pinecone/core-2020-05-10-deduplication huggingface dataset:
Opportunistic Contention-Based Feedback Protocol for Downlink OFDMA Systems with Mixed Traffic

An opportunistic feedback protocol is proposed for orthogonal frequency division multiple access (OFDMA) systems with mixed realtime (RT) and non-realtime (NRT) traffic. This protocol allocates a dedicated feedback channel for each RT user, while it allows all NRT users to compete to send feedback via a contention-based random access feedback channel. The proposed feedback protocol requires a fixed amount of resource to carry feedback from all RT users and some selected NRT users. The probability that NRT users attempt to send feedback is based on two parameters: a channel quality threshold, and the number of subcarriers (or clusters) whose channel quality is above the threshold. Simulation results demonstrate that allocating a dedicated feedback channel for RT user improves the achievable throughput significantly compared with allocating a dedicated traffic channel. For NRT users, the achievable sum rate of the proposed protocol when the number of users is moderate (20-50 users) approaches the ideal case when the channel quality of all users is known at the base station. The sum rate also is close to the ideal case when the number contention minislots per subcarrier is as low as 2 minislot per subcarrier
An Adaptive Cutaway with Volume Context Preservation

Knowledge expressiveness of scientific data is one of the most\ud
important visualization goals. However, current volume visualization systems\ud
require a lot of expertise from the final user. In this paper, we\ud
present a GPU-based ray casting interactive framework that computes\ud
two initial complementary camera locations and allows to select the focus\ud
interactively, on interesting structures keeping the volume’s context\ud
information with an adaptive cutaway technique. The adaptive cutaway\ud
surrounds the focused structure while preserving a depth immersive impression\ud
in the data set. Finally, we present a new brush widget to edit\ud
interactively the opening of the cutaway and to graduate the context in\ud
the final image.Peer ReviewedPostprint (published version
Autonomous Lighting Agents in Photon Mapping

proceedings of ISVC'05International audienceIn computer graphics, global illumination algorithms such as photon mapping require to gather large volumes of data which can be heavily redundant.We propose both a new characterization of useful data and a new optimization method for the photon mapping algorithm using structures borrowed from Artificial Intelligence such as autonomous agents. Our autonomous lighting agents efficiently gather large amounts of useful data and are used to make decisions during rendering. It induces less photons being cast and shorter rendering times in both photon casting and rendering phase of the photon mapping algorithm which leads to an important decrease of memory occupation and slightly shorter rendering times for equal image quality
Incremental context creation and its effects on semantic query precision

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-10543-2_19Proceedings of 4th International Conference on Semantic and Digital Media Technologies, SAMT 2009 Graz, Austria, December 2-4, 2009We briefly describe the results of an experimental study on the incremental creation of context out of the results of targeted queries, and discuss the increase in retrieval precision that results from the incremental enrichment of context.This work was supported in part by Consejería de Educación, Comunidad Autónoma de
Madrid, under the grant CCG08-UAM/TIC/4303, Búsqueda basada en contexto como alternativa
semántica al modelo ontológico. Simone Santini was in part supported by the Ramón
y Cajal initiative of the Ministero de educación y ciencia. Alexandra Dumitrescu was in part
supported by the European Social Fund, Universidad Autónoma de Madrid
On the coöperative creation of multimedia meaning

The final publication is available at Springer via http://dx.doi.org/110.1007/978-3-642-10543-2_5Proceedings of 4th International Conference on Semantic and Digital Media Technologies, SAMT 2009 Graz, Austria, December 2-4, 2009In this paper, we propose a content-based method the for semi- automatic organization of photo albums based on the analysis of how different users organize their own pictures. The goal is to help the user in dividing his pictures into groups characterized by a similar semantic content. The method is semi-automatic: the user starts to assign labels to the pictures and unlabeled pictures are tagged with proposed labels. The user can accept the recommendation or make a correction. The method is conceptually articulated in two parts. First, we use a suitable feature representation of the images to model the different classes that the users have collected, second, we look for correspondences between the criteria used by the different users. A quantitative evaluation of the proposed approach is proposed based on pictures of a set of members of the flickr® photo-sharing community
Spin-lattice coupling in frustrated antiferromagnets

We review the mechanism of spin-lattice coupling in relieving the geometrical
frustration of pyrochlore antiferromagnets, in particular spinel oxides. The
tetrahedral unit, which is the building block of the pyrochlore lattice,
undergoes a spin-driven Jahn-Teller instability when lattice degrees of freedom
are coupled to the antiferromagnetism. By restricting our considerations to
distortions which preserve the translational symmetries of the lattice, we
present a general theory of the collective spin-Jahn-Teller effect in the
pyrochlore lattice. One of the predicted lattice distortions breaks the
inversion symmetry and gives rise to a chiral pyrochlore lattice, in which
frustrated bonds form helices with a definite handedness. The chirality is
transferred to the spin system through spin-orbit coupling, resulting in a
long-period spiral state, as observed in spinel CdCr2O4. We discuss explicit
models of spin-lattice coupling using local phonon modes, and their
applications in other frustrated magnets.Comment: 23 pages, 6 figures. Lecture notes for Trieste Summer School, August
  2007. To appear as a chapter in "Highly Frustrated Magnetism", Eds. C.
  Lacroix, P. Mendels, F. Mil
Strong-coupling expansion and effective hamiltonians

When looking for analytical approaches to treat frustrated quantum magnets,
it is often very useful to start from a limit where the ground state is highly
degenerate. This chapter discusses several ways of deriving {effective
Hamiltonians} around such limits, starting from standard {degenerate
perturbation theory} and proceeding to modern approaches more appropriate for
the derivation of high-order effective Hamiltonians, such as the perturbative
continuous unitary transformations or contractor renormalization. In the course
of this exposition, a number of examples taken from the recent literature are
discussed, including frustrated ladders and other dimer-based Heisenberg models
in a field, as well as the mapping between frustrated Ising models in a
transverse field and quantum dimer models.Comment: To appear as a chapter in "Highly Frustrated Magnetism", Eds. C.
  Lacroix, P. Mendels, F. Mil
Lectures on inflation and cosmological perturbations

The purpose of these lectures is to give a pedagogical introduction to
inflation and the production of primordial perturbations, as well as a review
of some of the latest developments in this domain. After a short introduction,
we review the main principles of the Hot Big Bang model, as well as its
limitations. This motivates the study of cosmological inflation induced by a
slow-rolling scalar field. We then turn to the analysis of cosmological
perturbations, and explain how the vacuum quantum fluctuations are amplified
during an inflationary phase. The next step consists in relating the
perturbations generated during inflation to the perturbations of the
cosmological fluid in the radiation dominated phase. The final part of these
lectures gives a review of more general models of inflation, involving multiple
fields or non standard kinetic terms. Although more complicated, these models
are usually motivated by high energy physics and they can lead to specific
signatures that are not expected in the simplest models of inflation. After
introducing a very general formalism to describe perturbations in multi-field
models with arbitrary kinetic terms, several interesting cases are presented.
We also stress the role of entropy perturbations in the context of multi-field
models. Finally, we discuss in detail the non-Gaussianities of the primordial
perturbations and some models that could produce a detectable level of
non-Gaussianities.Comment: 56 pages, 5 figures; Lectures given at the Second TRR33 Winter School
  on cosmology, Passo del Tonale (Italy), December 200
Statistical methods in cosmology

The advent of large data-set in cosmology has meant that in the past 10 or 20
years our knowledge and understanding of the Universe has changed not only
quantitatively but also, and most importantly, qualitatively. Cosmologists rely
on data where a host of useful information is enclosed, but is encoded in a
non-trivial way. The challenges in extracting this information must be overcome
to make the most of a large experimental effort. Even after having converged to
a standard cosmological model (the LCDM model) we should keep in mind that this
model is described by 10 or more physical parameters and if we want to study
deviations from it, the number of parameters is even larger. Dealing with such
a high dimensional parameter space and finding parameters constraints is a
challenge on itself. Cosmologists want to be able to compare and combine
different data sets both for testing for possible disagreements (which could
indicate new physics) and for improving parameter determinations. Finally,
cosmologists in many cases want to find out, before actually doing the
experiment, how much one would be able to learn from it. For all these reasons,
sophisiticated statistical techniques are being employed in cosmology, and it
has become crucial to know some statistical background to understand recent
literature in the field. I will introduce some statistical tools that any
cosmologist should know about in order to be able to understand recently
published results from the analysis of cosmological data sets. I will not
present a complete and rigorous introduction to statistics as there are several
good books which are reported in the references. The reader should refer to
those.Comment: 31, pages, 6 figures, notes from 2nd Trans-Regio Winter school in
  Passo del Tonale. To appear in Lectures Notes in Physics, "Lectures on
  cosmology: Accelerated expansion of the universe" Feb 201
Reusing static keys in key agreement protocols

10th International Conference on Cryptology in India, INDOCRYPT 2009; New Delhi; India; 13 December 2009 through 16 December 2009Contrary to conventional cryptographic wisdom, the NIST SP 800-56A standard explicitly allows the use of a static key pair in more than one of the key establishment protocols described in the standard. In this paper, we give examples of key establishment protocols that are individually secure, but which are insecure when static key pairs are reused in two of the protocols. We also propose an enhancement of the extended Canetti-Krawczyk security model and definition for the situation where static public keys are reused in two or more key agreement protocols. © 2009 Springer-Verlag
Exploring narrative presentation for large multimodal lifelog collections through card sorting

Using lifelogging tools, personal digital artifacts are collected continuously and passively throughout each day. The wealth of information such an archive contains on our life history provides novel opportunities for the creation of digital life narratives. However, the complexity, volume and multimodal nature of such collections create barriers to achieving this. Nine participants engaged in a card-sorting activity designed to explore practices of content reduction and presentation for narrative composition. We found the visual modalities to be most fluent in communicating experience with other modalities serving to support them and that the users employed the salient themes of the story to organise, arrange and facilitate filtering of the content
Controlling narrative generation with planning trajectories: the role of constraints

AI planning has featured in a number of Interactive story-telling prototypes: since narratives can be naturally modelled as a sequence of actions it has been possible to exploit state of the art planners in the task of narrative generation. However the characteristics of a "good" plan, such as optimality, aren't necessarily the same as those of a "good" narrative, where errors and convoluted sequences may offer more reader interest, so some narrative structuring is required. In their work the authors have looked at injecting narrative control into plan generation through the use of PDDL3.0 state trajectory constraints which enable them to express narrative control information within the planning representation. As part of this the authors have developed an approach to planning\ud
with such trajectory constraints. The approach decomposes the problem into a set of smaller subproblems using the temporal orderings described by the constraints and then solves these subproblems incrementally. In this paper the authors outline their method and present results that illustrate the potential of the approach
Taking Fuzzy-Rough Application to Mars: Fuzzy-Rough Feature Selection for Mars Terrain Image Classification

C. Shang, D. Barnes and Q. Shen. Taking fuzzy-rough application to Mars: Fuzzy-rough feature selection for Mars terrain image classification. Proceedings of the 12th International Conference on Rough Sets, LNAI 5908, pp. 209-216, 2009. Sponsorship: Daphne Jackson Trust and Royal Academy of EngineeringThis paper presents a novel application of fuzzy-rough setbased feature selection (FRFS) for Mars terrain image classification. The work allows the induction of low-dimensionality feature sets from sample descriptions of feature patterns of a much higher dimensionality. In particular, FRFS is applied in conjunction with multi-layer perceptron and K-nearest neighbor based classifiers. Supported with comparative studies, the paper demonstrates that FRFS helps to enhance the effectiveness and efficiency of conventional classification systems, by minimizing redundant and noisy features. This is of particular significance for on-board image classification in future Mars rover missions.preprintpreprin
Fuzzy Sets and Rough Sets for Scenario Modelling and Analysis

Q. Shen. Fuzzy sets and rough sets for scenario modelling and analysis. Proceedings of the 12th International Conference on Rough Sets, LNAI 5908, pp. 45-58, 2009. Sponsorship: EPSRCBoth fuzzy set theory and rough set theory play an important role in data-driven, systems modelling and analysis. They have been successfully applied to building various intelligent decision support systems (amongst many others). This paper presents an integrated utilisation of some recent advances in these theories for detection and prevention of serious crime (e.g. terrorism). It is shown that the use of these advanced theories offers an effective means for the generation and assessment of plausible scenarios which can each provide an explanation for the given intelligence data. The resulting systems have the potential to facilitate rapid response in devising and deploying preventive measures. The paper also suggests a number of important further challenges in consolidating and refining such systems.preprintpreprin
Community Cloud Computing

Cloud Computing is rising fast, with its data centres growing at an
unprecedented rate. However, this has come with concerns over privacy,
efficiency at the expense of resilience, and environmental sustainability,
because of the dependence on Cloud vendors such as Google, Amazon and
Microsoft. Our response is an alternative model for the Cloud
conceptualisation, providing a paradigm for Clouds in the community, utilising
networked personal computers for liberation from the centralised vendor model.
Community Cloud Computing (C3) offers an alternative architecture, created by
combing the Cloud with paradigms from Grid Computing, principles from Digital
Ecosystems, and sustainability from Green Computing, while remaining true to
the original vision of the Internet. It is more technically challenging than
Cloud Computing, having to deal with distributed computing issues, including
heterogeneous nodes, varying quality of service, and additional security
constraints. However, these are not insurmountable challenges, and with the
need to retain control over our digital lives and the potential environmental
consequences, it is a challenge we must pursue.Comment: 11 Pages, 5 figure
Selecting, optimizing and fusing ‘salient’ Gabor \ud
features for facial expression recognition

This paper describes a novel framework for facial expression recognition from still images by selecting, optimizing and fusing ‘salient’ Gabor feature layers to recognize six universal facial expressions using the K nearest neighbor classifier. The recognition comparisons with all layer approach using JAFFE and Cohn-Kanade (CK) databases confirm that using ‘salient’ Gabor feature layers with optimized sizes can achieve better recognition performance and dramatically reduce computational time. Moreover, comparisons with the state of the art performances demonstrate the effectiveness of our approach
Exploring early classification strategies of streaming data with delayed attributes

In contrast to traditional machine learning algorithms, where all data are available in batch mode, the new paradigm of streaming data poses additional difficulties, since data samples arrive in a sequence and many hard decisions have to be made on-line. The problem addressed here consists of classifying streaming data which not only are unlabeled, but also have a number l of attributes arriving after some time delay. In this context, the main issues are what to do when the unlabeled incomplete samples and, later on, their missing attributes arrive; when and how to classify these incoming samples; and when and how to update the training set. Three different strategies (for l = 1 and constant) are explored and evaluated in terms of the accumulated classification error. The results reveal that the proposed on-line strategies, despite their simplicity, may outperform classifiers using only the original, labeled-and-complete samples as a fixed training set. In other words, learning is possible by properly tapping into the unlabeled, incomplete samples, and their delayed attributes. The many research issues identified include a better understanding of the link between the inherent properties of the data set and the design of the most suitable on-line classification strateg
A novel evolving clustering algorithm with polynomial regression for chaotic time-series prediction

Time-series prediction has been a very well researched topic in recent studies. Some popular approaches to this problem are the traditional statistical methods e.g. multiple linear regression and moving average, and neural network with the Multi Layer Perceptron which has shown its supremacy in time-series prediction. In this study, we used a different approach based on evolving clustering algorithm with polynomial regressions to find repeating local patterns in a time-series data. To illustrate chaotic time-series data we have taken into account the use of stock price data from Indonesian stock exchange market and currency exchange rate data. In addition, we have also conducted a benchmark test using the Mackey Glass data set. Results showed that the algorithm offers a considerably high accuracy in time-series prediction and could also reveal repeating patterns of movement from the past
Quantum Online Memory Checking

The problem of memory checking considers storing files on an unreliable
public server whose memory can be modified by a malicious party. The main task
is to design an online memory checker with the capability to verify that the
information on the server has not been corrupted. To store n bits of public
information, the memory checker has s private reliable bits for verification
purpose; while to retrieve each bit of public information the checker
communicates t bits with the public memory. Earlier work showed that, for
classical memory checkers, the lower bound s*t \in Omega(n) holds. In this
article we study quantum memory checkers that have s private qubits and that
are allowed to quantum query the public memory using t qubits. We prove an
exponential improvement over the classical setting by showing the existence of
a quantum checker that, using quantum fingerprints, requires only s \in O(log
n) qubits of local memory and t \in O(polylog n) qubits of communication with
the public memory.Comment: 12 pages; Theory of Quantum Computation, Communication, and
  Cryptography: Fourth Workshop, TQC 200
Speeding-Up Expensive Evaluations in High-Level Synthesis Using Solution Modeling and Fitness Inheritance

High-Level Synthesis (HLS) is the process of developing digital circuits from behavioral specifications. It involves three interdependent and NP-complete optimization problems: (i) the operation scheduling, (ii) the resource allocation, and (iii) the controller synthesis. Evolutionary Algorithms have been already effectively applied to HLS to find good solution in presence of conflicting design objectives. In this paper, we present an evolutionary approach to HLS that extends previous works in three respects: (i) we exploit the NSGA-II, a multi-objective genetic algorithm, to fully automate the design space exploration without the need of any human intervention, (ii) we replace the expensive evaluation process of candidate solutions with a quite accurate regression model, and (iii) we reduce the number of evaluations with a fitness inheritance scheme. We tested our approach on several benchmark problems. Our results suggest that all the enhancements introduced improve the overall performance of the evolutionary search
Lectures on Spectrum Generating Symmetries and U-duality in
  Supergravity, Extremal Black Holes, Quantum Attractors and Harmonic
  Superspace

We review the underlying algebraic structures of supergravity theories with
symmetric scalar manifolds in five and four dimensions, orbits of their
extremal black hole solutions and the spectrum generating extensions of their
U-duality groups. For 5D, N=2 Maxwell-Einstein supergravity theories (MESGT)
defined by Euclidean Jordan algebras, J, the spectrum generating symmetry
groups are the conformal groups Conf(J) of J which are isomorphic to their
U-duality groups in four dimensions. Similarly, the spectrum generating
symmetry groups of 4D, N=2 MESGTs are the quasiconformal groups QConf(J)
associated with J that are isomorphic to their U-duality groups in three
dimensions. We then review the work on spectrum generating symmetries of
spherically symmetric stationary 4D BPS black holes, based on the equivalence
of their attractor equations and the equations for geodesic motion of a
fiducial particle on the target spaces of corresponding 3D supergravity
theories obtained by timelike reduction. We also discuss the connection between
harmonic superspace formulation of 4D, N=2 sigma models coupled to supergravity
and the minimal unitary representations of their isometry groups obtained by
quantizing their quasiconformal realizations. We discuss the relevance of this
connection to spectrum generating symmetries and conclude with a brief summary
of more recent results.Comment: 55 pages; Latex fil