Abstract:
  The Gamow shell model has shown to efficiently describe weakly bound and
unbound nuclear systems, as internucleon correlations and continuum coupling
are both taken into account in this model. In the present work, we study
neutron-dripline oxygen isotopes. It is hereby demonstrated that the presence
of continuum coupling is important for the description of oxygen isotopes at
dripline, and especially to assess the eventual bound or unbound character of
$^{28}$O. Our results suggest that the ground state of $^{28}$O is weakly
unbound and is similar to the narrow resonant $^{26}$O ground state.
Predictions of weakly bound and resonance excited states in $^{24\text-26}$O
are also provided. The asymptotes of the studied many-body states are analyzed
via one-body densities, whereby the different radial properties of well bound,
loosely bound, resonance states are clearly depicted.


Abstract:
  In the field of human-robot interaction, teaching learning agents from human
demonstrations via supervised learning has been widely studied and successfully
applied to multiple domains such as self-driving cars and robot manipulation.
However, the majority of the work on learning from human demonstrations
utilizes only behavioral information from the demonstrator, i.e. what actions
were taken, and ignores other useful information. In particular, eye gaze
information can give valuable insight towards where the demonstrator is
allocating their visual attention, and leveraging such information has the
potential to improve agent performance. Previous approaches have only studied
the utilization of attention in simple, synchronous environments, limiting
their applicability to real-world domains. This work proposes a novel imitation
learning architecture to learn concurrently from human action demonstration and
eye tracking data to solve tasks where human gaze information provides
important context. The proposed method is applied to a visual navigation task,
in which an unmanned quadrotor is trained to search for and navigate to a
target vehicle in a real-world, photorealistic simulated environment. When
compared to a baseline imitation learning architecture, results show that the
proposed gaze augmented imitation learning model is able to learn policies that
achieve significantly higher task completion rates, with more efficient paths,
while simultaneously learning to predict human visual attention. This research
aims to highlight the importance of multimodal learning of visual attention
information from additional human input modalities and encourages the community
to adopt them when training agents from human demonstrations to perform
visuomotor tasks.


Abstract:
  Adaptive control approaches yield high-performance controllers when a precise
system model or suitable parametrizations of the controller are available.
Existing data-driven approaches for adaptive control mostly augment standard
model-based methods with additional information about uncertainties in the
dynamics or about disturbances. In this work, we propose a purely data-driven,
model-free approach for adaptive control. Tuning low-level controllers based
solely on system data raises concerns on the underlying algorithm safety and
computational performance. Thus, our approach builds on GoOSE, an algorithm for
safe and sample-efficient Bayesian optimization. We introduce several
computational and algorithmic modifications in GoOSE that enable its practical
use on a rotational motion system. We numerically demonstrate for several types
of disturbances that our approach is sample efficient, outperforms constrained
Bayesian optimization in terms of safety, and achieves the performance optima
computed by grid evaluation. We further demonstrate the proposed adaptive
control approach experimentally on a rotational motion system.


Abstract:
  This paper proposes two important contributions for conditional Generative
Adversarial Networks (cGANs) to improve the wide variety of applications that
exploit this architecture. The first main contribution is an analysis of cGANs
to show that they are not explicitly conditional. In particular, it will be
shown that the discriminator and subsequently the cGAN does not automatically
learn the conditionality between inputs. The second contribution is a new
method, called a contrario cGAN, that explicitly models conditionality for both
parts of the adversarial architecture via a novel a contrario loss that
involves training the discriminator to learn unconditional (adverse) examples.
This leads to a novel type of data augmentation approach for GANs (a contrario
learning) which allows to restrict the search space of the generator to
conditional outputs using adverse examples. Extensive experimentation is
carried out to evaluate the conditionality of the discriminator by proposing a
probability distribution analysis. Comparisons with the cGAN architecture for
different applications show significant improvements in performance on well
known datasets including, semantic image synthesis, image segmentation,
monocular depth prediction and "single label"-to-image using different metrics
including Fr\'echet Inception Distance (FID), mean Intersection over Union
(mIoU), Root Mean Square Error log (RMSE log) and Number of
statistically-Different Bins (NDB).


Abstract:
  We study how the typical gradient and typical height of a random surface are
modified by the addition of quenched disorder in the form of a random
independent external field. The results provide quantitative estimates, sharp
up to multiplicative constants, in the following cases.
  It is shown that for real-valued disordered random surfaces of the $\nabla
\phi$ type with a uniformly convex interaction potential: (i) The gradient of
the surface delocalizes in dimensions $1\le d\le 2$ and localizes in dimensions
$d\ge3$. (ii) The surface delocalizes in dimensions $1\le d\le 4$ and localizes
in dimensions $d\ge 5$.
  It is further shown that for the integer-valued disordered Gaussian free
field: (i) The gradient of the surface delocalizes in dimensions $d=1,2$ and
localizes in dimensions $d\ge3$. (ii) The surface delocalizes in dimensions
$d=1,2$. (iii) The surface localizes in dimensions $d\ge 3$ at weak disorder
strength. The behavior in dimensions $d\ge 3$ at strong disorder is left open.
  The proofs rely on several tools: explicit identities satisfied by the
expectation of the random surface, the Efron--Stein concentration inequality, a
coupling argument for Langevin dynamics (originally due to Funaki and Spohn)
and the Nash--Aronson estimate.


Abstract:
  Deep Learning has become a very valuable tool in different fields, and no one
doubts the learning capacity of these models. Nevertheless, since Deep Learning
models are often seen as black boxes due to their lack of interpretability,
there is a general mistrust in their decision-making process. To find a balance
between effectiveness and interpretability, Explainable Artificial Intelligence
(XAI) is gaining popularity in recent years, and some of the methods within
this area are used to generate counterfactual explanations. The process of
generating these explanations generally consists of solving an optimization
problem for each input to be explained, which is unfeasible when real-time
feedback is needed. To speed up this process, some methods have made use of
autoencoders to generate instant counterfactual explanations. Recently, a
method called Deep Guided Counterfactual Explanations (DGCEx) has been
proposed, which trains an autoencoder attached to a classification model, in
order to generate straightforward counterfactual explanations. However, this
method does not ensure that the generated counterfactual instances are close to
the data manifold, so unrealistic counterfactual instances may be generated. To
overcome this issue, this paper presents Distribution Aware Deep Guided
Counterfactual Explanations (DA-DGCEx), which adds a term to the DGCEx cost
function that penalizes out of distribution counterfactual instances.


Abstract:
  The one-dimensional compressible Navier-Stokes-Vlasov-Fokker-Planck system
with density-dependent viscosity and drag force coefficients is investigated in
the present paper. The existence, uniqueness, and regularity of global weak
solution to the initial value problem for general initial data are established
in spatial periodic domain. Moreover, the long time behavior of the weak
solution is analyzed. It is shown that as the time grows, the distribution
function of the particles converges to the global Maxwellian, and both the
fluid velocity and the macroscopic velocity of the particles converge to the
same speed.


Abstract:
  Approximate inference in Bayesian deep networks exhibits a dilemma of how to
yield high fidelity posterior approximations while maintaining computational
efficiency and scalability. We tackle this challenge by introducing a novel
variational structured approximation inspired by the Bayesian interpretation of
Dropout regularization. Concretely, we focus on the inflexibility of the
factorized structure in Dropout posterior and then propose an improved method
called Variational Structured Dropout (VSD). VSD employs an orthogonal
transformation to learn a structured representation on the variational Gaussian
noise with plausible complexity, and consequently induces statistical
dependencies in the approximate posterior. Theoretically, VSD successfully
addresses the pathologies of previous Variational Dropout methods and thus
offers a standard Bayesian justification. We further show that VSD induces an
adaptive regularization term with several desirable properties which contribute
to better generalization. Finally, we conduct extensive experiments on standard
benchmarks to demonstrate the effectiveness of VSD over state-of-the-art
variational methods on predictive accuracy, uncertainty estimation, and
out-of-distribution detection.


Abstract:
  We prove a weak version of the Perdomo Conjecture, namely, there is a
positive constant $\delta(n)>0$ depending only on $n$ such that on any closed
embedded, non-totally geodesic, minimal hypersurface $M^n$ in
$\mathbb{S}^{n+1}$,
  $$\int_{M}S \geq \delta(n){\rm Vol}(M^n),$$ where $S$ is the squared length
of the second fundamental form of $M^n$. The Perdomo Conjecture asserts that
$\delta(n)=n$ which is still open in general. As byproducts, we also obtain
some integral inequalities and Simons-type pinching results on closed embedded
(or immersed) minimal hypersurfaces, with the first positive eigenvalue
$\lambda_1(M)$ of the Laplacian involved.


Abstract:
  Initiated by Davis, Nelson, Petersen and Tenner (2018), the enumerative study
of pinnacle sets of permutations has attracted a fair amount of attention
recently. In this article, we provide a recurrence that can be used to compute
efficiently the number $|\sym_n(P)|$ of permutations of size $n$ with a given
pinnacle set $P$, with arithmetic complexity $O(k^4 + k\log n)$ for $P$ of size
$k$. A symbolic expression can also be computed in this way for pinnacle sets
of fixed size. A weighted sum $q_n(P)$ of $|\sym_n(P)|$ proposed in Davis,
Nelson, Petersen and Tenner (2018) seems to have a simple form, and a
conjectural form is given recently by Flaque, Novelli and Thibon (2021+). We
settle the problem by providing and proving an alternative form of $q_n(P)$,
which has a strong combinatorial flavor. We also study admissible orderings of
a given pinnacle set, first considered by Rusu (2020) and characterized by Rusu
and Tenner (2021), and we give an efficient algorithm for their counting.


Abstract:
  In this paper we study the cohomology of PEL-type Rapoport-Zink spaces
associated to unramified unitary similitude groups over $\Q_p$ in an odd number
of variables. We extend the results of Kaletha-Minguez-Shin-White to construct
a local Langlands correspondence for these groups and prove an averaging
formula relating the cohomology of Rapport-Zink spaces to this correspondence.
We use this formula to prove the Kottwitz conjecture for the groups we
consider.


Abstract:
  Many real life situations require a set of items to be repeatedly placed in a
random sequence. In such circumstances, it is often desirable to test whether
such randomization indeed obtains, yet this problem has received very limited
attention in the literature. This paper articulates the key features of this
problem and presents three "untargeted" tests that require no a priori
information from the analyst. These methods are used to analyze the order in
which lottery numbers are drawn in Powerball, the order in which contestants
perform on American Idol, and the order of candidates on primary election
ballots in Texas and West Virginia. In this last application, multiple
deviations from full randomization are detected, with potentially serious
political and legal consequences. The form these deviations take varies,
depending on institutional factors, which sometimes necessitates the use of
tests that exchange power for increased robustness.


Abstract:
  We systematically study the hyperon global polarization's sensitivity to the
collision systems' initial longitudinal flow velocity in hydrodynamic
simulations. By explicitly imposing local energy-momentum conservation when
mapping the initial collision geometry to macroscopic hydrodynamic fields, we
study the evolution of systems' orbital angular momentum (OAM) and fluid
vorticity. We find that a simultaneous description of the $\Lambda$ hyperons'
global polarization and the slope of pion's directed flow can strongly
constrain the size of longitudinal flow at the beginning of hydrodynamic
evolution. We extract the size of the initial longitudinal flow and the
fraction of orbital angular momentum in the produced QGP fluid as a function of
collision energy with the STAR measurements in the RHIC Beam Energy Scan
program. We find that there is about 100-200 $\hbar$ OAM that remains in the
mid-rapidity fluid at the beginning of hydrodynamic evolution. We further exam
the effects of different hydrodynamic gradients on the spin polarization of
$\Lambda$ and $\bar{\Lambda}$. The gradients of $\mu_B/T$ can change the
ordering between $\Lambda$'s and $\bar{\Lambda}$'s polarization.


Abstract:
  Tree path minimum query problem is a fundamental problem while processing
trees, and is used widely in minimum spanning tree verification and randomized
minimum spanning tree algorithms. In this paper, we study the possibility of
building an oracle in advance, which is able to answer the queries efficiently.
We present an algorithm based on Boruvka trees. Our algorithm is the first to
achieve a near-optimal bound on query time, while matching the currently
optimal trade-off between construction time and the number of comparisons
required at query. Particularly, in order to answer each query within $2k$
comparisons, our algorithm requires $O(n \log \lambda_k(n))$ time and space to
construct the oracle, and the oracle can answer queries in $O(k + \log
\lambda_k(n))$ time. Here $\lambda_k(n)$ is the inverse of the Ackermann
function along the $k$-th column. This algorithm not only is simpler than the
previous ones, but also gives a completely different method of solving this
problem.


Abstract:
  In this paper, we provide rigorous justification of the hydrostatic
approximation and the derivation of primitive equations as the small aspect
ratio limit of the incompressible three-dimensional Navier-Stokes equations in
the anisotropic horizontal viscosity regime. Setting $\varepsilon >0$ to be the
small aspect ratio of the vertical to the horizontal scales of the domain, we
investigate the case when the horizontal and vertical viscosities in the
incompressible three-dimensional Navier-Stokes equations are of orders $O(1)$
and $O(\varepsilon^\alpha)$, respectively, with $\alpha>2$, for which the
limiting system is the primitive equations with only horizontal viscosity as
$\varepsilon$ tends to zero. In particular we show that for "well prepared"
initial data the solutions of the scaled incompressible three-dimensional
Navier-Stokes equations converge strongly, in any finite interval of time, to
the corresponding solutions of the anisotropic primitive equations with only
horizontal viscosities, as $\varepsilon$ tends to zero, and that the
convergence rate is of order $O\left(\varepsilon^\frac\beta2\right)$, where
$\beta=\min\{\alpha-2,2\}$. Note that this result is different from the case
$\alpha=2$ studied in [Li, J.; Titi, E.S.: \emph{The primitive equations as the
small aspect ratio limit of the Navier-Stokes equations: Rigorous justification
of the hydrostatic approximation}, J. Math. Pures Appl., \textbf{124}
\rm(2019), 30--58], where the limiting system is the primitive equations with
full viscosities and the convergence is globally in time and its rate of order
$O\left(\varepsilon\right)$.


Abstract:
  Transformer based architectures are recently used for the task of answering
questions over tables. In order to improve the accuracy on this task,
specialized pre-training techniques have been developed and applied on millions
of open-domain web tables. In this paper, we propose two novel approaches
demonstrating that one can achieve superior performance on table QA task
without even using any of these specialized pre-training techniques. The first
model, called RCI interaction, leverages a transformer based architecture that
independently classifies rows and columns to identify relevant cells. While
this model yields extremely high accuracy at finding cell values on recent
benchmarks, a second model we propose, called RCI representation, provides a
significant efficiency advantage for online QA systems over tables by
materializing embeddings for existing tables. Experiments on recent benchmarks
prove that the proposed methods can effectively locate cell values on tables
(up to ~98% Hit@1 accuracy on WikiSQL lookup questions). Also, the interaction
model outperforms the state-of-the-art transformer based approaches,
pre-trained on very large table corpora (TAPAS and TaBERT), achieving ~3.4% and
~18.86% additional precision improvement on the standard WikiSQL benchmark.


Abstract:
  The majority of scientific papers are distributed in PDF, which pose
challenges for accessibility, especially for blind and low vision (BLV)
readers. We characterize the scope of this problem by assessing the
accessibility of 11,397 PDFs published 2010--2019 sampled across various fields
of study, finding that only 2.4% of these PDFs satisfy all of our defined
accessibility criteria. We introduce the SciA11y system to offset some of the
issues around inaccessibility. SciA11y incorporates several machine learning
models to extract the content of scientific PDFs and render this content as
accessible HTML, with added novel navigational features to support screen
reader users. An intrinsic evaluation of extraction quality indicates that the
majority of HTML renders (87%) produced by our system have no or only some
readability issues. We perform a qualitative user study to understand the needs
of BLV researchers when reading papers, and to assess whether the SciA11y
system could address these needs. We summarize our user study findings into a
set of five design recommendations for accessible scientific reader systems.
User response to SciA11y was positive, with all users saying they would be
likely to use the system in the future, and some stating that the system, if
available, would become their primary workflow. We successfully produce HTML
renders for over 12M papers, of which an open access subset of 1.5M are
available for browsing at https://scia11y.org/


Abstract:
  We prove new vanishing results on the growth of higher torsion homologies for
suitable arithmetic lattices, Artin groups and mapping class groups. The growth
is understood along Farber sequences, in particular, along residual chains. For
principal congruence subgroups, we also obtain strong asymptotic bounds for the
torsion growth.
  As a central tool, we introduce a quantitative homotopical method called
effective rebuilding. This constructs small classifying spaces of finite index
subgroups, at the same time controlling the complexity of the homotopy. The
method easily applies to free abelian groups and then extends recursively to a
wide class of residually finite groups.


Abstract:
  We discuss the impact of the new LHCb fixed-target $p\!+\!^{4}\!He$ data for
$D^0/{\bar D}^0$ production on the intrinsic $c \bar c$ component in the
nucleon wave function. Within the scenario presented here neither the
traditional gluon-gluon fusion or quark-antiquark annihilation mechanisms
calculated in the $k_T$-factorization approach nor their counterparts from the
collinear next-to-leading order collinear framework are not sufficient to
describe the transverse momentum and rapidity distributions of $D^0/{\bar D}^0$
mesons. First the $c{\bar c}$-pair production within the standard frameworks is
considered. Here a crucial role of the $c \to D$ hadronization effects at low
energies and low transverse momenta is found and discussed, which was not
analyzed in previous studies. A contribution related to intrinsic $c \bar c$
component in the nucleon wave function is included in addition. Two models of
the symmetric ($c(x) = \bar c(x)$) intrinsic charm (IC) component are
considered. At present the intrinsic charm $g^* c \to g c$ (or $g^* {\bar c}
\to g {\bar c}$) contribution can be calculated only in the massless framework
and needs to be regularized due to collinear singularity. We show that the
regularization parameter can be obtained from the fit to the LHCb fixed-target
data under consideration here. According to our model the $1 \%$ intrinsic
charm component allows to significantly improve description of the LHCb data.


Abstract:
  It is well known that, with a particular choice of norm, the classical
double-layer potential operator $D$ has essential norm $<1/2$ as an operator on
the natural trace space $H^{1/2}(\Gamma)$ whenever $\Gamma$ is the boundary of
a bounded Lipschitz domain. This implies, for the standard second-kind boundary
integral equations for the interior and exterior Dirichlet and Neumann problems
in potential theory, convergence of the Galerkin method in $H^{1/2}(\Gamma)$
for any sequence of finite-dimensional subspaces $(\mathcal{H}_N)_{N=1}^\infty$
that is asymptotically dense in $H^{1/2}(\Gamma)$. Long-standing open questions
are whether the essential norm is also $<1/2$ for $D$ as an operator on
$L^2(\Gamma)$ for all Lipschitz $\Gamma$ in 2-d; or whether, for all Lipschitz
$\Gamma$ in 2-d and 3-d, or at least for the smaller class of Lipschitz
polyhedra in 3-d, the weaker condition holds that the operators $\pm
\frac{1}{2}I+D$ are compact perturbations of coercive operators -- this a
necessary and sufficient condition for the convergence of the Galerkin method
for every sequence of subspaces $(\mathcal{H}_N)_{N=1}^\infty$ that is
asymptotically dense in $L^2(\Gamma)$. We settle these open questions
negatively. We give examples of 2-d and 3-d Lipschitz domains with Lipschitz
constant equal to one for which the essential norm of $D$ is $\geq 1/2$, and
examples with Lipschitz constant two for which the operators $\pm \frac{1}{2}I
+D$ are not coercive plus compact. We also give, for every $C>0$, examples of
Lipschitz polyhedra for which the essential norm is $\geq C$ and for which
$\lambda I+D$ is not a compact perturbation of a coercive operator for any real
or complex $\lambda$ with $|\lambda|\leq C$. Finally, we resolve negatively a
related open question in the convergence theory for collocation methods.


Abstract:
  We propose a new solvable class of multidimensional quantum harmonic
oscillators for a linear diffusive particle and a quadratic energy absorbing
well associated with a semi-definite positive matrix force. Under natural and
easily checked controllability conditions, the ground state and the zero-point
energy are explicitly computed in terms of a positive fixed point of a
continuous time algebraic Riccati matrix equation. We also present an explicit
solution of normalized and time dependent Feynman-Kac measures in terms of a
time varying linear dynamical system coupled with a differential Riccati matrix
equation. A refined non asymptotic analysis of the stability of these models is
developed based on a recently developed Floquet-type representation of time
varying exponential semigroups of Riccati matrices. We provide explicit and non
asymptotic estimates of the exponential decays to equilibrium of Feynman-Kac
semigroups in terms of Wasserstein distances or Boltzmann-relative entropy. For
reversible models we develop a series of functional inequalities including de
Bruijn identity, Fisher's information decays, log-Sobolev inequalities, and
entropy contraction estimates. In this context, we also provide a complete and
explicit description of all the spectrum and the excited states of the
Hamiltonian, yielding what seems to be the first result of this type for this
class of models. We illustrate these formulae with the traditional harmonic
oscillator associated with real time Brownian particles and Mehler's formula.
The analysis developed in this article can also be extended to solve time
dependent Schrodinger equations equipped with time varying linear diffusions
and quadratic potential functions.


Abstract:
  Approximate agreement is one of the few variants of consensus that can be
solved in a wait-free manner in asynchronous systems where processes
communicate by reading and writing to shared memory. In this work, we consider
a natural generalisation of approximate agreement on arbitrary undirected
connected graphs. Each process is given a vertex of the graph as input and, if
non-faulty, must output a vertex such that
  - all the outputs are within distance 1 of one another, and
  - each output value lies on a shortest path between two input values.
  From prior work, it is known that there is no wait-free algorithm among $n
\ge 3$ processes for this problem on any cycle of length $c \ge 4$, by
reduction from 2-set agreement (Casta\~neda et al., 2018).
  In this work, we investigate the solvability and complexity of this task on
general graphs. We give a new, direct proof of the impossibility of approximate
agreement on cycles of length $c \ge 4$, via a generalisation of Sperner's
Lemma to convex polygons. We also extend the reduction from 2-set agreement to
a larger class of graphs, showing that approximate agreement on on these graphs
is unsolvable. Furthermore, we show that combinatorial arguments, used by both
existing proofs, are necessary, by showing that the impossibility of a
wait-free algorithm in the nonuniform iterated snapshot model cannot be proved
via an extension-based proof. On the positive side, we present a wait-free
algorithm for a class of graphs that properly contains the class of chordal
graphs.


Abstract:
  Image-based geometric modeling and novel view synthesis based on sparse,
large-baseline samplings are challenging but important tasks for emerging
multimedia applications such as virtual reality and immersive telepresence.
Existing methods fail to produce satisfactory results due to the limitation on
inferring reliable depth information over such challenging reference
conditions. With the popularization of commercial light field (LF) cameras,
capturing LF images (LFIs) is as convenient as taking regular photos, and
geometry information can be reliably inferred. This inspires us to use a sparse
set of LF captures to render high-quality novel views globally. However, fusion
of LF captures from multiple angles is challenging due to the scale
inconsistency caused by various capture settings. To overcome this challenge,
we propose a novel scale-consistent volume rescaling algorithm that robustly
aligns the disparity probability volumes (DPV) among different captures for
scale-consistent global geometry fusion. Based on the fused DPV projected to
the target camera frustum, novel learning-based modules have been proposed
(i.e., the attention-guided multi-scale residual fusion module, and the
disparity field guided deep re-regularization module) which comprehensively
regularize noisy observations from heterogeneous captures for high-quality
rendering of novel LFIs. Both quantitative and qualitative experiments over the
Stanford Lytro Multi-view LF dataset show that the proposed method outperforms
state-of-the-art methods significantly under different experiment settings for
disparity inference and LF synthesis.


Abstract:
  E-commerce companies have to face abnormal sellers who sell potentially-risky
products. Typically, the risk can be identified by jointly considering product
content (e.g., title and image) and seller behavior. This work focuses on
behavior feature extraction as behavior sequences can provide valuable clues
for the risk discovery by reflecting the sellers' operation habits. Traditional
feature extraction techniques heavily depend on domain experts and adapt poorly
to new tasks. In this paper, we propose a self-supervised method InfoBehavior
to automatically extract meaningful representations from ultra-long raw
behavior sequences instead of the costly feature selection procedure.
InfoBehavior utilizes Bidirectional Transformer as feature encoder due to its
excellent capability in modeling long-term dependency. However, it is
intractable for commodity GPUs because the time and memory required by
Transformer grow quadratically with the increase of sequence length. Thus, we
propose a hierarchical grouping strategy to aggregate ultra-long raw behavior
sequences to length-processable high-level embedding sequences. Moreover, we
introduce two types of pretext tasks. Sequence-related pretext task defines a
contrastive-based training objective to correctly select the masked-out
coarse-grained/fine-grained behavior sequences against other "distractor"
behavior sequences; Domain-related pretext task designs a classification
training objective to correctly predict the domain-specific statistical results
of anomalous behavior. We show that behavior representations from the
pre-trained InfoBehavior can be directly used or integrated with features from
other side information to support a wide range of downstream tasks.
Experimental results demonstrate that InfoBehavior significantly improves the
performance of Product Risk Management and Intellectual Property Protection.


Abstract:
  This work is devoted to show a sufficient and necessary description for the
most probable transition paths of stochastic dynamical systems with Brownian
noise. The equivalence is established by studying the relations between
Markovian bridge measures and the Onsgaer-Machlup action functional. This
cannot be achieved by existing methods because Markovian bridge measures are
not quasi translation invariant. We overcome this problem by showing that the
Onsager-Machlup action functional can be derived from a Markovian bridge
process. We then demonstrate that, for some special cases of which one is a
class of linear stochastic systems and another is a class of nonlinear
stochastic systems with small noise, the most probable transition path can be
determined by a first order deterministic differential equation with an initial
value.Finally, we illustrate our results with several examples.


Abstract:
  The statistical convergence is defined for sequences with the asymptotic
density on the natural numbers, in general. In this paper, we introduce the
statistical convergence for nets in Riesz spaces by using the finite additive
measures on directed sets. Moreover, we give some relations among the
statistical convergence and the lattice properties such as the order
convergence and lattice operators.


Abstract:
  The ability of Machine-Learning (ML) based model components to generalize to
the previously unseen inputs, and the resulting stability of the models that
use these components, has been receiving a lot of recent attention, especially
when it comes to ML-based parameterizations. At the same time, ML-based
emulators of existing parameterizations can be stable, accurate, and fast when
used in the model they were specifically designed for. In this work we show
that shallow-neural-network-based emulators of radiative transfer
parameterizations developed almost a decade ago for a state-of-the-art GCM are
robust with respect to the substantial structural and parametric change in the
host model: when used in the AMIP-like experiment with the new model, they not
only remain stable, but generate realistic output. Aspects of neural network
architecture and training set design potentially contributing to stability of
ML-based model components are discussed.


Abstract:
  We present a new RF fingerprinting technique for wireless emitters that is
based on a simple, easily and efficiently retrainable Ridge Regression (RR)
classifier. The RR learns to identify devices using bursts of waveform samples,
conveniently transformed and preprocessed by delay-loop reservoirs. Deep delay
Loop Reservoir Computing (DLR) is our processing architecture that supports
general machine learning algorithms on resource-constrained devices by
leveraging delay-loop reservoir computing (RC) and innovative architectures of
loop trees. In prior work, we trained and evaluated DLR using high SNR device
emissions in clean channels. We here demonstrate how to use DLR for IoT
authentication by performing RF-based Specific Emitter Identification (SEI),
even in the presence of fading channels and heavy in-band jamming by leveraging
a matched filter (MF) extension, dubbed MF-DLR. We show that the MF processing
improves the SEI performance of RR without the RC transformation (MF-RR), but
the MF-DLR is more robust and applicable for addressing signatures beyond
waveform transients (e.g. turn-on).


Abstract:
  We explore the notion of a category with coproducts $\cup$ and a monoidal
structure $\otimes$ distributing over it, endowed with an endo-functor
$\partial$ which is "linear and Leibniz". Such $\partial$ can be legitimately
called a *derivation* on $\mathcal{C}$, and the pair $(\mathcal{C},\partial)$
appears as the categorification of a differential ri(n)g: a differential 2-rig.
  We explore a number of possible special examples: when $\mathcal{C}$ is
semicartesian (i.e., its $\otimes$-monoidal unit is the terminal object),
derivations are better behaved in some respect; interesting examples arise when
$\mathcal{C}=[\mathcal{A},\textsf{Set}]$ is a presheaf topos endowed with a
convolution product: among these, the operation of Brzozowski's derivative in
formal language theory; we retrieve the "chain rule" in a category of
"non-symmetric species" $[\textsf{Fin},\textsf{Set}]$, obtained when a
substitution product $F\diamond G$ is interpreted as composition $T_F\circ T_G$
of the associated finitary functors, regarded as formal power series.
  Derivations on a 2-rig are tightly related to *tensorial strengths*; this
draws a connection between the present theory and applicative structures in
functional programming. Moreover, given a derivation on $\mathcal{C}$, the
derivative of the $\otimes$-monoidal unit $\partial I$ is a *self-similar*
object, i.e. a solution to the equation $X\cong X\cup X$; we explore the many
consequences of this apparently innocuous observation. This has connection with
the category-theoretic analysis of dynamical systems.


Abstract:
  Existing video super-resolution methods often utilize a few neighboring
frames to generate a higher-resolution image for each frame. However, the
redundant information between distant frames has not been fully exploited in
these methods: corresponding patches of the same instance appear across distant
frames at different scales. Based on this observation, we propose a video
super-resolution method with long-term cross-scale aggregation that leverages
similar patches (self-exemplars) across distant frames. Our model also consists
of a multi-reference alignment module to fuse the features derived from similar
patches: we fuse the features of distant references to perform high-quality
super-resolution. We also propose a novel and practical training strategy for
referenced-based super-resolution. To evaluate the performance of our proposed
method, we conduct extensive experiments on our collected CarCam dataset and
the Waymo Open dataset, and the results demonstrate our method outperforms
state-of-the-art methods. Our source code will be publicly available.


Abstract:
  A recent 3-XORSAT challenge required to minimize a very complex and rough
energy function, typical of glassy models with a random first order transition
and a golf course like energy landscape. We present the ideas beyond the
quasi-greedy algorithm and its very efficient implementation on GPUs that are
allowing us to rank first in such a competition. We suggest a better protocol
to compare algorithmic performances and we also provide analytical predictions
about the exponential growth of the times to find the solution in terms of
free-energy barriers.


Abstract:
  ECM components, such as the Perineuronal net (PNN), one of the most prevalent
parts surrounding the neuronal cell. PNN is a protective net-like structure
regulating neuronal activity such as neurotransmission, charge balance and
generates an action potential. Shock induced damage of this essential component
may cause neuronal cell death and potentially leads to CTE, AD diseases, PTSD,
etc. The shock generated possibly during an accident, improvised devie
explosion or collision between NFL players may lead to damage to this safety
net. The goal is to investigate the mechanics of PNN under shock wave. To
understand the mechanics of PNN, mechanical properties of different PNN
components such as glycan, GAG, and protein need to be evaluated. In this
study, we evaluated the mechanical strength of PNN molecules and the
interfacial strength between the components of PNN. Afterward, we have assessed
the PNN molecules' damage efficiency at various conditions such as shock speed,
preexisting bubble, and boundary conditions. The secondary structure
altercation of the protein molecules of the PNN has been analyzed to evaluate
damage intensity under varying shock loading. At higher shock speed, damage
intensity is more elevated, and hyaluronan is most likely to break at the rigid
junction. The primary structure of the protein molecules is most unlikely to
fail. Instead, the molecules' secondary bonds will be altered. Our study
suggests that the number of hydrogen bonds during the shock wave propagation
decreased.


Abstract:
  Roboticists usually test new control software in simulation environments
before evaluating its functionality on real-world robots. Simulations reduce
the risk of damaging the hardware and can significantly increase the
development process's efficiency in the form of automated system tests.
  However, many flaws in the software remain undetected in simulation data,
revealing their harmful effects on the system only in time-consuming
experiments. In reality, such irregularities are often easily recognized solely
by the robot's airborne noise during operation. We propose a simulated noise
estimate for the detection of failures in automated system tests of robots. The
classification of flaws uses classical machine learning - a support vector
machine - to identify different failure classes from the scalar noise estimate.
  The methodology is evaluated on simulation data from the humanoid robot LOLA.
The approach yields high failure detection accuracy with a low false-positive
rate, enabling its use for stricter automated system tests. Results indicate
that a single trained model may work for different robots. The proposed
technique is provided to the community in the form of the open-source tool
NoisyTest, making it easy to test data from any robot. In a broader scope, the
technique may empower real-world automated system tests without human
evaluation of success or failure.


Abstract:
  A wide range of n- and p-type doping levels in GaAs layers grown by chemical
beam epitaxy is achieved using H$_{2}$-diluted DTBSi and CBr$_{4}$ as gas
precursors for Si and C. We show that the doping level can be varied by
modifying either the concentration or the flux of the diluted precursor.
Specifically, we demonstrate carrier concentrations of
6$\times$10$^{17}$-1.2$\times$10$^{19}$ cm$^{-3}$ for Si, and
9$\times$10$^{16}$-3.7$\times$10$^{20}$ cm$^{-3}$ for C, as determined by Hall
effect measurements. The incorporation of Si and C as a function of the flux of
the corresponding diluted precursor is found to follow, respectively, a first
and a fourth order power law. The dependence of the electron and hole mobility
values on the carrier concentration as well as the analysis of the layers by
low-temperature (12 K) photoluminescence spectroscopy indicate that the use of
H$_{2}$ for diluting DTBSi or CBr$_{4}$ has no effect on the electrical and
optical properties of GaAs.


Abstract:
  This paper pays close attention to the cross-modality visible-infrared person
re-identification (VI Re-ID) task, which aims to match human samples between
visible and infrared modes. In order to reduce the discrepancy between features
of different modalities, most existing works usually use constraints based on
Euclidean metric. Since the Euclidean based distance metric cannot effectively
measure the internal angles between the embedded vectors, the above methods
cannot learn the angularly discriminative feature embedding. Because the most
important factor affecting the classification task based on embedding vector is
whether there is an angularly discriminativ feature space, in this paper, we
propose a new loss function called Enumerate Angular Triplet (EAT) loss. Also,
motivated by the knowledge distillation, to narrow down the features between
different modalities before feature embedding, we further present a new
Cross-Modality Knowledge Distillation (CMKD) loss. The experimental results on
RegDB and SYSU-MM01 datasets have shown that the proposed method is superior to
the other most advanced methods in terms of impressive performance.


Abstract:
  Probing quantum mechanics, quantum aspects of general relativity along with
the sensing and the constraining of classical gravity can all be enabled by
unprecedented spatial sizes of superpositions of massive objects. In this
paper, we show that there is a feasible setup sourced by realizable magnetic
field gradients ${\cal O}(10-100)$ Tm$^{-1}$ to construct a large spatial
superposition of ${\cal O}(10^{-4}-10^{-8})$ m for masses ${\cal
O}(10^{-16}-10^{-14})$ kg over a time period of up to $0.1-10$ seconds.


Abstract:
  We study the thermal conductivity in the excitonic insulator using a simple
quasi one-dimensional two-band model consisting of electron and hole bands with
the Coulomb interactions between these bands. Based on the linear response
theory within a mean-field scheme, we develop a method to identify the
contributions to thermal conductivity driven by excitonic insulator. It is
found that there is an additional heat current operator owing to the excitonic
phase transition, and that it gives contributions to thermal conductivity which
are not expressed in the form of Sommerfeld-Bethe relations written in the form
of an imaginary-time derivative of the electric current operator. Finally, we
discuss the relationship between the newly-found additional contribution and
the heat current carried by excitons.


Abstract:
  In this paper, we present a control-design method based on the energy-Casimir
method for infinite-dimensional, boundary-actuated port-Hamiltonian systems
with two-dimensional spatial domain and second-order Hamiltonian. The resulting
control law depends on distributed system states that cannot be measured, and
therefore, we additionally design an infinite-dimensional observer by
exploiting the port-Hamiltonian system representation. A Kirchhoff-Love plate
serves as an example in order to demonstrate the proposed approaches.


Abstract:
  Objectively quantifying the value of player actions in football (soccer) is a
challenging problem. To date, studies in football analytics have mainly focused
on the attacking side of the game, while there has been less work on
event-driven metrics for valuing defensive actions (e.g., tackles and
interceptions). Therefore in this paper, we use deep learning techniques to
define a novel metric that values such defensive actions by studying the threat
of passages of play that preceded them. By doing so, we are able to value
defensive actions based on what they prevented from happening in the game. Our
Defensive Action Expected Threat (DAxT) model has been validated using
real-world event-data from the 2017/2018 and 2018/2019 English Premier League
seasons, and we combine our model outputs with additional features to derive an
overall rating of defensive ability for players. Overall, we find that our
model is able to predict the impact of defensive actions allowing us to better
value defenders using event-data.


Abstract:
  The cosmic distance duality relation (CDDR), eta(z)=(1+z)^2 d_A(z)/d_L(z)=1,
is one of the most fundamental and crucial formulae in cosmology. This relation
couples the luminosity and angular diameter distances, two of the most often
used measures of structure in the Universe. We here propose a new
model-independent method to test this relation, using strong gravitational
lensing (SGL) and the high-redshift quasar Hubble diagram reconstructed with a
Bezier parametric fit. We carry out this test without pre-assuming a zero
spatial curvature, adopting instead the value Omega_K=0.001 +/- 0.002 optimized
by Planck in order to improve the reliability of our result. We parametrize the
CDDR using eta(z)=1 + eta_0 z, 1 + eta_1 z + eta_2 z^2 and 1 + eta_3 z/(1+z),
and consider both the SIS and non-SIS lens models for the strong lensing. Our
best fit results are: eta_0=-0.021^{+0.068}_{-0.048},
eta_1=-0.404^{+0.123}_{-0.090}, eta_2=0.106^{+0.028}_{-0.034}, and
eta_3=-0.507^{+0.193}_{-0.133} for the SIS model, and
eta_0=-0.109^{+0.044}_{-0.031} for the non-SIS model. The measured eta(z),
based on the Planck parameter Omega_K, is essentially consistent with the value
(=1) expected if the CDDR were fully respected. For the sake of comparison, we
also carry out the test for other values of Omega_K, but find that deviations
of spatial flatness beyond the Planck optimization are in even greater tension
with the CDDR. Future measurements of SGL may improve the statistics and alter
this result but, as of now, we conclude that the CDDR favours a flat Universe.


Abstract:
  Prior work has shown that Twitter users use skin-toned emoji as an act of
self-representation to express their racial/ethnic identity. We test whether
this signal of identity can influence readers' perceptions about the content of
a post containing that signal. In a large scale (n=944) pre-registered
controlled experiment, we manipulate the presence of skin-toned emoji and
profile photos in a task where readers rate obscure trivia facts (presented as
tweets) as true or false. Using a Bayesian statistical analysis, we find that
neither emoji nor profile photo has an effect on how readers rate these facts.
This result will be of some comfort to anyone concerned about the manipulation
of online users through the crafting of fake profiles.


Abstract:
  American and Bermudan-type financial instruments are often priced with
specific Monte Carlo techniques whose efficiency critically depends on the
effective dimensionality of the problem and the available computational power.
In our work we focus on Bermudan Swaptions, well-known interest rate
derivatives embedded in callable debt instruments or traded in the OTC market
for hedging or speculation purposes, and we adopt an original pricing approach
based on Supervised Learning (SL) algorithms. In particular, we link the price
of a Bermudan Swaption to its natural hedges, i.e. the underlying European
Swaptions, and other sound financial quantities through SL non-parametric
regressions. We test different algorithms, from linear models to decision
tree-based models and Artificial Neural Networks (ANN), analyzing their
predictive performances. All the SL algorithms result to be reliable and fast,
allowing to overcome the computational bottleneck of standard Monte Carlo
simulations; the best performing algorithms for our problem result to be Ridge,
ANN and Gradient Boosted Regression Tree. Moreover, using feature importance
techniques, we are able to rank the most important driving factors of a
Bermudan Swaption price, confirming that the value of the maximum underlying
European Swaption is the prevailing feature.


Abstract:
  We introduce a ramified monoid, attached to each Brauer--type monoid, that
is, to the symmetric group, to the Jones and Brauer monoids among others.
Ramified monoids correspond to a class of tied monoids which arise from knot
theory and are interesting in itself. The ramified monoid attached to the
symmetric group is the Coxeter-like version of the so--called tied braid
monoid. We give a presentation of the ramified monoid attached to the Brauer
monoid. Also, we introduce and studied two tied-like monoids that cannot be
described as ramified monoids. However, these monoids can also be regarded as
tied versions of the Jones and Brauer monoids.


Abstract:
  The NA62 experiment reports the branching ratio measurement BR$(K^+
\rightarrow \pi^+ \nu\bar{\nu}) = (10.6^{+4.0}_{-3.4} |_{\rm stat} \pm 0.9_{\rm
syst}) \times 10 ^{-11}$ at 68% CL, based on the observation of 20 signal
candidates with an expected background of 7.0 events from the total data sample
collected at the CERN SPS during 2016-2018. This provides evidence for the very
rare $K^+ \rightarrow \pi^+ \nu\bar{\nu}$ decay, observed with a significance
of 3.4$\sigma$. The experiment achieves a single event sensitivity of
$(0.839\pm 0.054)\times 10^{-11}$, corresponding to 10.0 events assuming the
Standard Model branching ratio of $(8.4\pm1.0)\times10^{-11}$. This measurement
is also used to set limits on BR($K^+ \to \pi^+ X$), where $X$ is a scalar or
pseudo-scalar particle. Details are given of the analysis of the 2018 data
sample, which corresponds to about 80% of the total data sample.


Abstract:
  In [Haglund, Remmel, Wilson 2018] the authors state two versions of the so
called Delta conjecture, the rise version and the valley version. Of the
former, they also give a more general statement in which zero labels are also
allowed. In [Qiu, Wilson 2020], the corresponding generalisation of the valley
version is also formulated.
  In [D'Adderio, Iraci, Vanden Wyngaerd 2020], the authors use a pushing
algorithm to prove the generalised version of the shuffle theorem. An extension
of that argument is used in [Iraci, Vanden Wyngaerd 2020] to formulate a valley
version of the (generalised) Delta square conjecture, and to suggest a
symmetric function identity later stated and proved in [D'Adderio, Romero
2020].
  In this paper, we use the pushing algorithm together with the aforementioned
symmetric function identity in order to prove that the valley version of the
Delta conjecture implies the valley version of the generalised Delta
conjecture, which means that they are actually equivalent.
  Combining this with the results in [Iraci, Vanden Wyngaerd 2020], we prove
that the valley version of the Delta conjecture also implies the corresponding
generalised Delta square conjecture.


Abstract:
  The susceptibility of deep neural networks to untrustworthy predictions,
including out-of-distribution (OOD) data and adversarial examples, still
prevent their widespread use in safety-critical applications. Most existing
methods either require a re-training of a given model to achieve robust
identification of adversarial attacks or are limited to out-of-distribution
sample detection only. In this work, we propose a geometric gradient analysis
(GGA) to improve the identification of untrustworthy predictions without
retraining of a given model. GGA analyzes the geometry of the loss landscape of
neural networks based on the saliency maps of their respective input. To
motivate the proposed approach, we provide theoretical connections between
gradients' geometrical properties and local minima of the loss function.
Furthermore, we demonstrate that the proposed method outperforms prior
approaches in detecting OOD data and adversarial attacks, including
state-of-the-art and adaptive attacks.


Abstract:
  This paper presents comprehensive studies on frequency response optimized
integrators considering second order derivative regarding their numerical
error, numerical stability and transient performance. Frequency domain error
analysis is conducted on these numerical integrators to reveal their accuracy.
Numerical stability of the numerical integrators is investigated. Interesting
new types of numerical stability are recognized. Transient performance of the
numerical integrators is defined to qualitatively characterize their ability to
track fast decaying transients. This property is related to unsatisfactory
phenomena such as numerical oscillation which frequently appear in time domain
simulation of circuits and systems. Transient performance analysis of the
numerical integrators is provided. Theoretical observations from the analysis
of the numerical integrators are verified via time domain case studies.


Abstract:
  Modern data-intensive applications demand high computation capabilities with
strict power constraints. Unfortunately, such applications suffer from a
significant waste of both execution cycles and energy in current computing
systems due to the costly data movement between the computation units and the
memory units. Genome analysis and weather prediction are two examples of such
applications. Recent FPGAs couple a reconfigurable fabric with high-bandwidth
memory (HBM) to enable more efficient data movement and improve overall
performance and energy efficiency. This trend is an example of a paradigm shift
to near-memory computing. We leverage such an FPGA with high-bandwidth memory
(HBM) for improving the pre-alignment filtering step of genome analysis and
representative kernels from a weather prediction model. Our evaluation
demonstrates large speedups and energy savings over a high-end IBM POWER9
system and a conventional FPGA board with DDR4 memory. We conclude that
FPGA-based near-memory computing has the potential to alleviate the data
movement bottleneck for modern data-intensive applications.


Abstract:
  Music Genres, as a popular meta-data of music, are very useful to organize,
explore or search music datasets. Soft music genres are weighted multiple-genre
annotations to songs. In this initial work, we propose horizontally stacked bar
charts to represent a music dataset annotated by these soft music genres. For
this purpose, we take an example of a toy dataset consisting of songs labelled
with help of three music genres; Blues, Jazz and Country. We demonstrate how
such a stacked bar chart can be used as a slider for user-input in an
interface. We implement this by embedding this genre bar in a streaming
application prototype and show its utility in choosing playlists. We finally
conclude by proposing further work and future explorations on our proposed
preliminary research.


Abstract:
  Recent numerical studies indicate that the antiferromagnetic Kitaev honeycomb
lattice model undergoes a magnetic-field-induced quantum phase transition into
a new spin-liquid phase. This intermediate-field phase has been previously
characterized as a gapless spin liquid. By implementing a recently developed
variational approach based on the exact fractionalized excitations of the
zero-field model, we demonstrate that the field-induced spin liquid is gapped
and belongs to Kitaev's 16-fold way. Specifically, the low-field non-Abelian
liquid with Chern number $C=\pm 1$ transitions into an Abelian liquid with
$C=\pm 4$. The critical field and the field-dependent behaviors of key physical
quantities are in good quantitative agreement with published numerical results.
Furthermore, we derive an effective field theory for the field-induced critical
point which readily explains the ostensibly gapless nature of the
intermediate-field spin liquid.


Abstract:
  We evaluated recent CLAS Collaboration measurements for the $90^\circ$ meson
photoproduction off the nucleon using a tagged photon beam spanning the energy
interval $s = 3 - 11$ GeV$^2$. The results are compared with the "Quark
Counting Rules" predictions.


Abstract:
  This document is an introduction to designing a multiple degree of freedom
robotic manipulator. The goal of the robot is to sort an object based upon the
objects color. The robot will be a synthesis of several linkages, servo motors,
an Arduino system, and an end-effector. The design must be able to autonomously
determine what object to move and where to place it. The robot will have a
maximum reach of 24 horizontally. The operating conditions include being able
to sort several different colored spheres within the area of a 180{\deg} up to
16 from the base of the arm. Sorting consists of picking up the object from a
random position near the base and moving it to a storing location near the edge
of the defined operating range. With the following performance criteria and a
limited budget, the final design will enter a competition to determine the most
robust, and effective robotic manipulator.


Abstract:
  We consider the problem of sinusoidal parameter estimation using signed
observations obtained via one-bit sampling with fixed as well as time-varying
thresholds. In a previous paper, a relaxation-based algorithm, referred to as
1bRELAX, has been proposed to iteratively maximize the likelihood function.
However, the exhaustive search procedure used in each iteration of 1bRELAX is
time-consuming. In this paper, we present a majorization-minimization (MM)
based 1bRELAX algorithm, referred to as 1bMMRELAX, to enhance the computational
efficiency of 1bRELAX. Using the MM technique, 1bMMRELAX maximizes the
likelihood function iteratively using simple FFT operations instead of the more
computationally intensive search used by 1bRELAX. Both simulated and
experimental results are presented to show that 1bMMRELAX can significantly
reduce the computational cost of 1bRELAX while maintaining its excellent
estimation accuracy.


Abstract:
  We have developed a method that maps large astronomical images onto a
two-dimensional map and clusters them. A combination of various
state-of-the-art machine learning (ML) algorithms is used to develop a fully
unsupervised image quality assessment and clustering system. Our pipeline
consists of a data pre-processing step where individual image objects are
identified in a large astronomical image and converted to smaller pixel images.
This data is then fed to a deep convolutional autoencoder jointly trained with
a self-organizing map (SOM). This part can be used as a recommendation system.
The resulting output is eventually mapped onto a two-dimensional grid using a
second, deep, SOM. We use data taken from ground-based telescopes and, as a
case study, compare the system's ability and performance with the results
obtained by supervised methods presented by Teimoorinia et al. (2020). The
availability of target labels in this data allowed a comprehensive performance
comparison between our unsupervised and supervised methods. In addition to
image-quality assessments performed in this project, our method can have
various other applications. For example, it can help experts label images in a
considerably shorter time with minimum human intervention. It can also be used
as a content-based recommendation system capable of filtering images based on
the desired content.


Abstract:
  We present a residual-based a posteriori error estimator for the hybrid
high-order (HHO) method for the Stokes model problem. Both the proposed HHO
method and error estimator are valid in two and three dimensions and support
arbitrary approximation orders on fairly general meshes. The upper bound and
lower bound of the error estimator are proved, in which proof, the key
ingredient is a novel stabilizer employed in the discrete scheme. By using the
given estimator, adaptive algorithm of HHO method is designed to solve model
problem. Finally, the expected theoretical results are numerically demonstrated
on a variety of meshes for model problem.


Abstract:
  We prove that vector fields described by the generalized Proca class of
theories do not admit a consistent coupling to a gravitational sector defined
by a scalar-tensor theory of the degenerate type. Under the assumption that
there exists a frame in which the Proca field interacts with gravity only
through the metric tensor, our analysis shows that at least one of the
constraints associated with the degeneracy of the scalar-tensor sector is
inevitably lost whenever the vector theory includes a coupling to the
Christoffel connection.


Abstract:
  In superconductors, the motion of vortices introduces unwanted dissipation
that is disruptive to applications. Fortunately, material defects can
immobilize vortices, acting as vortex pinning centers, which engenders dramatic
improvements in superconductor material properties and device operation. This
has motivated decades of research into developing methods of tailoring the
disorder landscape in superconductors to increase the strength of vortex
pinning. Yet efficacious materials engineering still alludes us. The
electromagnetic properties of real (disordered) superconducting materials
cannot yet be reliably predicted, such that designing superconductors for
applications remains a largely inefficient process of trial and error. This is
ultimately due to large gaps in our knowledge of vortex dynamics: the field is
challenged by the extremely complex interplay between vortex elasticity,
vortex-vortex interactions, and material disorder.
  In this Perspective, we review obstacles and recent successes in
understanding and controlling vortex dynamics in superconducting materials and
devices. We further identify major open questions and discuss opportunities for
transformative research in the field. This includes improving our understanding
of vortex creep, determining and reaching the ceiling for the critical current,
advanced microscopy to garner accurate structure-property relationships,
frontiers in predictive simulations and the benefits of artificial
intelligence, as well as controlling and exploiting vortices in quantum
information applications.


Abstract:
  We examine the stochastic dynamics of two enzymes that are mechanically
coupled to each other, e.g., through an elastic substrate or a fluid medium.
The enzymes undergo conformational changes during their catalytic cycle, which
itself is driven by stochastic steps along a biased chemical free energy
landscape. We find conditions under which the enzymes can synchronize their
catalytic steps, and discover that the coupling can lead to a significant
enhancement in their overall catalytic rate. Both effects can be understood as
arising from a global bifurcation in the underlying dynamical system at
sufficiently strong coupling. Our findings suggest that, despite their
molecular scale, enzymes can be cooperative and improve their performance in
metabolic clusters.


Abstract:
  We study multiply recurrent and hypercyclic operators as a special case of
$\mathcal F$-hypercyclicity, where $\mathcal F$ is the family of subsets of the
natural numbers containing arbitrarily long arithmetic progressions. We prove
several properties of hypercyclic multiply recurrent operators, we characterize
those operators which are weakly mixing and multiply recurrent, and we show
that there are operators that are multiply recurrent and hypercyclic but not
weakly mixing.


Abstract:
  Secondary animation effects are essential for liveliness. We propose a
simple, real-time solution for adding them on top of standard skinning,
enabling artist-driven stylization of skeletal motion. Our method takes a
standard skeleton animation as input, along with a skin mesh and rig weights.
It then derives per-vertex deformations from the different linear and angular
velocities along the skeletal hierarchy. We highlight two specific applications
of this general framework, namely the cartoonlike "squashy" and "floppy"
effects, achieved from specific combinations of velocity terms. As our results
show, combining these effects enables to mimic, enhance and stylize
physical-looking behaviours within a standard animation pipeline, for arbitrary
skinned characters. Interactive on CPU, our method allows for GPU
implementation, yielding real-time performances even on large meshes. Animator
control is supported through a simple interface toolkit, enabling to refine the
desired type and magnitude of deformation at relevant vertices by simply
painting weights. The resulting rigged character automatically responds to new
skeletal animation, without further input.


Abstract:
  The Gibbs state is widely taken to be the equilibrium state of a system in
contact with an environment at temperature $T$. However, non-negligible
interactions between system and environment can give rise to an altered state.
Here we derive general expressions for this mean force Gibbs state, valid for
any system that interacts with a bosonic reservoir. First, we derive the state
in the weak coupling limit and find that, in general, it maintains coherences
with respect to the bare system Hamiltonian. Second, we develop a new expansion
method suited to investigate the ultrastrong coupling regime. This allows us to
derive the explicit form for the mean force Gibbs state, and we find that it
becomes diagonal in the basis set by the system-reservoir interaction instead
of the system Hamiltonian. Several examples are discussed including a single
qubit, a three-level V-system and two coupled qubits all interacting with
bosonic reservoirs. The results shed light on the presence of coherences in the
strong coupling regime, and provide key tools for nanoscale thermodynamics
investigations.


Abstract:
  Cyber-attacks on Industrial Automation and Control Systems (IACS) are rising
in numbers and sophistication. Embedded controller devices such as Programmable
Logic Controllers (PLCs), which are central to controlling physical processes,
must be secured against attacks on confidentiality, integrity and availability.
The focus of this paper is to add design-level support for security in IACS
applications, especially around inter-PLC communications. We propose an
end-to-end solution to develop IACS applications with inherent, and parametric
support for security. Built using the IEC 61499 Function Blocks standard, this
solution allows us to annotate certain communications as 'secure' during design
time. When the application is compiled, these annotations are transformed into
a security layer that implements encrypted communication between PLCs. In this
paper, we implement a part of this security layer focussed on confidentiality,
called Confidentiality Layer for Function Blocks (CL4FB), which provides a
range of encryption/decryption and secure key exchange functionalities. We
study the impact of using CL4FB in IACS applications with real-time
constraints. Through a case study focussing on protection functions in
smart-grids, we show that varying levels of confidentiality can be achieved
while also meeting hard real-time deadlines.


Abstract:
  Motivated by the variation in reported lattice parameters of
floating-zone-grown Nd$_{2}$Zr$_{2}$O$_{7}$ crystals, we have performed a
detailed study of the relationship between synthesis environment, structural
disorder, and magnetic properties. Using a combination of polycrystalline
standards, electron-probe microanalysis and scattering techniques, we show that
crystals grown under atmospheric conditions have a reduced lattice parameter -
relative to pristine polycrystalline powders - due to occupation of the Nd-site
by excess Zr (i.e. "negative" stuffing). In contrast, crystals grown under
high-pressure Ar are nearly stoichiometric with an average lattice parameter
approaching the polycrystalline value. While minimal disorder of the oxygen
sublattices is observed on the scale of the average structure, neutron
pair-distribution function analysis indicates a highly local disorder of the
oxygen coordination, which is only weakly dependent on growth environment. Most
importantly, our magnetization, heat capacity and single-crystal neutron
scattering data show that the magnetic properties of crystals grown under
high-pressure Ar match closely with those of stoichiometric powders. Neutron
scattering measurements reveal that the signature of magnetic moment
fragmentation - the coexistence of all-in-all-out (AIAO) magnetic Bragg peaks
and diffuse pinch-point scattering due to spin-ice correlations - persists in
these nearly stoichiometric crystals. However, in addition to an increased AIAO
transition temperature, the diffuse signal is seemingly stabilized and remains
nearly unchanged upon warming to 800 mK. This behavior indicates that both the
AIAO magnetic order and spin-ice correlations are sensitive to deviations of
the Nd stoichiometry.


Abstract:
  Code search is a common practice for developers during software
implementation. The challenges of accurate code search mainly lie in the
knowledge gap between source code and natural language (i.e., queries). Due to
the limited code-query pairs and large code-description pairs available, the
prior studies based on deep learning techniques focus on learning the semantic
matching relation between source code and corresponding description texts for
the task, and hypothesize that the semantic gap between descriptions and user
queries is marginal. In this work, we found that the code search models trained
on code-description pairs may not perform well on user queries, which indicates
the semantic distance between queries and code descriptions. To mitigate the
semantic distance for more effective code search, we propose QueCos, a
Query-enriched Code search model. QueCos learns to generate semantic enriched
queries to capture the key semantics of given queries with reinforcement
learning (RL). With RL, the code search performance is considered as a reward
for producing accurate semantic enriched queries. The enriched queries are
finally employed for code search. Experiments on the benchmark datasets show
that QueCos can significantly outperform the state-of-the-art code search
models.


Abstract:
  Agent Based Modelling (ABM) is a computational framework for simulating the
behaviours and interactions of autonomous agents. As Agent Based Models are
usually representative of complex systems, obtaining a likelihood function of
the model parameters is nearly always intractable. There is a necessity to
conduct inference in a likelihood free context in order to understand the model
output. Approximate Bayesian Computation is a suitable approach for this
inference. It can be applied to an Agent Based Model to both validate the
simulation and infer a set of parameters to describe the model. Recent research
in ABC has yielded increasingly efficient algorithms for calculating the
approximate likelihood. These are investigated and compared using a pedestrian
model in the Hamilton CBD.


Abstract:
  We review spherical and inhomogeneous analytic solutions of the field
equations of Einstein and of scalar-tensor gravity, including Brans-Dicke
theory, non-minimally (possibly conformally) coupled scalar fields, Horndeski,
and beyond Horndeski/DHOST gravity. The zoo includes both static and dynamic
solutions, asymptotically flat, and asymptotically
Friedmann-Lema\^itre-Robertson-Walker ones. We minimize overlap with existing
books and reviews and we place emphasis on scalar field spacetimes and on
geometries that are "general" within certain classes. Relations between various
solutions, which have largely emerged during the last decade, are pointed out.


Abstract:
  We study the gravitational radiation emitted during the scattering of two
spinless bodies in the post-Minkowskian Effective Field Theory approach. We
derive the conserved stress-energy tensor linearly coupled to gravity and the
classical probability amplitude of graviton emission at leading and
next-to-leading order in the Newton's constant $G$. The amplitude can be
expressed in compact form as one-dimensional integrals over a Feynman parameter
involving Bessel functions. We use it to recover the leading-order radiated
angular momentum expression. Upon expanding it in the relative velocity between
the two bodies $v$, we compute the total four-momentum radiated into
gravitational waves at leading-order in $G$ and up to an order $v^8$, finding
agreement with what was recently computed using scattering amplitude methods.
Our results also allow us to investigate the zero frequency limit of the
emitted energy spectrum.


Abstract:
  We consider the problem of estimating volatility based on high-frequency data
when the observed price process is a continuous It\^o semimartingale
contaminated by microstructure noise. Assuming that the noise process is
compatible across different sampling frequencies, we argue that it typically
has a similar local behavior to fractional Brownian motion. For the resulting
class of processes, which we call mixed semimartingales, we derive consistent
estimators and asymptotic confidence intervals for the roughness parameter of
the noise and the integrated price and noise volatilities, in all cases where
these quantities are identifiable. Our model can explain key features of recent
stock price data, most notably divergence rates in volatility signature plots
that vary considerably over time and between assets.


Abstract:
  Many studies have proposed a relationship between COVID-19 transmissibility
and ambient pollution levels. However, a major limitation in establishing such
associations is to adequately account for complex disease dynamics, influenced
by e.g. significant differences in control measures and testing policies.
Another difficulty is appropriately controlling the effects of other
potentially important factors, due to both their mutual correlations and a
limited dataset. To overcome these difficulties, we will here use the basic
reproduction number ($R_0$) that we estimate for USA states using non-linear
dynamics methods. To account for a large number of predictors (many of which
are mutually strongly correlated), combined with a limited dataset, we employ
machine-learning methods. Specifically, to reduce dimensionality without
complicating the variable interpretation, we employ Principal Component
Analysis on subsets of mutually related (and correlated) predictors. Methods
that allow feature (predictor) selection, and ranking their importance, are
then used, including both linear regressions with regularization and feature
selection (Lasso and Elastic Net) and non-parametric methods based on ensembles
of weak-learners (Random Forest and Gradient Boost). Through these
substantially different approaches, we robustly obtain that PM$_{2.5}$ is a
major predictor of $R_0$ in USA states, with corrections from factors such as
other pollutants, prosperity measures, population density, chronic disease
levels, and possibly racial composition. As a rough magnitude estimate, we
obtain that a relative change in $R_0$, with variations in pollution levels
observed in the USA, is typically ~30%, which further underscores the
importance of pollution in COVID-19 transmissibility.


Abstract:
  The paper introduces a general mathematical framework for action based field
theories on Finsler spacetimes. As most often fields on Finsler spacetime
(e.g., the Finsler fundamental function or the resulting metric tensor) have a
homogeneous dependence on the tangent directions of spacetime, we construct the
appropriate configuration bundles whose sections are such homogeneous fields;
on these configuration bundles, the tools of coordinate free calculus of
variations can be consistently applied to obtain field equations. Moreover, we
prove that general covariance of natural Finsler field Lagrangians leads to an
averaged energy-momentum conservation law which, in the particular case of
Lorentzian spacetimes, is equivalent to the usual, pointwise energy-momentum
covariant conservation law.


Abstract:
  In e-commerce industry, user behavior sequence data has been widely used in
many business units such as search and merchandising to improve their products.
However, it is rarely used in financial services not only due to its 3V
characteristics - i.e. Volume, Velocity and Variety - but also due to its
unstructured nature. In this paper, we propose a Financial Service scenario
Deep learning based Behavior data representation method for Clustering
(FinDeepBehaviorCluster) to detect fraudulent transactions. To utilize the
behavior sequence data, we treat click stream data as event sequence, use time
attention based Bi-LSTM to learn the sequence embedding in an unsupervised
fashion, and combine them with intuitive features generated by risk experts to
form a hybrid feature representation. We also propose a GPU powered HDBSCAN
(pHDBSCAN) algorithm, which is an engineering optimization for the original
HDBSCAN algorithm based on FAISS project, so that clustering can be carried out
on hundreds of millions of transactions within a few minutes. The computation
efficiency of the algorithm has increased 500 times compared with the original
implementation, which makes flash fraud pattern detection feasible. Our
experimental results show that the proposed FinDeepBehaviorCluster framework is
able to catch missed fraudulent transactions with considerable business values.
In addition, rule extraction method is applied to extract patterns from risky
clusters using intuitive features, so that narrative descriptions can be
attached to the risky clusters for case investigation, and unknown risk
patterns can be mined for real-time fraud detection. In summary,
FinDeepBehaviorCluster as a complementary risk management strategy to the
existing real-time fraud detection engine, can further increase our fraud
detection and proactive risk defense capabilities.


Abstract:
  In the emerging field of molecular communication (MC), testbeds are needed to
validate theoretical concepts, motivate applications, and guide further
modeling efforts. To this end, this paper presents a flexible and extendable
in-vessel MC testbed based on superparamagnetic iron oxide nanoparticles
(SPIONs) dispersed in an aqueous suspension as they are also used for drug
targeting in biotechnology. The transmitter is realized by an electronic pump
for injection of the SPIONs stored in a syringe via a Y-tubing-connector. A
second pump generates a background flow for signal propagation in the main
tube, e.g., modeling a part of a chemical reactor or a blood vessel. For signal
reception, we employ a commercial susceptometer, an electronic device including
a coil, through which the magnetic particles move and non-intrusively generate
an electrical signal. We identify the physical mechanisms governing
transmission, propagation, and reception of SPIONs as signal carriers and
propose a simple two-parameter mathematical model for the system's channel
impulse response (CIR). Reliable communication is demonstrated for
model-agnostic and model-based detection methods for experiments with 400
random symbols transmitted via on-off keying modulation with a 1 s symbol
interval. Moreover, the proposed CIR model is shown to consistently capture the
experimentally observed distance-dependent impulse response peak heights and
peak decays for transmission distances from 5 to 40 cm.


Abstract:
  Three decades of intensive research in molecular nanomagnets have brought the
magnetic memory in molecules from liquid helium to liquid nitrogen temperature.
The enhancement of this operational temperature relies on a wise choice of the
magnetic ion and the coordination environment. However, serendipity,
oversimplified theories and chemical intuition have played the main role. In
order to establish a powerful framework for statistically driven chemical
design, we collected chemical and physical data for lanthanide-based
nanomagnets to create a catalogue of over 1400 published experiments, developed
an interactive dashboard (SIMDAVIS) to visualise the dataset, and applied
inferential statistical analysis to it. We found that the effective energy
barrier derived from the Arrhenius equation displays an excellent correlation
with the magnetic memory, and that among all chemical families studied, only
terbium bis-phthalocyaninato sandwiches and dysprosium metallocenes
consistently present magnetic memory up to high temperature, but that there are
some promising strategies for improvement.


Abstract:
  We present Self-Ensembling Single-Stage object Detector (SE-SSD) for accurate
and efficient 3D object detection in outdoor point clouds. Our key focus is on
exploiting both soft and hard targets with our formulated constraints to
jointly optimize the model, without introducing extra computation in the
inference. Specifically, SE-SSD contains a pair of teacher and student SSDs, in
which we design an effective IoU-based matching strategy to filter soft targets
from the teacher and formulate a consistency loss to align student predictions
with them. Also, to maximize the distilled knowledge for ensembling the
teacher, we design a new augmentation scheme to produce shape-aware augmented
samples to train the student, aiming to encourage it to infer complete object
shapes. Lastly, to better exploit hard targets, we design an ODIoU loss to
supervise the student with constraints on the predicted box centers and
orientations. Our SE-SSD attains top performance compared with all prior
published works. Also, it attains top precisions for car detection in the KITTI
benchmark (ranked 1st and 2nd on the BEV and 3D leaderboards, respectively)
with an ultra-high inference speed. The code is available at
https://github.com/Vegeta2020/SE-SSD.


Abstract:
  In the last few years, Motor Current Signature Analysis (MCSA) has proven to
be an effective method for electrical machines condition monitoring. Indeed,
many mechanical and electrical faults manifest as side-band spectral components
generated around the fundamental frequency component of the motor current.
These components are called interharmonics and they are a major focus of fault
detection using MCSA. However, the main drawback of this approach is that the
interference of other more prevalent components can obstruct the effect of
interharmonics in the spectrum and may therefore impede fault detection
accuracy. Thus, we propose in this paper an alternative approach that
decomposes the different current components based on the Vandermonde model and
implements the tracking of each distinct component in time and spectral
domains. This is achieved by estimating their respective relevant parameters
using the Gradient Descent algorithm. The results of this work prove to be
promising and establish the parametric tracking of the electrical current
components using the Gradient Descent algorithm as a reliable monitoring
approach.


Abstract:
  The photospheric emission in the prompt phase is the natural prediction of
the original fireball model for gamma-ray burst (GRB) due to the large optical
depth ($\tau >1$) at the base of the outflow, which is supported by the
quasi-thermal components detected in several Fermi GRBs. However, which
radiation mechanism (photosphere or synchrotron) dominates in most GRB spectra
is still under hot debate. The shape of the observed photosphere spectrum from
a pure hot fireball or a pure Poynting-flux-dominated outflow has been
investigated before. In this work, we further study the photosphere spectrum
from a hybrid outflow containing both a thermal component and a magnetic
component with moderate magnetization ($\sigma_{0}=L_{P}/L_{\text{Th}}\sim
1-10$), by invoking the probability photosphere model. The high-energy spectrum
from such a hybrid outflow is a power law rather than an exponential cutoff,
which is compatible with the observed Band function in large amounts of GRBs.
Also, the distribution of the low-energy indices (corresponding to the
peak-flux spectra) is found to be quite consistent with the statistical result
for the peak-flux spectra of GRBs best-fitted by the Band function, with
similar angular profiles of structured jet in our previous works. Finally, the
observed distribution of the high-energy indices can be well understood after
considering the different magnetic acceleration (due to magnetic reconnection
and kink instability) and the angular profiles of dimensionless entropy with
the narrower core.


Abstract:
  Bayesian parameter estimation of gravitational waves from compact binary
coalescence (CBC) typically requires more than millions of evaluations of
computationally expensive template waveforms. We propose a technique to reduce
the cost of waveform generation by exploiting the chirping behavior of CBC
signal. Our technique does not require waveforms at all frequencies in the
frequency range used in the analysis, and does not suffer from the fixed cost
due to the upsampling of waveforms. Our technique speeds up the parameter
estimation of typical binary neutron star signal by a factor of
$\mathcal{O}(10)$ for the low-frequency cutoff of $20\,\mathrm{Hz}$, and
$\mathcal{O}(10^2)$ for $5\,\mathrm{Hz}$. It does not require any offline
preparations or accurate estimates of source parameters provided by detection
pipelines.


Abstract:
  Background: By the beginning of December 2020, some vaccines against COVID-19
already presented efficacy and security, which qualify them to be used in mass
vaccination campaigns. Thus, setting up strategies of vaccination became
crucial to control the COVID19 pandemic. Methods: We use daily COVID-19 reports
from Chicago and NYC from 01-Mar2020 to 28- Nov-2020 to estimate the parameters
of an SEIR-like epidemiological model that accounts for different severity
levels. To achieve data adherent predictions, we let the model parameters to be
time-dependent. The model is used to forecast different vaccination scenarios,
where the campaign starts at different dates, from 01-Oct-2020 to 01-Apr-2021.
To generate realistic scenarios, disease control strategies are implemented
whenever the number of predicted daily hospitalizations reaches a preset
threshold. Results: The model reproduces the empirical data with remarkable
accuracy. Delaying the vaccination severely affects the mortality,
hospitalization, and recovery projections. In Chicago, the disease spread was
under control, reducing the mortality increment as the start of the vaccination
was postponed. In NYC, the number of cases was increasing, thus, the estimated
model predicted a much larger impact, despite the implementation of contention
measures. The earlier the vaccination campaign begins, the larger is its
potential impact in reducing the COVID-19 cases, as well as in the
hospitalizations and deaths. Moreover, the rate at which cases,
hospitalizations and deaths increase with the delay in the vaccination
beginning strongly depends on the shape of the incidence of infection in each
city.


Abstract:
  In this paper we introduce a $(p, q)$-deformed analogues of the generalized
Touchard polynomials and Stirling numbers, the post-quantum analogues of the
$q$-deformed generalized Touchard polynomials and Stirling numbers. The
connection between these deformations is established. A recurrence relation for
the $(p, q)$-deformed generalized Touchard polynomials is expounded,
elucidating a $(p, q)$-deformation of Spivey's relation.


Abstract:
  On July 16, 1945, the Trinity nuclear test exploded in the desert near
Alamogordo, NM. A variety of new diagnostic experiments were fielded in an
effort to understand the detailed performance of the nuclear device. This
article describes a series of radiochemical experiments that were designed to
measure the efficiency and neutron fluence of the test. These experiments, and
the scientists who led them, laid the foundation of weapons radiochemistry for
decades to come.


Abstract:
  We consider the fundamental problem of assigning distinct labels to agents in
the probabilistic model of population protocols. Our protocols operate under
the assumption that the size $n$ of the population is embedded in the
transition function. Our labeling protocols are silent w.h.p., i.e., eventually
each agent reaches its final state and remains in it forever w.h.p., as well as
safe, i.e., never update the label assigned to any single agent. We first
present a fast, silent w.h.p.and safe labeling protocol for which the required
number of interactions is asymptotically optimal, i.e., $O(n \log n/\epsilon)$
w.h.p. It uses $(2+\epsilon)n+O(n^c)$ states, for any $c<1,$ and the label
range $1,\dots,(1+\epsilon)n.$ Furthermore, we consider the so-called pool
labeling protocols that include our fast protocol. We show that the expected
number of interactions required by any pool protocol is $\ge \frac{n^2}{r+1}$,
when the labels range is $1,\dots, n+r<2n.$ Next, we provide a protocol which
is silent and safe once a unique leader is provided, and uses only $n+5\sqrt n
+O(n^c)$ states, for any $c<1,$ and draws labels from the range $1,\dots,n.$
The expected number of interactions required by the protocol is $O(n^3).$ On
the other hand, we show that (even if a unique leader is given in advance) any
silent protocol that produces a valid labeling and is safe with probability
$>1-\frac 1n$, uses $\ge n+\sqrt {n-1} -1$ states. Hence, our protocol is
almost state-optimal. We also present a generalization of the protocol to
include a trade-off between the number of states and the expected number of
interactions. Furthermore, we show that for any silent and safe labeling
protocol utilizing $n+t<2n$ states the expected number of interactions required
to achieve a valid labeling is $\ge \frac{n^2}{t+1}$.


Abstract:
  How can a text corpus stored in a customer relationship management (CRM)
database be used for data mining and segmentation? In order to answer this
question we inherited the state of the art methods commonly used in natural
language processing (NLP) literature, such as word embeddings, and deep
learning literature, such as recurrent neural networks (RNN). We used the text
notes from a CRM system which are taken by customer representatives of an
internet ads consultancy agency between years 2009 and 2020. We trained word
embeddings by using the corresponding text corpus and showed that these word
embeddings can not only be used directly for data mining but also be used in
RNN architectures, which are deep learning frameworks built with long short
term memory (LSTM) units, for more comprehensive segmentation objectives. The
results prove that structured text data in a CRM can be used to mine out very
valuable information and any CRM can be equipped with useful NLP features once
the problem definitions are properly built and the solution methods are
conveniently implemented.


Abstract:
  Describing properties of a strongly interacting quantum many-body system
poses a serious challenge both for theory and experiment. In this work, we
study excitations of one-dimensional repulsive Bose gas for arbitrary
interaction strength using a hydrodynamic approach. We use linearization to
study particle (type-I) excitations and numerical minimization to study hole
(type-II) excitations. We observe a good agreement between our approach and
exact solutions of the Lieb-Liniger model for the particle modes and
discrepancies for the hole modes. Therefore, the hydrodynamical equations find
to be useful for long-wave structures like phonons and of a limited range of
applicability for short-wave ones like narrow solitons. We discuss potential
further applications of the method.


Abstract:
  Solving partial differential equations (PDEs) is the canonical approach for
understanding the behavior of physical systems. However, large scale solutions
of PDEs using state of the art discretization techniques remains an expensive
proposition. In this work, a new physics-constrained neural network (NN)
approach is proposed to solve PDEs without labels, with a view to enabling
high-throughput solutions in support of design and decision-making. Distinct
from existing physics-informed NN approaches, where the strong form or weak
form of PDEs are used to construct the loss function, we write the loss
function of NNs based on the discretized residual of PDEs through an efficient,
convolutional operator-based, and vectorized implementation. We explore an
encoder-decoder NN structure for both deterministic and probabilistic models,
with Bayesian NNs (BNNs) for the latter, which allow us to quantify both
epistemic uncertainty from model parameters and aleatoric uncertainty from
noise in the data. For BNNs, the discretized residual is used to construct the
likelihood function. In our approach, both deterministic and probabilistic
convolutional layers are used to learn the applied boundary conditions (BCs)
and to detect the problem domain. As both Dirichlet and Neumann BCs are
specified as inputs to NNs, a single NN can solve for similar physics, but with
different BCs and on a number of problem domains. The trained surrogate PDE
solvers can also make interpolating and extrapolating (to a certain extent)
predictions for BCs that they were not exposed to during training. Such
surrogate models are of particular importance for problems, where similar types
of PDEs need to be repeatedly solved for many times with slight variations. We
demonstrate the capability and performance of the proposed framework by
applying it to steady-state diffusion, linear elasticity, and nonlinear
elasticity.


Abstract:
  Topological phases of matter became a new standard to classify quantum
systems in many cases, yet key quantities like the quantum geometric tensor
providing local information about topological properties are still
experimentally hard to access. In non-Abelian systems this accessibility to
geometric properties can be even more restrictive due to the degeneracy of the
states. We propose universal protocols to determine quantum geometric
properties in non-Abelian systems. First, we show that for a weak resonant
driving of the local parameters the coherent Rabi oscillations are related to
the quantum geometric tensor. Second, we derive that in a Landau-Zener like
transition the final probability of an avoided energy crossing is proportional
to elements of the non-Abelian quantum geometric tensor. Our schemes suggest a
way to prepare eigenstates of the quantum metric, a task that is difficult
otherwise in a degenerate subspace.


Abstract:
  Recent research has shown that numerous human-interpretable directions exist
in the latent space of GANs. In this paper, we develop an automatic procedure
for finding directions that lead to foreground-background image separation, and
we use these directions to train an image segmentation model without human
supervision. Our method is generator-agnostic, producing strong segmentation
results with a wide range of different GAN architectures. Furthermore, by
leveraging GANs pretrained on large datasets such as ImageNet, we are able to
segment images from a range of domains without further training or finetuning.
Evaluating our method on image segmentation benchmarks, we compare favorably to
prior work while using neither human supervision nor access to the training
data. Broadly, our results demonstrate that automatically extracting
foreground-background structure from pretrained deep generative models can
serve as a remarkably effective substitute for human supervision.


Abstract:
  We show that a popular self-supervised learning method, InfoNCE, is a special
case of a new family of unsupervised learning methods, the self-supervised
variational autoencoder (SSVAE). SSVAEs circumvent the usual VAE requirement to
reconstruct the data by using a carefully chosen implicit decoder. The InfoNCE
objective was motivated as a simplified parametric mutual information
estimator. Under one choice of prior, the SSVAE objective (i.e. the ELBO) is
exactly equal to the mutual information (up to constants). Under an alternative
choice of prior, the SSVAE objective is exactly equal to the simplified
parametric mutual information estimator used in InfoNCE (up to constants).
Importantly, the use of simplified parametric mutual information estimators is
believed to be critical to obtain good high-level representations, and the
SSVAE framework naturally provides a principled justification for using prior
information to choose these estimators.


Abstract:
  In this article, we investigate the convergence rate of the discrete-time
Clark--Ocone formula provided by Akahori--Amaba--Okuma [1]. In that paper, they
mainly focus on the $L_{2}$-convergence rate of the first-order error estimate
related to the tracking error of the delta hedge in mathematical finance. Here,
as two extensions, we estimate "the higher order error" for Wiener functionals
with an integrability index $2$ and "an arbitrary differentiability index."


Abstract:
  Simple graph algorithms such as PageRank have been the target of numerous
hardware accelerators. Yet, there also exist much more complex graph mining
algorithms for problems such as clustering or maximal clique listing. These
algorithms are memory-bound and thus could be accelerated by hardware
techniques such as Processing-in-Memory (PIM). However, they also come with
nonstraightforward parallelism and complicated memory access patterns. In this
work, we address this problem with a simple yet surprisingly powerful
observation: operations on sets of vertices, such as intersection or union,
form a large part of many complex graph mining algorithms, and can offer rich
and simple parallelism at multiple levels. This observation drives our
cross-layer design, in which we (1) expose set operations using a novel
programming paradigm, (2) express and execute these operations efficiently with
carefully designed set-centric ISA extensions called SISA, and (3) use PIM to
accelerate SISA instructions. The key design idea is to alleviate the bandwidth
needs of SISA instructions by mapping set operations to two types of PIM:
in-DRAM bulk bitwise computing for bitvectors representing high-degree
vertices, and near-memory logic layers for integer arrays representing
low-degree vertices. Set-centric SISA-enhanced algorithms are efficient and
outperform hand-tuned baselines, offering more than 10x speedup over the
established Bron-Kerbosch algorithm for listing maximal cliques. We deliver
more than 10 SISA set-centric algorithm formulations, illustrating SISA's wide
applicability.