pinecone/core-2020-05-10-deduplication huggingface dataset:
Gravitational waves from rapidly rotating neutron stars

Rapidly rotating neutron stars in Low Mass X-ray Binaries have been proposed
as an interesting source of gravitational waves. In this chapter we present
estimates of the gravitational wave emission for various scenarios, given the
(electromagnetically) observed characteristics of these systems. First of all
we focus on the r-mode instability and show that a 'minimal' neutron star model
(which does not incorporate exotica in the core, dynamically important magnetic
fields or superfluid degrees of freedom), is not consistent with observations.
We then present estimates of both thermally induced and magnetically sustained
mountains in the crust. In general magnetic mountains are likely to be
detectable only if the buried magnetic field of the star is of the order of
$B\approx 10^{12}$ G. In the thermal mountain case we find that gravitational
wave emission from persistent systems may be detected by ground based
interferometers. Finally we re-asses the idea that gravitational wave emission
may be balancing the accretion torque in these systems, and show that in most
cases the disc/magnetosphere interaction can account for the observed spin
periods.Comment: To appear in 'Gravitational Waves Astrophysics: 3rd Session of the
  Sant Cugat Forum on Astrophysics, 2014', Editor: Carlos F. Sopuert
Supermassive Black Hole Binaries: The Search Continues

Gravitationally bound supermassive black hole binaries (SBHBs) are thought to
be a natural product of galactic mergers and growth of the large scale
structure in the universe. They however remain observationally elusive, thus
raising a question about characteristic observational signatures associated
with these systems. In this conference proceeding I discuss current theoretical
understanding and latest advances and prospects in observational searches for
SBHBs.Comment: 17 pages, 4 figures. To appear in the Proceedings of 2014 Sant Cugat
  Forum on Astrophysics. Astrophysics and Space Science Proceedings, ed.
  C.Sopuerta (Berlin: Springer-Verlag
Weak Singular Hybrid Automata

The framework of Hybrid automata, introduced by Alur, Courcourbetis,
Henzinger, and Ho, provides a formal modeling and analysis environment to
analyze the interaction between the discrete and the continuous parts of
cyber-physical systems. Hybrid automata can be considered as generalizations of
finite state automata augmented with a finite set of real-valued variables
whose dynamics in each state is governed by a system of ordinary differential
equations. Moreover, the discrete transitions of hybrid automata are guarded by
constraints over the values of these real-valued variables, and enable
discontinuous jumps in the evolution of these variables. Singular hybrid
automata are a subclass of hybrid automata where dynamics is specified by
state-dependent constant vectors. Henzinger, Kopke, Puri, and Varaiya showed
that for even very restricted subclasses of singular hybrid automata, the
fundamental verification questions, like reachability and schedulability, are
undecidable. In this paper we present \emph{weak singular hybrid automata}
(WSHA), a previously unexplored subclass of singular hybrid automata, and show
the decidability (and the exact complexity) of various verification questions
for this class including reachability (NP-Complete) and LTL model-checking
(PSPACE-Complete). We further show that extending WSHA with a single
unrestricted clock or extending WSHA with unrestricted variable updates lead to
undecidability of reachability problem
Time Petri Nets with Dynamic Firing Dates: Semantics and Applications

We define an extension of time Petri nets such that the time at which a
transition can fire, also called its firing date, may be dynamically updated.
Our extension provides two mechanisms for updating the timing constraints of a
net. First, we propose to change the static time interval of a transition each
time it is newly enabled; in this case the new time interval is given as a
function of the current marking. Next, we allow to update the firing date of a
transition when it is persistent, that is when a concurrent transition fires.
We show how to carry the widely used state class abstraction to this new kind
of time Petri nets and define a class of nets for which the abstraction is
exact. We show the usefulness of our approach with two applications: first for
scheduling preemptive task, as a poor man's substitute for stopwatch, then to
model hybrid systems with non trivial continuous behavior
The Power of Proofs: New Algorithms for Timed Automata Model Checking
  (with Appendix)

This paper presents the first model-checking algorithm for an expressive
modal mu-calculus over timed automata, $L^{\mathit{rel},
\mathit{af}}_{\nu,\mu}$, and reports performance results for an implementation.
This mu-calculus contains extended time-modality operators and can express all
of TCTL. Our algorithmic approach uses an "on-the-fly" strategy based on proof
search as a means of ensuring high performance for both positive and negative
answers to model-checking questions. In particular, a set of proof rules for
solving model-checking problems are given and proved sound and complete; we
encode our algorithm in these proof rules and model-check a property by
constructing a proof (or showing none exists) using these rules. One noteworthy
aspect of our technique is that we show that verification performance can be
improved with \emph{derived rules}, whose correctness can be inferred from the
more primitive rules on which they are based. In this paper, we give the basic
proof rules underlying our method, describe derived proof rules to improve
performance, and compare our implementation of this model checker to the UPPAAL
tool.Comment: This is the preprint of the FORMATS 2014 paper, but this is the full
  version, containing the Appendix. The final publication is published from
  Springer, and is available at
  http://link.springer.com/chapter/10.1007%2F978-3-319-10512-3_9 on the
  Springer webpag
On Symbolic Solutions of Algebraic Partial Differential Equations

The final version of this paper appears in Grasegger G., Lastra A., Sendra J.R. and\ud
Winkler F. (2014). On symbolic solutions of algebraic partial differential equations, Proc.\ud
CASC 2014 SpringerVerlag LNCS 8660 pp. 111-120. DOI 10.1007/978-3-319-10515-4_9\ud
and it is available at at Springer via http://DOI 10.1007/978-3-319-10515-4_9In this paper we present a general procedure for solving  rst-order autonomous\ud
algebraic partial di erential equations in two independent variables.\ud
The method uses proper rational parametrizations of algebraic surfaces\ud
and generalizes a similar procedure for  rst-order autonomous ordinary\ud
di erential equations. We will demonstrate in examples that, depending on\ud
certain steps in the procedure, rational, radical or even non-algebraic solutions\ud
can be found. Solutions computed by the procedure will depend on\ud
two arbitrary independent constants
Quality-of-Service Data Warehouse for the selection of cloud services : a recent trend

Cloud Computing presents an efficient, on-demand and scalable way to integrate computational resources. However, existing Cloud paradigm is increasingly transforming the information technology landscape, and organizations and businesses are exhibiting strong interest in software-as-a-service (SaaS) delivery model. This enables application service providers to lease data centre capabilities for deploying applications depending on quality of service (QoS) requirements. However, it still remains a challenging task to provide QoS assured services to serve customers with best quality, while also guaranteeing the maximization of the business objectives to service provider and infrastructure provider within certain constraints. To address these issues, this chapter proposes building a data warehouse of QoS to achieve better service matching and enhance dynamic service composition. The proposed QoS data warehouse (QoSDW) model supports the following: ensures a deep analysis of the service’s interior structure and properties through online database analysis; facilitates reasoning about complex service weakness points; supports visual representation of analysis results; and introduces a new QoS factor for study
Solyanik estimates in harmonic analysis

Let $\mathcal{B}$ denote a collection of open bounded sets in $\mathbb{R}^n$,
and define the associated maximal operator $M_{\mathcal{B}}$ by $$
M_{\mathcal{B}}f(x) := \sup_{x \in R \in \mathcal{B}} \frac{1}{|R|}\int_R |f|.
$$ The sharp Tauberian constant of $M_{\mathcal{B}}$ associated to $\alpha$,
denoted by $C_{\mathcal{B}}(\alpha)$, is defined as $$ C_{\mathcal{B}}(\alpha)
:= \sup_{E :\, 0 < |E| < \infty}\frac{1}{|E|}\big|\big\{x \in \mathbb{R}^n:\,
M_{\mathcal{B}}\chi_E (x) > \alpha\big\}\big|.$$ Motivated by previous work of
A. A. Solyanik, we show that if $M_{\mathcal{B}}$ is the uncentered
Hardy-Littlewood maximal operator associated to balls, the estimate $$
\lim_{\alpha \rightarrow 1^-}C_{\mathcal{B}}(\alpha) = 1 $$ holds. Similar
results for iterated maximal functions are obtained, and open problems in the
field of Solyanik estimates are also discussed.Comment: 17 pages, 2 figures, minor typos corrected, to appear in Springer
  Proceedings in Mathematics & Statistic
Riemannian Sparse Coding for Positive Definite Matrices

International audienceInspired by the great success of sparse coding for vector valued data, our goal is to represent symmetric positive definite (SPD) data matrices as sparse linear combinations of atoms from a dictionary, where each atom itself is an SPD matrix. Since SPD matrices follow a non-Euclidean (in fact a Riemannian) geometry, existing sparse coding techniques for Euclidean data cannot be directly extended. Prior works have approached this problem by defining a sparse coding loss function using either extrinsic similarity measures (such as the log-Euclidean distance) or kernelized variants of statistical measures (such as the Stein divergence, Jeffrey's divergence, etc.). In contrast, we propose to use the intrinsic Riemannian distance on the manifold of SPD matrices. Our main contribution is a novel mathematical model for sparse coding of SPD matrices; we also present a computationally simple algorithm for optimizing our model. Experiments on several computer vision datasets showcase superior classification and retrieval performance compared with state-of-the-art approaches
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual
  Recognition

Existing deep convolutional neural networks (CNNs) require a fixed-size
(e.g., 224x224) input image. This requirement is "artificial" and may reduce
the recognition accuracy for the images or sub-images of an arbitrary
size/scale. In this work, we equip the networks with another pooling strategy,
"spatial pyramid pooling", to eliminate the above requirement. The new network
structure, called SPP-net, can generate a fixed-length representation
regardless of image size/scale. Pyramid pooling is also robust to object
deformations. With these advantages, SPP-net should in general improve all
CNN-based image classification methods. On the ImageNet 2012 dataset, we
demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures
despite their different designs. On the Pascal VOC 2007 and Caltech101
datasets, SPP-net achieves state-of-the-art classification results using a
single full-image representation and no fine-tuning.
  The power of SPP-net is also significant in object detection. Using SPP-net,
we compute the feature maps from the entire image only once, and then pool
features in arbitrary regions (sub-images) to generate fixed-length
representations for training the detectors. This method avoids repeatedly
computing the convolutional features. In processing test images, our method is
24-102x faster than the R-CNN method, while achieving better or comparable
accuracy on Pascal VOC 2007.
  In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our
methods rank #2 in object detection and #3 in image classification among all 38
teams. This manuscript also introduces the improvement made for this
competition.Comment: This manuscript is the accepted version for IEEE Transactions on
  Pattern Analysis and Machine Intelligence (TPAMI) 2015. See Changelo
Recognizing City Identity via Attribute Analysis of Geo-tagged Images

After hundreds of years of human settlement, each city has formed a distinct identity, distinguishing itself from other cities. In this work, we propose to characterize the identity of a city via an attribute analysis of 2 million geo-tagged images from 21 cities over 3 continents. First, we estimate the scene attributes of these images and use this representation to build a higher-level set of 7 city attributes, tailored to the form and function of cities. Then, we conduct the city identity recognition experiments on the geo-tagged images and identify images with salient city identity on each city attribute. Based on the misclassification rate of the city identity recognition, we analyze the visual similarity among different cities. Finally, we discuss the potential application of computer vision to urban planning.National Science Foundation (U.S.) (Grant 1016862)Google (Firm) (Research Award
A Graph Theoretic Approach for Object Shape Representation in
  Compositional Hierarchies Using a Hybrid Generative-Descriptive Model

A graph theoretic approach is proposed for object shape representation in a
hierarchical compositional architecture called Compositional Hierarchy of Parts
(CHOP). In the proposed approach, vocabulary learning is performed using a
hybrid generative-descriptive model. First, statistical relationships between
parts are learned using a Minimum Conditional Entropy Clustering algorithm.
Then, selection of descriptive parts is defined as a frequent subgraph
discovery problem, and solved using a Minimum Description Length (MDL)
principle. Finally, part compositions are constructed by compressing the
internal data representation with discovered substructures. Shape
representation and computational complexity properties of the proposed approach
and algorithms are examined using six benchmark two-dimensional shape image
datasets. Experiments show that CHOP can employ part shareability and indexing
mechanisms for fast inference of part compositions using learned shape
vocabularies. Additionally, CHOP provides better shape retrieval performance
than the state-of-the-art shape retrieval methods.Comment: Paper : 17 pages. 13th European Conference on Computer Vision (ECCV
  2014), Zurich, Switzerland, September 6-12, 2014, Proceedings, Part III, pp
  566-581. Supplementary material can be downloaded from
  http://link.springer.com/content/esm/chp:10.1007/978-3-319-10578-9_37/file/MediaObjects/978-3-319-10578-9_37_MOESM1_ESM.pd
Spatio-Temporal Object Detection Proposals

International audienceSpatio-temporal detection of actions and events in video is a challenging problem. Besides the difficulties related to recognition, a major challenge for detection in video is the size of the search space defined by spatio-temporal tubes formed by sequences of bounding boxes along the frames. Recently methods that generate unsupervised detection proposals have proven to be very effective for object detection in still images. These methods open the possibility to use strong but computationally expensive features since only a relatively small number of detection hypotheses need to be assessed. In this paper we make two contributions towards exploiting detection proposals for spatio-temporal detection problems. First, we extend a recent 2D object proposal method, to produce spatio-temporal proposals by a randomized supervoxel merging process. We introduce spatial, temporal, and spatio-temporal pairwise supervoxel features that are used to guide the merging process. Second, we propose a new efficient supervoxel method. We experimentally evaluate our detection proposals, in combination with our new supervoxel method as well as existing ones. This evaluation shows that our supervoxels lead to more accurate proposals when compared to using existing state-of-the-art supervoxel methods
Refraction Wiggles for Measuring Fluid Depth and Velocity from Video

We present principled algorithms for measuring the velocity and 3D location of refractive fluids, such as hot air or gas, from natural videos with textured backgrounds. Our main observation is that intensity variations related to movements of refractive fluid elements, as observed by one or more video cameras, are consistent over small space-time volumes. We call these intensity variations “refraction wiggles”, and use them as features for tracking and stereo fusion to recover the fluid motion and depth from video sequences. We give algorithms for 1) measuring the (2D, projected) motion of refractive fluids in monocular videos, and 2) recovering the 3D position of points on the fluid from stereo cameras. Unlike pixel intensities, wiggles can be extremely subtle and cannot be known with the same level of confidence for all pixels, depending on factors such as background texture and physical properties of the fluid. We thus carefully model uncertainty in our algorithms for robust estimation of fluid motion and depth. We show results on controlled sequences, synthetic simulations, and natural videos. Different from previous approaches for measuring refractive flow, our methods operate directly on videos captured with ordinary cameras, do not require auxiliary sensors, light sources or designed backgrounds, and can correctly detect the motion and location of refractive fluids even when they are invisible to the naked eye.Shell ResearchMotion Sensing Wi-Fi Sensor Networks Co. (Grant 6925133)National Science Foundation (U.S.). Graduate Research Fellowship (Grant 1122374)Microsoft Research (PhD Fellowship
An Analysis of Errors in Graph-Based Keypoint Matching and Proposed Solutions

International audienceAn error occurs in graph-based keypoint matching when key-points in two different images are matched by an algorithm but do not correspond to the same physical point. Most previous methods acquire keypoints in a black-box manner, and focus on developing better algorithms to match the provided points. However to study the complete performance of a matching system one has to study errors through the whole matching pipeline, from keypoint detection, candidate selection to graph optimisation. We show that in the full pipeline there are six different types of errors that cause mismatches. We then present a matching framework designed to reduce these errors. We achieve this by adapting keypoint detectors to better suit the needs of graph-based matching, and achieve better graph constraints by exploiting more information from their keypoints. Our framework is applicable in general images and can handle clutter and motion discontinuities. We also propose a method to identify many mismatches a posteriori based on Left-Right Consistency inspired by stereo matching due to the asymmetric way we detect keypoints and define the graph
Dense Semi-Rigid Scene Flow Estimation from RGBD images

International audienceScene flow is defined as the motion field in 3D space, and can be computed from a single view when using an RGBD sensor. We propose a new scene flow approach that exploits the local and piecewise rigidity of real world scenes. By modeling the motion as a field of twists, our method encourages piecewise smooth solutions of rigid body motions. We give a general formulation to solve for local and global rigid motions by jointly using intensity and depth data. In order to deal efficiently with a moving camera, we model the motion as a rigid component plus a non-rigid residual and propose an alternating solver. The evaluation demonstrates that the proposed method achieves the best results in the most commonly used scene flow benchmark. Through additional experiments we indicate the general applicability of our approach in a variety of different scenarios
A Generative Model for the Joint Registration of Multiple Point Sets

International audienceThis paper describes a probabilistic generative model and its associated algorithm to jointly register multiple point sets. The vast majority of state-of-the-art registration techniques select one of the sets as the ''model" and perform pairwise alignments between the other sets and this set. The main drawback of this mode of operation is that there is no guarantee that the model-set is free of noise and outliers, which contaminates the estimation of the registration parameters. Unlike previous work, the proposed method treats all the point sets on an equal footing: they are realizations of a Gaussian mixture (GMM) and the registration is cast into a clustering problem. We formally derive an EM algorithm that estimates both the GMM parameters and the rotations and translations that map each individual set onto the ''central" model. The mixture means play the role of the registered set of points while the variances provide rich information about the quality of the registration. We thoroughly validate the proposed method with challenging datasets, we compare it with several state-of-the-art methods, and we show its potential for fusing real depth data
An introduction to description logics and query rewriting

This chapter gives an overview of the description logics underlying the OWL 2 Web Ontology Language and its three tractable profiles, OWL 2 RL, OWL 2 EL and OWL 2 QL. We consider the syntax and semantics of these description logics as well as main reasoning tasks and their computational complexity. We also discuss the semantical foundations for fist-order and datalog rewritings of conjunctive queries over knowledge bases given in the OWL2 profiles, and outline the architecture of the ontology-based data access system Ontop
Sub-pixel Layout for Super-Resolution with Images in the Octic Group

This paper presents a novel super-resolution framework by exploring the properties of non-conventional pixel layouts and shapes. We show that recording multiple images, transformed in the octic group, with a sensor of asymmetric sub-pixel layout increases the spatial sampling compared to a conventional sensor with a rectilinear grid of pixels and hence increases the image resolution. We further prove a theoretical bound for achieving well-posed super-resolution with a designated magnification factor w.r.t. the number and distribution of sub-pixels. We also propose strategies for selecting good sub-pixel layouts and effective super-resolution algorithms for our setup. The experimental results validate the proposed theory and solution, which have the potential to guide the future CCD layout design with super-resolution functionality.13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part IUnited States. Air Force (Assistant Secretary of Defense for Research & Engineering Contract #FA8721-05-C-0002)SUTD-MIT International Design Centre (Joint Postdoctoral Programme)Singapore University of Technology and Design (SUTD StartUp Grant ISTD 2011 016)Singapore. Ministry of Education (MOE Academic Research Fund MOE2013-T2-1-159
Read my lips: Continuous signer independent weakly supervised viseme recognition

This work presents a framework to recognise signer independent mouthings in continuous sign language, with no manual annotations needed. Mouthings represent lip-movements that correspond to pronunciations of words or parts of them during signing. Research on sign language recognition has focused extensively on the hands as features. But sign language is multi-modal and a full understanding particularly with respect to its lexical variety, language idioms and grammatical structures is not possible without further exploring the remaining information channels. To our knowledge no previous work has explored dedicated viseme recognition in the context of sign language recognition. The approach is trained on over 180.000 unlabelled frames and reaches 47.1% precision on the frame level. Generalisation across individuals and the influence of context-dependent visemes are analysed. © 2014 Springer International Publishing