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Title: Experimental Evaluation of Book Drawing Algorithms,
Abstract: A $k$-page book drawing of a graph $G=(V,E)$ consists of a linear ordering of
its vertices along a spine and an assignment of each edge to one of the $k$
pages, which are half-planes bounded by the spine. In a book drawing, two edges
cross if and only if they are assigned to the same page and their vertices
alternate along the spine. Crossing minimization in a $k$-page book drawing is
NP-hard, yet book drawings have multiple applications in visualization and
beyond. Therefore several heuristic book drawing algorithms exist, but there is
no broader comparative study on their relative performance. In this paper, we
propose a comprehensive benchmark set of challenging graph classes for book
drawing algorithms and provide an extensive experimental study of the
performance of existing book drawing algorithms. | [
1,
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] |
Title: Nonlinear Flexoelectricity in Non-centrosymmetric Crystals,
Abstract: We analytically derive the elastic, dielectric, piezoelectric, and the
flexoelectric phenomenological coefficients as functions of microscopic model
parameters such as ionic positions and spring constants in the two-dimensional
square-lattice model with rock-salt-type ionic arrangement. Monte-Carlo
simulation reveals that a difference in the given elastic constants of the
diagonal springs, each of which connects the same cations or anions, is
responsible for the linear flexoelectric effect in the model. We show the
quadratic flexoelectric effect is present only in non-centrosymmetric systems
and it can overwhelm the linear effect in feasibly large strain gradients. | [
0,
1,
0,
0,
0,
0
] |
Title: Massively parallel lattice-Boltzmann codes on large GPU clusters,
Abstract: This paper describes a massively parallel code for a state-of-the art thermal
lattice- Boltzmann method. Our code has been carefully optimized for
performance on one GPU and to have a good scaling behavior extending to a large
number of GPUs. Versions of this code have been already used for large-scale
studies of convective turbulence. GPUs are becoming increasingly popular in HPC
applications, as they are able to deliver higher performance than traditional
processors. Writing efficient programs for large clusters is not an easy task
as codes must adapt to increasingly parallel architectures, and the overheads
of node-to-node communications must be properly handled. We describe the
structure of our code, discussing several key design choices that were guided
by theoretical models of performance and experimental benchmarks. We present an
extensive set of performance measurements and identify the corresponding main
bot- tlenecks; finally we compare the results of our GPU code with those
measured on other currently available high performance processors. Our results
are a production-grade code able to deliver a sustained performance of several
tens of Tflops as well as a design and op- timization methodology that can be
used for the development of other high performance applications for
computational physics. | [
1,
0,
0,
0,
0,
0
] |
Title: Smart materials and structures for energy harvesters,
Abstract: Vibrational energy harvesters capture mechanical energy from ambient
vibrations and convert the mechanical energy into electrical energy to power
wireless electronic systems. Challenges exist in the process of capturing
mechanical energy from ambient vibrations. For example, resonant harvesters may
be used to improve power output near their resonance, but their narrow
bandwidth makes them less suitable for applications with varying vibrational
frequencies. Higher operating frequencies can increase harvesters power output,
but many vibrational sources are characterized by lower frequencies, such as
human motions. This paper provides a thorough review of state of the art energy
harvesters based on various energy sources such as solar, thermal,
electromagnetic and mechanical energy, as well as smart materials including
piezoelectric materials and carbon nanotubes. The paper will then focus on
vibrational energy harvesters to review harvesters using typical transduction
mechanisms and various techniques to address the challenges in capturing
mechanical energy and delivering it to the transducers. | [
0,
1,
0,
0,
0,
0
] |
Title: A Biomechanical Study on the Use of Curved Drilling Technique for Treatment of Osteonecrosis of Femoral Head,
Abstract: Osteonecrosis occurs due to the loss of blood supply to the bone, leading to
spontaneous death of the trabecular bone. Delayed treatment of the involved
patients results in collapse of the femoral head, which leads to a need for
total hip arthroplasty surgery. Core decompression, as the most popular
technique for treatment of the osteonecrosis, includes removal of the lesion
area by drilling a straight tunnel to the lesion, debriding the dead bone and
replacing it with bone substitutes. However, there are two drawbacks for this
treatment method. First, due to the rigidity of the instruments currently used
during core decompression, lesions cannot be completely removed and/or
excessive healthy bone may also be removed with the lesion. Second, the use of
bone substitutes, despite its biocompatibility and osteoconductivity, may not
provide sufficient mechanical strength and support for the bone. To address
these shortcomings, a novel robot-assisted curved core decompression (CCD)
technique is introduced to provide surgeons with direct access to the lesions
causing minimal damage to the healthy bone. In this study, with the aid of
finite element (FE) simulations, we investigate biomechanical performance of
core decompression using the curved drilling technique in the presence of
normal gait loading. In this regard, we compare the result of the CCD using
bone substitutes and flexible implants with other conventional core
decompression techniques. The study finding shows that the maximum principal
stress occurring at the superior domain of the neck is smaller in the CCD
techniques (i.e. 52.847 MPa) compared to the other core decompression methods. | [
0,
0,
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0
] |
Title: Output feedback exponential stabilization for 1-D unstable wave equations with boundary control matched disturbance,
Abstract: We study the output feedback exponential stabilization of a one-dimensional
unstable wave equation, where the boundary input, given by the Neumann trace at
one end of the domain, is the sum of the control input and the total
disturbance. The latter is composed of a nonlinear uncertain feedback term and
an external bounded disturbance. Using the two boundary displacements as output
signals, we design a disturbance estimator that does not use high gain. It is
shown that the disturbance estimator can estimate the total disturbance in the
sense that the estimation error signal is in $L^2[0,\infty)$. Using the
estimated total disturbance, we design an observer whose state is exponentially
convergent to the state of original system. Finally, we design an
observer-based output feedback stabilizing controller. The total disturbance is
approximately canceled in the feedback loop by its estimate. The closed-loop
system is shown to be exponentially stable while guaranteeing that all the
internal signals are uniformly bounded. | [
0,
0,
1,
0,
0,
0
] |
Title: Bio-inspired Tensegrity Soft Modular Robots,
Abstract: In this paper, we introduce a design principle to develop novel soft modular
robots based on tensegrity structures and inspired by the cytoskeleton of
living cells. We describe a novel strategy to realize tensegrity structures
using planar manufacturing techniques, such as 3D printing. We use this
strategy to develop icosahedron tensegrity structures with programmable
variable stiffness that can deform in a three-dimensional space. We also
describe a tendon-driven contraction mechanism to actively control the
deformation of the tensegrity mod-ules. Finally, we validate the approach in a
modular locomotory worm as a proof of concept. | [
1,
0,
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] |
Title: Diffusive Tidal Evolution for Migrating hot Jupiters,
Abstract: I consider a Jovian planet on a highly eccentric orbit around its host star,
a situation produced by secular interactions with its planetary or stellar
companions. The tidal interactions at every periastron passage exchange energy
between the orbit and the planet's degree-2 fundamental-mode. Starting from
zero energy, the f-mode can diffusively grow to large amplitudes if its
one-kick energy gain > 10^-5 of the orbital energy. This requires a pericentre
distance of < 4 tidal radii (or 1.6 Roche radii). If the f-mode has a
non-negligible initial energy, diffusive evolution can occur at a lower
threshold. The first effect can stall the secular migration as the f-mode can
absorb orbital energy and decouple the planet from its secular perturbers,
parking all migrating jupiters safely outside the zone of tidal disruption. The
second effect leads to rapid orbit circularization as it allows an excited
f-mode to continuously absorb orbital energy as the orbit eccentricity
decreases. So without any explicit dissipation, other than the fact that the
f-mode will damp nonlinearly when its amplitude reaches unity, the planet can
be transported from a few AU to ~ 0.2 AU in ~ 10^4 yrs. Such a rapid
circularization is equivalent to a dissipation factor Q ~ 1, and it explains
the observed deficit of super-eccentric Jovian planets. Lastly, the repeated
f-mode breaking likely deposit energy and angular momentum in the outer
envelope, and avoid thermally ablating the planet.
Overall, this work boosts the case for forming hot Jupiters through
high-eccentricity secular migration. | [
0,
1,
0,
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0,
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] |
Title: Harmonic analysis and distribution-free inference for spherical distributions,
Abstract: Fourier analysis and representation of circular distributions in terms of
their Fourier coefficients, is quite commonly discussed and used for model-free
inference such as testing uniformity and symmetry etc. in dealing with
2-dimensional directions. However a similar discussion for spherical
distributions, which are used to model 3-dimensional directional data, has not
been fully developed in the literature in terms of their harmonics. This paper,
in what we believe is the first such attempt, looks at the probability
distributions on a unit sphere, through the perspective of spherical harmonics,
analogous to the Fourier analysis for distributions on a unit circle. Harmonic
representations of many currently used spherical models are presented and
discussed. A very general family of spherical distributions is then introduced,
special cases of which yield many known spherical models. Through the prism of
harmonic analysis, one can look at the mean direction, dispersion, and various
forms of symmetry for these models in a generic setting. Aspects of
distribution free inference such as estimation and large-sample tests for these
symmetries, are provided. The paper concludes with a real-data example
analyzing the longitudinal sunspot activity. | [
0,
0,
0,
1,
0,
0
] |
Title: Free-form modelling of galaxy clusters: a Bayesian and data-driven approach,
Abstract: A new method is presented for modelling the physical properties of galaxy
clusters. Our technique moves away from the traditional approach of assuming
specific parameterised functional forms for the variation of physical
quantities within the cluster, and instead allows for a 'free-form'
reconstruction, but one for which the level of complexity is determined
automatically by the observational data and may depend on position within the
cluster. This is achieved by representing each independent cluster property as
some interpolating or approximating function that is specified by a set of
control points, or 'nodes', for which the number of nodes, together with their
positions and amplitudes, are allowed to vary and are inferred in a Bayesian
manner from the data. We illustrate our nodal approach in the case of a
spherical cluster by modelling the electron pressure profile Pe(r) in analyses
both of simulated Sunyaev-Zel'dovich (SZ) data from the Arcminute MicroKelvin
Imager (AMI) and of real AMI observations of the cluster MACS J0744+3927 in the
CLASH sample. We demonstrate that one may indeed determine the complexity
supported by the data in the reconstructed Pe(r), and that one may constrain
two very important quantities in such an analysis: the cluster total volume
integrated Comptonisation parameter (Ytot) and the extent of the gas
distribution in the cluster (rmax). The approach is also well-suited to
detecting clusters in blind SZ surveys. | [
0,
1,
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] |
Title: Multi-focus Attention Network for Efficient Deep Reinforcement Learning,
Abstract: Deep reinforcement learning (DRL) has shown incredible performance in
learning various tasks to the human level. However, unlike human perception,
current DRL models connect the entire low-level sensory input to the
state-action values rather than exploiting the relationship between and among
entities that constitute the sensory input. Because of this difference, DRL
needs vast amount of experience samples to learn. In this paper, we propose a
Multi-focus Attention Network (MANet) which mimics human ability to spatially
abstract the low-level sensory input into multiple entities and attend to them
simultaneously. The proposed method first divides the low-level input into
several segments which we refer to as partial states. After this segmentation,
parallel attention layers attend to the partial states relevant to solving the
task. Our model estimates state-action values using these attended partial
states. In our experiments, MANet attains highest scores with significantly
less experience samples. Additionally, the model shows higher performance
compared to the Deep Q-network and the single attention model as benchmarks.
Furthermore, we extend our model to attentive communication model for
performing multi-agent cooperative tasks. In multi-agent cooperative task
experiments, our model shows 20% faster learning than existing state-of-the-art
model. | [
1,
0,
0,
1,
0,
0
] |
Title: Equilibrium selection via Optimal transport,
Abstract: We propose a new dynamics for equilibrium selection of finite player discrete
strategy games. The dynamics is motivated by optimal transportation, and models
individual players' myopicity, greedy and uncertainty when making decisions.
The stationary measure of the dynamics provides each pure Nash equilibrium a
probability by which it is ranked. For potential games, its dynamical
properties are characterized by entropy and Fisher information. | [
0,
0,
1,
0,
0,
0
] |
Title: Using Continuous Power Modulation for Exchanging Local Channel State Information,
Abstract: This letter provides a simple but efficient technique, which allows each
transmitter of an interference network, to exchange local channel state
information with the other transmitters. One salient feature of the proposed
technique is that a transmitter only needs measurements of the signal power at
its intended receiver to implement it, making direct inter-transmitter
signaling channels unnecessary. The key idea to achieve this is to use a
transient period during which the continuous power level of a transmitter is
taken to be the linear combination of the channel gains to be exchanged. | [
1,
0,
0,
0,
0,
0
] |
Title: Lattice Rescoring Strategies for Long Short Term Memory Language Models in Speech Recognition,
Abstract: Recurrent neural network (RNN) language models (LMs) and Long Short Term
Memory (LSTM) LMs, a variant of RNN LMs, have been shown to outperform
traditional N-gram LMs on speech recognition tasks. However, these models are
computationally more expensive than N-gram LMs for decoding, and thus,
challenging to integrate into speech recognizers. Recent research has proposed
the use of lattice-rescoring algorithms using RNNLMs and LSTMLMs as an
efficient strategy to integrate these models into a speech recognition system.
In this paper, we evaluate existing lattice rescoring algorithms along with new
variants on a YouTube speech recognition task. Lattice rescoring using LSTMLMs
reduces the word error rate (WER) for this task by 8\% relative to the WER
obtained using an N-gram LM. | [
1,
0,
0,
1,
0,
0
] |
Title: Vanishing of Littlewood-Richardson polynomials is in P,
Abstract: J. DeLoera-T. McAllister and K. D. Mulmuley-H. Narayanan-M. Sohoni
independently proved that determining the vanishing of Littlewood-Richardson
coefficients has strongly polynomial time computational complexity. Viewing
these as Schubert calculus numbers, we prove the generalization to the
Littlewood-Richardson polynomials that control equivariant cohomology of
Grassmannians. We construct a polytope using the edge-labeled tableau rule of
H. Thomas-A. Yong. Our proof then combines a saturation theorem of D.
Anderson-E. Richmond-A. Yong, a reading order independence property, and E.
Tardos' algorithm for combinatorial linear programming. | [
1,
0,
1,
0,
0,
0
] |
Title: Multi-task memory networks for category-specific aspect and opinion terms co-extraction,
Abstract: In aspect-based sentiment analysis, most existing methods either focus on
aspect/opinion terms extraction or aspect terms categorization. However, each
task by itself only provides partial information to end users. To generate more
detailed and structured opinion analysis, we propose a finer-grained problem,
which we call category-specific aspect and opinion terms extraction. This
problem involves the identification of aspect and opinion terms within each
sentence, as well as the categorization of the identified terms. To this end,
we propose an end-to-end multi-task attention model, where each task
corresponds to aspect/opinion terms extraction for a specific category. Our
model benefits from exploring the commonalities and relationships among
different tasks to address the data sparsity issue. We demonstrate its
state-of-the-art performance on three benchmark datasets. | [
1,
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] |
Title: Discussion on Computationally Efficient Multivariate Spatio-Temporal Models for High-Dimensional Count-Valued Data by Bradley et al,
Abstract: I begin my discussion by summarizing the methodology proposed and new
distributional results on multivariate log-Gamma derived in the paper. Then, I
draw an interesting connection between their work with mean field variational
Bayes. Lastly, I make some comments on the simulation results and the
performance of the proposed Poisson multivariate spatio-temporal mixed effects
model (P-MSTM). | [
0,
0,
1,
1,
0,
0
] |
Title: Elucidation of the helical spin structure of FeAs,
Abstract: We present the results of resonant x-ray scattering measurements and
electronic structure calculations on the monoarsenide FeAs. We elucidate
details of the magnetic structure, showing the ratio of ellipticity of the spin
helix is larger than previously thought, at 2.58(3), and reveal both a
right-handed chirality and an out of plane component of the magnetic moments in
the spin helix. We find that electronic structure calculations and analysis of
the spin-orbit interaction are able to qualitatively account for this canting. | [
0,
1,
0,
0,
0,
0
] |
Title: Iron Intercalated Covalent-Organic Frameworks: First Crystalline Porous Thermoelectric Materials,
Abstract: Covalent-organic frameworks (COFs) are intriguing platforms for designing
functional molecular materials. Here, we present a computational study based on
van der Waals dispersion-corrected hybrid density functional theory
calculations to analyze the material properties of boroxine-linked and
triazine-linked intercalated-COFs. The effect of Fe atoms on the electronic
band structures near the Fermi energy level of the intercalated-COFs have been
investigated. The density of states (DOSs) computations have been performed to
analyze the material properties of these kind of intercalated-COFs. We predict
that COFs's electronic properties can be fine tuned by adding Fe atoms between
two organic layers in their structures. The new COFs are predicted to be
thermoelectric materials. These intercalated-COFs provide a new strategy to
create thermoelectric materials within a rigid porous network in a highly
controlled and predictable manner. | [
0,
1,
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0,
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] |
Title: Reversible Sequences of Cardinals, Reversible Equivalence Relations, and Similar Structures,
Abstract: A relational structure ${\mathbb X}$ is said to be reversible iff every
bijective endomorphism $f:X\rightarrow X$ is an automorphism. We define a
sequence of non-zero cardinals $\langle \kappa_i :i\in I\rangle$ to be
reversible iff each surjection $f :I\rightarrow I$ such that $\kappa_j
=\sum_{i\in f^{-1}[\{ j \}]}\kappa_i$, for all $j\in I $, is a bijection, and
characterize such sequences: either $\langle \kappa_i :i\in I\rangle$ is a
finite-to-one sequence, or $\kappa_i\in {\mathbb N}$, for all $i\in I$, $K:=\{
m\in {\mathbb N} : \kappa_i =m $, for infinitely many $i\in I \}$ is a
non-empty independent set, and $\gcd (K)$ divides at most finitely many
elements of the set $\{ \kappa_i :i\in I \}$. We isolate a class of binary
structures such that a structure from the class is reversible iff the sequence
of cardinalities of its connectivity components is reversible. In particular,
we characterize reversible equivalence relations, reversible posets which are
disjoint unions of cardinals $\leq \omega$, and some similar structures. In
addition, we show that a poset with linearly ordered connectivity components is
reversible, if the corresponding sequence of cardinalities is reversible and,
using this fact, detect a wide class of examples of reversible posets and
topological spaces. | [
0,
0,
1,
0,
0,
0
] |
Title: A Longitudinal Study of Google Play,
Abstract: The difficulty of large scale monitoring of app markets affects our
understanding of their dynamics. This is particularly true for dimensions such
as app update frequency, control and pricing, the impact of developer actions
on app popularity, as well as coveted membership in top app lists. In this
paper we perform a detailed temporal analysis on two datasets we have collected
from the Google Play Store, one consisting of 160,000 apps and the other of
87,223 newly released apps. We have monitored and collected data about these
apps over more than 6 months. Our results show that a high number of these apps
have not been updated over the monitoring interval. Moreover, these apps are
controlled by a few developers that dominate the total number of app downloads.
We observe that infrequently updated apps significantly impact the median app
price. However, a changing app price does not correlate with the download
count. Furthermore, we show that apps that attain higher ranks have better
stability in top app lists. We show that app market analytics can help detect
emerging threat vectors, and identify search rank fraud and even malware.
Further, we discuss the research implications of app market analytics on
improving developer and user experiences. | [
1,
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0,
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] |
Title: Effects of Images with Different Levels of Familiarity on EEG,
Abstract: Evaluating human brain potentials during watching different images can be
used for memory evaluation, information retrieving, guilty-innocent
identification and examining the brain response. In this study, the effects of
watching images, with different levels of familiarity, on subjects'
Electroencephalogram (EEG) have been studied. Three different groups of images
with three familiarity levels of "unfamiliar", "familiar" and "very familiar"
have been considered for this study. EEG signals of 21 subjects (14 men) were
recorded. After signal acquisition, pre-processing, including noise and
artifact removal, were performed on epochs of data. Features, including
spatial-statistical, wavelet, frequency and harmonic parameters, and also
correlation between recording channels, were extracted from the data. Then, we
evaluated the efficiency of the extracted features by using p-value and also an
orthogonal feature selection method (combination of Gram-Schmitt method and
Fisher discriminant ratio) for feature dimensional reduction. As the final step
of feature selection, we used 'add-r take-away l' method for choosing the most
discriminative features. For data classification, including all two-class and
three-class cases, we applied Support Vector Machine (SVM) on the extracted
features. The correct classification rates (CCR) for "unfamiliar-familiar",
"unfamiliar-very familiar" and "familiar-very familiar" cases were 85.6%,
92.6%, and 70.6%, respectively. The best results of classifications were
obtained in pre-frontal and frontal regions of brain. Also, wavelet, frequency
and harmonic features were among the most discriminative features. Finally, in
three-class case, the best CCR was 86.8%. | [
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] |
Title: Image synthesis with graph cuts: a fast model proposal mechanism in probabilistic inversion,
Abstract: Geophysical inversion should ideally produce geologically realistic
subsurface models that explain the available data. Multiple-point statistics is
a geostatistical approach to construct subsurface models that are consistent
with site-specific data, but also display the same type of patterns as those
found in a training image. The training image can be seen as a conceptual model
of the subsurface and is used as a non-parametric model of spatial variability.
Inversion based on multiple-point statistics is challenging due to high
nonlinearity and time-consuming geostatistical resimulation steps that are
needed to create new model proposals. We propose an entirely new model proposal
mechanism for geophysical inversion that is inspired by texture synthesis in
computer vision. Instead of resimulating pixels based on higher-order patterns
in the training image, we identify a suitable patch of the training image that
replace a corresponding patch in the current model without breaking the
patterns found in the training image, that is, remaining consistent with the
given prior. We consider three cross-hole ground-penetrating radar examples in
which the new model proposal mechanism is employed within an extended
Metropolis Markov chain Monte Carlo (MCMC) inversion. The model proposal step
is about 40 times faster than state-of-the-art multiple-point statistics
resimulation techniques, the number of necessary MCMC steps is lower and the
quality of the final model realizations is of similar quality. The model
proposal mechanism is presently limited to 2-D fields, but the method is
general and can be applied to a wide range of subsurface settings and
geophysical data types. | [
0,
1,
0,
0,
0,
0
] |
Title: Graded super duality for general linear Lie superalgebras,
Abstract: We provide a new proof of the super duality equivalence between infinite-rank
parabolic BGG categories of general linear Lie (super) algebras conjectured by
Cheng and Wang and first proved by Cheng and Lam. We do this by establishing a
new uniqueness theorem for tensor product categorifications motivated by work
of Brundan, Losev, and Webster. Moreover we show that these BGG categories have
Koszul graded lifts and super duality can be lifted to a graded equivalence. | [
0,
0,
1,
0,
0,
0
] |
Title: On a Formal Model of Safe and Scalable Self-driving Cars,
Abstract: In recent years, car makers and tech companies have been racing towards self
driving cars. It seems that the main parameter in this race is who will have
the first car on the road. The goal of this paper is to add to the equation two
additional crucial parameters. The first is standardization of safety assurance
--- what are the minimal requirements that every self-driving car must satisfy,
and how can we verify these requirements. The second parameter is scalability
--- engineering solutions that lead to unleashed costs will not scale to
millions of cars, which will push interest in this field into a niche academic
corner, and drive the entire field into a "winter of autonomous driving". In
the first part of the paper we propose a white-box, interpretable, mathematical
model for safety assurance, which we call Responsibility-Sensitive Safety
(RSS). In the second part we describe a design of a system that adheres to our
safety assurance requirements and is scalable to millions of cars. | [
1,
0,
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1,
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0
] |
Title: Randomness-induced quantum spin liquid on honeycomb lattice,
Abstract: We present a quantu spin liquid state in a spin-1/2 honeycomb lattice with
randomness in the exchange interaction. That is, we successfully introduce
randomness into the organic radial-based complex and realize a random-singlet
(RS) state. All magnetic and thermodynamic experimental results indicate the
liquid-like behaviors, which are consistent with those expected in the RS
state. These results demonstrate that the randomness or inhomogeneity in the
actual systems stabilize the RS state and yield liquid-like behavior. | [
0,
1,
0,
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0,
0
] |
Title: Cantor series and rational numbers,
Abstract: The article is devoted to the investigation of representation of rational
numbers by Cantor series. Necessary and sufficient conditions for a rational
number to be representable by a positive Cantor series are formulated for the
case of an arbitrary sequence $(q_k)$ and some its corollaries are considered.
Results of this article were presented by the author of this article on the
International Conference on Algebra dedicated to 100th anniversary of S. M.
Chernikov (www.researchgate.net/publication/311415815,
www.researchgate.net/publication/301849984). This investigation was also
presented in some reports (links to the reports:
www.researchgate.net/publication/303736670,
www.researchgate.net/publication/303720573, etc.). | [
0,
0,
1,
0,
0,
0
] |
Title: PAFit: an R Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks,
Abstract: Many real-world systems are profitably described as complex networks that
grow over time. Preferential attachment and node fitness are two simple growth
mechanisms that not only explain certain structural properties commonly
observed in real-world systems, but are also tied to a number of applications
in modeling and inference. While there are statistical packages for estimating
various parametric forms of the preferential attachment function, there is no
such package implementing non-parametric estimation procedures. The
non-parametric approach to the estimation of the preferential attachment
function allows for comparatively finer-grained investigations of the
`rich-get-richer' phenomenon that could lead to novel insights in the search to
explain certain nonstandard structural properties observed in real-world
networks. This paper introduces the R package PAFit, which implements
non-parametric procedures for estimating the preferential attachment function
and node fitnesses in a growing network, as well as a number of functions for
generating complex networks from these two mechanisms. The main computational
part of the package is implemented in C++ with OpenMP to ensure scalability to
large-scale networks. We first introduce the main functionalities of PAFit
through simulated examples, and then use the package to analyze a collaboration
network between scientists in the field of complex networks. The results
indicate the joint presence of `rich-get-richer' and `fit-get-richer' phenomena
in the collaboration network. The estimated attachment function is observed to
be near-linear, which we interpret as meaning that the chance an author gets a
new collaborator is proportional to their current number of collaborators.
Furthermore, the estimated author fitnesses reveal a host of familiar faces
from the complex networks community among the field's topmost fittest network
scientists. | [
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1,
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] |
Title: z-Classes and Rational Conjugacy Classes in Alternating Groups,
Abstract: In this paper, we compute the number of z-classes (conjugacy classes of
centralizers of elements) in the symmetric group S_n, when n is greater or
equal to 3 and alternating group A_n, when n is greater or equal to 4. It turns
out that the difference between the number of conjugacy classes and the number
of z-classes for S_n is determined by those restricted partitions of n-2 in
which 1 and 2 do not appear as its part. And, in the case of alternating
groups, it is determined by those restricted partitions of n-3 which has all
its parts distinct, odd and in which 1 (and 2) does not appear as its part,
along with an error term. The error term is given by those partitions of n
which have each of its part distinct, odd and perfect square. Further, we prove
that the number of rational-valued irreducible complex characters for A_n is
same as the number of conjugacy classes which are rational. | [
0,
0,
1,
0,
0,
0
] |
Title: A Simulator for Hedonic Games,
Abstract: Hedonic games are meant to model how coalitions of people form and break
apart in the real world. However, it is difficult to run simulations when
everything must be done by hand on paper. We present an online software that
allows fast and visual simulation of several types of hedonic games.
this http URL | [
1,
0,
0,
0,
0,
0
] |
Title: Estimating Large Precision Matrices via Modified Cholesky Decomposition,
Abstract: We introduce the $k$-banded Cholesky prior for estimating a high-dimensional
bandable precision matrix via the modified Cholesky decomposition. The bandable
assumption is imposed on the Cholesky factor of the decomposition. We obtained
the P-loss convergence rate under the spectral norm and the matrix
$\ell_{\infty}$ norm and the minimax lower bounds. Since the P-loss convergence
rate (Lee and Lee (2017)) is stronger than the posterior convergence rate, the
rates obtained are also posterior convergence rates. Furthermore, when the true
precision matrix is a $k_0$-banded matrix with some finite $k_0$, the obtained
P-loss convergence rates coincide with the minimax rates. The established
convergence rates are slightly slower than the minimax lower bounds, but these
are the fastest rates for bandable precision matrices among the existing
Bayesian approaches. A simulation study is conducted to compare the performance
to the other competitive estimators in various scenarios. | [
0,
0,
1,
1,
0,
0
] |
Title: High order conformal symplectic and ergodic schemes for stochastic Langevin equation via generating functions,
Abstract: In this paper, we consider the stochastic Langevin equation with additive
noises, which possesses both conformal symplectic geometric structure and
ergodicity. We propose a methodology of constructing high weak order conformal
symplectic schemes by converting the equation into an equivalent autonomous
stochastic Hamiltonian system and modifying the associated generating function.
To illustrate this approach, we construct a specific second order numerical
scheme, and prove that its symplectic form dissipates exponentially. Moreover,
for the linear case, the proposed scheme is also shown to inherit the
ergodicity of the original system, and the temporal average of the numerical
solution is a proper approximation of the ergodic limit over long time.
Numerical experiments are given to verify these theoretical results. | [
0,
0,
1,
0,
0,
0
] |
Title: Advanced LSTM: A Study about Better Time Dependency Modeling in Emotion Recognition,
Abstract: Long short-term memory (LSTM) is normally used in recurrent neural network
(RNN) as basic recurrent unit. However,conventional LSTM assumes that the state
at current time step depends on previous time step. This assumption constraints
the time dependency modeling capability. In this study, we propose a new
variation of LSTM, advanced LSTM (A-LSTM), for better temporal context
modeling. We employ A-LSTM in weighted pooling RNN for emotion recognition. The
A-LSTM outperforms the conventional LSTM by 5.5% relatively. The A-LSTM based
weighted pooling RNN can also complement the state-of-the-art emotion
classification framework. This shows the advantage of A-LSTM. | [
1,
0,
0,
1,
0,
0
] |
Title: On the number of circular orders on a group,
Abstract: We give a classification and complete algebraic description of groups
allowing only finitely many (left multiplication invariant) circular orders. In
particular, they are all solvable groups with a specific semi-direct product
decomposition. This allows us to also show that the space of circular orders of
any group is either finite or uncountable. As a special case and first step, we
show that the space of circular orderings of an infinite Abelian group has no
isolated points, hence is homeomorphic to a cantor set. | [
0,
0,
1,
0,
0,
0
] |
Title: Preparation and Measurement in Quantum Memory Models,
Abstract: Quantum Cognition has delivered a number of models for semantic memory, but
to date these have tended to assume pure states and projective measurement.
Here we relax these assumptions. A quantum inspired model of human word
association experiments will be extended using a density matrix representation
of human memory and a POVM based upon non-ideal measurements. Our formulation
allows for a consideration of key terms like measurement and contextuality
within a rigorous modern approach. This approach both provides new conceptual
advances and suggests new experimental protocols. | [
0,
0,
0,
0,
1,
0
] |
Title: Behavior of l-bits near the many-body localization transition,
Abstract: Eigenstates of fully many-body localized (FMBL) systems are described by
quasilocal operators $\tau_i^z$ (l-bits), which are conserved exactly under
Hamiltonian time evolution. The algebra of the operators $\tau_i^z$ and
$\tau_i^x$ associated with l-bits ($\boldsymbol{\tau}_i$) completely defines
the eigenstates and the matrix elements of local operators between eigenstates
at all energies. We develop a non-perturbative construction of the full set of
l-bit algebras in the many-body localized phase for the canonical model of MBL.
Our algorithm to construct the Pauli-algebra of l-bits combines exact
diagonalization and a tensor network algorithm developed for efficient
diagonalization of large FMBL Hamiltonians. The distribution of localization
lengths of the l-bits is evaluated in the MBL phase and used to characterize
the MBL-to-thermal transition. | [
0,
1,
0,
0,
0,
0
] |
Title: ROPE: high-dimensional network modeling with robust control of edge FDR,
Abstract: Network modeling has become increasingly popular for analyzing genomic data,
to aid in the interpretation and discovery of possible mechanistic components
and therapeutic targets. However, genomic-scale networks are high-dimensional
models and are usually estimated from a relatively small number of samples.
Therefore, their usefulness is hampered by estimation instability. In addition,
the complexity of the models is controlled by one or more penalization (tuning)
parameters where small changes to these can lead to vastly different networks,
thus making interpretation of models difficult. This necessitates the
development of techniques to produce robust network models accompanied by
estimation quality assessments.
We introduce Resampling of Penalized Estimates (ROPE): a novel statistical
method for robust network modeling. The method utilizes resampling-based
network estimation and integrates results from several levels of penalization
through a constrained, over-dispersed beta-binomial mixture model. ROPE
provides robust False Discovery Rate (FDR) control of network estimates and
each edge is assigned a measure of validity, the q-value, corresponding to the
FDR-level for which the edge would be included in the network model. We apply
ROPE to several simulated data sets as well as genomic data from The Cancer
Genome Atlas. We show that ROPE outperforms state-of-the-art methods in terms
of FDR control and robust performance across data sets. We illustrate how to
use ROPE to make a principled model selection for which genomic associations to
study further. ROPE is available as an R package on CRAN. | [
0,
0,
0,
1,
0,
0
] |
Title: Developing Robot Driver Etiquette Based on Naturalistic Human Driving Behavior,
Abstract: Automated vehicles can change the society by improved safety, mobility and
fuel efficiency. However, due to the higher cost and change in business model,
over the coming decades, the highly automated vehicles likely will continue to
interact with many human-driven vehicles. In the past, the control/design of
the highly automated (robotic) vehicles mainly considers safety and efficiency
but failed to address the "driving culture" of surrounding human-driven
vehicles. Thus, the robotic vehicles may demonstrate behaviors very different
from other vehicles. We study this "driving etiquette" problem in this paper.
As the first step, we report the key behavior parameters of human driven
vehicles derived from a large naturalistic driving database. The results can be
used to guide future algorithm design of highly automated vehicles or to
develop realistic human-driven vehicle behavior model in simulations. | [
1,
0,
0,
0,
0,
0
] |
Title: Solvability of curves on surfaces,
Abstract: In this article, we study subloci of solvable curves in $\mathcal{M}_g$ which
are contained in either a K3-surface or a quadric or a cubic surface. We give a
bound on the dimension of such subloci. In the case of complete intersection
genus $g$ curves in a cubic surface, we show that a general such curve is
solvable. | [
0,
0,
1,
0,
0,
0
] |
Title: Adversarial Variational Inference and Learning in Markov Random Fields,
Abstract: Markov random fields (MRFs) find applications in a variety of machine
learning areas, while the inference and learning of such models are challenging
in general. In this paper, we propose the Adversarial Variational Inference and
Learning (AVIL) algorithm to solve the problems with a minimal assumption about
the model structure of an MRF. AVIL employs two variational distributions to
approximately infer the latent variables and estimate the partition function,
respectively. The variational distributions, which are parameterized as neural
networks, provide an estimate of the negative log likelihood of the MRF. On one
hand, the estimate is in an intuitive form of approximate contrastive free
energy. On the other hand, the estimate is a minimax optimization problem,
which is solved by stochastic gradient descent in an alternating manner. We
apply AVIL to various undirected generative models in a fully black-box manner
and obtain better results than existing competitors on several real datasets. | [
1,
0,
0,
1,
0,
0
] |
Title: Geometric k-nearest neighbor estimation of entropy and mutual information,
Abstract: Nonparametric estimation of mutual information is used in a wide range of
scientific problems to quantify dependence between variables. The k-nearest
neighbor (knn) methods are consistent, and therefore expected to work well for
large sample size. These methods use geometrically regular local volume
elements. This practice allows maximum localization of the volume elements, but
can also induce a bias due to a poor description of the local geometry of the
underlying probability measure. We introduce a new class of knn estimators that
we call geometric knn estimators (g-knn), which use more complex local volume
elements to better model the local geometry of the probability measures. As an
example of this class of estimators, we develop a g-knn estimator of entropy
and mutual information based on elliptical volume elements, capturing the local
stretching and compression common to a wide range of dynamical systems
attractors. A series of numerical examples in which the thickness of the
underlying distribution and the sample sizes are varied suggest that local
geometry is a source of problems for knn methods such as the
Kraskov-Stögbauer-Grassberger (KSG) estimator when local geometric effects
cannot be removed by global preprocessing of the data. The g-knn method
performs well despite the manipulation of the local geometry. In addition, the
examples suggest that the g-knn estimators can be of particular relevance to
applications in which the system is large, but data size is limited. | [
0,
0,
1,
1,
0,
0
] |
Title: Resonant Electron Impact Excitation of 3d levels in Fe$^{14+}$ and Fe$^{15+}$,
Abstract: We present laboratory spectra of the $3p$--$3d$ transitions in Fe$^{14+}$ and
Fe$^{15+}$ excited with a mono-energetic electron beam. In the energy dependent
spectra obtained by sweeping the electron energy, resonant excitation is
confirmed as an intensity enhancement at specific electron energies. The
experimental results are compared with theoretical cross sections calculated
based on fully relativistic wave functions and the distorted-wave
approximation. Comparisons between the experimental and theoretical results
show good agreement for the resonance strength. A significant discrepancy is,
however, found for the non-resonant cross section in Fe$^{14+}$. %, which can
be considered as a fundamental cause of the line intensity ratio problem that
has often been found in both observatory and laboratory measurements. This
discrepancy is considered to be the fundamental cause of the previously
reported inconsistency of the model with the observed intensity ratio between
the $^3\!P_2$ -- $^3\!D_3$ and $^1\!P_1$ -- $^1\!D_2$ transitions. | [
0,
1,
0,
0,
0,
0
] |
Title: Automated Top View Registration of Broadcast Football Videos,
Abstract: In this paper, we propose a novel method to register football broadcast video
frames on the static top view model of the playing surface. The proposed method
is fully automatic in contrast to the current state of the art which requires
manual initialization of point correspondences between the image and the static
model. Automatic registration using existing approaches has been difficult due
to the lack of sufficient point correspondences. We investigate an alternate
approach exploiting the edge information from the line markings on the field.
We formulate the registration problem as a nearest neighbour search over a
synthetically generated dictionary of edge map and homography pairs. The
synthetic dictionary generation allows us to exhaustively cover a wide variety
of camera angles and positions and reduce this problem to a minimal per-frame
edge map matching procedure. We show that the per-frame results can be improved
in videos using an optimization framework for temporal camera stabilization. We
demonstrate the efficacy of our approach by presenting extensive results on a
dataset collected from matches of football World Cup 2014. | [
1,
0,
0,
0,
0,
0
] |
Title: Fast swaption pricing in Gaussian term structure models,
Abstract: We propose a fast and accurate numerical method for pricing European
swaptions in multi-factor Gaussian term structure models. Our method can be
used to accelerate the calibration of such models to the volatility surface.
The pricing of an interest rate option in such a model involves evaluating a
multi-dimensional integral of the payoff of the claim on a domain where the
payoff is positive. In our method, we approximate the exercise boundary of the
state space by a hyperplane tangent to the maximum probability point on the
boundary and simplify the multi-dimensional integration into an analytical
form. The maximum probability point can be determined using the gradient
descent method. We demonstrate that our method is superior to previous methods
by comparing the results to the price obtained by numerical integration. | [
0,
0,
0,
0,
0,
1
] |
Title: Humanoid Robots as Agents of Human Consciousness Expansion,
Abstract: The "Loving AI" project involves developing software enabling humanoid robots
to interact with people in loving and compassionate ways, and to promote
people' self-understanding and self-transcendence. Currently the project
centers on the Hanson Robotics robot "Sophia" -- specifically, on supplying
Sophia with personality content and cognitive, linguistic, perceptual and
behavioral content aimed at enabling loving interactions supportive of human
self-transcendence. In September 2017 a small pilot study was conducted,
involving the Sophia robot leading human subjects through dialogues and
exercises focused on meditation, visualization and relaxation. The pilot was an
apparent success, qualitatively demonstrating the viability of the approach and
the ability of appropriate human-robot interaction to increase human well-being
and advance human consciousness. | [
1,
0,
0,
0,
0,
0
] |
Title: Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training,
Abstract: In this paper we study leveraging confidence information induced by
adversarial training to reinforce adversarial robustness of a given
adversarially trained model. A natural measure of confidence is $\|F({\bf
x})\|_\infty$ (i.e. how confident $F$ is about its prediction?). We start by
analyzing an adversarial training formulation proposed by Madry et al.. We
demonstrate that, under a variety of instantiations, an only somewhat good
solution to their objective induces confidence to be a discriminator, which can
distinguish between right and wrong model predictions in a neighborhood of a
point sampled from the underlying distribution. Based on this, we propose
Highly Confident Near Neighbor (${\tt HCNN}$), a framework that combines
confidence information and nearest neighbor search, to reinforce adversarial
robustness of a base model. We give algorithms in this framework and perform a
detailed empirical study. We report encouraging experimental results that
support our analysis, and also discuss problems we observed with existing
adversarial training. | [
1,
0,
0,
1,
0,
0
] |
Title: Multi-Period Flexibility Forecast for Low Voltage Prosumers,
Abstract: Near-future electric distribution grids operation will have to rely on
demand-side flexibility, both by implementation of demand response strategies
and by taking advantage of the intelligent management of increasingly common
small-scale energy storage. The Home energy management system (HEMS), installed
at low voltage residential clients, will play a crucial role on the flexibility
provision to both system operators and market players like aggregators.
Modeling and forecasting multi-period flexibility from residential prosumers,
such as battery storage and electric water heater, while complying with
internal constraints (comfort levels, data privacy) and uncertainty is a
complex task. This papers describes a computational method that is capable of
efficiently learn and define the feasibility flexibility space from
controllable resources connected to a HEMS. An Evolutionary Particle Swarm
Optimization (EPSO) algorithm is adopted and reshaped to derive a set of
feasible temporal trajectories for the residential net-load, considering
storage, flexible appliances, and predefined costumer preferences, as well as
load and photovoltaic (PV) forecast uncertainty. A support vector data
description (SVDD) algorithm is used to build models capable of classifying
feasible and non-feasible HEMS operating trajectories upon request from an
optimization/control algorithm operated by a DSO or market player. | [
1,
0,
0,
0,
0,
0
] |
Title: Assessing the Economics of Customer-Sited Multi-Use Energy Storage,
Abstract: This paper presents an approach to assess the economics of customer-sited
energy storage systems (ESSs) which are owned and operated by a customer. The
ESSs can participate in frequency regulation and spinning reserve markets, and
are used to help the customer consume available renewable energy and reduce
electricity bill. A rolling-horizon approach is developed to optimize the
service schedule, and the resulting costs and revenues are used to assess
economics of the ESSs. The economic assessment approach is illustrated with
case studies, from which we obtain some new observations on profitability of
the customer- sited multi-use ESSs. | [
0,
0,
1,
0,
0,
0
] |
Title: A Hamiltonian approach for the Thermodynamics of AdS black holes,
Abstract: In this work we study the Thermodynamics of D-dimensional Schwarzschild-anti
de Sitter (SAdS) black holes. The minimal Thermodynamics of the SAdS spacetime
is briefly discussed, highlighting some of its strong points and shortcomings.
The minimal SAdS Thermodynamics is extended within a Hamiltonian approach, by
means of the introduction of an additional degree of freedom. We demonstrate
that the cosmological constant can be introduced in the thermodynamic
description of the SAdS black hole with a canonical transformation of the
Schwarzschild problem, closely related to the introduction of an anti-de Sitter
thermodynamic volume. The treatment presented is consistent, in the sense that
it is compatible with the introduction of new thermodynamic potentials, and
respects the laws of black hole Thermodynamics. By demanding homogeneity of the
thermodynamic variables, we are able to construct a new equation of state that
completely characterizes the Thermodynamics of SAdS black holes. The treatment
naturally generates phenomenological constants that can be associated with
different boundary conditions in underlying microscopic theories. A whole new
set of phenomena can be expected from the proposed generalization of SAdS
Thermodynamics. | [
0,
1,
1,
0,
0,
0
] |
Title: Dzyaloshinskii Moriya interaction across antiferromagnet / ferromagnet interface,
Abstract: The antiferromagnet (AFM) / ferromagnet (FM) interfaces are of central
importance in recently developed pure electric or ultrafast control of FM
spins, where the underlying mechanisms remain unresolved. Here we report the
direct observation of Dzyaloshinskii Moriya interaction (DMI) across the AFM/FM
interface of IrMn/CoFeB thin films. The interfacial DMI is quantitatively
measured from the asymmetric spin wave dispersion in the FM layer using
Brillouin light scattering. The DMI strength is enhanced by a factor of 7 with
increasing IrMn layer thickness in the range of 1- 7.5 nm. Our findings provide
deeper insight into the coupling at AFM/FM interface and may stimulate new
device concepts utilizing chiral spin textures such as magnetic skyrmions in
AFM/FM heterostructures. | [
0,
1,
0,
0,
0,
0
] |
Title: Temporal Markov Processes for Transport in Porous Media: Random Lattice Networks,
Abstract: Monte Carlo (MC) simulations of transport in random porous networks indicate
that for high variances of the log-normal permeability distribution, the
transport of a passive tracer is non-Fickian. Here we model this non-Fickian
dispersion in random porous networks using discrete temporal Markov models. We
show that such temporal models capture the spreading behavior accurately. This
is true despite the fact that the slow velocities are strongly correlated in
time, and some studies have suggested that the persistence of low velocities
would render the temporal Markovian model inapplicable. Compared to previously
proposed temporal stochastic differential equations with case specific drift
and diffusion terms, the models presented here require fewer modeling
assumptions. Moreover, we show that discrete temporal Markov models can be used
to represent dispersion in unstructured networks, which are widely used to
model porous media. A new method is proposed to extend the state space of
temporal Markov models to improve the model predictions in the presence of
extremely low velocities in particle trajectories and extend the applicability
of the model to higher temporal resolutions. Finally, it is shown that by
combining multiple transitions, temporal models are more efficient for
computing particle evolution compared to correlated CTRW with spatial
increments that are equal to the lengths of the links in the network. | [
1,
1,
0,
0,
0,
0
] |
Title: Defect Properties of Na and K in Cu2ZnSnS4 from Hybrid Functional Calculation,
Abstract: In-growth or post-deposition treatment of $Cu_{2}ZnSnS_{4}$ (CZTS) absorber
layer had led to improved photovoltaic efficiency, however, the underlying
physical mechanism of such improvements are less studied. In this study, the
thermodynamics of Na and K related defects in CZTS are investigated from first
principle approach using hybrid functional, with chemical potential of Na and K
established from various phases of their polysulphides. Both Na and K
predominantly substitute on Cu sites similar to their behavior in
$Cu(In,Ga)Se_{2}$, in contrast to previous results using the generalized
gradient approximation (GGA). All substitutional and interstitial defects are
shown to be either shallow levels or highly energetically unfavorable. Defect
complexing between Na and abundant intrinsic defects did not show possibility
of significant incorporation enhancement or introducing deep n-type levels. The
possible benefit of Na incorporation on enhancing photovoltaic efficiency is
discussed. The negligible defect solubility of K in CZTS also suggests possible
surfactant candidate. | [
0,
1,
0,
0,
0,
0
] |
Title: Global weak solutions in a three-dimensional Keller-Segel-Navier-Stokes system with nonlinear diffusion,
Abstract: The coupled quasilinear Keller-Segel-Navier-Stokes system is considered under
Neumann boundary conditions for $n$ and $c$ and no-slip boundary conditions for
$u$ in three-dimensional bounded domains $\Omega\subseteq \mathbb{R}^3$ with
smooth boundary, where $m>0,\kappa\in \mathbb{R}$ are given constants, $\phi\in
W^{1,\infty}(\Omega)$. If $ m> 2$, then for all reasonably regular initial
data, a corresponding initial-boundary value problem for $(KSNF)$ possesses a
globally defined weak solution. | [
0,
0,
1,
0,
0,
0
] |
Title: On Consistency of Compressive Spectral Clustering,
Abstract: Spectral clustering is one of the most popular methods for community
detection in graphs. A key step in spectral clustering algorithms is the eigen
decomposition of the $n{\times}n$ graph Laplacian matrix to extract its $k$
leading eigenvectors, where $k$ is the desired number of clusters among $n$
objects. This is prohibitively complex to implement for very large datasets.
However, it has recently been shown that it is possible to bypass the eigen
decomposition by computing an approximate spectral embedding through graph
filtering of random signals. In this paper, we analyze the working of spectral
clustering performed via graph filtering on the stochastic block model.
Specifically, we characterize the effects of sparsity, dimensionality and
filter approximation error on the consistency of the algorithm in recovering
planted clusters. | [
1,
0,
0,
1,
0,
0
] |
Title: Story of the Developments in Statistical Physics of Fracture, Breakdown \& Earthquake: A Personal Account,
Abstract: We review the developments of the statistical physics of fracture and
earthquake over the last four decades. We argue that major progress has been
made in this field and that the key concepts should now become integral part of
the (under-) graduate level text books in condensed matter physics. For arguing
in favor of this, we compare the development (citations) with the same for some
other related topics in condensed matter, for which Nobel prizes have already
been awarded. | [
0,
1,
0,
0,
0,
0
] |
Title: LocalNysation: A bottom up approach to efficient localized kernel regression,
Abstract: We consider a localized approach in the well-established setting of
reproducing kernel learning under random design. The input space $X$ is
partitioned into local disjoint subsets $X_j$ ($j=1,...,m$) equipped with a
local reproducing kernel $K_j$. It is then straightforward to define local KRR
estimates. Our first main contribution is in showing that minimax optimal rates
of convergence are preserved if the number $m$ of partitions grows sufficiently
slowly with the sample size, under locally different degrees on smoothness
assumptions on the regression function. As a byproduct, we show that low
smoothness on exceptional sets of small probability does not contribute,
leading to a faster rate of convergence. Our second contribution lies in
showing that the partitioning approach for KRR can be efficiently combined with
local Nyström subsampling, improving computational cost twofold. If the
number of locally subsampled inputs grows sufficiently fast with the sample
size, minimax optimal rates of convergence are maintained. | [
0,
0,
1,
1,
0,
0
] |
Title: Evolutionary Acyclic Graph Partitioning,
Abstract: Directed graphs are widely used to model data flow and execution dependencies
in streaming applications. This enables the utilization of graph partitioning
algorithms for the problem of parallelizing computation for multiprocessor
architectures. However due to resource restrictions, an acyclicity constraint
on the partition is necessary when mapping streaming applications to an
embedded multiprocessor. Here, we contribute a multi-level algorithm for the
acyclic graph partitioning problem. Based on this, we engineer an evolutionary
algorithm to further reduce communication cost, as well as to improve load
balancing and the scheduling makespan on embedded multiprocessor architectures. | [
1,
0,
0,
0,
0,
0
] |
Title: Endomorphism Algebras of Abelian varieties with special reference to Superelliptic Jacobians,
Abstract: This is (mostly) a survey article. We use an information about Galois
properties of points of small order on an abelian variety in order to describe
its endomorphism algebra over an algebraic closure of the ground field. We
discuss in detail applications to jacobians of cyclic covers of the projective
line. | [
0,
0,
1,
0,
0,
0
] |
Title: A Generalized Function defined by the Euler first kind integral and its connection with the Dirac delta function,
Abstract: We have shown that in some region where the Euler integral of the first kind
diverges, the Euler formula defines a generalized function. The connected of
this generalized function with the Dirac delta function is found. | [
0,
0,
1,
0,
0,
0
] |
Title: Optical emission of graphene and electron-hole pair production induced by a strong THz field,
Abstract: We report on the first experimental observation of graphene optical emission
induced by the intense THz pulse. P-doped CVD graphene with the initial Fermi
energy of about 200 meV was used, optical photons was detected in the
wavelength range of 340-600 nm. Emission started when THz field amplitude
exceeded 100 kV/cm. For THz fields from 200 to 300 kV/cm the temperature of
optical radiation was constant, while the number of emitted photons increased
several dozen times. This fact clearly indicates multiplication of
electron-hole pairs induced by an external field itself and not due to electron
heating. The experimental data are in a good agreement with the theory of
Landau-Zener interband transitions. It is shown theoretically that Landau-Zener
transitions are possible even in the case of heavily doped graphene because the
strong THz field removes quasiparticles from the region of interband
transitions during several femtoseconds, which cancels the Pauli blocking
effect. | [
0,
1,
0,
0,
0,
0
] |
Title: Floquet Analysis of Kuznetsov--Ma breathers: A Path Towards Spectral Stability of Rogue Waves,
Abstract: In the present work, we aim at taking a step towards the spectral stability
analysis of Peregrine solitons, i.e., wave structures that are used to emulate
extreme wave events. Given the space-time localized nature of Peregrine
solitons, this is a priori a non-trivial task. Our main tool in this effort
will be the study of the spectral stability of the periodic generalization of
the Peregrine soliton in the evolution variable, namely the Kuznetsov--Ma
breather. Given the periodic structure of the latter, we compute the
corresponding Floquet multipliers, and examine them in the limit where the
period of the orbit tends to infinity. This way, we extrapolate towards the
stability of the limiting structure, namely the Peregrine soliton. We find that
multiple unstable modes of the background are enhanced, yet no additional
unstable eigenmodes arise as the Peregrine limit is approached. We explore the
instability evolution also in direct numerical simulations. | [
0,
1,
0,
0,
0,
0
] |
Title: Photon propagation through linearly active dimers,
Abstract: We provide an analytic propagator for non-Hermitian dimers showing linear
gain or losses in the quantum regime. In particular, we focus on experimentally
feasible realizations of the $\mathcal{PT}$-symmetric dimer and provide their
mean photon number and second order two-point correlation. We study the
propagation of vacuum, single photon spatially-separable, and two-photon
spatially-entangled states. We show that each configuration produces a
particular signature that might signal their possible uses as photon switches,
semi-classical intensity-tunable sources, or spatially entangled sources to
mention a few possible applications. | [
0,
1,
0,
0,
0,
0
] |
Title: End-to-End Learning of Geometry and Context for Deep Stereo Regression,
Abstract: We propose a novel deep learning architecture for regressing disparity from a
rectified pair of stereo images. We leverage knowledge of the problem's
geometry to form a cost volume using deep feature representations. We learn to
incorporate contextual information using 3-D convolutions over this volume.
Disparity values are regressed from the cost volume using a proposed
differentiable soft argmin operation, which allows us to train our method
end-to-end to sub-pixel accuracy without any additional post-processing or
regularization. We evaluate our method on the Scene Flow and KITTI datasets and
on KITTI we set a new state-of-the-art benchmark, while being significantly
faster than competing approaches. | [
1,
0,
0,
0,
0,
0
] |
Title: Variational Inference via Transformations on Distributions,
Abstract: Variational inference methods often focus on the problem of efficient model
optimization, with little emphasis on the choice of the approximating
posterior. In this paper, we review and implement the various methods that
enable us to develop a rich family of approximating posteriors. We show that
one particular method employing transformations on distributions results in
developing very rich and complex posterior approximation. We analyze its
performance on the MNIST dataset by implementing with a Variational Autoencoder
and demonstrate its effectiveness in learning better posterior distributions. | [
1,
0,
0,
1,
0,
0
] |
Title: VLSI Computational Architectures for the Arithmetic Cosine Transform,
Abstract: The discrete cosine transform (DCT) is a widely-used and important signal
processing tool employed in a plethora of applications. Typical fast algorithms
for nearly-exact computation of DCT require floating point arithmetic, are
multiplier intensive, and accumulate round-off errors. Recently proposed fast
algorithm arithmetic cosine transform (ACT) calculates the DCT exactly using
only additions and integer constant multiplications, with very low area
complexity, for null mean input sequences. The ACT can also be computed
non-exactly for any input sequence, with low area complexity and low power
consumption, utilizing the novel architecture described. However, as a
trade-off, the ACT algorithm requires 10 non-uniformly sampled data points to
calculate the 8-point DCT. This requirement can easily be satisfied for
applications dealing with spatial signals such as image sensors and biomedical
sensor arrays, by placing sensor elements in a non-uniform grid. In this work,
a hardware architecture for the computation of the null mean ACT is proposed,
followed by a novel architectures that extend the ACT for non-null mean
signals. All circuits are physically implemented and tested using the Xilinx
XC6VLX240T FPGA device and synthesized for 45 nm TSMC standard-cell library for
performance assessment. | [
1,
0,
0,
0,
0,
0
] |
Title: Simulated Tornado Optimization,
Abstract: We propose a swarm-based optimization algorithm inspired by air currents of a
tornado. Two main air currents - spiral and updraft - are mimicked. Spiral
motion is designed for exploration of new search areas and updraft movements is
deployed for exploitation of a promising candidate solution. Assignment of just
one search direction to each particle at each iteration, leads to low
computational complexity of the proposed algorithm respect to the conventional
algorithms. Regardless of the step size parameters, the only parameter of the
proposed algorithm, called tornado diameter, can be efficiently adjusted by
randomization. Numerical results over six different benchmark cost functions
indicate comparable and, in some cases, better performance of the proposed
algorithm respect to some other metaheuristics. | [
1,
0,
1,
0,
0,
0
] |
Title: On a backward problem for multidimensional Ginzburg-Landau equation with random data,
Abstract: In this paper, we consider a backward in time problem for Ginzburg-Landau
equation in multidimensional domain associated with some random data. The
problem is ill-posed in the sense of Hadamard. To regularize the instable
solution, we develop a new regularized method combined with statistical
approach to solve this problem. We prove a upper bound, on the rate of
convergence of the mean integrated squared error in $L^2 $ norm and $H^1$ norm. | [
0,
0,
1,
0,
0,
0
] |
Title: Network analysis of the COSMOS galaxy field,
Abstract: The galaxy data provided by COSMOS survey for 1 by 1 degree field of sky are
analysed by methods of complex networks. Three galaxy samples (slices) with
redshifts ranging within intervals 0.88-0.91, 0.91-0.94 and 0.94-0.97 are
studied as two-dimensional projections for the spatial distributions of
galaxies. We construct networks and calculate network measures for each sample,
in order to analyse the network similarity of different samples, distinguish
various topological environments, and find associations between galaxy
properties (colour index and stellar mass) and their topological environments.
Results indicate a high level of similarity between geometry and topology for
different galaxy samples and no clear evidence of evolutionary trends in
network measures. The distribution of local clustering coefficient C manifests
three modes which allow for discrimination between stand-alone singlets and
dumbbells (0 <= C <= 0.1), intermediately (0 < C < 0.9) and clique (0.9 <= C <=
1) like galaxies. Analysing astrophysical properties of galaxies (colour index
and stellar masses), we show that distributions are similar in all slices,
however weak evolutionary trends can also be seen across redshift slices. To
specify different topological environments we have extracted selections of
galaxies from each sample according to different modes of C distribution. We
have found statistically significant associations between evolutionary
parameters of galaxies and selections of C: the distribution of stellar mass
for galaxies with interim C differ from the corresponding distributions for
stand-alone and clique galaxies, and this difference holds for all redshift
slices. The colour index realises somewhat different behaviour. | [
0,
1,
0,
0,
0,
0
] |
Title: Control Synthesis for Permutation-Symmetric High-Dimensional Systems With Counting Constraints,
Abstract: General purpose correct-by-construction synthesis methods are limited to
systems with low dimensionality or simple specifications. In this work we
consider highly symmetrical counting problems and exploit the symmetry to
synthesize provably correct controllers for systems with tens of thousands of
states. The key ingredients of the solution are an aggregate abstraction
procedure for mildly heterogeneous systems and a formulation of counting
constraints as linear inequalities. | [
1,
0,
1,
0,
0,
0
] |
Title: Commutativity and Commutative Pairs of Some Differential Equations,
Abstract: In this study, explicit differential equations representing commutative pairs
of some well-known second-order linear time-varying systems have been derived.
The commutativity of these systems are investigated by considering 30
second-order linear differential equations with variable coefficients. It is
shown that the system modeled by each one of these equations has a commutative
pair with (or without) some conditions or not. There appear special cases such
that both, only one or neither of the original system and its commutative pair
has explicit analytic solution. Some benefits of commutativity have already
been mentioned in the literature but a new application for in cryptology for
obscuring transmitted signals in telecommunication is illustrated in this
paper. | [
1,
0,
0,
0,
0,
0
] |
Title: Testing for Balance in Social Networks,
Abstract: Friendship and antipathy exist in concert with one another in real social
networks. Despite the role they play in social interactions, antagonistic ties
are poorly understood and infrequently measured. One important theory of
negative ties that has received relatively little empirical evaluation is
balance theory, the codification of the adage `the enemy of my enemy is my
friend' and similar sayings. Unbalanced triangles are those with an odd number
of negative ties, and the theory posits that such triangles are rare. To test
for balance, previous works have utilized a permutation test on the edge signs.
The flaw in this method, however, is that it assumes that negative and positive
edges are interchangeable. In reality, they could not be more different. Here,
we propose a novel test of balance that accounts for this discrepancy and show
that our test is more accurate at detecting balance. Along the way, we prove
asymptotic normality of the test statistic under our null model, which is of
independent interest. Our case study is a novel dataset of signed networks we
collected from 32 isolated, rural villages in Honduras. Contrary to previous
results, we find that there is only marginal evidence for balance in social tie
formation in this setting. | [
1,
0,
0,
0,
0,
0
] |
Title: Empirical Recurrence Rates for Seismic Signals on Planetary Surfaces,
Abstract: We review the recurrence intervals as a function of ground motion amplitude
at several terrestrial locations, and make the first interplanetary comparison
with measurements on the Moon, Mars, Venus and Titan. This empirical approach
gives an intuitive guide to the relative seismicity of these locations, without
invoking interior models and specific sources: for example a Venera-14
observation of possible ground motion indicates a microseismic environment
mid-way between noisy and quiet terrestrial locations; quiet terrestrial
regions see a peak velocity amplitude in mm/s roughly equal to 0.4*N(-0.7),
where N is the number of events observed per year. The Apollo data show signals
for a given recurrence rate are typically about 10,000 times smaller in
amplitude than a quiet site on Earth, while Viking data masked for low-wind
periods appears comparable with a quiet terrestrial site. Recurrence rate plots
from in-situ measurements provide a convenient guide to expectations for
seismic instrumentation on future planetary missions : while small geophones
can discriminate terrestrial activity rates, observations with guidance
accelerometers are typically too insensitive to provide meaningful constraints
unless operated for long periods. | [
0,
1,
0,
0,
0,
0
] |
Title: On Algebraic Characterization of SSC of the Jahangir's Graph $\mathcal{J}_{n,m}$,
Abstract: In this paper, some algebraic and combinatorial characterizations of the
spanning simplicial complex $\Delta_s(\mathcal{J}_{n,m})$ of the Jahangir's
graph $\mathcal{J}_{n,m}$ are explored. We show that
$\Delta_s(\mathcal{J}_{n,m})$ is pure, present the formula for $f$-vectors
associated to it and hence deduce a recipe for computing the Hilbert series of
the Face ring $k[\Delta_s(\mathcal{J}_{n,m})]$. Finaly, we show that the face
ring of $\Delta_s(\mathcal{J}_{n,m})$ is Cohen-Macaulay and give some open
scopes of the current work. | [
0,
0,
1,
0,
0,
0
] |
Title: Escaping the Curse of Dimensionality in Similarity Learning: Efficient Frank-Wolfe Algorithm and Generalization Bounds,
Abstract: Similarity and metric learning provides a principled approach to construct a
task-specific similarity from weakly supervised data. However, these methods
are subject to the curse of dimensionality: as the number of features grows
large, poor generalization is to be expected and training becomes intractable
due to high computational and memory costs. In this paper, we propose a
similarity learning method that can efficiently deal with high-dimensional
sparse data. This is achieved through a parameterization of similarity
functions by convex combinations of sparse rank-one matrices, together with the
use of a greedy approximate Frank-Wolfe algorithm which provides an efficient
way to control the number of active features. We show that the convergence rate
of the algorithm, as well as its time and memory complexity, are independent of
the data dimension. We further provide a theoretical justification of our
modeling choices through an analysis of the generalization error, which depends
logarithmically on the sparsity of the solution rather than on the number of
features. Our experiments on datasets with up to one million features
demonstrate the ability of our approach to generalize well despite the high
dimensionality as well as its superiority compared to several competing
methods. | [
1,
0,
0,
1,
0,
0
] |
Title: Automata-Guided Hierarchical Reinforcement Learning for Skill Composition,
Abstract: Skills learned through (deep) reinforcement learning often generalizes poorly
across domains and re-training is necessary when presented with a new task. We
present a framework that combines techniques in \textit{formal methods} with
\textit{reinforcement learning} (RL). The methods we provide allows for
convenient specification of tasks with logical expressions, learns hierarchical
policies (meta-controller and low-level controllers) with well-defined
intrinsic rewards, and construct new skills from existing ones with little to
no additional exploration. We evaluate the proposed methods in a simple grid
world simulation as well as a more complicated kitchen environment in AI2Thor | [
1,
0,
0,
0,
0,
0
] |
Title: Emergent transport in a many-body open system driven by interacting quantum baths,
Abstract: We analyze an open many-body system that is strongly coupled at its
boundaries to interacting quantum baths. We show that the two-body interactions
inside the baths induce emergent phenomena in the spin transport. The system
and baths are modeled as independent spin chains resulting in a global
non-homogeneous XXZ model. The evolution of the system-bath state is simulated
using matrix-product-states methods. We present two phase transitions induced
by bath interactions. For weak bath interactions we observe ballistic and
insulating phases. However, for strong bath interactions a diffusive phase
emerges with a distinct power-law decay of the time-dependent spin current
$Q\propto t^{-\alpha}$. Furthermore, we investigate long-lasting current
oscillations arising from the non-Markovian dynamics in the homogeneous case,
and find a sharp change in their frequency scaling coinciding with the triple
point of the phase diagram. | [
0,
1,
0,
0,
0,
0
] |
Title: GdPtPb: A non collinear antiferromagnet with distorted Kagomé lattice,
Abstract: In the spirit of searching for Gd-based, frustrated, rare earth magnets, we
have found antiferomagnetism (AF) in GdPtPb which crystallizes in the
ZrNiAl-type structure that has a distorted Kagomé lattice of Gd-triangles.
Single crystals were grown and investigated using structural, magnetic,
transport and thermodynamic measurements. GdPtPb orders antiferromagnetically
at 15.5 K arguably with a planar, non-collinear structure. The high temperature
magnetic susceptibility data reveal an "anti-frustration" behavior having a
frustration parameter, $|f|$ = $|\Theta|$/ $T_N$ = 0.25, which can be explained
by mean field theory (MFT) within a two sub-lattice model. Study of the
magnetic phase diagram down to $T$ = 1.8 K reveals a change of magnetic
structure through a metamagnetic transition at around 20 kOe and the
disappearance of the AF ordering near 140 kOe. In total, our work indicates
that, GdPtPb can serve as an example of a planar, non collinear, AF with a
distorted Kagomé magnetic sub-lattice. | [
0,
1,
0,
0,
0,
0
] |
Title: Weakly nonergodic dynamics in the Gross--Pitaevskii lattice,
Abstract: The microcanonical Gross--Pitaevskii (aka semiclassical Bose-Hubbard) lattice
model dynamics is characterized by a pair of energy and norm densities. The
grand canonical Gibbs distribution fails to describe a part of the density
space, due to the boundedness of its kinetic energy spectrum. We define
Poincare equilibrium manifolds and compute the statistics of microcanonical
excursion times off them. The tails of the distribution functions quantify the
proximity of the many-body dynamics to a weakly-nonergodic phase, which occurs
when the average excursion time is infinite. We find that a crossover to
weakly-nonergodic dynamics takes place inside the nonGibbs phase, being
unnoticed by the largest Lyapunov exponent. In the ergodic part of the
non-Gibbs phase, the Gibbs distribution should be replaced by an unknown
modified one. We relate our findings to the corresponding integrable limit,
close to which the actions are interacting through a short range coupling
network. | [
0,
1,
0,
0,
0,
0
] |
Title: Minimax Optimal Rates of Estimation in Functional ANOVA Models with Derivatives,
Abstract: We establish minimax optimal rates of convergence for nonparametric
estimation in functional ANOVA models when data from first-order partial
derivatives are available. Our results reveal that partial derivatives can
improve convergence rates for function estimation with deterministic or random
designs. In particular, for full $d$-interaction models, the optimal rates with
first-order partial derivatives on $p$ covariates are identical to those for
$(d-p)$-interaction models without partial derivatives. For additive models,
the rates by using all first-order partial derivatives are root-$n$ to achieve
the "parametric rate". We also investigate the minimax optimal rates for
first-order partial derivative estimations when derivative data are available.
Those rates coincide with the optimal rate for estimating the first-order
derivative of a univariate function. | [
0,
0,
1,
1,
0,
0
] |
Title: Phase Retrieval via Randomized Kaczmarz: Theoretical Guarantees,
Abstract: We consider the problem of phase retrieval, i.e. that of solving systems of
quadratic equations. A simple variant of the randomized Kaczmarz method was
recently proposed for phase retrieval, and it was shown numerically to have a
computational edge over state-of-the-art Wirtinger flow methods. In this paper,
we provide the first theoretical guarantee for the convergence of the
randomized Kaczmarz method for phase retrieval. We show that it is sufficient
to have as many Gaussian measurements as the dimension, up to a constant
factor. Along the way, we introduce a sufficient condition on measurement sets
for which the randomized Kaczmarz method is guaranteed to work. We show that
Gaussian sampling vectors satisfy this property with high probability; this is
proved using a chaining argument coupled with bounds on VC dimension and metric
entropy. | [
1,
0,
1,
1,
0,
0
] |
Title: Beyond perturbation 1: de Rham spaces,
Abstract: It is shown that if one uses the notion of infinity nilpotent elements due to
Moerdijk and Reyes, instead of the usual definition of nilpotents to define
reduced $C^\infty$-schemes, the resulting de Rham spaces are given as quotients
by actions of germs of diagonals, instead of the formal neighbourhoods of the
diagonals. | [
0,
0,
1,
0,
0,
0
] |
Title: Semantic Web Prefetching Using Semantic Relatedness between Web pages,
Abstract: Internet as become the way of life in the fast growing digital life.Even with
the increase in the internet speed, higher latency time is still a challenge.
To reduce latency, caching and pre fetching techniques can be used. However,
caching fails for dynamic websites which keeps on changing rapidly. Another
technique is web prefetching, which prefetches the web pages that the user is
likely to request for in the future. Semantic web prefetching makes use of
keywords and descriptive texts like anchor text, titles, text surrounding
anchor text of the present web pages for predicting users future requests.
Semantic information is embedded within the web pages during their designing
for the purpose of reflecting the relationship between the web pages. The
client can fetch this information from the server. However, this technique
involves load on web designers for adding external tags and on server for
providing this information along with the desired page, which is not desirable.
This paper is an effort to find the semantic relation between web pages using
the keywords provided by the user and the anchor texts of the hyperlinks on the
present web page.It provides algorithms for sequential and similar semantic
relations. These algorithms will be implemented on the client side which will
not cause overhead on designers and load on server for semantic information. | [
1,
0,
0,
0,
0,
0
] |
Title: Learning Certifiably Optimal Rule Lists for Categorical Data,
Abstract: We present the design and implementation of a custom discrete optimization
technique for building rule lists over a categorical feature space. Our
algorithm produces rule lists with optimal training performance, according to
the regularized empirical risk, with a certificate of optimality. By leveraging
algorithmic bounds, efficient data structures, and computational reuse, we
achieve several orders of magnitude speedup in time and a massive reduction of
memory consumption. We demonstrate that our approach produces optimal rule
lists on practical problems in seconds. Our results indicate that it is
possible to construct optimal sparse rule lists that are approximately as
accurate as the COMPAS proprietary risk prediction tool on data from Broward
County, Florida, but that are completely interpretable. This framework is a
novel alternative to CART and other decision tree methods for interpretable
modeling. | [
0,
0,
0,
1,
0,
0
] |
Title: Piezoresponse of ferroelectric films in ferroionic states: time and voltage dynamics,
Abstract: The interplay between electrochemical surface charges and bulk
ferroelectricity in thin films gives rise to a continuum of coupled ferro-ionic
states. These states are exquisitely sensitive to chemical and electric
conditions at the surfaces, applied voltage, and oxygen pressure. Using the
analytical approach combining the Ginzburg-Landau-Devonshire description of the
ferroelectricity with Langmuir adsorption isotherm for the ions at the film
surface, we have studied the temperature-, time- and field- dependent
polarization changes and electromechanical response of the ferro-ionic states.
The responses are found to be inseparable in thermodynamic equilibrium and at
low frequencies of applied voltage. The states become separable in high
frequency dynamic mode due to the several orders of magnitude difference in the
relaxation times of ferroelectric polarization and surface ions charge density.
These studies provide an insight into dynamic behavior of nanoscale
ferroelectrics with open surface exposed to different kinds of
electrochemically active gaseous surrounding. | [
0,
1,
0,
0,
0,
0
] |
Title: Penalty Alternating Direction Methods for Mixed-Integer Optimization: A New View on Feasibility Pumps,
Abstract: Feasibility pumps are highly effective primal heuristics for mixed-integer
linear and nonlinear optimization. However, despite their success in practice
there are only few works considering their theoretical properties. We show that
feasibility pumps can be seen as alternating direction methods applied to
special reformulations of the original problem, inheriting the convergence
theory of these methods. Moreover, we propose a novel penalty framework that
encompasses this alternating direction method, which allows us to refrain from
random perturbations that are applied in standard versions of feasibility pumps
in case of failure. We present a convergence theory for the new penalty based
alternating direction method and compare the new variant of the feasibility
pump with existing versions in an extensive numerical study for mixed-integer
linear and nonlinear problems. | [
0,
0,
1,
0,
0,
0
] |
Title: Asymptotic control theory for a closed string,
Abstract: We develop an asymptotical control theory for one of the simplest distributed
oscillating systems, namely, for a closed string under a bounded load applied
to a single distinguished point. We find exact classes of string states that
admit complete damping and an asymptotically exact value of the required time.
By using approximate reachable sets instead of exact ones, we design a
dry-friction like feedback control, which turns out to be asymptotically
optimal. We prove the existence of motion under the control using a rather
explicit solution of a nonlinear wave equation. Remarkably, the solution is
determined via purely algebraic operations. The main result is a proof of
asymptotic optimality of the control thus constructed. | [
0,
0,
1,
0,
0,
0
] |
Title: Intrinsic pinning by naturally occurring correlated defects in FeSe$_\text{1-x}$Te$_\text{x}$ superconductors,
Abstract: We study the angular dependence of the dissipation in the superconducting
state of FeSe and Fe(Se$_\text{1-x}$Te$_\text{x}$) through electrical transport
measurements, using crystalline intergrown materials. We reveal the key role of
the inclusions of the non superconducting magnetic phase
Fe$_\text{1-y}$(Se$_\text{1-x}$Te$_\text{x}$), growing into the
Fe(Se$_\text{1-x}$Te$_\text{x}$) pure $\beta$-phase, in the development of a
correlated defect structure. The matching of both atomic structures defines the
growth habit of the crystalline material as well as the correlated planar
defects orientation. | [
0,
1,
0,
0,
0,
0
] |
Title: Models for the Displacement Calculus,
Abstract: The displacement calculus $\mathbf{D}$ is a conservative extension of the
Lambek calculus $\mathbf{L1}$ (with empty antecedents allowed in sequents).
$\mathbf{L1}$ can be said to be the logic of concatenation, while $\mathbf{D}$
can be said to be the logic of concatenation and intercalation. In many senses,
it can be claimed that $\mathbf{D}$ mimics $\mathbf{L1}$ in that the proof
theory, generative capacity and complexity of the former calculus are natural
extensions of the latter calculus. In this paper, we strengthen this claim. We
present the appropriate classes of models for $\mathbf{D}$ and prove some
completeness results; strikingly, we see that these results and proofs are
natural extensions of the corresponding ones for $\mathbf{L1}$. | [
1,
0,
0,
0,
0,
0
] |
Title: A Maximum Matching Algorithm for Basis Selection in Spectral Learning,
Abstract: We present a solution to scale spectral algorithms for learning sequence
functions. We are interested in the case where these functions are sparse (that
is, for most sequences they return 0). Spectral algorithms reduce the learning
problem to the task of computing an SVD decomposition over a special type of
matrix called the Hankel matrix. This matrix is designed to capture the
relevant statistics of the training sequences. What is crucial is that to
capture long range dependencies we must consider very large Hankel matrices.
Thus the computation of the SVD becomes a critical bottleneck. Our solution
finds a subset of rows and columns of the Hankel that realizes a compact and
informative Hankel submatrix. The novelty lies in the way that this subset is
selected: we exploit a maximal bipartite matching combinatorial algorithm to
look for a sub-block with full structural rank, and show how computation of
this sub-block can be further improved by exploiting the specific structure of
Hankel matrices. | [
1,
0,
0,
1,
0,
0
] |
Title: Spin inversion in fluorinated graphene n-p junction,
Abstract: We consider a dilute fluorinated graphene nanoribbon as a spin-active
element. The fluorine adatoms introduce a local spin-orbit Rashba interaction
that induces spin-precession for electron passing by. In the absence of the
external magnetic field the transport is dominated by multiple scattering by
adatoms which cancels the spin precession effects, since the direction of the
spin precession depends on the electron momentum. Accumulation of the spin
precession effects is possible provided that the Fermi level electron passes
many times near the same adatom with the same momentum. In order to arrange for
these conditions a circular n-p junction can be introduced to the ribbon by
e.g. potential of the tip of an atomic force microscope. In the quantum Hall
conditions the electron current gets confined along the junction. The electron
spin interaction with the local Rashba field changes with the lifetime of the
quasi-bound states that is controlled with the coupling of the junction to the
edge of the ribbon. We demonstrate that the spin-flip probability can be
increased in this manner by as much as three orders of magnitude. | [
0,
1,
0,
0,
0,
0
] |
Title: Degenerate cyclotomic Hecke algebras and higher level Heisenberg categorification,
Abstract: We associate a monoidal category $\mathcal{H}^\lambda$ to each dominant
integral weight $\lambda$ of $\widehat{\mathfrak{sl}}_p$ or
$\mathfrak{sl}_\infty$. These categories, defined in terms of planar diagrams,
act naturally on categories of modules for the degenerate cyclotomic Hecke
algebras associated to $\lambda$. We show that, in the $\mathfrak{sl}_\infty$
case, the level $d$ Heisenberg algebra embeds into the Grothendieck ring of
$\mathcal{H}^\lambda$, where $d$ is the level of $\lambda$. The categories
$\mathcal{H}^\lambda$ can be viewed as a graphical calculus describing
induction and restriction functors between categories of modules for degenerate
cyclotomic Hecke algebras, together with their natural transformations. As an
application of this tool, we prove a new result concerning centralizers for
degenerate cyclotomic Hecke algebras. | [
0,
0,
1,
0,
0,
0
] |
Title: The spectra of harmonic layer potential operators on domains with rotationally symmetric conical points,
Abstract: We study the adjoint of the double layer potential associated with the
Laplacian (the adjoint of the Neumann-Poincaré operator), as a map on the
boundary surface $\Gamma$ of a domain in $\mathbb{R}^3$ with conical points.
The spectrum of this operator directly reflects the well-posedness of related
transmission problems across $\Gamma$. In particular, if the domain is
understood as an inclusion with complex permittivity $\epsilon$, embedded in a
background medium with unit permittivity, then the polarizability tensor of the
domain is well-defined when $(\epsilon+1)/(\epsilon-1)$ belongs to the
resolvent set in energy norm. We study surfaces $\Gamma$ that have a finite
number of conical points featuring rotational symmetry. On the energy space, we
show that the essential spectrum consists of an interval. On $L^2(\Gamma)$,
i.e. for square-integrable boundary data, we show that the essential spectrum
consists of a countable union of curves, outside of which the Fredholm index
can be computed as a winding number with respect to the essential spectrum. We
provide explicit formulas, depending on the opening angles of the conical
points. We reinforce our study with very precise numerical experiments,
computing the energy space spectrum and the spectral measures of the
polarizability tensor in two different examples. Our results indicate that the
densities of the spectral measures may approach zero extremely rapidly in the
continuous part of the energy space spectrum. | [
0,
0,
1,
0,
0,
0
] |
Title: Onset of a modulational instability in trapped dipolar Bose-Einstein condensates,
Abstract: We explore the phase diagram of a finite-sized dysprosium dipolar
Bose-Einstein condensate in a cylindrical harmonic trap. We monitor the final
state after the scattering length is lowered from the repulsive BEC regime to
the quantum droplet regime. Either an adiabatic transformation between a BEC
and a quantum droplet is obtained or, above a critical trap aspect ratio
$\lambda_{\rm c}=1.87(14)$, a modulational instability results in the formation
of multiple droplets. This is in full agreement with the predicted structure of
the phase diagram with a crossover region below $\lambda_{\rm c}$ and a
multistable region above. Our results provide the missing piece connecting the
previously explored regimes resulting in a single or multiple dipolar quantum
droplets. | [
0,
1,
0,
0,
0,
0
] |
Title: Visual Detection of Structural Changes in Time-Varying Graphs Using Persistent Homology,
Abstract: Topological data analysis is an emerging area in exploratory data analysis
and data mining. Its main tool, persistent homology, has become a popular
technique to study the structure of complex, high-dimensional data. In this
paper, we propose a novel method using persistent homology to quantify
structural changes in time-varying graphs. Specifically, we transform each
instance of the time-varying graph into metric spaces, extract topological
features using persistent homology, and compare those features over time. We
provide a visualization that assists in time-varying graph exploration and
helps to identify patterns of behavior within the data. To validate our
approach, we conduct several case studies on real world data sets and show how
our method can find cyclic patterns, deviations from those patterns, and
one-time events in time-varying graphs. We also examine whether
persistence-based similarity measure as a graph metric satisfies a set of
well-established, desirable properties for graph metrics. | [
1,
0,
0,
0,
0,
0
] |
Title: Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion,
Abstract: Computed Tomography (CT) reconstruction is a fundamental component to a wide
variety of applications ranging from security, to healthcare. The classical
techniques require measuring projections, called sinograms, from a full
180$^\circ$ view of the object. This is impractical in a limited angle
scenario, when the viewing angle is less than 180$^\circ$, which can occur due
to different factors including restrictions on scanning time, limited
flexibility of scanner rotation, etc. The sinograms obtained as a result, cause
existing techniques to produce highly artifact-laden reconstructions. In this
paper, we propose to address this problem through implicit sinogram completion,
on a challenging real world dataset containing scans of common checked-in
luggage. We propose a system, consisting of 1D and 2D convolutional neural
networks, that operates on a limited angle sinogram to directly produce the
best estimate of a reconstruction. Next, we use the x-ray transform on this
reconstruction to obtain a "completed" sinogram, as if it came from a full
180$^\circ$ measurement. We feed this to standard analytical and iterative
reconstruction techniques to obtain the final reconstruction. We show with
extensive experimentation that this combined strategy outperforms many
competitive baselines. We also propose a measure of confidence for the
reconstruction that enables a practitioner to gauge the reliability of a
prediction made by our network. We show that this measure is a strong indicator
of quality as measured by the PSNR, while not requiring ground truth at test
time. Finally, using a segmentation experiment, we show that our reconstruction
preserves the 3D structure of objects effectively. | [
0,
0,
0,
1,
0,
0
] |
Title: Introduction to Tensor Decompositions and their Applications in Machine Learning,
Abstract: Tensors are multidimensional arrays of numerical values and therefore
generalize matrices to multiple dimensions. While tensors first emerged in the
psychometrics community in the $20^{\text{th}}$ century, they have since then
spread to numerous other disciplines, including machine learning. Tensors and
their decompositions are especially beneficial in unsupervised learning
settings, but are gaining popularity in other sub-disciplines like temporal and
multi-relational data analysis, too.
The scope of this paper is to give a broad overview of tensors, their
decompositions, and how they are used in machine learning. As part of this, we
are going to introduce basic tensor concepts, discuss why tensors can be
considered more rigid than matrices with respect to the uniqueness of their
decomposition, explain the most important factorization algorithms and their
properties, provide concrete examples of tensor decomposition applications in
machine learning, conduct a case study on tensor-based estimation of mixture
models, talk about the current state of research, and provide references to
available software libraries. | [
1,
0,
0,
1,
0,
0
] |
Title: Concurrent Coding: A Reason to Think Differently About Encoding Against Noise, Burst Errors and Jamming,
Abstract: Concurrent coding is an unconventional encoding technique that simultaneously
provides protection against noise, burst errors and interference. This
simple-to-understand concept is investigated by distinguishing 2 types of code,
open and closed, with the majority of the investigation concentrating on closed
codes. Concurrent coding is shown to possess an inherent method of
synchronisation thus requiring no additional synchronisation signals to be
added. This enables an isolated codeword transmission to be synchronised and
decoded in the presence of noise and burst errors. Comparisons are made with
the spread spectrum technique CDMA. With a like-for-like comparison concurrent
coding performs comparably against random noise effects, performs better
against burst errors and is far superior in terms of transmitted energy
efficiency | [
1,
0,
0,
0,
0,
0
] |
Title: Traveling dark-bright solitons in a reduced spin-orbit coupled system: application to Bose-Einstein condensates,
Abstract: In the present work, we explore the potential of spin-orbit (SO) coupled
Bose-Einstein condensates to support multi-component solitonic states in the
form of dark-bright (DB) solitons. In the case where Raman linear coupling
between components is absent, we use a multiscale expansion method to reduce
the model to the integrable Mel'nikov system. The soliton solutions of the
latter allow us to reconstruct approximate traveling DB solitons for the
reduced SO coupled system. For small values of the formal perturbation
parameter, the resulting waveforms propagate undistorted, while for large
values thereof, they shed some dispersive radiation, and subsequently distill
into a robust propagating structure. After quantifying the relevant radiation
effect, we also study the dynamics of DB solitons in a parabolic trap,
exploring how their oscillation frequency varies as a function of the bright
component mass and the Raman laser wavenumber. | [
0,
1,
0,
0,
0,
0
] |
Title: On methods to determine bounds on the Q-factor for a given directivity,
Abstract: This paper revisit and extend the interesting case of bounds on the Q-factor
for a given directivity for a small antenna of arbitrary shape. A higher
directivity in a small antenna is closely connected with a narrow impedance
bandwidth. The relation between bandwidth and a desired directivity is still
not fully understood, not even for small antennas. Initial investigations in
this direction has related the radius of a circumscribing sphere to the
directivity, and bounds on the Q-factor has also been derived for a partial
directivity in a given direction. In this paper we derive lower bounds on the
Q-factor for a total desired directivity for an arbitrarily shaped antenna in a
given direction as a convex problem using semi-definite relaxation techniques
(SDR). We also show that the relaxed solution is also a solution of the
original problem of determining the lower Q-factor bound for a total desired
directivity.
SDR can also be used to relax a class of other interesting non-convex
constraints in antenna optimization such as tuning, losses, front-to-back
ratio. We compare two different new methods to determine the lowest Q-factor
for arbitrary shaped antennas for a given total directivity. We also compare
our results with full EM-simulations of a parasitic element antenna with high
directivity. | [
0,
1,
1,
0,
0,
0
] |
Title: Electron paramagnetic resonance and photochromism of $\mathrm{N}_{3}\mathrm{V}^{0}$ in diamond,
Abstract: The defect in diamond formed by a vacancy surrounded by three
nearest-neighbor nitrogen atoms and one carbon atom,
$\mathrm{N}_{3}\mathrm{V}$, is found in $\approx98\%$ of natural diamonds.
Despite $\mathrm{N}_{3}\mathrm{V}^{0}$ being the earliest electron paramagnetic
resonance spectrum observed in diamond, to date no satisfactory simulation of
the spectrum for an arbitrary magnetic field direction has been produced due to
its complexity. In this work, $\mathrm{N}_{3}\mathrm{V}^{0}$ is identified in
$^{15}\mathrm{N}$-doped synthetic diamond following irradiation and annealing.
The $\mathrm{^{15}N}_{3}\mathrm{V}^{0}$ spin Hamiltonian parameters are revised
and used to refine the parameters for $\mathrm{^{14}N}_{3}\mathrm{V}^{0}$,
enabling the latter to be accurately simulated and fitted for an arbitrary
magnetic field direction. Study of $\mathrm{^{15}N}_{3}\mathrm{V}^{0}$ under
excitation with green light indicates charge transfer between
$\mathrm{N}_{3}\mathrm{V}$ and $\mathrm{N_s}$. It is argued that this charge
transfer is facilitated by direct ionization of $\mathrm{N}_{3}\mathrm{V}^{-}$,
an as-yet unobserved charge state of $\mathrm{N}_{3}\mathrm{V}$. | [
0,
1,
0,
0,
0,
0
] |
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