on the intractable integrand. Moreover our
approach leads to lower and upper bounds on the Jaeger integral itself, which
may be compared with other approximation methods. The shot noise method
involves a truncated infinite series of decreasing random variables, and as
such is approximate, although the series are found to be rapidly convergent in
most cases. We note that the GIG process is the required Brownian motion
subordinator for the generalised hyperbolic (GH) L\'{e}vy process and so our
simulation approach will straightforwardly extend also to the simulation of
these intractable proceses. Our new methods will find application in forward
simulation of processes of GIG and GH type, in financial and engineering data,
for example, as well as inference for states and parameters of stochastic
processes driven by GIG and GH L\'{e}vy processes.


Abstract:
  Neural networks training on edge terminals is essential for edge AI
computing, which needs to be adaptive to evolving environment. Quantised models
can efficiently run on edge devices, but existing training methods for these
compact models are designed to run on powerful servers with abundant memory and
energy budget. For example, quantisation-aware training (QAT) method involves
two copies of model parameters, which is usually beyond the capacity of on-chip
memory in edge devices. Data movement between off-chip and on-chip memory is
energy demanding as well. The resource requirements are trivial for powerful
servers, but critical for edge devices. To mitigate these issues, We propose
Resource Constrained Training (RCT). RCT only keeps a quantised model
throughout the training, so that the memory requirements for model parameters
in training is reduced. It adjusts per-layer bitwidth dynamically in order to
save energy when a model can learn effectively with lower precision. We carry
out experiments with representative models and tasks in image application and
natural language processing. Experiments show that RCT saves more than 86\%
energy for General Matrix Multiply (GEMM) and saves more than 46\% memory for
model parameters, with limited accuracy loss. Comparing with QAT-based method,
RCT saves about half of energy on moving model parameters.


Abstract:
  We present the results of a temporal and spectral analysis of the transient
source ULX-4 in the galaxy M51. The data used were drawn from Chandra,
XMM-Newton and Swift-XRT archives, spanning the years 2000-2019. The X-ray flux
of the source is seen to vary by two orders of magnitudes within a month but a
short-term variability was not observed over the time intervals of 100-2000
second in the 0.3-10 keV energy band. We find some evidence for the existence
of bi-modality feature in the flux distribution of ULX-4. We identified two
optical sources as possible counterparts within an error radius of 0."18 at 95%
confidence level for ULX-4 based on the archival HST/ACS and HST/WFC3 data.
Blackbody fits of the spectral energy distributions (SEDs) indicate the
spectral type to be B-type stars. One of these counterparts exhibits a
low-amplitude optical periodicity of 264 days in the F606W filter; if we assume
this apparent periodicity is associated with the orbital motion of the donor,
then it is more likely that the donor is a red supergiant satisfying the long
periodicity and accretion via Roche-lobe overflow. Consequently, the SED would
then have to be interpreted as a superposition of emissions from a cold donor
and a hot flow component, most likely from an accretion disk. If, on the other
hand, the periodicity is super orbital in nature i.e., due to possible
interactions of the compact object with a circumstellar disk, the donor could
then be a Be/X star hosting a neutron star.


Abstract:
  With current high precision collider data, the reliable estimation of
theoretical uncertainties due to missing higher orders (MHOs) in perturbation
theory has become a pressing issue for collider phenomenology. Traditionally,
the size of the MHOs is estimated through scale variation, a simple but ad hoc
method without probabilistic interpretation. Bayesian approaches provide a
compelling alternative to estimate the size of the MHOs, but it is not clear
how to interpret the perturbative scales, like the factorisation and
renormalisation scales, in a Bayesian framework. Recently, it was proposed that
the scales can be incorporated as hidden parameters into a Bayesian model. In
this paper, we thoroughly scrutinise Bayesian approaches to MHO estimation and
systematically study the performance of different models on an extensive set of
high-order calculations. We extend the framework in two significant ways.
First, we define a new model that allows for asymmetric probability
distributions. Second, we introduce a prescription to incorporate information
on perturbative scales without interpreting them as hidden model parameters. We
clarify how the two scale prescriptions bias the result towards specific scale
choice, and we discuss and compare different Bayesian MHO estimates among
themselves and to the traditional scale variation approach. Finally, we provide
a practical prescription of how existing perturbative results at the standard
scale variation points can be converted to 68%/95% credibility intervals in the
Bayesian approach using the new public code MiHO.


Abstract:
  We consider the self-collision of portals in classical general relativity.
Portals are wormholes supported by a single loop of negative mass cosmic
string, and being wormholes, portals have a nontrivial topology. Portals can be
constructed so that the curvature is zero everywhere outside the cosmic string,
with vanishing ADM mass. The conical singularities of these wormholes can be
smoothed, yielding a spatial topology of $S^2 \times S^1$ with a point
corresponding to spatial infinity removed. If one attempts to collide the
mouths of a smoothed portal to induce self-annihilation, one naively might
think that a Euclidean topology is recovered, which would violate the classical
no topology change theorems. We consider a particular limit of smoothed portals
supported by an anisotropic fluid, and find that while the portal mouths do not
experience an acceleration as they are brought close together, a curvature
singularity forms in the limit that the separation distance vanishes. We find
that in general relativity, the interaction between portal mouths is not
primarily gravitational in nature, but depends critically on matter
interactions.


Abstract:
  To ensure accountability and mitigate harm, it is critical that diverse
stakeholders can interrogate black-box automated systems and find information
that is understandable, relevant, and useful to them. In this paper, we eschew
prior expertise- and role-based categorizations of interpretability
stakeholders in favor of a more granular framework that decouples stakeholders'
knowledge from their interpretability needs. We characterize stakeholders by
their formal, instrumental, and personal knowledge and how it manifests in the
contexts of machine learning, the data domain, and the general milieu. We
additionally distill a hierarchical typology of stakeholder needs that
distinguishes higher-level domain goals from lower-level interpretability
tasks. In assessing the descriptive, evaluative, and generative powers of our
framework, we find our more nuanced treatment of stakeholders reveals gaps and
opportunities in the interpretability literature, adds precision to the design
and comparison of user studies, and facilitates a more reflexive approach to
conducting this research.


Abstract:
  We present a new method to model the mass distribution of galaxy clusters
that combines a parametric and a free-form approach to reconstruct cluster
cores with strong lensing constraints. It aims at combining the advantages of
both approaches, by keeping the robustness of the parametric component with an
increased flexibility thanks to a free-form surface of B-spline functions. We
demonstrate the capabilities of this new approach on the simulated cluster
Hera, which has been used to evaluate lensing codes for the analysis of the
Frontier Fields clusters. The method leads to better reproduction of the
constraints, with an improvement by a factor $\sim3-4$ on the root-mean-square
error on multiple-image positions, when compared to parametric-only approaches.
The resulting models show a better accuracy in the reconstruction of the
amplitude of the convergence field while conserving a high fidelity on other
lensing observables already well reproduced. We make this method publicly
available through its implementation in the Lenstool software.


Abstract:
  Let $X$ be a compact, hyperbolic surface of genus $g\geq 2$. In this paper,
we prove that the twisted Selberg and Ruelle zeta functions, associated with an
arbitrary, finite-dimensional, complex representation $\chi$ of $\pi_1(X)$
admit a meromorphic continuation to $\C$. Moreover, we study the behaviour of
the twisted Ruelle zeta function at $s=0$ and prove that at this point, it has
a zero of order $\dim(\chi)(2g-2)$.


Abstract:
  By exploiting properties of boundaries associated with Coxeter groups we
obtain a complete characterization of simple right-angled multi-parameter Hecke
C$^{\ast}$-algebras. This extends previous results by Caspers, Larsen and the
author. Based on work by Raum and Skalski, we further describe the center and
the character space of right-angled Hecke C$^{\ast}$-algebras.


Abstract:
  Galton's rank order statistic is one of the oldest statistical tools for
two-sample comparisons. It is also a very natural index to measure departures
from stochastic dominance. Yet, its asymptotic behaviour has been investigated
only partially, under restrictive assumptions. This work provides a
comprehensive {study} of this behaviour, based on the analysis of the so-called
contact set (a modification of the set in which the quantile functions
coincide). We show that a.s. convergence to the population counterpart holds if
and only if {the} contact set has zero Lebesgue measure. When this set is
finite we show that the asymptotic behaviour is determined by the local
behaviour of a suitable reparameterization of the quantile functions in a
neighbourhood of the contact points. Regular crossings result in standard rates
and Gaussian limiting distributions, but higher order contacts (in the sense
introduced in this work) or contacts at the extremes of the supports may result
in different rates and non-Gaussian limits.


Abstract:
  An Euler tour in a hypergraph $H$ is a closed walk that traverses each edge
of $H$ exactly once, and an Euler family is a family of closed walks that
jointly traverse each edge of $H$ exactly once. An $\ell$-covering
$k$-hypergraph, for $2 \le \ell < k$, is a $k$-uniform hypergraph in which
every $\ell$-subset of vertices lie together in at least one edge.
  In this paper we prove that every $\ell$-covering $k$-hypergraph, for $k \ge
3$, admits an Euler family.


Abstract:
  As the quality of mobile cameras starts to play a crucial role in modern
smartphones, more and more attention is now being paid to ISP algorithms used
to improve various perceptual aspects of mobile photos. In this Mobile AI
challenge, the target was to develop an end-to-end deep learning-based image
signal processing (ISP) pipeline that can replace classical hand-crafted ISPs
and achieve nearly real-time performance on smartphone NPUs. For this, the
participants were provided with a novel learned ISP dataset consisting of
RAW-RGB image pairs captured with the Sony IMX586 Quad Bayer mobile sensor and
a professional 102-megapixel medium format camera. The runtime of all models
was evaluated on the MediaTek Dimensity 1000+ platform with a dedicated AI
processing unit capable of accelerating both floating-point and quantized
neural networks. The proposed solutions are fully compatible with the above NPU
and are capable of processing Full HD photos under 60-100 milliseconds while
achieving high fidelity results. A detailed description of all models developed
in this challenge is provided in this paper.


Abstract:
  The beyond-mean-field Lee-Huang-Yang (LHY) correction is ubiquitous in dilute
ultracold quantum gases. However, its effects are often elusive due to the
typically much larger influence of the mean-field energy. In this work, we
study an ultracold mixture of $^{23}$Na and $^{87}$Rb with tunable attractive
interspecies interactions. The LHY effects manifest in the formation of
self-bound quantum liquid droplets and the expansion dynamics of the gas-phase
sample. A liquid-to-gas phase diagram is obtained by measuring the critical
atom numbers below which the self-bound behavior disappears. In stark contrast
to trapped gas-phase condensates, the gas-phase mixture formed following the
liquid-to-gas phase transition shows an anomalous expansion featuring a larger
release energy for increasing mean-field attractions.


Abstract:
  Biclustering is a method for detecting homogeneous submatrices in a given
observed matrix, and it is an effective tool for relational data analysis.
Although there are many studies that estimate the underlying bicluster
structure of a matrix, few have enabled us to determine the appropriate number
of biclusters in an observed matrix. Recently, a statistical test on the number
of biclusters has been proposed for a regular-grid bicluster structure, where
we assume that the latent bicluster structure can be represented by row-column
clustering. However, when the latent bicluster structure does not satisfy such
regular-grid assumption, the previous test requires a larger number of
biclusters than necessary (i.e., a finer bicluster structure than necessary)
for the null hypothesis to be accepted, which is not desirable in terms of
interpreting the accepted bicluster structure. In this study, we propose a new
statistical test on the number of biclusters that does not require the
regular-grid assumption and derive the asymptotic behavior of the proposed test
statistic in both null and alternative cases. We illustrate the effectiveness
of the proposed method by applying it to both synthetic and practical
relational data matrices.


Abstract:
  The onset of frictional motion is characterized by the transition from
quasi-static stable slip growth to dynamic unstable crack propagation. Here, we
investigate how microscopic heterogeneities influence the macroscopic stability
of disordered interfaces. Building on the physics of depinning, we propose an
analytical framework to estimate the critical length $L_\mathrm{c}$ of the
instability nucleus, and benchmark it against 2D numerical dynamic
calculations. The interplay between material properties and disorder gives
birth to three instability regimes (local, extremal, and homogenized), each
related to different nucleation scenarios.


Abstract:
  Natural language processing (NLP) is becoming an important means for
automatic recognition of human traits and states, such as intoxication,
presence of psychiatric disorders, presence of airway disorders and states of
stress. Such applications have the potential to be an important pillar for
online help lines, and may gradually be introduced into eHealth modules.
However, NLP is language specific and for languages such as Dutch, NLP models
are scarce. As a result, recent Dutch NLP models have a low capture of long
range semantic dependencies over sentences. To overcome this, here we present
belabBERT, a new Dutch language model extending the RoBERTa architecture.
belabBERT is trained on a large Dutch corpus (+32 GB) of web crawled texts. We
applied belabBERT to the classification of psychiatric illnesses. First, we
evaluated the strength of text-based classification using belabBERT, and
compared the results to the existing RobBERT model. Then, we compared the
performance of belabBERT to audio classification for psychiatric disorders.
Finally, a brief exploration was performed, extending the framework to a hybrid
text- and audio-based classification. Our results show that belabBERT
outperformed the current best text classification network for Dutch, RobBERT.
belabBERT also outperformed classification based on audio alone.


Abstract:
  We propose an analytical parametrization of the comoving distance and Hubble
parameter to study the cosmic expansion history beyond the vanilla $\Lambda$CDM
model. The parametrization is generalized enough to include the contribution of
spatial curvature and to capture the higher redshift behaviors. With this
parameterization, we study the late time cosmic behavior and put constraints on
the cosmological parameters like present values of Hubble parameter ($H_{0}$),
matter energy density parameter ($\Omega_{m0}$), spatial curvature energy
density parameter ($\Omega_{k0}$) and baryonic matter energy density parameter
($\Omega_{b0}$) using different combinations data like CMB (Cosmic microwave
background), BAO (baryon acoustic oscillation), and SN (Pantheon sample for
type Ia supernovae). We also rigorously study the Hubble tension in the
framework of late time modification from the standard $\Lambda$CDM model. We
find that the late time modification of the cosmic expansion can solve the
Hubble tension between CMB $\&$ SHOES (local distance ladder observation for
$H_{0}$), between CMB+BAO $\&$ SHOES and between CMB+SN $\&$ SHOES, but the
late time modification can not solve the Hubble tension between CMB+BAO+SN and
SHOES. That means CMB, BAO, and SN data combined put strong enough constraints
on $H_{0}$ (even with varying $\Omega_{k0}$) and on other background
cosmological parameters so that the addition of $H_{0}$ prior from SHOES (or
from similar other local distance observations) can not significantly pull the
$H_{0}$ value towards the corresponding SHOES value.


Abstract:
  It is pointed out that the solutions of the Klein-Gordon and the Dirac
equation derived in the paper addressed in this Comment (and many more
solutions) may be obtained from generating functions.


Abstract:
  Autonomous experiments are excellent tools to increase the efficiency of
material discovery. Indeed, AI and ML methods can help optimizing valuable
experimental resources as, for example, beam time in neutron scattering
experiments, in addition to scientists' knowledge and experience. Active
learning methods form a particular class of techniques that acquire knowledge
on a specific quantity of interest by autonomous decisions on what or where to
investigate next based on previous measurements. For instance, Gaussian Process
Regression (GPR) is a well-known technique that can be exploited to accomplish
active learning tasks for scattering experiments as was recently demonstrated.
Gaussian processes are not only capable to approximate functions by their
posterior mean function, but can also quantify uncertainty about the
approximation itself. Hence, if we perform function evaluations at locations of
highest uncertainty, the function can be "optimally" learned in an iterative
manner. We suggest the use of log-Gaussian processes, being a natural approach
to successfully conduct autonomous neutron scattering experiments in general
and TAS experiments with the instrument PANDA at MLZ in particular.


Abstract:
  This article is based on lecture notes for the Marie Curie Training school
"Initial Training on Numerical Methods for Active Matter". It provides an
introductory overview of modeling approaches for active matter and is primarily
targeted at PhD students (or other readers) who encounter some of these
approaches for the first time. The aim of the article is to help put the
described modeling approaches into perspective.


Abstract:
  Data labeling is a time intensive process. As such, many data scientists use
various tools to aid in the data generation and labeling process. While these
tools help automate labeling, many still require user interaction throughout
the process. Additionally, most target only a few network frameworks. Any
researchers exploring multiple frameworks must find additional tools orwrite
conversion scripts. This paper presents an automated tool for generating
synthetic data in arbitrary network formats. It uses Robot Operating System
(ROS) and Gazebo, which are common tools in the robotics community. Through ROS
paradigms, it allows extensive user customization of the simulation environment
and data generation process. Additionally, a plugin-like framework allows the
development of arbitrary data format writers without the need to change the
main body of code. Using this tool, the authors were able to generate an
arbitrarily large image dataset for three unique training formats using
approximately 15 min of user setup time and a variable amount of hands-off run
time, depending on the dataset size. The source code for this data generation
tool is available at https://github.com/Navy-RISE-Lab/nn_data_collection


Abstract:
  This paper presents a simple decidable logic of functional dependence LFD,
based on an extension of classical propositional logic with dependence atoms
plus dependence quantifiers treated as modalities, within the setting of
generalized assignment semantics for first order logic. The expressive
strength, complete proof calculus and meta-properties of LFD are explored.
Various language extensions are presented as well, up to undecidable
modal-style logics for independence and dynamic logics of changing dependence
models. Finally, more concrete settings for dependence are discussed:
continuous dependence in topological models, linear dependence in vector
spaces, and temporal dependence in dynamical systems and games.


Abstract:
  We study the asymptotic behaviour of the renormalised $s$-fractional Gaussian
perimeter of a set $E$ inside a domain $\Omega$ as $s\to 0^+$. Contrary to the
Euclidean case, as the Gaussian measure is finite, the shape of the set at
infinity does not matter, but, surprisingly, the limit set function is never
additive.


Abstract:
  Designing optimal control pulses that drive a noisy qubit to a target state
is a challenging and crucial task for quantum engineering. In a situation where
the properties of the quantum noise affecting the system are dynamic, a
periodic characterization procedure is essential to ensure the models are
updated. As a result, the operation of the qubit is disrupted frequently. In
this paper, we propose a protocol that addresses this challenge by making use
of a spectator qubit to monitor the noise in real-time. We develop a quantum
machine-learning-based quantum feature engineering approach for designing the
protocol. The complexity of the protocol is front-loaded in a characterization
phase, which allow real-time execution during the quantum computations. We
present the results of numerical simulations that showcase the favorable
performance of the protocol.


Abstract:
  Recurrent Neural Networks (RNNs), more specifically their Long Short-Term
Memory (LSTM) variants, have been widely used as a deep learning tool for
tackling sequence-based learning tasks in text and speech. Training of such
LSTM applications is computationally intensive due to the recurrent nature of
hidden state computation that repeats for each time step. While sparsity in
Deep Neural Nets has been widely seen as an opportunity for reducing
computation time in both training and inference phases, the usage of non-ReLU
activation in LSTM RNNs renders the opportunities for such dynamic sparsity
associated with neuron activation and gradient values to be limited or
non-existent. In this work, we identify dropout induced sparsity for LSTMs as a
suitable mode of computation reduction. Dropout is a widely used regularization
mechanism, which randomly drops computed neuron values during each iteration of
training. We propose to structure dropout patterns, by dropping out the same
set of physical neurons within a batch, resulting in column (row) level hidden
state sparsity, which are well amenable to computation reduction at run-time in
general-purpose SIMD hardware as well as systolic arrays. We conduct our
experiments for three representative NLP tasks: language modelling on the PTB
dataset, OpenNMT based machine translation using the IWSLT De-En and En-Vi
datasets, and named entity recognition sequence labelling using the CoNLL-2003
shared task. We demonstrate that our proposed approach can be used to translate
dropout-based computation reduction into reduced training time, with
improvement ranging from 1.23x to 1.64x, without sacrificing the target metric.


Abstract:
  Few-shot learning aims to generalize unseen classes that appear during
testing but are unavailable during training. Prototypical networks incorporate
few-shot metric learning, by constructing a class prototype in the form of a
mean vector of the embedded support points within a class. The performance of
prototypical networks in extreme few-shot scenarios (like one-shot) degrades
drastically, mainly due to the desuetude of variations within the clusters
while constructing prototypes. In this paper, we propose to replace the typical
prototypical loss function with an Episodic Triplet Mining (ETM) technique. The
conventional triplet selection leads to overfitting, because of all possible
combinations being used during training. We incorporate episodic training for
mining the semi hard positive and the semi hard negative triplets to overcome
the overfitting. We also propose an adaptation to make use of unlabeled
training samples for better modeling. Experimenting on two different audio
processing tasks, namely speaker recognition and audio event detection; show
improved performances and hence the efficacy of ETM over the prototypical loss
function and other meta-learning frameworks. Further, we show improved
performances when unlabeled training samples are used.


Abstract:
  Long waves bring many important challenges in the ocean and coastal
engineering, including but are not limited to harbor resonance and run-up.
Therefore, understanding and modeling their dynamics is crucially important.
Although their dynamics over various types of geometries are well-studied in
the literature, the study of the geometries with power-law variations remains
an open problem in this setting. With this motivation, in this paper, we derive
the exact analytical solutions of the long-wave equation over nonlinear depth
and breadth profiles having power-law forms given by $h(x)=c_1 x^a$ and
$b(x)=c_2 x^c$, where the parameters $c_1, c_2, a, c$ are some constants. We
show that for these types of power-law forms of depth and breadth profiles, the
long-wave equation admits solutions in terms of Bessel functions and
Cauchy-Euler series. We also derive the seiching periods and resonance
conditions for these forms of depth and breadth variations. Our results can be
used to investigate the long-wave dynamics and their envelope characteristics
over equilibrium beach profiles, the effects of nonlinear harbor entrances and
angled nonlinear seawall breadth variations in the power-law forms on these
dynamics, and the effects of reconstruction, geomorphological changes,
sedimentation, and dredging to harbor resonance, to the shift in resonance
periods and to the seiching characteristics in lakes and barrages.


Abstract:
  Analyzing the relation between intelligence and neural activity is of the
utmost importance in understanding the working principles of the human brain in
health and disease. In existing literature, functional brain connectomes have
been used successfully to predict cognitive measures such as intelligence
quotient (IQ) scores in both healthy and disordered cohorts using machine
learning models. However, existing methods resort to flattening the brain
connectome (i.e., graph) through vectorization which overlooks its topological
properties. To address this limitation and inspired from the emerging graph
neural networks (GNNs), we design a novel regression GNN model (namely RegGNN)
for predicting IQ scores from brain connectivity. On top of that, we introduce
a novel, fully modular sample selection method to select the best samples to
learn from for our target prediction task. However, since such deep learning
architectures are computationally expensive to train, we further propose a
\emph{learning-based sample selection} method that learns how to choose the
training samples with the highest expected predictive power on unseen samples.
For this, we capitalize on the fact that connectomes (i.e., their adjacency
matrices) lie in the symmetric positive definite (SPD) matrix cone. Our results
on full-scale and verbal IQ prediction outperforms comparison methods in autism
spectrum disorder cohorts and achieves a competitive performance for
neurotypical subjects using 3-fold cross-validation. Furthermore, we show that
our sample selection approach generalizes to other learning-based methods,
which shows its usefulness beyond our GNN architecture.


Abstract:
  The success of deep learning sparked interest in whether the brain learns by
using similar techniques for assigning credit to each synaptic weight for its
contribution to the network output. However, the majority of current attempts
at biologically-plausible learning methods are either non-local in time,
require highly specific connectivity motives, or have no clear link to any
known mathematical optimization method. Here, we introduce Deep Feedback
Control (DFC), a new learning method that uses a feedback controller to drive a
deep neural network to match a desired output target and whose control signal
can be used for credit assignment. The resulting learning rule is fully local
in space and time and approximates Gauss-Newton optimization for a wide range
of feedback connectivity patterns. To further underline its biological
plausibility, we relate DFC to a multi-compartment model of cortical pyramidal
neurons with a local voltage-dependent synaptic plasticity rule, consistent
with recent theories of dendritic processing. By combining dynamical system
theory with mathematical optimization theory, we provide a strong theoretical
foundation for DFC that we corroborate with detailed results on toy experiments
and standard computer-vision benchmarks.


Abstract:
  This paper offers a finite-state abstraction of traffic coordination and
congestion in a network of interconnected roads (NOIR). By applying mass
conservation, we model traffic coordination as a Markov process. Model
Predictive Control (MPC) is applied to control traffic congestion through the
boundary of the traffic network. The optimal boundary inflow is assigned as the
solution of a constrained quadratic programming problem. Additionally, the
movement phases commanded by traffic signals are determined using receding
horizon optimization. In simulation, we show how traffic congestion can be
successfully controlled through optimizing boundary inflow and movement phases
at traffic network junctions.


Abstract:
  In this paper, based on neural networks, we develop a data-driven model for
extremely fast prediction of steady-state heat convection of a hot object with
arbitrary complex geometry in a two-dimensional space. According to the
governing equations, the steady-state heat convection is dominated by
convection and thermal diffusion terms, thus the distribution of the physical
fields would exhibit stronger correlations between adjacent points. Therefore,
the proposed neural network model uses Convolutional Neural Network (CNN)
layers as the encoder and Deconvolutional Neural Network (DCNN) layers as the
decoder. Compared with fully connected (FC) network model, the CNN based model
is good for capturing and reconstructing the spatial relationships of low-rank
feature spaces, such as edge intersections, parallelism and symmetry.
Furthermore, we applied the signed distance function (SDF) as the network input
for representing the problem geometry, which contains more information compared
to a binary image. For displaying the strong learning and generalization
ability of the proposed network model, the training dataset only contains hot
objects with simple geometries: triangles, quadrilaterals, pentagons, hexagons
and dodecagons; while the validating cases use arbitrary and complex
geometries. According to the study, the trained network model can accurately
predict the velocity and temperature field of the problems with complex
geometries which has never been seen by the network model during the model
training; and the prediction speed is four orders faster than the CFD. The
ability of the accurate and extremely faster prediction of the network model
suggests the potentials of applying such kind of models to the applications of
real-time control, optimization, and design in future.


Abstract:
  Let $f$ be a $C^{1+\alpha}$ diffeomorphism of a compact manifold $M$
preserving a smooth measure $\mu$. We show that if $f:(M,\mu)\to (M,\mu)$ is
exponentially mixing then it is Bernoulli.


Abstract:
  Coronary artery disease leading up to stenosis, the partial or total blocking
of coronary arteries, is a severe condition that affects millions of patients
each year. Automated identification and classification of stenosis severity
from minimally invasive procedures would be of great clinical value, but
existing methods do not match the accuracy of experienced cardiologists, due to
the complexity of the task. Although a number of computational approaches for
quantitative assessment of stenosis have been proposed to date, the performance
of these methods is still far from the required levels for clinical
applications. In this paper, we propose a two-step deep-learning framework to
partially automate the detection of stenosis from X-ray coronary angiography
images. In the two steps, we used two distinct convolutional neural network
architectures, one to automatically identify and classify the angle of view,
and another to determine the bounding boxes of the regions of interest in
frames where stenosis is visible. Transfer learning and data augmentation
techniques were used to boost the performance of the system in both tasks. We
achieved a 0.97 accuracy on the task of classifying the Left/Right Coronary
Artery (LCA/RCA) angle view and 0.68/0.73 recall on the determination of the
regions of interest, for LCA and RCA, respectively. These results compare
favorably with previous results obtained using related approaches, and open the
way to a fully automated method for the identification of stenosis severity
from X-ray angiographies.


Abstract:
  Property inference attacks consider an adversary who has access to the
trained model and tries to extract some global statistics of the training data.
In this work, we study property inference in scenarios where the adversary can
maliciously control part of the training data (poisoning data) with the goal of
increasing the leakage.
  Previous work on poisoning attacks focused on trying to decrease the accuracy
of models either on the whole population or on specific sub-populations or
instances. Here, for the first time, we study poisoning attacks where the goal
of the adversary is to increase the information leakage of the model. Our
findings suggest that poisoning attacks can boost the information leakage
significantly and should be considered as a stronger threat model in sensitive
applications where some of the data sources may be malicious.
  We describe our \emph{property inference poisoning attack} that allows the
adversary to learn the prevalence in the training data of any property it
chooses. We theoretically prove that our attack can always succeed as long as
the learning algorithm used has good generalization properties.
  We then verify the effectiveness of our attack by experimentally evaluating
it on two datasets: a Census dataset and the Enron email dataset. We were able
to achieve above $90\%$ attack accuracy with $9-10\%$ poisoning in all of our
experiments.


Abstract:
  We study ribbons of vanishing Gaussian curvature, i.e., flat ribbons,
constructed along a curve in $\mathbb{R}^{3}$. In particular, we first
investigate to which extent the ruled structure determines a flat ribbon: in
other words, we ask whether for a given curve $\gamma$ and ruling angle (angle
between the ruling line and the curve's tangent) there exists a well-defined
flat ribbon. It turns out that the answer is positive only up to an initial
condition, expressed by a choice of normal vector at a point. We then study the
set of infinitely narrow flat ribbons along a fixed curve $\gamma$ in terms of
energy. By extending a well-known formula for the bending energy of the
rectifying developable, introduced in the literature by Sadowsky in 1930, we
obtain an upper bound for the ratio between the bending energies of two
solutions of the initial value problem. We finally draw further conclusions
under some additional assumptions on the ruling angle and the curve $\gamma$.


Abstract:
  It is well-known that liquid and saturated vapor, separated by a flat
interface in an unbounded space, are in equilibrium. One would similarly expect
a liquid drop, sitting on a flat substrate, to be in equilibrium with the vapor
surrounding it. Yet, it is not: as shown in this work, the drop evaporates.
Mathematically, this conclusion is deduced using the diffuse-interface model,
but it can also be reformulated in terms of the maximum-entropy principle,
suggesting model independence. Physically, evaporation of drops is due to the
so-called Kelvin effect, which gives rise to a liquid-to-vapor mass flux in all
cases where the boundary of the liquid phase is convex.


Abstract:
  StarCraft II (SC2) is a real-time strategy game in which players produce and
control multiple units to fight against opponent's units. Due to its
difficulties, such as huge state space, various action space, a long time
horizon, and imperfect information, SC2 has been a research hotspot in
reinforcement learning. Recently, an agent called AlphaStar (AS) has been
proposed, which shows good performance, obtaining a high win rate of 99.8%
against human players. We implemented a mini-scaled version of it called
mini-AlphaStar (mAS) based on AS's paper and pseudocode. The difference between
AS and mAS is that we substituted the hyper-parameters of AS with smaller ones
for mini-scale training. Codes of mAS are all open-sourced
(https://github.com/liuruoze/mini-AlphaStar) for future research.


Abstract:
  Graph Neural Networks (GNNs) are the first choice methods for graph machine
learning problems thanks to their ability to learn state-of-the-art level
representations from graph-structured data. However, centralizing a massive
amount of real-world graph data for GNN training is prohibitive due to
user-side privacy concerns, regulation restrictions, and commercial
competition. Federated Learning is the de-facto standard for collaborative
training of machine learning models over many distributed edge devices without
the need for centralization. Nevertheless, training graph neural networks in a
federated setting is vaguely defined and brings statistical and systems
challenges. This work proposes SpreadGNN, a novel multi-task federated training
framework capable of operating in the presence of partial labels and absence of
a central server for the first time in the literature. SpreadGNN extends
federated multi-task learning to realistic serverless settings for GNNs, and
utilizes a novel optimization algorithm with a convergence guarantee,
Decentralized Periodic Averaging SGD (DPA-SGD), to solve decentralized
multi-task learning problems. We empirically demonstrate the efficacy of our
framework on a variety of non-I.I.D. distributed graph-level molecular property
prediction datasets with partial labels. Our results show that SpreadGNN
outperforms GNN models trained over a central server-dependent federated
learning system, even in constrained topologies. The source code is publicly
available at https://github.com/FedML-AI/SpreadGNN


Abstract:
  Imaging surveys of CO and other molecular transition lines are fundamental to
measuring the large-scale distribution of molecular gas in the Milky Way. Due
to finite angular resolution and sensitivity, however, observational effects
are inevitable in the surveys, but few studies are available on the extent of
uncertainties involved. The purpose of this work is to investigate the
dependence of observations on angular resolution (beam sizes), sensitivity
(noise levels), distances, and molecular tracers. To this end, we use
high-quality CO images of a large-scale region (25.8 <l< 49.7 deg and |b|<5
deg) mapped by the Milky Way Imaging Scroll Painting (MWISP) survey as a
benchmark to simulate observations with larger beam sizes and higher noise
levels, deriving corresponding beam filling and sensitivity clip factors. The
sensitivity clip factor is defined to be the completeness of observed flux.
Taking the entire image as a whole object, we found that 12CO has the largest
beam filling and sensitivity clip factors and C18O has the lowest. For
molecular cloud samples extracted from images, the beam filling factor can be
described by a characteristic size, $l_{1/4}$=0.762 (in beam size), at which
the beam filling factor is approximately 1/4. The sensitivity clip factor shows
a similar relationship but is more correlated with the mean voxel
signal-to-noise ratio of molecular clouds. This result may serve as a practical
reference on beam filling and sensitivity clip factors in further analyses of
the MWISP data and other observations.


Abstract:
  MAROON-X is a fiber-fed, red-optical, high precision radial velocity
spectrograph recently commissioned at the Gemini North telescope on Mauna Kea,
Hawai'i. With a resolving power of 85,000 and a wavelength coverage of 500-920
nm, it delivers radial velocity measurements for late K and M dwarfs with
sub-50 cm s$^{-1}$ precision. MAROON-X is currently the only optical EPRV
spectrograph on a 8m-class telescope in the northern hemisphere and the only
EPRV instrument on a large telescope with full access by the entire US
community. We report here on the results of the commissioning campaign in
December 2019 and early science results.


Abstract:
  We present POD, a smartphone that flies, as a new way to achieve hands-free,
eyes-up mobile computing. Unlike existing drone-carried user interfaces, POD
features a smartphone-sized display and the computing and sensing power of a
modern smartphone. We share our experience in building a prototype of POD,
discuss the technical challenges facing it, and describe early results toward
addressing them.


Abstract:
  The purpose of this manuscript is to prove the conjectures made in
\cite{Tosatti2011,Tosatti2012} for families of Calabi--Yau manifolds. In
particular, these results confirm the conjectural understanding of collapsed
Gromov--Hausdorff limits which arise in the study of the long-time behavior of
the K\"ahler--Ricci flow on compact K\"ahler manifolds with semi-ample
canonical bundle and intermediate Kodaira dimension.


Abstract:
  The recent advent of various forms of Federated Knowledge Distillation (FD)
paves the way for a new generation of robust and communication-efficient
Federated Learning (FL), where mere soft-labels are aggregated, rather than
whole gradients of Deep Neural Networks (DNN) as done in previous FL schemes.
This security-per-design approach in combination with increasingly performant
Internet of Things (IoT) and mobile devices opens up a new realm of
possibilities to utilize private data from industries as well as from
individuals as input for artificial intelligence model training. Yet in
previous FL systems, lack of trust due to the imbalance of power between
workers and a central authority, the assumption of altruistic worker
participation and the inability to correctly measure and compare contributions
of workers hinder this technology from scaling beyond small groups of already
entrusted entities towards mass adoption. This work aims to mitigate the
aforementioned issues by introducing a novel decentralized federated learning
framework where heavily compressed 1-bit soft-labels, resembling 1-hot label
predictions, are aggregated on a smart contract. In a context where workers'
contributions are now easily comparable, we modify the Peer Truth Serum for
Crowdsourcing mechanism (PTSC) for FD to reward honest participation based on
peer consistency in an incentive compatible fashion. Due to heavy reductions of
both computational complexity and storage, our framework is a fully
on-blockchain FL system that is feasible on simple smart contracts and
therefore blockchain agnostic. We experimentally test our new framework and
validate its theoretical properties.


Abstract:
  Waveplates having spatially varying fast-axis orientation and retardance
provide an elegant and easy way to locally manipulate different attributes of
light beams namely, polarization, amplitude and phase, leading to the
generation of exotic structured light beams. The fabrication of such doubly
inhomogeneous waveplates (d-plates) is more complex, compared to that of singly
inhomogeneous waveplates (s-plates) having uniform retardance, which can be
easily fabricated by different means such as photoalignment of liquid crystals,
metasurfaces etc. Here, exploiting the SU(2) formalism, we establish
analytically that any d-plate can be equivalently implemented using a pair of
quarter-wave s-plates and a half-wave s-plate. An important advantage of this
method is that it gives the flexibility to realize a whole family of distinct
d-plates using the same triplet of s-plates. To underline the scope of this
method, we propose novel d-plates for spatially tailoring the phase and complex
amplitudes of light beams. Towards complex amplitude shaping, we present a
generic method for carving out higher-order eigenmodes of light using a d-plate
in conjugation with a polarizer. A generalized q-plate-like gadget, for
imparting a polarization-dependent phase profile to a scalar light beam, is
proposed as a demonstration of phase-polarization interplay. For these two
illustrations, the corresponding three-s-plate gadget is constructed, and its
functioning is validated with extensive numerical simulations. The main result
and its illustrations are generic and agnostic to the way the s-plates are
fabricated and we believe they carry the potential to push the current state of
the art in interdisciplinary applications involving structured light beams.