title: Weakly-top groups

abstract:
  A finite group is weakly-top if the order of every abelian section is bounded
by the order of the abelianization. In particular, it is top if equality is not
achieved by any proper abelian section. In this article, we prove that every
weakly-top group is the direct product of weakly-top $p$-groups, but there
exist weakly-top groups of arbitrarily large nilpotency class. Furthermore, we
show that the properties of being weakly-top and top are invariant under
isoclinism.


title: Compound Optics

abstract:
  Simple optics are defined using actions of monoidal categories. Compound
optics arise, for instance, as natural transformations between polynomial
functors. Since a monoidal category is a special case of a bicategory, we
formulate complex optics using the action of a bicategory. We show that
polynomial optics are a special case of complex optics defined by the action of
bicategory $\mathbf{Prof}$ on co-presheaves.


title: Generative Modeling Helps Weak Supervision (and Vice Versa)

abstract:
  Many promising applications of supervised machine learning face hurdles in
the acquisition of labeled data in sufficient quantity and quality, creating an
expensive bottleneck. To overcome such limitations, techniques that do not
depend on ground truth labels have been developed, including weak supervision
and generative modeling. While these techniques would seem to be usable in
concert, improving one another, how to build an interface between them is not
well-understood. In this work, we propose a model fusing weak supervision and
generative adversarial networks. It captures discrete variables in the data
alongside the weak supervision derived label estimate. Their alignment allows
for better modeling of sample-dependent accuracies of the weak supervision
sources, improving the unobserved ground truth estimate. It is the first
approach to enable data augmentation through weakly supervised synthetic images
and pseudolabels. Additionally, its learned discrete variables can be inspected
qualitatively. The model outperforms baseline weak supervision label models on
a number of multiclass classification datasets, improves the quality of
generated images, and further improves end-model performance through data
augmentation with synthetic samples.


title:
Strategy Complexity of Reachability in Countable Stochastic 2-Player
  Games

abstract:
  We study countably infinite stochastic 2-player games with reachability
objectives. Our results provide a complete picture of the memory requirements
of $\varepsilon$-optimal (resp. optimal) strategies. These results depend on
whether the game graph is infinitely branching and on whether one requires
strategies that are uniform (i.e., independent of the start state). Our main
result is that $\varepsilon$-optimal (resp. optimal) Maximizer strategies in
infinitely branching turn-based reachability games require infinite memory.
This holds even under very strong restrictions. Even if all states have an
almost surely winning Maximizer strategy, strategies with a step counter plus
finite private memory are still useless. Regarding uniformity, we show that for
Maximizer there need not exist positional uniformly $\varepsilon$-optimal
strategies even in finitely branching turn-based games, whereas there always
exists one that uses one bit of public memory, even in concurrent games with
finite action sets.


title: Dents in the Veil: Protostellar feedback in Orion

abstract:
  Interest in stellar feedback has recently increased because new studies
suggest that radiative and mechanical feedback from young massive stars
regulate the physical and chemical composition of the interstellar medium (ISM)
significantly. Recent SOFIA [CII] 158 micron observations of the Orion Veil
revealed that the expanding bubble is powered by stellar winds and influenced
by previously active molecular outflows of ionizing massive stars. We aim to
investigate the mechanical feedback on the whole Veil shell by searching for
jets/outflows interacting with the Veil shell and determining the
origin/driving mechanisms of these collisions. In the light of these findings,
as well as the momenta of the dents and their dynamical timescales, we propose
that the dents are created by the interaction of collimated jets/outflows from
protostars with luminosities ranging from 10$^3$ to 10$^4$ $L_\odot$ indicating
B-type stars in the Orion star-forming cloud with the surrounding Veil shell.
However, it is challenging to pinpoint the driving stars as they may have moved
from the original ejection points of the jets/outflows. We conclude that the
dynamics of the expanding Veil shell is influenced not just by the O-type stars
in the Trapezium cluster, but also by less massive stars, especially B-type, in
the Orion Nebula. Mechanical feedback from protostars with a range of masses
appears to play an important role in determining the morphology of [HII]
regions and injecting turbulence into the medium.


title:
Machine Learning Testing in an ADAS Case Study Using
  Simulation-Integrated Bio-Inspired Search-Based Testing

abstract:
  This paper presents an extended version of Deeper, a search-based
simulation-integrated test solution that generates failure-revealing test
scenarios for testing a deep neural network-based lane-keeping system. In the
newly proposed version, we utilize a new set of bio-inspired search algorithms,
genetic algorithm (GA), $({\mu}+{\lambda})$ and $({\mu},{\lambda})$ evolution
strategies (ES), and particle swarm optimization (PSO), that leverage a quality
population seed and domain-specific cross-over and mutation operations tailored
for the presentation model used for modeling the test scenarios. In order to
demonstrate the capabilities of the new test generators within Deeper, we carry
out an empirical evaluation and comparison with regard to the results of five
participating tools in the cyber-physical systems testing competition at SBST
2021. Our evaluation shows the newly proposed test generators in Deeper not
only represent a considerable improvement on the previous version but also
prove to be effective and efficient in provoking a considerable number of
diverse failure-revealing test scenarios for testing an ML-driven lane-keeping
system. They can trigger several failures while promoting test scenario
diversity, under a limited test time budget, high target failure severity, and
strict speed limit constraints.


title:
Gravastar under the framework of braneworld gravity II: effect of the
  Kuchowicz metric function

abstract:
  In recent years, a class of compact objects called gravastars have drawn
immense interest as regular solutions to end state stellar collapse. Since the
energy density involved in collapse process is expected to be high, it is a
natural choice to study such compact objects in context of modified gravity
theories which reduce to General Relativity (GR) in the low energy regime. We
have already framed a model of gravastar in such a modified gravity framework
involving extra dimensional Randall-Sundrum (RS) single brane gravity~[Phys.
Rev. D \textbf{102}, 024037 (2020)] \cite{Sengupta1}. As a sequel in the
present paper we substantially improve our previous model by choosing the
Kuchowicz function as one of the metric potentials, which leads to many new
interesting results and physical features from our analysis as discussed in
this paper. Also, we provide essential additional stability checks on our
gravastar model to investigate the possibility of any instability creeping in
due to the higher dimensional framework. Our present improved gravastar model
is found to clear all the stability checks successfully. Very interestingly,
the static spherically symmetric matter distributions are found to accommodate
both classes of solutions obeying and violating the modified energy conditions
on the RS brane as we find in this work. We can conclude from our analysis that
the Kuchowicz metric potential is very effective for describing regular
solutions to compact objects at substantially high energies on the 3-brane.


title: Dynamics of wave packets in the functional mechanics

abstract:
  In these notes we consider the mathematical aspects of the functional
mechanics proposed and developed by I. V. Volovich.


title:
Cosmic ray-driven galactic winds: transport modes of cosmic rays and
  Alfv\'en-wave dark regions

abstract:
  Feedback mediated by cosmic rays (CRs) is an important process in galaxy
formation. Because CRs are long-lived and because they are transported along
magnetic field lines independently of any gas flow, they can efficiently
distribute their feedback energy within the galaxy. We present an in-depth
investigation of (i) how CRs launch galactic winds from a disc that is forming
in a $10^{11} \mathrm{M}_\odot$ halo and (ii) how CR transport affects the
dynamics in a galactic outflow. To this end, we use the Arepo moving-mesh code
and model CR transport with the two-moment description of CR hydrodynamics.
This model includes the CR interaction with gyroresonant Alfv\'en waves that
enables us to self-consistently calculate the CR diffusion coefficient and CR
transport speeds based on coarse-grained models for plasma physical effects.
This delivers insight into key questions such as whether the effective CR
transport is streaming-like or diffusive-like, how the CR diffusion coefficient
and transport speed change inside the circumgalactic medium (CGM), and to what
degree the two-moment approximation is needed to faithfully capture these
effects. We find that the CR-diffusion coefficient reaches a steady-state in
most environments with the notable exception of our newly discovered
Alfv\'en-wave dark regions where the toroidal wind magnetic field is nearly
perpendicular to the CR pressure gradient so that CRs are unable to excite
gyroresonant Alfv\'en waves. However, CR transport itself cannot reach a
steady-state and is not well described by either the CR streaming paradigm, the
CR diffusion paradigm or a combination of both.


title:
Historical curiosity: how asymptotic freedom of the Yang-Mills theory
  could have been discovered three times before Gross, Wilczek, and Politzer,
  but was not

abstract:
  This article was published in {\bf 2001} in Festschrift "At the Frontier of
Particle Physics; Handbook of QCD", Ed. M. Shifman, (World Scientific,
Singapore, 2001), Vol. 1, page 126. Asymptotic freedom as the basic property of
QCD was discovered by Gross, Wilczek, and Politzer in 1973. Personal
recollections of David Gross which are being published in this Volume vividly
describe the historical background and the chain of events which led to this
fundamental breakthrough. Unfortunately, I failed to obtain Politzer's side of
the story. Some details can be found in an interview which Prof. Politzer gave
to R. Crease and C. Mann on February 21, 1985 \cite{DP}. Below I acquaint the
reader with the pre-1973 appearances of asymptotic freedom which,
unfortunately, went unnoticed


title: MemComputing vs. Quantum Computing: some analogies and major differences

abstract:
  Quantum computing employs some quantum phenomena to process information. It
has been hailed as the future of computing but it is plagued by serious hurdles
when it comes to its practical realization. MemComputing is a new paradigm that
instead employs non-quantum dynamical systems and exploits time non-locality
(memory) to compute. It can be efficiently emulated in software and its path
towards hardware is more straightforward. I will discuss some analogies between
these two computing paradigms, and the major differences that set them apart.


title: Rheology and Viscoelasticity of Proteins and Nucleic Acids Condensates

abstract:
  Phase separation is as familiar as watching vinegar separating from oil in
vinegrette. The observation that phase separation of proteins and nucleic acids
is widespread in living cells has opened an entire field of research into the
biological significance and the biophysical mechanisms of phase separation and
protein condensation in biology. Recent evidence indicate that certain proteins
and nucleic acids condensates are not simple liquids and instead display both
viscous and elastic behaviours, which in turn may have biological significance.
The aim of this perspective is to review the state-of-the-art of this quickly
emerging field focusing on the material and rheological properties of protein
condensates. Finally, we discuss the different techniques that can be employed
to quantify the viscoelasticity of condensates and highlight potential future
directions and opportunities for interdisciplinary cross-talk between chemists,
physicists and biologists.


title: Bioplastic Design using Multitask Deep Neural Networks

abstract:
  Non-degradable plastic waste stays for decades on land and in water,
jeopardizing our environment; yet our modern lifestyle and current technologies
are impossible to sustain without plastics. Bio-synthesized and biodegradable
alternatives such as the polymer family of polyhydroxyalkanoates (PHAs) have
the potential to replace large portions of the world's plastic supply with
cradle-to-cradle materials, but their chemical complexity and diversity limit
traditional resource-intensive experimentation. In this work, we develop
multitask deep neural network property predictors using available experimental
data for a diverse set of nearly 23000 homo- and copolymer chemistries. Using
the predictors, we identify 14 PHA-based bioplastics from a search space of
almost 1.4 million candidates which could serve as potential replacements for
seven petroleum-based commodity plastics that account for 75% of the world's
yearly plastic production. We discuss possible synthesis routes for these
identified promising materials. The developed multitask polymer property
predictors are made available as a part of the Polymer Genome project at
https://PolymerGenome.org.


title: Connecting Dark Gauge Symmetry to the Standard Model

abstract:
  Dark matter is postulated to be a neutral Dirac fermion, charged under a dark
$U(1)_D$ gauge symmetry. Scalar partners of the quarks and leptons are also
charged under $U(1)_D$. The dark gauge boson $Z_D$ and the dark Higgs boson
$h_D$ enable either freeze-out or freeze-in mechanisms to account for the
correct dark matter relic abundance. Dark number $D$ is connected to baryon
number $B$ and lepton number $L$ through $D=3B+L-(2j)_{[mod~2]}$ where $j$ is
the intrinsic spin of the particle.


title:
Displaying dark matter constraints from colliders with varying
  simplified model parameters

abstract:
  The search for dark matter is one of the main science drivers of the particle
and astroparticle physics communities. Determining the nature of dark matter
will require a broad approach, with a range of experiments pursuing different
experimental hypotheses. Within this search program, collider experiments
provide insights on dark matter which are complementary to direct/indirect
detection experiments and to astrophysical evidence. To compare results from a
wide variety of experiments, a common theoretical framework is required. The
ATLAS and CMS experiments have adopted a set of simplified models which
introduce two new particles, a dark matter particle and a mediator, and whose
interaction strengths are set by the couplings of the mediator.
  So far, the presentation of LHC and future hadron collider results has
focused on four benchmark scenarios with specific coupling values within these
simplified models. In this work, we describe ways to extend those four
benchmark scenarios to arbitrary couplings, and release the corresponding code
for use in further studies. This will allow for more straightforward comparison
of collider searches to accelerator experiments that are sensitive to smaller
couplings, such as those for the US Community Study on the Future of Particle
Physics (Snowmass 2021), and will give a more complete picture of the coupling
dependence of dark matter collider searches when compared to direct and
indirect detection searches. By using semi-analytical methods to rescale
collider limits, we drastically reduce the computing resources needed relative
to traditional approaches based on the generation of additional simulated
signal samples.


title: Cosmic Flexion

abstract:
  Cosmic flexion, like cosmic shear, is a correlation function whose signal
originates from the large-scale structure of the universe. Building on the
observational success of cosmic shear, along with the unprecedented quality of
large-scale cosmological datasets, the time is ripe to explore the practical
constraints from cosmic flexion. Unlike cosmic shear, which has a broad window
function for power, cosmic flexion is only measurable on small scales and
therefore can uniquely place constraints on the small-scale matter power
spectrum. Here, we present a full theoretical formalism for cosmic flexion,
including both flexion-flexion and shear-flexion two-point correlations. We
present forecasts for measuring cosmic flexion in the Dark Energy Survey (DES),
a Stage III cosmological survey, and comment on the future prospects of
measuring these cosmological flexion signals in the upcoming era of Stage IV
experiments.


title:
Quality Control of Mass-Produced GEM Detectors for the CMS GE1/1 Muon
  Upgrade

abstract:
  The series of upgrades to the Large Hadron Collider, culminating in the High
Luminosity Large Hadron Collider, will enable a significant expansion of the
physics program of the CMS experiment. However, the accelerator upgrades will
also make the experimental conditions more challenging, with implications for
detector operations, triggering, and data analysis. The luminosity of the
proton-proton collisions is expected to exceed
$2-3\times10^{34}$~cm$^{-2}$s$^{-1}$ for Run 3 (starting in 2022), and it will
be at least $5\times10^{34}$~cm$^{-2}$s$^{-1}$ when the High Luminosity Large
Hadron Collider is completed for Run 4. These conditions will affect muon
triggering, identification, and measurement, which are critical capabilities of
the experiment. To address these challenges, additional muon detectors are
being installed in the CMS endcaps, based on Gas Electron Multiplier
technology. For this purpose, 161 large triple-Gas Electron Multiplier
detectors have been constructed and tested. Installation of these devices began
in 2019 with the GE1/1 station and will be followed by two additional stations,
GE2/1 and ME0, to be installed in 2023 and 2026, respectively. The assembly and
quality control of the GE1/1 detectors were distributed across several
production sites around the world. We motivate and discuss the quality control
procedures that were developed to standardize the performance of the detectors,
and we present the final results of the production. Out of 161 detectors
produced, 156 detectors passed all tests, and 144 detectors are now installed
in the CMS experiment. The various visual inspections, gas tightness tests,
intrinsic noise rate characterizations, and effective gas gain and response
uniformity tests allowed the project to achieve this high success rate.


title:
Search for Gravitational Waves Associated with Fast Radio Bursts
  Detected by CHIME/FRB During the LIGO--Virgo Observing Run O3a

abstract:
  We search for gravitational-wave transients associated with fast radio bursts
(FRBs) detected by the Canadian Hydrogen Intensity Mapping Experiment Fast
Radio Burst Project (CHIME/FRB), during the first part of the third observing
run of Advanced LIGO and Advanced Virgo (1 April 2019 15:00 UTC-1 Oct 2019
15:00 UTC). Triggers from 22 FRBs were analyzed with a search that targets
compact binary coalescences with at least one neutron star component. A
targeted search for generic gravitational-wave transients was conducted on 40
FRBs. We find no significant evidence for a gravitational-wave association in
either search. Given the large uncertainties in the distances of the FRBs
inferred from the dispersion measures in our sample, however, this does not
conclusively exclude any progenitor models that include emission of a
gravitational wave of the types searched for from any of these FRB events. We
report $90\%$ confidence lower bounds on the distance to each FRB for a range
of gravitational-wave progenitor models. By combining the inferred maximum
distance information for each FRB with the sensitivity of the
gravitational-wave searches, we set upper limits on the energy emitted through
gravitational waves for a range of emission scenarios. We find values of order
$10^{51}$-$10^{57}$ erg for a range of different emission models with central
gravitational wave frequencies in the range 70-3560 Hz. Finally, we also found
no significant coincident detection of gravitational waves with the repeater,
FRB 20200120E, which is the closest known extragalactic FRB.


title:
Prediction of resistance induced by surface complexity in lubricating
  layers: Application to super-hydrophobic surfaces

abstract:
  Super Hydrophobic (SH) coatings are widely used to mitigate drag in various
applications. Numerous studies have demonstrated that the beneficial wall-slip
effect produced by these materials disappears in laminar flow regimes. The main
mechanisms considered to be behind the decrease in performance are
Marangoni-induced stresses and air/liquid interface deformation. In the present
study, a new mechanism is proposed to explain the loss of performances of
SH-surfaces in laminar flow regimes. Here we consider the flow of air inside
the plastron and the associated momentum loses induced by roughness elements
with different geometric characteristics. The effects of air motion within the
plastron is coupled to the outer fluid with a homogenised boundary condition
approach. To this end, numerical simulations at the scale of the roughness
element were conducted as a function of the porosity and the tortuosity of the
domain to determine the slip velocity at the air-liquid interface.
  The homogenised boundary condition is then implemented in a theoretical model
for the outer flow to compute drag on SH-spheres at low $Re$ numbers.
Experiments of laminar SH falling spheres indicate that high values of the
tortuosity and low values of porosity lead to a loss of performances when
considering drag reduction. As anticipated, a 3D printed sphere with low
tortuosity and similar porosity demonstrated near-optimal drag reductions. A
comparative study between the predicted values and experiments shows that the
homogenised model is able to accurately predict the drag on SH surfaces for
values of the porosity and tortuosity estimated from microscopy images of the
SH textured surface.


title: Adiabatic lapse rate of real gases

abstract:
  We derive a formula for the dry adiabatic lapse rate of atmospheres composed
of real gases. We restrict our study to those described by a family of
two-parameter cubic equations of state and the recent Guevara non-cubic
equation. Since our formula depends on the adiabatic curves, we compute them
all at once considering molecules that can move, rotate, and vibrate, for any
equation of state. To illustrate our results, we estimate the lapse rate of the
troposphere of Titan, obtaining a better approximation to the observed data in
some instances, when compared to the estimation provided by the virial
expansion up to third order.


title:
Disentangling high-order mechanisms and high-order behaviours in complex
  systems

abstract:
  Battiston et al. (arXiv:2110.06023) provide a comprehensive overview of how
investigations of complex systems should take into account interactions between
more than two elements, which can be modelled by hypergraphs and studied via
topological data analysis. Following a separate line of enquiry, a broad
literature has developed information-theoretic tools to characterize high-order
interdependencies from observed data. While these could seem to be competing
approaches aiming to address the same question, in this correspondence we
clarify that this is not the case, and that a complete account of higher-order
phenomena needs to embrace both.


title: Price Manipulability in First-Price Auctions

abstract:
  First-price auctions have many desirable properties, including uniquely
possessing some, like credibility. However, first-price auctions are also
inherently non-truthful, and non-truthfulness may result in instability and
inefficiencies. Given these pros and cons, we seek to quantify the extent to
which first-price auctions are susceptible to manipulation. In this work we
adopt a metric that was introduced in the context of bitcoin fee design
markets: the percentage change in payment that can be achieved by being
strategic. We study the behavior of this metric for single-unit and $k$-unit
auction environments with $n$ i.i.d. buyers, and seek conditions under which
the percentage change tends to zero as $n$ grows large. To the best of our
knowledge, ours is the first rigorous study of the extent to which large
multi-unit first price auctions are susceptible to manipulation. We provide an
almost complete picture of the conditions under which they are truthful in the
large, and exhibit some surprising boundaries.


title: High Efficiency, Low Cost, RF Sources for Accelerators and Colliders

abstract:
  Several high efficiency, low cost, RF sources are in development or recently
completed. All are designed to provide operating efficiencies exceeding 80% and
provide more than 100 kW of output power with a focus on high average power or
CW operation. The sources include (1) a magnetron system with amplitude and
phase control, a multiple beam, power grid-tube based source, a multiple beam
inductive output tube, and a klystron using the core oscillation method. The
estimated cost for the magnetron system and multiple beam power grid-tube
source are one dollar per Watt and 75 cents per Watt, respectively. Operating
frequencies span the range from 300 MHz (power grid tube) to 1.3 GHz (magnetron
and klystron). This paper describes the basic operation of the sources,
indicates the status and schedule, and provides available experimental results.


title:
Data-driven optimal control of affine systems: A linear programming
  perspective

abstract:
  In this letter, we discuss the problem of optimal control for affine systems
in the context of data-driven linear programming. First, we introduce a unified
framework for the fixed point characterization of the value function,
Q-function and relaxed Bellman operators. Then, in a model-free setting, we
show how to synthesize and estimate Bellman inequalities from a small but
sufficiently rich dataset. To guarantee exploration richness, we complete the
extension of Willem's fundamental lemma to affine systems.


title:
On the Spectral Evolution of Hot White Dwarf Stars. III. The PG
  1159$-$DO$-$DB$-$DQ Evolutionary Channel Revisited

abstract:
  We continue our comprehensive theoretical investigation of the spectral
evolution of white dwarfs based on sophisticated simulations of element
transport. In this paper, we focus on the transformation of PG 1159 stars into
DO/DB white dwarfs due to the gravitational settling of heavy elements, and
then into DQ white dwarfs through the convective dredge-up of carbon. We study
the impact of several physical parameters on the evolution of the surface
carbon abundance over a wide range of effective temperature. In the hot PG 1159
and DO phases, our calculations confirm that the temperature of the PG
1159-to-DO transition depends sensitively on the stellar mass and the wind
mass-loss rate. We show that measured carbon abundances of DOZ white dwarfs are
mostly accounted for by our models, with the notable exception of the coolest
DOZ stars. In the cooler DB and DQ phases, the predicted atmospheric
composition is strongly influenced by the stellar mass, the thickness of the
envelope, the initial carbon content, the efficiency of convective overshoot,
and the presence of residual hydrogen. We demonstrate that, under reasonable
assumptions, our simulations reproduce very well the observed carbon abundance
pattern of DQ stars, which thus allows us to constrain the extent of the
overshoot region in cool helium-rich white dwarfs. We also argue that our
calculations naturally explain a number of recent empirical results, such as
the relative excess of low-mass DQ stars and the presence of trace hydrogen
and/or carbon at the surface of most DC and DZ stars.


title:
NNReArch: A Tensor Program Scheduling Framework Against Neural Network
  Architecture Reverse Engineering

abstract:
  Architecture reverse engineering has become an emerging attack against deep
neural network (DNN) implementations. Several prior works have utilized
side-channel leakage to recover the model architecture while the target is
executing on a hardware acceleration platform. In this work, we target an
open-source deep-learning accelerator, Versatile Tensor Accelerator (VTA), and
utilize electromagnetic (EM) side-channel leakage to comprehensively learn the
association between DNN architecture configurations and EM emanations. We also
consider the holistic system -- including the low-level tensor program code of
the VTA accelerator on a Xilinx FPGA and explore the effect of such low-level
configurations on the EM leakage. Our study demonstrates that both the
optimization and configuration of tensor programs will affect the EM
side-channel leakage.
  Gaining knowledge of the association between the low-level tensor program and
the EM emanations, we propose NNReArch, a lightweight tensor program scheduling
framework against side-channel-based DNN model architecture reverse
engineering. Specifically, NNReArch targets reshaping the EM traces of
different DNN operators, through scheduling the tensor program execution of the
DNN model so as to confuse the adversary. NNReArch is a comprehensive
protection framework supporting two modes, a balanced mode that strikes a
balance between the DNN model confidentiality and execution performance, and a
secure mode where the most secure setting is chosen. We implement and evaluate
the proposed framework on the open-source VTA with state-of-the-art DNN
architectures. The experimental results demonstrate that NNReArch can
efficiently enhance the model architecture security with a small performance
overhead. In addition, the proposed obfuscation technique makes reverse
engineering of the DNN architecture significantly harder.


title: AES as Error Correction

abstract:
  In this paper, we show that the Advanced Encryption Standard (AES)
cryptosystem can be used as an error-correcting code to obtain reliability over
noisy communication and data systems. We empirically contrast the performance
of the proposed approach using AES as error correction with that of Random
Linear Codes (RLC) and CA-Polar codes and show that in practical scenarios,
they achieve almost the same performance.


title:
Review of Metrics to Measure the Stability, Robustness and Resilience of
  Reinforcement Learning

abstract:
  Reinforcement learning has received significant interest in recent years, due
primarily to the successes of deep reinforcement learning at solving many
challenging tasks such as playing Chess, Go and online computer games. However,
with the increasing focus on reinforcement learning, applications outside of
gaming and simulated environments require understanding the robustness,
stability, and resilience of reinforcement learning methods. To this end, we
conducted a comprehensive literature review to characterize the available
literature on these three behaviors as they pertain to reinforcement learning.
We classify the quantitative and theoretical approaches used to indicate or
measure robustness, stability, and resilience behaviors. In addition, we
identified the action or event to which the quantitative approaches were
attempting to be stable, robust, or resilient. Finally, we provide a decision
tree useful for selecting metrics to quantify the behaviors. We believe that
this is the first comprehensive review of stability, robustness and resilience
specifically geared towards reinforcement learning.


title: Kemeny's constant for non-backtracking random walks

abstract:
  Kemeny's constant for a connected graph $G$ is the expected time for a random
walk to reach a randomly-chosen vertex $u$, regardless of the choice of the
initial vertex. We extend the definition of Kemeny's constant to
non-backtracking random walks and compare it to Kemeny's constant for simple
random walks. We explore the relationship between these two parameters for
several families of graphs and provide closed-form expressions for regular and
biregular graphs. In nearly all cases, the non-backtracking variant yields the
smaller Kemeny's constant.


title: Mouse Sensitivity Effects in First-Person Targeting Tasks

abstract:
  Despite billions of hours of play and copious discussion online, mouse
sensitivity recommendations for first-person targeting tasks vary by a factor
of 10x or more and remain an active topic of debate in both competitive and
recreational gaming communities. Inspired by previous academic literature in
pointer-based gain optimization, we conduct the first user study of mouse
sensitivity in first person targeting tasks, reporting a statistically
significant range of optimal values in both task completion time and
throughput. Due to inherent incompatibility (i.e., lack of convert-ability)
between sensitivity metrics adopted for prior pointer-based gain literature and
those describing first-person targeting, we provide the first analytically
demonstrated, statistically significant optimal sensitivity range useful for
first-person camera controls. Furthermore, we demonstrate that this optimal
sensitivity range arises (at least in part) from a speed-precision trade-off
impacted by spatial task difficulty, similar to results reported in
pointer-based sensitivity literature previously.


title:
On decay of entropy solutions to degenerate nonlinear parabolic
  equations with perturbed periodic initial data

abstract:
  Under a precise nonlinearity-diffusivity assumption we establish the decay of
entropy solutions of a degenerate nonlinear parabolic equation with initial
data being a sum of periodic function and a function vanishing at infinity (in
the sense of measure).


title: Thermodynamic stability of a cosmological SU(2)-weak gauge field

abstract:
  The CGF cosmology is a complete theory of cosmology from the electroweak
transition onward. It is semi-classical. At leading order the only matter is
dark matter -- a cosmological SU(2)-weak gauge field (the CGF). Ordinary matter
is a subleading correction from fluctuations around the classical state. The
CGF is periodic in imaginary time. It acts as thermal bath for the fluctuations
of the Standard Model fields. Here, the initial thermal state of the SU(2)
gauge field fluctuations is constructed and shown to be thermodynamically
stable. This is a warm-up for (1) constructing the initial thermal state of all
the fluctuations in order to calculate its time evolution and (2) showing that
initial state to be thermodynamically stable in order to show that the CGF
cosmology is physically natural.


title:
Upmixing via style transfer: a variational autoencoder for disentangling
  spatial images and musical content

abstract:
  In the stereo-to-multichannel upmixing problem for music, one of the main
tasks is to set the directionality of the instrument sources in the
multichannel rendering results. In this paper, we propose a modified
variational autoencoder model that learns a latent space to describe the
spatial images in multichannel music. We seek to disentangle the spatial images
and music content, so the learned latent variables are invariant to the music.
At test time, we use the latent variables to control the panning of sources. We
propose two upmixing use cases: transferring the spatial images from one song
to another and blind panning based on the generative model. We report objective
and subjective evaluation results to empirically show that our model captures
spatial images separately from music content and achieves transfer-based
interactive panning.


title:
Self-supervision through Random Segments with Autoregressive Coding
  (RandSAC)

abstract:
  Inspired by the success of self-supervised autoregressive representation
learning in natural language (GPT and its variants), and advances in recent
visual architecture design with Vision Transformers (ViTs), in this paper, we
explore the effects various design choices have on the success of applying such
training strategies for visual feature learning. Specifically, we introduce a
novel strategy that we call Random Segments with Autoregressive Coding
(RandSAC). In RandSAC, we group patch representations (image tokens) into
hierarchically arranged segments; within each segment, tokens are predicted in
parallel, similar to BERT, while across segment predictions are sequential,
similar to GPT. We illustrate that randomized serialization of the segments
significantly improves the performance and results in distribution over
spatially-long (across-segments) and -short (within-segment) predictions which
are effective for feature learning. We illustrate the pertinence of these
design choices and explore alternatives on a number of datasets (e.g., CIFAR10,
ImageNet). While our pre-training strategy works with vanilla Transformer, we
also propose a conceptually simple, but highly effective, addition to the
decoder that allows learnable skip-connections to encoder feature layers, which
further improves the performance. Our final model, trained on ImageNet,
achieves new state-of-the-art linear probing performance 68.3% among
comparative predictive self-supervised learning approaches.


title:
Energy-optimal Three-dimensional Path-following Control of Autonomous
  Underwater Vehicles under Ocean Currents

abstract:
  This paper presents a three-dimensional (3D) energy-optimal path-following
control design for autonomous underwater vehicles subject to ocean currents.
The proposed approach has a two-stage control architecture consisting of the
setpoint computation and the setpoint tracking. In the first stage, the surge
velocity, heave velocity, and pitch angle setpoints are optimized by minimizing
the required vehicle propulsion energy under currents, and the line-of-sight
(LOS) guidance law is used to generate the yaw angle setpoint that ensures path
following. In the second stage, two model predictive controllers are designed
to control the vehicle motion in the horizontal and vertical planes by tracking
the optimal setpoints. The proposed controller is compared with a conventional
LOS-based control that maintains zero heave velocity relative to the current
(i.e., relative heave velocity) and derives pitch angle setpoint using LOS
guidance to reach the desired depth. Through simulations, we show that the
proposed approach can achieve more than $13\%$ energy saving on a
lawnmower-type and an inspection mission under different ocean current
conditions. The simulation results demonstrate that allowing motions with
non-zero relative heave velocity improves energy efficiency in 3D
path-following applications.


title: On Last-Iterate Convergence Beyond Zero-Sum Games

abstract:
  Most existing results about \emph{last-iterate convergence} of learning
dynamics are limited to two-player zero-sum games, and only apply under rigid
assumptions about what dynamics the players follow. In this paper we provide
new results and techniques that apply to broader families of games and learning
dynamics. First, we use a regret-based analysis to show that in a class of
games that includes constant-sum polymatrix and strategically zero-sum games,
dynamics such as \emph{optimistic mirror descent (OMD)} have \emph{bounded
second-order path lengths}, a property which holds even when players employ
different algorithms and prediction mechanisms. This enables us to obtain
$O(1/\sqrt{T})$ rates and optimal $O(1)$ regret bounds. Our analysis also
reveals a surprising property: OMD either reaches arbitrarily close to a Nash
equilibrium, or it outperforms the \emph{robust price of anarchy} in
efficiency. Moreover, for potential games we establish convergence to an
$\epsilon$-equilibrium after $O(1/\epsilon^2)$ iterations for mirror descent
under a broad class of regularizers, as well as optimal $O(1)$ regret bounds
for OMD variants. Our framework also extends to near-potential games, and
unifies known analyses for distributed learning in Fisher's market model.
Finally, we analyze the convergence, efficiency, and robustness of
\emph{optimistic gradient descent (OGD)} in general-sum continuous games.


title: Cat-inspired Gaits for A Tilt-rotor -- from Symmetrical to Asymmetrical

abstract:
  Among the tilt-rotors (quadrotors) developed in the last decades, Rylls model
with eight inputs (four magnitudes of the thrusts and four tilting angles)
attracted great attention. Typical feedback linearization maneuvers all the
eight inputs with a united control rule to stabilize this tilt-rotor. Instead
of assigning the tilting angles by the control rule, the recent research
predetermined the tilting angles and left the magnitudes of the thrusts the
only control signals. These tilting angles are designed to mimic the cat-trot
gait, avoiding the singular decoupling matrix feedback linearization. To
complete the discussions of the cat-gaits inspired tilt-rotor gaits, this
research addresses the analyses on the rest of the common cat gaits, walk, run,
transverse gallop, and rotary gallop. It is found that the singular decoupling
matrix exist in walk gait and rotary gallop. Further modifications are
conducted to these two gaits to accommodate the application of feedback
linearization. The modified gaits with different periods are then applied to
the tilt-rotor in tracking experiments, in which the references are uniform
rectilinear motion and uniform circular motion. All the experiments are
simulated in Simulink, MATLAB. The result shows that.


title:
Parametrically enhanced interactions and non-trivial bath dynamics in a
  photon-pressure Kerr amplifier

abstract:
  Photon-pressure coupling between two superconducting circuits is a promising
platform for investigating radiation-pressure coupling in novel parameter
regimes and for the development of radio-frequency (RF) quantum photonics and
quantum-limited RF sensing. So far, the intrinsic Josephson nonlinearity of
photon-pressure coupled circuits has not been considered a potential resource
for enhanced devices or novel experimental schemes. Here, we implement
photon-pressure coupling between a RF circuit and a microwave cavity containing
a superconducting quantum interference device (SQUID) which can be operated as
a Josephson parametric amplifier (JPA). We demonstrate a Kerr-based enhancement
of the photon-pressure single-photon coupling rate and an increase of the
cooperativity by one order of magnitude in the amplifier regime. In addition,
we characterize the upconverted and Kerr-amplified residual thermal
fluctuations of the RF circuit, and observe that the intracavity amplification
reduces the measurement imprecision. Finally, we demonstrate that RF mode
sideband-cooling is surprisingly not limited to the effective amplifier mode
temperature arising from quantum noise amplification, which we explain by
non-trivial bath dynamics due to a two-stage amplification process. Our results
demonstrate how Kerr nonlinearities and in particular Josephson parametric
amplification can be utilized as resource for enhanced photon-pressure systems
and Kerr cavity optomechanics.


title: Slender body theories for rotating filaments

abstract:
  Slender fibers are ubiquitous in biology, physics, and engineering, with
prominent examples including bacterial flagella and cytoskeletal fibers. In
this setting, slender body theories (SBTs), which give the resistance on the
fiber asymptotically in its slenderness $\epsilon$, are useful tools for both
analysis and computations. However, a difficulty arises when accounting for
twist and cross-sectional rotation: because the angular velocity of the cross
section can range from $\mathcal{O}\left(\epsilon^{-2}\right)$ to
$\mathcal{O}(1)$, asymptotic theories must give accurate results for rotational
dynamics and rotation-translation coupling over a range of angular velocities.
In this paper, we first survey the challenges in applying existing SBTs, which
are based on either singularity or full boundary integral representations, to
rotating filaments. We then provide an alternative approach which uses
regularized Rotne-Prager-Yamakawa (RPY) singularities to represent the fiber
centerline. We show that, unlike existing SBTs, this approach gives a grand
mobility with symmetric rotation-translation and translation-rotation coupling,
making it fit to handle rotational velocity of arbitrary order. Our asymptotics
reveal that the regularized singularity radius $\hat{a}$ can be chosen to match
either the slender body expression for translation from force or rotation from
torque, but not both.


title: Risk-averse design of tall buildings for uncertain wind conditions

abstract:
  Reducing the intensity of wind excitation via aerodynamic shape modification
is a major strategy to mitigate the reaction forces on supertall buildings,
reduce construction and maintenance costs, and improve the comfort of future
occupants. To this end, computational fluid dynamics (CFD) combined with
state-of-the-art stochastic optimization algorithms is more promising than the
trial and error approach adopted by the industry. The present study proposes
and investigates a novel approach to risk-averse shape optimization of tall
building structures that incorporates site-specific uncertainties in the wind
velocity, terrain conditions, and wind flow direction. A body-fitted finite
element approximation is used for the CFD with different wind directions
incorporated by re-meshing the fluid domain. The bending moment at the base of
the building is minimized, resulting in a building with reduced cost, material,
and hence, a reduced carbon footprint. Both risk-neutral and risk-averse
optimization of the twist and tapering of a representative building are
presented under uncertain inflow wind conditions that have been calibrated to
fit freely-available site-specific data from Basel, Switzerland. The
risk-averse strategy uses the conditional value-at-risk to optimize for the
low-probability high-consequence events appearing in the worst 10% of loading
conditions. Adaptive sampling is used to accelerate the gradient-based
stochastic optimization pipeline. The adaptive method is easy to implement and
particularly helpful for compute-intensive simulations because the number of
gradient samples grows only as the optimal design algorithm converges. The
performance of the final risk-averse building geometry is exceptionally
favorable when compared to the risk-neutral optimized geometry, thus,
demonstrating the effectiveness of the risk-averse design approach in
computational wind engineering.


title:
Generation of Super Intense Isolated Attosecond Pulses from Trapped
  Electrons in Metal Surfaces

abstract:
  Generation of ultrabroadband isolated attosecond pulses (IAPs) is essential
for time-resolved applications in chemical and material sciences, as they have
the potential to access the spectral water window region of chemical elements,
which yet has to be established. Here we propose a numerical scheme for highly
efficient high-order harmonic generation (HHG) and hence the generation of
ultrabroadband IAPs in the XUV and soft x-ray regions. The scheme combines the
use of chirped pulses with trapped electrons in copper transition-metal
surfaces and takes advantage of the characteristic features of an infrared (IR)
single-cycle pulse to achieve high conversion efficiencies and large spectral
bandwidths. In particular, we show that ultrabroad IAPs with a duration of 370
as and with a bandwidth covering the photon energy range of 50-250 and 350-450
eV can be produced. We further show that introducing an additional IR single
cycle pulse permits to enhance the harmonic yield in the soft x-ray photon
energy region by almost 7 order of magnitude. Our findings thus elucidate the
relevance of trapped electrons in metal surfaces for developing stable and
highly efficient attosecond light sources in compact solid-state devices.


title:
Distributionally Robust Model Predictive Control with Total Variation
  Distance

abstract:
  This paper studies the problem of distributionally robust model predictive
control (MPC) using total variation distance ambiguity sets. For a
discrete-time linear system with additive disturbances, we provide a
conditional value-at-risk reformulation of the MPC optimization problem that is
distributionally robust in the expected cost and chance constraints. The
distributionally robust chance constraint is over-approximated as a tightened
chance constraint, wherein the tightening for each time step in the MPC can be
computed offline, hence reducing the computational burden. We conclude with
numerical experiments to support our results on the probabilistic guarantees
and computational efficiency.


title: Enumeration of interval graphs and $d$-representable complexes

abstract:
  For each fixed $d\ge 1$, we obtain asymptotic estimates for the number of
$d$-representable simplicial complexes on $n$ vertices as a function of $n$.
The case $d=1$ corresponds to counting interval graphs, and we obtain new
results in this well-studied case as well. Our results imply that the
$d$-representable complexes comprise a vanishingly small fraction of
$d$-collapsible complexes.


title: Effective Seed Scheduling for Fuzzing with Graph Centrality Analysis

abstract:
  Seed scheduling, the order in which seeds are selected, can greatly affect
the performance of a fuzzer. Existing approaches schedule seeds based on their
historical mutation data, but ignore the structure of the underlying Control
Flow Graph (CFG). Examining the CFG can help seed scheduling by revealing the
potential edge coverage gain from mutating a seed. An ideal strategy will
schedule seeds based on a count of all reachable and feasible edges from a seed
through mutations, but computing feasibility along all edges is prohibitively
expensive. Therefore, a seed scheduling strategy must approximate this count.
We observe that an approximate count should have 3 properties -- (i) it should
increase if there are more edges reachable from a seed; (ii) it should decrease
if mutation history information suggests an edge is hard to reach or is located
far away from currently visited edges; and (iii) it should be efficient to
compute over large CFGs. We observe that centrality measures from graph
analysis naturally provide these three properties and therefore can efficiently
approximate the likelihood of reaching unvisited edges by mutating a seed. We
therefore build a graph called the edge horizon graph that connects seeds to
their closest unvisited nodes and compute the seed node's centrality to measure
the potential edge coverage gain from mutating a seed. We implement our
approach in K-scheduler and compare with many popular seed scheduling
strategies. We find that K-scheduler increases feature coverage by 25.89%
compared to Entropic and edge coverage by 4.21% compared to the next-best
AFL-based seed scheduler, in arithmetic mean on 12 Google FuzzBench programs.
It also finds 3 more previously-unknown bugs than the next-best AFL-based seed
scheduler.


title:
Dozer: Migrating Shell Commands to Ansible Modules via Execution
  Profiling and Synthesis

abstract:
  Software developers frequently use the system shell to perform configuration
management tasks. Unfortunately, the shell does not scale well to large
systems, and configuration management tools like Ansible are more difficult to
learn. We address this problem with Dozer, a technique to help developers push
their shell commands into Ansible task definitions. It operates by tracing and
comparing system calls to find Ansible modules with similar behaviors to shell
commands, then generating and validating migrations to find the task which
produces the most similar changes to the system. Dozer is syntax agnostic,
which should allow it to generalize to other configuration management
platforms. We evaluate Dozer using datasets from open source configuration
scripts.


title: A Unified Substrate for Body-Brain Co-evolution

abstract:
  The discovery of complex multicellular organism development took millions of
years of evolution. The genome of such a multicellular organism guides the
development of its body from a single cell, including its control system. Our
goal is to imitate this natural process using a single neural cellular
automaton (NCA) as a genome for modular robotic agents. In the introduced
approach, called Neural Cellular Robot Substrate (NCRS), a single NCA guides
the growth of a robot and the cellular activity which controls the robot during
deployment. We also introduce three benchmark environments, which test the
ability of the approach to grow different robot morphologies. In this paper,
NCRSs are trained with covariance matrix adaptation evolution strategy
(CMA-ES), and covariance matrix adaptation MAP-Elites (CMA-ME) for quality
diversity, which we show leads to more diverse robot morphologies with higher
fitness scores. While the NCRS can solve the easier tasks from our benchmark
environments, the success rate reduces when the difficulty of the task
increases. We discuss directions for future work that may facilitate the use of
the NCRS approach for more complex domains.


title:
Building Robust Spoken Language Understanding by Cross Attention between
  Phoneme Sequence and ASR Hypothesis

abstract:
  Building Spoken Language Understanding (SLU) robust to Automatic Speech
Recognition (ASR) errors is an essential issue for various voice-enabled
virtual assistants. Considering that most ASR errors are caused by phonetic
confusion between similar-sounding expressions, intuitively, leveraging the
phoneme sequence of speech can complement ASR hypothesis and enhance the
robustness of SLU. This paper proposes a novel model with Cross Attention for
SLU (denoted as CASLU). The cross attention block is devised to catch the
fine-grained interactions between phoneme and word embeddings in order to make
the joint representations catch the phonetic and semantic features of input
simultaneously and for overcoming the ASR errors in downstream natural language
understanding (NLU) tasks. Extensive experiments are conducted on three
datasets, showing the effectiveness and competitiveness of our approach.
Additionally, We also validate the universality of CASLU and prove its
complementarity when combining with other robust SLU techniques.


title: Groupoid models for diagrams of groupoid correspondences

abstract:
  A diagram of groupoid correspondences is a homomorphism to the bicategory of
\'etale groupoid correspondences. We study examples of such diagrams, including
complexes of groups and self-similar higher-rank graphs. We encode the diagram
in a single groupoid, which we call its groupoid model. The groupoid model is
defined so that there is a natural bijection between its actions on a space and
suitably defined actions of the diagram. We describe the groupoid model in
several cases, including a complex of groups or a self-similar group. We show
that the groupoid model is a bilimit in the bicategory of groupoid
correspondences.


title:
ANOSY: Approximated Knowledge Synthesis with Refinement Types for
  Declassification

abstract:
  Non-interference is a popular way to enforce confidentiality of sensitive
data. However, declassification of sensitive information is often needed in
realistic applications but breaks non-interference. We present ANOSY, an
approximate knowledge synthesizer for quantitative declassification policies.
ANOSY uses refinement types to automatically construct machine checked over-
and under-approximations of attacker knowledge for boolean queries on
multi-integer secrets. It also provides an AnosyT monad to track the attacker
knowledge over multiple declassification queries and checks for violations
against user-specified policies in information flow control applications. We
implement a prototype of ANOSY and show that it is precise and permissive: up
to 14 declassification queries are permitted before a policy violation occurs
using the powerset of intervals domain.


title: A Friedlander type estimate for Stokes operators

abstract:
  Let $\Omega \subset \mathbb{R}^d$ be a bounded open connected set with
Lipschitz boundary. Let $A^N$ and $A^D$ be the Neumann Stokes operator and
Dirichlet Stokes operator on $\Omega$, respectively. Further let $\lambda_1^N
\leq \lambda_2^N \leq \ldots$ and $\lambda_1^D \leq \lambda_2^D \leq \ldots$ be
the eigenvalues of $A^N$ and $A^D$ repeated with multiplicity, respectively.
Then \[ \lambda_{n+1}^N < \lambda_n^D \] for all $n \in \mathbb{N}$.


title: WayFAST: Traversability Predictive Navigation for Field Robots

abstract:
  We present a self-supervised approach for learning to predict traversable
paths for wheeled mobile robots that require good traction to navigate. Our
algorithm, termed WayFAST (Waypoint Free Autonomous Systems for
Traversability), uses RGB and depth data, along with navigation experience, to
autonomously generate traversable paths in outdoor unstructured environments.
Our key inspiration is that traction can be estimated for rolling robots using
kinodynamic models. Using traction estimates provided by an online receding
horizon estimator, we are able to train a traversability prediction neural
network in a self-supervised manner, without requiring heuristics utilized by
previous methods. We demonstrate the effectiveness of WayFAST through extensive
field testing in varying environments, ranging from sandy dry beaches to forest
canopies and snow covered grass fields. Our results clearly demonstrate that
WayFAST can learn to avoid geometric obstacles as well as untraversable
terrain, such as snow, which would be difficult to avoid with sensors that
provide only geometric data, such as LiDAR. Furthermore, we show that our
training pipeline based on online traction estimates is more data-efficient
than other heuristic-based methods.


title: A hybrid quantum image edge detector for the NISQ era

abstract:
  Edges are image locations where the gray value intensity changes suddenly.
They are among the most important features to understand and segment an image.
Edge detection is a standard task in digital image processing, solved for
example using filtering techniques. However, the amount of data to be processed
grows rapidly and pushes even supercomputers to their limits. Quantum computing
promises exponentially lower memory usage in terms of the number of qubits
compared to the number of classical bits. In this paper, we propose a hybrid
method for quantum edge detection based on the idea of a quantum artificial
neuron. Our method can be practically implemented on quantum computers,
especially on those of the current noisy intermediate-scale quantum era. We
compare six variants of the method to reduce the number of circuits and thus
the time required for the quantum edge detection. Taking advantage of the
scalability of our method, we can practically detect edges in images
considerably larger than reached before.


title:
Experimental demonstration of robustness under scaling errors for
  superadiabatic population transfer in a superconducting circuit

abstract:
  We study experimentally and theoretically the transfer of population between
the ground state and the second excited state in a transmon circuit by the use
of superadiabatic stimulated Raman adiabatic passage (saSTIRAP). We show that
the transfer is remarkably resilient against variations in the amplitudes of
the pulses (scaling errors), thus demostrating that the superadiabatic process
inherits certain robustness features from the adiabatic one. In particular, we
put in evidence a new plateau that appears at high values of the
counterdiabatic pulse strength, which goes beyond the usual framework of
saSTIRAP.


title:
Optimistic Mirror Descent Either Converges to Nash or to Strong Coarse
  Correlated Equilibria in Bimatrix Games

abstract:
  We show that, for any sufficiently small fixed $\epsilon > 0$, when both
players in a \emph{general-sum two-player (bimatrix) game} employ
\emph{optimistic mirror descent (OMD)} with smooth regularization, learning
rate $\eta = O(\epsilon^2)$ and $T = \Omega(\text{poly}(1/\epsilon))$
repetitions, either the dynamics reach an \emph{$\epsilon$-approximate Nash
equilibrium (NE)}, or the average correlated distribution of play is an
\emph{$\Omega(\text{poly}(\epsilon))$-strong coarse correlated equilibrium
(CCE)}: any possible unilateral deviation does not only leave the player worse,
but will decrease its utility by $\Omega(\text{poly}(\epsilon))$. As an
immediate consequence, when the iterates of OMD are bounded away from being
Nash equilibria in a bimatrix game, we guarantee convergence to an \emph{exact}
CCE after only $O(1)$ iterations. Our results reveal that uncoupled no-regret
learning algorithms can converge to CCE in general-sum games remarkably faster
than to NE in, for example, zero-sum games. To establish this, we show that
when OMD does not reach arbitrarily close to a NE, the \emph{(cumulative)
regret} of \emph{both} players is not only \emph{negative}, but \emph{decays
linearly} with time. Given that regret is the canonical measure of performance
in online learning, our results suggest that cycling behavior of no-regret
learning algorithms in games can be justified in terms of efficiency.


title: Non-recursive Approach for Sort-Merge Join Operation

abstract:
  Several algorithms have been developed over the years to perform join
operation which is executed frequently and affects the efficiency of the
database system. Some of these efforts prove that join performance mainly
depends on the sequences of execution of relations in addition to the hardware
architecture. In this paper, we present a method that processes a many-to-many
multi join operation by using a non-recursive reverse polish notation tree for
sort-merge join. Precisely, this paper sheds more light on main memory join
operation of two types of sort-merge join sequences: sequential join sequences
(linear tree) and general join sequences (wide bushy tree, also known as
composite inner) and also tests their performance and functionality. We will
also provide the algorithm of the proposed system that shows the implementation
steps.


title: Improving Quality of Service for Users of DAG-based Distributed Ledgers

abstract:
  An outstanding problem in the design of distributed ledgers concerns policies
that govern the manner in which users interact with the network. Network
usability is crucial to the mainstream adoption of distributed ledgers,
particularly for enterprise applications in which most users do not wish to
operate full node. For DAG-based ledgers such as IOTA, we propose a user-node
interaction mechanism that is designed to ensure the risk of a user
experiencing a poor quality of service is low. Our mechanism involves users
selecting nodes to issue their transactions to the ledger based on quality of
service indicators advertised by the nodes. Simulation results are presented to
illustrate the efficacy of the proposed policies.


title:
Counting Invariant Curves: a theory of Gopakumar-Vafa invariants for
  Calabi-Yau threefolds with an involution

abstract:
  We develop a theory of Gopakumar-Vafa (GV) invariants for a Calabi-Yau
threefold (CY3) $X$ which is equipped with an involution $\imath$ preserving
the holomorphic volume form. We define integers $n_{g,h}(\beta) $ which give a
virtual count of the number of genus $g$ curves $C$ on $X$, in the class $\beta
\in H_{2}(X)$, which are invariant under $\imath$, and whose quotient
$C/\imath$ has genus $h$. We give two definitions of $n_{g,h}(\beta) $ which we
conjecture to be equivalent: one in terms of a version of Pandharipande-Thomas
theory and one in terms of a version of Maulik-Toda theory.
  We compute our invariants and give evidence for our conjecture in several
cases. In particular, we compute our invariants when $X=S\times \mathbb{C}$
where $S$ is an Abelian surface with $\imath (a)=-a$ or a $K3$ surface with a
symplectic involution (a Nikulin $K3$ surface). For these cases, we give
formulas for our invariants in terms of Jacobi modular forms. For the Abelian
surface case, the specialization of our invariants $n_{g,h}(\beta) $ to $h=0$
recovers the count of hyperelliptic curves on an Abelian surface first computed
by Bryan-Oberdieck-Pandharipande-Yin.


title:
Modelling and Analysis of Car Following Algorithms for Fuel Economy
  Improvement in Connected and Autonomous Vehicles (CAVs)

abstract:
  Connectivity in ground vehicles allows vehicles to share crucial vehicle
data, such as vehicle acceleration, with each other. Using sensors such as
cameras, radars and lidars, on the other hand, the intravehicular distance
between a leader vehicle and a host vehicle can be detected, as well as the
relative speed. Cooperative Adaptive Cruise Control (CACC) builds upon ground
vehicle connectivity and sensor information to form convoys with automated car
following. CACC can also be used to improve fuel economy and mobility
performance of vehicles in the said convoy. In this paper, 3 car following
algorithms for fuel economy of CAVs are presented. An Adaptive Cruise Control
(ACC) algorithm was designed as the benchmark model for comparison. A
Cooperative Adaptive Cruise Control (CACC) was designed, which uses lead
vehicle acceleration received through V2V in car following. an Ecological
Cooperative Adaptive Cruise Control (Eco-CACC) model was developed that takes
the erratic lead vehicle acceleration as a disturbance to be attenuated. A High
Level (HL) controller was designed for decision making when the lead vehicle
was an erratic driver. Model-in-the-Loop (MIL) and Hardware-in-the-Loop (HIL)
simulations were run to test these car following algorithms for fuel economy
performance. The results show that the HL controller was able to attain a
smooth speed profile that consumed less fuel through using CACC and Eco-CACC
than its ACC counterpart when the lead vehicle was erratic.


title:
Entropy, single-particle occupation probabilities, and short-range
  correlations

abstract:
  Both theoretical and experimental studies have shown that the fermion
momentum distribution has a generic behavior $n(k)=C/k^4$ at momenta larger
than the Fermi momentum, due to their short-range interactions, with
approximately 20\% of the particles having momenta larger than the Fermi
momentum. It is shown here that short-range correlations, which induce
high-momentum tails of the single-particle occupation probabilities, increase
the entropy of fermionic systems, which in its turn will affect the dynamics of
many reactions, such as heavy-ion collisions and nuclear fission.


title:
Born-Oppenheimer potentials for $\Pi$, $\Delta$, and $\Phi$ states of
  the hydrogen molecule

abstract:
  We report on accurate variational calculations of the Born-Oppenheimer
potential for excited states of the hydrogen molecule with $\Pi$, $\Delta$, and
$\Phi$ symmetries. The obtained potential energy curves reach the relative
precision of $10^{-9}$ or better along internuclear distances of 0.01 -- 20 au.
Calculations rely on recursive evaluation of two-center two-electron molecular
integrals with exponential functions in arbitrary precision arithmetics. Our
results for most of the states are the first ever reported, and for the
previously calculated states constitute an improvement by several orders of
magnitude.


title:
DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning
  for Histopathology Whole Slide Image Classification

abstract:
  Multiple instance learning (MIL) has been increasingly used in the
classification of histopathology whole slide images (WSIs). However, MIL
approaches for this specific classification problem still face unique
challenges, particularly those related to small sample cohorts. In these, there
are limited number of WSI slides (bags), while the resolution of a single WSI
is huge, which leads to a large number of patches (instances) cropped from this
slide. To address this issue, we propose to virtually enlarge the number of
bags by introducing the concept of pseudo-bags, on which a double-tier MIL
framework is built to effectively use the intrinsic features. Besides, we also
contribute to deriving the instance probability under the framework of
attention-based MIL, and utilize the derivation to help construct and analyze
the proposed framework. The proposed method outperforms other latest methods on
the CAMELYON-16 by substantially large margins, and is also better in
performance on the TCGA lung cancer dataset. The proposed framework is ready to
be extended for wider MIL applications. The code is available at:
https://github.com/hrzhang1123/DTFD-MIL


title: PlaneMVS: 3D Plane Reconstruction from Multi-View Stereo

abstract:
  We present a novel framework named PlaneMVS for 3D plane reconstruction from
multiple input views with known camera poses. Most previous learning-based
plane reconstruction methods reconstruct 3D planes from single images, which
highly rely on single-view regression and suffer from depth scale ambiguity. In
contrast, we reconstruct 3D planes with a multi-view-stereo (MVS) pipeline that
takes advantage of multi-view geometry. We decouple plane reconstruction into a
semantic plane detection branch and a plane MVS branch. The semantic plane
detection branch is based on a single-view plane detection framework but with
differences. The plane MVS branch adopts a set of slanted plane hypotheses to
replace conventional depth hypotheses to perform plane sweeping strategy and
finally learns pixel-level plane parameters and its planar depth map. We
present how the two branches are learned in a balanced way, and propose a
soft-pooling loss to associate the outputs of the two branches and make them
benefit from each other. Extensive experiments on various indoor datasets show
that PlaneMVS significantly outperforms state-of-the-art (SOTA) single-view
plane reconstruction methods on both plane detection and 3D geometry metrics.
Our method even outperforms a set of SOTA learning-based MVS methods thanks to
the learned plane priors. To the best of our knowledge, this is the first work
on 3D plane reconstruction within an end-to-end MVS framework.


title: Composition of low-lying $\mathbf{J=\tfrac{3}{2}^\pm \,\Delta}$-baryons

abstract:
  A Poincar\'e-covariant quark+diquark Faddeev equation is used to develop
insights into the structure of the four lightest
$(I,J^P=\tfrac{3}{2},\tfrac{3}{2}^\pm)$ baryon multiplets. Whilst these systems
can contain isovector-axialvector and isovector-vector diquarks, one may
neglect the latter and still arrive at a reliable description. The
$(\tfrac{3}{2},\tfrac{3}{2}^+)$ states are the simpler systems, with features
that bear some resemblance to quark model pictures, \emph{e.g}., their most
prominent rest-frame orbital angular momentum component is $\mathsf S$-wave and
the $\Delta(1600)\tfrac{3}{2}^+$ may reasonably be viewed as a radial
excitation of the $\Delta(1232)\tfrac{3}{2}^+$. The
$(\tfrac{3}{2},\tfrac{3}{2}^-)$ states are more complex: the
$\Delta(1940)\tfrac{3}{2}^-$ expresses little of the character of a radial
excitation of the $\Delta(1700)\tfrac{3}{2}^-$; and whilst the rest-frame wave
function of the latter is predominantly $\mathsf P$-wave, the leading piece in
the $\Delta(1940)\tfrac{3}{2}^-$ wave function is $\mathsf S$-wave, in conflict
with quark model expectations. Experiments that can test these predictions,
such as large momentum transfer resonance electroexcitation, may shed light on
the nature of emergent hadron mass.


title: Time-domain Generalization of Kron Reduction

abstract:
  Kron reduction is a network-reduction method that eliminates nodes with zero
current injections from electrical networks operating in sinusoidal steady
state. In the time domain, the state-of-the-art application of Kron reduction
has been in networks with transmission lines that have constant R/L ratios.
This paper considers RL networks without such restriction and puts forth a
provably exact time-domain generalization of Kron reduction. Exemplifying
empirical tests on a wye-delta network are provided to validate the analytical
results.


title: Characterizing High-Quality Test Methods: A First Empirical Study

abstract:
  To assess the quality of a test suite, one can rely on mutation testing,
which computes whether the overall test cases are adequately exercising the
covered lines. However, this high level of granularity may overshadow the
quality of individual test methods. In this paper, we propose an empirical
study to assess the quality of test methods by relying on mutation testing at
the method level. We find no major differences between high-quality and
low-quality test methods in terms of size, number of asserts, and
modifications. In contrast, high-quality test methods are less affected by
critical test smells. Finally, we discuss practical implications for
researchers and practitioners.


title: Pattern recovery by SLOPE

abstract:
  LASSO and SLOPE are two popular methods for dimensionality reduction in the
high-dimensional regression. LASSO can eliminate redundant predictors by
setting the corresponding regression coefficients to zero, while SLOPE can
additionally identify clusters of variables with the same absolute values of
regression coefficients. It is well known that LASSO Irrepresentability
Condition is sufficient and necessary for the proper estimation of the sign of
sufficiently large regression coefficients. In this article we formulate an
analogous Irrepresentability Condition for SLOPE, which is sufficient and
necessary for the proper identification of the SLOPE pattern, i.e. of the
proper sign as well as of the proper ranking of the absolute values of
individual regression coefficients, while proper ranking guarantees a proper
clustering. We also provide asymptotic results on the strong consistency of
pattern recovery by SLOPE when the number of columns in the design matrix is
fixed while the sample size diverges to infinity.


title:
Monadic forgetful functors and (non-)presentability for $C^*$- and
  $W^*$-algebras

abstract:
  We prove that the forgetful functors from the categories of $C^*$- and
$W^*$-algebras to Banach $*$-algebras, Banach algebras or Banach spaces are all
monadic, answering a question of J.Rosick\'{y}, and that the categories of
unital (commutative) $C^*$-algebras are not locally-isometry
$\aleph_0$-generated either as plain or as metric-enriched categories,
answering a question of I. Di Liberti and Rosick\'{y}.
  We also prove a number of negative presentability results for the category of
von Neumann algebras: not only is that category not locally presentable, but in
fact its only presentable objects are the two algebras of dimension $\le 2$.
For the same reason, for a locally compact abelian group $\mathbb{G}$ the
category of $\mathbb{G}$-graded von Neumann algebras is not locally
presentable.


title: Deep Portrait Delighting

abstract:
  We present a deep neural network for removing undesirable shading features
from an unconstrained portrait image, recovering the underlying texture. Our
training scheme incorporates three regularization strategies: masked loss, to
emphasize high-frequency shading features; soft-shadow loss, which improves
sensitivity to subtle changes in lighting; and shading-offset estimation, to
supervise separation of shading and texture. Our method demonstrates improved
delighting quality and generalization when compared with the state-of-the-art.
We further demonstrate how our delighting method can enhance the performance of
light-sensitive computer vision tasks such as face relighting and semantic
parsing, allowing them to handle extreme lighting conditions.


title:
Optimal Control of Connected Automated Vehicles with Event-Triggered
  Control Barrier Functions

abstract:
  We address the problem of controlling Connected and Automated Vehicles (CAVs)
in conflict areas of a traffic network subject to hard safety constraints. It
has been shown that such problems can be solved through a combination of
tractable optimal control problem formulations and the use of Control Barrier
Functions (CBFs) that guarantee the satisfaction of all constraints. These
solutions can be reduced to a sequence of Quadratic Programs (QPs) which are
efficiently solved on line over discrete time steps. However, the feasibility
of each such QP cannot be guaranteed over every time step. To overcome this
limitation, we develop an event-driven approach such that the next QP is
triggered by properly defined events and show that this approach can eliminate
infeasible cases due to time-driven inter-sampling effects. Simulation examples
show how overall infeasibilities can be significantly reduced with the proposed
event-triggering scheme, while also reducing the need for communication among
CAVs without compromising performance.


title:
Low-Dimensional Analysis of a Kuramoto Model with Inertia and Hebbian
  Learning

abstract:
  We study low-dimensional dynamics of a Kuramoto model with inertia with
Hebbian learning. In this model, the coupling strength between oscillators
depends on the phase differences between oscillators and changes according to a
Hebbian learning rule. We analyze the special case of two coupled oscillators,
which yields a three-dimensional dynamical system, and classify the stability
of its equilibrium points using linear stability analysis. We show that the
system is dissipative and that all of its trajectories are eventually confined
to a bounded region. We approximate the limiting behavior and demarcate the
parameter regions of three qualitatively different behaviors of the system.
Using insights from our analysis of the low-dimensional dynamics, we study the
original high-dimensional system when we draw the intrinsic frequencies of the
oscillators from Gaussian distributions with different variances.


title:
FxP-QNet: A Post-Training Quantizer for the Design of Mixed
  Low-Precision DNNs with Dynamic Fixed-Point Representation

abstract:
  Deep neural networks (DNNs) have demonstrated their effectiveness in a wide
range of computer vision tasks, with the state-of-the-art results obtained
through complex and deep structures that require intensive computation and
memory. Now-a-days, efficient model inference is crucial for consumer
applications on resource-constrained platforms. As a result, there is much
interest in the research and development of dedicated deep learning (DL)
hardware to improve the throughput and energy efficiency of DNNs. Low-precision
representation of DNN data-structures through quantization would bring great
benefits to specialized DL hardware. However, the rigorous quantization leads
to a severe accuracy drop. As such, quantization opens a large hyper-parameter
space at bit-precision levels, the exploration of which is a major challenge.
In this paper, we propose a novel framework referred to as the Fixed-Point
Quantizer of deep neural Networks (FxP-QNet) that flexibly designs a mixed
low-precision DNN for integer-arithmetic-only deployment. Specifically, the
FxP-QNet gradually adapts the quantization level for each data-structure of
each layer based on the trade-off between the network accuracy and the
low-precision requirements. Additionally, it employs post-training
self-distillation and network prediction error statistics to optimize the
quantization of floating-point values into fixed-point numbers. Examining
FxP-QNet on state-of-the-art architectures and the benchmark ImageNet dataset,
we empirically demonstrate the effectiveness of FxP-QNet in achieving the
accuracy-compression trade-off without the need for training. The results show
that FxP-QNet-quantized AlexNet, VGG-16, and ResNet-18 reduce the overall
memory requirements of their full-precision counterparts by 7.16x, 10.36x, and
6.44x with less than 0.95%, 0.95%, and 1.99% accuracy drop, respectively.


title: Toward Physically Realizable Quantum Neural Networks

abstract:
  There has been significant recent interest in quantum neural networks (QNNs),
along with their applications in diverse domains. Current solutions for QNNs
pose significant challenges concerning their scalability, ensuring that the
postulates of quantum mechanics are satisfied and that the networks are
physically realizable. The exponential state space of QNNs poses challenges for
the scalability of training procedures. The no-cloning principle prohibits
making multiple copies of training samples, and the measurement postulates lead
to non-deterministic loss functions. Consequently, the physical realizability
and efficiency of existing approaches that rely on repeated measurement of
several copies of each sample for training QNNs are unclear. This paper
presents a new model for QNNs that relies on band-limited Fourier expansions of
transfer functions of quantum perceptrons (QPs) to design scalable training
procedures. This training procedure is augmented with a randomized quantum
stochastic gradient descent technique that eliminates the need for sample
replication. We show that this training procedure converges to the true minima
in expectation, even in the presence of non-determinism due to quantum
measurement. Our solution has a number of important benefits: (i) using QPs
with concentrated Fourier power spectrum, we show that the training procedure
for QNNs can be made scalable; (ii) it eliminates the need for resampling, thus
staying consistent with the no-cloning rule; and (iii) enhanced data efficiency
for the overall training process since each data sample is processed once per
epoch. We present a detailed theoretical foundation for our models and methods'
scalability, accuracy, and data efficiency. We also validate the utility of our
approach through a series of numerical experiments.


title:
Enhancing Mobile App Bug Reporting via Real-time Understanding of
  Reproduction Steps

abstract:
  One of the primary mechanisms by which developers receive feedback about
in-field failures of software from users is through bug reports. Unfortunately,
the quality of manually written bug reports can vary widely due to the effort
required to include essential pieces of information, such as detailed
reproduction steps (S2Rs). Despite the difficulty faced by reporters, few
existing bug reporting systems attempt to offer automated assistance to users
in crafting easily readable, and conveniently reproducible bug reports. To
address the need for proactive bug reporting systems that actively aid the user
in capturing crucial information, we introduce a novel bug reporting approach
called EBug. EBug assists reporters in writing S2Rs for mobile applications by
analyzing natural language information entered by reporters in real-time, and
linking this data to information extracted via a combination of static and
dynamic program analyses. As reporters write S2Rs, EBug is capable of
automatically suggesting potential future steps using predictive models trained
on realistic app usages. To evaluate EBug, we performed two user studies based
on 20 failures from $11$ real-world apps. The empirical studies involved ten
participants that submitted ten bug reports each and ten developers that
reproduced the submitted bug reports. In the studies, we found that reporters
were able to construct bug reports 31 faster with EBug as compared to the
state-of-the-art bug reporting system used as a baseline. EBug's reports were
also more reproducible with respect to the ones generated with the baseline.
Furthermore, we compared EBug's prediction models to other predictive modeling
approaches and found that, overall, the predictive models of our approach
outperformed the baseline approaches. Our results are promising and demonstrate
the potential benefits provided by proactively assistive bug reporting systems.


title: Learning curves for the multi-class teacher-student perceptron

abstract:
  One of the most classical results in high-dimensional learning theory
provides a closed-form expression for the generalisation error of binary
classification with the single-layer teacher-student perceptron on i.i.d.
Gaussian inputs. Both Bayes-optimal estimation and empirical risk minimisation
(ERM) were extensively analysed for this setting. At the same time, a
considerable part of modern machine learning practice concerns multi-class
classification. Yet, an analogous analysis for the corresponding multi-class
teacher-student perceptron was missing. In this manuscript we fill this gap by
deriving and evaluating asymptotic expressions for both the Bayes-optimal and
ERM generalisation errors in the high-dimensional regime. For Gaussian teacher
weights, we investigate the performance of ERM with both cross-entropy and
square losses, and explore the role of ridge regularisation in approaching
Bayes-optimality. In particular, we observe that regularised cross-entropy
minimisation yields close-to-optimal accuracy. Instead, for a binary teacher we
show that a first-order phase transition arises in the Bayes-optimal
performance.


title: Guts in Sutured Decompositions and the Thurston Norm

abstract:
  We construct an invariant called guts for second homology classes in
irreducible 3-manifolds with toral boundary and non-degenerate Thurston norm.
We prove that the guts of second homology classes in each Thurston cone are
invariant under a natural condition. We show that the guts of different
homology classes are related by sutured decompositions. As an application, an
invariant of knot complements is given and is computed in a few interesting
cases. Besides, we show that the dimension of a maximal simplex in a Kakimizu
Complex is an invariant.


title: Derived Algebraic Cobordism

abstract:
  We construct and study a theory of bivariant cobordism of derived schemes.
Our theory provides a vast generalization of the algebraic bordism theory of
characteristic 0 algebraic schemes, constructed earlier by Levine and Morel,
and a (partial) non-$\Ab^1$-invariant refinement of the motivic cohomology
theory $MGL$ in Morel--Voevodsky's stable motivic homotopy theory. Our main
result is that bivariant cobordism satisfies the projective bundle formula. As
applications of this, we construct cobordism Chern classes of vector bundles,
and establish a strong connection between the cobordism cohomology rings and
the Grothendieck ring of vector bundles. We also provide several universal
properties for our theory. Additionally, our algebraic cobordism is also used
to construct a candidate for the elusive theory of Chow cohomology of schemes.


title:
Hardware Watermarking for Finite State Machines, with Symmetric Circuit
  Encryption

abstract:
  Putting a watermark into digital circuitry has its own set of challenges.
Creating a secure watermark in printed matter usually involves including
graphics that are difficult to reproduce. In circuitry, including additional
circuitry that is hard to produce, one must contend with the prospect of
increasing power consumption of the circuit, decreasing the speed of the
circuit, and introducing watermark circuitry that is easily reproduced. In this
paper we present a watermark method for sequential circuitry. It allows for
several degrees of calibration, allowing the user to tune the complexity of the
watermark to requirements of speed, power, and efficacy. Our method uses an
encryption technique to introduce secrets about the watermark circuit, at
several levels. It also employs a boundary scan testing protocol as a means to
protect the watermark circuitry. Our discussion starts by describing the
different tools needed in our watermark scheme. We then discuss the difficulty
of the problem associated with cracking our watermark circuit. This analysis
shows that, with full implementation, our method can be made quite secure.


title:
Learning by non-interfering feedback chemical signaling in physical
  networks

abstract:
  Both non-neural and neural biological systems can learn. So rather than
focusing on purely brain-like learning, efforts are underway to study learning
in physical systems. Such efforts include equilibrium propagation (EP) and
coupled learning (CL), which require storage of two different states-the free
state and the perturbed state-during the learning process to retain information
about gradients. Inspired by slime mold, we propose a new learning algorithm
rooted in chemical signaling that does not require storage of two different
states. Rather, the output error information is encoded in a chemical signal
that diffuses into the network in a similar way as the activation/feedforward
signal. The steady state feedback chemical concentration, along with the
activation signal, stores the required gradient information locally. We apply
our algorithm using a physical, linear flow network and test it using the Iris
data set with 93% accuracy. We also prove that our algorithm performs gradient
descent. Finally, in addition to comparing our algorithm directly with EP and
CL, we address the biological plausibility of the algorithm.


title:
A cylindrical implosion platform for the study of highly magnetized
  plasmas at LMJ

abstract:
  The potential benefits of the use of magnetic fields in Inertial Confinement
Fusion (ICF) experiments have been investigated for several years, and
exploring them has given rise to new experimental platforms such as the
Magnetized Liner Inertial Fusion (MagLIF) approach at the Z-machine, or its
laser-driven equivalent at the OMEGA 60 laser. Implementing these experimental
platforms at MJ-scale laser facilities such as the National Ignition Facility
(NIF) or the Laser MegaJoule (LMJ) enlarges the range of plasma parameters that
can be explored, in particular the level of magnetization that can be achieved
through a more efficient compression. In addition, these facilities are crucial
to reaching self-sustained nuclear fusion, including laser-based
magneto-inertial approach. In this paper, we present a complete design of an
experimental platform for magnetized implosions using cylindrical targets at
LMJ. In this case, the initial magnetic field is generated along the axis of
the cylinder using laser-driven coil targets, thus minimizing debris and
increasing diagnostic access compared with pulsed power field generators. We
present a comprehensive simulation study of the initial B-field generated with
these coil targets, as well as 2-dimensional advanced magneto-hydrodynamics
(MHD) simulations showing the achievable plasma conditions in the cylindrical
implosions in presence (or not) of this seed B-field. Additionally, we discuss
how these conditions can be diagnosed using X-ray and particle diagnostics
within the current facility capabilities, and present a plan for future
experiments of this kind at LMJ.


title: Anomalous Hall effect in electrolytically reduced PdCoO$_2$ thin films

abstract:
  PdCoO$_2$, being highly conductive and anisotropic, is a promising material
for fundamental and technological applications. Recently, reduced PdCoO$_2$
thin films were shown to exhibit ferromagnetism after hydrogen annealing. Here,
we demonstrate that when PdCoO$_2$ film is used as a cathode for dissociation
of water, hydrogen generated around the film reduces the material and leads to
the emergence of anomalous Hall effect (AHE). Moreover, we demonstrate that the
sign of the AHE signal can also be changed with the electrolytic process.
Electrolytically-modified PdCoO$_2$ films may open a door to applications in
spintronics.


title:
Adaptive movement strategy may promote biodiversity in the
  rock-paper-scissors model

abstract:
  We study the role of the adaptive movement strategy in promoting biodiversity
in cyclic models described by the rock-paper-scissors game rules. We assume
that individuals of one out of the species may adjust their movement to escape
hostile regions and stay longer in their comfort zones. Running a series of
stochastic simulations, we calculate the alterations in the spatial patterns
and population densities in scenarios where not all organisms are physically or
cognitively conditioned to perform the behavioural strategy. Although the
adaptive movement strategy is not profitable in terms of territorial dominance
for the species, it may promote biodiversity. Our findings show that if all
individuals are apt to move adaptively, coexistence probability increases for
intermediary mobility. The outcomes also show that even if not all individuals
can react to the signals received from the neighbourhood, biodiversity is still
benefited, but for a shorter mobility range. We find that the improvement in
the coexistence conditions is more accentuated if organisms adjust their
movement intensely and can receive sensory information from longer distances.
We also discover that biodiversity is slightly promoted for high mobility if
the proportion of individuals participating in the strategy is low. Our results
may be helpful for biologists and data scientists to understand adaptive
process learning in system biology.

categories: q-bio.PE nlin.AO nlin.PS physics.bio-ph physics.data-an


title:
Effect of ECAP and heat treatment on mechanical properties, stress
  relaxation behavior and corrosion resistance of a 321-type austenitic steel
  with increased delta-ferrite content

abstract:
  Hot rolled commercial metastable austenitic steel 0.8C-18Cr-10Ni-0.1Ti
(Russian industrial name 08X18H10T, analog 321L) with strongly elongated thin
delta-ferrite particles in its microstructure was the object of investigations.
The lengths of these delta-particles were up to 500 mkm, the thickness was 10
mkm. The formation of the strain-induced martensite as well as the grinding of
the austenite and of the delta-ferrite grains take place during ECAP. During
the annealing of the UFG steel, the formation of the delta-phase particles
takes place. These particles affect the grain boundary migration and the
strength of the steel. However, a reduction of the Hall-Petch coefficient as
compared to the coarse-grained (CG) steel due to the fragmentation of the
delta-ferrite particles was observed. The samples of the UFG steel were found
to have 2-3 times higher stress relaxation resistance as compared to the CG
steel (a higher macroelasticity stress and a lower stress relaxation
magnitude). The differences in the stress relaxation resistance of the UFG and
CG steels were investigated. ECAP was shown to result in an increase in the
corrosion rate and in an increased tendency to the intergranular corrosion
(IGC). The reduction of the corrosion resistance of the UFG steel was found to
originate from the increase in the fraction of the strain-induced martensite
during ECAP.


title: New Theory for Cooper Pair Formation and Superconductivity

abstract:
  A new theory for Cooper pair formation and superconductivity is derived from
quantum statistical mechanics. It is shown that zero momentum Cooper pairs have
non-local permutations and behave as effective bosons with an internal weight
close to unity when bound by a primary minimum in the potential of mean force.
For a short-ranged, shallow, and highly curved minimum there is no
thermodynamic barrier to condensation. The size of the condensing Cooper pairs
found here is orders of magnitude smaller than those found in BCS theory. The
new statistical theory is applicable to high temperature superconductors.


title: Fast on-line signature recognition based on VQ with time modeling

abstract:
  This paper proposes a multi-section vector quantization approach for on-line
signature recognition. We have used the MCYT database, which consists of 330
users and 25 skilled forgeries per person performed by 5 different impostors.
This database is larger than those typically used in the literature.
Nevertheless, we also provide results from the SVC database.
  Our proposed system outperforms the winner of SVC with a reduced
computational requirement, which is around 47 times lower than DTW. In
addition, our system improves the database storage requirements due to vector
compression, and is more privacy-friendly as it is not possible to recover the
original signature using the codebooks. Experimental results with MCYT provide
a 99.76% identification rate and 2.46% EER (skilled forgeries and individual
threshold). Experimental results with SVC are 100% of identification rate and
0% (individual threshold) and 0.31% (general threshold) when using a
two-section VQ approach.


title: Music Generation Using an LSTM

abstract:
  Over the past several years, deep learning for sequence modeling has grown in
popularity. To achieve this goal, LSTM network structures have proven to be
very useful for making predictions for the next output in a series. For
instance, a smartphone predicting the next word of a text message could use an
LSTM. We sought to demonstrate an approach of music generation using Recurrent
Neural Networks (RNN). More specifically, a Long Short-Term Memory (LSTM)
neural network. Generating music is a notoriously complicated task, whether
handmade or generated, as there are a myriad of components involved. Taking
this into account, we provide a brief synopsis of the intuition, theory, and
application of LSTMs in music generation, develop and present the network we
found to best achieve this goal, identify and address issues and challenges
faced, and include potential future improvements for our network.


title:
An Empirical Study on Learning and Improving the Search Objective for
  Unsupervised Paraphrasing

abstract:
  Research in unsupervised text generation has been gaining attention over the
years. One recent approach is local search towards a heuristically defined
objective, which specifies language fluency, semantic meanings, and other
task-specific attributes. Search in the sentence space is realized by
word-level edit operations including insertion, replacement, and deletion.
However, such objective function is manually designed with multiple components.
Although previous work has shown maximizing this objective yields good
performance in terms of true measure of success (i.e. BLEU and iBLEU), the
objective landscape is considered to be non-smooth with significant noises,
posing challenges for optimization. In this dissertation, we address the
research problem of smoothing the noise in the heuristic search objective by
learning to model the search dynamics. Then, the learned model is combined with
the original objective function to guide the search in a bootstrapping fashion.
Experimental results show that the learned models combined with the original
search objective can indeed provide a smoothing effect, improving the search
performance by a small margin.