Abstract:
  We consider a one-phase free boundary problem governed by doubly degenerate
fully non-linear elliptic PDEs with non-zero right hand side, which should be
understood as an analog (non-variational) of certain double phase functionals
in the theory of non-autonomous integrals. By way of brief elucidating example,
such non-linear problems in force appear in the mathematical theory of
combustion, as well as in the study of some flame propagation problems. In such
an environment we prove that solutions are Lipschitz continuous and they fulfil
a non-degeneracy property. Furthermore, we address the Caffarelli's
classification scheme: Flat and Lipschitz free boundaries are locally $C^{1,
\beta}$ for some $0 < \beta (universal) < 1$.


Abstract:
  In order to improve performance of photovoltaic/thermal (or PV/T for
simplicity) collectors, this paper firstly validated a previous computational
thermal model and then introduced an improved computational thermal model to
investigate the effects of the major control parameters on the thermal
performance of PV/T collectors, including solar cell temperature, back surface
temperature, and outlet water temperature. Besides, a computational electrical
model of PV/T system was also introduced to elaborate the relationship of
voltage, current and power of a PV module (MSX60 polycrystalline solar cell)
used in an experiment in the literature. Simulation results agree with the
experimental data very well. The effects of the time-steps from 1 hour to
minute, which is closed to the real time, were also reported. At last, several
suggestions to improve the efficiency of PV/T system were illustrated.


Abstract:
  Machine learning based interatomic potential energy surface (PES) models are
revolutionizing the field of molecular modeling. However, although much faster
than electronic structure schemes, these models suffer from a lower efficiency
as compared to typical empirical force fields due to more sophisticated
computations involved. Herein, we report a model compression scheme for
boosting the performance of the Deep Potential (DP) model, a deep learning
based PES model. This scheme, we call DP Compress, is an efficient
post-processing step after the training of DP models (DP Train). DP Compress
combines several DP-specific compression techniques, which typically speed up
DP- based molecular dynamics simulations by an order of magnitude faster, and
consume an order of magnitude less memory. We demonstrate that DP Compress is
sufficiently accurate by testing a variety of physical properties of Cu, H2O,
and Al-Cu-Mg systems. DP Compress applies to both CPU and GPU machines and is
publicly available at https://github.com/deepmodeling/deepmd-kit.


Abstract:
  Creating virtual avatars with realistic rendering is one of the most
essential and challenging tasks to provide highly immersive virtual reality
(VR) experiences. It requires not only sophisticated deep neural network (DNN)
based codec avatar decoders to ensure high visual quality and precise motion
expression, but also efficient hardware accelerators to guarantee smooth
real-time rendering using lightweight edge devices, like untethered VR
headsets. Existing hardware accelerators, however, fail to deliver sufficient
performance and efficiency targeting such decoders which consist of
multi-branch DNNs and require demanding compute and memory resources. To
address these problems, we propose an automation framework, called F-CAD
(Facebook Codec avatar Accelerator Design), to explore and deliver optimized
hardware accelerators for codec avatar decoding. Novel technologies include 1)
a new accelerator architecture to efficiently handle multi-branch DNNs; 2) a
multi-branch dynamic design space to enable fine-grained architecture
configurations; and 3) an efficient architecture search for picking the
optimized hardware design based on both application-specific demands and
hardware resource constraints. To the best of our knowledge, F-CAD is the first
automation tool that supports the whole design flow of hardware acceleration of
codec avatar decoders, allowing joint optimization on decoder designs in
popular machine learning frameworks and corresponding customized accelerator
design with cycle-accurate evaluation. Results show that the accelerators
generated by F-CAD can deliver up to 122.1 frames per second (FPS) and 91.6%
hardware efficiency when running the latest codec avatar decoder. Compared to
the state-of-the-art designs, F-CAD achieves 4.0X and 2.8X higher throughput,
62.5% and 21.2% higher efficiency than DNNBuilder and HybridDNN by targeting
the same hardware device.


Abstract:
  Affective computing with Electroencephalogram (EEG) is a challenging task
that requires cumbersome models to effectively learn the information contained
in large-scale EEG signals, causing difficulties for real-time smart-device
deployment. In this paper, we propose a novel knowledge distillation pipeline
to distill EEG representations via capsule-based architectures for both
classification and regression tasks. Our goal is to distill information from a
heavy model to a lightweight model for subject-specific tasks. To this end, we
first pre-train a large model (teacher network) on large number of training
samples. Then, we employ the teacher network to learn the discriminative
features embedded in capsules by adopting a lightweight model (student network)
to mimic the teacher using the privileged knowledge. Such privileged
information learned by the teacher contain similarities among capsules and are
only available during the training stage of the student network. We evaluate
the proposed architecture on two large-scale public EEG datasets, showing that
our framework consistently enables student networks with different compression
ratios to effectively learn from the teacher, even when provided with limited
training samples. Lastly, our method achieves state-of-the-art results on one
of the two datasets.


Abstract:
  Dimensionality reduction (DR) and manifold learning (ManL) have been applied
extensively in many machine learning tasks, including signal processing, speech
recognition, and neuroinformatics. However, the understanding of whether DR and
ManL models can generate valid learning results remains unclear. In this work,
we investigate the validity of learning results of some widely used DR and ManL
methods through the chart mapping function of a manifold. We identify a
fundamental problem of these methods: the mapping functions induced by these
methods violate the basic settings of manifolds, and hence they are not
learning manifold in the mathematical sense. To address this problem, we
provide a provably correct algorithm called fixed points Laplacian mapping
(FPLM), that has the geometric guarantee to find a valid manifold
representation (up to a homeomorphism). Combining one additional
condition(orientation preserving), we discuss a sufficient condition for an
algorithm to be bijective for any d-simplex decomposition result on a
d-manifold. However, constructing such a mapping function and its computational
method satisfying these conditions is still an open problem in mathematics.


Abstract:
  Single-beam scanning electron microscopes (SEM) are widely used to acquire
massive data sets for biomedical study, material analysis, and fabrication
inspection. Datasets are typically acquired with uniform acquisition: applying
the electron beam with the same power and duration to all image pixels, even if
there is great variety in the pixels' importance for eventual use. Many SEMs
are now able to move the beam to any pixel in the field of view without delay,
enabling them, in principle, to invest their time budget more effectively with
non-uniform imaging.
  In this paper, we show how to use deep learning to accelerate and optimize
single-beam SEM acquisition of images. Our algorithm rapidly collects an
information-lossy image (e.g. low resolution) and then applies a novel learning
method to identify a small subset of pixels to be collected at higher
resolution based on a trade-off between the saliency and spatial diversity. We
demonstrate the efficacy of this novel technique for active acquisition by
speeding up the task of collecting connectomic datasets for neurobiology by up
to an order of magnitude.


Abstract:
  We improve the integrability of the state space of the $\Phi ^4_3$-process
and the support of the $\Phi ^4_3$-measure on the torus obtained in [Albeverio,
Kusuoka, Ann. Sc. Norm. Super. Pisa Cl. Sci. (5), 2020]. For the improvement,
we improve the estimates of the H\"older continuity in time of the solutions to
approximation equations. In the present paper, we only discuss the estimates
different from those in [Albeverio, Kusuoka, Ann. Sc. Norm. Super. Pisa Cl.
Sci. (5), 2020].


Abstract:
  Machine Learning (ML) provides important techniques for classification and
predictions. Most of these are black-box models for users and do not provide
decision-makers with an explanation. For the sake of transparency or more
validity of decisions, the need to develop explainable/interpretable ML-methods
is gaining more and more importance. Certain questions need to be addressed:
  How does an ML procedure derive the class for a particular entity? Why does a
particular clustering emerge from a particular unsupervised ML procedure? What
can we do if the number of attributes is very large? What are the possible
reasons for the mistakes for concrete cases and models?
  For binary attributes, Formal Concept Analysis (FCA) offers techniques in
terms of intents of formal concepts, and thus provides plausible reasons for
model prediction. However, from the interpretable machine learning viewpoint,
we still need to provide decision-makers with the importance of individual
attributes to the classification of a particular object, which may facilitate
explanations by experts in various domains with high-cost errors like medicine
or finance.
  We discuss how notions from cooperative game theory can be used to assess the
contribution of individual attributes in classification and clustering
processes in concept-based machine learning. To address the 3rd question, we
present some ideas on how to reduce the number of attributes using similarities
in large contexts.


Abstract:
  Reinforcement learning, which acquires a policy maximizing long-term rewards,
has been actively studied. Unfortunately, this learning type is too slow and
difficult to use in practical situations because the state-action space becomes
huge in real environments. The essential factor for learning efficiency is
rewards. Potential-based reward shaping is a basic method for enriching
rewards. This method is required to define a specific real-value function
called a potential function for every domain. It is often difficult to
represent the potential function directly. SARSA-RS learns the potential
function and acquires it. However, SARSA-RS can only be applied to the simple
environment. The bottleneck of this method is the aggregation of states to make
abstract states since it is almost impossible for designers to build an
aggregation function for all states. We propose a trajectory aggregation that
uses subgoal series. This method dynamically aggregates states in an episode
during trial and error with only the subgoal series and subgoal identification
function. It makes designer effort minimal and the application to environments
with high-dimensional observations possible. We obtained subgoal series from
participants for experiments. We conducted the experiments in three domains,
four-rooms(discrete states and discrete actions), pinball(continuous and
discrete), and picking(both continuous). We compared our method with a baseline
reinforcement learning algorithm and other subgoal-based methods, including
random subgoal and naive subgoal-based reward shaping. As a result, our reward
shaping outperformed all other methods in learning efficiency.


Abstract:
  Liquid crystal elastomers (LCEs) can undergo large reversible contractions
along their nematic director upon heating or illumination. A spatially
patterned director within a flat LCE sheet thus encodes a pattern of
contraction on heating, which can morph the sheet into a curved shell, akin to
how a pattern of growth sculpts a developing organism. Here we consider,
theoretically, numerically and experimentally, patterns constructed from
regions of radial and circular director, which, in isolation, would form cones
and anticones. The resultant surfaces contain curved ridges with sharp V-shaped
cross-sections, associated with the boundaries between regions in the patterns.
Such ridges may be created in positively and negatively curved variants and,
since they bear Gauss curvature (quantified here via the Gauss-Bonnet theorem),
they cannot be flattened without energetically prohibitive stretch. Our
experiments and numerics highlight that, although such ridges cannot be
flattened isometrically, they can deform isometrically by trading the
(singular) curvature of the V angle against the (finite) curvature of the ridge
line. Furthermore, in finite thickness sheets, the sharp ridges are inevitably
non-isometrically blunted to relieve bend, resulting in a modest smearing out
of the encoded singular Gauss curvature. We close by discussing the use of such
features as actuating linear features, such as probes, tongues and limbs, and
highlighting the similarities between these patterns of shape change and those
found during the morphogenesis of several biological systems.


Abstract:
  Given a metric space $X$ and a subspace $A\subset X$, we prove $A$ can
generate various algebraic elements in persistent homology of $X$. We call such
elements (algebraic) footprints of $A$. Our results imply that footprints
typically appear in dimensions above the dimension of $A$. Higher-dimensional
persistent homology thus encodes lower-dimensional geometric features of $X$.
  We pay special attention to a specific type of geodesics in a geodesic
surface $X$ called geodesic circles. We explain how they may generate
non-trivial odd-dimensional and two-dimensional footprints. In particular, we
can detect even some contractible geodesics using two- and three-dimensional
persistent homology. This provides a link between persistent homology and
length spectrum in Riemannian geometry.


Abstract:
  Designing robotic tasks for co-manipulation necessitates to exploit not only
proprioceptive but also exteroceptive information for improved safety and
autonomy. Following such instinct, this research proposes to formulate
intuitive robotic tasks following human viewpoint by incorporating
visuo-tactile perception. The visual data using depth cameras surveils and
determines the object dimensions and human intentions while the tactile sensing
ensures to maintain the desired contact to avoid slippage. Experiment performed
on robot platform with human assistance under industrial settings validates the
performance and applicability of proposed intuitive task formulation.


Abstract:
  This paper presents an overview on how the PAL Robotics ARI robot is
participating in the European SHAPES project to promote healthy and active
living among older people, by integrating digital solutions from project
partners and adapting the system in order to improve human-robot interaction
and user acceptability in a wide range of tasks.


Abstract:
  In this paper, we study the impact of non-trivial sound on the evolution of
cosmological complexity in inflationary period. The vacuum state of curvature
perturbation could be treated as squeezed states with two modes, characterized
by the two most essential parameters: angle parameter $\phi_k$ and squeezing
parameter $r_k$. Through $Schr\ddot{o}dinger$ equation, one can obtain the
corresponding evolution equation of $\phi_k$ and $r_k$. Subsequently, the
quantum circuit complexity between a squeezed vacuum state and squeezed states
are evaluated in scalar curvature perturbation with a type of non-trivial sound
speed. Our results reveal that the evolution of complexity at early times shows
the rapid solution comparing with $c_S=1$, in which we implement the resonant
sound speed with various values of $\xi$. In these cases, it shows that the
scrambling time will be lagged with non-vanishing $\xi$. Further, our
methodology sheds a new way of distinguishing various inflationary models.


Abstract:
  The thermal properties of ice, liquid water and steam are at odds with
statistical theories applied to many-body systems. Here, these properties are
quantitatively explained with a bulk-scale matter field emerging from the
indefinite status of the microscopic constituents. Such a field is
characterized by its symmetry in spacetime, its degree of degeneracy and its
eigenstates. There are several one-to-one correspondences bridging outcomes of
classical and quantum measurements. (i) The heat capacities are linked to the
symmetry of the field for each phase of water. (ii) The latent heats are linked
to the change of the degree of degeneracy for each transition. (iii) The
critical temperatures are linked to the eigenstates of the potential operator.
The matter field leads to a complete representation of the phases of water,
free of hidden parameters and statistical ignorance


Abstract:
  Cryptocurrencies have emerged as a new form of digital money that has not
escaped the eyes of cyber-attackers. Traditionally, they have been maliciously
used as a medium of exchange for proceeds of crime in the cyber dark-market by
cyber-criminals. However, cyber-criminals have devised an exploitative
technique of directly acquiring cryptocurrencies from benign users' CPUs
without their knowledge through a process called crypto mining. The presence of
crypto mining activities in a network is often an indicator of compromise of
illegal usage of network resources for crypto mining purposes. Crypto mining
has had a financial toll on victims such as corporate networks and individual
home users. This paper addresses the detection of crypto mining attacks in a
generic network environment using dynamic network characteristics. It tackles
an in-depth overview of crypto mining operational details and proposes a
semi-supervised machine learning approach to detection using various crypto
mining features derived from complex network characteristics. The results
demonstrate that the integration of semi-supervised learning with complex
network theory modeling is effective at detecting crypto mining activities in a
network environment. Such an approach is helpful during security mitigation by
network security administrators and law enforcement agencies.


Abstract:
  We have studied polarized image of a Schwarzschild black hole with an
equatorial thin accretion disk as photon couples to Weyl tensor. The
birefringence of photon originating from the coupling affect the black hole
shadow, the thin disk pattern and its luminosity distribution. We also analyze
the observed polarized intensity in the sky plane. The observed polarized
intensity in the bright region is stronger than that in the darker region. The
stronger effect of the coupling on the observed polarized vector appears only
in the bright region close to black hole. These features in the polarized image
could help us to understand black hole shadow, the thin accretion disk and the
coupling between photon and Weyl tensor.


Abstract:
  A multiple operator integral (MOI) is an indispensable tool in several
branches of noncommutative analysis. However, there are substantial technical
issues with the existing literature on the "separation of variables" approach
to defining MOIs, especially when the Hilbert spaces on which they act are not
separable. In this paper, we provide a detailed development of this approach in
a very general setting that resolves existing technical issues. Along the way,
we characterize several kinds of "weak" operator-valued integrals in terms of
easily checkable conditions and prove a highly useful Minkowski-type integral
inequality for maps with values in a semifinite von Neumann algebra.


Abstract:
  The K x-ray spectra of different targets (Cu, Zn, and Ge) induced by 3 to 5
MeV/u Si projectile ions have been measured to determine the K-shell ionization
cross-section. A significant difference is observed between the measurements
and theoretical estimates, with the latter being about 50% below the
experimental results. This underestimation} is attributed to the
charge-exchange from target K-shell to projectile K- and L-shells. Such
observation can only be possible if the projectile ions attain up to H- and
He-like charge states. Corresponding projectile charge state fractions have
been evaluated from the Lorentzian charge state distribution, where mean charge
state is taken from the Fermi gas model [Phys. Rev. Lett. 30, 358 (1973)] and
width from the Novikov and Teplova approach [Phys. Lett. A378, 1286-1289
(2014)]. The sum of the direct ionization cross-section and K-K + K-L capture
cross-sections gives a good agreement with the measured cross-sections.
Furthermore, we have validated this methodology with available data for Si-ion
on Ti target. Such results may be useful in many solid target based
applications.


Abstract:
  In this paper, we present a novel tone mapping algorithm that can be used for
displaying wide dynamic range (WDR) images on low dynamic range (LDR) devices.
The proposed algorithm is mainly motivated by the logarithmic response and
local adaptation features of the human visual system (HVS). HVS perceives
luminance differently when under different adaptation levels, and therefore our
algorithm uses functions built upon different scales to tone map pixels to
different values. Functions of large scales are used to maintain image
brightness consistency and functions of small scales are used to preserve local
detail and contrast. An efficient method using local variance has been proposed
to fuse the values of different scales and to remove artifacts. The algorithm
utilizes integral images and integral histograms to reduce computation
complexity and processing time. Experimental results show that the proposed
algorithm can generate high brightness, good contrast, and appealing images
that surpass the performance of many state-of-the-art tone mapping algorithms.
This project is available at
https://github.com/jieyang1987/ToneMapping-Based-on-Multi-scale-Histogram-Synthesis.


Abstract:
  UVscope is an instrument, based on a multi-pixel photon detector, developed
to support experimental activities for high-energy astrophysics and cosmic ray
research. The instrument, working in single photon counting mode, is designed
to directly measure light flux in the wavelengths range 300-650~nm. The
instrument can be used in a wide field of applications where the knowledge of
the nocturnal environmental luminosity is required. Currently, one UVscope
instrument is allocated onto the external structure of the ASTRI-Horn Cherenkov
telescope devoted to the gamma-ray astronomy at very high energies. Being
co-aligned with the ASTRI-Horn camera axis, UVscope can measure the diffuse
emission of the night sky background simultaneously with the ASTRI-Horn camera,
without any interference with the main telescope data taking procedures.
UVscope is properly calibrated and it is used as an independent reference
instrument for test and diagnostic of the novel ASTRI-Horn telescope.


Abstract:
  We construct a canonical chain of formality quasiisomorphisms for the operad
of chains on framed little disks and the operad of chains on little disks. The
construction is done in terms of logarithmic algebraic geometry and is
remarkable for being rational (and indeed definable integrally) in de Rham
cohomology.


Abstract:
  We describe the order type of range sets of compact ultrametrics and show
that an ultrametrizable infinite topological space $(X, \tau)$ is compact iff
the range sets are order isomorphic for any two ultrametrics compatible with
the topology $\tau$. It is also shown that an ultrametrizable topology is
separable iff every compatible with this topology ultrametric has at most
countable range set.


Abstract:
  We provide a direct proof of Weyl's law for the buckling eigenvalues of the
biharmonic operator on a wide class of domains of $\mathbb R^d$ including
bounded Lipschitz domains. The proof relies on asymptotically sharp lower and
upper bounds that we develop for the Riesz mean $R_2(z)$. Lower bounds are
obtained by making use of the so-called "averaged variational principle". Upper
bounds are obtained in the spirit of Berezin-Li-Yau. Moreover, we state a
conjecture for the second term in Weyl's law and prove its correctness in two
special cases: balls in $\mathbb R^d$ and bounded intervals in $\mathbb R$.


Abstract:
  In this study, we report the first Cu-filled through silicon via (TSV)
integrated ion trap. TSVs are placed directly underneath electrodes as vertical
interconnections between ion trap and a glass interposer, facilitating the
arbitrary geometry design with increasing electrodes numbers and evolving
complexity. The integration of TSVs reduces the form factor of ion trap by more
than 80%, minimizing parasitic capacitance from 32 to 3 pF. A low RF
dissipation is achieved in spite of the absence of ground screening layer. The
entire fabrication process is on 12-inch wafer and compatible with established
CMOS back end process. We demonstrate the basic functionality of the trap by
loading and laser-cooling single 88Sr+ ions. It is found that both heating rate
(17 quanta/ms for an axial frequency of 300 kHz) and lifetime (~30 minutes) are
comparable with traps of similar dimensions. This work pioneers the development
of TSV-integrated ion traps, enriching the toolbox for scalable quantum
computing.


Abstract:
  We develop further the recently proposed event-by-event cumulants of
azimuthal angles. The role of reflection symmetry, permutation symmetry, frame
independence, and relabeling of particle indices in the cumulant expansion is
discussed in detail. We argue that mathematical and statistical properties of
cumulants are preserved if cumulants of azimuthal angles are defined
event-by-event in terms of single-event averages of azimuthal angles, while
they are violated in the traditional approach in which cumulants are defined in
terms of all-event averages. We derive for the first time the analytic
solutions for the contribution of combinatorial background in the measured 2-
and 3-particle correlations. We demonstrate that these solutions for the
combinatorial background are universal as they can be written generically in
terms of multiplicity-dependent combinatorial weights and marginal probability
density functions of starting multivariate distribution. The new general
results between multiparticle azimuthal correlators and flow amplitudes and
symmetry planes are presented.


Abstract:
  With the goal of predicting the future rainfall intensity in a local region
over a relatively short period time, precipitation nowcasting has been a
long-time scientific challenge with great social and economic impact. The radar
echo extrapolation approaches for precipitation nowcasting take radar echo
images as input, aiming to generate future radar echo images by learning from
the historical images. To effectively handle complex and high non-stationary
evolution of radar echoes, we propose to decompose the movement into optical
flow field motion and morphologic deformation. Following this idea, we
introduce Flow-Deformation Network (FDNet), a neural network that models flow
and deformation in two parallel cross pathways. The flow encoder captures the
optical flow field motion between consecutive images and the deformation
encoder distinguishes the change of shape from the translational motion of
radar echoes. We evaluate the proposed network architecture on two real-world
radar echo datasets. Our model achieves state-of-the-art prediction results
compared with recent approaches. To the best of our knowledge, this is the
first network architecture with flow and deformation separation to model the
evolution of radar echoes for precipitation nowcasting. We believe that the
general idea of this work could not only inspire much more effective approaches
but also be applied to other similar spatiotemporal prediction tasks


Abstract:
  Transformer is important for text modeling. However, it has difficulty in
handling long documents due to the quadratic complexity with input text length.
In order to handle this problem, we propose a hierarchical interactive
Transformer (Hi-Transformer) for efficient and effective long document
modeling. Hi-Transformer models documents in a hierarchical way, i.e., first
learns sentence representations and then learns document representations. It
can effectively reduce the complexity and meanwhile capture global document
context in the modeling of each sentence. More specifically, we first use a
sentence Transformer to learn the representations of each sentence. Then we use
a document Transformer to model the global document context from these sentence
representations. Next, we use another sentence Transformer to enhance sentence
modeling using the global document context. Finally, we use hierarchical
pooling method to obtain document embedding. Extensive experiments on three
benchmark datasets validate the efficiency and effectiveness of Hi-Transformer
in long document modeling.


Abstract:
  Compressive sensing (CS) is a signal processing technique that enables
sub-Nyquist sampling and near lossless reconstruction of a sparse signal. The
technique is particularly appealing for neural signal processing since it
avoids the issues relevant to high sampling rate and large data storage. In
this project, different CS reconstruction algorithms were tested on raw action
potential signals recorded in our lab. Two numerical criteria were set to
evaluate the performance of different CS algorithms: Compression Ratio (CR) and
Signal-to-Noise Ratio (SNR). In order to do this, individual CS algorithm
testing platforms for the EEG data were constructed within MATLAB scheme. The
main considerations for the project were the following. 1) Feasibility of the
dictionary 2) Tolerance to non-sparsity 3) Applicability of thresholding or
interpolation.


Abstract:
  In this study, we investigated the influence of the topological defects
space-time with a spiral dislocation on a spin-zero boson field by using the
Duffin-Kemmer-Petiau (DKP) equation. To be more specific, we solved the
generalized spin-zero DKP equation in the presence of a spiral dislocation
exactly. We derived the wave function and corresponding energy eigenvalues for
two cases, in the absence and presence of a static potential by using
analytical methods. We numerically demonstrated the effect of the spiral
dislocation on the solutions.


Abstract:
  A bad point of a positive semidefinite real polynomial f is a point at which
a pole appears in all expressions of f as a sum of squares of rational
functions. We show that quartic polynomials in three variables never have bad
points. We give examples of positive semidefinite polynomials with a bad point
at the origin, that are nevertheless sums of squares of formal power series,
answering a question of Brumfiel. We also give an example of a positive
semidefinite polynomial in three variables with a complex bad point that is not
real, answering a question of Scheiderer.


Abstract:
  The beneficial use of sewage sludge for valorization of carbon and nutrients
is of increasing interest while micropollutants in sludge are of concern to the
environment and human health. This study investigates the hydrothermal
liquefaction (HTL) of sewage sludge in a continuous flow pilot scale reactor at
conditions expected to reflect future industrial installations. The processing
is evaluated in terms of energy efficiency, bio-crude yields and quality. The
raw sludge and post-HTL process water and solid residues were analyzed
extensively for micropollutants via HPLC-MS/MS for target pharmaceuticals
including antibiotics, blood pressure medicine, antidepressants, analgesics,
x-ray contrast media, angiotensin II receptor blockers, immunosuppressant drugs
and biocides including triazines, triazoles, carbamates, a carboxamide, an
organophosphate and a cationic surfactant. The results show that a positive
energy return on investment was achieved for all three HTL processing
temperatures of 300, 325 and 350 {\deg}C with the most beneficial temperature
identified as 325 {\deg}C. The analysis of the HTL by-products, process water
and solids, indicates that HTL is indeed a suitable technology for the
destruction of micropollutants. However, due to the large matrix effect of the
HTL process water it can only be stated with certainty that 9 out of 30
pharmaceuticals and 5 out of 7 biocides products were destroyed successfully
(over 98% removal). One compound, the antidepressant citalopram, was shown to
be moderately recalcitrant at 300 {\deg}C with 87% removal and was only
destroyed at temperatures $\geq$325 {\deg}C ($>$99% removal). Overall, the
results suggest that HTL is a suitable technology for energy efficient and
value added sewage sludge treatment enabling destruction of micropollutants.


Abstract:
  A formula for the class number $h$ of the imaginary quadratic field
$Q(\sqrt{-p}$ is obtained by counting on a specific way the quadratic residues
of a prime number of the form $p=4n-1.$ Formulas for the sum of the quadratic
residues are also found.


Abstract:
  Ensemble classifiers have been investigated by many in the artificial
intelligence and machine learning community. Majority voting and weighted
majority voting are two commonly used combination schemes in ensemble learning.
However, understanding of them is incomplete at best, with some properties even
misunderstood. In this paper, we present a group of properties of these two
schemes formally under a dataset-level geometric framework. Two key factors,
every component base classifier's performance and dissimilarity between each
pair of component classifiers are evaluated by the same metric - the Euclidean
distance. Consequently, ensembling becomes a deterministic problem and the
performance of an ensemble can be calculated directly by a formula. We prove
several theorems of interest and explain their implications for ensembles. In
particular, we compare and contrast the effect of the number of component
classifiers on these two types of ensemble schemes. Empirical investigation is
also conducted to verify the theoretical results when other metrics such as
accuracy are used. We believe that the results from this paper are very useful
for us to understand the fundamental properties of these two combination
schemes and the principles of ensemble classifiers in general. The results are
also helpful for us to investigate some issues in ensemble classifiers, such as
ensemble performance prediction, selecting a small number of base classifiers
to obtain efficient and effective ensembles.


Abstract:
  A method involving intensity correlation measurements is described, which
allows for the complete removal of Doppler broadening in the emission of
electromagnetic radiation from far-away sources that are inaccessible to
conventional Doppler-free measurements. The technique, relying on a correction
to g(2) of order N-1, probes the separation between neighboring spectral lines
and is also applicable to the elimination of broadening due to collisions (N is
the number of emitting particles and g(2) is the second-order field correlation
function). Possible applications include a determination of cosmological
parameters from red shifts of gravitationally-lensed quasars.


Abstract:
  Can we define what it means to be "creative," and if so, can our definition
drive artificial intelligence (AI) systems to feats of creativity
indistinguishable from human efforts? This mixed question is considered from
technological and social perspectives. Beginning with an exploration of the
value we attach to authenticity in works of art, the article considers the
ability of AI to detect forgeries of renowned paintings and, in so doing,
somehow reveal the quiddity of a work of art. We conclude by considering
whether evolving technical capability can revise traditional relationships
among art, artist, and the market.


Abstract:
  We investigate a well-known phenomenon of the appearance of the crossover
points, corresponding to the intersections of the solubility isotherms of the
solid compound in supercritical fluid. Opposed to the accepted understanding of
the existence of two fixed crossover points, which confine the region of the
inverse isobaric temperature dependence of the solubility, we have found that
these points tend to shift with the change of the temperature and in the limit
of the certain threshold value they converge to a single point. We demonstrate
this analyzing the solubility data of a set of poorly soluble drug compounds,
which have been computed in a wide area of the phase diagram via the approach,
based on the classical density functional theory. Thorough analysis of the
available in the literature experimental solubility data is found to be in an
agreement with our conclusions, as one can find that the wider temperature
region of the experimental study is, the more pronounced effect of the
crossover points drift can be observed.


Abstract:
  We study projective fourfolds of $K3^{[2]}$-type with a symplectic involution
and the deformations of their quotients, called orbifolds of Nikulin types;
they are IHS orbifolds. We compute the Riemann--Roch formula for Weil divisors
on such orbifolds and describe the first complete family of orbifolds of
Nikulin type with a polarization of degree $2$ as double covers of special
complete intersections $(3,4)$ in $\mathbb{P}^6$.


Abstract:
  Word embeddings have been shown adept at capturing the semantic and syntactic
regularities of the natural language text, as a result of which these
representations have found their utility in a wide variety of downstream
content analysis tasks. Commonly, these word embedding techniques derive the
distributed representation of words based on the local context information.
However, such approaches ignore the rich amount of explicit information present
in knowledge-bases. This is problematic, as it might lead to poor
representation for words with insufficient local context such as domain
specific words. Furthermore, the problem becomes pronounced in domain such as
bio-medicine where the presence of these domain specific words are relatively
high. Towards this end, in this project, we propose a new word embedding based
model for biomedical domain that jointly leverages the information from
available corpora and domain knowledge in order to generate knowledge-base
powered embeddings. Unlike existing approaches, the proposed methodology is
simple but adept at capturing the precise knowledge available in domain
resources in an accurate way. Experimental results on biomedical concept
similarity and relatedness task validates the effectiveness of the proposed
approach.


Abstract:
  Scientific literature contain important information related to cutting-edge
innovations in diverse domains. Advances in natural language processing have
been driving the fast development in automated information extraction from
scientific literature. However, scientific literature is often available in
unstructured PDF format. While PDF is great for preserving basic visual
elements, such as characters, lines, shapes, etc., on a canvas for presentation
to humans, automatic processing of the PDF format by machines presents many
challenges. With over 2.5 trillion PDF documents in existence, these issues are
prevalent in many other important application domains as well.
  Our ICDAR 2021 Scientific Literature Parsing Competition (ICDAR2021-SLP) aims
to drive the advances specifically in document understanding. ICDAR2021-SLP
leverages the PubLayNet and PubTabNet datasets, which provide hundreds of
thousands of training and evaluation examples. In Task A, Document Layout
Recognition, submissions with the highest performance combine object detection
and specialised solutions for the different categories. In Task B, Table
Recognition, top submissions rely on methods to identify table components and
post-processing methods to generate the table structure and content. Results
from both tasks show an impressive performance and opens the possibility for
high performance practical applications.


Abstract:
  The development of neuromorphic hardware and modeling of biological neural
networks requires algorithms with local learning rules. Artificial neural
networks using local learning rules to perform principal subspace analysis
(PSA) and clustering have recently been derived from principled objective
functions. However, no biologically plausible networks exist for minor subspace
analysis (MSA), a fundamental signal processing task. MSA extracts the
lowest-variance subspace of the input signal covariance matrix. Here, we
introduce a novel similarity matching objective for extracting the minor
subspace, Minor Subspace Similarity Matching (MSSM). Moreover, we derive an
adaptive MSSM algorithm that naturally maps onto a novel neural network with
local learning rules and gives numerical results showing that our method
converges at a competitive rate.


Abstract:
  Human societies are characterized, besides others, by three constituent
features. (A) Options, as for jobs and societal positions, differ with respect
to their associated monetary and non-monetary payoffs. (B) Competition leads to
reduced payoffs when individuals compete for the same option with others. (C)
People care how they are doing relatively to others. The latter trait, the
propensity to compare one's own success with that of others, expresses itself
as envy.
  It is shown that the combination of (A)-(C) leads to spontaneous class
stratification. Societies of agents split endogenously into two social classes,
an upper and a lower class, when envy becomes relevant. A comprehensive
analysis of the Nash equilibria characterizing a basic reference game is
presented. Class separation is due to the condensation of the strategies of
lower-class agents, which play an identical mixed strategy. Upper class agents
do not condense, following individualist pure strategies.
  Model and results are size-consistent, holding for arbitrary large numbers of
agents and options. Analytic results are confirmed by extensive numerical
simulations. An analogy to interacting confined classical particles is
discussed.


Abstract:
  Analog quantum simulation - the technique of using one experimentally
well-controlled physical system to mimic the behavior of another - has quickly
emerged as one of the most promising near term strategies for studying strongly
correlated quantum many-body systems. In particular, systems of interacting
photons, realizable in solid-state cavity and circuit QED frameworks, for
example, hold tremendous promise for the study of nonequilibrium many-body
phenomena due to the capability to locally create and destroy photons. These
systems are typically modeled using a Jaynes-Cummings-Hubbard (JCH)
Hamiltonian, named due to similarities with the Bose-Hubbard (BH) model. Here,
we present a non-perturbative procedure for transforming the JC Hamiltonian
into a dressed operator representation that, in its most general form, admits
an infinite sum of bosonic $k$-body terms where $k$ is bound only by the number
of excitations in the system. We closely examine this result in both the
dispersive and resonant coupling regimes, finding rapid convergence in the
former and contributions from $k\gg1$ in the latter. Through extension to a
two-site JCH system, we demonstrate that this approach facilitates close
inspection of the analogy between the JCH and BH models and its breakdown for
resonant light-matter coupling. Finally, we use this framework to survey the
many-body character of a two-site JCH for general system parameters,
identifying four unique quantum phases and the parameter regimes in which they
are realized, thus highlighting phenomena realizable with finite JCH-based
quantum simulators beyond the BH model. More broadly, this work is intended to
serve as a clear mathematical exposition of bosonic many-body interactions
underlying JC-type systems, often postulated through analogy to Kerr-like
nonlinear susceptibilities or by matching coefficients to obtain the
appropriate eigenvalue spectrum.


Abstract:
  Tables in Web documents are pervasive and can be directly used to answer many
of the queries searched on the Web, motivating their integration in question
answering. Very often information presented in tables is succinct and hard to
interpret with standard language representations. On the other hand, tables
often appear within textual context, such as an article describing the table.
Using the information from an article as additional context can potentially
enrich table representations. In this work we aim to improve question answering
from tables by refining table representations based on information from
surrounding text. We also present an effective method to combine text and
table-based predictions for question answering from full documents, obtaining
significant improvements on the Natural Questions dataset.


Abstract:
  In this paper, for a degenerate higher order equation with a fractional
derivative in the sense of Caputo, a nonlocal problem with conjugation
conditions in a rectangular domain is studied. The solution is constructed in
the form of a Fourier series in the eigenfunctions of a one-dimensional
problem. A criterion for the uniqueness of a solution is given.


Abstract:
  The estimation of periodicity is a fundamental task in many scientific areas
of study. Existing methods rely on theoretical assumptions that the observation
times have equal or i.i.d. spacings, and that common estimators, such as the
periodogram peak, are consistent and asymptotically normal. In practice,
however, these assumptions are unrealistic as observation times usually exhibit
deterministic patterns -- e.g., the nightly observation cycle in astronomy --
that imprint nuisance periodicities in the data. These nuisance signals also
affect the finite-sample distribution of estimators, which can substantially
deviate from normality. Here, we propose a set identification method, fusing
ideas from randomization inference and partial identification. In particular,
we develop a sharp test for any periodicity value, and then invert the test to
build a confidence set. This approach is appropriate here because the
construction of confidence sets does not rely on assumptions of regular or
well-behaved asymptotics. Notably, our inference is valid in finite samples
when our method is fully implemented, while it can be asymptotically valid
under an approximate implementation designed to ease computation. Empirically,
we validate our method in exoplanet detection using radial velocity data. In
this context, our method correctly identifies the periodicity of the confirmed
exoplanets in our sample. For some other, yet unconfirmed detections, we show
that the statistical evidence is weak, which illustrates the failure of
traditional statistical techniques. Last but not least, our method offers a
constructive way to resolve these identification issues via improved
observation designs. In exoplanet detection, these designs suggest meaningful
improvements in identifying periodicity even when a moderate amount of
randomization is introduced in scheduling radial velocity measurements.


Abstract:
  In [Hut16], Hutchings uses embedded contact homology to show the following
for area-preserving disc diffeomorphisms that are a rotation near the boundary
of the disc: If the asymptotic mean action on the boundary is greater than the
Calabi invariant, then the infimum of the mean action of the periodic points is
less than or equal to the Calabi invariant. In this article, we extend this to
all area-preserving disc diffeomorphisms. Our strategy is to extend the
diffeomorphism to a larger disc with nice properties and apply Hutchings'
theorem. As an application, we show that the Calabi invariant of any smooth
pseudo-rotation is equal to its rotation number.


Abstract:
  We present a gravitational theory that interpolates between JT gravity, and a
gravity theory with a fixed boundary Hamiltonian. For this, we consider a
matrix integral with the insertion of a Gaussian with variance $\sigma^2$,
centered around a matrix $\textsf{H}_0$. Tightening the Gaussian renders the
matrix integral less random, and ultimately it collapses the ensemble to one
Hamiltonian $\textsf{H}_0$. This model provides a concrete setup to study
factorization, and what the gravity dual of a single member of the ensemble is.
We find that as $\sigma^2$ is decreased, the JT gravity dilaton potential gets
modified, and ultimately the gravity theory goes through a series of phase
transitions, corresponding to a proliferation of extra macroscopic holes in the
spacetime. Furthermore, we observe that in the Efetov model approach to random
matrices, the non-averaged factorizing theory is described by one simple saddle
point.


Abstract:
  We present a heterogeneous localization framework for solving radar global
localization and pose tracking on pre-built lidar maps. To bridge the gap of
sensing modalities, deep neural networks are constructed to create shared
embedding space for radar scans and lidar maps. Herein learned feature
embeddings are supportive for similarity measurement, thus improving map
retrieval and data matching respectively. In RobotCar and MulRan datasets, we
demonstrate the effectiveness of the proposed framework with the comparison to
Scan Context and RaLL. In addition, the proposed pose tracking pipeline is with
less neural networks compared to the original RaLL.


Abstract:
  The frequencies of the analytic normal modes for N identical particles are
studied as a function of system parameters from the weakly interacting BCS
regime to the strongly interacting unitary regime. The normal modes were
obtained previously from a first-order L=0 group theoretic solution of a
three-dimensional Hamiltonian with a general two-body interaction for confined,
identical particles. In a precursor to this study, the collective behavior of
these normal modes was investigated as a function of N from few-body systems to
many-body systems analyzing the contribution of individual particles to the
collective macroscopic motions. A specific case, the Hamiltonian for Fermi
gases in the unitary regime was studied in more detail. This regime is known to
support collective behavior in the form of superfluidity and has previously
been successfully described using normal modes. Two phenomena that could
sustain the emergence and stability of superfluid behavior were revealed,
including the behavior of the normal mode frequencies as N increases. In this
paper, I focus on a more detailed analysis of these analytic frequencies,
extending my investigation to include Hamiltonians with a range of
interparticle interaction strengths from the BCS regime to the unitary regime
and analyzing the microscopic dynamics that lead to large gaps at unitarity.
The results of the current study suggest that in regimes where higher-order
effects are small, normal modes an be used to describe the physics of
superfluidity from the weakly interacting BCS regime with the emergence of
small excitation gaps to unitarity with its large gaps, and can offer insight
into a possible microscopic understanding of the behavior at unitarity. This
approach could thus offer an alternative to the two-body pairing models
commonly used to describe superfluidity along this transition.


Abstract:
  In this work, the aim is to study the diffusion of innovation of two
competing products. The main focus has been to understand the effects of the
competitive dynamic market on the diffusion of innovation. The global
smartphone operating system sales are chosen as an example. The availability of
the sales and the number of users data, as well as the predictions for the
future number of users, make the smartphone diffusion a new laboratory to test
the innovation of diffusion models for the competitive markets. In this work,
the Bass model and its extensions which incorporate the competition between the
brands are used. The diffusion of smartphones can be considered on two levels:
the product level and the brand level. The diffusion of the smartphone as a
category is studied by using the Bass equation (category-level diffusion). The
diffusion of each competing operating system (iOS and Android) are considered
as the competition of the brands, and it is studied in the context of
competitive market models (product-level diffusion). It is shown that the
effects of personal interactions play the dominant role in the diffusion
process. Moreover, the volume of near future sales can be predicted by
introducing appropriate dynamic market potential which helps to extrapolate the
model results for the future.


Abstract:
  Integrating efficient connectivity, positioning and sensing functionalities
into 5G New Radio (NR) and beyond mobile cellular systems is one timely
research paradigm, especially at mm-wave and sub-THz bands. In this article, we
address the radio-based sensing and environment mapping prospect with specific
emphasis on the user equipment (UE) side. We first describe an efficient
l1-regularized least-squares (LS) approach to obtain sparse range--angle charts
at individual measurement or sensing locations. For the subsequent environment
mapping, we then introduce a novel state model for mapping diffuse and specular
scattering, which allows efficient tracking of individual scatterers over time
using interacting multiple model (IMM) extended Kalman filter and smoother. We
provide extensive numerical indoor mapping results at the 28~GHz band deploying
OFDM-based 5G NR uplink waveform with 400~MHz channel bandwidth, covering both
accurate ray-tracing based as well as actual RF measurement results. The
results illustrate the superiority of the dynamic tracking-based solutions,
compared to static reference methods, while overall demonstrate the excellent
prospects of radio-based mobile environment sensing and mapping in future
mm-wave networks.


Abstract:
  The Zernike wavefront sensor (ZWFS) is a concept belonging to the wide class
Fourier-filtering wavefront sensor (FFWFS). The ZWFS is known for its extremely
high sensitivity while having a low dynamic range, which makes it a unique
sensor for second stage adaptive optics (AO) systems or quasi-static
aberrations calibration sensor. This sensor is composed of a focal plane mask
made of a phase shifting dot fully described by two parameters: its diameter
and depth. In this letter, we aim to improve the performance of this sensor by
changing the diameter of its phase shifting dot. We begin with a general
theoretical framework providing an analytical description of the FFWFS
properties, then we predict the expected ZWFS sensitivity for different
configurations of dot diameters and depths. The analytical predictions are then
validated with end-to-end simulations. From this, we propose a variation of the
classical ZWFS shape which exhibits extremely appealing properties. We show
that the ZWFS sensitivity can be optimized by modifying the dot diameter and
even reach the optimal theoretical limit, with a trade-off for low spatial
frequencies sensitivity. As an example, we show that a ZWFS with a 2{\lambda}/D
dot diameter (where {\lambda} is the sensing wavelength and D the telescope
diameter), hereafter called Z2WFS, exhibits a sensitivity twice higher than the
classical 1.06{\lambda}/D ZWFS for all the phase spatial components except for
tip-tilt modes. Furthermore, this gain in sensitivity does not impact the
dynamic range of the sensor, and the Z2WFS exhibits a similar dynamical range
as the classical 1.06{\lambda}/D ZWFS. This study opens the path to the
conception of diameter-optimized ZWFS.


Abstract:
  For an integer $r\ge0$, we prove the $r$th order Reshetnyak formula for the
ray transform of rank $m$ symmetric tensor fields on $\mathbb{R}^n$. Certain
differential operators $A^{(m,r,l)}\ (0\le l\le r)$ on the sphere
$\mathbb{S}^{n-1}$ are main ingredients of the formula. The operators are
defined by an algorithm that can be applied for any $r$ although the volume of
calculations grows fast with $r$. The algorithm is realized for small values of
$r$ and Reshetnyak formulas of orders $0,1,2$ are presented in an explicit
form.


Abstract:
  We prove an asymptotic result on the maximum number of k-vertex subtrees in
binary trees of given order. This problem turns out to be equivalent to
determine the maximum number of k+2-cycles in n-vertex outerplanar graphs, thus
we settle the generalized outerplanar Tur\'an number for all cycles. We also
determine the exponential growth of the generalized outerplanar Tur\'an number
of paths Pk as a function of k which implies the order of magnitude of the
generalized outerplanar Tur\'an number of arbitrary trees. The bounds are
strongly related to the sequence of Catalan numbers.


Abstract:
  The weighted maximal bisection problem is, given an edge weighted graph, to
find a bipartition of the vertex set into two sets such that their cardinality
differs by at most one and the sum of the weight of the edges between vertices
that are not in the same set is maximized. This problem is known to be NP-hard,
even when a tree decomposition of width $t$ and $\mathcal O(n)$ nodes is given
as an advice as part of the input, where $n$ is the number of vertices of the
input graph. But, given such an advice, the problem is decidable in FPT time in
$n$ parametrized by $t$. In particular Jansen et al. presented an algorithm
with running time $\mathcal O(2^tn^3)$. Hanaka, Kobayashi, and Sone enhanced
the analysis of the complexity to $\mathcal O(2^t(nt)^2)$. By slightly
modifying the approach, we improve the running time to $\mathcal O(2^tn^2)$ in
the RAM model, which is asymptotically optimal in $n$ under the hardness of
MinConv.
  We proof that this is also asymptotically optimal in its dependence on $t$
assuming SETH by showing for a slightly easier problem (maximal cut) that there
is no $\mathcal O(2^{\epsilon t} \operatorname{poly} n)$ algorithm for any
$\varepsilon < 1$ under SETH. This was already claimed by Hanaka, Kobayashi,
and Sone but without a correct proof.
  We also present a hardness result (no $\mathcal O(2^t n^{2-\varepsilon})$
algorithm for any $\varepsilon > 0$) for a broad family of subclasses of the
weighted maximal bisection problem that are characterized only by the
dependence of $t$ from $n$, more precisely, all instances with $t = f(n)$ for
an arbitrary but fixed $f(n) \in \operatorname o(\log n)$. This holds even when
only considering planar graphs.
  Moreover we present a detailed description of the implementation details and
assumptions that are necessary to achieve the optimal running time.


Abstract:
  Deep neural network (DNN) accelerators received considerable attention in
recent years due to the potential to save energy compared to mainstream
hardware. Low-voltage operation of DNN accelerators allows to further reduce
energy consumption significantly, however, causes bit-level failures in the
memory storing the quantized DNN weights. Furthermore, DNN accelerators have
been shown to be vulnerable to adversarial attacks on voltage controllers or
individual bits. In this paper, we show that a combination of robust
fixed-point quantization, weight clipping, as well as random bit error training
(RandBET) or adversarial bit error training (AdvBET) improves robustness
against random or adversarial bit errors in quantized DNN weights
significantly. This leads not only to high energy savings for low-voltage
operation as well as low-precision quantization, but also improves security of
DNN accelerators. Our approach generalizes across operating voltages and
accelerators, as demonstrated on bit errors from profiled SRAM arrays, and
achieves robustness against both targeted and untargeted bit-level attacks.
Without losing more than 0.8%/2% in test accuracy, we can reduce energy
consumption on CIFAR10 by 20%/30% for 8/4-bit quantization using RandBET.
Allowing up to 320 adversarial bit errors, AdvBET reduces test error from above
90% (chance level) to 26.22% on CIFAR10.


Abstract:
  Among the properties shaping the light of a galaxy, the star formation
history (SFH) is one of the most challenging to model due to the variety of
correlated physical processes regulating star formation. In this work, we
leverage the stellar population synthesis model FSPS, together with SFHs
predicted by the hydrodynamical simulation IllustrisTNG and the empirical model
UNIVERSEMACHINE, to study the impact of star formation variability on galaxy
colours. We start by introducing a model-independent metric to quantify the
burstiness of a galaxy formation model, and we use this metric to demonstrate
that UNIVERSEMACHINE predicts SFHs with more burstiness relative to
IllustrisTNG. Using this metric and principal component analysis, we construct
families of SFH models with adjustable variability, and we show that the
precision of broad-band optical and near-infrared colours degrades as the level
of unresolved short-term variability increases. We use the same technique to
demonstrate that variability in metallicity and dust attenuation presents a
practically negligible impact on colours relative to star formation
variability. We additionally provide a model-independent fitting function
capturing how the level of unresolved star formation variability translates
into imprecision in predictions for galaxy colours; our fitting function can be
used to determine the minimal SFH model that reproduces colours with some
target precision. Finally, we show that modelling the colours of individual
galaxies with percent-level precision demands resorting to complex SFH models,
while producing precise colours for galaxy populations can be achieved using
models with just a few degrees of freedom.


Abstract:
  Magnetic structures and the relationship between spin and charge-orbital
orderings of an A-site ordered double-perovskite manganite SmBaMn2O6, an
anticipated multiferroic material, were investigated by means of neutron
diffraction. The spin arrangement in MnO2 planes perpendicular to the c axis is
revealed to be the same as that in the A-site disordered half-doped manganites
CE-type but the stacking pattern is found to be different displaying a unique
twofold period. The temperature dependence of the superlattice magnetic and
nuclear reflections clarifies that the antiferromagnetic spin ordering occurs
at a temperature slightly lower than the temperature at which a rearrangement
of the charge-orbital orderings occurs. The result evidences that the
rearrangement leads the spin ordering. The intensities of the magnetic
reflections are found to change across Tf = 10 K, suggesting a spin-flop by 90
[deg.] while keeping the Mn spin ordering pattern unchanged.


Abstract:
  We study Fourier and Laplace transforms for Fourier hyperfunctions with
values in a complex locally convex Hausdorff space. Since any hyperfunction
with values in a wide class of locally convex Hausdorff spaces can be extended
to a Fourier hyperfunction, this gives simple notions of asymptotic Fourier and
Laplace transforms for vector-valued hyperfunctions, which improves the
existing models of Komatsu, B\"aumer, Lumer and Neubrander and Langenbruch.


Abstract:
  The decentralized state estimation is one of the most fundamental components
for autonomous aerial swarm systems in GPS-denied areas, which still remains a
highly challenging research topic. To address this research niche, the
Omni-swarm, a decentralized omnidirectional visual-inertial-UWB state
estimation system for the aerial swarm is proposed in this paper. In order to
solve the issues of observability, complicated initialization, insufficient
accuracy and lack of global consistency, we introduce an omnidirectional
perception system as the front-end of the Omni-swarm, consisting of
omnidirectional sensors, which includes stereo fisheye cameras and
ultra-wideband (UWB) sensors, and algorithms, which includes fisheye visual
inertial odometry (VIO), multi-drone map-based localization and visual object
detector. A graph-based optimization and forward propagation working as the
back-end of the Omni-swarm to fuse the measurements from the front-end.
According to the experiment result, the proposed decentralized state estimation
method on the swarm system achieves centimeter-level relative state estimation
accuracy while ensuring global consistency. Moreover, supported by the
Omni-swarm, inter-drone collision avoidance can be accomplished in a whole
decentralized scheme without any external device, demonstrating the potential
of Omni-swarm to be the foundation of autonomous aerial swarm flights in
different scenarios.


Abstract:
  A family of reversible deterministic cellular automata, including the rules
54 and 201 of [Bobenko et al., Commun. Math. Phys. 158, 127 (1993)] as well as
their kinetically constrained quantum (unitary) or stochastic deformations, is
shown to correspond to integrable Floquet circuit models with local
interactions round-a-face. Using inhomogeneous solutions of the star-triangle
relation with a one or two dimensional spectral parameter, changing their
functional form depending on the orientation, we provide an explicit
construction of the transfer matrix and establish its conservation law and
involutivity properties. Integrability is independently demonstrated by
numerically exploring the spectral statistics via the Berry-Tabor conjecture.
Curiously, we find that the deformed rule 54 model generically possesses no
other local conserved quantities besides the net soliton current.


Abstract:
  We propose an approach for unsupervised domain adaptation for the task of
estimating someone's age from a given face image. In order to avoid the
propagation of racial bias in most publicly available face image datasets into
the inefficacy of models trained on them, we perform domain adaptation to
motivate the predictor to learn features that are invariant to ethnicity,
enhancing the generalization performance across faces of people from different
ethnic backgrounds. Exploiting the ordinality of age, we also impose ranking
constraints on the prediction of the model and design our model such that it
takes as input a pair of images, and outputs both the relative age difference
and the rank of the first identity with respect to the other in terms of their
ages. Furthermore, we implement Multi-Dimensional Scaling to retrieve absolute
ages from the predicted age differences from as few as two labeled images from
the domain to be adapted to. We experiment with a publicly available dataset
with age labels, dividing it into subsets based on the ethnicity labels, and
evaluating the performance of our approach on the data from an ethnicity
different from the one that the model is trained on. Additionally, we impose a
constraint to preserve the sanity of the predictions with respect to relative
and absolute ages, and another to ensure the smoothness of the predictions with
respect to the input. We experiment extensively and compare various domain
adaptation approaches for the task of regression.


Abstract:
  We investigate potential correlations between radio source counts (after
background corrections) of 22 Galactic globular clusters (GCs) from the MAVERIC
survey, and stellar encounter rates ($\Gamma$) and masses ($M$) of the GCs.
Applying a radio luminosity limit of $L_\mathrm{lim}=5.0\times
10^{27}~\mathrm{erg~s^{-1}}$, we take a census of radio sources in the core and
those within the half-light radius. By following a maximum likelihood method
and adopting a simplified linear model, we find an unambiguous dependence of
core radio source counts on $\Gamma$ and/or $M$ at 90% confidence, but no clear
dependence of source counts within the half-light radius on either $\Gamma$ or
$M$. Five of the identified radio sources in cores above our adopted limit are
millisecond pulsars or neutron star X-ray binaries (XRBs), the dependence of
which on $\Gamma$ is well-known, but another is a published black hole (BH) XRB
candidate, and ten others are not identified. Accounting for the verified
cluster members increases the significance of correlation with $M$ and/or
$\Gamma$ (to 99% confidence), for fits to core and half-light region source
counts, while excluding a dependence on $\Gamma$ alone at 90% (core) and 68%
(half-light) confidence. This is consistent with published dynamical
simulations of GC BH interactions that argue $\Gamma$ will be a poor predictor
of the distribution of accreting BHs in GCs. Future multiwavelength follow-up
to verify cluster membership will enable stronger constraints on the dependence
of radio source classes on cluster properties, promising a new view on the
dynamics of BHs in GCs.


Abstract:
  Intelligent reflecting surfaces (IRSs) are promising enablers for
next-generation wireless communications due to their reconfigurability and high
energy efficiency in improving poor propagation condition of channels, e.g.,
limited scattering environment. However, most existing works assumed full-rank
channels requiring rich scatters, which may not be available in practice. To
analyze the impact of rank-deficient channels and mitigate the ensued
performance loss, we consider a large-scale IRS-aided MIMO system with
statistical channel state information (CSI), where the double-scattering
channel is adopted to model rank deficiency. By leveraging random matrix theory
(RMT), we first derive a deterministic approximation (DA) of the ergodic rate
with low computational complexity and prove the existence and uniqueness of the
DA parameters. Then, we propose an alternating optimization algorithm for
maximizing the DA with respect to phase shifts and signal covariance matrices.
Numerical results will show that the DA is tight and our proposed method can
effectively mitigate the performance loss induced by channel rank deficiency.


Abstract:
  We survey recent studies and results on the following problem: which
numerical functions can be the depth function of powers and symbolic powers of
homogeneous ideals.


Abstract:
  In December 2019, the first case of SARS-CoV-2 infection was identified in
Wuhan, China. Since that day, COVID-19 has spread worldwide, affecting 153
million people. Education, as many other sectors, has managed to adapt to the
requirements and barriers implied by the impossibility to teach students
face-to-face as it was done before. Yet, little is known about the implications
of emergency remote teaching (ERT) during the pandemic. This study describes
and analyzes the impact of the pandemic on the study patterns of higher
education students. The analysis was performed by the integration of three main
components: (1) interaction with the learning management system (LMS), (2)
Assignment submission rate, and (3) Teachers' perspective. Several variables
were created to analyze the study patterns, clicks on different LMS components,
usage during the day, week and part of the term, the time span of interaction
with the LMS, and grade categories. The results showed significant differences
in study patterns depending on the year of study, and the variables reflecting
the effect of teachers' changes in the course structure are identified. This
study outlines the first insights of higher education's new normality,
providing important implications for supporting teachers in creating academic
material that adequately addresses students' particular needs depending on
their year of study, changes in study pattern, and distribution of time and
activity through the term.


Abstract:
  The reinforcement learning problem of finding a control policy that minimizes
the minimum time objective for the Mountain Car environment is considered.
Particularly, a class of parameterized nonlinear feedback policies is optimized
over to reach the top of the highest mountain peak in minimum time. The
optimization is carried out using quasi-Stochastic Gradient Descent (qSGD)
methods. In attempting to find the optimal minimum time policy, a new
parameterized policy approach is considered that seeks to learn an optimal
policy parameter for different regions of the state space, rather than rely on
a single macroscopic policy parameter for the entire state space. This
partitioned parameterized policy approach is shown to outperform the uniform
parameterized policy approach and lead to greater generalization than prior
methods, where the Mountain Car became trapped in circular trajectories in the
state space.


Abstract:
  For a graph $G$, and two distinct vertices $u$ and $v$ of $G$, let $n_G(u,v)$
be the number of vertices of $G$ that are closer in $G$ to $u$ than to $v$.
Miklavi\v{c} and \v{S}parl (arXiv:2011.01635v1) define the
distance-unbalancedness ${\rm uB}(G)$ of $G$ as the sum of
$|n_G(u,v)-n_G(v,u)|$ over all unordered pairs of distinct vertices $u$ and $v$
of $G$. For positive integers $n$ up to $15$, they determine the trees $T$ of
fixed order $n$ with the smallest and the largest values of ${\rm uB}(T)$,
respectively. While the smallest value is achieved by the star $K_{1,n-1}$ for
these $n$, which we then proved for general $n$ (Minimum
distance-unbalancedness of trees, Journal of Mathematical Chemistry, DOI
10.1007/s10910-021-01228-4), the structure of the trees maximizing the
distance-unbalancedness remained unclear. For $n$ up to $15$ at least, all
these trees were subdivided stars. Contributing to problems posed by
Miklavi\v{c} and \v{S}parl, we show $$\max\Big\{{\rm uB}(T):T\mbox{ is a tree
of order }n\Big\} =\frac{n^3}{2}+o(n^3)$$ and $$\max\Big\{{\rm
uB}(S(n_1,\ldots,n_k)):1+n_1+\cdots+n_k=n\Big\}
=\left(\frac{1}{2}-\frac{5}{6k}+\frac{1}{3k^2}\right)n^3+O(kn^2),$$ where
$S(n_1,\ldots,n_k)$ is the subdivided star such that removing its center vertex
leaves paths of orders $n_1,\ldots,n_k$.


Abstract:
  We consider families of parameterizations of reduced curve singularities over
a Noetherian base scheme and prove that the delta invariant is semicontinuous.
In our setting, each curve singularity in the family is the image of a
parameterization and not the fiber of a morphism. The problem came up in
connection with the right-left classification of parameterizations of curve
singularities defined over a field of positive characteristic. We prove a bound
for right-left determinacy of a parameterization in terms of delta and the
semicontinuity theorem provides a simultaneous bound for the determinacy in a
family. The fact that the base space can be an arbitrary Noetherian scheme
causes some difficulties but is (not only) of interest for computational
purposes.


Abstract:
  We suggest a mathematical model for computing and regularly updating the next
preventive maintenance plan for a wind farm. Our optimization criterium takes
into account the current ages of the key components, the major maintenance
costs including eventual energy production losses as well as the available data
monitoring the condition of the wind turbines. We illustrate our approach with
a case study based on data collected from several wind farms located in Sweden.
Our results show that preventive maintenance planning gives some effect, if the
wind turbine components in question live significantly shorter than the turbine
itself.


Abstract:
  We consider a general tractable model for default contagion and systemic risk
in a heterogeneous financial network, subject to an exogenous macroeconomic
shock. We show that, under some regularity assumptions, the default cascade
model could be transferred to a death process problem represented by
balls-and-bins model. We also reduce the dimension of the problem by
classifying banks according to different types, in an appropriate type space.
These types may be calibrated to real-world data by using machine learning
techniques. We then state various limit theorems regarding the final size of
default cascade over different types. In particular, under suitable assumptions
on the degree and threshold distributions, we show that the final size of
default cascade has asymptotically Gaussian fluctuations. We next state limit
theorems for different system-wide wealth aggregation functions and show how
the systemic risk measure, in a given stress test scenario, could be related to
the structure and heterogeneity of financial networks. We finally show how
these results could be used by a social planner to optimally target
interventions during a financial crisis, with a budget constraint and under
partial information of the financial network.


Abstract:
  The signatures of topological superconductivity (TSC) in the superconducting
materials with topological nontrivial states prompt intensive researches
recently. Utilizing high-resolution angle-resolved photoemission spectroscopy
and first-principles calculations, we demonstrate multiple Dirac fermions and
surface states in superconductor BaSn$_3$ with a critical transition
temperature of about 4.4 K. We predict and then unveil the existence of two
pairs of type-\uppercase\expandafter{\romannumeral1} topological Dirac fermions
residing on the rotational axis. Type-\uppercase\expandafter{\romannumeral2}
Dirac fermions protected by screw axis are confirmed in the same compound.
Further calculation for the spin helical texture of the observed surface states
originating from the Dirac fermions give an opportunity for realization of TSC
in one single material. Hosting multiple Dirac fermions and topological surface
states, the intrinsic superconductor BaSn$_3$ is expected to be a new platform
for further investigation of the topological quantum materials as well as TSC.


Abstract:
  Sleeping problems have become one of the major diseases all over the world.
To tackle this issue, the basic tool used by specialists is the Polysomnogram,
which is a collection of different signals recorded during sleep. After its
recording, the specialists have to score the different signals according to one
of the standard guidelines. This process is carried out manually, which can be
highly time-consuming and very prone to annotation errors. Therefore, over the
years, many approaches have been explored in an attempt to support the
specialists in this task. In this paper, an approach based on convolutional
neural networks is presented, where an in-depth comparison is performed in
order to determine the convenience of using more than one signal simultaneously
as input. Additionally, the models were also used as parts of an ensemble model
to check whether any useful information can be extracted from signal processing
a single signal at a time which the dual-signal model cannot identify. Tests
have been performed by using a well-known dataset called expanded sleep-EDF,
which is the most commonly used dataset as the benchmark for this problem. The
tests were carried out with a leave-one-out cross-validation over the patients,
which ensures that there is no possible contamination between training and
testing. The resulting proposal is a network smaller than previously published
ones, but which overcomes the results of any previous models on the same
dataset. The best result shows an accuracy of 92.67\% and a Cohen's Kappa value
over 0.84 compared to human experts.


Abstract:
  We study generalized symmetries of quantum field theories in 1+1D generated
by topological defect lines with no inverse. This paper follows our companion
paper on gapped phases and anomalies associated with these symmetries. In the
present work we focus on identifying fusion category symmetries, using both
specialized 1+1D methods such as the modular bootstrap and (rational) conformal
field theory (CFT), as well as general methods based on gauging finite
symmetries, that extend to all dimensions. We apply these methods to $c = 1$
CFTs and uncover a rich structure. We find that even those $c = 1$ CFTs with
only finite group-like symmetries can have continuous fusion category
symmetries, and prove a Noether theorem that relates such symmetries in general
to non-local conserved currents. We also use these symmetries to derive new
constraints on RG flows between 1+1D CFTs.


Abstract:
  Diagnosing pre-existing heart diseases early in life is important as it helps
prevent complications such as pulmonary hypertension, heart rhythm problems,
blood clots, heart failure and sudden cardiac arrest. To identify such
diseases, phonocardiogram (PCG) and electrocardiogram (ECG) waveforms convey
important information. Therefore, effectively using these two modalities of
data has the potential to improve the disease screening process. We evaluate
this hypothesis on a subset of the PhysioNet Challenge 2016 Dataset which
contains simultaneously acquired PCG and ECG recordings. Our novel
Dual-Convolutional Neural Network based approach uses transfer learning to
tackle the problem of having limited amounts of simultaneous PCG and ECG data
that is publicly available, while having the potential to adapt to larger
datasets. In addition, we introduce two main evaluation frameworks named
record-wise and sample-wise evaluation which leads to a rich performance
evaluation for the transfer learning approach. Comparisons with methods which
used single or dual modality data show that our method can lead to better
performance. Furthermore, our results show that individually collected ECG or
PCG waveforms are able to provide transferable features which could effectively
help to make use of a limited number of synchronized PCG and ECG waveforms and
still achieve significant classification performance.


Abstract:
  In part I (arXiv:2006.12393) (accepted in JPP), we simulated individual
dynamics of a rising bubble and a falling droplet in an incompressible limit of
a strongly coupled dusty plasma (SCDP) using a generalized hydrodynamic
viscoelastic fluid model. In interest to understand hetero- (bubble-drop)
interactions between them, we extend this study to their combined evolution
through two arrangements: First, both are placed side-by-side in a row at the
same height. We observe that the overall dynamic governs by the competition
between the rotational motion induces due to the pairing between two
co-rotating inner vorticity lobes and net vertical motion of two
dipolar-vorticities (upward for bubble and downward for droplet) induces due to
the gravity. In second arrangement where the vertically aligned bubble (below)
and droplet (above) after collision exchange their blobs/lobes and subsequently
start to move horizontally in opposite directions away from each other. This
horizontal movement gets slower with increasing coupling strength. For the
these arrangement, we consider the varying distance between the fixed-size
bubble and droplet, and varying coupling strength which represents the nature
of our medium. A series of 2D numerical simulations have been conducted to
visualize these interactions


Abstract:
  We show that the weakest versions of Foreman's minimal generic hugeness
axioms cannot hold simultaneously on adjacent cardinals. Moreover, conventional
forcing techniques cannot produce a model of one of these axioms.


Abstract:
  Chan, Har-Peled, and Jones [2020] recently developed locality-sensitive
ordering (LSO), a new tool that allows one to reduce problems in the Euclidean
space $\mathbb{R}^d$ to the $1$-dimensional line. They used LSO's to solve a
host of problems. Later, Buchin, Har-Peled, and Ol{\'{a}}h [2019,2020] used the
LSO of Chan {\em et al. } to construct very sparse \emph{reliable spanners} for
the Euclidean space. A highly desirable feature of a reliable spanner is its
ability to withstand a massive failure: the network remains functioning even if
90\% of the nodes fail. In a follow-up work, Har-Peled, Mendel, and Ol{\'{a}}h
[2021] constructed reliable spanners for general and topologically structured
metrics. Their construction used a different approach, and is based on sparse
covers.
  In this paper, we develop the theory of LSO's to non-Euclidean metrics by
introducing new types of LSO's suitable for general and topologically
structured metrics. We then construct such LSO's, as well as constructing
considerably improved LSO's for doubling metrics. Afterwards, we use our new
LSO's to construct reliable spanners with improved stretch and sparsity
parameters. Most prominently, we construct $\tilde{O}(n)$-size reliable
spanners for trees and planar graphs with the optimal stretch of $2$. Along the
way to the construction of LSO's and reliable spanners, we introduce and
construct ultrametric covers, and construct $2$-hop reliable spanners for the
line.


Abstract:
  As is known, for each fixed $\nu\in\{0,1\},$ the spectra of two operators
generated by $-y''(x)+q(x)y(x-a)$ and the boundary conditions
$y^{(\nu)}(0)=y^{(j)}(\pi)=0,$ $j=0,1,$ uniquely determine the complex-valued
square-integrable potential $q(x)$ vanishing on $(0,a)$ as soon as
$a\in[2\pi/5,\pi).$ Meanwhile, it actually became the main question of the
inverse spectral theory for Sturm-Liouville operators with constant delay
whether the uniqueness holds also for smaller values of $a.$ Recently, a
negative answer was given by the authors [Appl. Math. Lett. 113 (2021) 106862]
for $a\in[\pi/3,2\pi/5)$ in the case $\nu=0$ by constructing an infinite family
of iso-bispectral potentials. Moreover, an essential and dramatic reason was
established why this strategy, generally speaking, fails in the remarkable case
when $\nu=1.$ Here we construct a counterexample giving a negative answer for
$\nu=1,$ which is an important subcase of the Robin boundary condition at zero.
We also refine the former counterexample for $\nu=0$ to $W_2^1$-potentials.


Abstract:
  Acoustic wave resonators are promising for gravimetric biosensing. However,
they generally suffer from strong acoustic radiation in liquid, which limits
their quality factor and increases their frequency noise. This article presents
an acoustic-radiation-free gravimetric biosensor based on a locally-resonant
surface phononic crystal (SPC) consisting of periodic high aspect ratio
electrodes to ad-dress the above issue. The acoustic wave generated in the SPC
is slower than the sound wave in water, hence preventing acoustic propagation
in the fluid and resulting in energy confinement near the electrode surface.
This energy confinement results in a significant quality factor improvement and
thus reduces the frequency noise. The proposed SPC resonator is numerically
studied by finite element analysis and experimentally implemented by an
electroplating based fabrication process. Experimental results show that the
SPC resonator exhibits an in-liquid quality factor 15 times higher than a
conventional Rayleigh wave resonator with a similar operating frequency. The
proposed radiation suppression method using SPC can also be applied in other
types of acoustic wave resonators. It can thus serve as a general technique for
boosting the in-liquid quality factor and the sensing performance of many
acoustic biosensors.


Abstract:
  A fundamental premise of SMS One-Time Password (OTP) is that the used
pseudo-random numbers (PRNs) are uniquely unpredictable for each login session.
Hence, the process of generating PRNs is the most critical step in the OTP
authentication. An improper implementation of the pseudo-random number
generator (PRNG) will result in predictable or even static OTP values, making
them vulnerable to potential attacks. In this paper, we present a vulnerability
study against PRNGs implemented for Android apps. A key challenge is that PRNGs
are typically implemented on the server-side, and thus the source code is not
accessible. To resolve this issue, we build an analysis tool, \sysname, to
assess implementations of the PRNGs in an automated manner without the source
code requirement. Through reverse engineering, \sysname identifies the apps
using SMS OTP and triggers each app's login functionality to retrieve OTP
values. It further assesses the randomness of the OTP values to identify
vulnerable PRNGs. By analyzing 6,431 commercially used Android apps downloaded
from \tool{Google Play} and \tool{Tencent Myapp}, \sysname identified 399
vulnerable apps that generate predictable OTP values. Even worse, 194
vulnerable apps use the OTP authentication alone without any additional
security mechanisms, leading to insecure authentication against guessing
attacks and replay attacks.


Abstract:
  We study maximal operators associated to singular averages along finite
subsets $\Sigma$ of the Grassmannian $\mathrm{Gr}(d,n)$ of $d$-dimensional
subspaces of $\mathbb R^n$. The well studied $d=1$ case corresponds to the the
directional maximal function with respect to arbitrary finite subsets of
$\mathrm{Gr}(1,n)=\mathbb S^{n-1}$. We provide a systematic study of all cases
$1\leq d<n$ and prove essentially sharp $L^2(\mathbb R^n)$ bounds for the
maximal subspace averaging operator in terms of the cardinality of $\Sigma$,
with no assumption on the structure of $\Sigma$. In the codimension $1$ case,
that is $n=d+1$, we prove the precise critical weak $(2,2)$-bound.
  Drawing on the analogy between maximal subspace averages and $(d,n)$-Nikodym
maximal averages, we also formulate the appropriate maximal Nikodym conjecture
for general $1<d<n$ by providing examples that determine the critical
$L^p$-space for the $(d,n)$-Nikodym problem. Unlike the $d=1$ case, the maximal
Kakeya and Nikodym problems are shown not to be equivalent when $d>1$. In this
context, we prove the best possible $L^2(\mathbb R^n)$-bound for the
$(d,n)$-Nikodym maximal function for all combinations of dimension and
codimension.
  Our estimates rely on Fourier analytic almost orthogonality principles,
combined with polynomial partitioning, but we also use spatial analysis based
on the precise calculation of intersections of $d$-dimensional plates in
$\mathbb R^n$.


Abstract:
  In the process of studying a conjecture of Holly M. Green and Martin W.
Liebeck, we obtain two interesting identities by elementary methods, one is a
combinatorial identity, and the other is a number theoretic identity.


Abstract:
  We propose injective generative models called Trumpets that generalize
invertible normalizing flows. The proposed generators progressively increase
dimension from a low-dimensional latent space. We demonstrate that Trumpets can
be trained orders of magnitudes faster than standard flows while yielding
samples of comparable or better quality. They retain many of the advantages of
the standard flows such as training based on maximum likelihood and a fast,
exact inverse of the generator. Since Trumpets are injective and have fast
inverses, they can be effectively used for downstream Bayesian inference. To
wit, we use Trumpet priors for maximum a posteriori estimation in the context
of image reconstruction from compressive measurements, outperforming
competitive baselines in terms of reconstruction quality and speed. We then
propose an efficient method for posterior characterization and uncertainty
quantification with Trumpets by taking advantage of the low-dimensional latent
space.


Abstract:
  We consider quantum many-body dynamics under quantum measurements, where the
measurement-induced phase transitions (MIPs) occur when changing the frequency
of the measurement. In this work, we consider the robustness of the MIP for
long-range interaction that decays as $r^{-\alpha}$ with distance $r$. The
effects of long-range interactions are classified into two regimes: (i) the MIP
is observed $(\alpha > \alpha_c)$, and (ii) the MIP is absent even for
arbitrarily strong measurements $(\alpha<\alpha_c)$. Using fermion models, we
demonstrate both regimes in integrable and non-integrable cases. We identify
the underlying mechanism and propose sufficient conditions to observe the MIP,
that is, $\alpha > d/2+1$ for general bilinear systems and $\alpha > d+1$ for
general non-integrable systems ($d$: spatial dimension). Numerical calculation
indicates that these conditions are optimal.


Abstract:
  We study confinement in 4d $\mathcal{N}=1$ $SU(N)$ Super-Yang Mills (SYM)
from a holographic point of view, focusing on the 1-form symmetry and its
relation to chiral symmetry breaking. In the 5d supergravity dual, obtained by
truncation of the Klebanov-Strassler solution, we identify the topological
couplings that determine the 1-form symmetry and its 't Hooft anomalies. One
such coupling is a mixed 0-form/1-form symmetry anomaly closely related to
chiral symmetry breaking in gapped confining vacua. From the dual gravity
description we also identify the infra-red (IR) 4d topological field theory
(TQFT), which realises chiral symmetry breaking and matches the mixed anomaly.
Finally, complementing this, we derive the chiral and mixed anomalies from the
Little String Theory realization of pure SYM.


Abstract:
  In classical approaches of dynamic network identification, in order to
identify a system (module) embedded in a dynamic network, one has to formulate
a Multi-input-Single-output (MISO) identification problem that requires
identification of a parametric model for all the modules constituting the MISO
setup including (possibly) the noise model, and determine their model order.
This requirement leads to model order selection steps for modules that are of
no interest to the experimenter which increases the computational complexity
for large-sized networks. Also, identification using a parametric noise model
(like BJ method) can suffer from local minima, however neglecting the noise
model has its impact on the variance of the estimates. In this paper, we
provide a two-step identification approach to avoid these problems. The first
step involves performing a non-parametric indirect approach for a MISO
identification problem to get the non-parametric frequency response function
(FRF) estimates and its variance as a function of frequency. In the second
step, the estimated non-parametric FRF of the target module is smoothed using a
parametric frequency domain estimator with the estimated variance from the
previous step as the non-parametric noise model. The developed approach is
practical with weak assumptions on noise, uses the available toolbox, requires
a parametric model only for the target module of interest, and uses a
non-parametric noise model to reduce the variance of the estimates. Numerical
simulations illustrate the potentials of the introduced method in comparison
with the classical identification methods.


Abstract:
  This paper pushes the envelope on decomposing camouflaged regions in an image
into meaningful components, namely, camouflaged instances. To promote the new
task of camouflaged instance segmentation of in-the-wild images, we introduce a
dataset, dubbed CAMO++, that extends our preliminary CAMO dataset (camouflaged
object segmentation) in terms of quantity and diversity. The new dataset
substantially increases the number of images with hierarchical pixel-wise
ground truths. We also provide a benchmark suite for the task of camouflaged
instance segmentation. In particular, we present an extensive evaluation of
state-of-the-art instance segmentation methods on our newly constructed CAMO++
dataset in various scenarios. We also present a camouflage fusion learning
(CFL) framework for camouflaged instance segmentation to further improve the
performance of state-of-the-art methods. The dataset, model, evaluation suite,
and benchmark will be made publicly available on our project page:
https://sites.google.com/view/ltnghia/research/camo_plus_plus


Abstract:
  Nowadays, Light Detection And Ranging (LiDAR) has been widely used in
autonomous vehicles for perception and localization. However, the cost of a
high-resolution LiDAR is still prohibitively expensive, while its
low-resolution counterpart is much more affordable. Therefore, using
low-resolution LiDAR for autonomous driving perception tasks instead of
high-resolution LiDAR is an economically feasible solution. In this paper, we
propose a novel framework for 3D object detection in Bird-Eye View (BEV) using
a low-resolution LiDAR and a monocular camera. Taking the low-resolution LiDAR
point cloud and the monocular image as input, our depth completion network is
able to produce dense point cloud that is subsequently processed by a
voxel-based network for 3D object detection. Evaluated with KITTI dataset, the
experimental results shows that the proposed approach performs significantly
better than directly applying the 16-line LiDAR point cloud for object
detection. For both easy and moderate cases, our detection results are
comparable to those from 64-line high-resolution LiDAR. The network
architecture and performance evaluations are analyzed in detail.


Abstract:
  The Latent River Problem has emerged as a flagship problem for causal
discovery in extreme value statistics. This paper gives QTree, a simple and
efficient algorithm to solve the Latent River Problem that outperforms existing
methods. QTree returns a directed graph and achieves almost perfect recovery on
the Upper Danube, the existing benchmark dataset, as well as on new data from
the Lower Colorado River in Texas. It can handle missing data, has an automated
parameter tuning procedure, and runs in time $O(n |V|^2)$, where $n$ is the
number of observations and $|V|$ the number of nodes in the graph. In addition,
under a Bayesian network model for extreme values with propagating noise, we
show that the QTree estimator returns for $n\to\infty$ a.s. the correct tree.


Abstract:
  Interconnectivity, fault tolerance, and dynamic evolution of the circuitry
are long sought-after objectives of bio-inspired engineering. Here, we propose
dendritic transistors composed of organic semiconductors as building blocks for
neuromorphic computing. These devices, owning to their voltage-triggered growth
and resemblance to neural structures, respond to action potentials to achieve
complex brain-like features, such as Pavlovian learning, pattern recognition,
and spike-timing-dependent plasticity. The dynamic formation of the connections
is reminiscent of a biological learning mechanism known as synaptogenesis, and
it is carried out by an electrochemical reaction that we name field-directed
polymerization. We employ it to dendritic connections and, by modulating the
growth parameters, control material properties such as the resistance and the
time constants relevant for plasticity. We believe these results will inspire
further research towards the complex integration of polymerized synapses for
brain-inspired computing.