OpenRaiser/Intern-Atlas huggingface dataset:
On the Capacity and Design of Limited Feedback Multiuser MIMO Uplinks
Venue: arXiv cs.IT 2008
Year: 2008

The theory of multiple-input multiple-output (MIMO) technology has been well-developed to increase fading channel capacity over single-input single-output (SISO) systems. This capacity gain can often be leveraged by utilizing channel state information at the transmitter and the receiver. Users make use of this channel state information for transmit signal adaptation. In this correspondence, we derive the capacity region for the MIMO multiple access channel (MIMO MAC) when partial channel state information is available at the transmitters, where we assume a synchronous MIMO multiuser uplink. The partial channel state information feedback has a cardinality constraint and is fed back from the basestation to the users using a limited rate feedback channel. Using this feedback information, we propose a finite codebook design method to maximize sum-rate. In this correspondence, the codebook is a set of transmit signal covariance matrices. We also derive the capacity region and codebook design methods in the case that the covariance matrix is rank-one (i.e., beamforming). This is motivated by the fact that beamforming is optimal in certain conditions. The simulation results show that when the number of feedback bits increases, the capacity also increases. Even with a small number of feedback bits, the performance of the proposed system is close to an optimal solution with the full feedback.
An integrated model of traffic, geography and economy in the Internet
Venue: arXiv cs.NI 2008
Year: 2008

Modeling Internet growth is important both for understanding the current network and to predict and improve its future. To date, Internet models have typically attempted to explain a subset of the following characteristics: network structure, traffic flow, geography, and economy. In this paper we present a discrete, agent-based model, that integrates all of them. We show that the model generates networks with topologies, dynamics, and (more speculatively) spatial distributions that are similar to the Internet.
Concerning the Feasibility of Example-driven Modelling Techniques
Venue: arXiv cs.HC 2008
Year: 2008

We report on a series of experiments concerning the feasibility of example driven modelling. The main aim was to establish experimentally within an academic environment: the relationship between error and task complexity using a) Traditional spreadsheet modelling; b) example driven techniques. We report on the experimental design, sampling, research methods and the tasks set for both control and treatment groups. Analysis of the completed tasks allows comparison of several different variables. The experimental results compare the performance indicators for the treatment and control groups by comparing accuracy, experience, training, confidence measures, perceived difficulty and perceived completeness. The various results are thoroughly tested for statistical significance using: the Chi squared test, Fisher's exact test for significance, Cochran's Q test and McNemar's test on difficulty.
Neural Networks and Database Systems
Venue: arXiv cs.DB 2008
Year: 2008

Object-oriented database systems proved very valuable at handling and administrating complex objects. In the following guidelines for embedding neural networks into such systems are presented. It is our goal to treat networks as normal data in the database system. From the logical point of view, a neural network is a complex data value and can be stored as a normal data object. It is generally accepted that rule-based reasoning will play an important role in future database applications. The knowledge base consists of facts and rules, which are both stored and handled by the underlying database system. Neural networks can be seen as representation of intensional knowledge of intelligent database systems. So they are part of a rule based knowledge pool and can be used like conventional rules. The user has a unified view about his knowledge base regardless of the origin of the unique rules.
Preventing Coordinated Attacks Via Distributed Alert Exchange
Venue: arXiv cs.CR 2008
Year: 2008

Attacks on information systems followed by intrusions may cause large revenue losses. The prevention of both is not always possible by just considering information from isolated sources of the network. A global view of the whole system is necessary to recognize and react to the different actions of such an attack. The design and deployment of a decentralized system targeted at detecting as well as reacting to information system attacks might benefit from the loose coupling realized by publish/subscribe middleware. In this paper, we present the advantages and convenience in using this communication paradigm for a general decentralized attack prevention framework. Furthermore, we present the design and implementation of our approach based on existing publish/subscribe middleware and evaluate our approach for GNU/Linux systems.
Information Hiding Techniques: A Tutorial Review
Venue: arXiv cs.CR 2008
Year: 2008

The purpose of this tutorial is to present an overview of various information hiding techniques. A brief history of steganography is provided along with techniques that were used to hide information. Text, image and audio based information hiding techniques are discussed. This paper also provides a basic introduction to digital watermarking.
Pattern-Oriented Analysis and Design (POAD) Theory
Venue: arXiv cs.SE 2008
Year: 2008

Pattern-Oriented Analysis and Design (POAD) is the practice of building complex software by applying proven designs to specific problem domains. Although a great deal of research and practice has been devoted to formalizing existing design patterns and discovering new ones, there has been relatively little research into methods for combining these patterns into software applications. This is partly because the creation of complex software applications is so expensive. This paper proposes a mathematical model of POAD that may allow future research in pattern-oriented techniques to be performed using less expensive formal techniques rather than expensive, complex software development.
Joint Source Channel Coding with Side Information Using Hybrid Digital Analog Codes
Venue: arXiv cs.IT 2008
Year: 2008

We study the joint source channel coding problem of transmitting an analog source over a Gaussian channel in two cases - (i) the presence of interference known only to the transmitter and (ii) in the presence of side information known only to the receiver. We introduce hybrid digital analog forms of the Costa and Wyner-Ziv coding schemes. Our schemes are based on random coding arguments and are different from the nested lattice schemes by Kochman and Zamir that use dithered quantization. We also discuss superimposed digital and analog schemes for the above problems which show that there are infinitely many schemes for achieving the optimal distortion for these problems. This provides an extension of the schemes by Bross et al to the interference/side information case. We then discuss applications of the hybrid digital analog schemes for transmitting under a channel signal-to-noise ratio mismatch and for broadcasting a Gaussian source with bandwidth compression.
A Qualitative Modal Representation of Quantum Register Transformations
Venue: arXiv cs.LO 2008
Year: 2008

We introduce two modal natural deduction systems that are suitable to represent and reason about transformations of quantum registers in an abstract, qualitative, way. Quantum registers represent quantum systems, and can be viewed as the structure of quantum data for quantum operations. Our systems provide a modal framework for reasoning about operations on quantum registers (unitary transformations and measurements), in terms of possible worlds (as abstractions of quantum registers) and accessibility relations between these worlds. We give a Kripke--style semantics that formally describes quantum register transformations and prove the soundness and completeness of our systems with respect to this semantics.
Hospital Case Cost Estimates Modelling - Algorithm Comparison
Venue: arXiv cs.CE 2008
Year: 2008

Ontario (Canada) Health System stakeholders support the idea and necessity of the integrated source of data that would include both clinical (e.g. diagnosis, intervention, length of stay, case mix group) and financial (e.g. cost per weighted case, cost per diem) characteristics of the Ontario healthcare system activities at the patient-specific level. At present, the actual patient-level case costs in the explicit form are not available in the financial databases for all hospitals. The goal of this research effort is to develop financial models that will assign each clinical case in the patient-specific data warehouse a dollar value, representing the cost incurred by the Ontario health care facility which treated the patient. Five mathematical models have been developed and verified using real dataset. All models can be classified into two groups based on their underlying method: 1. Models based on using relative intensity weights of the cases, and 2. Models based on using cost per diem.
Opportunistic Scheduling and Beamforming for MIMO-OFDMA Downlink Systems with Reduced Feedback
Venue: arXiv cs.IT 2008
Year: 2008

Opportunistic scheduling and beamforming schemes with reduced feedback are proposed for MIMO-OFDMA downlink systems. Unlike the conventional beamforming schemes in which beamforming is implemented solely by the base station (BS) in a per-subcarrier fashion, the proposed schemes take advantages of a novel channel decomposition technique to perform beamforming jointly by the BS and the mobile terminal (MT). The resulting beamforming schemes allow the BS to employ only {\em one} beamforming matrix (BFM) to form beams for {\em all} subcarriers while each MT completes the beamforming task for each subcarrier locally. Consequently, for a MIMO-OFDMA system with $Q$ subcarriers, the proposed opportunistic scheduling and beamforming schemes require only one BFM index and $Q$ supportable throughputs to be returned from each MT to the BS, in contrast to $Q$ BFM indices and $Q$ supportable throughputs required by the conventional schemes. The advantage of the proposed schemes becomes more evident when a further feedback reduction is achieved by grouping adjacent subcarriers into exclusive clusters and returning only cluster information from each MT. Theoretical analysis and computer simulation confirm the effectiveness of the proposed reduced-feedback schemes.
SINR Analysis of Opportunistic MIMO-SDMA Downlink Systems with Linear Combining
Venue: arXiv cs.IT 2008
Year: 2008

Opportunistic scheduling (OS) schemes have been proposed previously by the authors for multiuser MIMO-SDMA downlink systems with linear combining. In particular, it has been demonstrated that significant performance improvement can be achieved by incorporating low-complexity linear combining techniques into the design of OS schemes for MIMO-SDMA. However, this previous analysis was performed based on the effective signal-to-interference ratio (SIR), assuming an interference-limited scenario, which is typically a valid assumption in SDMA-based systems. It was shown that the limiting distribution of the effective SIR is of the Frechet type. Surprisingly, the corresponding scaling laws were found to follow $ε\log K$ with $0<ε<1$, rather than the conventional $\log\log K$ form. Inspired by this difference between the scaling law forms, in this paper a systematic approach is developed to derive asymptotic throughput and scaling laws based on signal-to-interference-noise ratio (SINR) by utilizing extreme value theory. The convergence of the limiting distribution of the effective SINR to the Gumbel type is established. The resulting scaling law is found to be governed by the conventional $\log\log K$ form. These novel results are validated by simulation results. The comparison of SIR and SINR-based analysis suggests that the SIR-based analysis is more computationally efficient for SDMA-based systems and it captures the asymptotic system performance with higher fidelity.
Estimating the entropy of binary time series: Methodology, some theory and a simulation study
Venue: arXiv cs.IT 2008
Year: 2008

Partly motivated by entropy-estimation problems in neuroscience, we present a detailed and extensive comparison between some of the most popular and effective entropy estimation methods used in practice: The plug-in method, four different estimators based on the Lempel-Ziv (LZ) family of data compression algorithms, an estimator based on the Context-Tree Weighting (CTW) method, and the renewal entropy estimator. **Methodology. Three new entropy estimators are introduced. For two of the four LZ-based estimators, a bootstrap procedure is described for evaluating their standard error, and a practical rule of thumb is heuristically derived for selecting the values of their parameters. ** Theory. We prove that, unlike their earlier versions, the two new LZ-based estimators are consistent for every finite-valued, stationary and ergodic process. An effective method is derived for the accurate approximation of the entropy rate of a finite-state HMM with known distribution. Heuristic calculations are presented and approximate formulas are derived for evaluating the bias and the standard error of each estimator. ** Simulation. All estimators are applied to a wide range of data generated by numerous different processes with varying degrees of dependence and memory. Some conclusions drawn from these experiments include: (i) For all estimators considered, the main source of error is the bias. (ii) The CTW method is repeatedly and consistently seen to provide the most accurate results. (iii) The performance of the LZ-based estimators is often comparable to that of the plug-in method. (iv) The main drawback of the plug-in method is its computational inefficiency.
From a set of parts to an indivisible whole. Part I: Operations in a closed mode
Venue: arXiv cs.OH 2008
Year: 2008

This paper provides a description of a new method for information processing based on holistic approach wherein analysis is a direct product of synthesis. The core of the method is iterative averaging of all the elements of a system according to all the parameters describing the elements. Contrary to common logic, the iterative averaging of a system's elements does not result in homogenization of the system; instead, it causes an obligatory subdivision of the system into two alternative subgroups, leaving no outliers. Within each of the formed subgroups, similarity coefficients between the elements reach the value of 1, whereas similarity coefficients between the elements of different subgroups equal a certain constant value greater than 0 but lower than 1. When subjected to iterative averaging, any system consisting of three or more elements of which at least two elements are not completely identical undergo such a process of bifurcation that occurs non-linearly. Successive iterative averaging of each of the forming subgroups eventually provides a hierarchical system that reflects relationships between the elements of an input system under analysis. We propose a definition of a natural hierarchy that can exist only in conditions of closeness of a system and can be discovered upon providing such an effect onto a system which allows its elements interact with each other based on the principle of self-organization. Self-organization can be achieved through an overall and total cross-averaging of a system's elements. We demonstrate the application potentials of the proposed technology on a number of examples, including a system of scattered points, randomized datasets, as well as meteorological and demographical datasets.
A Survey on Deep Packet Inspection for Intrusion Detection Systems
Venue: arXiv cs.CR 2008
Year: 2008

Deep packet inspection is widely recognized as a powerful way which is used for intrusion detection systems for inspecting, deterring and deflecting malicious attacks over the network. Fundamentally, almost intrusion detection systems have the ability to search through packets and identify contents that match with known attacks. In this paper, we survey the deep packet inspection implementations techniques, research challenges and algorithms. Finally, we provide a comparison between the different applied systems.
Considering Functional Spreadsheet Operator Usage Suggests the Value of Example Driven Modelling for Decision Support Systems
Venue: arXiv cs.HC 2008
Year: 2008

Most spreadsheet surveys both for reporting use and error focus on the practical application of the spreadsheet in a particular industry. Typically these studies will illustrate that a particular percentage of spreadsheets are used for optimisation and a further percentage are used for 'What if' analysis. Much less common is examining the classes of function, as defined by the vendor, used by modellers to build their spreadsheet models. This alternative analysis allows further insight into the programming nature of spreadsheets and may assist researchers in targeting particular structures in spreadsheet software for further investigation. Further, understanding the functional make-up of spreadsheets allows effective evaluation of novel approaches from a programming point of view. It allows greater insight into studies that report what spreadsheets are used for since it is explicit which functional structures are in use in spreadsheets. We conclude that a deeper understanding of the use of operators and the operator's relationship to error would provide fresh insight into the spreadsheet error problem. Considering functional spreadsheet operator usage suggests the value of Example Driven Modelling for Decision Support Systems
Inferring Neuronal Network Connectivity from Spike Data: A Temporal Datamining Approach
Venue: arXiv cs.DB 2008
Year: 2008

Understanding the functioning of a neural system in terms of its underlying circuitry is an important problem in neuroscience. Recent developments in electrophysiology and imaging allow one to simultaneously record activities of hundreds of neurons. Inferring the underlying neuronal connectivity patterns from such multi-neuronal spike train data streams is a challenging statistical and computational problem. This task involves finding significant temporal patterns from vast amounts of symbolic time series data. In this paper we show that the frequent episode mining methods from the field of temporal data mining can be very useful in this context. In the frequent episode discovery framework, the data is viewed as a sequence of events, each of which is characterized by an event type and its time of occurrence and episodes are certain types of temporal patterns in such data. Here we show that, using the set of discovered frequent episodes from multi-neuronal data, one can infer different types of connectivity patterns in the neural system that generated it. For this purpose, we introduce the notion of mining for frequent episodes under certain temporal constraints; the structure of these temporal constraints is motivated by the application. We present algorithms for discovering serial and parallel episodes under these temporal constraints. Through extensive simulation studies we demonstrate that these methods are useful for unearthing patterns of neuronal network connectivity.
Network Coding for Distributed Storage Systems
Venue: arXiv cs.NI 2008
Year: 2008

Distributed storage systems provide reliable access to data through redundancy spread over individually unreliable nodes. Application scenarios include data centers, peer-to-peer storage systems, and storage in wireless networks. Storing data using an erasure code, in fragments spread across nodes, requires less redundancy than simple replication for the same level of reliability. However, since fragments must be periodically replaced as nodes fail, a key question is how to generate encoded fragments in a distributed way while transferring as little data as possible across the network. For an erasure coded system, a common practice to repair from a node failure is for a new node to download subsets of data stored at a number of surviving nodes, reconstruct a lost coded block using the downloaded data, and store it at the new node. We show that this procedure is sub-optimal. We introduce the notion of regenerating codes, which allow a new node to download \emph{functions} of the stored data from the surviving nodes. We show that regenerating codes can significantly reduce the repair bandwidth. Further, we show that there is a fundamental tradeoff between storage and repair bandwidth which we theoretically characterize using flow arguments on an appropriately constructed graph. By invoking constructive results in network coding, we introduce regenerating codes that can achieve any point in this optimal tradeoff.
Constant-Rank Codes
Venue: arXiv cs.IT 2008
Year: 2008

Constant-dimension codes have recently received attention due to their significance to error control in noncoherent random network coding. In this paper, we show that constant-rank codes are closely related to constant-dimension codes and we study the properties of constant-rank codes. We first introduce a relation between vectors in $\mathrm{GF}(q^m)^n$ and subspaces of $\mathrm{GF}(q)^m$ or $\mathrm{GF}(q)^n$, and use it to establish a relation between constant-rank codes and constant-dimension codes. We then derive bounds on the maximum cardinality of constant-rank codes with given rank weight and minimum rank distance. Finally, we investigate the asymptotic behavior of the maximal cardinality of constant-rank codes with given rank weight and minimum rank distance.
On the Topology of the Restricted Delaunay Triangulation and Witness Complex in Higher Dimensions
Venue: arXiv cs.CG 2008
Year: 2008

It is a well-known fact that, under mild sampling conditions, the restricted Delaunay triangulation provides good topological approximations of 1- and 2-manifolds. We show that this is not the case for higher-dimensional manifolds, even under stronger sampling conditions. Specifically, it is not true that, for any compact closed submanifold M of R^n, and any sufficiently dense uniform sampling L of M, the Delaunay triangulation of L restricted to M is homeomorphic to M, or even homotopy equivalent to it. Besides, it is not true either that, for any sufficiently dense set W of witnesses, the witness complex of L relative to M contains or is contained in the restricted Delaunay triangulation of L.
A Quantifier Elimination Algorithm for Linear Real Arithmetic
Venue: arXiv cs.LO 2008
Year: 2008

We propose a new quantifier elimination algorithm for the theory of linear real arithmetic. This algorithm uses as subroutine satisfiability modulo this theory, a problem for which there are several implementations available. The quantifier elimination algorithm presented in the paper is compared, on examples arising from program analysis problems, to several other implementations, all of which cannot solve some of the examples that our algorithm solves easily.
A Queueing System for Modeling a File Sharing Principle
Venue: arXiv cs.NI 2008
Year: 2008

We investigate in this paper the performance of a simple file sharing principle. For this purpose, we consider a system composed of N peers becoming active at exponential random times; the system is initiated with only one server offering the desired file and the other peers after becoming active try to download it. Once the file has been downloaded by a peer, this one immediately becomes a server. To investigate the transient behavior of this file sharing system, we study the instant when the system shifts from a congested state where all servers available are saturated by incoming demands to a state where a growing number of servers are idle. In spite of its apparent simplicity, this queueing model (with a random number of servers) turns out to be quite difficult to analyze. A formulation in terms of an urn and ball model is proposed and corresponding scaling results are derived. These asymptotic results are then compared against simulations.