title:
Robust limit analysis theory for computing worst-case limit loads under
  uncertainties

abstract:
  This work proposes a novel theoretical framework of robust limit analysis
i.e. the computation of limit loads of structures in presence of uncertainties
using limit analysis and robust optimization theories. We first derive generic
robust limit analysis formulations in the case of uncertain material strength
properties. We discuss various ways of modeling uncertain strength properties
and introduce the notion of robust strength criteria. We formulate static and
adjustable robust counterparts of the corresponding uncertain limit analysis
problems. Depending on the chosen strength uncertainty model, we also discuss
tractable reformulations or approximations which can be implemented numerically
using conic programming solvers. Building upon these results, we also derive
robust limit analysis formulations in presence of loading uncertainties.
Finally, various applications illustrate the versatility of the proposed
framework including the derivation of a robust Mohr-Coulomb criterion with
uncertain cohesion and friction angle, the computation of limit load of a
structure subject to the partial loss of some components, or the limit load of
a truss structure under multiple load uncertainties.


title:
Origami in N dimensions: How feed-forward networks manufacture linear
  separability

abstract:
  Neural networks can implement arbitrary functions. But, mechanistically, what
are the tools at their disposal to construct the target? For classification
tasks, the network must transform the data classes into a linearly separable
representation in the final hidden layer. We show that a feed-forward
architecture has one primary tool at hand to achieve this separability:
progressive folding of the data manifold in unoccupied higher dimensions. The
operation of folding provides a useful intuition in low-dimensions that
generalizes to high ones. We argue that an alternative method based on shear,
requiring very deep architectures, plays only a small role in real-world
networks. The folding operation, however, is powerful as long as layers are
wider than the data dimensionality, allowing efficient solutions by providing
access to arbitrary regions in the distribution, such as data points of one
class forming islands within the other classes. We argue that a link exists
between the universal approximation property in ReLU networks and the
fold-and-cut theorem (Demaine et al., 1998) dealing with physical paper
folding. Based on the mechanistic insight, we predict that the progressive
generation of separability is necessarily accompanied by neurons showing mixed
selectivity and bimodal tuning curves. This is validated in a network trained
on the poker hand task, showing the emergence of bimodal tuning curves during
training. We hope that our intuitive picture of the data transformation in deep
networks can help to provide interpretability, and discuss possible
applications to the theory of convolutional networks, loss landscapes, and
generalization.
  TL;DR: Shows that the internal processing of deep networks can be thought of
as literal folding operations on the data distribution in the N-dimensional
activation space. A link to a well-known theorem in origami theory is provided.


title: Algebraically generated groups and their lie algebras

abstract:
  The automorphism group Aut$(X)$ of an affine variety $X$ is an ind-group. Its
Lie algebra is canonically embedded into the Lie algebra Vec$(X)$ of vector
fields on $X$. We study the relations between closed subgroups $G$ of Aut$(X)$
and the corresponding Lie subalgebras of Vec$(X)$. We show that a subgroup $G
\subset $Aut$(X)$ generated by a family of connected algebraic subgroups $G_i$
of Aut$(X)$ is algebraic if and only if the Lie $G_i \subset$Vec$(X)$ generate
a finite dimensional Lie subalgebra of Vec$(X)$. Extending a result by
Cohen-Draisma (2003) we prove that a locally finite Lie algebra $L
\subset$Vec$(X)$ generated by locally nilpotent vector fields is algebraic,
i.e. $L =$Lie $G$ of some algebraic subgroup $G \subset$Aut$(X)$. Along the
same lines we prove that if a subgroup $G \subset$Aut$(X)$ generated by
finitely many unipotent algebraic groups is solvable, then it is a unipotent
algebraic group. Furthermore, we give an example of a free subgroup $F
\subset$Aut$(\mathbb{A}^2)$ generated by two algebraic elements such that the
Zariski closure of $F$ is a free product of two nested commutative closed
unipotent ind-subgroups. To any ind-group $\mathfrak{G}$ one can associate a
canonical ideal $L_{\mathfrak{G}} \subset$ Lie $\mathfrak{G}$ generated by the
Lie algebras of algebraic subgroups of $\mathfrak{G}$, and another one
associated to the set of all algebraic subvarieties of $\mathfrak{G}$. We study
functorial properties of these ideals, ind-subgroups of finite codimension in
$\mathfrak{G}$ and the corresponding Lie subalgebras of Lie $\mathfrak{G}$.


title:
Chiral symmetry breaking and catalytic effect induced by monopole and
  instanton creations in QCD

abstract:
  In QCD, magnetic monopoles are crucial elements for the mechanism of color
confinement, and instantons induce spontaneous chiral symmetry breaking.
However, showing the relations among the monopoles, instantons, and chiral
symmetry breaking is difficult because of the strong interaction in the
low-energy QCD. Therefore, we perform numerical simulations and show the
relations that experiments can detect. First, we add a monopole and an
anti-monopole varying their magnetic charges to the QCD vacua by applying the
monopole creation operator. Second, we estimate the monopole and instanton
effects on observables using the eigenvalues and eigenvectors of the overlap
Dirac operator. Finally, we compare the outcomes with the predictions. In the
previous research, we found that the added monopoles and anti-monopoles create
instantons and anti-instantons. We have discovered the following effects on the
observables by increasing the number density of instantons and anti-instantons.
First, the chiral condensate (negative values) decreases. Second, the masses of
light quarks and mesons become heavy, and the decay constants of light mesons
increase. Third, the decay width of the charged pion becomes wide; therefore,
its lifetime becomes short. We have demonstrated quantitative relations among
these observables and the number density of instantons and anti-instantons.
These results, however, were obtained using the QCD vacuum of one lattice
volume of one lattice spacing. Therefore, this research examines the influences
of finite lattice volume and discretization. Finally, we obtain the
interpolated results at the continuum limit and show the quantitative
relations.


title:
Segmenting Medical Instruments in Minimally Invasive Surgeries using
  AttentionMask

abstract:
  Precisely locating and segmenting medical instruments in images of minimally
invasive surgeries, medical instrument segmentation, is an essential first step
for several tasks in medical image processing. However, image degradations,
small instruments, and the generalization between different surgery types make
medical instrument segmentation challenging. To cope with these challenges, we
adapt the object proposal generation system AttentionMask and propose a
dedicated post-processing to select promising proposals. The results on the
ROBUST-MIS Challenge 2019 show that our adapted AttentionMask system is a
strong foundation for generating state-of-the-art performance. Our evaluation
in an object proposal generation framework shows that our adapted AttentionMask
system is robust to image degradations, generalizes well to unseen types of
surgeries, and copes well with small instruments.


title: Deploying an inter-European quantum network

abstract:
  Around forty years have passed since the first pioneering works have
introduced the possibility of using quantum physics to strongly enhance
communications safety. Nowadays Quantum Cryptography, and in particular,
Quantum Key Distribution (QKD) exited the physics laboratories to become
commercial technologies that increasingly trigger the attention of States,
military forces, banks, and private corporations. This work takes on the
challenge of bringing QKD closer to a consumer technology: optical fibers
deployed and used by telecommunication companies of different States have been
used to realize a quantum network, the first-ever connecting three different
countries. This pushes towards the necessary coexistence of QKD and classical
communications on the same infrastructure, which currently represents a main
limit of this technology. Our network connects Trieste to Rijeka and Ljubljana
via a trusted node in Postojna; a key rate of over 3 kbps has been achieved in
the shortest link, and a 7-hour long measurement has demonstrated the system
stability and reliability. Finally, the network has been used for a public
demonstration of QKD at the G20 Digital Ministers' Meeting in Trieste. The
reported experimental results, together with the significant interest that one
of the most important events of international politics has attracted, showcase
the maturity of the QKD technology bundle, placing it in the spotlight for
consumer applications in the near term.


title: Bootstrap for Lattice Yang-Mills theory

abstract:
  We study the $SU(\infty)$ lattice Yang-Mills theory at the dimensions
$D=2,3,4$ via the numerical bootstrap method. It combines the Makeenko-Migdal
loop equations, with a cut-off $L_{\mathrm{max}}$ on the maximal length of
loops, and positivity conditions on certain matrices of Wilson loops. Our
algorithm is inspired by the pioneering paper of P.Anderson and M.Kruczenski
but it is significantly more efficient, as it takes into account the symmetries
of the lattice theory and uses the relaxation procedure in line with our
previous work on matrix bootstrap. We thus obtain rigorous upper and lower
bounds on the plaquette average at various couplings and dimensions. For $D=4$,
the lower bound data appear to be close to the MC data in the strong coupling
phase and the upper bound data in the weak coupling phase reproduce well the
3-loop perturbation theory. Our results suggest that this bootstrap approach
can provide a tangible alternative to the, so far uncontested, Monte Carlo
approach.


title:
Complexity of limit cycles with block-sequential update schedules in
  conjunctive networks

abstract:
  In this paper, we deal the following decision problem: given a conjunctive
Boolean network defined by its interaction digraph, does it have a limit cycle
of a given length k? We prove that this problem is NP-complete in general if k
is a parameter of the problem and in P if the interaction digraph is strongly
connected. The case where $k$ is a constant, but the interaction digraph is not
strongly connected remains open. Furthermore, we study the variation of the
decision problem: given a conjunctive Boolean network, does there exist a
block-sequential (resp. sequential) update schedule such that there exists a
limit cycle of length k? We prove that this problem is NP-complete for any
constant k >= 2.


title:
Practical guide to replica exchange transition interface sampling and
  forward flux sampling

abstract:
  Path sampling approaches have become invaluable tools to explore the
mechanisms and dynamics of so-called rare events that are characterized by
transitions between metastable states separated by sizeable free energy
barriers. Their practical application, in particular to ever more complex
molecular systems, is, however, not entirely trivial. Focusing on replica
exchange transition interface sampling (RETIS) and forward flux sampling (FFS),
we discuss a range of analysis tools that can be used to assess the quality and
convergence of such simulations which is crucial to obtain reliable results.
The basic ideas of a step-wise evaluation are exemplified for the study of
nucleation in several systems with different complexity, providing a general
guide for the critical assessment of RETIS and FFS simulations.


title:
PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic
  differential equations

abstract:
  We propose a new class of physics-informed neural networks, called
physics-informed Variational Autoencoder (PI-VAE), to solve stochastic
differential equations (SDEs) or inverse problems involving SDEs. In these
problems the governing equations are known but only a limited number of
measurements of system parameters are available. PI-VAE consists of a
variational autoencoder (VAE), which generates samples of system variables and
parameters. This generative model is integrated with the governing equations.
In this integration, the derivatives of VAE outputs are readily calculated
using automatic differentiation, and used in the physics-based loss term. In
this work, the loss function is chosen to be the Maximum Mean Discrepancy (MMD)
for improved performance, and neural network parameters are updated iteratively
using the stochastic gradient descent algorithm. We first test the proposed
method on approximating stochastic processes. Then we study three types of
problems related to SDEs: forward and inverse problems together with mixed
problems where system parameters and solutions are simultaneously calculated.
The satisfactory accuracy and efficiency of the proposed method are numerically
demonstrated in comparison with physics-informed generative adversarial network
(PI-WGAN).


title:
An Information-theoretic Approach to Prompt Engineering Without Ground
  Truth Labels

abstract:
  Pre-trained language models derive substantial linguistic and factual
knowledge from the massive corpora on which they are trained, and prompt
engineering seeks to align these models to specific tasks. Unfortunately,
existing prompt engineering methods require significant amounts of labeled
data, access to model parameters, or both. We introduce a new method for
selecting prompt templates \textit{without labeled examples} and
\textit{without direct access to the model}. Specifically, over a set of
candidate templates, we choose the template that maximizes the mutual
information between the input and the corresponding model output. Across 8
datasets representing 7 distinct NLP tasks, we show that when a template has
high mutual information, it also has high accuracy on the task. On the largest
model, selecting prompts with our method gets 90\% of the way from the average
prompt accuracy to the best prompt accuracy and requires no ground truth
labels.


title: Towards a Change Taxonomy for Machine Learning Systems

abstract:
  Machine Learning (ML) research publications commonly provide open-source
implementations on GitHub, allowing their audience to replicate, validate, or
even extend machine learning algorithms, data sets, and metadata.
  However, thus far little is known about the degree of collaboration activity
happening on such ML research repositories, in particular regarding (1) the
degree to which such repositories receive contributions from forks, (2) the
nature of such contributions (i.e., the types of changes), and (3) the nature
of changes that are not contributed back to forks, which might represent missed
opportunities. In this paper, we empirically study contributions to 1,346 ML
research repositories and their 67,369 forks, both quantitatively and
qualitatively (by building on Hindle et al.'s seminal taxonomy of code
changes). We found that while ML research repositories are heavily forked, only
9% of the forks made modifications to the forked repository. 42% of the latter
sent changes to the parent repositories, half of which (52%) were accepted by
the parent repositories. Our qualitative analysis on 539 contributed and 378
local (fork-only) changes, extends Hindle et al.'s taxonomy with one new
top-level change category related to ML (Data), and 15 new sub-categories,
including nine ML-specific ones (input data, output data, program data,
sharing, change evaluation, parameter tuning, performance, pre-processing,
model training). While the changes that are not contributed back by the forks
mostly concern domain-specific customizations and local experimentation (e.g.,
parameter tuning), the origin ML repositories do miss out on a non-negligible
15.4% of Documentation changes, 13.6% of Feature changes and 11.4% of Bug fix
changes. The findings in this paper will be useful for practitioners,
researchers, toolsmiths, and educators.


title: Integral points on cubic twists of Mordell curves

abstract:
  Fix a non-square integer $k\neq 0$. We show that the the number of curves
$E_B:y^2=x^3+kB^2$ containing an integral point, where $B$ ranges over positive
integers less than $N$, is bounded by $\ll_k N(\log
N)^{-\frac{1}{2}+\epsilon}$. In particular, this implies that the number of
positive integers $B\leq N$ such that $-3kB^2$ is the discriminant of an
elliptic curve over $\mathbb{Q}$ is $o(N)$. The proof involves a
discriminant-reducing procedure on integral binary cubic forms.


title:
Inference of B cell clonal families using heavy/light chain pairing
  information

abstract:
  Next generation sequencing of B cell receptor (BCR) repertoires has become a
ubiquitous tool for understanding the antibody-mediated immune response: it is
now common to have large volumes of sequence data coding for both the heavy and
light chain subunits of the BCR. However, until the recent development of high
throughput methods of preserving heavy/light chain pairing information, these
samples contained no explicit information on which heavy chain sequence pairs
with which light chain sequence. One of the first steps in analyzing such BCR
repertoire samples is grouping sequences into clonally related families, where
each stems from a single rearrangement event. Many methods of accomplishing
this have been developed, however, none so far has taken full advantage of the
newly-available pairing information. This information can dramatically improve
clustering performance, especially for the case of the light chain; the light
chain has traditionally been challenging for clonal family inference because of
its low diversity and consequent abundance of non-clonal families with
indistinguishable naive rearrangements. Here we present a method of
incorporating this pairing information into the clustering process in order to
arrive at a more accurate partition of the data into clonally related families.
We also demonstrate two methods of fixing imperfect pairing information, which
may allow for simplified sample preparation and increased sequencing depth.
Finally, we describe several other improvements to the partis software package
(https://github.com/psathyrella/partis).


title:
Audio visual character profiles for detecting background characters in
  entertainment media

abstract:
  An essential goal of computational media intelligence is to support
understanding how media stories -- be it news, commercial or entertainment
media -- represent and reflect society and these portrayals are perceived.
People are a central element of media stories. This paper focuses on
understanding the representation and depiction of background characters in
media depictions, primarily movies and TV shows. We define the background
characters as those who do not participate vocally in any scene throughout the
movie and address the problem of localizing background characters in videos. We
use an active speaker localization system to extract high-confidence
face-speech associations and generate audio-visual profiles for talking
characters in a movie by automatically clustering them. Using a face
verification system, we then prune all the face-tracks which match any of the
generated character profiles and obtain the background character face-tracks.
We curate a background character dataset which provides annotations for
background character for a set of TV shows, and use it to evaluate the
performance of the background character detection framework.


title:
Temporal Abstractions-Augmented Temporally Contrastive Learning: An
  Alternative to the Laplacian in RL

abstract:
  In reinforcement learning, the graph Laplacian has proved to be a valuable
tool in the task-agnostic setting, with applications ranging from skill
discovery to reward shaping. Recently, learning the Laplacian representation
has been framed as the optimization of a temporally-contrastive objective to
overcome its computational limitations in large (or continuous) state spaces.
However, this approach requires uniform access to all states in the state
space, overlooking the exploration problem that emerges during the
representation learning process. In this work, we propose an alternative method
that is able to recover, in a non-uniform-prior setting, the expressiveness and
the desired properties of the Laplacian representation. We do so by combining
the representation learning with a skill-based covering policy, which provides
a better training distribution to extend and refine the representation. We also
show that a simple augmentation of the representation objective with the
learned temporal abstractions improves dynamics-awareness and helps
exploration. We find that our method succeeds as an alternative to the
Laplacian in the non-uniform setting and scales to challenging continuous
control environments. Finally, even if our method is not optimized for skill
discovery, the learned skills can successfully solve difficult continuous
navigation tasks with sparse rewards, where standard skill discovery approaches
are no so effective.


title: Language modeling via stochastic processes

abstract:
  Modern language models can generate high-quality short texts. However, they
often meander or are incoherent when generating longer texts. These issues
arise from the next-token-only language modeling objective. To address these
issues, we introduce Time Control (TC), a language model that implicitly plans
via a latent stochastic process. TC does this by learning a representation
which maps the dynamics of how text changes in a document to the dynamics of a
stochastic process of interest. Using this representation, the language model
can generate text by first implicitly generating a document plan via a
stochastic process, and then generating text that is consistent with this
latent plan. Compared to domain-specific methods and fine-tuning GPT2 across a
variety of text domains, TC improves performance on text infilling and
discourse coherence. On long text generation settings, TC preserves the text
structure both in terms of ordering (up to +40% better) and text length
consistency (up to +17% better). Human evaluators also prefer TC's output 28.6%
more than the baselines.


title:
Soliton dynamics for the 1D quadratic Klein-Gordon equation with
  symmetry

abstract:
  We establish the conditional asymptotic stability in a local energy norm of
the unstable soliton for the one-dimensional quadratic Klein-Gordon equation
under even perturbations. A key feature of the problem is the positive gap
eigenvalue exhibited by the linearized operator around the soliton. Our proof
is based on several virial-type estimates, combining techniques from the series
of works [23-26, 28], and an explicitly verified Fermi Golden Rule. The
approach hinges on the fact that even perturbations are orthogonal to the odd
threshold resonance of the linearized operator.


title: Sensitivity of Single-Pulse Radar Detection to Radar State Uncertainty

abstract:
  Mission planners for aircraft operating under threat of detection from
ground-based radar systems are often concerned with the probability of
detection. Current approaches to path planning in such environments consider
the radar state (i.e. radar position and parameters) to be deterministic and
known. In practice, there is uncertainty in the radar state which induces
uncertainty in the probability of detection. This paper presents a method to
incorporate the uncertainty of the radar state in a single-pulse radar
detection model. The method linearizes the radar detection model with respect
to the the radar state and uses the linearized models to estimate, to the first
order, the variance of the probability of detection. The results in this paper
validate the linearization using Monte Carlo analysis and illustrate the
sensitivity of the probability of detection to radar state uncertainty.


title:
Two methods for Jamming Identification in UAVs Networks using New
  Synthetic Dataset

abstract:
  Unmanned aerial vehicle (UAV) systems are vulnerable to jamming from
self-interested users who utilize radio devices for their benefits during UAV
transmissions. The vulnerability occurs due to the open nature of air-to-ground
(A2G) wireless communication networks, which may enable network-wide attacks.
This paper presents two strategies to identify Jammers in UAV networks. The
first strategy is based on time series approaches for anomaly detection where
the signal available in resource blocks are decomposed statistically to find
trend, seasonality, and residues, while the second is based on newly designed
deep networks. The joined technique is suitable for UAVs because the
statistical model does not require heavy computation processing but is limited
in generalizing possible attack's identification. On the other hand, the deep
network can classify attacks accurately but requires more resources. The
simulation considers the location and power of the jamming attacks and the UAV
position related to the base station. The statistical method technique made it
feasible to identify 84.38 % of attacks when the attacker was at 30 m from the
UAV. Furthermore, the Deep network's accuracy was approximately 99.99 % for
jamming powers greater than two and jammer distances less than 200 meters.


title: The randomized measurement toolbox

abstract:
  Increasingly sophisticated programmable quantum simulators and quantum
computers are opening unprecedented opportunities for exploring and exploiting
the properties of highly entangled complex quantum systems. The complexity of
large quantum systems is the source of their power, but also makes them
difficult to control precisely or characterize accurately using measured
classical data. We review recently developed protocols for probing the
properties of complex many-qubit systems using measurement schemes that are
practical using today's quantum platforms. In all these protocols, a quantum
state is repeatedly prepared and measured in a randomly chosen basis; then a
classical computer processes the measurement outcomes to estimate the desired
property. The randomization of the measurement procedure has distinct
advantages; for example, a single data set can be employed multiple times to
pursue a variety of applications, and imperfections in the measurements are
mapped to a simplified noise model that can more easily be mitigated. We
discuss a range of use cases that have already been realized in quantum
devices, including Hamiltonian simulation tasks, probes of quantum chaos,
measurements of nonlocal order parameters, and comparison of quantum states
produced in distantly separated laboratories. By providing a workable method
for translating a complex quantum state into a succinct classical
representation that preserves a rich variety of relevant physical properties,
the randomized measurement toolbox strengthens our ability to grasp and control
the quantum world.


title:
Robust Model Predictive Control with Polytopic Model Uncertainty through
  System Level Synthesis

abstract:
  We propose a novel method for robust model predictive control (MPC) of
uncertain systems subject to both polytopic model uncertainty and additive
disturbances. In our method, we over-approximate the actual uncertainty by a
surrogate additive disturbance which simplifies constraint tightening of the
robust optimal control problem. Using System Level Synthesis, we can optimize
over a robust linear state feedback control policy and the uncertainty
over-approximation parameters jointly and in a convex manner. The proposed
method is demonstrated to achieve feasible domains close to the maximal robust
control invariant set for a wide range of uncertainty parameters and
significant improvement in conservatism compared with tube-based MPC through
numerical examples.


title:
Characterization of irradiated RD53A pixel modules with passive CMOS
  sensors

abstract:
  We are investigating the feasibility of using CMOS foundries to fabricate
silicon detectors, both for pixels and for large-area strip sensors. The
availability of multi-layer routing will provide the freedom to optimize the
sensor geometry and the performance, with biasing structures in poly-silicon
layers and MIM-capacitors allowing for AC coupling. A prototyping production of
strip test-structures and RD53A compatible pixel sensors was recently completed
at LFoundry in a 150$\,$nm CMOS process. This paper will focus on the
characterization of irradiated and non-irradiated pixel modules, composed by a
CMOS passive sensor interconnected to a RD53A chip. The sensors are designed
with a pixel cell of $25\times100\,\mu \mathrm{m}^2$ in case of DC coupled
devices and $50\times50\,\mu \mathrm{m}^2$ for the AC coupled ones. Their
performance in terms of charge collection, position resolution, and hit
efficiency was studied with measurements performed in the laboratory and with
beam tests. The RD53A modules with LFoundry silicon sensors were irradiated to
fluences up to
$1.0\times10^{16}\,\frac{\mathrm{n}_\mathrm{eq}}{\mathrm{cm}^2}$.


title: Sequential algorithmic modification with test data reuse

abstract:
  After initial release of a machine learning algorithm, the model can be
fine-tuned by retraining on subsequently gathered data, adding newly discovered
features, or more. Each modification introduces a risk of deteriorating
performance and must be validated on a test dataset. It may not always be
practical to assemble a new dataset for testing each modification, especially
when most modifications are minor or are implemented in rapid succession.
Recent works have shown how one can repeatedly test modifications on the same
dataset and protect against overfitting by (i) discretizing test results along
a grid and (ii) applying a Bonferroni correction to adjust for the total number
of modifications considered by an adaptive developer. However, the standard
Bonferroni correction is overly conservative when most modifications are
beneficial and/or highly correlated. This work investigates more powerful
approaches using alpha-recycling and sequentially-rejective graphical
procedures (SRGPs). We introduce novel extensions that account for correlation
between adaptively chosen algorithmic modifications. In empirical analyses, the
SRGPs control the error rate of approving unacceptable modifications and
approve a substantially higher number of beneficial modifications than previous
approaches.


title: HyperShot: Few-Shot Learning by Kernel HyperNetworks

abstract:
  Few-shot models aim at making predictions using a minimal number of labeled
examples from a given task. The main challenge in this area is the one-shot
setting where only one element represents each class. We propose HyperShot -
the fusion of kernels and hypernetwork paradigm. Compared to reference
approaches that apply a gradient-based adjustment of the parameters, our model
aims to switch the classification module parameters depending on the task's
embedding. In practice, we utilize a hypernetwork, which takes the aggregated
information from support data and returns the classifier's parameters
handcrafted for the considered problem. Moreover, we introduce the kernel-based
representation of the support examples delivered to hypernetwork to create the
parameters of the classification module. Consequently, we rely on relations
between embeddings of the support examples instead of direct feature values
provided by the backbone models. Thanks to this approach, our model can adapt
to highly different tasks.


title:
A Bayesian Deep Learning Technique for Multi-Step Ahead Solar Generation
  Forecasting

abstract:
  In this paper, we propose an improved Bayesian bidirectional long-short term
memory (BiLSTM) neural networks for multi-step ahead (MSA) solar generation
forecasting. The proposed technique applies alpha-beta divergence for a more
appropriate consideration of outliers in the solar generation data and
resulting variability of the weight parameter distribution in the neural
network. The proposed method is examined on highly granular solar generation
data from Ausgrid using probabilistic evaluation metrics such as Pinball loss
and Winkler score. Moreover, a comparative analysis between MSA and the
single-step ahead (SSA) forecasting is provided to test the effectiveness of
the proposed method on variable forecasting horizons. The numerical results
clearly demonstrate that the proposed Bayesian BiLSTM with alpha-beta
divergence outperforms standard Bayesian BiLSTM and other benchmark methods for
MSA forecasting in terms of error performance.


title: Energy Efficient PON Backhaul Network for a VLC Based Fog Architecture

abstract:
  In this paper, an energy efficient passive optical network (PON) architecture
is proposed for backhaul connectivity in indoor visible light communication
(VLC) systems. The proposed network is used to support a fog computing
architecture allowing users with processing demands to access dedicated fog
nodes and idle processing resources in other user devices within a building.
The fog resources within a building complement fog nodes at higher layers of
the access and metro networks and the central cloud data center. A mixed
integer linear programming (MILP) model is developed to minimize the total
power consumption associated with serving demands over the proposed
architecture. The results show that the PON backhaul network improves the
energy efficiency of fog computing by 66% for networking and 12% for processing
compared to an architecture based on state-of-the-art Spine-and-Leaf
connectivity.


title: Canonical vertex formalism in DT theory of toric Calabi-Yau 4-folds

abstract:
  Motivated by previous computations of Y. Cao, M. Kool and the author, we
propose square roots and sign rules for the vertex and edge terms that compute
Donaldson-Thomas invariants of a toric Calabi-Yau 4-fold, and prove that they
are canonical, exploiting the combinatorics of plane and solid partitions.


title:
Preference Exploration for Efficient Bayesian Optimization with Multiple
  Outcomes

abstract:
  We consider Bayesian optimization of expensive-to-evaluate experiments that
generate vector-valued outcomes over which a decision-maker (DM) has
preferences. These preferences are encoded by a utility function that is not
known in closed form but can be estimated by asking the DM to express
preferences over pairs of outcome vectors. To address this problem, we develop
Bayesian optimization with preference exploration, a novel framework that
alternates between interactive real-time preference learning with the DM via
pairwise comparisons between outcomes, and Bayesian optimization with a learned
compositional model of DM utility and outcomes. Within this framework, we
propose preference exploration strategies specifically designed for this task,
and demonstrate their performance via extensive simulation studies.


title: DIANES: A DEI Audit Toolkit for News Sources

abstract:
  Professional news media organizations have always touted the importance that
they give to multiple perspectives. However, in practice the traditional
approach to all-sides has favored people in the dominant culture. Hence it has
come under ethical critique under the new norms of diversity, equity, and
inclusion (DEI). When DEI is applied to journalism, it goes beyond conventional
notions of impartiality and bias and instead democratizes the journalistic
practice of sourcing -- who is quoted or interviewed, who is not, how often,
from which demographic group, gender, and so forth. There is currently no
real-time or on-demand tool in the hands of reporters to analyze the persons
they quote. In this paper, we present DIANES, a DEI Audit Toolkit for News
Sources. It consists of a natural language processing pipeline on the backend
to extract quotes, speakers, titles, and organizations from news articles in
real time. On the frontend, DIANES offers the WordPress plugins, a Web monitor,
and a DEI annotation API service, to help news media monitor their own quoting
patterns and push themselves towards DEI norms.


title:
A Note on the Critical Groups of Strongly Regular Graphs and Their
  Generalizations

abstract:
  We determine the maximum order of an element in the critical group of a
strongly regular graph, and show that it achieves the spectral bound due to
Lorenzini. We extend the result to all graphs with exactly two non-zero
Laplacian eigenvalues, and study the signed graph version of the problem. We
also study the monodromy pairing on the critical groups, and suggest an
approach to study the structure of these groups using the pairing.


title: Cluster Hadronisation in Sherpa

abstract:
  We present the Sherpa cluster hadronization model and a simple model for
non-perturbative colour reconnections. Using two different parton shower
implementations we tuned the model to data and we show typical resulting
distributions that are sensitive to hadronization effects in
$e^+e^-$--annihilations into hadrons.


title: Optimizing Binary Decision Diagrams with MaxSAT for classification

abstract:
  The growing interest in explainable artificial intelligence (XAI) for
critical decision making motivates the need for interpretable machine learning
(ML) models. In fact, due to their structure (especially with small sizes),
these models are inherently understandable by humans. Recently, several exact
methods for computing such models are proposed to overcome weaknesses of
traditional heuristic methods by providing more compact models or better
prediction quality.
  Despite their compressed representation of Boolean functions, Binary decision
diagrams (BDDs) did not gain enough interest as other interpretable ML models.
In this paper, we first propose SAT-based models for learning optimal BDDs (in
terms of the number of features) that classify all input examples. Then, we
lift the encoding to a MaxSAT model to learn optimal BDDs in limited depths,
that maximize the number of examples correctly classified. Finally, we tackle
the fragmentation problem by introducing a method to merge compatible subtrees
for the BDDs found via the MaxSAT model. Our empirical study shows clear
benefits of the proposed approach in terms of prediction quality and
intrepretability (i.e., lighter size) compared to the state-of-the-art
approaches.


title:
Privacy Rarely Considered: Exploring Considerations in the Adoption of
  Third-Party Services by Websites

abstract:
  Modern websites frequently use and embed third-party services to facilitate
web development, connect to social media, or for monetization. This often
introduces privacy issues as the inclusion of third-party services on a website
can allow the third party to collect personal data about the website's
visitors. While the prevalence and mechanisms of third-party web tracking have
been widely studied, little is known about the decision processes that lead to
websites using third-party functionality and whether efforts are being made to
protect their visitors' privacy.
  We report results from an online survey with 395 participants involved in the
creation and maintenance of websites. For ten common website functionalities we
investigated if privacy has played a role in decisions about how the
functionality is integrated, if specific efforts for privacy protection have
been made during integration, and to what degree people are aware of data
collection through third parties. We find that ease of integration drives
third-party adoption but visitor privacy is considered if there are legal
requirements or respective guidelines. Awareness of data collection and privacy
risks is higher if the collection is directly associated with the purpose for
which the third-party service is used.


title:
The Gor'kov and Melik-Barkhudarov correction to an imbalanced Fermi gas
  in the presence of impurities

abstract:
  The effects of induced interactions are calculated in both clean and dirty
situations, for balanced and imbalanced Fermi gases. We investigate the effects
of nonmagnetic impurities on the induced interactions corrections to the
transition temperature in the case of a balanced gas, and to the tricritical
point in the case of an imbalanced Fermi gas at unitarity. We find that
impurities act in detriment of the induced interactions, or particle-hole
fluctuations, for the transition temperature and the tricritical point. For
large impurity parameter, the particle-hole fluctuations are strongly
suppressed. We have also found the Chandrasekhar-Clogston limit of an
imbalanced Fermi gas at unitarity considering the effects of the induced
interactions, both in the pure and impurity regimes.

categories: cond-mat.quant-gas cond-mat.str-el cond-mat.supr-con nucl-th


title: The VoiceMOS Challenge 2022

abstract:
  We present the first edition of the VoiceMOS Challenge, a scientific event
that aims to promote the study of automatic prediction of the mean opinion
score (MOS) of synthetic speech. This challenge drew 22 participating teams
from academia and industry who tried a variety of approaches to tackle the
problem of predicting human ratings of synthesized speech. The listening test
data for the main track of the challenge consisted of samples from 187
different text-to-speech and voice conversion systems spanning over a decade of
research, and the out-of-domain track consisted of data from more recent
systems rated in a separate listening test. Results of the challenge show the
effectiveness of fine-tuning self-supervised speech models for the MOS
prediction task, as well as the difficulty of predicting MOS ratings for unseen
speakers and listeners, and for unseen systems in the out-of-domain setting.


title: Librational Feedback Cooling

abstract:
  Librational motion, whereby a rigid body undergoes angular oscillation around
a preferred direction, can be observed in optically trapped, silica
microspheres. We demonstrate the cooling of one librational degree of freedom
for $\sim 5~\mu$m diameter spheres that have been induced to rotate with an
external electric field coupled to their electric dipole moment. Cooling is
accomplished by adding a phase modulation to the rotating field. The degree of
cooling is quantified by applying a $\pi/2$ shift to the phase of the electric
field and fitting the resulting exponential decay of the librational motion to
obtain a damping time, as well as estimating a mode temperature from the
observed libration in equilibrium. The result is an important step in the study
of the dynamics of trapped microspheres, crucial to cooling the mechanical
motion to its ground state, as well as providing insights regarding the charge
mobility in the material at microscopic scales.


title:
Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and
  Incomplete Clinical Data

abstract:
  Idiopathic Pulmonary Fibrosis (IPF) is an inexorably progressive fibrotic
lung disease with a variable and unpredictable rate of progression. CT scans of
the lungs inform clinical assessment of IPF patients and contain pertinent
information related to disease progression. In this work, we propose a
multi-modal method that uses neural networks and memory banks to predict the
survival of IPF patients using clinical and imaging data. The majority of
clinical IPF patient records have missing data (e.g. missing lung function
tests). To this end, we propose a probabilistic model that captures the
dependencies between the observed clinical variables and imputes missing ones.
This principled approach to missing data imputation can be naturally combined
with a deep survival analysis model. We show that the proposed framework yields
significantly better survival analysis results than baselines in terms of
concordance index and integrated Brier score. Our work also provides insights
into novel image-based biomarkers that are linked to mortality.


title:
Twisted Modules of a Vertex Operator Algebra and Associators as
  Classifying Morphisms

abstract:
  The monoidal category of twisted modules of a Vertex Operator Algebra $V$ is
defined and reduced to its 2-group of invertible objects $G_\alpha$, which can
be described by a 3-cocycle $\alpha$ on its 0-truncation $G$ with values in the
group of units $A$ of the field of definition of $V$ serving as its associator.
This cocycle also presents the classifying morphism of an $\infty$-group
extension of $G$ by the delooping $BA$. Motivated by this, it is proven that
the $\infty$-group extension classified by a 3-cocycle $\alpha$ is presented by
the skeletal 2-group $G_\alpha$ with associator $\alpha$. The results are
discussed in light of current developments in Moonshine and $(\infty,1)$-topos
theory.


title:
Role of the electromagnetic vacuum in the transition from classical to
  quantum mechanics

abstract:
  We revisit the nonrelativistic problem of a bound, charged particle subject
to the random zero-point radiation field (ZPF), with the purpose of revealing
the mechanism that takes it from the initially classical description to the
final quantum-mechanical one. The combined effect of the ZPF and the radiation
reaction force results, after a characteristic time lapse, in the loss of the
initial conditions and the concomitant irreversible transition of the dynamics
to a stationary regime controlled by the field. In this regime, the canonical
variables x,p become expressed in terms of the dipolar response functions to a
set of field modes. A proper ordering of the response coefficients leads to the
matrix representation of quantum mechanics, as was proposed in the early days
of the theory, and to the basic commutator \left[\hat{x},\hat{p}\right]=i\hbar.
Further, the connection with the corresponding Fokker-Planck equation valid in
the Markov approximation, allows to obtain the (nonrelativistic) radiative
corrections of QED. These results reaffirm the essentially electrodynamic and
stochastic nature of the quantum phenomenon, as proposed by stochastic
electrodynamics.


title:
Minimum-Time Reorientation of Axisymmetric Rigid Spacecraft Using Three
  Controls

abstract:
  A minimum-time reorientation of an axisymmetric rigid spacecraft controlled
by three torques is studied. The orientation of the body is modeled such that
the attitude kinematics are representative of a spin-stabilized spacecraft. The
optimal control problem considered is shown to have a switching control
structure. Moreover, under certain assumptions, the solutions contain segments
that lie on a singular arc. A numerical optimization study is performed using a
recently developed method that is designed to accurately solve bang-bang and
singular optimal control problems. The optimality conditions for the resulting
optimal control problem are derived and analyzed for a variety of cases. Also,
the results obtained in this study are compared to a previous method existing
in the literature. The key features of the optimized trajectories and controls
are identified, and the aforementioned method for solving bang-bang and
singular optimal control problems is shown to efficiently and accurately solve
the problem under consideration.


title:
Multiple Convex Objects Image Segmentation via Proximal Alternating
  Direction Method of Multipliers

abstract:
  This paper focuses on the issue of image segmentation with convex shape
prior. Firstly, we use binary function to represent convex object(s). The
convex shape prior turns out to be a simple quadratic inequality constraint on
the binary indicator function associated with each object. An image
segmentation model incorporating convex shape prior into a probability-based
method is proposed. Secondly, a new algorithm is designed to solve involved
optimization problem, which is a challenging task because of the quadratic
inequality constraint. To tackle this difficulty, we relax and linearize the
quadratic inequality constraint to reduce it to solve a sequence of convex
minimization problems. For each convex problem, an efficient proximal
alternating direction method of multipliers is developed to solve it. The
convergence of the algorithm follows some existing results in the optimization
literature. Moreover, an interactive procedure is introduced to improve the
accuracy of segmentation gradually. Numerical experiments on natural and
medical images demonstrate that the proposed method is superior to some
existing methods in terms of segmentation accuracy and computational time.


title: Towards Textual Out-of-Domain Detection without In-Domain Labels

abstract:
  In many real-world settings, machine learning models need to identify user
inputs that are out-of-domain (OOD) so as to avoid performing wrong actions.
This work focuses on a challenging case of OOD detection, where no labels for
in-domain data are accessible (e.g., no intent labels for the intent
classification task). To this end, we first evaluate different language model
based approaches that predict likelihood for a sequence of tokens. Furthermore,
we propose a novel representation learning based method by combining
unsupervised clustering and contrastive learning so that better data
representations for OOD detection can be learned. Through extensive
experiments, we demonstrate that this method can significantly outperform
likelihood-based methods and can be even competitive to the state-of-the-art
supervised approaches with label information.


title: A Real World Dataset for Multi-view 3D Reconstruction

abstract:
  We present a dataset of 371 3D models of everyday tabletop objects along with
their 320,000 real world RGB and depth images. Accurate annotations of camera
poses and object poses for each image are performed in a semi-automated fashion
to facilitate the use of the dataset for myriad 3D applications like shape
reconstruction, object pose estimation, shape retrieval etc. We primarily focus
on learned multi-view 3D reconstruction due to the lack of appropriate real
world benchmark for the task and demonstrate that our dataset can fill that
gap. The entire annotated dataset along with the source code for the annotation
tools and evaluation baselines will be made publicly available.


title:
A Hybrid Lagrangian-Eulerian Model for the Structural Analysis of
  Multifield Datasets

abstract:
  Multifields datasets are common in a large number of research and engineering
applications of computational science. The effective visualization of the
corresponding datasets can facilitate their analysis by elucidating the complex
and dynamic interactions that exist between the attributes that describe the
physics of the phenomenon. We present in this paper a new hybrid
Lagrangian-Eulerian model that extends existing Lagrangian visualization
techniques to the analysis of multifields problems. In particular, our approach
factors in the entire data space to reveal the structure of multifield
datasets, thereby combining both Eulerian and Lagrangian perspectives. We
evaluate our technique in the context of several fluid dynamics applications.
Our results indicate that our proposed approach is able to characterize
important structural features that are missed by existing methods.


title:
Achieving Conversational Goals with Unsupervised Post-hoc Knowledge
  Injection

abstract:
  A limitation of current neural dialog models is that they tend to suffer from
a lack of specificity and informativeness in generated responses, primarily due
to dependence on training data that covers a limited variety of scenarios and
conveys limited knowledge. One way to alleviate this issue is to extract
relevant knowledge from external sources at decoding time and incorporate it
into the dialog response. In this paper, we propose a post-hoc
knowledge-injection technique where we first retrieve a diverse set of relevant
knowledge snippets conditioned on both the dialog history and an initial
response from an existing dialog model. We construct multiple candidate
responses, individually injecting each retrieved snippet into the initial
response using a gradient-based decoding method, and then select the final
response with an unsupervised ranking step. Our experiments in goal-oriented
and knowledge-grounded dialog settings demonstrate that human annotators judge
the outputs from the proposed method to be more engaging and informative
compared to responses from prior dialog systems. We further show that
knowledge-augmentation promotes success in achieving conversational goals in
both experimental settings.


title: VLSP 2021 - ViMRC Challenge: Vietnamese Machine Reading Comprehension

abstract:
  One of the emerging research trends in natural language understanding is
machine reading comprehension (MRC) which is the task to find answers to human
questions based on textual data. Existing Vietnamese datasets for MRC research
concentrate solely on answerable questions. However, in reality, questions can
be unanswerable for which the correct answer is not stated in the given textual
data. To address the weakness, we provide the research community with a
benchmark dataset named UIT-ViQuAD 2.0 for evaluating the MRC task and question
answering systems for the Vietnamese language. We use UIT-ViQuAD 2.0 as a
benchmark dataset for the challenge on Vietnamese MRC at the Eighth Workshop on
Vietnamese Language and Speech Processing (VLSP 2021). This task attracted 77
participant teams from 34 universities and other organizations. In this
article, we present details of the organization of the challenge, an overview
of the methods employed by shared-task participants, and the results. The
highest performances are 77.24% in F1-score and 67.43% in Exact Match on the
private test set. The Vietnamese MRC systems proposed by the top 3 teams use
XLM-RoBERTa, a powerful pre-trained language model based on the transformer
architecture. The UIT-ViQuAD 2.0 dataset motivates researchers to further
explore the Vietnamese machine reading comprehension task and related tasks
such as question answering, question generation, and natural language
inference.


title:
Suum Cuique: Studying Bias in Taboo Detection with a Community
  Perspective

abstract:
  Prior research has discussed and illustrated the need to consider linguistic
norms at the community level when studying taboo (hateful/offensive/toxic etc.)
language. However, a methodology for doing so, that is firmly founded on
community language norms is still largely absent. This can lead both to biases
in taboo text classification and limitations in our understanding of the causes
of bias. We propose a method to study bias in taboo classification and
annotation where a community perspective is front and center. This is
accomplished by using special classifiers tuned for each community's language.
In essence, these classifiers represent community level language norms. We use
these to study bias and find, for example, biases are largest against African
Americans (7/10 datasets and all 3 classifiers examined). In contrast to
previous papers we also study other communities and find, for example, strong
biases against South Asians. In a small scale user study we illustrate our key
idea which is that common utterances, i.e., those with high alignment scores
with a community (community classifier confidence scores) are unlikely to be
regarded taboo. Annotators who are community members contradict taboo
classification decisions and annotations in a majority of instances. This paper
is a significant step toward reducing false positive taboo decisions that over
time harm minority communities.


title: Vitamin K content of cheese, yoghurt and meat products in Australia

abstract:
  Vitamin K is vital for normal blood coagulation, and may influence bone,
neurological and vascular health. Data on the vitamin K content of Australian
foods are limited, preventing estimation of vitamin K intakes in the Australian
population. We measured phylloquinone (PK) and menaquinone (MK) -4 to -10 in
cheese, yoghurt and meat products (48 composite samples from 288 primary
samples) by liquid chromatography with electrospray ionisation-tandem mass
spectrometry. At least one K vitamer was found in every sample. The greatest
mean concentrations of PK, MK-4 and MK-9 were found in lamb liver, chicken leg
meat and Cheddar cheese, respectively. Cheddar cheese and cream cheese
contained MK-5. MK-8 was found in Cheddar cheese only. As the K vitamer profile
and concentrations appear to vary considerably by geographical location,
Australia needs a vitamin K food composition dataset that is representative of
foods consumed in Australia.


title:
Quantum Gravity Constraints on Fine Structure Constant from GUP in
  Braneworlds

abstract:
  The Generalized Uncertainty Principle (GUP) has been discussed in the thick
braneworld scenario. By considering Rydberg atoms in this background, we show
that the spacetime geometry affects Maxwell equations inducing an effective
dielectric constant on the space. In its turn, the corrected Coulomb potential
by the gravitational interaction yields a deviation on the $3$-dimensional Bohr
radius. Then, we compute the corrections on the fine structure constant owing
to the GUP in higher-dimensional spacetime. We also found constraints for the
deformation parameter $\beta$ and $D$-dimensional Planck length $l_{D}$ by
comparing the predicted deviations with the recent empirical data of the fine
structure constant. We compute the intermediate length scale, which in
principle may be larger than the Planck length scale. It is conjectured that
below such scale Quantum Gravity effects should take place.


title: Enhanced Preamble Based MAC Mechanism for IIoT-oriented PLC Network

abstract:
  In this paper, we propose an enhanced preamble based media access control
mechanism (E-PMAC), which can be applied in power line communication (PLC)
network for Industrial Internet of Things (IIoT). We introduce detailed
technologies used in E-PMAC, including delay calibration mechanism, preamble
design, and slot allocation algorithm. With these technologies, E-PMAC is more
robust than existing preamble based MAC mechanism (P-MAC). Besides, we analyze
the disadvantage of P-MAC in multi-layer networking and design the networking
process of E-PMAC to accelerate networking process. We analyze the complexity
of networking process in P-MAC and E-PMAC and prove that E-PMAC has lower
complexity than P-MAC. Finally, we simulate the single-layer networking and
multi-layer networking of E-PMAC, P-MAC, and existing PLC protocol, i.e. ,
IEEE1901.1. The simulation results indicate that E-PMAC spends much less time
in networking than IEEE1901.1 and P-MAC. Finally, with our work, a PLC network
based on E-PMAC mechanism can be realized.


title: Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception

abstract:
  Self-driving cars must detect vehicles, pedestrians, and other traffic
participants accurately to operate safely. Small, far-away, or highly occluded
objects are particularly challenging because there is limited information in
the LiDAR point clouds for detecting them. To address this challenge, we
leverage valuable information from the past: in particular, data collected in
past traversals of the same scene. We posit that these past data, which are
typically discarded, provide rich contextual information for disambiguating the
above-mentioned challenging cases. To this end, we propose a novel, end-to-end
trainable Hindsight framework to extract this contextual information from past
traversals and store it in an easy-to-query data structure, which can then be
leveraged to aid future 3D object detection of the same scene. We show that
this framework is compatible with most modern 3D detection architectures and
can substantially improve their average precision on multiple autonomous
driving datasets, most notably by more than 300% on the challenging cases.


title: Snowmass2021 Whitepaper: Muonium to antimuonium conversion

abstract:
  The spontaneous muonium to antimuonium conversion is one of the interesting
charged lepton flavor violation processes. It serves as a clear indication of
new physics and plays an important role in constraining the parameter space
beyond Standard Model. MACE is a proposed experiment to probe such a phenomenon
and expected to enhance the sensitivity to the conversion probability by more
than two orders of magnitude from the current best upper constraint obtained by
the PSI experiment two decades ago. Recent developments in the theoretical and
experimental aspects to search for such a rare process are summarized.


title: Causal inference in time series in terms of R\'enyi transfer entropy

abstract:
  Uncovering causal interdependencies from observational data is one of the
great challenges of nonlinear time series analysis. In this paper, we discuss
this topic with the help of information-theoretic concept known as R\'enyi
information measure. In particular, we tackle the directional information flow
between bivariate time series in terms of R\'enyi transfer entropy. We show
that by choosing R\'enyi $\alpha$ parameter appropriately we can control
information that is transferred only between selected parts of underlying
distributions. This, in turn, provides particularly potent tool for quantifying
causal interdependencies in time series, where the knowledge of "black swan"
events such as spikes or sudden jumps are of a key importance. In this
connection, we first prove that for Gaussian variables, Granger causality and
R\'enyi transfer entropy are entirely equivalent. Moreover, we also partially
extend this results to heavy-tailed $\alpha$-Gaussian variables. These results
allow to establish connection between autoregressive and R\'enyi entropy based
information-theoretic approaches to data-driven causal inference. To aid our
intuition we employ Leonenko et al. entropy estimator and analyze R\'enyi
information flow between bivariate time series generated from two
unidirectionally coupled R\"ossler systems. Notably, we find that R\'enyi
transfer entropy not only allowed us to detect a threshold of synchronization
but it also provided a non-trivial insight into the structure of a transient
regime that exists between region of chaotic correlations and synchronization
threshold. In addition, from R\'enyi transfer entropy we could reliably infer
the direction of coupling - and hence causality, only for coupling strengths
smaller that the onset value of transient regime, i.e. when two R\"ossler
systems were coupled, but have not yet entered a synchronization.

categories: cs.IT math-ph math.IT math.MP nlin.CD physics.data-an stat.AP


title:
Combined Optimal Routing and Coordination of Connected and Automated
  Vehicles

abstract:
  In this letter, we consider a transportation network with a 100\% penetration
rate of connected and automated vehicles (CAVs), and present an optimal routing
approach which takes into account the efficiency achieved in the network by
coordinating the CAVs at specific traffic scenarios, e.g., intersections,
merging roadways, roundabouts, etc. To derive the optimal route of a travel
request, we use the information of the CAVs that have already received a
routing solution. This enables each CAV to consider the traffic conditions on
the roads. Given the trajectories of CAVs resulting by the routing solutions,
the solution of any new travel request determines the optimal travel time at
each traffic scenario while satisfying all state, control, and safety
constraints. We validate the performance of our framework through numerical
simulations. To the best of our knowledge, this is the first attempt to
consider the coordination of CAVs in a routing problem.


title: A Primer on Maximum Causal Entropy Inverse Reinforcement Learning

abstract:
  Inverse Reinforcement Learning (IRL) algorithms infer a reward function that
explains demonstrations provided by an expert acting in the environment.
Maximum Causal Entropy (MCE) IRL is currently the most popular formulation of
IRL, with numerous extensions. In this tutorial, we present a compressed
derivation of MCE IRL and the key results from contemporary implementations of
MCE IRL algorithms. We hope this will serve both as an introductory resource
for those new to the field, and as a concise reference for those already
familiar with these topics.


title:
Dazzle: Using Optimized Generative Adversarial Networks to Address
  Security Data Class Imbalance Issue

abstract:
  Background: Machine learning techniques have been widely used and demonstrate
promising performance in many software security tasks such as software
vulnerability prediction. However, the class ratio within software
vulnerability datasets is often highly imbalanced (since the percentage of
observed vulnerability is usually very low). Goal: To help security
practitioners address software security data class imbalanced issues and
further help build better prediction models with resampled datasets. Method: We
introduce an approach called Dazzle which is an optimized version of
conditional Wasserstein Generative Adversarial Networks with gradient penalty
(cWGAN-GP). Dazzle explores the architecture hyperparameters of cWGAN-GP with a
novel optimizer called Bayesian Optimization. We use Dazzle to generate
minority class samples to resample the original imbalanced training dataset.
Results: We evaluate Dazzle with three software security datasets, i.e., Moodle
vulnerable files, Ambari bug reports, and JavaScript function code. We show
that Dazzle is practical to use and demonstrates promising improvement over
existing state-of-the-art oversampling techniques such as SMOTE (e.g., with an
average of about 60% improvement rate over SMOTE in recall among all datasets).
Conclusion: Based on this study, we would suggest the use of optimized GANs as
an alternative method for security vulnerability data class imbalanced issues.


title:
Linear Response Theory of Scale-Dependent Viscoelasticity for Overdamped
  Brownian Particle Systems

abstract:
  We show the linear response theory of spatial-scale-dependent relaxation
moduli for overdamped Brownian particle systems. We employ the Irving-Kirkwood
stress tensor field as the microscopic stress tensor field. We show that the
scale-dependent relaxation modulus tensor, which characterizes the response of
the stress tensor field to the applied velocity gradient field, can be
expressed by using the correlation function of the Irving-Kirkwood stress
tensor field. The spatial Fourier transform of the relaxation modulus tensor
gives the wavenumber-dependent relaxation modulus. For isotropic and
homogeneous systems, the relaxation modulus tensor has only two independent
components. The transverse and longitudinal deformation modes give the
wavenumber-dependent shear relaxation modulus and the wavenumber-dependent bulk
relaxation modulus. As simple examples, we derive the explicit expressions for
the relaxation moduli for two simple models the non-interacting Brownian
particles and the harmonic dumbbell model.


title:
Robust Pivoting: Exploiting Frictional Stability Using Bilevel
  Optimization

abstract:
  Generalizable manipulation requires that robots be able to interact with
novel objects and environment. This requirement makes manipulation extremely
challenging as a robot has to reason about complex frictional interaction with
uncertainty in physical properties of the object. In this paper, we study
robust optimization for control of pivoting manipulation in the presence of
uncertainties. We present insights about how friction can be exploited to
compensate for the inaccuracies in the estimates of the physical properties
during manipulation. In particular, we derive analytical expressions for
stability margin provided by friction during pivoting manipulation. This margin
is then used in a bilevel trajectory optimization algorithm to design a
controller that maximizes this stability margin to provide robustness against
uncertainty in physical properties of the object. We demonstrate our proposed
method using a 6 DoF manipulator for manipulating several different objects.


title: Learning Confidence for Transformer-based Neural Machine Translation

abstract:
  Confidence estimation aims to quantify the confidence of the model
prediction, providing an expectation of success. A well-calibrated confidence
estimate enables accurate failure prediction and proper risk measurement when
given noisy samples and out-of-distribution data in real-world settings.
However, this task remains a severe challenge for neural machine translation
(NMT), where probabilities from softmax distribution fail to describe when the
model is probably mistaken. To address this problem, we propose an unsupervised
confidence estimate learning jointly with the training of the NMT model. We
explain confidence as how many hints the NMT model needs to make a correct
prediction, and more hints indicate low confidence. Specifically, the NMT model
is given the option to ask for hints to improve translation accuracy at the
cost of some slight penalty. Then, we approximate their level of confidence by
counting the number of hints the model uses. We demonstrate that our learned
confidence estimate achieves high accuracy on extensive sentence/word-level
quality estimation tasks. Analytical results verify that our confidence
estimate can correctly assess underlying risk in two real-world scenarios: (1)
discovering noisy samples and (2) detecting out-of-domain data. We further
propose a novel confidence-based instance-specific label smoothing approach
based on our learned confidence estimate, which outperforms standard label
smoothing.


title:
BESSIE: A Behavior and Epidemic Simulator for Use With Synthetic
  Populations

abstract:
  In this paper, we present BESSIE (Behavior and Epidemic Simulator for
Synthetic Information Environments), an open source, agent-based simulator for
COVID-type epidemics. BESSIE uses a synthetic population where each person has
demographic attributes, belong to a household, and has a base activity- and
visit schedule covering seven days. The simulated disease spreads through
contacts that arise from joint visits to the locations where activities take
place. The simulation model has a plugin-type programmable behavioral model
where, based on the dynamics and observables tracked by the simulator, agents
decide on actions such as wearing a mask, engaging in social distancing, or
refraining from certain activity types by staying at home instead. The plugins
are supplied as Python code. To the best of our knowledge, BESSIE is a unique
simulator supporting this feature set, and most certainly as open software.
  To illustrate the use of BESSIE, we provide a COVID-relevant example
demonstrating some of its capabilities. The example uses a synthetic population
for the City of Charlottesville, Virginia. Both this population and the Python
plugin modules used in the example are made available. The Python
implementation, which can run on anything from a laptop to a cluster, is made
available under the Apache 2.0 license
(https://www.apache.org/licenses/LICENSE-2.0.html). The example population
accompanying this publication is made available under the CC BY 4.0 license
(https://creativecommons.org/licenses/by/4.0/).


title:
All-optical pulse switching with a periodically driven dissipative
  quantum system

abstract:
  All-optical switching used to switch the input optical signals without any
electro-optical conversion plays a vital role in the next generation of optical
information processing devices. Even all-optical switchings (AOSs) with
continuous input signals have been widely studied, all-optical pulse switchings
(AOPSs) whose input signals are pulse sequences have rarely been investigated
because of the time-dependent Hamiltonian, especially for dissipative quantum
systems. In this paper, we propose an AOPS scheme, where a strong pulsed field
is used to switch another pulsed input signal. With the help of
Floquet-Lindblad theory, we identify the control field that can effectively
turn on/off the input signal whose amplitude envelope is a square-wave (SW)
pulse train in a three-level dissipative system. By comparing the properties of
the AOPSs controlled by a continuous-wave (CW) field and an SW control field,
we find that the SW field is more suitable to be a practical tool for
controlling the input SW signal. It is interesting to impress that the
switching efficacy is robust against pulse errors. The proposed protocol is
readily implemented in atomic gases or superconducting circuits and corresponds
to AOPSs or all-microwave pulse switchings.


title: Statistics on Almost-Fibonacci Pattern-Avoiding Permutations

abstract:
  We prove that $|Av_n(231,312,1432)|$, $|Av_n(312,321,1342)|$
$|Av_n(231,312,4321,21543)|$, and $ |Av_n(321,231,4123,21534)|$, are all equal
to $F_{n+1} - 1$ where $F_n$ is the $n$-th Fibonacci number using the
convention $F_0 = F_1 = 1$ and $Av_n(S)$ is the set of all permutations of
length $n$ that avoid all of the patterns in the set $S$. To do this, we
characterize the structures of the permutations in these sets in terms of
Fibonacci permutations. Then, we further quantify the structures using
statistics such as inversion number and a statistic that measures the length of
Fibonacci subsequences. Finally, we encode these statistics in generating
functions written in terms of the generating function for Fibonacci
permutations. We use these generating functions to find analogs about
recurrence relation and addition formulae of Fibonacci identities.


title:
Quantum and classical annealing in a continuous space with multiple
  local minima

abstract:
  The protocol of quantum annealing is applied to an optimization problem with
a one-dimensional continuous degree of freedom, a variant of the problem
proposed by Shinomoto and Kabashima. The energy landscape has a number of local
minima, and the classical approach of simulated annealing is predicted to have
a logarithmically slow convergence to the global minimum. We show by extensive
numerical analyses that quantum annealing yields a power law convergence, thus
an exponential improvement over simulated annealing. The power is larger, and
thus the convergence is faster, than a prediction by an existing
phenomenological theory for this problem. Performance of simulated annealing is
shown to be enhanced by introducing quasi-global searches across energy
barriers, leading to a power-law convergence but with a smaller power than in
the quantum case and thus a slower convergence classically even with
quasi-global search processes. We also reveal how diabatic quantum dynamics,
quantum tunneling in particular, steers the systems toward the global minimum
by a meticulous choice of annealing schedule. This latter result explicitly
contrasts the role of tunneling in quantum annealing against the classical
counterpart of stochastic optimization by simulated annealing.


title:
The hydrostatic approximation of the Boussinesq equations with rotation
  in a thin domain

abstract:
  In this paper, we improve the global existence result in [9] slightly. More
precisely, the global existence of strong solutions to the primitive equations
with only horizontal viscosity and diffusivity is obtained under the assumption
of initial data $(v_0,T_0) \in H^1$ with $\partial_z v_0 \in L^4$. Moreover, we
prove that the scaled Boussinesq equations with rotation strongly converge to
the primitive equations with only horizontal viscosity and diffusivity, in the
cases of $H^1$ initial data, $H^1$ initial data with additional regularity
$\partial_z v_0 \in L^4$ and $H^2$ initial data, respectively, as the aspect
ration parameter $\lambda$ goes to zero, and the rate of convergence is of the
order $O(\lambda^{{\eta}/2})$ with
$\eta=\min\{2,\beta-2,\gamma-2\}(2<\beta,\gamma<\infty)$. The convergence
result implies a rigorous justification of the hydrostatic approximation.


title: Embedded Code Generation with CVXPY

abstract:
  We introduce CVXPYgen, a tool for generating custom C code, suitable for
embedded applications, that solves a parametrized class of convex optimization
problems. CVXPYgen is based on CVXPY, a Python-embedded domain-specific
language that supports a natural syntax (that follows the mathematical
description) for specifying convex optimization problems. Along with the C
implementation of a custom solver, CVXPYgen creates a Python wrapper for
prototyping and desktop (non-embedded) applications. We give two examples,
position control of a quadcopter and back-testing a portfolio optimization
model. CVXPYgen outperforms a state-of-the-art code generation tool in terms of
problem size it can handle, binary code size, and solve times. CVXPYgen and the
generated solvers are open-source.


title: Consent as a Foundation for Responsible Autonomy

abstract:
  This paper focuses on a dynamic aspect of responsible autonomy, namely, to
make intelligent agents be responsible at run time. That is, it considers
settings where decision making by agents impinges upon the outcomes perceived
by other agents. For an agent to act responsibly, it must accommodate the
desires and other attitudes of its users and, through other agents, of their
users.
  The contribution of this paper is twofold. First, it provides a conceptual
analysis of consent, its benefits and misuses, and how understanding consent
can help achieve responsible autonomy. Second, it outlines challenges for AI
(in particular, for agents and multiagent systems) that merit investigation to
form as a basis for modeling consent in multiagent systems and applying consent
to achieve responsible autonomy.


title: Mobility Equity from a Game-Theoretic Perspective

abstract:
  In this letter, we consider a multi-modal mobility system of travelers, and
propose a game-theoretic framework to efficiently assign each traveler to a
mobility service (e.g., different modes of transportation). Our focus in this
framework is to maximize the "mobility equity" in the sense of respecting the
mobility budgets of the travelers. Each traveler seeks to travel using only one
service (e.g., car, bus, train, bike). The services are capacitated and can
serve up to a fixed number of travelers at any instant of time. Thus, our
problem falls under the category of many-to-one assignment problems, where the
goal is to find the conditions that guarantee the stability of assignments. We
formulate a linear program of maximizing the mobility equity of travelers and
we fully characterize the optimal solution. We also show that our framework
under the proposed pricing scheme induces truthfulness from the strategic
travelers, while they have incentives to voluntarily participate under
informational asymmetry.


title: Topological Iwasawa invariants and Arithmetic Statistics

abstract:
  Given a prime number $p$, we study topological analogues of Iwasawa
invariants associated to $\mathbb{Z}_p$-covers of the $3$-sphere that are
branched along a link. We prove explicit criteria to detect these Iwasawa
invariants, and apply them to the study of links consisting of $2$ component
knots. Fixing the prime $p$, we prove statistical results for the average
behaviour of $p$-primary Iwasawa invariants for $2$-bridge links that are in
Schubert normal form. Our main result, which is entirely unconditional, shows
that the density of $2$-bridge links for which the $\mu$-invariant vanishes,
and the $\lambda$-invariant is equal to $1$, is $(1-\frac{1}{p})$. We also
conjecture that the density of $2$-bridge links for which the $\mu$-invariant
vanishes is $1$, and this is significantly backed by computational evidence.
Our results are proven in a topological setting, yet have arithmetic
significance, as we set out new directions in arithmetic statistics and
arithmetic topology.


title:
RT-Bench: an Extensible Benchmark Framework for the Analysis and
  Management of Real-Time Applications

abstract:
  Benchmarking is crucial for testing and validating any system, even more so
in real-time systems. Typical real-time applications adhere to well-understood
abstractions: they exhibit a periodic behavior, operate on a well-defined
working set, and strive for stable response time avoiding non-predicable
factors such as page faults. Unfortunately, available benchmark suites fail to
reflect key characteristics of real-time applications. Practitioners and
researchers must resort to either benchmark heavily approximated real-time
environments, or to re-engineer available benchmarks to add -- if possible --
the sought-after features. Additionally, the measuring and logging capabilities
provided by most benchmark suites are not tailored "out-of-the-box" to
real-time environments, and changing basic parameters such as the scheduling
policy often becomes a tiring and error-prone exercise.
  In this paper, we present RT-bench, an open-source framework adding standard
real-time features to virtually any existing benchmark. Furthermore, RT-bench
provides an easy-to-use, unified command line interface to customize key
aspects of the real-time execution of a set of benchmarks. Our framework is
guided by four main criteria: 1) cohesive interface, 2) support for periodic
application behavior and deadline semantics, 3) controllable memory footprint,
and 4) extensibility and portability. We have integrated within the framework
applications from the widely used SD-VBS and IsolBench suites. We showcase a
set of use-cases that are representative of typical real-time system evaluation
scenarios and that can be easily conducted via RT-Bench.


title:
Accelerating Model-Free Policy Optimization Using Model-Based Gradient:
  A Composite Optimization Perspective

abstract:
  We develop an algorithm that combines model-based and model-free methods for
solving a nonlinear optimal control problem with a quadratic cost in which the
system model is given by a linear state-space model with a small additive
nonlinear perturbation. We decompose the cost into a sum of two functions, one
having an explicit form obtained from the approximate linear model, the other
being a black-box model representing the unknown modeling error. The
decomposition allows us to formulate the problem as a composite optimization
problem. To solve the optimization problem, our algorithm performs gradient
descent using the gradient obtained from the approximate linear model until
backtracking line search fails, upon which the model-based gradient is compared
with the exact gradient obtained from a model-free algorithm. The difference
between the model gradient and the exact gradient is then used for compensating
future gradient-based updates. Our algorithm is shown to decrease the number of
function evaluations compared with traditional model-free methods both in
theory and in practice.


title: Towards Abstractive Grounded Summarization of Podcast Transcripts

abstract:
  Podcasts have recently shown a rapid rise in popularity. Summarization of
podcast transcripts is of practical benefit to both content providers and
consumers. It helps consumers to quickly decide whether they will listen to the
podcasts and reduces the cognitive load of content providers to write
summaries. Nevertheless, podcast summarization faces significant challenges
including factual inconsistencies with respect to the inputs. The problem is
exacerbated by speech disfluencies and recognition errors in transcripts of
spoken language. In this paper, we explore a novel abstractive summarization
method to alleviate these challenges. Specifically, our approach learns to
produce an abstractive summary while grounding summary segments in specific
portions of the transcript to allow for full inspection of summary details. We
conduct a series of analyses of the proposed approach on a large podcast
dataset and show that the approach can achieve promising results. Grounded
summaries bring clear benefits in locating the summary and transcript segments
that contain inconsistent information, and hence significantly improve
summarization quality in both automatic and human evaluation metrics.


title:
Bianchi type-V Transitioning model in Brans-Dicke theory with
  Observational Constraint

abstract:
  In this paper, we have examined the viability of the Bianchi type-V universe
in Brans-Dicke (BD) theory of gravitation. We have discussed the interacting
and non-interacting scenarios between dark matter (DM) and dark energy (DE) of
the derived universe within the framework of BD theory. CCA technique has been
applied to constrain the model parameters using 46 values of observational
Hubble data (OHD), Pantheon data (the latest compilation of SNIa with 40 binned
in the redshift range $0.014 \leq z \leq 1.62)$ and their combined datasets. We
establish an exact solution of the field equations to derive the dynamics of
the derived universe and the obtained results are found to agree with the
observations. We also noted a distinctive change in the sign of the
deceleration parameter from positive to negative, as well as the presence of a
transition red-shift exists. Using various observational data points, the
evolution trajectories for $(r~- s)$ diagnostic planes are shown to understand
the geometrical behavior of the Bianchi-V model. Some physical properties of
the universe are also discussed. It's also worth noting that the conclusions of
the cosmological parameter are consistent with modern observational data.


title:
Arithmetic crosscorrelation of pseudorandom binary sequences of coprime
  periods

abstract:
  The (classical) crosscorrelation is an important measure of pseudorandomness
of two binary sequences for applications in communications. The arithmetic
crosscorrelation is another figure of merit introduced by Goresky and Klapper
generalizing Mandelbaum's arithmetic autocorrelation.
  First we observe that the arithmetic crosscorrelation is constant for two
binary sequences of coprime periods which complements the analogous result for
the classical crosscorrelation.
  Then we prove upper bounds for the constant arithmetic crosscorrelation of
two Legendre sequences of different periods and of two binary $m$-sequences of
coprime periods, respectively.


title: The Angell Plot from the Potential Energy Landscape Perspective

abstract:
  Within the scenario of the potential energy landscape (PEL), a thermodynamic
model has been developed to uncover the physics behind the Angell plot. In our
model, by separating the barrier distribution in PELs into a Gaussian-like and
a power-law form, we obtain a general relationship between the relaxation time
and the temperature. The wide range of the experimental data in the Angell
plot, as well as the molecular-dynamics data, can be excellently fitted by two
characteristic parameters, the effective barrier ({\omega}) and the effective
width ({\sigma}) of a Gaussian-like distribution. More importantly, the fitted
{\omega} and {\sigma}^2 for all glasses are found to have a simple linear
relationship within a very narrow band, and fragile and strong glasses are well
separated in the {\omega}-{\sigma}^2 plot, which indicates that glassy states
only appear in a specific region of the PEL.


title:
Forecast of Neutrino Cosmology from the CSST Photometric Galaxy
  Clustering and Cosmic Shear Surveys

abstract:
  China Space Station Telescope (CSST) is a forthcoming powerful Stage IV
space-based optical survey equipment. It is expected to explore a number of
important cosmological problems in extremely high precision. In this work, we
focus on investigating the constraints on neutrino mass and other cosmological
parameters under the model of cold dark matter with a constant equation of
state of dark energy ($w$CDM), using the mock data from the CSST photometric
galaxy clustering and cosmic shear surveys (i.e. 3$\times$2pt). The systematics
from galaxy bias, photometric redshift uncertainties, intrinsic alignment,
shear calibration, baryonic feedback, non-linear, and instrumental effects are
also included in the analysis. We generate the mock data based on the COSMOS
catalog considering the instrumental and observational effects of the CSST, and
make use of the Markov Chain Monte Carlo (MCMC) method to perform the
constraints. Comparing to the results from current similar measurements, we
find that CSST 3$\times$2pt surveys can improve the constraints on the
cosmological parameters by one order of magnitude at least. We can obtain an
upper limit for the sum of neutrino mass $\Sigma m_{\nu} \lesssim 0.36$ (0.56)
eV at 68\% (95\%) confidence level, and $\Sigma m_{\nu} \lesssim 0.23$ (0.29)
eV at 68\% (95\%) confidence level if ignore the baryonic effect, which is
comparable to the {\it Planck} results and much better than the current
photometric surveys. This indicates that the CSST photometric surveys can
provide stringent constraints on the neutrino mass and other cosmological
parameters, and the results also can be further improved by including data from
other kinds of CSST cosmological surveys.


title:
CP violation in $B_s$ decays mediated by two nearly-degenerate Majorana
  neutrinos

abstract:
  We study the CP violation in the lepton-number-violating process $B_s^0
\rightarrow D_s^- \mu^+ \mu^+ \pi^-$ and its CP-conjugate process $\bar{B_s^0}
\rightarrow D_s^+ \mu^- \mu^- \pi^+$ that are induced by two GeV-scale Majorana
neutrinos with nearly degenerate masses. Our result shows that the size of the
CP violation could become considerable if the mass difference between the two
Majorana neutrinos is around the decay width of the neutrinos. We perform
experimental analysis on the CP-violating processes at LHCb within its upgrade
II. The analysis shows that under current constraint on the heavy-light
neutrino mixing parameter, it is possible that such CP violation is observed
with the LHCb experimental ability. We also give the upper bound on the
heavy-light mixing parameter $|U_{\mu N}|^2$ under the assumption that no
positive signal of the process is observed. The result shows that such modes
can give complementary constraint compared with previous experiments such as
NuTeV, BEBC, etc. in the mass region 1 GeV $< m_N <$ 3 GeV.


title: Task-guided Disentangled Tuning for Pretrained Language Models

abstract:
  Pretrained language models (PLMs) trained on large-scale unlabeled corpus are
typically fine-tuned on task-specific downstream datasets, which have produced
state-of-the-art results on various NLP tasks. However, the data discrepancy
issue in domain and scale makes fine-tuning fail to efficiently capture
task-specific patterns, especially in the low data regime. To address this
issue, we propose Task-guided Disentangled Tuning (TDT) for PLMs, which
enhances the generalization of representations by disentangling task-relevant
signals from the entangled representations. For a given task, we introduce a
learnable confidence model to detect indicative guidance from context, and
further propose a disentangled regularization to mitigate the over-reliance
problem. Experimental results on GLUE and CLUE benchmarks show that TDT gives
consistently better results than fine-tuning with different PLMs, and extensive
analysis demonstrates the effectiveness and robustness of our method. Code is
available at https://github.com/lemon0830/TDT.


title:
Gated Domain-Invariant Feature Disentanglement for Domain Generalizable
  Object Detection

abstract:
  For Domain Generalizable Object Detection (DGOD), Disentangled Representation
Learning (DRL) helps a lot by explicitly disentangling Domain-Invariant
Representations (DIR) from Domain-Specific Representations (DSR). Considering
the domain category is an attribute of input data, it should be feasible for
networks to fit a specific mapping which projects DSR into feature channels
exclusive to domain-specific information, and thus much cleaner disentanglement
of DIR from DSR can be achieved simply on channel dimension. Inspired by this
idea, we propose a novel DRL method for DGOD, which is termed Gated
Domain-Invariant Feature Disentanglement (GDIFD). In GDIFD, a Channel Gate
Module (CGM) learns to output channel gate signals close to either 0 or 1,
which can mask out the channels exclusive to domain-specific information
helpful for domain recognition. With the proposed GDIFD, the backbone in our
framework can fit the desired mapping easily, which enables the channel-wise
disentanglement. In experiments, we demonstrate that our approach is highly
effective and achieves state-of-the-art DGOD performance.


title:
Making DeepFakes more spurious: evading deep face forgery detection via
  trace removal attack

abstract:
  DeepFakes are raising significant social concerns. Although various DeepFake
detectors have been developed as forensic countermeasures, these detectors are
still vulnerable to attacks. Recently, a few attacks, principally adversarial
attacks, have succeeded in cloaking DeepFake images to evade detection.
However, these attacks have typical detector-specific designs, which require
prior knowledge about the detector, leading to poor transferability. Moreover,
these attacks only consider simple security scenarios. Less is known about how
effective they are in high-level scenarios where either the detectors or the
attacker's knowledge varies. In this paper, we solve the above challenges with
presenting a novel detector-agnostic trace removal attack for DeepFake
anti-forensics. Instead of investigating the detector side, our attack looks
into the original DeepFake creation pipeline, attempting to remove all
detectable natural DeepFake traces to render the fake images more "authentic".
To implement this attack, first, we perform a DeepFake trace discovery,
identifying three discernible traces. Then a trace removal network (TR-Net) is
proposed based on an adversarial learning framework involving one generator and
multiple discriminators. Each discriminator is responsible for one individual
trace representation to avoid cross-trace interference. These discriminators
are arranged in parallel, which prompts the generator to remove various traces
simultaneously. To evaluate the attack efficacy, we crafted heterogeneous
security scenarios where the detectors were embedded with different levels of
defense and the attackers' background knowledge of data varies. The
experimental results show that the proposed attack can significantly compromise
the detection accuracy of six state-of-the-art DeepFake detectors while causing
only a negligible loss in visual quality to the original DeepFake samples.


title: Non-linear Embeddings in Hilbert Simplex Geometry

abstract:
  A key technique of machine learning and computer vision is to embed discrete
weighted graphs into continuous spaces for further downstream processing.
Embedding discrete hierarchical structures in hyperbolic geometry has proven
very successful since it was shown that any weighted tree can be embedded in
that geometry with arbitrary low distortion. Various optimization methods for
hyperbolic embeddings based on common models of hyperbolic geometry have been
studied. In this paper, we consider Hilbert geometry for the standard simplex
which is isometric to a vector space equipped with the variation polytope norm.
We study the representation power of this Hilbert simplex geometry by embedding
distance matrices of graphs. Our findings demonstrate that Hilbert simplex
geometry is competitive to alternative geometries such as the Poincar\'e
hyperbolic ball or the Euclidean geometry for embedding tasks while being fast
and numerically robust.


title:
Coincidence Points of Parameterized Generalized Equations with
  Applications to Optimal Value Functions

abstract:
  The paper studies coincidence points of parameterized set-valued mappings
(multifunctions), which provide an extended framework to cover several
important topics in variational analysis and optimization that include the
existence of solutions of parameterized generalized equations, implicit
function and fixed-point theorems, optimal value functions in parametric
optimization, etc. Using the advanced machinery of variational analysis and
generalized differentiation that furnishes complete characterizations of
well-posedness properties of multifunctions, we establish a general theorem
ensuring the existence of parameter-dependent coincidence point mappings with
explicit error bounds for parameterized multifunctions between
infinite-dimensional spaces. The obtained major result yields a new implicit
function theorem and allows us to derive efficient conditions for
semicontinuity and continuity of optimal value functions associated with
parametric minimization problems subject to constraints governed by
parameterized generalized equations.


title: Evaluating the quantum Ziv-Zakai bound in noisy environments

abstract:
  In the highly non-Gaussian regime, the quantum Ziv-Zakai bound (QZZB)
provides a lower bound on the available precision, demonstrating the better
performance compared with the quantum Cram\'er-Rao bound. However, evaluating
the impact of a noisy environment on the QZZB without applying certain
approximations proposed by Tsang [Phys. Rev. Lett. 108, 230401 (2012)] remains
a difficult challenge. In this paper, we not only derive the general form of
the QZZB with the photon loss and the phase diffusion by invoking the technique
of integration within an ordered product of operators, but also show its
estimation performance for several different Gaussian resources, such as a
coherent state (CS), a single-mode squeezed vacuum state (SMSVS) and a two-mode
squeezed vacuum state (TMSVS). Our results indicate that compared with the
SMSVS and the TMSVS, the QZZB for the CS always shows the better estimation
performance under the photon-loss environment. More interestingly, for the
phase-diffusion environment, the estimation performance of the QZZB for the
TMSVS can be better than that for the CS throughout a wide range of
phase-diffusion strength. Our findings will provide a useful guidance for
investigating the noisy quantum parameter estimation.