Abstract:
  The existence of adversarial examples poses a real danger when deep neural
networks are deployed in the real world. The go-to strategy to quantify this
vulnerability is to evaluate the model against specific attack algorithms. This
approach is however inherently limited, as it says little about the robustness
of the model against more powerful attacks not included in the evaluation. We
develop a unified mathematical framework to describe relaxation-based
robustness certification methods, which go beyond adversary-specific robustness
evaluation and instead provide provable robustness guarantees against attacks
by any adversary. We discuss the fundamental limitations posed by single-neuron
relaxations and show how the recent ``k-ReLU'' multi-neuron relaxation
framework of Singh et al. (2019) obtains tighter correlation-aware activation
bounds by leveraging additional relational constraints among groups of neurons.
Specifically, we show how additional pre-activation bounds can be mapped to
corresponding post-activation bounds and how they can in turn be used to obtain
tighter robustness certificates. We also present an intuitive way to visualize
different relaxation-based certification methods. By approximating multiple
non-linearities jointly instead of separately, the k-ReLU method is able to
bypass the convex barrier imposed by single neuron relaxations.


Abstract:
  We present a new model of collective decision making that captures important
crowd-funding and donor coordination scenarios. In the setting, there is a set
of projects (each with its own cost) and a set of agents (that have their
budgets as well as preferences over the projects). An outcome is a set of
projects that are funded along with the specific contributions made by the
agents. For the model, we identify meaningful axioms that capture concerns
including fairness, efficiency, and participation incentives. We then propose
desirable rules for the model and study, which sets of axioms can be satisfied
simultaneously. An experimental study indicates the relative performance of
different rules as well as the price of enforcing fairness axioms.


Abstract:
  The '$S_8$ tension' is a longstanding discrepancy between the cosmic
microwave background (CMB) and weak gravitational lensing determination of the
amplitude of matter fluctuations, parametrized as
$S_8\equiv\sigma_8(\Omega_m/0.3)^{0.5}$, where $\sigma_8$ is the root mean
square of matter fluctuations on a 8 $h^{-1}$Mpc scale, and $\Omega_m$ is the
total matter abundance. It was recently shown that dark matter (DM) decaying
into a massless (dark radiation) and a massive (warm DM) species, with a
lifetime $\Gamma^{-1} \simeq 55~(\varepsilon/0.007)^{1.4}$ Gyrs -- where
$\varepsilon$ represent the mass-energy fraction transferred to the massless
component -- can ease the tension. Thanks to a fast and accurate fluid
approximation scheme for the warm species, we perform a comprehensive study of
this 2-body decaying DM scenario, discussing in detail its dynamics and its
impact on the CMB and linear matter power spectra. We then investigate the
implications for the '$S_8$ tension' against a number of changes in the
analysis: different $S_8$ priors, marginalization over the lensing information
in Planck data, trading Planck high$-\ell$ polarization data for those from the
SPTpol and ACTPol surveys, and the inclusion of the recent results from the
Xenon1T collaboration. We conclude that the preference for decaying DM,
apparent only when the $S_8$ value determined from weak lensing data is added
to the analysis, does not sensibly degrade the fit to any of the cosmological
data-sets considered, and that the model could potentially explain the
anomalous electron recoil excess reported by the Xenon1T collaboration.
Furthermore, we explictly show that while current CMB data alone are not
sensitive enough to distinguish between standard CDM and decaying DM,
next-generation CMB observations (CMB-S4) can unambiguously detect its
signature.


Abstract:
  While adversarial neural networks have been shown successful for static image
attacks, very few approaches have been developed for attacking online image
streams while taking into account the underlying physical dynamics of
autonomous vehicles, their mission, and environment. This paper presents an
online adversarial machine learning framework that can effectively misguide
autonomous vehicles' missions. In the existing image attack methods devised
toward autonomous vehicles, optimization steps are repeated for every image
frame. This framework removes the need for fully converged optimization at
every frame to realize image attacks in real-time. Using reinforcement
learning, a generative neural network is trained over a set of image frames to
obtain an attack policy that is more robust to dynamic and uncertain
environments. A state estimator is introduced for processing image streams to
reduce the attack policy's sensitivity to physical variables such as unknown
position and velocity. A simulation study is provided to validate the results.


Abstract:
  A critical question in astrobiology is whether exoEarth candidates (EECs) are
Earth-like, in that they originate life that progressively oxygenates their
atmospheres similarly to Earth. We propose answering this question
statistically by searching for O2 and O3 on EECs with missions such as HabEx or
LUVOIR. We explore the ability of these missions to constrain the fraction, fE,
of EECs that are Earth-like in the event of a null detection of O2 or O3 on all
observed EECs. We use the Planetary Spectrum Generator to simulate observations
of EECs with O2 and O3 levels based on Earth's history. We consider four
instrument designs: LUVOIR-A (15m), LUVOIR-B (8m), HabEx with a starshade (4m,
"HabEx/SS"), HabEx without a starshade (4m, "HabEx/no-SS"); as well as three
estimates of the occurrence rate of EECs (eta_earth): 24%, 5%, and 0.5%. In the
case of a null-detection, we find that for eta_earth = 24%, LUVOIR-A, LUVOIR-B,
and HabEx/SS would constrain fE to <= 0.094, <= 0.18, and <= 0.56,
respectively. This also indicates that if fE is greater than these upper
limits, we are likely to detect O3 on at least 1 EEC. Conversely, we find that
HabEx/no-SS cannot constrain fE, due to the lack of an coronagraph ultraviolet
channel. For eta_earth = 5%, only LUVOIR-A and LUVOIR-B would be able to
constrain fE, to <= 0.45 and <= 0.85, respectively. For eta_earth = 0.5%, none
of the missions would allow us to constrain fE, due to the low number of
detectable EECs. We conclude that the ability to constrain fE is more robust to
uncertainties in eta_earth for missions with larger aperture mirrors. However
all missions are susceptible to an inconclusive null detection if eta_earth is
sufficiently low.


Abstract:
  Visual perception of the objects in a 3D environment is a key to successful
performance in autonomous driving and simultaneous localization and mapping
(SLAM). In this paper, we present a real time approach for estimating the
distances to multiple objects in a scene using only a single-shot image. Given
a 2D Bounding Box (BBox) and object parameters, a 3D distance to the object can
be calculated directly using 3D reprojection; however, such methods are prone
to significant errors because an error from the 2D detection can be amplified
in 3D. In addition, it is also challenging to apply such methods to a real-time
system due to the computational burden. In the case of the traditional
multi-object detection methods, %they mostly pay attention to existing works
have been developed for specific tasks such as object segmentation or 2D BBox
regression. These methods introduce the concept of anchor BBox for elaborate 2D
BBox estimation, and predictors are specialized and trained for specific 2D
BBoxes. In order to estimate the distances to the 3D objects from a single 2D
image, we introduce the notion of \textit{anchor distance} based on an object's
location and propose a method that applies the anchor distance to the
multi-object detector structure. We let the predictors catch the distance prior
using anchor distance and train the network based on the distance. The
predictors can be characterized to the objects located in a specific distance
range. By propagating the distance prior using a distance anchor to the
predictors, it is feasible to perform the precise distance estimation and
real-time execution simultaneously. The proposed method achieves about 30 FPS
speed, and shows the lowest RMSE compared to the existing methods.


Abstract:
  Unsupervised Domain Adaptation (UDA) aims to align the labeled source
distribution with the unlabeled target distribution to obtain domain invariant
predictive models. However, the application of well-known UDA approaches does
not generalize well in Semi-Supervised Domain Adaptation (SSDA) scenarios where
few labeled samples from the target domain are available. In this paper, we
propose a simple Contrastive Learning framework for semi-supervised Domain
Adaptation (CLDA) that attempts to bridge the intra-domain gap between the
labeled and unlabeled target distributions and inter-domain gap between source
and unlabeled target distribution in SSDA. We suggest employing class-wise
contrastive learning to reduce the inter-domain gap and instance-level
contrastive alignment between the original (input image) and strongly augmented
unlabeled target images to minimize the intra-domain discrepancy. We have shown
empirically that both of these modules complement each other to achieve
superior performance. Experiments on three well-known domain adaptation
benchmark datasets namely DomainNet, Office-Home, and Office31 demonstrate the
effectiveness of our approach. CLDA achieves state-of-the-art results on all
the above datasets.


Abstract:
  A key issue in system design is the lack of communication between hardware,
software and domain expert. Recent research work shows progress in automatic
HW/SW co-design flows of neural accelerators that seems to make this kind of
communication obsolete. Most real-world systems, however, are a composition of
multiple processing units, communication networks and memories. A HW/SW
co-design process of (reconfigurable) neural accelerators, therefore, is an
important sub-problem towards a common co-design methodology. The ultimate
challenge is to define the constraints for the design space exploration on
system level - a task which requires deep knowledge and understanding of
hardware architectures, mapping of workloads onto hardware and the application
domain, e.g. artificial intelligence.
  For most projects, these skills are distributed among several people or even
different teams which is one of the major reasons why there is no established
end-to-end development methodology for digital systems. This position paper
discusses possibilities how to establish such a methodology for systems that
include (reconfigurable) dedicated accelerators and outlines the central role
that languages and tools play in the process.


Abstract:
  The Pr$_{0.50}$Sr$_{0.5}$0CoO$_3$ perovskite exhibits unique
magnetostructural properties among the rest of ferromagnetic/metallic
Ln$_{0.50}$Sr$_{0.50}$CoO$_3$ compounds. The sudden orthorhombic-tetragonal
(Imma $\to$ I4/mcm) structural transition produces an unusual magnetic behavior
versus temperature and external magnetic fields. In particular, the symmetry
change is responsible for a spontaneous spin rotation in this metallic oxide.
We have studied half-doped Ln$_{0.50}$(Sr$_{1-x}$A$_x$)$_{0.50}$CoO$_3$
cobaltites varying the ionic radius rA of A-site cations (divalent cations and
lanthanides) in order to complete the T-rA phase diagram. The influence of the
structural distortion and the A-cations size for the occurrence of a
spontaneous spin reorientation in the metallic state has been investigated. As
the reorientation of the magnetization is driven by the temperature induced
collapse of the orthorhombic distortion, a careful investigation of the
structural symmetry is presented varying the structural distortion of the
Sr-rich half-doped cobaltites by means of both compositional and temperature
changes. The region in the phase diagram of these perovskites where the phase
of magnetic symmetry Fm'm'm replaces that of Im'm'a symmetry was determined in
this family of ferromagnetic/metallic cobaltites. In that region the
magnetization direction has rotated 45 degrees within the a-b plane with
respect to the second.


Abstract:
  The new experiment data of muon g-2 is reported by the workers at Fermilab
National Accelerator Laboratory(FNAL). Combined with the previous Brookhaven
National Laboratory(BNL) E821 result, the departure from the standard model
prediction is about 4.2 $\sigma$. It strengthens our faith in the new physics.
$U(1)_X$SSM is the U(1) extension of the minimal supersymmetric standard model,
where we study the electroweak corrections to the anomalous magnetic dipole
moment of muon from the one-loop diagrams and some two-loop diagrams possessing
important contributions. These two-loop diagrams include Barr-Zee type, rainbow
type and diamond type. The virtual supersymmetric particles in these two-loop
diagrams are chargino, scalar neutrino, neutralino, scalar lepton, which are
supposed not very heavy to make relatively large corrections. We obtain the
Wilson coefficients of the dimension 6 operators inducing the anomalous
magnetic dipole moment of muon. The numerical results can reach $25\times
10^{-10}$ and even larger.


Abstract:
  Starting from the idea of Quantum Computing which is a concept that dates
back to 80s, we come to the present day where we can perform calculations on
real quantum computers. This sudden development of technology opens up new
scenarios that quickly lead to the desire and the real possibility of
integrating this technology into current software architectures. The usage of
frameworks that allow computation to be performed directly on quantum hardware
poses a series of challenges. This document describes a an architectural
framework that addresses the problems of integrating an API exposed Quantum
provider in an existing Enterprise architecture and it provides a minimum
viable product (MVP) solution that really merges classical quantum computers on
a basic scenario with reusable code on GitHub repository. The solution
leverages a web-based frontend where user can build and select applications/use
cases and simply execute it without any further complication. Every triggered
run leverages on multiple backend options, that include a scheduler managing
the queuing mechanism to correctly schedule jobs and final results retrieval.
The proposed solution uses the up-to-date cloud native technologies (e.g. Cloud
Functions, Containers, Microservices) and serves as a general framework to
develop multiple applications on the same infrastructure.


Abstract:
  The two-loop correction to the mass of the $\phi^4$ kink is $0.0126\lambda/m$
in terms of the coupling $\lambda$ and the meson mass $m$ evaluated at the
minimum of the potential. This is calculated using a recently proposed
alternative to collective coordinates. Both the kink energy and the vacuum
energy are IR divergent at this order. To cancel the divergence, the two energy
densities are subtracted before integrating over space, or equivalently a
finite counterterm is added to the Hamiltonian density to cancel the vacuum
energy density. All spatial integrals are performed analytically. However in
the last step of our calculation, integrals over virtual momenta are performed
numerically.


Abstract:
  We tackle the problem of generalization to unseen configurations for dynamic
tasks in the real world while learning from high-dimensional image input. The
family of nonlinear dynamical system-based methods have successfully
demonstrated dynamic robot behaviors but have difficulty in generalizing to
unseen configurations as well as learning from image inputs. Recent works
approach this issue by using deep network policies and reparameterize actions
to embed the structure of dynamical systems but still struggle in domains with
diverse configurations of image goals, and hence, find it difficult to
generalize. In this paper, we address this dichotomy by leveraging embedding
the structure of dynamical systems in a hierarchical deep policy learning
framework, called Hierarchical Neural Dynamical Policies (H-NDPs). Instead of
fitting deep dynamical systems to diverse data directly, H-NDPs form a
curriculum by learning local dynamical system-based policies on small regions
in state-space and then distill them into a global dynamical system-based
policy that operates only from high-dimensional images. H-NDPs additionally
provide smooth trajectories, a strong safety benefit in the real world. We
perform extensive experiments on dynamic tasks both in the real world (digit
writing, scooping, and pouring) and simulation (catching, throwing, picking).
We show that H-NDPs are easily integrated with both imitation as well as
reinforcement learning setups and achieve state-of-the-art results. Video
results are at https://shikharbahl.github.io/hierarchical-ndps/


Abstract:
  The internal conversion from the optically bright S$_2$
($^1$B$_{2\mathrm{u}}$, $\pi\pi^*$) state to the dark S$_1$
($^1$B$_{3\mathrm{u}}$, n$\pi^*$) state in pyrazine is a standard benchmark for
experimental and theoretical studies on ultrafast radiationless decay. Since
2008 a few theoretical groups have suggested significant contributions of other
dark states S$_3$ ($^1$A$_\mathrm{u}$, n$\pi^*$) and S$_4$
($^1$B$_{2\mathrm{g}}$, n$\pi^*$) to the decay of S$_2$. We have previously
reported the results of nuclear wave packet simulations [Phys. Chem. Chem.
Phys. 17, 2012 (2015)] and photoelectron spectrum calculations [Chem. Phys.
515, 704 (2018)] that support the conventional two-state picture. In this
article, the two different approaches, i.e., wave packet simulation and
photoelectron spectrum calculation are combined: We computed the time-resolved
vacuum ultraviolet photoelectron spectrum and photoelectron angular
distribution for the ionization of the wave packet transferred from S$_2$ to
S$_1$. The present results reproduce almost all the characteristic features of
the corresponding experimental time-resolved spectrum [T. Horio et al., J.
Chem. Phys. 145, 044306 (2016)] such as a rapid change from a three-band to
two-band structure. This further supports the existence and character of the
widely accepted pathway (S$_2$ $\rightarrow$ S$_1$) of ultrafast internal
conversion in pyrazine.


Abstract:
  We consider the classical uncoded caching problem from an online learning
point-of-view. A cache of limited storage capacity can hold $C$ files at a time
from a large catalog. A user requests an arbitrary file from the catalog at
each time slot. Before the file request from the user arrives, a caching policy
populates the cache with any $C$ files of its choice. In the case of a
cache-hit, the policy receives a unit reward and zero rewards otherwise. In
addition to that, there is a cost associated with fetching files to the cache,
which we refer to as the switching cost. The objective is to design a caching
policy that incurs minimal regret while considering both the rewards due to
cache-hits and the switching cost due to the file fetches. The main
contribution of this paper is the switching regret analysis of a Follow the
Perturbed Leader-based anytime caching policy, which is shown to have an order
optimal switching regret. In this pursuit, we improve the best-known switching
regret bound for this problem by a factor of $\Theta(\sqrt{C}).$ We conclude
the paper by comparing the performance of different popular caching policies
using a publicly available trace from a commercial CDN server.


Abstract:
  Bulk nanobubbles (NBs) generated electrochemically by short voltage pulses of
alternating polarity behave differently from those produced by regular methods.
Only bubbles smaller than $200\;$nm are formed in the process and their
concentration is very high. Moreover, the bubbles containing both H2 and O2
gases disappear quickly via the combustion reaction, although the reaction in
such a small volume cannot happen according to the classical combustion theory.
Experimental facts about these unusual NBs are reviewed and current
understanding of the observed phenomena is provided. Visualisation methods of a
cloud of NBs above the electrodes are briefly discussed. Experimental
signatures demonstrating the reaction between the gases in NBs are considered.
A surface-assisted mechanism proposed for the combustion reactions in
restricted volumes with a high surface-to-volume ratio is discussed. It it
explained how the same mechanism may describe the audible explosion of
microbubbles, that is observed in certain circumstances.


Abstract:
  Accurate localization is of crucial importance for autonomous driving tasks.
Nowadays, we have seen a lot of sensor-rich vehicles (e.g. Robo-taxi) driving
on the street autonomously, which rely on high-accurate sensors (e.g. Lidar and
RTK GPS) and high-resolution map. However, low-cost production cars cannot
afford such high expenses on sensors and maps. How to reduce costs? How do
sensor-rich vehicles benefit low-cost cars? In this paper, we proposed a
light-weight localization solution, which relies on low-cost cameras and
compact visual semantic maps. The map is easily produced and updated by
sensor-rich vehicles in a crowd-sourced way. Specifically, the map consists of
several semantic elements, such as lane line, crosswalk, ground sign, and stop
line on the road surface. We introduce the whole framework of on-vehicle
mapping, on-cloud maintenance, and user-end localization. The map data is
collected and preprocessed on vehicles. Then, the crowd-sourced data is
uploaded to a cloud server. The mass data from multiple vehicles are merged on
the cloud so that the semantic map is updated in time. Finally, the semantic
map is compressed and distributed to production cars, which use this map for
localization. We validate the performance of the proposed map in real-world
experiments and compare it against other algorithms. The average size of the
semantic map is $36$ kb/km. We highlight that this framework is a reliable and
practical localization solution for autonomous driving.


Abstract:
  Chest radiographs are one of the most common diagnostic modalities in
clinical routine. It can be done cheaply, requires minimal equipment, and the
image can be diagnosed by every radiologists. However, the number of chest
radiographs obtained on a daily basis can easily overwhelm the available
clinical capacities. We propose RATCHET: RAdiological Text Captioning for Human
Examined Thoraces. RATCHET is a CNN-RNN-based medical transformer that is
trained end-to-end. It is capable of extracting image features from chest
radiographs, and generates medically accurate text reports that fit seamlessly
into clinical work flows. The model is evaluated for its natural language
generation ability using common metrics from NLP literature, as well as its
medically accuracy through a surrogate report classification task. The model is
available for download at: http://www.github.com/farrell236/RATCHET.


Abstract:
  Supervised machine learning models and their evaluation strongly depends on
the quality of the underlying dataset. When we search for a relevant piece of
information it may appear anywhere in a given passage. However, we observe a
bias in the position of the correct answer in the text in two popular Question
Answering datasets used for passage re-ranking. The excessive favoring of
earlier positions inside passages is an unwanted artefact. This leads to three
common Transformer-based re-ranking models to ignore relevant parts in unseen
passages. More concerningly, as the evaluation set is taken from the same
biased distribution, the models overfitting to that bias overestimate their
true effectiveness. In this work we analyze position bias on datasets, the
contextualized representations, and their effect on retrieval results. We
propose a debiasing method for retrieval datasets. Our results show that a
model trained on a position-biased dataset exhibits a significant decrease in
re-ranking effectiveness when evaluated on a debiased dataset. We demonstrate
that by mitigating the position bias, Transformer-based re-ranking models are
equally effective on a biased and debiased dataset, as well as more effective
in a transfer-learning setting between two differently biased datasets.


Abstract:
  Widespread adoption of deep models has motivated a pressing need for
approaches to interpret network outputs and to facilitate model debugging.
Instance attribution methods constitute one means of accomplishing these goals
by retrieving training instances that (may have) led to a particular
prediction. Influence functions (IF; Koh and Liang 2017) provide machinery for
doing this by quantifying the effect that perturbing individual train instances
would have on a specific test prediction. However, even approximating the IF is
computationally expensive, to the degree that may be prohibitive in many cases.
Might simpler approaches (e.g., retrieving train examples most similar to a
given test point) perform comparably? In this work, we evaluate the degree to
which different potential instance attribution agree with respect to the
importance of training samples. We find that simple retrieval methods yield
training instances that differ from those identified via gradient-based methods
(such as IFs), but that nonetheless exhibit desirable characteristics similar
to more complex attribution methods. Code for all methods and experiments in
this paper is available at:
https://github.com/successar/instance_attributions_NLP.


Abstract:
  The quadratic computational and memory complexities of the Transformer's
attention mechanism have limited its scalability for modeling long sequences.
In this paper, we propose Luna, a linear unified nested attention mechanism
that approximates softmax attention with two nested linear attention functions,
yielding only linear (as opposed to quadratic) time and space complexity.
Specifically, with the first attention function, Luna packs the input sequence
into a sequence of fixed length. Then, the packed sequence is unpacked using
the second attention function. As compared to a more traditional attention
mechanism, Luna introduces an additional sequence with a fixed length as input
and an additional corresponding output, which allows Luna to perform attention
operation linearly, while also storing adequate contextual information. We
perform extensive evaluations on three benchmarks of sequence modeling tasks:
long-context sequence modeling, neural machine translation and masked language
modeling for large-scale pretraining. Competitive or even better experimental
results demonstrate both the effectiveness and efficiency of Luna compared to a
variety


Abstract:
  We show that molecular spin qudits provide an ideal platform to simulate the
quantum dynamics of photon fields strongly interacting with matter. The basic
unit of the proposed molecular quantum simulator can be realized by a simple
dimer of a spin 1/2 and a spin $S$ transition metal ion, solely controlled by
microwave pulses. The spin $S$ ion is exploited to encode the photon field in a
flexible architecture, which enables the digital simulation of a wide range of
spin-boson models much more efficiently than by using a multi-qubit register.
The effectiveness of our proposal is demonstrated by numerical simulations
using realistic molecular parameters, whose prerequisites delineating possible
chemical approaches are also discussed.


Abstract:
  Extreme debris disks (EDDs) are rare systems with peculiarly large amounts of
warm dust that may stem from recent giant impacts between planetary embryos
during the final phases of terrestrial planet growth. Here we report on the
identification and characterization of six new EDDs. These disks surround F5-G9
type main-sequence stars with ages >100 Myr, have dust temperatures higher than
300K and fractional luminosities between 0.01 and 0.07. Using time-domain
photometric data at 3.4 and 4.6$\mu$m from the WISE all sky surveys, we
conclude that four of these disks exhibited variable mid-infrared emission
between 2010 and 2019. Analyzing the sample of all known EDDs, now expanded to
17 objects, we find that 14 of them showed changes at 3-5$\mu$m over the past
decade suggesting that mid-infrared variability is an inherent characteristic
of EDDs. We also report that wide-orbit pairs are significantly more common in
EDD systems than in the normal stellar population. While current models of
rocky planet formation predict that the majority of giant collisions occur in
the first 100 Myr, we find that the sample of EDDs is dominated by systems
older than this age. This raises the possibility that the era of giant impacts
may be longer than we think, or that some other mechanism(s) can also produce
EDDs. We examine a scenario where the observed warm dust stems from the
disruption and/or collisions of comets delivered from an outer reservoir into
the inner regions, and explore what role the wide companions could play in this
process.


Abstract:
  Variational autoencoders (VAEs) often suffer from posterior collapse, which
is a phenomenon in which the learned latent space becomes uninformative. This
is often related to a hyperparameter resembling the data variance. It can be
shown that an inappropriate choice of this parameter causes oversmoothness and
leads to posterior collapse in the linearly approximated case and can be
empirically verified for the general cases. Therefore, we propose AR-ELBO
(Adaptively Regularized Evidence Lower BOund), which controls the smoothness of
the model by adapting this variance parameter. In addition, we extend VAE with
alternative parameterizations on the variance parameter to deal with
non-uniform or conditional data variance. The proposed VAE extensions trained
with AR-ELBO show improved Fr\'echet inception distance (FID) on images
generated from the MNIST and CelebA datasets.


Abstract:
  We introduce Metatheory.jl: a lightweight and performant general purpose
symbolics and metaprogramming framework meant to simplify the act of writing
complex Julia metaprograms and to significantly enhance Julia with a native
term rewriting system, based on state-of-the-art equality saturation
techniques, and a dynamic first class Abstract Syntax Tree (AST) pattern
matching system that is dynamically composable in an algebraic fashion, taking
full advantage of the language's powerful reflection capabilities. Our
contribution allows to perform general purpose symbolic mathematics,
manipulation, optimization, synthesis or analysis of syntactically valid Julia
expressions with a clean and concise programming interface, both during
compilation or execution of programs.


Abstract:
  Background. Developers use Automated Static Analysis Tools (ASATs) to control
for potential quality issues in source code, including defects and technical
debt. Tool vendors have devised quite a number of tools, which makes it harder
for practitioners to select the most suitable one for their needs. To better
support developers, researchers have been conducting several studies on ASATs
to favor the understanding of their actual capabilities.
  Aims. Despite the work done so far, there is still a lack of knowledge
regarding (1) which source quality problems can actually be detected by static
analysis tool warnings, (2) what is their agreement, and (3) what is the
precision of their recommendations. We aim at bridging this gap by proposing a
large-scale comparison of six popular static analysis tools for Java projects:
Better Code Hub, CheckStyle, Coverity Scan, Findbugs, PMD, and SonarQube.
  Method. We analyze 47 Java projects and derive a taxonomy of warnings raised
by 6 state-of-the-practice ASATs. To assess their agreement, we compared them
by manually analyzing - at line-level - whether they identify the same issues.
Finally, we manually evaluate the precision of the tools.
  Results. The key results report a comprehensive taxonomy of ASATs warnings,
show little to no agreement among the tools and a low degree of precision.
  Conclusions. We provide a taxonomy that can be useful to researchers,
practitioners, and tool vendors to map the current capabilities of the tools.
Furthermore, our study provides the first overview on the agreement among
different tools as well as an extensive analysis of their precision.


Abstract:
  We introduce a simplified model of the electron-beam/plasma system to model
the electrical breakdown caused by the inductive electric field created by a
rapidly rising electron beam current. The rigid-beam model is a reduction to
the problem geometry to cylindrical coordinated and simplifications to
Maxwell's equations that are driven by a prescribed electron beam current
density. The model is very convenient for comparing various reductions of the
plasma dynamics and air chemistry equation while maintaining a good
approximation to the overall magnitude of the beam-created electric field. The
usefulness of this model is demonstrated by comparing results for two different
fluid reductions of the plasma dynamics: one where the collision rates are
computed from the local reduced electric field (E/p) and another where the
collision rates are determined from the mean energy per particle. We find that
the two methods give similar results at higher pressures where the energy
relation rate is large but differs significantly at lower pressures where the
characteristic inelastic energy loss time scale is comparable to or greater
than the rise time of the electron beam current.


Abstract:
  For hypocoercive linear kinetic equations we first formulate an optimisation
problem on a spatially dependent jump rate in order to find the fastest decay
rate of perturbations. In the Goldstein-Taylor model we show (i) that for a
locally optimal jump rate the spectral gap is determined by multiple, possible
degenerate, eigenvectors and (ii) that globally the fastest decay is obtained
with a spatially homogeneous jump rate. Our proofs rely on a connection to
damped wave equations and a relationship to the spectral theory of
Schr{\"o}dinger operators.


Abstract:
  Generative Adversarial Networks (GANs) are capable of generating convincing
imitations of elements from a training set, but the distribution of elements in
the training set affects to difficulty of properly training the GAN and the
quality of the outputs it produces. This paper looks at six different GANs
trained on different subsets of data from the game Lode Runner. The quality
diversity algorithm MAP-Elites was used to explore the set of quality levels
that could be produced by each GAN, where quality was defined as being beatable
and having the longest solution path possible. Interestingly, a GAN trained on
only 20 levels generated the largest set of diverse beatable levels while a GAN
trained on 150 levels generated the smallest set of diverse beatable levels,
thus challenging the notion that more is always better when training GANs.


Abstract:
  Lattice structure can dictate electronic and magnetic properties of a
material. Especially, reconstruction at a surface or heterointerface can create
properties that are fundamentally different from those of the corresponding
bulk material. We have investigated the lattice structure on the surface and in
the thin films of epitaxial SrRuO3 with the film thickness up to 22
pseudo-cubic unit cells (u.c.), using the combination of surface sensitive low
energy electron diffraction and bulk sensitive scanning transmission electron
microscopy. Our analysis indicates that, in contrast to many perovskite oxides,
the RuO6 tilt and rotational distortions appear even in single unit cell SrRuO3
thin films on cubic SrTiO3, while the full relaxation to the bulk-like
orthorhombic structure takes 3-4 u.c. from the interface for thicker films. Yet
the TiO6 octahedra of the substrate near the interface with SrRuO3 films show
no sign of distortion, unlike those near the interface with CaRuO3 films. Two
orthogonal in-plane rotated structural domains are identified. These structural
distortions are essential for the nature of the thickness dependent transport
and magnetism in ultrathin films.


Abstract:
  We present a detailed low-energy muon spin rotation and x-ray magnetic
circular dichroism (XMCD) investigation of the magnetic structure in ultra-thin
tetragonal (T)-CuO films. The measured muon-spin polarization decay indicates
an antiferromagnetic (AFM) order with a transition temperature higher than
200K. The XMCD signal obtained around the Cu $L_{2,3}$ edges indicates the
presence of pinned Cu$^{2+}$ moments that are parallel to the sample surface,
and additionally, isotropic paramagnetic moments. The pinning of some of the Cu
moments is caused by an AFM ordering consisting of moments that lie most likely
in the plane of the film. Moreover, pinned moments show a larger orbital
magnetic moment contribution with an approximate ratio of $m_{orb}/m_{spin} =
2$, indicating that these spins are located at sites with reduced symmetry.
Some fractions of the pinned moments remain pinned from an AFM background even
at 360K, indicating that $T_N >$ 360K. A simple model could explain
qualitatively these experimental findings; however, it is in contrast to
theoretical predictions, showing that the magnetic properties of ultra-thin
T-CuO films differ from bulk expectations and is more complex.


Abstract:
  We analyze a dataset of 51 current (2019-2020) Distributed Systems syllabi
from top Computer Science programs, focusing on finding the prevalence and
context in which topics related to performance are being taught in these
courses. We also study the scale of the infrastructure mentioned in DS courses,
from small client-server systems to cloud-scale, peer-to-peer, global-scale
systems. We make eight main findings, covering goals such as performance, and
scalability and its variant elasticity; activities such as performance
benchmarking and monitoring; eight selected performance-enhancing techniques
(replication, caching, sharding, load balancing, scheduling, streaming,
migrating, and offloading); and control issues such as trade-offs that include
performance and performance variability.


Abstract:
  This study explores the impact of high PV penetration on the inter-area
oscillation modes of large-scale power grids. A series of dynamic models with
various PV penetration levels are developed based on a detailed model
representing the U.S. Eastern Interconnection (EI). Transient simulations are
performed to investigate the change of inter-area oscillation modes with PV
penetration. The impact of PV control strategies and parameter settings on
inter-area oscillations is studied. This study finds that as PV increases, the
damping of the dominant oscillation mode decreases monotonically. It is also
observed that the mode shape varies with the PV control strategy and new
oscillation modes may emerge under inappropriate parameter settings in PV plant
controls.


Abstract:
  Urban green space has been regarded as contributing to citizen happiness by
promoting physical and mental health. However, how urban green space and
happiness are related across many countries of different socioeconomic
conditions has not been explained well. By measuring urban green space score
(UGS) from high-resolution Sentinel-2 satellite imagery of 90 global cities
that in total cover 179,168 km$^2$ and include 230 million people in 60
developed countries, we reveal that the amount of urban green space and the GDP
can explain the happiness level of the country. More precisely, urban green
space and GDP are each individually associated with happiness; happiness in the
30 wealthiest countries is explained only by urban green space, whereas GDP
alone explains happiness in the 30 other countries in this study. Lastly, we
further show that the relationship between urban green space and happiness is
mediated by social support and that GDP moderates the relationship between
social support and happiness, which underlines the importance of maintaining
urban green space as a place for social cohesion in promoting people's
happiness.


Abstract:
  Context: The Sun is by far a privileged target for testing stellar models
with unique precision. A recent concern appeared with the progress in the solar
surface abundances derivation that has led to a decrease of the solar
metallicity. While the ancient high-metallicity models were in fair agreement
with other solar observational indicators, it is no longer the case for
low-metallicity models. Recent collection of data are however promising to shed
a new light on it. For instance, the Borexino collaboration released in 2020
the first-ever complete estimate of neutrinos emitted in the CNO cycle. It has
reaffirmed the role of the neutrino constraints in the solar modelling process
and its associated issues. In parallel, newly claimed detection of solar
gravity modes of oscillations offers another opportunity of probing the
stratification in the Sun's central layers. Aims: We propose to combine the
diagnoses from neutrinos and helioseismology, both from pressure and gravity
modes, for assessing the predictions of solar models. We compare in detail the
different physical prescriptions currently at disposal for stellar model
computations. Results: The CNO neutrino flux confirms a preference for
high-metallicity models. Nevertheless, we found that mild modification of the
nuclear screening factors can re-match low-metallicity model predictions to
observed fluxes, although it does not restore the agreement with the
helioseismic frequency ratios. Neither the high-metallicity or low-metallicity
models are able to reproduce the gravity-mode period spacing. The disagreement
is huge, more than 100$\sigma$ to the reported value. Reversely, the family of
standard models narrows the expected range of the Sun's period spacing: between
$\sim$2150 to $\sim$2190~s. Moreover, we show this indicator can constrain the
chemical mixture, opacity, and to a lower extent nuclear reactions in solar
models.


Abstract:
  This work is an analytical study of the main differences between classical
inflationary effects of a fast-roll regime and imprints of hybrid Loop Quantum
Cosmology (LQC) on the primordial power spectrum. Effective LQC solutions of
phenomenological interest typically contain a classical period of kinetic
dominance prior to slow-roll inflation, with consequences on the power spectrum
that here we try and tell apart from those of phenomena occuring in the
vicinity of a bounce in hybrid LQC. For this comparison, we use the same
criterion for the choice of a vacuum state of the primordial perturbations,
both in the context of relativistic cosmology and on the LQC cosmological
background. As a first approximation to the problem, our study ignores the
influence of a quasi-de Sitter evolution (rather than de Sitter) in the
slow-roll inflationary regime, and of a typically short transition between the
kinetically dominated era and the slow-roll period. Our results show that,
while the two studied types of effects can lead to a drastic power suppression
in the infrared sector of the spectrum, the mode scale at which this happens is
much larger for LQC than for classical relativistic cosmology, the
corresponding scales being related to the spacetime curvature at the bounce and
at the onset of inflation, respectively.


Abstract:
  The pairwise velocity generating function $G$ has deep connection with both
the pairwise velocity probability distribution function and modeling of
redshift space distortion (RSD). Its implementation into RSD modeling is often
faciliated by expansion into series of pairwise velocity moments $\langle
v_{12}^n\rangle$. Motivated by the logrithmic transformation of the cosmic
density field, we investigate an alternative expansion into series of pairwise
velocity cumulants $\langle v_{12}^n\rangle_c$ . We numerically evaluate the
convergence rate of the two expansions, with three $3072^3$ particle
simulations of the CosmicGrowth N-body simulation series. (1) We find that the
cumulant expansion performs significantly better, for all the halo samples and
redshifts investigated. (2) For modeling RSD at $k_{\|}<0.1 h$ Mpc$^{-1}$,
including only the $n=1,2$ cumulants is sufficient. (3) But for modeling RSD at
$k_\parallel=0.2 h$ Mpc$^{-1}$, we need and only need the $n=1,2,3,4$
cumulants. These results provide specific requirements on RSD modeling in terms
of $m$-th order statistics of the large scale strucure.


Abstract:
  Bayesian optimization (BO) is a sample efficient approach to automatically
tune the hyperparameters of machine learning models. In practice, one
frequently has to solve similar hyperparameter tuning problems sequentially.
For example, one might have to tune a type of neural network learned across a
series of different classification problems. Recent work on multi-task BO
exploits knowledge gained from previous tuning tasks to speed up a new tuning
task. However, previous approaches do not account for the fact that BO is a
sequential decision making procedure. Hence, there is in general a mismatch
between the number of evaluations collected in the current tuning task compared
to the number of evaluations accumulated in all previously completed tasks. In
this work, we enable multi-task BO to compensate for this mismatch, such that
the transfer learning procedure is able to handle different data regimes in a
principled way. We propose a new multi-task BO method that learns a set of
ordered, non-linear basis functions of increasing complexity via nested
drop-out and automatic relevance determination. Experiments on a variety of
hyperparameter tuning problems show that our method improves the sample ef


Abstract:
  Context. Bulge globular clusters (BGCs) are exceptional tracers of the
formation and chemodynamical evolution of this oldest Galactic component.
However, until now, observational difficulties have prevented us from taking
full advantage of these powerful Galactic archeological tools. Aims. CAPOS, the
bulge Cluster APOgee Survey, addresses this key topic by observing a large
number of BGCs, most of which have only been poorly studied previously. Even
their most basic parameters, such as metallicity, [{\alpha}/Fe], and radial
velocity, are generally very uncertain. We aim to obtain accurate mean values
for these parameters, as well as abundances for a number of other elements, and
explore multiple populations. In this first paper, we describe the CAPOS
project and present initial results for seven BGCs. Methods. CAPOS uses the
APOGEE-2S spectrograph observing in the H band to penetrate obscuring dust
toward the bulge. For this initial paper, we use abundances derived from
ASPCAP, the APOGEE pipeline. Results. We derive mean [Fe/H] values of
$-$0.85$\pm$0.04 (Terzan 2), $-$1.40$\pm$0.05 (Terzan 4), $-$1.20$\pm$0.10 (HP
1), $-$1.40$\pm$0.07 (Terzan 9), $-$1.07$\pm$0.09 (Djorg 2), $-$1.06$\pm$0.06
(NGC 6540), and $-$1.11$\pm$0.04 (NGC 6642) from three to ten stars per
cluster. We determine mean abundances for eleven other elements plus the mean
[$\alpha$/Fe] and radial velocity. CAPOS clusters significantly increase the
sample of well-studied Main Bulge globular clusters (GCs) and also extend them
to lower metallicity. We reinforce the finding that Main Bulge and Main Disk
GCs, formed in situ, have [Si/Fe] abundances slightly higher than their
accreted counterparts at the same metallicity. We investigate multiple
populations and find our clusters generally follow the light-element
(anti)correlation trends of previous studies of GCs of similar metallicity. We
finally explore the abundances ...


Abstract:
  We show that the Euclidean and Lorentzian EPRL vertex amplitudes of covariant
Loop Quantum Gravity are related through a ``Wick rotation'' of the real
Immirzi parameter to purely imaginary values. Our result follows from the
simultaneous analytic continuation of the algebras, group elements and unitary
irreducible representations of the gauge groups $Spin(4)$ and
$SL(2,\mathbb{C})$, applied to the decomposition of the two models in terms of
$SU(2)$ invariants and booster functions.


Abstract:
  Data privacy concerns often prevent the use of cloud-based machine learning
services for sensitive personal data. While homomorphic encryption (HE) offers
a potential solution by enabling computations on encrypted data, the challenge
is to obtain accurate machine learning models that work within the
multiplicative depth constraints of a leveled HE scheme. Existing approaches
for encrypted inference either make ad-hoc simplifications to a pre-trained
model (e.g., replace hard comparisons in a decision tree with soft comparators)
at the cost of accuracy or directly train a new depth-constrained model using
the original training set. In this work, we propose a framework to transfer
knowledge extracted by complex decision tree ensembles to shallow neural
networks (referred to as DTNets) that are highly conducive to encrypted
inference. Our approach minimizes the accuracy loss by searching for the best
DTNet architecture that operates within the given depth constraints and
training this DTNet using only synthetic data sampled from the training data
distribution. Extensive experiments on real-world datasets demonstrate that
these characteristics are critical in ensuring that DTNet accuracy approaches
that of the original tree ensemble. Our system is highly scalable and can
perform efficient inference on batched encrypted (134 bits of security) data
with amortized time in milliseconds. This is approximately three orders of
magnitude faster than the standard approach of applying soft comparison at the
internal nodes of the ensemble trees.


Abstract:
  The wireless communications landscape is anticipated to offer new service
levels following the introduction of the millimeter-wave (mmWave) spectrum to
consumer electronics. With their broad bandwidths and corresponding multi-Gbps
data rates, these mmWaves are a perfect fit for data hungry applications, such
as streaming video to extended reality devices. However, the latter are also
bound by maximal latency constraints as low as 1 ms. Understanding where such
minuscule time delays lurk requires a close-up study of individual layers in
the network stack. Starting from the bottom up, the present work describes an
endeavor at uncloaking the origins of physical layer (PHY) latency in mmWave
Wi-Fi networks. It proposes a newly designed simulation framework and sheds
light on how any conventional laboratory can be turned into a virtual
experiment setting, speeding up computation. A case study based on the IEEE
802.11ad standard demonstrates the framework's ability to track packet latency
at the PHY-level and identify individual bottlenecks. In particular, it
evaluates the impact of the number of LDPC decoding iterations on latency in
short transmission sequences.


Abstract:
  Probabilistic machine learning models are often insufficient to help with
decisions on interventions because those models find correlations - not causal
relationships. If observational data is only available and experimentation are
infeasible, the correct approach to study the impact of an intervention is to
invoke Pearl's causality framework. Even that framework assumes that the
underlying causal graph is known, which is seldom the case in practice. When
the causal structure is not known, one may use out-of-the-box algorithms to
find causal dependencies from observational data. However, there exists no
method that also accounts for the decision-maker's prior knowledge when
developing the causal structure either. The objective of this paper is to
develop rational approaches for making decisions from observational data in the
presence of causal graph uncertainty and prior knowledge from the
decision-maker. We use ensemble methods like Bayesian Model Averaging (BMA) to
infer set of causal graphs that can represent the data generation process. We
provide decisions by computing the expected value and risk of potential
interventions explicitly. We demonstrate our approach by applying them in
different example contexts.


Abstract:
  Federated learning (FL) is a paradigm that allows distributed clients to
learn a shared machine learning model without sharing their sensitive training
data. While largely decentralized, FL requires resources to fund a central
orchestrator or to reimburse contributors of datasets to incentivize
participation. Inspired by insights from prior-independent auction design, we
propose a mechanism, FIPIA (Federated Incentive Payments via Prior-Independent
Auctions), to collect monetary contributions from self-interested clients. The
mechanism operates in the semi-honest trust model and works even if clients
have a heterogeneous interest in receiving high-quality models, and the server
does not know the clients' level of interest. We run experiments on the MNIST,
FashionMNIST, and CIFAR-10 datasets to test clients' model quality under FIPIA
and FIPIA's incentive properties.


Abstract:
  In this paper we develop the theory of train track maps on graphs of groups.
We prove the existence of CTs representing outer automorphisms of free
products. We generalize an index inequality due to Feighn-Handel to the graph
of groups setting, sharpening a result of Martino.


Abstract:
  We formulate a mean field game where each player stops a privately observed
Brownian motion with absorption. Players are ranked according to their level of
stopping and rewarded as a function of their relative rank. There is a unique
mean field equilibrium and it is shown to be the limit of associated $n$-player
games. Conversely, the mean field strategy induces $n$-player
$\varepsilon$-Nash equilibria for any continuous reward function -- but not for
discontinuous ones. In a second part, we study the problem of a principal who
can choose how to distribute a reward budget over the ranks and aims to
maximize the performance of the median player. The optimal reward design
(contract) is found in closed form, complementing the merely partial results
available in the $n$-player case. We then analyze the quality of the mean field
design when used as a proxy for the optimizer in the $n$-player game.
Surprisingly, the quality deteriorates dramatically as $n$ grows. We explain
this with an asymptotic singularity in the induced $n$-player equilibrium
distributions.


Abstract:
  While ultrahigh-baud-rate optical signals are effective for extending the
transmission distance of large capacity signals, they also reduce the number of
wavelengths that can be arranged in a band because of their wider bandwidth.
This reduces the flexibility of optical path configuration in reconfigurable
optical add/drop multiplexing (ROADM) networks. In colorless, directionless and
contentionless (CDC)-ROADM in particular, the effect reduces the add/drop ratio
at a node. Multiband ROADM systems are an effective countermeasure for
overcoming this issue, but they make the node configuration more complicated
and its operation more difficult. In this paper, we analyze the challenges of C
+ L band CDC-ROADM and show that optical switch devices that operate over
multiple bands are effective in meeting them. For this purpose, we built a C +
L band CDC-ROADM node based on C + L band wavelength selective switches (WSSs)
and multicast switches (MCSs) and confirmed its effectiveness experimentally.
In particular, to simplify the node configuration, we propose a reduction in
the number of optical amplifiers used for node loss compensation and
experimentally verify its feasibility.


Abstract:
  Federated learning is promising for its ability to collaboratively train
models with multiple clients without accessing their data, but vulnerable when
clients' data distributions diverge from each other. This divergence further
leads to a dilemma: "Should we prioritize the learned model's generic
performance (for future use at the server) or its personalized performance (for
each client)?" These two, seemingly competing goals have divided the community
to focus on one or the other, yet in this paper we show that it is possible to
approach both at the same time. Concretely, we propose a novel federated
learning framework that explicitly decouples a model's dual duties with two
prediction tasks. On the one hand, we introduce a family of losses that are
robust to non-identical class distributions, enabling clients to train a
generic predictor with a consistent objective across them. On the other hand,
we formulate the personalized predictor as a lightweight adaptive module that
is learned to minimize each client's empirical risk on top of the generic
predictor. With this two-loss, two-predictor framework which we name Federated
Robust Decoupling Fed-RoD, the learned model can simultaneously achieve
state-of-the-art generic and personalized performance, essentially bridging the
two tasks.


Abstract:
  We study the free data in the Fefferman-Graham expansion of asymptotically
Einstein metrics with non-zero cosmological constant. We prove that if
$\mathscr{I}$ is conformally flat, the rescaled Weyl tensor at $\mathscr{I}$
agrees up to a constant with the free data at $\mathscr{I}$ , namely the
traceless part of the $n$-th order coefficient of the expansion. In the
non-conformally flat case, the rescaled Weyl tensor is generically divergent at
$\mathscr{I}$ but one can still extract the free data in terms of the
difference of the Weyl tensors of suitably constructed metrics, in full
generality when the spacetime dimension $D$ is even and provided the so-called
obstruction tensor at $\mathscr{I}$ is identically zero when $D$ is odd. These
results provide a geometric definition of the data, particularly relevant for
the asymptotic Cauchy problem of even dimensional Einstein metrics with
positive $\Lambda$ and also for the odd dimensional analytic case
irrespectively of the sign of $\Lambda$. We establish a Killing initial data
equation at spacelike $\mathscr{I}$ in all dimension for analytic data. These
results are used to find a geometric characterization of the Kerr-de Sitter
metrics in all dimensions in terms of its geometric data at null infinity.


Abstract:
  The d-band center descriptor based on the adsorption strength of adsorbate
has been widely used in understanding and predicting the catalytic activity in
various metal catalysts. However, its applicability is unsure for the
single-atom-anchored two-dimensional (2D) catalysts. Here, taking the hydrogen
(H) adsorption on the single-atom-anchored 2D basal plane as example, we
examine the influence of orbitals interaction on the bond strength of hydrogen
adsorption. We find that the adsorption of H is formed mainly via the
hybridization between the 1s orbital of H and the vertical dz2 orbital of
anchored atoms. The other four projected d orbitals (dxy/dx2-y2, dxz/dyz) have
no contribution to the H chemical bond. There is an explicit linear relation
between the dz2-band center and the H bond strength. The dz2-band center is
proposed as an activity descriptor for hydrogen evolution reaction (HER). We
demonstrate that the dz2-band center is valid for the single-atom active sites
on a single facet, such as the basal plane of 2D nanosheets. For the surface
with multiple facets, such as the surface of three-dimensional (3D) polyhedral
nanoparticles, the d-band center is more suitable.


Abstract:
  Inspired by the idea that quantum computers can be useful in advancing basic
science, we use a quantum processor to experimentally validate a number of
theoretical results in non-equilibrium quantum thermodynamics, that were not
(or were very little) corroborated so far. In order to do so, we first put
forward a novel method to implement the so called two point measurement scheme,
which is at the basis of the study of non-equilibrium energetic exchanges in
quantum systems. Like the well-established interferometric method, our method
uses an ancillary system, but at variance with it, it provides direct access to
the energy exchange statistics, rather than its Fourier transform, thus being
extremely more effective. We first experimentally validate our ancilla-assisted
two point measurement scheme, and then apply it to i) experimentally verify
that fluctuation theorems are robust against projective measurements, a
theoretical prediction which was not validated so far, ii) experimentally
verify the so called heat engine fluctuation relation, by implementing a SWAP
quantum heat engine. iii) experimentally verify that the heat engine
fluctuation relation continues to hold in presence of intermediate
measurements, by implementing the design at the basis of the so called
quantum-measurement-cooling concept. For both engines, we report the measured
average heat and work exchanged and single out their operation mode. Our
experiments constitute the experimental basis for the understanding of the
non-equilibrium energetics of quantum computation and for the implementation of
energy management devices on quantum processors.


Abstract:
  In this paper, we hypothesize that the effects of the degree of typicality in
natural semantic categories can be generated based on the structure of
artificial categories learned with deep learning models. Motivated by the human
approach to representing natural semantic categories and based on the Prototype
Theory foundations, we propose a novel Computational Prototype Model (CPM) to
represent the internal structure of semantic categories. Unlike other prototype
learning approaches, our mathematical framework proposes a first approach to
provide deep neural networks with the ability to model abstract semantic
concepts such as category central semantic meaning, typicality degree of an
object's image, and family resemblance relationship. We proposed several
methodologies based on the typicality's concept to evaluate our CPM-model in
image semantic processing tasks such as image classification, a global semantic
description, and transfer learning. Our experiments on different image
datasets, such as ImageNet and Coco, showed that our approach might be an
admissible proposition in the effort to endow machines with greater power of
abstraction for the semantic representation of objects' categories.


Abstract:
  In e-commerce, opinion tags refer to a ranked list of tags provided by the
e-commerce platform that reflect characteristics of reviews of an item. To
assist consumers to quickly grasp a large number of reviews about an item,
opinion tags are increasingly being applied by e-commerce platforms. Current
mechanisms for generating opinion tags rely on either manual labelling or
heuristic methods, which is time-consuming and ineffective. In this paper, we
propose the abstractive opinion tagging task, where systems have to
automatically generate a ranked list of opinion tags that are based on, but
need not occur in, a given set of user-generated reviews.
  The abstractive opinion tagging task comes with three main challenges: (1)
the noisy nature of reviews; (2) the formal nature of opinion tags vs. the
colloquial language usage in reviews; and (3) the need to distinguish between
different items with very similar aspects. To address these challenges, we
propose an abstractive opinion tagging framework, named AOT-Net, to generate a
ranked list of opinion tags given a large number of reviews. First, a
sentence-level salience estimation component estimates each review's salience
score. Next, a review clustering and ranking component ranks reviews in two
steps: first, reviews are grouped into clusters and ranked by cluster size;
then, reviews within each cluster are ranked by their distance to the cluster
center. Finally, given the ranked reviews, a rank-aware opinion tagging
component incorporates an alignment feature and alignment loss to generate a
ranked list of opinion tags. To facilitate the study of this task, we create
and release a large-scale dataset, called eComTag, crawled from real-world
e-commerce websites. Extensive experiments conducted on the eComTag dataset
verify the effectiveness of the proposed AOT-Net in terms of various evaluation
metrics.


Abstract:
  Coronal extreme-ultraviolet (EUV) waves are large-scale disturbances
propagating in the corona, whose physical nature and origin have been discussed
for decades. We report the first three dimensional (3D) radiative
magneto-hydrodynamic (RMHD) simulation of a coronal EUV wave and the
accompanying quasi-periodic wave trains. The numerical experiment is conducted
with the MURaM code and simulates the formation of solar active regions through
magnetic flux emergence from the convection zone to the corona. The coronal EUV
wave is driven by the eruption of a magnetic flux rope that also gives rise to
a C-class flare. It propagates in a semi-circular shape with an initial speed
ranging from about 550 to 700 km s$^{-1}$, which corresponds to an average Mach
number (relative to fast magnetoacoustic waves) of about 1.2. Furthermore, the
abrupt increase of the plasma density, pressure and tangential magnetic field
at the wavefront confirms fast-mode shock nature of the coronal EUV wave.
Quasi-periodic wave trains with a period of about 30 s are found as multiple
secondary wavefronts propagating behind the leading wavefront and ahead of the
erupting magnetic flux rope. We also note that the true wavefront in the 3D
space can be very inhomogeneous, however, the line-of-sight integration of EUV
emission significantly smoothes the sharp structures in 3D and leads to a more
diffuse wavefront.


Abstract:
  This Colloquium is a fast journey through the build-up of key thermodynamical
concepts, i.e. work, heat and irreversibility -- and how they relate to
information. Born at the time of industrial revolution to optimize the
exploitation of thermal resources, these concepts have been adapted to small
systems where thermal fluctuations are predominant. Extending the framework to
quantum fluctuations is a great challenge of quantum thermodynamics, that opens
exciting research lines e.g. measurement fueled engines or thermodynamics of
driven-dissipative systems. On a more applied side, it provides the tools to
optimize the energetic consumption of future quantum computers.


Abstract:
  In this work we address the issue of validating the monodomain equation used
in combination with the Bueno-Orovio ionic model for the prediction of the
activation times in cardiac electro-physiology of the left ventricle. To this
aim, we consider our patients who suffered from Left Bundle Branch Block
(LBBB). We use activation maps performed at the septum as input data for the
model and maps at the epicardial veins for the validation. In particular, a
first set (half) of the latter are used to estimate the conductivities of the
patient and a second set (the remaining half) to compute the errors of the
numerical simulations. We find an excellent agreement between measures and
numerical results. Our validated computational tool could be used to accurately
predict activation times at the epicardial veins with a short mapping, i.e. by
using only a part (the most proximal) of the standard acquisition points, thus
reducing the invasive procedure and exposure to radiation.


Abstract:
  Understanding how the dynamics of a given quantum system with many degrees of
freedom is altered by the presence of a generic perturbation is a notoriously
difficult question. Recent works predict that, in the overwhelming majority of
cases, the unperturbed dynamics is just damped by a simple function, e.g.,
exponentially as expected from Fermi's golden rule. While these predictions
rely on random-matrix arguments and typicality, they can only be verified for a
specific physical situation by comparing to the actual solution or measurement.
Crucially, it also remains unclear how frequent and under which conditions
counterexamples to the typical behavior occur. In this work, we discuss this
question from the perspective of projection-operator techniques, where
exponential damping of a density matrix occurs in the interaction picture but
not necessarily in the Schr\"odinger picture. We show that a nontrivial damping
in the Schr\"odinger picture can emerge if the dynamics in the unperturbed
system possesses rich features, for instance due to the presence of strong
interactions. This suggestion has consequences for the time dependence of
correlation functions. We substantiate our theoretical arguments by large-scale
numerical simulations of charge transport in the extended Fermi-Hubbard chain,
where the nearest-neighbor interactions are treated as a perturbation to the
integrable reference system.


Abstract:
  Continuous practices are a staple of the modern software development
workflow. Automation, in particular, is widely adopted due to its benefits
related to quality and productivity. However, automation, similarly to all
other aspects of the software development workflow, interacts with humans (in
this case developers). While some work has investigated the impact of
automation on developers, it is not clear to what extent context and process
influence that impact. We present our ADEPT theory of developers and
automation, in an attempt to bridge this gap and identify the possible ways
context, process, and other factors may influence how developers perceive,
interpret, and interact with automation.


Abstract:
  The goal of electronic monitoring (EM) of longline fishing is to monitor the
fish catching activities on fishing vessels, either for the regulatory
compliance or catch counting. Hierarchical classification based on videos
allows for inexpensive and efficient fish species identification of catches
from longline fishing, where fishes are under severe deformation and
self-occlusion during the catching process. More importantly, the flexibility
of hierarchical classification mitigates the laborious efforts of human reviews
by providing confidence scores in different hierarchical levels. Some related
works either use cascaded models for hierarchical classification or make
predictions per image or predict one overlapping hierarchical data structure of
the dataset in advance. However, with a known non-overlapping hierarchical data
structure provided by fisheries scientists, our method enforces the
hierarchical data structure and introduces an efficient training and inference
strategy for video-based fisheries data. Our experiments show that the proposed
method outperforms the classic flat classification system significantly and our
ablation study justifies our contributions in CNN model design, training
strategy, and the video-based inference schemes for the hierarchical fish
species classification task.


Abstract:
  Anomaly detection for time-series data becomes an essential task for many
data-driven applications fueled with an abundance of data and out-of-the-box
machine-learning algorithms. In many real-world settings, developing a reliable
anomaly model is highly challenging due to insufficient anomaly labels and the
prohibitively expensive cost of obtaining anomaly examples. It imposes a
significant bottleneck to evaluate model quality for model selection and
parameter tuning reliably. As a result, many existing anomaly detection
algorithms fail to show their promised performance after deployment.
  In this paper, we propose LaF-AD, a novel anomaly detection algorithm with
label-free model selection for unlabeled times-series data. Our proposed
algorithm performs a fully unsupervised ensemble learning across a large number
of candidate parametric models. We develop a model variance metric that
quantifies the sensitivity of anomaly probability with a bootstrapping method.
Then it makes a collective decision for anomaly events by model learners using
the model variance. Our algorithm is easily parallelizable, more robust for
ill-conditioned and seasonal data, and highly scalable for a large number of
anomaly models. We evaluate our algorithm against other state-of-the-art
methods on a synthetic domain and a benchmark public data set.


Abstract:
  We introduce a new type of estimator for the spectral tail process of a
regularly varying time series. The approach is based on a characterizing
invariance property of the spectral tail process, which is incorporated into
the new estimator via a projection technique. We show uniform asymptotic
normality of this estimator, both in the case of known and of unknown index of
regular variation. In a simulation study the new procedure shows a more stable
performance than previously proposed estimators.


Abstract:
  We propose a universal building block of Convolutional Neural Network
(ConvNet) to improve the performance without any inference-time costs. The
block is named Diverse Branch Block (DBB), which enhances the representational
capacity of a single convolution by combining diverse branches of different
scales and complexities to enrich the feature space, including sequences of
convolutions, multi-scale convolutions, and average pooling. After training, a
DBB can be equivalently converted into a single conv layer for deployment.
Unlike the advancements of novel ConvNet architectures, DBB complicates the
training-time microstructure while maintaining the macro architecture, so that
it can be used as a drop-in replacement for regular conv layers of any
architecture. In this way, the model can be trained to reach a higher level of
performance and then transformed into the original inference-time structure for
inference. DBB improves ConvNets on image classification (up to 1.9% higher
top-1 accuracy on ImageNet), object detection and semantic segmentation. The
PyTorch code and models are released at
https://github.com/DingXiaoH/DiverseBranchBlock.


Abstract:
  Attributed network representation learning aims at learning node embeddings
by integrating network structure and attribute information. It is a challenge
to fully capture the microscopic structure and the attribute semantics
simultaneously, where the microscopic structure includes the one-step, two-step
and multi-step relations, indicating the first-order, second-order and
high-order proximity of nodes, respectively. In this paper, we propose a deep
attributed network representation learning via attribute enhanced neighborhood
(DANRL-ANE) model to improve the robustness and effectiveness of node
representations. The DANRL-ANE model adopts the idea of the autoencoder, and
expands the decoder component to three branches to capture different order
proximity. We linearly combine the adjacency matrix with the attribute
similarity matrix as the input of our model, where the attribute similarity
matrix is calculated by the cosine similarity between the attributes based on
the social homophily. In this way, we preserve the second-order proximity to
enhance the robustness of DANRL-ANE model on sparse networks, and deal with the
topological and attribute information simultaneously. Moreover, the sigmoid
cross-entropy loss function is extended to capture the neighborhood character,
so that the first-order proximity is better preserved. We compare our model
with the state-of-the-art models on five real-world datasets and two network
analysis tasks, i.e., link prediction and node classification. The DANRL-ANE
model performs well on various networks, even on sparse networks or networks
with isolated nodes given the attribute information is sufficient.


Abstract:
  Let $\Sigma$ be a surface equipped with an area form. There is an long
standing open question by Katok, which, in particular, asks whether every
entropy-zero Hamiltonian diffeomorphism of a surface lies in the $C^0$-closure
of the set of integrable diffeomorphisms. A slightly weaker version of this
question asks: ``Does every entropy-zero Hamiltonian diffeomorphism of a
surface lie in the $C^0$-closure of the set of autonomous diffeomorphisms?''
  In this paper we answer in negative the later question. In particular, we
show that on a surface $\Sigma$ the set of autonomous Hamiltonian
diffeomorphisms is not $C^0$-dense in the set of entropy-zero Hamiltonians. We
explicitly construct examples of such Hamiltonians which cannot be approximated
by autonomous diffeomorphisms.


Abstract:
  We consider different generalizations of the Brachistochrone Problem in the
context of fundamental concepts of classical mechanics. The correct statement
for the Brachistochrone problem for nonholonomic systems is proposed. It is
shown that the Brachistochrone problem is closely related to vakonomic
mechanics.


Abstract:
  Biomonitoring wearable sensors based on two-dimensional nanomaterials have
lately elicited keen research interest and potential for a new range of
flexible nanoelectronic devices. Practical nanomaterial-based devices suited
for real-world service, which have first-rate performance while being an
attractive accessory, are still distant. We report a multifunctional flexible
wearable sensor fabricated using an ultra-thin percolative layer of microwave
exfoliated graphene nanosheets on laser-patterned gold circular inter-digitated
electrodes for monitoring vital human physiological parameters. This Graphene
on Laser-patterned Electrodes (GLE) sensor displays an ultra-high strain
resolution of 0.024% and a record gauge factor of 6.3e7 and exceptional
stability and repeatability in its operating range. The sensor was subjected to
biomonitoring experiments like measurement of heart rate, breathing rate, body
temperature, and hydration level, which are vital health parameters, especially
considering the current pandemic scenario. The sensor also served in
applications such as a pedometer, limb movement tracking, and control switch
for human interaction. The innovative laser-etch process used to pattern gold
thin-film electrodes and shapes, with the multifunctional incognizable graphene
layer, provides a technique for integrating multiple sensors in a wearable
fashion accessory. The reported work marks a giant leap from the conventional
banal devices to a highly marketable multifunctional sensor array as a
biomonitoring fashion accessory.


Abstract:
  Research collaborations provide the foundation for scientific advances, but
we have only recently begun to understand how they form and grow on a global
scale. Here we analyze a model of the growth of research collaboration networks
to explain the empirical observations that the number of collaborations scales
superlinearly with institution size, though at different rates (heterogeneous
densification), the number of institutions grows as a power of the number of
researchers (Heaps' law) and institution sizes approximate Zipf's law. This
model has three mechanisms: (i) researchers are preferentially hired by large
institutions, (ii) new institutions trigger more potential institutions, and
(iii) researchers collaborate with friends-of-friends. We show agreement
between these assumptions and empirical data, through analysis of co-authorship
networks spanning two centuries. We then develop a theoretical understanding of
this model, which reveals emergent heterogeneous scaling such that the number
of collaborations between institutions scale with an institution's size.


Abstract:
  Parallelization of A* path planning is mostly limited by the number of
possible motions, which is far less than the level of parallelism that modern
processors support. In this paper, we go beyond the limitations of traditional
parallelism of A* and propose Speculative Path Planning to accelerate the
search when there are abundant idle resources. The key idea of our approach is
predicting future state expansions relying on patterns among expansions and
aggressively parallelize the computations of prospective states (i.e.
pre-evaluate the expensive collision checking operation of prospective nodes).
This method allows us to maintain the same search order as of vanilla A* and
safeguard any optimality guarantees. We evaluate our method on various
configurations and show that on a machine with 32 physical cores, our method
improves the performance around 11x and 10x on average over counterpart
single-threaded and multi-threaded implementations respectively. The code to
our paper can be found here:
https://github.com/bakhshalipour/speculative-path-planning.


Abstract:
  In this paper, we explore a canonical connection between the algebra of
$q$-difference operators $\widetilde{V}_{q}$, affine Lie algebra and affine
vertex algebras associated to certain subalgebra $\mathcal{A}$ of the Lie
algebra $\mathfrak{gl}_{\infty}$. We also introduce and study a category
$\mathcal{O}$ of $\widetilde{V}_{q}$-modules. More precisely, we obtain a
realization of $\widetilde{V}_{q}$ as a covariant algebra of the affine Lie
algebra $\widehat{\mathcal{A}^{*}}$, where $\mathcal{A}^{*}$ is a 1-dimensional
central extension of $\mathcal{A}$. We prove that restricted
$\widetilde{V_{q}}$-modules of level $\ell_{12}$ correspond to
$\mathbb{Z}$-equivariant $\phi$-coordinated quasi-modules for the vertex
algebra $V_{\widetilde{\mathcal{A}}}(\ell_{12},0)$, where
$\widetilde{\mathcal{A}}$ is a generalized affine Lie algebra of $\mathcal{A}$.
In the end, we show that objects in the category $\mathcal{O}$ are restricted
$\widetilde{V_{q}}$-modules, and we classify simple modules in the category
$\mathcal{O}$.


Abstract:
  Cooling the centre-of-mass motion is an important tool for levitated
optomechanical systems, but it is often not clear which method can practically
reach lower temperatures for a particular experiment. We directly compare the
parametric and velocity feedback damping methods, which are used extensively
for cooling the motion of single trapped particles in a range of traps. By
performing experiments on the same particle, and with the same detection
system, we demonstrate that velocity damping cools the oscillator to lower
temperatures and is more resilient to imperfect experimental conditions. We
show that these results are consistent with analytical limits as well as
numerical simulations that include experimental noise.


Abstract:
  Given access to a single long trajectory generated by an unknown irreducible
Markov chain $M$, we simulate an $\alpha$-lazy version of $M$ which is ergodic.
This enables us to generalize recent results on estimation and identity testing
that were stated for ergodic Markov chains in a way that allows fully empirical
inference. In particular, our approach shows that the pseudo spectral gap
introduced by Paulin [2015] and defined for ergodic Markov chains may be given
a meaning already in the case of irreducible but possibly periodic Markov
chains.


Abstract:
  Let $D^\alpha, \alpha>0$, be the Vladimirov-Taibleson fractional
differentiation operator acting on complex-valued functions on a
non-Archimedean local field. The identity $D^\alpha D^{-\alpha}f=f$ was known
only for the case where $f$ has a compact support. Following a result by Samko
about the fractional Laplacian of real analysis, we extend the above identity
in terms of $L^p$-convergence of truncated integrals. Differences between real
and non-Archimedean cases are discussed.


Abstract:
  Music is often considered as the language of emotions. It has long been known
to elicit emotions in human being and thus categorizing music based on the type
of emotions they induce in human being is a very intriguing topic of research.
When the task comes to classify emotions elicited by Indian Classical Music
(ICM), it becomes much more challenging because of the inherent ambiguity
associated with ICM. The fact that a single musical performance can evoke a
variety of emotional response in the audience is implicit to the nature of ICM
renditions. With the rapid advancements in the field of Deep Learning, this
Music Emotion Recognition (MER) task is becoming more and more relevant and
robust, hence can be applied to one of the most challenging test case i.e.
classifying emotions elicited from ICM. In this paper we present a new dataset
called JUMusEmoDB which presently has 400 audio clips (30 seconds each) where
200 clips correspond to happy emotions and the remaining 200 clips correspond
to sad emotion. For supervised classification purposes, we have used 4 existing
deep Convolutional Neural Network (CNN) based architectures (resnet18,
mobilenet v2.0, squeezenet v1.0 and vgg16) on corresponding music spectrograms
of the 2000 sub-clips (where every clip was segmented into 5 sub-clips of about
5 seconds each) which contain both time as well as frequency domain
information. The initial results are quite inspiring, and we look forward to
setting the baseline values for the dataset using this architecture. This type
of CNN based classification algorithm using a rich corpus of Indian Classical
Music is unique even in the global perspective and can be replicated in other
modalities of music also. This dataset is still under development and we plan
to include more data containing other emotional features as well. We plan to
make the dataset publicly available soon.


Abstract:
  Unsupervised domain adaptation has been widely adopted to generalize models
for unlabeled data in a target domain, given labeled data in a source domain,
whose data distributions differ from the target domain. However, existing works
are inapplicable to face recognition under privacy constraints because they
require sharing sensitive face images between two domains. To address this
problem, we propose a novel unsupervised federated face recognition approach
(FedFR). FedFR improves the performance in the target domain by iteratively
aggregating knowledge from the source domain through federated learning. It
protects data privacy by transferring models instead of raw data between
domains. Besides, we propose a new domain constraint loss (DCL) to regularize
source domain training. DCL suppresses the data volume dominance of the source
domain. We also enhance a hierarchical clustering algorithm to predict pseudo
labels for the unlabeled target domain accurately. To this end, FedFR forms an
end-to-end training pipeline: (1) pre-train in the source domain; (2) predict
pseudo labels by clustering in the target domain; (3) conduct
domain-constrained federated learning across two domains. Extensive experiments
and analysis on two newly constructed benchmarks demonstrate the effectiveness
of FedFR. It outperforms the baseline and classic methods in the target domain
by over 4% on the more realistic benchmark. We believe that FedFR will shed
light on applying federated learning to more computer vision tasks under
privacy constraints.


Abstract:
  In this paper we show that generic continuous Lebesgue measure preserving
circle maps have the s-limit shadowing property. In addition we obtain that
s-limit shadowing is a generic property also for continuous circle maps. In
particular, this implies that classical shadowing, periodic shadowing and limit
shadowing are generic in these two settings as well.


Abstract:
  In this study, the standard IEEE 754 2008 and modulo-based floor functions
for rounding non-integers have been presented. Their effects on the accuracy of
the bilinear interpolation algorithm have been demonstrated. The improved floor
uses the modulo operator in an effort to make each non-integer addend an
integer when the remainder between the multiplicative inverse of the fractional
factor and the numerator is greater than zero. The experiments demonstrated
relatively positive effects with the improved floor function and the
alternativeness to the standard round function.


Abstract:
  To predict line emission in the solar atmosphere requires models which are
fundamentally different depending on whether the emission is from the
chromosphere or the corona. At some point between the two regions, there must
be a change between the two modelling regimes. Recent extensions to the coronal
modelling for carbon and oxygen lines in the solar transition region have shown
improvements in the emission of singly- and doubly-charged ions, along with
Li-like ions. However, discrepancies still remain, particularly for
singly-charged ions and intercombination lines. The aim of this work is to
explore additional atomic processes that could further alter the charge state
distribution and the level populations within ions, in order to resolve some of
the discrepancies. To this end, excitation and ionisation caused by both the
radiation field and by atom-ion collisions have been included, along with
recombination through charge transfer. The modelling is carried out using
conditions which would be present in the quiet Sun, which allows an assessment
of the part atomic processes play in changing coronal modelling, separately
from dynamic and transient events taking place in the plasma. The effect the
processes have on the fractional ion populations are presented, as well as the
change in level populations brought about by the new excitation mechanisms.
Contribution functions of selected lines from low charge states are also shown,
to demonstrate the extent to which line emission in the lower atmosphere could
be affected by the new modelling.


Abstract:
  Researchers help operators of vulnerable and non-compliant internet services
by individually notifying them about security and privacy issues uncovered in
their research. To improve efficiency and effectiveness of such efforts,
dedicated notification studies are imperative. As of today, there is no
comprehensive documentation of pitfalls and best practices for conducting such
notification studies, which limits validity of results and impedes
reproducibility. Drawing on our experience with such studies and guidance from
related work, we present a set of guidelines and practical recommendations,
including initial data collection, sending of notifications, interacting with
the recipients, and publishing the results. We note that future studies can
especially benefit from extensive planning and automation of crucial processes,
i.e., activities that take place well before the first notifications are sent.


Abstract:
  This paper investigates the simultaneous wireless information and power
transfer (SWIPT) precoding scheme for K-user multiple-input-multiple-output
(MIMO) interference channels (IC), for which interference alignment (IA)
schemes provide optimal precoders to achieve full degrees-of-freedom (DoF)
gain. However, harvesting RF energy simultaneously reduces the achievable DoFs.
To study a trade-off between harvested energy and sum rate, the transceiver
design problem is suboptimally formulated in literature via convex relaxations,
which is still computationally intensive, especially for battery limited nodes
running on harvested energy. In this paper, we propose a systematic method
using chordal distance (CD) decomposition to obtain the balanced precoding,
which improves the trade-off. Analysis shows that given the nonnegative value
of CD, the achieved harvested energy for the proposed precoder is higher than
that for perfect IA precoder. Moreover, energy constraints can be achieved,
while maintaining a constant rate loss without losing DoFs via tuning the CD
value and splitting factor. Simulation results verify the analysis and add that
the IA schemes based on max-SINR or mean-squared error are better suited for
SWIPT maximization than subspace or leakage minimization methods.


Abstract:
  This paper analyzes the discretization of a Neumann boundary control problem
with a stochastic parabolic equation, where an additive noise occurs in the
Neumann boundary condition. The convergence is established for general
filtrations, and the convergence rate $ O(\tau^{1/4-\epsilon} +
h^{3/2-\epsilon}) $ is derived for the natural filtration of the Q-Wiener
process.


Abstract:
  Aligning partial views of a scene into a single whole is essential to
understanding one's environment and is a key component of numerous robotics
tasks such as SLAM and SfM. Recent approaches have proposed end-to-end systems
that can outperform traditional methods by leveraging pose supervision.
However, with the rising prevalence of cameras with depth sensors, we can
expect a new stream of raw RGB-D data without the annotations needed for
supervision. We propose UnsupervisedR&R: an end-to-end unsupervised approach to
learning point cloud registration from raw RGB-D video. The key idea is to
leverage differentiable alignment and rendering to enforce photometric and
geometric consistency between frames. We evaluate our approach on indoor scene
datasets and find that we outperform existing traditional approaches with
classic and learned descriptors while being competitive with supervised
geometric point cloud registration approaches.


Abstract:
  Locally recoverable codes were introduced by Gopalan et al. in 2012, and in
the same year Prakash et al. introduced the concept of codes with locality,
which are a type of locally recoverable codes. In this work we introduce a new
family of codes with locality, which are subcodes of a certain family of
evaluation codes. We determine the dimension of these codes, and also bounds
for the minimum distance. We present the true values of the minimum distance in
special cases, and also show that elements of this family are "optimal codes",
as defined by Prakash et al.


Abstract:
  Environmental Microorganism Data Set Fifth Version (EMDS-5) is a microscopic
image dataset including original Environmental Microorganism (EM) images and
two sets of Ground Truth (GT) images. The GT image sets include a single-object
GT image set and a multi-object GT image set. The EMDS-5 dataset has 21 types
of EMs, each of which contains 20 original EM images, 20 single-object GT
images and 20 multi-object GT images. EMDS-5 can realize to evaluate image
preprocessing, image segmentation, feature extraction, image classification and
image retrieval functions. In order to prove the effectiveness of EMDS-5, for
each function, we select the most representative algorithms and price
indicators for testing and evaluation. The image preprocessing functions
contain two parts: image denoising and image edge detection. Image denoising
uses nine kinds of filters to denoise 13 kinds of noises, respectively. In the
aspect of edge detection, six edge detection operators are used to detect the
edges of the images, and two evaluation indicators, peak-signal to noise ratio
and mean structural similarity, are used for evaluation. Image segmentation
includes single-object image segmentation and multi-object image segmentation.
Six methods are used for single-object image segmentation, while k-means and
U-net are used for multi-object segmentation.We extract nine features from the
images in EMDS-5 and use the Support Vector Machine classifier for testing. In
terms of image classification, we select the VGG16 feature to test different
classifiers. We test two types of retrieval approaches: texture feature
retrieval and deep learning feature retrieval. We select the last layer of
features of these two deep learning networks as feature vectors. We use mean
average precision as the evaluation index for retrieval.


Abstract:
  Sparse adversarial attacks can fool deep neural networks (DNNs) by only
perturbing a few pixels (regularized by l_0 norm). Recent efforts combine it
with another l_infty imperceptible on the perturbation magnitudes. The
resultant sparse and imperceptible attacks are practically relevant, and
indicate an even higher vulnerability of DNNs that we usually imagined.
However, such attacks are more challenging to generate due to the optimization
difficulty by coupling the l_0 regularizer and box constraints with a
non-convex objective. In this paper, we address this challenge by proposing a
homotopy algorithm, to jointly tackle the sparsity and the perturbation bound
in one unified framework. Each iteration, the main step of our algorithm is to
optimize an l_0-regularized adversarial loss, by leveraging the nonmonotone
Accelerated Proximal Gradient Method (nmAPG) for nonconvex programming; it is
followed by an l_0 change control step, and an optional post-attack step
designed to escape bad local minima. We also extend the algorithm to handling
the structural sparsity regularizer. We extensively examine the effectiveness
of our proposed homotopy attack for both targeted and non-targeted attack
scenarios, on CIFAR-10 and ImageNet datasets. Compared to state-of-the-art
methods, our homotopy attack leads to significantly fewer perturbations, e.g.,
reducing 42.91% on CIFAR-10 and 75.03% on ImageNet (average case, targeted
attack), at similar maximal perturbation magnitudes, when still achieving 100%
attack success rates. Our codes are available at:
https://github.com/VITA-Group/SparseADV_Homotopy.


Abstract:
  Data augmentation has been widely used to improve deep neural networks in
many research fields, such as computer vision. However, less work has been done
in the context of text, partially due to its discrete nature and the complexity
of natural languages. In this paper, we propose to improve the standard maximum
likelihood estimation (MLE) paradigm by incorporating a self-imitation-learning
phase for automatic data augmentation. Unlike most existing sentence-level
augmentation strategies, which are only applied to specific models, our method
is more general and could be easily adapted to any MLE-based training
procedure. In addition, our framework allows task-specific evaluation metrics
to be designed to flexibly control the generated sentences, for example, in
terms of controlling vocabulary usage and avoiding nontrivial repetitions.
Extensive experimental results demonstrate the superiority of our method on two
synthetic and several standard real datasets, significantly improving related
baselines.


Abstract:
  There has been tremendous interest in the development of formal trust models
and metrics through the use of analytics (e.g., Belief Theory and Bayesian
models), logics (e.g., Epistemic and Subjective Logic) and other mathematical
models. The choice of trust metric will depend on context, circumstance and
user requirements and there is no single best metric for use in all
circumstances. Where different users require different trust metrics to be
employed the trust score calculations should still be based on all available
trust evidence. Trust is normally computed using past experiences but, in
practice (especially in centralised systems), the validity and accuracy of
these experiences are taken for granted. In this paper, we provide a formal
framework and practical blockchain-based implementation that allows independent
trust providers to implement different trust metrics in a distributed manner
while still allowing all trust providers to base their calculations on a common
set of trust evidence. Further, our design allows experiences to be provably
linked to interactions without the need for a central authority. This leads to
the notion of evidence-based trust with provable interactions. Leveraging
blockchain allows the trust providers to offer their services in a competitive
manner, charging fees while users are provided with payments for recording
experiences. Performance details of the blockchain implementation are provided.