title:
Resource allocation for reconfigurable intelligent surface aided
  broadcast channels

abstract:
  A two-user downlink network aided by a reconfigurable intelligent surface is
considered. The weighted sum signal to interference plus noise ratio
maximization and the sum rate maximization models are presented, where the
precoding vectors and the RIS matrix are jointly optimized. Since the
optimization problem is non-convex and difficult, new approximation models are
proposed. The upper bounds of the corresponding objective functions are derived
and maximized. Two new algorithms based on the alternating direction method of
multiplier are proposed. It is proved that the proposed algorithms converge to
the KKT points of the approximation models as long as the iteration points
converge. Simulation results show the good performances of the proposed models
compared to state of the art algorithms.


title:
Path-integral approach to information processing in complex networks:
  information core emerges from entropic transitions

abstract:
  Complex networks usually exhibit a rich architecture organized over multiple
intertwined scales. Information pathways are expected to pervade these scales
reflecting structural insights that are not manifest from analyses of the
network topology. Moreover, small-world effects correlate the different network
hierarchies complicating the identification of coexisting mesoscopic structures
and functional cores. We present a communicability analysis of effective
information pathways throughout complex networks based on information diffusion
to shed further light on these issues. We employ a variety of brand-new
theoretical techniques allowing for: (i) bring the theoretical framework to
quantify the probability of information diffusion among nodes, (iii) identify
critical scales and structures of complex networks regardless of their
intrinsic properties, and (iii) introduce future renormalization group
approaches based on diffusion distances. By harmoniously combining these ideas,
we find the information flow of complex networks at different resolution
scales. We focus on entropic transitions using computational techniques,
uncovering generic mesoscale functional structures controlling information
processing in complex networks. Altogether, this study sheds much light on
allowing new theoretical techniques to renormalize complex spatial structures
at different scales and offers a basis for exploring structural
scale-invariance and universality in complex networks.

categories: physics.soc-ph cond-mat.dis-nn nlin.AO q-bio.NC


title: Learning Weakly-Supervised Contrastive Representations

abstract:
  We argue that a form of the valuable information provided by the auxiliary
information is its implied data clustering information. For instance,
considering hashtags as auxiliary information, we can hypothesize that an
Instagram image will be semantically more similar with the same hashtags. With
this intuition, we present a two-stage weakly-supervised contrastive learning
approach. The first stage is to cluster data according to its auxiliary
information. The second stage is to learn similar representations within the
same cluster and dissimilar representations for data from different clusters.
Our empirical experiments suggest the following three contributions. First,
compared to conventional self-supervised representations, the
auxiliary-information-infused representations bring the performance closer to
the supervised representations, which use direct downstream labels as
supervision signals. Second, our approach performs the best in most cases, when
comparing our approach with other baseline representation learning methods that
also leverage auxiliary data information. Third, we show that our approach also
works well with unsupervised constructed clusters (e.g., no auxiliary
information), resulting in a strong unsupervised representation learning
approach.


title:
Neighborhood Contrastive Learning for Scientific Document
  Representations with Citation Embeddings

abstract:
  Learning scientific document representations can be substantially improved
through contrastive learning objectives, where the challenge lies in creating
positive and negative training samples that encode the desired similarity
semantics. Prior work relies on discrete citation relations to generate
contrast samples. However, discrete citations enforce a hard cut-off to
similarity. This is counter-intuitive to similarity-based learning, and ignores
that scientific papers can be very similar despite lacking a direct citation -
a core problem of finding related research. Instead, we use controlled nearest
neighbor sampling over citation graph embeddings for contrastive learning. This
control allows us to learn continuous similarity, to sample hard-to-learn
negatives and positives, and also to avoid collisions between negative and
positive samples by controlling the sampling margin between them. The resulting
method SciNCL outperforms the state-of-the-art on the SciDocs benchmark.
Furthermore, we demonstrate that it can train (or tune) models
sample-efficiently, and that it can be combined with recent training-efficient
methods. Perhaps surprisingly, even training a general-domain language model
this way outperforms baselines pretrained in-domain.


title:
Transport Model Comparison Studies of Intermediate-Energy Heavy-Ion
  Collisions

abstract:
  Transport models are the main method to obtain physics information from low
to relativistic-energy heavy-ion collisions. The Transport Model Evaluation
Project (TMEP) has been pursued to test the robustness of transport model
predictions in reaching consistent conclusions from the same type of physical
model. Calculations under controlled conditions of physical input and set-up
were performed with various participating codes. These included both
calculations of nuclear matter in a box with periodic boundary conditions, and
more realistic calculations of heavy-ion collisions. In this intermediate
review, we summarize and discuss the present status of the project. We also
provide condensed descriptions of the 26 participating codes, which contributed
to some part of the project. These include the major codes in use today. We
review the main results of the studies completed so far. They show, that in box
calculations the differences between the codes can be well understood and a
convergence of the results can be reached. These studies also highlight the
systematic differences between the two families of transport codes, known as
BUU and QMD type codes. However, when the codes were compared in full heavy-ion
collisions using different physical models, as recently for pion production,
they still yielded substantially different results. This calls for further
comparisons of heavy-ion collisions with controlled models and of box
comparisons of important ingredients, like momentum-dependent fields, which are
currently underway. We often indicate improved strategies in performing
transport simulations and thus provide guidance to code developers. Results of
transport simulations of heavy-ion collisions from a given code will have more
significance if the code can be validated against benchmark calculations such
as the ones summarized in this review.


title:
Convolutional Neural Network with Convolutional Block Attention Module
  for Finger Vein Recognition

abstract:
  Convolutional neural networks have become a popular research in the field of
finger vein recognition because of their powerful image feature representation.
However, most researchers focus on improving the performance of the network by
increasing the CNN depth and width, which often requires high computational
effort. Moreover, we can notice that not only the importance of pixels in
different channels is different, but also the importance of pixels in different
positions of the same channel is different. To reduce the computational effort
and to take into account the different importance of pixels, we propose a
lightweight convolutional neural network with a convolutional block attention
module (CBAM) for finger vein recognition, which can achieve a more accurate
capture of visual structures through an attention mechanism. First, image
sequences are fed into a lightweight convolutional neural network we designed
to improve visual features. Afterwards, it learns to assign feature weights in
an adaptive manner with the help of a convolutional block attention module. The
experiments are carried out on two publicly available databases and the results
demonstrate that the proposed method achieves a stable, highly accurate, and
robust performance in multimodal finger recognition.


title: Learning to Ground Objects for Robot Task and Motion Planning

abstract:
  Task and motion planning (TAMP) algorithms have been developed to help robots
plan behaviors in discrete and continuous spaces. Robots face complex
real-world scenarios, where it is hardly possible to model all objects or their
physical properties for robot planning (e.g., in kitchens or shopping centers).
In this paper, we define a new object-centric TAMP problem, where the TAMP
robot does not know object properties (e.g., size and weight of blocks). We
then introduce Task-Motion Object-Centric planning ({\bf TMOC}), a grounded
TAMP algorithm that learns to ground objects and their physical properties with
a physics engine. TMOC is particularly useful for those tasks that involve
dynamic complex robot-multi-object interactions that can hardly be modeled
beforehand. We have demonstrated and evaluated TMOC in simulation and using a
real robot. Results show that TMOC outperforms competitive baselines from the
literature in cumulative utility.


title:
Can Machines Help Us Answering Question 16 in Datasheets, and In Turn
  Reflecting on Inappropriate Content?

abstract:
  Large datasets underlying much of current machine learning raise serious
issues concerning inappropriate content such as offensive, insulting,
threatening, or might otherwise cause anxiety. This calls for increased dataset
documentation, e.g., using datasheets. They, among other topics, encourage to
reflect on the composition of the datasets. So far, this documentation,
however, is done manually and therefore can be tedious and error-prone,
especially for large image datasets. Here we ask the arguably "circular"
question of whether a machine can help us reflect on inappropriate content,
answering Question 16 in Datasheets. To this end, we propose to use the
information stored in pre-trained transformer models to assist us in the
documentation process. Specifically, prompt-tuning based on a dataset of
socio-moral values steers CLIP to identify potentially inappropriate content,
therefore reducing human labor. We then document the inappropriate images found
using word clouds, based on captions generated using a vision-language model.
The documentations of two popular, large-scale computer vision datasets --
ImageNet and OpenImages -- produced this way suggest that machines can indeed
help dataset creators to answer Question 16 on inappropriate image content.


title: On the Computation of General Vector-valued Modular Forms

abstract:
  We present and discuss an algorithm and its implementation that is capable of
directly determining Fourier expansions of any vector-valued modular form of
weight at least $2$ associated with representations whose kernel is a
congruence subgroup. It complements two available algorithms that are limited
to inductions of Dirichlet characters and to Weil representations, thus
covering further applications like Moonshine or Jacobi forms for congruence
subgroups. We examine the calculation of invariants in specific representations
via techniques from permutation groups, which greatly aids runtime performance.
We explain how a generalization of cusp expansions of classical modular forms
enters our implementation. After a heuristic consideration of time complexity,
we relate the formulation of our algorithm to the two available ones, to
highlight the compromises between level of generality and performance that each
them makes.


title: Secure-by-Construction Synthesis of Cyber-Physical Systems

abstract:
  Correct-by-construction synthesis is a cornerstone of the confluence of
formal methods and control theory towards designing safety-critical systems.
Instead of following the time-tested, albeit laborious
(re)design-verify-validate loop, correct-by-construction methodology advocates
the use of continual refinements of formal requirements -- connected by chains
of formal proofs -- to build a system that assures the correctness by design. A
remarkable progress has been made in scaling the scope of applicability of
correct-by-construction synthesis -- with a focus on cyber-physical systems
that tie discrete-event control with continuous environment -- to enlarge
control systems by combining symbolic approaches with principled state-space
reduction techniques.
  Unfortunately, in the security-critical control systems, the security
properties are verified ex post facto the design process in a way that
undermines the correct-by-construction paradigm. We posit that, to truly
realize the dream of correct-by-construction synthesis for security-critical
systems, security considerations must take center-stage with the safety
considerations. Moreover, catalyzed by the recent progress on the opacity
sub-classes of security properties and the notion of hyperproperties capable of
combining security with safety properties, we believe that the time is ripe for
the research community to holistically target the challenge of
secure-by-construction synthesis. This paper details our vision by highlighting
the recent progress and open challenges that may serve as bricks for providing
a solid foundation for secure-by-construction synthesis of cyber-physical
systems.


title: Reproduction Capabilities of Penalized Hyperbolic-polynomial Splines

abstract:
  This paper investigates two important analytical properties of
hyperbolic-polynomial penalized splines, HP-splines for short. HP-splines,
obtained by combining a special type of difference penalty with
hyperbolic-polynomial B-splines (HB-splines), were recently introduced by the
authors as a generalization of P-splines. HB-splines are bell-shaped basis
functions consisting of segments made of real exponentials $e^{\alpha x},\,
e^{-\alpha x}$ and linear functions multiplied by these exponentials,
$xe^{+\alpha x}$ and $xe^{-\alpha x}$. Here, we show that these type of
penalized splines reproduce function in the space $\{e^{-\alpha x},\ x
e^{-\alpha x}\}$, that is they fit exponential data exactly. Moreover, we show
that they conserve the first and second 'exponential' moments.


title: Liveness and Latency of Byzantine State-Machine Replication

abstract:
  Byzantine state-machine replication (SMR) ensures the consistency of
replicated state in the presence of malicious replicas and lies at the heart of
the modern blockchain technology. Byzantine SMR protocols often guarantee
safety under all circumstances and liveness only under synchrony. However,
guaranteeing liveness even under this assumption is nontrivial. So far we have
lacked systematic ways of incorporating liveness mechanisms into Byzantine SMR
protocols, which often led to subtle bugs. To close this gap, we introduce a
modular framework to facilitate the design of provably live and efficient
Byzantine SMR protocols. Our framework relies on a view abstraction generated
by a special SMR synchronizer primitive to drive the agreement on command
ordering. We present a simple formal specification of an SMR synchronizer and
its bounded-space implementation under partial synchrony. We also apply our
specification to prove liveness and analyze the latency of three Byzantine SMR
protocols via a uniform methodology. In particular, one of these results yields
what we believe is the first rigorous liveness proof for the algorithmic core
of the seminal PBFT protocol.


title:
Quantitative estimate of diameter for weighted manifolds under integral
  curvature bounds and $\varepsilon$-range

abstract:
  In this article, we extend the compactness theorems proved by Sprouse and
Hwang-Lee to a weighted manifold under the assumption that the weighted Ricci
curvature is bounded below in terms of its weight function. With the help of
the $\varepsilon$-range, we treat the case that the effective dimension is at
most $1$ in addition to the case that the effective dimension is at least the
dimension of the manifold. To show these theorems, we extend the segment
inequality of Cheeger-Colding to a weighted manifold.


title: Binary orbital evolution driven by a circumbinary disc

abstract:
  The question whether the interaction of a circumbinary disc with the central
binary system leads to shrinking or expansion of the binary orbit has attracted
considerable interest as it impacts the evolution of binary black holes and
stellar binary stars in their formation phase. We performed two-dimensional
hydrodynamical simulations of circumbinary discs for a large parameter set of
disc viscosities and thicknesses and two different binary mass ratios for
binaries on circular orbits. For those we measured carefully the net angular
momentum and mass transfer between disc and binary system, and evaluate the
normalised specific angular momentum accretion, $j_\mathrm{s}$ . This is
compared to the theoretical, critical specific angular momentum change
$j_\mathrm{s,crit}$ that separates contracting from expanding cases which
depends on the the binary's mass ratio and the relative accretion onto the two
stars. Using finite and infinite disc models we show that the inferred binary
evolution is very similar for both setups and confirm that $j_\mathrm{s}$ can
be measured accurately with cylindrical simulations that do not include the
central binary. However, to obtain the relative accretion onto the stars for
non-equal mass binaries, simulations that cover the whole domain including the
binary are required. We find that for thick discs with aspect ratio $h = 0.1$
the binaries expand for all viscosities, while discs with $h = 0.05$ lead to an
expansion only for larger viscosities with $\alpha$ exceeding $\sim 0.005$.
Overall, the regime of binary expansion extends to a much wider parameter space
than previously anticipated, but for thin, low viscosity discs the orbits
shrink.


title: Client Monitoring Software: A Monitoring Tool for Greatleaf Land Inc

abstract:
  Monitoring typically supports greater analysis and allows for a lot deeper
data collection on a Web browser level. Analysts may usually see the use of
web-based monitoring software within an entire client context when it comes to
client-side monitoring, on the other hand. In the case of Greatleaf Land Inc.,
their monitoring is somehow traditional. Traditional means that the paper
method of monitoring is being utilized. When arranging piles of paper and
afterwards forgetting about it, users lose track of where the information is
situated. Another notable issue is that some information situating on a written
on paper isn't easily cant be detected beyond skimming, which is a process
normally being used to fast track records on a flipping pages, and sometimes
there's also the fact that written records situating on papers can only situate
in one location at a certain time unless the custodian has given up and made
copies for everything. This study will check the feasibility of a web-based
client monitoring software for Greatleaf Land Inc. By using ISO 25010, the
study will determine if it is beneficial for Greatleaf Land Inc. to have an
online monitoring software rather than settling to the traditional method that
is currently being used by their company.


title:
Collective wind farm operation based on a predictive model increases
  utility-scale energy production

abstract:
  Wind turbines located in wind farms are operated to maximize only their own
power production. Individual operation results in wake losses that reduce farm
energy. In this study, we operate a wind turbine array collectively to maximize
total array production through wake steering. The selection of the farm control
strategy relies on the optimization of computationally efficient flow models.
We develop a physics-based, data-assisted flow control model to predict the
optimal control strategy. In contrast to previous studies, we first design and
implement a multi-month field experiment at a utility-scale wind farm to
validate the model over a range of control strategies, most of which are
suboptimal. The flow control model is able to predict the optimal yaw
misalignment angles for the array within +/- 5 degrees for most wind directions
(11-32% power gains). Using the validated model, we design a control protocol
which increases the energy production of the farm in a second multi-month
experiment by 2.7% and 1.0%, for the wind directions of interest and for wind
speeds between 6 and 8 m/s and all wind speeds, respectively. The developed and
validated predictive model can enable a wider adoption of collective wind farm
operation.


title:
Partially Fake Audio Detection by Self-attention-based Fake Span
  Discovery

abstract:
  The past few years have witnessed the significant advances of speech
synthesis and voice conversion technologies. However, such technologies can
undermine the robustness of broadly implemented biometric identification models
and can be harnessed by in-the-wild attackers for illegal uses. The ASVspoof
challenge mainly focuses on synthesized audios by advanced speech synthesis and
voice conversion models, and replay attacks. Recently, the first Audio Deep
Synthesis Detection challenge (ADD 2022) extends the attack scenarios into more
aspects. Also ADD 2022 is the first challenge to propose the partially fake
audio detection task. Such brand new attacks are dangerous and how to tackle
such attacks remains an open question. Thus, we propose a novel framework by
introducing the question-answering (fake span discovery) strategy with the
self-attention mechanism to detect partially fake audios. The proposed fake
span detection module tasks the anti-spoofing model to predict the start and
end positions of the fake clip within the partially fake audio, address the
model's attention into discovering the fake spans rather than other shortcuts
with less generalization, and finally equips the model with the discrimination
capacity between real and partially fake audios. Our submission ranked second
in the partially fake audio detection track of ADD 2022.


title:
Thermal processing of Jupiter Family Comets during their chaotic orbital
  evolution

abstract:
  Evidence for cometary activity beyond Jupiter and Saturn's orbits -- such as
that observed for Centaurs and long period comets -- suggests that the thermal
processing of comet nuclei starts long before they enter the inner Solar
System, where they are typically observed and monitored. Such observations
raise questions as to the depth of unprocessed material, and whether the
activity of JFCs can be representative of any primitive material. Here we model
the coupled thermal and dynamical evolution of Jupiter Family Comets (JFCs),
from the moment they leave their outer Solar System reservoirs until their
ejection into interstellar space. We apply a thermal evolution model to a
sample of simulated JFCs obtained from dynamical simulations (arXiv:1706.07447)
that successfully reproduce the orbital distribution of observed JFCs. We show
that due to the stochastic nature of comet trajectories toward the inner solar
system, all simulated JFCs undergo multiple heating episodes resulting in
significant modifications of their initial volatile contents. A statistical
analysis constrains the extent of such processing. We suggest that primordial
condensed hypervolatile ices should be entirely lost from the layers that
contribute to cometary activity observed today. Our results demonstrate that
understanding the orbital (and thus, heating) history of JFCs is essential when
putting observations in a broader context.


title:
On the mystery of the absence of a spin-orbit gap in scanning tunneling
  microscopy spectra of germanene

abstract:
  Germanene, the germanium analogue of graphene, shares many properties with
its carbon counterpart. Both materials are two-dimensional materials that host
Dirac fermions. There are, however, also a few important differences between
these two materials: (1) graphene has a planar honeycomb lattice, whereas
germanene's honeycomb lattice is buckled and (2) the spin-orbit gap in
germanene is predicted to be about three orders of magnitude larger than the
spin-orbit gap in graphene (24 meV for germanene versus 20 $\mu$eV for
graphene). Surprisingly, scanning tunneling spectra recorded on germanene
layers synthesized on different substrates do not show any sign of the presence
of a spin-orbit gap in germanene. To date the exact origin of the absence of
this spin-orbit gap in the scanning tunneling spectra of germanene has remained
a mystery. In this work we show that the absence of the spin-orbit can be
explained by germanene's exceptionally low work function of only 3.8 eV. The
difference in work function between germanene and the scanning tunneling
microscopy tip (the work functions of most commonly used STM tips are in the
range of 4.5 to 5.5 eV) gives rise to an electric field in the tunnel junction.
This electric field results in a strong suppression of the size of the
spin-orbit gap.


title: Domain Adaptation via Prompt Learning

abstract:
  Unsupervised domain adaption (UDA) aims to adapt models learned from a
well-annotated source domain to a target domain, where only unlabeled samples
are given. Current UDA approaches learn domain-invariant features by aligning
source and target feature spaces. Such alignments are imposed by constraints
such as statistical discrepancy minimization or adversarial training. However,
these constraints could lead to the distortion of semantic feature structures
and loss of class discriminability. In this paper, we introduce a novel prompt
learning paradigm for UDA, named Domain Adaptation via Prompt Learning (DAPL).
In contrast to prior works, our approach makes use of pre-trained
vision-language models and optimizes only very few parameters. The main idea is
to embed domain information into prompts, a form of representations generated
from natural language, which is then used to perform classification. This
domain information is shared only by images from the same domain, thereby
dynamically adapting the classifier according to each domain. By adopting this
paradigm, we show that our model not only outperforms previous methods on
several cross-domain benchmarks but also is very efficient to train and easy to
implement.


title: Geometric Transformer for Fast and Robust Point Cloud Registration

abstract:
  We study the problem of extracting accurate correspondences for point cloud
registration. Recent keypoint-free methods bypass the detection of repeatable
keypoints which is difficult in low-overlap scenarios, showing great potential
in registration. They seek correspondences over downsampled superpoints, which
are then propagated to dense points. Superpoints are matched based on whether
their neighboring patches overlap. Such sparse and loose matching requires
contextual features capturing the geometric structure of the point clouds. We
propose Geometric Transformer to learn geometric feature for robust superpoint
matching. It encodes pair-wise distances and triplet-wise angles, making it
robust in low-overlap cases and invariant to rigid transformation. The
simplistic design attains surprisingly high matching accuracy such that no
RANSAC is required in the estimation of alignment transformation, leading to
$100$ times acceleration. Our method improves the inlier ratio by $17{\sim}30$
percentage points and the registration recall by over $7$ points on the
challenging 3DLoMatch benchmark. Our code and models are available at
\url{https://github.com/qinzheng93/GeoTransformer}.


title:
CodeFill: Multi-token Code Completion by Jointly Learning from Structure
  and Naming Sequences

abstract:
  Code completion is an essential feature of IDEs, yet current autocompleters
are restricted to either grammar-based or NLP-based single token completions.
Both approaches have significant drawbacks: grammar-based autocompletion is
restricted in dynamically-typed language environments, whereas NLP-based
autocompleters struggle to understand the semantics of the programming language
and the developer's code context.
  In this work, we present CodeFill, a language model for autocompletion that
combines learned structure and naming information. Using a parallel Transformer
architecture and multi-task learning, CodeFill consumes sequences of source
code token names and their equivalent AST token types. Uniquely, CodeFill is
trained both for single-token and multi-token (statement) prediction, which
enables it to learn long-range dependencies among grammatical and naming
elements. We train CodeFill on two datasets, consisting of 29M and 425M lines
of code, respectively. To make the evaluation more realistic, we develop a
method to automatically infer points in the source code at which completion
matters. We compare CodeFill against four baselines and two state-of-the-art
models, GPT-C and TravTrans+.CodeFill surpasses all baselines in single token
prediction (MRR: 70.9% vs. 66.2% and 67.8%) and outperforms the state of the
art for multi-token prediction (ROUGE-L: 63.7% vs. 52.4% and 59.2%, for n=4
tokens). We publicly release our source code and datasets.


title: ArgSciChat: A Dataset for Argumentative Dialogues on Scientific Papers

abstract:
  The applications of conversational agents for scientific disciplines (as
expert domains) are understudied due to the lack of dialogue data to train such
agents. While most data collection frameworks, such as Amazon Mechanical Turk,
foster data collection for generic domains by connecting crowd workers and task
designers, these frameworks are not much optimized for data collection in
expert domains. Scientists are rarely present in these frameworks due to their
limited time budget. Therefore, we introduce a novel framework to collect
dialogues between scientists as domain experts on scientific papers. Our
framework lets scientists present their scientific papers as groundings for
dialogues and participate in dialogue they like its paper title. We use our
framework to collect a novel argumentative dialogue dataset, ArgSciChat. It
consists of 498 messages collected from 41 dialogues on 20 scientific papers.
Alongside extensive analysis on ArgSciChat, we evaluate a recent conversational
agent on our dataset. Experimental results show that this agent poorly performs
on ArgSciChat, motivating further research on argumentative scientific agents.
We release our framework and the dataset.


title:
Vanishing results for the coherent cohomology of automorphic vector
  bundles over the Siegel variety in positive characteristic

abstract:
  We prove vanishing results for the coherent cohomology of the good reduction
modulo $p$ of the Siegel variety with coefficients in some automorphic bundles.
We show that for an automorphic bundle with highest weight $\lambda$ near the
walls of the anti-dominant Weyl chamber, there is an integer $e \geq 0$ such
that the cohomology is concentrated in degrees $[0, e]$. The accessible weights
with our method are not necessarily regular and not necessarily $p$-small.
Since our method is technical, we also provide an algorithm written in Sage
that computes explicitly the vanishing results.


title: TRIP: Trustless Coercion-Resistant In-Person Voter Registration

abstract:
  Most existing remote electronic voting systems are vulnerable to voter
coercion and vote buying. While coercion-resistant voting systems address this
challenge, current schemes assume that the voter has access to an untappable,
incorruptible device during voter registration. We present TRIP, an in-person
voter registration scheme enabling voters to create verifiable and
indistinguishable real and fake credentials using an untrusted kiosk inside a
privacy booth at a supervised location, e.g., the registrar's office. TRIP
ensures the integrity of the voter's real credential while enabling the
creation of fake credentials using interactive zero-knowledge proofs between
the voter as the verifier and the kiosk as the prover, unbeknownst to the
average voter. TRIP ensures that even voters who are under extreme coercion,
and cannot leave the booth with a real credential, can delegate their vote to a
political party, with the caveat that they must then trust the kiosk. TRIP
optimizes the tallying process by limiting the number of credentials a voter
can receive and capping the number of votes that a credential can cast per
election. We conduct a preliminary usability study among 41 participants at a
university and found that 42.5% of participants rated TRIP a B or higher in
usability, a promising result for a voter registration scheme that
substantially reduces trust in the registrar.


title: On Payment Channels in Asynchronous Money Transfer Systems

abstract:
  Money transfer is an abstraction that realizes the core of cryptocurrencies.
It has been shown that, contrary to common belief, money transfer in the
presence of Byzantine faults can be implemented in asynchronous networks and
does not require consensus. Nonetheless, existing implementations of money
transfer still require a quadratic message complexity per payment, making
attempts to scale hard. In common blockchains, such as Bitcoin and Ethereum,
this cost is mitigated by payment channels implemented as a second layer on top
of the blockchain allowing to make many off-chain payments between two users
who share a channel. Such channels only require on-chain transactions for
channel opening and closing, while the intermediate payments are done off-chain
with constant message complexity. But payment channels in-use today require
synchrony, therefore they are inadequate for asynchronous money transfer
systems.
  In this paper, we provide a series of possibility and impossibility results
for payment channels in asynchronous money transfer systems. We first prove a
quadratic lower bound on the message complexity of on-chain transfers. Then, we
explore two types of payment channels, unidirectional and bidirectional. We
define them as shared memory abstractions and prove that in certain cases they
can be implemented as a second layer on top of an asynchronous money transfer
system whereas in other cases it is impossible.


title:
Versatile Dueling Bandits: Best-of-both-World Analyses for Online
  Learning from Preferences

abstract:
  We study the problem of $K$-armed dueling bandit for both stochastic and
adversarial environments, where the goal of the learner is to aggregate
information through relative preferences of pair of decisions points queried in
an online sequential manner. We first propose a novel reduction from any
(general) dueling bandits to multi-armed bandits and despite the simplicity, it
allows us to improve many existing results in dueling bandits. In particular,
\emph{we give the first best-of-both world result for the dueling bandits
regret minimization problem} -- a unified framework that is guaranteed to
perform optimally for both stochastic and adversarial preferences
simultaneously. Moreover, our algorithm is also the first to achieve an optimal
$O(\sum_{i = 1}^K \frac{\log T}{\Delta_i})$ regret bound against the
Condorcet-winner benchmark, which scales optimally both in terms of the
arm-size $K$ and the instance-specific suboptimality gaps $\{\Delta_i\}_{i =
1}^K$. This resolves the long-standing problem of designing an instancewise
gap-dependent order optimal regret algorithm for dueling bandits (with matching
lower bounds up to small constant factors). We further justify the robustness
of our proposed algorithm by proving its optimal regret rate under
adversarially corrupted preferences -- this outperforms the existing
state-of-the-art corrupted dueling results by a large margin. In summary, we
believe our reduction idea will find a broader scope in solving a diverse class
of dueling bandits setting, which are otherwise studied separately from
multi-armed bandits with often more complex solutions and worse guarantees. The
efficacy of our proposed algorithms is empirically corroborated against the
existing dueling bandit methods.


title: Tunable Excitons in Rhombohedral Trilayer Graphene

abstract:
  Trilayer graphene is receiving an increasing level of attention due to its
stacking--dependent magnetoelectric and optoelectric properties, and its more
robust ferromagnetism relative to monolayer and bilayer variants.
  Additionally, rhombohedral stacked trilayer graphene presents the possibility
of easily opening a gap via either an external electric field perpendicular to
the layers, or via the application of external strain.
  In this paper, we consider an external electric field to open a bandgap in
rhombohedral trilayer graphene and study the excitonic optical response of the
system.
  This is done via the combination of a tight binding model with the
Bethe--Salpeter equation, solved semi--analytically and requiring only a simple
numerical quadrature.
  We then discuss the valley--dependent optical selection rules, followed by
the computation of the excitonic linear optical conductivity for the case of a
rhombohedral graphene trilayer encapsulated in hexagonal boron nitride.
  The tunability of the excitonic resonances via an external field is also
discussed, together with the increasing localization of the excitonic states as
the field increases.


title: Tuning quantum-classical correspondence of molecular systems in a cavity

abstract:
  We show that the correspondence between quantum and classical mechanics can
be tuned by varying the coupling strength between the cavity modes and an atom
or a molecule. In the acceleration gauge the cavity-matter system is
represented by an effective Hamiltonian with a non-trivial coupling appearing
in the potential, and a renormaized mass. Importantly, the acceleration-gauge
coupling is non-monotonic with the strength cavity-matter interaction. As a
result one obtain effective approximately decoupled field-matter dynamics for
weak and strong interactions. In the weak coupling regime the effective mass is
essentially the original mass. In contrast, the renormalized mass is increased
as the interaction is increased. This results in acceleration gauge dynamics of
atom/molecule with the original Hamiltonian and effective Planck's constant
that is reduced when the interaction is increased. This approach might lead in
particular to the possibility of studying the correspondence of "quantum-chaos"
(quantum stochasticity) with classical chaos, as well as either enhancement or
suppression of tunneling, by varying a controllable physical parameter.
Physical realization of our findings is briefly discussed.


title:
Study of KNO scaling in pp collisions at $\sqrt{s}$ from 0.9 to 13 TeV
  using results of the ATLAS at the LHC

abstract:
  The comparisons of the charged-particle multiplicity and the average
transverse momentum distributions on the scaled multiplicity, KNO scale, using
the results of the ATLAS collaboration at the LHC are presented. These
distributions were measured in proton-proton collisions at centre-of-mass
energies from 0.9 to 13 TeV for the absolute pseudorapidity region less than
2.5 and two samples of events with each charged-particle transverse momentum
greater than 100 and 500 MeV, respectively. The shape evolution of the
multiplicity distributions with a collision energy is studied in terms of KNO
scaling variables. The charged-particle multiplicity distributions on the KNO
scale have the similar shape and decrease with increasing collision energy. The
KNO distributions tend to be independent of energy for the highest energies.
The average transverse momentum distributions on the KNO scale have a similar
shape and increase with increasing collision energy.


title: Digital Contact Tracing Solutions: Promises, Pitfalls and Challenges

abstract:
  The COVID-19 pandemic has caused many countries to deploy novel digital
contact tracing (DCT) systems to boost the efficiency of manual tracing of
infection chains. In this paper, we systematically analyze DCT solutions and
categorize them based on their design approaches and architectures. We analyze
them with regard to effectiveness, security, privacy, and ethical aspects and
compare prominent solutions with regard to these requirements. In particular,
we discuss the shortcomings of the Google and Apple Exposure Notification API
(GAEN) that is currently widely adopted all over the world. We find that the
security and privacy of GAEN have considerable deficiencies as it can be
compromised by severe, large-scale attacks. We also discuss other proposed
approaches for contact tracing, including our proposal TRACECORONA, that are
based on Diffie-Hellman (DH) key exchange and aim at tackling shortcomings of
existing solutions. Our extensive analysis shows thatTRACECORONA fulfills the
above security requirements better than deployed state-of-the-art approaches.
We have implementedTRACECORONA, and its beta test version has been used by more
than 2000 users without any major functional problems, demonstrating that there
are no technical reasons requiring to make compromises with regard to the
requirements of DCTapproaches.


title: Inverse-designed metaphotonics for hypersensitive detection

abstract:
  Controlling the flow of broadband electromagnetic energy at the nanoscale
remains a critical challenge in optoelectronics. Surface plasmon polaritons (or
plasmons) provide subwavelength localization of light, but are affected by
significant losses. On the contrary, dielectrics lack a sufficiently robust
response in the visible to trap photons similar to metallic structures.
Overcoming these limitations appears elusive, as it implies devising a path to
circumvent causality in the quantum-mechanical form of matter. Here we
demonstrate that addressing this problem is possible if we employ a novel
approach based on suitably deformed reflective metaphotonic structures. The
complex geometrical shape engineered in these reflectors emulates nondispersive
index responses, which can be inverse-designed following arbitrary form
factors. We discuss the realization of essential components such as resonators
with an ultra-high refractive index of $n=100$ in diverse profiles. These
structures support localization of light in the form of bound states in the
continuum (BIC), fully localized in air, in a platform in which all refractive
index regions are physically accessible. We discuss our approach to sensing
applications, designing a class of sensors where the analyte directly contacts
areas of ultra-high refractive index. Leveraging this feature, we report
differential sensitivities up to $350$~nm/RIU in structures with footprints of
approximately one micron. These performances are two times better than the
closest competitor with a similar form factor. Inversely designed reflective
metaphotonics offers a flexible technology for controlling broadband light,
supporting optoelectronics' integration with large bandwidths in circuitry with
miniaturized footprints.


title:
Superlinear convergence of Anderson accelerated Newton's method for
  solving stationary Navier-Stokes equations

abstract:
  This paper studies the performance Newton's iteration applied with Anderson
acceleration for solving the incompressible steady Navier-Stokes equations. We
manifest that this method converges superlinearly with a good initial guess,
and moreover, a large Anderson depth decelerates the convergence speed
comparing to a small Anderson depth. We observe that the numerical tests
confirm these analytical convergence results, and in addition, Anderson
acceleration sometimes enlarges the domain of convergence for Newton's method.


title: UA-FedRec: Untargeted Attack on Federated News Recommendation

abstract:
  News recommendation is critical for personalized news distribution. Federated
news recommendation enables collaborative model learning from many clients
without sharing their raw data. It is promising for privacy-preserving news
recommendation. However, the security of federated news recommendation is still
unclear. In this paper, we study this problem by proposing an untargeted attack
called UA-FedRec. By exploiting the prior knowledge of news recommendation and
federated learning, UA-FedRec can effectively degrade the model performance
with a small percentage of malicious clients. First, the effectiveness of news
recommendation highly depends on user modeling and news modeling. We design a
news similarity perturbation method to make representations of similar news
farther and those of dissimilar news closer to interrupt news modeling, and
propose a user model perturbation method to make malicious user updates in
opposite directions of benign updates to interrupt user modeling. Second,
updates from different clients are typically aggregated by weighted-averaging
based on their sample sizes. We propose a quantity perturbation method to
enlarge sample sizes of malicious clients in a reasonable range to amplify the
impact of malicious updates. Extensive experiments on two real-world datasets
show that UA-FedRec can effectively degrade the accuracy of existing federated
news recommendation methods, even when defense is applied. Our study reveals a
critical security issue in existing federated news recommendation systems and
calls for research efforts to address the issue.


title: Dynamical architecture of the HD 107148 system

abstract:
  We present an independent Doppler validation and dynamical orbital analysis
of the two-planet system HD 107148, which was recently announced in Rosenthal
et al. (2021). Our detailed analyses are based on literature HIRES data and
newly obtained HARPS and CARMENES radial velocity (RV) measurements as part of
our survey in search for additional planets around single planet systems. We
perform a periodogram analysis of the available HIRES and HARPS precise RVs and
stellar activity indicators. We do not find any apparent correlation between
the RV measurements and the stellar activity indicators, thus linking the two
strong periodicities to a moderately compact multiple-planet system. We carry
out orbital fitting analysis by testing various one- and two-planet orbital
configurations and studying the posterior probability distribution of the
fitted parameters. Our results solidify the existence of a Saturn-mass planet
(HD 107148 b, discovered first) with a period $P_b\sim77.2$ d, and a second,
eccentric ($e_c \sim$ 0.4), Neptune-mass exoplanet (HD 107148 c), with an
orbital period of $P_c\sim18.3$ d. Finally, we investigate the two-planet
system's long-term stability and overall orbital dynamics with the posterior
distribution of our preferred orbital configuration. Our N-body stability
simulations show that the system is long-term stable and exhibits large secular
osculations in eccentricity but in no particular mean-motion resonance
configuration. The HD 107148 system, consisting of a Solar-type main sequence
star with two giant planets in a rare configuration, features a common proper
motion white dwarf companion and is, therefore, a valuable target for
understanding the formation and evolution of planetary systems.


title: Heavy neutrinos at future linear e$^+$e$^-$ colliders

abstract:
  Neutrinos are among the most mysterious particles in nature. Their mass
hierarchy and oscillations, as well as their antiparticle properties, are being
intensively studied in experiments around the world. Moreover, in many models
of physics beyond the Standard Model, the baryon asymmetry or the dark matter
density in the Universe are explained by introducing new species of neutrinos.
Among others, heavy neutrinos of Dirac or Majorana nature were proposed to
solve open questions in High Energy Physics. Such neutrinos with masses above
the EW scale could be produced at future linear e$^+$e$^-$ colliders, like the
Compact LInear Collider (CLIC) or the International Linear Collider (ILC).
  We studied the possibility of observing decays of heavy Dirac and Majorana
neutrinos in the $qq\ell$ final state with ILC running at 500 GeV and 1 TeV,
and CLIC at 3 TeV. The analysis is based on the Whizard event generation and
fast simulation of detector response with Delphes. Neutrinos with masses from
200 GeV to 3.2 TeV were considered. We estimated the limits on the production
cross sections, interpreted them in terms of the neutrino-lepton coupling
parameter $V_{\ell N}^{2}$ (effectively the neutrino mixing angle) and compared
them with current limits coming from the LHC running at 13 TeV, as well as the
expected limits from future hadron colliders. The limits for the future lepton
colliders, extending down to the coupling values of $10^{-7} - 10^{-6}$, are
stricter than any other limit estimates published so far.


title: An Edge-Based Interface-Tracking Method for Multiphase Flows

abstract:
  We propose a novel class of Edge-Based Interface-Tracking (EBIT) methods in
the field of multiphase flows for advecting the interface. The position of the
interface is tracked by marker points located on the edges of the underlying
grid, making the method flexible with respect to the choice of spatial
discretization and suitable for parallel computation. In this paper we present
a simple EBIT method based on two-dimensional Cartesian grids and on a linear
interface representation.


title:
Uncovering the non-equilibrium stationary properties in sparse Boolean
  networks

abstract:
  Dynamic processes of interacting units on a network are out of equilibrium in
general. In the case of a directed tree, the dynamic cavity method provides an
efficient tool that characterises the dynamic trajectory of the process for the
linear threshold model. However, because of the computational complexity of the
method, the analysis has been limited to systems where the largest number of
neighbours is small. We devise an efficient implementation of the dynamic
cavity method which substantially reduces the computational complexity of the
method for systems with discrete couplings. Our approach opens up the
possibility to investigate the dynamic properties of networks with fat-tailed
degree distribution. We exploit this new implementation to study properties of
the non-equilibrium steady-state. We extend the dynamical cavity approach to
calculate the pairwise correlations induced by different motifs in the network.
Our results suggest that just two basic motifs of the network are able to
accurately describe the entire statistics of observed correlations. Finally, we
investigate models defined on networks containing bi-directional interactions.
We observe that the stationary state associated with networks with symmetric or
anti-symmetric interactions is biased towards the active or inactive state
respectively, even if independent interaction entries are drawn from a
symmetric distribution. This phenomenon, which can be regarded as a form of
spontaneous symmetry-breaking, is peculiar to systems formulated in terms of
Boolean variables, as opposed to Ising spins.


title: (A)dS Taub-NUT and exact black bounces with scalar hair

abstract:
  We present a new family of exact four-dimensional Taub-NUT spacetimes in
Einstein-$\Lambda$ theory supplemented with a conformally coupled scalar field
exhibiting a power-counting super-renormalizable potential. The construction
proceeds as follows: A solution of a conformally coupled theory with a
conformal potential, henceforth the seed $(g_{\mu\nu},\phi)$, is transformed by
the action of a specific change of frame in addition with a simultaneous shift
of the seed scalar. The new configuration, $(\bar{g}_{\mu\nu},\bar{\phi})$,
solves the field equations of a conformally coupled theory with the
aforementioned super-renormalizable potential. The solution spectrum of the
seed is notoriously enhanced. We highlight the existence of two types of exact
black bounces given by de Sitter and anti-de Sitter geometries that transit
across three different configurations each. The de Sitter geometries transit
from a regular black hole with event and cosmological horizons to a bouncing
cosmology connecting two de Sitter Universes with different values of the
asymptotic cosmological constant. An intermediate phase represented by a de
Sitter wormhole or by a bouncing cosmology that connects two de Sitter
Universes is shown, this under the presence of a cosmological horizon. On the
other hand, the anti-de Sitter geometries transit from a regular black hole
with inner and event horizons to a wormhole that connects two asymptotic
boundaries with different constant curvatures. The intermediate phase is given
by an anti-de Sitter regular black hole with a single event horizon that
appears in two different settings. As a regular anti-de Sitter black hole
inside of an anti-de Sitter wormhole or as an anti-de Sitter regular black hole
with an internal cosmological bounce. These geometries are smoothly connected
by the mass parameter only. Other black holes, bouncing cosmologies and
wormholes are also found.


title: Spiking Cochlea with System-level Local Automatic Gain Control

abstract:
  Including local automatic gain control (AGC) circuitry into a silicon cochlea
design has been challenging because of transistor mismatch and model
complexity. To address this, we present an alternative system-level algorithm
that implements channel-specific AGC in a silicon spiking cochlea by measuring
the output spike activity of individual channels. The bandpass filter gain of a
channel is adapted dynamically to the input amplitude so that the average
output spike rate stays within a defined range. Because this AGC mechanism only
needs counting and adding operations, it can be implemented at low hardware
cost in a future design. We evaluate the impact of the local AGC algorithm on a
classification task where the input signal varies over 32 dB input range. Two
classifier types receiving cochlea spike features were tested on a speech
versus noise classification task. The logistic regression classifier achieves
an average of 6% improvement and 40.8% relative improvement in accuracy when
the AGC is enabled. The deep neural network classifier shows a similar
improvement for the AGC case and achieves a higher mean accuracy of 96%
compared to the best accuracy of 91% from the logistic regression classifier.

categories: eess.SP cs.CV cs.LG cs.SD cs.SY eess.AS eess.SY


title: On Triangle Counting Parameterized by Twin-Width

abstract:
  In this report we present an algorithm solving Triangle Counting in time
$O(d^2n+m)$, where n and m, respectively, denote the number of vertices and
edges of a graph G and d denotes its twin-width, a recently introduced graph
parameter. We assume that a compact representation of a d-contraction sequence
of G is given.


title: How Do Vision Transformers Work?

abstract:
  The success of multi-head self-attentions (MSAs) for computer vision is now
indisputable. However, little is known about how MSAs work. We present
fundamental explanations to help better understand the nature of MSAs. In
particular, we demonstrate the following properties of MSAs and Vision
Transformers (ViTs): (1) MSAs improve not only accuracy but also generalization
by flattening the loss landscapes. Such improvement is primarily attributable
to their data specificity, not long-range dependency. On the other hand, ViTs
suffer from non-convex losses. Large datasets and loss landscape smoothing
methods alleviate this problem; (2) MSAs and Convs exhibit opposite behaviors.
For example, MSAs are low-pass filters, but Convs are high-pass filters.
Therefore, MSAs and Convs are complementary; (3) Multi-stage neural networks
behave like a series connection of small individual models. In addition, MSAs
at the end of a stage play a key role in prediction. Based on these insights,
we propose AlterNet, a model in which Conv blocks at the end of a stage are
replaced with MSA blocks. AlterNet outperforms CNNs not only in large data
regimes but also in small data regimes. The code is available at
https://github.com/xxxnell/how-do-vits-work.


title: On the Cauchy problem for quasi-linear Hamiltonian KdV-type equations

abstract:
  We prove local in time well-posedness for a class of quasilinear Hamiltonian
KdV-type equations with periodic boundary conditions, more precisely we show
existence, uniqueness and continuity of the solution map. We improve the
previous result in \cite{Mietka}, generalising the considered class of
equations and improving the regularity assumption on the initial data.


abstract:
  In this paper we introduce $n\mathbb{Z}$-abelian and $n\mathbb{Z}$-exact
categories by axiomatising properties of $n\mathbb{Z}$-cluster tilting
subcategories. We study this categories and show that every
$n\mathbb{Z}$-cluster tilting subcategory of an abelian (resp., exact) category
has a natural structure of an $n\mathbb{Z}$-abelian (resp.,
$n\mathbb{Z}$-exact) category. Also we show that every small
$n\mathbb{Z}$-abelian category arise in this way, and discuss the problem for
$n\mathbb{Z}$-exact categories.


title: The Merger Dynamics of the X-ray Emitting Plasma in Clusters of Galaxies

abstract:
  As the formation of cosmic structure continues to proceed, we observe one of
the latest stages of this process in mergers of clusters of galaxies with other
clusters, groups, and galaxies. The X-ray emitting hot plasma of these systems
can be dramatically affected by these mergers, producing cold and shock fronts,
gas sloshing, bulk motions, and turbulence. Combined with numerical
simulations, observations of these features can be used to constrain the plasma
physics of the hot gas as well as its interaction with high-energy cosmic rays.
In this chapter, we review these topics and point forward to what the
capabilities of future observatories will reveal.


title: Branched covers and rational homology balls

abstract:
  The concordance group of knots in the three-sphere contains an infinite
subgroup generated by elements of order two, each one of which is represented
by a knot K with the property that for every n > 0, the n-fold cyclic cover of
S^3 branched over K bounds a rational homology ball. This implies that the
kernel of the canonical homomorphism from the knot concordance group to the
infinite direct sum of rational homology cobordism groups (defined via
prime-power branched covers) contains an infinitely generated two-torsion
subgroup.


title: Local law and rigidity for unitary Brownian motion

abstract:
  We establish high probability estimates on the eigenvalue locations of
Brownian motion on the $N$-dimensional unitary group, as well as estimates on
the number of eigenvalues lying in any interval on the unit circle. These
estimates are optimal up to arbitrarily small polynomial factors in $N$. Our
results hold at the spectral edges (showing that the extremal eigenvalues are
within $\mathcal{O} (N^{-2/3+})$ of the edges of the limiting spectral
measure), in the spectral bulk, as well as for times near $4$ at which point
the limiting spectral measure forms a cusp. Our methods are dynamical and are
based on analyzing the evolution of the Borel transform of the empirical
spectral measure along the characteristics of the PDE satisfied by the limiting
spectral measure, that of the free unitary Brownian motion.


title: A classical analog of the quantum Zeeman effect

abstract:
  We extend a recent classical mechanical analog of Bohr's atom consisting of a
scalar field coupled to a massive point-like particle [P. Jamet, A. Drezet, ``A
mechanical analog of Bohr's atom based on de Broglie's double-solution
approach'', Chaos 31, 103120 (2021)] by adding and studying the contribution of
a uniform weak magnetic field on their dynamics. In doing so, we are able to
recover the splitting of the energy levels of the atom called Zeeman's effect
within the constraints of our model and in agreement with the semiclassical
theory of Sommerfeld. This result is obtained using Larmor's theorem for both
the field and the particle, associating magnetic effects with inertial Coriolis
forces in a rotating frame of reference. Our work, based on the old `double
solution' theory of de Broglie, shows that a dualistic model involving a
particle guided by a scalar field can reproduce the normal Zeeman effect.


title:
Sintering of Alumina Nanoparticles: Comparison of Interatomic
  Potentials, Molecular Dynamics Simulations, and Data Analysis

abstract:
  Sintering of alumina nanoparticles is of interest both from the view of
fundamental research as well as for industrial applications. Atomistic
simulations are tailor-made for understanding and predicting the time- and
temperature-dependent sintering behaviour. However, the quality and
predictability of such analysis is strongly dependent on the performance of the
underlying interatomic potentials. In this work, we investigate and benchmark
four empirical interatomic potentials and discuss the resulting properties and
drawbacks based on experimental and density functional theory data from the
literature. The potentials, which have different origins and formulations, are
then used in molecular dynamics simulations to perform a systematic study of
the sintering process. To analyse the results, we develop a number of tailored
data analysis approaches that are able to characterise and quantify the
sintering process. Subsequently, the disparities in the sintering behaviour
predicted by the potentials are critically discussed. Finally, we conclude by
providing explanations for the differences in performance of the potentials,
together with recommendations for molecular dynamics sintering simulations of
alumina.


title:
A Data-Centric Approach to Generate Invariants for a Smart Grid Using
  Machine Learning

abstract:
  Cyber-Physical Systems (CPS) have gained popularity due to the increased
requirements on their uninterrupted connectivity and process automation. Due to
their connectivity over the network including intranet and internet, dependence
on sensitive data, heterogeneous nature, and large-scale deployment, they are
highly vulnerable to cyber-attacks. Cyber-attacks are performed by creating
anomalies in the normal operation of the systems with a goal either to disrupt
the operation or destroy the system completely. The study proposed here focuses
on detecting those anomalies which could be the cause of cyber-attacks. This is
achieved by deriving the rules that govern the physical behavior of a process
within a plant. These rules are called Invariants. We have proposed a
Data-Centric approach (DaC) to generate such invariants. The entire study was
conducted using the operational data of a functional smart power grid which is
also a living lab.


title: Some New Inequalities for Beta Distributions

abstract:
  This note provides some new inequalities and approximations for beta
distributions, including exponential tail inequalities, inequalities of
Hoeffding type, and Gaussian tail inequalities and approximations. Some of the
results are extended to Gamma distributions.


title: Turbulent flame speed based on mass flow rate of reactants

abstract:
  Starting with an integral formulation of mass flow rate of reactants through
an ensemble of isotherms within a statistically planar, turbulent premixed
flame, a scaling for the corresponding turbulent flame speed is derived.
Closure relations for the local flame displacement speed and scalar dissipation
rate anomaly are invoked and a new length scale quantifying the fluctuation
distance of the isotherms within the premixed flame structure is introduced.
Damk{\"o}hler's hypotheses are not used at any stage. Eventually, the scaling
is extended to expanding turbulent premixed flames.


title: SAMPL9 blind predictions using nonequilibrium alchemical approaches

abstract:
  We present our blind predictions for the Statistical Assessment of the
Modeling of Proteins and Ligands (SAMPL), 9th challenge, focusing on binding of
WP6 (carboxy-pillar[6]arene) with ammonium/diammonium cationic guests.
Host-guest binding free energies have been calculated using the recently
developed virtual double system single box approach, based on the enhanced
sampling of the bound and unbound end-states followed by fast switching
nonequilibrium alchemical simulations [M Macchiagodena, M Pagliai, M
Karrenbrock, G Guarnieri, F
  Iannone, Procacci, J Chem Theory Comput, 16, 7260, 2020]. As far as Pearson
and Kendall coefficients are concerned, performances were acceptable and, in
general, better than those we submitted for calixarenes, cucurbituril-like open
cavitand, and beta-cyclodextrines in previous SAMPL host-guest challenges,
confirming the reliability of nonequilibrium approaches for absolute binding
free energy calculations. In comparison with previous submissions, we found,
nonetheless, a rather large mean signed error that we attribute to the way the
finite charge correction was addressed through the assumption of a neutralizing
background plasma.


title: Generalized para-Bose coherent states

abstract:
  In this paper, we construct integrals of motion in a para-Bose formulation
for a general time-dependent quadratic Hamiltonian, which, in its turn,
commutes with the reflection operator. In this context, we obtain
generalizations for the squeezed vacuum states (SS) and coherent states (CS) in
terms of the Wigner parameter. Furthermore, we show that there is a positive
completeness relation for the SS owing to the Wigner parameter. In the study of
the probability transition, we found that the displacement parameter acts as a
transition parameter by allowing access to odd states, while the Wigner
parameter controls the dispersion of the distribution


title:
Active and Passive Hybrid Detection Method for Power CPS False Data
  Injection Attacks with Improved AKF and GRU-CNN

abstract:
  Influenced by deep penetration of the new generation of information
technology, power systems have gradually evolved into highly coupled
cyber-physical systems (CPS). Among many possible power CPS network attacks, a
false data injection attacks (FDIAs) is the most serious. Taking account of the
fact that the existing knowledge-driven detection process for FDIAs has been in
a passive detection state for a long time and ignores the advantages of
data-driven active capture of features, an active and passive hybrid detection
method for power CPS FDIAs with improved adaptive Kalman filter (AKF) and
convolutional neural networks (CNN) is proposed in this paper. First, we
analyze the shortcomings of the traditional AKF algorithm in terms of filtering
divergence and calculation speed. The state estimation algorithm based on
non-negative positive-definite adaptive Kalman filter (NDAKF) is improved, and
a passive detection method of FDIAs is constructed, with similarity Euclidean
distance detection and residual detection at its core. Then, combined with the
advantages of gate recurrent unit (GRU) and CNN in terms of temporal memory and
feature-expression ability, an active detection method of FDIAs based on a
GRU-CNN hybrid neural network is proposed. Finally, the results of joint
knowledge-driven and data-driven parallel detection are used to define a mixed
fixed-calculation formula, and an active and passive hybrid detection method of
FDIAs is established, considering the characteristic constraints of the
parallel mode. A simulation system example of power CPS FDIAs verifies the
effectiveness and accuracy of the method proposed in this paper.


title:
An Experimental Study of Wind Resistance and Power Consumption in MAVs
  with a Low-Speed Multi-Fan Wind System

abstract:
  This paper discusses a low-cost, open-source and open-hardware design and
performance evaluation of a low-speed, multi-fan wind system dedicated to micro
air vehicle (MAV) testing. In addition, a set of experiments with a flapping
wing MAV and rotorcraft is presented, demonstrating the capabilities of the
system and the properties of these different types of drones in response to
various types of wind. We performed two sets of experiments where a MAV is
flying into the wake of the fan system, gathering data about states, battery
voltage and current. Firstly, we focus on steady wind conditions with wind
speeds ranging from 0.5 m/s to 3.4 m/s. During the second set of experiments,
we introduce wind gusts, by periodically modulating the wind speed from 1.3 m/s
to 3.4 m/s with wind gust oscillations of 0.5 Hz, 0.25 Hz and 0.125 Hz. The
"Flapper" flapping wing MAV requires much larger pitch angles to counter wind
than the "CrazyFlie" quadrotor. This is due to the Flapper's larger wing
surface. In forward flight, its wings do provide extra lift, considerably
reducing the power consumption. In contrast, the CrazyFlie's power consumption
stays more constant for different wind speeds. The experiments with the varying
wind show a quicker gust response by the CrazyFlie compared with the Flapper
drone, but both their responses could be further improved. We expect that the
proposed wind gust system will provide a useful tool to the community to
achieve such improvements.


title:
Thermal release tape-assisted semiconductor membrane transfer process
  for hybrid photonic devices embedding quantum emitters

abstract:
  Being able to combine different materials allows taking advantage of
different properties and device engineering that cannot be found or exploited
within a single material system. In quantum nano-photonics, one might want to
increase the device functionalities by, for instance, combining efficient
classical and quantum light emission available in III-V semiconductors,
low-loss light propagation accessible in silicon-based materials, fast
electro-optical properties of lithium niobate and broadband reflectors and/or
buried metallic contacts for local electric field application or electrical
injection of emitters. We propose a transfer printing technique based on the
removal of arrays of free-standing membranes and their deposition onto a host
material using a thermal release adhesive tape-assisted process. This approach
is versatile, in that it poses limited restrictions on the transferred and host
materials. In particular, we transfer 190 nm-thick GaAs membranes, with
dimensions up to about 260$\mu$m x 80$\mu$m, containing InAs quantum dots, onto
a gold substrate. We show that the presence of a back reflector combined with
the etching of micro-pillars significantly increases the extraction efficiency
of quantum light, reaching photon fluxes, from a single quantum dot line,
exceeding 8 x 10$^5$ photons per second, which is four times higher than the
highest count rates measured, on the same chip, from emitters outside the
pillars. Given the versatility and the ease of the process, this technique
opens the path to the realisation of hybrid quantum and nano-photonic devices
that can combine virtually any material that can be undercut to realise
free-standing membranes that are then transferred onto any host substrate,
without specific compatibility issues and/or requirements.


title: A Graph-based U-Net Model for Predicting Traffic in unseen Cities

abstract:
  Accurate traffic prediction is a key ingredient to enable traffic management
like rerouting cars to reduce road congestion or regulating traffic via dynamic
speed limits to maintain a steady flow. A way to represent traffic data is in
the form of temporally changing heatmaps visualizing attributes of traffic,
such as speed and volume. In recent works, U-Net models have shown SOTA
performance on traffic forecasting from heatmaps. We propose to combine the
U-Net architecture with graph layers which improves spatial generalization to
unseen road networks compared to a Vanilla U-Net. In particular, we specialize
existing graph operations to be sensitive to geographical topology and
generalize pooling and upsampling operations to be applicable to graphs.


title: Experimental Augmented Reality User Experience

abstract:
  Augmented Reality (AR) is an emerging field ripe for experimentation,
especially when it comes to developing the kinds of applications and
experiences that will drive mass adoption of the technology. While we aren't
aware of any current consumer product that realize a wearable, wide Field of
View (FoV), AR Head Mounted Display (HMD), such devices will certainly come. In
order for these sophisticated, likely high-cost hardware products to succeed,
it is important they provide a high quality user experience. To that end, we
prototyped 4 experimental applications for wide FoV displays that will likely
exist in the future. Given current AR HMD limitations, we used a AR simulator
built on web technology and VR headsets to demonstrate these applications,
allowing users and designers to peer into the future.


title:
Graph-GAN: A spatial-temporal neural network for short-term passenger
  flow prediction in urban rail transit systems

abstract:
  Short-term passenger flow prediction plays an important role in better
managing the urban rail transit (URT) systems. Emerging deep learning models
provide good insights to improve short-term prediction accuracy. However, a
large number of existing prediction models combine diverse neural network
layers to improve accuracy, making their model structures extremely complex and
difficult to be applied to the real world. Therefore, it is necessary to trade
off between the model complexity and prediction performance from the
perspective of real-world applications. To this end, we propose a deep
learning-based Graph-GAN model with a simple structure and high prediction
accuracy to predict short-term passenger flows of the URT network. The
Graph-GAN consists of two major parts: (1) a simplified and static version of
the graph convolution network (GCN) used to extract network topological
information; (2) a generative adversarial network (GAN) used to predict
passenger flows, with generators and discriminators in GAN just composed of
simple fully connected neural networks. The Graph-GAN is tested on two
large-scale real-world datasets from Beijing Subway. A comparison of the
prediction performance of Graph-GAN with those of several state-of-the-art
models illustrates its superiority and robustness. This study can provide
critical experience in conducting short-term passenger flow predictions,
especially from the perspective of real-world applications.


title: Learning Branch Probabilities in Compiler from Datacenter Workloads

abstract:
  Estimating the probability with which a conditional branch instruction is
taken is an important analysis that enables many optimizations in modern
compilers. When using Profile Guided Optimizations (PGO), compilers are able to
make a good estimation of the branch probabilities. In the absence of profile
information, compilers resort to using heuristics for this purpose. In this
work, we propose learning branch probabilities from a large corpus of data
obtained from datacenter workloads.
  Using metrics including Root Mean Squared Error, Mean Absolute Error and
cross-entropy, we show that the machine learning model improves branch
probability estimation by 18-50% in comparison to compiler heuristics. This
translates to performance improvement of up to 8.1% on 24 out of a suite of 40
benchmarks with a 1% geomean improvement on the suite. This also results in
greater than 1.2% performance improvement in an important search application.


title:
Random walk informed community detection reveals heterogeneities in
  large networks

abstract:
  Random walks on networks are widely used to model stochastic processes such
as search strategies, transportation problems or disease propagation. A
prominent biological example of search by random walkers on a network is the
guiding of naive T cells by the lymphatic conduits network in the lymph node.
Motivated by this case study, we propose a general framework to find network
heterogeneities, which we define as connectivity patterns that affect the
random walk. We propose to characterize and measure these heterogeneities by i)
ranking nodes and ii) detecting communities in a way that is interpretable in
terms of random walk, moreover, we propose iii) an approximation to accurately
and efficiently compute these quantities on large networks. The ranking
parameter we propose is the probability of presence field, and the community
detection method adapts previously defined diffusion coordinates. In addition,
we propose an interactive data visualization platform to follow the dynamics of
the random walks and their characteristics on our datasets, and a ready-to-use
pipeline for other datasets upon download. We first showcase the properties of
our method on toy models. We highlight this way the efficiency of our methods
at contrasting two highly similar networks (identical degree distribution, same
number of nodes). Moreover, we show numerically that the ranking and
communities defined in this way are not redundant with any other classical
methods (centralities, global mean first passage, louvain, node2vec). We then
use our methods to characterize the lymph node conduits network. We show that
the lymph node conduits network appears homogeneous and therefore has a global
structure that promotes a uniform exploration of space by T-cells.


title:
Spontaneous symmetry breaking induced by interaction in linearly coupled
  binary Bose Einstein condensates

abstract:
  We analyze the spontaneous symmetry breaking (SSB) induced by one specific
component of a linearly coupled binary Bose-Einstein condensate (BEC). The
model is based on linearly coupled Schr\"odinger equations with cubic
nonlinearity and with a double-well (DW) potential acting on only one of the
atomic components. By numerical simulations, symmetric and asymmetric
ground-states were obtained, and an induced asymmetry in the partner field was
observed. In this sense, we properly demonstrated that the linear coupling
mixing the two-field component (Rabi coupling) promotes the (in)balance between
atomic species, as well as the appearance of the Josephson and SSB phases.


title: On the symmetries of electrodynamic interactions

abstract:
  While mechanics was developed under the idea of reciprocal action
(interactions), electromagnetism, as we know it today, takes a form more akin
to unilateral action. Interactions call for spatial relations, unilateral
action calls for space, just one reference centre. In contrast, interactions
are matters of relations that require at least two centres. The development of
the relational electromagnetism encouraged by Gauss appears to stop around 1870
for reasons that are not completely clear but are certainly not solely
scientific. By the same time, Maxwell recognised the equivalence in formulae of
his electromagnetism and the one advocated by Gauss and called for an
explanation of why such theories so differently conceived have such a large
part in common. In this work we reconstruct and update the relational
electromagnetism up to the contributions of Lorentz guided by the
non-arbitrariness principle (NAP) that requests arbitrary choices to be
accompanied by groups of symmetries. We show that a-priori there must be two
more symmetries in electromagnetism, one related to the breaking (in the
description) of the relation source/detector and one relating all the
perceptions of the same source by detectors moving with different (constant)
relative velocities.


title: A Mutation Threshold for Cooperative Takeover

abstract:
  One of the leading theories for the origin of life includes the hypothesis
according to which life would have evolved as cooperative networks of
molecules. Explaining cooperation$-$and particularly, its emergence in favoring
the evolution of life-bearing molecules$-$is thus a key element in describing
the transition from nonlife to life. Using agent-based modeling of the iterated
prisoner's dilemma, we investigate the emergence of cooperative behavior in a
stochastic and spatially extended setting and characterize the effects of
inheritance and variability. We demonstrate that there is a mutation threshold
above which cooperation is$-$counterintuitively$-$selected, which drives a
dramatic and robust cooperative takeover of the whole system sustained
consistently up to the error catastrophe, in a manner reminiscent of typical
phase transition phenomena in statistical physics. Moreover, our results also
imply that one of the simplest conditional cooperative strategies,
"Tit-for-Tat", plays a key role in the emergence of cooperative behavior
required for the origin of life.


title:
Some recent results for $SU(3)$ and Octonions within the Geometric
  Algebra approach to the fundamental forces of nature

abstract:
  Different ways of representing the group $SU(3)$ within a Geometric Algebra
approach are explored. As part of this we consider characteristic multivectors
for $SU(3)$, and how these are linked with decomposition of generators into
commuting bivectors. The setting for this work is within a 6d Euclidean
Clifford Algebra. We then go on to consider whether the fundamental forces of
particle physics might arise from symmetry considerations in just the 4d
geometric algebra of spacetime -- the STA. As part of this, a representation of
$SU(3)$ is found wholly within the STA, involving preservation of a bivector
norm. We also show how Octonions can be fully represented within the Spacetime
Algebra, which we believe will be useful in making them understandable and
accessible to a new community in Physics and Engineering. The two strands of
the paper are drawn together in showing how preserving the octonion norm is the
same as preserving the timelike part of the Dirac current of a particle. This
suggests a new model for the symmetries preserved in particle physics.
Following on from work by G\"unaydin and G\"ursey on the link between quarks,
and octonions, and by Furey on chains of octonionic multiplications, we show
how both of these fit well within our scheme, and give some wholly STA versions
of the operations involved, which in the cases considered have easily
understandable equivalents in terms of 4d geometry. Links with larger groups
containing $SU(3)$, such as $G_2$ and $SU(8)$, are also considered.


title: Lavaurs algorithm for cubic symmetric polynomials

abstract:
  To investigate the degree $d$ connectedness locus, Thurston studied
\emph{$\sigma_d$-invariant laminations}, where $\sigma_d$ is the $d$-tupling
map on the unit circle, and built a topological model for the space of
quadratic polynomials $f_c(z) = z^2 +c$. In the same spirit, we consider the
space of all \emph{cubic symmetric polynomials} $f_\lambda(z)=z^3+\lambda^2 z$
in three articles. In the first one we construct the lamination $C_sCL$
together with the induced factor space $\mathbb{S}/C_sCL$ of the unit circle
$\mathbb{S}$. As will be verified in the third paper, $\mathbb{S}/C_sCL$ is a
monotone model of the \emph{cubic symmetric connectedness locus}, i.e., the
space of all cubic symmetric polynomials with connected Julia sets. In the
present paper, the second in the series, we develop an algorithm for
constructing $C_sCL$ analogous to the Lavaurs algorithm for constructing a
combinatorial model $\mathcal{M}^{comb}_2$ of the Mandelbrot set
$\mathcal{M}_2$.


title: Quantum spin-flavour memory of ultrahigh-energy neutrino

abstract:
  There are two types of uncertainties related to the measurements done on a
quantum system: statistical and those related to non-commuting observables and
incompatible measurements. The latter indicates the quantum system's inherent
nature and is in the scope of the present study. We explore uncertainties
related to the interstellar ultrahigh-energy neutrino and introduce a novel
concept: quantum spin-flavour memory. Advanced uncertainty measures are
entropic measures, and the effect of the quantum memory reduces the
uncertainty. The problem in question corresponds to a real physical event:
high-energy Dirac neutrinos emitted by some distant source and propagating
towards the earth. The neutrino has a finite magnetic moment and interacts with
both deterministic and stochastic interstellar magnetic fields. To describe the
effect of a noisy environment, we exploit the Lindblad master equation for the
neutrino density matrix. Quantum spin-flavour memory we quantify in terms of
the generalized Kraus's trade-off relation. This trade-off relation converts to
the equality when quantum memory is absent. We discovered that while most
measures of quantum correlations show their irrelevance, the quantum
spin-flavour discord is the quantifier of the quantum spin-flavour memory.


title:
Minimizing Entropy to Discover Good Solutions to Recurrent Mixed Integer
  Programs

abstract:
  Current state-of-the-art solvers for mixed-integer programming (MIP) problems
are designed to perform well on a wide range of problems. However, for many
real-world use cases, problem instances come from a narrow distribution. This
has motivated the development of specialized methods that can exploit the
information in historical datasets to guide the design of heuristics. Recent
works have shown that machine learning (ML) can be integrated with an MIP
solver to inject domain knowledge and efficiently close the optimality gap.
This hybridization is usually done with deep learning (DL), which requires a
large dataset and extensive hyperparameter tuning to perform well. This paper
proposes an online heuristic that uses the notion of entropy to efficiently
build a model with minimal training data and tuning. We test our method on the
locomotive assignment problem (LAP), a recurring real-world problem that is
challenging to solve at scale. Experimental results show a speed up of an order
of magnitude compared to a general purpose solver (CPLEX) with a relative gap
of less than 2%. We also observe that for some instances our method can
discover better solutions than CPLEX within the time limit.


title:
Holographic dark energy in Gauss-Bonnet gravity with Granda-Oliveros
  cut-off

abstract:
  In this paper, we have studied a model of holographic dark energy (HDE) with
a homogeneous and anisotropic Bianchi type-I Universe in the framework of
Gauss-Bonnet (GB) gravity or $f(G)$ gravity. To find an exact solution of the
field equations, we assume that the deceleration parameter varies with cosmic
time $\left( t\right) $. We plot some physical quantities and give
interpretations of the results obtained. Finally, we compared our model with
the $\Lambda CDM$ model by analyzing the Jerk parameter, and we examine the
equivalence between the HDE of the present work and the generalized HDE.


title:
Attention-based Deep Neural Networks for Battery Discharge Capacity
  Forecasting

abstract:
  Battery discharge capacity forecasting is critically essential for the
applications of lithium-ion batteries. The capacity degeneration can be treated
as the memory of the initial battery state of charge from the data point of
view. The streaming sensor data collected by battery management systems (BMS)
reflect the usable battery capacity degradation rates under various operational
working conditions. The battery capacity in different cycles can be measured
with the temporal patterns extracted from the streaming sensor data based on
the attention mechanism. The attention-based similarity regarding the first
cycle can describe the battery capacity degradation in the following cycles.
The deep degradation network (DDN) is developed with the attention mechanism to
measure similarity and predict battery capacity. The DDN model can extract the
degeneration-related temporal patterns from the streaming sensor data and
perform the battery capacity prediction efficiently online in real-time. Based
on the MIT-Stanford open-access battery aging dataset, the root-mean-square
error of the capacity estimation is 1.3 mAh. The mean absolute percentage error
of the proposed DDN model is 0.06{\%}. The DDN model also performance well in
the Oxford Battery Degradation Dataset with dynamic load profiles. Therefore,
the high accuracy and strong robustness of the proposed algorithm are verified.


title:
Local recovery of a piecewise constant anisotropic conductivity in EIT
  on domains with exposed corners

abstract:
  We study the local recovery of an unknown piecewise constant anisotropic
conductivity in EIT (electric impedance tomography) on certain bounded
Lipschitz domains $\Omega$ in $\mathbb{R}^2$ with corners. The measurement is
conducted on a connected open subset of the boundary $\partial\Omega$ of
$\Omega$ containing corners and is given as a localized Neumann-to-Dirichlet
map. The above unknown conductivity is defined via a decomposition of $\Omega$
into polygonal cells. Specifically, we consider a parallelogram-based
decomposition and a trapezoid-based decomposition. We assume that the
decomposition is known, but the conductivity on each cell is unknown. We prove
the local recovery near a known piecewise constant anisotropic conductivity
$\gamma_0$. We do so by proving the injectivity of the Fr\'echet derivative
$F'(\gamma_0)$ of the forward map $F$, say, at $\gamma_0$. The proof presented,
here, involves defining different classes of decompositions for $\gamma_0$ and
a perturbation or contrast $H$ in a proper way so that we can find in the
interior of a cell for $\gamma_0$ exposed single or double corners of a cell of
$\mathrm{supp}H$ for the former decomposition and latter decomposition,
respectively. Then, by adapting the usual proof near such corners, we establish
the mentioned injectivity.


title: Universal self-scalings in a micro-co-flowing

abstract:
  On hypothesis of self-scaling co-flows in tapered rectanglar PMMA
micro-channels for producing mono-dispersed liquid cells, universal scalings
through all liquid detaching regimes are found under a self-similarity frame.
Pan-dripping and Pan-jetting regimes are calculated clearly to border at the
Weber number approximately $1$ and the capillary number approximately $0.28$ by
machine learning classification. The sizes, as well as detaching frequencies,
of liquid cells in different flow regimes behave at the same manner and submit
to the same law, and such highly consistent behaviors breaking through physical
barriers among flow regimes have never been reported.


title:
Electrochemical Degradation of Per- and Polyfluoroalkyl Substances
  (PFAS) using Low-cost Graphene Sponge Electrodes

abstract:
  Boron-doped, graphene sponge anode was synthesized and applied for the
electrochemical oxidation of C4-C8 per and polyfluoroalkyl substances (PFASs).
Removal efficiencies, obtained in low conductivity electrolyte (1 mS cm-1) and
one-pass flow-through mode, were in the range 16.7-67% at 230 A m-2 of anodic
current density, and with the energy consumption of 10.1+-0.7 kWh m-3. Their
removal was attributed to electrosorption (7.4-35%), and electrooxidation
(9.3-32 %). Defluorination efficiencies of C4-C8 perfluoroalkyl sulfonates and
acids were 8-24% due to a fraction of PFAS being electrosorbed only at the
anode surface. Yet, the recovery of fluoride was 74-87% relative to the
electrooxidized fraction, suggesting that once the degradation of the PFAS is
initiated, the C-F bond cleavage is very efficient. The nearly stoichiometric
sulfate recoveries obtained for perfluoroalkyl sulfonates (91-98%) relative to
the electrooxidized fraction demonstrated an efficient cleavage of the
sulfonate head-group. Adsorbable organic fluoride (AOF) analysis showed that
the remaining partially defluorinated byproducts are electrosorbed at the
graphene sponge anode during current application and are released into the
solution after the current is switched off. This proof-of-concept study
demonstrated that the developed graphene sponge anode is capable of C-F bond
cleavage and defluorination of PFAS. Given that the graphene sponge anode is
electrochemically inert towards chloride and does not form any chlorate and
perchlorate even in brackish solutions, the developed material may unlock the
electrochemical degradation of PFAS complex wastewaters and brines.


title: Trace norm regularization for multi-task learning with scarce data

abstract:
  Multi-task learning leverages structural similarities between multiple tasks
to learn despite very few samples. Motivated by the recent success of neural
networks applied to data-scarce tasks, we consider a linear low-dimensional
shared representation model. Despite an extensive literature, existing
theoretical results either guarantee weak estimation rates or require a large
number of samples per task. This work provides the first estimation error bound
for the trace norm regularized estimator when the number of samples per task is
small. The advantages of trace norm regularization for learning data-scarce
tasks extend to meta-learning and are confirmed empirically on synthetic
datasets.


title:
An Atomistic-based Finite Deformation Continuum Membrane Model for
  Monolayer Transition Metal Dichalcogenides

abstract:
  A finite-deformation crystal-elasticity membrane model for Transition Metal
Dichalcogenide (TMD) monolayers is presented. Monolayer TMDs are
multi-atom-thick two-dimensional (2D) crystalline membranes having atoms
arranged in three parallel surfaces. In the present formulation, the deformed
configuration of a TMD-membrane is represented through the deformation map of
its middle surface and two stretches normal to the middle surface.
Crystal-elasticity based kinematic rules are employed to express the deformed
bond lengths and bond angles of TMDs in terms of the continuum strains. The
continuum hyper-elastic strain energy of the TMD membrane is formulated from
its inter-atomic potential. The relative shifts between two simple lattices of
TMDs are also considered in the constitutive relation. A smooth finite element
framework using B-splines is developed to numerically implement the present
continuum membrane model. The proposed model generalizes the
crystal-elasticity-based membrane theory of purely 2D membranes, such as
graphene, to the multi-atom-thick TMD crystalline membranes. The significance
of relative shifts and two normal stretches are demonstrated through numerical
results. The proposed atomistic-based continuum model accurately matches the
material moduli, complex post-buckling deformations, and the equilibrium
energies predicted by the purely atomistic simulations. It also accurately
reproduces the experimental results for large-area TMD samples containing tens
of millions of atoms.


title: Overhead Management in Multi-Core Environment

abstract:
  In multi-core systems, various factors like inter-process communication,
dependency, resource sharing and scheduling, level of parallelism,
synchronization, number of available cores etc. influence the extent of
possible High Performance Computing parallelization. These parameters if not
managed to the root level, later surface as overheads during execution. This
paper emphasizes on these parameters of parallelism, their overheads of
parallelization and its effective management for optimal parallel execution
under any domain. As a whole, we focus on the Dense Linear Algebra (DLA) domain
and specifically on Matrix Multiplication and sorting domains. These domains
are chosen as they find application in various sectors of scientific and
mathematical applications. The comparative analysis of results obtained
clarifies the trade-off between serial and parallel execution of DLA problems
the surfacing overheads and their possible and effective management.


title: Photovoltaic Surfaces to Reverse Global Warming

abstract:
  Climate changes and its many associated impacts are one of the most critical
global challenges. Photovoltaics has been instrumental in mitigation of CO$_2$
through the generation of electricity. However, the goal of limiting global
warming to 1.5 $^\circ$C increasingly requires additional approaches. The paper
presents how PV surfaces can be designed to reverse the Earth's radiative
imbalance from increased greenhouse gasses that lead to higher global
temperatures. The new PV surface generate electricity, reflect sub-band gap
radiation, minimize their temperature, generate thermal radiation and emit
additional IR through the atmospheric, with these processes totaling 650
Wm$^{-2}$. This is realized by: (1) PV system efficiency at operating
temperature $>$ 20 \% and sub-band gap reflection of 150 Wm$^{-2}$ for a total
of 350 Wm$^{-2}$ (2) Thermally emitted radiation (radiative cooling) of 150
Wm$^{-2}$; and (3) Active IR emission through an atmospheric window at 1.5
$\mu$ of 150 Wm$^{-2}$. With such PV surfaces, we show that 10 TW of installed
PV can reverse global warming. Using PV to balance global temperatures
introduces additional considerations for PV, focusing on high efficiency,
particularly high efficiency at operating temperatures, radiative cooling, and
new processes for 1.5 $\mu$ emission. We find that depending on their design,
PV panels can increase or decrease global temperatures.


title:
Single nanosized graphene/TiOx multi-shells on TiO2 core via
  rapid-concomitant reaction pathway on metal oxide/polymer interface

abstract:
  A novel design has been proposed for a facile and rapid build-up of highly
tailorable nanostructured multishells on metal oxide particles
(graphene/TiOx@TiO2) under a dry inert atmosphere to maximize the visible-light
photocatalytic performance. We also thoroughly and systematically investigated
the as-prepared nanostructured particle surface and the mechanisms of the
extraordinary in-situ graphene growth on the TiOx@TiO2 nanoparticles achieved
by a rapid-concomitant reaction in the metal oxide (TiO2)/polymer (polymethyl
methacrylate) interface under microwave irradiation. The as-prepared composite
materials are also found to perform a better photocatalytic activity in
comparison to the traditional synthesis pathway for degradation of organic
pollutants under visible light, associated with the synergetic effects of
homo-/hetero-junction on TiO2.