title:
Distances of Galactic Radio Pulsars; First Quadrant: $-2^\circ < \ell <
  90^\circ$ and $-2^\circ < b < 2^\circ$

abstract:
  Distance versus dispersion measure relations are constructed for Galactic
radio pulsars in small solid angle intervals. The calculations are based on
some basic criteria as well as using the independent distance measurements of
well examined pulsars for the first Galactic quadrant including Galactic
central directions. Values of average free electron density for these regions
are derived from the fits to distance versus dispersion measure relations and
checked for consistency and smoothness. The effects of plasma in the Galactic
arms and within the central parts of the Galactic bulge region are also
compared and discussed. Our adopted distances for the radio pulsars are
compared with the ones given in some other models. Some basic results on
distributions of the radio pulsars and the plasma are presented.


title:
On the isomorphism class of $q$-Gaussian C$^\ast$-algebras for infinite
  variables

abstract:
  For a real Hilbert space $H_{\mathbb{R}}$ and $-1 < q < 1$ Bozejko and
Speicher introduced the C$^\ast$-algebra $A_q(H_{\mathbb{R}})$ and von Neumann
algebra $M_q(H_{\mathbb{R}})$ of $q$-Gaussian variables. We prove that if
$\dim(H_{\mathbb{R}}) = \infty$ and $-1 < q < 1, q \not = 0$ then
$M_q(H_{\mathbb{R}})$ does not have the Akemann-Ostrand property with respect
to $A_q(H_{\mathbb{R}})$. It follows that $A_q(H_{\mathbb{R}})$ is not
isomorphic to $A_0(H_{\mathbb{R}})$. This gives an answer to the
C$^\ast$-algebraic part of Question 1.1 and Question 1.2 in [NeZe18].


title: Quantum Tanner codes

abstract:
  Tanner codes are long error correcting codes obtained from short codes and a
graph, with bits on the edges and parity-check constraints from the short codes
enforced at the vertices of the graph. Combining good short codes together with
a spectral expander graph yields the celebrated expander codes of Sipser and
Spielman, which are asymptotically good classical LDPC codes.
  In this work we apply this prescription to the left-right Cayley complex that
lies at the heart of the recent construction of a $c^3$ locally testable code
by Dinur et al. Specifically, we view this complex as two graphs that share the
same set of edges. By defining a Tanner code on each of those graphs we obtain
two classical codes that together define a quantum code. This construction can
be seen as a simplified variant of the Panteleev and Kalachev asymptotically
good quantum LDPC code, with improved estimates for its minimum distance. This
quantum code is closely related to the Dinur et al. code in more than one
sense: indeed, we prove a theorem that simultaneously gives a linearly growing
minimum distance for the quantum code and recovers the local testability of the
Dinur et al. code.


title:
Quality Monitoring and Assessment of Deployed Deep Learning Models for
  Network AIOps

abstract:
  Artificial Intelligence (AI) has recently attracted a lot of attention,
transitioning from research labs to a wide range of successful deployments in
many fields, which is particularly true for Deep Learning (DL) techniques.
Ultimately, DL models being software artifacts, they need to be regularly
maintained and updated: AIOps is the logical extension of the DevOps software
development practices to AI-software applied to network operation and
management. In the lifecycle of a DL model deployment, it is important to
assess the quality of deployed models, to detect "stale" models and prioritize
their update. In this article, we cover the issue in the context of network
management, proposing simple yet effective techniques for (i) quality
assessment of individual inference, and for (ii) overall model quality tracking
over multiple inferences, that we apply to two use cases, representative of the
network management and image recognition fields.


title: Logic Blog 2021

abstract:
  The blog has several entries on group theory interacting with computability
and wider logic, several open questions, and an entry on undecidability in
physics.


title:
Ten more times precision improved method for surface roughness
  estimation with weak measurement

abstract:
  High-precision surface roughness estimation plays an important role in many
applications. However, the classical estimating methods are limited by shot
noise and only can achieve the precision of 0.1 nm with white light
interferometer. Here, we propose two weak measurement schemes to estimate
surface roughness through spectrum analysis and intensity analysis. The
estimating precision with spectrum analysis is about $10 ^{-5}$ nm by using a
currently available spectrometer with the resolution of $\Delta \lambda= 0.04$
pm and the corresponding sensitivity is better than 0.1 THz/nm. And the
precision and sensitivity of the light intensity analysis scheme achieve as
high as 0.07 nm and 1/nm, respectively. By introducing a modulated phase, we
show that the sensitivity and precision achieved in our schemes can be
effectively retained in a wider dynamic range. We further provide the
experimental design of the surface profiler based on our schemes. It
simultaneously meets the requirements of high precision, high sensitivity, and
wide measurement range, making it to be a promising practical tool.


title: MSCTD: A Multimodal Sentiment Chat Translation Dataset

abstract:
  Multimodal machine translation and textual chat translation have received
considerable attention in recent years. Although the conversation in its
natural form is usually multimodal, there still lacks work on multimodal
machine translation in conversations. In this work, we introduce a new task
named Multimodal Chat Translation (MCT), aiming to generate more accurate
translations with the help of the associated dialogue history and visual
context. To this end, we firstly construct a Multimodal Sentiment Chat
Translation Dataset (MSCTD) containing 142,871 English-Chinese utterance pairs
in 14,762 bilingual dialogues and 30,370 English-German utterance pairs in
3,079 bilingual dialogues. Each utterance pair, corresponding to the visual
context that reflects the current conversational scene, is annotated with a
sentiment label. Then, we benchmark the task by establishing multiple baseline
systems that incorporate multimodal and sentiment features for MCT. Preliminary
experiments on four language directions (English-Chinese and English-German)
verify the potential of contextual and multimodal information fusion and the
positive impact of sentiment on the MCT task. Additionally, as a by-product of
the MSCTD, it also provides two new benchmarks on multimodal dialogue sentiment
analysis. Our work can facilitate research on both multimodal chat translation
and multimodal dialogue sentiment analysis.


title:
The intertwined physics of active chemical reactions and phase
  separation

abstract:
  Phase separation is the thermodynamic process that explains how droplets form
in multicomponent fluids. These droplets can provide controlled compartments to
localize chemical reactions, and reactions can also affect the droplets'
dynamics. This review focuses on the tight interplay between phase separation
and chemical reactions originating from thermodynamic constraints. In
particular, simple mass action kinetics cannot describe chemical reactions
since phase separation requires non-ideal fluids. Instead, thermodynamics
implies that passive chemical reactions reduce the complexity of phase diagrams
and provide only limited control over the system's behavior. However, driven
chemical reactions, which use external energy input to create spatial fluxes,
can circumvent thermodynamic constraints. Such active systems can suppress the
typical droplet coarsening, control droplet size, and localize droplets. This
review provides an extensible framework for describing active chemical
reactions in phase separating systems, which forms a basis for improving
control in technical applications and understanding self-organized structures
in biological cells.


title: Loop integral evaluation and asymptotic expansion with pySecDec

abstract:
  The evaluation of higher-loop Feynman integrals is at the core of the quest
to reduce the uncertainty of theoretical predictions and match experimental
data from the LHC and future colliders. pySecDec is a program to evaluate such
integrals numerically based on the sector decomposition approach; its new
release version 1.5 introduces features significantly improving its
performance: automatic adaptive evaluation of weighted sums of integrals (e.g.
amplitudes) and asymptotic expansion in kinematic ratios. Here we briefly
review both, illustrating the expected performance benefits.


title: Inverse moment of $B$-meson quasi distribution amplitude

abstract:
  We perform a study on the structure of inverse moment (IM) of quasi
distributions, by taking $B$-meson quasi distribution amplitude (quasi-DA) as
an example. Based on a one-loop calculation, we derive the renormalization
group equation and velocity evolution equation for the first IM of quasi-DA. We
find that, in the large velocity limit, the first IM of $B$-meson quasi-DA can
be factorized into IM as well as logarithmic moments of light-cone distribution
amplitude (LCDA), accompanied with short distance coefficients. Our results can
be useful either in understanding the patterns of perturbative matching in
Large Momentum Effective Theory or evaluating inverse moment of $B$-meson LCDA
on the lattice.


title: GausSetExpander: A Simple Approach for Entity Set Expansion

abstract:
  Entity Set Expansion is an important NLP task that aims at expanding a small
set of entities into a larger one with items from a large pool of candidates.
In this paper, we propose GausSetExpander, an unsupervised approach based on
optimal transport techniques. We propose to re-frame the problem as choosing
the entity that best completes the seed set. For this, we interpret a set as an
elliptical distribution with a centroid which represents the mean and a spread
that is represented by the scale parameter. The best entity is the one that
increases the spread of the set the least. We demonstrate the validity of our
approach by comparing to state-of-the art approaches.


title:
Improved Sensing and Positioning via 5G and mmWave radar for Airport
  Surveillance

abstract:
  This paper explores an integrated approach for improved sensing and
positioning with applications in air traffic management (ATM) and in the
Advanced Surface Movement Guidance and Control System (A-SMGCS). The integrated
approach includes the synergy of 3D Vector Antenna with the novel
time-of-arrival and angle-of-arrival estimate methods for accurate positioning,
combining the sensing on the sub-6GHz and mmWave spectrum for the enhanced
non-cooperative surveillance. For the positioning scope, both uplink and
downlink 5G reference signals are investigated and their performance is
evaluated. For the non-cooperative sensing scope, a novel 5G-signal-based
imaging function is proposed and verified with realistic airport
radio-propagation modelling and the AI-based targets tracking-and-motion
recognition are investigated. The 5G-based imaging and mmWave radar based
detection can be potentially fused to enhance surveillance in the airport. The
work is being done within the European-funded project NewSense and it delves
into the 5G, Vector Antennas, and mmWave capabilities for future ATM solutions.


title:
Signal-to-noise ratio is more important than sampling rate in
  beat-to-beat interval estimation from optical sensors

abstract:
  Photoplethysmographic Imaging (PPGI) allows the determination of pulse rate
variability from sequential beat-to-beat intervals (BBI) and pulse wave
velocity from spatially resolved recorded pulse waves. In either case,
sufficient temporal accuracy is essential.
  The presented work investigates the temporal accuracy of BBI estimation from
photoplethysmographic signals. Within comprehensive numerical simulation, we
systematically assess the impact of sampling rate, signal-to-noise ratio (SNR),
and beat-to-beat shape variations on the root mean square error (RMSE) between
real and estimated BBI.
  Our results show that at sampling rates beyond 14 Hz only small errors exist
when interpolation is used. For example, the average RMSE is 3 ms for a
sampling rate of 14 Hz and an SNR of 18 dB. Further increasing the sampling
rate only results in marginal improvements, e.g. more than tripling the
sampling rate to 50 Hz reduces the error by approx. 14%. The most important
finding relates to the SNR, which is shown to have a much stronger influence on
the error than the sampling rate. For example, increasing the SNR from 18 dB to
24 dB at 14 Hz sampling rate reduced the error by almost 50% to 1.5 ms. Subtle
beat-to-beat shape variations, moreover, increase the error decisively by up to
800%.
  Our results are highly relevant in three regards: first, they partially
explain different results in the literature on minimum sampling rates. Second,
they emphasize the importance to consider SNR and possibly shape variation in
investigations on the minimal sampling rate. Third, they underline the
importance of appropriate processing techniques to increase SNR. Importantly,
though our motivation is PPGI, the presented work immediately applies to
contact PPG and PPG in other settings such as wearables. To enable further
investigations, we make the scripts used in modelling and simulation freely
available.


title:
Hierarchical Multi-Agent DRL-Based Framework for Joint Multi-RAT
  Assignment and Dynamic Resource Allocation in Next-Generation HetNets

abstract:
  This paper considers the problem of cost-aware downlink sum-rate maximization
via joint optimal radio access technologies (RATs) assignment and power
allocation in next-generation heterogeneous wireless networks (HetNets). We
consider a future HetNet comprised of multi-RATs and serving multi-connectivity
edge devices (EDs), and we formulate the problem as mixed-integer non-linear
programming (MINP) problem. Due to the high complexity and combinatorial nature
of this problem and the difficulty to solve it using conventional methods, we
propose a hierarchical multi-agent deep reinforcement learning (DRL)-based
framework, called DeepRAT, to solve it efficiently and learn system dynamics.
In particular, the DeepRAT framework decomposes the problem into two main
stages; the RATs-EDs assignment stage, which implements a single-agent Deep Q
Network (DQN) algorithm, and the power allocation stage, which utilizes a
multi-agent Deep Deterministic Policy Gradient (DDPG) algorithm. Using
simulations, we demonstrate how the various DRL agents efficiently interact to
learn system dynamics and derive the global optimal policy. Furthermore, our
simulation results show that the proposed DeepRAT algorithm outperforms
existing state-of-the-art heuristic approaches in terms of network utility.
Finally, we quantitatively show the ability of the DeepRAT model to quickly and
dynamically adapt to abrupt changes in network dynamics, such as EDs mobility.


title:
Traveling edge states in massive Dirac equations along slowly varying
  edges

abstract:
  Edge states attract more and more research interests owing to the novel
topologically protected properties. In this work, we studied edge modes and
traveling edge states via the linear Dirac equation with so-called
edge-admissible masses. The unidirectional edge state provides a heuristic
approach to more general traveling edge states through the localized behavior
along slowly varying edges. We show the dominated asymptotic solutions of two
typical edge states that follow circular and curved edges with small curvature
by the analytic and quantitative arguments.


title:
Cross-Lingual Text Classification with Multilingual Distillation and
  Zero-Shot-Aware Training

abstract:
  Multilingual pre-trained language models (MPLMs) not only can handle tasks in
different languages but also exhibit surprising zero-shot cross-lingual
transferability. However, MPLMs usually are not able to achieve comparable
supervised performance on rich-resource languages compared to the
state-of-the-art monolingual pre-trained models. In this paper, we aim to
improve the multilingual model's supervised and zero-shot performance
simultaneously only with the resources from supervised languages. Our approach
is based on transferring knowledge from high-performance monolingual models
with a teacher-student framework. We let the multilingual model learn from
multiple monolingual models simultaneously. To exploit the model's
cross-lingual transferability, we propose MBLM (multi-branch multilingual
language model), a model built on the MPLMs with multiple language branches.
Each branch is a stack of transformers. MBLM is trained with the
zero-shot-aware training strategy that encourages the model to learn from the
mixture of zero-shot representations from all the branches. The results on two
cross-lingual classification tasks show that, with only the task's supervised
data used, our method improves both the supervised and zero-shot performance of
MPLMs.


title:
Blow-up solutions for non-scale-invariant nonlinear Schr\"odinger
  equation in one dimension

abstract:
  In this paper, we consider the mass-critical nonlinear Schr\"odinger equation
in one dimension. Ogawa--Tsutsumi [19] proved a blow-up result for negative
energy solution by using a scaling argument for initial data. By the reason,
the method cannot be used to an equation with a linear potential. So, we modify
the proof and get that for the equation with the linear potential.


title:
Mercury and OrbFit packages for the numerical integration of planetary
  systems: implementation of the Yarkovsky and YORP effects

abstract:
  For studies of the long-term evolution of small Solar System objects, it is
fundamental to add the Yarkovsky and Yarkovsky-O'Keefe-Radzievskii-Paddack
(YORP) effects in the dynamical model. Still, implementations of these effects
in publicly available N-body codes are often lacking, or the effects are
implemented using significantly simplified models. In this paper, we present an
implementation of the coupled Yarkovsky/YORP effects in the mercury and OrbFit
$N$-body codes. Along with these two effects, we also included the effects of
non-destructive collisions and rotationally induced breakups to model the
asteroid spin state properly. Given the stochastic nature of many incorporated
effects, the software is suitable for statistical dynamical studies. Here we
explained the scientific aspect of the implementation, while technical details
will be made freely available along with the source codes.


title: Avalanche RL: a Continual Reinforcement Learning Library

abstract:
  Continual Reinforcement Learning (CRL) is a challenging setting where an
agent learns to interact with an environment that is constantly changing over
time (the stream of experiences). In this paper, we describe Avalanche RL, a
library for Continual Reinforcement Learning which allows to easily train
agents on a continuous stream of tasks. Avalanche RL is based on PyTorch and
supports any OpenAI Gym environment. Its design is based on Avalanche, one of
the more popular continual learning libraries, which allow us to reuse a large
number of continual learning strategies and improve the interaction between
reinforcement learning and continual learning researchers. Additionally, we
propose Continual Habitat-Lab, a novel benchmark and a high-level library which
enables the usage of the photorealistic simulator Habitat-Sim for CRL research.
Overall, Avalanche RL attempts to unify under a common framework continual
reinforcement learning applications, which we hope will foster the growth of
the field.


title: Self-buckling of filamentous cyanobacteria reveals gliding forces

abstract:
  Filamentous cyanobacteria are one of the oldest and today still most abundant
lifeforms on earth, with manifold implications in ecology and economics. These
phototrophic organisms form long and flexible filaments that exhibit gliding
motility in contact with solid surfaces. The underlying force generating
mechanism is not yet understood. We measure their bending rigidity with
micropipette force sensors, and investigate how filaments buckle after gliding
onto an obstacle. Comparing Kirchhoff theory to the experiments, we derive the
active forces and the friction coefficients associated with gliding from the
observed critical filament length for buckling. Remarkably, we find that these
two quantities are strongly correlated to each other, while dependencies on
other observables are largely absent. The observed critical lengths align with
the peaks of natural length distributions: small changes in active forces or
length distributions may determine whether the majority of a colony undergoes
buckling instabilities or not.


title:
Homotopy Equivalent Algebraic Structures in Multicategories and
  Permutative Categories

abstract:
  We show that the free construction from multicategories to permutative
categories is a categorically-enriched non-symmetric multifunctor. Our main
result then shows that the induced functor between categories of algebras is an
equivalence of homotopy theories. We describe an application to ring
categories.


title: FusionCount: Efficient Crowd Counting via Multiscale Feature Fusion

abstract:
  State-of-the-art crowd counting models follow an encoder-decoder approach.
Images are first processed by the encoder to extract features. Then, to account
for perspective distortion, the highest-level feature map is fed to extra
components to extract multiscale features, which are the input to the decoder
to generate crowd densities. However, in these methods, features extracted at
earlier stages during encoding are underutilised, and the multiscale modules
can only capture a limited range of receptive fields, albeit with considerable
computational cost. This paper proposes a novel crowd counting architecture
(FusionCount), which exploits the adaptive fusion of a large majority of
encoded features instead of relying on additional extraction components to
obtain multiscale features. Thus, it can cover a more extensive scope of
receptive field sizes and lower the computational cost. We also introduce a new
channel reduction block, which can extract saliency information during decoding
and further enhance the model's performance. Experiments on two benchmark
databases demonstrate that our model achieves state-of-the-art results with
reduced computational complexity.


title:
Edge-Cut Width: An Algorithmically Driven Analogue of Treewidth Based on
  Edge Cuts

abstract:
  Decompositional parameters such as treewidth are commonly used to obtain
fixed-parameter algorithms for NP-hard graph problems. For problems that are
W[1]-hard parameterized by treewidth, a natural alternative would be to use a
suitable analogue of treewidth that is based on edge cuts instead of vertex
separators. While tree-cut width has been coined as such an analogue of
treewidth for edge cuts, its algorithmic applications have often led to
disappointing results: out of twelve problems where one would hope for
fixed-parameter tractability parameterized by an edge-cut based analogue to
treewidth, eight were shown to be W[1]-hard parameterized by tree-cut width.
  As our main contribution, we develop an edge-cut based analogue to treewidth
called edge-cut width. Edge-cut width is, intuitively, based on measuring the
density of cycles passing through a spanning tree of the graph. Its benefits
include not only a comparatively simple definition, but mainly that it has
interesting algorithmic properties: it can be computed by a fixed-parameter
algorithm, and it yields fixed-parameter algorithms for all the aforementioned
problems where tree-cut width failed to do so.


title:
Bina-Rep Event Frames: a Simple and Effective Representation for
  Event-based cameras

abstract:
  This paper presents "Bina-Rep", a simple representation method that converts
asynchronous streams of events from event cameras to a sequence of sparse and
expressive event frames. By representing multiple binary event images as a
single frame of $N$-bit numbers, our method is able to obtain sparser and more
expressive event frames thanks to the retained information about event orders
in the original stream. Coupled with our proposed model based on a
convolutional neural network, the reported results achieve state-of-the-art
performance and repeatedly outperforms other common event representation
methods. Our approach also shows competitive robustness against common image
corruptions, compared to other representation techniques.


title:
Confidence Based Bidirectional Global Context Aware Training Framework
  for Neural Machine Translation

abstract:
  Most dominant neural machine translation (NMT) models are restricted to make
predictions only according to the local context of preceding words in a
left-to-right manner. Although many previous studies try to incorporate global
information into NMT models, there still exist limitations on how to
effectively exploit bidirectional global context. In this paper, we propose a
Confidence Based Bidirectional Global Context Aware (CBBGCA) training framework
for NMT, where the NMT model is jointly trained with an auxiliary conditional
masked language model (CMLM). The training consists of two stages: (1)
multi-task joint training; (2) confidence based knowledge distillation. At the
first stage, by sharing encoder parameters, the NMT model is additionally
supervised by the signal from the CMLM decoder that contains bidirectional
global contexts. Moreover, at the second stage, using the CMLM as teacher, we
further pertinently incorporate bidirectional global context to the NMT model
on its unconfidently-predicted target words via knowledge distillation.
Experimental results show that our proposed CBBGCA training framework
significantly improves the NMT model by +1.02, +1.30 and +0.57 BLEU scores on
three large-scale translation datasets, namely WMT'14 English-to-German, WMT'19
Chinese-to-English and WMT'14 English-to-French, respectively.


title: Neural Adaptive SCEne Tracing

abstract:
  Neural rendering with implicit neural networks has recently emerged as an
attractive proposition for scene reconstruction, achieving excellent quality
albeit at high computational cost. While the most recent generation of such
methods has made progress on the rendering (inference) times, very little
progress has been made on improving the reconstruction (training) times. In
this work, we present Neural Adaptive Scene Tracing (NAScenT), the first neural
rendering method based on directly training a hybrid explicit-implicit neural
representation. NAScenT uses a hierarchical octree representation with one
neural network per leaf node and combines this representation with a two-stage
sampling process that concentrates ray samples where they matter most near
object surfaces. As a result, NAScenT is capable of reconstructing challenging
scenes including both large, sparsely populated volumes like UAV captured
outdoor environments, as well as small scenes with high geometric complexity.
NAScenT outperforms existing neural rendering approaches in terms of both
quality and training time.


title: Restless Multi-Armed Bandits under Exogenous Global Markov Process

abstract:
  We consider an extension to the restless multi-armed bandit (RMAB) problem
with unknown arm dynamics, where an unknown exogenous global Markov process
governs the rewards distribution of each arm. Under each global state, the
rewards process of each arm evolves according to an unknown Markovian rule,
which is non-identical among different arms. At each time, a player chooses an
arm out of N arms to play, and receives a random reward from a finite set of
reward states. The arms are restless, that is, their local state evolves
regardless of the player's actions. The objective is an arm-selection policy
that minimizes the regret, defined as the reward loss with respect to a player
that knows the dynamics of the problem, and plays at each time t the arm that
maximizes the expected immediate value. We develop the Learning under Exogenous
Markov Process (LEMP) algorithm, that achieves a logarithmic regret order with
time, and a finite-sample bound on the regret is established. Simulation
results support the theoretical study and demonstrate strong performances of
LEMP.


title: Over-the-Air Computation with Imperfect Channel State Information

abstract:
  This paper investigates the effect of imperfect channel state information
(CSI) on the over-the-air computation (AirComp) system, in which multiple
wireless devices (WDs) send individual messages to one access point (AP) for
distributed functional computation. By particularly considering the channel
estimation errors, we jointly design the transmit coefficients at the WDs and
the receive strategy at the AP, for minimizing the computation mean squared
error (MSE). First, we consider the single-input single-output (SISO) case with
each WD and AP equipped with one single antenna, in which the globally optimal
solution to the computation MSE minimization problem is obtained in closed
form. Next, we consider the single-input multiple-output (SIMO) case with
multiple receive antennas at the AP, in which a high-quality solution is
obtained based on alternating optimization and convex optimization. For both
cases, the optimized power control solution at the WDs follows a
threshold-based regularized channel inversion structure; while for the SIMO
case, the receive beamforming at the AP follows a sum-minimum MSE (MMSE)
structure. It is shown that with finite receive antennas, a non-zero
computation MSE is inevitable due to the channel estimation errors even when
the WDs' transmit powers become infinity; while with massive receive antennas,
a vanishing MSE is achievable when the channel vectors are independent and
identically distributed. Finally, numerical results are provided to demonstrate
the effectiveness of the proposed designs.


title:
General Indefinite Backward Stochastic Linear-Quadratic Optimal Control
  Problems

abstract:
  A general backward stochastic linear-quadratic optimal control problem is
studied, in which both the state equation and the cost functional contain the
nonhomogeneous terms. The main feature of the problem is that the weighting
matrices in the cost functional are allowed to be indefinite and cross-product
terms in the control and the state processes are present. Necessary and
sufficient conditions for the solvability of the problem are obtained, and a
characterization of the optimal control in terms of forward-backward stochastic
differential equations is derived. By a Riccati equation approach, a general
procedure for constructing optimal controls is developed and the value function
is obtained explicitly.


title:
Reflection-mode optical diffraction tomography for label-free imaging of
  thick biological specimens

abstract:
  Optical diffraction tomography (ODT) has emerged as a powerful label-free
three-dimensional (3D) bioimaging techniques for observing living cells and
thin tissue layers. We report a new reflection-mode ODT (rODT) method for
imaging thick biological specimens with 500 nm lateral resolution and 1 {\mu}m
axial resolution. In rODT, multiple scattering background is rejected through
spatio-temporal gating provided by dynamic speckle-field interferometry, while
depth-resolved refractive index maps are reconstructed by developing a
comprehensive inverse scattering model that also considers specimen-induced
aberration. Benefiting from the high-resolution and full-field quantitative
imaging capabilities of rODT, we succeeded in imaging red blood cells and
quantifying their membrane fluctuations behind a turbid sample with a thickness
of 2.8 scattering mean-free-paths. We further realized volumetric imaging of
cornea inside an ex vivo rat eye and quantified its optical properties,
including mapping the topography of Dua's and Descemet's membrane surfaces on
the nanometer scale.


title:
LiLT: A Simple yet Effective Language-Independent Layout Transformer for
  Structured Document Understanding

abstract:
  Structured document understanding has attracted considerable attention and
made significant progress recently, owing to its crucial role in intelligent
document processing. However, most existing related models can only deal with
the document data of specific language(s) (typically English) included in the
pre-training collection, which is extremely limited. To address this issue, we
propose a simple yet effective Language-independent Layout Transformer (LiLT)
for structured document understanding. LiLT can be pre-trained on the
structured documents of a single language and then directly fine-tuned on other
languages with the corresponding off-the-shelf monolingual/multilingual
pre-trained textual models. Experimental results on eight languages have shown
that LiLT can achieve competitive or even superior performance on diverse
widely-used downstream benchmarks, which enables language-independent benefit
from the pre-training of document layout structure. Code and model are publicly
available at https://github.com/jpWang/LiLT.


title:
FedDrive: Generalizing Federated Learning to Semantic Segmentation in
  Autonomous Driving

abstract:
  Semantic Segmentation is essential to make self-driving vehicles autonomous,
enabling them to understand their surroundings by assigning individual pixels
to known categories. However, it operates on sensible data collected from the
users' cars; thus, protecting the clients' privacy becomes a primary concern.
For similar reasons, Federated Learning has been recently introduced as a new
machine learning paradigm aiming to learn a global model while preserving
privacy and leveraging data on millions of remote devices. Despite several
efforts on this topic, no work has explicitly addressed the challenges of
federated learning in semantic segmentation for driving so far. To fill this
gap, we propose FedDrive, a new benchmark consisting of three settings and two
datasets, incorporating the real-world challenges of statistical heterogeneity
and domain generalization. We benchmark state-of-the-art algorithms from the
federated learning literature through an in-depth analysis, combining them with
style transfer methods to improve their generalization ability. We demonstrate
that correctly handling normalization statistics is crucial to deal with the
aforementioned challenges. Furthermore, style transfer improves performance
when dealing with significant appearance shifts. We plan to make both the code
and the benchmark publicly available to the research community.


title:
A minimal one-dimensional model of "bad metal" behavior from fast
  particle-hole scattering

abstract:
  A strongly interacting plasma of linearly dispersing electron and hole
excitations in two spatial dimensions (2D), also known as a Dirac fluid, can be
captured by relativistic hydrodynamics and shares many universal features with
other quantum critical systems. We propose a one-dimensional (1D) model to
capture key aspects of the 2D Dirac fluid while including lattice effects and
being amenable to non-perturbative computation. When interactions are added to
the Dirac-like 1D dispersion without opening a gap, we show that this kind of
irrelevant interaction is able to preserve Fermi-liquid-like quasi-particle
features while relaxing a zero-momentum charge current via collisions between
particle-hole excitations, leading to resistivity that is linear in temperature
via a mechanism previously discussed for large-diameter metallic carbon
nanotubes. We further provide a microscopic lattice model and obtain numerical
results via density-matrix renormalization group (DMRG) simulations, which
support the above physical picture. The limits on such fast relaxation at
strong coupling are of considerable interest because of the ubiquity of bad
metals in experiments.


title: Molecular and colloidal transport in bacterial cellulose biofilms

abstract:
  Bacterial cellulose biofilms are complex networks of strong entangled
nanofibers that control transport and protect bacterial colonies in the film.
Design of diverse applications of bacterial cellulose films also relies on
understanding and controlling transport through the fiber mesh, and transport
simulations of the films are most accurate when guided by experimental
characterization of the structures and the resultant diffusion inside.
Diffusion through such films is a function of their key microstructural length
scales, determining how molecules, as well as particles and microorganisms,
permeate them. We use microscopy to study the unique bacterial cellulose film
structure and quantify the mobility dynamics of various sizes of tracer
particles and macromolecules. Mobility is hindered within the films, as
confinement and local movement strongly depend on void size relative to
diffusing tracers. The biofilms have a naturally periodic structure of
alternating dense and porous layers of nanofiber mesh, and we tune the
magnitude of the spacing via fermentation conditions. Micron-sized particles
can diffuse through the porous layers, but can not penetrate the dense layers.
Tracer mobility in the porous layers is isotropic, indicating a largely random
pore structure there. Molecular diffusion through the whole film is only
slightly reduced by the structural tortuosity. Knowledge of transport
variations within bacterial cellulose networks can be used to guide design of
symbiotic cultures in these structures and enhance their use in applications
biomedical implants, wound dressings, lab-grown meat, and sensors.


title: Recent Advances and Challenges in Deep Audio-Visual Correlation Learning

abstract:
  Audio-visual correlation learning aims to capture essential correspondences
and understand natural phenomena between audio and video. With the rapid growth
of deep learning, an increasing amount of attention has been paid to this
emerging research issue. Through the past few years, various methods and
datasets have been proposed for audio-visual correlation learning, which
motivate us to conclude a comprehensive survey. This survey paper focuses on
state-of-the-art (SOTA) models used to learn correlations between audio and
video, but also discusses some tasks of definition and paradigm applied in AI
multimedia. In addition, we investigate some objective functions frequently
used for optimizing audio-visual correlation learning models and discuss how
audio-visual data is exploited in the optimization process. Most importantly,
we provide an extensive comparison and summarization of the recent progress of
SOTA audio-visual correlation learning and discuss future research directions.


title:
Symmetries, Length Scales, Magnetic Response and Skyrmion Chains in
  Nematic Superconductors

abstract:
  Nematic systems are two component superconductors that break rotational
symmetry, but exhibit a mixed symmetry that couples spatial rotations and phase
difference rotations. We show that a consequence of this induced spatial
anisotropy is mixed normal modes, that is the linear response to a small
perturbation of the system about its ground state, generally couples magnetic
and condensate degrees of freedom. We will study the effect of mode mixing on
the magnetic response of a nematic system as the strength of applied field is
increased. In general we show that the coupled modes generate magnetic field
perpendicular to the applied field, causing the magnetic response to
spontaneously twist direction. We will study this for the Meissner effect with
weak fields and also for stronger applied fields, which produce a mixture of
Skyrmions and composite vortices, forming orientation dependent bound states.
We will also calculate the anisotropies of the resulting first and second
critical fields $H_{c_1}$ and $H_{c_2}$. The Skyrmion lattices for $H_{c_1}
\leq H \leq H_{c_2}$ in nematic superconductors are shown to be structurally
complicated, in contrast to the triangular or square vortex lattices in
conventional superconductors. For low fields the magnetic response of the
system involves a loosely bound collection of parallel Skyrmion chains. As the
external field is increased the chains attract one another, causing a
transition where the unit cell becomes triangular for high applied fields. This
unique Skyrmion lattice and the magnetic twisting are clear indicators that
could be used experimentally to identify materials that exhibit nematic
superconductivity. To obtain these results we develop and present a novel
method to find the unit cell of a vortex lattice that can be applied to other
kinds of superconducting systems.


title:
A Survey on Recent Advances and Challenges in Reinforcement
  LearningMethods for Task-Oriented Dialogue Policy Learning

abstract:
  Dialogue Policy Learning is a key component in a task-oriented dialogue
system (TDS) that decides the next action of the system given the dialogue
state at each turn. Reinforcement Learning (RL) is commonly chosen to learn the
dialogue policy, regarding the user as the environment and the system as the
agent. Many benchmark datasets and algorithms have been created to facilitate
the development and evaluation of dialogue policy based on RL. In this paper,
we survey recent advances and challenges in dialogue policy from the
prescriptive of RL. More specifically, we identify the major problems and
summarize corresponding solutions for RL-based dialogue policy learning.
Besides, we provide a comprehensive survey of applying RL to dialogue policy
learning by categorizing recent methods into basic elements in RL. We believe
this survey can shed a light on future research in dialogue management.


title: An EF2X Allocation Protocol for Restricted Additive Valuations

abstract:
  We study the problem of fairly allocating a set of $m$ indivisible goods to a
set of $n$ agents. Envy-freeness up to any good (EFX) criteria -- which
requires that no agent prefers the bundle of another agent after removal of any
single good -- is known to be a remarkable analogous of envy-freeness when the
resource is a set of indivisible goods. In this paper, we investigate EFX
notion for the restricted additive valuations, that is, every good has some
non-negative value, and every agent is interested in only some of the goods.
  We introduce a natural relaxation of EFX called EFkX which requires that no
agent envies another agent after removal of any $k$ goods. Our main
contribution is an algorithm that finds a complete (i.e., no good is discarded)
EF2X allocation for the restricted additive valuations. In our algorithm we
devise new concepts, namely "configuration" and "envy-elimination" that might
be of independent interest.
  We also use our new tools to find an EFX allocation for restricted additive
valuations that discards at most $\lfloor n/2 \rfloor -1$ goods. This improves
the state of the art for the restricted additive valuations by a factor of $2$.


title: The Complexity of Evaluating nfer

abstract:
  Nfer is a rule-based language for abstracting event streams into a hierarchy
of intervals with data. Nfer has multiple implementations and has been applied
in the analysis of spacecraft telemetry and autonomous vehicle logs. This work
provides the first complexity analysis of nfer evaluation, i.e., the problem of
deciding whether a given interval is generated by applying rules.
  We show that the full nfer language is undecidable and that this depends on
both recursion in the rules and an infinite data domain. By restricting either
or both of those capabilities, we obtain tight decidability results. We also
examine the impact on complexity of exclusive rules and minimality. For the
most practical case, which is minimality with finite data, we provide a
polynomial time algorithm.


title: Collective photon emission patterns from two atoms in free space

abstract:
  Modification of spontaneous decay in space and time is a central topic of
quantum physics. It has been predominantly investigated in the context of
cavity quantum electrodynamics (QED), gaining new interest recently in the
domain of nano-optics. Beyond cavity-QED, spontaneous emission may be modified
also in free space due to correlations among the photon emitters, a phenomenon
known as super- and sub-radiance. Correlations may stem either from direct
interactions between the particles, from long-range exchange of photons, or by
measuring single photons in a common mode. Yet, the genuine spatial spontaneous
emission pattern of an atomic ensemble in an entangled quantum state has not
been observed so far, due to the lack of ultra-fast cameras with high spatial
resolution suited for recording single photons from single atoms. Preparing two
trapped ions in free space in entangled Dicke states via photon detection, we
study the resulting collective spontaneous emission patterns. Depending on the
symmetry of the Dicke states, associated with the direction of detection of the
first state-determining photon, we observe fundamentally different emission
patterns for the subsequently scattered photon, including super- and
sub-radiance. Our results demonstrate that the detection of a single photon can
profoundly modify the collective emission of an atomic array, here represented
by its most elementary building block of two atoms in free space.


title: On some realizable metabelian $5$-groups

abstract:
  Let $G$ be a $5$-group of maximal class and $\gamma_2(G) = [G, G]$ its
derived group. Assume that the abelianization $G/\gamma_2(G)$ is of type $(5,
5)$ and the transfers $V_{H_1\to \gamma_2(G)}$ and $V_{H_2\to \gamma_2(G)}$ are
trivial, where $H_1$ and $H_2$ are two maximal normal subgroups of $G$. Then
$G$ is completely determined with the isomorphism class groups of maximal
class. Moreover the group $G$ is realizable with some fields $k$, which is the
normal closure of a pure quintic field.


title: Hierarchical Policy Learning for Mechanical Search

abstract:
  Retrieving objects from clutters is a complex task, which requires multiple
interactions with the environment until the target object can be extracted.
These interactions involve executing action primitives like grasping or pushing
as well as setting priorities for the objects to manipulate and the actions to
execute. Mechanical Search (MS) is a framework for object retrieval, which uses
a heuristic algorithm for pushing and rule-based algorithms for high-level
planning. While rule-based policies profit from human intuition in how they
work, they usually perform sub-optimally in many cases. Deep reinforcement
learning (RL) has shown great performance in complex tasks such as taking
decisions through evaluating pixels, which makes it suitable for training
policies in the context of object-retrieval. In this work, we first formulate
the MS problem in a principled formulation as a hierarchical POMDP. Based on
this formulation, we propose a hierarchical policy learning approach for the MS
problem. For demonstration, we present two main parameterized sub-policies: a
push policy and an action selection policy. When integrated into the
hierarchical POMDP's policy, our proposed sub-policies increase the success
rate of retrieving the target object from less than 32% to nearly 80%, while
reducing the computation time for push actions from multiple seconds to less
than 10 milliseconds.


title:
The Effect of Micromotion and Local Stress in Quantum simulation with
  Trapped Ions in Optical Tweezers

abstract:
  The ability to program and control interactions provides the key to
implementing large-scale quantum simulation and computation in trapped ion
systems. Adding optical tweezers, which can tune the phonon spectrum and thus
modify the phonon-mediated spin-spin interaction, was recently proposed as a
way of programming quantum simulators for a broader range of spin models [Arias
Espinoza et al., Phys. Rev. A {\bf 103}, 052437]. In this work we study the
robustness of our findings in the presence of experimental imperfections:
micromotion, local stress, and intensity noise. We show that the effects of
micromotion can be easily circumvented when designing and optimizing tweezer
patterns to generate a target interaction. Furthermore, while local stress,
whereby the tweezers apply small forces on individual ions, may appear to
enable further tuning of the spin-spin interactions, any additional flexibility
is negligible. We conclude that optical tweezers are a useful method for
controlling interactions in trapped ion quantum simulators in the presence of
micromotion and imperfections in the tweezer alignment, but require intensity
stabilization on the sub-percent level.


title: Compatible, split and family Loday-algebras

abstract:
  Given a nonsymmetric operad $\mathcal{O}$, we first construct two new
nonsymmetric operads $\mathcal{O}^{\mathrm{comp}}$ and
$\mathcal{O}^{\mathrm{Dend}}$. These operads are respectively useful to study
compatible and split Loday-algebras. As an application of the operad
$\mathcal{O}^{\mathrm{comp}}$, we show that the cohomology of a compatible
associative algebra carries a Gerstenhaber structure. We give an application of
the operad $\mathcal{O}^\mathrm{Dend}$ to dendriform algebras and find
generalizations to other Loday-algebras. In the end, we construct another
operad $\mathrm{Fam}(\mathcal{O}^\Omega)^\mathrm{Dend}$ to study
dendriform-family algebras recently introduced in the literature. We also
define and study homotopy dendriform-family algebras.


title:
Estimating Model Performance on External Samples from Their Limited
  Statistical Characteristics

abstract:
  Methods that address data shifts usually assume full access to multiple
datasets. In the healthcare domain, however, privacy-preserving regulations as
well as commercial interests limit data availability and, as a result,
researchers can typically study only a small number of datasets. In contrast,
limited statistical characteristics of specific patient samples are much easier
to share and may be available from previously published literature or focused
collaborative efforts.
  Here, we propose a method that estimates model performance in external
samples from their limited statistical characteristics. We search for weights
that induce internal statistics that are similar to the external ones; and that
are closest to uniform. We then use model performance on the weighted internal
sample as an estimation for the external counterpart.
  We evaluate the proposed algorithm on simulated data as well as electronic
medical record data for two risk models, predicting complications in ulcerative
colitis patients and stroke in women diagnosed with atrial fibrillation. In the
vast majority of cases, the estimated external performance is much closer to
the actual one than the internal performance. Our proposed method may be an
important building block in training robust models and detecting potential
model failures in external environments.


title:
Rate-Distortion Problems of the Poisson Process based on a
  Group-Theoretic Approach

abstract:
  We study rate-distortion problems of a Poisson process using a group
theoretic approach. By describing a realization of a Poisson point process with
either point timings or inter-event (inter-point) intervals and by choosing
appropriate distortion measures, we establish rate-distortion problems of a
homogeneous Poisson process as ball- or sphere-covering problems for
realizations of the hyperoctahedral group in $\mathbb{R}^n$. Specifically, the
realizations we investigate are a hypercube and a hyperoctahedron. Thereby we
unify three known rate-distortion problems of a Poisson process (with different
distortion measures, but resulting in the same rate-distortion function) with
the Laplacian-$\ell_1$ rate-distortion problem.


title:
Assortative pairing alone can lead to a structured biota in organisms
  with cultural transmission

abstract:
  Spatial separation is often included in models of ethnic divergence but it
has also been realised that urban subcultures can, and frequently do, emerge in
sympatry. Previous research tended to attribute this phenomenon to the human
tendency to imitate self-similar individuals and actively differentiate oneself
from individuals recognized as members of an outgroup. Application of such a
model to non-human animals has been, however, viewed as problematic. We present
a parsimonious model of subculture emergence where the algorithm of social
learning does not require the assumption of an 'imitation threshold'. All it
takes is a slight modification of Galton-Pearson's biometric model previously
used to approximate cultural inheritance. The new model includes
proportionality between the variance of inputs (cultural 'parents') and the
variance of outputs (cultural 'offspring'). In this model, assortment alone can
lead to the formation of distinct cohesive clusters of individuals
(subcultures) with a low within-group and large between-group variability even
in absence of a spatial separation or disruptive natural selection. Sympatric
emergence of arbitrary behavioural varieties preceding ecological divergence
may thus represent the norm, not the exception, in all cultural animals.


title:
Points-of-Interest Relationship Inference with Spatial-enriched Graph
  Neural Networks

abstract:
  As a fundamental component in location-based services, inferring the
relationship between points-of-interests (POIs) is very critical for service
providers to offer good user experience to business owners and customers. Most
of the existing methods for relationship inference are not targeted at POI,
thus failing to capture unique spatial characteristics that have huge effects
on POI relationships. In this work we propose PRIM to tackle POI relationship
inference for multiple relation types. PRIM features four novel components,
including a weighted relational graph neural network, category taxonomy
integration, a self-attentive spatial context extractor, and a
distance-specific scoring function. Extensive experiments on two real-world
datasets show that PRIM achieves the best results compared to state-of-the-art
baselines and it is robust against data sparsity and is applicable to unseen
cases in practice.


title:
AGMR-Net: Attention Guided Multiscale Recovery framework for stroke
  segmentation

abstract:
  Automatic and accurate lesion segmentation is critical for clinically
estimating the lesion statuses of stroke diseases and developing appropriate
diagnostic systems. Although existing methods have achieved remarkable results,
further adoption of the models is hindered by: (1) inter-class indistinction,
the normal brain tissue resembles the lesion in appearance. (2) intra-class
inconsistency, large variability exists between different areas of the lesion.
To solve these challenges in stroke segmentation, we propose a novel method,
namely Attention Guided Multiscale Recovery framework (AGMR-Net) in this paper.
Firstly, a coarse-grained patch attention module in the encoding is adopted to
get a patch-based coarse-grained attention map in a multi-stage explicitly
supervised way, enabling target spatial context saliency representation with a
patch-based weighting technique that eliminates the effect of intra-class
inconsistency. Secondly, to obtain a more detailed boundary partitioning to
solve the challenge of the inter-class indistinction, a newly designed
cross-dimensional feature fusion module is used to capture global contextual
information to further guide the selective aggregation of 2D and 3D features,
which can compensate for the lack of boundary learning capability of 2D
convolution. Lastly, in the decoding stage, an innovative designed multi-scale
deconvolution upsampling instead of linear interpolation enhances the recovery
of target space and boundary information. The AGMR-Net is evaluated on the open
dataset Anatomical Tracings of Lesions-After-Stroke (ATLAS), achieving the
highest dice similarity coefficient (DSC) score of 0.594, Hausdorff distance of
27.005 mm, and average symmetry surface distance of 7.137 mm, which demonstrate
that our proposed method outperforms other state-of-the-art methods and has
great potential in the diagnosis of stroke.


title:
Differential cross sections and photon beam asymmetries of $\eta$
  photoproduction on the proton at $E_\gamma$ = 1.3-2.4 GeV

abstract:
  We have carried out exclusive measurements for the photoproduction of an
$\eta$ meson from a proton target with an egg-shaped calorimeter made of BGO
crystals (BGOegg) and forward charged-particle detectors at the SPring-8 LEPS2
beamline. The differential cross sections and photon beam asymmetries of the
$\gamma p \to \eta p$ reaction are measured in a center-of-mass energy ($W$)
range of $1.82$-$2.32$ GeV and a polar angle range of $-1.0 <
\cos{\theta^{\eta}_{\mathrm{c.m.}}} < 0.6$. The reaction is identified by
selecting a proton and two $\gamma$'s produced by an $\eta$-meson decay. The
kinematic fit method was employed to select the reaction candidate with the
confidence level larger than $1$\%. A bump structure at $W$ = $2.0$-$2.3$ GeV
in the differential cross section is confirmed at extremely backward $\eta$
polar angles, where the existing data are inconsistent with each other. This
bump structure is likely associated with high-spin resonances that couple with
$s\bar{s}$ quarks. The results of the photon beam asymmetries in a wide $\eta$
polar angle range are new for the photon beam energies exceeding $2.1$ GeV.
These results are not reproduced by the existing partial wave analyses. They
provide an additional constraint to nucleon resonance studies at high energies.


title: Bayesian Hierarchical Copula Model for Financial Time series

abstract:
  We discuss a Bayesian hierarchical copula model for clusters of financial
time series. A similar approach has been developed in Zhuang et al. (2020).
However, the prior distributions proposed there do not always provide a proper
posterior. In order to circumvent the problem, we adopt a proper global-local
shrinkage prior, which is also able to account for potential dependence
structure among different clusters. The performance of the proposed model is
presented through simulations and a real data analysis.


title: Computational Experiments: Past, Present and Future

abstract:
  Powered by advanced information technology, more and more complex systems are
exhibiting characteristics of the Cyber-Physical-Social Systems (CPSS).
Understanding the mechanism of CPSS is essential to our ability to control
their actions, reap their benefits and minimize their harms. In consideration
of the cost, legal and institutional constraints on the study of CPSS in real
world, computational experiments have emerged as a new method for quantitative
analysis of CPSS. This paper outlines computational experiments from several
key aspects, including origin, characteristics, methodological framework, key
technologies, and some typical applications. Finally, this paper highlights
some challenges of computational experiments to provide a roadmap for its rapid
development and widespread application.


title: On the quadrature exactness in hyperinterpolation

abstract:
  This paper investigates the role of quadrature exactness in the approximation
scheme of hyperinterpolation. Constructing a hyperinterpolant of degree $n$
requires an $m$-point positive-weight quadrature rule with exactness degree
$2n$. Aided by the Marcinkiewicz--Zygmund inequality, we affirm that when the
required exactness degree $2n$ is relaxed to $n+k$ with $0<k\leq n$, the $L^2$
norm of the hyperinterpolation operator is bounded by a constant independent of
$n$. The resulting scheme is convergent as $n\rightarrow\infty$ if $k$ is
positively correlated to $n$. Thus, the family of candidate quadrature rules
for constructing hyperinterpolants can be significantly enriched, and the
number of quadrature points can be considerably reduced. As a potential cost,
this relaxation may slow the convergence rate of hyperinterpolation in terms of
the reduced degrees of quadrature exactness.


title:
Automated generation of large-scale distribution grid models based on
  open data and open source software using an optimization approach

abstract:
  The increasing share of renewable energy sources on distribution grid level
as well as the emerging active role of prosumers lead to both higher
distribution grid utilization, and at the same time greater unpredictability of
energy generation and consumption. This poses major problems for grid operators
in view of, e.g., voltage stability and line (over)loading. Thus, detailed and
comprehensive simulation models are essential for planning future distribution
grid expansion in view of the expected strong electrification of society. In
this context, the contribution of the present paper is a new, more refined
method for automated creation of large-scale detailed distribution grid models
based solely on publicly available GIS and statistical data. Utilizing the
street layouts in Open Street Maps as potential cable routes, a graph
representation is created and complemented by residential units that are
extracted from the same data source. This graph structure is adjusted to match
electrical low-voltage grid topology by solving a variation of the minimum cost
flow linear optimization problem with provided data on secondary substations.
In a final step, the generated grid representation is transferred to a
DIgSILENT PowerFactory model with photovoltaic systems. The presented workflow
uses open source software and is fully automated and scalable that allows the
generation of ready-to-use distribution grid simulation models for given 20kV
substation locations and additional data on residential unit properties for
improved results. The performance of the developed method with respect to grid
utilization is presented for a selected suburban residential area with power
flow simulations for eight scenarios including current residential PV
installation and a future scenario with full PV expansion. Furthermore, the
suitability of the generated models for quasi-dynamic simulations is shown.


title:
Explainable deepfake and spoofing detection: an attack analysis using
  SHapley Additive exPlanations

abstract:
  Despite several years of research in deepfake and spoofing detection for
automatic speaker verification, little is known about the artefacts that
classifiers use to distinguish between bona fide and spoofed utterances. An
understanding of these is crucial to the design of trustworthy, explainable
solutions. In this paper we report an extension of our previous work to better
understand classifier behaviour to the use of SHapley Additive exPlanations
(SHAP) to attack analysis. Our goal is to identify the artefacts that
characterise utterances generated by different attacks algorithms. Using a pair
of classifiers which operate either upon raw waveforms or magnitude
spectrograms, we show that visualisations of SHAP results can be used to
identify attack-specific artefacts and the differences and consistencies
between synthetic speech and converted voice spoofing attacks.


title: Quotients of Palindromic and Antipalindromic Numbers

abstract:
  A natural number N is said to be palindromic if its binary representation
reads the same forwards and backwards. In this paper we study the quotients of
two palindromic numbers and answer some basic questions about the resulting
sets of integers and rational numbers. For example, we show that the following
problem is algorithmically decidable: given an integer N, determine if we can
write N = A/B for palindromic numbers A and B. Given that N is representable,
we find a bound on the size of the numerator of the smallest representation. We
prove that the set of unrepresentable integers has positive density in N. We
also obtain similar results for quotients of antipalindromic numbers (those for
which the first half of the binary representation is the reverse complement of
the second half). We also provide examples, numerical data, and a number of
intriguing conjectures and open problems.


title: Taxpayer deductions and the endogenous probability of tax penalisation

abstract:
  We propose a parametric specification of the probability of tax penalisation
faced by a taxpayer, based on the amount of deduction chosen by her to reduce
total taxation. Comparative analyses lead to a closed-form solution for the
optimum tax deduction, and provide the maximising conditions with respect to
the probability parameters.


title: Effects of Mesh Topology on MHD Solution Features in Coronal Simulations

abstract:
  Magnetohydrodynamic (MHD) simulations of the solar corona have become more
popular with the increased availability of computational power. Modern
computational plasma codes, relying upon Computational Fluid Dynamics (CFD)
methods, allow for resolving the coronal features using solar surface
magnetograms as inputs. These computations are carried out in a full 3D domain
and thus selection of the right mesh configuration is essential to save
computational resources and enable/speed up convergence. In addition, it has
been observed that for MHD simulations close to the hydrostatic equilibrium,
spurious numerical artefacts might appear in the solution following the mesh
structure, which makes the selection of the grid also a concern for accuracy.
The purpose of this paper is to discuss and trade off two main mesh topologies
when applied to global solar corona simulations using the unstructured ideal
MHD solver from the COOLFluiD platform. The first topology is based on the
geodesic polyhedron and the second on UV mapping. Focus will be placed on
aspects such as mesh adaptability, resolution distribution, resulting spurious
numerical fluxes and convergence performance. For this purpose, firstly a
rotating dipole case is investigated, followed by two simulations using real
magnetograms from the solar minima (1995) and solar maxima (1999). It is
concluded that the most appropriate mesh topology for the simulation depends on
several factors, such as the accuracy requirements, the presence of features
near the polar regions and/or strong features in the flow field in general. If
convergence is of concern and the simulation contains strong dynamics, then
grids which are based on the geodesic polyhedron are recommended compared to
more conventionally used UV-mapped meshes.


title: Metric, Schauder and Operator-Valued Frames

abstract:
  Notion of frames and Bessel sequences for metric spaces have been introduced.
This notion is related with the notion of Lipschitz free Banach spaces. \ It is
proved that every separable metric space admits a metric $\mathcal{M}_d$-frame.
Through Lipschitz-free Banach spaces it is showed that there is a
correspondence between frames for metric spaces and frames for subsets of
Banach spaces. Several characterizations of metric frames are obtained.
Stability results are also presented. Non linear multipliers are introduced and
studied. This notion is connected with the notion of Lipschitz compact
operators. Continuity properties of multipliers are discussed.
  For a subclass of approximated Schauder frames for Banach spaces,
characterization result is derived using standard Schauder basis for standard
sequence spaces. Duals of a subclass of approximate Schauder frames are
completely described. Similarity of this class is characterized and
interpolation result is derived using orthogonality. A dilation result is
obtained. A new identity is derived for Banach spaces which admit a homogeneous
semi-inner product. Some stability results are obtained for this class.
  A generalization of operator-valued frames for Hilbert spaces are introduced
which unifies all the known generalizations of frames for Hilbert spaces. This
notion has been studied in depth by imposing factorization property of the
frame operator. Its duality, similarity and orthogonality are addressed.
Connections between this notion and unitary representations of groups and
group-like unitary systems are derived. Paley-Wiener theorem for this class are
derived.


title:
Naked singularity in 4D Einstein-Gauss-Bonnet novel gravity: Echoes and
  (in)-stability

abstract:
  We study the stability of an asymptotically flat, static, spherically
symmetric naked singularity spacetime in the novel four dimensional
Einstein-Gauss-Bonnet (EGB) gravity. The four dimensional EGB black hole for
large enough values of the coupling parameter leads to such a naked
singularity. The stability and the response of the spacetime is studied against
the perturbations by test scalar, electromagnetic and Dirac fields and the time
evolution of these perturbations were observed numerically. Implementing a null
Dirichlet boundary condition near the singularity, we observed that for l=1
modes of scalar, electromagnetic perturbation and l=0,1 modes of Dirac
perturbation the time-domain profile give rise to distinct echoes. However, as
the coupling constant is increased, the echoes align and the QNM structure of
the 4D-EGB naked singularity-spacetime becomes prominent. For higher values of
the multipole number, the spacetime becomes unstable, thereby restricting the
parameter space for the coupling parameter.


title:
Determining holographic wave functions from Wilsonian renormalization
  group

abstract:
  We show a possible way to build the AdS/CFT correspondence starting from the
quantum field theory side based on renormalization group approach. An extra
dimension is naturally introduced in our scheme as the renomalization scale.
The holographic wave equations are derived, with the potential term being
determined by the QFT properties. We discover that only around the fixed point,
i.e. the conformal limit, the potential in the bulk equations can be fully
constrained, and upon this foundation, the correspondence is build. We
demonstrate this fact using a 3$d$ scalar theory in which, besides the trivial
fixed point, there exists the Wilson-Fisher fixed point. From the energy
scalings around those fixed points, we determine the behavior of the potential
in the bulk equations.


title:
A GNSS Aided Initial Alignment Method for MEMS-IMU Based on Backtracking
  Algorithm and Backward Filtering

abstract:
  To obtain a high-accuracy position with SINS(Strapdown Inertial Navigation
System), initial alignment needs to determine initial attitude rapidly and
accurately. High-accuracy grade IMU(Inertial Measurement Uint) can obtain the
initial attitude indenpendently, however, the low-accuracy grade gyroscope
doesn't adapt to determine the heading angle, hence the initial attitude matrix
will not be obtained. If using large misalignment angle model to estiamting
heading angle, the convergence time will become much longer. For solving these
two problems, a novel alignment algorithm combined backtracking algorithm and
reverse navigation updating method with GNSS(Global Navigation Satellite
System) aiding is proposed herein. The simulation and land vehicle test were
finished to evaluate the alignment accuracy of the proposed algorithm. The
horizontal misalignment is less than 2.3 arcmin and the heading misalignment is
less than 10.1 arcmin in test. The proposed algorithm is a feasible and
practical alignment method for low-cost IMU to obtain initial attitude in short
term and large misalignment condition aided by GNSS.


title: How do interfaces alter the dynamics of supercooled water?

abstract:
  The structure of liquid water in the proximity of an interface can deviate
significantly from that of bulk water, with surface-induced structural
perturbations typically converging to bulk values at about ~1 nm from the
interface. While these structural changes are well established it is, in
contrast, less clear how an interface perturbs the dynamics of water molecules
within the liquid. Here, through an extensive set of molecular dynamics
simulations of supercooled bulk and interfacial water films and nano-droplets,
we observe the formation of persistent, spatially extended dynamical domains in
which the average mobility varies as a function of the distance from the
interface. This is in stark contrast with the dynamical heterogeneity observed
in bulk water, where these domains average out spatially over time. We also
find that the dynamical response of water to an interface depends critically on
the nature of the interface and on the choice of interface definition. Overall
these results reveal a richness in the dynamics of interfacial water that opens
up the prospect of tuning the dynamical response of water through specific
modifications of the interface structure or confining material.


title: On the period of Li, Pertusi and Zhao's symplectic variety

abstract:
  We extend classical results of Perego and Rapagnetta on moduli spaces of
sheaves of type OG10 to moduli spaces of Bridgeland semistable objects on the
Kuznetsov component of a cubic fourfold. In particular, we determine the period
of this class of varieties and use it to understand when they become birational
to moduli spaces of sheaves on a K3 surface.


title: Spectroscopic evolution of very massive stars at Z = 1/2.5 Zsun

abstract:
  Stars with masses in excess of 100 Msun are observed in the Local Universe,
but they remain rare objects. Because of the shape of the mass function, they
are expected to be present only in the most massive and youngest clusters. They
may thus be formed in number in highly star-forming galaxies. Very massive
stars (VMSs) experience strong stellar winds that are stronger than those of
their less massive OB-type counterparts. These strong winds therefore need to
be taken into account in evolutionary models and synthetic spectra to properly
predict the appearance of VMS. We present evolutionary models computed with the
code STAREVOL. They include a recent mass-loss recipe that is relevant for
VMSs. We subsequently calculated atmosphere models and synthetic spectra along
the resulting tracks with the code CMFGEN. We studied stars with masses between
150 and 400 Msun and focused on a metallicity Z=1/2.5Zsun. We studied the
impact of our VMS spectra on the spectral energy distribution of young
starbursts. We show that the optical and UV range is dominated by HeII 4686 and
HeII 1640 emission for almost the entire main-sequence evolution of VMSs, in
contrast to less massive stars. In the UV spectral range, carbon, nitrogen, and
iron lines shape the spectra of VMSs, which appear for most of their evolution
as WNh objects. The morphology of the synthetic spectra is similar to that of
VMSs in the Large Magellanic Cloud. We show that stars with masses higher than
100 Msun emit nearly as much light as all other stars in young starbursts. The
integrated UV spectrum of these starbursts is significantly affected by the
presence of VMSs.


title:
Time-resolved chiral X-Ray photoelectron spectroscopy with transiently
  enhanced atomic site-selectivity: a Free Electron Laser investigation of
  electronically excited fenchone enantiomers

abstract:
  Chiral molecules are widespread in nature, playing a fundamental role in
bio-chemical processes and in the origin of life itself. The observation of
dynamics in chiral molecules is crucial for the understanding and control of
the chiral activity of photo-excited states. One of the most promising
techniques for the study of photo-excited chiral systems is time-resolved
photoelectron circular dichroism (TR-PECD), which offers an intense and
sensitive probe for vibronic and geometric molecular structure as well as
electronic structures, and their evolution on a femtosecond timescale. However,
the non-local character of the PECD effect, which is imprinted during the
electron scattering off the molecule, makes the interpretation of TR-PECD
experiments challenging. In this respect, core-photoionization is known to
allow site- and chemical-sensitivity to photelectron spectroscopy. Here we
demonstrate that TR-PECD utilising core-level photoemission enables probing the
chiral electronic structure and its relaxation dynamics with atomic site
sensitivity. Following UV pumped excitation to a 3s Rydberg state, fenchone
enantiomers (C 10 H 16 O) were probed on a femtosecond scale using circularly
polarized soft X-ray light pulses provided by the free-electron laser FERMI. C
1s binding energy shifts caused by the redistribution of valence electron
density in this 3s-valence-Rydberg excitation allowed us to measure transient
PECD chiral responses with an enhanced C-atom site-selectivity compared to that
achievable in the ground state molecule. These results represent the first
chemical-specific and site-specific, enantio-sensitive observations on the
electronic structure of a photo-excited chiral molecule and pave the way
towards chiral femtochemistry probed by core-level photoemission.


title: Mechanical mode engineering with orthotropic metamaterial membranes

abstract:
  Metamaterials are structures engineered at a small scale with respect to the
wavelength of the excitations they interact with. These structures behave as
artificial materials whose properties can be chosen by design, mocking and even
outperforming natural materials and making them the quintessential tool for
manipulation of wave systems. In this Letter we show how the acoustic
properties of a silicon nitride membrane can be affected by nanopatterning. The
degree of asymmetry in the pattern geometry induces an artificial anisotropic
elasticity, resulting in the splitting of otherwise degenerate mechanical
modes. The artificial material we introduce has a maximum Ledbetter-Migliori
anisotropy of 1.568, favorably comparing to most bulk natural crystals. With an
additional freedom in defining arbitrary asymmetry axes by pattern rotation,
our approach can be useful for fundamental investigation of material properties
as well as for devising improved sensors of light, mass or acceleration based
on micromechanical resonators.


title:
Monkey Business: Reinforcement learning meets neighborhood search for
  Virtual Network Embedding

abstract:
  In this article, we consider the Virtual Network Embedding (VNE) problem for
5G networks slicing. This problem requires to allocate multiple Virtual
Networks (VN) on a substrate virtualized physical network while maximizing
among others, resource utilization, maximum number of placed VNs and network
operator's benefit. We solve the online version of the problem where slices
arrive over time. Inspired by the Nested Rollout Policy Adaptation (NRPA)
algorithm, a variant of the well known Monte Carlo Tree Search (MCTS) that
learns how to perform good simulations over time, we propose a new algorithm
that we call Neighborhood Enhanced Policy Adaptation (NEPA). The key feature of
our algorithm is to observe NRPA cannot exploit knowledge acquired in one
branch of the state tree for another one which starts differently. NEPA learns
by combining NRPA with Neighbordhood Search in a frugal manner which improves
only promising solutions while keeping the running time low. We call this
technique a monkey business because it comes down to jumping from one
interesting branch to the other, similar to how monkeys jump from tree to tree
instead of going down everytime. NEPA achieves better results in terms of
acceptance ratio and revenue-to-cost ratio compared to other state-of-the-art
algorithms, both on real and synthetic topologies.


title:
Stable discretizations and IETI-DP solvers for the Stokes system in
  multi-patch Isogeometric Analysis

abstract:
  We are interested in a fast solver for the Stokes equations, discretized with
multi-patch Isogeometric Analysis. In the last years, several inf-sup stable
discretizations for the Stokes problem have been proposed, often the analysis
was restricted to single-patch domains. We focus on one of the simplest
approaches, the isogeometric Taylor--Hood element. We show how stability
results for single-patch domains can be carried over to multi-patch domains.
While this is possible, the stability strongly depends on the shape of the
geometry. We construct a Dual-Primal Isogeometric Tearing and Interconnecting
(IETI-DP) solver that does not suffer from that effect. We give a convergence
analysis and provide numerical tests.


title: Joint Camera Intrinsic and LiDAR-Camera Extrinsic Calibration

abstract:
  Sensor-based environmental perception is a crucial step for autonomous
driving systems, for which an accurate calibration between multiple sensors
plays a critical role. For the calibration of LiDAR and camera, the existing
method is generally to calibrate the intrinsic of the camera first and then
calibrate the extrinsic of the LiDAR and camera. If the camera's intrinsic is
not calibrated correctly in the first stage, it isn't easy to calibrate the
LiDAR-camera extrinsic accurately. Due to the complex internal structure of the
camera and the lack of an effective quantitative evaluation method for the
camera's intrinsic calibration, in the actual calibration, the accuracy of
extrinsic parameter calibration is often reduced due to the tiny error of the
camera's intrinsic parameters. To this end, we propose a novel target-based
joint calibration method of the camera intrinsic and LiDAR-camera extrinsic
parameters. Firstly, we design a novel calibration board pattern, adding four
circular holes around the checkerboard for locating the LiDAR pose.
Subsequently, a cost function defined under the reprojection constraints of the
checkerboard and circular holes features is designed to solve the camera's
intrinsic parameters, distortion factor, and LiDAR-camera extrinsic parameter.
In the end, quantitative and qualitative experiments are conducted in actual
and simulated environments, and the result shows the proposed method can
achieve accuracy and robustness performance. The open-source code is available
at https://github.com/OpenCalib/JointCalib.


title:
Remote trajectory tracking of a rigid body in an incompressible fluid at
  low Reynolds number

abstract:
  In this paper we study the motion of a rigid body driven by Newton's law
immersed in a stationary incompressible Stokes flow occupying a bounded simply
connected domain. The aim is that of trajectory tracking of the solid by the
means of a control in the form of Dirichlet boundary data on the outside
boundary of the fluid domain. We show that it is possible to exactly achieve
any smooth trajectory for the solid that stays away from the external boundary,
by the means of such a remote control. The proof relies on some density methods
for the Stokes system, as well as a reformulation of the solid equations into
an ODE.


title: Online Learning with Knapsacks: the Best of Both Worlds

abstract:
  We study online learning problems in which a decision maker wants to maximize
their expected reward without violating a finite set of $m$ resource
constraints. By casting the learning process over a suitably defined space of
strategy mixtures, we recover strong duality on a Lagrangian relaxation of the
underlying optimization problem, even for general settings with non-convex
reward and resource-consumption functions. Then, we provide the first
best-of-both-worlds type framework for this setting, with no-regret guarantees
both under stochastic and adversarial inputs. Our framework yields the same
regret guarantees of prior work in the stochastic case. On the other hand, when
budgets grow at least linearly in the time horizon, it allows us to provide a
constant competitive ratio in the adversarial case, which improves over the
$O(m \log T)$ competitive ratio of Immorlica at al. (2019). Moreover, our
framework allows the decision maker to handle non-convex reward and cost
functions. We provide two game-theoretic applications of our framework to give
further evidence of its flexibility.


title: Evaluating the Adversarial Robustness of Adaptive Test-time Defenses

abstract:
  Adaptive defenses that use test-time optimization promise to improve
robustness to adversarial examples. We categorize such adaptive test-time
defenses and explain their potential benefits and drawbacks. In the process, we
evaluate some of the latest proposed adaptive defenses (most of them published
at peer-reviewed conferences). Unfortunately, none significantly improve upon
static models when evaluated appropriately. Some even weaken the underlying
static model while simultaneously increasing inference cost. While these
results are disappointing, we still believe that adaptive test-time defenses
are a promising avenue of research and, as such, we provide recommendations on
evaluating such defenses. We go beyond the checklist provided by Carlini et al.
(2019) by providing concrete steps that are specific to this type of defense.


title: Near-field radiative heat transfer between hybrid polaritonic structures

abstract:
  Near-field radiative heat transfer between close objects may exceed the
far-field blackbody radiation in orders of magnitude when exploiting
polaritonic materials. Great efforts have been made to experimentally measure
this fundamental stochastic effect but mostly based on simple materials. In
this work, we foster an all-optical method to characterize the heat transfer
between less explored plasmon-phonon hybrid polaritonic systems made of
graphene-SiC heterostructures. A large heat flux about 26 times of the
blackbody radiation limit is obtained over a 150-nm vacuum gap, attributed to
the couplings of three different surface modes (plasmon, phonon polaritons and
frustrated mode). The interaction of polaritonic modes in the hybrid system is
also explored to build a switchable thermophotonic device with nearly unity
heat flux tunability. This work paves the way for understanding mode-mediated
near-field heat transfer and provides a platform for building thermophotonic or
thermo-optoelectronic blocks for various applications.


title:
Effect of tax dynamics on linearly growing processes under stochastic
  resetting: a possible economic model

abstract:
  We study a system of $N$ agents, whose wealth grows linearly, under the
effect of stochastic resetting and interacting via a tax-like dynamics -- all
agents donate a part of their wealth, which is, in turn, redistributed equally
among all others. This mimics a socio-economic scenario where people have fixed
incomes, suffer individual economic setbacks, and pay taxes to the state. The
system always reaches a stationary state, which shows a trivial exponential
wealth distribution in the absence of tax dynamics. The introduction of the tax
dynamics leads to several interesting features in the stationary wealth
distribution. In particular, we analytically find that an increase in taxation
for a homogeneous system (where all agents are alike) results in a transition
from a society where agents are most likely poor to another where rich agents
are more common. We also study inhomogeneous systems, where the growth rates of
the agents are chosen from a distribution, and the taxation is proportional to
the individual growth rates. We find an optimal taxation, which produces a
complete economic equality (average wealth is independent of the individual
growth rates), beyond which there is a reverse disparity, where agents with low
growth rates are more likely to be rich. We consider three income distributions
observed in the real world and show that they exhibit the same qualitative
features. Our analytical results are in the $N\to\infty$ limit and backed by
numerical simulations.


title:
Robust resource-efficient quantum variational ansatz through
  evolutionary algorithm

abstract:
  Variational quantum algorithms (VQAs) are promising methods to demonstrate
quantum advantage on near-term devices as the required resources are divided
between a quantum simulator and a classical optimizer. As such, designing a VQA
which is resource-efficient and robust against noise is a key factor to achieve
potential advantage with the existing noisy quantum simulators. It turns out
that a fixed VQA circuit design, such as the widely-used hardware efficient
ansatz, is not necessarily robust against imperfections. In this work, we
propose a genome-length-adjustable evolutionary algorithm to design a robust
VQA circuit that is optimized over variations of both circuit ansatz and gate
parameters, without any prior assumptions on circuit structure or depth.
Remarkably, our method not only generates a noise-effect-minimized circuit with
shallow depth, but also accelerates the classical optimization by substantially
reducing the number of parameters. In this regard, the optimized circuit is far
more resource-efficient with respect to both quantum and classical resources.
As applications, based on two typical error models in VQA, we apply our method
to calculating the ground energy of the hydrogen molecule and the Heisenberg
model. Simulations suggest that compared with conventional hardware efficient
ansatz, our circuit-structure-tunable method can generate circuits apparently
more robust against both coherent and incoherent noise, and hence more likely
to be implemented on near-term devices.


title:
Fast and Compute-efficient Sampling-based Local Exploration Planning via
  Distribution Learning

abstract:
  Exploration is a fundamental problem in robotics. While sampling-based
planners have shown high performance, they are oftentimes compute intensive and
can exhibit high variance. To this end, we propose to directly learn the
underlying distribution of informative views based on the spatial context in
the robot's map. We further explore a variety of methods to also learn the
information gain. We show in thorough experimental evaluation that our proposed
system improves exploration performance by up to 28\% over classical methods,
and find that learning the gains in addition to the sampling distribution can
provide favorable performance vs. compute trade-offs for compute-constrained
systems. We demonstrate in simulation and on a low-cost mobile robot that our
system generalizes well to varying environments.


title: SFIP: Coarse-Grained Syscall-Flow-Integrity Protection in Modern Systems

abstract:
  Growing code bases of modern applications have led to a steady increase in
the number of vulnerabilities. Control-Flow Integrity (CFI) is one promising
mitigation that is more and more widely deployed and prevents numerous
exploits. CFI focuses purely on one security domain. That is, transitions
between user space and kernel space are not protected by CFI. Furthermore, if
user space CFI is bypassed, the system and kernel interfaces remain
unprotected, and an attacker can run arbitrary transitions.
  In this paper, we introduce the concept of syscall-flow-integrity protection
(SFIP) that complements the concept of CFI with integrity for user-kernel
transitions. Our proof-of-concept implementation relies on static analysis
during compilation to automatically extract possible syscall transitions. An
application can opt-in to SFIP by providing the extracted information to the
kernel for runtime enforcement. The concept is built on three fully-automated
pillars: First, a syscall state machine, representing possible transitions
according to a syscall digraph model. Second, a syscall-origin mapping, which
maps syscalls to the locations at which they can occur. Third, an efficient
enforcement of syscall-flow integrity in a modified Linux kernel. In our
evaluation, we show that SFIP can be applied to large scale applications with
minimal slowdowns. In a micro- and a macrobenchmark, it only introduces an
overhead of 13.1% and 1.8%, respectively. In terms of security, we discuss and
demonstrate its effectiveness in preventing control-flow-hijacking attacks in
real-world applications. Finally, to highlight the reduction in attack surface,
we perform an analysis of the state machines and syscall-origin mappings of
several real-world applications. On average, SFIP decreases the number of
possible transitions by 38.6% compared to seccomp and 90.9% when no protection
is applied.


title: Models for Self-resonant Dark Matter

abstract:
  We consider a new mechanism for enhancing the self-scattering and
annihilation cross sections for dark matter with multiple components but
without a light mediator. The lighter dark matter component plays a role of the
$u$-channel pole in the elastic co-scattering for dark matter, leading to a
large self-scattering cross section and a Sommerfeld enhancement for
semi-annihilation processes. Taking the effective theory approach for
self-resonant dark matter, we present various combinations of multiple dark
matter components with spins and parities, showing a $u$-channel pole in the
co-scattering processes. Adopting dark photon and dark Higgs portals for
self-resonant dark matter, we impose the relic density condition as well as
indirect detection bounds on semi-annihilation channels with a Sommerfeld
enhancement and discuss potential signals for direct detection experiments.


title: Fast Feature Selection with Fairness Constraints

abstract:
  We study the fundamental problem of selecting optimal features for model
construction. This problem is computationally challenging on large datasets,
even with the use of greedy algorithm variants. To address this challenge, we
extend the adaptive query model, recently proposed for the greedy forward
selection for submodular functions, to the faster paradigm of Orthogonal
Matching Pursuit for non-submodular functions. Our extension also allows the
use of downward-closed constraints, which can be used to encode certain
fairness criteria into the feature selection process. The proposed algorithm
achieves exponentially fast parallel run time in the adaptive query model,
scaling much better than prior work. The proposed algorithm also handles
certain fairness constraints by design. We prove strong approximation
guarantees for the algorithm based on standard assumptions. These guarantees
are applicable to many parametric models, including Generalized Linear Models.
Finally, we demonstrate empirically that the proposed algorithm competes
favorably with state-of-the-art techniques for feature selection, on real-world
and synthetic datasets.


title: Guarding Polygons with Holes

abstract:
  We study the Cooperative Guarding problem for polygons with holes in a mobile
multi-agents setting. Given a set of agents, initially deployed at a point in a
polygon with $n$ vertices and $h$ holes, we require the agents to
collaboratively explore and position themselves in such a way that every point
in the polygon is visible to at least one agent and that the set of agents are
visibly connected. We study the problem under two models of computation, one in
which the agents can compute exact distances and angles between two points in
its visibility, and one in which agents can only compare distances and angles.
In the stronger model, we provide a deterministic $O(n)$ round algorithm to
compute such a cooperative guard set while not requiring more than $\frac{n +
h}{2}$ agents and $O(\log n)$ bits of persistent memory per agent. In the
weaker model, we provide an $O(n^4)$ round algorithm, that does not require
more than $\frac{n+2h}{2}$ agents.


title:
A Market Mechanism for Trading Flexibility Between Interconnected
  Electricity Markets

abstract:
  Electricity markets differ in their ability to meet power imbalances in short
notice in a controlled fashion. Relatively flexible markets have the ability to
ramp up (or down) power flows across interties without compromising their
ability to reliably meet internal demand. In this paper, a market mechanism to
enable flexibility trading amongst market operators is introduced. In the
proposed market mechanism, market operators exchange information regarding
optimal terms of trade (nodal prices and flows) along interconnection lines at
every trading round. Equipped with this information, each market operator then
independently solves its own internal chance-constrained economic dispatch
problem and broadcasts the updated optimal terms of trade for flows across
markets. We show the proposed decentralized market mechanism for flexibility
trading converges to a Nash equilibrium of the intraday market coupling game,
i.e. a combination of internal market clearing solutions (one for each
participating market) and flows and prices along interconnection lines so that
no individual market operator has an incentive to modify its own internal
solution and/or the terms of trade along interties. For a specific class of
chance constraints, we show that the limiting equilibrium outcome is efficient,
i.e. it corresponds to the solution of the single market clearing problem for
all participating markets. The proposed market mechanism is illustrated with an
application to the three-area IEEE Reliability Test System.


title: Positive solutions for a critical elliptic equation

abstract:
  In this paper, we are concerned with the following elliptic equation
\begin{equation*} \begin{cases} -\Delta u= Q(x)u^{2^*-1 }+\varepsilon u^{s},~
&{\text{in}~\Omega},\\[1mm] u>0,~ &{\text{in}~\Omega},\\[1mm] u=0,
&{\text{on}~\partial \Omega}, \end{cases} \end{equation*} where $N\geq 3$,
$s\in [1,2^*-1)$ with $2^*=\frac{2N}{N-2}$, $\varepsilon>0$, $\Omega$ is a
smooth bounded domain in $\mathbb{R}^N$.
  Under some conditions on $Q(x)$, Cao and Zhong in Nonlin. Anal. TMA (Vol 29,
1997, 461--483) proved that there exists a single-peak solution for small
$\varepsilon$ if $N\geq 4$ and $s\in (1,2^*-1)$. And they proposed in Remark
1.7 of their paper that
  \vskip 0.1cm\begin{center} \emph{``it is interesting to know the existence of
single-peak solutions for small $\varepsilon$ and $s=1$''.} \end{center}\vskip
0.1cm
  \noindent Also it was addressed in Remark 1.8 of their paper that
  \vskip 0.1cm \begin{center} \emph{``the question of solutions concentrated at
several points at the same time is still open''.} \end{center}\vskip 0.1cm
  \noindent
  Here we give some confirmative answers to the above two questions.
Furthermore, we prove the local uniqueness of the multi-peak solutions. And our
results show that the concentration of the solutions to above problem is
delicate whether $s=1$ or $s>1$.


title: Combinatorial Cobordism Theory

abstract:
  We introduce a formalism based on a combinatorial notion of cell complex
subject to an inclusion-reversing duality operation. Our main goal is to open
the way for a functorial definition of field theories in a context where no
manifold or topological structure is assumed. This is achieved via a discrete
notion of cobordism for which a composition operation is defined. Our main
theorem enables the composition of cobordisms by showing that certain sequences
of maps between cell complexes are in bijective correspondence with a cell
complex of dimension one higher. As a result we obtain a category whose
morphisms are cobordisms having a causal structure generalizing that of Causal
Dynamical Triangulations as well as dualities inherited from the duality map
defined on cell complexes.