ppxscal/embedding_network huggingface dataset:
Is region-of-interest overlap comparison a reliable measure of category specificity?
Authors: Chun-Chia Kung, Jessie J. Peissig, Michael J. Tarr
Venue: Journal of Cognitive Neuroscience (2007)

Analysis of the degree of overlap between functional magnetic resonance imaging--derived regions of interest (ROIs) has been used to assess the functional convergence and/or segregation of category-selective brain areas. An examination of the extant literature reveals no consistent usage for how such overlap is calculated, nor any systematic comparison between different methods. We argue that how ROI overlap is computed, especially the choice of the denominator in the formula, can profoundly affect the results and interpretation of such an analysis. To do this, we compared the overlap of the FFA-FFA (fusiform face area) and FFA-FGA (fusiform Greeble-selective area) in a localizer study testing both Greeble novices and experts. When using a single ROI as the denominator, we found a significant difference in FFA-FFA versus FFA-FGA overlap, consistent with the result of a previous study arguing for face specificity of the FFA [Rhodes, G., Byatt, G., Michie, P. T., & Puce, A. Is the fusiform face area specialized for faces, individuation, or expert individuation? J Cogn Neurosci, 16, 189--203, 2004]. However, these ROI overlap differences disappeared when the denominator combined both of the involved ROIs, and the patterns of such overlap comparisons were dependent on given statistical thresholds. We also found proportionally decreasing FFA-FFA overlap with increasing center-of-FFA distance, resolving an apparent contradiction between the consistency of the location of the FFA and the seemingly low FFA-FFA overlap. Finally, Monte Carlo simulations revealed the most stable formula---the most resistant to ROI size variations---to be the average of the two single-ROI-denominator-based overlap indices. In sum, ROI overlap analysis is not a reliable tool for assessing category specificity, and caution should be exercised with regard to ROI overlap definition, underlying assumptions, and interpretation.
Configurable AMBA On-Chip Real-Time Signal Tracer
Authors: Chung-Fu Kao, Chi-Hung Lin, Ing-Jer Huang
Venue: Proceedings of the 2007 Asia and South Pacific Design Automation Conference (2007)

This paper purpose an embedded AMBA signal tracer for microprocessor-based SoC's. This tracer provides five trace resolution modes that can perform a cycle-accurate or a transaction-based trace collection in an unlimited time. Also this tracer is implemented in a Soft-IP style. It provides four parameters for tracing configuration. The experimental results show that the bus tracer can reach a good compression ratio of 96%.
Pattern replication in polypropylene films by hot embossing
Authors: Patrick W. Leech
Venue: Microelectronic Engineering (2008)

The hot embossing of electron beam generated structures with linewidths of 0.35-3.25@mm has been examined in biaxially-oriented polypropylene (BOPP). The individual test elements consisted of pixels of 30x30@mm containing a specific configuration of symbols and lettering. The embossing of these features has been performed as a function of temperature (80-140^oC) and applied pressure (2-20kPa). Significant increases in both embossed depth and sidewall angle were evident over the temperature range 100-130^oC with a leveling off at higher temperatures attributed to the onset of a regime of viscous liquid flow. At temperatures within this regime, a critical level of pressure was required to fill the mold features. Accurate embossing of medium and coarse lettering (0.50-0.65@mm linewidth) and geometric symbols (1.25-3.25@mm linewidth) has been demonstrated at a temperature of 130^oC and an applied pressure >=13kPa. The depth of the finest lettering (0.35@mm linewidth) was incompletely embossed under these conditions.
A new and reliable Poisson noise estimator for radiographic images
Authors: I. Frosio, M. Lucchese, N. A. Borghese
Venue: Proceedings of the 14th International Conference on Image Analysis and Processing (2007)

Digital images in general, and radiographies in particular, are mainly affected by photon counting noise. In this paper, we provide a method to estimate the gain of the imaging sensor and the variance of the photon counting noise associated to each image grey level. This is useful to multiple scopes, like reverse engineering and denoising.
The Performance Appraising of Language Models and the Entropy Estimation of Chinese
Authors: Yangsen Zhang, Gaijuan Huang, Miao Mai
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02 (2007)

We give a quantified reasoning and description of the perplexity for evaluating language models using the concept of entropy in information theory: The smaller the entropy of the language estimated by the language model is, the more precise the language model is; an interpolated model based on two (n-1)-gram models is better than the (n-1)-gram component models, but not a n-gram model. We also explore the methods to estimating the entropy of Chinese using language models.
A Feature based on Encoding the Relative Position of a Point in the Character for Online Handwritten Character Recognition
Authors: Dinesh M, Murali Krishna Sridhar
Venue: Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02 (2007)

Feature extraction is a very important step in the process of character recognition. The features extracted from the character should encode the local, global and the structural characteristics of the character shape. In this paper we propose a new feature for recognition of online handwritten characters called the star feature. The star feature encodes the local, global and structural characteristics of a character. The star feature describes every point of the character, in terms of its relative position with respect to the other points in the character. We have evaluated the performance of the star feature on IRONOFF data set and IWFHR-06 Tamil competition data set. The experimental results show that the star feature achieves high accuracy on both the data sets.
Real-Time Computation by n-Dimensional Iterative Arrays of Finite-State Machines
Authors: S. N. Cole
Venue: IEEE Transactions on Computers (1969)

An n-dimensional iterative array of finite-state machines is formally introduced as a real-time tape acceptor. The computational characteristics of iterative arrays are illuminated by establishing several results concerning the sets of tapes that they recognize. Intercommunication between machines in an array is characterized by specifying a stencil for the array. The computing capability of the array is preserved even if its stencil is reduced to a simple form in which machines communicate only with their nearest neighbors. An increase of computing speed by a constant factor k is defined by encoding k-length blocks of the input tapes, which reduces the lengths of the tapes by 1/k; the time available for computation is correspondingly reduced since the computation must be real time. The computation speed of iterative arrays can be increased by any constant factor k. Two examples of one-dimensional arrays are provided. The first accepts the set of palindromes; the second accepts the set of all tapes of the form tt (for any tape t). The latter set of tapes is not a context-free language; therefore, the sets of tapes accepted by iterative arrays are not all contained in the class of context-free languages. Conversely, the class of context-free languages is not contained in the class of sets of tapes accepted by iterative arrays. The sets of tapes accepted by iterative arrays are closed under the operations: union, intersection, and complement; therefore, they form a Boolean algebra. They are not closed under the reflection or concatenation-product operations.
Real-time implementation of a GIS-based localization system for intelligent vehicles
Authors: Philippe Bonnifait, Maged Jabbour, Gérald Dherbomez
Venue: EURASIP Journal on Embedded Systems (2007)

This paper presents a loosely coupled fusion approach that merges GPS data, dead-reckoned sensors, and GIS (geographical information system) data. The GPS latency is compensated for by the DR sensors and the use of an xPPS signal. The fusion of the estimate with the map data is spatial-triggered, while the fusion with the GPS is time-triggered. We present a strategy that relies on pose tracking which is reinitialized when GPS data become incoherent. Particular attention is given to the representation of the road map and to the management of a cache memory for efficiency purposes. We report experiments carried out with our equipped car and a GIS whose characteristics are well adapted to embedded constraints.
A Run-Time Memory Protection Methodology
Authors: Udaya Seshua, Nagaraju Bussa, Bart Vermeulen
Venue: Proceedings of the 2007 Asia and South Pacific Design Automation Conference (2007)

In this paper we present a novel methodology to help debug memory corruption errors during application debug. In this methodology an optimal balance between hardware and software instrumentation is chosen to check at run-time all memory accesses made by an application. To achieve this balance a set of benchmark applications is first analyzed to determine their memory access patterns. The analysis results are used to make our approach low-cost both from a software performance penalty and a hardware area point-of-view. Experimental results show that our innovative approach typically requires less than 2% of a CPU in silicon area for a less than 1% run-time performance overhead. Our method is both low-cost and applicable to high performance microprocessors as well as time-constrained embedded systems.
An Improved Ant Algorithm for VRP with the Shortest Delivery Time In Distribution Center
Authors: Zixia Chen
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 04 (2007)

An improved ant algorithm is brought up to minimize the delivery time of vehicle routing problem(VRP) in distribution center. Firstly VRP whose objective is to minimize the delivery time is given, then the optimal vehicle distribution route division approach of distribution center for given customer array is defined, and the ant colony algorithm for VRP with objective to minimize the delivery time is presented. The research of this paper is based on the information and application case of tobacco network in Hangzhou city. By investigating, we created two different system models. One is division of distribution route of distribution center, and the other is optimization of single vehicle route. The computational example tests and verifys the solution.
Ontology based complex object recognition
Authors: Nicolas Eric Maillot, Monique Thonnat
Venue: Image and Vision Computing (2008)

This paper presents a new approach for object categorization involving the following aspects of cognitive vision: learning, recognition and knowledge representation. A major element of our approach is a visual concept ontology composed of several types of concepts (spatial concepts and relations, color concepts and texture concepts). Visual concepts contained in this ontology can be seen as an intermediate layer between domain knowledge and image processing procedures. Machine learning techniques are used to solve the symbol grounding problem (i.e. linking meaningfully symbols to sensory information). This paper shows, how a new object categorization system is set up by a knowledge acquisition and learning phase and then used by an object categorization phase.
A Next-Generation Hybrid Computer System
Authors: G. F. Graber
Venue: Computer (1976)

In introducing the concept of an advanced hybrid computer system it is appropriate to describe briefly the current-generation hybrid system from which the advance is being made. The true significance of the advanced system can then be appreciated-namely that it retains all the advantages and at the same time removes the short-comings of the present generation of hybrid computers.
Fast Lexicon-Based Scene Text Recognition with Sparse Belief Propagation
Authors: J. Weinman, E. Learned-Miller, A. Hanson
Venue: Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02 (2007)

Using a lexicon can often improve character recognition under challenging conditions, such as poor image quality or unusual fonts. We propose a flexible probabilistic model for character recognition that integrates local language prop- erties, such as bigrams, with lexical decision, having open and closed vocabulary modes that operate simultaneously. Lexical processing is accelerated by performing inference with sparse belief propagation, a bottom-up method for hy- pothesis pruning. We give experimental results on recogniz- ing text from images of signs in outdoor scenes. Incorpo- rating the lexicon reduces word recognition error by 42% and sparse belief propagation reduces the number of lexi- con words considered by 97%.
Solving a real-world problem using an evolving heuristically driven schedule builder
Authors: Emma Hart, Peter Ross, Jeremy Nelson
Venue: Evolutionary Computation (1998)

This work addresses the real-life scheduling problem of a Scottish company that must produce daily schedules for the catching and transportation of large numbers of live chickens. The problem is complex and highly constrained. We show that it can be successfully solved by division into two subproblems and solving each using a separate genetic algorithm (GA). We address the problem of whether this produces locally optimal solutions and how to overcome this. We extend the traditional approach of evolving a “permutation + schedule builder” by concentrating on evolving the schedule builder itself. This results in a unique schedule builder being built for each daily scheduling problem, each individually tailored to deal with the particular features of that problem. This results in a robust, fast, and flexible system that can cope with most of the circumstances imaginable at the factory. We also compare the performance of a GA approach to several other evolutionary methods and show that population-based methods are superior to both hill-climbing and simulated annealing in the quality of solutions produced. Population-based methods also have the distinct advantage of producing multiple, equally fit solutions, which is of particular importance when considering the practical aspects of the problem.
Technology for developing regions
Authors: Eric Brewer
Venue: Proceedings of the 33rd international conference on Very large data bases (2007)

Moore's Law and the wave of technologies it enabled have led to tremendous improvements in productivity and the quality of life in industrialized nations. Yet, technology has had almost no effect on the other five billion people on our planet. In this talk I argue that decreasing costs of computing and wireless networking make this the right time to spread the benefits of technology, and that the biggest missing piece is a lack of focus on the problems that matter. After covering some example applications that have shown very high impact, I present some our own preliminary results, including the use of novel low-cost telemedicine to improve the vision of real people, with over 20,000 patients examined so far. I conclude with some discussion on the role of database researchers in this new area.
IC Design Capability Conversion from Mainframe to Workstation Environment
Authors: Dave Palmer, John Wisniewski
Venue: IEEE Design Test (1986)

Over the last eighteen months, Sandia National Laboratories has converted its IC design facility from a mainframe to a workstationenvironment. Although workstations were advertised as turnkey systems, augmentation of vendor software with Sandia-developedsoftware greatly enhanced Sandia's specific IC design capabilities. This article examines advantages of the workstation approachfor sophisticated CAD tool users, discussing complete hardware, software, and networking capacities and capabilities.
Robust Clustering on Rule Usage of Probability Reasoning with Raw Rule Score
Authors: Yuan-Horng Lin, Wen-Liang Hung
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02 (2007)

The purpose of this study is to analyze the rule usage of probability reasoning items by a robust clustering method proposed by Yang and Wu [15]. Based on raw rule scoring of four rules, each subject has four scores as to rule usage. The clustering results show that the optimal group number is four and each group corresponds to a unique rule usage. Thanks to the robustness, patterns of rule usage for each cluster display the cognitive structures of those who belong to that group. It also shows that patterns of rule usage vary with different grade of subjects, but not with gender.
1 Tutorial Series Perspectives on Large-Scale Scientific Computation
Authors: G. Rodrigue
Venue: Computer (1980)

An in-depth study of the computational environment at Lawrence Livermore National Laboratory provides insights into the types ofsystems neededfor detailed numeric modeling.
Wide-coverage efficient statistical parsing with ccg and log-linear models
Authors: Stephen Clark, James R. Curran
Venue: Computational Linguistics (2007)

This article describes a number of log-linear parsing models for an automatically extracted lexicalized grammar. The models are “full” parsing models in the sense that probabilities are defined for complete parses, rather than for independent events derived by decomposing the parse tree. Discriminative training is used to estimate the models, which requires incorrect parses for each sentence in the training data as well as the correct parse. The lexicalized grammar formalism used is Combinatory Categorial Grammar (CCG), and the grammar is automatically extracted from CCGbank, a CCG version of the Penn Treebank. The combination of discriminative training and an automatically extracted grammar leads to a significant memory requirement (up to 25 GB), which is satisfied using a parallel implementation of the BFGS optimization algorithm running on a Beowulf cluster. Dynamic programming over a packed chart, in combination with the parallel implementation, allows us to solve one of the largest-scale estimation problems in the statistical parsing literature in under three hours. A key component of the parsing system, for both training and testing, is a Maximum Entropy supertagger which assigns CCG lexical categories to words in a sentence. The supertagger makes the discriminative training feasible, and also leads to a highly efficient parser. Surprisingly, given CCG's “spurious ambiguity,” the parsing speeds are significantly higher than those reported for comparable parsers in the literature. We also extend the existing parsing techniques for CCG by developing a new model and efficient parsing algorithm which exploits all derivations, including CCG's nonstandard derivations. This model and parsing algorithm, when combined with normal-form constraints, give state-of-the-art accuracy for the recovery of predicate-argument dependencies from CCGbank. The parser is also evaluated on DepBank and compared against the RASP parser, outperforming RASP overall and on the majority of relation types. The evaluation on DepBank raises a number of issues regarding parser evaluation. This article provides a comprehensive blueprint for building a wide-coverage CCG parser. We demonstrate that both accurate and highly efficient parsing is possible with CCG.
An Algorithm for the Optimal Solution of Linear Inequalities and its Application to Pattern Recognition
Authors: R. E. Warmack
Venue: IEEE Transactions on Computers (1973)

An algorithm for the optimal solution of consistent and inconsistent linear inequalities is presented, where the optimality criterion is the maximization of the number of satisfied constraints. The algorithm is developed as a nonenumerative search procedure based on two new theorems established in this paper. It is shown that the number of iterative steps before termination is strictly less than that required by an exhaustive search. Experimental results with various types of data establish the computational tractability of the procedure under nontrivial conditions.
Data cache locking for tight timing calculations
Authors: Xavier Vera, Björn Lisper, Jingling Xue
Venue: ACM Transactions on Embedded Computing Systems (TECS) (2007)

Caches have become increasingly important with the widening gap between main memory and processor speeds. Small and fast cache memories are designed to bridge this discrepancy. However, they are only effective when programs exhibit sufficient data locality. In addition, caches are a source of unpredictability, resulting in programs sometimes behaving in a different way than expected. Detailed information about the number of cache misses and their causes allows us to predict cache behavior and to detect bottlenecks. Small modifications in the source code may change memory patterns, thereby altering the cache behavior. Code transformations, which take the cache behavior into account, might result in a high cache performance improvement. However, cache memory behavior is very hard to predict, thus making the task of optimizing and timing cache behavior very difficult. This article proposes and evaluates a new compiler framework that times cache behavior for multitasking systems. Our method explores the use of cache partitioning and dynamic cache locking to provide worst-case performance estimates in a safe and tight way for multitasking systems. We use cache partitioning, which divides the cache among tasks to eliminate intertask cache interferences. We combine static cache analysis and cache-locking mechanisms to ensure that all intratask conflicts, and consequently, memory access times, are exactly predictable. The results of our experiments demonstrate the capability of our framework to describe cache behavior at compile time. We compare our timing approach with a system equipped with a nonpartitioned, but statically, locked data cache. Our method outperforms static cache locking for all analyzed task sets under various cache architectures, demonstrating that our fully predictable scheme does not compromise the performance of the transformed programs.
E-learning content management: an ontology-based approach
Authors: Nophadol Jekjantuk, Md Maruf Hasan
Venue: Proceedings of the third conference on IASTED International Conference: Advances in Computer Science and Technology (2007)

Scarcity of E-Learning content being a barrier for E-Learning is no longer true on today's Internet. The current concerns are how to effectively annotate and organize available content (both textual and non-textual) to facilitate effective sharing, reusability and customization in an intelligent fashion. In this paper, we explain a component-oriented approach to organize content in an ontology. We also illustrate our 3-Tier E-Learning Content Management Architecture and relevant Web Services and Interfaces. We use a simple yet intuitive example to successfully demonstrate the current working prototype which is capable of compiling personalized course materials on a particular topic (e.g., "Database") on-the-fly. The prototype uses the Pellet Semantic Web Reasoner as an inference engine to satisfy the constraints and criteria specified by a user (through browser based interface) or an agent (via Web Service API), and retrieves relevant content from the domain ontology in an organized fashion.