Abstract:
  Melanoma is an aggressive neoplasm responsible for the majority of deaths
from skin cancer. Specifically, spitzoid melanocytic tumors are one of the most
challenging melanocytic lesions due to their ambiguous morphological features.
The gold standard for its diagnosis and prognosis is the analysis of skin
biopsies. In this process, dermatopathologists visualize skin histology slides
under a microscope, in a high time-consuming and subjective task. In the last
years, computer-aided diagnosis (CAD) systems have emerged as a promising tool
that could support pathologists in daily clinical practice. Nevertheless, no
automatic CAD systems have yet been proposed for the analysis of spitzoid
lesions. Regarding common melanoma, no proposed system allows both the
selection of the tumoral region and the prediction of the diagnosis as benign
or malignant. Motivated by this, we propose a novel end-to-end
weakly-supervised deep learning model, based on inductive transfer learning
with an improved convolutional neural network (CNN) to refine the embedding
features of the latent space. The framework is composed of a source model in
charge of finding the tumor patch-level patterns, and a target model focuses on
the specific diagnosis of a biopsy. The latter retrains the backbone of the
source model through a multiple instance learning workflow to obtain the
biopsy-level scoring. To evaluate the performance of the proposed methods, we
perform extensive experiments on a private skin database with spitzoid lesions.
Test results reach an accuracy of 0.9231 and 0.80 for the source and the target
models, respectively. Besides, the heat map findings are directly in line with
the clinicians' medical decision and even highlight, in some cases, patterns of
interest that were overlooked by the pathologist due to the huge workload.


Abstract:
  We introduce a new pair of mutually dual bases of noncommutative symmetric
functions and quasi-symmetric functions, and use it to derive generalizations
of several results on the reduced incidence algebra of the lattice of
noncrossing partitions. As a consequence, we obtain a quasi-symmetric version
of the Farahat-Higman algebra.


Abstract:
  We report Coulomb blockade transport studies of InAs nanowires grown with
epitaxial superconducting Al and ferromagnetic insulator EuS on overlapping
facets. By comparing experimental results to a theoretical model, we associate
cotunneling features in even-odd bias spectra with spin-polarized Andreev
levels, indicating that spin splitting exceeding the induced superconducting
gap at zero applied magnetic field. Energies of the polarized subgap states can
be tuned on either side of zero by electrostatic gates.


Abstract:
  The empirical Archie's law, relating the measured electrical resistivity of
an electrolyte-saturated rock to its connected porosity, is derived by
consideration of the fractal character of the pore space. The derivation thus
imposes some conditions on the implementation and meaning of Archie's law.


Abstract:
  Quantum cryptography exploits principles of quantum physics for the secure
processing of information. A prominent example is secure communication, i.e.,
the task of transmitting confidential messages from one location to another.
The cryptographic requirement here is that the transmitted messages remain
inaccessible to anyone other than the designated recipients, even if the
communication channel is untrusted. In classical cryptography, this can usually
only be guaranteed under computational hardness assumptions, e.g., that
factoring large integers is infeasible. In contrast, the security of quantum
cryptography relies entirely on the laws of quantum mechanics. Here we review
this physical notion of security, focusing on quantum key distribution and
secure communication.


Abstract:
  Many real-world applications, such as those in medical domains,
recommendation systems, etc, can be formulated as large state space
reinforcement learning problems with only a small budget of the number of
policy changes, i.e., low switching cost. This paper focuses on the linear
Markov Decision Process (MDP) recently studied in [Yang et al 2019, Jin et al
2020] where the linear function approximation is used for generalization on the
large state space. We present the first algorithm for linear MDP with a low
switching cost. Our algorithm achieves an
$\widetilde{O}\left(\sqrt{d^3H^4K}\right)$ regret bound with a near-optimal
$O\left(d H\log K\right)$ global switching cost where $d$ is the feature
dimension, $H$ is the planning horizon and $K$ is the number of episodes the
agent plays. Our regret bound matches the best existing polynomial algorithm by
[Jin et al 2020] and our switching cost is exponentially smaller than theirs.
When specialized to tabular MDP, our switching cost bound improves those in
[Bai et al 2019, Zhang et al 20020]. We complement our positive result with an
$\Omega\left(dH/\log d\right)$ global switching cost lower bound for any
no-regret algorithm.


Abstract:
  In the new era of Extremely Large Telescopes (ELTs) currently under
construction, challenging requirements drive spectrograph designs towards
techniques that efficiently use a facility's light collection power. Operating
in the single-mode (SM) regime, close to the diffraction limit, reduces the
footprint of the instrument compared to a conventional high-resolving power
spectrograph. The custom built injection fiber system with 3D-printed
micro-lenses on top of it for the replicable high-resolution exoplanet and
asteroseismology spectrograph at Subaru in combination with extreme adaptive
optics of SCExAO, proved its high efficiency in a lab environment, manifesting
up to ~77% of the theoretical predicted performance.


Abstract:
  In this paper we investigate equilibria of continuous differential equation
models of network dynamics. The motivation comes from gene regulatory networks
where each directed edge represents either down- or up-regulation, and is
modeled by a sigmoidal nonlinear function. We show that the existence and
stability of equilibria of a sigmoidal system is determined by a combinatorial
analysis of the limiting switching system with piece-wise constant
non-linearities. In addition, we describe a local decomposition of a switching
system into a product of simpler cyclic feedback systems, where the cycles in
each decomposition correspond to a particular subset of network loops.


Abstract:
  It is conjectured that stationary black holes are characterized by the
inverse hoop relation ${\cal A}\leq {\cal C}^2/\pi$, where ${\cal A}$ and
${\cal C}$ are respectively the black-hole surface area and the circumference
length of the smallest ring that can engulf the black-hole horizon in every
direction. We explicitly prove that generic Kerr-Newman-(anti)-de Sitter black
holes conform to this conjectured area-circumference relation.


Abstract:
  Given that there are a variety of stakeholders involved in, and affected by,
decisions from machine learning (ML) models, it is important to consider that
different stakeholders have different transparency needs. Previous work found
that the majority of deployed transparency mechanisms primarily serve technical
stakeholders. In our work, we want to investigate how well transparency
mechanisms might work in practice for a more diverse set of stakeholders by
conducting a large-scale, mixed-methods user study across a range of
organizations, within a particular industry such as health care, criminal
justice, or content moderation. In this paper, we outline the setup for our
study.


Abstract:
  We present a scalable tree search planning algorithm for large multi-agent
sequential decision problems that require dynamic collaboration. Teams of
agents need to coordinate decisions in many domains, but naive approaches fail
due to the exponential growth of the joint action space with the number of
agents. We circumvent this complexity through an anytime approach that allows
us to trade computation for approximation quality and also dynamically
coordinate actions. Our algorithm comprises three elements: online planning
with Monte Carlo Tree Search (MCTS), factored representations of local agent
interactions with coordination graphs, and the iterative Max-Plus method for
joint action selection. We evaluate our approach on the benchmark SysAdmin
domain with static coordination graphs and achieve comparable performance with
much lower computation cost than our MCTS baselines. We also introduce a
multi-drone delivery domain with dynamic, i.e., state-dependent coordination
graphs, and demonstrate how our approach scales to large problems on this
domain that are intractable for other MCTS methods. We provide an open-source
implementation of our algorithm at
https://github.com/JuliaPOMDP/FactoredValueMCTS.jl.


Abstract:
  We classify finite-dimensional Nichols algebras over finite nilpotent groups
of odd order in group-theoretical terms. The main step is to show that the
conjugacy classes of such finite groups are either abelian or of type C; this
property also holds for finite conjugacy classes of finitely generated
nilpotent groups whose torsion has odd order. To extend our approach to the
setting of finite GK-dimension, we propose a new Conjecture on racks of type C.
We also prove that the bosonization a of Nichols algebra of a Yetter-Drinfeld
module over a group whose support is an infinite conjugacy class has infinite
GK-dimension. We apply this to the study of the finite GK-dimensional pointed
Hopf algebras over finitely generated torsion-free nilpotent groups.


Abstract:
  Collective orders and photo-induced phase transitions in quantum matter can
evolve on timescales which are orders of magnitude slower than the femtosecond
processes related to electronic motion in the solid. Quantum Boltzmann
equations can potentially resolve this separation of timescales, but are often
constructed within a perturbative framework. Here we derive a quantum Boltzmann
equation which only assumes a separation of timescales (taken into account
through the gradient approximation for convolutions in time), but is based on a
non-perturbative scattering integral, and makes no assumption on the spectral
function such as the quasiparticle approximation. In particular, a scattering
integral corresponding to non-equilibrium dynamical mean-field theory is
evaluated in terms of an Anderson impurity model in a non-equilibrium steady
state with prescribed distribution functions. This opens the possibility to
investigate dynamical processes in correlated solids with quantum impurity
solvers designed for the study of non-equilibrium steady states.


Abstract:
  Artificial intelligence has changed our day to day life in multitude ways. AI
technology is rearing itself as a driving force to be reckoned with in the
largest industries in the world. AI has already engulfed our educational
system, our businesses and our financial establishments. The future is definite
that machines with artificial intelligence will soon be captivating over
trained manual work that now is mostly cared by humans. Machines can carry out
human-like tasks by new inputs as artificial intelligence makes it possible for
machines to learn from experience. AI data from web of science database from
2008 to 2017 have been mapped to depict the average growth rate, relative
growth rate, contribution made by authors in the view of research productivity,
authorship pattern and collaboration of AI literature. The Lotka's law on
authorship productivity of AI literature has been tested to confirm the
applicability of the law to the present data set. A K-S test was applied to
measure the degree of agreement between the distribution of the observed set of
data against the inverse general power relationship and the theoretical value
of {\alpha} =2. It is found that the inverse square law of Lotka follow as
such.


Abstract:
  Video super-resolution (VSR), with the aim to restore a high-resolution video
from its corresponding low-resolution version, is a spatial-temporal sequence
prediction problem. Recently, Transformer has been gaining popularity due to
its parallel computing ability for sequence-to-sequence modeling. Thus, it
seems to be straightforward to apply the vision Transformer to solve VSR.
However, the typical block design of Transformer with a fully connected
self-attention layer and a token-wise feed-forward layer does not fit well for
VSR due to the following two reasons. First, the fully connected self-attention
layer neglects to exploit the data locality because this layer relies on linear
layers to compute attention maps. Second, the token-wise feed-forward layer
lacks the feature alignment which is important for VSR since this layer
independently processes each of the input token embeddings without any
interaction among them. In this paper, we make the first attempt to adapt
Transformer for VSR. Specifically, to tackle the first issue, we present a
spatial-temporal convolutional self-attention layer with a theoretical
understanding to exploit the locality information. For the second issue, we
design a bidirectional optical flow-based feed-forward layer to discover the
correlations across different video frames and also align features. Extensive
experiments on several benchmark datasets demonstrate the effectiveness of our
proposed method. The code will be available at
https://github.com/caojiezhang/VSR-Transformer.


Abstract:
  This paper proposes a novel monitoring methodology for car-following control
of automated vehicles that uses real-time measurements of spacing and velocity
obtained through vehicle sensors. This study focuses on monitoring the time
gap, a key parameter that dictates the desired following spacing of the
controlled vehicle. The goal is to monitor deviations in actual time gap from a
desired setting and detect when it deviates beyond a control limit. A random
coefficient modeling is developed to systematically capture the stochastic
distribution of the time gap and derive a closed-form Bayesian updating scheme
for real-time inference. A control chart is then adopted to systematically set
the control limits and inform when the time gap setting should be changed.
Simulation experiments are performed to demonstrate the effectiveness of the
proposes method for monitoring the time gap and alerting when the parameter
setting needs to be changed.


Abstract:
  We report the formation of self-assembled monolayers of a molecular
photoswitch (azobenzene-bithiophene derivative, AzBT) on cobalt via a thiol
covalent bond. We study the electrical properties of the molecular junctions
formed with the tip of a conductive atomic force microscope under ultra-high
vacuum. The statistical analysis of the current-voltage curves shows two
distinct states of the molecule conductance, suggesting the coexistence of both
the trans and cis azobenzene isomers on the surface. The cis isomer population
(trans isomer) increases (decreases) upon UV light irradiation. The situation
is reversed under blue light irradiation. The experiments are confronted to
first-principle calculations performed on the molecular junctions with the
Non-Equilibrium Green's Function formalism combined with Density Functional
Theory (NEGF/DFT). The theoretical results consider two different molecular
orientations for each isomer. Whereas the orientation does not affect the
conductance of the trans isomer, it significantly modulates the conductance of
the cis isomer and the resulting conductance ON/OFF ratio of the molecular
junction. This helps identifying the molecular orientation at the origin of the
observed current differences between the trans and cis forms. The ON state is
associated to the trans isomer irrespective of its orientation in the junction,
while the OFF state is identified as a cis isomer with its azobenzene moiety
folded upward with respect to the bithiophene core. The experimental and
calculated ON/OFF conductance ratios have a similar order of magnitude. This
conductance ratio seems reasonable to make these Co-AzBT molecular junctions a
good test-bed to further explore the relationship between the spin-polarized
charge transport, the molecule conformation and the molecule-Co spinterface.


Abstract:
  Metallic structures interacting with electromagnetic fields are known to
exhibit properties similar to those found in atoms and molecules, such as
multi-photon and tunnel ionization. Developing this similarity beyond the
electron emission current, we generalize the wellknown Fowler-Nordheim model,
and predict heretofore unrecognized source of nonlinear optical response from
nano-structures exposed to illumination with intense optical pulses.


Abstract:
  We address the problem of form understanding: finding text entities and the
relationships/links between them in form images. The proposed FUDGE model
formulates this problem on a graph of text elements (the vertices) and uses a
Graph Convolutional Network to predict changes to the graph. The initial
vertices are detected text lines and do not necessarily correspond to the final
text entities, which can span multiple lines. Also, initial edges contain many
false-positive relationships. FUDGE edits the graph structure by combining text
segments (graph vertices) and pruning edges in an iterative fashion to obtain
the final text entities and relationships. While recent work in this area has
focused on leveraging large-scale pre-trained Language Models (LM), FUDGE
achieves almost the same level of entity linking performance on the FUNSD
dataset by learning only visual features from the (small) provided training
set. FUDGE can be applied on forms where text recognition is difficult (e.g.
degraded or historical forms) and on forms in resource-poor languages where
pre-training such LMs is challenging. FUDGE is state-of-the-art on the
historical NAF dataset.


Abstract:
  We make available some results about model theory cyclically ordered groups.
We start with a classification of complete theories of divisible abelian
cyclically ordered groups. Then we look at the cyclically ordered groups where
the only parametrically definable subsets are finite unions of singletons and
open intervals, and those where the definable subsets are finite union of
singletons and c-convex subsets, where being c-convex is the analogue of being
convex in the linearly ordered case.


Abstract:
  Real-world data is often unbalanced and long-tailed, but deep models struggle
to recognize rare classes in the presence of frequent classes. To address
unbalanced data, most studies try balancing the data, the loss, or the
classifier to reduce classification bias towards head classes. Far less
attention has been given to the latent representations learned with unbalanced
data. We show that the feature extractor part of deep networks suffers greatly
from this bias. We propose a new loss based on robustness theory, which
encourages the model to learn high-quality representations for both head and
tail classes. While the general form of the robustness loss may be hard to
compute, we further derive an easy-to-compute upper bound that can be minimized
efficiently. This procedure reduces representation bias towards head classes in
the feature space and achieves new SOTA results on CIFAR100-LT, ImageNet-LT,
and iNaturalist long-tail benchmarks. We find that training with robustness
increases recognition accuracy of tail classes while largely maintaining the
accuracy of head classes. The new robustness loss can be combined with various
classifier balancing techniques and can be applied to representations at
several layers of the deep model.


Abstract:
  In this article, we study a class of semilinear scalar field equations on the
real line with variable coefficients in the linear terms. We establish
asymptotic stability for the vacuum state with respect to perturbations in
$H^1\times L^2$, without placing any parity assumptions on the coefficients,
potential, or initial data. Next, under a parity assumption, we show asymptotic
stability for steady states that are "almost constant" in the spatial variable,
which can be constructed because the of the non-translation-invariant linear
part of our equation.


Abstract:
  We consider the pricing of VIX options in the rough Bergomi model [Bayer,
Friz, and Gatheral, Pricing under rough volatility, Quantitative Finance 16(6),
887-904, 2016]. In this setting, the VIX random variable is defined by the
one-dimensional integral of the exponential of a Gaussian process with
correlated increments, hence approximate samples of the VIX can be constructed
via discretization of the integral and simulation of a correlated Gaussian
vector. A Monte-Carlo estimator of VIX options based on a rectangle
discretization scheme and exact Gaussian sampling via the Cholesky method has a
computational complexity of order $\mathcal O(\varepsilon^{-4})$ when the
mean-squared error is set to $\varepsilon^2$. We demonstrate that this cost can
be reduced to $\mathcal O(\varepsilon^{-2} \log^2(\varepsilon))$ combining the
scheme above with the multilevel method [Giles, Multilevel Monte Carlo path
simulation, Oper. Res. 56(3), 607-617, 2008], and further reduced to the
asymptotically optimal cost $\mathcal O(\varepsilon^{-2})$ when using a
trapezoidal discretization. We provide numerical experiments highlighting the
efficiency of the multilevel approach in the pricing of VIX options in such a
rough forward variance setting.


Abstract:
  Given a collection of graphs $\mathbf{G}=(G_1, \ldots, G_m)$ with the same
vertex set, an $m$-edge graph $H\subset \cup_{i\in [m]}G_i$ is a transversal if
there is a bijection $\phi:E(H)\to [m]$ such that $e\in E(G_{\phi(e)})$ for
each $e\in E(H)$. We give asymptotically-tight minimum degree conditions for a
graph collection on an $n$-vertex set to have a transversal which is a copy of
a graph $H$, when $H$ is an $n$-vertex graph which is an $F$-factor or a tree
with maximum degree $o(n/\log n)$.


Abstract:
  Many representative graph neural networks, e.g., GPR-GNN and ChebNet,
approximate graph convolutions with graph spectral filters. However, existing
work either applies predefined filter weights or learns them without necessary
constraints, which may lead to oversimplified or ill-posed filters. To overcome
these issues, we propose BernNet, a novel graph neural network with theoretical
support that provides a simple but effective scheme for designing and learning
arbitrary graph spectral filters. In particular, for any filter over the
normalized Laplacian spectrum of a graph, our BernNet estimates it by an
order-$K$ Bernstein polynomial approximation and designs its spectral property
by setting the coefficients of the Bernstein basis. Moreover, we can learn the
coefficients (and the corresponding filter weights) based on observed graphs
and their associated signals and thus achieve the BernNet specialized for the
data. Our experiments demonstrate that BernNet can learn arbitrary spectral
filters, including complicated band-rejection and comb filters, and it achieves
superior performance in real-world graph modeling tasks. Code is available at
https://github.com/ivam-he/BernNet.


Abstract:
  We consider methods for transporting a prediction model and assessing its
performance for use in a new target population, when outcome and covariate
information for model development is available from a simple random sample from
the source population, but only covariate information is available on a simple
random sample from the target population. We discuss how to tailor the
prediction model for use in the target population, how to assess model
performance in the target population (e.g., by estimating the target population
mean squared error), and how to perform model and tuning parameter selection in
the context of the target population. We provide identifiability results for
the target population mean squared error of a potentially misspecified
prediction model under a sampling design where the source study and the target
population samples are obtained separately. We also introduce the concept of
prediction error modifiers that can be used to reason about the need for
tailoring measures of model performance to the target population and provide an
illustration of the methods using simulated data.


Abstract:
  3D object detection is a key perception component in autonomous driving. Most
recent approaches are based on Lidar sensors only or fused with cameras. Maps
(e.g., High Definition Maps), a basic infrastructure for intelligent vehicles,
however, have not been well exploited for boosting object detection tasks. In
this paper, we propose a simple but effective framework - MapFusion to
integrate the map information into modern 3D object detector pipelines. In
particular, we design a FeatureAgg module for HD Map feature extraction and
fusion, and a MapSeg module as an auxiliary segmentation head for the detection
backbone. Our proposed MapFusion is detector independent and can be easily
integrated into different detectors. The experimental results of three
different baselines on large public autonomous driving dataset demonstrate the
superiority of the proposed framework. By fusing the map information, we can
achieve 1.27 to 2.79 points improvements for mean Average Precision (mAP) on
three strong 3d object detection baselines.


Abstract:
  In order to study the circumgalactic medium (CGM) of galaxies we develop an
automated pipeline to estimate the optical continuum of quasars and detect
intervening metal absorption line systems with a matched kernel convolution
technique and adaptive S/N criteria. We process $\sim$ one million quasars in
the latest Data Release 16 (DR16) of the Sloan Digital Sky Survey (SDSS) and
compile a large sample of $\sim$ 160,000 MgII absorbers, together with $\sim$
70,000 FeII systems, in the redshift range $0.35<z_{abs}<2.3$. Combining these
with the SDSS DR16 spectroscopy of $\sim1.1$ million luminous red galaxies
(LRGs) and $\sim 200,000$ emission line galaxies (ELGs), we investigate the
nature of cold gas absorption at $0.5<z<1$. These large samples allow us to
characterize the scale dependence of MgII with greater accuracy than in
previous work. We find that there is a strong enhancement of MgII absorption
within $\sim 50$ kpc of ELGs, and the covering fraction within $0.5r_{\rm vir}$
of ELGs is 2-5 times higher than for LRGs. Beyond 50 kpc, there is a sharp
decline in MgII for both kinds of galaxies, indicating a transition to the
regime where the CGM is tightly linked with the dark matter halo. The MgII
covering fraction correlates strongly with stellar mass for LRGs, but weakly
for ELGs, where covering fractions increase with star formation rate. Our
analysis implies that cool circumgalactic gas has a different physical origin
for star forming versus quiescent galaxies.


Abstract:
  In this paper, we study the long-time behavior of the solution for the
linearized ideal MHD around sheared velocity and magnetic field under Stern
stability condition. We prove that the velocity and magnetic field will
converge to sheared velocity and magnetic field as time approaches infinity.
Moreover a new depletion phenomenon is proved: the horizontal velocity and
magnetic field at the critical points will decay to 0 as time approaches
infinity.


Abstract:
  The MR-Linac is a combination of an MR-scanner and radiotherapy linear
accelerator (Linac) which holds the promise to increase the precision of
radiotherapy treatments with MR-guided radiotherapy by monitoring motion during
radiotherapy with MRI, and adjusting the radiotherapy plan accordingly. Optimal
MR-guidance for respiratory motion during radiotherapy requires MR-based 3D
motion estimation with a latency of 200-500 ms. Currently this is still
challenging since typical methods rely on MR-images, and are therefore limited
by the 3D MR-imaging latency. In this work, we present a method to perform
non-rigid 3D respiratory motion estimation with 170 ms latency, including both
acquisition and reconstruction. The proposed method called real-time low-rank
MR-MOTUS reconstructs motion-fields directly from k-space data, and leverages
an explicit low-rank decomposition of motion-fields to split the large scale
3D+t motion-field reconstruction problem posed in our previous work into two
parts: (I) a medium-scale offline preparation phase and (II) a small-scale
online inference phase which exploits the results of the offline phase for
real-time computations. The method was validated on free-breathing data of five
volunteers, acquired with a 1.5T Elekta Unity MR-Linac. Results show that the
reconstructed 3D motion-field are anatomically plausible, highly correlated
with a self-navigation motion surrogate (R = 0.975 +/- 0.0110), and can be
reconstructed with a total latency of 170 ms that is sufficient for real-time
MR-guided abdominal radiotherapy.


Abstract:
  Binary millisecond pulsars (MSPs) are detached binary systems consisting of a
MSP and a He white dwarf. If the initial orbital periods of binary MSPs are
less than 0.3 day, they would evolve toward ultra-compact binary pulsars due to
the rapid orbital shrinkage by the gravitational wave (GW) radiation. During
the orbital decay, the MSP with an ellipticity would spin down by the GW
radiation and the magnetic dipole radiation. Our calculations indicate that the
angular momentum loss is dominated by the GW radiation when the ellipticities
of the neutron stars (NSs) are in the range of $(1-50)\times 10^{-7}$, and the
frequencies of high-frequency GW signals from the rotating NSs are $10-100$ Hz
when the binary pulsars can be visible as low-frequency GW sources. These
high-frequency GW signals are possible to be detected by the aLIGO and the
third-generation GW detectors such as Einstein Telescope, depending on the
frequencies and the distances. Therefore, some ultra-compact binary pulsars
have an opportunity to become intriguing dual-line GW sources. By detecting the
low-frequency GW signals, the NS mass can be accurately derived. A dual-line
detection of two band GW signals could provide a constraint on the moment of
inertia and the ellipticity of the NS. Thus the dual-line GW sources can be
potentially applied to constrain the equation of state of the NS.


Abstract:
  We show that in the Dirac-Milne universe (a matter-antimatter symmetric
universe where the two components repel each other), rotation curves are
generically flat beyond the characteristic distance of about 3 virial radii,
and that a Tully-Fisher relation with exponent $\approx 3$ is satisfied. Using
3D simulations with a modified version of the RAMSES code, we show that the
Dirac-Milne cosmology presents a Faber-Jackson relation with a very small
scatter and an exponent equal to $\approx 3$ between the mass and the velocity
dispersion. We also show that the mass derived from the rotation curves
assuming Newtonian gravity is systematically overestimated compared to the mass
really present. We also show that the Dirac-Milne universe, featuring a
polarization between its matter and antimatter components, presents a behavior
similar to that of MOND (Modified Newtonian Dynamics), characterized by an
additional surface gravity compared to the Newtonian case. We show that in the
Dirac-Milne universe, at the present epoch, the intensity of the additional
gravitational field $g_{am}$ due to the presence of clouds of antimatter is of
the order of a few $10^{-11}$ m/s$^2$, similar to the characteristic
acceleration of MOND. We study the evolution of this additional acceleration
$g_{am}$ and show that it depends on the redshift, and is therefore not a
fundamental constant. Combined with its known concordance properties on SNIa
luminosity distance, age, nucleosynthesis and structure formation, the
Dirac-Milne cosmology may then represent an interesting alternative to the
$\Lambda$CDM, MOND, and other scenarios for explaining the Dark Matter and Dark
Energy conundrum.


Abstract:
  Cloth-Changing person re-identification (CC-ReID) aims at matching the same
person across different locations over a long-duration, e.g., over days, and
therefore inevitably meets challenge of changing clothing. In this paper, we
focus on handling well the CC-ReID problem under a more challenging setting,
i.e., just from a single image, which enables high-efficiency and latency-free
pedestrian identify for real-time surveillance applications. Specifically, we
introduce Gait recognition as an auxiliary task to drive the Image ReID model
to learn cloth-agnostic representations by leveraging personal unique and
cloth-independent gait information, we name this framework as GI-ReID. GI-ReID
adopts a two-stream architecture that consists of a image ReID-Stream and an
auxiliary gait recognition stream (Gait-Stream). The Gait-Stream, that is
discarded in the inference for high computational efficiency, acts as a
regulator to encourage the ReID-Stream to capture cloth-invariant biometric
motion features during the training. To get temporal continuous motion cues
from a single image, we design a Gait Sequence Prediction (GSP) module for
Gait-Stream to enrich gait information. Finally, a high-level semantics
consistency over two streams is enforced for effective knowledge
regularization. Experiments on multiple image-based Cloth-Changing ReID
benchmarks, e.g., LTCC, PRCC, Real28, and VC-Clothes, demonstrate that GI-ReID
performs favorably against the state-of-the-arts. Codes are available at
https://github.com/jinx-USTC/GI-ReID.


Abstract:
  Combinatorial Optimization (CO) has been a long-standing challenging research
topic featured by its NP-hard nature. Traditionally such problems are
approximately solved with heuristic algorithms which are usually fast but may
sacrifice the solution quality. Currently, machine learning for combinatorial
optimization (MLCO) has become a trending research topic, but most existing
MLCO methods treat CO as a single-level optimization by directly learning the
end-to-end solutions, which are hard to scale up and mostly limited by the
capacity of ML models given the high complexity of CO. In this paper, we
propose a hybrid approach to combine the best of the two worlds, in which a
bi-level framework is developed with an upper-level learning method to optimize
the graph (e.g. add, delete or modify edges in a graph), fused with a
lower-level heuristic algorithm solving on the optimized graph. Such a bi-level
approach simplifies the learning on the original hard CO and can effectively
mitigate the demand for model capacity. The experiments and results on several
popular CO problems like Directed Acyclic Graph scheduling, Graph Edit Distance
and Hamiltonian Cycle Problem show its effectiveness over manually designed
heuristics and single-level learning methods.


Abstract:
  The map $TC(\mathbb{S})^{\wedge}_{p}\to \text{holim }
TC(L_{n}\mathbb{S})^{\wedge}_{p}$ is a weak equivalence.


Abstract:
  This survey paper focuses on the estimation schemes in molecular
communication (MC) systems. The existing studies in estimation schemes can be
divided into parameter estimation (e.g., distance, diffusion coefficient, and
flow velocity) and channel estimation. In this paper, we present, for the first
time, a comprehensive survey on i) distance estimation, since distance is the
most widely estimated parameter in current studies, ii) estimation of other
parameters (i.e. the parameters excluding distance), and iii) channel
estimation that focuses on the channel impulse response (CIR). Moreover, we
examine the noise that may impact on the estimation performance and the metrics
applied to evaluate the performance of different estimation schemes. Numerical
results are provided to compare the performance of different distance
estimation schemes. In addition, future research directions in parameter
estimation and channel estimation are identified and discussed.


Abstract:
  In this paper, we use a variety of machine learning methods to quantify the
extent to which economic and technological factors are predictive of the
progression of Central Bank Digital Currencies (CBDC) within a country, using
as our measure of this progression the CBDC project index (CBDCPI). We find
that a financial development index is the most important feature for our model,
followed by the GDP per capita and an index of the voice and accountability of
the country's population. Our results are consistent with previous qualitative
research which finds that countries with a high degree of financial development
or digital infrastructure have more developed CBDC projects. Further, we obtain
robust results when predicting the CBDCPI at different points in time.


Abstract:
  In a Dyck path a peak which is (weakly) higher than all the preceding peaks
is called a strict (weak) left to right maximum. We obtain explicit generating
functions for both weak and strict left to right maxima in Dyck paths. The
proofs of the associated asymptotics make use of analytic techniques such as
Mellin transforms, singularity analysis and formal residue calculus.


Abstract:
  In the first part of this work we have established an efficient method to
obtain a topological classification of locally discrete, finitely generated,
virtually free subgroups of real-analytic circle diffeomorphisms. In this
second part we describe several consequences, among which the solution (within
this setting) to an old conjecture by P. R. Dippolito [Ann. Math. 107 (1978),
403-453] that actions with invariant Cantor sets must be semi-conjugate to
piecewise linear actions. In addition, we exhibit examples of locally discrete,
minimal actions which are not of Fuchsian type.


Abstract:
  Driven-dissipative many-body systems are difficult to analyze analytically
due to their non-equilibrium dynamics, dissipation and many-body interactions.
In this paper, we consider a driven-dissipative infinite-range Ising model with
local spontaneous emission, which naturally emerges from the open Dicke model
in the large-detuning limit. Utilizing an adaptation of the Suzuki-Trotter
quantum-to-classical mapping, we develop an exact field-theoretical analysis
and a diagrammatic representation of the spin model that can be understood from
a simple scattering picture. With this representation, we are able to analyze
critical behavior, finite-size scaling and the effective temperature near the
respective phase transition. Our formalism further allows a detailed study of
the ordered phase where we find a "heating" region within which the effective
temperature becomes negative, thereby exhibiting a truly non-equilibrium
behavior. At the phase transition, we find two distinct critical behaviors with
overdamped and underdamped critical dynamics at generic and weakly-dissipative
critical points, respectively. We further show that the underdamped critical
behavior is robust against short-range perturbations and is not an artifact of
the mean-field nature of the model. To treat such perturbations, we extend our
diagrammatic representation to include the coupling to spin waves due to the
short-range interactions. The field-theoretical approach and the diagrammatics
developed in this work should prove useful in applications to generic
short-range driven-dissipative spin systems.


Abstract:
  The two Zero Degree Calorimeters (ZDCs) of the CMS experiment are located at
$\pm 140~$m from the collision point and detect neutral particles in the
$|\eta| > 8.3$ pseudorapidity region. This paper presents a study on the
performance of the ZDC in the 2016 pPb run. The response of the detectors to
ultrarelativistic neutrons is studied using in-depth Monte Carlo simulations. A
method of signal extraction based on template fits is presented, along with a
dedicated calibration procedure. A deconvolution technique for the correction
of overlapping collision events is discussed.


Abstract:
  We prove explicit asymptotic formulae for some functions used in sieve
methods and show that there exists no odd multiperfect number of abundancy four
whose squared part is cubefree.


Abstract:
  In high-speed hyperconnected parcel logistics, intermediate hubs play a
critical role in reaching economies of scale through consolidating disperse
flows of goods. However, resorting small-size parcels at every hub is
resource-intensive and can increase hubs' workload and parcels' total travel
time. Such resorting can be reduced by smart containerized consolidation,
encapsulating together parcels sharing service level and a subsequent
destination. In this study, we introduce an optimization model enabling to
assess the impact of such consolidation in reducing the expected total
pickup-to-delivery times over a parcel logistic network and its consequences on
urban logistic operations sustainability. We provide empirical results for a
synthetic urban environment, contrasting consolidation performance over
different operational and tactical capabilities, network configurations and
demand patterns.


Abstract:
  Numerical methods for stochastic differential equations with non-globally
Lipschitz coefficients are currently studied intensively. This article gives an
overview of our work for the case that the drift coefficient is potentially
discontinuous complemented by other important results in this area. To make the
topic accessible to a broad audience, we begin with a heuristic on SDEs and a
motivation.


Abstract:
  This paper bridges discrete and continuous optimization approaches for
decomposable submodular function minimization, in both the standard and
parametric settings.
  We provide improved running times for this problem by reducing it to a number
of calls to a maximum flow oracle. When each function in the decomposition acts
on $O(1)$ elements of the ground set $V$ and is polynomially bounded, our
running time is up to polylogarithmic factors equal to that of solving maximum
flow in a sparse graph with $O(\vert V \vert)$ vertices and polynomial integral
capacities.
  We achieve this by providing a simple iterative method which can optimize to
high precision any convex function defined on the submodular base polytope,
provided we can efficiently minimize it on the base polytope corresponding to
the cut function of a certain graph that we construct. We solve this
minimization problem by lifting the solutions of a parametric cut problem,
which we obtain via a new efficient combinatorial reduction to maximum flow.
This reduction is of independent interest and implies some previously unknown
bounds for the parametric minimum $s,t$-cut problem in multiple settings.


Abstract:
  State-of-the-art (SOTA) neural machine translation (NMT) systems translate
texts at sentence level, ignoring context: intra-textual information, like the
previous sentence, and extra-textual information, like the gender of the
speaker. Because of that, some sentences are translated incorrectly.
Personalised NMT (PersNMT) and document-level NMT (DocNMT) incorporate this
information into the translation process. Both fields are relatively new and
previous work within them is limited. Moreover, there are no readily available
robust evaluation metrics for them, which makes it difficult to develop better
systems, as well as track global progress and compare different methods. This
thesis proposal focuses on PersNMT and DocNMT for the domain of dialogue
extracted from TV subtitles in five languages: English, Brazilian Portuguese,
German, French and Polish. Three main challenges are addressed: (1)
incorporating extra-textual information directly into NMT systems; (2)
improving the machine translation of cohesion devices; (3) reliable evaluation
for PersNMT and DocNMT.


Abstract:
  A uniform hypergraph $\mathcal{H}$ is corresponding to an adjacency tensor
$\mathcal{A}_\mathcal{H}$. We define an Estrada index of $\mathcal{H}$ by using
all the eigenvalues $\lambda_1,\dots,\lambda_k$ of $\mathcal{A}_\mathcal{H}$ as
$\sum_{i=1}^k e^{\lambda_i}$. The bounds for the Estrada indices of uniform
hypergraphs are given. And we characterize the Estrada indices of $m$-uniform
hypergraphs whose spectra of the adjacency tensors are $m$-symmetric.
Specially, we characterize the Estrada indices of uniform hyperstars.


Abstract:
  The interplay between topological electronic structure and superconductivity
has attracted tremendous research interests recently as they could induce
topological superconductivity (TSCs) which may be used to realize topological
qubits for quantum computation. Among various TSC candidates, superconducting
BaSn3 (Tc ~ 4.4 K) has been predicted to be a topological Dirac semimetal (TDS)
hosting two pairs of Dirac points along the G - A direction. Here, by combining
the use of angle-resolved photoemission spectroscopy and ab initio
calculations, we identified the predicted topological Dirac fermions and
confirmed the TDS nature of the compound. In addition, we observed surface
states connecting the Dirac points. Our observations demonstrate BaSn3 as a
superconductor with nontrivial topological electronic structures.


Abstract:
  We connect the Grover walk with sinks to the Grover walk with tails. The
survival probability of the Grover walk with sinks in the long time limit is
characterized by the centered generalized eigenspace of the Grover walk with
tails. The centered eigenspace of the Grover walk is the attractor eigenspace
of the Grover walk with sinks. It is described by the persistent eigenspace of
the underlying random walk whose support has no overlap to the boundaries of
the graph and combinatorial flow in the graph theory.


Abstract:
  Initiated in 2007, the INSPIRE Directive has set a legal framework to create
a European-wide Spatial Data Infrastructure (SDI) to support the European Union
(EU) environmental policies. This chapter analyses the INSPIRE infrastructure
from a Big Geospatial Data perspective, describing how data is shared in an
interoperable way by public sector organisations in the EU Member States and
how it is made available in and accessible within the infrastructure. The
INSPIRE Geoportal, which is the entry point to the whole infrastructure, is
presented in detail. To justify its nature of a Big Geospatial Data
infrastructure, the characteristics of INSPIRE data are mapped to those of Big
Data's six 'Vs'. Despite many good results achieved in terms of data sharing,
some challenges still remain related to data consumption from the user side.
The chapter concludes with a dedicated discussion on how INSPIRE, and
traditional SDIs in general, should evolve into modern data ecosystems to
address these challenges while also embracing the modern practices of data
sharing through the web.


Abstract:
  The formulation of national research policies would benefit greatly from
reliable strategic analysis of the scientific infrastructure, aimed at
identifying the relevant strengths and weaknesses at field level. Bibliometric
methodologies thus far proposed in the literature are not completely
satisfactory. This work proposes a novel "output-to-input-oriented" approach,
which permits identification of research strengths and weaknesses on the basis
of the ratios of top scientists and highly cited articles to research
expenditures in each field. The proposed approach is applied to the Italian
academic system. 2012-2016 scientific publications are analyzed, in the 218
research fields where bibliometric assessment is appropriate.


Abstract:
  It is possible that bosonic dark matter forms halos around the Sun or the
Earth. We discuss the possibility of probing such halos with atomic clocks.
Focusing on either a Higgs portal or photon portal interaction between the dark
matter and the Standard Model, we search the possible parameter space for which
a clock on Earth and a clock in space would have a discernible frequency
difference. Bosonic dark matter halos surrounding the Earth can potentially be
probed with current optical atomic clocks.


Abstract:
  In this note, we propose a novel technique to reduce the algorithmic
complexity of neural network training by using matrices of tropical real
numbers instead of matrices of real numbers. Since the tropical arithmetics
replaces multiplication with addition, and addition with max, we theoretically
achieve several order of magnitude better constant factors in time complexities
in the training phase. The fact that we replace the field of real numbers with
the tropical semiring of real numbers and yet achieve the same classification
results via neural networks come from deep results in topology and analysis,
which we verify in our note. We then explore artificial neural networks in
terms of tropical arithmetics and tropical algebraic geometry, and introduce
the multi-layered tropical neural networks as universal approximators. After
giving a tropical reformulation of the backpropagation algorithm, we verify the
algorithmic complexity is substantially lower than the usual backpropagation as
the tropical arithmetic is free of the complexity of usual multiplication.


Abstract:
  Tuberculosis (TB) is a top-10 cause of death worldwide. Though the WHO
recommends chest radiographs (CXRs) for TB screening, the limited availability
of CXR interpretation is a barrier. We trained a deep learning system (DLS) to
detect active pulmonary TB using CXRs from 9 countries across Africa, Asia, and
Europe, and utilized large-scale CXR pretraining, attention pooling, and noisy
student semi-supervised learning. Evaluation was on (1) a combined test set
spanning China, India, US, and Zambia, and (2) an independent mining population
in South Africa. Given WHO targets of 90% sensitivity and 70% specificity, the
DLS's operating point was prespecified to favor sensitivity over specificity.
On the combined test set, the DLS's ROC curve was above all 9 India-based
radiologists, with an AUC of 0.90 (95%CI 0.87-0.92). The DLS's sensitivity
(88%) was higher than the India-based radiologists (75% mean sensitivity),
p<0.001 for superiority; and its specificity (79%) was non-inferior to the
radiologists (84% mean specificity), p=0.004. Similar trends were observed
within HIV positive and sputum smear positive sub-groups, and in the South
Africa test set. We found that 5 US-based radiologists (where TB isn't endemic)
were more sensitive and less specific than the India-based radiologists (where
TB is endemic). The DLS also remained non-inferior to the US-based
radiologists. In simulations, using the DLS as a prioritization tool for
confirmatory testing reduced the cost per positive case detected by 40-80%
compared to using confirmatory testing alone. To conclude, our DLS generalized
to 5 countries, and merits prospective evaluation to assist cost-effective
screening efforts in radiologist-limited settings. Operating point flexibility
may permit customization of the DLS to account for site-specific factors such
as TB prevalence, demographics, clinical resources, and customary practice
patterns.


Abstract:
  In this paper we introduce the notion of Modal Software Engineering:
automatically turning sequential, deterministic programs into semantically
equivalent programs efficiently operating on inputs coming from multiple
overlapping worlds. We are drawing an analogy between modal logics, and
software application domains where multiple sets of inputs (multiple worlds)
need to be processed efficiently. Typically those sets highly overlap, so
processing them independently would involve a lot of redundancy, resulting in
lower performance, and in many cases intractability. Three application domains
are presented: reasoning about feature-based variability of Software Product
Lines (SPLs), probabilistic programming, and approximate programming.


Abstract:
  This work is meant to demonstrate new class of prime numbers -- cyclic prime
numbers, that can be derived from any prime number at certain numeric systems.
Cyclic prime numbers are also related to the cyclic numbers and full reptend
prime numbers. Cyclic prime numbers, derived from the same prime number, could
be grouped. Conditions and properties of those groups between different numeric
systems are also the subjects of this paper.


Abstract:
  Gold-associated pathfinder minerals have been investigated by identifying
host minerals of Au for samples collected from an artisanal mining site near a
potential gold mine (Kubi Gold Project) in Dunkwa-On-Offin in the central
region of Ghana. We find that for each composition of Au powder (impure) and
the residual black hematite/magnetite sand that remains after gold panning,
there is a unique set of associated diverse indicator minerals. These indicator
minerals are identified as SiO2 (quartz), Fe3O4 (magnetite), and Fe2O3
(hematite), while contributions from pyrite, arsenopyrites, iridosmine,
scheelite, tetradymite, garnet, gypsum, and other sulfate materials are
insignificant. This constitutes a confirmative identification of Au pathfinding
minerals in this particular mineralogical area. The findings suggest that X-ray
diffraction could also be applied in other mineralogical sites to aid in
identifying indicator minerals of Au and the location of ore bodies at reduced
environmental and exploration costs.


Abstract:
  In product description generation (PDG), the user-cared aspect is critical
for the recommendation system, which can not only improve user's experiences
but also obtain more clicks. High-quality customer reviews can be considered as
an ideal source to mine user-cared aspects. However, in reality, a large number
of new products (known as long-tailed commodities) cannot gather sufficient
amount of customer reviews, which brings a big challenge in the product
description generation task. Existing works tend to generate the product
description solely based on item information, i.e., product attributes or title
words, which leads to tedious contents and cannot attract customers
effectively. To tackle this problem, we propose an adaptive posterior network
based on Transformer architecture that can utilize user-cared information from
customer reviews. Specifically, we first extend the self-attentive Transformer
encoder to encode product titles and attributes. Then, we apply an adaptive
posterior distillation module to utilize useful review information, which
integrates user-cared aspects to the generation process. Finally, we apply a
Transformer-based decoding phase with copy mechanism to automatically generate
the product description. Besides, we also collect a large-scare Chinese product
description dataset to support our work and further research in this field.
Experimental results show that our model is superior to traditional generative
models in both automatic indicators and human evaluation.


Abstract:
  Automated semantic segmentation and object detection are of great importance
in geospatial data analysis. However, supervised machine learning systems such
as convolutional neural networks require large corpora of annotated training
data. Especially in the geospatial domain, such datasets are quite scarce.
Within this paper, we aim to alleviate this issue by introducing a new
annotated 3D dataset that is unique in three ways: i) The dataset consists of
both an Unmanned Aerial Vehicle (UAV) laser scanning point cloud and a 3D
textured mesh. ii) The point cloud features a mean point density of about 800
pts/sqm and the oblique imagery used for 3D mesh texturing realizes a ground
sampling distance of about 2-3 cm. This enables the identification of
fine-grained structures and represents the state of the art in UAV-based
mapping. iii) Both data modalities will be published for a total of three
epochs allowing applications such as change detection. The dataset depicts the
village of Hessigheim (Germany), henceforth referred to as H3D. It is designed
to promote research in the field of 3D data analysis on one hand and to
evaluate and rank existing and emerging approaches for semantic segmentation of
both data modalities on the other hand. Ultimately, we hope that H3D will
become a widely used benchmark dataset in company with the well-established
ISPRS Vaihingen 3D Semantic Labeling Challenge benchmark (V3D). The dataset can
be downloaded from
https://ifpwww.ifp.uni-stuttgart.de/benchmark/hessigheim/default.aspx.


Abstract:
  Polar active particles constitute a wide class of synthetic colloids that are
able to propel along a preferential direction, given by their polar axis. Here,
we demonstrate a generic self-phoretic mechanism that leads to their
spontaneous chiralization through a symmetry breaking instability. We find that
the transition of an active particle from a polar to a chiral symmetry is
characterized by the emergence of active rotation and of circular trajectories.
We show that the instability is driven by the advection of a solute that
interacts differently with the two portions of the particle surface and it
occurs through a supercritical pitchfork bifurcation.


Abstract:
  The breaking of reciprocity is a topic of great interest in fundamental
physics and optical information processing applications. We demonstrate
non-reciprocal light transport in a quantum system of hot atoms by engineering
the dissipative atomic reservoir. Our scheme is based on the phase-sensitive
light transport in a multi-channel photon-atom interaction configuration, where
the phase of collective atomic excitations is tunable through external driving
fields. Remarkably, we observe inter-channel quantum correlations which
originate from interactions with the judiciously engineered reservoir. The
non-reciprocal transport in a quantum optical atomic system constitutes a new
paradigm for atom-based, non-reciprocal optics, and offers opportunities for
quantum simulations with coupled optical channels.


Abstract:
  In this paper, we study a class of fully nonlinear contracting curvature
flows of closed, uniformly convex hypersurfaces in the Euclidean space $\mathbb
R^{n+1}$ with the normal speed $\Phi$ given by $r^\alpha F^\beta$ or $u^\alpha
F^\beta$, where $F$ is a monotone, symmetric, inverse-concave, homogeneous of
degree one function of the principal curvatures, $r$ is the distance from the
hypersurface to the origin and $u$ is the support function of hypersurface. If
$\alpha\geq \beta+1$ when $\Phi=r^\alpha F^\beta$ or $\alpha> \beta+1$ when
$\Phi=u^\alpha F^\beta$, we prove that the flow exists for all times and
converges to the origin. After proper rescaling, we prove that the normalized
flow converges exponentially in the $C^\infty$ topology to a sphere centered at
the origin. Furthermore, for special inverse concave curvature function
$F=K^{\frac{s}{n}}F_1^{1-s}(s\in(0, 1])$, where $K$ is Gauss curvature and
$F_1$ is inverse-concave, we obtain the asymptotic convergence for the flow
with $\Phi=u^\alpha F^\beta$ when $\alpha=\beta+1$. If $\alpha<\beta+1$, a
counterexample is given for the above convergence when speed equals to
$r^\alpha F^\beta$.


Abstract:
  Dust is an essential ingredient of galaxies, determining the physical and
chemical conditions in the interstellar medium. Several complementary
observational evidences indicate that the cosmic dust mass density
significantly drops from redshift $z=1$ to $z=0$. Clearly, and for the first
time during cosmic evolution, dust must be destroyed more rapidly than it is
formed. By considering the dust production/destruction processes acting in this
cosmic time lapse, we find that the drop can be explained if dust is mainly
destroyed by astration (49\% contribution in the fiducial case) and supernova
shocks within galaxies (42\%). Our results further imply that on average each
supernova destroys only $M_{d,sn} =0.45\, M_\odot$ of dust, i.e. $5-10$ times
less than usually assumed, with a hard upper limit of $M_{d,sn} < {3.0}
M_\odot$ set by the available metal budget and maximal grain growth. The lower
efficiency might be explained by effective shielding of dust against shock
processing in pre-supernova wind shells.


Abstract:
  End-to-end Automatic Speech Recognition (ASR) models are commonly trained
over spoken utterances using optimization methods like Stochastic Gradient
Descent (SGD). In distributed settings like Federated Learning, model training
requires transmission of gradients over a network. In this work, we design the
first method for revealing the identity of the speaker of a training utterance
with access only to a gradient. We propose Hessian-Free Gradients Matching, an
input reconstruction technique that operates without second derivatives of the
loss function (required in prior works), which can be expensive to compute. We
show the effectiveness of our method using the DeepSpeech model architecture,
demonstrating that it is possible to reveal the speaker's identity with 34%
top-1 accuracy (51% top-5 accuracy) on the LibriSpeech dataset. Further, we
study the effect of two well-known techniques, Differentially Private SGD and
Dropout, on the success of our method. We show that a dropout rate of 0.2 can
reduce the speaker identity accuracy to 0% top-1 (0.5% top-5).


Abstract:
  We give a construction of a set $A \subset \mathbb N$ such that any subset
$A' \subset A$ with $|A'| \gg |A|^{2/3}$ is neither an additive nor
multiplicative Sidon set. In doing so, we refute a conjecture of Klurman and
Pohoata.


Abstract:
  Continual learning is known for suffering from catastrophic forgetting, a
phenomenon where earlier learned concepts are forgotten at the expense of more
recent samples. In this work, we challenge the assumption that continual
learning is inevitably associated with catastrophic forgetting by presenting a
set of tasks that surprisingly do not suffer from catastrophic forgetting when
learned continually. We provide evidence that these reconstruction-type tasks
exhibit positive forward transfer and that single-view 3D shape reconstruction
improves the performance on learned and novel categories over time. We provide
the novel analysis of knowledge transfer ability by looking at the output
distribution shift across sequential learning tasks. Finally, we show that the
robustness of these tasks leads to the potential of having a proxy
representation learning task for continual classification. The codebase,
dataset, and pre-trained models released with this article can be found at
https://github.com/rehg-lab/CLRec.


Abstract:
  Most ultraluminous X-ray sources (ULXs) are now thought to be powered by
stellar-mass compact objects accreting at super-Eddington rates. While the
discovery of evolutionary cycles have marked a breakthrough in our
understanding of the accretion flow changes in the sub-Eddington regime in
Galactic Black Hole Binaries, their evidence in the super-Eddington regime
remained elusive. However, recent circumstantial evidence had hinted the
presence of a recurrent evolutionary cycle in two archetypal ULXs, Holmberg II
X-1 and NGC 5204 X-1. Here we build on our previous work and exploit the
long-term high-cadence monitoring of Swift-XRT in order to provide evidence of
the evolutionary cycle in these two sources and investigate the main physical
parameters inducing their spectral transitions. We study the long-term
evolution of both sources using hardness-intensity diagrams (HID) and by means
of Lomb-Scargle periodograms and Gaussian processes modelling to look for
periodic variability. We show that both sources follow a recurrent evolutionary
pattern in the HID that can be characterized by the hard ultraluminous (HUL)
and soft ultraluminous (SUL) spectral regimes, and a third state with
characteristics similar to the supersoft ultraluminous (SSUL) state. The
transitions between the soft states seem aperiodic, as revealed by timing
analysis of the light curve of Holmberg II X-1, albeit further investigation is
warranted. The light curve of NGC 5204 X-1 shows a periodicity of $\sim$ 200
days, possibly associated with the duration of the evolutionary cycle. We
support a scenario in which the spectral changes from HUL to SUL are due to a
periodic increase of the mass-transfer rate and subsequent narrowing of the
opening angle of the supercritical funnel. The narrower funnel, combined with
stochastic variability imprinted by the wind, might explain the SUL--SSUL
spectral changes.


Abstract:
  Landfall of a tropical cyclone is the event when it moves over the land after
crossing the coast of the ocean. It is important to know the characteristics of
the landfall in terms of location and time, well advance in time to take
preventive measures timely. In this article, we develop a deep learning model
based on the combination of a Convolutional Neural network and a Long
Short-Term memory network to predict the landfall's location and time of a
tropical cyclone in six ocean basins of the world with high accuracy. We have
used high-resolution spacial reanalysis data, ERA5, maintained by European
Center for Medium-Range Weather Forecasting (ECMWF). The model takes any 9
hours, 15 hours, or 21 hours of data, during the progress of a tropical cyclone
and predicts its landfall's location in terms of latitude and longitude and
time in hours. For 21 hours of data, we achieve mean absolute error for
landfall's location prediction in the range of 66.18 - 158.92 kilometers and
for landfall's time prediction in the range of 4.71 - 8.20 hours across all six
ocean basins. The model can be trained in just 30 to 45 minutes (based on ocean
basin) and can predict the landfall's location and time in a few seconds, which
makes it suitable for real time prediction.


Abstract:
  This paper gives the cohomology classification of finitistic spaces X
equipped with free actions of the group G = S3 and the orbit space X/G is the
integral or mod 2 cohomology quaternion projective space HPn. We have proved
that X is the integral or mod 2 cohomology (4n+3)-sphere or the product of
3-sphere and quaternion projective space HPn.
  Similar results for G = S1 actions are also discussed.


Abstract:
  We present an example of the string correspondence: a relation between two
classical string solutions - one in Minkowski spacetime and another one in the
AdS spacetime. The first solution describes a $2$d motion of the string with
massive ends and was derived in (https://doi.org/10.1103/PhysRevD.13.2364). The
second solution is the accelerating string solution in the $\textrm{AdS}_3$
spacetime and describes a heavy quark-antiquark pair (arXiv:0804.1343).


Abstract:
  Advances in artificial intelligence are driven by technologies inspired by
the brain, but these technologies are orders of magnitude less powerful and
energy efficient than biological systems. Inspired by the nonlinear dynamics of
neural networks, new unconventional computing hardware has emerged with the
potential for extreme parallelism and ultra-low power consumption. Physical
reservoir computing demonstrates this with a variety of unconventional systems
from optical-based to spintronic. Reservoir computers provide a nonlinear
projection of the task input into a high-dimensional feature space by
exploiting the system's internal dynamics. A trained readout layer then
combines features to perform tasks, such as pattern recognition and time-series
analysis. Despite progress, achieving state-of-the-art performance without
external signal processing to the reservoir remains challenging. Here we show,
through simulation, that magnetic materials in thin-film geometries can realise
reservoir computers with greater than or similar accuracy to digital recurrent
neural networks. Our results reveal that basic spin properties of magnetic
films generate the required nonlinear dynamics and memory to solve machine
learning tasks. Furthermore, we show that neuromorphic hardware can be reduced
in size by removing the need for discrete neural components and external
processing. The natural dynamics and nanoscale size of magnetic thin-films
present a new path towards fast energy-efficient computing with the potential
to innovate portable smart devices, self driving vehicles, and robotics.


Abstract:
  We introduce the quantum implementation of a binary classifier based on
cosine similarity between data vectors. The proposed quantum algorithm
evaluates the classifier on a set of data vectors with time complexity that is
logarithmic in the product of the set cardinality and the dimension of the
vectors. It is based just on a suitable state preparation like the retrieval
from a QRAM, a SWAP test circuit (two Hadamard gates and one Fredkin gate), and
a measurement process on a single qubit. Furthermore we present a simple
implementation of the considered classifier on the IBM quantum processor
ibmq_16_melbourne. Finally we describe the combination of the classifier with
the quantum version of a K-nearest neighbors algorithm within a hybrid
quantum-classical structure.


Abstract:
  We study new classes of generic off-diagonal and diagonal cosmological
solutions for effective Einstein equations in modified gravity theories, MGTs,
with modified dispersion relations, MDRs, encoding possible violations of
(local) Lorentz invariance, LIVs. Such MGTs are constructed for Lagrange
densities with two non-Riemannian volume forms (two-measure theories, TMTs) and
associated bimetric/ biconnection geometric structures. For conventional 2+2
splitting, we can describe such models in Finsler like variables, which is
important for elaborating geometric methods of constructing exact and
parametric solutions. Such formulations of general relativity, GR, and MGTs are
considered for Lorentz manifolds and their (co) tangent bundles, in brief,
FTMT. Off-diagonal metrics solving gravitational field equations in FTMTs are
determined by generating functions, effective sources and integration constants
and characterized by nonholonomic frame torsion effects. Restricting the class
of integration functions, we can extract torsionless and diagonal
configurations and model emergent cosmological theories with square scalar
curvature, $R^2$, when the global Weyl-scale symmetry is broken via nonlinear
dynamical interactions with nonholonomic constraints. In the physical
Einstein-Finsler frame, the constructions involve (i) nonlinear
re-parametrization symmetries of the generating functions and effective
sources; (ii) effective potentials for the scalar field with possible two flat
regions which allows a unified description of locally anisotropic and/or
isotropic early universe inflation related to acceleration cosmology and dark
energy; (iii) there are "emergent universes" described by (off-) diagonal
solutions for certain nonholonomic phases and parametric cosmological evolution
resulting in various inflationary phases; (iv) we can reproduce in two-measure
theories massive gravity effects.


Abstract:
  This study aims to describe the response of two persistent chlorophyll-a
maxima to physical processes affecting the thermocline and nitracline position
in the Eastern Tropical North Pacific (ETNP). We focused on Long Rossby Waves
given their relevance to the ETNP circulation and its potential as a mechanism
introducing nutrients into the euphotic zone. We found the shallower
chlorophyll-a maximum in oxygenated waters became more intense when denser
waters (more nutrients) moved toward the surface. It suggests that isopycnals
and nitracline displacements modify the nutrient supply in the euphotic zone,
which produces changes in phytoplankton growth. The suboxic and deeper
chlorophyll a maximum showed a strong association with the 26 kg m^{-3}
isopycnal being only mechanically displaced, and its chlorophyll-a content does
not seem to covary with irradiance or nutrients. The different responses of the
chlorophyll-a maxima could be explained if different phytoplankton groups are
associated with them. Long Rossby Waves can affect the position of the
thermocline, nitracline and isopycnals in an annual cycle, but it seems to be a
background signal modulated by higher frequency processes such as mesoscale
eddies and other Rossby waves. The co-occurrence of processes can control the
nitracline depth, and hence the input of nutrients into the euphotic zone that
can cause sporadic enhancements of the chlorophyll-a concentration of one
maximum.


Abstract:
  In this paper we establish the convergence case of Khintchine's theorem for
affine hyperplanes in function field of positive characteristic. Along with
that, we also prove a quantitative version of the same. The main technique used
in the proof is a dynamical result called `Quantitative nondivergence' due to
D. Y. Kleinbock and G. A. Margulis [17].


Abstract:
  We study the structure of profinite polyadic groups and we prove that a
polyadic topological group $(G, f)$ is profinite, if and only if, it is
compact, Hausdorff, totally disconnected. More generally, for a pseudo-variety
(or a formation) of finite groups $\mathfrak{X}$, we define the class of
$\mathfrak{X}$-polyadic groups, and we show that a polyadic group $(G, f)$ is
pro-$\mathfrak{X}$, if and only if, it is compact, Hausdorff, totally
disconnected and for every open congruence $R$, the quotient $(G/R, f_R)$ is
$\mathfrak{X}$-polyadic.


Abstract:
  We study the problem of Safe Policy Improvement (SPI) under constraints in
the offline Reinforcement Learning (RL) setting. We consider the scenario
where: (i) we have a dataset collected under a known baseline policy, (ii)
multiple reward signals are received from the environment inducing as many
objectives to optimize. We present an SPI formulation for this RL setting that
takes into account the preferences of the algorithm's user for handling the
trade-offs for different reward signals while ensuring that the new policy
performs at least as well as the baseline policy along each individual
objective. We build on traditional SPI algorithms and propose a novel method
based on Safe Policy Iteration with Baseline Bootstrapping (SPIBB, Laroche et
al., 2019) that provides high probability guarantees on the performance of the
agent in the true environment. We show the effectiveness of our method on a
synthetic grid-world safety task as well as in a real-world critical care
context to learn a policy for the administration of IV fluids and vasopressors
to treat sepsis.


Abstract:
  The time evolution of a two-component collisionless plasma is modeled by the
Vlasov-Poisson system. In this work, the setting is two and one-half
dimensional, that is, the distribution functions of the particles species are
independent of the third space dimension. We consider the case that an external
magnetic field is present in order to confine the plasma in a given infinitely
long cylinder. After discussing global well-posedness of the corresponding
Cauchy problem, we construct stationary solutions which indeed have support
away from their confinement device. Then, in the main part of this work we
investigate the stability of such steady states, both with respect to
perturbations in the initial data, where we employ the energy-Casimir method,
and also with respect to perturbations in the external magnetic field.


Abstract:
  Hyperspectral image (HSI) contains both spatial pattern and spectral
information which has been widely used in food safety, remote sensing, and
medical detection. However, the acquisition of hyperspectral images is usually
costly due to the complicated apparatus for the acquisition of optical
spectrum. Recently, it has been reported that HSI can be reconstructed from
single RGB image using convolution neural network (CNN) algorithms. Compared
with the traditional hyperspectral cameras, the method based on CNN algorithms
is simple, portable and low cost. In this study, we focused on the influence of
the RGB camera spectral sensitivity (CSS) on the HSI. A Xenon lamp incorporated
with a monochromator were used as the standard light source to calibrate the
CSS. And the experimental results show that the CSS plays a significant role in
the reconstruction accuracy of an HSI. In addition, we proposed a new HSI
reconstruction network where the dimensional structure of the original
hyperspectral datacube was modified by 3D matrix transpose to improve the
reconstruction accuracy.


Abstract:
  Mid- to far-infrared (IR) lines are suited to study dust obscured regions in
galaxies, because IR spectroscopy allows us to explore the most hidden regions
where heavily obscured star formation as well as accretion onto supermassive
black-holes occur. This is mostly important at redshifts of 1<z<3, when most of
the baryonic mass in galaxies has been assembled. We provide reliable
calibrations of the mid- to far-IR ionic fine structure lines, the brightest H2
pure rotational lines and the Polycyclic Aromatic Hydrocarbons (PAHs) features,
that will be used to analyse current and future observations in the mm/submm
range from the ground, as well as mid-IR spectroscopy from the upcoming James
Webb Space Telescope. We use three samples of galaxies observed in the local
Universe: star forming galaxies, AGN and low-metallicity dwarf galaxies. For
each population we derive different calibrations of the observed line
luminosities versus the total IR luminosities. We derive spectroscopic
measurements of SFR and BHAR using mid- and far-IR fine structure lines, H2
pure rotational lines and PAH features. We derive robust star-formation tracers
based on the [CII]158 $\mu$m line; the sum of the [OI]63$\mu$m and
[OIII]88$\mu$m lines; a combination of the neon and sulfur mid-IR lines; the
bright PAH features at 6.2 and 11.3 $\mu$m, and the H2 rotational lines at 9.7,
12.3 and 17 $\mu$m. We propose the [CII]158$\mu$m line, the combination of two
neon lines and, for solar-like metallicity galaxies that may harbor an AGN, the
PAH11.3$\mu$m feature as the best SFR tracers. A reliable measure of the BHAR
can be obtained using the [OIV]25.9 $\mu$m and the [NeV]14.3 and 24.3 $\mu$m
lines. For the most commonly observed fine-structure lines in the far-IR we
compare our calibration with the existing ALMA observations of high redshift
galaxies finding overall a good agreement with local results.


Abstract:
  Partizan subtraction games are combinatorial games where two players, say
Left and Right, alternately remove a number n of tokens from a heap of tokens,
with $n \in S_L$ (resp. $n \in S_R$) when it is Left's (resp. Right's) turn.
The first player unable to move loses. These games were introduced by Fraenkel
and Kotzig in 1987, where they introduced the notion of dominance, i.e. an
asymptotic behavior of the outcome sequence where Left always wins if the heap
is sufficiently large. In the current paper, we investigate the other kinds of
behaviors for the outcome sequence. In addition to dominance, three other
disjoint behaviors are defined, namely weak dominance, fairness and ultimate
impartiality. We consider the problem of computing this behavior with respect
to $S_L$ and $S_R$, which is connected to the well-known Frobenius coin
problem. General results are given, together with arithmetic and geometric
characterizations when the sets $S_L$ and $S_R$ have size at most 2.


Abstract:
  Quantum entanglement plays a crucial role in quantum information processing
tasks and quantum mechanics, hence quantifying unknown entanglement is a
fundamental task. However, this is also challenging, as entanglement cannot be
measured by any observables directly. In this paper, we train neural networks
to quantify unknown entanglement, where the input features of neural networks
are the outcome statistics data produced by locally measuring target quantum
states, and the training labels are well-chosen quantities. For bipartite
quantum states, this quantity is coherent information, which is a lower bound
for the entanglement of formation and the entanglement of distillation. For
multipartite quantum states, we choose this quantity as the geometric measure
of entanglement. It turns out that the neural networks we train have very good
performance in quantifying unknown quantum states, and can beat previous
approaches like semi-device-independent protocols for this problem easily in
both precision and application range. We also observe a surprising phenomenon
that on quantum states with stronger quantum nonlocality, the neural networks
tend to have better performance, though we do not provide them any knowledge on
quantum nonlocality.


Abstract:
  High-performing machine translation (MT) systems can help overcome language
barriers while making it possible for everyone to communicate and use language
technologies in the language of their choice. However, such systems require
large amounts of parallel sentences for training, and translators can be
difficult to find and expensive. Here, we present a data collection strategy
for MT which, in contrast, is cheap and simple, as it does not require
bilingual speakers. Based on the insight that humans pay specific attention to
movements, we use graphics interchange formats (GIFs) as a pivot to collect
parallel sentences from monolingual annotators. We use our strategy to collect
data in Hindi, Tamil and English. As a baseline, we also collect data using
images as a pivot. We perform an intrinsic evaluation by manually evaluating a
subset of the sentence pairs and an extrinsic evaluation by finetuning mBART on
the collected data. We find that sentences collected via GIFs are indeed of
higher quality.


Abstract:
  Tezos is a smart-contract blockchain. Tezos smart contracts are written in a
low-level stack-based language called Michelson. This article gives an overview
of efforts using the Coq proof assistant to have stronger guarantees on
Michelson smart contracts: the Mi-Cho-Coq framework, a Coq library defining
formal semantics of Michelson, as well as an interpreter, a simple optimiser
and a weakest-precondition calculus to reason about Michelson smart contracts;
Albert, an intermediate language that abstracts Michelson stacks with a
compiler written in Coq that targets Mi-Cho-Coq.


Abstract:
  False information and true information fact checking it, often co-exist in
social networks, each competing to influence people in their spread paths. An
efficient strategy here to contain false information is to proactively identify
if nodes in the spread path are likely to endorse false information (i.e.
further spread it) or refutation information (thereby help contain false
information spreading). In this paper, we propose SCARLET (truSt and
Credibility bAsed gRaph neuraL nEtwork model using aTtention) to predict likely
action of nodes in the spread path. We aggregate trust and credibility features
from a node's neighborhood using historical behavioral data and network
structure and explain how features of a spreader's neighborhood vary. Using
real world Twitter datasets, we show that the model is able to predict false
information spreaders with an accuracy of over 87%.


Abstract:
  Multiple imputation is a well-established general technique for analyzing
data with missing values. A convenient way to implement multiple imputation is
sequential regression multiple imputation (SRMI), also called chained equations
multiple imputation. In this approach, we impute missing values using
regression models for each variable, conditional on the other variables in the
data. This approach, however, assumes that the missingness mechanism is missing
at random, and it is not well-justified under not-at-random missingness without
additional modification. In this paper, we describe how we can generalize the
SRMI imputation procedure to handle not-at-random missingness (MNAR) in the
setting where missingness may depend on other variables that are also missing.
We provide algebraic justification for several generalizations of standard SRMI
using Taylor series and other approximations of the target imputation
distribution under MNAR. Resulting regression model approximations include
indicators for missingness, interactions, or other functions of the MNAR
missingness model and observed data. In a simulation study, we demonstrate that
the proposed SRMI modifications result in reduced bias in the final analysis
compared to standard SRMI, with an approximation strategy involving inclusion
of an offset in the imputation model performing the best overall. The method is
illustrated in a breast cancer study, where the goal is to estimate the
prevalence of a specific genetic pathogenic variant.


Abstract:
  We present a caricature generation framework based on shape and style
manipulation using StyleGAN. Our framework, dubbed StyleCariGAN, automatically
creates a realistic and detailed caricature from an input photo with optional
controls on shape exaggeration degree and color stylization type. The key
component of our method is shape exaggeration blocks that are used for
modulating coarse layer feature maps of StyleGAN to produce desirable
caricature shape exaggerations. We first build a layer-mixed StyleGAN for
photo-to-caricature style conversion by swapping fine layers of the StyleGAN
for photos to the corresponding layers of the StyleGAN trained to generate
caricatures. Given an input photo, the layer-mixed model produces detailed
color stylization for a caricature but without shape exaggerations. We then
append shape exaggeration blocks to the coarse layers of the layer-mixed model
and train the blocks to create shape exaggerations while preserving the
characteristic appearances of the input. Experimental results show that our
StyleCariGAN generates realistic and detailed caricatures compared to the
current state-of-the-art methods. We demonstrate StyleCariGAN also supports
other StyleGAN-based image manipulations, such as facial expression control.


Abstract:
  We investigate the entanglement dynamics in a free-fermion chain initially
prepared in a Fermi sea and subjected to localized losses (dissipative
impurity). We derive a formula describing the dynamics of the entanglement
entropies in the hydrodynamic limit of long times and large intervals. The
result depends only on the absorption coefficient of the effective delta
potential describing the impurity in the hydrodynamic limit. Genuine
dissipation-induced entanglement is certified by the linear growth of the
logarithmic negativity. Finally, in the quantum Zeno regime at strong
dissipation the entanglement growth is arrested (Zeno entanglement death).


Abstract:
  Neutrino oscillation physics has entered a new precision era, which poses
major challenges to the level of control and diagnostics of the neutrino beams.
In this paper, we review the design of high-precision beams, their current
limitations, and the latest techniques envisaged to overcome such limits. We
put emphasis on "monitored neutrino beams" and advanced diagnostics to
determine the flux and flavor of the neutrinos produced at the source at the
per-cent level. We also discuss ab-initio measurements of the neutrino energy
-- i.e. measurements performed without relying on the event reconstruction at
the neutrino detector -- to remove any flux-induced bias in the determination
of the cross sections.


Abstract:
  In this paper, a new Discontinuity Capturing Shallow Neural Network (DCSNN)
for approximating $d$-dimensional piecewise continuous functions and for
solving elliptic interface problems is developed. There are three novel
features in the present network; namely, (i) jump discontinuity is captured
sharply, (ii) it is completely shallow consisting of only one hidden layer,
(iii) it is completely mesh-free for solving partial differential equations
(PDEs). We first continuously extend the $d$-dimensional piecewise continuous
function in $(d+1)$-dimensional space by augmenting one coordinate variable to
label the pieces of discontinuous function, and then construct a shallow neural
network to express this new augmented function. Since only one hidden layer is
employed, the number of training parameters (weights and biases) scales
linearly with the dimension and the neurons used in the hidden layer. For
solving elliptic interface equations, the network is trained by minimizing the
mean squared error loss that consists of the residual of governing equation,
boundary condition, and the interface jump conditions. We perform a series of
numerical tests to compare the accuracy and efficiency of the present network.
Our DCSNN model is comparably efficient due to only moderate number of
parameters needed to be trained (a few hundreds of parameters used throughout
all numerical examples here), and the result shows better accuracy (and less
parameters) than other method using piecewise deep neural network in
literature. We also compare the results obtained by the traditional grid-based
immersed interface method (IIM) which is designed particularly for elliptic
interface problems. Again, the present results show better accuracy than the
ones obtained by IIM. We conclude by solving a six-dimensional problem to show
the capability of the present network for high-dimensional applications.


Abstract:
  We give a coding based perspective, on a result of Erd\'{o}s, on a lower
bound for the diagonal ramsey numbers.


Abstract:
  Two-photon interference is widely used in quantum information processing,
e.g., state engineering and designing quantum gates, as well as in quantum
sensing and metrology. This includes the generation and characterization of
N00N states, which provide a phase measurement sensitivity that is beyond the
shot-noise limit. There have been numerous efforts in generating N00N states
with a photon number higher than 2, since their measurement sensitivity
increases with N. Both photon number state and modal (temporal, spectral, and
spatial) properties of light offer advantages in sensing, where the latter is
achieved through mode-multiplexed measurements. Here, we experimentally
demonstrate, with the aid of a recently developed photon-detection technology,
measurement and characterization of up to tera-mode spatiotemporal correlations
in two-photon interference and use it to generate high-dimensional two-photon
N00N-states, which can be advantageous for multiphase estimation. We observe a
high bi-photon interference and coalescence visibility of $\sim64\%$ and
$\sim88\%$ for a tera ($10^{12}$) and 0.2 tera ($2\times 10^{11}$)
spatiotemporal modes, respectively. These results open up a route for practical
applications of using the spatiotemporal degrees of freedom in two-photon
interference, and in particular, for quantum sensing and communication.


Abstract:
  MagnetoMyoGraphy (MMG) is a method of studying muscle function via weak
magnetic fields generated from human active organs and tissues. The
correspondence between MMG and electromyography means directly derived from the
Maxwell-Amp\`ere law. Here, upon briefly describing the principles of voltage
distribution inside skeletal muscles due to the electrical stimulation, we
provide a protocol to determine the effects of the magnetic field generated
from a time-changing action potential propagating in a group of skeletal muscle
cells. The position-dependent and the magnetic field behaviour on account of
the different currents in muscle fibres are performed in temporal, spectral and
spatial domains. The procedure covers identification of the fibre
subpopulations inside the fascicles of a given nerve section, characterization
of soleus skeletal muscle currents, check of axial intracellular currents,
calculation of the generated magnetic field ultimately. We expect this protocol
to take approximately 2-3 hours to complete for the whole finite-element
analysis.


Abstract:
  Current abstractive summarization systems outperform their extractive
counterparts, but their widespread adoption is inhibited by the inherent lack
of interpretability. To achieve the best of both worlds, we propose EASE, an
extractive-abstractive framework for evidence-based text generation and apply
it to document summarization. We present an explainable summarization system
based on the Information Bottleneck principle that is jointly trained for
extraction and abstraction in an end-to-end fashion. Inspired by previous
research that humans use a two-stage framework to summarize long documents
(Jing and McKeown, 2000), our framework first extracts a pre-defined amount of
evidence spans as explanations and then generates a summary using only the
evidence. Using automatic and human evaluations, we show that explanations from
our framework are more relevant than simple baselines, without substantially
sacrificing the quality of the generated summary.


Abstract:
  Training Reinforcement Learning (RL) agents in high-stakes applications might
be too prohibitive due to the risk associated to exploration. Thus, the agent
can only use data previously collected by safe policies. While previous work
considers optimizing the average performance using offline data, we focus on
optimizing a risk-averse criteria, namely the CVaR. In particular, we present
the Offline Risk-Averse Actor-Critic (O-RAAC), a model-free RL algorithm that
is able to learn risk-averse policies in a fully offline setting. We show that
O-RAAC learns policies with higher CVaR than risk-neutral approaches in
different robot control tasks. Furthermore, considering risk-averse criteria
guarantees distributional robustness of the average performance with respect to
particular distribution shifts. We demonstrate empirically that in the presence
of natural distribution-shifts, O-RAAC learns policies with good average
performance.


Abstract:
  The low water content of the terrestrial planets in the solar system suggests
that the protoplanets formed within the water snow line. Accurate prediction of
the snow line location moving with time provides a clue to constrain the
formation process of the planets. In this paper, we investigate the migration
of the snow line in protoplanetary disks whose accretion is controlled by
laminar magnetic fields, which have been proposed by various nonideal
magnetohydrodynamic (MHD) simulations. We propose an empirical model of the
disk temperature based on our nonideal MHD simulations, which show that the
accretion heating is significantly less efficient than in turbulent disks, and
calculate the snow line location over time. We find that the snow line in the
magnetically accreting laminar disks moves inside the current Earth's orbit
within 1 Myr after star formation, whereas the time for the conventional
turbulent disk is much longer than 1 Myr. This result suggests that either the
rocky protoplanets formed in such an early phase of the disk evolution, or the
protoplanets moved outward to the current orbits after they formed close to the
protosun.


Abstract:
  Spin glasses are notoriously difficult to study both analytically and
numerically due to the presence of frustration and metastability. Their highly
non-convex landscapes require collective updates to explore efficiently.
Currently, most state-of-the-art algorithms rely on stochastic spin clusters to
perform non-local updates, but such "cluster algorithms" lack general
efficiency. Here, we introduce a non-equilibrium approach for simulating spin
glasses based on classical dynamics with memory. By simulating various classes
of 3d spin glasses (Edwards-Anderson, partially-frustrated, and
fully-frustrated models), we find that memory dynamically promotes critical
spin clusters during time evolution, in a self-organizing manner. This
facilitates an efficient exploration of the low-temperature phases of spin
glasses.


Abstract:
  This paper describes a system developed to help University students get more
from their online lectures, tutorials, laboratory and other live sessions. We
do this by logging their attention levels on their laptops during live Zoom
sessions and providing them with personalised video summaries of those live
sessions. Using facial attention analysis software we create personalised video
summaries composed of just the parts where a student's attention was below some
threshold. We can also factor in other criteria into video summary generation
such as parts where the student was not paying attention while others in the
class were, and parts of the video that other students have replayed
extensively which a given student has not. Attention and usage based video
summaries of live classes are a form of personalised content, they are
educational video segments recommended to highlight important parts of live
sessions, useful in both topic understanding and in exam preparation. The
system also allows a Professor to review the aggregated attention levels of
those in a class who attended a live session and logged their attention levels.
This allows her to see which parts of the live activity students were paying
most, and least, attention to. The Help-Me-Watch system is deployed and in use
at our University in a way that protects student's personal data, operating in
a GDPR-compliant way.


Abstract:
  We consider the infinite-horizon, discrete-time full-information control
problem. Motivated by learning theory, as a criterion for controller design we
focus on regret, defined as the difference between the LQR cost of a causal
controller (that has only access to past and current disturbances) and the LQR
cost of a clairvoyant one (that has also access to future disturbances). In the
full-information setting, there is a unique optimal non-causal controller that
in terms of LQR cost dominates all other controllers. Since the regret itself
is a function of the disturbances, we consider the worst-case regret over all
possible bounded energy disturbances, and propose to find a causal controller
that minimizes this worst-case regret. The resulting controller has the
interpretation of guaranteeing the smallest possible regret compared to the
best non-causal controller, no matter what the future disturbances are. We show
that the regret-optimal control problem can be reduced to a Nehari problem,
i.e., to approximate an anticausal operator with a causal one in the operator
norm. In the state-space setting, explicit formulas for the optimal regret and
for the regret-optimal controller (in both the causal and the strictly causal
settings) are derived. The regret-optimal controller is the sum of the
classical $H_2$ state-feedback law and a finite-dimensional controller obtained
from the Nehari problem. The controller construction simply requires the
solution to the standard LQR Riccati equation, in addition to two Lyapunov
equations. Simulations over a range of plants demonstrates that the
regret-optimal controller interpolates nicely between the $H_2$ and the
$H_\infty$ optimal controllers, and generally has $H_2$ and $H_\infty$ costs
that are simultaneously close to their optimal values. The regret-optimal
controller thus presents itself as a viable option for control system design.