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FA*IR: A Fair Top-k Ranking Algorithm | In this work, we define and solve the Fair Top-k Ranking problem, in which we
want to determine a subset of k candidates from a large pool of n >> k
candidates, maximizing utility (i.e., select the "best" candidates) subject to
group fairness criteria. Our ranked group fairness definition extends group
fairness using the standard notion of protected groups and is based on ensuring
that the proportion of protected candidates in every prefix of the top-k
ranking remains statistically above or indistinguishable from a given minimum.
Utility is operationalized in two ways: (i) every candidate included in the
top-$k$ should be more qualified than every candidate not included; and (ii)
for every pair of candidates in the top-k, the more qualified candidate should
be ranked above. An efficient algorithm is presented for producing the Fair
Top-k Ranking, and tested experimentally on existing datasets as well as new
datasets released with this paper, showing that our approach yields small
distortions with respect to rankings that maximize utility without considering
fairness criteria.
To the best of our knowledge, this is the first algorithm grounded in
statistical tests that can mitigate biases in the representation of an
under-represented group along a ranked list.
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A novel delayed-choice experimental proposal testing local decisions | Entangled states are notoriously non-separable, their sub-ensembles being
only statistical mixtures yielding no coherences and no quantum interference
phenomena. The interesting features of entangled states can be revealed only by
coincidence counts over the (typically) two sub-ensembles of the system. In
this paper we show that this feature extends to properties thought to be local,
for example the transmissivity coefficient of a beam splitter. We discuss a
well-known experimental setup and propose modifications, so that delayed-choice
can be added and this new feature of entanglement tested.
| 0 | 1 | 0 | 0 | 0 | 0 |
Better accuracy with quantified privacy: representations learned via reconstructive adversarial network | The remarkable success of machine learning, especially deep learning, has
produced a variety of cloud-based services for mobile users. Such services
require an end user to send data to the service provider, which presents a
serious challenge to end-user privacy. To address this concern, prior works
either add noise to the data or send features extracted from the raw data. They
struggle to balance between the utility and privacy because added noise reduces
utility and raw data can be reconstructed from extracted features. This work
represents a methodical departure from prior works: we balance between a
measure of privacy and another of utility by leveraging adversarial learning to
find a sweeter tradeoff. We design an encoder that optimizes against the
reconstruction error (a measure of privacy), adversarially by a Decoder, and
the inference accuracy (a measure of utility) by a Classifier. The result is
RAN, a novel deep model with a new training algorithm that automatically
extracts features for classification that are both private and useful. It turns
out that adversarially forcing the extracted features to only conveys the
intended information required by classification leads to an implicit
regularization leading to better classification accuracy than the original
model which completely ignores privacy. Thus, we achieve better privacy with
better utility, a surprising possibility in machine learning! We conducted
extensive experiments on five popular datasets over four training schemes, and
demonstrate the superiority of RAN compared with existing alternatives.
| 1 | 0 | 0 | 1 | 0 | 0 |
Compositional descriptor-based recommender system accelerating the materials discovery | Structures and properties of many inorganic compounds have been collected
historically. However, it only covers a very small portion of possible
inorganic crystals, which implies the presence of numerous currently unknown
compounds. A powerful machine-learning strategy is mandatory to discover new
inorganic compounds from all chemical combinations. Herein we propose a
descriptor-based recommender-system approach to estimate the relevance of
chemical compositions where stable crystals can be formed [i.e., chemically
relevant compositions (CRCs)]. As well as data-driven compositional similarity
used in the literature, the use of compositional descriptors as a prior
knowledge can accelerate the discovery of new compounds. We validate our
recommender systems in two ways. Firstly, one database is used to construct a
model, while another is used for the validation. Secondly, we estimate the
phase stability for compounds at expected CRCs using density functional theory
calculations.
| 0 | 1 | 0 | 0 | 0 | 0 |
Structure Formation and Microlensing with Axion Miniclusters | If the symmetry breaking responsible for axion dark matter production occurs
during the radiation-dominated epoch in the early Universe, then this produces
large amplitude perturbations that collapse into dense objects known as axion
miniclusters. The characteristic minicluster mass, $M_0$, is set by the mass
inside the horizon when axion oscillations begin. For the QCD axion $M_0\sim
10^{-10}M_\odot$, however for an axion-like particle $M_0$ can approach
$M_\odot$ or higher. Using the Press-Schechter formalism we compute the mass
function of halos formed by hierarchical structure formation from these seeds.
We compute the concentrations and collapse times of these halos and show that
they can grow to be as massive as $10^6M_0$. Within the halos, miniclusters
likely remain tightly bound, and we compute their gravitational microlensing
signal taking the fraction of axion dark matter collapsed into miniclusters,
$f_{\rm MC}$, as a free parameter. A large value of $f_{\rm MC}$ severely
weakens constraints on axion scenarios from direct detection experiments. We
take into account the non-Gaussian distribution of sizes of miniclusters and
determine how this effects the number of microlensing events. We develop the
tools to consider microlensing by an extended mass function of non-point-like
objects, and use microlensing data to place the first observational constraints
on $f_{\rm MC}$. This opens a new window for the potential discovery of the
axion.
| 0 | 1 | 0 | 0 | 0 | 0 |
Clustering Residential Electricity Load Curves via Community Detection in Network | Performing analytic of household load curves (LCs) has significant value in
predicting individual electricity consumption patterns, and hence facilitate
developing demand-response strategy, and finally achieve energy efficiency
improvement and emission reduction. LC clustering is a widely used analytic
technology, which discovers electricity consumption patterns by grouping
similar LCs into same sub-groups. However, previous clustering methods treat
each LC in the data set as an individual time series, ignoring the inherent
relationship among different LCs, restraining the performance of the clustering
methods. What's more, due to the significant volatility and uncertainty of LCs,
the previous LC clustering approaches inevitably result in either lager number
of clusters or huge variances within a cluster, which is unacceptable for
actual application needs. In this paper, we proposed an integrated approach to
address this issue. First, we converted the LC clustering task into the
community detection task in network. Second, we proposed a clustering approach
incorporated with community detection to improve the performance of LC
clustering, which includes network construction, community detection and
typical load profile extraction. The method convert the data set into a network
in which the inherent relationship among LCs is represented by the edges of the
network. Third, we construct a multi-layer typical load profile directory to
make the trade-off between variances within a cluster and the number of the
clusters, enabling the researchers to assess the LCs and the customers in
different layers according to practical application requirements. The
experiments demonstrate that our integrated approach outperform the
state-of-the-art methods.
| 1 | 0 | 0 | 0 | 0 | 0 |
Lie and Noether point Symmetries for a Class of Nonautonomous Dynamical Systems | We prove two general theorems which determine the Lie and the Noether point
symmetries for the equations of motion of a dynamical system which moves in a
general Riemannian space under the action of a time dependent potential
$W(t,x)=\omega(t)V(x)$. We apply the theorems to the case of a time dependent
central potential and the harmonic oscillator and determine all Lie and Noether
point symmetries. Finally we prove that these theorems also apply to the case
of a dynamical system with linear dumping and study two examples.
| 0 | 0 | 1 | 0 | 0 | 0 |
Thermal Inflation with a Thermal Waterfall Scalar Field Coupled to a Light Spectator Scalar Field | A new model of thermal inflation is introduced, in which the mass of the
thermal waterfall field is dependent on a light spectator scalar field. Using
the $\delta N$ formalism, the "end of inflation" scenario is investigated in
order to ascertain whether this model is able to produce the dominant
contribution to the primordial curvature perturbation. A multitude of
constrains are considered so as to explore the parameter space, with particular
emphasis to key observational signatures. For natural values of the parameters,
the model is found to yield a sharp prediction for the scalar spectral index
and its running, well within the current observational bounds.
| 0 | 1 | 0 | 0 | 0 | 0 |
DeepMoTIon: Learning to Navigate Like Humans | We present a novel human-aware navigation approach, where the robot learns to
mimic humans to navigate safely in crowds. The presented model referred to as
DeepMoTIon, is trained with pedestrian surveillance data to predict human
velocity. The robot processes LiDAR scans via the trained network to navigate
to the target location. We conduct extensive experiments to assess the
different components of our network and prove the necessity of each to imitate
humans. Our experiments show that DeepMoTIon outperforms state-of-the-art in
terms of human imitation and reaches the target on 100% of the test cases
without breaching humans' safe distance.
| 1 | 0 | 0 | 1 | 0 | 0 |
Multidimensional Free Poisson Limits on Free Stochastic Integral Algebras | In this paper, we prove four-moment theorems for multidimensional free
Poisson limits on free Wigner chaos or the free Poisson algebra. We prove that,
under mild technical conditions, a bi-indexed sequence of free stochastic
integrals in free Wigner algebra or free Poisson algebra converges to a free
sequence of free Poisson random variables if and only if the moments with order
not greater than four of the sequence converge to the corresponding moments of
the limit sequence of random variables. Similar four-moment theorems hold when
the limit sequence is not free, but has a multidimensional free Poisson
distribution with parameters $\lambda>0$ and $\alpha=\{\alpha_i: 0\ne
\alpha_i\in \mathbb{R}, i=1, 2, \cdots\}$.
| 0 | 0 | 1 | 0 | 0 | 0 |
On the finite $W$-algebra for the Lie superalgebra Q(N) in the non-regular case | In this paper we study the finite W-algebra for the queer Lie superalgebra
Q(n) associated with the non-regular even nilpotent coadjoint orbits in the
case when the corresponding nilpotent element has Jordan blocks each of size l.
We prove that this finite W-algebra is isomorphic to a quotient of the
super-Yangian of Q({n/l})
| 0 | 0 | 1 | 0 | 0 | 0 |
SNeCT: Scalable network constrained Tucker decomposition for integrative multi-platform data analysis | Motivation: How do we integratively analyze large-scale multi-platform
genomic data that are high dimensional and sparse? Furthermore, how can we
incorporate prior knowledge, such as the association between genes, in the
analysis systematically? Method: To solve this problem, we propose a Scalable
Network Constrained Tucker decomposition method we call SNeCT. SNeCT adopts
parallel stochastic gradient descent approach on the proposed parallelizable
network constrained optimization function. SNeCT decomposition is applied to
tensor constructed from large scale multi-platform multi-cohort cancer data,
PanCan12, constrained on a network built from PathwayCommons database. Results:
The decomposed factor matrices are applied to stratify cancers, to search for
top-k similar patients, and to illustrate how the matrices can be used for
personalized interpretation. In the stratification test, combined twelve-cohort
data is clustered to form thirteen subclasses. The thirteen subclasses have a
high correlation to tissue of origin in addition to other interesting
observations, such as clear separation of OV cancers to two groups, and high
clinical correlation within subclusters formed in cohorts BRCA and UCEC. In the
top-k search, a new patient's genomic profile is generated and searched against
existing patients based on the factor matrices. The similarity of the top-k
patient to the query is high for 23 clinical features, including
estrogen/progesterone receptor statuses of BRCA patients with average precision
value ranges from 0.72 to 0.86 and from 0.68 to 0.86, respectively. We also
provide an illustration of how the factor matrices can be used for
interpretable personalized analysis of each patient.
| 1 | 0 | 0 | 1 | 0 | 0 |
CANAL: A Cache Timing Analysis Framework via LLVM Transformation | A unified modeling framework for non-functional properties of a program is
essential for research in software analysis and verification, since it reduces
burdens on individual researchers to implement new approaches and compare
existing approaches. We present CANAL, a framework that models the cache
behaviors of a program by transforming its intermediate representation in the
LLVM compiler. CANAL inserts auxiliary variables and instructions over these
variables, to allow standard verification tools to handle a new class of cache
related properties, e.g., for computing the worst-case execution time and
detecting side-channel leaks. We demonstrate the effectiveness of CANAL using
three verification tools: KLEE, SMACK and Crab-llvm. We confirm the accuracy of
our cache model by comparing with CPU cycle-accurate simulation results of
GEM5. CANAL is available on GitHub and YouTube.
| 1 | 0 | 0 | 0 | 0 | 0 |
Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging | Obtaining magnetic resonance images (MRI) with high resolution and generating
quantitative image-based biomarkers for assessing tissue biochemistry is
crucial in clinical and research applications. How- ever, acquiring
quantitative biomarkers requires high signal-to-noise ratio (SNR), which is at
odds with high-resolution in MRI, especially in a single rapid sequence. In
this paper, we demonstrate how super-resolution can be utilized to maintain
adequate SNR for accurate quantification of the T2 relaxation time biomarker,
while simultaneously generating high- resolution images. We compare the
efficacy of resolution enhancement using metrics such as peak SNR and
structural similarity. We assess accuracy of cartilage T2 relaxation times by
comparing against a standard reference method. Our evaluation suggests that SR
can successfully maintain high-resolution and generate accurate biomarkers for
accelerating MRI scans and enhancing the value of clinical and research MRI.
| 0 | 0 | 0 | 1 | 0 | 0 |
Inverse problems in models of resource distribution | We continue to study the problem of modeling of substitution of production
factors motivated by the need for computable mathematical models of economics
that could be used as a basis in applied developments. This problem has been
studied for several decades, and several connections to complex analysis and
geometry has been established. We describe several models of resource
distribution and discuss the inverse problems for the generalized Radon
transform arising is these models. We give a simple explicit range
characterization for a particular case of the generalized Radon transform, and
we apply it to show that the most popular production functions are compatible
with these models. Besides, we give a necessary condition and a sufficient
condition for solvability of the model identification problem in the form of an
appropriate moment problem. These conditions are formulated in terms of rhombic
tilings.
| 0 | 0 | 1 | 0 | 0 | 0 |
Critical behaviour in one dimension: unconventional pairing, phase separation, BEC-BCS crossover and magnetic Lifshitz transition | We study the superconducting properties of population-imbalanced ultracold
Fermi mixtures in one-dimensional (1D) optical lattices that can be effectively
described by the spin-imbalanced attractive Hubbard model (AHM) in the presence
of a Zeeman magnetic field. We use the mean-field theory approach to obtain the
ground state phase diagrams including some unconventional superconducting
phases such as the Fulde--Ferrell--Larkin--Ovchinnikov (FFLO) phase, and the
$\eta$ phase (an extremal case of the FFLO phase), both for the case of a fixed
chemical potential and for a fixed number of particles. It allows to determine
optimal regimes for the FFLO phase as well as $\eta$-pairing stability. We also
investigate the evolution from the weak coupling (BCS-like limit) to the strong
coupling limit of tightly bound local pairs (BEC) with increasing attraction,
at $T=0$. Finally, the obtained results show that despite of the occurrence of
the Lifshitz transition induced by an external magnetic field, the
superconducting state can still exist in the system, at higher magnetic field
values.
| 0 | 1 | 0 | 0 | 0 | 0 |
Andreev reflections and the quantum physics of black holes | We establish an analogy between superconductor-metal interfaces and the
quantum physics of a black hole, using the proximity effect. We show that the
metal-superconductor interface can be thought of as an event horizon and
Andreev reflection from the interface is analogous to the Hawking radiation in
black holes. We describe quantum information transfer in Andreev reflection
with a final state projection model similar to the Horowitz-Maldacena model for
black hole evaporation. We also propose the Andreev reflection-analogue of
Hayden and Preskill's description of a black hole final state, where the black
hole is described as an information mirror. The analogy between Crossed Andreev
Reflections and Einstein-Rosen bridges is discussed: our proposal gives a
precise mechanism for the apparent loss of quantum information in a black hole
by the process of nonlocal Andreev reflection, transferring the quantum
information through a wormhole and into another universe. Given these
established connections, we conjecture that the final quantum state of a black
hole is exactly the same as the ground state wavefunction of the
superconductor/superfluid in the Bardeen-Cooper-Schrieffer (BCS) theory of
superconductivity; in particular, the infalling matter and the infalling
Hawking quanta, described in the Horowitz-Maldacena model, forms a Cooper
pair-like singlet state inside the black hole. A black hole evaporating and
shrinking in size can be thought of as the analogue of Andreev reflection by a
hole where the superconductor loses a Cooper pair. Our model does not suffer
from the black hole information problem since Andreev reflection is unitary. We
also relate the thermodynamic properties of a black hole to that of a
superconductor, and propose an experiment which can demonstrate the negative
specific heat feature of black holes in a growing/evaporating condensate.
| 0 | 1 | 0 | 0 | 0 | 0 |
Wind, Sand and Water. The Orientation of the Late Roman Forts in the Kharga Oasis (Egyptian Western Desert) | The chain of late Roman fortified settlements built in the Kharga Oasis, in
Egypt Western Desert, represents an interesting case study to analyse how the
ancient Roman town planners interacted with the landscape. A peculiar feature
of the site is the existence of a prevailing, north westerly wind, and it is
possible to identify the average azimuth of the wind by measuring the central
axes of the halfmoon shaped sand dunes which characterize the landscape. Using
the methods of Archaeoastronomy, we compared these azimuths with the orthogonal
layout of both the settlements and the agricultural installations and showed
that these are oriented on the prevailing wind. A description and the possible
implications of this <<weathervane orientation>> are discussed in this article.
| 0 | 1 | 0 | 0 | 0 | 0 |
On generalized Dold manifolds | Let $X$ be a smooth manifold with a (smooth) involution $\sigma:X\to X$ such
that $Fix(\sigma)\ne \emptyset$. We call the space $P(m,X):=\mathbb{S}^m\times
X/\!\sim$ where $(v,x)\sim (-v,\sigma(x))$ a generalized Dold manifold. When
$X$ is an almost complex manifold and the differential $T\sigma: TX\to TX$ is
conjugate complex linear on each fibre, we obtain a formula for the
Stiefel-Whitney polynomial of $P(m,X)$ when $H^1(X;\mathbb{Z}_2)=0$. We obtain
results on stable parallelizability of $P(m,X)$ and a very general criterion
for the (non) vanishing of the unoriented cobordism class $[P(m,X)]$ in terms
of the corresponding properties for $X$. These results are applied to the case
when $X$ is a complex flag manifold.
| 0 | 0 | 1 | 0 | 0 | 0 |
Pilot system development in metre-scale laboratory discharge | The pilot system development in metre-scale negative laboratory discharges is
studied with ns-fast photography. The systems appear as bipolar structures in
the vicinity of the negative high-voltage electrode. They appear as a result of
a single negative streamer propagation and determine further discharge
development. Such systems possess features like glowing beads, bipolarity,
different brightness of the top and bottom parts, and mutual reconnection. A 1D
model of the ionization evolution in the spark gap is proposed. In the process
of the nonlinear development of ionization growth, the model shows features
similar to those observed. The visual similarities between high-altitude
sprites and laboratory pilots are striking and may indicate that they are two
manifestations of the same natural phenomenon.
| 0 | 1 | 0 | 0 | 0 | 0 |
Abstract Interpretation of Binary Code with Memory Accesses using Polyhedra | In this paper we propose a novel methodology for static analysis of binary
code using abstract interpretation. We use an abstract domain based on
polyhedra and two mapping functions that associate polyhedra variables with
registers and memory. We demonstrate our methodology to the problem of
computing upper bounds to loop iterations in the code. This problem is
particularly important in the domain of Worst-Case Execution Time (WCET)
analysis of safety-critical real-time code. However, our approach is general
and it can applied to other static analysis problems.
| 1 | 0 | 0 | 0 | 0 | 0 |
Optimal learning via local entropies and sample compression | The aim of this paper is to provide several novel upper bounds on the excess
risk with a primal focus on classification problems. We suggest two approaches
and the obtained bounds are represented via the distribution dependent local
entropies of the classes or the sizes of specific sample com- pression schemes.
We show that in some cases, our guarantees are optimal up to constant factors
and outperform previously known results. As an application of our results, we
provide a new tight PAC bound for the hard-margin SVM, an extended analysis of
certain empirical risk minimizers under log-concave distributions, a new
variant of an online to batch conversion, and distribution dependent localized
bounds in the aggregation framework. We also develop techniques that allow to
replace empirical covering number or covering numbers with bracketing by the
coverings with respect to the distribution of the data. The proofs for the
sample compression schemes are based on the moment method combined with the
analysis of voting algorithms.
| 0 | 0 | 1 | 1 | 0 | 0 |
Domain Specific Semantic Validation of Schema.org Annotations | Since its unveiling in 2011, schema.org has become the de facto standard for
publishing semantically described structured data on the web, typically in the
form of web page annotations. The increasing adoption of schema.org facilitates
the growth of the web of data, as well as the development of automated agents
that operate on this data. Schema.org is a large heterogeneous vocabulary that
covers many domains. This is obviously not a bug, but a feature, since
schema.org aims to describe almost everything on the web, and the web is huge.
However, the heterogeneity of schema.org may cause a side effect, which is the
challenge of picking the right classes and properties for an annotation in a
certain domain, as well as keeping the annotation semantically consistent. In
this work, we introduce our rule based approach and an implementation of it for
validating schema.org annotations from two aspects: (a) the completeness of the
annotations in terms of a specified domain, (b) the semantic consistency of the
values based on pre-defined rules. We demonstrate our approach in the tourism
domain.
| 1 | 0 | 0 | 0 | 0 | 0 |
Vector-valued extensions of operators through multilinear limited range extrapolation | We give an extension of Rubio de Francia's extrapolation theorem for
functions taking values in UMD Banach function spaces to the multilinear
limited range setting. In particular we show how boundedness of an
$m$-(sub)linear operator \[T:L^{p_1}(w_1^{p_1})\times\cdots\times
L^{p_m}(w_m^{p_m})\to L^p(w^p) \] for a certain class of Muckenhoupt weights
yields an extension of the operator to Bochner spaces $L^{p}(w^p;X)$ for a wide
class of Banach function spaces $X$, which includes certain Lebesgue, Lorentz
and Orlicz spaces.
We apply the extrapolation result to various operators, which yields new
vector-valued bounds. Our examples include the bilinear Hilbert transform,
certain Fourier multipliers and various operators satisfying sparse domination
results.
| 0 | 0 | 1 | 0 | 0 | 0 |
Phase Diagram of Carbon Nickel Tungsten: Superatom Model | Carbon solubility in face-centered cubic Ni-W alloys and the phase diagram of
C-Ni-W are investigated by means of first principle calculations and semi-grand
canonical Monte Carlo simulations. With density functional theory (DFT) total
energies as fitting data, we build a superatom model for efficient simulation.
Multi-histogram analysis is utilized to predict free energies for different
compositions and temperatures. By comparing free energies of competing phases,
we are able to predict carbon solubility and phase diagrams of C-Ni-W at
different temperatures. A simple ideal mixing approximation gives qualitatively
similar predictions.
| 0 | 1 | 0 | 0 | 0 | 0 |
Classical solution for the linear sigma model | In this paper, the linear sigma model is studied using a method for finding
analytical solutions based on Padé approximants. Using the solutions of two
and three traveling waves in 1+3 dimensions we found, we are able to show a
solution that is valid for an arbitrary number of bosons and traveling waves.
| 0 | 1 | 0 | 0 | 0 | 0 |
The Importance of System-Level Information in Multiagent Systems Design: Cardinality and Covering Problems | A fundamental challenge in multiagent systems is to design local control
algorithms to ensure a desirable collective behaviour. The information
available to the agents, gathered either through communication or sensing,
naturally restricts the achievable performance. Hence, it is fundamental to
identify what piece of information is valuable and can be exploited to design
control laws with enhanced performance guarantees. This paper studies the case
when such information is uncertain or inaccessible for a class of submodular
resource allocation problems termed covering problems. In the first part of
this work we pinpoint a fundamental risk-reward tradeoff faced by the system
operator when conditioning the control design on a valuable but uncertain piece
of information, which we refer to as the cardinality, that represents the
maximum number of agents that can simultaneously select any given resource.
Building on this analysis, we propose a distributed algorithm that allows
agents to learn the cardinality while adjusting their behaviour over time. This
algorithm is proved to perform on par or better to the optimal design obtained
when the exact cardinality is known a priori.
| 1 | 0 | 0 | 0 | 0 | 0 |
Detecting Policy Preferences and Dynamics in the UN General Debate with Neural Word Embeddings | Foreign policy analysis has been struggling to find ways to measure policy
preferences and paradigm shifts in international political systems. This paper
presents a novel, potential solution to this challenge, through the application
of a neural word embedding (Word2vec) model on a dataset featuring speeches by
heads of state or government in the United Nations General Debate. The paper
provides three key contributions based on the output of the Word2vec model.
First, it presents a set of policy attention indices, synthesizing the semantic
proximity of political speeches to specific policy themes. Second, it
introduces country-specific semantic centrality indices, based on topological
analyses of countries' semantic positions with respect to each other. Third, it
tests the hypothesis that there exists a statistical relation between the
semantic content of political speeches and UN voting behavior, falsifying it
and suggesting that political speeches contain information of different nature
then the one behind voting outcomes. The paper concludes with a discussion of
the practical use of its results and consequences for foreign policy analysis,
public accountability, and transparency.
| 1 | 0 | 0 | 1 | 0 | 0 |
FLASH: A Faster Optimizer for SBSE Tasks | Most problems in search-based software engineering involve balancing
conflicting objectives. Prior approaches to this task have required a large
number of evaluations- making them very slow to execute and very hard to
comprehend. To solve these problems, this paper introduces FLASH, a decision
tree based optimizer that incrementally grows one decision tree per objective.
These trees are then used to select the next best sample. This paper compares
FLASH to state-of-the-art algorithms from search-based SE and machine learning.
This comparison uses multiple SBSE case studies for release planning,
configuration control, process modeling, and sprint planning for agile
development. FLASH was found to be the fastest optimizer (sometimes requiring
less than 1% of the evaluations used by evolutionary algorithms). Also,
measured in terms of model size, FLASH's reasoning was far more succinct and
comprehensible. Further, measured in terms of finding effective optimization,
FLASH's recommendations were highly competitive with other approaches. Finally,
FLASH scaled to more complex models since it always terminated (while
state-of-the-art algorithm did not).
| 1 | 0 | 0 | 0 | 0 | 0 |
On the complexity of generalized chromatic polynomials | J. Makowsky and B. Zilber (2004) showed that many variations of graph
colorings, called CP-colorings in the sequel, give rise to graph polynomials.
This is true in particular for harmonious colorings, convex colorings,
mcc_t-colorings, and rainbow colorings, and many more. N. Linial (1986) showed
that the chromatic polynomial $\chi(G;X)$ is #P-hard to evaluate for all but
three values X=0,1,2, where evaluation is in P. This dichotomy includes
evaluation at real or complex values, and has the further property that the set
of points for which evaluation is in P is finite. We investigate how the
complexity of evaluating univariate graph polynomials that arise from
CP-colorings varies for different evaluation points. We show that for some
CP-colorings (harmonious, convex) the complexity of evaluation follows a
similar pattern to the chromatic polynomial. However, in other cases (proper
edge colorings, mcc_t-colorings, H-free colorings) we could only obtain a
dichotomy for evaluations at non-negative integer points. We also discuss some
CP-colorings where we only have very partial results.
| 1 | 0 | 1 | 0 | 0 | 0 |
Hybrid SGP4 orbit propagator | Two-Line Elements (TLEs) continue to be the sole public source of orbiter
observations. The accuracy of TLE propagations through the Simplified General
Perturbations-4 (SGP4) software decreases dramatically as the propagation
horizon increases, and thus the period of validity of TLEs is very limited. As
a result, TLEs are gradually becoming insufficient for the growing demands of
Space Situational Awareness (SSA). We propose a technique, based on the hybrid
propagation methodology, aimed at extending TLE validity with minimal changes
to the current TLE-SGP4 system in a non-intrusive way. It requires that the
institution in possession of the osculating elements distributes hybrid TLEs,
HTLEs, which encapsulate the standard TLE and the model of its propagation
error. The validity extension can be accomplished when the end user processes
HTLEs through the hybrid SGP4 propagator, HSGP4, which comprises the standard
SGP4 and an error corrector.
| 0 | 1 | 0 | 0 | 0 | 0 |
PDE-Net: Learning PDEs from Data | In this paper, we present an initial attempt to learn evolution PDEs from
data. Inspired by the latest development of neural network designs in deep
learning, we propose a new feed-forward deep network, called PDE-Net, to
fulfill two objectives at the same time: to accurately predict dynamics of
complex systems and to uncover the underlying hidden PDE models. The basic idea
of the proposed PDE-Net is to learn differential operators by learning
convolution kernels (filters), and apply neural networks or other machine
learning methods to approximate the unknown nonlinear responses. Comparing with
existing approaches, which either assume the form of the nonlinear response is
known or fix certain finite difference approximations of differential
operators, our approach has the most flexibility by learning both differential
operators and the nonlinear responses. A special feature of the proposed
PDE-Net is that all filters are properly constrained, which enables us to
easily identify the governing PDE models while still maintaining the expressive
and predictive power of the network. These constrains are carefully designed by
fully exploiting the relation between the orders of differential operators and
the orders of sum rules of filters (an important concept originated from
wavelet theory). We also discuss relations of the PDE-Net with some existing
networks in computer vision such as Network-In-Network (NIN) and Residual
Neural Network (ResNet). Numerical experiments show that the PDE-Net has the
potential to uncover the hidden PDE of the observed dynamics, and predict the
dynamical behavior for a relatively long time, even in a noisy environment.
| 1 | 0 | 0 | 1 | 0 | 0 |
Stable components in the parameter plane of meromorphic functions of finite type | We study the parameter planes of certain one-dimensional, dynamically-defined
slices of holomorphic families of meromorphic transcendental maps of finite
type for which infinity is not an asymptotic value. Our planes are defined by
constraining the orbits of all but one of the asymptotic values. We study the
structure of the regions in the parameter plane where the dynamics have a free
asymptotic value that is attracted to an attracting periodic orbit. The tangent
family is an example that has been studied in detail, \cite{KK}, and our goal
is to understand in the general context of meromorphic functions to what extent
the structure one sees in parameter plane of the tangent family is generic.
For the analogous dynamically defined slices of parameter spaces for rational
maps, there are Mandelbrot-like components that have a unique {\em center} that
can be used to give a combinatorial descripton of the components. No such
center exists for our slices, no matter how carefully they are chosen. We can
show, however, that there is an analogous concept, a "virtual" center, that
plays essentially the same role as a center would have. It lies on the boundary
of the component and corresponds to a map for which there is a "virtual cycle",
namely, an iterate of the free asymptotic value that lands at a pole. These
virtual centers are dense in the bifurcation locus of our one-dimensional
families.
| 0 | 0 | 1 | 0 | 0 | 0 |
Optimal Regulation Response of Batteries Under Cycle Aging Mechanisms | When providing frequency regulation in a pay-for-performance market,
batteries need to carefully balance the trade-off between following regulation
signals and their degradation costs in real-time. Existing battery control
strategies either do not consider mismatch penalties in pay-for-performance
markets, or cannot accurately account for battery cycle aging mechanism during
operation. This paper derives an online control policy that minimizes a battery
owner's operating cost for providing frequency regulation in a
pay-for-performance market. The proposed policy considers an accurate
electrochemical battery cycle aging model, and is applicable to most types of
battery cells. It has a threshold structure, and achieves near-optimal
performance with respect to an offline controller that has complete future
information. We explicitly characterize this gap and show it is independent of
the duration of operation. Simulation results with both synthetic and real
regulation traces are conducted to illustrate the theoretical results.
| 0 | 0 | 1 | 0 | 0 | 0 |
Spin-structures on real Bott manifolds with Kähler structures | Let M be a real Bott manifold with Kähler structure. Using Ishida
characterization we give necessary and sufficient condition for the existence
of the Spin-structure on M. In proof we use the technic developed in Popko,
Szczepański "Cohomological rigity of oriented Hantzsche-Wendt manifolds"
(Adv. Math. 302 (2016), 1044 - 1068) and characteristic classes.
| 0 | 0 | 1 | 0 | 0 | 0 |
No difference in orbital parameters of RV-detected giant planets between 0.1 and 5 au in single vs multi-stellar systems | Our Keck/NIRC2 imaging survey searches for stellar companions around 144
systems with radial velocity (RV) detected giant planets to determine whether
stellar binaries influence the planets' orbital parameters. This survey, the
largest of its kind to date, finds eight confirmed binary systems and three
confirmed triple systems. These include three new multi-stellar systems (HD
30856, HD 86081, and HD 207832) and three multi-stellar systems with newly
confirmed common proper motion (HD 43691, HD 116029, and HD 164509). We combine
these systems with seven RV planet-hosting multi-stellar systems from the
literature in order to test for differences in the properties of planets with
semimajor axes ranging between 0.1-5 au in single vs multi-stellar systems. We
find no evidence that the presence or absence of stellar companions alters the
distribution of planet properties in these systems. Although the observed
stellar companions might influence the orbits of more distant planetary
companions in these systems, our RV observations currently provide only weak
constraints on the masses and orbital properties of planets beyond 5 au. In
order to aid future efforts to characterize long period RV companions in these
systems, we publish our contrast curves for all 144 targets. Using four years
of astrometry for six hierarchical triple star systems hosting giant planets,
we fit the orbits of the stellar companions in order to characterize the
orbital architecture in these systems. We find that the orbital plane of the
secondary and tertiary companions are inconsistent with an edge-on orbit in
four out of six cases.
| 0 | 1 | 0 | 0 | 0 | 0 |
On overfitting and post-selection uncertainty assessments | In a regression context, when the relevant subset of explanatory variables is
uncertain, it is common to use a data-driven model selection procedure.
Classical linear model theory, applied naively to the selected sub-model, may
not be valid because it ignores the selected sub-model's dependence on the
data. We provide an explanation of this phenomenon, in terms of overfitting,
for a class of model selection criteria.
| 0 | 0 | 1 | 1 | 0 | 0 |
Concept of multiple-cell cavity for axion dark matter search | In cavity-based axion dark matter search experiments exploring high mass
regions, multiple-cavity design is considered to increase the detection volume
within a given magnet bore. We introduce a new idea, referred to as
multiple-cell cavity, which provides various benefits including a larger
detection volume, simpler experimental setup, and easier phase-matching
mechanism. We present the characteristics of this concept and demonstrate the
experimental feasibility with an example of a double-cell cavity.
| 0 | 1 | 0 | 0 | 0 | 0 |
Detecting Recycled Commodity SoCs: Exploiting Aging-Induced SRAM PUF Unreliability | A physical unclonable function (PUF), analogous to a human fingerprint, has
gained an enormous amount of attention from both academia and industry. SRAM
PUF is among one of the popular silicon PUF constructions that exploits random
initial power-up states from SRAM cells to extract hardware intrinsic secrets
for identification and key generation applications. The advantage of SRAM PUFs
is that they are widely embedded into commodity devices, thus such a PUF is
obtained without a custom design and virtually free of implementation costs. A
phenomenon known as `aging' alters the consistent
reproducibility---reliability---of responses that can be extracted from a
readout of a set of SRAM PUF cells. Similar to how a PUF exploits undesirable
manufacturing randomness for generating a hardware intrinsic fingerprint, SRAM
PUF unreliability induced by aging can be exploited to detect recycled
commodity devices requiring no additional cost to the device. In this context,
the SRAM PUF itself acts as an aging sensor by exploiting responses sensitive
to aging. We use SRAMs available in pervasively deployed commercial
off-the-shelf micro-controllers for experimental validations, which complements
recent work demonstrated in FPGA platforms, and we present a simplified
detection methodology along experimental results. We show that less than 1,000
SRAM responses are adequate to guarantee that both false acceptance rate and
false rejection rate are no more than 0.001.
| 1 | 0 | 0 | 0 | 0 | 0 |
Ginzburg-Landau-type theory of non-polarized spin superconductivity | Since the concept of spin superconductor was proposed, all the related
studies concentrate on spin-polarized case. Here, we generalize the study to
spin-non-polarized case. The free energy of non-polarized spin superconductor
is obtained, and the Ginzburg-Landau-type equations are derived by using the
variational method. These Ginzburg-Landau-type equations can be reduced to the
spin-polarized case when the spin direction is fixed. Moreover, the expressions
of super linear and angular spin currents inside the superconductor are
derived. We demonstrate that the electric field induced by super spin current
is equal to the one induced by equivalent charge obtained from the second
Ginzburg-Landau-type equation, which shows self-consistency of our theory. By
applying these Ginzburg-Landau-type equations, the effect of electric field on
the superconductor is also studied. These results will help us get a better
understanding of the spin superconductor and the related topics such as
Bose-Einstein condensate of magnons and spin superfluidity.
| 0 | 1 | 0 | 0 | 0 | 0 |
Evolution of Anisotropic Displacement Parameters and Superconductivity with Chemical Pressure in BiS2-Based REO0.5F0.5BiS2 (RE = La, Ce, Pr, and Nd) | In order to understand the mechanisms behind the emergence of
superconductivity by the chemical pressure effect in REO0.5F0.5BiS2 (RE = La,
Ce, Pr, and Nd), where bulk superconductivity is induced by the substitutions
with a smaller-radius RE, we performed synchrotron powder X-ray diffraction,
and analyzed the crystal structure and anisotropic displacement parameters.
With the decrease of the RE3+ ionic radius, the in-plane disorder of the S1
sites significantly decreased, very similar to the trend observed in the
Se-substituted systems: LaO0.5F0.5BiS2-xSex and Eu0.5La0.5FBiS2-xSex.
Therefore, the emergence of bulk superconductivity upon the suppression of the
in-plane disorder at the chalcogen sites is a universal scenario for the
BiCh2-based superconductors. In addition, we indicated that the amplitude of
vibration along the c-axis of the in-plane chalcogen sites may be related to
the Tc in the BiCh2-based superconductors.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Proximal Block Coordinate Descent Algorithm for Deep Neural Network Training | Training deep neural networks (DNNs) efficiently is a challenge due to the
associated highly nonconvex optimization. The backpropagation (backprop)
algorithm has long been the most widely used algorithm for gradient computation
of parameters of DNNs and is used along with gradient descent-type algorithms
for this optimization task. Recent work have shown the efficiency of block
coordinate descent (BCD) type methods empirically for training DNNs. In view of
this, we propose a novel algorithm based on the BCD method for training DNNs
and provide its global convergence results built upon the powerful framework of
the Kurdyka-Lojasiewicz (KL) property. Numerical experiments on standard
datasets demonstrate its competitive efficiency against standard optimizers
with backprop.
| 0 | 0 | 0 | 1 | 0 | 0 |
Surface zeta potential and diamond seeding on gallium nitride films | Measurement of zeta potential of Ga and N-face gallium nitride has been
carried out as function of pH. Both the faces show negative zeta potential in
the pH range 5.5-9. The Ga face has an isoelectric point at pH 5.5. The N-face
shows higher negative zeta potential due to larger concentration of adsorbed
oxygen. Zeta potential data clearly showed that H-terminated diamond seed
solution at pH 8 will be optimal for the self assembly of a monolayer of
diamond nanoparticles on the GaN surface. Subsequent growth of thin diamond
films on GaN seeded with H-terminated diamond seeds produced fully coalesced
films confirming a seeding density in excess of 10$^{12}$ cm$^{-2}$. This
technique removes the requirement for a low thermal conduction seeding layer
like silicon nitride on GaN.
| 0 | 1 | 0 | 0 | 0 | 0 |
Analysing Temporal Evolution of Interlingual Wikipedia Article Pairs | Wikipedia articles representing an entity or a topic in different language
editions evolve independently within the scope of the language-specific user
communities. This can lead to different points of views reflected in the
articles, as well as complementary and inconsistent information. An analysis of
how the information is propagated across the Wikipedia language editions can
provide important insights in the article evolution along the temporal and
cultural dimensions and support quality control. To facilitate such analysis,
we present MultiWiki - a novel web-based user interface that provides an
overview of the similarities and differences across the article pairs
originating from different language editions on a timeline. MultiWiki enables
users to observe the changes in the interlingual article similarity over time
and to perform a detailed visual comparison of the article snapshots at a
particular time point.
| 1 | 0 | 0 | 0 | 0 | 0 |
Smith Ideals of Operadic Algebras in Monoidal Model Categories | Building upon Hovey's work on Smith ideals for monoids, we develop a homotopy
theory of Smith ideals for general operads in a symmetric monoidal category.
For a sufficiently nice stable monoidal model category and an operad satisfying
a cofibrancy condition, we show that there is a Quillen equivalence between a
model structure on Smith ideals and a model structure on algebra maps induced
by the cokernel and the kernel. For symmetric spectra this applies to the
commutative operad and all Sigma-cofibrant operads. For chain complexes over a
field of characteristic zero and the stable module category, this Quillen
equivalence holds for all operads.
| 0 | 0 | 1 | 0 | 0 | 0 |
Phase and Power Control in the RF Magnetron Power Stations of Superconducting Accelerators | Phase and power control methods that satisfy the requirements of
superconducting accelerators to magnetron RF sources were considered by a
simplified kinetic model of a magnetron driven by a resonant injected signal.
The model predicting and explaining stable, low noise operation of the tube
below the threshold of self-excitation (the Hatrree voltage in free run mode)
at a highest efficiency, a wide range of power control and a wide-band phase
control was well verified in experiments demonstrating capabilities of the
magnetron transmitters for powering of state of the art superconducting
accelerators. Descriptions of the kinetic model, the experimental verification
and a conceptual scheme of the highly-efficient magnetron RF transmitter for
the accelerators are presented and discussed.
| 0 | 1 | 0 | 0 | 0 | 0 |
Cohomologies of locally conformally symplectic manifolds and solvmanifolds | We study the Morse-Novikov cohomology and its almost-symplectic counterpart
on manifolds admitting locally conformally symplectic structures. More
precisely, we introduce lcs cohomologies and we study elliptic Hodge theory,
dualities, Hard Lefschetz Condition. We consider solvmanifolds and
Oeljeklaus-Toma manifolds. In particular, we prove that Oeljeklaus-Toma
manifolds with precisely one complex place, and under an additional arithmetic
condition, satisfy the Mostow property. This holds in particular for the Inoue
surface of type $S^0$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Frequency Offset Estimation for OFDM Systems with a Novel Frequency Domain Training Sequence | A novel frequency domain training sequence and the corresponding carrier
frequency offset (CFO) estimator are proposed for orthogonal frequency division
multiplexing (OFDM) systems over frequency-selective fading channels. The
proposed frequency domain training sequence comprises two types of pilot tones,
namely distinctively spaced pilot tones with high energies and uniformly spaced
ones with low energies. Based on the distinctively spaced pilot tones, integer
CFO estimation is accomplished. After the subcarriers occupied by the
distinctively spaced pilot tones and their adjacent subcarriers are nulled for
the sake of interference cancellation, fractional CFO estimation is executed
according to the uniformly spaced pilot tones. By exploiting a predefined
lookup table making the best of the structure of the distinctively spaced pilot
tones, computational complexity of the proposed CFO estimator can be decreased
considerably. With the aid of the uniformly spaced pilot tones generated from
Chu sequence with cyclically orthogonal property, the ability of the proposed
estimator to combat multipath effect is enhanced to a great extent. Simulation
results illustrate the good performance of the proposed CFO estimator.
| 1 | 0 | 0 | 0 | 0 | 0 |
Cusp shape and tunnel number | We show that the set of cusp shapes of hyperbolic tunnel number one manifolds
is dense in the Teichmuller space of the torus. A similar result holds for
tunnel number n manifolds. As a consequence, for fixed n, there are infinitely
many hyperbolic tunnel number n manifolds with at most one exceptional Dehn
filling. This is in contrast to large volume Berge knots, which are tunnel
number one manifolds, but with cusp shapes converging to a single point in
Teichmuller space.
| 0 | 0 | 1 | 0 | 0 | 0 |
Divergence-free positive symmetric tensors and fluid dynamics | We consider $d\times d$ tensors $A(x)$ that are symmetric, positive
semi-definite, and whose row-divergence vanishes identically. We establish
sharp inequalities for the integral of $(\det A)^{\frac1{d-1}}$. We apply them
to models of compressible inviscid fluids: Euler equations, Euler--Fourier,
relativistic Euler, Boltzman, BGK, etc... We deduce an {\em a priori} estimate
for a new quantity, namely the space-time integral of $\rho^{\frac1n}p$, where
$\rho$ is the mass density, $p$ the pressure and $n$ the space dimension. For
kinetic models, the corresponding quantity generalizes Bony's functional.
| 0 | 1 | 1 | 0 | 0 | 0 |
Admire vs. Safire: Objective comparison of CT reconstruction algorithms and their noise properties | Purpose: Siemens has developed several iterative reconstruction (IR)
algorithms on their CT scanners. SAFIRE is available on most of their CT
scanners. The latest algorithm, ADMIRE, is available on their newest high-end
CT scanners. The aim of our study was to compare the noise reduction properties
of the two IR algorithms using objective methods. Methods and Materials: The
homogeneous module of the Catphan phantom was scanned on a Siemens AS+ and a
Siemens Flash CT scanner using an axial abdomen protocol with fixed tube
current at two dose levels. The images were reconstructed with an abdomen
filter (B30) using filtered back projection (FBP) and a low, medium, and high
level of SAFIRE or ADMIRE. Noise Power Spectrum (NPS) curves were calculated
using these images. Then, an anthropomorphic abdomen phantom (Kyoto Kagaku
PH-5) was scanned using the same setup and exposure parameters. Fifty axial
images at the same slice location were used to calculate inter-image standard
deviation maps. Results: At full dose, the median values of the NPS curves were
similar for both scanners at all IR levels. At low dose the median values of
the NPS curves were generally shifted towards lower spatial frequencies,
usually resulting in a more blotchy image texture. This shift was more
prominent for ADMIRE compared to SAFIRE for all IR levels. Based on the
inter-image standard deviation maps of the anthropomorphic phantom, ADMIRE
removed noise near edges more efficiently than SAFIRE. Conclusion: No
significant improvement in maintaining noise structure were found for the
ADMIRE algorithm. Based on the inter-image standard deviation maps, ADMIRE
removed noise near edges more efficiently than SAFIRE.
| 0 | 1 | 0 | 0 | 0 | 0 |
Occupants in simplicial complexes | Let $M$ be a smooth manifold and $K\subset M$ be a simplicial complex of
codimension at least 3. Functor calculus methods lead to a homotopical formula
of $M\setminus K$ in terms of spaces $M\setminus T$ where $T$ is a finite
subset of $K$. This is a generalization of the author's previous work with
Michael Weiss where the subset $K$ is assumed to be a smooth submanifold of $M$
and uses his generalization of manifold calculus adapted for simplicial
complexes.
| 0 | 0 | 1 | 0 | 0 | 0 |
Temporal Stability in Predictive Process Monitoring | Predictive process monitoring is concerned with the analysis of events
produced during the execution of a business process in order to predict as
early as possible the final outcome of an ongoing case. Traditionally,
predictive process monitoring methods are optimized with respect to accuracy.
However, in environments where users make decisions and take actions in
response to the predictions they receive, it is equally important to optimize
the stability of the successive predictions made for each case. To this end,
this paper defines a notion of temporal stability for binary classification
tasks in predictive process monitoring and evaluates existing methods with
respect to both temporal stability and accuracy. We find that methods based on
XGBoost and LSTM neural networks exhibit the highest temporal stability. We
then show that temporal stability can be enhanced by hyperparameter-optimizing
random forests and XGBoost classifiers with respect to inter-run stability.
Finally, we show that time series smoothing techniques can further enhance
temporal stability at the expense of slightly lower accuracy.
| 1 | 0 | 0 | 1 | 0 | 0 |
Discrete Gradient Line Fields on Surfaces | A line field on a manifold is a smooth map which assigns a tangent line to
all but a finite number of points of the manifold. As such, it can be seen as a
generalization of vector fields. They model a number of geometric and physical
properties, e.g. the principal curvature directions dynamics on surfaces or the
stress flux in elasticity.
We propose a discretization of a Morse-Smale line field on surfaces,
extending Forman's construction for discrete vector fields. More general
critical elements and their indices are defined from local matchings, for which
Euler theorem and the characterization of homotopy type in terms of critical
cells still hold.
| 1 | 0 | 1 | 0 | 0 | 0 |
New Directions In Cellular Automata | We Propose A Novel Automaton Model which uses Arithmetic Operations as the
Evolving Rules, each cell has the states of the Natural Numbers k = (N), a
radius of r = 1/2 and operates on an arbitrary input size. The Automaton reads
an Arithmetic Expression as an input and outputs another Arithmetic Expression.
In Addition, we simulate a variety of One Dimensional Cellular Automata
Structures with different Dynamics including Elementary Cellular Automata.
| 1 | 1 | 0 | 0 | 0 | 0 |
Smart Fog: Fog Computing Framework for Unsupervised Clustering Analytics in Wearable Internet of Things | The increasing use of wearables in smart telehealth generates heterogeneous
medical big data. Cloud and fog services process these data for assisting
clinical procedures. IoT based ehealthcare have greatly benefited from
efficient data processing. This paper proposed and evaluated use of low
resource machine learning on Fog devices kept close to the wearables for smart
healthcare. In state of the art telecare systems, the signal processing and
machine learning modules are deployed in the cloud for processing physiological
data. We developed a prototype of Fog-based unsupervised machine learning big
data analysis for discovering patterns in physiological data. We employed Intel
Edison and Raspberry Pi as Fog computer in proposed architecture. We performed
validation studies on real-world pathological speech data from in home
monitoring of patients with Parkinson's disease (PD). Proposed architecture
employed machine learning for analysis of pathological speech data obtained
from smartwatches worn by the patients with PD. Results showed that proposed
architecture is promising for low-resource clinical machine learning. It could
be useful for other applications within wearable IoT for smart telehealth
scenarios by translating machine learning approaches from the cloud backend to
edge computing devices such as Fog.
| 1 | 0 | 0 | 0 | 0 | 0 |
Subspace Clustering via Optimal Direction Search | This letter presents a new spectral-clustering-based approach to the subspace
clustering problem. Underpinning the proposed method is a convex program for
optimal direction search, which for each data point d finds an optimal
direction in the span of the data that has minimum projection on the other data
points and non-vanishing projection on d. The obtained directions are
subsequently leveraged to identify a neighborhood set for each data point. An
alternating direction method of multipliers framework is provided to
efficiently solve for the optimal directions. The proposed method is shown to
notably outperform the existing subspace clustering methods, particularly for
unwieldy scenarios involving high levels of noise and close subspaces, and
yields the state-of-the-art results for the problem of face clustering using
subspace segmentation.
| 1 | 0 | 0 | 1 | 0 | 0 |
The Covering Principle: A New Approach to Address Multiplicity in Hypotheses Testing | The closure and the partitioning principles have been used to build various
multiple testing procedures in the past three decades. The essence of these two
principles is based on parameter space partitioning. In this article, we
propose a novel approach coined the covering principle from the perspective of
rejection region coverage in the sample space. The covering principle divides
the whole family of null hypotheses into a few overlapped sub-families when
there is a priority of making decisions for hypothesis testing. We have proven
that the multiple testing procedure constructed by the covering principle
strongly controls the familywise error rate as long as the multiple tests for
each sub-familiy strongly control the type I error. We have illustrated the
covering principle can be applied to solve the general gate-keeping problems.
| 0 | 0 | 0 | 1 | 0 | 0 |
Mechanisms of Lagrangian analyticity in fluids | Certain systems of inviscid fluid dynamics have the property that for
solutions that are only slightly better than differentiable in Eulerian
variables, the corresponding Lagrangian trajectories are analytic in time. We
elucidate the mechanisms in fluid dynamics systems that give rise to this
automatic Lagrangian analyticity, as well as mechanisms in some particular
fluids systems which prevent it from occurring.
We give a conceptual argument for a general fluids model which shows that the
fulfillment of a basic set of criteria results in the analyticity of the
trajectory maps in time. We then apply this to the incompressible Euler
equations to prove analyticity of trajectories for vortex patch solutions. We
also use the method to prove the Lagrangian trajectories are analytic for
solutions to the pressureless Euler-Poisson equations, for initial data with
moderate regularity.
We then examine the compressible Euler equations, and find that the finite
speed of propagation in the system is incompatible with the Lagrangian
analyticity property. By taking advantage of this finite speed we are able to
construct smooth initial data with the property that some corresponding
Lagrangian trajectory is not analytic in time. We also study the Vlasov-Poisson
system, uncovering another mechanism that deters the analyticity of
trajectories. In this instance, we find that a key nonlocal operator does not
preserve analytic dependence in time. For this system we can also construct
smooth initial data for which the corresponding solution has some non-analytic
Lagrangian trajectory. This provides a counterexample to Lagrangian analyticity
for a system in which there is an infinite speed of propagation, unlike the
compressible Euler equations.
| 0 | 0 | 1 | 0 | 0 | 0 |
How to Differentiate Collective Variables in Free Energy Codes: Computer-Algebra Code Generation and Automatic Differentiation | The proper choice of collective variables (CVs) is central to biased-sampling
free energy reconstruction methods in molecular dynamics simulations. The
PLUMED 2 library, for instance, provides several sophisticated CV choices,
implemented in a C++ framework; however, developing new CVs is still time
consuming due to the need to provide code for the analytical derivatives of all
functions with respect to atomic coordinates. We present two solutions to this
problem, namely (a) symbolic differentiation and code generation, and (b)
automatic code differentiation, in both cases leveraging open-source libraries
(SymPy and Stan Math respectively). The two approaches are demonstrated and
discussed in detail implementing a realistic example CV, the local radius of
curvature of a polymer. Users may use the code as a template to streamline the
implementation of their own CVs using high-level constructs and automatic
gradient computation.
| 0 | 1 | 0 | 0 | 0 | 0 |
Blocks of the category of smooth $\ell$-modular representations of $GL(n,F)$ and its inner forms: reduction to level-$0$ | Let $G$ be an inner form of a general linear group over a non-archimedean
locally compact field of residue characteristic $p$, let $R$ be an
algebraically closed field of characteristic different from $p$ and let
$\mathscr{R}_R(G)$ be the category of smooth representations of $G$ over $R$.
In this paper, we prove that a block (indecomposable summand) of
$\mathscr{R}_R(G)$ is equivalent to a level-$0$ block (a block in which every
object has non-zero invariant vectors for the pro-$p$-radical of a maximal
compact open subgroup) of $\mathscr{R}_R(G')$, where $G'$ is a direct product
of groups of the same type of $G$.
| 0 | 0 | 1 | 0 | 0 | 0 |
An Investigation of Newton-Sketch and Subsampled Newton Methods | The concepts of sketching and subsampling have recently received much
attention by the optimization and statistics communities. In this paper, we
study Newton-Sketch and Subsampled Newton (SSN) methods for the finite-sum
optimization problem. We consider practical versions of the two methods in
which the Newton equations are solved approximately using the conjugate
gradient (CG) method or a stochastic gradient iteration. We establish new
complexity results for the SSN-CG method that exploit the spectral properties
of CG. Controlled numerical experiments compare the relative strengths of
Newton-Sketch and SSN methods and show that for many finite-sum problems, they
are far more efficient than SVRG, a popular first-order method.
| 1 | 0 | 1 | 1 | 0 | 0 |
On angled bounce-off impact of a drop impinging on a flowing soap film | Small drops impinging angularly on thin flowing soap films frequently
demonstrate the rare emergence of bulk elastic effects working in-tandem with
the more common-place hydrodynamic interactions. Three collision regimes are
observable: (a) drop piercing through the film, (b) it coalescing with the
flow, and (c) it bouncing off the film surface. During impact, the drop deforms
along with a bulk elastic deformation of the film. For impacts that are
close-to-tangential, the bounce-off regime predominates. We outline a reduced
order analytical framework assuming a deformable drop and a deformable
three-dimensional film, and the idealization invokes a phase-based parametric
study. Angular inclination of the film and the ratio of post and pre impact
drop sizes entail the phase parameters. We also perform experiments with
vertically descending droplets impacting against an inclined soap film, flowing
under constant pressure head. Model predicted phase domain for bounce-off
compares well to our experimental findings. Additionally, the experiments
exhibit momentum transfer to the film in the form of shed vortex dipole, along
with propagation of free surface waves. On consulting prior published work, we
note that for locomotion of water-walking insects using an impulsive action,
the momentum distribution to the shed vortices and waves are both significant,
taking up respectively 2/3-rd and 1/3-rd of the imparted streamwise momentum.
In view of the potentially similar impulse actions, this theory is applied to
the bounce-off examples in our experiments, and the resultant shed vortex
dipole momenta are compared to the momenta computed from particle imaging
velocimetry data. The magnitudes reveal identical order ($10^{-7}$ N$\cdot$s),
suggesting that the bounce-off regime can be tapped as a simple analogue for
interfacial bio-locomotion relying on impulse reactions.
| 0 | 1 | 0 | 0 | 0 | 0 |
Fast evaluation of solid harmonic Gaussian integrals for local resolution-of-the-identity methods and range-separated hybrid functionals | An integral scheme for the efficient evaluation of two-center integrals over
contracted solid harmonic Gaussian functions is presented. Integral expressions
are derived for local operators that depend on the position vector of one of
the two Gaussian centers. These expressions are then used to derive the formula
for three-index overlap integrals where two of the three Gaussians are located
at the same center. The efficient evaluation of the latter is essential for
local resolution-of-the-identity techniques that employ an overlap metric. We
compare the performance of our integral scheme to the widely used Cartesian
Gaussian-based method of Obara and Saika (OS). Non-local interaction potentials
such as standard Coulomb, modified Coulomb and Gaussian-type operators, that
occur in range-separated hybrid functionals, are also included in the
performance tests. The speed-up with respect to the OS scheme is up to three
orders of magnitude for both, integrals and their derivatives. In particular,
our method is increasingly efficient for large angular momenta and highly
contracted basis sets.
| 0 | 1 | 0 | 0 | 0 | 0 |
A note on integrating products of linear forms over the unit simplex | Integrating a product of linear forms over the unit simplex can be done in
polynomial time if the number of variables n is fixed (V. Baldoni et al.,
2011). In this note, we highlight that this problem is equivalent to obtaining
the normalizing constant of state probabilities for a popular class of Markov
processes used in queueing network theory. In light of this equivalence, we
survey existing computational algorithms developed in queueing theory that can
be used for exact integration. For example, under some regularity conditions,
queueing theory algorithms can exactly integrate a product of linear forms of
total degree N by solving N systems of linear equations.
| 1 | 0 | 1 | 0 | 0 | 0 |
Linearly constrained Gaussian processes | We consider a modification of the covariance function in Gaussian processes
to correctly account for known linear constraints. By modelling the target
function as a transformation of an underlying function, the constraints are
explicitly incorporated in the model such that they are guaranteed to be
fulfilled by any sample drawn or prediction made. We also propose a
constructive procedure for designing the transformation operator and illustrate
the result on both simulated and real-data examples.
| 0 | 0 | 0 | 1 | 0 | 0 |
Racks as multiplicative graphs | We interpret augmented racks as a certain kind of multiplicative graphs and
show that this point of view is natural for defining rack homology. We also
define the analogue of the group algebra for these objects; in particular, we
see how discrete racks give rise to Hopf algebras and Lie algebras in the
Loday-Pirashvili category $\mathcal{LM}$. Finally, we discuss the integration
of Lie algebras in $\mathcal{LM}$ in the context of multiplicative graphs and
augmented racks.
| 0 | 0 | 1 | 0 | 0 | 0 |
Bielliptic intermediate modular curves | We determine which of the modular curves $X_\Delta(N)$, that is, curves lying
between $X_0(N)$ and $X_1(N)$, are bielliptic. Somewhat surprisingly, we find
that one of these curves has exceptional automorphisms. Finally we find all
$X_\Delta(N)$ that have infinitely many quadratic points over $\mathbb{Q}$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Behavior Revealed in Mobile Phone Usage Predicts Loan Repayment | Many households in developing countries lack formal financial histories,
making it difficult for banks to extend loans, and for potential borrowers to
receive them. However, many of these households have mobile phones, which
generate rich data about behavior. This paper shows that behavioral signatures
in mobile phone data predict loan default, using call records matched to loan
outcomes. In a middle income South American country, individuals in the highest
quintile of risk by our measure are 2.8 times more likely to default than those
in the lowest quintile. On our sample of individuals with (thin) financial
histories, our method outperforms models using credit bureau information, both
within time and when tested on a different time period. The method forms the
basis for new forms of lending that reach the unbanked.
| 1 | 0 | 0 | 0 | 0 | 0 |
Robust and structural ergodicity analysis of stochastic biomolecular networks involving synthetic antithetic integral controllers | Ergodicity and output controllability have been shown to be fundamental
concepts for the analysis and synthetic design of closed-loop stochastic
reaction networks, as exemplified by the use of antithetic integral feedback
controllers. In [Gupta, Briat & Khammash, PLoS Comput. Biol., 2014], some
ergodicity and output controllability conditions for unimolecular and certain
classes of bimolecular reaction networks were obtained and formulated through
linear programs. To account for context dependence, these conditions were later
extended in [Briat & Khammash, CDC, 2016] to reaction networks with uncertain
rate parameters using simple and tractable, yet potentially conservative,
methods. Here we develop some exact theoretical methods for verifying, in a
robust setting, the original ergodicity and output controllability conditions
based on algebraic and polynomial techniques. Some examples are given for
illustration.
| 1 | 0 | 1 | 0 | 0 | 0 |
A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering | We propose an effective method to solve the event sequence clustering
problems based on a novel Dirichlet mixture model of a special but significant
type of point processes --- Hawkes process. In this model, each event sequence
belonging to a cluster is generated via the same Hawkes process with specific
parameters, and different clusters correspond to different Hawkes processes.
The prior distribution of the Hawkes processes is controlled via a Dirichlet
distribution. We learn the model via a maximum likelihood estimator (MLE) and
propose an effective variational Bayesian inference algorithm. We specifically
analyze the resulting EM-type algorithm in the context of inner-outer
iterations and discuss several inner iteration allocation strategies. The
identifiability of our model, the convergence of our learning method, and its
sample complexity are analyzed in both theoretical and empirical ways, which
demonstrate the superiority of our method to other competitors. The proposed
method learns the number of clusters automatically and is robust to model
misspecification. Experiments on both synthetic and real-world data show that
our method can learn diverse triggering patterns hidden in asynchronous event
sequences and achieve encouraging performance on clustering purity and
consistency.
| 1 | 0 | 0 | 1 | 0 | 0 |
On the number of cyclic subgroups of a finite group | Let $G$ be a finite group and let $c(G)$ be the number of cyclic subgroups of
$G$. We study the function $\alpha(G) = c(G)/|G|$. We explore its basic
properties and we point out a connection with the probability of commutation.
For many families $\mathscr{F}$ of groups we characterize the groups $G \in
\mathscr{F}$ for which $\alpha(G)$ is maximal and we classify the groups $G$
for which $\alpha(G) > 3/4$. We also study the number of cyclic subgroups of a
direct power of a given group deducing an asymptotic result and we characterize
the equality $\alpha(G) = \alpha(G/N)$ when $G/N$ is a symmetric group.
| 0 | 0 | 1 | 0 | 0 | 0 |
Accelerated Extra-Gradient Descent: A Novel Accelerated First-Order Method | We provide a novel accelerated first-order method that achieves the
asymptotically optimal convergence rate for smooth functions in the first-order
oracle model. To this day, Nesterov's Accelerated Gradient Descent (AGD) and
variations thereof were the only methods achieving acceleration in this
standard blackbox model. In contrast, our algorithm is significantly different
from AGD, as it relies on a predictor-corrector approach similar to that used
by Mirror-Prox [Nemirovski, 2004] and Extra-Gradient Descent [Korpelevich,
1977] in the solution of convex-concave saddle point problems. For this reason,
we dub our algorithm Accelerated Extra-Gradient Descent (AXGD). Its
construction is motivated by the discretization of an accelerated
continuous-time dynamics [Krichene et al., 2015] using the classical method of
implicit Euler discretization. Our analysis explicitly shows the effects of
discretization through a conceptually novel primal-dual viewpoint. Moreover, we
show that the method is quite general: it attains optimal convergence rates for
other classes of objectives (e.g., those with generalized smoothness properties
or that are non-smooth and Lipschitz-continuous) using the appropriate choices
of step lengths. Finally, we present experiments showing that our algorithm
matches the performance of Nesterov's method, while appearing more robust to
noise in some cases.
| 1 | 0 | 1 | 0 | 0 | 0 |
Disruption of Alfvénic turbulence by magnetic reconnection in a collisionless plasma | We calculate the disruption scale $\lambda_{\rm D}$ at which sheet-like
structures in dynamically aligned Alfvénic turbulence are destroyed by the
onset of magnetic reconnection in a low-$\beta$ collisionless plasma. The
scaling of $\lambda_{\rm D}$ depends on the order of the statistics being
considered, with more intense structures being disrupted at larger scales. The
disruption scale for the structures that dominate the energy spectrum is
$\lambda_{\rm D}\sim L_\perp^{1/9}(d_e\rho_s)^{4/9}$, where $d_e$ is the
electron inertial scale, $\rho_s$ is the ion sound scale, and $L_\perp$ is the
outer scale of the turbulence. When $\beta_e$ and $\rho_s/L_\perp$ are
sufficiently small, the scale $\lambda_{\rm D}$ is larger than $\rho_s$ and
there is a break in the energy spectrum at $\lambda_{\rm D}$, rather than at
$\rho_s$. We propose that the fluctuations produced by the disruption are
circularised flux ropes, which may have already been observed in the solar
wind. We predict the relationship between the amplitude and radius of these
structures and quantify the importance of the disruption process to the cascade
in terms of the filling fraction of undisrupted structures and the fractional
reduction of the energy contained in them at the ion sound scale $\rho_s$. Both
of these fractions depend strongly on $\beta_e$, with the disrupted structures
becoming more important at lower $\beta_e$. Finally, we predict that the energy
spectrum between $\lambda_{\rm D}$ and $\rho_s$ is steeper than $k_\perp^{-3}$,
when this range exists. Such a steep "transition range" is sometimes observed
in short intervals of solar-wind turbulence. The onset of collisionless
magnetic reconnection may therefore significantly affect the nature of plasma
turbulence around the ion gyroscale.
| 0 | 1 | 0 | 0 | 0 | 0 |
New neutrino physics and the altered shapes of solar neutrino spectra | Neutrinos coming from the Sun's core are now measured with a high precision,
and fundamental neutrino oscillations parameters are determined with a good
accuracy. In this work, we estimate the impact that a new neutrino physics
model, the so-called generalized Mikheyev-Smirnov-Wolfenstein (MSW) oscillation
mechanism, has on the shape of some of leading solar neutrino spectra, some of
which will be partially tested by the next generation of solar neutrino
experiments. In these calculations, we use a high-precision standard solar
model in good agreement with helioseismology data. We found that the neutrino
spectra of the different solar nuclear reactions of the proton-proton chains
and carbon-nitrogen-oxygen cycle have quite distinct sensitivities to the new
neutrino physics. The $HeP$ and $^8B$ neutrino spectra are the ones for which
their shapes are more affected when neutrinos interact with quarks in addition
to electrons. The shape of the $^{15}O$ and $^{17}F$ neutrino spectra are also
modified, although in these cases the impact is much smaller. Finally, the
impact in the shape of the $PP$ and $^{13}N$ neutrino spectra is practically
negligible.
| 0 | 1 | 0 | 0 | 0 | 0 |
Searching for a Single Community in a Graph | In standard graph clustering/community detection, one is interested in
partitioning the graph into more densely connected subsets of nodes. In
contrast, the "search" problem of this paper aims to only find the nodes in a
"single" such community, the target, out of the many communities that may
exist. To do so , we are given suitable side information about the target; for
example, a very small number of nodes from the target are labeled as such.
We consider a general yet simple notion of side information: all nodes are
assumed to have random weights, with nodes in the target having higher weights
on average. Given these weights and the graph, we develop a variant of the
method of moments that identifies nodes in the target more reliably, and with
lower computation, than generic community detection methods that do not use
side information and partition the entire graph. Our empirical results show
significant gains in runtime, and also gains in accuracy over other graph
clustering algorithms.
| 1 | 0 | 0 | 1 | 0 | 0 |
Superinjective Simplicial Maps of the Two-sided Curve Complexes on Nonorientable Surfaces | Let $N$ be a compact, connected, nonorientable surface of genus $g$ with $n$
boundary components with $g \geq 5$, $n \geq 0$. Let $\mathcal{T}(N)$ be the
two-sided curve complex of $N$. If $\lambda :\mathcal{T}(N) \rightarrow
\mathcal{T}(N)$ is a superinjective simplicial map, then there exists a
homeomorphism $h : N \rightarrow N$ unique up to isotopy such that $H(\alpha) =
\lambda(\alpha)$ for every vertex $\alpha$ in $\mathcal{T}(N)$ where $H=[h]$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Geometry-Oblivious FMM for Compressing Dense SPD Matrices | We present GOFMM (geometry-oblivious FMM), a novel method that creates a
hierarchical low-rank approximation, "compression," of an arbitrary dense
symmetric positive definite (SPD) matrix. For many applications, GOFMM enables
an approximate matrix-vector multiplication in $N \log N$ or even $N$ time,
where $N$ is the matrix size. Compression requires $N \log N$ storage and work.
In general, our scheme belongs to the family of hierarchical matrix
approximation methods. In particular, it generalizes the fast multipole method
(FMM) to a purely algebraic setting by only requiring the ability to sample
matrix entries. Neither geometric information (i.e., point coordinates) nor
knowledge of how the matrix entries have been generated is required, thus the
term "geometry-oblivious." Also, we introduce a shared-memory parallel scheme
for hierarchical matrix computations that reduces synchronization barriers. We
present results on the Intel Knights Landing and Haswell architectures, and on
the NVIDIA Pascal architecture for a variety of matrices.
| 1 | 0 | 0 | 0 | 0 | 0 |
Deep learning for universal linear embeddings of nonlinear dynamics | Identifying coordinate transformations that make strongly nonlinear dynamics
approximately linear is a central challenge in modern dynamical systems. These
transformations have the potential to enable prediction, estimation, and
control of nonlinear systems using standard linear theory. The Koopman operator
has emerged as a leading data-driven embedding, as eigenfunctions of this
operator provide intrinsic coordinates that globally linearize the dynamics.
However, identifying and representing these eigenfunctions has proven to be
mathematically and computationally challenging. This work leverages the power
of deep learning to discover representations of Koopman eigenfunctions from
trajectory data of dynamical systems. Our network is parsimonious and
interpretable by construction, embedding the dynamics on a low-dimensional
manifold that is of the intrinsic rank of the dynamics and parameterized by the
Koopman eigenfunctions. In particular, we identify nonlinear coordinates on
which the dynamics are globally linear using a modified auto-encoder. We also
generalize Koopman representations to include a ubiquitous class of systems
that exhibit continuous spectra, ranging from the simple pendulum to nonlinear
optics and broadband turbulence. Our framework parametrizes the continuous
frequency using an auxiliary network, enabling a compact and efficient
embedding at the intrinsic rank, while connecting our models to half a century
of asymptotics. In this way, we benefit from the power and generality of deep
learning, while retaining the physical interpretability of Koopman embeddings.
| 1 | 0 | 0 | 1 | 0 | 0 |
Mean-field modeling of the basal ganglia-thalamocortical system. II. Dynamics of parkinsonian oscillations | Neuronal correlates of Parkinson's disease (PD) include a slowing of the
electroencephalogram (EEG) and enhanced synchrony at 3-7 and 7-30 Hz in the
basal ganglia, thalamus, and cortex. This study describes the dynamics of a
physiologically based mean-field model of the basal ganglia-thalamocortical
system, and shows how it accounts for key electrophysiological correlates of
PD. Its connectivity comprises partially segregated direct and indirect
pathways through the striatum, a hyperdirect pathway involving a
corticosubthalamic projection, thalamostriatal feedback, and local inhibition
in striatum and external pallidum (GPe). In a companion paper, realistic
steady-state firing rates were obtained for the healthy state, and after
dopamine loss modeled by weaker direct and stronger indirect pathways, reduced
intrapallidal inhibition, lower firing thresholds of the GPe and subthalamic
nucleus (STN), a stronger striato-GPe projection, and weaker cortical
interactions. Here we show that oscillations around 5 and 20 Hz can arise with
a strong indirect pathway, which also increases synchrony throughout the basal
ganglia. Further, increased theta power with nigrostriatal degeneration
correlates with reduced alpha power and peak frequency, matching experiments.
Unlike the hyperdirect pathway, the indirect pathway sustains oscillations with
realistic phase relationships. Changes in basal ganglia responses to transient
stimuli accord with experimental data. Reduced cortical gains due to both
nigrostriatal and mesocortical dopamine loss lead to slower cortical activity
changes and may be related to bradykinesia. Finally, increased EEG power found
in some studies may be partly explained by a lower effective GPe firing
threshold, reduced GPe-GPe inhibition, and/or weaker intracortical connections
in PD. Strict separation of the direct and indirect pathways is not necessary
for these results.
| 0 | 0 | 0 | 0 | 1 | 0 |
Applying Machine Learning To Maize Traits Prediction | Heterosis is the improved or increased function of any biological quality in
a hybrid offspring. We have studied yet the largest maize SNP dataset for
traits prediction. We develop linear and non-linear models which consider
relationships between different hybrids as well as other effect. Specially
designed model proved to be efficient and robust in prediction maize's traits.
| 0 | 0 | 0 | 1 | 0 | 0 |
Perceptual Compressive Sensing based on Contrast Sensitivity Function: Can we avoid non-visible redundancies acquisition? | In this paper, we propose a novel CS approach in which the acquisition of
non-visible information is also avoided.
| 1 | 0 | 0 | 0 | 0 | 0 |
Poincaré surfaces of section around a 3-D irregular body: The case of asteroid 4179 Toutatis | In general, small bodies of the solar system, e.g., asteroids and comets,
have a very irregular shape. This feature affects significantly the
gravitational potential around these irregular bodies, which hinders dynamical
studies. The Poincaré surface of sec- tion technique is often used to look
for stable and chaotic regions in two-dimensional dynamic cases. In this work,
we show that this tool can be useful for exploring the surroundings of
irregular bodies such as the asteroid 4179 Toutatis. Considering a rotating
system with a particle, under the effect of the gravitational field computed
three-dimensionally, we define a plane in the phase space to build the
Poincaré surface of sections. Despite the extra dimension, the sections
created allow us to find trajec- tories and classify their stabilities. Thus,
we have also been able to map stable and chaotic regions, as well as to find
correlations between those regions and the contri- bution of the third
dimension of the system to the trajectory dynamics as well. As examples, we
show details of periodic(resonant or not) and quasi-periodic trajectories.
| 0 | 1 | 0 | 0 | 0 | 0 |
On the second Dirichlet eigenvalue of some nonlinear anisotropic elliptic operators | Let $\Omega$ be a bounded open set of $\mathbb R^{n}$, $n\ge 2$. In this
paper we mainly study some properties of the second Dirichlet eigenvalue
$\lambda_{2}(p,\Omega)$ of the anisotropic $p$-Laplacian \[ -\mathcal
Q_{p}u:=-\textrm{div} \left(F^{p-1}(\nabla u)F_\xi (\nabla u)\right), \] where
$F$ is a suitable smooth norm of $\mathbb R^{n}$ and $p\in]1,+\infty[$. We
provide a lower bound of $\lambda_{2}(p,\Omega)$ among bounded open sets of
given measure, showing the validity of a Hong-Krahn-Szego type inequality.
Furthermore, we investigate the limit problem as $p\to+\infty$.
| 0 | 0 | 1 | 0 | 0 | 0 |
A parametric level-set method for partially discrete tomography | This paper introduces a parametric level-set method for tomographic
reconstruction of partially discrete images. Such images consist of a
continuously varying background and an anomaly with a constant (known)
grey-value. We represent the geometry of the anomaly using a level-set
function, which we represent using radial basis functions. We pose the
reconstruction problem as a bi-level optimization problem in terms of the
background and coefficients for the level-set function. To constrain the
background reconstruction we impose smoothness through Tikhonov regularization.
The bi-level optimization problem is solved in an alternating fashion; in each
iteration we first reconstruct the background and consequently update the
level-set function. We test our method on numerical phantoms and show that we
can successfully reconstruct the geometry of the anomaly, even from limited
data. On these phantoms, our method outperforms Total Variation reconstruction,
DART and P-DART.
| 1 | 0 | 0 | 0 | 0 | 0 |
Using PCA and Factor Analysis for Dimensionality Reduction of Bio-informatics Data | Large volume of Genomics data is produced on daily basis due to the
advancement in sequencing technology. This data is of no value if it is not
properly analysed. Different kinds of analytics are required to extract useful
information from this raw data. Classification, Prediction, Clustering and
Pattern Extraction are useful techniques of data mining. These techniques
require appropriate selection of attributes of data for getting accurate
results. However, Bioinformatics data is high dimensional, usually having
hundreds of attributes. Such large a number of attributes affect the
performance of machine learning algorithms used for classification/prediction.
So, dimensionality reduction techniques are required to reduce the number of
attributes that can be further used for analysis. In this paper, Principal
Component Analysis and Factor Analysis are used for dimensionality reduction of
Bioinformatics data. These techniques were applied on Leukaemia data set and
the number of attributes was reduced from to.
| 1 | 0 | 0 | 0 | 0 | 0 |
Three-dimensional vortex structures and dynamics in hexagonal manganites | Hexagonal manganites REMnO3 (RE, rare earths) have attracted significant
attention due to their potential applications as multiferroic materials and the
intriguing physics associated with the topological defects. The two-dimensional
(2D) and 3D domain and vortex structure evolution of REMnO3 is predicted using
the phase-field method based on a thermodynamic potential constructed from
first-principles calculations. In 3D spaces, vortex lines show three types of
topological changes, i.e. shrinking, coalescence, and splitting, with the
latter two caused by the interaction and exchange of vortex loops. Compared to
the coarsening rate of the isotropic XY model, the six-fold degeneracy gives
rise to negligible differences with the vortex-antivortex annihilation
controlling the scaling dynamics, whereas the anisotropy of interfacial energy
results in a deviation. The temporal evolution of domain and vortex structures
serves as a platform to fully explore the mesoscale mechanisms for the 0-D and
1-D topological defects.
| 0 | 1 | 0 | 0 | 0 | 0 |
A spectral-Galerkin turbulent channel flow solver for large-scale simulations | A fully (pseudo-)spectral solver for direct numerical simulations of
large-scale turbulent channel flows is described. The solver utilizes the
Chebyshev base functions suggested by J. Shen [SIAM J. Sci. Comput., 16, 1,
1995], that lead to stable and robust numerical schemes, even at very large
scale. New and fast algorithms for the direct solution of the linear systems
are devised, and algorithms and matrices for all required scalar products and
transforms are provided. We validate the solver for very high Reynolds numbers.
Specifically, the solver is shown to reproduce the first order statistics of
Hoyas and Jiménez [Phys. Fluids, 18(1), 2006], for a channel flow at
$Re_{\tau}=2000$. The solver is available through the open source project
spectralDNS [this https URL].
| 1 | 1 | 1 | 0 | 0 | 0 |
Bi-Lagrangian structures and Teichmüller theory | This paper has two purposes: the first is to study several structures on
manifolds in the general setting of real and complex differential geometry; the
second is to apply this study to Teichmüller theory. We primarily focus on
bi-Lagrangian structures, which are the data of a symplectic structure and a
pair of transverse Lagrangian foliations, and are equivalent to para-Kähler
structures. First we carefully study real and complex bi-Lagrangian structures
and discuss other closely related structures and their interrelationships. Next
we prove the existence of a canonical complex bi-Lagrangian structure in the
complexification of any real-analytic Kähler manifold and showcase its
properties. We later use this bi-Lagrangian structure to construct a natural
almost hyper-Hermitian structure. We then specialize our study to moduli spaces
of geometric structures on closed surfaces, which tend to have a rich
symplectic structure. We show that some of the recognized geometric features of
these moduli spaces are formal consequences of the general theory, while
revealing other new geometric features. We also gain clarity on several
well-known results of Teichmüller theory by deriving them from pure
differential geometric machinery.
| 0 | 0 | 1 | 0 | 0 | 0 |
Proof of a conjecture of Abdollahi-Akbari-Maimani concerning the non-commutative graph of finite groups | The non--commuting graph $\Gamma(G)$ of a non--abelian group $G$ is defined
as follows. The vertex set $V(\Gamma(G))$ of $\Gamma(G)$ is $G\setminus Z(G)$
where $Z(G)$ denotes the center of $G$ and two vertices $x$ and $y$ are
adjacent if and only if $xy\neq yx$. For non--abelian finite groups $G$ and $H$
it is conjectured that if $\Gamma(G) \cong \Gamma(H)$, then $|G|=|H|$. We prove
the conjecture.
| 0 | 0 | 1 | 0 | 0 | 0 |
LQG Control and Sensing Co-design | Linear-Quadratic-Gaussian (LQG) control is concerned with the design of an
optimal controller and estimator for linear Gaussian systems with imperfect
state information. Standard LQG control assumes the set of sensor measurements
to be fed to the estimator to be given. However, in many problems in networked
systems and robotics, one is interested in designing a suitable set of sensors
for LQG control. In this paper, we introduce the LQG control and sensing
co-design problem, where one has to jointly design a suitable sensing,
estimation, and control policy. We consider two dual instances of the co-design
problem: the sensing-constrained LQG control problem, where the design
maximizes the control performance subject to sensing constraints, and the
minimum-sensing LQG control, where the design minimizes the amount of sensing
subject to performance constraints. We focus on the case in which the sensing
design has to be selected among a finite set of possible sensing modalities,
where each modality is associated with a different cost. While we observe that
the computation of the optimal sensing design is intractable in general, we
present the first scalable LQG co-design algorithms to compute near-optimal
policies with provable sub-optimality guarantees. To this end, (i) we show that
a separation principle holds, which partially decouples the design of sensing,
estimation, and control; (ii) we frame LQG co-design as the optimization of
(approximately) supermodular set functions; (iii) we develop novel algorithms
to solve the resulting optimization problems; (iv) we prove original results on
the performance of these algorithms and establish connections between their
sub-optimality gap and control-theoretic quantities. We conclude the paper by
discussing two practical applications of the co-design problem, namely,
sensing-constrained formation control and resource-constrained robot
navigation.
| 1 | 0 | 0 | 0 | 0 | 0 |
Subspace Learning in The Presence of Sparse Structured Outliers and Noise | Subspace learning is an important problem, which has many applications in
image and video processing. It can be used to find a low-dimensional
representation of signals and images. But in many applications, the desired
signal is heavily distorted by outliers and noise, which negatively affect the
learned subspace. In this work, we present a novel algorithm for learning a
subspace for signal representation, in the presence of structured outliers and
noise. The proposed algorithm tries to jointly detect the outliers and learn
the subspace for images. We present an alternating optimization algorithm for
solving this problem, which iterates between learning the subspace and finding
the outliers. This algorithm has been trained on a large number of image
patches, and the learned subspace is used for image segmentation, and is shown
to achieve better segmentation results than prior methods, including least
absolute deviation fitting, k-means clustering based segmentation in DjVu, and
shape primitive extraction and coding algorithm.
| 1 | 0 | 0 | 0 | 0 | 0 |
Trends in scientific research in Online Information Review. Part 2. Mapping the scientific knowledge through bibliometric and social network analyses | Objective. The purpose of this work is to analyse the knowledge structure and
trends in scientific research in the Online Information Reviews journal by
bibliometric analysis of key words and social network analysis of co-words.
Methods. Key words included in a set of 758 papers included in the Web of
Science database from 2000 to 2014 were analysed. We conducted a subject
analysis considering the key words assigned to papers. A social network
analysis was also conducted to identify the number of co-occurrences between
key words (co-words). The Pajek software was used to create and graphically
visualize the networks. Results. Internet is the most frequent key word (n=219)
and the most central in the network of co-words, strongly associated with
Information retrieval, search engines, the World Wide Web, libraries and users
Conclusions. Information science, as represented by Online Information Review
in the present study, is an evolving discipline that draws on literature from a
relatively wide range of subjects. Although Online Information Review appears
to have well-defined and established research topics, the journal also changes
rapidly to embrace new lines of research.
| 1 | 0 | 0 | 0 | 0 | 0 |
Balance between quantum Markov semigroups | The concept of balance between two state preserving quantum Markov semigroups
on von Neumann algebras is introduced and studied as an extension of conditions
appearing in the theory of quantum detailed balance. This is partly motivated
by the theory of joinings. Balance is defined in terms of certain correlated
states (couplings), with entangled states as a specific case. Basic properties
of balance are derived and the connection to correspondences in the sense of
Connes is discussed. Some applications and possible applications, including to
non-equilibrium statistical mechanics, are briefly explored.
| 0 | 0 | 1 | 0 | 0 | 0 |
Deep Stochastic Configuration Networks with Universal Approximation Property | This paper develops a randomized approach for incrementally building deep
neural networks, where a supervisory mechanism is proposed to constrain the
random assignment of the weights and biases, and all the hidden layers have
direct links to the output layer. A fundamental result on the universal
approximation property is established for such a class of randomized leaner
models, namely deep stochastic configuration networks (DeepSCNs). A learning
algorithm is presented to implement DeepSCNs with either specific architecture
or self-organization. The read-out weights attached with all direct links from
each hidden layer to the output layer are evaluated by the least squares
method. Given a set of training examples, DeepSCNs can speedily produce a
learning representation, that is, a collection of random basis functions with
the cascaded inputs together with the read-out weights. An empirical study on a
function approximation is carried out to demonstrate some properties of the
proposed deep learner model.
| 1 | 0 | 0 | 0 | 0 | 0 |
On convergence for graphexes | We study four different notions of convergence for graphexes, recently
introduced by Borgs, Chayes, Cohn and Holden, and by Veitch and Roy. We give
some properties of them and some relations between them. We also extend results
by Veitch and Roy on convergence of empirical graphons.
| 0 | 0 | 1 | 0 | 0 | 0 |
Improving text classification with vectors of reduced precision | This paper presents the analysis of the impact of a floating-point number
precision reduction on the quality of text classification. The precision
reduction of the vectors representing the data (e.g. TF-IDF representation in
our case) allows for a decrease of computing time and memory footprint on
dedicated hardware platforms. The impact of precision reduction on the
classification quality was performed on 5 corpora, using 4 different
classifiers. Also, dimensionality reduction was taken into account. Results
indicate that the precision reduction improves classification accuracy for most
cases (up to 25% of error reduction). In general, the reduction from 64 to 4
bits gives the best scores and ensures that the results will not be worse than
with the full floating-point representation.
| 1 | 0 | 0 | 0 | 0 | 0 |
On ramification in transcendental extensions of local fields | Let $L/K$ be an extension of complete discrete valuation fields, and assume
that the residue field of $K$ is perfect and of positive characteristic. The
residue field of $L$ is not assumed to be perfect.
In this paper, we prove a formula for the Swan conductor of the image of a
character $\chi \in H^1(K, \mathbb{Q}/\mathbb{Z})$ in $H^1(L,
\mathbb{Q}/\mathbb{Z})$ for $\chi$ sufficiently ramified. Further, we define
generalizations $\psi_{L/K}^{\mathrm{ab}}$ and $\psi_{L/K}^{\mathrm{AS}}$ of
the classical Hasse-Herbrand $\psi$-function and prove a formula for
$\psi_{L/K}^{\mathrm{ab}}(t)$ for sufficiently large $t\in \mathbb{R}$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Local Formulas for Ehrhart Coefficients from Lattice Tiles | As shown by McMullen in 1983, the coefficients of the Ehrhart polynomial of a
lattice polytope can be written as a weighted sum of facial volumes. The
weights in such a local formula depend only on the outer normal cones of faces,
but are far from being unique. In this paper, we develop an infinite class of
such local formulas. These are based on choices of fundamental domains in
sublattices and obtained by polyhedral volume computations. We hereby also give
a kind of geometric interpretation for the Ehrhart coefficients. Since our
construction gives us a great variety of possible local formulas, these can,
for instance, be chosen to fit well with a given polyhedral symmetry group. In
contrast to other constructions of local formulas, ours does not rely on
triangulations of rational cones into simplicial or even unimodular ones.
| 0 | 0 | 1 | 0 | 0 | 0 |
Atypicality for Heart Rate Variability Using a Pattern-Tree Weighting Method | Heart rate variability (HRV) is a vital measure of the autonomic nervous
system functionality and a key indicator of cardiovascular condition. This
paper proposes a novel method, called pattern tree which is an extension of
Willem's context tree to real-valued data, to investigate HRV via an
atypicality framework. In a previous paper atypicality was developed as method
for mining and discovery in "Big Data," which requires a universal approach.
Using the proposed pattern tree as a universal source coder in this framework
led to discovery of arrhythmias and unknown patterns in HRV Holter Monitoring.
| 1 | 0 | 0 | 0 | 0 | 0 |
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