ppxscal/embedding_network huggingface dataset:
Data structures and spatial similarities for vector images
Authors: Jong P. Yoon
Venue:  (2006)

The World Wide Web (WWW) contains a large quantity of images and other visual information such as videos, movies, and comic strips. These images either belong to structured collections (e.g., museum collections) or are independent (e.g., images found in Web pages in the form of individual photographs, logos, and so on). The great paradox of the Web is that the difficulty in locating accurate and relevant information about a subject increases with the amount of available information. To help mitigate this problem, many information retrieval engines (or systems) have been proposed and implemented. The need for such tools is clearly shown by the success they have achieved. However, despite the visual nature of the Web, only a few engines have focused on retrieving visual information such as images and videos. Indeed, while there has been tremendous success in developing search engines for text, search engines for other media on the Web (e.g., images, audio, and video) are still rare and not as powerful. In this dissertation, we are primarily concerned with the retrieval of visual information from the Web. Visual information is published both embedded in Web documents and as stand-alone objects. It exists in the form of images, graphics, bitmaps, animations, and videos. One of the expressive and powerful languages for WWW images is XML-based Scalable Vector Graphics (SVG). SVG markup language is useful in many applications including law enforcement, image copyright protection, filtering of inappropriately mature content, criminal tracking, home entertainment, education, and training. SVG is becoming increasingly important for displaying and exchanging multimedia data over the Internet. Although SVG is powerful in the exchange, transformation, and rendering of image data, it still has the weakness of content-based component-sensitive image search mainly because it does not yet provide the descriptors of component image objects and their spatial relationships. Additionally, although similarity matches have been studied for a long time, the similarity measures of images on the component images and their relationships are not satisfactory.
Fusion of multitemporal contextual information by neural networks for multisensor remote sensing image classification
Authors: Farid Melgani, Sebastiano B. Serpico, Gianni Vernazza
Venue: Integrated Computer-Aided Engineering (2003)

The contextual analysis of a multitemporal sequence of images of a given site represents a way to improve the accuracy with respect to the non-contextual single-time classification. The proposed contextual multitemporal classification scheme consists of two stages of multilayer perceptron (MLP) neural networks for each single-time image of the multitemporal sequence. The first stage is a one-hidden layer MLP whose role is to estimate the single-time posterior probability of each class, given the feature vector. These probability estimates represent spectral information; in addition, they are utilized to generate a non-contextual classification map. The neighboring class labels of a given pixel in the non-contextual classification map are exploited to extract spatial information, while temporal information is deduced from the non-contextual maps produced by the remaining single-time images. Spatial and temporal contextual information, together with spectral information, serves as input for the second stage network where the fusion takes place. As the network configuration can influence the classification performances, three MLP-based configurations are investigated. Experimental results on a multitemporal data set consisting of two multisensor (Landsat TM and ERS-1 SAR) images are presented and the performances of the proposed method are compared with those of both a classifier based on Markov random fields and a statistical contextual classifier.
Shared randomness and entanglement in communication complexity
Authors: Dmitry Gavinsky
Venue:  (2006)

We study the role of shared randomness and shared entanglement in the context of communication complexity, both classical and quantum. In the first part of this thesis we present two exponential separations in the simultaneous message passing model of communication complexity. First, we describe a relation that can be computed with O(log n) classical bits of communication in the presence of shared randomness, but needs &Omega;(n1/3) communication if the parties do not share randomness, even if communication is quantum. Second, we describe a relation that can be computed with O(log n) classical bits of communication in the presence of shared entanglement, but needs &Omega;((n/log n)1/3) communication if the parties share randomness but no entanglement, even if communication is quantum. In the second part of this thesis we establish a wide range of tight (up to a logarithmic factor) entanglement vs. communication tradeoffs for relational problems. The low end is: for any t > 2, reducing shared entanglement from logtn to o(logt-1n ) qubits can increase the communication required for solving a problem almost exponentially, from O(logtn ) to &omega;( n ). The high end is: for any &epsiv; > 0, reducing shared entanglement from n1-&epsiv;log n to o(n1-&epsiv;) qubits can increase the required communication from O(n 1-&epsiv;log n) to &Omega;(n1-&epsiv;/2). The upper bounds are demonstrated via protocols which are exact and work in the simultaneous message passing model, while the lower bounds hold for bounded-error protocols, even in the more powerful model of one-way communication. Our protocols use shared EPR pairs while the lower bounds apply to any sort of prior entanglement. Finally, we demonstrate that every many-round quantum protocol with unlimited shared entanglement can be simulated (with exponential overhead) in the model of quantum SMP protocols without shared randomness (Q||). We apply our technique to obtain an efficient Q||-protocol for a function that cannot be efficiently solved through more restricted simulations.
Imposing Biological Constraints onto an Abstract Neocortical Attractor Network Model
Authors: Christopher Johansson, Anders Lansner
Venue: Neural Computation (2007)

In this letter, we study an abstract model of neocortex based on its modularization into mini- and hypercolumns. We discuss a full-scale instance of this model and connect its network properties to the underlying biological properties of neurons in cortex. In particular, we discuss how the biological constraints put on the network determine the network's performance in terms of storage capacity. We show that a network instantiating the model scales well given the biologically constrained parameters on activity and connectivity, which makes this network interesting also as an engineered system. In this model, the minicolumns are grouped into hypercolumns that can be active or quiescent, and the model predicts that only a few percent of the hypercolumns should be active at any one time. With this model, we show that at least 20 to 30 pyramidal neurons should be aggregated into a minicolumn and at least 50 to 60 minicolumns should be grouped into a hypercolumn in order to achieve high storage capacity.
Identification of multivariate FIR systems using higher-order statistics
Authors: Lang Tong
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 05 (1996)

The identification of multichannel moving average (MA) parameter matrices {H(k)} using fourth-order output cumulants is considered. By analysing the eigenstructures of the cumulant matrices, it is shown that the MA parameter matrices can be identified uniquely up to a post multiplication of monomial matrices if H(0) does not have columns that are pairwise colinear and H=[H/sup t/(0),...,H/sup t/(L)]/sup t/ has full column rank. The constructive proof of this condition leads to a closed-form identification algorithm.
Efficient parallelization of the iterative solution of a coupled fluid&ndash;structure interaction problem: Research Articles
Authors: Martyn D. Hughes, Ke Chen
Venue: Concurrency and Computation: Practice Experience (2007)

In this paper we consider the parallelization of the generation and iterative solution of coupled linear systems modelling the interaction of an acoustic field in a fluid medium with an elastic structure immersed in the fluid. The particular case studied is that of a hollow steel sphere in water. The aim of the work is to speed up the generation and solution of the systems. We describe the methods used, which involve special sparse storage arrangements and a novel application of a sparse approximate inverse preconditioning technique, and present results showing that the methods are very effective in terms of speeding up the generation and iterative solution of the systems. Copyright &copy; 2007 John Wiley & Sons, Ltd.
Characterizing Workflow Nets Using Regions
Authors: Nadia Busi, G. Michele Pinna
Venue: Proceedings of the Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (2006)

The target of workflow mining is to obtain a workflow management systems from a possibly complete set of event logs of the system. This gives a fruitful support for developing complex business transaction sequences and business collaboration applications. We investigate on how the theory of regions, used to synthesize nets from marking graphs, can be used to mine Workflow Nets. We show that using minimal regions we can mine the correct net. We also briefly discuss on how transitions systems can be obtained by event logs.
Sea ice monitoring using spaceborne multi-polarization and polarimetric sar imagery
Authors: Bernd Scheuchl
Venue:  (2006)

The imminent launch of RADARSAT-2, the most advanced of the second-generation spaceborne SARs, has stimulated renewed interest in polarization diversity for sea ice monitoring. The primary objective of this work is to assess the potential value of RADARSAT-2 multi-polarization and polarimetric C-band SAR imagery for classification of sea ice and to develop improved classifiers that account for the characteristics of such imagery. Our review of the ice information requirements of the Canadian Ice Service reveals the importance of daily revisit for operations. The need to determine the ice edge location, the ice concentration, and stage of development of ice can be addressed by accurate classification of ice types in SAP, imagery. Our application of target decomposition to AIRSAR airborne polarimetric imagery of sea ice reveals that surface scattering dominates the majority of the scene. Pixel by pixel application of target decomposition methods can be used to distinguish thin ice, first-year ice, and multi-year ice with some success. When classifying sea ice, we show that a recently proposed K-means clustering algorithm which uses a Wishart classifier can be substantially simplified by initializing it with a seed based solely on backscatter levels. Our analysis of AIRSAR airborne polarimetric imagery of sea ice suggests that classification accuracy obtained using dual-polarization imagery is similar to that of polarimetric imagery and better than that of single-polarization imagery. Our analysis of simulated RADARSAT-2 polarimetric imagery derived from airborne CV-580 imagery indicates that speckle noise degrades our ability to distinguish between ice types more than the increase in NESZ but can easily be reduced through spatial filtering. In simulated dual-polarization ScanSAR imagery, open water and sea ice can be easily distinguished by using both co- and cross-polarized image in spite of the high NESZ level. In our analysis of ENVISAT ASAR AP imagery that covers a full ice season, multi-year ice could be distinguished from other types in eight out of ten scenes available. When antenna pattern correction causes a variation of NESZ over the swath, we show that adaptive classification scheme can compensate for such variation.
On the Pitfalls of UML 2 Activity Modeling
Authors: Tim Schattkowsky, Alexander Forster
Venue: Proceedings of the International Workshop on Modeling in Software Engineering (2007)

With the introduction of new Petri Net-like semantics for Activities in UML 2.0, these have become a complete language for modeling behavior. Thus, UML Activities are nowadays investigated for application in many areas from embedded systems to business process modeling. However, some issues have been discovered that currently seem to limit the practical applicability of Activities. In this paper, we present an overview of the identified semantic and syntactic problems, and point at possible solutions and directions for future research.
Is Coordination a Means to Collaboration?
Authors: Chris Weaver
Venue: Proceedings of the Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization (2007)

Coordinated multiple view (CMV) approaches have become commonplace in the development of visual tools for information visualization and visual analytics. However, research and development in CMV has focused almost exclusively on the exploratory and analytic needs of individual users working in traditional interactive desktop environments. Current understanding of collaboration in terms of perception, interaction, conceptualization, and social organization begs for integrated approaches to exploration and analysis that support visualization of information in multiple coordinated views by multiple actors working across time and space in multiple roles and environments. This statement outlines two key aspects of a possible strategy for achieving acquisition, exploration, communication, and management of information in a collaborative exploratory visualization environment built on top of a solid foundation of well-established CMV techniques.
Spectral RTL Test Generation for Microprocessors
Authors: Nitin Yogi, Vishwani D. Agrawal
Venue: Proceedings of the 20th International Conference on VLSI Design held jointly with 6th International Conference: Embedded Systems (2007)

We introduce a novel method of test generation for microprocessors at the RTL using spectral methods. Test vectors are generated for RTL faults, which are the stuck-at faults on inputs-outputs of the different modules/registers in the circuit, and the vectors are analyzed using Hadamard matrices for Walsh functions and the random noise level at each primary input. This information then helps generate vector sequences. At the gate-level, a fault simulator and an integer linear program (ILP) compact the test sequences. RTL test appraisal also helps reveal the hard-to-test parts of the circuit. An XOR observability tree was used to im- prove the testability of those parts. We give results for a simple accumulator-based processor named Parwan. The RTL spectral vectors produced higher coverage in shorter CPU times as compared to a gate-level ATPG.
Outwit: Unix tool-based programming meets the windows world
Authors: Diomidis D. Spinellis
Venue: Proceedings of the annual conference on USENIX Annual Technical Conference (2000)

The ubiquity of Windows-based desktop environments has not been matched by a corresponding emergence of tools supporting the Unix tool composition paradigm. Outwit is a suite of tools based on the Unix tool design principles allowing the processing of Windows application data with sophisticated data manipulation pipelines. The outwit tools offer access to the Windows clipboard, the registry, relational databases, document properties, and shell links. We demonstrate a number of applications of the outwit tools used in conjuction with existing Unix commands, and discuss future directions of our work.
A fast full-search motion estimation method for programmable processors with a multiply-accumulator
Authors: Y. Naito
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06 (1996)

Motion estimation (ME) has widely been used in video codecs, such as MPEG and ITU-T H.261, to remove interframe redundancy. This paper describes a fast full-search ME method for programmable processors with a multiply-accumulator. It is based on the block matching method that evaluates block mismatch by the mean square error. In order to reduce the computational load of the ME, a fast two-dimensional FIR filtering algorithm is applied, and the redundancy of the overlapped area computation among the reference blocks is eliminated. When the block size is 16/spl times/16 and the number of reference blocks in the search window is 32/spl times/32, this method reduces the computational load to 22.6% without degrading the motion tracking capability.
Soft Competitive Learning and Growing Self-Organizing Neural Networks for Pattern Classification
Authors: Guojian Cheng, Tianshi Liu, Kuisheng Wang, Jiaxin Han
Venue: Proceedings of the Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (2006)

Competitive learning can be defined as an adaptive process in which the neurons in an artificial neural network gradually become sensitive to different input categories which are sets of patterns in a specific domain of the input space. By using competitive learning, Kohonen's Self-Organizing Maps (KSOM) can generate mappings from high-dimensional signal spaces to lower dimensional topological structures. The main features of KSOM are formation of topology preserving feature maps and approximation of input probability distribution. However, KSOM have some shortages, e.g., a fixed number of neural units and a fixed topology dimensionality which can result in problems if this dimensionality does not match the dimensionality of the feature manifold. Compared to KSOM, Growing Self-Organizing Neural Networks (GSONN) can change their topological structures during learning. The topology formation of both GSONN and KSOM is driven by soft competitive learning. This paper first gives an introduction to KSOM and neural gas network. Then, we discuss some GSONN without fixed dimensionality such as growing neural gas and the author's model: twin growing neural gas and it's application for pattern classification. It is ended with some conclusions.
Satisfiability of mixed Horn formulas
Authors: Stefan Porschen, Ewald Speckenmeyer
Venue: Discrete Applied Mathematics (2007)

In this paper the class of mixed Horn formulas is introduced that contain a Horn part and a 2-CNF (conjunctive normal form) (also called quadratic) part. We show that SAT remains NP-complete for such instances and also that any CNF formula can be encoded in terms of a mixed Horn formula in polynomial time. Further, we provide an exact deterministic algorithm showing that SAT for mixed Horn formulas containing n variables is solvable in time O(2^0^.^5^2^8^4^n). A strong argument showing that it is hard to improve a time bound of O(2^n^/^2) for mixed Horn formulas is provided. We also obtain a fixed-parameter tractability classification for SAT restricted to mixed Horn formulas C of at most k variables in its positive 2-CNF part providing the bound O(@?C@?2^0^.^5^2^8^4^k). We further show that the NP-hard optimization problem minimum weight SAT for mixed Horn formulas can be solved in time O(2^0^.^5^2^8^4^n) if non-negative weights are assigned to the variables. Motivating examples for mixed Horn formulas are level graph formulas [B. Randerath, E. Speckenmeyer, E. Boros, P. Hammer, A. Kogan, K. Makino, B. Simeone, O. Cepek, A satisfiability formulation of problems on level graphs, ENDM 9 (2001)] and graph colorability formulas.
A one-shot configurable-cache tuner for improved energy and performance
Authors: Ann Gordon-Ross, Pablo Viana, Frank Vahid, Walid Najjar, Edna Barros
Venue: Proceedings of the conference on Design, automation and test in Europe (2007)

We introduce a new non-intrusive on-chip cache-tuning hardware module capable of accurately predicting the best configuration of a configurable cache for an executing application. Previous dynamic cache tuning approaches change the cache configuration several times as part of the tuning search process, executing the application using inferior configurations and temporarily causing energy and performance overhead. The introduced tuner uses a different approach, which non-intrusively collects data on addresses issued by the microprocessor, analyzes that data to predict the best cache configuration, and then updates the cache to the new best configuration in "one-shot," without ever having to examine inferior configurations. The result is less energy and less performance overhead, meaning that cache tuning can be applied more frequently. We show through experiments that the one-shot cache tuner can reduce memory-access related energy for instructions by 35% and comes within 4% of a previous intrusive approach, and results in 4.6 times less energy overhead and a 7.7 times speedup in tuning time compared to a previous intrusive approach, at the main expense of 12% larger size.
Introduction to the Minitrack on Knowledge Flows: Knowledge Transfer, Sharing and Exchange in Organizations
Authors: K. D. Joshi, Mark E. Nissen, Saonee Sarker
Venue: Proceedings of the 40th Annual Hawaii International Conference on System Sciences (2007)

This short paper serves to introduce the minitrack on knowledge flows and to summarize its constituent proceedings articles.
PRESTO: a predictive storage architecture for sensor networks
Authors: Peter Desnoyers, Deepak Ganesan, Huan Li, Ming Li, Prashant Shenoy
Venue: Proceedings of the 10th conference on Hot Topics in Operating Systems - Volume 10 (2005)

We describe PRESTO, a predictive storage architecture for emerging large-scale, hierarchical sensor networks. In contrast to existing techniques, PRESTO is a proxy-centric architecture, where tethered proxies balance the need for interactive querying from users with the energy optimization needs of the remote sensors. The main novelty in this work lies in extensive use of predictive techniques that are a natural fit to the correlated behavior of the physical world. PRESTO exploits technology trends in storage to build an architecture that emphasizes archival at remote sensors and intelligent caching at proxies. The system also addresses user needs for querying such sensor networks by exposing a unified, easy to use data abstraction across numerous proxies and remote sensors.
Peer-to-Peer Scheduling System with Scalable Information Sharing Protocol
Authors: Norihiro Umeda, Hidemoto Nakada, Satoshi Matsuoka
Venue: Proceedings of the 2007 International Symposium on Applications and the Internet Workshops (2007)

In traditional job scheduling systems for the Grid, a single or a few machines handle information of all computing resources and scheduling tasks. This centralized approach is not scalable, since it introduces single point of failure and bottleneck. Some decentralized scheduling systems have been proposed to improve scalability. They avoid concentration of scheduling costs by broadcasting job execution requests in a peer-to-peer style. However, resource utilization tends to be low, because most of them are not aware of the dynamic states of computing resources before throwing execution requests. This paper introduces a decentralized scheduling system that improves resource utilization by using a Gossip-based multicast protocol. With this protocol, peers can gather information of each other efficiently and schedule jobs individually. The simulation shows that our system is scalable and it handles many jobs efficiently in large scale Grid environments.