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cs/0503033
An Introduction to the Summarization of Evolving Events: Linear and Non-linear Evolution
[ "cs.CL", "cs.IR" ]
This paper examines the summarization of events that evolve through time. It discusses different types of evolution taking into account the time in which the incidents of an event are happening and the different sources reporting on the specific event. It proposes an approach for multi-document summarization which empl...
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cs/0503037
Mining Top-k Approximate Frequent Patterns
[ "cs.DB", "cs.AI" ]
Frequent pattern (itemset) mining in transactional databases is one of the most well-studied problems in data mining. One obstacle that limits the practical usage of frequent pattern mining is the extremely large number of patterns generated. Such a large size of the output collection makes it difficult for users to un...
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cs/0503038
On a Kronecker products sum distance bounds
[ "cs.IT", "math.IT" ]
A binary linear error correcting codes represented by two code families Kronecker products sum are considered. The dimension and distance of new code is investigated. Upper and lower bounds of distance are obtained. Some examples are given. It is shown that some classic constructions are the private cases of considered...
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cs/0503040
Uplink Throughput in a Single-Macrocell/Single-Microcell CDMA System, with Application to Data Access Points
[ "cs.IT", "math.IT" ]
This paper studies a two-tier CDMA system in which the microcell base is converted into a data access point (DAP), i.e., a limited-range base station that provides high-speed access to one user at a time. The microcell (or DAP) user operates on the same frequency as the macrocell users and has the same chip rate. Howev...
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cs/0503041
Soft Handoff and Uplink Capacity in a Two-Tier CDMA System
[ "cs.IT", "math.IT" ]
This paper examines the effect of soft handoff on the uplink user capacity of a CDMA system consisting of a single macrocell in which a single hotspot microcell is embedded. The users of these two base stations operate over the same frequency band. In the soft handoff scenario studied here, both macrocell and microcell...
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cs/0503042
Uplink User Capacity in a CDMA System with Hotspot Microcells: Effects of Finite Transmit Power and Dispersion
[ "cs.IT", "math.IT" ]
This paper examines the uplink user capacity in a two-tier code division multiple access (CDMA) system with hotspot microcells when user terminal power is limited and the wireless channel is finitely-dispersive. A finitely-dispersive channel causes variable fading of the signal power at the output of the RAKE receiver....
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cs/0503043
Complexity Issues in Finding Succinct Solutions of PSPACE-Complete Problems
[ "cs.AI", "cs.CC", "cs.LO" ]
We study the problem of deciding whether some PSPACE-complete problems have models of bounded size. Contrary to problems in NP, models of PSPACE-complete problems may be exponentially large. However, such models may take polynomial space in a succinct representation. For example, the models of a QBF are explicitely rep...
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cs/0503044
Generating Hard Satisfiable Formulas by Hiding Solutions Deceptively
[ "cs.AI", "cond-mat.other", "cond-mat.stat-mech" ]
To test incomplete search algorithms for constraint satisfaction problems such as 3-SAT, we need a source of hard, but satisfiable, benchmark instances. A simple way to do this is to choose a random truth assignment A, and then choose clauses randomly from among those satisfied by A. However, this method tends to produ...
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cs/0503046
Hiding Satisfying Assignments: Two are Better than One
[ "cs.AI", "cond-mat.dis-nn", "cond-mat.stat-mech", "cs.CC" ]
The evaluation of incomplete satisfiability solvers depends critically on the availability of hard satisfiable instances. A plausible source of such instances consists of random k-SAT formulas whose clauses are chosen uniformly from among all clauses satisfying some randomly chosen truth assignment A. Unfortunately, in...
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cs/0503047
On Multiflows in Random Unit-Disk Graphs, and the Capacity of Some Wireless Networks
[ "cs.IT", "math.IT" ]
We consider the capacity problem for wireless networks. Networks are modeled as random unit-disk graphs, and the capacity problem is formulated as one of finding the maximum value of a multicommodity flow. In this paper, we develop a proof technique based on which we are able to obtain a tight characterization of the s...
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cs/0503052
Zeta-Dimension
[ "cs.CC", "cs.IT", "math.IT" ]
The zeta-dimension of a set A of positive integers is the infimum s such that the sum of the reciprocals of the s-th powers of the elements of A is finite. Zeta-dimension serves as a fractal dimension on the positive integers that extends naturally usefully to discrete lattices such as the set of all integer lattice ...
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cs/0503053
A hybrid MLP-PNN architecture for fast image superresolution
[ "cs.CV", "cs.MM" ]
Image superresolution methods process an input image sequence of a scene to obtain a still image with increased resolution. Classical approaches to this problem involve complex iterative minimization procedures, typically with high computational costs. In this paper is proposed a novel algorithm for super-resolution th...
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cs/0503056
Semi-automatic vectorization of linear networks on rasterized cartographic maps
[ "cs.CV", "cs.MM" ]
A system for semi-automatic vectorization of linear networks (roads, rivers, etc.) on rasterized cartographic maps is presented. In this system, human intervention is limited to a graphic, interactive selection of the color attributes of the information to be obtained. Using this data, the system performs a preliminary...
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cs/0503058
On the Stopping Distance and the Stopping Redundancy of Codes
[ "cs.IT", "cs.DM", "math.IT" ]
It is now well known that the performance of a linear code $C$ under iterative decoding on a binary erasure channel (and other channels) is determined by the size of the smallest stopping set in the Tanner graph for $C$. Several recent papers refer to this parameter as the \emph{stopping distance} $s$ of $C$. This is s...
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cs/0503059
Les repr\'{e}sentations g\'{e}n\'{e}tiques d'objets : simples analogies ou mod\`{e}les pertinents ? Le point de vue de l' "\'{e}volutique".<br>&ndash;&ndash;&ndash;<br>Genetic representations of objects : simple analogies or efficient models ? The "evolutic" point of view
[ "cs.AI", "nlin.AO" ]
Depuis une trentaine d'ann\'{e}es, les ing\'{e}nieurs utilisent couramment des analogies avec l'\'{e}volution naturelle pour optimiser des dispositifs techniques. Le plus souvent, ces m\'{e}thodes "g\'{e}n\'{e}tiques" ou "\'{e}volutionnaires" sont consid\'{e}r\'{e}es uniquement du point de vue pratique, comme des m\'{e...
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cs/0503061
Integrity Constraints in Trust Management
[ "cs.CR", "cs.DB" ]
We introduce the use, monitoring, and enforcement of integrity constraints in trust management-style authorization systems. We consider what portions of the policy state must be monitored to detect violations of integrity constraints. Then we address the fact that not all participants in a trust management system can b...
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cs/0503062
On the Complexity of Nonrecursive XQuery and Functional Query Languages on Complex Values
[ "cs.DB", "cs.CC" ]
This paper studies the complexity of evaluating functional query languages for complex values such as monad algebra and the recursion-free fragment of XQuery. We show that monad algebra with equality restricted to atomic values is complete for the class TA[2^{O(n)}, O(n)] of problems solvable in linear exponential ti...
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cs/0503063
Randomly Spread CDMA: Asymptotics via Statistical Physics
[ "cs.IT", "math.IT" ]
This paper studies randomly spread code-division multiple access (CDMA) and multiuser detection in the large-system limit using the replica method developed in statistical physics. Arbitrary input distributions and flat fading are considered. A generic multiuser detector in the form of the posterior mean estimator is a...
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cs/0503064
Minimum-Cost Multicast over Coded Packet Networks
[ "cs.IT", "cs.NI", "math.IT" ]
We consider the problem of establishing minimum-cost multicast connections over coded packet networks, i.e. packet networks where the contents of outgoing packets are arbitrary, causal functions of the contents of received packets. We consider both wireline and wireless packet networks as well as both static multicast ...
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cs/0503070
Improved message passing for inference in densely connected systems
[ "cs.IT", "cond-mat.dis-nn", "math.IT" ]
An improved inference method for densely connected systems is presented. The approach is based on passing condensed messages between variables, representing macroscopic averages of microscopic messages. We extend previous work that showed promising results in cases where the solution space is contiguous to cases where ...
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cs/0503071
Consistency in Models for Distributed Learning under Communication Constraints
[ "cs.IT", "cs.LG", "math.IT" ]
Motivated by sensor networks and other distributed settings, several models for distributed learning are presented. The models differ from classical works in statistical pattern recognition by allocating observations of an independent and identically distributed (i.i.d.) sampling process amongst members of a network of...
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cs/0503072
Distributed Learning in Wireless Sensor Networks
[ "cs.IT", "cs.LG", "math.IT" ]
The problem of distributed or decentralized detection and estimation in applications such as wireless sensor networks has often been considered in the framework of parametric models, in which strong assumptions are made about a statistical description of nature. In certain applications, such assumptions are warranted a...
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cs/0503076
Geometric Models of Rolling-Shutter Cameras
[ "cs.CV", "cs.RO" ]
Cameras with rolling shutters are becoming more common as low-power, low-cost CMOS sensors are being used more frequently in cameras. The rolling shutter means that not all scanlines are exposed over the same time interval. The effects of a rolling shutter are noticeable when either the camera or objects in the scene a...
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cs/0503077
Weighted Automata in Text and Speech Processing
[ "cs.CL", "cs.HC" ]
Finite-state automata are a very effective tool in natural language processing. However, in a variety of applications and especially in speech precessing, it is necessary to consider more general machines in which arcs are assigned weights or costs. We briefly describe some of the main theoretical and algorithmic aspec...
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cs/0503078
Obtaining Membership Functions from a Neuron Fuzzy System extended by Kohonen Network
[ "cs.NE" ]
This article presents the Neo-Fuzzy-Neuron Modified by Kohonen Network (NFN-MK), an hybrid computational model that combines fuzzy system technique and artificial neural networks. Its main task consists in the automatic generation of membership functions, in particular, triangle forms, aiming a dynamic modeling of a sy...
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cs/0503079
Space-time databases modeling global semantic networks
[ "cs.IT", "cs.IR", "math.IT" ]
This paper represents an approach to creating global knowledge systems, using new philosophy and infrastructure of global distributed semantic network (frame knowledge representation system) based on the space-time database construction. The main idea of the space-time database environment introduced in the paper is to...
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cs/0503081
An Optimization Model for Outlier Detection in Categorical Data
[ "cs.DB", "cs.AI" ]
The task of outlier detection is to find small groups of data objects that are exceptional when compared with rest large amount of data. Detection of such outliers is important for many applications such as fraud detection and customer migration. Most existing methods are designed for numeric data. They will encounter ...
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cs/0503082
Spines of Random Constraint Satisfaction Problems: Definition and Connection with Computational Complexity
[ "cs.CC", "cond-mat.dis-nn", "cs.AI" ]
We study the connection between the order of phase transitions in combinatorial problems and the complexity of decision algorithms for such problems. We rigorously show that, for a class of random constraint satisfaction problems, a limited connection between the two phenomena indeed exists. Specifically, we extend the...
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cs/0503084
The Peculiarities of Nonstationary Formation of Inhomogeneous Structures of Charged Particles in the Electrodiffusion Processes
[ "cs.CE" ]
In this paper the distribution of charged particles is constructed under the approximation of ambipolar diffusion. The results of mathematical modelling in two-dimensional case taking into account the velocities of the system are presented.
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cs/0503085
Dynamic Shannon Coding
[ "cs.IT", "math.IT" ]
We present a new algorithm for dynamic prefix-free coding, based on Shannon coding. We give a simple analysis and prove a better upper bound on the length of the encoding produced than the corresponding bound for dynamic Huffman coding. We show how our algorithm can be modified for efficient length-restricted coding, a...
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cs/0503087
Dynamic Simulation of Construction Machinery: Towards an Operator Model
[ "cs.CE" ]
In dynamic simulation of complete wheel loaders, one interesting aspect, specific for the working task, is the momentary power distribution between drive train and hydraulics, which is balanced by the operator. This paper presents the initial results to a simulation model of a human operator. Rather than letting the ...
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cs/0503088
General non-asymptotic and asymptotic formulas in channel resolvability and identification capacity and their application to wire-tap channel
[ "cs.IT", "math.IT" ]
Several non-asymptotic formulas are established in channel resolvability and identification capacity, and they are applied to wire-tap channel. By using these formulas, the $\epsilon$ capacities of the above three problems are considered in the most general setting, where no structural assumptions such as the stationar...
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cs/0503089
Second order asymptotics in fixed-length source coding and intrinsic randomness
[ "cs.IT", "math.IT" ]
Second order asymptotics of fixed-length source coding and intrinsic randomness is discussed with a constant error constraint. There was a difference between optimal rates of fixed-length source coding and intrinsic randomness, which never occurred in the first order asymptotics. In addition, the relation between unifo...
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cs/0503092
Monotonic and Nonmonotonic Preference Revision
[ "cs.DB", "cs.AI" ]
We study here preference revision, considering both the monotonic case where the original preferences are preserved and the nonmonotonic case where the new preferences may override the original ones. We use a relational framework in which preferences are represented using binary relations (not necessarily finite). We i...
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cs/0504001
Probabilistic and Team PFIN-type Learning: General Properties
[ "cs.LG" ]
We consider the probability hierarchy for Popperian FINite learning and study the general properties of this hierarchy. We prove that the probability hierarchy is decidable, i.e. there exists an algorithm that receives p_1 and p_2 and answers whether PFIN-type learning with the probability of success p_1 is equivalent ...
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cs/0504003
Multiple Description Quantization via Gram-Schmidt Orthogonalization
[ "cs.IT", "math.IT" ]
The multiple description (MD) problem has received considerable attention as a model of information transmission over unreliable channels. A general framework for designing efficient multiple description quantization schemes is proposed in this paper. We provide a systematic treatment of the El Gamal-Cover (EGC) achiev...
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cs/0504005
Fast Codes for Large Alphabets
[ "cs.IT", "math.IT" ]
We address the problem of constructing a fast lossless code in the case when the source alphabet is large. The main idea of the new scheme may be described as follows. We group letters with small probabilities in subsets (acting as super letters) and use time consuming coding for these subsets only, whereas letters in ...
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cs/0504006
Using Information Theory Approach to Randomness Testing
[ "cs.IT", "math.IT" ]
We address the problem of detecting deviations of binary sequence from randomness,which is very important for random number (RNG) and pseudorandom number generators (PRNG). Namely, we consider a null hypothesis $H_0$ that a given bit sequence is generated by Bernoulli source with equal probabilities of 0 and 1 and the ...
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cs/0504010
Reversible Fault-Tolerant Logic
[ "cs.IT", "math.IT", "quant-ph" ]
It is now widely accepted that the CMOS technology implementing irreversible logic will hit a scaling limit beyond 2016, and that the increased power dissipation is a major limiting factor. Reversible computing can potentially require arbitrarily small amounts of energy. Recently several nano-scale devices which have t...
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cs/0504011
Average Coset Weight Distribution of Combined LDPC Matrix Ensemble
[ "cs.IT", "math.IT" ]
In this paper, the average coset weight distribution (ACWD) of structured ensembles of LDPC (Low-density Parity-Check) matrix, which is called combined ensembles, is discussed. A combined ensemble is composed of a set of simpler ensembles such as a regular bipartite ensemble. Two classes of combined ensembles have prim...
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cs/0504013
Pseudocodewords of Tanner graphs
[ "cs.IT", "math.IT" ]
This papers presents a detailed analysis of pseudocodewords of Tanner graphs. Pseudocodewords arising on the iterative decoder's computation tree are distinguished from pseudocodewords arising on finite degree lifts. Lower bounds on the minimum pseudocodeword weight are presented for the BEC, BSC, and AWGN channel. Som...
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cs/0504014
Network Information Flow with Correlated Sources
[ "cs.IT", "math.IT" ]
In this paper, we consider a network communications problem in which multiple correlated sources must be delivered to a single data collector node, over a network of noisy independent point-to-point channels. We prove that perfect reconstruction of all the sources at the sink is possible if and only if, for all partiti...
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cs/0504015
Design of Block Transceivers with Decision Feedback Detection
[ "cs.IT", "math.IT" ]
This paper presents a method for jointly designing the transmitter-receiver pair in a block-by-block communication system that employs (intra-block) decision feedback detection. We provide closed-form expressions for transmitter-receiver pairs that simultaneously minimize the arithmetic mean squared error (MSE) at the ...
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cs/0504016
Shortened Array Codes of Large Girth
[ "cs.DM", "cs.IT", "math.IT" ]
One approach to designing structured low-density parity-check (LDPC) codes with large girth is to shorten codes with small girth in such a manner that the deleted columns of the parity-check matrix contain all the variables involved in short cycles. This approach is especially effective if the parity-check matrix of a ...
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cs/0504017
A new SISO algorithm with application to turbo equalization
[ "cs.IT", "math.IT" ]
In this paper we propose a new soft-input soft-output equalization algorithm, offering very good performance/complexity tradeoffs. It follows the structure of the BCJR algorithm, but dynamically constructs a simplified trellis during the forward recursion. In each trellis section, only the M states with the strongest f...
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cs/0504020
The Viterbi Algorithm: A Personal History
[ "cs.IT", "math.IT" ]
The story of the Viterbi algorithm (VA) is told from a personal perspective. Applications both within and beyond communications are discussed. In brief summary, the VA has proved to be an extremely important algorithm in a surprising variety of fields.
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cs/0504021
Near Perfect Decoding of LDPC Codes
[ "cs.IT", "math.IT" ]
Cooperative optimization is a new way for finding global optima of complicated functions of many variables. It has some important properties not possessed by any conventional optimization methods. It has been successfully applied in solving many large scale optimization problems in image processing, computer vision, an...
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cs/0504022
A Matter of Opinion: Sentiment Analysis and Business Intelligence (position paper)
[ "cs.CL" ]
A general-audience introduction to the area of "sentiment analysis", the computational treatment of subjective, opinion-oriented language (an example application is determining whether a review is "thumbs up" or "thumbs down"). Some challenges, applications to business-intelligence tasks, and potential future direction...
{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
cs/0504024
Constraint-Based Qualitative Simulation
[ "cs.AI", "cs.LO" ]
We consider qualitative simulation involving a finite set of qualitative relations in presence of complete knowledge about their interrelationship. We show how it can be naturally captured by means of constraints expressed in temporal logic and constraint satisfaction problems. The constraints relate at each stage the ...
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cs/0504028
On Extrinsic Information of Good Codes Operating Over Discrete Memoryless Channels
[ "cs.IT", "math.IT" ]
We show that the Extrinsic Information about the coded bits of any good (capacity achieving) code operating over a wide class of discrete memoryless channels (DMC) is zero when channel capacity is below the code rate and positive constant otherwise, that is, the Extrinsic Information Transfer (EXIT) chart is a step fun...
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cs/0504030
Sufficient conditions for convergence of the Sum-Product Algorithm
[ "cs.IT", "cs.AI", "math.IT" ]
We derive novel conditions that guarantee convergence of the Sum-Product algorithm (also known as Loopy Belief Propagation or simply Belief Propagation) to a unique fixed point, irrespective of the initial messages. The computational complexity of the conditions is polynomial in the number of variables. In contrast wit...
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cs/0504031
Convexity Analysis of Snake Models Based on Hamiltonian Formulation
[ "cs.CV", "cs.GR" ]
This paper presents a convexity analysis for the dynamic snake model based on the Potential Energy functional and the Hamiltonian formulation of the classical mechanics. First we see the snake model as a dynamical system whose singular points are the borders we seek. Next we show that a necessary condition for a singul...
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cs/0504032
Critical Point for Maximum Likelihood Decoding of Linear Block Codes
[ "cs.IT", "math.IT" ]
In this letter, the SNR value at which the error performance curve of a soft decision maximum likelihood decoder reaches the slope corresponding to the code minimum distance is determined for a random code. Based on this value, referred to as the critical point, new insight about soft bounded distance decoding of rando...
{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 1, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
cs/0504035
Fitness Uniform Deletion: A Simple Way to Preserve Diversity
[ "cs.NE", "cs.AI" ]
A commonly experienced problem with population based optimisation methods is the gradual decline in population diversity that tends to occur over time. This can slow a system's progress or even halt it completely if the population converges on a local optimum from which it cannot escape. In this paper we present the Fi...
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cs/0504036
Scientific impact quantity and quality: Analysis of two sources of bibliographic data
[ "cs.IR", "cs.DL" ]
Attempts to understand the consequence of any individual scientist's activity within the long-term trajectory of science is one of the most difficult questions within the philosophy of science. Because scientific publications play such as central role in the modern enterprise of science, bibliometric techniques which m...
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cs/0504037
Bayesian Restoration of Digital Images Employing Markov Chain Monte Carlo a Review
[ "cs.CV", "cond-mat.stat-mech", "physics.comp-ph" ]
A review of Bayesian restoration of digital images based on Monte Carlo techniques is presented. The topics covered include Likelihood, Prior and Posterior distributions, Poisson, Binay symmetric channel, and Gaussian channel models of Likelihood distribution,Ising and Potts spin models of Prior distribution, restorati...
{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
cs/0504041
Learning Polynomial Networks for Classification of Clinical Electroencephalograms
[ "cs.AI", "cs.NE" ]
We describe a polynomial network technique developed for learning to classify clinical electroencephalograms (EEGs) presented by noisy features. Using an evolutionary strategy implemented within Group Method of Data Handling, we learn classification models which are comprehensively described by sets of short-term polyn...
{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 1, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
cs/0504042
The Bayesian Decision Tree Technique with a Sweeping Strategy
[ "cs.AI", "cs.LG" ]
The uncertainty of classification outcomes is of crucial importance for many safety critical applications including, for example, medical diagnostics. In such applications the uncertainty of classification can be reliably estimated within a Bayesian model averaging technique that allows the use of prior information. De...
{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
cs/0504043
Experimental Comparison of Classification Uncertainty for Randomised and Bayesian Decision Tree Ensembles
[ "cs.AI", "cs.LG" ]
In this paper we experimentally compare the classification uncertainty of the randomised Decision Tree (DT) ensemble technique and the Bayesian DT technique with a restarting strategy on a synthetic dataset as well as on some datasets commonly used in the machine learning community. For quantitative evaluation of class...
{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
cs/0504046
On the Entropy Rate of Pattern Processes
[ "cs.IT", "math.IT" ]
We study the entropy rate of pattern sequences of stochastic processes, and its relationship to the entropy rate of the original process. We give a complete characterization of this relationship for i.i.d. processes over arbitrary alphabets, stationary ergodic processes over discrete alphabets, and a broad family of st...
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cs/0504047
Pushdown dimension
[ "cs.IT", "cs.CC", "math.IT" ]
This paper develops the theory of pushdown dimension and explores its relationship with finite-state dimension. Pushdown dimension is trivially bounded above by finite-state dimension for all sequences, since a pushdown gambler can simulate any finite-state gambler. We show that for every rational 0 < d < 1, there exis...
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cs/0504049
Bounds on the Entropy of Patterns of I.I.D. Sequences
[ "cs.IT", "math.IT" ]
Bounds on the entropy of patterns of sequences generated by independently identically distributed (i.i.d.) sources are derived. A pattern is a sequence of indices that contains all consecutive integer indices in increasing order of first occurrence. If the alphabet of a source that generated a sequence is unknown, the ...
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cs/0504051
A Scalable Stream-Oriented Framework for Cluster Applications
[ "cs.DC", "cs.DB", "cs.NI", "cs.OS", "cs.PL" ]
This paper presents a stream-oriented architecture for structuring cluster applications. Clusters that run applications based on this architecture can scale to tenths of thousands of nodes with significantly less performance loss or reliability problems. Our architecture exploits the stream nature of the data flow and ...
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cs/0504052
Learning Multi-Class Neural-Network Models from Electroencephalograms
[ "cs.NE", "cs.LG" ]
We describe a new algorithm for learning multi-class neural-network models from large-scale clinical electroencephalograms (EEGs). This algorithm trains hidden neurons separately to classify all the pairs of classes. To find best pairwise classifiers, our algorithm searches for input variables which are relevant to the...
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cs/0504053
A Neural-Network Technique for Recognition of Filaments in Solar Images
[ "cs.NE" ]
We describe a new neural-network technique developed for an automated recognition of solar filaments visible in the hydrogen H-alpha line full disk spectroheliograms. This technique allows neural networks learn from a few image fragments labelled manually to recognize the single filaments depicted on a local background...
{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 1, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
cs/0504054
Learning from Web: Review of Approaches
[ "cs.NE", "cs.LG" ]
Knowledge discovery is defined as non-trivial extraction of implicit, previously unknown and potentially useful information from given data. Knowledge extraction from web documents deals with unstructured, free-format documents whose number is enormous and rapidly growing. The artificial neural networks are well suitab...
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cs/0504055
A Learning Algorithm for Evolving Cascade Neural Networks
[ "cs.NE", "cs.AI" ]
A new learning algorithm for Evolving Cascade Neural Networks (ECNNs) is described. An ECNN starts to learn with one input node and then adding new inputs as well as new hidden neurons evolves it. The trained ECNN has a nearly minimal number of input and hidden neurons as well as connections. The algorithm was successf...
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cs/0504056
Self-Organizing Multilayered Neural Networks of Optimal Complexity
[ "cs.NE", "cs.AI" ]
The principles of self-organizing the neural networks of optimal complexity is considered under the unrepresentative learning set. The method of self-organizing the multi-layered neural networks is offered and used to train the logical neural networks which were applied to the medical diagnostics.
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cs/0504057
Diagnostic Rule Extraction Using Neural Networks
[ "cs.NE", "cs.AI" ]
The neural networks have trained on incomplete sets that a doctor could collect. Trained neural networks have correctly classified all the presented instances. The number of intervals entered for encoding the quantitative variables is equal two. The number of features as well as the number of neurons and layers in trai...
{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 1, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
cs/0504058
Polynomial Neural Networks Learnt to Classify EEG Signals
[ "cs.NE", "cs.AI" ]
A neural network based technique is presented, which is able to successfully extract polynomial classification rules from labeled electroencephalogram (EEG) signals. To represent the classification rules in an analytical form, we use the polynomial neural networks trained by a modified Group Method of Data Handling (GM...
{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 1, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
cs/0504059
A Neural Network Decision Tree for Learning Concepts from EEG Data
[ "cs.NE", "cs.AI" ]
To learn the multi-class conceptions from the electroencephalogram (EEG) data we developed a neural network decision tree (DT), that performs the linear tests, and a new training algorithm. We found that the known methods fail inducting the classification models when the data are presented by the features some of them ...
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cs/0504060
Universal Minimax Discrete Denoising under Channel Uncertainty
[ "cs.IT", "math.IT" ]
The goal of a denoising algorithm is to recover a signal from its noise-corrupted observations. Perfect recovery is seldom possible and performance is measured under a given single-letter fidelity criterion. For discrete signals corrupted by a known discrete memoryless channel, the DUDE was recently shown to perform th...
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cs/0504061
Summarization from Medical Documents: A Survey
[ "cs.CL", "cs.IR" ]
Objective: The aim of this paper is to survey the recent work in medical documents summarization. Background: During the last decade, documents summarization got increasing attention by the AI research community. More recently it also attracted the interest of the medical research community as well, due to the en...
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cs/0504063
Selection in Scale-Free Small World
[ "cs.LG", "cs.IR" ]
In this paper we compare the performance characteristics of our selection based learning algorithm for Web crawlers with the characteristics of the reinforcement learning algorithm. The task of the crawlers is to find new information on the Web. The selection algorithm, called weblog update, modifies the starting URL l...
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cs/0504064
Neural-Network Techniques for Visual Mining Clinical Electroencephalograms
[ "cs.AI" ]
In this chapter we describe new neural-network techniques developed for visual mining clinical electroencephalograms (EEGs), the weak electrical potentials invoked by brain activity. These techniques exploit fruitful ideas of Group Method of Data Handling (GMDH). Section 2 briefly describes the standard neural-network ...
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cs/0504065
Estimating Classification Uncertainty of Bayesian Decision Tree Technique on Financial Data
[ "cs.AI" ]
Bayesian averaging over classification models allows the uncertainty of classification outcomes to be evaluated, which is of crucial importance for making reliable decisions in applications such as financial in which risks have to be estimated. The uncertainty of classification is determined by a trade-off between the ...
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cs/0504066
Comparison of the Bayesian and Randomised Decision Tree Ensembles within an Uncertainty Envelope Technique
[ "cs.AI" ]
Multiple Classifier Systems (MCSs) allow evaluation of the uncertainty of classification outcomes that is of crucial importance for safety critical applications. The uncertainty of classification is determined by a trade-off between the amount of data available for training, the classifier diversity and the required pe...
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cs/0504067
An Evolving Cascade Neural Network Technique for Cleaning Sleep Electroencephalograms
[ "cs.NE", "cs.AI" ]
Evolving Cascade Neural Networks (ECNNs) and a new training algorithm capable of selecting informative features are described. The ECNN initially learns with one input node and then evolves by adding new inputs as well as new hidden neurons. The resultant ECNN has a near minimal number of hidden neurons and inputs. The...
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cs/0504068
Self-Organization of the Neuron Collective of Optimal Complexity
[ "cs.NE", "cs.AI" ]
The optimal complexity of neural networks is achieved when the self-organization principles is used to eliminate the contradictions existing in accordance with the K. Godel theorem about incompleteness of the systems based on axiomatics. The principle of S. Beer exterior addition the Heuristic Group Method of Data Hand...
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cs/0504069
A Neural-Network Technique to Learn Concepts from Electroencephalograms
[ "cs.NE", "cs.AI", "cs.LG" ]
A new technique is presented developed to learn multi-class concepts from clinical electroencephalograms. A desired concept is represented as a neuronal computational model consisting of the input, hidden, and output neurons. In this model the hidden neurons learn independently to classify the electroencephalogram segm...
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cs/0504070
The Combined Technique for Detection of Artifacts in Clinical Electroencephalograms of Sleeping Newborns
[ "cs.NE", "cs.AI", "cs.LG" ]
In this paper we describe a new method combining the polynomial neural network and decision tree techniques in order to derive comprehensible classification rules from clinical electroencephalograms (EEGs) recorded from sleeping newborns. These EEGs are heavily corrupted by cardiac, eye movement, muscle and noise artif...
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cs/0504071
Proceedings of the Pacific Knowledge Acquisition Workshop 2004
[ "cs.AI" ]
Artificial intelligence (AI) research has evolved over the last few decades and knowledge acquisition research is at the core of AI research. PKAW-04 is one of three international knowledge acquisition workshops held in the Pacific-Rim, Canada and Europe over the last two decades. PKAW-04 has a strong emphasis on incre...
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cs/0504072
Knowledge Representation Issues in Semantic Graphs for Relationship Detection
[ "cs.AI", "physics.soc-ph" ]
An important task for Homeland Security is the prediction of threat vulnerabilities, such as through the detection of relationships between seemingly disjoint entities. A structure used for this task is a "semantic graph", also known as a "relational data graph" or an "attributed relational graph". These graphs encode ...
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cs/0504074
Metalinguistic Information Extraction for Terminology
[ "cs.CL", "cs.AI", "cs.IR" ]
This paper describes and evaluates the Metalinguistic Operation Processor (MOP) system for automatic compilation of metalinguistic information from technical and scientific documents. This system is designed to extract non-standard terminological resources that we have called Metalinguistic Information Databases (or MI...
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cs/0504075
Dichotomy for Voting Systems
[ "cs.GT", "cs.CC", "cs.MA" ]
Scoring protocols are a broad class of voting systems. Each is defined by a vector $(\alpha_1,\alpha_2,...,\alpha_m)$, $\alpha_1 \geq \alpha_2 \geq >... \geq \alpha_m$, of integers such that each voter contributes $\alpha_1$ points to his/her first choice, $\alpha_2$ points to his/her second choice, and so on, and any ...
{ "Other": 1, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 1, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
cs/0504078
Adaptive Online Prediction by Following the Perturbed Leader
[ "cs.AI", "cs.LG" ]
When applying aggregating strategies to Prediction with Expert Advice, the learning rate must be adaptively tuned. The natural choice of sqrt(complexity/current loss) renders the analysis of Weighted Majority derivatives quite complicated. In particular, for arbitrary weights there have been no results proven so far. T...
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cs/0504079
Prediction of Large Alphabet Processes and Its Application to Adaptive Source Coding
[ "cs.IT", "math.IT" ]
The problem of predicting a sequence $x_1,x_2,...$ generated by a discrete source with unknown statistics is considered. Each letter $x_{t+1}$ is predicted using information on the word $x_1x_2... x_t$ only. In fact, this problem is a classical problem which has received much attention. Its history can be traced back t...
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cs/0504080
Performance of Gaussian Signalling in Non Coherent Rayleigh Fading Channels
[ "cs.IT", "math.IT" ]
The mutual information of a discrete time memoryless Rayleigh fading channel is considered, where neither the transmitter nor the receiver has the knowledge of the channel state information except the fading statistics. We present the mutual information of this channel in closed form when the input distribution is comp...
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cs/0504081
A Decomposition Approach to Multi-Vehicle Cooperative Control
[ "cs.RO" ]
We present methods that generate cooperative strategies for multi-vehicle control problems using a decomposition approach. By introducing a set of tasks to be completed by the team of vehicles and a task execution method for each vehicle, we decomposed the problem into a combinatorial component and a continuous compone...
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cs/0504085
Capacity per Unit Energy of Fading Channels with a Peak Constraint
[ "cs.IT", "math.IT" ]
A discrete-time single-user scalar channel with temporally correlated Rayleigh fading is analyzed. There is no side information at the transmitter or the receiver. A simple expression is given for the capacity per unit energy, in the presence of a peak constraint. The simple formula of Verdu for capacity per unit cost ...
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cs/0504086
Componentwise Least Squares Support Vector Machines
[ "cs.LG", "cs.AI" ]
This chapter describes componentwise Least Squares Support Vector Machines (LS-SVMs) for the estimation of additive models consisting of a sum of nonlinear components. The primal-dual derivations characterizing LS-SVMs for the estimation of the additive model result in a single set of linear equations with size growing...
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cs/0504089
Universal Similarity
[ "cs.IR", "cs.AI", "cs.CL", "physics.data-an" ]
We survey a new area of parameter-free similarity distance measures useful in data-mining, pattern recognition, learning and automatic semantics extraction. Given a family of distances on a set of objects, a distance is universal up to a certain precision for that family if it minorizes every distance in the family bet...
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cs/0504091
A Probabilistic Upper Bound on Differential Entropy
[ "cs.IT", "math.IT" ]
A novel, non-trivial, probabilistic upper bound on the entropy of an unknown one-dimensional distribution, given the support of the distribution and a sample from that distribution, is presented. No knowledge beyond the support of the unknown distribution is required, nor is the distribution required to have a density....
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cs/0504099
The Capacity of Random Ad hoc Networks under a Realistic Link Layer Model
[ "cs.IT", "cs.NI", "math.IT" ]
The problem of determining asymptotic bounds on the capacity of a random ad hoc network is considered. Previous approaches assumed a threshold-based link layer model in which a packet transmission is successful if the SINR at the receiver is greater than a fixed threshold. In reality, the mapping from SINR to packet su...
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cs/0504100
A DNA Sequence Compression Algorithm Based on LUT and LZ77
[ "cs.IT", "math.IT" ]
This article introduces a new DNA sequence compression algorithm which is based on LUT and LZ77 algorithm. Combined a LUT-based pre-coding routine and LZ77 compression routine,this algorithm can approach a compression ratio of 1.9bits \slash base and even lower.The biggest advantage of this algorithm is fast execution,...
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cs/0504101
Single-solution Random 3-SAT Instances
[ "cs.AI", "cs.CC", "cs.DM" ]
We study a class of random 3-SAT instances having exactly one solution. The properties of this ensemble considerably differ from those of a random 3-SAT ensemble. It is numerically shown that the running time of several complete and stochastic local search algorithms monotonically increases as the clause density is dec...
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cs/0504102
Spectral Orbits and Peak-to-Average Power Ratio of Boolean Functions with respect to the {I,H,N}^n Transform
[ "cs.IT", "math.IT" ]
We enumerate the inequivalent self-dual additive codes over GF(4) of blocklength n, thereby extending the sequence A090899 in The On-Line Encyclopedia of Integer Sequences from n = 9 to n = 12. These codes have a well-known interpretation as quantum codes. They can also be represented by graphs, where a simple graph op...
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cs/0504108
Cooperative Game Theory within Multi-Agent Systems for Systems Scheduling
[ "cs.AI", "cs.MA" ]
Research concerning organization and coordination within multi-agent systems continues to draw from a variety of architectures and methodologies. The work presented in this paper combines techniques from game theory and multi-agent systems to produce self-organizing, polymorphic, lightweight, embedded agents for system...
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cs/0505001
Modelling investment in artificial stock markets: Analytical and Numerical Results
[ "cs.CE" ]
In this article we study the behavior of a group of economic agents in the context of cooperative game theory, interacting according to rules based on the Potts Model with suitable modifications. Each agent can be thought of as belonging to a chain, where agents can only interact with their nearest neighbors (periodic ...
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cs/0505002
Tight Lower Bounds for Query Processing on Streaming and External Memory Data
[ "cs.DB", "cs.CC" ]
We study a clean machine model for external memory and stream processing. We show that the number of scans of the external data induces a strict hierarchy (as long as work space is sufficiently small, e.g., polylogarithmic in the size of the input). We also show that neither joins nor sorting are feasible if the produc...
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