OpenRaiser/Intern-Atlas huggingface dataset:
Optimal power allocation for downstream xDSL with per-modem total power constraints : Broadcast Channel Optimal Spectrum Balancing (BC-OSB)
Venue: arXiv cs.IT 2010
Year: 2010

Recently, the duality between Multiple Input Multiple Output (MIMO) Multiple Access Channels (MAC) and MIMO Broadcast Channels (BC) has been established under a total power constraint. The same set of rates for MAC can be achieved in BC exploiting the MAC-BC duality formulas while preserving the total power constraint. In this paper, we describe the BC optimal power allo- cation applying this duality in a downstream x-Digital Subscriber Lines (xDSL) context under a total power constraint for all modems over all tones. Then, a new algorithm called BC-Optimal Spectrum Balancing (BC-OSB) is devised for a more realistic power allocation under per-modem total power constraints. The capacity region of the primal BC problem under per-modem total power constraints is found by the dual optimization problem for the BC under per-modem total power constraints which can be rewritten as a dual optimization problem in the MAC by means of a precoder matrix based on the Lagrange multipliers. We show that the duality gap between the two problems is zero. The multi-user power allocation problem has been solved for interference channels and MAC using the OSB algorithm. In this paper we solve the problem of multi-user power allocation for the BC case using the OSB algorithm as well and we derive a computational efficient algorithm that will be referred to as BC-OSB. Simulation results are provided for two VDSL2 scenarios: the first one with Differential-Mode (DM) transmission only and the second one with both DM and Phantom- Mode (PM) transmissions.
Joint Bandwidth and Power Allocation with Admission Control in Wireless Multi-User Networks With and Without Relaying
Venue: arXiv cs.IT 2010
Year: 2010

Equal allocation of bandwidth and/or power may not be efficient for wireless multi-user networks with limited bandwidth and power resources. Joint bandwidth and power allocation strategies for wireless multi-user networks with and without relaying are proposed in this paper for (i) the maximization of the sum capacity of all users; (ii) the maximization of the worst user capacity; and (iii) the minimization of the total power consumption of all users. It is shown that the proposed allocation problems are convex and, therefore, can be solved efficiently. Moreover, the admission control based joint bandwidth and power allocation is considered. A suboptimal greedy search algorithm is developed to solve the admission control problem efficiently. The conditions under which the greedy search is optimal are derived and shown to be mild. The performance improvements offered by the proposed joint bandwidth and power allocation are demonstrated by simulations. The advantages of the suboptimal greedy search algorithm for admission control are also shown.
Local Strategy Improvement for Parity Game Solving
Venue: arXiv cs.GT 2010
Year: 2010

The problem of solving a parity game is at the core of many problems in model checking, satisfiability checking and program synthesis. Some of the best algorithms for solving parity game are strategy improvement algorithms. These are global in nature since they require the entire parity game to be present at the beginning. This is a distinct disadvantage because in many applications one only needs to know which winning region a particular node belongs to, and a witnessing winning strategy may cover only a fractional part of the entire game graph. We present a local strategy improvement algorithm which explores the game graph on-the-fly whilst performing the improvement steps. We also compare it empirically with existing global strategy improvement algorithms and the currently only other local algorithm for solving parity games. It turns out that local strategy improvement can outperform these others by several orders of magnitude.
On the Expressiveness of Markovian Process Calculi with Durational and Durationless Actions
Venue: arXiv cs.LO 2010
Year: 2010

Several Markovian process calculi have been proposed in the literature, which differ from each other for various aspects. With regard to the action representation, we distinguish between integrated-time Markovian process calculi, in which every action has an exponentially distributed duration associated with it, and orthogonal-time Markovian process calculi, in which action execution is separated from time passing. Similar to deterministically timed process calculi, we show that these two options are not irreconcilable by exhibiting three mappings from an integrated-time Markovian process calculus to an orthogonal-time Markovian process calculus that preserve the behavioral equivalence of process terms under different interpretations of action execution: eagerness, laziness, and maximal progress. The mappings are limited to classes of process terms of the integrated-time Markovian process calculus with restrictions on parallel composition and do not involve the full capability of the orthogonal-time Markovian process calculus of expressing nondeterministic choices, thus elucidating the only two important differences between the two calculi: their synchronization disciplines and their ways of solving choices.
Mean-payoff Automaton Expressions
Venue: arXiv cs.LO 2010
Year: 2010

Quantitative languages are an extension of boolean languages that assign to each word a real number. Mean-payoff automata are finite automata with numerical weights on transitions that assign to each infinite path the long-run average of the transition weights. When the mode of branching of the automaton is deterministic, nondeterministic, or alternating, the corresponding class of quantitative languages is not robust as it is not closed under the pointwise operations of max, min, sum, and numerical complement. Nondeterministic and alternating mean-payoff automata are not decidable either, as the quantitative generalization of the problems of universality and language inclusion is undecidable. We introduce a new class of quantitative languages, defined by mean-payoff automaton expressions, which is robust and decidable: it is closed under the four pointwise operations, and we show that all decision problems are decidable for this class. Mean-payoff automaton expressions subsume deterministic mean-payoff automata, and we show that they have expressive power incomparable to nondeterministic and alternating mean-payoff automata. We also present for the first time an algorithm to compute distance between two quantitative languages, and in our case the quantitative languages are given as mean-payoff automaton expressions.
Extending scientific computing system with structural quantum programming capabilities
Venue: arXiv cs.PL 2010
Year: 2010

We present a basic high-level structures used for developing quantum programming languages. The presented structures are commonly used in many existing quantum programming languages and we use quantum pseudo-code based on QCL quantum programming language to describe them. We also present the implementation of introduced structures in GNU Octave language for scientific computing. Procedures used in the implementation are available as a package quantum-octave, providing a library of functions, which facilitates the simulation of quantum computing. This package allows also to incorporate high-level programming concepts into the simulation in GNU Octave and Matlab. As such it connects features unique for high-level quantum programming languages, with the full palette of efficient computational routines commonly available in modern scientific computing systems. To present the major features of the described package we provide the implementation of selected quantum algorithms. We also show how quantum errors can be taken into account during the simulation of quantum algorithms using quantum-octave package. This is possible thanks to the ability to operate on density matrices.
Towards a Conceptual Framework for Innate Immunity
Venue: arXiv cs.AI 2010
Year: 2010

Innate immunity now occupies a central role in immunology. However, artificial immune system models have largely been inspired by adaptive not innate immunity. This paper reviews the biological principles and properties of innate immunity and, adopting a conceptual framework, asks how these can be incorporated into artificial models. The aim is to outline a meta-framework for models of innate immunity.
Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret
Venue: arXiv cs.NI 2010
Year: 2010

The problem of distributed learning and channel access is considered in a cognitive network with multiple secondary users. The availability statistics of the channels are initially unknown to the secondary users and are estimated using sensing decisions. There is no explicit information exchange or prior agreement among the secondary users. We propose policies for distributed learning and access which achieve order-optimal cognitive system throughput (number of successful secondary transmissions) under self play, i.e., when implemented at all the secondary users. Equivalently, our policies minimize the regret in distributed learning and access. We first consider the scenario when the number of secondary users is known to the policy, and prove that the total regret is logarithmic in the number of transmission slots. Our distributed learning and access policy achieves order-optimal regret by comparing to an asymptotic lower bound for regret under any uniformly-good learning and access policy. We then consider the case when the number of secondary users is fixed but unknown, and is estimated through feedback. We propose a policy in this scenario whose asymptotic sum regret which grows slightly faster than logarithmic in the number of transmission slots.
The MUSIC Algorithm for Sparse Objects: A Compressed Sensing Analysis
Venue: arXiv cs.IT 2010
Year: 2010

The MUSIC algorithm, with its extension for imaging sparse {\em extended} objects, is analyzed by compressed sensing (CS) techniques. The notion of restricted isometry property (RIP) and an upper bound on the restricted isometry constant (RIC) are employed to establish sufficient conditions for the exact localization by MUSIC with or without the presence of noise. In the noiseless case, the sufficient condition gives an upper bound on the numbers of random sampling and incident directions necessary for exact localization. In the noisy case, the sufficient condition assumes additionally an upper bound for the noise-to-object ratio in terms of the RIC and the condition number of objects. Rigorous comparison of performance between MUSIC and the CS minimization principle, Lasso, is given. In general, the MUSIC algorithm guarantees to recover, with high probability, $s$ scatterers with $n=\cO(s^2)$ random sampling and incident directions and sufficiently high frequency. For the favorable imaging geometry where the scatterers are distributed on a transverse plane MUSIC guarantees to recover, with high probability, $s$ scatterers with a median frequency and $n=\cO(s)$ random sampling/incident directions. Numerical results confirm that the Lasso outperforms MUSIC in the well-resolved case while the opposite is true for the under-resolved case. The latter effect indicates the superresolution capability of the MUSIC algorithm. Another advantage of MUSIC over the Lasso as applied to imaging is the former's flexibility with grid spacing and guarantee of {\em approximate} localization of sufficiently separated objects in an arbitrarily fine grid. The error can be bounded from above by $\cO(λs)$ for general configurations and $\cO(λ)$ for objects distributed in a transverse plane.
Rancangan Infrastruktur E-Bisnis Business Intelligence Pada Perguruan Tinggi
Venue: arXiv cs.OH 2010
Year: 2010

In order to compete with others, high education need complete their infrastructure with Information technology support. High level management as a decision maker need something that can boost the system to compete with other high education, they need IT knowledge that can support them to view the future and can help the whole system to improve their services. Business Intelligence is one of term of Decision Support System which can help the management by something that they can forecast and decide. High Education need infrastructure design to make good foundation for business intelligent implementation which will be implemented on internet or e-business.
Towards the Design of Heuristics by Means of Self-Assembly
Venue: arXiv cs.AI 2010
Year: 2010

The current investigations on hyper-heuristics design have sprung up in two different flavours: heuristics that choose heuristics and heuristics that generate heuristics. In the latter, the goal is to develop a problem-domain independent strategy to automatically generate a good performing heuristic for the problem at hand. This can be done, for example, by automatically selecting and combining different low-level heuristics into a problem specific and effective strategy. Hyper-heuristics raise the level of generality on automated problem solving by attempting to select and/or generate tailored heuristics for the problem at hand. Some approaches like genetic programming have been proposed for this. In this paper, we explore an elegant nature-inspired alternative based on self-assembly construction processes, in which structures emerge out of local interactions between autonomous components. This idea arises from previous works in which computational models of self-assembly were subject to evolutionary design in order to perform the automatic construction of user-defined structures. Then, the aim of this paper is to present a novel methodology for the automated design of heuristics by means of self-assembly.
Full-Duplex Relay based on Zero-Forcing Beamforming
Venue: arXiv cs.IT 2010
Year: 2010

In this paper, we propose a full-duplex relay (FDR) based on a zero-forcing beamforming (ZFBF) for a multiuser MIMO relay system. The ZFBF is employed at the base station to suppress both the self-interference of the relay and the multiuser interference at the same time. Numerical results show that the proposed FDR can enhance the sum rate performance as compared to the half-duplex relay (HDR), if sufficient isolation between the transmit and receive antennas is ensured at the relay.
Multidimensional Datawarehouse with Combination Formula
Venue: arXiv cs.DB 2010
Year: 2010

Multidimensional in data warehouse is a compulsion and become the most important for information delivery, without multidimensional Multidimensional in data warehouse is a compulsion and become the most important for information delivery, without multidimensional datawarehouse is incomplete. Multidimensional give ability to analyze business measurement in many different ways. Multidimensional is also synonymous with online analytical processing (OLAP). By using some concepts in datawarehouse like slice-dice,drill down and roll up will increase the ability of multidimensional datawarehouse. The research question and the discussing for this paper are how much deepest the multidimensional ability from each fact table in datawarehouse. By using the statistic combination formula we try to explore the combination that can be yielded from each dimension in hypercubes, the entire of dimensi combination, minimum combination and maximum combination.
"Birds of a Feather": Does User Homophily Impact Information Diffusion in Social Media?
Venue: arXiv cs.CY 2010
Year: 2010

This article investigates the impact of user homophily on the social process of information diffusion in online social media. Over several decades, social scientists have been interested in the idea that similarity breeds connection: precisely known as "homophily". Homophily has been extensively studied in the social sciences and refers to the idea that users in a social system tend to bond more with ones who are similar to them than to ones who are dissimilar. The key observation is that homophily structures the ego-networks of individuals and impacts their communication behavior. It is therefore likely to effect the mechanisms in which information propagates among them. To this effect, we investigate the interplay between homophily along diverse user attributes and the information diffusion process on social media. In our approach, we first extract diffusion characteristics---corresponding to the baseline social graph as well as graphs filtered on different user attributes (e.g. location, activity). Second, we propose a Dynamic Bayesian Network based framework to predict diffusion characteristics at a future time. Third, the impact of attribute homophily is quantified by the ability of the predicted characteristics in explaining actual diffusion, and external variables, including trends in search and news. Experimental results on a large Twitter dataset demonstrate that choice of the homophilous attribute can impact the prediction of information diffusion, given a specific metric and a topic. In most cases, attribute homophily is able to explain the actual diffusion and external trends by ~15-25% over cases when homophily is not considered.
Efficient Decomposition of Dense Matrices over GF(2)
Venue: arXiv cs.MS 2010
Year: 2010

In this work we describe an efficient implementation of a hierarchy of algorithms for the decomposition of dense matrices over the field with two elements (GF(2)). Matrix decomposition is an essential building block for solving dense systems of linear and non-linear equations and thus much research has been devoted to improve the asymptotic complexity of such algorithms. In this work we discuss an implementation of both well-known and improved algorithms in the M4RI library. The focus of our discussion is on a new variant of the M4RI algorithm - denoted MMPF in this work -- which allows for considerable performance gains in practice when compared to the previously fastest implementation. We provide performance figures on x86_64 CPUs to demonstrate the viability of our approach.
Measuring Meaning on the World-Wide Web
Venue: arXiv cs.AI 2010
Year: 2010

We introduce the notion of the 'meaning bound' of a word with respect to another word by making use of the World-Wide Web as a conceptual environment for meaning. The meaning of a word with respect to another word is established by multiplying the product of the number of webpages containing both words by the total number of webpages of the World-Wide Web, and dividing the result by the product of the number of webpages for each of the single words. We calculate the meaning bounds for several words and analyze different aspects of these by looking at specific examples.
Roomy: A System for Space Limited Computations
Venue: arXiv cs.DC 2010
Year: 2010

There are numerous examples of problems in symbolic algebra in which the required storage grows far beyond the limitations even of the distributed RAM of a cluster. Often this limitation determines how large a problem one can solve in practice. Roomy provides a minimally invasive system to modify the code for such a computation, in order to use the local disks of a cluster or a SAN as a transparent extension of RAM. Roomy is implemented as a C/C++ library. It provides some simple data structures (arrays, unordered lists, and hash tables). Some typical programming constructs that one might employ in Roomy are: map, reduce, duplicate elimination, chain reduction, pair reduction, and breadth-first search. All aspects of parallelism and remote I/O are hidden within the Roomy library.
Augmented Rotation-Based Transformation for Privacy-Preserving Data Clustering
Venue: arXiv cs.CR 2010
Year: 2010

Multiple rotation-based transformation (MRBT) was introduced recently for mitigating the apriori-knowledge independent component analysis (AK-ICA) attack on rotation-based transformation (RBT), which is used for privacy-preserving data clustering. MRBT is shown to mitigate the AK-ICA attack but at the expense of data utility by not enabling conventional clustering. In this paper, we extend the MRBT scheme and introduce an augmented rotation-based transformation (ARBT) scheme that utilizes linearity of transformation and that both mitigates the AK-ICA attack and enables conventional clustering on data subsets transformed using the MRBT. In order to demonstrate the computational feasibility aspect of ARBT along with RBT and MRBT, we develop a toolkit and use it to empirically compare the different schemes of privacy-preserving data clustering based on data transformation in terms of their overhead and privacy.
Timed Games for Computing Worst-Case Execution-Times
Venue: arXiv cs.SE 2010
Year: 2010

In this paper we introduce a framework for computing upper bounds yet accurate WCET for hardware platforms with caches and pipelines. The methodology we propose consists of 3 steps: 1) given a program to analyse, compute an equivalent (WCET-wise) abstract program; 2) build a timed game by composing this abstract program with a network of timed automata modeling the architecture; and 3) compute the WCET as the optimal time to reach a winning state in this game. We demonstrate the applicability of our framework on standard benchmarks for an ARM9 processor with instruction and data caches, and compute the WCET with UPPAAL-TiGA. We also show that this framework can easily be extended to take into account dynamic changes in the speed of the processor during program execution. %
Stochastic Approximation Algorithm for Optimal Throughput Performance of Wireless LANs
Venue: arXiv cs.NI 2010
Year: 2010

Throughput improvement of the Wireless LANs has been a constant area of research. Most of the work in this area, focuses on designing throughput optimal schemes for fully connected networks (no hidden nodes). But, we demonstrate that the proposed schemes, though perform optimally in fully connected network, achieve significantly lesser throughput even than that of standard IEEE 802.11 in a network with hidden nodes. This motivates the need for designing schemes that provide near optimal performance even when hidden nodes are present. The primary reason for the failure of existing protocols in the presence of hidden nodes is that these protocols are based on the model developed by Bianchi. However this model does not hold when hidden nodes exist. Moreover, analyzing networks with hidden nodes is still an open problem. Thus, designing throughput optimal schemes in networks with hidden nodes is particularly challenging. The novelty of our approach is that it is not based on any underlying mathematical model, rather it directly tunes the control variables so as to maximize the throughput. We demonstrate that this model independent approach achieves maximum throughput in networks with hidden terminals as well. Apart from this major contribution, we present stochastic approximation based algorithms for achieving weighted fairness in a connected networks. We also present a throughput optimal exponential backoff based random access algorithm. We demonstrate that the exponential backoff based scheme may outperform an optimal p-persistent scheme in networks with hidden terminals. This demonstrates the merit of exponential backoff based random access schemes which was deemed unnecessary by results shown by Bianchi.
Efektifitas Teknologi Informasi Dalam Proses Belajar Mengajar Pada Universitas Budi Luhur
Venue: arXiv cs.OH 2010
Year: 2010

In general, however, IT will empower students to have greater control over the learning process, with all the benefits associated with active learning and personal responsibility. Not only will students decide when to learn and how to learn, increasingly they will also decide what to learn and how that learning is to be certified. Traditionally, higher education institutions have combined several functions in their faculty. Faculty are architects as they design learning programs; navigators as they help advise students in their course of study; instructors when they lecture; mentors when they help students form a sense of connectedness to the world; and evaluators and certifiers as they decide to grant students grades or degrees.