title: A Unified Prediction Framework for Signal Maps

abstract:
  Signal maps are essential for the planning and operation of cellular
networks. However, the measurements needed to create such maps are expensive,
often biased, not always reflecting the metrics of interest, and posing privacy
risks. In this paper, we develop a unified framework for predicting cellular
signal maps from limited measurements. Our framework builds on a
state-of-the-art random-forest predictor, or any other base predictor. We
propose and combine three mechanisms that deal with the fact that not all
measurements are equally important for a particular prediction task. First, we
design quality-of-service functions ($Q$), including signal strength (RSRP) but
also other metrics of interest to operators, i.e., coverage and call drop
probability. By implicitly altering the loss function employed in learning,
quality functions can also improve prediction for RSRP itself where it matters
(e.g., MSE reduction up to 27% in the low signal strength regime, where errors
are critical). Second, we introduce weight functions ($W$) to specify the
relative importance of prediction at different locations and other parts of the
feature space. We propose re-weighting based on importance sampling to obtain
unbiased estimators when the sampling and target distributions are different.
This yields improvements up to 20% for targets based on spatially uniform loss
or losses based on user population density. Third, we apply the Data Shapley
framework for the first time in this context: to assign values ($\phi$) to
individual measurement points, which capture the importance of their
contribution to the prediction task. This improves prediction (e.g., from 64%
to 94% in recall for coverage loss) by removing points with negative values,
and can also enable data minimization. We evaluate our methods and demonstrate
significant improvement in prediction performance, using several real-world
datasets.


title:
Exploring Inter-Channel Correlation for Diversity-preserved
  KnowledgeDistillation

abstract:
  Knowledge Distillation has shown very promising abil-ity in transferring
learned representation from the largermodel (teacher) to the smaller one
(student).Despitemany efforts, prior methods ignore the important role
ofretaining inter-channel correlation of features, leading tothe lack of
capturing intrinsic distribution of the featurespace and sufficient diversity
properties of features in theteacher network.To solve the issue, we propose
thenovel Inter-Channel Correlation for Knowledge Distillation(ICKD), with which
the diversity and homology of the fea-ture space of the student network can
align with that ofthe teacher network. The correlation between these
twochannels is interpreted as diversity if they are irrelevantto each other,
otherwise homology. Then the student isrequired to mimic the correlation within
its own embed-ding space. In addition, we introduce the grid-level
inter-channel correlation, making it capable of dense predictiontasks.
Extensive experiments on two vision tasks, includ-ing ImageNet classification
and Pascal VOC segmentation,demonstrate the superiority of our ICKD, which
consis-tently outperforms many existing methods, advancing thestate-of-the-art
in the fields of Knowledge Distillation. Toour knowledge, we are the first
method based on knowl-edge distillation boosts ResNet18 beyond 72% Top-1
ac-curacy on ImageNet classification. Code is available
at:https://github.com/ADLab-AutoDrive/ICKD.


title:
Breakdown of Linear Spin-wave Theory and Existence of Spinon Bound
  States in the Frustrated Kagome Lattice Antiferromagnet

abstract:
  Spin dynamics of the spin-1/2 kagome lattice antiferromagnet
Cs$_2$Cu$_3$SnF$_{12}$ was studied using high-resolution, time-of-flight
inelastic neutron scattering. The flat mode, a characteristic of the frustrated
kagome antiferromagnet, and the low-energy dispersive mode, which is dominated
by magnons, can be well described by the linear spin-wave theory. However, the
theory fails to describe three weakly dispersive modes between 9 and 14 meV.
These modes could be attributed to two-spinon bound states, which decay into
free spinons away from the zone center and at a high temperature, giving rise
to continuum scattering.


title: The Legacy of Authoritarianism in a Democracy

abstract:
  Recent democratic backsliding and the rise of authoritarian regimes around
the world have rekindled interest in understanding the causes and consequences
of authoritarian rule in democracies. In this paper, I study the long-run
political consequences of authoritarianism in the context of India, the world's
largest democracy. Utilizing the unexpected timing of the authoritarian rule
imposed in the 1970s and the variation in a draconian policy implemented during
this period, I document a sharp decline in the share of the then incumbent
party's, the Indian National Congress, votes and the probability of its
candidates winning in subsequent elections. The decline in the incumbent
party's political dominance was not at the expense of a lower voter turnout
rate. Instead, a sharp rise in the number of opposition candidates contesting
for election in subsequent years played an important role. Finally, I examine
the enduring consequences, revealing that confidence in politicians remains low
in states where the draconian policy was high. Together, the evidence suggests
that authoritarianism in a democracy has a persistent effect on voting
behavior, political representation, and confidence in institutions.


title:
On the almost everywhere and norm convergences of N\"orlund means with
  respect to Vilenkin systems

abstract:
  Unlike the classical theory of Fourier series which deals with decomposition
of a function into sinusoidal waves the Vilenkin (Walsh) functions are
rectangular waves. The development of the theory of Vilenkin-Fourier series has
been strongly influenced by the classical theory of trigonometric series but
there are a lot of differences also. The aim of my master thesis is to discuss,
develop and apply the newest developments of this fascinating theory connected
to modern harmonic analysis. In particular, we investigate N\"orlund means but
only in the case when their coefficients are monotone and prove convergence in
Lebesgue and Vilenkin-Lebesgue points. Since almost everywhere points are
Lebesgue and Vilenkin-Lebesgue points for any integrable functions we obtain
almost everywhere convergence of such summability methods.


title: Efficiently Escaping Saddle Points in Bilevel Optimization

abstract:
  Bilevel optimization is one of the fundamental problems in machine learning
and optimization. Recent theoretical developments in bilevel optimization focus
on finding the first-order stationary points for nonconvex-strongly-convex
cases. In this paper, we analyze algorithms that can escape saddle points in
nonconvex-strongly-convex bilevel optimization. Specifically, we show that the
perturbed approximate implicit differentiation (AID) with a warm start strategy
finds $\epsilon$-approximate local minimum of bilevel optimization in
$\tilde{O}(\epsilon^{-2})$ iterations with high probability. Moreover, we
propose an inexact NEgative-curvature-Originated-from-Noise Algorithm (iNEON),
a pure first-order algorithm that can escape saddle point and find local
minimum of stochastic bilevel optimization. As a by-product, we provide the
first nonasymptotic analysis of perturbed multi-step gradient descent ascent
(GDmax) algorithm that converges to local minimax point for minimax problems.


title:
A Tale of Two Datasets: Representativeness and Generalisability of
  Inference for Samples of Networks

abstract:
  The last two decades have witnessed considerable progress on foundational
aspects of statistical network analysis, but less attention has been paid to
the complex statistical issues arising in real-world applications. Here, we
consider two samples of within-household contact networks in Belgium generated
by different but complementary sampling designs: one smaller but with all
contacts in each household observed, the other larger and more representative
but recording contacts of only one person per household. We wish to combine
their strengths to learn the social forces that shape household contact
formation and facilitate simulation for prediction of disease spread, while
generalising to the population of households in the region.
  To accomplish this, we introduce a flexible framework for specifying
multi-network models in the exponential family class and identify the
requirements for inference and prediction under this framework to be
consistent, identifiable, and generalisable, even when data are incomplete;
explore how these requirements may be violated in practice; and develop a suite
of quantitative and graphical diagnostics for detecting violations and
suggesting improvements to a candidate model. We report on the effects of
network size, geography, and household roles on household contact patterns
(activity, heterogeneity in activity, and triadic closure).


title:
Possible Origin of Preformed Hole Pairs and Superconductivity in
  Cuprates

abstract:
  This paper addresses the long standing and controversial issue of the origin
of superconductivity in cuprates. Their superconductivity can be attributed to
amphoteric defects associated with vacancy sites in copper oxide planes. A
local defect lattice relaxation results in a negative-$U$ energy binding two
holes on amphoteric defects in the donor configuration that act as preformed
boson pair. Thermodynamic equilibrium between defects in the donor and acceptor
configurations stabilizes Fermi energy at the amphoteric defect charge
transition state assuring resonant coupling between free holes and the
localized hole pairs. Model calculations show that the critical temperature is
primarily determined by the density of the amphoteric defects in the donor
configuration, explaining the ubiquity of dome-like dependence of the critical
temperature on the doping as well as its universal dependence on the superfluid
density. Intentional doping with chemical acceptors or donors is neither
necessary nor sufficient condition for superconductivity that is fully
determined by the amphoteric defects whose concentration can be controlled by
crystal nonstoichiometry. The only role of chemical doping is changing the
balance between concentrations of amphoteric defects in the donor and acceptor
configurations resulting in an increase of the superfluid density and thus also
the critical temperature for acceptor and a decrease for donor doping. This
accounts for the experimentally observed distinct asymmetry between the dome
structures for the chemical doping with acceptors and donors. The unusual
sensitivity of the critical temperature to external perturbations is explained
by the resonant nature of the coupling between free holes and preformed hole
pairs. The work has broader implications as it could be applicable to other
superconductors with dome-like dependence of the critical temperature on
doping.


title: CyberOps: Situational Awareness in Cybersecurity Operations

abstract:
  Cybersecurity operations, CyberOps, is the use and application of
cybersecurity capabilities to a domain, department, organisation or nation. It
is fundamentally to protect digital investments, contribute to national
economic wellbeing by providing a safe, secure and conducive environment to
conduct business and to protect national critical national infrastructures and
citizens welfare. In this paper, we investigate operational factors that
influence situational awareness of CyberOps, specifically, the features that
deals with understanding and comprehension of operational and human factors
aspects and that helps with insights on human operator decision making such as
cognition, teamwork, knowledge, skills and abilities. The operational factors
discussed in this paper range from tools, techniques, integration, architecture
to automation, cognition, people, policy, process and procedures.


title: Is interpolation benign for random forests?

abstract:
  Statistical wisdom suggests that very complex models, interpolating training
data, will be poor at prediction on unseen examples. Yet, this aphorism has
been recently challenged by the identification of benign overfitting regimes,
specially studied in the case of parametric models: generalization capabilities
may be preserved despite model high complexity. While it is widely known that
fully-grown decision trees interpolate and, in turn, have bad predictive
performances, the same behavior is yet to be analyzed for random forests. In
this paper, we study the trade-off between interpolation and consistency for
several types of random forest algorithms. Theoretically, we prove that
interpolation regimes and consistency cannot be achieved for non-adaptive
random forests. Since adaptivity seems to be the cornerstone to bring together
interpolation and consistency, we introduce and study interpolating Adaptive
Centered Forests, which are proved to be consistent in a noiseless scenario.
Numerical experiments show that Breiman's random forests are consistent while
exactly interpolating, when no bootstrap step is involved. We theoretically
control the size of the interpolation area, which converges fast enough to
zero, so that exact interpolation and consistency occur in conjunction.


title: A Decision Support Design Framework for Selecting a Robotic Interface

abstract:
  The design and development of robots involve the essential step of selecting
and testing robotic interfaces. This interface selection requires careful
consideration as the robot's physical embodiment influences and adds to the
traditional interfaces' complexities. Our paper presents a decision support
design framework for the a priori selection of robotic interface that was
inductively formulated from our case study of designing a robot to collaborate
with employees with cognitive disabilities. Our framework outlines the
interface requirements according to User, Robot, Tasks and Environment and
facilitates a structured comparison of interfaces against those requirements.
The framework is assessed for its potential applicability and usefulness
through a qualitative study with HRI experts. The framework is appreciated as a
systematic tool that enables documentation and discussion, and identified
issues inform the frameworks' iteration. The themes of ownership of this
process in interdisciplinary teams and its role in iteratively designing
interfaces are discussed.


title:
Probing a dissipative phase transition with a trapped ion through
  reservoir engineering

abstract:
  Dissipation is often considered as a detrimental effect in quantum systems
for unitary quantum operations. However, it has been shown that suitable
dissipation can be useful resources both in quantum information and quantum
simulation. Here, we propose and experimentally simulate a dissipative phase
transition (DPT) model using a single trapped ion with an engineered reservoir.
We show that the ion's spatial oscillation mode reaches a steady state after
the alternating application of unitary evolution under a quantum Rabi model
Hamiltonian and sideband cooling of the oscillator. The average phonon number
of the oscillation mode is used as the order parameter to provide evidence for
the DPT. Our work highlights the suitability of trapped ions for simulating
open quantum systems and shall facilitate further investigations of DPT with
various dissipation terms.


title:
Challenges towards Building an effective Cyber Security Operations
  Centre

abstract:
  The increasing dependency of modern society on IT systems and infrastructures
for essential services (e.g. internet banking, vehicular network, health-IT,
etc.) coupled with the growing number of cyber incidents and security
vulnerabilities have made Cyber Security Operations Centre (CSOC) undoubtedly
vital. As such security operations monitoring is now an integral part of most
business operations. SOCs (used interchangeably as CSOCs) are responsible for
continuously and protectively monitoring business services, IT systems and
infrastructures to identify vulnerabilities, detect cyber-attacks, security
breaches, policy violations, and to respond to cyber incidents swiftly. They
must also ensure that security events and alerts are triaged and analysed,
while coordinating and managing cyber incidents to resolution. Because SOCs are
vital, it is also necessary that SOCs are effective. But unfortunately, the
effectiveness of SOCs are a widespread concern and a focus of boundless debate.
In this paper, we identify and discuss some of the pertinent challenges to
building an effective SOC. We investigate some of the factors contributing to
the inefficiencies in SOCs and explain some of the challenges they face.
Further, we provide and prioritise recommendations to addressing the identified
issues.


title:
The factorization method and Capon's method for random source
  identification in experimental aeroacoustics

abstract:
  Experimental aeroacoustics is concerned with the estimation of acoustic
source power distributions, which are for instance caused by fluid structure
interactions on scaled aircraft models inside a wind tunnel, from microphone
array measurements of associated sound pressure fluctuations. In the frequency
domain aeroacoustic sound propagation can be modelled as a random source
problem for a convected Helmholtz equation. This article is concerned with the
inverse random source problem to recover the support of an uncorrelated
aeroacoustic source from correlations of observed pressure signals. We show a
variant of the factorization method from inverse scattering theory can be used
for this purpose. We also discuss a surprising relation between the
factorization method and a commonly used beamforming algorithm from
experimental aeroacoustics, which is known as Capon's method or as the minimum
variance method. Numerical examples illustrate our theoretical findings.


title:
Consistency between black hole and mimetic gravity -- Case of
  $(2+1)$-dimensional gravity

abstract:
  We show that the mimetic theory with the constraint $g^{\rho
\sigma}\partial_\rho\phi \partial_\sigma\phi=1$ cannot realize the black hole
geometry with the horizon(s). To overcome such issue, we may change the mimetic
constraint a little bit by $\omega(\phi) g^{\rho \sigma}\partial_\rho\phi
\partial_\sigma\phi=-1,$ where $\omega(\phi)$ is a function of the scalar field
$\phi$. As an example, we consider $(2+1)$-dimensional mimetic gravity with the
mimetic potential and construct black hole (BH) solutions by using this
modified constraint. We study three different classes: In the first class, we
assume the Lagrange multiplier and mimetic potential are vanishing and obtain a
BH solution that fully matches the BH of GR despite the non-triviality of the
mimetic field which ensures the study presented in {\it JCAP 01 (2019) 058}. In
the second class, we obtain a BH having constant mimetic potential and a
non-trivial form of the Lagrange multiplier. In the third class, we obtain a
new BH solution with non-vanishing values of the mimetic field, the Lagrange
multiplier, and the mimetic potential. In any case, the solutions correspond to
the space-time with only one horizon but we show that the formalism for the
constraint works.


title:
Determining the potential and the gradient coupling of two-state quantum
  systems in an infinite waveguide

abstract:
  We consider the inverse coefficient problem of simultaneously determining the
space dependent electric potential, the zero-th order coupling term and the
first order coupling vector of a two-state Schr\"odinger equation in an
infinite cylindrical domain of $\mathbb{R}^n$, $n \ge 2$, from finitely many
partial boundary measurements of the solution. We prove that these $n+1$
unknown scalar coefficients can be H\"older stably retrieved by $(n+1)$-times
suitably changing the initial condition attached at the system.


title:
Network Comparison Study of Deep Activation Feature Discriminability
  with Novel Objects

abstract:
  Feature extraction has always been a critical component of the computer
vision field. More recently, state-of-the-art computer visions algorithms have
incorporated Deep Neural Networks (DNN) in feature extracting roles, creating
Deep Convolutional Activation Features (DeCAF). The transferability of DNN
knowledge domains has enabled the wide use of pretrained DNN feature extraction
for applications with novel object classes, especially those with limited
training data. This study analyzes the general discriminability of novel object
visual appearances encoded into the DeCAF space of six of the leading visual
recognition DNN architectures. The results of this study characterize the
Mahalanobis distances and cosine similarities between DeCAF object manifolds
across two visual object tracking benchmark data sets. The backgrounds
surrounding each object are also included as an object classes in the manifold
analysis, providing a wider range of novel classes. This study found that
different network architectures led to different network feature focuses that
must to be considered in the network selection process. These results are
generated from the VOT2015 and UAV123 benchmark data sets; however, the
proposed methods can be applied to efficiently compare estimated network
performance characteristics for any labeled visual data set.


title:
Hybrid Kinetic/Fluid numerical method for the Vlasov-BGK equation in the
  diffusive scaling

abstract:
  This paper presents a hybrid numerical method for linear collisional kinetic
equations with diffusive scaling. The aim of the method is to reduce the
computational cost of kinetic equations by taking advantage of the lower
dimensionality of the asymptotic fluid model while reducing the error induced
by the latter approach. It relies on two criteria motivated by a pertubative
approach to obtain a dynamic domain decomposition. The first criterion
quantifies how far from a local equilibrium in velocity the distribution
function of particles is. The second one depends only on the macroscopic
quantities that are available on the whole computing domain. Interface
conditions are dealt with using a micro-macro decomposition and the method is
significantly more efficient than a standard full kinetic approach. Some
properties of the hybrid method are also investigated, such as the conservation
of mass.


title:
DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via
  online learning in a structured generative model

abstract:
  Visual servoing enables robotic systems to perform accurate closed-loop
control, which is required in many applications. However, existing methods
either require precise calibration of the robot kinematic model and cameras or
use neural architectures that require large amounts of data to train. In this
work, we present a method for unsupervised learning of visual servoing that
does not require any prior calibration and is extremely data-efficient. Our key
insight is that visual servoing does not depend on identifying the veridical
kinematic and camera parameters, but instead only on an accurate generative
model of image feature observations from the joint positions of the robot. We
demonstrate that with our model architecture and learning algorithm, we can
consistently learn accurate models from less than 50 training samples (which
amounts to less than 1 min of unsupervised data collection), and that such
data-efficient learning is not possible with standard neural architectures.
Further, we show that by using the generative model in the loop and learning
online, we can enable a robotic system to recover from calibration errors and
to detect and quickly adapt to possibly unexpected changes in the robot-camera
system (e.g. bumped camera, new objects).


title: Functional convergence to the local time of sticky diffusions

abstract:
  We prove the convergence of a class of high frequency path-functionals of a
sticky diffusion to its local time. First, we prove this for the sticky
Brownian motion. Then, we extend the result to sticky stochastic differential
equations. We combine the local time approximation with an approximation of the
occupation time to set up a consistent stickiness estimator. Last, we perform
numerical experiments to assess the properties of the stickiness estimator and
the local time approximation.


title:
Assessing Planetary Complexity and Potential Agnostic Biosignatures
  using Epsilon Machines

abstract:
  We present a new approach to exoplanet characterisation using techniques from
complexity science, with potential applications to biosignature detection. This
agnostic method makes use of the temporal variability of light reflected or
emitted from a planet. We use a technique known as epsilon machine
reconstruction to compute the statistical complexity, a measure of the minimal
model size for time series data. We demonstrate that statistical complexity is
an effective measure of the complexity of planetary features. Increasing levels
of qualitative planetary complexity correlate with increases in statistical
complexity and Shannon entropy, demonstrating that our approach can identify
planets with the richest dynamics. We also compare Earth time series with
Jupiter data, and find that for the three wavelengths considered, Earth's
average complexity and entropy rate are approximately 50% and 43% higher than
Jupiter's, respectively. The majority of schemes for the detection of
extraterrestrial life rely upon biochemical signatures and planetary context.
However, it is increasingly recognised that extraterrestrial life could be very
different to life on Earth. Under the hypothesis that there is a correlation
between the presence of a biosphere and observable planetary complexity, our
technique offers an agnostic and quantitative method for the measurement
thereof.


title: Bounds for regular induced subgraphs of strongly regular graphs

abstract:
  Given feasible strongly regular graph parameters $(v,k,\lambda,\mu)$ and a
non-negative integer $d$, we determine upper and lower bounds on the order of a
$d$-regular induced subgraph of any strongly regular graph with parameters
$(v,k,\lambda,\mu)$. Our new bounds are at least as good as the bounds on the
order of a $d$-regular induced subgraph of a $k$-regular graph determined by
Haemers. Further, we prove that for each non-negative integer $d$, our new
upper bound improves on Haemers' upper bound for infinitely many strongly
regular graphs.


title:
Distinguishing reionization models using the largest cluster statistics
  of the 21-cm maps

abstract:
  The evolution of topology and morphology of ionized or neutral hydrogen
during different stages of the Epoch of Reionization (EoR) have the potential
to provide us a great amount of information about the properties of the
ionizing sources during this era. We compare a variety of reionization source
models in terms of the geometrical properties of the ionized regions. We show
that the percolation transition in the ionized hydrogen, as studied by tracing
the evolution of the Largest Cluster Statistics (LCS), is a robust statistic
that can distinguish the fundamentally different scenarios -- inside-out and
outside-in reionization. Particularly, the global neutral fraction at the onset
of percolation is significantly higher for the inside-out scenario as compared
to that for the outside-in reionization. In complementary to percolation
analysis, we explore the shape and morphology of the ionized regions as they
evolve in different reionization models in terms of the Shapefinders (SFs) that
are ratios of the Minkowski functionals (MFs). The shape distribution can
readily discern the reionization scenario with extreme non-uniform
recombination in the IGM, such as the clumping model. In the rest of the
reionization models, the largest ionized region abruptly grows only in terms of
its third SF - 'length' - during percolation while the first two SFs -
'thickness' and 'breadth' - remain stable. Thus the ionized hydrogen in these
scenarios becomes highly filamentary near percolation and exhibit a
'characteristic cross-section' that varies among the source models. Therefore,
the geometrical studies based on SFs, together with the percolation analysis
can shed light on the reionization sources.


title:
Human-Robot Creative Interactions (HRCI): Exploring Creativity in
  Artificial Agents Using a Story-Telling Game

abstract:
  Creativity in social robots requires further attention in the
interdisciplinary field of Human-Robot Interaction (HRI). This paper
investigates the hypothesised connection between the perceived creative agency
and the animacy of social robots. The goal of this work is to assess the
relevance of robot movements in the attribution of creativity to robots. The
results of this work inform the design of future Human-Robot Creative
Interactions (HRCI). The study uses a storytelling game based on visual imagery
inspired by the game 'Story Cubes' to explore the perceived creative agency of
social robots. This game is used to tell a classic story for children with an
alternative ending. A 2x2 experiment was designed to compare two conditions:
the robot telling the original version of the story and the robot plot-twisting
the end of the story. A Robotis Mini humanoid robot was used for the
experiment. As a novel contribution, we propose an adaptation of the Short
Scale Creative Self scale (SSCS) to measure perceived creative agency in
robots. We also use the Godspeed scale to explore different attributes of
social robots in this setting. We did not obtain significant main effects of
the robot movements or the story in the participants' scores. However, we
identified significant main effects of the robot movements in features of
animacy, likeability, and perceived safety. This initial work encourages
further studies experimenting with different robot embodiment and movements to
evaluate the perceived creative agency in robots and inform the design of
future robots that participate in creative interactions.


title: Self-Paced Imbalance Rectification for Class Incremental Learning

abstract:
  Exemplar-based class-incremental learning is to recognize new classes while
not forgetting old ones, whose samples can only be saved in limited memory. The
ratio fluctuation of new samples to old exemplars, which is caused by the
variation of memory capacity at different environments, will bring challenges
to stabilize the incremental optimization process. To address this problem, we
propose a novel self-paced imbalance rectification scheme, which dynamically
maintains the incremental balance during the representation learning phase.
Specifically, our proposed scheme consists of a frequency compensation strategy
that adjusts the logits margin between old and new classes with the
corresponding number ratio to strengthen the expression ability of the old
classes, and an inheritance transfer strategy to reduce the representation
confusion by estimating the similarity of different classes in the old
embedding space. Furthermore, a chronological attenuation mechanism is proposed
to mitigate the repetitive optimization of the older classes at multiple
step-wise increments. Extensive experiments on three benchmarks demonstrate
stable incremental performance, significantly outperforming the
state-of-the-art methods.


title: Budgeted Combinatorial Multi-Armed Bandits

abstract:
  We consider a budgeted combinatorial multi-armed bandit setting where, in
every round, the algorithm selects a super-arm consisting of one or more arms.
The goal is to minimize the total expected regret after all rounds within a
limited budget. Existing techniques in this literature either fix the budget
per round or fix the number of arms pulled in each round. Our setting is more
general where based on the remaining budget and remaining number of rounds, the
algorithm can decide how many arms to be pulled in each round. First, we
propose CBwK-Greedy-UCB algorithm, which uses a greedy technique, CBwK-Greedy,
to allocate the arms to the rounds. Next, we propose a reduction of this
problem to Bandits with Knapsacks (BwK) with a single pull. With this
reduction, we propose CBwK-LPUCB that uses PrimalDualBwK ingeniously. We
rigorously prove regret bounds for CBwK-LP-UCB. We experimentally compare the
two algorithms and observe that CBwK-Greedy-UCB performs incrementally better
than CBwK-LP-UCB. We also show that for very high budgets, the regret goes to
zero.


title: A Survey on Sematic Communications for Intelligent Wireless Networks

abstract:
  With deployment of 6G technology, it is envisioned that competitive edge of
wireless networks will be sustained and next decade's communication
requirements will be stratified. Also 6G will aim to aid development of a human
society which is ubiquitous and mobile, simultaneously providing solutions to
key challenges such as, coverage, capacity, etc. In addition, 6G will focus on
providing intelligent use-cases and applications using higher data-rates over
mill-meter waves and Tera-Hertz frequency. However, at higher frequencies
multiple non-desired phenomena such as atmospheric absorption, blocking, etc.,
occur which create a bottleneck owing to resource (spectrum and energy)
scarcity. Hence, following same trend of making efforts towards reproducing at
receiver, exact information which was sent by transmitter, will result in a
never ending need for higher bandwidth. A possible solution to such a challenge
lies in semantic communications which focuses on meaning (context) of received
data as opposed to only reproducing correct transmitted data. This in turn will
require less bandwidth, and will reduce bottleneck due to various undesired
phenomenon. In this respect, current article presents a detailed survey on
recent technological trends in regard to semantic communications for
intelligent wireless networks. We focus on semantic communications architecture
including model, and source and channel coding. Next, we detail cross-layer
interaction, and various goal-oriented communication applications. We also
present overall semantic communications trends in detail, and identify
challenges which need timely solutions before practical implementation of
semantic communications within 6G wireless technology. Our survey article is an
attempt to significantly contribute towards initiating future research
directions in area of semantic communications for intelligent 6G wireless
networks.


title: Temporal Walk Centrality: Ranking Nodes in Evolving Networks

abstract:
  We propose the Temporal Walk Centrality, which quantifies the importance of a
node by measuring its ability to obtain and distribute information in a
temporal network. In contrast to the widely-used betweenness centrality, we
assume that information does not necessarily spread on shortest paths but on
temporal random walks that satisfy the time constraints of the network. We show
that temporal walk centrality can identify nodes playing central roles in
dissemination processes that might not be detected by related betweenness
concepts and other common static and temporal centrality measures. We propose
exact and approximation algorithms with different running times depending on
the properties of the temporal network and parameters of our new centrality
measure. A technical contribution is a general approach to lift existing
algebraic methods for counting walks in static networks to temporal networks.
Our experiments on real-world temporal networks show the efficiency and
accuracy of our algorithms. Finally, we demonstrate that the rankings by
temporal walk centrality often differ significantly from those of other
state-of-the-art temporal centralities.


title: A modal approach to quantum temporal imaging

abstract:
  We consider the problem of quantum temporal imaging in the case where the
time lens is implemented by a Sum Frequency Generation nonlinear process, in
particular when the device is operated close to 100% conversion efficiency. In
the general case where the time lens also presents a finite aperture and a
non-perfect phase-matching the relevant figures of merit, as for example the
temporal resolution, do not have an explicit expression. As a consequence, the
performances of imaging scheme are difficult to assess. Having a reliable
estimation of these figures of merits is particularly important because they
can be significantly different from the regime of low conversion efficiency
usually considered in classical temporal imaging. We show that this problem can
be approached in terms of the eigenmodes of the imaging scheme and we show how
its relevant figures of merit can be extracted from the modal description of
the imaging scheme. As a consequence we obtain criteria allowing to design
imaging schemes with close to unity efficiencies.


title:
Electric and Magnetic Properties of Higher-Spin Kondo-Heisenberg Models
  at Strong Coupling

abstract:
  We study higher-spin ($S \geq 1$) generalization of the one-dimensional
Kondo-Heisenberg model, in which the local spin-$S$ moments of the Kondo
lattice model interact with each other via the antiferromagnetic Heisenberg
interaction ($J_{\text{H}}$), by analytical and numerical methods. The
strong-coupling (i.e., large Kondo-coupling) expansion maps out an insulating
phase at half-filling whose magnetic correlation depends on the parity of $2S$
as well as a ferromagnetic metallic phase which dominates the strong-coupling
region at generic fillings. Then, we carried out the Density-Matrix
Renormalization Group (DMRG) simulations for $S=1$ to closely investigate the
phase structure at large but finite Kondo coupling. At half-filling, the Kondo
coupling and $J_{\text{H}}$ do not compete and the insulating spin-gapless
phase is stable, while the competition of the two leads to a stepwise collapse
of the strong-coupling ferromagnetism via an intervening dimerized insulating
phase with power-law spin correlation at quarter-filling.


title:
Symmetric states for $C^*$-Fermi systems II: Klein transformation and
  their structure

abstract:
  In the present note, which is the second part of a work concerning the study
of the set of the symmetric states, we introduce the extension of the Klein
transformation for general Fermi tensor product of two $\bz^2$ graded
$C^*$-algebras, under the condition that the grading of one of the involved
algebras is inner. After extending the construction to $C^*$-inductive limits,
such a Klein transformation realises a canonical $*$-isomorphism between two
$\bz^2$-graded $C^*$-algebras made of the infinite Fermi $C^*$-tensor product
and the infinite $C^*$-tensor product of a single $\bz^2$-graded $C^*$-algebra,
both built with respect to the corresponding minimal $C^*$-cross norms. It
preserves the grading, and its transpose sends even product states of $\ga_{\rm
X}$ in (necessarily even) product states on $\ga_{\rm F}$, and therefore
induces an isomorphism of simplexes $$ \cs_\bp(\ga_{\rm
F})=\cs_{\bp\times\bz^2}(\ga_{\rm F})\sim\cs_{\bp\times\bz^2}(\ga_{\rm X})\,,
$$ which allows to reduce the study of the structure of the symmetric states
for $C^*$-Fermi systems to the corresponding even symmetric states on the usual
infinite $C^*$-tensor product. Other relevant properties of symmetric states on
the Fermi algebra will be proved without the use of the Klein transformation.
  We end with an example for which such a Klein transformation is not
implementable, simply because the Fermi tensor product does not generate a
usual tensor product. Therefore, in general, the study of the symmetric states
on the Fermi algebra cannot be reduced to that of the corresponding symmetric
states on the usual infinite tensor product, even if both share many common
properties.


title: Magnetic quantum currents in the presence of a Neumann wall

abstract:
  We consider the Schr\"odinger operator with constant transverse magnetic
field on a half-plane, endowed with Neumann boundary conditions. We study the
low energy currents flowing along the boundary and we establish a Limiting
Absorption Principle for the magnetic Neumann Laplacian under perturbation of
an electric potential.


title: Rate-Distortion Theory for Strategic Semantic Communication

abstract:
  This paper analyzes the fundamental limit of the strategic semantic
communication problem in which a transmitter obtains a limited number of
indirect observation of an intrinsic semantic information source and can then
influence the receiver's decoding by sending a limited number of messages to an
imperfect channel. The transmitter and the receiver can have different
distortion measures and can make rational decision about their encoding and
decoding strategies, respectively. The decoder can also have some side
information (e.g., background knowledge and/or information obtained from
previous communications) about the semantic source to assist its interpretation
of the semantic information. We focus particularly on the case that the
transmitter can commit to an encoding strategy and study the impact of the
strategic decision making on the rate distortion of semantic communication.
Three equilibrium solutions including the strong Stackelberg equilibrium, weak
Stackelberg equilibrium, as well as Nash equilibrium have been studied and
compared. The optimal encoding and decoding strategy profiles under various
equilibrium solutions have been derived. We prove that committing to an
encoding strategy cannot always bring benefit to the encoder. We therefore
propose a feasible condition under which committing to an encoding strategy can
always reduce the distortion performance of semantic communication.


title:
Fourier Representations for Black-Box Optimization over Categorical
  Variables

abstract:
  Optimization of real-world black-box functions defined over purely
categorical variables is an active area of research. In particular,
optimization and design of biological sequences with specific functional or
structural properties have a profound impact in medicine, materials science,
and biotechnology. Standalone search algorithms, such as simulated annealing
(SA) and Monte Carlo tree search (MCTS), are typically used for such
optimization problems. In order to improve the performance and sample
efficiency of such algorithms, we propose to use existing methods in
conjunction with a surrogate model for the black-box evaluations over purely
categorical variables. To this end, we present two different representations, a
group-theoretic Fourier expansion and an abridged one-hot encoded Boolean
Fourier expansion. To learn such representations, we consider two different
settings to update our surrogate model. First, we utilize an adversarial online
regression setting where Fourier characters of each representation are
considered as experts and their respective coefficients are updated via an
exponential weight update rule each time the black box is evaluated. Second, we
consider a Bayesian setting where queries are selected via Thompson sampling
and the posterior is updated via a sparse Bayesian regression model (over our
proposed representation) with a regularized horseshoe prior. Numerical
experiments over synthetic benchmarks as well as real-world RNA sequence
optimization and design problems demonstrate the representational power of the
proposed methods, which achieve competitive or superior performance compared to
state-of-the-art counterparts, while improving the computation cost and/or
sample efficiency, substantially.


title:
On the minimum of independent collecting processes via the Stirling
  numbers of the second kind

abstract:
  We consider the combinatorial problem where $p$ players aim to a complete set
of $N$ different types of items (species) which are uniformly distributed. Let
the random variables $T_{N(i)},\,\,i=1,2,\cdots,p$ denoting the number of
trials needed until all $N$ types are detected (at least once), respectively
for each player. This paper studies the impact of the number $p$ in the
asymptotics of the expectation, the second moment, and the variance of the
random variable \begin{equation*} M_{N(p)}: = \bigwedge_{i=1}^p
T_{N(i)},\,\,\,\,\,\,N\rightarrow \infty. \end{equation*} The main ingredient
in the expression of these quantittes are sums involving the Stirling numbers
of the second kind; for which the asymptotics are explored. At the end of the
paper we conjecture on a remarkable \textit{combinatorial identity}, regarding
alternating binomial sums. These sums have been studied (mainly) by P. Flajolet
due to their applications to digital search trees and quadtrees.


title:
What's Cracking? A Review and Analysis of Deep Learning Methods for
  Structural Crack Segmentation, Detection and Quantification

abstract:
  Surface cracks are a very common indicator of potential structural faults.
Their early detection and monitoring is an important factor in structural
health monitoring. Left untreated, they can grow in size over time and require
expensive repairs or maintenance. With recent advances in computer vision and
deep learning algorithms, the automatic detection and segmentation of cracks
for this monitoring process have become a major topic of interest. This review
aims to give researchers an overview of the published work within the field of
crack analysis algorithms that make use of deep learning. It outlines the
various tasks that are solved through applying computer vision algorithms to
surface cracks in a structural health monitoring setting and also provides
in-depth reviews of recent fully, semi and unsupervised approaches that perform
crack classification, detection, segmentation and quantification. Additionally,
this review also highlights popular datasets used for cracks and the metrics
that are used to evaluate the performance of those algorithms. Finally,
potential research gaps are outlined and further research directions are
provided.


title:
Evidence for Cold-stream to Hot-accretion Transition as Traced by
  Ly{\alpha} Emission from Groups and Clusters at 2 < z < 3.3

abstract:
  We present Keck Cosmic Web Imager (KCWI) observations of giant Lya halos
surrounding 9 galaxy groups and clusters at 2<z<3.3, including five new
detections and one upper limit. We find observational evidence for the
cold-stream to hot-accretion transition predicted by theory by measuring a
decrease in the ratio between the spatially extended Lya luminosity and the
expected baryonic accretion rate (BAR), with increasing elongation above the
transition mass Mstream). This implies a modulation of the share of BAR that
remains cold diminishing quasi-linearly (logarithmic slope of 0.97+-0.19, 5
sigma significance) with the halo to Mstream mass ratio. The integrated
star-formation rates (SFRs) and AGN bolometric luminosities display a
potentially consistent decrease, albeit significant only at 2.6 sigma and 1.3
sigma, respectively. The higher scatter in these tracers suggests the Lya
emission might be mostly a direct product of cold accretion in these structures
rather than indirect, mediated by outflows and photo-ionization from SFR and
AGNs; this is also supported by energetics considerations. Below Mstream
(cold-stream regime) we measure LLya/BAR=10^{40.51+-0.16}~erg/s/Msun*yr,
consistent with predictions, and SFR/BAR=10^{-0.54+-0.23}: on average
30_{-10}^{+20}% of the cold streams go into stars. Above Mstream (hot-accretion
regime), LLya is set by Mstream (within 0.2~dex scatter in our sample),
independent of the halo mass but rising tenfold from z=2 to 3.


title:
Binary Neural Networks as a general-propose compute paradigm for
  on-device computer vision

abstract:
  For binary neural networks (BNNs) to become the mainstream on-device computer
vision algorithm, they must achieve a superior speed-vs-accuracy tradeoff than
8-bit quantization and establish a similar degree of general applicability in
vision tasks. To this end, we propose a BNN framework comprising 1) a
minimalistic inference scheme for hardware-friendliness, 2) an
over-parameterized training scheme for high accuracy, and 3) a simple procedure
to adapt to different vision tasks. The resultant framework overtakes 8-bit
quantization in the speed-vs-accuracy tradeoff for classification, detection,
segmentation, super-resolution and matching: our BNNs not only retain the
accuracy levels of their 8-bit baselines but also showcase 1.3-2.4$\times$
faster FPS on mobile CPUs. Similar conclusions can be drawn for prototypical
systolic-array-based AI accelerators, where our BNNs promise 2.8-7$\times$
fewer execution cycles than 8-bit and 2.1-2.7$\times$ fewer cycles than
alternative BNN designs. These results suggest that the time for large-scale
BNN adoption could be upon us.


title: Asymptotic Growth and Decay of Two-Dimensional Symmetric Plasmas

abstract:
  We study the large time behavior of classical solutions to the
two-dimensional Vlasov-Poisson (VP) and relativistic Vlasov-Poisson (RVP)
systems launched by radially-symmetric initial data with compact support. In
particular, we prove that particle positions and momenta grow unbounded as $t
\to \infty$ and obtain sharp rates on the maximal values of these quantities on
the support of the distribution function for each system. Furthermore, we
establish nearly sharp rates of decay for the associated electric field, as
well as upper and lower bounds on the decay rate of the charge density in the
large time limit. We prove that, unlike (VP) in higher dimensions, smooth
solutions do not scatter to their free-streaming profiles as $t \to \infty$
because nonlinear, long-range field interactions dominate the behavior of
characteristics due to the exchange of energy from the potential to the kinetic
term. In this way, the system "forgets" any previous configuration of
particles.


title: Spectrum, algebraicity and normalization in alternate bases

abstract:
  The first aim of this article is to give information about the algebraic
properties of alternate bases $\boldsymbol{\beta}=(\beta_0,\dots,\beta_{p-1})$
determining sofic systems. We show that a necessary condition is that the
product $\delta=\prod_{i=0}^{p-1}\beta_i$ is an algebraic integer and all of
the bases $\beta_0,\ldots,\beta_{p-1}$ belong to the algebraic field ${\mathbb
Q}(\delta)$. On the other hand, we also give a sufficient condition: if
$\delta$ is a Pisot number and $\beta_0,\ldots,\beta_{p-1}\in {\mathbb
Q}(\delta)$, then the system associated with the alternate base
$\boldsymbol{\beta}=(\beta_0,\dots,\beta_{p-1})$ is sofic. The second aim of
this paper is to provide an analogy of Frougny's result concerning
normalization of real bases representations. We show that given an alternate
base $\boldsymbol{\beta}=(\beta_0,\dots,\beta_{p-1})$ such that $\delta$ is a
Pisot number and $\beta_0,\ldots,\beta_{p-1}\in {\mathbb Q}(\delta)$, the
normalization function is computable by a finite B\"uchi automaton, and
furthermore, we effectively construct such an automaton. An important tool in
our study is the spectrum of numeration systems associated with alternate
bases. The spectrum of a real number $\delta>1$ and an alphabet $A\subset
{\mathbb Z}$ was introduced by Erd\H{o}s et al. For our purposes, we use a
generalized concept with $\delta\in{\mathbb C}$ and $A\subset{\mathbb C}$ and
study its topological properties.


title:
Well-posedness for a class of compressible non-Newtonian fluids
  equations

abstract:
  The purpose of this paper is to deal with the issue of well-posedness for a
class of non-Newtonian fluid dynamics equations. These sets of equations are
commonly used to describe various complex models that appear in nature,
industry, and biology. The equations describing the motion of such fluids are
characterized by a non-linear constitutive law relating the state of stress to
the rate of deformation. We show the local-in-time existence and uniqueness of
strong solutions to two important models: the Power Law model and the Bingham
model. While our result for the first model holds over a periodic domain
$\Omega=\mathbb{R}^3,$ the result obtained on the second model is limited to
the one-dimensional case. This is because Bingham's constitutive law is
discontinuous due to phase transition that may appear during the time when
flows change nature, particularly from liquid motion to rigid motion and
vice-versa. This property reduces the probability of showing smooth solutions
to such a system in higher dimension space.


title: Levy geometric graphs

abstract:
  We present a new family of graphs with remarkable properties. They are
obtained by connecting the points of a random walk when their distance is
smaller than a given scale. Their degree (number of neighbors) does not depend
on the graphs' size but only on the considered scale. It follows a Gamma
distribution and thus presents an exponential decay. Levy flights are
particular random walks with some power-law increments of infinite variance.
When building the geometric graphs from them, we show from dimensional
arguments, that the number of clusters follows an inverse power of the scale.
When the scale increases, these graphs never tend towards a single cluster (the
giant component). In other words, they do not undergo a phase transition of
percolation type. Moreover, the distribution of the size of the clusters,
properly normalized, is scale-invariant, which reflects the self-similar nature
of the underlying process. This invariance makes it possible to link them to
more abstract graphs without a metric (like social ones) characterized only by
the size of their clusters. The Levy graphs may find applications in community
structure analysis, and in modeling power-law interacting systems which,
although inherently scale-free, are still analyzed at some resolution.


title:
Predicting and Visualizing Daily Mood of People Using Tracking Data of
  Consumer Devices and Services

abstract:
  Users can easily export personal data from devices (e.g., weather station and
fitness tracker) and services (e.g., screentime tracker and commits on GitHub)
they use but struggle to gain valuable insights. To tackle this problem, we
present the self-tracking meta app called InsightMe, which aims to show users
how data relate to their wellbeing, health, and performance. This paper focuses
on mood, which is closely associated with wellbeing. With data collected by one
person, we show how a person's sleep, exercise, nutrition, weather, air
quality, screentime, and work correlate to the average mood the person
experiences during the day. Furthermore, the app predicts the mood via multiple
linear regression and a neural network, achieving an explained variance of 0.55
and 0.50, respectively. We strive for explainability and transparency by
showing the users p-values of the correlations, drawing prediction intervals.
In addition, we conducted a small A-B test on illustrating how the original
data influence predictions. The source code and app are available online.


title: Exact Decomposition of Multifrequency Discrete Real and Complex Signals

abstract:
  'The spectral leakage from windowing and the picket fence effect from
discretization' have been among the standard contents in textbooks for many
decades. The spectral leakage and picket fence effect would cause the
distortions in amplitude, frequency, and phase of signals, which have always
been of concern, and attempts have been made to solve them. This paper proposes
two novel decomposition theorems that can totally eliminate the spectral
leakage and picket fence effect, and could broaden the knowledge of signal
processing. First, two generalized eigenvalue equations are constructed for
multifrequency discrete real signals and complex signals. The two decomposition
theorems are then proved. On these bases, exact decomposition methods for real
and complex signals are proposed. For a noise-free multifrequency real signal
with m sinusoidal components, the frequency, amplitude, and phase of each
component can be exactly calculated by using just 4m-1 discrete values and its
second-order derivatives. For a multifrequency complex signal, only 2m-1
discrete values and its first-order derivatives are needed. The numerical
experiments show that the proposed methods have very high resolution, and the
sampling rate does not necessarily obey the Nyquist sampling theorem. With
noisy signals, the proposed methods have extraordinary accuracy.


title: Hair Color Digitization through Imaging and Deep Inverse Graphics

abstract:
  Hair appearance is a complex phenomenon due to hair geometry and how the
light bounces on different hair fibers. For this reason, reproducing a specific
hair color in a rendering environment is a challenging task that requires
manual work and expert knowledge in computer graphics to tune the result
visually. While current hair capture methods focus on hair shape estimation
many applications could benefit from an automated method for capturing the
appearance of a physical hair sample, from augmented/virtual reality to hair
dying development. Building on recent advances in inverse graphics and material
capture using deep neural networks, we introduce a novel method for hair color
digitization. Our proposed pipeline allows capturing the color appearance of a
physical hair sample and renders synthetic images of hair with a similar
appearance, simulating different hair styles and/or lighting environments.
Since rendering realistic hair images requires path-tracing rendering, the
conventional inverse graphics approach based on differentiable rendering is
untractable. Our method is based on the combination of a controlled imaging
device, a path-tracing renderer, and an inverse graphics model based on
self-supervised machine learning, which does not require to use differentiable
rendering to be trained. We illustrate the performance of our hair digitization
method on both real and synthetic images and show that our approach can
accurately capture and render hair color.


title:
Odderon observation: explanations and answers to questions/objections
  regarding the PRL publication

abstract:
  The odderon observation recently published by the D0 and TOTEM collaborations
has been widely accepted by a majority of the particle physics community and
its importance recognized through dedicated physics seminars in the world major
labs and physics institute. Naturally also some questions and objections have
been raised, either privately or publicly, in discussion sessions and articles.
In this proceedings article, a comprehensive list of these questions and
objections are answered and supplementary material is provided. The methods and
assumptions used in the extrapolation of the $pp$ elastic differential cross
section to $\sqrt{s}$ = 1.96 TeV and its comparison to the D0 measurement in
$p\bar{p}$ are shown to be valid and reasonable. Likewise, the methods and
choices used for the $\rho$ measurement at LHC. Furthermore, objections against
the odderon interpretation are demonstrated not to be valid. Finally, the
combination of the different odderon significances, leading to the first
experimental observation of odderon exchange, is shown to be well founded.


title:
A two-step approach to leverage contextual data: speech recognition in
  air-traffic communications

abstract:
  Automatic Speech Recognition (ASR), as the assistance of speech communication
between pilots and air-traffic controllers, can significantly reduce the
complexity of the task and increase the reliability of transmitted information.
ASR application can lead to a lower number of incidents caused by
misunderstanding and improve air traffic management (ATM) efficiency.
Evidently, high accuracy predictions, especially, of key information, i.e.,
callsigns and commands, are required to minimize the risk of errors. We prove
that combining the benefits of ASR and Natural Language Processing (NLP)
methods to make use of surveillance data (i.e. additional modality) helps to
considerably improve the recognition of callsigns (named entity). In this
paper, we investigate a two-step callsign boosting approach: (1) at the 1 step
(ASR), weights of probable callsign n-grams are reduced in G.fst and/or in the
decoding FST (lattices), (2) at the 2 step (NLP), callsigns extracted from the
improved recognition outputs with Named Entity Recognition (NER) are correlated
with the surveillance data to select the most suitable one. Boosting callsign
n-grams with the combination of ASR and NLP methods eventually leads up to
53.7% of an absolute, or 60.4% of a relative, improvement in callsign
recognition.


title: Cyrus 2D Simulation Team Description Paper 2016

abstract:
  This description includes some explanation about algorithms and also
algorithms that are being implemented by Cyrus team members. The objectives of
this description are to express a brief explanation about shoot, block, mark
and defensive decision will be given. It also explained about the parts that
has been implemented. The base code that Cyrus used is agent 3.11.


title:
Assessment of nanoparticle immersion depth at liquid interfaces from
  chemically equivalent macroscopic surfaces

abstract:
  Hypothesis: We test whether the wettability of nanoparticles (NPs) straddling
at an air/water surface or oil/water interface can be extrapolated from sessile
drop-derived macroscopic contact angles (mCAs) on planar substrates, assuming
that both the nanoparticles and the macroscopic substrates are chemically
equivalent and feature the same electrokinetic potential. Experiments: Pure
silica (SiO2) and amino-terminated silica (APTES-SiO2) NPs are compared to
macroscopic surfaces with extremely low roughness (root mean square [RMS]
roughness <= 2 nm) or a roughness determined by a close-packed layer of NPs
(RMS roughness about 35 nm). Equivalence of the surface chemistry is assessed
by comparing the electrokinetic potentials of the NPs via electrophoretic light
scattering and of the macroscopic substrates via streaming current analysis.
The wettability of the macroscopic substrates is obtained from advancing (ACAs)
and receding contact angles (RCAs) and in situ synchrotron X-ray reflectivity
(XRR) provided by the NP wettability at the liquid interfaces. Findings:
Generally, the RCA on smooth surfaces provides a good estimate of NP wetting
properties. However, mCAs alone cannot predict adsorption barriers that prevent
NP segregation to the interface, as is the case with the pure SiO2
nanoparticles. This strategy greatly facilitates assessing the wetting
properties of NPs for applications such as emulsion formulation, flotation, or
water remediation.


title:
The Weights can be Harmful: Pareto Search versus Weighted Search in
  Multi-Objective Search-Based Software Engineering

abstract:
  In presence of multiple objectives to be optimized in Search-Based Software
Engineering (SBSE), Pareto search has been commonly adopted. It searches for a
good approximation of the problem's Pareto optimal solutions, from which the
stakeholders choose the most preferred solution according to their preferences.
However, when clear preferences of the stakeholders (e.g., a set of weights
which reflect relative importance between objectives) are available prior to
the search, weighted search is believed to be the first choice since it
simplifies the search via converting the original multi-objective problem into
a single-objective one and enable the search to focus on what only the
stakeholders are interested in.
  This paper questions such a "weighted search first" belief. We show that the
weights can, in fact, be harmful to the search process even in the presence of
clear preferences. Specifically, we conduct a large scale empirical study which
consists of 38 systems/projects from three representative SBSE problems,
together with two types of search budget and nine sets of weights, leading to
604 cases of comparisons. Our key finding is that weighted search reaches a
certain level of solution quality by consuming relatively less resources at the
early stage of the search; however, Pareto search is at the majority of the
time (up to 77% of the cases) significantly better than its weighted
counterpart, as long as we allow a sufficient, but not unrealistic search
budget. This, together with other findings and actionable suggestions in the
paper, allows us to codify pragmatic and comprehensive guidance on choosing
weighted and Pareto search for SBSE under the circumstance that clear
preferences are available. All code and data can be accessed at:
https://github.com/ideas-labo/pareto-vs-weight-for-sbse.


title: On two questions of James

abstract:
  64 years ago, I. M. James raised two fundamental questions about octonionic
Stiefel spaces. The prime objective of this paper is to figure out partial
answers to both of them.


title: Computing H-Partitions in ASP and Datalog

abstract:
  A $H$-partition of a finite undirected simple graph $G$ is a labeling of
$G$'s vertices such that the constraints expressed by the model graph $H$ are
satisfied. For every model graph $H$, it can be decided in non-deterministic
polynomial time whether a given input graph $G$ admits a $H$-partition.
Moreover, it has been shown by Dantas et al. that for most model graphs, this
decision problem is in deterministic polynomial time. In this paper, we show
that these polynomial-time algorithms for finding $H$-partitions can be
expressed in Datalog with stratified negation. Moreover, using the answer set
solver Clingo, we have conducted experiments to compare straightforward
guess-and-check programs with Datalog programs. Our experiments indicate that
in Clingo, guess-and-check programs run faster than their equivalent Datalog
programs.


title:
Expanding Edges of Quantum Hall Systems in a Cosmology Language --
  Hawking Radiation from de Sitter Horizon in Edge Modes

abstract:
  Expanding edge experiments are promising to open new physics windows of
quantum Hall systems. In a static edge, the edge excitation, which is described
by free fields decoupled with the bulk dynamics, is gapless, and the dynamics
preserve conformal symmetry. When the edge expands, such properties need not be
preserved. We formulate a quantum field theory in 1+1 dimensional curved
spacetimes to analyze the edge dynamics. We propose methods to address the
following questions using edge waveforms from the expanding region: Does the
conformal symmetry survive? Is the nonlinear interaction of the edge
excitations induced by edge expansion? Do the edge excitations interact with
the bulk excitations? We additionally show that the expanding edges can be
regarded as expanding universe simulators of two-dimensional dilaton-gravity
models, including the Jackiw-Teitelboim gravity model. As an application, we
point out that our theoretical setup might simulate emission of analog Hawking
radiation with the Gibbons-Hawking temperature from the future de Sitter
horizon formed in the expanding edge region.


title: Defective incidence coloring of graphs

abstract:
  We define the $d$-defective incidence chromatic number of a graph,
generalizing the notion of incidence chromatic number, and determine it for
some classes of graphs including trees, complete bipartite graphs, complete
graphs, and outerplanar graphs. Fast algorithms for constructing the optimal
$d$-defective incidence colorings of those graphs are presented.


title:
Phase retrieval of entire functions and its implications for Gabor phase
  retrieval

abstract:
  In this paper, we consider the full or partial recovery of entire functions
from magnitude measurements on different subsets of the complex plane. This
so-called phase retrieval for entire functions is inspired by its manifold
connections to other phase retrieval problems. A particular connection,
illuminated in more detail in this paper, is that to Gabor phase retrieval
which can be made by using the Bargmann transform and the Fock space.
  By applying well-known techniques from complex analysis -- in particular, the
famous Hadamard factorisation theorem -- we develop many known and numerous new
results for the phase retrieval of entire functions. Among other things, we
provide a full classification of all (finite order) entire functions whose
magnitudes agree on two arbitrary lines in the complex plane as well as a full
classification of all entire functions of exponential type whose magnitudes
agree on infinitely many equidistant parallel lines.
  Our results have interesting implications for Gabor phase retrieval such as
giving a full classification of all signals whose Gabor magnitudes agree on two
arbitrary lines in the time-frequency plane; or, yielding a machinery with
which to generate signals whose Gabor magnitudes agree on infinitely many
equidistant lines. The latter has already been harnessed to propose certain
counterexamples to sampled Gabor phase retrieval in the literature. We show
here that one may also apply this machinery to generate so-called "universal
counterexamples": signals which cannot be recovered (up to global phase) from
magnitude measurements of their Gabor transforms on infinitely many equidistant
parallel lines -- no matter how small the distance between those lines.


title:
Cascaded Debiasing : Studying the Cumulative Effect of Multiple
  Fairness-Enhancing Interventions

abstract:
  Understanding the cumulative effect of multiple fairness enhancing
interventions at different stages of the machine learning (ML) pipeline is a
critical and underexplored facet of the fairness literature. Such knowledge can
be valuable to data scientists/ML practitioners in designing fair ML pipelines.
This paper takes the first step in exploring this area by undertaking an
extensive empirical study comprising 60 combinations of interventions, 9
fairness metrics, 2 utility metrics (Accuracy and F1 Score) across 4 benchmark
datasets. We quantitatively analyze the experimental data to measure the impact
of multiple interventions on fairness, utility and population groups. We found
that applying multiple interventions results in better fairness and lower
utility than individual interventions on aggregate. However, adding more
interventions do no always result in better fairness or worse utility. The
likelihood of achieving high performance (F1 Score) along with high fairness
increases with larger number of interventions. On the downside, we found that
fairness-enhancing interventions can negatively impact different population
groups, especially the privileged group. This study highlights the need for new
fairness metrics that account for the impact on different population groups
apart from just the disparity between groups. Lastly, we offer a list of
combinations of interventions that perform best for different fairness and
utility metrics to aid the design of fair ML pipelines.


title: Navigating to Objects in Unseen Environments by Distance Prediction

abstract:
  Object Goal Navigation (ObjectNav) task is to navigate an agent to an object
instance in unseen environments. The traditional navigation paradigm plans the
shortest path on a pre-built map. Inspired by this, we propose an object goal
navigation framework, which could directly perform path planning based on an
estimated distance map. Specifically, our model takes a birds-eye-view semantic
map as input, and estimates the distance from the map cells to the target
object based on the learned prior knowledge. With the estimated distance map,
the agent could explore the environment and navigate to the target objects
based on either human-designed or learned navigation policy. Empirical results
in visually realistic simulation environments show that the proposed method
outperforms a wide range of baselines on success rate and efficiency.


title:
Effect of an External Electric Field on the Propagation Velocity of
  Premixed Flames

abstract:
  There have been many experimental investigations into the ability of electric
fields to enhance combustion by acting upon ion species present in flames [1].
In this work, we examine this phenomenon using a one-dimensional model of a
lean premixed flame under the influence of a longitudinal electric field. We
expand upon prior two-step chain-branching reaction laminar models with
reactions to model the creation and consumption of both a positively-charged
radical species and free electrons. Also included are the electromotive force
in the conservation equation for ion species and the electrostatic form of the
Maxwell equations in order to resolve ion transport by externally applied and
internally induced electric fields. The numerical solution of these equations
allows us to compute changes in flame speed due to electric fields. Further,
the variation of key kinetic and transport parameters modifies the electrical
sensitivity of the flame. From changes in flame speed and reactant profiles we
are able to gain novel, valuable insight into how and why combustion can be
controlled by electric fields.


title:
A Survey of Breast Cancer Screening Techniques: Thermography and
  Electrical Impedance Tomography

abstract:
  Breast cancer is a disease that threatens many women's life, thus, early and
accurate detection plays a key role in reducing the mortality rate. Mammography
stands as the reference technique for breast cancer screening; nevertheless,
many countries still lack access to mammograms due to economic, social, and
cultural issues. Last advances in computational tools, infrared cameras, and
devices for bio-impedance quantification allowed the development of parallel
techniques like thermography, infrared imaging, and electrical impedance
tomography, these being faster, reliable and cheaper. In the last decades,
these have been considered as complement procedures for breast cancer
diagnosis, where many studies concluded that false positive and false negative
rates are greatly reduced. This work aims to review the last breakthroughs
about the three above-mentioned techniques describing the benefits of mixing
several computational skills to obtain a better global performance. In
addition, we provide a comparison between several machine learning techniques
applied to breast cancer diagnosis going from logistic regression, decision
trees, and random forest to artificial, deep, and convolutional neural
networks. Finally, it is mentioned several recommendations for 3D breast
simulations, pre-processing techniques, biomedical devices in the research
field, prediction of tumor location and size.


title: Conflict-free incidence coloring and two-way radio networks

abstract:
  In this paper, we introduce the conflict-free incidence coloring of graphs to
model a problem of designing two-way radio networks efficiently and
economically. Specifically, we call the vertex-edge pair $(v,e)$ an incidence
of a graph. A conflict-free incidence coloring of a graph is a coloring of the
incidences in such a way that two incidences $(u,e)$ and $(v,f)$ get distinct
colors if and only if they conflicts each other, i.e.,(i) $u=v$, (ii) $uv$ is
$e$ or $f$, or (iii) there is a vertex $w$ such that $uw=e$ and $vw=f$. The
minimum number of colors used among all conflict-free incidence colorings of a
graph is the conflict-free incidence chromatic number. For a simple graph with
maximum degree $\Delta$, we claim that its conflict-free incidence chromatic
number is either $2\Delta$, $2\Delta+1$, or $2\Delta+2$, and each of them can
be attained by infinite many graphs. We also show that the conflict-free
incidence chromatic number of an outer-1-planar graph with maximum degree
$\Delta$ is either $2\Delta$ or $2\Delta+1$, and moreover, characterize all
outer-1-planar graphs whose conflict-free incidence chromatic numbers are
exactly $2\Delta$ or $2\Delta+1$.


title:
Regimes of boundary layer ignition by heat release from a localized
  energy source

abstract:
  This paper investigates the initiation of a deflagration in a premixed
boundary-layer stream by continuous heat deposition from a line energy source
placed perpendicular to the flow on the wall surface, a planar flow
configuration relevant for small-scale combustion applications, including
portable rotary engines. Ignition is investigated in the constant density
approximation with a one-step irreversible reaction with large activation
energy adopted for the chemistry description. The ratio of the characteristic
strain time, given by the inverse of the wall velocity gradient, to the
characteristic deflagration residence time defines the relevant controlling
Damk\"ohler number $D$. The time-dependent evolution following the activation
of the heat source is obtained by numerical integration of the energy and fuel
conservation equations. For sufficiently small values of $D$, the solution is
seen to evolve towards a steady flow in which the chemical reaction remains
confined to a finite near-source reactive kernel. This becomes increasingly
slender for increasing values of $D$, corresponding to smaller near-wall
velocities, until a critical value $D_{c1}$ is reached at which the confined
kernel is replaced by a steady anchored deflagration, assisted by the source
heating rate, which develops indefinitely downstream. As the boundary-layer
velocity gradient is further decreased, a second critical Damk\"ohler number
$D_{c2}>D_{c1}$ is reached at which the energy deposition results in a
flashback deflagration propagating upstream against the incoming flow along the
base of the boundary layer. The computations are extended to investigate the
dependence of $D_{c1}$ and $D_{c2}$ on the fuel diffusivity as well as the
additional dependence of $D_{c1}$ on the source heating rate, thereby
delineating the boundaries that define the relevant regime diagram for these
combustion systems.


title:
Consistency-Regularized Region-Growing Network for Semantic Segmentation
  of Urban Scenes with Point-Level Annotations

abstract:
  Deep learning algorithms have obtained great success in semantic segmentation
of very high-resolution (VHR) images. Nevertheless, training these models
generally requires a large amount of accurate pixel-wise annotations, which is
very laborious and time-consuming to collect. To reduce the annotation burden,
this paper proposes a consistency-regularized region-growing network (CRGNet)
to achieve semantic segmentation of VHR images with point-level annotations.
The key idea of CRGNet is to iteratively select unlabeled pixels with high
confidence to expand the annotated area from the original sparse points.
However, since there may exist some errors and noises in the expanded
annotations, directly learning from them may mislead the training of the
network. To this end, we further propose the consistency regularization
strategy, where a base classifier and an expanded classifier are employed.
Specifically, the base classifier is supervised by the original sparse
annotations, while the expanded classifier aims to learn from the expanded
annotations generated by the base classifier with the region-growing mechanism.
The consistency regularization is thereby achieved by minimizing the
discrepancy between the predictions from both the base and the expanded
classifiers. We find such a simple regularization strategy is yet very useful
to control the quality of the region-growing mechanism. Extensive experiments
on two benchmark datasets demonstrate that the proposed CRGNet significantly
outperforms the existing state-of-the-art methods. Codes and pre-trained models
will be available online.


title: Thermodynamic Model for the Oxygen Ion Mobility in Dense Argon Gas

abstract:
  We report experimental data of the mobility of O\(_2^-\) ions in argon gas as
a function of the density in the temperature range between \(180\,\mbox{K}\le
T\le 300\,\mbox{K}\). At the intermediate, though fairly large, densities of
the experiment both the kinetic theory and the hydrodynamic theory fail at
describing the experimental data. By contrast, the free volume model,
originally developed to describe electron and ion mobility in superfluid
helium, gives a satisfactory agreement with the data as previously obtained for
the O\(_2^-\) ion mobility in both dense neon and helium gases.


title:
Chandra view of Abell 407: the central compact group of galaxies and the
  interaction between the radio AGN and the ICM

abstract:
  Abell 407 (A407) is a unique galaxy cluster hosting a central compact group
of nine galaxies (named as 'Zwicky's Nonet'; G1 - G9 in this work) within a 30
kpc radius region. The cluster core also hosts a luminous radio active galactic
nucleus (AGN), 4C 35.06 with helically twisted jets extending over 200 kpc.
With a 44 ks Chandra observation of A407, we characterize the X-ray properties
of its intracluster medium (ICM) and central galaxies. The mean X-ray
temperature of A407 is 2.7 keV and the $M_{200}$ is $1.9 \times 10^{14}
{M_{\odot}}$. We suggest that A407 has a weak cool core at $r < 60$ kpc scales
and at its very center, $< 1$-2 kpc radius, a small galaxy corona associated
with the strong radio AGN. We also conclude that the AGN 4C 35.06 host galaxy
is most likely G3. We suggest that the central group of galaxies is undergoing
a `slow merge' procedure. The range of the merging time-scale is $0.3\sim2.3$
Gyr and the stellar mass of the future brightest cluster galaxy (BCG) will be
$7.4\times10^{11} M_{\odot}$. We find that the regions which overlap with the
radio jets have higher temperature and metallicity. This is consistent with AGN
feedback activity. The central entropy is higher than that for other clusters,
which may be due to the AGN feedback and/or merging activity. With all these
facts, we suggest that A407 is a unique and rare system in the local universe
that could help us to understand the formation of a massive BCG.


title:
Odd-distance and right-equidistant sets in the maximum and Manhattan
  metrics

abstract:
  We solve two related extremal-geometric questions in the $n-$dimensional
space $\mathbb{R}^n_{\infty}$ equipped with the maximum metric. First, we prove
that the maximum size of a right-equidistant sequence of points in
$\mathbb{R}^n_{\infty}$ equals $2^{n+1}-1$. A sequence is right-equidistant if
each of the points is at the same distance from all the succeeding points.
Second, we prove that the maximum number of points in $\mathbb{R}^n_{\infty}$
with pairwise odd distances equals $2^n$. We also obtain partial results for
both questions in the $n-$dimensional space $\mathbb{R}^n_1$ with the Manhattan
distance.


title:
Experiments demonstrating the creation of elements via pulsed electric
  field in water and deuterated water: application to the production of
  fluorine

abstract:
  A relatively simple experiment, which has been proven to be fully
reproducible, shows that in solutions of water and deuterated water, sensible
quantities of fluorine are generated when a triggering energy is supplied in
form of electric field with suitable characteristics leading to discharges in
the material between the electrodes. The novelty of the experiment is the
simplicity of the layout, the full reproducibility and the concrete proof of
the events based on simple techniques to detect elements that, as it was
accurately checked, were not present in the system before energy supply.
Modularity is another interesting feature of the solution presented here. The
experimental apparatus used (named STORM REACTOR(R) and described here for the
first time in scientific literature) consists of reaction chamber, control
system and instrumentation to detect fluorine. It is illustrated together with
the measured results and with discussions on the issue of reproducibility and
on possible application areas, including energy production. The case of
fluorine production is of potential major importance at industrial level being
fluorine widely employed in medical, materials and agrochemical sciences (in
particular artificial radioactive fluorine isotopes in medical diagnosis).
Moreover, its production with traditional methods can be considered difficult
and expensive as compared to generation in a safe and inexpensive way using the
device presented here.


title: Holographic Supersolids

abstract:
  A supersolid is a system that presents long-range order and shear rigidity as
a solid but which also supports a non-dissipative superflow as a superfluid.
From an effective perspective, supersolids are identified with phases of matter
that break spontaneously translational invariance together with a global $U(1)$
symmetry. By using this symmetry prescription, we build a holographic bottom-up
model for supersolids and we start the investigation of its thermodynamic and
mechanical properties. More precisely, we analyze the behaviour of the critical
temperature, the condensate and the shear modulus across all the phase diagram.
Finally, we successfully compare our results with a simple Ginzburg-Landau
model for supersolids deriving some universal physical correlations between the
observables mentioned above.


title: Two-closure of rank 3 groups in polynomial time

abstract:
  A finite permutation group $G$ on $\Omega$ is called a rank 3 group if it has
precisely three orbits in its induced action on $\Omega \times \Omega$. The
largest permutation group on $\Omega$ having the same orbits as $G$ on $\Omega
\times \Omega$ is called the 2-closure of $G$. We construct a polynomial-time
algorithm which given generators of a rank 3 group computes generators of its
2-closure.


title:
STC: Spatio-Temporal Contrastive Learning for Video Instance
  Segmentation

abstract:
  Video Instance Segmentation (VIS) is a task that simultaneously requires
classification, segmentation, and instance association in a video. Recent VIS
approaches rely on sophisticated pipelines to achieve this goal, including
RoI-related operations or 3D convolutions. In contrast, we present a simple and
efficient single-stage VIS framework based on the instance segmentation method
CondInst by adding an extra tracking head. To improve instance association
accuracy, a novel bi-directional spatio-temporal contrastive learning strategy
for tracking embedding across frames is proposed. Moreover, an instance-wise
temporal consistency scheme is utilized to produce temporally coherent results.
Experiments conducted on the YouTube-VIS-2019, YouTube-VIS-2021, and OVIS-2021
datasets validate the effectiveness and efficiency of the proposed method. We
hope the proposed framework can serve as a simple and strong alternative for
many other instance-level video association tasks. Code will be made available.


title:
On the implementation of an adaptive multirate framework for coupled
  transport and flow

abstract:
  In this work, a multirate in time approach resolving the different time
scales of a convection-dominated transport and coupled fluid flow is developed
and studied in view of goal-oriented error control by means of the Dual
Weighted Residual (DWR) method. Key ingredients are an arbitrary degree
discontinuous Galerkin time discretization of the underlying subproblems, an a
posteriori error representation for the transport problem coupled with flow and
its implementation using space-time tensor-product spaces. The error
representation allows the separation of the temporal and spatial discretization
error which serve as local error indicators for adaptive mesh refinement. The
performance of the approach and its software implementation are studied by
numerical convergence examples as well as an example of physical interest for
convection-dominated transport.


title: CVA6's Data cache: Structure and Behavior

abstract:
  Since Spectre and Meltdown's disclosure in 2018, a new category of attacks
has been identified and characterized by the scientific community. The
Foreshadow attack, which was the first one to target Intel's secure enclave
technology (namely SGX) has been developed shortly after. It opened the way to
micro architectural attacks on Intel's architecture, and led to the quick
development of micro architectural attacks until today. While Spectre and
Meltdown are often considered as the first micro architectural attacks, one can
argue that cache attacks, as introduced by Osvik et al. in 2006, can be seen as
the first types of micro architectural attacks that were developed. Now, even
though there are many variants, they are still the most prominent type of micro
architectural attacks. One example of cache micro architectural covert-channel
is the Prime+Probe. Lately targeting the Intel architecture, the micro
architectural attacks are now challenging a wider variety of CPUs. Recently,
CPUs running the RISC-V Instruction Set Architecture have been targeted. One
famous and widely used RISC-V CPU is the ETH Zurich's CVA6 (formerly Ariane)
core. CVA6 is a 6-stage, single issue, in-order CPU. To the best of our
knowledge, there is no existing document presenting very detailed aspects of
the CVA6's micro architecture, especially with respect to the data cache. Such
information is mandatory to deeply understand any architectural or micro
architectural study successfully, such as the replication of the Prime+Probe
attack on the CVA6 CPU proposed by Nils Wistoff. This paper presents the
implementation of the Data cache in the CVA6 CPU from OpenHW Group by focusing
on its memory structure and explaining through several examples what happens
when a request for memory allocation occurs.


title:
Critical behavior of active Brownian particles: Connection to field
  theories

abstract:
  We explore the relation between active Brownian particles, a minimal
particle-based model for active matter, and scalar field theories. Both show a
liquid-gas-like phase transition towards stable coexistence of a dense liquid
with a dilute active gas that terminates in a critical point. However, a
comprehensive mapping between the particle-based model parameters and the
effective coefficients governing the field theories has not been established
yet. We discuss conflicting recent numerical results for the critical exponents
of active Brownian particles in two dimensions. Starting from the intermediate
effective hydrodynamic equations, we then present a novel construction for a
scalar order parameter for active Brownian particles that yields the "active
model B+". We argue that a crucial ingredient is the coupling between density
and polarization in the particle current. The renormalization flow close to two
dimensions exhibits a pair of perturbative fixed points that limit the
attractive basin of the Wilson-Fisher fixed point, with the perspective that
the critical behavior of active Brownian particles in two dimensions is
governed by a strong-coupling fixed point different from Wilson-Fisher and not
necessarily corresponding to Ising universality.


title:
InferGrad: Improving Diffusion Models for Vocoder by Considering
  Inference in Training

abstract:
  Denoising diffusion probabilistic models (diffusion models for short) require
a large number of iterations in inference to achieve the generation quality
that matches or surpasses the state-of-the-art generative models, which
invariably results in slow inference speed. Previous approaches aim to optimize
the choice of inference schedule over a few iterations to speed up inference.
However, this results in reduced generation quality, mainly because the
inference process is optimized separately, without jointly optimizing with the
training process. In this paper, we propose InferGrad, a diffusion model for
vocoder that incorporates inference process into training, to reduce the
inference iterations while maintaining high generation quality. More
specifically, during training, we generate data from random noise through a
reverse process under inference schedules with a few iterations, and impose a
loss to minimize the gap between the generated and ground-truth data samples.
Then, unlike existing approaches, the training of InferGrad considers the
inference process. The advantages of InferGrad are demonstrated through
experiments on the LJSpeech dataset showing that InferGrad achieves better
voice quality than the baseline WaveGrad under same conditions while
maintaining the same voice quality as the baseline but with $3$x speedup ($2$
iterations for InferGrad vs $6$ iterations for WaveGrad).


title:
Carleson and Sampling measures on Bernstein spaces on Siegel CR
  Manifolds

abstract:
  In this paper we introduce and study Carleson and sampling measures on
Bernstein spaces on a class of quadratic CR manifold called Siegel CR
manifolds. These are spaces of entire functions of exponential type whose
restrictions to the given Siegel CR manifold are $L^p$-integrable with respect
to a natural measure. For these spaces, we prove necessary and sufficients
conditions for a Radon measure to be a Carleson or a sampling measure. We also
provide sufficient conditions for sampling sequences.


title: Semantic features of object concepts generated with GPT-3

abstract:
  Semantic features have been playing a central role in investigating the
nature of our conceptual representations. Yet the enormous time and effort
required to empirically sample and norm features from human raters has
restricted their use to a limited set of manually curated concepts. Given
recent promising developments with transformer-based language models, here we
asked whether it was possible to use such models to automatically generate
meaningful lists of properties for arbitrary object concepts and whether these
models would produce features similar to those found in humans. To this end, we
probed a GPT-3 model to generate semantic features for 1,854 objects and
compared automatically-generated features to existing human feature norms.
GPT-3 generated many more features than humans, yet showed a similar
distribution in the types of generated features. Generated feature norms
rivaled human norms in predicting similarity, relatedness, and category
membership, while variance partitioning demonstrated that these predictions
were driven by similar variance in humans and GPT-3. Together, these results
highlight the potential of large language models to capture important facets of
human knowledge and yield a new approach for automatically generating
interpretable feature sets, thus drastically expanding the potential use of
semantic features in psychological and linguistic studies.


title:
Direct measurement of the ionization quenching factor of nuclear recoils
  in germanium in the keV energy range

abstract:
  This article reports the measurement of the ionization quenching factor in
germanium for nuclear recoil energies between 0.4 and 6.3 keV$_{nr}$. Precise
knowledge of this factor in this energy range is relevant for coherent elastic
neutrino-nucleus scattering and low mass dark matter searches with
germanium-based detectors. Nuclear recoils were produced in a thin high-purity
germanium target with a very low energy threshold via irradiation with
monoenergetic neutron beams. The energy dependence of the ionization quenching
factor was directly measured via kinematically constrained coincidences with
surrounding liquid scintillator based neutron detectors. The systematic
uncertainties of the measurements are discussed in detail. With measured
quenching factors between 0.16 and 0.23 in the [0.4, 6.3] keV$_{nr}$ energy
range, the data are compatible with the Lindhard theory with a parameter $k$ of
0.162 $\pm$ 0.004 (stat+sys).


title: Can We Generate Shellcodes via Natural Language? An Empirical Study

abstract:
  Writing software exploits is an important practice for offensive security
analysts to investigate and prevent attacks. In particular, shellcodes are
especially time-consuming and a technical challenge, as they are written in
assembly language. In this work, we address the task of automatically
generating shellcodes, starting purely from descriptions in natural language,
by proposing an approach based on Neural Machine Translation (NMT). We then
present an empirical study using a novel dataset (Shellcode_IA32), which
consists of 3,200 assembly code snippets of real Linux/x86 shellcodes from
public databases, annotated using natural language. Moreover, we propose novel
metrics to evaluate the accuracy of NMT at generating shellcodes. The empirical
analysis shows that NMT can generate assembly code snippets from the natural
language with high accuracy and that in many cases can generate entire
shellcodes with no errors.


title: Consensus on Demand

abstract:
  Digital money can be implemented efficiently by avoiding consensus. However,
no-consensus implementations have drawbacks, as they cannot support smart
contracts, and (even more fundamentally) they cannot deal with conflicting
transactions. We present a novel protocol that combines the benefits of an
asynchronous, broadcast-based digital currency, with the capacity to perform
consensus. This is achieved by selectively performing consensus a posteriori,
i.e., only when absolutely necessary. Our on-demand consensus comes at the
price of restricting the Byzantine participants to be less than a one-fifth
minority in the system, which is the optimal threshold. We formally prove the
correctness of our system and present an open-source implementation, which
inherits many features from the Ethereum ecosystem.


title: 3-D Ti/TiN Interface

abstract:
  Interface by definition is two-dimensional (2-D) as it separates 2 phases
with an abrupt change in structure and chemistry across the interface. The
interface between a metal and its nitride is expected to be atomically sharp,
as chemical gradation would require the creation of N vacancies in nitrides and
N interstitials in metal. Contrary to this belief, using first-principles
density functional theory (DFT), we establish that the chemically graded Ti/TiN
interface is thermodynamically preferred over the sharp interface. DFT
calculated N vacancy formation energy in TiN is 2.4 eV, and N interstitial in
Ti is -3.8 eV. Thus, diffusion of N from TiN to Ti by the formation of N
vacancy in TiN and N interstitial in Ti would reduce the internal energy of the
Ti-TiN heterostructure. We show that diffusion of N is thermodynamically
favorable till ~23% of N has diffused from TiN to Ti, resulting in an
atomically chemically graded interface, which we refer to as a 3-D interface.
Experiments' inability to identify a 3-D interface in Ti/TiN could be
attributed to limitations in identifying chemical composition and structure
with atomic-level resolution at interfaces. We define the sum of N vacancy
formation energy and N interstitial formation energy as driving-force, which
could be used as a convenient way to assess the possibility of forming a 3-D
interface in metal/ceramic heterostructures. We also show gradual variation in
lattice parameters and mechanical properties (like bulk modulus, shear modulus,
Young's modulus, and hardness) across the Ti/TiN interface. 3-D interfaces open
a new way to control properties of metal/ceramic heterostructures, in line with
the already established advantage of gradation at interfaces in micrometer
length scale. For widely explored Ti/TiN multilayer nano-heterostructures, the
possibility of forming 3-D interface could lead to enhanced wear and erosion
resistance.