title:
DualSC: Automatic Generation and Summarization of Shellcode via
  Transformer and Dual Learning

abstract:
  A shellcode is a small piece of code and it is executed to exploit a software
vulnerability, which allows the target computer to execute arbitrary commands
from the attacker through a code injection attack. Similar to the purpose of
automated vulnerability generation techniques, the automated generation of
shellcode can generate attack instructions, which can be used to detect
vulnerabilities and implement defensive measures. While the automated
summarization of shellcode can help users unfamiliar with shellcode and network
information security understand the intent of shellcode attacks. In this study,
we propose a novel approach DualSC to solve the automatic shellcode generation
and summarization tasks. Specifically, we formalize automatic shellcode
generation and summarization as dual tasks, use a shallow Transformer for model
construction, and design a normalization method Adjust QKNorm to adapt these
low-resource tasks (i.e., insufficient training data). Finally, to alleviate
the out-of-vocabulary problem, we propose a rulebased repair component to
improve the performance of automatic shellcode generation. In our empirical
study, we select a highquality corpus Shellcode IA32 as our empirical subject.
This corpus was gathered from two real-world projects based on the line-by-line
granularity. We first compare DualSC with six state-of-the-art baselines from
the code generation and code summarization domains in terms of four performance
measures. The comparison results show the competitiveness of DualSC. Then, we
verify the effectiveness of the component setting in DualSC. Finally, we
conduct a human study to further verify the effectiveness of DualSC.


title:
Dual-mode microresonators as straightforward access to octave-spanning
  dissipative Kerr solitons

abstract:
  The Kerr soliton frequency comb is a revolutionary compact ruler of coherent
light that allows applications, from precision metrology to quantum information
technology. The universal, reliable, and low-cost soliton microcomb source is
key to these applications. In this work, we thoroughly present an innovative
design strategy for realizing optical microresonators with two adjacent modes,
separated by approximately 10 GHz, which stabilizes soliton formation without
using additional auxiliary laser or RF components. We demonstrate the
deterministic generation of the single-solitons that span 1.5-octaves, i.e.,
near 200 THz, via adiabatic pump wavelength tuning. The ultra-wide soliton
existence ranges up to 17 GHz not only suggests the robustness of the system
but will also extend the applications of soliton combs. Moreover, the proposed
scheme is found to easily give rise to multi-solitons as well as the soliton
crystals featuring enhanced repetition rate (2 and 3 THz) and conversion
efficiency greater than 10%. We also show the effective thermal tuning of mode
separation for stably accessing single-soliton. Our results are crucial for the
chip-scale self-referenced frequency combs with a simplified configuration.


title:
A new operational matrix based on Boubaker polynomials for solving
  fractional Emden-Fowler problem

abstract:
  In this paper the singular Emden-Fowler equation of fractional order is
introduced and a computational method is proposed for its numerical solution.
For the approximation of the solutions we have used Boubaker polynomials and
defined the formulation for its fractional derivative operational matrix. This
tool was not used yet, however, this area has not found many practical
applications yet, and here introduced for the first time. The operational
matrix of the Caputo fractional derivative tool converts these problems to a
system of algebraic equations whose solutions are simple and easy to compute.
Numerical examples are examined to prove the validity and the effectiveness of
the proposed method to find approximate and precise solutions.


title:
How to Physically Verify a Rectangle in a Grid: A Physical ZKP for
  Shikaku

abstract:
  Shikaku is a pencil puzzle consisting of a rectangular grid, with some cells
containing a number. The player has to partition the grid into rectangles such
that each rectangle contains exactly one number equal to the area of that
rectangle. In this paper, we propose two physical zero-knowledge proof
protocols for Shikaku using a deck of playing cards, which allow a prover to
physically show that he/she knows a solution of the puzzle without revealing
it. Most importantly, in our second protocol we develop a general technique to
physically verify a rectangle-shaped area with a certain size in a rectangular
grid, which can be used to verify other puzzles with similar constraints.


title:
SOTitle: A Transformer-based Post Title Generation Approach for Stack
  Overflow

abstract:
  On Stack Overflow, developers can not only browse question posts to solve
their programming problems but also gain expertise from the question posts to
help improve their programming skills. Therefore, improving the quality of
question posts in Stack Overflow has attracted the wide attention of
researchers. A concise and precise title can play an important role in helping
developers understand the key information of the question post, which can
improve the post quality. However, the quality of the generated title is not
high due to the lack of professional knowledge related to their questions or
the poor presentation ability of developers. A previous study aimed to
automatically generate the title by analyzing the code snippets in the question
post. However, this study ignored the useful information in the corresponding
problem description. Therefore, we propose an approach SOTitle for automatic
post title generation by leveraging the code snippets and the problem
description in the question post (i.e., the multi-modal input). SOTitle follows
the Transformer structure, which can effectively capture long-term dependencies
through a multi-head attention mechanism. To verify the effectiveness of
SOTitle, we construct a large-scale high-quality corpus from Stack Overflow,
which includes 1,168,257 high-quality question posts for four popular
programming languages. Experimental results show that SOTitle can significantly
outperform six state-of-the-art baselines in both automatic evaluation and
human evaluation. To encourage follow-up studies, we make our corpus and
approach publicly available


title:
A Potential Cathode Material for Rechargeable Potassium-Ion Batteries
  Inducing Manganese Cation and Oxygen Anion Redox Chemistry:
  Potassium-Deficient $\rm K_{0.4}Fe_{0.5}Mn_{0.5}O_2$

abstract:
  Potassium-ion ($\rm K$-ion) rechargeable batteries; considered to be
lucrative low-cost battery options for large-scale and capacious energy storage
systems, have been garnering tremendous attention in recent years. However, due
to the scarcity of cathode materials that can condone the reversible
re-insertion of the large $\rm K$-ions at feasible capacities, the viability of
$\rm K$-ion batteries has been greatly undercut. In this paper, we explore a
potential cathode material in the $\rm K_2O$-$\rm Fe_2O_3$-$\rm MnO_2$ ternary
phase system, that not only demonstrates reversible $\rm K$-ion reinsertion but
also manifests relatively fast rate capabilities. The titled cathode compound,
$\rm K_{0.4}Fe_{0.5}Mn_{0.5}O_2$, demonstrates a reversible capacity of
approximately $\rm120 $ mAh g$^{-1}$ at $\rm 10$ hours of (dis)charge ($viz.$,
$\rm C/10$ rate) with $ca.$ $\rm 85$% of the capacity being retained at a $\rm
1$ hour of (dis)charge ($\rm 1 C$ rate) which is considered to be good capacity
retention. Additionally, both hard and soft X-rays have been employed to
unravel the mechanism by which $\rm K$-ion is reversibly inserted into $\rm
K_{0.4}Fe_{0.5}Mn_{0.5}O_2$. The results revealed a cumulative participation of
both manganese cations and oxygen anions in $\rm K_{0.4}Fe_{0.5}Mn_{0.5}O_2$
illustrating its potential as a high-capacity $\rm K$-ion battery cathode
material that relies on both anion and cation redox. Further development of
related high-capacity cathode compositions can be anticipated.


title: Contextual Semantic Embeddings for Ontology Subsumption Prediction

abstract:
  Automating ontology curation is a crucial task in knowledge engineering.
Prediction by machine learning techniques such as semantic embedding is a
promising direction, but the relevant research is still preliminary. In this
paper, we present a class subsumption prediction method named BERTSubs, which
uses the pre-trained language model BERT to compute contextual embeddings of
the class labels and customized input templates to incorporate contexts of
surrounding classes. The evaluation on two large-scale real-world ontologies
has shown its better performance than the state-of-the-art.


title: Hierarchical Interpretation of Neural Text Classification

abstract:
  Recent years have witnessed increasing interests in developing interpretable
models in Natural Language Processing (NLP). Most existing models aim at
identifying input features such as words or phrases important for model
predictions. Neural models developed in NLP however often compose word
semantics in a hierarchical manner. Interpretation by words or phrases only
thus cannot faithfully explain model decisions. This paper proposes a novel
Hierarchical INTerpretable neural text classifier, called Hint, which can
automatically generate explanations of model predictions in the form of
label-associated topics in a hierarchical manner. Model interpretation is no
longer at the word level, but built on topics as the basic semantic unit.
Experimental results on both review datasets and news datasets show that our
proposed approach achieves text classification results on par with existing
state-of-the-art text classifiers, and generates interpretations more faithful
to model predictions and better understood by humans than other interpretable
neural text classifiers.


title:
Controlling nonautonomous matter waves in "smart" transient trap
  variations

abstract:
  In this paper, we study the controllable behavior of nonautonomous matter
waves in different "smart" transient trap variations in the context of the
cigar-shaped Bose-Einstein condensates. By utilizing a self-similarity
transformation we reduce the nonautonomous Gross-Pitaevskii (GP) equation to
the elliptic equation that admits soliton solutions. This procedure leads to a
consistency equation which is in the form of Riccati equation. The connection
between the Riccati and the linear Schr\"odinger equation, through the
Cole-Hopf transformation, is exploited profitably here to introduce temporal
trap variations. For our study, we explore the possibility of using one
dimensional exactly solvable (ES) potentials and their newly constructed
rational extensions, as functions of time to introduce interesting temporal
trap modulations. The fact that the regular potentials and their rational
extensions being structurally different, leads to different temporal
modulations. It is exhibited that the soliton behavior with respect to
compression in both these cases is quite different.


title: Evolution of primordial black holes in a dissipative FLRW universe

abstract:
  This paper deals with the study of the evolution of PBHs in an FLRW universe
with dissipation due to bulk viscosity which is considered to be in the form of
isentropic gravitational particle creation. Assuming that the process of
evaporation is quite suppressed during the early radiation era, we obtain an
analytic solution for the evolution of PBH mass by accretion during this era,
subject to an initial condition. We also obtain an upper bound on the accretion
efficiency $\epsilon$ for $a \sim a_r$, where $a_r$ is the point of transition
from the early de Sitter to the radiation era. Furthermore, aided by Maple 13,
we obtain numerical solutions for the mass of a hypothetical PBH with intial
mass 100 g assumed to be formed at an epoch when the value of the Hubble
parameter was, say, 1 km/s/Mpc. We consider three values of the accretion
efficiency, $\epsilon=0.23,0.5$, and $0.89$ for our study. The analysis reveals
that the mass of the PBH increases rapidly due to the accretion of radiation in
the early stages of its evolution. The accretion continues but its rate
decreases gradually with the evolution of the Universe. Finally, Hawking
radiation comes into play and the rate of evaporation surpasses the rate of
accretion so that the PBH mass starts to decrease. As the Universe grows,
evaporation becomes the dominant phenomenon, and the mass of the PBH decreases
at a faster rate.


title: Accountable Javascript Code Delivery

abstract:
  The Internet is a major distribution platform for applications, but there are
no effective transparency and audit mechanisms. Due to the ephemeral nature of
web applications, a client visiting a website has no guarantee that the code it
receives today is the same as yesterday, or the same as others receive. Despite
advances in web security, it is thus challenging to audit web applications
before they are rendered in the browser. We propose Accountable JS, a browser
extension and opt-in protocol for accountable delivery of active content on a
web page. We prototype our protocol, formally model its security properties
with the Tamarin Prover, and evaluate its compatibility and performance impact
with case studies including WhatsApp Web, AdSense and Nimiq.


title: Microwave Sensing of Elemental Sulfur Deposition in Gas Pipelines

abstract:
  A non-intrusive conformal electromagnetic-based monitoring of elemental
sulfur deposition within a natural gas-carrying pipeline is presented. The
deposited sulfur behaves as a superstrate layer above a sensing microstrip
patch antenna that is optimally placed on the inner wall of the gas pipeline.
Increasing the superstrate thickness by the sulfur deposition alters the
antenna resonance behavior, which can be monitored externally. The effect of
uneven or bumpy sulfur deposition is studied. Sensing antennas positioned
outside a plexiglass pipeline are also investigated to observe the change in
the antenna impedance matching with accumulating sulfur superstrate. Lab-based
measured results agreed well with the simulated responses using commercial
electromagnetic software. The proposed low-cost and easy-to-implement detection
technique exhibits an accurate estimation of the deposited sulfur thickness
inside the natural gas-carrying pipelines.


title:
Tight Bounds for Sketching the Operator Norm, Schatten Norms, and
  Subspace Embeddings

abstract:
  We consider the following oblivious sketching problem: given $\epsilon \in
(0,1/3)$ and $n \geq d/\epsilon^2$, design a distribution $\mathcal{D}$ over
$\mathbb{R}^{k \times nd}$ and a function $f: \mathbb{R}^k \times
\mathbb{R}^{nd} \rightarrow \mathbb{R}$, so that for any $n \times d$ matrix
$A$, $$\Pr_{S \sim \mathcal{D}} [(1-\epsilon) \|A\|_{op} \leq f(S(A),S) \leq
(1+\epsilon)\|A\|_{op}] \geq 2/3,$$ where $\|A\|_{op}$ is the operator norm of
$A$ and $S(A)$ denotes $S \cdot A$, interpreting $A$ as a vector in
$\mathbb{R}^{nd}$. We show a tight lower bound of $k = \Omega(d^2/\epsilon^2)$
for this problem. Our result considerably strengthens the result of Nelson and
Nguyen (ICALP, 2014), as it (1) applies only to estimating the operator norm,
which can be estimated given any OSE, and (2) applies to distributions over
general linear operators $S$ which treat $A$ as a vector and compute $S(A)$,
rather than the restricted class of linear operators corresponding to matrix
multiplication. Our technique also implies the first tight bounds for
approximating the Schatten $p$-norm for even integers $p$ via general linear
sketches, improving the previous lower bound from $k = \Omega(n^{2-6/p})$
[Regev, 2014] to $k = \Omega(n^{2-4/p})$. Importantly, for sketching the
operator norm up to a factor of $\alpha$, where $\alpha - 1 = \Omega(1)$, we
obtain a tight $k = \Omega(n^2/\alpha^4)$ bound, matching the upper bound of
Andoni and Nguyen (SODA, 2013), and improving the previous $k =
\Omega(n^2/\alpha^6)$ lower bound. Finally, we also obtain the first lower
bounds for approximating Ky Fan norms.


title:
Image quality assessment by overlapping task-specific and task-agnostic
  measures: application to prostate multiparametric MR images for cancer
  segmentation

abstract:
  Image quality assessment (IQA) in medical imaging can be used to ensure that
downstream clinical tasks can be reliably performed. Quantifying the impact of
an image on the specific target tasks, also named as task amenability, is
needed. A task-specific IQA has recently been proposed to learn an
image-amenability-predicting controller simultaneously with a target task
predictor. This allows for the trained IQA controller to measure the impact an
image has on the target task performance, when this task is performed using the
predictor, e.g. segmentation and classification neural networks in modern
clinical applications. In this work, we propose an extension to this
task-specific IQA approach, by adding a task-agnostic IQA based on
auto-encoding as the target task. Analysing the intersection between
low-quality images, deemed by both the task-specific and task-agnostic IQA, may
help to differentiate the underpinning factors that caused the poor target task
performance. For example, common imaging artefacts may not adversely affect the
target task, which would lead to a low task-agnostic quality and a high
task-specific quality, whilst individual cases considered clinically
challenging, which can not be improved by better imaging equipment or
protocols, is likely to result in a high task-agnostic quality but a low
task-specific quality. We first describe a flexible reward shaping strategy
which allows for the adjustment of weighting between task-agnostic and
task-specific quality scoring. Furthermore, we evaluate the proposed algorithm
using a clinically challenging target task of prostate tumour segmentation on
multiparametric magnetic resonance (mpMR) images, from 850 patients. The
proposed reward shaping strategy, with appropriately weighted task-specific and
task-agnostic qualities, successfully identified samples that need
re-acquisition due to defected imaging process.


title: Redactable Signature with Compactness from Set-Commitment

abstract:
  Redactable signature allows anyone to remove parts of a signed message
without invalidating the signature. The need to prove the validity of digital
documents issued by governments is increasing. When governments disclose
documents, they must remove private information concerning individuals.
Redactable signature is useful for such a situation. However, in most
redactable signature schemes, to remove parts of the signed message, we need
pieces of information for each part we want to remove. If a signed message
consists of l elements, the number of elements in an original signature is at
least linear in l. As far as we know, in some redactable signature schemes, the
number of elements in an original signature is constant, regardless of the
number of elements in a message to be signed. However, these constructions have
drawbacks in that the use of the random oracle model or generic group model. In
this paper, we construct an efficient redactable signature to overcome these
drawbacks. Our redactable signature is obtained by combining set-commitment
proposed in the recent work by Fuchsbauer et al. (JoC 2019) and digital
signatures.


title:
Marginally trapped surfaces in ${\mathbb{L}}^{4}$ and three Weierstrass
  representations

abstract:
  We construct new integrable systems to present Weierstrass type
representations for spacelike surfaces whose mean curvature vector $\mathbf{H}$
satisfies the null condition $\langle \mathbf{H}, \mathbf{H} \rangle=0$ in the
four dimensional Lorentz-Minkowski space ${\mathbb{L}}^{4}$. Our new
Weierstrass presentations extend simultaneously classical Weierstrass
representations (of the first kind and the second kind) for maximal surfaces in
${\mathbb{L}}^{3}$ and minimal surfaces in ${\mathbb{R}}^{3}$. We solve a
linear partial differental equation to construct explicit examples of
marginally trapped surfaces with nowhere vanishing mean curvature vector.


title:
Normalized ground states for 3D dipolar Bose-Einstein condensate with
  attractive three-body interactions

abstract:
  We study the existence of normalized ground states for the 3D dipolar
Bose-Einstein condensate equation with attractive three-body interactions:
\begin{align}\label{1} -\Delta u+\beta u+\lambda_1|u|^2 u+\lambda_2
(K*|u|^2)u-|u|^4u=0.\tag{DBEC} \end{align} When $\lambda_2=0$ or $u$ is radial,
(\ref{1}) reduces to the cubic-quintic NLS \begin{align}\label{2} -\Delta
u+\beta u+\lambda_1|u|^2 u-|u|^4u=0\tag{CQNLS}, \end{align} which has been
recently studied by Soave in [31]. In particular, it was shown that for any
$\lambda_1<0$ and $c>0$, (\ref{2}) possesses a radially symmetric ground state
solution with mass $c$ and for $\lambda_1\geq 0$, (\ref{2}) has no non-trivial
solution. We show that by adding a dipole-dipole interaction to (\ref{2}), the
geometric nature of (\ref{2}) changes dramatically and techniques as the ones
from [31] cannot be used anymore to obtain similar results. More precisely, due
to the axisymmetric nature of the dipole-dipole interaction potential, the
energy corresponding to (\ref{1}) is not stable under symmetric rearrangements,
hence conventional arguments based on the radial symmetry of solutions are
inapplicable. We will overcome this difficulty by appealing to subtle
variational and perturbative methods and prove the following:
  (i) If the pair $(\lambda_1,\lambda_2)$ is unstable and $\lambda_1<0$, then
for any $c>0$, (\ref{1}) has a ground state solution with mass $c$.
  (ii) If the pair $(\lambda_1,\lambda_2)$ is unstable and $\lambda_1\geq 0$,
then there exists some $c^*=c^*(\lambda_1,\lambda_2)\geq 0$ such that for all
$c>c^*$, (\ref{1}) has a ground state solution with mass $c$. Moreover, any
non-trivial solution of (\ref{1}) in this case must be non-radial.
  (iii) If the pair $(\lambda_1,\lambda_2)$ is \textit{stable}, then (\ref{1})
has no non-trivial solutions.


title:
Distortion-Aware Loop Filtering of Intra 360^o Video Coding with
  Equirectangular Projection

abstract:
  In this paper, we propose a distortion-aware loop filtering model to improve
the performance of intra coding for 360$^o$ videos projected via
equirectangular projection (ERP) format. To enable the awareness of distortion,
our proposed module analyzes content characteristics based on a coding unit
(CU) partition mask and processes them through partial convolution to activate
the specified area. The feature recalibration module, which leverages cascaded
residual channel-wise attention blocks (RCABs) to adjust the inter-channel and
intra-channel features automatically, is capable of adapting with different
quality levels. The perceptual geometry optimization combining with weighted
mean squared error (WMSE) and the perceptual loss guarantees both the local
field of view (FoV) and global image reconstruction with high quality.
Extensive experimental results show that our proposed scheme achieves
significant bitrate savings compared with the anchor (HM + 360Lib), leading to
8.9%, 9.0%, 7.1% and 7.4% on average bit rate reductions in terms of PSNR,
WPSNR, and PSNR of two viewports for luminance component of 360^o videos,
respectively.


title: Trapping of HII regions in Population III star formation

abstract:
  Radiative feedback from massive Population III (Pop III) stars in the form of
ionising and photodissociating photons is widely believed to play a central
role in shutting off accretion onto these stars. Understanding whether and how
this occurs is vital for predicting the final masses reached by these stars and
the form of the Pop III stellar initial mass function. To help us better
understand the impact of UV radiation from massive Pop III stars on the gas
surrounding them, we carry out high resolution simulations of the formation and
early evolution of these stars, using the AREPO moving-mesh code coupled with
the innovative radiative transfer module SPRAI. Contrary to most previous
results, we find that the ionising radiation from these stars is trapped in the
dense accretion disk surrounding them. Consequently, the inclusion of radiative
feedback has no significant impact on either the number or the total mass of
protostars formed during the 20 kyr period that we simulate. We show that the
reason that we obtain qualitatively different results from previous studies of
Pop III stellar feedback lies in how the radiation is injected into the
simulation. HII region trapping only occurs if the photons are injected on
scales smaller than the local scale height of the accretion disk, a criterion
not fulfilled in previous 3D simulations of this process. Finally, we speculate
as to whether outflows driven by the magnetic field or by Lyman-alpha radiation
pressure may be able to clear enough gas away from the star to allow the HII
region to escape from the disk.


title: Gradient Tracking: A Unified Approach to Smooth Distributed Optimization

abstract:
  In this work, we study the classical distributed optimization problem over
digraphs, where the objective function is a sum of smooth local functions.
Inspired by the implicit tracking mechanism proposed in our earlier work, we
develop a unified algorithmic framework from a pure primal perspective, i.e.,
UGT, which is essentially a generalized gradient tracking method and can unify
most existing distributed optimization algorithms with constant step-sizes. It
is proved that two variants of UGT can both achieve linear convergence if the
global objective function is strongly convex. Finally, the performance of UGT
is evaluated by numerical experiments.


title: Mahler discrete residues and summability for rational functions

abstract:
  We construct Mahler discrete residues for rational functions and show that
they comprise a complete obstruction to the Mahler summability problem of
deciding whether a given rational function $f(x)$ is of the form $g(x^p)-g(x)$
for some rational function $g(x)$ and an integer $p > 1$. This extends to the
Mahler case the analogous notions, properties, and applications of discrete
residues (in the shift case) and $q$-discrete residues (in the $q$-difference
case) developed by Chen and Singer. Along the way we define several additional
notions that promise to be useful for addressing related questions involving
Mahler difference fields of rational functions, including in particular
telescoping problems and problems in the (differential) Galois theory of Mahler
difference equations.


title: Learning logic programs by discovering where not to search

abstract:
  The goal of inductive logic programming (ILP) is to search for a hypothesis
that generalises training examples and background knowledge (BK). To improve
performance, we introduce an approach that, before searching for a hypothesis,
first discovers `where not to search'. We use given BK to discover constraints
on hypotheses, such as that a number cannot be both even and odd. We use the
constraints to bootstrap a constraint-driven ILP system. Our experiments on
multiple domains (including program synthesis and inductive general game
playing) show that our approach can substantially reduce learning times.


title: A t-out-of-n Redactable Signature Scheme

abstract:
  A redactable signature scheme allows removing parts of a signed message
without invalidating the signature. Currently, the need to prove the validity
of digital documents issued by governments and enterprises is increasing.
However, when disclosing documents, governments and enterprises must remove
privacy information concerning individuals. A redactable signature scheme is
useful for such a situation. In this paper, we introduce the new notion of the
t-out-of-n redactable signature scheme. This scheme has a signer, n redactors,
a combiner, and a verifier. The signer designates n redactors and a combiner in
advance and generates a signature of a message M. Each redactor decides parts
that he or she wants to remove from the message and generates a piece of
redaction information. The combiner collects pieces of redaction information
from all redactors, extracts parts of the message that more than t redactors
want to remove, and generate a redacted message. We consider the one-time
redaction model which allows redacting signatures generated by the signer only
once. We formalize the one-time redaction t-out-of-n redactable signature
scheme, define security, and give a construction using the pairing based
aggregate signature scheme in the random oracle model.


title:
Improved Security Proof for the Camenisch-Lysyanskaya Signature-Based
  Synchronized Aggregate Signature Scheme

abstract:
  The Camenisch-Lysyanskaya signature scheme in CRYPTO 2004 is a useful
building block to construct privacy-preserving schemes such as anonymous
credentials, group signatures or ring signatures. However, the security of this
signature scheme relies on the interactive assumption called the LRSW
assumption. Even if the interactive assumptions are proven in the generic group
model or bilinear group model, the concerns about these assumptions arise in a
cryptographic community. This fact caused a barrier to the use of cryptographic
schemes whose security relies on these assumptions. Recently, Pointcheval and
Sanders proposed the modified Camenisch-Lysyanskaya signature scheme in CT-RSA
2018. This scheme satisfies the EUF-CMA security under the new q-type
assumption called the Modified-q-Strong Diffie-Hellman-2 (q-MSDH-2) assumption.
However, the size of a q- type assumptions grows dynamically and this fact
leads to inefficiency of schemes. In this work, we revisit the
Camenisch-Lysyanskaya signature-based synchronized aggregate signature scheme
in FC 2013. This scheme is one of the most efficient synchronized aggregate
signature schemes with bilinear groups. However, the security of this
synchronized aggregate scheme was proven under the one-time LRSW assumption in
the random oracle model. We give the new security proof for this synchronized
aggregate scheme under the 1-MSDH-2 (static) assumption in the random oracle
model with little loss of efficiency.


title: The stable uniqueness theorem for equivariant Kasparov theory

abstract:
  This paper examines and strengthens the Cuntz-Thomsen picture of equivariant
Kasparov theory for arbitrary second-countable locally compact groups, in which
elements are given by certain pairs of cocycle representations between
C*-dynamical systems. The main result is a stable uniqueness theorem that
generalizes a fundamental characterization of ordinary $KK$-theory by Lin and
Dadarlat-Eilers. Along the way, we prove an equivariant Cuntz-Thomsen picture
analog of the fact that the equivalence relation of homotopy agrees with the (a
priori stronger) equivalence relation of stable operator homotopy. The results
proved in this paper will be employed as the technical centerpiece in
forthcoming work of the authors to classify certain amenable group actions on
Kirchberg algebras by equivariant Kasparov theory.


title: Alternative design of DeepPDNet in the context of image restoration

abstract:
  This work designs an image restoration deep network relying on unfolded
Chambolle-Pock primal-dual iterations. Each layer of our network is built from
Chambolle-Pock iterations when specified for minimizing a sum of a
$\ell_2$-norm data-term and an analysis sparse prior. The parameters of our
network are the step-sizes of the Chambolle-Pock scheme and the linear operator
involved in sparsity-based penalization, including implicitly the
regularization parameter. A backpropagation procedure is fully described.
Preliminary experiments illustrate the good behavior of such a deep primal-dual
network in the context of image restoration on BSD68 database.


title: One-cycles on Gushel-Mukai fourfolds and the Beauville-Voisin filtration

abstract:
  We prove that the invariant locus of the involution associated to a general
double EPW sextic is a constant surface and introduce a filtration on $\CH_1$
of a Gushel-Mukai fourfold. We verify the sheaf/cycle correspondence for
sheaves supported on low degree rational curves, parallel to the cubic
fourfolds case.


title: Explicit Solvent Theory of Salt-Induced Dielectric Decrement

abstract:
  We introduce a field-theoretic electrolyte model composed of structured
solvent molecules and salt ions coupled by electrostatic and hard-core
interactions. Within this explicit solvent theory, we characterize the
salt-driven dielectric decrement beyond weak-coupling (WC) electrostatics. The
WC approximation of prior formalisms is relaxed by treating the salt charges
via a virial expansion. This virial approach enables the explicit inclusion of
the many-body salt-solvent interactions, and directly leads to the
experimentally observed linear decay of the electrolyte permittivity with added
dilute salt. The permittivity formula emerging from our approach indicates that
the reduction of the solvent permittivity is induced by the salt screening of
the polarization charges suppressing the dielectric response of the solvent. By
comparison with experiments, we also show that the salt-dressed permittivity
formula can equally reproduce the attenuation of the electrolyte permittivity
with rising temperature, the thermal decay of the dielectric decrement, and its
intensification with the salt valency. The consistent qualitative agreement of
our theory with this wide range of experimental trends points out the
electrostatic ion-solvent correlations as the primary mechanism behind the
salt-induced dielectric decrement.


title:
Real-time Emotion Appraisal with Circumplex Model for Human-Robot
  Interaction

abstract:
  Emotions are the intrinsic or extrinsic representations of our experiences.
The importance of emotions during a human-human interaction is immense as it
formulates the basis of our interaction framework. There are several approaches
in psychology to evaluate emotional states in humans based on the perceived
stimuli. However, the topic has been less explored as far as human-robot
interaction is concerned. This paper uses an appropriate emotion appraisal
mechanism from psychology, generating an emotional state in a humanoid robot
on-the-fly during human-robot interaction. Since the exhibition of only six
basic emotions is not sufficient to cater to diverse situations, the use of the
Circumplex Model in this work has allowed the life-sized robot called ROBIN to
experience 28 emotional states in different interaction scenarios. Realistic
robot behaviour has been generated based on the proposed appraisal system in
various interaction scenarios.


title: Streak-less wall-bounded turbulence

abstract:
  The effect of damping the longest streaks in wall-bounded turbulence is
explored using numerical experiments. It is found that long streaks are not
required for the self-sustenance of the bursting process, which is relatively
little affected by their absence. The only perturbations of the streamwise
velocity required to sustain turbulence are shown to be those associated with
the bursts themselves. This suggests that long streaks are byproducts, rather
than active parts of the process.


title:
Micro-scale investigations of temperature-dependent Microbial-Induced
  Carbonate Precipitation (MICP) in the temperature range 4-50 {\deg}C

abstract:
  Microbially-Induced Carbonate Precipitation (MICP) involves a series of
bio-geochemical reactions whereby microbes alter the surrounding aqueous
environment and induce calcium carbonate precipitation. MICP has a broad range
of applications, including in-situ soil stabilization. However, the reliability
of this process is dependent on a number of environmental conditions. In
particular, bacterial growth, bacterial activity, and precipitation kinetics
all depend on temperature. Batch test and microfluidic chip experiments were
performed in this study to investigate the effects of temperature on bacterial
density and activity and the MICP processes occurring at different temperatures
(4-50{\deg}C). Spatial and temporal variations in the formation and development
of calcium carbonate precipitates, including their amount, type, growth rate,
formation, and deformation characteristics, were monitored. Results show that
different types of calcium carbonate precipitates with varying sizes and
quantities were produced by varying the temperature. Low temperature (4{\deg}C)
did not reduce bacterial activity, but limited the final amount of cementation;
low temperature reduced bacterial growth and attachment ratio, as well as
calcium carbonate precipitation rate. High temperature (50{\deg}C) conditions
significantly reduced bacterial activity within a short period of time, whilst
a repeated injection of bacteria before every two injections of cementation
solution increased the final amount of cementation. The findings made from this
paper provide insight into how MICP processes vary across a range of
temperatures and could be valuable for optimising the MICP process for
different applications.


title:
Contextual Intelligent Decisions: Expert Moderation of Machine Outputs
  for Fair Assessment of Commercial Driving

abstract:
  Commercial driving is a complex multifaceted task influenced by personal
traits and external contextual factors, such as weather, traffic, road
conditions, etc. Previous intelligent commercial driver-assessment systems do
not consider these factors when analysing the impact of driving behaviours on
road safety, potentially producing biased, inaccurate, and unfair assessments.
In this paper, we introduce a methodology (Expert-centered Driver Assessment)
towards a fairer automatic road safety assessment of drivers' behaviours,
taking into consideration behaviours as a response to contextual factors. The
contextual moderation embedded within the intelligent decision-making process
is underpinned by expert input, comprising of a range of associated
stakeholders in the industry. Guided by the literature and expert input, we
identify critical factors affecting driving and develop an interval-valued
response-format questionnaire to capture the uncertainty of the influence of
factors and variance amongst experts' views. Questionnaire data are modelled
and analysed using fuzzy sets, as they provide a suitable computational
approach to be incorporated into decision-making systems with uncertainty. The
methodology has allowed us to identify the factors that need to be considered
when moderating driver sensor data, and to effectively capture experts'
opinions about the effects of the factors. An example of our methodology using
Heavy Goods Vehicles professionals input is provided to demonstrate how the
expert-centred moderation can be embedded in intelligent driver assessment
systems.


title:
$\mathcal{Y}$-Tuning: An Efficient Tuning Paradigm for Large-Scale
  Pre-Trained Models via Label Representation Learning

abstract:
  With the success of large-scale pre-trained models (PTMs), how efficiently
adapting PTMs to downstream tasks has attracted tremendous attention,
especially for PTMs with billions of parameters. Although some
parameter-efficient tuning paradigms have been proposed to address this
problem, they still require large resources to compute the gradients in the
training phase. In this paper, we propose $\mathcal{Y}$-Tuning, an efficient
yet effective paradigm to adapt frozen large-scale PTMs to specific downstream
tasks. $\mathcal{Y}$-tuning learns dense representations for labels
$\mathcal{Y}$ defined in a given task and aligns them to fixed feature
representation. Without tuning the features of input text and model parameters,
$\mathcal{Y}$-tuning is both parameter-efficient and training-efficient. For
$\text{DeBERTa}_\text{XXL}$ with 1.6 billion parameters, $\mathcal{Y}$-tuning
achieves performance more than $96\%$ of full fine-tuning on GLUE Benchmark
with only $2\%$ tunable parameters and much fewer training costs.


title: The Lambda Number of the Power Graph of Finite Simple Groups

abstract:
  For a finite group $G$, the power graph $\Gamma_G$ is a graph whose set of
vertices is equal to $G$ and two distinct elements of $G$ are adjacent if and
only if one of them is a positive integer power of the other. An
$L(2,1)$-labelling of $\Gamma_G$ is an integer-valued function defined on $G$
so that the distance between the images of two adjacent vertices (resp.
vertices with distance two) are at least two (resp. at least one). The lambda
number $\lambda(G)$ is defined to be the least difference between the largest
and the smallest integer values assigned to the vertices of $\Gamma_G$ for all
possible $L(2,1)$-labellings of $\Gamma_G$. It is known that $\lambda(G) \geq
|G|$. In this paper, we prove that if $G$ is a finite simple group, then
$\lambda(G) = |G|$ except when $G$ is cyclic of prime order. This settles a
partial classification of finite groups $G$ that achieve the lower bound on of
lambda number.


title:
$d$-dimensional partitions of n as words of length $n-1$ on an alphabet
  of $d+1$ letters

abstract:
  The enumeration of higher-dimensional partitions has proved to be a difficult
problem. Here we demonstrate a method for listing and recursively enumerating
such partitions as words. We use this to estimate some elementary universal
bounds on all such partitions in each dimension. The method allows us to
recover generating function expression for ordinary partitions through the
combinatorics of words. We also show that the words for ordinary partitions
form a connected bipartite subgraph of the hypercube $Q_{n-1}$ for each $n$.


title:
Chimeric forecasting: combining probabilistic predictions from
  computational models and human judgment

abstract:
  Forecasts of the trajectory of an infectious agent can help guide public
health decision making. A traditional approach to forecasting fits a
computational model to structured data and generates a predictive distribution.
However, human judgment has access to the same data as computational models
plus experience, intuition, and subjective data. We propose a chimeric ensemble
-- a combination of computational and human judgment forecasts -- as a novel
approach to predicting the trajectory of an infectious agent. Each month from
January, 2021 to June, 2021 we asked two generalist crowds, using the same
criteria as the COVID-19 Forecast Hub, to submit a predictive distribution over
incident cases and deaths at the US national level either two or three weeks
into the future and combined these human judgment forecasts with forecasts from
computational models submitted to the COVID-19 Forecasthub into a chimeric
ensemble. We find a chimeric ensemble compared to an ensemble including only
computational models improves predictions of incident cases and shows similar
performance for predictions of incident deaths. A chimeric ensemble is a
flexible, supportive public health tool and shows promising results for
predictions of the spread of an infectious agent.


title:
Generating Quality Grasp Rectangle using Pix2Pix GAN for Intelligent
  Robot Grasping

abstract:
  Intelligent robot grasping is a very challenging task due to its inherent
complexity and non availability of sufficient labelled data. Since making
suitable labelled data available for effective training for any deep learning
based model including deep reinforcement learning is so crucial for successful
grasp learning, in this paper we propose to solve the problem of generating
grasping Poses/Rectangles using a Pix2Pix Generative Adversarial Network
(Pix2Pix GAN), which takes an image of an object as input and produces the
grasping rectangle tagged with the object as output. Here, we have proposed an
end-to-end grasping rectangle generating methodology and embedding it to an
appropriate place of an object to be grasped. We have developed two modules to
obtain an optimal grasping rectangle. With the help of the first module, the
pose (position and orientation) of the generated grasping rectangle is
extracted from the output of Pix2Pix GAN, and then the extracted grasp pose is
translated to the centroid of the object, since here we hypothesize that like
the human way of grasping of regular shaped objects, the center of
mass/centroids are the best places for stable grasping. For other irregular
shaped objects, we allow the generated grasping rectangles as it is to be fed
to the robot for grasp execution. The accuracy has significantly improved for
generating the grasping rectangle with limited number of Cornell Grasping
Dataset augmented by our proposed approach to the extent of 87.79%. Experiments
show that our proposed generative model based approach gives the promising
results in terms of executing successful grasps for seen as well as unseen
objects.


title: Odd Covers of Graphs

abstract:
  Given a finite simple graph $G$, an odd cover of $G$ is a collection of
complete bipartite graphs, or bicliques, in which each edge of $G$ appears in
an odd number of bicliques and each non-edge of $G$ appears in an even number
of bicliques. We denote the minimum cardinality of an odd cover of $G$ by
$b_2(G)$ and prove that $b_2(G)$ is bounded below by half of the rank over
$\mathbb{F}_2$ of the adjacency matrix of $G$. We show that this lower bound is
tight in the case when $G$ is a bipartite graph and almost tight when $G$ is an
odd cycle. However, we also present an infinite family of graphs which shows
that this lower bound can be arbitrarily far away from $b_2(G)$.
  Babai and Frankl (1992) proposed the "odd cover problem," which in our
language is equivalent to determining $b_2(K_n)$. Radhakrishnan, Sen, and
Vishwanathan (2000) determined $b_2(K_n)$ for an infinite but density zero
subset of positive integers $n$. In this paper, we determine $b_2(K_n)$ for a
density $3/8$ subset of the positive integers.


title:
Direct reconstruction of the band structure of a 1D optical lattice with
  thermal atoms

abstract:
  We report on a simple method to reconstruct the band structure of a 1D
optical lattice using a thermal cloud with a momentum spread of about
two-photon recoils. We image the momentum distribution of a thermal cloud
exposed to a standing wave potential using time-of-flight absorption images and
observe unique features. With the support of numerical calculations, we explain
their appearance and show how they can be used to reconstruct the full
band-structure directly. While this can serve as a precise lattice depth
calibration tool, we additionally propose a method to estimate the lattice
depth in a single-shot manner.


title: Mean Curvature Rigidity and Non-rigidity Results on Spherical Caps

abstract:
  We prove that a hemisphere in the Euclidean space $R^{n+1}$, viewed as the
graph of a function, admits no smooth perturbations as graphs with mean
curvature $H\ge 1$ whose boundary equator is fixed up to $C^2$. This is an
extension of the \emph{Mean Curvature Rigidity} phenomenon discovered by Gromov
and Souam on non-compact totally umbilic hypersurfaces in space forms. The
proof uses a Tangency Principle. On the other hand, we show that there exist
nontrivial smooth perturbations with $H\ge 1$ on a great spherical cap whose
boundary is fixed up to $C^2$. Similar results hold true for perturbations
decreasing $H$, and for the $r$ mean curvature function $H_r$. This contrast
between rigidity and non-rigidity is even true in the 1-dimensional case for
circles and for discrete objects (polygons inscribed in a circle).


title:
A new flow-kinematics-based model for time-dependent effective
  dispersion in mixing-limited reactions

abstract:
  A new upscaling procedure that provides 1D representations of 2D
mixing-limited reactive transport systems is developed and applied. A key
complication with upscaled models in this setting is that the procedure must
differentiate between interface spreading, driven by the spatially variable
velocity field, and mixing, in which components contact one another and react.
Our model captures the enhanced mixing caused by spreading through use of a
time-dependent effective dispersion term. The early-time behavior of this
dispersion is driven by flow kinematics, while at late times it reaches a
Taylor-dispersion-like limit. The early-time behavior is modeled here using a
very fast (purely advective) particle tracking procedure, while late-time
effects are estimated from scaling arguments. The only free parameter in the
model is the asymptotic effective dispersion. This quantity is determined for a
few cases by calibrating 1D results to reference 2D results. For most cases, it
is estimated using a fit involving a dimensionless grouping of system
variables. Numerical results for bimolecular reaction systems are generated
using a pseudo-spectral approach capable of resolving fronts at high Peclet
numbers. Results are presented for two different types of 2D velocity fields
over a wide range of parameters. The upscaled model is shown to provide highly
accurate results for conversion factor, along with reasonable approximations of
the spatial distribution of reaction occurrence. The model is also shown to be
valid for non-reacting systems, and results for such cases can be used in the
calibration step to achieve computational savings.


title: Efficient Continual Learning Ensembles in Neural Network Subspaces

abstract:
  A growing body of research in continual learning focuses on the catastrophic
forgetting problem. While many attempts have been made to alleviate this
problem, the majority of the methods assume a single model in the continual
learning setup. In this work, we question this assumption and show that
employing ensemble models can be a simple yet effective method to improve
continual performance. However, the training and inference cost of ensembles
can increase linearly with the number of models. Motivated by this limitation,
we leverage the recent advances in the deep learning optimization literature,
such as mode connectivity and neural network subspaces, to derive a new method
that is both computationally advantageous and can outperform the
state-of-the-art continual learning algorithms.


title:
Dissecting graph measure performance for node clustering in LFR
  parameter space

abstract:
  Graph measures that express closeness or distance between nodes can be
employed for graph nodes clustering using metric clustering algorithms. There
are numerous measures applicable to this task, and which one performs better is
an open question. We study the performance of 25 graph measures on generated
graphs with different parameters. While usually measure comparisons are limited
to general measure ranking on a particular dataset, we aim to explore the
performance of various measures depending on graph features. Using an LFR graph
generator, we create a dataset of 11780 graphs covering the whole LFR parameter
space. For each graph, we assess the quality of clustering with k-means
algorithm for each considered measure. Based on this, we determine the best
measure for each area of the parameter space. We find that the parameter space
consists of distinct zones where one particular measure is the best. We analyze
the geometry of the resulting zones and describe it with simple criteria. Given
particular graph parameters, this allows us to recommend a particular measure
to use for clustering.


title: On projective evolutes of polygons

abstract:
  The evolute of a curve is the envelope of its normals. In this note we
consider a projectively natural discrete analog of this construction: we define
projective perpendicular bisectors of the sides of a polygon in the projective
plane, and study the map that sends a polygon to the new polygon formed by the
projective perpendicular bisectors of its sides. We consider this map acting on
the moduli space of projective polygons. We analyze the case of pentagons; the
moduli space is 2-dimensional in this case. The second iteration of the map has
one integral whose level curves are cubic curves, and the transformation on
these level curves is conjugated to the map $x\mapsto -4x$ mod 1. We also
present the results of an experimental study in the case of hexagons.


title: Finiteness properties of the algebra of invariants

abstract:
  Given a base field $\Bbbk$ of characteristic zero, an arbitrary
$\Bbbk$-algebra $R$, a semisimple linear algebraic $\Bbbk$-group $G$, and a
$G$-$R$ module $M$ free of finite rank, we prove that the algebra of invariants
$(S_{R}^{\bullet}((M^{\vee})^{\oplus m}))^{G_{R}}$ is a finitely presented
$R$-algebra. In case $R$ is noetherian, we recover Seshadri's generalization of
Nagata's finiteness theorem. Furthermore, if $T$ is an $R$-algebra and $N$ a
$T$-module, we prove that the algebra of invariants
$(S_{T}^{\bullet}(M^{\vee}\otimes_{T}N))^{G_{T}}$ is also finitely presented as
long as $N$ is. If $N$ is an arbitrary $T$-module and $G$ is classical, we show
that the algebra of invariants is generated by elements of bounded degree whose
ideal of relations is generated by elements with bounded degree as well. The
bounds depend on $G$ but not on $N$. Additionally, we prove a Chevalley
compactification type theorem for the algebra of invariants.


title:
Practical Interference Exploitation Precoding without Symbol-by-Symbol
  Optimization: A Block-Level Approach

abstract:
  In this paper, we propose a constructive interference (CI)-based block-level
precoding (CI-BLP) approach for the downlink of a multi-user multiple-input
single-output (MU-MISO) communication system. Contrary to existing CI precoding
approaches which have to be designed on a symbol-by-symbol level, here a
constant precoding matrix is applied to a block of symbol slots within a
channel coherence interval, thus significantly reducing the computational costs
over traditional CI-based symbol-level precoding (CI-SLP) as the CI-BLP
optimization problem only needs to be solved once per block. For both PSK and
QAM modulation, we formulate an optimization problem to maximize the minimum CI
effect over the block subject to a block- rather than symbol-level power
budget. We mathematically derive the optimal precoding matrix for CI-BLP as a
function of the Lagrange multipliers in closed form. By formulating the dual
problem, the original CI-BLP optimization problem is further shown to be
equivalent to a quadratic programming (QP) optimization. Numerical results
validate our derivations, and show that the proposed CI-BLP scheme achieves
improved performance over the traditional CI-SLP method, thanks to the relaxed
power constraint over the considered block of symbol slots.


title:
Behind Closed Doors: Process-Level Rootkit Attacks in Cyber-Physical
  Microgrid Systems

abstract:
  Embedded controllers, sensors, actuators, advanced metering infrastructure,
etc. are cornerstone components of cyber-physical energy systems such as
microgrids (MGs). Harnessing their monitoring and control functionalities,
sophisticated schemes enhancing MG stability can be deployed. However, the
deployment of `smart' assets increases the threat surface. Power systems
possess mechanisms capable of detecting abnormal operations. Furthermore, the
lack of sophistication in attack strategies can render them detectable since
they blindly violate power system semantics. On the other hand, the recent
increase of process-aware rootkits that can attain persistence and compromise
operations in undetectable ways requires special attention. In this work, we
investigate the steps followed by stealthy rootkits at the process level of
control systems pre- and post-compromise. We investigate the rootkits'
precompromise stage involving the deployment to multiple system locations and
aggregation of system-specific information to build a neural network-based
virtual data-driven model (VDDM) of the system. Then, during the weaponization
phase, we demonstrate how the VDDM measurement predictions are paramount, first
to orchestrate crippling attacks from multiple system standpoints, maximizing
the impact, and second, impede detection blinding system operator situational
awareness.


title: Surgery sequences and self-similarity of the Mandelbrot set

abstract:
  We introduce an analog in the context of rational maps of the idea of
hyperbolic Dehn surgery from the theory of Kleinian groups. A surgery sequence
is a sequence of postcritically finite maps limiting (in a precise manner) to a
postcritically finite map. As an application of this idea we give an elementary
proof of Tan Lei's theorem on the asymptotic self-similarity of Julia Sets and
the Mandelbrot Set at Misiurewicz points.


title:
Topology change with Morse functions: progress on the Borde-Sorkin
  conjecture

abstract:
  Topology change is considered to be a necessary feature of quantum gravity by
some authors, and impossible by others. One of the main arguments against it is
that spacetimes with changing spatial topology have bad causal properties.
Borde and Sorkin proposed a way to avoid this dilemma by considering topology
changing spacetimes constructed from Morse functions, where the metric is
allowed to vanish at isolated points. They conjectured that these Morse
spacetimes are causally continuous (hence quite well behaved), as long as the
index of the Morse points is different from $1$ and $n-1$. In this paper, we
prove a special case of this conjecture. We also argue, heuristically, that the
original conjecture is actually false, and formulate a refined version of it.


title:
Real-time Model Predictive Control and System Identification Using
  Differentiable Physics Simulation

abstract:
  Developing robot controllers in a simulated environment is advantageous but
transferring the controllers to the target environment presents challenges,
often referred to as the "sim-to-real gap". We present a method for continuous
improvement of modeling and control after deploying the robot to a
dynamically-changing target environment. We develop a differentiable physics
simulation framework that performs online system identification and optimal
control simultaneously, using the incoming observations from the target
environment in real time. To ensure robust system identification against noisy
observations, we devise an algorithm to assess the confidence of our estimated
parameters, using numerical analysis of the dynamic equations. To ensure
real-time optimal control, we adaptively schedule the optimization window in
the future so that the optimized actions can be replenished faster than they
are consumed, while staying as up-to-date with new sensor information as
possible. The constant re-planning based on a constantly improved model allows
the robot to swiftly adapt to the changing environment and utilize real-world
data in the most sample-efficient way. Thanks to a fast differentiable physics
simulator, the optimization for both system identification and control can be
solved efficiently for robots operating in real time. We demonstrate our method
on a set of examples in simulation and show that our results are favorable
compared to baseline methods.


title:
Active diffusion of self-propelled particles in flexible polymer
  networks

abstract:
  Biopolymer networks having a meshwork topology, e.g., extracellular matrix
and mucus gels, are ubiquitous. It is an open question to understand how
self-propelled agents such as Janus colloidal particles diffuse through such a
biopolymer network. Here, we computationally explore this issue in-depth by
explicitly modeling three-dimensional biopolymer networks and performing
Langevin dynamics simulations of active diffusion of the self-propelled tracers
therein. We show that the active tracer performs distinct diffusion dynamics
depending on the mesh-to-particle size and P\'eclet number (Pe). When the
particle is smaller than the mesh size, it moves as if in free space with a
decreased mobility depending on the polymer occupation density and Pe. However,
when the particle size is increased to be comparable to the mesh size, the
active particles explore the polymer network using the trapped-and-hopping
mechanism. We study the trapped time distribution, flight length distribution,
the mean-squared displacement, and the long-time diffusivity at varying Pe. If
the particle is larger than the mesh, it captures the collective viscoelastic
dynamics from the polymer network at short times and the simple diffusion of
the total system at large times. Finally, we discuss the scaling behavior of
the long-time diffusivity with Pe, where we find a range of Pe that yields a
nontrivial power law. The latter turns out to arise from a large fluctuation of
trapped, activated tracers in conjugation with responsive polymer networks.


title: Automated Reasoning in Non-classical Logics in the TPTP World

abstract:
  Non-classical logics are used in a wide spectrum of disciplines, including
artificial intelligence, computer science, mathematics, and philosophy. The
de-facto standard infrastructure for automated theorem proving, the TPTP World,
currently supports only classical logics. Similar standards for non-classical
logic reasoning do not exist (yet). This hampers practical development of
reasoning systems, and limits their interoperability and application. This
paper describes the latest extension of the TPTP World, which provides
languages and infrastructure for reasoning in non-classical logics. The
extensions integrate seamlessly with the existing TPTP World.


title:
BCJ relations in ${AdS}_5 \times S^3$ and the double-trace spectrum of
  super gluons

abstract:
  We revisit the four-point function of super gluons in $AdS_5 \times S^3$ in
the spirit of the large $p$ formalism and show how the integrand of a
generalised Mellin transform satisfies various non-trivial properties such as
$U(1)$ decoupling identity, BCJ relations and colour-kinematic duality, in a
way that directly mirrors the analogous relations in flat space. We unmix the
spectrum of double-trace operators at large $N$ and find all anomalous
dimensions at leading order. The anomalous dimensions follow a very simple
pattern, resembling those of other theories with hidden conformal symmetries.


title:
Extensions of two classical Poisson limit laws to non-stationary
  independent data

abstract:
  In earlier stages in the introduction to asymptotic methods in probability
theory, the weak convergence of sequences $(X_n)_{n\geq 1}$ of Binomial of
random variables (\textit{rv}'s) to a Poisson law is classical and easy-to
prove. A version of such a result concerning sequences $(Y_n)_{n\geq 1}$ of
negative binomial \textit{rv}'s also exists. In both cases, $X_n$ and $Y_n-n$
are by-row sums $S_n[X]$ and $S_n[Y]$ of arrays of Bernoulli \textit{rv}'s and
corrected geometric \textit{rv}'s respectively. When considered in the general
frame of asymptotic theorems of by-row sums of \textit{rv}'s of arrays, these
two simple results in the independent and identically distributed scheme can be
generalized to non-stationary data and beyond to non-stationary and dependent
data. Further generalizations give interesting results that would not be found
by direct methods. In this paper, we focus on generalizations to the
non-stationary independent data. Extensions to dependent data will addressed
later.


title: Singularities and diffeomorphisms

abstract:
  Comparing and recognizing metrics can be extraordinarily difficult because of
the group of diffeomorphisms. Two metrics, that could even be the same, could
look completely different in different coordinates. This is the gauge problem.
The general gauge problem is extremely subtle, especially for non-compact
spaces. Often it can be avoided if one uses some additional structure of the
particular situation. However, in many problems there is no additional
structure. Instead we solve the gauge problem directly in great generality. The
techniques and ideas apply to many problems. We use them to solve a well-known
open problem in Ricci flow. We solve the gauge problem by solving a nonlinear
system of PDEs. The PDE produces a diffeomorphism that fixes an appropriate
gauge in the spirit of the slice theorem for group actions. We then show
optimal bounds for the displacement function of the diffeomorphism.


title: Signatures of Light Massive Relics on nonlinear structure formation

abstract:
  Cosmologies with Light Massive Relics (LiMRs) as a subdominant component of
the dark sector are well-motivated from a particle physics perspective, and can
also have implications for the $\sigma_8$ tension between early and late time
probes of matter clustering. The effects of LiMRs on the Cosmic Microwave
Background (CMB) and structure formation on large (linear) scales have been
investigated extensively. In this paper, we initiate a systematic study of the
effects of LiMRs on smaller, nonlinear scales using cosmological $N$-body
simulations; focusing on quantities relevant for photometric galaxy surveys.
For most of our study, we use a particular model of nonthermal LiMRs but the
methods developed easily generalize to a large class of models of LiMRs -- we
explicitly demonstrate this by considering the Dodelson-Widrow form of the
velocity distribution. We find that, in general, the effects of LiMR on small
scales are distinct from those of a $\Lambda$CDM universe, even when the value
of $\sigma_8$ is matched between the models. We show that weak lensing
measurements around massive clusters, between $\sim 0.1 h^{-1}$Mpc and $\sim 10
h^{-1}$Mpc, should have sufficient signal-to-noise in future surveys to
distinguish between $\Lambda$CDM and LiMR models that are tuned to fit both CMB
data and large (linear) scale structure data at late times. Furthermore, we
find that different LiMR cosmologies which are indistinguishable by
conventional linear probes can be distinguished by these probes if their
velocity distributions are sufficiently different. LiMR models can, therefore,
be best tested and constrained by jointly analyzing data from CMB and late-time
structure formation on both large \textit{and} small scales.


title: RFTacho: Non-intrusive RF monitoring of rotating machines

abstract:
  Measuring rotation speed is essential to many engineering applications; it
elicits faults undetectable by vibration monitoring alone and enhances the
vibration signal analysis of rotating machines. Optical, magnetic or mechanical
Tachometers are currently state-of-art. Their limitations are they require
line-of-sight, direct access to the rotating object. This paper proposes
RFTacho, a rotation speed measurement \emph{system} that leverages novel
hardware and signal processing algorithms to produce highly accurate readings
conveniently. RFTacho uses RF Orbital Angular Momentum (OAM) waves to measure
rotation speed of multiple machines simultaneously with no requirements from
the machine's properties. OAM antennas allow it to operate in high-scattering
environments, commonly found in industries, as they are resilient to
de-polarization compared to linearly polarized antennas. RFTacho achieves this
by using two novel signal processing algorithms to extract the rotation speed
of several rotating objects simultaneously amidst noise arising from
high-scattering environments, non-line-of-sight scenarios and dynamic
environmental conditions with a resolution of $1 rpm$. We test RFTacho on
several real-world machines like fans, motors, air conditioners. Results show
that RFTacho has avg. error of $<0.5\%$ compared to ground truth. We
demonstrate RFTacho's simultaneous multiple-object measurement capability that
other tachometers do not have. Initial experiments show that RFTacho can
measure speeds as high as 7000 rpm (theoretically 60000 rpm) with high
resiliency at different coverage distances and orientation angles, requiring
only 150 mW transmit power while operating in the 5 GHz license-exempt band.
RFTacho is the first RF-based sensing system that combines OAM waves and novel
processing approaches to measure the rotation speed of multiple machines
simultaneously in a non-intrusive way.


title:
Bimodule connections for relative line modules over the irreducible
  quantum flag manifolds

abstract:
  It was recently shown (by the second author and D\'iaz Garc\'ia, Krutov,
\linebreak Somberg, and Strung) that every relative line module over an
irreducible quantum flag manifold $\mathcal{O}_q(G/L_S)$ admits a unique
$\mathcal{O}_q(G)$-covariant connection with respect to the Heckenberger--Kolb
differential calculus $\Omega^1_q(G/L_S)$. In this paper we show that these
connections are bimodule connections with an invertible associated bimodule
map. This is proved by applying general results of Beggs and Majid, on
principal connections for quantum principal bundles, to the quantum principal
bundle presentation of the Heckenberger--Kolb calculi recently constructed by
the authors and D\'iaz Garc\'ia. Explicit presentations of the associated
bimodule maps are given first in terms of generalised quantum determinants,
then in terms of the FRT presentation of $\mathcal{O}_q(G)$, and finally in
terms of Takeuchi's categorical equivalence for relative Hopf modules.


title: Multimode soliton collisions in graded-index optical fibers

abstract:
  In this work, we unveil the unique complex dynamics of multimode soliton
interactions in graded-index optical fibers through simulations and
experiments. By generating two multimode solitons from the fission of an input
femtosecond pulse, we examine the evolution of their Raman-induced red-shift
when the input pulse energy grows larger. Remarkably, we find that the output
red-shift of the trailing multimode soliton may be reduced, so that it
accelerates until it collides with the leading multimode soliton. As a result
of the inelastic collision, a significant energy transfer occurs between the
two multimode solitons: the trailing soliton captures energy from the leading
soliton, which ultimately enhances its red-shift, thus increasing temporal
separation between the two multimode solitons.


title:
Sparsity Winning Twice: Better Robust Generalization from More Efficient
  Training

abstract:
  Recent studies demonstrate that deep networks, even robustified by the
state-of-the-art adversarial training (AT), still suffer from large robust
generalization gaps, in addition to the much more expensive training costs than
standard training. In this paper, we investigate this intriguing problem from a
new perspective, i.e., injecting appropriate forms of sparsity during
adversarial training. We introduce two alternatives for sparse adversarial
training: (i) static sparsity, by leveraging recent results from the lottery
ticket hypothesis to identify critical sparse subnetworks arising from the
early training; (ii) dynamic sparsity, by allowing the sparse subnetwork to
adaptively adjust its connectivity pattern (while sticking to the same sparsity
ratio) throughout training. We find both static and dynamic sparse methods to
yield win-win: substantially shrinking the robust generalization gap and
alleviating the robust overfitting, meanwhile significantly saving training and
inference FLOPs. Extensive experiments validate our proposals with multiple
network architectures on diverse datasets, including CIFAR-10/100 and
Tiny-ImageNet. For example, our methods reduce robust generalization gap and
overfitting by 34.44% and 4.02%, with comparable robust/standard accuracy
boosts and 87.83%/87.82% training/inference FLOPs savings on CIFAR-100 with
ResNet-18. Besides, our approaches can be organically combined with existing
regularizers, establishing new state-of-the-art results in AT. Codes are
available in https://github.com/VITA-Group/Sparsity-Win-Robust-Generalization.


title: On the Dynamics of Solid, Liquid and Digital Gold Futures

abstract:
  This paper examines the determinants of the volatility of futures prices and
basis for three commodities: gold, oil and bitcoin -- often dubbed solid,
liquid and digital gold -- by using contract-by-contract analysis which has
been previously applied to crude oil futures volatility investigations. By
extracting the spot and futures daily prices as well as the maturity, trading
volume and open interest data for the three assets from 18th December 2017 to
30th November 2021, we find a positive and significant role for trading volume
and a possible negative influence of open interest, when significant, in
shaping the volatility in all three assets, supporting earlier findings in the
context of oil futures. Additionally, we find maturity has a relatively
positive significance for bitcoin and oil futures price volatility.
Furthermore, our analysis demonstrates that maturity affects the basis of
bitcoin and gold positively -- confirming the general theory that the basis
converges to zero as maturity nears for bitcoin and gold -- while oil is
affected in both directions.


title: Maximally Chaotic Dynamical Systems and Fundamental Interactions

abstract:
  We give a general review on the application of Ergodic theory to the
investigation of dynamics of the Yang-Mills gauge fields and of the
gravitational systems, as well as its application in the Monte Carlo method and
fluid dynamics. In ergodic theory the maximally chaotic dynamical systems
(MCDS) can be defined as dynamical systems that have nonzero Kolmogorov
entropy. The hyperbolic dynamical systems that fulfil the Anosov C-condition
belong to the MCDS insofar as they have exponential instability of their phase
trajectories and positive Kolmogorov entropy. It follows that the C-condition
defines a rich class of MCDS that span over an open set in the space of all
dynamical systems. The large class of Anosov-Kolmogorov MCDS is realised on
Riemannian manifolds of negative sectional curvatures and on high-dimensional
tori. The interest in MCDS is rooted in the attempts to understand the
relaxation phenomena, the foundations of the statistical mechanics, the
appearance of turbulence in fluid dynamics, the non-linear dynamics of
Yang-Mills field and gravitating N-body systems as well as black hole
thermodynamics. Our aim is to investigate classical- and quantum-mechanical
properties of MCDS and their role in the theory of fundamental interactions.

categories: hep-th astro-ph.GA math-ph math.MP nlin.CD


title: Evolution and Mutations of Beta 2 Microglobulin

abstract:
  Here we examine the evolution of beta-2 microglobulin in terms of its
hydropathic shapes, a theoretical construct that has revealed important trends.
The dynamics of many proteins are largely driven by interactions between the
protein itself and the thin water film that covers it. \b{eta}2m constitutes
the basic building unit of the immunoglobulin superfamily; the evolution of its
amino acid sequences from chickens to mice to humans provides new information
about its multiple functions. Our hydrodynamic method involves concepts of
topological shape evolution towards a critical point for optimized functions.
The results are in excellent agreement with experiment for the details of the
mouse-human evolution, as well as both the dangerous natural amyloid
aggregation mutation D76N, and six other DN test mutations.


title: Personalized Federated Learning with Exact Stochastic Gradient Descent

abstract:
  In Federated Learning (FL), datasets across clients tend to be heterogeneous
or personalized, and this poses challenges to the convergence of standard FL
schemes that do not account for personalization. To address this, we present a
new approach for personalized FL that achieves exact stochastic gradient
descent (SGD) minimization. We start from the FedPer (Arivazhagan et al., 2019)
neural network (NN) architecture for personalization, whereby the NN has two
types of layers: the first ones are the common layers across clients, while the
few final ones are client-specific and are needed for personalization. We
propose a novel SGD-type scheme where, at each optimization round, randomly
selected clients perform gradient-descent updates over their client-specific
weights towards optimizing the loss function on their own datasets, without
updating the common weights. At the final update, each client computes the
joint gradient over both client-specific and common weights and returns the
gradient of common parameters to the server. This allows to perform an exact
and unbiased SGD step over the full set of parameters in a distributed manner,
i.e. the updates of the personalized parameters are performed by the clients
and those of the common ones by the server. Our method is superior to FedAvg
and FedPer baselines in multi-class classification benchmarks such as Omniglot,
CIFAR-10, MNIST, Fashion-MNIST, and EMNIST and has much lower computational
complexity per round.


title:
Realistic non-Gaussian operations scheme in parity detection based
  Mach-Zehnder quantum interferometry

abstract:
  We theoretically analyze phase sensitivity using parity detection based Mach
Zehnder interferometer (MZI) with the input states generated by performing
non-Gaussian operations, viz., photon subtraction, photon addition, and photon
catalysis on a two-mode squeezed vacuum (TMSV) state. Since these non-Gaussian
operations are probabilistic, it is of utmost importance to take the success
probability into account. To this end, we consider the realistic model of
photon subtraction, addition, and catalysis and derive a single expression of
the Wigner function for photon subtracted, added, and catalyzed TMSV state. The
Wigner function is used to evaluate the lower bound on the phase sensitivity
via quantum Cramer-Rao bound and parity detection based phase sensitivity in
MZI. We identify the ranges of squeezing and transmissivity parameters where
the non-Gaussian states provide better phase sensitivity than the TMSV state.
On qualitatively taking the success probability into account, it turns out that
the photon addition is the most advantageous non-Gaussian operation. We hope
that the generalized Wigner function derived in this work will be useful in
various quantum information protocols and state characterization.


title:
A Novel Framework for Brain Tumor Detection Based on Convolutional
  Variational Generative Models

abstract:
  Brain tumor detection can make the difference between life and death.
Recently, deep learning-based brain tumor detection techniques have gained
attention due to their higher performance. However, obtaining the expected
performance of such deep learning-based systems requires large amounts of
classified images to train the deep models. Obtaining such data is usually
boring, time-consuming, and can easily be exposed to human mistakes which
hinder the utilization of such deep learning approaches. This paper introduces
a novel framework for brain tumor detection and classification. The basic idea
is to generate a large synthetic MRI images dataset that reflects the typical
pattern of the brain MRI images from a small class-unbalanced collected
dataset. The resulted dataset is then used for training a deep model for
detection and classification. Specifically, we employ two types of deep models.
The first model is a generative model to capture the distribution of the
important features in a set of small class-unbalanced brain MRI images. Then by
using this distribution, the generative model can synthesize any number of
brain MRI images for each class. Hence, the system can automatically convert a
small unbalanced dataset to a larger balanced one. The second model is the
classifier that is trained using the large balanced dataset to detect brain
tumors in MRI images. The proposed framework acquires an overall detection
accuracy of 96.88% which highlights the promise of the proposed framework as an
accurate low-overhead brain tumor detection system.


title:
Matter Effect of Sterile Neutrino in Light of Renormalization-Group
  Equations

abstract:
  The renormalization-group equation (RGE) approach to neutrino matter effect
is further developed in this work. We derive a complete set of differential
equations in presence of a light sterile neutrino. The evolution of mixing
elements is obtained by numerically solving these differential equations. We
calculate terrestrial matter effect in long-baseline (LBL) experiments, taking
NOvA, T2K and DUNE as examples. In both three-flavor and four-flavor
frameworks, electron-neutrino survival probabilities as well as the day-night
asymmetry of solar neutrino are also evaluated as a further examination of the
RGE approach.


title: Cross-Task Knowledge Distillation in Multi-Task Recommendation

abstract:
  Multi-task learning (MTL) has been widely used in recommender systems,
wherein predicting each type of user feedback on items (e.g, click, purchase)
are treated as individual tasks and jointly trained with a unified model. Our
key observation is that the prediction results of each task may contain
task-specific knowledge about user's fine-grained preference towards items.
While such knowledge could be transferred to benefit other tasks, it is being
overlooked under the current MTL paradigm. This paper, instead, proposes a
Cross-Task Knowledge Distillation framework that attempts to leverage
prediction results of one task as supervised signals to teach another task.
However, integrating MTL and KD in a proper manner is non-trivial due to
several challenges including task conflicts, inconsistent magnitude and
requirement of synchronous optimization. As countermeasures, we 1) introduce
auxiliary tasks with quadruplet loss functions to capture cross-task
fine-grained ranking information and avoid task conflicts, 2) design a
calibrated distillation approach to align and distill knowledge from auxiliary
tasks, and 3) propose a novel error correction mechanism to enable and
facilitate synchronous training of teacher and student models. Comprehensive
experiments are conducted to verify the effectiveness of our framework in
real-world datasets.


title:
Normalized Volumes of Type-PQ Adjacency Polytopes for Certain Classes of
  Graphs

abstract:
  The type-PQ adjacency polytope associated to a simple graph is a
$0/1$-polytope containing valuable information about an underlying power
network. Chen and the first author have recently demonstrated that, when the
underlying graph $G$ is connected, the normalized volumes of the adjacency
polytopes can be computed by counting sequences of nonnegative integers
satisfying restrictions determined by $G$. This article builds upon their work,
namely by showing that one of their main results -- the so-called "triangle
recurrence" -- applies in a more general setting. Formulas for the normalized
volumes when $G$ is obtained by deleting a path or a cycle from a complete
graph are also established.


title:
Score Driven Generalized Fitness Model for Sparse and Weighted Temporal
  Networks

abstract:
  While the vast majority of the literature on models for temporal networks
focuses on binary graphs, often one can associate a weight to each link. In
such cases the data are better described by a weighted, or valued, network. An
important well known fact is that real world weighted networks are typically
sparse. We propose a novel time varying parameter model for sparse and weighted
temporal networks as a combination of the fitness model, appropriately
extended, and the score driven framework. We consider a zero augmented
generalized linear model to handle the weights and an observation driven
approach to describe time varying parameters. The result is a flexible approach
where the probability of a link to exist is independent from its expected
weight. This represents a crucial difference with alternative specifications
proposed in the recent literature, with relevant implications for the
flexibility of the model.
  Our approach also accommodates for the dependence of the network dynamics on
external variables. We present a link forecasting analysis to data describing
the overnight exposures in the Euro interbank market and investigate whether
the influence of EONIA rates on the interbank network dynamics has changed over
time.


title: ChemTab: A Physics Guided Chemistry Modeling Framework

abstract:
  Modeling of turbulent combustion system requires modeling the underlying
chemistry and the turbulent flow. Solving both systems simultaneously is
computationally prohibitive. Instead, given the difference in scales at which
the two sub-systems evolve, the two sub-systems are typically (re)solved
separately. Popular approaches such as the Flamelet Generated Manifolds (FGM)
use a two-step strategy where the governing reaction kinetics are pre-computed
and mapped to a low-dimensional manifold, characterized by a few reaction
progress variables (model reduction) and the manifold is then "looked-up"
during the run-time to estimate the high-dimensional system state by the flow
system. While existing works have focused on these two steps independently, we
show that joint learning of the progress variables and the look-up model, can
yield more accurate results. We propose a deep neural network architecture,
called ChemTab, customized for the joint learning task and experimentally
demonstrate its superiority over existing state-of-the-art methods.


title: Non-Deterministic Face Mask Removal Based On 3D Priors

abstract:
  This paper presents a novel image inpainting framework for face mask removal.
Although current methods have demonstrated their impressive ability in
recovering damaged face images, they suffer from two main problems: the
dependence on manually labeled missing regions and the deterministic result
corresponding to each input. The proposed approach tackles these problems by
integrating a multi-task 3D face reconstruction module with a face inpainting
module. Given a masked face image, the former predicts a 3DMM-based
reconstructed face together with a binary occlusion map, providing dense
geometrical and textural priors that greatly facilitate the inpainting task of
the latter. By gradually controlling the 3D shape parameters, our method
generates high-quality dynamic inpainting results with different expressions
and mouth movements. Qualitative and quantitative experiments verify the
effectiveness of the proposed method.


title:
Multi-objective optimization: basic approaches and moving beyond them
  through flexible skyline queries

abstract:
  The area of scientific research that deals with the simultaneous optimization
of several (possibly conflicting) criteria is named multi-objective
optimization. The ability to efficiently filter and extract interesting data
out of large datasets is one of the key tasks in modern database systems. This
paper provides a general overview of the most common approaches employed to
handle the problem in the field of databases, and describes a novel framework
named flexible skylines. After analyzing the main differences between single
and multi-optimization problems, I will discuss the three main basic approaches
used to handle multi-optimization problems: lexicographic approach, top-k
queries and skylines. Each methodology will be discussed, analyzing the pros,
the range of applicability and the main issues, which motivate the need to
introduce the flexible skylines innovative framework. A review of this approach
will show its superiority with respect to the basic approaches, as well as the
capability to overcome the majority of their drawbacks.


title: The real equiangular tight frames obtained from rank $3$ graphs

abstract:
  We present all nontrivial real equiangular tight frames
$\{\varphi_m\}_{m=1}^M$ in $\mathbb{R}^N$ obtained as spherical embeddings of
primitive rank $3$ graphs on $M$ vertices, and those such that one of their
associated $M$ strongly regular graphs on $M-1$ vertices is a primitive rank
$3$ graph.


title: Cooperative Artificial Intelligence

abstract:
  In the future, artificial learning agents are likely to become increasingly
widespread in our society. They will interact with both other learning agents
and humans in a variety of complex settings including social dilemmas. We argue
that there is a need for research on the intersection between game theory and
artificial intelligence, with the goal of achieving cooperative artificial
intelligence that can navigate social dilemmas well. We consider the problem of
how an external agent can promote cooperation between artificial learners by
distributing additional rewards and punishments based on observing the actions
of the learners. We propose a rule for automatically learning how to create the
right incentives by considering the anticipated parameter updates of each
agent. Using this learning rule leads to cooperation with high social welfare
in matrix games in which the agents would otherwise learn to defect with high
probability. We show that the resulting cooperative outcome is stable in
certain games even if the planning agent is turned off after a given number of
episodes, while other games require ongoing intervention to maintain mutual
cooperation. Finally, we reflect on what the goals of multi-agent reinforcement
learning should be in the first place, and discuss the necessary building
blocks towards the goal of building cooperative AI.


title: Isometry groups of skewed $\Gamma$-complexes

abstract:
  Let $A_\Gamma$ be a right-angled Artin group. Charney, Vogtmann and the
author constructed an outer space for $\text{Out}(A_\Gamma)$ generalizing both
$CV_n$ for $\text{Out}(F_n)$ and the symmetric space
$\text{SL}_n(\mathbb{R})/\text{SO}_n(\mathbb{R})$ for
$\text{GL}_n(\mathbb{Z})$. Points in this space are equivalence classes of
pairs $(X,\rho)$ where $\rho\colon X\rightarrow \mathbb{S}_\Gamma$ is a
homotopy equivalence from $X$ to the Salvetti complex $\mathbb{S}_\Gamma$ and
$X$ is a locally CAT(0) space called a skewed $\Gamma$-complex. In this note we
show that any isometry of a skewed $\Gamma$-complex which is homotopic to the
identity lies in the identity component of $\text{Isom}(X)$. As a corollary, we
prove that the group of path components of $\text{Isom}(X)$ is finite and
injects into $\text{Out}(A_\Gamma)$.


title:
How might Driver Licensing and Vehicle Registration evolve if we adopt
  Autonomous Cars and Digital Identification?

abstract:
  It has been said that fewer or greater numbers of driver's licenses may be
issued in future. However, when we extrapolate based on comparisons to other
transport domains that have high levels of automation like aviation, the future
for driver's licenses may not be so up in the air. Introduction of autonomous
systems in aviation frequently leads to pilot retraining, and while modern
commercial jets are generally sufficiently capable to take off, fly to their
destination and land with little in the way of human intervention, pilots are
still required and every pilot must hold a current license with ratings for the
types of plane they command. Contemporary in-service training for commercial
pilots has shifted focus from extended practicing of general flight skills like
navigation and radio use. Pilots now spend many hours each year in simulators
training to building knowledge, experience and thus muscle memory for how to
respond when the airplane's extensive interconnected collection of semi- and
fully-autonomous fly-by-wire systems go awry. While the plane itself is
actually flying for much of the time during most flights today, the pilot's
license still attests to their ability to assume full control and complete the
flight where it becomes necessary. We contend a similar future may exist for
driver training and licensure.


title: Experimental Verification of Illusion Optics with Optical Null Media

abstract:
  We experimentally show the results of the illusion device designed by the
transformation optics (TO) technique. The coordinate transformation maps a zero
thickness in virtual space to a finite region in physical space, yielding
extreme material properties called optical null media (ONM). As a result, an
object placed inside the core media will look larger to an outside observer.
ONM media is realized using metal-dielectric layered structures in cylindrical
coordinates. Also closed-form solutions for the scattering field from a
cylindrical object are given in detail to prove illusion effect analytically.
ONM media is realized using air and iron sheets in radial structure using
effective medium theory. We experimentally fabricate the ONM and show the
scattering effect using the ONM. For achieving illusion validation, a metallic
pipe has been inserted into the center of the core medium made of Plexiglas and
surrounded by ONM.


title: Four classic problems

abstract:
  In this work we survey four classic problems: Borsuk's partition problem,
Tarski's plank problem, the Kneser--Poulsen problem on the monotonicity of the
union of balls under a contraction of their centers, and the Hadwiger--Levi
problem on covering convex bodies by their smaller positively homothetic
copies.


title: R\'esolution du jeu de Juniper Green

abstract:
  In this article, we describe the solution of the Juniper Green game for every
integer, with techniques that are elementary enough to be explained to students
who have mastered the notions of divisors and multiples. A general method
allows us to solve the game for almost every integer, then there are less than
fifty cases left to deal with. Each of these cases can be solved within a few
minutes, with a paper and a pencil.
  --
  Dans cet article, nous d\'ecrivons la r\'esolution du jeu de Juniper Green
pour tout entier, avec des techniques suffisamment \'el\'ementaires pour \^etre
expliqu\'ees \`a des \'el\`eves ma\^itrisant les notions de diviseurs et
multiples. Une m\'ethode g\'en\'erale permet de r\'esoudre le jeu pour presque
tout entier, si bien qu'il reste alors moins de cinquante cas \`a traiter,
chacun pouvant \^etre r\'esolu au prix d'une \'etude sur papier de quelques
minutes.


title:
On the usefulness of lattice approximations for fractional Gaussian
  fields

abstract:
  Fractional Gaussian fields provide a rich class of spatial models and have a
long history of applications in multiple branches of science. However,
estimation and inference for fractional Gaussian fields present significant
challenges. This book chapter investigates the use of the fractional Laplacian
differencing on regular lattices to approximate to continuum fractional
Gaussian fields. Emphasis is given on model based geostatistics and likelihood
based computations. For a certain range of the fractional parameter, we
demonstrate that there is considerable agreement between the continuum models
and their lattice approximations. For that range, the parameter estimates and
inferences about the continuum fractional Gaussian fields can be derived from
the lattice approximations. Interestingly, regular lattice approximations
facilitate fast matrix-free computations and enable anisotropic
representations. We illustrate the usefulness of lattice approximations via
simulation studies and by analyzing sea surface temperature on the Indian
Ocean.


title: Number of $k$-normal elements over a finite field

abstract:
  An element $\alpha \in \mathbb{F}_{q^n}$ is a normal element over
$\mathbb{F}_q$ if the conjugates $\alpha^{q^i}$, $0 \leq i \leq n-1$, are
linearly independent over $\mathbb{F}_q$. Hence a normal basis for
$\mathbb{F}_{q^n}$ over $\mathbb{F}_q$ is of the form $\{\alpha,\alpha^q,
\ldots, \alpha^{q^{n-1}}\}$, where $\alpha \in \mathbb{F}_{q^n}$ is normal over
$\mathbb{F}_q$. In 2013, Huczynska, Mullen, Panario and Thomson introduce the
concept of k-normal elements, as a generalization of the notion of normal
elements. In the last few years, several results have been known about these
numbers. In this paper, we give an explicit combinatorial formula for the
number of $k$-normal elements in the general case, answering an open problem
proposed by Huczynska et al. (2013).


title: Interacting Contour Stochastic Gradient Langevin Dynamics

abstract:
  We propose an interacting contour stochastic gradient Langevin dynamics
(ICSGLD) sampler, an embarrassingly parallel multiple-chain contour stochastic
gradient Langevin dynamics (CSGLD) sampler with efficient interactions. We show
that ICSGLD can be theoretically more efficient than a single-chain CSGLD with
an equivalent computational budget. We also present a novel random-field
function, which facilitates the estimation of self-adapting parameters in big
data and obtains free mode explorations. Empirically, we compare the proposed
algorithm with popular benchmark methods for posterior sampling. The numerical
results show a great potential of ICSGLD for large-scale uncertainty estimation
tasks.


title: ExAIS: Executable AI Semantics

abstract:
  Neural networks can be regarded as a new programming paradigm, i.e., instead
of building ever-more complex programs through (often informal) logical
reasoning in the programmers' mind, complex 'AI' systems are built by
optimising generic neural network models with big data. In this new paradigm,
AI frameworks such as TensorFlow and PyTorch play a key role, which is as
essential as the compiler for traditional programs. It is known that the lack
of a proper semantics for programming languages (such as C), i.e., a
correctness specification for compilers, has contributed to many problematic
program behaviours and security issues. While it is in general hard to have a
correctness specification for compilers due to the high complexity of
programming languages and their rapid evolution, we have a unique opportunity
to do it right this time for neural networks (which have a limited set of
functions, and most of them have stable semantics). In this work, we report our
effort on providing a correctness specification of neural network frameworks
such as TensorFlow. We specify the semantics of almost all TensorFlow layers in
the logical programming language Prolog. We demonstrate the usefulness of the
semantics through two applications. One is a fuzzing engine for TensorFlow,
which features a strong oracle and a systematic way of generating valid neural
networks. The other is a model validation approach which enables consistent bug
reporting for TensorFlow models.