title:
A Simple but Effective Pluggable Entity Lookup Table for Pre-trained
  Language Models

abstract:
  Pre-trained language models (PLMs) cannot well recall rich factual knowledge
of entities exhibited in large-scale corpora, especially those rare entities.
In this paper, we propose to build a simple but effective Pluggable Entity
Lookup Table (PELT) on demand by aggregating the entity's output
representations of multiple occurrences in the corpora. PELT can be compatibly
plugged as inputs to infuse supplemental entity knowledge into PLMs. Compared
to previous knowledge-enhanced PLMs, PELT only requires 0.2%-5% pre-computation
with capability of acquiring knowledge from out-of-domain corpora for domain
adaptation scenario. The experiments on knowledge-related tasks demonstrate
that our method, PELT, can flexibly and effectively transfer entity knowledge
from related corpora into PLMs with different architectures.


title:
Transformer-based Knowledge Distillation for Efficient Semantic
  Segmentation of Road-driving Scenes

abstract:
  For scene understanding in robotics and automated driving, there is a growing
interest in solving semantic segmentation tasks with transformer-based methods.
However, effective transformers are always too cumbersome and computationally
expensive to solve semantic segmentation in real time, which is desired for
robotic systems. Moreover, due to the lack of inductive biases compared to
Convolutional Neural Networks (CNNs), pre-training on a large dataset is
essential but it takes a long time. Knowledge Distillation (KD) speeds up
inference and maintains accuracy while transferring knowledge from a
pre-trained cumbersome teacher model to a compact student model. Most
traditional KD methods for CNNs focus on response-based knowledge and
feature-based knowledge. In contrast, we present a novel KD framework according
to the nature of transformers, i.e., training compact transformers by
transferring the knowledge from feature maps and patch embeddings of large
transformers. To this purpose, two modules are proposed: (1) the Selective
Kernel Fusion (SKF) module, which helps to construct an efficient
relation-based KD framework, Selective Kernel Review (SKR); (2) the Patch
Embedding Alignment (PEA) module, which performs the dimensional transformation
of patch embeddings. The combined KD framework is called SKR+PEA. Through
comprehensive experiments on Cityscapes and ACDC datasets, it indicates that
our proposed approach outperforms recent state-of-the-art KD frameworks and
rivals the time-consuming pre-training method. Code will be made publicly
available at https://github.com/RuipingL/SKR_PEA.git


title: Some Arguments for the Wave Equation in Quantum Theory 2

abstract:
  We prove that if the frame S is decaying surface non-radiating, in the sense
of Definition 1.1, then if the charge and current are analytic, either they
both vanish, or S is non-radiating, in the sense of a previous paper. In
particularly, by the result there, the charge and current satisfy certain wave
equations in all frames S', connected to S by a real velocity vector, whose
magnitude is less than the speed of light.


title:
Granular media equation with double-well external landscape: limiting
  steady state

abstract:
  In this paper, we give a simple condition on the initial state of the
granular media equation which ensures that the limit as the time goes to
infinity is the unique steady state with positive center of mass. To do so, we
use functional inequalities, Laplace method and McKean-Vlasov diffusion (which
corresponds to the probabilistic interpretation of the granular media
equation).


title:
A new infinite family of star normal quotient graphs of twisted wreath
  type

abstract:
  We construct the first infinite families of locally arc transitive graphs
with the property that the automorphism group has two orbits on vertices and is
quasiprimitive on exactly one orbit, of twisted wreath type. This work
contributes to Giudici, Li and Praeger's program for the classification of
locally arc transitive graphs by showing that the star normal quotient twisted
wreath category also contains infinitely many graphs.


title:
Superconducting boron doped nanocrystalline diamond microwave coplanar
  resonator

abstract:
  A superconducting boron doped nanocrystalline diamond (B-NCD) coplanar
waveguide resonator (CPR) is presented for kinetic inductance ($L_k$) and
penetration depth ($\lambda_{\rm{L}}$) measurements at microwave frequencies of
0.4 to 1.2 GHz and at temperatures below 3 K. Using a simplified effective
medium CPR approach, this work demonstrates that thin granular B-NCD films
($t\approx $ 500 nm) on Si have a large penetration depth
($\lambda_{\rm{L}}\approx 4.3$ to 4.4 $\mu$m), and therefore an associated high
kinetic inductance ($L_{k,\square} \approx $ 670 to 690 pH/$\square$). These
values are much larger than those typically obtained for films on single
crystal diamond which is likely due to the significant granularity of the
nanocrystalline films. Based on the measured Q factors of the structure, the
calculated surface resistance in this frequency range is found to be as low as
$\approx$ 2 to 4 $\mu\Omega$ at $T<2$ K, demonstrating the potential for
granular B-NCD for high quality factor superconducting microwave resonators and
highly sensitive kinetic inductance detectors.


title: Topological theories and automata

abstract:
  The paper explains the connection between topological theories for
one-manifolds with defects and values in the Boolean semiring and automata and
their generalizations. Finite state automata are closely related to regular
languages. To each pair of a regular language and a circular regular language
we associate a topological theory for one-dimensional manifolds with
zero-dimensional defects labelled by letters of the language. This theory takes
values in the Boolean semiring. Universal construction of topological theories
gives rise in this case to a monoidal category of Boolean semilinear
combinations of one-dimensional cobordisms with defects modulo skein relations.
The latter category can be interpreted as a semilinear rigid monoidal closure
of standard structures associated to a regular language, including minimal
deterministic and nondeterministic finite state automata for the language and
the syntactic monoid. The circular language plays the role of a regularizer,
allowing to define the rigid closure of these structures. When the state space
of a single point for a regular language describes a distributive lattice,
there is a unique associated circular language such that the resulting theory
is a Boolean TQFT.


title: Conservative Random Walk

abstract:
  Recently, in \cite{EVW2020}, the "coin-turning walk" was introduced on $\Z$.
It is a non-Markovian process where the steps form a (possibly)
time-inhomogeneous Markov chain. In this article, we follow up the
investigation by introducing analogous processes in $\Z^d,\ d\ge 2$: at time
$n$ the direction of the process is "updated" with probability $p_n$; otherwise
the next step repeats the previous one. We study some of the fundamental
properties of these walks, such as transience/recurrence and scaling limits.


title: Taylor-Culick retractions and the influence of the surroundings

abstract:
  When a freely suspended liquid film ruptures, it retracts spontaneously under
the action of surface tension. If the film is surrounded by air, the retraction
velocity is known to approach the constant Taylor-Culick velocity. However,
when surrounded by an external viscous medium, the dissipation within that
medium dictates the magnitude of the retraction velocity. In the present work,
we study the retraction of a liquid (water) film in a viscous oil ambient
(\emph{two-phase} Taylor-Culick retractions), and that sandwiched between air
and a viscous oil (\emph{three-phase} Taylor-Culick retractions). In the latter
case, the experimentally-measured retraction velocity is observed to have a
weaker dependence on the viscosity of the oil phase as compared to the
configuration where the water film is surrounded completely by oil. Numerical
simulations indicate that this weaker dependence arises from the localization
of viscous dissipation near the three-phase contact line. The speed of
retraction only depends on the viscosity of the surrounding medium and not on
that of the film. From the experiments and the numerical simulations, we reveal
unprecedented regimes for the scaling of the Weber number $We_f$ of the film or
the capillary number $Ca_s$ of the surroundings vs. the Ohnesorge number $Oh_s$
of the surroundings in the regime of large viscosity of the surroundings ($Oh_s
\gg 1$), namely $We_f \sim Oh_s^{-2}$ and $Ca_s \sim Oh_s^{0}$ for the
two-phase Taylor-Culick configuration, and $We_f \sim Oh_s^{-1}$ and $Ca_s \sim
Oh_s^{1/2}$ for the three-phase Taylor-Culick configuration.


title:
MOCA-S: A Sensitive Mobile Collaborative Robotic Assistant exploiting
  Low-Cost Capacitive Tactile Cover and Whole-Body Control

abstract:
  Safety is one of the most fundamental aspects of robotics, especially when it
comes to collaborative robots (cobots) that are expected to physically interact
with humans. Although a large body of literature has focused on safety-related
aspects for fixed-based cobots, a low effort has been put into developing
collaborative mobile manipulators. In response to this need, this work presents
MOCA-S, i.e., Sensitive Mobile Collaborative Robotic Assistant, that integrates
a low-cost, capacitive tactile cover to measure interaction forces applied to
the robot base. The tactile cover comprises a set of 11 capacitive large-area
tactile sensors distributed as a 1-D tactile array around the base.
Characterization of the tactile sensors with different materials is included.
Moreover, two expanded whole-body controllers that exploit the platform's
tactile cover and the loco-manipulation features are proposed. These
controllers are tested in two experiments, demonstrating the potential of
MOCA-S for safe physical Human-Robot Interaction (pHRI). Finally, an experiment
is carried out in which an undesired collision occurs between MOCA-S and a
human during a loco-manipulation task. The results demonstrate the intrinsic
safety of MOCA-S and the proposed controllers, suggesting a new step towards
creating safe mobile manipulators.


title: Concept Graph Neural Networks for Surgical Video Understanding

abstract:
  We constantly integrate our knowledge and understanding of the world to
enhance our interpretation of what we see.
  This ability is crucial in application domains which entail reasoning about
multiple entities and concepts, such as AI-augmented surgery. In this paper, we
propose a novel way of integrating conceptual knowledge into temporal analysis
tasks via temporal concept graph networks. In the proposed networks, a global
knowledge graph is incorporated into the temporal analysis of surgical
instances, learning the meaning of concepts and relations as they apply to the
data. We demonstrate our results in surgical video data for tasks such as
verification of critical view of safety, as well as estimation of Parkland
grading scale. The results show that our method improves the recognition and
detection of complex benchmarks as well as enables other analytic applications
of interest.


title:
A Multimodal German Dataset for Automatic Lip Reading Systems and
  Transfer Learning

abstract:
  Large datasets as required for deep learning of lip reading do not exist in
many languages. In this paper we present the dataset GLips (German Lips)
consisting of 250,000 publicly available videos of the faces of speakers of the
Hessian Parliament, which was processed for word-level lip reading using an
automatic pipeline. The format is similar to that of the English language LRW
(Lip Reading in the Wild) dataset, with each video encoding one word of
interest in a context of 1.16 seconds duration, which yields compatibility for
studying transfer learning between both datasets. By training a deep neural
network, we investigate whether lip reading has language-independent features,
so that datasets of different languages can be used to improve lip reading
models. We demonstrate learning from scratch and show that transfer learning
from LRW to GLips and vice versa improves learning speed and performance, in
particular for the validation set.


title:
Improving Candidate Retrieval with Entity Profile Generation for
  Wikidata Entity Linking

abstract:
  Entity linking (EL) is the task of linking entity mentions in a document to
referent entities in a knowledge base (KB). Many previous studies focus on
Wikipedia-derived KBs. There is little work on EL over Wikidata, even though it
is the most extensive crowdsourced KB. The scale of Wikidata can open up many
new real-world applications, but its massive number of entities also makes EL
challenging. To effectively narrow down the search space, we propose a novel
candidate retrieval paradigm based on entity profiling. Wikidata entities and
their textual fields are first indexed into a text search engine (e.g.,
Elasticsearch). During inference, given a mention and its context, we use a
sequence-to-sequence (seq2seq) model to generate the profile of the target
entity, which consists of its title and description. We use the profile to
query the indexed search engine to retrieve candidate entities. Our approach
complements the traditional approach of using a Wikipedia anchor-text
dictionary, enabling us to further design a highly effective hybrid method for
candidate retrieval. Combined with a simple cross-attention reranker, our
complete EL framework achieves state-of-the-art results on three Wikidata-based
datasets and strong performance on TACKBP-2010.


title: $OSp(n|2m)$ quantum chains with free boundaries

abstract:
  In this paper we investigate the spectrum of $OSp(n|2m)$ quantum spin chains
with free boundary conditions. We compute the surface free energy of these
models which, similar to other properties in the thermodynamic limit including
the effective central charge of the underlying conformal field theory, depends
on $n-2m$ only. For several models in the regime $n-2m< 2$ we have studied the
finite-size properties including the subleading logarithmic corrections to
scaling. As in the case of periodic boundary conditions we find the existence
of a tower of states with the same conformal dimension as the identity
operator. As expected the amplitudes of the corresponding logarithmic
corrections differ from those found previously for the models with periodic
boundary conditions. We point out however the existence of simple relations
connecting such amplitudes for free and periodic boundaries. Based on our
findings we formulate a conjecture on the long distance behaviour of the bulk
and surface watermelon correlators.


title: Towards Unifying Logical Entailment and Statistical Estimation

abstract:
  This paper gives a generative model of the interpretation of formal logic for
data-driven logical reasoning. The key idea is to represent the interpretation
as likelihood of a formula being true given a model of formal logic. Using the
likelihood, Bayes' theorem gives the posterior of the model being the case
given the formula. The posterior represents an inverse interpretation of formal
logic that seeks models making the formula true. The likelihood and posterior
cause Bayesian learning that gives the probability of the conclusion being true
in the models where all the premises are true. This paper looks at statistical
and logical properties of the Bayesian learning. It is shown that the
generative model is a unified theory of several different types of reasoning in
logic and statistics.


title: Average shadowing and gluing property

abstract:
  The purpose of this work is threefold: (i) extend shadowing theory for
discontinuous and non-invertible systems, (ii) consider more general classes of
perturbations (for example, small only on average), (iii) establish a general
theory based on the property that the shadowing holds for the case of a single
perturbation. The "gluing" construction used in the analysis of the last
property turns out to be the key point of this theory.


title: Scalable Byzantine Fault Tolerance via Partial Decentralization

abstract:
  Byzantine consensus is a critical component in many permissioned Blockchains
and distributed ledgers. We propose a new paradigm for designing BFT protocols
called DQBFT that addresses three major performance and scalability challenges
that plague past protocols: (i) high communication costs to reach
geo-distributed agreement, (ii) uneven resource utilization hampering
performance, and (iii) performance degradation under varying node and network
conditions and high-contention workloads. Specifically, DQBFT divides consensus
into two parts: 1) durable command replication without a global order, and 2)
consistent global ordering of commands across all replicas. DQBFT achieves this
by decentralizing the heavy task of replicating commands while centralizing the
ordering process.
  Under the new paradigm, we develop a new protocol, Destiny that uses a
combination of three techniques to achieve high performance and scalability:
using a trusted subsystem to decrease consensus's quorum size, using threshold
signatures to attain linear communication costs, reducing client communication.
Our evaluations on 300-replica geo-distributed deployment reveal that DQBFT
protocols achieve significant performance gains over prior art: $\approx$3x
better throughput and $\approx$50\% better latency.


title:
CoPA: Cold Page Awakening to Overcome Retention Failures in STT-MRAM
  Based I/O Buffers

abstract:
  Performance and reliability are two prominent factors in the design of data
storage systems. To achieve higher performance, recently storage system
designers use DRAM-based buffers. The volatility of DRAM brings up the
possibility of data loss, so a part of the main storage is conventionally used
as the journal area to be able of recovering unflushed data pages in the case
of power failure. Moreover, periodically flushing buffered data pages to the
main storage is a common mechanism to preserve a high level of reliability,
which leads to an increase in storage write traffic. To address this
shortcoming, recent studies offer a small NVM as the Persistent Journal Area
(PJA) along with DRAM as an efficient approach, named NVM-Backed Buffer
(NVB-Buffer). This approach aims to address DRAM vulnerability against power
failure while reducing storage write traffic. In this paper, we use the most
promising technologies for PJA among the emerging technologies, which is
STT-MRAM to meet the requirements of PJA (high endurance, non-volatility, and
DRAM-like latency). However, STT-MRAM faces major reliability challenges, i.e.
Retention Failure, Read Disturbance, and Write Failure. In this paper, we first
show that retention failure is the dominant source of errors in NVB-Buffers as
it suffers from long and unpredictable page idle intervals. Then, we propose a
novel NVB-Buffer management scheme, named, Cold Page Awakening (CoPA), which
predictably reduces the idle time of PJA pages. To this aim, CoPA employs
Distant Refreshing to periodically overwrite the vulnerable PJA page contents
by using their replica in DRAM-based buffer. We compare CoPA with the
state-of-the-art schemes over several workloads based on physical journaling.
Our evaluations show that employing CoPA leads to three orders of magnitude
lower failure rate with negligible performance degradation (1.1%) and memory
overhead (1.2%).


title:
Investigating the Role of Pedestrian Groups in Shared Spaces through
  Simulation Modeling

abstract:
  In shared space environments, urban space is shared among different types of
road users, who frequently interact with each other to negotiate priority and
coordinate their trajectories. Instead of traffic rules, interactions among
them are conducted by informal rules like speed limitations and by social
protocols e.g., courtesy behavior. Social groups (socially related road users
who walk together) are an essential phenomenon in shared spaces and affect the
safety and efficiency of such environments. To replicate group phenomena and
systematically study their influence in shared spaces; realistic models of
social groups and the integration of these models into shared space simulations
are required. In this work, we focus on pedestrian groups and adopt an extended
version of the social force model in conjunction with a game-theoretic model to
simulate their movements. The novelty of our paper is in the modeling of
interactions between social groups and vehicles. We validate our model by
simulating scenarios involving interaction between social groups and also
group-to-vehicle interaction.


title:
Regularization of the Factorization Method with Applications to Inverse
  Scattering

abstract:
  Here we discuss a regularized version of the factorization method for
positive operators acting on a Hilbert Space. The factorization method is a
qualitative reconstruction method that has been used to solve many inverse
shape problems. In general, qualitative methods seek to reconstruct the shape
of an unknown object using little to no a priori information. The regularized
factorization method presented here seeks to avoid numerical instabilities in
the inversion algorithm. This allows one to recover unknown structures in a
computationally simple and analytically rigorous way. We will discuss the
theory and application of the regularized factorization method to examples
coming from acoustic inverse scattering. Numerical examples will also be
presented using synthetic data to show the applicability of the method.


title:
A Decade of Information Architecture in HCI: A Systematic Literature
  Review

abstract:
  Information Architecture (IA) is a blueprint for the information system in
websites or other information-rich environments. It corresponds to how we
organize, label and structure information. The importance of Information
Architecture and its influence on a system's usability is vastly discussed in
literature. Because of the inherent connection between Information Architecture
concepts and the Human Computer Interaction (HCI) field, we decided to
investigate how previous research has used Information Architecture in the
context of Human Computer Interaction (IAinHCI). In order to do that, we
followed a two phase process. First, we conducted a Systematic Literature
Review (SLR). We queried both the ACM and IEEE databases. We filtered and
assessed 311 papers that spanned a decade of research on Information
Architecture. We found 25 papers that utilized Information Architecture in the
context of Human Computer Interaction. Then, we followed a Background Reference
Search process using the SLR resulting papers as a starting set. We assessed
the eligibility of the reference list of all 25 papers and found eight
additional papers that were relevant to our research question. Results of our
review show that, IAinHCI papers fall under seven main categories, from IoT to
the semantic web and ubiquitous technology. The website category, however, was
both the most consistent over the years and the most prevalent category
accounting for 67% of the papers. Our findings suggest that IA has not yet
uncovered its full potential and there is still room for research to leverage
and expend the IA knowledge base promising a prosperous future for Information
Architecture.


title:
An isogeometric finite element formulation for surface and shell
  viscoelasticity based on a multiplicative surface deformation split

abstract:
  This work presents a numerical formulation to model isotropic viscoelastic
material behavior for membranes and thin shells. The surface and the shell
theory are formulated within a curvilinear coordinate system, which allows the
representation of general surfaces and deformations. The kinematics follow from
Kirchhoff-Love theory and the discretization makes use of isogeometric shape
functions. A multiplicative split of the surface deformation gradient is
employed, such that an intermediate surface configuration is introduced. The
surface metric and curvature of this intermediate configuration follow from the
solution of nonlinear evolution laws - ordinary differential equations (ODEs) -
that stem from a generalized viscoelastic solid model. The evolution laws are
integrated numerically with the implicit Euler scheme and linearized within the
Newton-Raphson scheme of the nonlinear finite element framework. The
implementation of surface and bending viscosity is verified with the help of
analytical solutions and shows ideal convergence behavior. The chosen numerical
examples capture large deformations and typical viscoelasticity behavior, such
as creep, relaxation, and strain rate dependence. It is shown that the proposed
formulation can also be straightforwardly applied to model boundary
viscoelasticity of 3D bodies.


title: Quantum computing with differentiable quantum transforms

abstract:
  We present a framework for differentiable quantum transforms. Such transforms
are metaprograms capable of manipulating quantum programs in a way that
preserves their differentiability. We highlight their potential with a set of
relevant examples across quantum computing (gradient computation, circuit
compilation, and error mitigation), and implement them using the transform
framework of PennyLane, a software library for differentiable quantum
programming. In this framework, the transforms themselves are differentiable
and can be parametrized and optimized, which opens up the possibility of
improved quantum resource requirements across a spectrum of tasks.


title: Conformal prediction beyond exchangeability

abstract:
  Conformal prediction is a popular, modern technique for providing valid
predictive inference for arbitrary machine learning models. Its validity relies
on the assumptions of exchangeability of the data, and symmetry of the given
model fitting algorithm as a function of the data. However, exchangeability is
often violated when predictive models are deployed in practice. For example, if
the data distribution drifts over time, then the data points are no longer
exchangeable; moreover, in such settings, we might want to use an algorithm
that treats recent observations as more relevant, which would violate the
assumption that data points are treated symmetrically. This paper proposes new
methodology to deal with both aspects: we use weighted quantiles to introduce
robustness against distribution drift, and design a new technique to allow for
algorithms that do not treat data points symmetrically. Our algorithms are
provably robust, with substantially less loss of coverage when exchangeability
is violated due to distribution drift or other challenging features of real
data, while also achieving the same coverage guarantees as existing conformal
prediction methods if the data points are in fact exchangeable. Finally, we
demonstrate the practical utility of these new tools with simulations and
real-data experiments.


title:
Soft gluon resummation for associated squark-electroweakino production
  at the LHC

abstract:
  We perform a threshold resummation calculation for the associated production
of squarks and electroweakinos at the LHC to the next-to-leading logarithmic
(NLL) accuracy. Analytical results for the process-dependent soft anomalous
dimension and the hard matching coefficient are presented. The resummed results
are matched to fixed-order predictions at next-to-leading order (NLO) in QCD,
which are generalised to scenarios with non-universal squark masses and
mixings. Numerically, the NLL contributions increase the total NLO cross
section by $2\%$ to $6\%$ for squark masses ranging from 1 TeV to 3 TeV,
respectively, and they reduce the dependence of the predictions on the
factorisation and renormalisation scales from typically $\pm10\%$ to below
$\pm5\%$. Our NLO and NLO+NLL calculations have been implemented in the
publicly available program Resummino.


title:
The political economy of big data leaks: Uncovering the skeleton of tax
  evasion

abstract:
  After the leak of 11.5 million documents from the Panamanian corporation
Mossack Fonseca, an intricate network of offshore business entities has been
revealed. The emerging picture is that of legal entities, either individuals or
companies, involved in offshore activities and transactions with several tax
havens simultaneously which establish, indirectly, an effective network of
countries acting on tax evasion. The analysis of this network quantitatively
uncovers a strongly connected core (a rich-club) of countries whose indirect
interactions, mediated by legal entities, form the skeleton for tax evasion
worldwide. Intriguingly, the rich-club mainly consists of well-known tax havens
such as British Virgin Islands and Hong Kong, and major global powers such as
China, Russia, United Kingdom and United States of America. The analysis
provides a new way to rank tax havens because of the role they play in this
network, and the results call for an international coordination on taxation
policies that take into account the complex interconnected structure of tax
evaders in a globalized economy.


title: Taming the Long Tail of Deep Probabilistic Forecasting

abstract:
  Deep probabilistic forecasting is gaining attention in numerous applications
ranging from weather prognosis, through electricity consumption estimation, to
autonomous vehicle trajectory prediction. However, existing approaches focus on
improvements on the most common scenarios without addressing the performance on
rare and difficult cases. In this work, we identify a long tail behavior in the
performance of state-of-the-art deep learning methods on probabilistic
forecasting. We present two moment-based tailedness measurement concepts to
improve performance on the difficult tail examples: Pareto Loss and Kurtosis
Loss. Kurtosis loss is a symmetric measurement as the fourth moment about the
mean of the loss distribution. Pareto loss is asymmetric measuring right
tailedness, modeling the loss using a generalized Pareto distribution (GPD). We
demonstrate the performance of our approach on several real-world datasets
including time series and spatiotemporal trajectories, achieving significant
improvements on the tail examples.


title:
On Intercultural Transferability and Calibration of Heterogeneous Shared
  Space Motion Models

abstract:
  Modelling and simulation of mixed-traffic zones is an important tool for
transportation planners to assess safety, efficiency, and human-friendliness of
future urban areas. This paper addresses problems of calibration and
transferability of existing shared space models when applied to scenarios that
differ in terms of cultural aspects, traffic conditions, and spatial layout. In
particular, the first contribution of this work is an enhancement of the
Game-Theoretic Social Force Model (GSFM) by a generic methodology for largely
automated model calibration; we illustrate the use of the calibration method
for a shared space environment in Germany. The second contribution is an
investigation into transferability of shared space models. We define criteria
for model transferability and present a case study, in which we analyse and
evaluate transferability of the model we constructed based on the ``German
dataset'' to a different shared space environment from China. Our results
indicate that although -- as to be expected -- the model faces difficulties to
replicate the movement behaviours of road users from a new environment, by
adding social norms (derived through analysis) of that environment to our
model, satisfactory improvement of model accuracy can be obtained with limited
effort.


title: Curvature tensor of smoothable Alexandrov spaces

abstract:
  We prove weak convergence of curvature tensors of Riemannian manifolds for
converging noncollapsing sequences with a lower bound on sectional curvature.


title:
Using birefringent elements and imaging Michelsons for calibration of
  high precision planet finding spectrographs

abstract:
  One of the main methods used for finding extrasolar planets is the radial
velocity technique, in which the Doppler shift of a star due to an orbiting
planet is measured. These measurements are typically performed using
cross-dispersed echelle spectrographs. Unfortunately such spectrographs are
large and expensive and their accurate calibration continues to be challenging.
The aim is to develop a different way to provide a calibration signal. A
commonly used way to introduce a calibration signal is to insert an iodine cell
in the beam. Disadvantages of this include that the lines are narrow, do not
cover the entire spectrum and that light is absorbed. Here I show that
inserting a birefringent element or an imaging Michelson, combined with
Wollaston prisms eliminates these three shortcomings, while maintaining most of
the benefits of the iodine approach. The proposed designs can be made very
compact, thereby providing a convenient way of calibrating a spectrograph.
Similar to the iodine cell approach, the calibration signal travels with the
stellar signal, thereby reducing the sensitivity to spectrograph stability. The
imposed signal covers the entire visible range and any temperature drifts will
be consistent and describable by a single number. Based on experience with
similar devices used, in a different configuration, by the Helioseismic and
Magnetic Imager, it is shown that the calibration device can be made stable at
the 0.1 m/s level, over a significant wavelength range, on short to medium time
scales. While promising, many details still need to be worked out. In
particular a number of laboratory measurements are required in order to
finalize a design and estimate actual performance and it would be desirable to
make a proof of concept.


title:
Thermal Modelling and Controller Design of an Alkaline Electrolysis
  System under Dynamic Operating Conditions

abstract:
  Thermal management is vital for the efficient and safe operation of alkaline
electrolysis systems. Traditional alkaline electrolysis systems use simple
proportional-integral-differentiation (PID) controllers to maintain the stack
temperature near the rated value. However, in renewable-to-hydrogen scenarios,
the stack temperature is disturbed by load fluctuations, and the temperature
overshoot phenomenon occurs which can exceed the upper limit and harm the
stack. This paper focuses on the thermal modelling and controller design of an
alkaline electrolysis system under dynamic operating conditions. A
control-oriented thermal model is established in the form of a third-order
time-delay process, which is used for simulation and controller design. Based
on this model, we propose two novel controllers to reduce temperature
overshoot: one is a current feed-forward PID controller (PID-I), the other is a
model predictive controller (MPC). Their performances are tested on a lab-scale
system and the experimental results are satisfying: the temperature overshoot
is reduced by 2.2 degree with the PID-I controller, and no obvious overshoot is
observed with the MPC controller. Furthermore, the thermal dynamic performance
of an MW-scale alkaline electrolysis system is analyzed by simulation, which
shows that the temperature overshoot phenomenon is more general in large
systems. The proposed method allows for higher temperature set points which can
improve system efficiency by 1%.


title: Strong Consistency for a Class of Adaptive Clustering Procedures

abstract:
  We introduce a class of clustering procedures which includes $k$-means and
$k$-medians, as well as variants of these where the domain of the cluster
centers can be chosen adaptively (for example, $k$-medoids) and where the
number of cluster centers can be chosen adaptively (for example, according to
the elbow method). In the non-parametric setting and assuming only the
finiteness of certain moments, we show that all clustering procedures in this
class are strongly consistent under IID samples. Our method of proof is to
directly study the continuity of various deterministic maps associated with
these clustering procedures, and to show that strong consistency simply
descends from analogous strong consistency of the empirical measures. In the
adaptive setting, our work provides a strong consistency result that is the
first of its kind. In the non-adaptive setting, our work strengthens Pollard's
classical result by dispensing with various unnecessary technical hypotheses,
by upgrading the particular notion of strong consistency, and by using the same
methods to prove further limit theorems.


title:
The Art of Manipulation: Threat of Multi-Step Manipulative Attacks in
  Security Games

abstract:
  This paper studies the problem of multi-step manipulative attacks in
Stackelberg security games, in which a clever attacker attempts to orchestrate
its attacks over multiple time steps to mislead the defender's learning of the
attacker's behavior. This attack manipulation eventually influences the
defender's patrol strategy towards the attacker's benefit. Previous work along
this line of research only focuses on one-shot games in which the defender
learns the attacker's behavior and then designs a corresponding strategy only
once. Our work, on the other hand, investigates the long-term impact of the
attacker's manipulation in which current attack and defense choices of players
determine the future learning and patrol planning of the defender. This paper
has three key contributions. First, we introduce a new multi-step manipulative
attack game model that captures the impact of sequential manipulative attacks
carried out by the attacker over the entire time horizon. Second, we propose a
new algorithm to compute an optimal manipulative attack plan for the attacker,
which tackles the challenge of multiple connected optimization components
involved in the computation across multiple time steps. Finally, we present
extensive experimental results on the impact of such misleading attacks,
showing a significant benefit for the attacker and loss for the defender.


title: Opacity effect on core-shift and the spectral properties of jets

abstract:
  There is theoretical and observational evidence that the jet core position
changes with frequency. However, the core position for a given frequency may
vary with time in the case of flares or for a precessing jet. In this work, we
want to explore the changes in core position as a function of frequency,
magnetic field alignment, relativistic electron density, and jet inclination
angle. We use a physical model of a synchrotron-emitting jet. Two cases of the
jet are analysed, namely with magnetic field parallel and perpendicular to the
jet axis. The evolution of the related spectrum is monitored over the radio
band. We find that a smaller jet inclination angle or a higher electron density
causes the jet core position to move downstream of the jet and we demonstrate
that this displacement of the core along the jet gives rise to a spectral
flattening.


title: Bayesian Active Learning for Discrete Latent Variable Models

abstract:
  Active learning seeks to reduce the number of samples required to estimate
the parameters of a model, thus forming an important class of techniques in
modern machine learning. However, past work on active learning has largely
overlooked latent variable models, which play a vital role in neuroscience,
psychology, and a variety of other engineering and scientific disciplines. Here
we address this gap in the literature and propose a novel framework for
maximum-mutual-information input selection for learning discrete latent
variable regression models. We first examine a class of models known as
"mixtures of linear regressions" (MLR). This example is striking because it is
well known that active learning confers no advantage for standard least-squares
regression. However, we show -- both in simulations and analytically using
Fisher information -- that optimal input selection can nevertheless provide
dramatic gains for mixtures of regression models; we also validate this on a
real-world application of MLRs. We then consider a powerful class of temporally
structured latent variable models known as Input-Output Hidden Markov Models
(IO-HMMs), which have recently gained prominence in neuroscience. We show that
our method substantially speeds up learning, and outperforms a variety of
approximate methods based on variational and amortized inference.


title:
Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory
  Prediction

abstract:
  Spatio-temporal graphs (ST-graphs) have been used to model time series tasks
such as traffic forecasting, human motion modeling, and action recognition. The
high-level structure and corresponding features from ST-graphs have led to
improved performance over traditional architectures. However, current methods
tend to be limited by simple features, despite the rich information provided by
the full graph structure, which leads to inefficiencies and suboptimal
performance in downstream tasks. We propose the use of features derived from
meta-paths, walks across different types of edges, in ST-graphs to improve the
performance of Structural Recurrent Neural Network. In this paper, we present
the Meta-path Enhanced Structural Recurrent Neural Network (MESRNN), a generic
framework that can be applied to any spatio-temporal task in a simple and
scalable manner. We employ MESRNN for pedestrian trajectory prediction,
utilizing these meta-path based features to capture the relationships between
the trajectories of pedestrians at different points in time and space. We
compare our MESRNN against state-of-the-art ST-graph methods on standard
datasets to show the performance boost provided by meta-path information. The
proposed model consistently outperforms the baselines in trajectory prediction
over long time horizons by over 32\%, and produces more socially compliant
trajectories in dense crowds. For more information please refer to the project
website at https://sites.google.com/illinois.edu/mesrnn/home.


title: Distribution Preserving Graph Representation Learning

abstract:
  Graph neural network (GNN) is effective to model graphs for distributed
representations of nodes and an entire graph. Recently, research on the
expressive power of GNN attracted growing attention. A highly-expressive GNN
has the ability to generate discriminative graph representations. However, in
the end-to-end training process for a certain graph learning task, a
highly-expressive GNN risks generating graph representations overfitting the
training data for the target task, while losing information important for the
model generalization. In this paper, we propose Distribution Preserving GNN
(DP-GNN) - a GNN framework that can improve the generalizability of expressive
GNN models by preserving several kinds of distribution information in graph
representations and node representations. Besides the generalizability, by
applying an expressive GNN backbone, DP-GNN can also have high expressive
power. We evaluate the proposed DP-GNN framework on multiple benchmark datasets
for graph classification tasks. The experimental results demonstrate that our
model achieves state-of-the-art performances.


title:
DeepPropNet -- A Recursive Deep Propagator Neural Network for Learning
  Evolution PDE Operators

abstract:
  In this paper, we propose a deep neural network approximation to the
evolution operator for time dependent PDE systems over long time period by
recursively using one single neural network propagator, in the form of
POD-DeepONet with built-in causality feature, for a small time interval. The
trained DeepPropNet of moderate size is shown to give accurate prediction of
wave solutions over the whole time interval.


title:
Turbulent magnetic fields in merging clusters: A case study of Abell
  2146

abstract:
  Kelvin-Helmholtz Instabilities (KHI) along contact discontinuities in galaxy
clusters have been used to constrain the strength of magnetic fields in galaxy
clusters, following the assumption that, as magnetic field lines drape around
the interface between the cold and hot phases, their magnetic tension resists
the growth of perturbations. This has been observed in simulations of rigid
objects moving through magnetised media and sloshing galaxy clusters, and then
applied in interpreting observations of merger cold fronts. Using a suite of
MHD simulations of binary cluster mergers, we show that even magnetic field
strengths stronger than yet observed ($\beta = P_{\rm th}/P_B = 50$) show
visible KHI features. This is because our initial magnetic field is tangled,
producing Alfven waves and associated velocity fluctuations in the ICM;
stronger initial fields therefore seed larger fluctuations, so that even a
reduced growth rate due to magnetic tension produces a significant KHI. The net
result is that a stronger initial magnetic field produces more dramatic
fluctuations in surface brightness and temperature, not the other way around.
We show that this is hard to distinguish from the evolution of turbulent
perturbations of the same initial magnitude. Therefore, in order to use
observations of KHI in the ICM to infer magnetic field strengths by comparing
to idealized simulations, the perturbations which seed the KHI must be
well-understood and (if possible) carefully controlled.


title: O6-plane backreaction on scale-separated Type IIA AdS$_3$ vacua

abstract:
  We evaluate the backreaction of O6-planes in scale-separated $\text{AdS}_3$
flux vacua of massive Type IIA. Using the appropriate flux scaling we show that
the corrections to the various background fields and moduli are controlled and
subleading when going from smeared to localized sources. Similarly, the
backreaction corrections to the scalar potential are parametrically small in
the scale-separation limit, assuming always that the near-O6-plane
singularities will find a resolution within string theory, even in the presence
of a Romans mass. Our analysis is based on the equations of motion and
therefore applies also to the non-supersymmetric vacua.


title:
Electrical and thermal transport in van der Waals magnets
  2H-M$_x$TaS$_2$ (M = Mn, Co)

abstract:
  We report a detailed study of electrical and thermal transport properties in
2H-M$_x$TaS$_2$ (M = Mn, Co) magnets where M atoms are intercalated in the van
der Waals gap. The intercalation induces ferromagentism with an easy-plane
anisotropy in 2H-Mn$_x$TaS$_2$, but ferromagnetism with a strong uniaxial
anisotropy in 2H-Co$_{0.22}$TaS$_2$, which finally evolves into a
three-dimensional antiferromagnetism in 2H-Co$_{0.34}$TaS$_2$.
Temperature-dependent electrical resistivity shows metallic behavior for all
samples. Thermopower is negative in the whole temperature range for
2H-Co$_x$TaS$_2$, whereas the sign changes from negative to positive with
increasing Mn for 2H-Mn$_x$TaS$_2$. The diffusive thermoelectric response
dominates in both high- and low-temperature ranges for all samples. A clear
kink in electrical resistivity, a weak anomaly in thermal conductivity, as well
as a slope change in thermopower were observed at the magnetic transitions for
2H-Mn$_{0.28}$TaS$_2$ ($T_\textrm{c}$ $\approx$ 82 K) and 2H-Co$_{0.34}$TaS$_2$
($T_\textrm{N}$ $\approx$ 36 K), respectively, albeit weaker for lower $x$
crystals. Co-intercalation promoted ferromagnetic to antiferromagnetic
transition is further confirmed by the Hall resistivity; the sign change of the
ordinary Hall coefficient indicates a multi-band behavior in 2H-Co$_x$TaS$_2$.


title:
Feasibility and Acceptability of Remote Neuromotor Rehabilitation
  Interactions Using Social Robot Augmented Telepresence: A Case Study

abstract:
  There is a growing need to deliver rehabilitation care to patients remotely.
Long term demographic changes, geographic shortages of care providers, and now
a global pandemic contribute to this need. Telepresence provides an option for
delivering this care. However, telepresence using video and audio alone does
not provide an interaction of the same quality as in-person. To bridge this
gap, we propose the use of social robot augmented telepresence (SRAT). We have
constructed a demonstration SRAT system for upper extremity rehab, in which a
humanoid, with a head, body, face, and arms, is attached to a mobile
telepresence system, to collaborate with the patient and clinicians as an
independent social entity. The humanoid can play games with the patient and
demonstrate activities.These activities could be used both to perform
assessments in support of self-directed rehab and to perform exercises.
  In this paper, we present a case series with six subjects who completed
interactions with the robot, three subjects who have previously suffered a
stroke and three pediatric subjects who are typically developing. Subjects
performed a Simon Says activity and a target touch activity in person, using
classical telepresence (CT), and using SRAT. Subjects were able to effectively
work with the social robot guiding interactions and 5 of 6 rated SRAT better
than CT. This study demonstrates the feasibility of SRAT and some of its
benefits.


title: Negation-Closure for JSON Schema

abstract:
  JSON Schema is an evolving standard for describing families of JSON
documents. It is a logical language, based on a set of assertions that describe
features of the JSON value under analysis and on logical or structural
combinators for these assertions, including a negation operator. Most logical
languages with negation enjoy negation closure, that is, for every operator
they have a negation dual that expresses its negation. We show that this is not
the case for JSON Schema, we study how that changed with the latest versions of
the Draft, and we discuss how the language may be enriched accordingly. In the
process, we define an algebraic reformulation of JSON Schema, which we
successfully employed in a prototype system for generating schema witnesses.


title:
Thermoelectricity and electronic correlations enhancement in FeS by
  slight Se substitution

abstract:
  We report thermoelectric studies of FeS$_{1-x}$Se$_x$ ($x$ = 0, 0.06)
superconducting single crystals that feature high irreversibility fields and
critical current density $J_c$ comparable to materials with much higher
superconducting critical temperatures ($T_c$'s). The ratio of $T_c$ to the
Fermi temperature $T_F$ is very small indicating weak electronic correlations.
With a slight selenium substitution on sulfur site in FeS both $T_c$/$T_F$ and
the effective mass $m^*$ rise considerably, implying increase in electronic
correlation of the bulk conducting states. The first-principle calculations
show rise of the density of states at the Fermi level in
FeS$_{0.94}$Se$_{0.06}$ when compared to FeS which is related not only to Fe
but also to chalcogen-derived electronic states.


title:
Neural-Progressive Hedging: Enforcing Constraints in Reinforcement
  Learning with Stochastic Programming

abstract:
  We propose a framework, called neural-progressive hedging (NP), that
leverages stochastic programming during the online phase of executing a
reinforcement learning (RL) policy. The goal is to ensure feasibility with
respect to constraints and risk-based objectives such as conditional
value-at-risk (CVaR) during the execution of the policy, using probabilistic
models of the state transitions to guide policy adjustments. The framework is
particularly amenable to the class of sequential resource allocation problems
since feasibility with respect to typical resource constraints cannot be
enforced in a scalable manner. The NP framework provides an alternative that
adds modest overhead during the online phase. Experimental results demonstrate
the efficacy of the NP framework on two continuous real-world tasks: (i) the
portfolio optimization problem with liquidity constraints for financial
planning, characterized by non-stationary state distributions; and (ii) the
dynamic repositioning problem in bike sharing systems, that embodies the class
of supply-demand matching problems. We show that the NP framework produces
policies that are better than deep RL and other baseline approaches, adapting
to non-stationarity, whilst satisfying structural constraints and accommodating
risk measures in the resulting policies. Additional benefits of the NP
framework are ease of implementation and better explainability of the policies.


title:
A Unified Wasserstein Distributional Robustness Framework for
  Adversarial Training

abstract:
  It is well-known that deep neural networks (DNNs) are susceptible to
adversarial attacks, exposing a severe fragility of deep learning systems. As
the result, adversarial training (AT) method, by incorporating adversarial
examples during training, represents a natural and effective approach to
strengthen the robustness of a DNN-based classifier. However, most AT-based
methods, notably PGD-AT and TRADES, typically seek a pointwise adversary that
generates the worst-case adversarial example by independently perturbing each
data sample, as a way to "probe" the vulnerability of the classifier. Arguably,
there are unexplored benefits in considering such adversarial effects from an
entire distribution. To this end, this paper presents a unified framework that
connects Wasserstein distributional robustness with current state-of-the-art AT
methods. We introduce a new Wasserstein cost function and a new series of risk
functions, with which we show that standard AT methods are special cases of
their counterparts in our framework. This connection leads to an intuitive
relaxation and generalization of existing AT methods and facilitates the
development of a new family of distributional robustness AT-based algorithms.
Extensive experiments show that our distributional robustness AT algorithms
robustify further their standard AT counterparts in various settings.


title: Thermal transport and mixed valence in ZrTe$_3$ doped with Hf and Se

abstract:
  Two-dimensional transition metal trichalcogenides (TMTC's) feature covalently
bonded metal-chalcogen layers separated by the van der Waals (vdW) gap. Similar
to transition metal dichalcogenides (TMDCs), TMTCs often host charge density
waves (CDWs) and superconductivity but unlike TMDCs atomic chains in the
crystal structure give rise to quasi one-dimensional (quasi 1D) conduction.
ZrTe$_3$ features CDW below $T_{\textrm{CDW}}$ = 63 K and filamentary
superconductivity below 2 K that can be enhanced by pressure or chemical
substitution. Here we report the presence of mixed valent Zr$^{2+}$ and
Zr$^{4+}$ atoms in ZrTe$_3$ crystals that is reduced by doping in
ZrTe$_{3-x}$Se$_x$ and Zr$_{1-y}$Hf$_y$Te$_3$. Superconductivity is enhanced
via disorder in Te2-Te3 atomic chains that are associated with CDW formation.
Hf substitution on Zr atomic site enhances $T_{\textrm{CDW}}$ due to
unperturbed Te2-Te3 chain periodicity and enhanced electron-phonon coupling.
Weak electronic correlations in ZrTe$_{3-x}$Se$_x$ are likely governed by the
lattice contraction effects.


title: (Looking For) The Heart of Abelian Polish Groups

abstract:
  We prove that the category $\mathcal{M}$ of abelian groups with a Polish
cover introduced in collaboration with Bergfalk and Panagiotopoulos is the left
heart of (the derived category of) the quasi-abelian category $\mathcal{A}$ of
abelian Polish groups in the sense of Beilinson--Bernstein--Deligne and
Schneiders. Thus, $\mathcal{M}$ is an abelian category containing $\mathcal{A}$
as a full subcategory such that the inclusion functor $\mathcal{A}\rightarrow
\mathcal{M}$ is exact and finitely continuous. Furthermore, $\mathcal{M}$ is
uniquely characterized up to equivalence by the following universal property:
for every abelian category $\mathcal{B}$, a functor $\mathcal{A}\rightarrow
\mathcal{B}$ is exact and finitely continuous if and only if it extends to an
exact (and necessarily finitely continuous) functor $\mathcal{M}\rightarrow
\mathcal{B}$. We provide similar descriptions of the left heart of the
following categories: non-Archimedean abelian Polish groups; (non-Archimedean)
Polish $G$-modules, for a given Polish group or Polish ring $G$; Fr\'{e}chet
spaces over $K$ for a given separable complete non-Archimedean valued field
$K$.


title: Robust Control of Partially Specified Boolean Networks

abstract:
  Regulatory networks (RNs) are a well-accepted modelling formalism in
computational systems biology. The control of RNs is currently receiving a lot
of attention because it provides a computational basis for cell reprogramming
-- an attractive technology developed in regenerative medicine. By solving the
control problem, we learn which parts of a biological system should be
perturbed to stabilise the system in the desired phenotype.
  We allow the specification of the Boolean model representing a given RN to be
incomplete. To that end, we utilise the formalism of partially specified
Boolean networks which covers every possible behaviour of unspecified parts of
the system. Such an approach causes a significant state explosion. This problem
is addressed by using symbolic methods to represent both the unspecified model
parts and all possible perturbations of the system.
  Additionally, to make the control design efficient and practically
applicable, the optimal control should be minimal in terms of size. Moreover,
in a partially specified model, a control may achieve the desired stabilisation
only for a subset of the possible fully specified model instantiations. To
address these aspects, we utilise several quantitative measures. Apart from the
size of perturbation, we also examine its robustness -- a portion of
instantiations for which the control is applicable.
  We show that proposed symbolic methods solving the control problem for
partially specified BNs are efficient and scale well. We also evaluate the
robustness metrics in cases of all three studied control types. The robustness
metric tells us how big a proportion of fully defined systems the given
perturbation works. Our experiments support the hypothesis that one-step
perturbations may be less robust than temporary or permanent perturbations.
  This is a full version of a paper that is submitted to a journal.


title:
Stability vs Implicit Bias of Gradient Methods on Separable Data and
  Beyond

abstract:
  An influential line of recent work has focused on the generalization
properties of unregularized gradient-based learning procedures applied to
separable linear classification with exponentially-tailed loss functions. The
ability of such methods to generalize well has been attributed to the their
implicit bias towards large margin predictors, both asymptotically as well as
in finite time. We give an additional unified explanation for this
generalization and relate it to two simple properties of the optimization
objective, that we refer to as realizability and self-boundedness. We introduce
a general setting of unconstrained stochastic convex optimization with these
properties, and analyze generalization of gradient methods through the lens of
algorithmic stability. In this broader setting, we obtain sharp stability
bounds for gradient descent and stochastic gradient descent which apply even
for a very large number of gradient steps, and use them to derive general
generalization bounds for these algorithms. Finally, as direct applications of
the general bounds, we return to the setting of linear classification with
separable data and establish several novel test loss and test accuracy bounds
for gradient descent and stochastic gradient descent for a variety of loss
functions with different tail decay rates. In some of these case, our bounds
significantly improve upon the existing generalization error bounds in the
literature.


title:
Is ferroelectric polarization spontaneous? The hidden effect of oxygen
  vacancies

abstract:
  The abrupt transition from a disordered phase to collective ion behavior in
ferroelectrics serves as a model system to study spontaneous symmetry breaking
in nature. First-principle calculations combined with atomic-scale imaging
demonstrate that oxygen vacancies that serve as pinning sites for the ferroic
domain walls remain immobile above the Curie temperature in the seminal
ferroelectric barium titanate. Thus, upon cooling, these oxygen vacancies
dictate reproducible domain-wall patterning. Domain-scale imaging with
variable-temperature piezoresponse force microscopy confirmed the polarization
memory effect, questioning the spontaneity of the ferroelectric transition.


title:
Molecular rectifiers with very high rectification ratio enabled by
  oxidative damage in double-stranded DNA

abstract:
  In this work, we report a novel strategy to construct molecular diodes with a
record tunable rectification ratio of as high as 10^6 using oxidatively damaged
DNA molecules. Being exposed to several endogenous and exogenous events, DNA
suffers constant oxidative damages leading to oxidation of guanine to
8-Oxoguanine (8oxoG). Here, we study the charge migration properties of native
and oxidatively damaged DNA using a multiscale multiconfigurational methodology
comprising of molecular dynamics, density functional theory and kinetic Monte
Carlo simulations. We perform a comprehensive study to understand the effect of
different concentrations and locations of 8oxoG in a dsDNA sequence on its CT
properties and find tunable rectifier properties having potential applications
in molecular electronics such as molecular switches and molecular rectifiers.
We also discover the negative differential resistance properties of fully
oxidized Drew-Dickerson sequence. The presence of 8oxoG guanine leads to the
trapping of charge, thus operates as a charge sink, which reveals how oxidized
guanine saves the rest of the genome from further oxidative damage.

categories: cond-mat.soft physics.bio-ph physics.chem-ph physics.comp-ph


title:
The Impact of Explanations on Layperson Trust in Artificial
  Intelligence-Driven Symptom Checker Apps: Experimental Study

abstract:
  To achieve the promoted benefits of an AI symptom checker, laypeople must
trust and subsequently follow its instructions. In AI, explanations are seen as
a tool to communicate the rationale behind black-box decisions to encourage
trust and adoption. However, the effectiveness of the types of explanations
used in AI-driven symptom checkers has not yet been studied. Social theories
suggest that why-explanations are better at communicating knowledge and
cultivating trust among laypeople. This study ascertains whether explanations
provided by a symptom checker affect explanatory trust among laypeople (N=750)
and whether this trust is impacted by their existing knowledge of disease.
  Results suggest system builders developing explanations for symptom-checking
apps should consider the recipient's knowledge of a disease and tailor
explanations to each user's specific need. Effort should be placed on
generating explanations that are personalized to each user of a symptom checker
to fully discount the diseases that they may be aware of and to close their
information gap.


title:
Goodness-of-fit tests for stochastic frontier models based on the
  characteristic function

abstract:
  We consider goodness-of-fit tests for the distribution of the composed error
in Stochastic Frontier Models. The proposed test statistic utilizes the
characteristic function of the composed error term, and is formulated as a
weighted integral of properly standardized data. The new test statistic is
shown to be consistent and computationally convenient. Simulation results are
presented whereby resampling versions of the new tests are compared to
classical goodness-of-fit methods.


title: The Unfairness of Popularity Bias in Book Recommendation

abstract:
  Recent studies have shown that recommendation systems commonly suffer from
popularity bias. Popularity bias refers to the problem that popular items
(i.e., frequently rated items) are recommended frequently while less popular
items are recommended rarely or not at all. Researchers adopted two approaches
to examining popularity bias: (i) from the users' perspective, by analyzing how
far a recommendation system deviates from user's expectations in receiving
popular items, and (ii) by analyzing the amount of exposure that long-tail
items receive, measured by overall catalog coverage and novelty. In this paper,
we examine the first point of view in the book domain, although the findings
may be applied to other domains as well. To this end, we analyze the well-known
Book-Crossing dataset and define three user groups based on their tendency
towards popular items (i.e., Niche, Diverse, Bestseller-focused). Further, we
evaluate the performance of nine state-of-the-art recommendation algorithms and
two baselines (i.e., Random, MostPop) from both the accuracy (e.g., NDCG,
Precision, Recall) and popularity bias perspectives. Our results indicate that
most state-of-the-art recommendation algorithms suffer from popularity bias in
the book domain, and fail to meet users' expectations with Niche and Diverse
tastes despite having a larger profile size. Conversely, Bestseller-focused
users are more likely to receive high-quality recommendations, both in terms of
fairness and personalization. Furthermore, our study shows a tradeoff between
personalization and unfairness of popularity bias in recommendation algorithms
for users belonging to the Diverse and Bestseller groups, that is, algorithms
with high capability of personalization suffer from the unfairness of
popularity bias.


title: Graph-Assisted Communication-Efficient Ensemble Federated Learning

abstract:
  Communication efficiency arises as a necessity in federated learning due to
limited communication bandwidth. To this end, the present paper develops an
algorithmic framework where an ensemble of pre-trained models is learned. At
each learning round, the server selects a subset of pre-trained models to
construct the ensemble model based on the structure of a graph, which
characterizes the server's confidence in the models. Then only the selected
models are transmitted to the clients, such that certain budget constraints are
not violated. Upon receiving updates from the clients, the server refines the
structure of the graph accordingly. The proposed algorithm is proved to enjoy
sub-linear regret bound. Experiments on real datasets demonstrate the
effectiveness of our novel approach.


title: Federated Online Sparse Decision Making

abstract:
  This paper presents a novel federated linear contextual bandits model, where
individual clients face different K-armed stochastic bandits with
high-dimensional decision context and coupled through common global parameters.
By leveraging the sparsity structure of the linear reward , a collaborative
algorithm named \texttt{Fedego Lasso} is proposed to cope with the
heterogeneity across clients without exchanging local decision context vectors
or raw reward data. \texttt{Fedego Lasso} relies on a novel multi-client
teamwork-selfish bandit policy design, and achieves near-optimal regrets for
shared parameter cases with logarithmic communication costs. In addition, a new
conceptual tool called federated-egocentric policies is introduced to delineate
exploration-exploitation trade-off. Experiments demonstrate the effectiveness
of the proposed algorithms on both synthetic and real-world datasets.


title:
The reconstructed thermal lattice Boltzmann flux solver and its
  applications for simulations of thermal flows

abstract:
  The thermal lattice Boltzmann flux solver (TLBFS) has been proposed to
overcome the drawbacks of the thermal lattice Boltzmann models. However, as a
weakly compressible model, its mechanism of good numerical stability for high
Rayleigh number thermal flows is still unclear. To reveal the mechanism, the
present paper firstly derives the macroscopic equations of TLBFS (MEs-TLBFS)
with actual numerical dissipation terms by approximating its computational
process. By solving MEs-TLBFS with the finite volume method, the reconstructed
TLBFS (RTLBFS) is proposed. Detailed analyses prove that these actual numerical
dissipation terms are the mechanism of the good numerical stability of TLBFS
for high Rayleigh number thermal flows. More detailed numerical tests indicate
RTLBFS has similar performances as TLBFS for stability, accuracy, and
efficiency. Moreover, the present RTLBFS shows a clear mechanism to achieve
good numerical stability for high Rayleigh number flows.


title: ZipZap: A Blockchain Solution for Local Energy Trading

abstract:
  In the last few years, electric utility companies have increasingly invested
into transactive energy systems. This trend was primarily caused by the
integration of distributed energy resources (DERs) and internet-of-things (IoT)
devices into their existing distribution networks. Influenced by the general
interest in blockchain technologies, many industry specialists are considering
new, more efficient peer-to-peer market structures for DERs. Since
blockchain-based energy exchanges can automate transactions between their
members and provide increased levels of security thanks to smart contracts,
these new initiatives may eventually revolutionize how customers interact with
utility companies. In this paper, we explore the trade-off between cost and
traceability in the form of on-chain and off-chain solutions. We also propose
ZipZap, a first step towards a blockchain-based local smart grid system. ZipZap
is an ERC-1155 compliant solution with four different prototypes: Heavyweight,
Featherweight, Lightweight and Weightless. The first three prototypes were
developed in Solidity and deployed using Ethereum. Heavyweight is fully
on-chain, whereas Featherweight and Lightweight showcase various levels of
hybridization. Weightless, in turn, was deployed using Quorum, a gas-free
alternative to Ethereum. Our evaluation uses realistic parameters and measures
the impact of different types of metadata storage scopes, with some Ethereum
prototypes showcasing gas cost reductions of more than 97% in comparison to our
fully on-chain baseline.


title: A note on infinite partitions of free products of Boolean algebras

abstract:
  If $A$ is an infinite Boolean algebra the cardinal invariant
$\mathfrak{a}(A)$ is defined as the smallest size of an infinite partition of
$A$. The cardinal $\mathfrak{a}(A\oplus B)$, where $A\oplus B$ is the free
product of the Boolean algebras $A$ and $B$ (whose dual topological space is
the product of the dual topological spaces of $A$ and $B$), is below both
$\mathfrak{a}(A)$ and $\mathfrak{a}(B)$. The equality $\mathfrak{a}(A\oplus
B)=\min\lbrace\mathfrak{a}(A),\mathfrak{a}(B)\rbrace$ is not known to hold for
all infinite Boolean algebras $A$ and $B$. Here some lower bounds of
$\mathfrak{a}(A\oplus B)$ are provided.


title: Byzantine Agreement in Polynomial Time with Near-Optimal Resilience

abstract:
  It has been known since the early 1980s that Byzantine Agreement in the full
information, asynchronous model is impossible to solve deterministically
against even one crash fault [FLP85], but that it can be solved with
probability 1 [Ben83], even against an adversary that controls the scheduling
of all messages and corrupts up to $f<n/3$ players [Bra87]. The main downside
of [Ben83, Bra87] is that they terminate in $2^{\Theta(n)}$ rounds in
expectation whenever $f=\Theta(n)$.
  King and Saia [KS16, KS18(arXiv:1812.10169)] developed a polynomial protocol
(polynomial rounds, polynomial computation) that is resilient to $f <
(1.14\times 10^{-9})n$ Byzantine faults. The new idea in their protocol is to
detect -- and blacklist -- coalitions of likely-bad players by analyzing the
deviations of random variables generated by those players over many rounds.
  In this work we design a simple collective coin-flipping protocol such that
if any coalition of faulty players repeatedly does not follow protocol, then
they will eventually be detected by one of two simple statistical tests. Using
this coin-flipping protocol, we solve Byzantine Agreement in a polynomial
number of rounds, even in the presence of up to $f<n/4$ Byzantine faults. This
comes close to the $f<n/3$ upper bound on the maximum number of faults
[BT85,FLM86,LSP82].


title: Fourier--Hermite Dynamic Programming for Optimal Control

abstract:
  In this paper, we propose a novel computational method for solving non-linear
optimal control problems. The method is based on the use of Fourier--Hermite
series for approximating the action-value function arising in dynamic
programming instead of the conventional Taylor series expansion used in
differential dynamic programming (DDP). The coefficients of the
Fourier--Hermite series can be numerically computed by using sigma-point
methods, which leads to a novel class of sigma-point based dynamic programming
methods. We also prove the quadratic convergence of the method and
experimentally test its performance against other methods.


title:
Hamiltonian field theory close to the wave equation: from
  Fermi-Pasta-Ulam to water waves

abstract:
  In the present work we analyse the structure of the Hamiltonian field theory
in the neighbourhood of the wave equation $q_{tt}=q_{xx}$. We show that,
restricting to "graded" polynomial perturbations in $q_x$, $p$ and their space
derivatives of higher order, the local field theory is equivalent, in the sense
of the Hamiltonian normal form, to that of the Korteweg-de Vries hierarchy of
second order. Within this framework, we explain the connection between the
theory of water waves and the Fermi-Pasta-Ulam system.


title: New description of perverse sheaves on a disc

abstract:
  There is a connection between the category of perverse sheaves on a disc and
different notions related to spherical functors. We introduce a category whose
objects are analogous to 4-periodic semiorthogonal decompositions and prove
that it is equivalent to the category of perverse sheaves on a disc stratified
by the origin and its complement.


title:
PheroCom: Decentralised and asynchronous swarm robotics coordination
  based on virtual pheromone and vibroacoustic communication

abstract:
  Representation and control of the dynamics of stigmergic substances used by
bio-inspired approaches is a challenge when applied to robotics. In order to
overcome this challenge, this work proposes a model to coordinate swarms of
robots based on the virtualisation and control of these substances in a local
scope. The model presents a new pheromone modelling, which enables the
decentralisation and asynchronicity of navigation decisions. Each robot
maintains an independent virtual pheromone map, which is continuously updated
with the robot's deposits and pheromone evaporation. Moreover, the individual
pheromone map is also updated by aggregating information from other robots that
are exploring nearby areas. Thus, individual and independent maps replace the
need of a centralising agent that controls and distributes the pheromone
information, which is not always practicable. Pheromone information propagation
is inspired by ants' vibroacoustic communication, which, in turn, is
characterised as an indirect communication through a type of gossip protocol.
The proposed model was evaluated through an agent simulation software,
implemented by the authors, and in the Webots platform. Experiments were
carried out to validate the model in different environments, with different
shapes and sizes, as well as varying the number of robots. The analysis of the
results has shown that the model was able to perform the coordination of the
swarm, and the robots have exhibited an expressive performance executing the
surveillance task.


title:
Enhancing Legal Argument Mining with Domain Pre-training and Neural
  Networks

abstract:
  The contextual word embedding model, BERT, has proved its ability on
downstream tasks with limited quantities of annotated data. BERT and its
variants help to reduce the burden of complex annotation work in many
interdisciplinary research areas, for example, legal argument mining in digital
humanities. Argument mining aims to develop text analysis tools that can
automatically retrieve arguments and identify relationships between
argumentation clauses. Since argumentation is one of the key aspects of case
law, argument mining tools for legal texts are applicable to both academic and
non-academic legal research. Domain-specific BERT variants (pre-trained with
corpora from a particular background) have also achieved strong performance in
many tasks. To our knowledge, previous machine learning studies of argument
mining on judicial case law still heavily rely on statistical models. In this
paper, we provide a broad study of both classic and contextual embedding models
and their performance on practical case law from the European Court of Human
Rights (ECHR). During our study, we also explore a number of neural networks
when being combined with different embeddings. Our experiments provide a
comprehensive overview of a variety of approaches to the legal argument mining
task. We conclude that domain pre-trained transformer models have great
potential in this area, although traditional embeddings can also achieve strong
performance when combined with additional neural network layers.


title:
The Large Dispersion and Scattering of FRB 20190520B are Dominated by
  the Host Galaxy

abstract:
  The repeating FRB 20190520B is localized to a galaxy at $z=0.241$, much
closer than expected given its dispersion measure $\rm DM=1205\pm4 pc\
cm^{-3}$. Here we assess implications of the large DM and scattering observed
from FRB 20190520B for the host galaxy's plasma properties. A sample of 75
bursts detected with the Five-hundred-meter Aperture Spherical radio Telescope
shows scattering on two scales: a mean temporal delay $\tau(1.41\ {\rm
GHz})=10.9\pm1.5$ ms, which is attributed to the host galaxy, and a mean
scintillation bandwidth $\nu_{\rm d}(1.41\ {\rm GHz})=0.21\pm0.01$ MHz, which
is attributed to the Milky Way. Balmer line measurements for the host imply an
H$\alpha$ emission measure (galaxy frame) $\rm EM_s=620$ pc cm$^{-6} \times
(T/10^4 {\rm K})^{0.9}$, implying $\rm DM_{\rm H\alpha}$ of order the value
inferred from the FRB DM budget, $\rm DM_h=1121^{+89}_{-138}$ pc cm$^{-3}$ for
plasma temperatures greater than the typical value $10^4$ K. Combining $\tau$
and $\rm DM_h$ yields a nominal constraint on the scattering amplification from
the host galaxy $\tilde{F} G=1.5^{+0.8}_{-0.3}$ (pc$^2$ km)$^{-1/3}$, where
$\tilde{F}$ describes turbulent density fluctuations and $G$ represents the
geometric leverage to scattering that depends on the location of the scattering
material. For a two-screen scattering geometry where $\tau$ arises from the
host galaxy and $\Delta \nu_{\rm d}$ from the Milky Way, the implied distance
between the FRB source and dominant scattering material is $\lesssim100$ pc.
The host galaxy scattering and DM contributions support a novel technique for
estimating FRB redshifts using the $\tau-\rm DM$ relation, and are consistent
with previous findings that scattering of localized FRBs is largely dominated
by plasma within host galaxies and the Milky Way.


title:
Improving the Efficiency of Variationally Enhanced Sampling with
  Wavelet-Based Bias Potentials

abstract:
  Collective variable-based enhanced sampling methods are routinely used on
systems with metastable states, where high free energy barriers impede proper
sampling of the free energy landscapes when using conventional molecular
dynamics simulations. One such method is variationally enhanced sampling (VES),
which is based on a variational principle where a bias potential in the space
of some chosen slow degrees of freedom, or collective variables, is constructed
by minimizing a convex functional. In practice, the bias potential is taken as
a linear expansion in some basis function set. So far, primarily basis
functions delocalized in the collective variable space, like plane waves,
Chebyshev, or Legendre polynomials, have been used. However, there has not been
an extensive study of how the convergence behavior is affected by the choice of
the basis functions. In particular, it remains an open question if localized
basis functions might perform better. In this work, we implement, tune, and
validate Daubechies wavelets as basis functions for VES\@. The wavelets
construct orthogonal and localized bases that exhibit an attractive
multiresolution property. We evaluate the performance of wavelet and other
basis functions on various systems, going from model potentials to the calcium
carbonate association process in water. We observe that wavelets exhibit
excellent performance and much more robust convergence behavior than all other
basis functions, as well as better performance than metadynamics. In
particular, using wavelet bases yields far smaller fluctuations of the bias
potential within individual runs and smaller differences between independent
runs. Based on our overall results, we can recommend wavelets as basis
functions for VES.


title: Conditional Simulation Using Diffusion Schr\"odinger Bridges

abstract:
  Denoising diffusion models have recently emerged as a powerful class of
generative models. They provide state-of-the-art results, not only for
unconditional simulation, but also when used to solve conditional simulation
problems arising in a wide range of inverse problems such as image inpainting
or deblurring. A limitation of these models is that they are computationally
intensive at generation time as they require simulating a diffusion process
over a long time horizon. When performing unconditional simulation, a
Schr\"odinger bridge formulation of generative modeling leads to a
theoretically grounded algorithm shortening generation time which is
complementary to other proposed acceleration techniques. We extend here the
Schr\"odinger bridge framework to conditional simulation. We demonstrate this
novel methodology on various applications including image super-resolution and
optimal filtering for state-space models.


title:
Configuration Control for Physical Coupling of Heterogeneous Robot
  Swarms

abstract:
  In this paper, we present a heterogeneous robot swarm system that can
physically couple with each other to form functional structures and dynamically
decouple to perform individual tasks. The connection between robots can be
formed with a passive coupling mechanism, ensuring minimum energy consumption
during coupling and decoupling behavior. The heterogeneity of the system
enables the robots to perform structural enhancement configurations based on
specific environmental requirements. We propose a connection-pair oriented
configuration control algorithm to form different assemblies. We show
experiments of up to nine robots performing the coupling, gap-crossing, and
decoupling behaviors.


title:
Temporal signals drive the emergence of multicellular information
  networks

abstract:
  Coordinated responses to environmental stimuli are critical for multicellular
organisms. To overcome the obstacles of cell-to-cell heterogeneity and noisy
signaling dynamics within individual cells, cells must effectively exchange
information with peers. However, the dynamics and mechanisms of collective
information transfer driven by external signals is poorly understood. Here we
investigate the calcium dynamics of neuronal cells that form confluent
monolayers and respond to cyclic ATP stimuli in microfluidic devices. Using
Granger inference to reconstruct the underlying causal relations between the
cells, we find that the cells self-organize into spatially decentralized and
temporally stationary networks to support information transfer via gap junction
channels. The connectivity of the causal networks depend on the temporal
profile of the external stimuli, where short periods, or long periods with
small duty fractions, lead to reduced connectivity and fractured network
topology. We build a theoretical model based on communicating excitable units
that reproduces our observations. The model further predicts that connectivity
of the causal network is maximal at an optimal communication strength, which is
confirmed by the experiments. Together, our results show that information
transfer between neuronal cells is externally regulated by the temporal profile
of the stimuli, and internally regulated by cell-cell communication.


title: Application of DatasetGAN in medical imaging: preliminary studies

abstract:
  Generative adversarial networks (GANs) have been widely investigated for many
potential applications in medical imaging. DatasetGAN is a recently proposed
framework based on modern GANs that can synthesize high-quality segmented
images while requiring only a small set of annotated training images. The
synthesized annotated images could be potentially employed for many medical
imaging applications, where images with segmentation information are required.
However, to the best of our knowledge, there are no published studies focusing
on its applications to medical imaging. In this work, preliminary studies were
conducted to investigate the utility of DatasetGAN in medical imaging. Three
improvements were proposed to the original DatasetGAN framework, considering
the unique characteristics of medical images. The synthesized segmented images
by DatasetGAN were visually evaluated. The trained DatasetGAN was further
analyzed by evaluating the performance of a pre-defined image segmentation
technique, which was trained by the use of the synthesized datasets. The
effectiveness, concerns, and potential usage of DatasetGAN were discussed.


title: Technical Debt and Maintainability: How do tools measure it?

abstract:
  The technical state of software, i.e., its technical debt (TD) and
maintainability are of increasing interest as ever more software is developed
and deployed. Since td and maintainability are neither uniformly defined, not
easy to understand, nor directly measurable, practitioners are likely to apply
readily available tools to assess TD or maintainability and they may rely on
the reported results without properly understanding what they embody. In this
paper, we: a) methodically identify 11 readily available tools that measure TD
or maintainability, b) present an in-depth investigation on how each of these
tools measures and computes TD or maintainability, and c) compare these tools
and their characteristics. We find that contemporary tools focus mainly on
internal qualities of software, i.e., quality of source code, that they define
and measure TD or maintainability in widely different ways, that most of the
tools measure TD or maintainability opaquely, and that it is not obvious why
the measure of one tool is more trustworthy or representative than the one of
another.


title: Ergodic theorems for algorithmically random points

abstract:
  This paper is a survey of applications of the theory of algorithmic
randomness to ergodic theory. We establish various degrees of constructivity
for asymptotic laws of probability theory. In the framework of the Kolmogorov
approach to the substantiation of the probability theory and information theory
on the base of the theory of algorithms, we formulate probabilistic laws, i.e.
statements which hold almost surely, in a pointwise form, i.e., for Martin-Lof
random points. It is shown in this paper that the main statement of ergodic
theory - Birkhoff ergodic theorem, is non-constructive in the strong
(classical) sense, but it is constructive in some weaker sense - in terms of
Martin-Lof randomness.


title:
Dynamical renormalization and universality in classical multifield
  cosmological models

abstract:
  We study the scaling behavior of classical multifield cosmological models
with complete scalar manifold $({\cal M},{\cal G})$ and positive smooth scalar
potential $\Phi$, introducing a dynamical renormalization group action which
relates their UV and IR limits. We show that the RG flow of such models
interpolates between a modification of the geodesic flow of $({\cal M},{\cal
G})$ (obtained in the UV limit) and the gradient flow of $({\cal M},{\cal
G},V)$ (obtained in the IR limit), where the classical effective potential $V$
is proportional to $\sqrt{2\Phi}$. Using this fact, we show that two-field
models whose scalar manifold has constant Gaussian curvature equal to $-1$, $0$
or $1$ are infrared universal in the sense that they suffice to describe the
first order IR approximants of cosmological orbits for all two-field models
with positive smooth scalar potential.


title: A Weyl geometric scalar field approach to the dark sector

abstract:
  This paper explores the dark sector (dark matter and dark energy) from the
perspective of Weyl geometric scalar tensor theory (integrable Weyl geometry).
In order to account for the galactic dynamics successfully modelled by MOND
("modified Newtonian dynamics"), the non-minimally coupled scalar field
considered here has a Lagrangian with two non-conventional contributions in
addition to a standard kinetic term: one is inspired by Bekenstein/Milgrom's
RAQUAL ("relativistic a-quadratic Lagrangian") from 1983, the other one by a
second order term introduced in cosmological studies by Novello et al. in 1993.
The original RAQUAL approach suffered from the defect that the energy-momentum
of the scalar field was far too small for the expected gravitational light
refraction; this is cured by the additional second order kinetic term. The
result is a {\em Weyl geometric dark scalar + tensor} theory (WdST) in which
the scalar field equation is a covariant generalization of the non-linear
Poisson equation known from MOND. The flat space limit of the weak field
approximation of WdST leads to a specific version of MOND for the quasi-static
case. Galactic and cluster dynamics ought to be covered by its dynamics. We
consider the transition to the Einstein gravity on one hand and to scalar field
cosmology in the FRW framework on the other. A bouncing cosmological model is
tentatively discussed at the end.


title:
Roadway Design Matters: Variation in Bicyclists' Psycho-Physiological
  Responses in Different Urban Roadway Designs

abstract:
  As a healthier and more sustainable way of mobility, cycling has been
advocated by literature and policy. However, current trends in bicyclist crash
fatalities suggest deficiencies in current roadway design in protecting these
vulnerable road users. The lack of cycling data is a common challenge for
studying bicyclists' safety, behavior, and comfort levels under different
design contexts. To understand bicyclists' behavioral and physiological
responses in an efficient and safe way, this study uses a bicycle simulator
within an immersive virtual environment (IVE). Off-the-shelf sensors are
utilized to evaluate bicyclists' cycling performance (speed and lane position)
and physiological responses (eye tracking and heart rate (HR)). Participants
bike in a simulated virtual environment modeled to scale from a real-world
street with a shared bike lane (sharrow) to evaluate how introduction of a bike
lane and a protected bike lane with pylons may impact perceptions of safety, as
well as behavioral and psycho-physiological responses. Results from 50
participants show that the protected bike lane design received the highest
perceived safety rating and exhibited the lowest average cycling speed.
Furthermore, both the bike lane and the protected bike lane scenarios show a
less dispersed gaze distribution than the as-built sharrow scenario, reflecting
a higher gaze focus among bicyclists on the biking task in the bike lane and
protected bike lane scenarios, compared to when bicyclists share right of way
with vehicles. Additionally, heart rate change point results from the study
suggest that creating dedicated zones for bicyclists (bike lanes or protected
bike lanes) has the potential to reduce bicyclists' stress levels.


title:
UCTopic: Unsupervised Contrastive Learning for Phrase Representations
  and Topic Mining

abstract:
  High-quality phrase representations are essential to finding topics and
related terms in documents (a.k.a. topic mining). Existing phrase
representation learning methods either simply combine unigram representations
in a context-free manner or rely on extensive annotations to learn
context-aware knowledge. In this paper, we propose UCTopic, a novel
unsupervised contrastive learning framework for context-aware phrase
representations and topic mining. UCTopic is pretrained in a large scale to
distinguish if the contexts of two phrase mentions have the same semantics. The
key to pretraining is positive pair construction from our phrase-oriented
assumptions. However, we find traditional in-batch negatives cause performance
decay when finetuning on a dataset with small topic numbers. Hence, we propose
cluster-assisted contrastive learning(CCL) which largely reduces noisy
negatives by selecting negatives from clusters and further improves phrase
representations for topics accordingly. UCTopic outperforms the
state-of-the-art phrase representation model by 38.2% NMI in average on four
entity cluster-ing tasks. Comprehensive evaluation on topic mining shows that
UCTopic can extract coherent and diverse topical phrases.


title: Attacks on Deidentification's Defenses

abstract:
  Quasi-identifier-based deidentification techniques (QI-deidentification) are
widely used in practice, including $k$-anonymity, $\ell$-diversity, and
$t$-closeness. We present three new attacks on QI-deidentification: two
theoretical attacks and one practical attack on a real dataset. In contrast to
prior work, our theoretical attacks work even if every attribute is a
quasi-identifier. Hence, they apply to $k$-anonymity, $\ell$-diversity,
$t$-closeness, and most other QI-deidentification techniques.
  First, we introduce a new class of privacy attacks called downcoding attacks,
and prove that every QI-deidentification scheme is vulnerable to downcoding
attacks if it is minimal and hierarchical. Second, we convert the downcoding
attacks into powerful predicate singling-out (PSO) attacks, which were recently
proposed as a way to demonstrate that a privacy mechanism fails to legally
anonymize under Europe's General Data Protection Regulation. Third, we use
LinkedIn.com to reidentify 3 students in a $k$-anonymized dataset published by
EdX (and show thousands are potentially vulnerable), undermining EdX's claimed
compliance with the Family Educational Rights and Privacy Act.
  The significance of this work is both scientific and political. Our
theoretical attacks demonstrate that QI-deidentification may offer no
protection even if every attribute is treated as a quasi-identifier. Our
practical attack demonstrates that even deidentification experts acting in
accordance with strict privacy regulations fail to prevent real-world
reidentification. Together, they rebut a foundational tenet of
QI-deidentification and challenge the actual arguments made to justify the
continued use of $k$-anonymity and other QI-deidentification techniques.


title:
ONE-NAS: An Online NeuroEvolution based Neural Architecture Search for
  Time Series Forecasting

abstract:
  Time series forecasting (TSF) is one of the most important tasks in data
science, as accurate time series (TS) predictions can drive and advance a wide
variety of domains including finance, transportation, health care, and power
systems. However, real-world utilization of machine learning (ML) models for
TSF suffers due to pretrained models being able to learn and adapt to
unpredictable patterns as previously unseen data arrives over longer time
scales. To address this, models must be periodically retained or redesigned,
which takes significant human and computational resources. This work presents
the Online NeuroEvolution based Neural Architecture Search (ONE-NAS) algorithm,
which to the authors' knowledge is the first neural architecture search
algorithm capable of automatically designing and training new recurrent neural
networks (RNNs) in an online setting. Without any pretraining, ONE-NAS utilizes
populations of RNNs which are continuously updated with new network structures
and weights in response to new multivariate input data. ONE-NAS is tested on
real-world large-scale multivariate wind turbine data as well a univariate Dow
Jones Industrial Average (DJIA) dataset, and is shown to outperform traditional
statistical time series forecasting, including naive, moving average, and
exponential smoothing methods, as well as state of the art online ARIMA
strategies.


title:
Synergistic Network Learning and Label Correction for Noise-robust Image
  Classification

abstract:
  Large training datasets almost always contain examples with inaccurate or
incorrect labels. Deep Neural Networks (DNNs) tend to overfit training label
noise, resulting in poorer model performance in practice. To address this
problem, we propose a robust label correction framework combining the ideas of
small loss selection and noise correction, which learns network parameters and
reassigns ground truth labels iteratively. Taking the expertise of DNNs to
learn meaningful patterns before fitting noise, our framework first trains two
networks over the current dataset with small loss selection. Based on the
classification loss and agreement loss of two networks, we can measure the
confidence of training data. More and more confident samples are selected for
label correction during the learning process. We demonstrate our method on both
synthetic and real-world datasets with different noise types and rates,
including CIFAR-10, CIFAR-100 and Clothing1M, where our method outperforms the
baseline approaches.


title: The Spectral Bias of Polynomial Neural Networks

abstract:
  Polynomial neural networks (PNNs) have been recently shown to be particularly
effective at image generation and face recognition, where high-frequency
information is critical. Previous studies have revealed that neural networks
demonstrate a $\textit{spectral bias}$ towards low-frequency functions, which
yields faster learning of low-frequency components during training. Inspired by
such studies, we conduct a spectral analysis of the Neural Tangent Kernel (NTK)
of PNNs. We find that the $\Pi$-Net family, i.e., a recently proposed
parametrization of PNNs, speeds up the learning of the higher frequencies. We
verify the theoretical bias through extensive experiments. We expect our
analysis to provide novel insights into designing architectures and learning
frameworks by incorporating multiplicative interactions via polynomials.