ppxscal/embedding_network huggingface dataset:
Low-Frequency Noise Characterization in Charge-Based Coherent Nanodevices
Authors: G. Falci, A. Mastellone, A. D'Arrigo, E. Paladino
Venue: Open Systems Information Dynamics (2006)

Low-frequency noise has been recognized as the main mechanism of decoherence in present-day solid state coherent nanodevices. Often low-frequency noise has a 1/f spectrum, thus the responsible degrees of freedom are almost static during the coherent time evolution of the device. Their effect is to reduce the amplitude of the measured signal by an effect analogous to inhomogeneous broadening in NMR. Here we present a way to characterize effects of adiabatic noise exploiting the tunability of nanodevices. The results apply in principle to many implementations of quantum bits, and are particularly suited for solid state devices. The accurate characterization of the physics of the noise sources represents the first step toward the design specific strategies to eliminate their effects.
Division of labor in a group of robots inspired by ants' foraging behavior
Authors: Thomas H. Labella, Marco Dorigo, Jean-Louis Deneubourg
Venue: ACM Transactions on Autonomous and Adaptive Systems (TAAS) (2006)

In this article, we analyze the behavior of a group of robots involved in an object retrieval task. The robots' control system is inspired by a model of ants' foraging. This model emphasizes the role of learning in the individual. Individuals adapt to the environment using only locally available information. We show that a simple parameter adaptation is an effective way to improve the efficiency of the group and that it brings forth division of labor between the members of the group. Moreover, robots that are best at retrieving have a higher probability of becoming active retrievers. This selection of the best members does not use any explicit representation of individual capabilities. We analyze this system and point out its strengths and its weaknesses.
Toward Broadcast Reliability in Mobile Ad Hoc Networks with Double Coverage
Authors: Wei Lou, Jie Wu
Venue: IEEE Transactions on Mobile Computing (2007)

The broadcast operation, as a fundamental service in mobile ad hoc networks (MANETs), is prone to the broadcast storm problem if forwarding nodes are not carefully designated. The objective of reducing broadcast redundancy while still providing high delivery ratio under high transmission error rate is a major challenge in MANETs. In this paper, we propose a simple broadcast algorithm, called double-covered broadcast (DCB), which takes advantage of broadcast redundancy to improve the delivery ratio in an environment that has rather high transmission error rate. Among the 1-hop neighbors of the sender, only selected forwarding nodes retransmit the broadcast message. Forwarding nodes are selected in such a way that 1) the sender's 2-hop neighbors are covered and 2) the sender's 1-hop neighbors are either forwarding nodes or nonforwarding nodes covered by at least two forwarding neighbors. The retransmissions of the forwarding nodes are received by the sender as the confirmation of their reception of the packet. The nonforwarding 1-hop neighbors of the sender do not acknowledge the reception of the broadcast. If the sender does not detect all its forwarding nodes' retransmissions, it will resend the packet until the maximum number of retries is reached. Simulation results show that the proposed broadcast algorithm provides good performance under a high transmission error rate environment.
An Analysis of Spatio-Temporal Query Processing in Sensor Networks
Authors: Alexandru Coman, Jorg Sander, Mario A. Nascimento
Venue: Proceedings of the 21st International Conference on Data Engineering Workshops (2005)

Sensor networks are an emerging technology that provide new means to monitor and understand various phenomena. Nodes in a sensor network typically have a limited power supply, thus energy-efficient processing of the queries over the network is a critical issue. We propose analytical models to evaluate the performance of three methods for processing historical spatio-temporal queries in sensor networks. The models allow us to better understand the tradeoffs of the investigated methods, as well as to recommend the most energy efficient one at query time.
Incorporating Gene Ontology in Clustering Gene Expression Data
Authors: Rafal Kustra, Adam Zagdanski
Venue: Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems (2006)

In this paper we consider a general framework for clustering expression data that permits integration of various biological data sources through combination of corresponding dissimilarity measures. In the paper we briefly review currently published attempts to genomic data fusion and discuss a problem of validating results from clustering expression data. We apply our approach to a real microarray expression dataset which induces a correlationbased dissimilarity matrix, and use Gene Ontology - Biological Process annotations to derive GO-based dissimilarity matrix. The proposed procedure is verified using a simple knowledge-based validation measure based on protein-protein interaction database. Obtained results reveal that combining experimental data with comprehensive and reliable biological repository may improve performance of cluster analysis and yield biologically meaningful gene clusters.
The Distance-Similarity Metaphor in Region-Display Spatializations
Authors: Sara Irina Fabrikant, Daniel R. Montello, David M. Mark
Venue: IEEE Computer Graphics and Applications (2006)

Region-display spatializations represent documents metaphorically as points within regions. Semantic interrelatedness is expressed by some combination of interpoint distance and region membership. In two experiments, the authors investigate judgments of document similarity as a function of these variables. Distance matters, but region membership largely determines judged similarity; hue further modifies it.
Proteome Profiling without Selection Bias
Authors: Annalisa Barla, Bettina Irler, Stefano Merler, Giuseppe Jurman, Silvano Paoli, Cesare Furlanello
Venue: Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems (2006)

In this paper, we present a method for predictive profiling of mass spectrometry data. The method integrates a spectra preprocessing pipeline with a complete validation setup aimed at identifying the discriminating peaks and at providing an unbiased estimate of the predictive classification error, based on SVM classifiers and on Entropy-based RFE procedure. A particular emphasis is placed upon avoiding selection bias effects throughout all the analysis steps, from preprocessing to peak importance ranking.
Using Communication Genre for searching with Small Display Devices
Authors: Carlos J. Costa, Manuela Aparicio
Venue: Proceedings of the International Multi-Conference on Computing in the Global Information Technology (2006)

The problem of searching the web by using small devices such as PDAs is the subject of great this paper. We present here a solution based in the assumption that use of communication genre may contribute to the reduction of processing and memory burden. Communication genre may also contribute to ease the access to information. It is in this context that we analysed in what extent communication genres are employed by users to describe digital resources, to be displayed in PDA.
An ontology for organizational functions: the recursive self-maintenance mechanism of the enterprise
Authors: David Aveiro, Jose Tribolet
Venue: Proceedings of the 10th IEEE on International Enterprise Distributed Object Computing Conference Workshops (2006)

Although there have been great advances in business process modeling as a means to better understand and control organizations and improve the development of information systems, the existence of functions - like IT, logistics, financial, etc. - is an inescapable reality in organizations, that strongly influences aspects like change processes, or the formation of organizational units, even if a culture of "process orientation" is established. This paper tries to clarify the concept of organizational function, discusses certain organizational artifacts that seem to be of a functional nature and proposes an ontology for the purpose of allowing a consistent and coherent modeling of this recursive self-maintenance mechanism of organizations.
A HW/SW Partitioner for Multi-Mode Multi-Task Embedded Applications
Authors: Young-Jun Kim, Taewhan Kim
Venue: Journal of VLSI Signal Processing Systems (2006)

An embedded system is called a multi-mode embedded system if it performs multiple applications by dynamically reconfiguring the system functionality. Further, the embedded system is called a multi-mode multi-task embedded system if it additionally supports multiple tasks to be executed in a mode. In this paper, we address an important HW/SW partitioning problem, that is, HW/SW partitioning of multi-mode multi-task embedded applications with timing constraints of tasks. The objective of the optimization problem is to find a minimal total system cost of allocation/mapping of processing resources to functional modules in tasks together with a schedule that satisfies the timing constraints. The key success of solving the problem is closely related to the degree of the amount of utilization of the potential parallelism among the executions of modules. However, due to an inherently excessively large search space of the parallelism, and to make the task of schedulability analysis easy, the prior HW/SW partitioning methods have not been able to fully exploit the potential parallel execution of modules. To overcome the limitation, we propose a set of comprehensive HW/SW partitioning techniques which solve the three subproblems of the partitioning problem simultaneously: (1) allocation of processing resources, (2) mapping the processing resources to the modules in tasks, and (3) determining an execution schedule of modules. Specifically, based on a precise measurement on the parallel execution and schedulability of modules, we develop a stepwise refinement partitioning technique for single-mode multi-task applications, which aims to solve the subproblems 1, 2 and 3 effectively in an integrated fashion. The proposed techniques is then extended to solve the HW/SW partitioning problem of multi-mode multi-task applications (i.e., to find a globally optimized allocation/mapping of processing resources with feasible execution schedule of modules). From experiments with a set of real-life applications, it is shown that the proposed techniques are able to reduce the implementation cost by 19.0 and 17.0% for single- and multi-mode multi-task applications over that by the conventional method, respectively.
Orthogonal Least Squares in Partition of Unity Surface Reconstruction with Radial Basis Function
Authors: Qi Xia, Michael Yu Wang, Xiaojun Wu
Venue: Proceedings of the conference on Geometric Modeling and Imaging: New Trends (2006)

In this paper, a least squares formulation with radial basis function for surface reconstruction is presented and OLS (Orthogonal Least Squares) algorithm is proposed to select centers and eliminate numerical ill-conditioning. The two objectives are fused into a single iterative process in OLS algorithm, which makes the reconstruction fast and robust. In the end, in order to deal with large point sets, we organize a point set with an octree; reconstruct surfaces in octree cells and blend them into a global surface by Partition of Unity (POU) method. To sum up, the first method is dedicated to reconstructing a surface with a smaller number of RBFs, and the last one is a local method to bypass impractical global reconstructions. Effectiveness of our proposed methods is demonstrated with results of real world point sets.
Distributed Groups Mutual Exclusion Based on Clients/Servers Model
Authors: Ousmane Thiare, Mourad Gueroui, Mohamed NAIMI
Venue: Proceedings of the Seventh International Conference on Parallel and Distributed Computing, Applications and Technologies (2006)

In this paper we present a new Distributed GroupMutual Exclusion(DGME) based on Clients/Servers model, and uses a dynamic data structures. Several processes (Clients) can access simultaneously to a same opened session (Server). The algorithm ensures that, at any time, at most one session is opened, and any requested Session will be opened in a finite time. The number of messages is between 0 and m, where m is the number of session in the network. In the average case, O(Log(m)) messages are necessary to open a session. The maximum concurrency is n, where n is the number of processes in the network.
Improving wireless sensor network lifetime through power aware organization
Authors: Mihaela Cardei, Ding-Zhu Du
Venue: Wireless Networks (2005)

A critical aspect of applications with wireless sensor networks is network lifetime. Battery-powered sensors are usable as long as they can communicate captured data to a processing node. Sensing and communications consume energy, therefore judicious power management and scheduling can effectively extend operational time. To monitor a set of targets with known locations when ground access in the monitored area is prohibited, one solution is to deploy the sensors remotely, from an aircraft. The loss of precise sensor placement would then be compensated by a large sensor population density in the drop zone, that would improve the probability of target coverage. The data collected from the sensors is sent to a central node for processing. In this paper we propose an efficient method to extend the sensor network operational time by organizing the sensors into a maximal number of disjoint set covers that are activated successively. Only the sensors from the current active set are responsible for monitoring all targets and for transmitting the collected data, while nodes from all other sets are in a low-energy sleep mode. In this paper we address the maximum disjoint set covers problem and we design a heuristic that computes the sets. Theoretical analysis and performance evaluation results are presented to verify our approach.
Automating Deep Space Network scheduling and conflict resolution
Authors: Mark D. Johnston, Bradley J. Clement
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

The Deep Space Network (DSN) is a central part of NASA's infrastructure for communicating with active space missions, from earth orbit to beyond the solar system. Consisting of more than a dozen major ground antennas at three sites spaced around the globe, it must be carefully scheduled to satisfy the requirements of the various mission users, subject to many constraints. Scheduling the communication services for these missions is a fairly large scale negotiation problem since each mission cannot give up control of its spacecraft. While we will give details of this problem, this paper focuses more on describing an agent that provides scheduling advice to a single user within a collaborative scheduling system being developed. This agent can provide guidance to the user about feasible and optimal solutions to the problem they are working on. We describe our recent work in modeling the complexities of user requirements, and then scheduling and resolving conflicts on that basis.
Supervised Learning of Edges and Object Boundaries
Authors: Piotr Dollar, Zhuowen Tu, Serge Belongie
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (2006)

Edge detection is one of the most studied problems in computer vision, yet it remains a very challenging task. It is difficult since often the decision for an edge cannot be made purely based on low level cues such as gradient, instead we need to engage all levels of information, low, middle, and high, in order to decide where to put edges. In this paper we propose a novel supervised learning algorithm for edge and object boundary detection which we refer to as Boosted Edge Learning or BEL for short. A decision of an edge point is made independently at each location in the image; a very large aperture is used providing significant context for each decision. In the learning stage, the algorithm selects and combines a large number of features across different scales in order to learn a discriminative model using an extended version of the Probabilistic Boosting Tree classification algorithm. The learning based framework is highly adaptive and there are no parameters to tune. We show applications for edge detection in a number of specific image domains as well as on natural images. We test on various datasets including the Berkeley dataset and the results obtained are very good.
Compiler-directed physical address generation for reducing dTLB power
Authors: I. Kadayif, P. Nath, M. Kandemir, A. Sivasubramaniam
Venue: Proceedings of the 2004 IEEE International Symposium on Performance Analysis of Systems and Software (2004)

Address translation using the Translation Lookaside Buffer (TLB) consumes as much as 16% of the chip power on some processors because of its high associativity and access frequency. While prior work has looked into optimizing this structure at the circuit and architectural levels, this paper takes a different approach of optimizing its power by reducing the number of data TLB (dTLB) lookups for data references. The main idea is to keep translations in a set of translation registers, and intelligently use them in software to directly generate the physical addresses without going through the dTLB. The software has to work within the confines of the translation registers provided by the hardware, and has to maximize the reuse of such translations to be effective. We propose strategies and code transformations for achieving this in array-based and pointer-based codes, looking to optimize data accesses. Results with a suite of Spec95 array-based and pointer-based codes show dTLB energy savings of up to 73% and 88%, respectively, compared to directly using the dTLB for all references. Despite the small increase in instructions executed with our mechanisms, the approach can in fact provide performance benefits in certain cases.
A Context-Based Adaptive Visualization Environment
Authors: Maria Golemati, Constantin Halatsis, Costas Vassilakis, Akrivi Katifori, University of Peloponnese
Venue: Proceedings of the conference on Information Visualization (2006)

Digital libraries and historical archives are increasingly employing visualization systems to facilitate the information retrieval and knowledge extraction tasks of their users. Typically, each organization employs a single visualization system, which may not suit best the needs of certain user groups, specific tasks, or properties of document collections to be visualized. In this paper we present a context-based adaptive visualization environment, which embeds a set of visualization methods into a visualization library, from which the most appropriate one is selected for presenting information to the user. Methods are selected by examining parameters related to the user profile, system configuration and the set of data to be visualized, and employing a set of rules to assess the suitability of each method. The presented environment additionally monitors user behavior and preferences to adapt the visualization method selection criteria.
Support versus corroboration
Authors: Mary G. Egan
Venue: Journal of Biomedical Informatics (2006)

Numerous metrics have been developed that attempt to assess the reliability of phylogenetic trees. Several of these commonly used measures of tree and tree branch support are described and discussed in the context of their relationship to Popperian corroboration. Claims that measures of support indicate the accuracy of phylogenetic trees or provide information for tree choice are rebutted. Measures of support are viewed as being of heuristic value within a given phylogenetic framework for describing the precision of the data based on perturbations to the data. However, no direct link is observed between the calculation of measures of support and corroboration. Direct measures of support, but not re-sampling or randomization methods, may play a more specific role in phylogenetic inference by providing the tools to search for falsifiers that could be the subject of future rounds of hypothesis testing.
Support vector machines for dyadic data
Authors: Sepp Hochreiter, Klaus Obermayer
Venue: Neural Computation (2006)

We describe a new technique for the analysis of dyadic data, where two sets of objects (row and column objects) are characterized by a matrix of numerical values that describe their mutual relationships. The new technique, called potential support vector machine (P-SVM), is a large-margin method for the construction of classifiers and regression functions for the column objects. Contrary to standard support vector machine approaches, the P-SVM minimizes a scale-invariant capacity measure and requires a new set of constraints. As a result, the P-SVM method leads to a usually sparse expansion of the classification and regression functions in terms of the row rather than the column objects and can handle data and kernel matrices that are neither positive definite nor square. We then describe two complementary regularization schemes. The first scheme improves generalization performance for classification and regression tasks; the second scheme leads to the selection of a small, informative set of row support objects and can be applied to feature selection. Benchmarks for classification, regression, and feature selection tasks are performed with toy data as well as with several real-world data sets. The results show that the new method is at least competitive with but often performs better than the benchmarked standard methods for standard vectorial as well as true dyadic data sets. In addition, a theoretical justification is provided for the new approach.
A conceptual architecture for static features in physical security simulation
Authors: Volkan Ustun, Haluk Yapicioglu, Skylab Gupta, Abishek Ramesh, Jeffrey S. Smith
Venue: Proceedings of the 37th conference on Winter simulation (2005)

The aim of this paper is twofold: First, to propose a data model that enables the user to model a physical facility at different levels of detail and explicitly incorporate interactions among the components of the facility. Second to suggest a methodology for line-of-sight, which is the primary factor in recognition of threats in physical security settings.
Productive failure
Authors: Manu Kapur
Venue: Proceedings of the 7th international conference on Learning sciences (2006)

Contrary to the fairly established notion in the learning sciences that un-scaffolded processes rarely lead to meaningful learning, this study reports a hidden efficacy of such processes and a method for extracting it. Compared to scaffolded, well-structured problem-solving groups, un-scaffolded, ill-structured problem-solving groups struggled with defining and solving the problems. Their discussions were chaotic and divergent, resulting in poor group performance. However, despite failing in their problem-solving efforts, these participants outperformed their counterparts in the well-structured condition on transfer measures, suggesting a latent productivity in the failure. The study's contrasting-case design provided participants in the un-scaffolded condition with an opportunity to contrast the ill-structured problems that they had solved in groups with the well-structured problems they solved individually afterwards. This contrast facilitated a spontaneous transfer, helping them perform significantly better on the individual ill-structured problem-solving tasks subsequently. Implications of productive failure for the development of adaptive expertise are discussed.