title:
A Kernel Method to Nonlinear Location Estimation with RSS-based
  Fingerprint

abstract:
  This paper presents a nonlinear location estimation to infer the position of
a user holding a smartphone. We consider a large location with $M$ number of
grid points, each grid point is labeled with a unique fingerprint consisting of
the received signal strength (RSS) values measured from $N$ number of Bluetooth
Low Energy (BLE) beacons. Given the fingerprint observed by the smartphone, the
user's current location can be estimated by finding the top-k similar
fingerprints from the list of fingerprints registered in the database. Besides
the environmental factors, the dynamicity in holding the smartphone is another
source to the variation in fingerprint measurements, yet there are not many
studies addressing the fingerprint variability due to dynamic smartphone
positions held by human hands during online detection. To this end, we propose
a nonlinear location estimation using the kernel method. Specifically, our
proposed method comprises of two steps: 1) a beacon selection strategy to
select a subset of beacons that is insensitive to the subtle change of holding
positions, and 2) a kernel method to compute the similarity between this subset
of observed signals and all the fingerprints registered in the database. The
experimental results based on large-scale data collected in a complex building
indicate a substantial performance gain of our proposed approach in comparison
to state-of-the-art methods. The dataset consisting of the signal information
collected from the beacons is available online.


title:
T4PdM: a Deep Neural Network based on the Transformer Architecture for
  Fault Diagnosis of Rotating Machinery

abstract:
  Deep learning and big data algorithms have become widely used in industrial
applications to optimize several tasks in many complex systems. Particularly,
deep learning model for diagnosing and prognosing machinery health has
leveraged predictive maintenance (PdM) to be more accurate and reliable in
decision making, in this way avoiding unnecessary interventions, machinery
accidents, and environment catastrophes. Recently, Transformer Neural Networks
have gained notoriety and have been increasingly the favorite choice for
Natural Language Processing (NLP) tasks. Thus, given their recent major
achievements in NLP, this paper proposes the development of an automatic fault
classifier model for predictive maintenance based on a modified version of the
Transformer architecture, namely T4PdM, to identify multiple types of faults in
rotating machinery. Experimental results are developed and presented for the
MaFaulDa and CWRU databases. T4PdM was able to achieve an overall accuracy of
99.98% and 98% for both datasets, respectively. In addition, the performance of
the proposed model is compared to other previously published works. It has
demonstrated the superiority of the model in detecting and classifying faults
in rotating industrial machinery. Therefore, the proposed Transformer-based
model can improve the performance of machinery fault analysis and diagnostic
processes and leverage companies to a new era of the Industry 4.0. In addition,
this methodology can be adapted to any other task of time series
classification.


title:
Decentralized Event-Triggered Federated Learning with Heterogeneous
  Communication Thresholds

abstract:
  A recent emphasis of distributed learning research has been on federated
learning (FL), in which model training is conducted by the data-collecting
devices. Existing research on FL has mostly focused on a star topology learning
architecture with synchronized (time-triggered) model training rounds, where
the local models of the devices are periodically aggregated by a centralized
coordinating node. However, in many settings, such a coordinating node may not
exist, motivating efforts to fully decentralize FL. In this work, we propose a
novel methodology for distributed model aggregations via asynchronous,
event-triggered consensus iterations over the network graph topology. We
consider heterogeneous communication event thresholds at each device that weigh
the change in local model parameters against the available local resources in
deciding the benefit of aggregations at each iteration. Through theoretical
analysis, we demonstrate that our methodology achieves asymptotic convergence
to the globally optimal learning model under standard assumptions in
distributed learning and graph consensus literature, and without restrictive
connectivity requirements on the underlying topology. Subsequent numerical
results demonstrate that our methodology obtains substantial improvements in
communication requirements compared with FL baselines.


abstract:
  Differential Dynamic Programming (DDP) is an efficient trajectory
optimization algorithm relying on second-order approximations of a system's
dynamics and cost function, and has recently been applied to optimize systems
with time-invariant parameters. Prior works include system parameter estimation
and identifying the optimal switching time between modes of hybrid dynamical
systems. This paper generalizes previous work by proposing a general
parameterized optimal control objective and deriving a parametric version of
DDP, titled Parameterized Differential Dynamic Programming (PDDP). A rigorous
convergence analysis of the algorithm is provided, and PDDP is shown to
converge to a minimum of the cost regardless of initialization. The effects of
varying the optimization to more effectively escape local minima are analyzed.
Experiments are presented applying PDDP on multiple robotics systems to solve
model predictive control (MPC) and moving horizon estimation (MHE) tasks
simultaneously. Finally, PDDP is used to determine the optimal transition point
between flight regimes of a complex urban air mobility (UAM) class vehicle
exhibiting multiple phases of flight.


title:
Evaluacion de un producto a base de aceite esencial de oregano sobre la
  integridad intestinal, la capacidad de absorcion de nutrientes y el
  comportamiento productivo de pollos de carne

abstract:
  Eight experiments were conducted to test an oregano-essential-oil-based
product (PRO) on performance, gut health, and nutrient absorption capacity in
broilers. The first three, up to 14d and without specific challenges, tested
the effects on intestinal histology, skeletal integrity, and growth. Other two
experiments, up to 28d birds were challenged to induce feed passage syndrome
(STR) and test the effect on the antioxidant status and performance,
contrasting later the results under a natural challenge in commercial
conditions. Finally, in two other experiments, up to 21 d, birds challenged
with coccidia were tested to determine the effects on coccidiosis, nutrient
digestibility, and feed efficiency (FCR). Corn-soybean-fish meal diets were
used. Under healthy conditions, PRO effects were observed on intestinal villus
height and surface area, mucosa density and structure, villus to crypt ratio,
goblet cells density, mucus production, body weight gain, and FCR (P<0.04), and
carcass yield and bone density (P<0.08). Birds affected by STR showed effects
(P<0.05) on superoxide dismutase activity, bodyweight gain, FCRE, non-normal
feces frequency, bursal diameter, and dysbacteriosis. Birds affected by
coccidia showed effects on cocci-positive ratio, cocci microscopic score,
oocysts count, bursal diameter, body weight gain, FCR, apparent digestibility
of dry matter and crude protein, protein and energy efficiencies (P<0.01), and
nitrogen apparent retention (P<0.07). Results showed PRO added value to the
diet favoring feed efficiency and performance.


title:
Martensitic transformation in V_3Si single crystal: ^51V NMR evidence
  for coexistence of cubic and tetragonal phases

abstract:
  The Martensitic transformation (MT) in A15 binary-alloy superconductor V_3Si
is a second-order, displacive structural transition from cubic to tetragonal
symmetry, at temperature T_m a few K above the superconducting transition
temperature T_c = 17 K. Though studied extensively, the MT has not yet been
conclusively linked with a transition to superconductivity, and remains
relevant, e.g. due to renewed interest in soft phonon modes, while V_3Si
continues to be of interest, e.g. due to similarities with Fe-As
superconductors. Previous NMR studies on the MT in V_3Si have mainly been on
powder samples, and with little emphasis on temperature dependence during the
transformation. Here we study a high-quality single crystal, where quadrupolar
splitting and Knight shift of NMR spectra for ^51V allowed us to distinguish
between spectra from transverse chains of V as a function of temperature. Our
data revealed that the MT is not abrupt, but rather there is a microscopic
coexistence of untransformed cubic phase and transformed tetragonal phase over
a few K below and above T_m, and that the Martensitic lengthening of one axis
occurs predominantly in a plane perpendicular to the crystal growth axis, as
twinned domains.


title: Multilevel Memetic Hypergraph Partitioning with Greedy Recombination

abstract:
  The Hypergraph Partitioning (HGP) problem is a well-studied problem that
finds applications in a variety of domains. The literature on the HGP problem
has heavily focused on developing fast heuristic approaches. In several
application domains, such as the VLSI design and database migration planning,
the quality of the solution is more of a concern than the running time of the
algorithm. KaHyPar-E is the first multilevel memetic algorithm designed for the
HGP problem and it returns better quality solutions, compared to the heuristic
algorithms, if sufficient computation time is given. In this work, we introduce
novel problem-specific recombination and mutation operators, and develop a new
multilevel memetic algorithm by combining KaHyPar-E with these operators. The
performance of our algorithm is compared with the state-of-the-art HGP
algorithms on $150$ real-life instances taken from the benchmark datasets used
in the literature. In the experiments, which would take $39,000$ hours in a
single-core computer, each algorithm is given $2, 4$, and $8$ hours to compute
a solution for each instance. Our algorithm outperforms all others and finds
the best solutions in $112$, $115$, and $125$ instances in $2, 4$, and $8$
hours, respectively.


title: GreaseVision: Rewriting the Rules of the Interface

abstract:
  Digital harms can manifest across any interface. Key problems in addressing
these harms include the high individuality of harms and the fast-changing
nature of digital systems. As a result, we still lack a systematic approach to
study harms and produce interventions for end-users. We put forward
GreaseVision, a new framework that enables end-users to collaboratively develop
interventions against harms in software using a no-code approach and recent
advances in few-shot machine learning. The contribution of the framework and
tool allow individual end-users to study their usage history and create
personalized interventions. Our contribution also enables researchers to study
the distribution of harms and interventions at scale.


title:
A Model-based Technique for Ad Hoc Correction of Instrumental
  Polarization in Solar Spectropolarimetry

abstract:
  We present a new approach for correcting instrumental polarization by
modeling the non-depolarizing effects of a complex series of optical elements
to determine physically realizable Mueller matrices. Provided that the Mueller
matrix of the optical system can be decomposed into a general elliptical
diattenuator and general elliptical retarder, it is possible to model the
cross-talk between both the polarized and unpolarized states of the Stokes
vector and then use the acquired science observations to determine the best-fit
free parameters. Here, we implement a minimization for solar
spectropolarimetric measurements containing photospheric spectral lines
sensitive to the Zeeman effect using physical constraints provided by polarized
line and continuum formation. This model-based approach is able to provide an
accurate correction even in the presence of large amounts of polarization
cross-talk and conserves the physically meaningful magnitude of the Stokes
vector, a significant improvement over previous ad hoc techniques.


title:
Demonstration of a Quantum Gate using Electromagnetically Induced
  Transparency

abstract:
  We demonstrate a native $\mathrm{CNOT}$ gate between two individually
addressed neutral atoms based on electromagnetically induced transparency
(EIT). This protocol utilizes the strong long-range interactions of Rydberg
states to enable conditional state transfer on the target qubit when operated
in the blockade regime. An advantage of this scheme is it enables
implementation of multi-qubit CNOT$^k$ gates using a pulse sequence independent
of qubit number, and provides an simple gate for efficient implementation of
quantum algorithms and error correction. We achieve a loss corrected gate
fidelity of $\mathcal{F}_\mathrm{CNOT}^\mathrm{cor} = 0.82(6)$, and prepare an
entangled Bell-state with $\mathcal{F}_\mathrm{Bell}^\mathrm{cor} = 0.66(5)$,
limited at present by laser power. We present a number of technical
improvements to advance this to a level required for fault-tolerant scaling.


title: MHMS: Multimodal Hierarchical Multimedia Summarization

abstract:
  Multimedia summarization with multimodal output can play an essential role in
real-world applications, i.e., automatically generating cover images and titles
for news articles or providing introductions to online videos. In this work, we
propose a multimodal hierarchical multimedia summarization (MHMS) framework by
interacting visual and language domains to generate both video and textual
summaries. Our MHMS method contains video and textual segmentation and
summarization module, respectively. It formulates a cross-domain alignment
objective with optimal transport distance which leverages cross-domain
interaction to generate the representative keyframe and textual summary. We
evaluated MHMS on three recent multimodal datasets and demonstrated the
effectiveness of our method in producing high-quality multimodal summaries.


title:
Editorial on Research Topic: High-Tc Superconductivity in Electron-Doped
  Iron Selenide and Related Compounds

abstract:
  In this editorial, we first give a brief survey of the field of iron-selenide
superconductivity, both in the case of bulk FeSe characterized by the
co-existence of superconductivity and nematic order, and in the case of
electron-doped FeSe characterized by high-temperature superconductivity. We
next review the 8 contributions to the Frontiers in Physics research topic
"High-Tc Superconductivity in Electron-Doped Iron Selenide and Related
Compounds".


title:
Generation of Highly Pure Single-Photon State at Telecommunication
  Wavelength

abstract:
  Telecommunication wavelength with well-developed optical communication
technologies and low losses in the waveguide are advantageous for quantum
applications. However, an experimental generation of non-classical states
called non-Gaussian states at the telecommunication wavelength is still
underdeveloped. Here, we generate highly-pure-single-photon states, one of the
most primitive non-Gaussian states, by using a heralding scheme with an optical
parametric oscillator and a superconducting nano-strip photon detector. The
Wigner negativity, the indicator of non-classicality, of the generated single
photon state is $-0.228\pm0.004$, corresponded to $85.1\pm0.7\%$ of single
photon and the best record of the minimum value at all wavelengths. The
quantum-optics-technology we establish can be easily applied to the generation
of various types of quantum states, opening up the possibility of
continuous-variable-quantum-information processing at telecommunication
wavelengths.


title:
Mixing Signals: Data Augmentation Approach for Deep Learning Based
  Modulation Recognition

abstract:
  With the rapid development of deep learning, automatic modulation recognition
(AMR), as an important task in cognitive radio, has gradually transformed from
traditional feature extraction and classification to automatic classification
by deep learning technology. However, deep learning models are data-driven
methods, which often require a large amount of data as the training support.
Data augmentation, as the strategy of expanding dataset, can improve the
generalization of the deep learning models and thus improve the accuracy of the
models to a certain extent. In this paper, for AMR of radio signals, we propose
a data augmentation strategy based on mixing signals and consider four specific
methods (Random Mixing, Maximum-Similarity-Mixing, $\theta-$Similarity Mixing
and n-times Random Mixing) to achieve data augmentation. Experiments show that
our proposed method can improve the classification accuracy of deep learning
based AMR models in the full public dataset RML2016.10a. In particular, for the
case of a single signal-to-noise ratio signal set, the classification accuracy
can be significantly improved, which verifies the effectiveness of the methods.


title: BankNote-Net: Open dataset for assistive universal currency recognition

abstract:
  Millions of people around the world have low or no vision. Assistive software
applications have been developed for a variety of day-to-day tasks, including
optical character recognition, scene identification, person recognition, and
currency recognition. This last task, the recognition of banknotes from
different denominations, has been addressed by the use of computer vision
models for image recognition. However, the datasets and models available for
this task are limited, both in terms of dataset size and in variety of
currencies covered. In this work, we collect a total of 24,826 images of
banknotes in variety of assistive settings, spanning 17 currencies and 112
denominations. Using supervised contrastive learning, we develop a machine
learning model for universal currency recognition. This model learns compliant
embeddings of banknote images in a variety of contexts, which can be shared
publicly (as a compressed vector representation), and can be used to train and
test specialized downstream models for any currency, including those not
covered by our dataset or for which only a few real images per denomination are
available (few-shot learning). We deploy a variation of this model for public
use in the last version of the Seeing AI app developed by Microsoft. We share
our encoder model and the embeddings as an open dataset in our BankNote-Net
repository.


title:
Brain-Inspired Hyperdimensional Computing: How Thermal-Friendly for Edge
  Computing?

abstract:
  Brain-inspired hyperdimensional computing (HDC) is an emerging machine
learning (ML) methods. It is based on large vectors of binary or bipolar
symbols and a few simple mathematical operations. The promise of HDC is a
highly efficient implementation for embedded systems like wearables. While fast
implementations have been presented, other constraints have not been considered
for edge computing. In this work, we aim at answering how thermal-friendly HDC
for edge computing is. Devices like smartwatches, smart glasses, or even mobile
systems have a restrictive cooling budget due to their limited volume. Although
HDC operations are simple, the vectors are large, resulting in a high number of
CPU operations and thus a heavy load on the entire system potentially causing
temperature violations. In this work, the impact of HDC on the chip's
temperature is investigated for the first time. We measure the temperature and
power consumption of a commercial embedded system and compare HDC with
conventional CNN. We reveal that HDC causes up to 6.8{\deg}C higher
temperatures and leads to up to 47% more CPU throttling. Even when both HDC and
CNN aim for the same throughput (i.e., perform a similar number of
classifications per second), HDC still causes higher on-chip temperatures due
to the larger power consumption.


title:
Successes and critical failures of neural networks in capturing
  human-like speech recognition

abstract:
  Natural and artificial audition can in principle evolve different solutions
to a given problem. The constraints of the task, however, can nudge the
cognitive science and engineering of audition to qualitatively converge,
suggesting that a closer mutual examination would improve artificial hearing
systems and process models of the mind and brain. Speech recognition - an area
ripe for such exploration - is inherently robust in humans to a number
transformations at various spectrotemporal granularities. To what extent are
these robustness profiles accounted for by high-performing neural network
systems? We bring together experiments in speech recognition under a single
synthesis framework to evaluate state-of-the-art neural networks as
stimulus-computable, optimized observers. In a series of experiments, we (1)
clarify how influential speech manipulations in the literature relate to each
other and to natural speech, (2) show the granularities at which machines
exhibit out-of-distribution robustness, reproducing classical perceptual
phenomena in humans, (3) identify the specific conditions where model
predictions of human performance differ, and (4) demonstrate a crucial failure
of all artificial systems to perceptually recover where humans do, suggesting a
key specification for theory and model building. These findings encourage a
tighter synergy between the cognitive science and engineering of audition.


title: Drivers' attention detection: a systematic literature review

abstract:
  Countless traffic accidents often occur because of the inattention of the
drivers. Many factors can contribute to distractions while driving, since
objects or events to physiological conditions, as drowsiness and fatigue, do
not allow the driver to stay attentive. The technological progress allowed the
development and application of many solutions to detect the attention in real
situations, promoting the interest of the scientific community in these last
years. Commonly, these solutions identify the lack of attention and alert the
driver, in order to help her/him to recover the attention, avoiding serious
accidents and preserving lives. Our work presents a Systematic Literature
Review (SLR) of the methods and criteria used to detect attention of drivers at
the wheel, focusing on those methods based on images. As results, 50 studies
were selected from the literature on drivers' attention detection, in which 22
contain solutions in the desired context. The results of SLR can be used as a
resource in the preparation of new research projects in drivers' attention
detection.


title:
Mitosis domain generalization in histopathology images -- The MIDOG
  challenge

abstract:
  The density of mitotic figures within tumor tissue is known to be highly
correlated with tumor proliferation and thus is an important marker in tumor
grading. Recognition of mitotic figures by pathologists is known to be subject
to a strong inter-rater bias, which limits the prognostic value.
State-of-the-art deep learning methods can support the expert in this
assessment but are known to strongly deteriorate when applied in a different
clinical environment than was used for training. One decisive component in the
underlying domain shift has been identified as the variability caused by using
different whole slide scanners. The goal of the MICCAI MIDOG 2021 challenge has
been to propose and evaluate methods that counter this domain shift and derive
scanner-agnostic mitosis detection algorithms. The challenge used a training
set of 200 cases, split across four scanning systems. As a test set, an
additional 100 cases split across four scanning systems, including two
previously unseen scanners, were given. The best approaches performed on an
expert level, with the winning algorithm yielding an F_1 score of 0.748 (CI95:
0.704-0.781). In this paper, we evaluate and compare the approaches that were
submitted to the challenge and identify methodological factors contributing to
better performance.


title:
Automatic inference of fault tree models via multi-objective
  evolutionary algorithms

abstract:
  Fault tree analysis is a well-known technique in reliability engineering and
risk assessment, which supports decision-making processes and the management of
complex systems. Traditionally, fault tree (FT) models are built manually
together with domain experts, considered a time-consuming process prone to
human errors. With Industry 4.0, there is an increasing availability of
inspection and monitoring data, making techniques that enable knowledge
extraction from large data sets relevant. Thus, our goal with this work is to
propose a data-driven approach to infer efficient FT structures that achieve a
complete representation of the failure mechanisms contained in the failure data
set without human intervention. Our algorithm, the FT-MOEA, based on
multi-objective evolutionary algorithms, enables the simultaneous optimization
of different relevant metrics such as the FT size, the error computed based on
the failure data set and the Minimal Cut Sets. Our results show that, for six
case studies from the literature, our approach successfully achieved automatic,
efficient, and consistent inference of the associated FT models. We also
present the results of a parametric analysis that tests our algorithm for
different relevant conditions that influence its performance, as well as an
overview of the data-driven methods used to automatically infer FT models.


title: The coupling of the hydrated proton to its first solvation shell

abstract:
  The transfer of a hydrated proton between water molecules in aqueous solution
is accompanied by the large-scale structural reorganization of the environment
as the proton relocates, giving rise to the Grotthus mechanism. The Zundel
(H5O2+) and Eigen (H9O4+) cations are the main intermediate structures in this
process. They exhibit radically different gas-phase infrared (IR) spectra,
indicating fundamentally different environments of the solvated proton in its
first solvation shell. The question arises: is there a least common denominator
structure that explains the IR spectra of the Zundel and Eigen cations, and
hence of the solvated proton? Full dimensional quantum simulations of these
protonated cations demonstrate that two dynamical water molecules embedded in
the static environment of the parent Eigen cation constitute this fundamental
subunit. It is sufficient to explain the spectral signatures and anharmonic
couplings of the solvated proton in its first solvation shell. In particular,
we identify the anharmonic vibrational modes that explain the large broadening
of the proton transfer peak in the experimental IR spectrum of the Eigen
cation, of which the origin remained so far unclear. Our findings about the
quantum mechanical structure of the first solvation shell provide a starting
point for further investigations of the larger protonated water clusters with
second and additional solvation shells.


title:
Merger rates of intermediate-mass black hole binaries in nuclear star
  clusters

abstract:
  Repeated mergers of stellar-mass black holes (BHs) in dense star clusters can
produce intermediate-mass black holes (IMBHs). In particular, nuclear star
clusters at the centers of galaxies have deep enough potential wells to retain
most of the BH merger products, in spite of the significant recoil kicks due to
anisotropic emission of gravitational radiation. These events can be detected
in gravitational waves (GWs), which represent an unprecedented opportunity to
reveal IMBHs. In this paper, we analyze the statistical results of a wide range
of numerical simulations, which encompass different cluster metallicities,
initial BH seed masses, and initial BH spins, and we compute the merger rate of
IMBH binaries. We find that merger rates are in the range
$0.01$-$10$\,Gpc$^{-3}$\,yr$^{-1}$ depending on IMBH masses. We also compute
the number of multi-band detections in ground-based and space-based
observatories. Our model predicts that a few merger events per year should be
detectable with LISA, DECIGO, ET, and LIGO for IMBHs with masses $\lesssim
1000\msun$, and a few tens of merger events per year with DECIGO, ET, and LIGO
only.


title:
Spontaneously-Induced Dirac Boundary State and Digitization in a
  Nonlinear Resonator Chain

abstract:
  The low-energy excitations in many condensed matter and metamaterial systems
can be well described by the Dirac equation. The mass term associated with
these collective excitations, also known as the Dirac mass, can take any value
and is directly responsible for determining whether the resultant band
structure exhibits a band gap or a Dirac point with linear dispersion.
Manipulation of this Dirac mass has inspired new methods of band structure
engineering and electron confinement. Notably, it has been shown that a
massless state necessarily localizes at any domain wall that divides regions
with Dirac masses of different signs. These localized states are known as
Jackiw-Rebbi-type (JR-type) Dirac boundary modes and their tunability and
localization features have valuable technological potential. In this study, we
experimentally demonstrate that nonlinearity within a 1D Dirac material can
result in the spontaneous appearance of a domain boundary for the Dirac mass.
Our experiments are performed in a dimerized magneto-mechanical metamaterial
that allows complete control of both the magnitude and sign of the local
material nonlinearity, as well as the sign of the Dirac mass. We find that the
massless bound state that emerges at the spontaneously appearing domain
boundary acts similarly to a dopant site within an insulator, causing the
material to exhibit a dramatic binary switch in its conductivity when driven
above an excitation threshold.


title:
Experimental Validation of DeeP-LCC for Dissipating Stop-and-Go Waves in
  Mixed Traffic

abstract:
  We present results on the experimental validation of leading cruise control
(LCC) for connected and autonomous vehicles (CAVs). In a mixed traffic
situation that is dominated by human-driven vehicles, LCC strategies are
promising to smooth undesirable stop-and-go waves. Our experiments are carried
out on a mini-scale traffic platform. We first reproduce stop-and-go traffic
waves in a miniature scale, and then show that these traffic instabilities can
be dissipated by one or a few CAVs that utilize Data-EnablEd Predicted Leading
Cruise Control (DeeP-LCC). Rather than identifying a parametric traffic model,
DeeP-LCC relies on a data-driven non-parametric behavior representation for
traffic prediction and CAV control. DeeP-LCC also incorporates input and output
constraints to achieve collision-free guarantees for CAVs. We experimentally
demonstrate that DeeP-LCC is able to dissipate traffic waves caused by
car-following behavior and significantly improve both driving safety and travel
efficiency. CAVs utilizing DeeP-LCC may bring additional societal benefits by
mitigating stop-and-go waves in practical traffic.


title: Energy self-sufficient systems for monitoring sewer networks

abstract:
  Underground infrastructure networks form the backbone of vital supply and
disposal systems. However, they are under-monitored in comparison to their
value. This is due, in large part, to the lack of energy supply for monitoring
and data transmission. In this paper, we investigate a novel, energy harvesting
system used to power underground sewer infrastructure monitoring networks. The
system collects the required energy from ambient sources, such as temperature
differences or residual light in sewer networks. A prototype was developed that
could use either a thermoelectric generator (TEG) or a solar cell to capture
the energy needed to acquire and transmit ultrasonic water level data via
LoRaWAN. Real-world field trials were satisfactory and showed the potential
power output, as well as, possibilities to improve the system. Using an
extrapolation model, we proved that the developed solution could work reliably
throughout the year.


title:
Powering Finetuning in Few-shot Learning: Domain-Agnostic Feature
  Adaptation with Rectified Class Prototypes

abstract:
  In recent works, utilizing a deep network trained on meta-training set serves
as a strong baseline in few-shot learning. In this paper, we move forward to
refine novel-class features by finetuning a trained deep network. Finetuning is
designed to focus on reducing biases in novel-class feature distributions,
which we define as two aspects: class-agnostic and class-specific biases.
Class-agnostic bias is defined as the distribution shifting introduced by
domain difference, which we propose Distribution Calibration Module(DCM) to
reduce. DCM owes good property of eliminating domain difference and fast
feature adaptation during optimization. Class-specific bias is defined as the
biased estimation using a few samples in novel classes, which we propose
Selected Sampling(SS) to reduce. Without inferring the actual class
distribution, SS is designed by running sampling using proposal distributions
around support-set samples. By powering finetuning with DCM and SS, we achieve
state-of-the-art results on Meta-Dataset with consistent performance boosts
over ten datasets from different domains. We believe our simple yet effective
method demonstrates its possibility to be applied on practical few-shot
applications.


title:
The transient behaviour of through-flowing gravity currents interacting
  with a roughness array

abstract:
  We present laboratory experiments that investigate the structure and flow
characteristics of gravity currents travelling through an array of roughness
elements. The roughness elements are of comparable height to the gravity
current such that the current flows through the roughness array rather than
over it. The frontal velocity and density structure are measured as the current
transitions from flowing along a smooth bed to flowing through the roughness
array, and then back to a smooth bed. We find that, upon entering the roughness
array, the gravity current decelerates and the density structure changes from
the head and tail structure typical of smooth bed gravity currents, to a wedge
shape. A model is presented that explains the deceleration and change in shape
based on a dynamic balance between a pressure gradient within the current tail
and a drag force associated with individual roughness elements. This model
accurately predicts the deceleration of the gravity current, supporting the
proposed dynamic balance.


title: Homotopy groups of shrinking wedges of non-simply connected CW-complexes

abstract:
  In this paper, we study the homotopy groups of a shrinking wedge $X$ of a
sequence $\{X_j\}$ of non-simply connected CW-complexes. Using a combination of
generalized covering space theory and shape theory, we construct a canonical
homomorphism
$$\Theta:\pi_n(X)\to\prod_{j\in\mathbb{N}}\bigoplus_{\pi_1(X)/\pi_1(X_j)}\pi_n(X_j),$$
characterize its image, and prove that $\Theta$ is injective whenever each
universal cover $\widetilde{X}_j$ is $(n-1)$-connected. These results (1)
provide a characterization of the $n$-th homotopy group of the shrinking wedge
of copies of $\mathbb{RP}^n$, (2) provide a characterization of $\pi_2$ of an
arbitrary shrinking wedge, and (3) imply that a shrinking wedge of aspherical
CW-complexes is aspherical.


title: Automated Isovist Computation for Minecraft

abstract:
  Procedural content generation for games is a growing trend in both research
and industry, even though there is no consensus of how good content looks, nor
how to automatically evaluate it. A number of metrics have been developed in
the past, usually focused on the artifact as a whole, and mostly lacking
grounding in human experience. In this study we develop a new set of automated
metrics, motivated by ideas from architecture, namely isovists and space
syntax, which have a track record of capturing human experience of space. These
metrics can be computed for a specific game state, from the player's
perspective, and take into account their embodiment in the game world. We show
how to apply those metrics to the 3d blockworld of Minecraft. We use a dataset
of generated settlements from the GDMC Settlement Generation Challenge in
Minecraft and establish several rank-based correlations between the isovist
properties and the rating human judges gave those settelements. We also produce
a range of heat maps that demonstrate the location based applicability of the
approach, which allows for development of those metrics as measures for a game
experience at a specific time and space.


title: Probing edge state conductance in ultra-thin topological insulator films

abstract:
  Quantum spin Hall (QSH) insulators have unique electronic properties,
comprising a band gap in their two-dimensional interior and one-dimensional
spin-polarized edge states in which current flows ballistically. In scanning
tunneling microscopy (STM), the edge states manifest themselves as a localized
density of states. However, there is a significant research gap between the
observation of edge states in nanoscale spectroscopy, and the detection of
ballistic transport in edge channels which typically relies on transport
experiments with microscale lithographic contacts. Here, we study few-layer
films of the three-dimensional topological insulator
(Bi$_{x}$Sb$_{1-x})_2$Te$_3$, for which a topological transition to a
two-dimensional topological QSH insulator phase has been proposed. Indeed, an
edge state in the local density of states is observed within the band gap. Yet,
in nanoscale transport experiments with a four-tip STM, 2 and 3 quintuple layer
films do not exhibit a ballistic conductance in the edge channels. This
demonstrates that the detection of edge states in spectroscopy can be
misleading with regard to the identification of a QSH phase. In contrast,
nanoscale multi-tip transport experiments are a robust method for effectively
pinpointing ballistic edge channels, as opposed to trivial edge states, in
quantum materials.


title:
Higher uniformity of arithmetic functions in short intervals I. All
  intervals

abstract:
  We study higher uniformity properties of the M\"obius function $\mu$, the von
Mangoldt function $\Lambda$, and the divisor functions $d_k$ on short intervals
$(X,X+H]$ with $X^{\theta+\varepsilon} \leq H \leq X^{1-\varepsilon}$ for a
fixed constant $0 \leq \theta < 1$ and any $\varepsilon>0$.
  More precisely, letting $\Lambda^\sharp$ and $d_k^\sharp$ be suitable
approximants of $\Lambda$ and $d_k$ and $\mu^\sharp = 0$, we show for instance
that, for any nilsequence $F(g(n)\Gamma)$, we have \[ \sum_{X < n \leq X+H}
(f(n)-f^\sharp(n)) F(g(n) \Gamma) \ll H \log^{-A} X \] when $\theta = 5/8$ and
$f \in \{\Lambda, \mu, d_k\}$ or $\theta = 1/3$ and $f = d_2$.
  As a consequence, we show that the short interval Gowers norms
$\|f-f^\sharp\|_{U^s(X,X+H]}$ are also asymptotically small for any fixed $s$
for these choices of $f,\theta$. As applications, we prove an asymptotic
formula for the number of solutions to linear equations in primes in short
intervals, and show that multiple ergodic averages along primes in short
intervals converge in $L^2$.
  Our innovations include the use of multi-parameter nilsequence
equidistribution theorems to control type $II$ sums, and an elementary
decomposition of the neighbourhood of a hyperbola into arithmetic progressions
to control type $I_2$ sums.


title:
Minimum Distance and Parameter Ranges of Locally Recoverable Codes with
  Availability from Fiber Products of Curves

abstract:
  We construct families of locally recoverable codes with availability $t\geq
2$ using fiber products of curves, determine the exact minimum distance of many
families, and prove a general theorem for minimum distance of such codes. The
paper concludes with an exploration of parameters of codes from these families
and the fiber product construction more generally. We show that fiber product
codes can achieve arbitrarily large rate and arbitrarily small relative defect,
and compare to known bounds and important constructions from the literature.


title:
Parallelized Domain Decomposition for Multi-Dimensional Lagrangian
  Random Walk, Mass-Transfer Particle Tracking Schemes

abstract:
  We develop a multi-dimensional, parallelized domain decomposition strategy
(DDC) for mass-transfer particle tracking (MTPT) methods. These methods are a
type of Lagrangian algorithm for simulating reactive transport and are able to
be parallelized by employing large numbers of CPU cores to accelerate run
times. In this work, we investigate different procedures for "tiling" the
domain in two and three dimensions, (2-d and 3-d), as this type of formal DDC
construction is currently limited to 1-d. An optimal tiling is prescribed based
on physical problem parameters and the number of available CPU cores, as each
tiling provides distinct results in both accuracy and run time. We further
extend the most efficient technique to 3-d for comparison, leading to an
analytical discussion of the effect of dimensionality on strategies for
implementing DDC schemes. Increasing computational resources (cores) within the
DDC method produces a trade-off between inter-node communication and on-node
work. For an optimally subdivided diffusion problem, the 2-d parallelized
algorithm achieves nearly perfect linear speedup in comparison with the serial
run up to around 2700 cores, reducing a 5-hour simulation to 8 seconds, and the
3-d algorithm maintains appreciable speedup up to 1700 cores.


title:
Generalizing forbidden induced subgraph characterizations of high
  throttling numbers

abstract:
  Zero forcing is a process that models the spread of information throughout a
graph as white vertices are forced to turn blue using a color change rule. The
idea of throttling, introduced in 2013 by Butler and Young, is to optimize the
trade-off between the number of initial blue vertices and the time taken to
force all vertices to become blue. The original throttling number of a graph
minimizes the sum of these two quantities and the product throttling number
minimizes their product. In addition, weighted throttling changes the weights
given to these two quantities when minimizing their sum. Since its
introduction, throttling has expanded to include many variants of zero forcing.
This motivates the study of zero forcing and throttling using abstract color
change rules. Recently, it has been shown that the graphs with high (sum)
throttling numbers are characterized by a finite family of forbidden induced
subgraphs. In this paper, we extend that result to throttling, product
throttling, and weighted throttling using abstract color change rules. To this
end, we define some important families of color change rules and explore their
properties.


title:
Compositional Generalization and Decomposition in Neural Program
  Synthesis

abstract:
  When writing programs, people have the ability to tackle a new complex task
by decomposing it into smaller and more familiar subtasks. While it is
difficult to measure whether neural program synthesis methods have similar
capabilities, what we can measure is whether they compositionally generalize,
that is, whether a model that has been trained on the simpler subtasks is
subsequently able to solve more complex tasks. In this paper, we focus on
measuring the ability of learned program synthesizers to compositionally
generalize. We first characterize several different axes along which program
synthesis methods would be desired to generalize, e.g., length generalization,
or the ability to combine known subroutines in new ways that do not occur in
the training data. Based on this characterization, we introduce a benchmark
suite of tasks to assess these abilities based on two popular existing
datasets, SCAN and RobustFill. Finally, we make first attempts to improve the
compositional generalization ability of Transformer models along these axes
through novel attention mechanisms that draw inspiration from a human-like
decomposition strategy. Empirically, we find our modified Transformer models
generally perform better than natural baselines, but the tasks remain
challenging.


title:
First Principles Study of the Structural, Mechanical, Electronic, and
  Lattice Dynamical Properties of the Half-Heusler Alloys ZrCoY (Y=Sb, Bi )

abstract:
  First-principles calculation has led to significant discoveries in materials
science. Half heusler (HH) alloys, which are potential thermoelectric materials
have demonstrated significant improvements in thermoelectric performance owing
to their thermal stability, mechanical strength, and moderate ZT. Using Density
Functional Theory (DFT), the structural, mechanical, electronic, and lattice
dynamical properties of cubic Half Heusler alloys ZrCoY (Y=Sb, Bi) have been
investigated. The unknown exchange-correlation functional is approximated using
the generalized gradient approximation (GGA) pseudopotential plane-wave
approach. The structural parameters, that is, equilibrium lattice constant,
elastic constants, and their derivatives are consistent with reported
experimental and theoretical studies where available. Mechanical properties
such as anisotropy factor A, shear modulus G, bulk modulus B, Youngs modulus E,
and Poisons ratio n, are calculated using the Voigt-Reuss-Hill average approach
based on elastic constants. The Debyes temperature, as well as longitudinal and
transverse sound velocities, are predicted from elastic constants at GGA-PBE
and GW approximations. The study of elastic constants showed that the compounds
are mechanically stable, and the phonon dispersion study showed that they are
dynamically stable as well. The ductility and anisotropic nature of the
compounds are confirmed by the elastic constants and mechanical properties.


title: The market drives ETFs or ETFs the market: causality without Granger

abstract:
  This paper develops a deep learning-based econometric methodology to
determine the causality of the financial time series. This method is applied to
the imbalances in daily transactions in individual stocks, as well as the ETFs
reported to SEC with a nanosecond time stamp. Based on our method, we conclude
that transaction imbalances of ETFs alone are more informative than the
transaction imbalances in the entire market. Characteristically, a sheer number
of imbalance messages related to the individual stocks dominates the imbalance
messages due to the ETF in the proportion of 8:1.


title:
Quantum version of the k-NN classifier based on a quantum sorting
  algorithm

abstract:
  In this work we introduce a quantum sorting algorithm with adaptable
requirements of memory and circuit depth, and then use it to develop a new
quantum version of the classical machine learning algorithm known as k-nearest
neighbors (k-NN). Both the efficiency and performance of this new quantum
version of the k-NN algorithm are compared to those of the classical k-NN and
another quantum version proposed by Schuld et al. \cite{Int13}. Results show
that the efficiency of both quantum algorithms is similar to each other and
superior to that of the classical algorithm. On the other hand, the performance
of our proposed quantum k-NN algorithm is superior to the one proposed by
Schuld et al. and similar to that of the classical k-NN.


title:
Normalized additional velocity distribution: a fast sample analysis for
  dark matter or modified gravity models

abstract:
  Here we propose a fast and complementary approach to study galaxy rotation
curves directly from the sample data, instead of first performing individual
rotation curve fits. The method is based on a dimensionless difference between
the observational rotation curve and the expected one from the baryonic matter
($\delta V^2$). It is named as Normalized Additional Velocity (NAV). Using 153
galaxies from the SPARC galaxy sample, we find the observational distribution
of $\delta V^2$. This result is used to compare with the model-inferred
distributions of the same quantity. We consider the following five models to
illustrate the method, which include a dark matter model and four modified
gravity models: Burkert profile, MOND, Palatini $f(R)$ gravity,
Eddington-inspired-Born-Infeld (EiBI) and general relativity with
renormalization group effects (RGGR). We find that the Burkert profile, MOND
and RGGR have reasonable agreement with the observational data, the Burkert
profile being the best model. The method also singles out specific difficulties
of each one of these models. Such indications can be useful for future
phenomenological improvements. The NAV method is sufficient to indicate that
Palatini $f(R)$ and EiBI gravities cannot be used to replace dark matter in
galaxies, since their results are in strong tension with the observational data
sample.


title: A classification of $G$-charge Thouless pumps in 1D invertible states

abstract:
  Recently, a theory has been proposed that classifies cyclic processes of
symmetry protected topological (SPT) quantum states. For the case of spin
chains, i.e.\ one-dimensional bosonic SPT's, this theory implies that cyclic
processes are classified by zero-dimensional SPT's. This is often described as
a generalization of Thouless pumps, with the original Thouless pump
corresponding to the case where the symmetry group is $U(1)$ and pumps are
classified by an integer that corresponds to the charge pumped per cycle. In
this paper, we review this one-dimensional theory in an explicit and rigorous
setting and we provide a proof for the completeness of the proposed
classification for compact symmetry groups $G$.


title: Backports: Change Types, Challenges and Strategies

abstract:
  Source code repositories allow developers to manage multiple versions (or
branches) of a software system. Pull-requests are used to modify a branch, and
backporting is a regular activity used to port changes from a current
development branch to other versions. In open-source software, backports are
common and often need to be adapted by hand, which motivates us to explore
backports and backporting challenges and strategies. In our exploration of
68,424 backports from 10 GitHub projects, we found that bug, test, document,
and feature changes are commonly backported. We identified a number of
backporting challenges, including that backports were inconsistently linked to
their original pull-request (49%), that backports had incompatible code (13%),
that backports failed to be accepted (10%), and that there were backporting
delays (16 days to create, 5 days to merge). We identified some general
strategies for addressing backporting issues. We also noted that backporting
strategies depend on the project type and that further investigation is needed
to determine their suitability. Furthermore, we created the first-ever
backports dataset that can be used by other researchers and practitioners for
investigating backports and backporting.


title:
One Star to Tag Them All (OSTTA): I. Radial velocities and chemical
  abundances for 20 poorly studied open clusters

abstract:
  Context: Open clusters are ideal laboratories to investigate a variety of
astrophysical topics, from the properties of the Galactic disc to stellar
evolution models. For this purpose, we need to know their chemical composition
in detail. Unfortunately, the number of systems with chemical abundances
determined from high resolution spectroscopy remains small. Aims: Our aim is to
increase the number of open clusters with radial velocities and chemical
abundances determined from high resolution spectroscopy by sampling a few stars
in clusters not studied previously. Methods: We obtained high resolution
spectra with the FIES spectrograph at NOT for 41 stars belonging to 20 open
clusters. These stars have high astrometric membership probabilities,
determined from the Gaia second data release. Results: We derived radial
velocities for all the observed stars, which were used to confirm their
membership to the corresponding clusters. For Gulliver\,37 we cannot be sure
the observed star is a real member. We derived atmospheric parameters for the
32 stars considered real cluster members. We discarded five stars because they
have very low gravity or atmospheric parameters were not properly constrained
due to low signal-to-noise ratio spectra. Therefore, detailed chemical
abundances were determined for 28 stars belonging to 17 clusters. For most of
them, this is the first chemical analysis available in the literature. Finally,
we compared the clusters in our sample to a large population of well studied
clusters. The studied systems follow the trends, both chemical and kinematical,
described by the majority of open clusters. Worth noticing that the three most
metal-poor studied clusters (NGC\,1027, NGC\,1750 and Trumpler 2) are enhanced
in Si but not in the other alpha-elements studied (Mg, Ca and Ti).


title:
Model of pulsar pair cascades in non uniform electric fields: growth
  rate, density profile and screening time

abstract:
  Time-dependent cascades of electron-positron pairs are thought to be the main
source of plasma in pulsar magnetospheres and a primary ingredient to explain
the nature of pulsar radio emission, a longstanding open problem in high-energy
astrophysics. During these cascades - positive feedback loops of gamma-ray
photon emission, via curvature radiation by TeV electrons and positrons, and
pair production -, the plasma self-consistently develops inductive waves that
couple to electromagnetic modes capable of escaping the pulsar dense plasma. In
this work, we present an analytical description of pair cascades relevant in
pulsars, including their onset, exponential growth and saturation stages. We
study this problem in the case of a background linear electric field, relevant
in pulsar polar caps, and using an heuristic model of the pair production
process. The analytical results are confirmed with particle-in-cell simulations
performed with OSIRIS including heuristic pair production.


title:
GeV antiproton/gamma-ray excesses and the $W$-boson mass anomaly: three
  faces of $\sim 60-70$ GeV dark matter particle?

abstract:
  For the newly discovered $W$-boson mass anomaly, one of the simplest dark
matter (DM) models that can account for the anomaly without violating other
astrophysical/experimental constraints is the inert two Higgs doublet model, in
which the DM mass ($m_{S}$) is found to be within $\sim 54-74$ GeV. In this
model, the annihilation of DM via $SS\to b\bar{b}$ and $SS\to WW^{*}$ would
produce antiprotons and gamma rays, and may account for the excesses identified
previously in both particles. Motivated by this, we re-analyze the AMS-02
antiproton and Fermi-LAT Galactic center gamma-ray data. For the antiproton
analysis, the novel treatment is the inclusion of the charge-sign-dependent
three-dimensional solar modulation model as constrained by the time-dependent
proton data. We find that the excess of antiprotons is more distinct than
previous results based on the force-field solar modulation model. The
interpretation of this excess as the annihilation of $SS\to WW^{*}$ ($SS\to
b\bar{b}$) requires a DM mass of $\sim 40-80$ ($40-60$) GeV and a
velocity-averaged cross section of $O(10^{-26})~{\rm cm^3~s^{-1}}$. As for the
$\gamma$-ray data analysis, rather than adopting the widely-used spatial
template fitting, we employ an orthogonal approach with a data-driven spectral
template analysis. The fitting to the GeV $\gamma$-ray excess yields DM model
parameters overlapped with those to fit the antiproton excess via the $WW^{*}$
channel. The consistency of the DM particle properties required to account for
the $W$-boson mass anomaly, the GeV antiproton excess, and the GeV $\gamma$-ray
excess suggest a common origin of them.


title:
Global ECG Classification by Self-Operational Neural Networks with
  Feature Injection

abstract:
  Objective: Global (inter-patient) ECG classification for arrhythmia detection
over Electrocardiogram (ECG) signal is a challenging task for both humans and
machines. The main reason is the significant variations of both normal and
arrhythmic ECG patterns among patients. Automating this process with utmost
accuracy is, therefore, highly desirable due to the advent of wearable ECG
sensors. However, even with numerous deep learning approaches proposed
recently, there is still a notable gap in the performance of global and
patient-specific ECG classification performances. This study proposes a novel
approach to narrow this gap and propose a real-time solution with shallow and
compact 1D Self-Organized Operational Neural Networks (Self-ONNs). Methods: In
this study, we propose a novel approach for inter-patient ECG classification
using a compact 1D Self-ONN by exploiting morphological and timing information
in heart cycles. We used 1D Self-ONN layers to automatically learn
morphological representations from ECG data, enabling us to capture the shape
of the ECG waveform around the R peaks. We further inject temporal features
based on RR interval for timing characterization. The classification layers can
thus benefit from both temporal and learned features for the final arrhythmia
classification. Results: Using the MIT-BIH arrhythmia benchmark database, the
proposed method achieves the highest classification performance ever achieved,
i.e., 99.21% precision, 99.10% recall, and 99.15% F1-score for normal (N)
segments; 82.19% precision, 82.50% recall, and 82.34% F1-score for the
supra-ventricular ectopic beat (SVEBs); and finally, 94.41% precision, 96.10%
recall, and 95.2% F1-score for the ventricular-ectopic beats (VEBs).


title:
ARIADNE: Measuring accurate and precise stellar parameters through SED
  fitting

abstract:
  Accurately measuring stellar parameters is a key goal to increase our
understanding of the observable universe. However, current methods are limited
by many factors, in particular, the biases and physical assumptions that are
the basis for the underlying evolutionary or atmospheric models, those that
these methods rely upon. Here we introduce our code spectrAl eneRgy
dIstribution bAyesian moDel averagiNg fittEr (ARIADNE), which tackles this
problem by using Bayesian Model Averaging to incorporate the information from
all stellar models to arrive at accurate and precise values. This code uses
spectral energy distribution fitting methods, combined with precise Gaia
distances, to measure the temperature, log g, [Fe/H], A$_{\text V}$, and radius
of a star. When compared with interferometrically measured radii ARIADNE
produces values in excellent agreement across a wide range of stellar
parameters, with a mean fractional difference of only 0.001 $\pm$ 0.070. We
currently incorporate six different models, and in some cases we find
significant offsets between them, reaching differences of up to 550 K and 0.6
R$_\odot$ in temperature and radius, respectively. For example, such offsets in
stellar radius would give rise to a difference in planetary radius of 60%,
negating homogeneity when combining results from different models. We also find
a trend for stars smaller than 0.4-0.5 R$_\odot$, which shows more work needs
to be done to better model these stars, even though the overall extent is
within the uncertainties of the interferometric measurements. We advocate for
the use of ARIADNE to provide improved bulk parameters of nearby A to M dwarfs
for future studies.


title:
Characterizing User Behaviors in Open-Source Software User Forums: An
  Empirical Study

abstract:
  User forums of Open Source Software (OSS) enable end-users to collaboratively
discuss problems concerning the OSS applications. Despite decades of research
on OSS, we know very little about how end-users engage with OSS communities on
these forums, in particular, the challenges that hinder their continuous and
meaningful participation in the OSS community. Many previous works are
developer-centric and overlook the importance of end-user forums. As a result,
end-users' expectations are seldom reflected in OSS development. To better
understand user behaviors in OSS user forums, we carried out an empirical study
analyzing about 1.3 million posts from user forums of four popular OSS
applications: Zotero, Audacity, VLC, and RStudio. Through analyzing the
contribution patterns of three common user types (end-users, developers, and
organizers), we observed that end-users not only initiated most of the threads
(above 96% of threads in three projects, 86% in the other), but also acted as
the significant contributors for responding to other users' posts, even though
they tended to lack confidence in their activities as indicated by
psycho-linguistic analyses. Moreover, we found end-users more open, reflecting
a more positive emotion in communication than organizers and developers in the
forums. Our work contributes new knowledge about end-users' activities and
behaviors in OSS user forums that the vital OSS stakeholders can leverage to
improve end-user engagement in the OSS development process.


title: Q-learning with online random forests

abstract:
  $Q$-learning is the most fundamental model-free reinforcement learning
algorithm. Deployment of $Q$-learning requires approximation of the
state-action value function (also known as the $Q$-function). In this work, we
provide online random forests as $Q$-function approximators and propose a novel
method wherein the random forest is grown as learning proceeds (through
expanding forests). We demonstrate improved performance of our methods over
state-of-the-art Deep $Q$-Networks in two OpenAI gyms (`blackjack' and
`inverted pendulum') but not in the `lunar lander' gym. We suspect that the
resilience to overfitting enjoyed by random forests recommends our method for
common tasks that do not require a strong representation of the problem domain.
We show that expanding forests (in which the number of trees increases as data
comes in) improve performance, suggesting that expanding forests are viable for
other applications of online random forests beyond the reinforcement learning
setting.


title:
Multi-objective optimization determines when, which and how to fuse deep
  networks: an application to predict COVID-19 outcomes

abstract:
  The COVID-19 pandemic has caused millions of cases and deaths and the
AI-related scientific community, after being involved with detecting COVID-19
signs in medical images, has been now directing the efforts towards the
development of methods that can predict the progression of the disease. This
task is multimodal by its very nature and, recently, baseline results achieved
on the publicly available AIforCOVID dataset have shown that chest X-ray scans
and clinical information are useful to identify patients at risk of severe
outcomes. While deep learning has shown superior performance in several medical
fields, in most of the cases it considers unimodal data only. In this respect,
when, which and how to fuse the different modalities is an open challenge in
multimodal deep learning. To cope with these three questions here we present a
novel approach optimizing the setup of a multimodal end-to-end model. It
exploits Pareto multi-objective optimization working with a performance metric
and the diversity score of multiple candidate unimodal neural networks to be
fused. We test our method on the AIforCOVID dataset, attaining state-of-the-art
results, not only outperforming the baseline performance but also being robust
to external validation. Moreover, exploiting XAI algorithms we figure out a
hierarchy among the modalities and we extract the features' intra-modality
importance, enriching the trust on the predictions made by the model.


title:
Permanence via invasion graphs: Incorporating community assembly into
  Modern Coexistence Theory

abstract:
  To understand the mechanisms underlying species coexistence, ecologists often
study invasion growth rates of theoretical and data-driven models. These growth
rates correspond to average per-capita growth rates of one species with respect
to an ergodic measure supporting other species. In the ecological literature,
coexistence often is equated with the invasion growth rates being positive.
Intuitively, positive invasion growth rates ensure that species recover from
being rare. To provide a mathematically rigorous framework for this approach,
we prove theorems that answer two questions: (i) When do the signs of the
invasion growth rates determine coexistence? (ii) When signs are sufficient,
which invasion growth rates need to be positive? We focus on deterministic
models and equate coexistence with permanence, i.e., a global attractor bounded
away from extinction. For models satisfying certain technical assumptions, we
introduce invasion graphs where vertices correspond to proper subsets of
species (communities) supporting an ergodic measure and directed edges
correspond to potential transitions between communities due to invasions by
missing species. These directed edges are determined by the signs of invasion
growth rates. When the invasion graph is acyclic (i.e. there is no sequence of
invasions starting and ending at the same community), we show that permanence
is determined by the signs of the invasion growth rates. In this case,
permanence is characterized by the invasibility of all -i communities, i.e.,
communities without species i where all other missing species having negative
invasion growth rates. We show that dissipative Lotka-Volterra models satisfy
our technical assumptions and computing their invasion graphs reduces to
solving systems of linear equations. We provide additional applications of the
results and discuss open problems.


title: Taking models seriously and being a linguistic realist

abstract:
  Carnap's conception of linguistic frameworks is widespread; however, it is
not entirely clear nor consensual to pinpoint what is the influence in his
stance within the traditional realist/anti-realist debate. In this paper, we
place Carnap as a proponent of a scientific realist stance, by presenting what
he called "linguistic realism". Some possible criticisms are considered, and a
case study is offered with wave function realism, a popular position in the
philosophy of quantum mechanics.


title: Massively scalable stencil algorithm

abstract:
  Stencil computations lie at the heart of many scientific and industrial
applications. Unfortunately, stencil algorithms perform poorly on machines with
cache based memory hierarchy, due to low re-use of memory accesses. This work
shows that for stencil computation a novel algorithm that leverages a localized
communication strategy effectively exploits the Cerebras WSE-2, which has no
cache hierarchy. This study focuses on a 25-point stencil finite-difference
method for the 3D wave equation, a kernel frequently used in earth modeling as
numerical simulation. In essence, the algorithm trades memory accesses for data
communication and takes advantage of the fast communication fabric provided by
the architecture. The algorithm -- historically memory bound -- becomes compute
bound. This allows the implementation to achieve near perfect weak scaling,
reaching up to 503 TFLOPs on WSE-2, a figure that only full clusters can
eventually yield.


title: TorMentor: Deterministic dynamic-path, data augmentations with fractals

abstract:
  We propose the use of fractals as a means of efficient data augmentation.
Specifically, we employ plasma fractals for adapting global image augmentation
transformations into continuous local transforms. We formulate the diamond
square algorithm as a cascade of simple convolution operations allowing
efficient computation of plasma fractals on the GPU. We present the TorMentor
image augmentation framework that is totally modular and deterministic across
images and point-clouds. All image augmentation operations can be combined
through pipelining and random branching to form flow networks of arbitrary
width and depth. We demonstrate the efficiency of the proposed approach with
experiments on document image segmentation (binarization) with the DIBCO
datasets. The proposed approach demonstrates superior performance to
traditional image augmentation techniques. Finally, we use extended synthetic
binary text images in a self-supervision regiment and outperform the same model
when trained with limited data and simple extensions.


title:
Predicting electrical conductivity in Cu/Nb composites: a combined
  model-experiment study

abstract:
  The generation of high magnetic fields requires materials with high electric
conductivity and good strength properties. Cu/Nb composites are considered to
be good candidates for this purpose. In this work we aim to predict, from
theory, the dependence of electric conductivity on the microstructure, most
notably on the layer thickness and grain sizes. We also conducted experiments
to calibrate and validate our simulations. Bimetal interfaces and grain
boundaries are confirmed to have the largest impact on conductivity in this
composite material. In this approach, a distribution of the layer thickness is
accounted for in order to better model the experimentally observed
microstructure. Because layer thicknesses below the mean free path of Cu
significantly degrade the conductivity, an average layer thickness larger than
expected may be needed to meet conductivity requirements in order to minimize
these smaller layers in the distribution. We also investigate the effect of
variations in volume fraction of Nb and temperature on the material's
conductivity.


title: Mitigating Mismatch Compression in Differential Local Field Potentials

abstract:
  Bidirectional deep brain stimulation (bdDBS) devices capable of recording
differential local field potentials (dLFP) enable neural recordings alongside
clinical therapy. Efforts to identify objective signals of various brain
disorders, or disease readouts, are challenging in dLFP, especially during
active DBS. In this report we identified, characterized, and mitigated a major
source of distortion in dLFP that we introduce as mismatch compression (MC). MC
occurs secondary to impedance mismatches across the dLFP channel resulting in
incomplete rejection of artifacts and downstream amplifier gain compression.
Using in silico and in vitro models we demonstrate that MC accounts for
impedance-related distortions sensitive to DBS amplitude. We then use these
models to develop and validate a mitigation strategy for MC that is provided as
an opensource library for more reliable oscillatory disease readouts.


title:
Autoencoder-based Unsupervised Intrusion Detection using Multi-Scale
  Convolutional Recurrent Networks

abstract:
  The massive growth of network traffic data leads to a large volume of
datasets. Labeling these datasets for identifying intrusion attacks is very
laborious and error-prone. Furthermore, network traffic data have complex
time-varying non-linear relationships. The existing state-of-the-art intrusion
detection solutions use a combination of various supervised approaches along
with fused features subsets based on correlations in traffic data. These
solutions often require high computational cost, manual support in fine-tuning
intrusion detection models, and labeling of data that limit real-time
processing of network traffic. Unsupervised solutions do reduce computational
complexities and manual support for labeling data but current unsupervised
solutions do not consider spatio-temporal correlations in traffic data. To
address this, we propose a unified Autoencoder based on combining multi-scale
convolutional neural network and long short-term memory (MSCNN-LSTM-AE) for
anomaly detection in network traffic. The model first employs Multiscale
Convolutional Neural Network Autoencoder (MSCNN-AE) to analyze the spatial
features of the dataset, and then latent space features learned from MSCNN-AE
employs Long Short-Term Memory (LSTM) based Autoencoder Network to process the
temporal features. Our model further employs two Isolation Forest algorithms as
error correction mechanisms to detect false positives and false negatives to
improve detection accuracy. %Additionally, covariance matrices forms a
Riemannian manifold that is naturally embedded with distance metrices that
facilitates descriminative patterns for detecting malicious network traffic. We
evaluated our model NSL-KDD, UNSW-NB15, and CICDDoS2019 dataset and showed our
proposed method significantly outperforms the conventional unsupervised methods
and other existing studies on the dataset.


title: On implicitly oscillatory quadrilinear integrals

abstract:
  For quadrilinear functionals $\int_B \prod_{j=1}^4 (f_j\circ\varphi_j)$,
where $B\subset{\mathbb R}^2$ is a ball, $\varphi_j:B\to{\mathbb R}^1$ are real
analytic submersions, and $f_j\in L^\infty({\mathbb R}^1)$ are bounded and
measurable, we seek a majorization of the integral by a product of negative
order Sobolev norms of the factors $f_j$. An obvious necessary condition is
that any smooth solution of $\sum_j (g_j\circ\varphi_j)\equiv 0$, in any
connected open set, must be constant. Assuming this condition and certain
auxiliary hypotheses, we establish an upper bound of the desired type. The
proof relies in part on a three term sublevel set inequality established in a
companion paper.


title: Color My World: Deterministic Tagging for Memory Safety

abstract:
  Hardware-assisted memory protection features are increasingly being deployed
in COTS processors. ARMv8.5 Memory Tagging Extensions (MTE) is a recent
example, which has been used to provide probabilistic checks for memory safety.
This use of MTE is not secure against the standard adversary with arbitrary
read/write access to memory. Consequently MTE is used as a software development
tool. In this paper we present the first design for deterministic memory
protection using MTE that can resist the standard adversary, and hence is
suitable for post-deployment memory safety. We describe our compiler extensions
for LLVM Clang implementing static analysis and subsequent MTE instrumentation.
Via a comprehensive evaluation we show that our scheme is effective.


title: Testing Positive Semidefiniteness Using Linear Measurements

abstract:
  We study the problem of testing whether a symmetric $d \times d$ input matrix
$A$ is symmetric positive semidefinite (PSD), or is $\epsilon$-far from the PSD
cone, meaning that $\lambda_{\min}(A) \leq - \epsilon \|A\|_p$, where $\|A\|_p$
is the Schatten-$p$ norm of $A$. In applications one often needs to quickly
tell if an input matrix is PSD, and a small distance from the PSD cone may be
tolerable. We consider two well-studied query models for measuring efficiency,
namely, the matrix-vector and vector-matrix-vector query models. We first
consider one-sided testers, which are testers that correctly classify any PSD
input, but may fail on a non-PSD input with a tiny failure probability. Up to
logarithmic factors, in the matrix-vector query model we show a tight
$\widetilde{\Theta}(1/\epsilon^{p/(2p+1)})$ bound, while in the
vector-matrix-vector query model we show a tight
$\widetilde{\Theta}(d^{1-1/p}/\epsilon)$ bound, for every $p \geq 1$. We also
show a strong separation between one-sided and two-sided testers in the
vector-matrix-vector model, where a two-sided tester can fail on both PSD and
non-PSD inputs with a tiny failure probability. In particular, for the
important case of the Frobenius norm, we show that any one-sided tester
requires $\widetilde{\Omega}(\sqrt{d}/\epsilon)$ queries. However we introduce
a bilinear sketch for two-sided testing from which we construct a Frobenius
norm tester achieving the optimal $\widetilde{O}(1/\epsilon^2)$ queries. We
also give a number of additional separations between adaptive and non-adaptive
testers. Our techniques have implications beyond testing, providing new methods
to approximate the spectrum of a matrix with Frobenius norm error using
dimensionality reduction in a way that preserves the signs of eigenvalues.


title: Does Simultaneous Speech Translation need Simultaneous Models?

abstract:
  In simultaneous speech translation (SimulST), finding the best trade-off
between high translation quality and low latency is a challenging task. To meet
the latency constraints posed by the different application scenarios, multiple
dedicated SimulST models are usually trained and maintained, generating high
computational costs. In this paper, motivated by the increased social and
environmental impact caused by these costs, we investigate whether a single
model trained offline can serve not only the offline but also the simultaneous
task without the need for any additional training or adaptation. Experiments on
en->{de, es} indicate that, aside from facilitating the adoption of
well-established offline techniques and architectures without affecting
latency, the offline solution achieves similar or better translation quality
compared to the same model trained in simultaneous settings, as well as being
competitive with the SimulST state of the art.


title: Free Energy Evaluation Using Marginalized Annealed Importance Sampling

abstract:
  The evaluation of the free energy of a stochastic model is considered to be a
significant issue in various fields of physics and machine learning. However,
the exact free energy evaluation is computationally infeasible because it
includes an intractable partition function. Annealed importance sampling (AIS)
is a type of importance sampling based on the Markov chain Monte Carlo method,
which is similar to a simulated annealing, and can effectively approximate the
free energy. This study proposes a new AIS-based approach, referred to as
marginalized AIS (mAIS). The statistical efficiency of mAIS is investigated in
detail based on a theoretical and numerical perspectives. Based on the
investigation, it has been proved that mAIS is more effective than AIS under a
certain condition.

categories: stat.ML cond-mat.dis-nn cond-mat.stat-mech cs.LG math.ST stat.TH


title:
Computational Understandings of the Cation Configuration Dependent Redox
  Activities and Oxygen Dimerizations in Li$_{1.22}$Ni$_{0.22}$Mn$_{0.56}$O$_2$
  Cathode

abstract:
  Understanding the lattice oxygen dimerization is quite essential for the
optimal design for the Li-rich Mn-based cathode materials. In this work, based
on the density functional theory (DFT) calculations, a Ni-honeycomb Li-Ni-Mn
cation configuration for Li$_{1.22}$Ni$_{0.22}$Mn$_{0.56}$O$_2$ cathode was
carefully proposed and examined, which can coexist with the well-known
Li-honeycomb structure in the experimentally synthesized
Li$_{1.2}$Ni$_{0.2}$Mn$_{0.6}$O$_2$ samples. Li-Ni-Mn cation configurations
have significant impacts on oxygen redox activities and oxygen dimerizations in
the delithiated Li$_x$Ni$_{0.22}$Mn$_{0.56}$O$_2$. There is no necessary
consistency between the high lattice oxygen redox activity and easy oxygen
dimerization, such as the Li-honeycomb structures showing higher redox
activities and higher activation energy barriers to prohibit oxygen
dimerizations than Ni-honeycomb structures. Avoiding the Ni-honeycomb
structures with more favorable lattice oxygen dimerization and making full use
of the Li-honeycomb structures with better redox activities is important to
optimally design the high-performance Li-rich Mn-based cathode materials.


title: Perlustration on Mobile Forensics Tools

abstract:
  Nowadays many people store more information in cellphones rather than do on
their computer which leads to the increase of crimes taking place in mobile.
This is also because of extensive use of mobile devices. People refer to store
more information in mobile than in computer. Therefore,scope of forensics in
mobile has increased in last few years as compared to computers. In this paper
, we have carried out a survey on tools and techniques which are in practice
for mobile forensics. The survey is the amalgamation of the in practice
technical methods and discussion of some of the tools for specific operating
systems. Some challenges faced by practitioners while performing forensics has
also been enlisted.


title: The generalized reciprocal distance matrix of graphs

abstract:
  Let $G$ be a simple undirected connected graph with the Harary matrix
$RD(G)$, which is also called the reciprocal distance matrix of $G$. The
reciprocal distance signless Laplacian matrix of $G$ is $RQ(G)=RT(G)+RD(G)$,
where $RT(G)$ denotes the diagonal matrix of the vertex reciprocal
transmissions of graph $G$. This paper intends to introduce a new matrix
$RD_{\alpha}(G)=\alpha RT(G)+(1-\alpha)RD(G)$, $\alpha\in [0,1]$, to track the
gradual change from $RD(G)$ to $RQ(G)$. First, we describe completely the
eigenvalues of $RD_{\alpha}(G)$ of some special graphs. Then we obtain serval
basic properties of $RD_{\alpha}(G)$ including inequalities that involve the
spectral radii of the reciprocal distance matrix, reciprocal distance signless
Laplacian matrix and $RD_{\alpha}$-matrix of $G$. We also provide some lower
and upper bounds of the spectral radius of $RD_{\alpha}$-matrix. Finally, we
depict the extremal graphs with maximal spectral radius of the
$RD_{\alpha}$-matrix among all connected graphs of fixed order and precise
vertex connectivity, edge connectivity, chromatic number and independence
number, respectively.


title: Role of inflexible minorities in the evolution of alcohol consumption

abstract:
  In this work we study a simple mathematical model for drinking behavior
evolution. For this purpose, we considered three compartments, namely
Susceptible individuals $S$ (nonconsumers), Moderated drinkers $M$ and Risk
drinkers $R$. Inside the $S$ and $R$ compartments, we considered the presence
of inflexible or zealot agents, i.e., individuals that never change their
behavior (never drink or always drink a lot). These inflexible agents are
described by fixed densities $s_I$ and $r_I$, for nonconsumer inflexible and
risk drinking inflexible individuals, respectively. We analyze the impact of
the presence of such special agents in the evolution of drinking behavior in
the population. Specifically, since the presence of inflexible agents are
similar to the introduction of quenched disorder in the model, we are
interested in the impact of such disorder in the critical behavior of the
system. Our analytical and numerical results indicate that the presence of only
one class of inflexible agents, $s_I$ or $r_I$, destroys one of the two
possible absorbing phases that are observed in the model without such
inflexibles, i.e., for $s_i=r_I=0$. In the presence of the both kinds of
inflexible agents simultaneously, there are no absorbing states anymore. Since
absorbing states are collective macroscopic states with the presence of only
one kind of individuals in the population, nonconsumers or risk drinkers, we
argue that the inclusion of inflexible agents in the population makes the model
more realistic. In addition, the work makes a contribution to studies on the
impact of quenched disorder in nonequilibrium phase transitions, that are a
subject of interest for Nonequilibrium Statistical Physics.


title: Broadening AI Ethics Narratives: An Indic Art View

abstract:
  Incorporating interdisciplinary perspectives is seen as an essential step
towards enhancing artificial intelligence (AI) ethics. In this regard, the
field of arts is perceived to play a key role in elucidating diverse historical
and cultural narratives, serving as a bridge across research communities. Most
of the works that examine the interplay between the field of arts and AI ethics
concern digital artworks, largely exploring the potential of computational
tools in being able to surface biases in AI systems. In this paper, we
investigate a complementary direction--that of uncovering the unique
socio-cultural perspectives embedded in human-made art, which in turn, can be
valuable in expanding the horizon of AI ethics. Through qualitative interviews
of sixteen artists, art scholars, and researchers of diverse Indian art forms
like music, sculpture, painting, floor drawings, dance, etc., we explore how
{\it non-Western} ethical abstractions, methods of learning, and participatory
practices observed in Indian arts, one of the most ancient yet perpetual and
influential art traditions, can inform the FAccT community. Insights from our
study suggest (1) the need for incorporating holistic perspectives (that are
informed both by data-driven observations and prior beliefs encapsulating the
structural models of the world) in designing ethical AI algorithms, (2) the
need for integrating multimodal data formats for design, development, and
evaluation of ethical AI systems, (3) the need for viewing AI ethics as a
dynamic, cumulative, shared process rather than as a self contained framework
to facilitate adaptability without annihilation of values, (4) the need for
consistent life-long learning to enhance AI accountability, and (5) the need
for identifying ethical commonalities across cultures and infusing the same
into AI system design, so as to enhance applicability across geographies.


title: High-Dimensional Geometric Streaming in Polynomial Space

abstract:
  Many existing algorithms for streaming geometric data analysis have been
plagued by exponential dependencies in the space complexity, which are
undesirable for processing high-dimensional data sets. In particular, once
$d\geq\log n$, there are no known non-trivial streaming algorithms for problems
such as maintaining convex hulls and L\"owner-John ellipsoids of $n$ points,
despite a long line of work in streaming computational geometry since [AHV04].
  We simultaneously improve these results to $\mathrm{poly}(d,\log n)$ bits of
space by trading off with a $\mathrm{poly}(d,\log n)$ factor distortion. We
achieve these results in a unified manner, by designing the first streaming
algorithm for maintaining a coreset for $\ell_\infty$ subspace embeddings with
$\mathrm{poly}(d,\log n)$ space and $\mathrm{poly}(d,\log n)$ distortion. Our
algorithm also gives similar guarantees in the \emph{online coreset} model.
Along the way, we sharpen results for online numerical linear algebra by
replacing a log condition number dependence with a $\log n$ dependence,
answering a question of [BDM+20]. Our techniques provide a novel connection
between leverage scores, a fundamental object in numerical linear algebra, and
computational geometry.
  For $\ell_p$ subspace embeddings, we give nearly optimal trade-offs between
space and distortion for one-pass streaming algorithms. For instance, we give a
deterministic coreset using $O(d^2\log n)$ space and $O((d\log n)^{1/2-1/p})$
distortion for $p>2$, whereas previous deterministic algorithms incurred a
$\mathrm{poly}(n)$ factor in the space or the distortion [CDW18].
  Our techniques have implications in the offline setting, where we give
optimal trade-offs between the space complexity and distortion of subspace
sketch data structures. To do this, we give an elementary proof of a "change of
density" theorem of [LT80] and make it algorithmic.


title: Quantifying the entanglement of quantum states under the geometry method

abstract:
  Quantifying entanglement is an important issue in quantum information theory.
A straightforward method to quantify entanglement is to measure the distance
between the entangled state and the separable sets under the fidelity, distance
norm and so on. However, there are few results on the entanglement measure in
terms of trace norm. Here we consider the entanglement measures through the
trace norm in terms of three methods, the standard measure, the modified
measure and the way proposed in [Phys. Rev. A 102. 062401 (2020)].
Specifically, we present some properties of the entanglement measures under the
three methods. We also present the analytical expressions for a class of mixed
states on two-qubit systems for the standard measure, the pure states for the
modified measure and the mixed states on two-qubit systems for the method
generated under the third way. And we also generalize the bipartite
entanglement measure to a measure for the tripartite states.


title: Deep water wave cloaking using a multi-layered buoyant plate

abstract:
  Trajectory of surface gravity waves in potential flow regime is affected by
the gravitational acceleration, water density, and seabed depth. While the
gravitational acceleration and water density are approximately constant, the
effect of water depth on surface gravity waves exponentially decreases as the
water depth increases. In shallow water, cloaking an object from surface waves
by varying the seabed topography is possible, however, as the water depth
increases, cloaking becomes a challenge since there is no physical parameter to
be engineered and subsequently affects the wave propagation. In order to create
an omnidirectional cylindrical cloaking device for finite-depth/deep-water
waves, we propose a multi-layered elastic plate that floats on the surface
around a to-be-cloaked cylinder. The buoyant elastic plate is made of
axisymmetric, homogeneous, and isotropic layers which provides adjustable
degrees of freedom to engineer and affect the wave propagation. We first
develop a pseudo-spectral method to efficiently determine the wave solution for
a multi-layered buoyant plate. Next, we optimize the physical parameters of the
buoyant plate (i.e. elasticity and mass of every layer) using a real-coded
evolutionary algorithm to minimize the energy of scattered-waves from the
object. We show that the optimized cloak reduces the energy of scattered-waves
as high as 99.2% for the target wave number. We also quantify the wave drift
force exerted on the structures and show that the buoyant plate reduces the
exerted force by 99.9%.


title:
Personal VAD 2.0: Optimizing Personal Voice Activity Detection for
  On-Device Speech Recognition

abstract:
  Personalization of on-device speech recognition (ASR) has seen explosive
growth in recent years, largely due to the increasing popularity of personal
assistant features on mobile devices and smart home speakers. In this work, we
present Personal VAD 2.0, a personalized voice activity detector that detects
the voice activity of a target speaker, as part of a streaming on-device ASR
system. Although previous proof-of-concept studies have validated the
effectiveness of Personal VAD, there are still several critical challenges to
address before this model can be used in production: first, the quality must be
satisfactory in both enrollment and enrollment-less scenarios; second, it
should operate in a streaming fashion; and finally, the model size should be
small enough to fit a limited latency and CPU/Memory budget. To meet the
multi-faceted requirements, we propose a series of novel designs: 1) advanced
speaker embedding modulation methods; 2) a new training paradigm to generalize
to enrollment-less conditions; 3) architecture and runtime optimizations for
latency and resource restrictions. Extensive experiments on a realistic speech
recognition system demonstrated the state-of-the-art performance of our
proposed method.


title: On the Importance of Performing App Analysis Within Peer Groups

abstract:
  The competing nature of the app market motivates us to shift our focus on
apps that provide similar functionalities and directly compete with each other
(i.e., peer apps). In this work, we study the ratings and the review text of
100 Android apps across 10 peer app groups. We highlight the importance of
performing peer-app analysis by showing that it can provide a unique
perspective over performing a global analysis of apps (i.e., mixing apps from
multiple categories). First, we observe that comparing user ratings within peer
groups can provide very different results from comparing user ratings from a
global perspective. Then, we show that peer-app analysis provides a different
perspective to spot the dominant topics in the user reviews, and to understand
the impact of the topics on user ratings. Our findings suggest that future
efforts may pay more attention to performing and supporting app analysis from a
peer group context. For example, app store owners may consider an additional
rating mechanism that normalizes app ratings within peer groups, and future
research may help developers understand the characteristics of specific peer
groups and prioritize their efforts.


title:
Semantic Representation and Dependency Learning for Multi-Label Image
  Recognition

abstract:
  Recently many multi-label image recognition (MLR) works have made significant
progress by introducing pre-trained object detection models to generate lots of
proposals or utilizing statistical label co-occurrence enhance the correlation
among different categories. However, these works have some limitations: (1) the
effectiveness of the network significantly depends on pre-trained object
detection models that bring expensive and unaffordable computation; (2) the
network performance degrades when there exist occasional co-occurrence objects
in images, especially for the rare categories. To address these problems, we
propose a novel and effective semantic representation and dependency learning
(SRDL) framework to learn category-specific semantic representation for each
category and capture semantic dependency among all categories. Specifically, we
design a category-specific attentional regions (CAR) module to generate
channel/spatial-wise attention matrices to guide model to focus on
semantic-aware regions. We also design an object erasing (OE) module to
implicitly learn semantic dependency among categories by erasing semantic-aware
regions to regularize the network training. Extensive experiments and
comparisons on two popular MLR benchmark datasets (i.e., MS-COCO and Pascal VOC
2007) demonstrate the effectiveness of the proposed framework over current
state-of-the-art algorithms.


title: Electroweak Precision Fit and New Physics in light of $W$ Boson Mass

abstract:
  The $W$ boson mass is one of the most important electroweak precision
observables for testing the Standard Model or its extensions. The very recent
measured $W$ boson mass at CDF shows about $7\sigma$ deviations from the SM
prediction, which may challenge the internal consistency of the SM. By
performing the global electroweak fit with the new $W$-boson, we present the
new values of the oblique parameters: $S = 0.06 \pm 0.10$, $T= 0.11 \pm 0.12$,
$U=0.13 \pm 0.09$, or $S=0.14 \pm 0.08$, $T= 0.26 \pm 0.06$ with $U =0$ and the
corresponding correlation matrices, which strongly indicates the need for the
non-degenerate multiplets beyond the SM. As a proof-of-concept, we show that
the new results can be accommodated in the two-Higgs doublet model, where the
charged Higgs boson has to be either heavier or lighter than both two heavy
neutral Higgs bosons. Therefore, searching for these non-SM Higgs bosons will
provide a complementary way to test the new physics for the $W$ boson mass
anomaly.


title: On the $KU_G$-local equivariant sphere

abstract:
  Equivariant complex $K$-theory and the equivariant sphere spectrum are two of
the most fundamental equivariant spectra. For an odd $p$-group, we calculate
the zeroth homotopy Green functor of the localization of the equivariant sphere
spectrum with respect to equivariant complex $K$-theory. Further, we calculate
the zeroth homotopy Tambara functor structure in the case of odd cyclic
$p$-groups.


title:
Log-optimal portfolio after a random time: Existence, description and
  sensitivity analysis

abstract:
  In this paper, we consider an informational market model with two flows of
informations. The smallest flow F, which is available to all agents, is the
filtration of the initial market model(S,F,P), where S is the assets' prices
and P is a probability measure. The largest flow G contains additional
information about the occurrence of a random time T. This setting covers credit
risk theory where T models the default time of a firm, and life insurance where
T represents the death time of an insured. For the model (S-S^T,G,P), we
address the log-optimal portfolio problem in many aspects. In particular, we
answer the following questions and beyond: 1) What are the necessary and
sufficient conditions for the existence of log-optimal portfolio of the model
under consideration? 2) what are the various type of risks induced by T that
affect this portfolio and how? 3) What are the factors that completely describe
the sensitivity of the log-portfolio to the parameters of T? The answers to
these questions and other related discussions definitely complement the work of
Choulli and Yansori [12] which deals with the stopped model (S^T,G).


title:
Optimal allocations to heterogeneous agents with an application to
  stimulus checks

abstract:
  A planner allocates discrete transfers of size $D_g$ to $N$ heterogeneous
groups labeled $g$ and has CES preferences over the resulting outcomes,
$H_g(D_g)$. We derive a closed-form solution for optimally allocating a fixed
budget subject to group-specific inequality constraints under the assumption
that increments in the $H_g$ functions are non-increasing. We illustrate our
method by studying allocations of "support checks" from the U.S. government to
households during both the Great Recession and the COVID-19 pandemic. We
compare the actual allocations to optimal ones under alternative constraints,
assuming the government focused on stimulating aggregate consumption during the
2008--2009 crisis and focused on welfare during the 2020--2021 crisis. The
inputs for this analysis are obtained from versions of a life-cycle model with
heterogeneous households, which predicts household-type-specific consumption
and welfare responses to tax rebates and cash transfers.


title: End-of-Life of Software How is it Defined and Managed?

abstract:
  The rapid development of new software and algorithms, fueled by the immense
amount of data available, has made the shelf life of software products a lot
shorter. With a rough estimate of more than 40,000 new software projects
developed every day, it is becoming quicker and cheaper to abandon old software
and acquire new software that meets rapidly changing needs and demands. What
happens to software that is abandoned and what consequences may arise from
'throwaway' culture (Cooper, 2005) are still open questions. This paper will
explore the systems engineering concept of end-of-life for software, it will
highlight the gaps in existing software engineering practices, it will bring
forward examples of software that has been abandoned in an attempt to
decommission and it will explore the repercussions of abandoned software
artefacts. A proposed way forward for addressing the identified research gaps
is also detailed.