OpenRaiser/Intern-Atlas huggingface dataset:
Characterization of Flexible RF Microcoil Dedicated to Surface Mri
Venue: arXiv cs.OH 2007
Year: 2007

In Magnetic Resonance Imaging (MRI), to achieve sufficient Signal to Noise Ratio (SNR), the electrical performance of the RF coil is critical. We developed a device (microcoil) based on the original concept of monolithic resonator. This paper presents the used fabrication process based on micromoulding. The dielectric substrates are flexible thin films of polymer, which allow the microcoil to be form fitted to none-plane surface. Electrical characterizations of the RF coils are first performed and results are compared to the attempted values. Proton MRI of a saline phantom using a flexible RF coil of 15 mm in diameter is performed. When the coil is conformed to the phantom surface, a SNR gain up to 2 is achieved as compared to identical but planar RF coil. Finally, the flexible coil is used in vivo to perform MRI with high spatial resolution on a mouse using a small animal dedicated scanner operating at in a 2.35 T.
New Internal Stress Driven on-Chip Micromachines for Extracting Mechanical Properties of Thin Films
Venue: arXiv cs.OH 2007
Year: 2007

A new concept of micromachines has been developed for measuring the mechanical properties of thin metallic films. The actuator is a beam undergoing large internal stresses built up during the deposition process. Al thin films are deposited partly on the actuator beam and on the substrate. By etching the structure, the actuator contracts and pulls the Al film. Full stress strain curves can be generated by designing a set of micromachines with various actuator lengths. In the present study, the displacements have been measured by scanning electronic microscopy. The stress is derived from simple continuum mechanics relationships. The tensile properties of Al films of various thicknesses have been tested. A marked increase of the strength with decreasing film thickness is observed.
Clustering with Transitive Distance and K-Means Duality
Venue: arXiv cs.LG 2007
Year: 2007

Recent spectral clustering methods are a propular and powerful technique for data clustering. These methods need to solve the eigenproblem whose computational complexity is $O(n^3)$, where $n$ is the number of data samples. In this paper, a non-eigenproblem based clustering method is proposed to deal with the clustering problem. Its performance is comparable to the spectral clustering algorithms but it is more efficient with computational complexity $O(n^2)$. We show that with a transitive distance and an observed property, called K-means duality, our algorithm can be used to handle data sets with complex cluster shapes, multi-scale clusters, and noise. Moreover, no parameters except the number of clusters need to be set in our algorithm.
The one-way function based on computational uncertainty principle
Venue: arXiv cs.CR 2007
Year: 2007

This paper presents how to make use of the advantage of round-off error effect in some research areas. The float-point operation complies with the reproduce theorem without the external random perturbation. The computation uncertainty principle and the high nonlinear of chaotic system guarantee the numerical error is random and departure from the analytical result. Combining these two properties we can produce unilateral one-way function and provide a case of utilizing this function to construct encryption algorithm. The multiple-precision (MP) library is used to analyze nonlinear dynamics systems and achieve the code. As an example, we provide a scheme of encrypting a plaintext by employing the one-way function with Lorenz system. Since the numerical solution used in this scheme is beyond the maximum effective computation time (MECT) and it cannot satisfy the requirements of return-map analysis and phase space reconstruction, it can block some existing attacks.
Observational semantics of the Prolog Resolution Box Model
Venue: arXiv cs.PL 2007
Year: 2007

This paper specifies an observational semantics and gives an original presentation of the Byrd box model. The approach accounts for the semantics of Prolog tracers independently of a particular Prolog implementation. Prolog traces are, in general, considered as rather obscure and difficult to use. The proposed formal presentation of its trace constitutes a simple and pedagogical approach for teaching Prolog or for implementing Prolog tracers. It is a form of declarative specification for the tracers. The trace model introduced here is only one example to illustrate general problems relating to tracers and observing processes. Observing processes know, from observed processes, only their traces. The issue is then to be able to reconstitute, by the sole analysis of the trace, part of the behaviour of the observed process, and if possible, without any loss of information. As a matter of fact, our approach highlights qualities of the Prolog resolution box model which made its success, but also its insufficiencies.
Contextual Information Retrieval based on Algorithmic Information Theory and Statistical Outlier Detection
Venue: arXiv cs.IR 2007
Year: 2007

The main contribution of this paper is to design an Information Retrieval (IR) technique based on Algorithmic Information Theory (using the Normalized Compression Distance- NCD), statistical techniques (outliers), and novel organization of data base structure. The paper shows how they can be integrated to retrieve information from generic databases using long (text-based) queries. Two important problems are analyzed in the paper. On the one hand, how to detect "false positives" when the distance among the documents is very low and there is actual similarity. On the other hand, we propose a way to structure a document database which similarities distance estimation depends on the length of the selected text. Finally, the experimental evaluations that have been carried out to study previous problems are shown.
Strategies for Addressing Spreadsheet Compliance Challenges
Venue: arXiv cs.CY 2007
Year: 2007

Most organizations today use spreadsheets in some form or another to support critical business processes. However the financial resources, and developmental rigor dedicated to them are often minor in comparison to other enterprise technology. The increasing focus on achieving regulatory and other forms of compliance over key technology assets has made it clear that organizations must regard spreadsheets as an enterprise resource and account for them when developing an overall compliance strategy. This paper provides the reader with a set of practical strategies for addressing spreadsheet compliance from an organizational perspective. It then presents capabilities offered in the 2007 Microsoft Office System which can be used to help customers address compliance challenges.
Finding the growth rate of a regular language in polynomial time
Venue: arXiv cs.DM 2007
Year: 2007

We give an O(n^3+n^2 t) time algorithm to determine whether an NFA with n states and t transitions accepts a language of polynomial or exponential growth. We also show that given a DFA accepting a language of polynomial growth, we can determine the order of polynomial growth in quadratic time.
Automatic Pattern Classification by Unsupervised Learning Using Dimensionality Reduction of Data with Mirroring Neural Networks
Venue: arXiv cs.LG 2007
Year: 2007

This paper proposes an unsupervised learning technique by using Multi-layer Mirroring Neural Network and Forgy's clustering algorithm. Multi-layer Mirroring Neural Network is a neural network that can be trained with generalized data inputs (different categories of image patterns) to perform non-linear dimensionality reduction and the resultant low-dimensional code is used for unsupervised pattern classification using Forgy's algorithm. By adapting the non-linear activation function (modified sigmoidal function) and initializing the weights and bias terms to small random values, mirroring of the input pattern is initiated. In training, the weights and bias terms are changed in such a way that the input presented is reproduced at the output by back propagating the error. The mirroring neural network is capable of reducing the input vector to a great degree (approximately 1/30th the original size) and also able to reconstruct the input pattern at the output layer from this reduced code units. The feature set (output of central hidden layer) extracted from this network is fed to Forgy's algorithm, which classify input data patterns into distinguishable classes. In the implementation of Forgy's algorithm, initial seed points are selected in such a way that they are distant enough to be perfectly grouped into different categories. Thus a new method of unsupervised learning is formulated and demonstrated in this paper. This method gave impressive results when applied to classification of different image patterns.
On Using Unsatisfiability for Solving Maximum Satisfiability
Venue: arXiv cs.AI 2007
Year: 2007

Maximum Satisfiability (MaxSAT) is a well-known optimization pro- blem, with several practical applications. The most widely known MAXS AT algorithms are ineffective at solving hard problems instances from practical application domains. Recent work proposed using efficient Boolean Satisfiability (SAT) solvers for solving the MaxSAT problem, based on identifying and eliminating unsatisfiable subformulas. However, these algorithms do not scale in practice. This paper analyzes existing MaxSAT algorithms based on unsatisfiable subformula identification. Moreover, the paper proposes a number of key optimizations to these MaxSAT algorithms and a new alternative algorithm. The proposed optimizations and the new algorithm provide significant performance improvements on MaxSAT instances from practical applications. Moreover, the efficiency of the new generation of unsatisfiability-based MaxSAT solvers becomes effectively indexed to the ability of modern SAT solvers to proving unsatisfiability and identifying unsatisfiable subformulas.
Dynamic Multilevel Graph Visualization
Venue: arXiv cs.GR 2007
Year: 2007

We adapt multilevel, force-directed graph layout techniques to visualizing dynamic graphs in which vertices and edges are added and removed in an online fashion (i.e., unpredictably). We maintain multiple levels of coarseness using a dynamic, randomized coarsening algorithm. To ensure the vertices follow smooth trajectories, we employ dynamics simulation techniques, treating the vertices as point particles. We simulate fine and coarse levels of the graph simultaneously, coupling the dynamics of adjacent levels. Projection from coarser to finer levels is adaptive, with the projection determined by an affine transformation that evolves alongside the graph layouts. The result is a dynamic graph visualizer that quickly and smoothly adapts to changes in a graph.
Non-linear and Linear Broadcasting with QoS Requirements: Tractable Approaches for Bounded Channel Uncertainties
Venue: arXiv cs.IT 2007
Year: 2007

We consider the downlink of a cellular system in which the base station employs multiple transmit antennas, each receiver has a single antenna, and the users specify. We consider communication schemes in which the users have certain Quality of Service (QoS) requirements. We study the design of robust broadcasting schemes that minimize the transmission power necessary to guarantee that the QoS requirements are satisfied for all channels within bounded uncertainty regions around the transmitter's estimate of each user's channel. Each user's QoS requirement is formulated as a constraint on the mean square error (MSE) in its received signal, and we show that these MSE constraints imply constraints on the received SINR. Using the MSE constraints, we present a unified design approach for robust linear and non-linear transceivers with QoS requirements. The proposed designs overcome the limitations of existing approaches that provide conservative designs or are only applicable to the case of linear precoding. Furthermore, we provide computationally-efficient design formulations for a rather general model of channel uncertainty that subsumes many natural choices for the uncertainty region. We also consider the problem of the robust counterpart to precoding schemes that maximize the fidelity of the weakest user's signal subject to a power constraint. For this problem, we provide quasi-convex formulations, for both linear and non-linear transceivers, that can be efficiently solved using a one-dimensional bisection search. Our numerical results demonstrate that in the presence of CSI uncertainty, the proposed designs provide guarantees for a larger range of QoS requirements than the existing approaches, and consume require less transmission power in providing these guarantees.
Delaunay Edge Flips in Dense Surface Triangulations
Venue: arXiv cs.CG 2007
Year: 2007

Delaunay flip is an elegant, simple tool to convert a triangulation of a point set to its Delaunay triangulation. The technique has been researched extensively for full dimensional triangulations of point sets. However, an important case of triangulations which are not full dimensional is surface triangulations in three dimensions. In this paper we address the question of converting a surface triangulation to a subcomplex of the Delaunay triangulation with edge flips. We show that the surface triangulations which closely approximate a smooth surface with uniform density can be transformed to a Delaunay triangulation with a simple edge flip algorithm. The condition on uniformity becomes less stringent with increasing density of the triangulation. If the condition is dropped completely, the flip algorithm still terminates although the output surface triangulation becomes "almost Delaunay" instead of exactly Delaunay.
Knowledge Engineering Technique for Cluster Development
Venue: arXiv cs.OH 2007
Year: 2007

After the concept of industry cluster was tangibly applied in many countries, SMEs trended to link to each other to maintain their competitiveness in the market. The major key success factors of the cluster are knowledge sharing and collaboration between partners. This knowledge is collected in form of tacit and explicit knowledge from experts and institutions within the cluster. The objective of this study is about enhancing the industry cluster with knowledge management by using knowledge engineering which is one of the most important method for managing knowledge. This work analyzed three well known knowledge engineering methods, i.e. MOKA, SPEDE and CommonKADS, and compares the capability to be implemented in the cluster context. Then, we selected one method and proposed the adapted methodology. At the end of this paper, we validated and demonstrated the proposed methodology with some primary result by using case study of handicraft cluster in Thailand.
Distributed Fair Scheduling Using Variable Transmission Lengths in Carrier-Sensing-based Wireless Networks
Venue: arXiv cs.NI 2007
Year: 2007

The fairness of IEEE 802.11 wireless networks (including Wireless LAN and Ad-hoc networks) is hard to predict and control because of the randomness and complexity of the MAC contentions and dynamics. Moreover, asymmetric channel conditions such as those caused by capture and channel errors often lead to severe unfairness among stations. In this paper we propose a novel distributed scheduling algorithm that we call VLS, for ``{\em variable-length scheduling}'', that provides weighted fairness to all stations despite the imperfections of the MAC layer and physical channels. Distinct features of VLS include the use of variable transmission lengths based on distributed observations, compatibility with 802.11's contention window algorithm, opportunistic scheduling to achieve high throughput in time-varying wireless environments, and flexibility and ease of implementation. Also, VLS makes the throughput of each station more smooth, which is appealing to real-time applications such as video and voice. Although the paper mostly assumes 802.11 protocol, the idea generally applies to wireless networks based on CSMA (Carrier Sensing Multiple Access).
Hinged Dissections Exist
Venue: arXiv cs.CG 2007
Year: 2007

We prove that any finite collection of polygons of equal area has a common hinged dissection. That is, for any such collection of polygons there exists a chain of polygons hinged at vertices that can be folded in the plane continuously without self-intersection to form any polygon in the collection. This result settles the open problem about the existence of hinged dissections between pairs of polygons that goes back implicitly to 1864 and has been studied extensively in the past ten years. Our result generalizes and indeed builds upon the result from 1814 that polygons have common dissections (without hinges). We also extend our common dissection result to edge-hinged dissections of solid 3D polyhedra that have a common (unhinged) dissection, as determined by Dehn's 1900 solution to Hilbert's Third Problem. Our proofs are constructive, giving explicit algorithms in all cases. For a constant number of planar polygons, both the number of pieces and running time required by our construction are pseudopolynomial. This bound is the best possible, even for unhinged dissections. Hinged dissections have possible applications to reconfigurable robotics, programmable matter, and nanomanufacturing.
Constructions for Difference Triangle Sets
Venue: arXiv cs.IT 2007
Year: 2007

Difference triangle sets are useful in many practical problems of information transmission. This correspondence studies combinatorial and computational constructions for difference triangle sets having small scopes. Our algorithms have been used to produce difference triangle sets whose scopes are the best currently known.
Experiments with a Convex Polyhedral Analysis Tool for Logic Programs
Venue: arXiv cs.PL 2007
Year: 2007

Convex polyhedral abstractions of logic programs have been found very useful in deriving numeric relationships between program arguments in order to prove program properties and in other areas such as termination and complexity analysis. We present a tool for constructing polyhedral analyses of (constraint) logic programs. The aim of the tool is to make available, with a convenient interface, state-of-the-art techniques for polyhedral analysis such as delayed widening, narrowing, "widening up-to", and enhanced automatic selection of widening points. The tool is accessible on the web, permits user programs to be uploaded and analysed, and is integrated with related program transformations such as size abstractions and query-answer transformation. We then report some experiments using the tool, showing how it can be conveniently used to analyse transition systems arising from models of embedded systems, and an emulator for a PIC microcontroller which is used for example in wearable computing systems. We discuss issues including scalability, tradeoffs of precision and computation time, and other program transformations that can enhance the results of analysis.
L'accessibilité des E-services aux personnes non-voyantes : difficultés d'usage et recommandations
Venue: arXiv cs.HC 2007
Year: 2007

While taking into account handicapped people in the design of technologies represents a social and political stake that becomes important (in particular with the recent law on equal rights for all the citizens, March 2004), this paper aims at evaluating the level of accessibility of two sites of E-services thanks to tests of use and proposing a set of recommendations in order to increase usability for the largest amount of people.
How to Complete a Doubling Metric
Venue: arXiv cs.DM 2007
Year: 2007

In recent years, considerable advances have been made in the study of properties of metric spaces in terms of their doubling dimension. This line of research has not only enhanced our understanding of finite metrics, but has also resulted in many algorithmic applications. However, we still do not understand the interaction between various graph-theoretic (topological) properties of graphs, and the doubling (geometric) properties of the shortest-path metrics induced by them. For instance, the following natural question suggests itself: \emph{given a finite doubling metric $(V,d)$, is there always an \underline{unweighted} graph $(V',E')$ with $V\subseteq V'$ such that the shortest path metric $d'$ on $V'$ is still doubling, and which agrees with $d$ on $V$.} This is often useful, given that unweighted graphs are often easier to reason about. We show that for any metric space $(V,d)$, there is an \emph{unweighted} graph $(V',E')$ with shortest-path metric $d':V'\times V' \to \R_{\geq 0}$ such that -- for all $x,y \in V$, the distances $d(x,y) \leq d'(x,y) \leq (1+\eps) \cdot d(x,y)$, and -- the doubling dimension for $d'$ is not much more than that of $d$, where this change depends only on $\e$ and not on the size of the graph. We show a similar result when both $(V,d)$ and $(V',E')$ are restricted to be trees: this gives a simpler proof that doubling trees embed into constant dimensional Euclidean space with constant distortion. We also show that our results are tight in terms of the tradeoff between distortion and dimension blowup.
Pattern Recognition System Design with Linear Encoding for Discrete Patterns
Venue: arXiv cs.IT 2007
Year: 2007

In this paper, designs and analyses of compressive recognition systems are discussed, and also a method of establishing a dual connection between designs of good communication codes and designs of recognition systems is presented. Pattern recognition systems based on compressed patterns and compressed sensor measurements can be designed using low-density matrices. We examine truncation encoding where a subset of the patterns and measurements are stored perfectly while the rest is discarded. We also examine the use of LDPC parity check matrices for compressing measurements and patterns. We show how more general ensembles of good linear codes can be used as the basis for pattern recognition system design, yielding system design strategies for more general noise models.
Improving the Performance of PieceWise Linear Separation Incremental Algorithms for Practical Hardware Implementations
Venue: arXiv cs.NE 2007
Year: 2007

In this paper we shall review the common problems associated with Piecewise Linear Separation incremental algorithms. This kind of neural models yield poor performances when dealing with some classification problems, due to the evolving schemes used to construct the resulting networks. So as to avoid this undesirable behavior we shall propose a modification criterion. It is based upon the definition of a function which will provide information about the quality of the network growth process during the learning phase. This function is evaluated periodically as the network structure evolves, and will permit, as we shall show through exhaustive benchmarks, to considerably improve the performance(measured in terms of network complexity and generalization capabilities) offered by the networks generated by these incremental models.
Tests of Machine Intelligence
Venue: arXiv cs.AI 2007
Year: 2007

Although the definition and measurement of intelligence is clearly of fundamental importance to the field of artificial intelligence, no general survey of definitions and tests of machine intelligence exists. Indeed few researchers are even aware of alternatives to the Turing test and its many derivatives. In this paper we fill this gap by providing a short survey of the many tests of machine intelligence that have been proposed.
Discrete logarithms in curves over finite fields
Venue: arXiv cs.CR 2007
Year: 2007

A survey on algorithms for computing discrete logarithms in Jacobians of curves over finite fields.