ppxscal/embedding_network huggingface dataset:
DiCAS: An Efficient Distributed Caching Mechanism for P2P Systems
Authors: Chen Wang, Li Xiao, Yunhao Liu, Pei Zheng
Venue: IEEE Transactions on Parallel and Distributed Systems (2006)

Peer-to-peer networks are widely criticized for their inefficient flooding search mechanism. Distributed Hash Table (DHT) algorithms have been proposed to improve the search efficiency by mapping the index of a file to a unique peer based on predefined hash functions. However, the tight coupling between indices and hosting peers incurs high maintenance cost in a highly dynamic network. To properly balance the tradeoff between the costs of indexing and searching, we propose the distributed caching and adaptive search (DiCAS) algorithm, where indices are passively cached in a group of peers based on a predefined hash function. Guided by the same function, adaptive search selectively forwards queries to "matched” peers with a high probability of caching the desired indices. The search cost is reduced due to shrunk searching space. Different from the DHT solutions, distributed caching loosely maps the index of a file to a group of peers in a passive fashion, which saves the cost of updating indices. Our simulation study shows that the DiCAS protocol can significantly reduce the network search traffic with the help of small cache space contributed by each individual peer.
Studying the current status of technology adoption
Authors: Pratyush Bharati, Abhijit Chaudhury
Venue: Communications of the ACM (2006)

Examining the extent and nature of adoption of technologies by micro, small, and medium-sized manufacturing firms in the greater Boston area.
Chaotic methods for image processing
Authors: T. W. Frison, H. D. I. Abarbanel, L. S. Tsimring
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1995. on International Conference - Volume 02 (1995)

We extend methods for processing chaotic time series to two-dimensional images. The motivation is to develop new tools for understanding physical systems that can be imaged, such as the ocean surface. The novel issue addressed is the computation of the average mutual information for images. The average mutual information provides insights into energy transport and information loss rates in the underlying system. It also provides a crucial parameter needed for further processing with chaotic methods.
Vision-Based Projection-Handwriting Integration in Classroom
Authors: Weihong Li, Hao Tang, Zhigang Zhu
Venue: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)

A projector based presentation and a whiteboard handwriting capture system, when monitored by a camera, could provide a better means to present a lecture in a classroom and later to deliver more effective e-lectures for off-campus students. When the PowerPoint© (PPT) slides are projected on the whiteboard where the instructor annotates, modifies, or expands the PPT presentation, geometrically registering high-resolution images from the two sources yields high quality digital presentations. Since the two sources do not share the same materials, one with printing notes and the other with handwriting notes, a digital camera is used as a bridge to align the two sources, as well as to record the whole lecture. We present a hybrid registration approach to align the PPT and handwriting images and then propose a coarse-to-fine content matching method to register handwriting contents captured by the video camera and the whiteboard capture system. We also show an integrated user-customized presentation system to provide students better lecturing context. Experimental results are presented to validate our approach, and future research directions are also discussed.
Towards a mechanized metatheory of standard ML
Authors: Daniel K. Lee, Karl Crary, Robert Harper
Venue: Proceedings of the 34th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages (2007)

We present an internal language with equivalent expressive power to Standard ML, and discuss its formalization in LF and the machine-checked verification of its type safety in Twelf. The internal language is intended to serve as the target of elaboration in an elaborative semantics for Standard ML in the style of Harper and Stone. Therefore, it includes all the programming mechanisms necessary to implement Standard ML, including translucent modules, abstraction, polymorphism, higher kinds, references, exceptions, recursive types, and recursive functions. Our successful formalization of the proof involved a careful interplay between the precise formulations of the various mechanisms, and required the invention of new representation and proof techniques of general interest.
Dynamics of information and emergent computation in generic neural microcircuit models
Authors: Thomas Natschläger, Wolfgang Maass
Venue: Neural Networks (2005)

Numerous methods have already been developed to estimate the information contained in single spike trains. In this article we explore efficient methods for estimating the information contained in the simultaneous firing activity of hundreds of neurons. Obviously such methods are needed to analyze data from multi-unit recordings. We test these methods on generic neural microcircuit models consisting of 800 neurons, and analyze the temporal dynamics of information about preceding spike inputs in such circuits. It turns out that information spreads with high speed in such generic neural microcircuit models, thereby supporting-without the postulation of any additional neural or synaptic mechanisms-the possibility of ultra-rapid computations on the first input spikes.
Real-time sound localization using field-programmable gate arrays
Authors: D. Nguyen, P. Aarabi, A. Sheikholeslami
Venue: Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1 (2003)

This paper presents a single FPGA implementation of a real-time sound localization system using two microphones. The implementation, utilizing a cross-correlation technique based on a modified version of the phase transform, successfully localizes sound sources in noisy environments with as low an SNR as 10 dB. Using the same algorithm and similar hardware architecture, it is shown that up to 5 parallel systems (using 10 microphones), all real-time, can be implemented on a single FPGA while only utilizing an estimated 77 mW-108 mW per microphone.
A Technique for Visualizing Dihedral Signal of Large Protein Sequences
Authors: Quang Vinh Nguyen, Mao Lin Huang, Yu Qian, Kang Zhang
Venue: Proceedings of the International Conference on Computer Graphics, Imaging and Visualisation (2006)

This paper presents a clustering and visualization technique for analyzing dihedral angles of large protein sequences. The clustering is used for discovering and grouping those similar dihedrals while the visualization can display and navigate sequences of dihedral angles of several proteins as well as their clustered property. In order to visualize a very large sequence of hundred thousands of dihedral signals, we plot them on a spiral coordinate system. This spiral visualization ensures the linear distribution without distortion or interruption of a very long sequence of points. A clustering algorithm is also provided to group those dihedral signals into different clusters so that it can enhance the analysis process. Our system can also zoom to display a number of selected proteins interactively.
Hierarchical stochastic modelling for speech compression
Authors: Kie-Bum Eom, R. Chellappa
Venue: Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04 (1994)

In this paper, we consider the hierarchical modeling of signals in a scale space using autoregressive and moving average (ARMA) models with applications to speech compression. We show that the AR polynomial can be uniquely determined when the scale is changed. When the scale changes from fine to coarse (aggregation) and from coarse to fine (disaggregation), the model parameters can be obtained from the parameters of the model at different scales. Data disaggregation is the estimation of data at a finer scale from data at a coarse scale. We present a data disaggregation algorithm based on the minimum mean square error (MMSE) criterion. The MMSE data disaggregation algorithm is computationally more efficient than the weighted least squares (WLS) approach. The data disaggregation algorithm is then applied to speech compression.
A Reflection Mechanism for Reusing Software Architecture
Authors: Shi Ying, ZaoQing Liang, JunLi Wang, FuDi Wang
Venue: Proceedings of the Sixth International Conference on Quality Software (2006)

Reusing software architecture and its constituents, which are a kind of coarse-grained software resources, are confronted with great difficulty. Reflection mechanism has been successfully applied in the reuse of code component, but scarcely applied in the reuse of architecture and its constituents. Architectural reflection is the computation performed by a system about its own architecture, and currently confined in the realm of the runtime management and dynamic evolution of software systems. This paper proposes a reflection mechanism supporting the reuse of architectural level design, which offers the information support for reusing the architecture and its constituents at design time of software architecture by modeling the meta-information about the base-level to construct a meta-level architecture. Moreover, this paper illustrates the approach to describing meta-level architecture of this reflection mechanism, and presents the solution of constructing this reflection mechanism in detail. Through this reflection mechanism, architects could accomplish the tasks of architecture design efficiently at software design stage, by reusing software architecture and its constituents.
Common Mechanisms for Working Memory and Attention: The Case of Perseveration with Visible Solutions
Authors: Jennifer Merva Stedron, Sarah Devi Sahni, Yuko Munakata
Venue: Journal of Cognitive Neuroscience (2005)

Everyone perseverates at one time or another, repeating previous behaviors when they are no longer appropriate. Such perseveration often occurs in situations with working memory demands, and the ability to overcome perseveration has been linked to brain regions critical for working memory. Many theories thus explain perseveration in terms of working memory deficits. However, perseveration also occurs in situations without apparent working memory demands, in which the visible environment specifies appropriate behavior. Such findings appear to challenge working memory accounts of perseveration. To evaluate this challenge, a neural network model of a working memory account of perseveration was tested on tasks with visible solutions. With advances in the mechanisms that support working memory, networks became increasingly able to attend to relevant information in the environment. These developments led to improvements in performance on tasks with visible solutions, paralleling the developmental progression observed in infants. The simulations demonstrate how mechanisms of working memory can subserve perseveration and success on tasks with and without obvious memory demands. In both types of tasks, controlled processing occurs through the activation of task-relevant representations, which provide top&ndash;down biasing of other processing pathways. More generally, the simulations demonstrate how common mechanisms can support working memory and attention.
A Study of Collaborative Dancing in Tele-immersive Environments
Authors: Zhenyu Yang, Bin Yu, Wanmin Wu, Klara Nahrstedt, Ross Diankov, Ruzena Bajscy
Venue: Proceedings of the Eighth IEEE International Symposium on Multimedia (2006)

We first present the tele-immersive environments developed jointly by University of Illinois at Urbana-Champaign and University of California at Berkeley. The environment features 3D full and real body capturing, wide field of view, multi-display 3D rendering, and attachment free participant. We then describe a study of collaborative dancing between remotely located dancers in the shared virtual space. Two professional dancers are invited to the tele-immersive site of each university. As a preliminary experiment, we let the dancers perform elementary body movements and coordinate their dancing. The coordination requires one dancer to take the lead while the other follows her by appropriate movements. During the experiment, the dancers are dancing at various motion rates to evaluate how well the collaborative dancing is supported with the current technical boundary. Our important findings indicate that 1) tele-immersive environments have strong potential impact on the concept of choreography and communication of live dance performance, 2) the presence of multi-display system, real body 3D rendering, audio channel, and less intrusiveness greatly enhances the immersive and dancing experience, and 3) the level of synchronization achieved by the dancers is higher than that expected from the video rate.
Autonomous predictive-adaptive simulation for operations support
Authors: Peter C. Bosch, Majdi Rajab
Venue: Proceedings of the 36th conference on Winter simulation (2004)

This paper describes a simulation system that monitors operations on a production floor, periodically creating a model of those operations, and running a simulation that predicts the next several shifts' worth of events, providing operators with new predictive analysis capabilities.As a set of procedures is carried out in the model and the real world, deviations are introduced by the variations between the expected activities and the actual occurrences. More deviations arise from explicit adaptations undertaken by operations staff in response to already-observed anomalies. With each cycle, those deviations are integrated into the model, heuristics are applied to estimate the likely future course of events, and after a simulation run, a new set of predictions is generated from that model, and it compares the new predictions with the last run's predictions. Differences between the pre-loop prediction and the post-loop prediction serve to indicate whether the situation is improving or degrading.
Calculation of a logistics equation parameter on experimental data with the miss
Authors: D. I. Yakushev
Venue: Proceedings of the 2003 International Conference on Physics and Control - Volume 2 (2003)

The iterative method of calculation of a logistic equation parameter on experimental data with the miss is described. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Tutorial on portfolio credit risk management
Authors: William J. Morokoff
Venue: Proceedings of the 36th conference on Winter simulation (2004)

The distribution of possible future losses for a portfolio of credit risky corporate assets, such as bonds or loans, shows strongly asymmetric behavior and a fat tail as the consequence of the limited upside of credit (the promised coupon payment) and substantial downside if the corporation defaults. Because of correlation, it is not possible to fully diversify away this fat tail. Detailed correlation models require Monte Carlo simulation to determine the loss distribution for a credit portfolio. This tutorial covers the basics of credit risk modeling including an overview of the credit markets, a summary of what data are available for defining and calibrating models, and a discussion of key modeling questions. Finally a detailed discussion of simulation methods used in calculation credit portfolio loss distribution and related credit risk measures is presented.
Modeling load address behaviour through recurrences
Authors: L. Ramos
Venue: Proceedings of the 2000 IEEE International Symposium on Performance Analysis of Systems and Software (2000)

Addresses of load instructions exhibit regularity in their behaviour which is modelled through several models (locality repetitive patterns, etc.) and exploited in processor and memory hierarchy design. Nevertheless, sparse and symbolic applications are intensive in addressing patterns not entirely covered by current models. In this work we introduce a new recurrence among load pairs called "linear link" in order to identify more regularity from such applications. A linear link is a type of recurrence between the value read by a (producer) load and the address issued by a (consumer) load, which is detected tracking on-the-fly dependencies among loads. We consider a broad workload (Nas, Olden, Perfect, Spec95 and IAbench) and conclude that linear links together with stride recurrences can identify many address streams in symbolic and scientific applications traversing either dense, linked data structures or compressed forms of sparse arrays. The two recurrence combinations identify more than 90% of the addresses in more than a half the programs (in 24 our of 55), and more than 75% of the addresses in 90% of the programs (50 our of 55). Finally, we show several measures related to the use of linear links as address predictors for executing loads speculatively and for issuing data prefetches (prediction distance ahead capacity, etc.).
Modelling and simulation of an autonomous variable speed micro hydropower station
Authors: A. Ansel, B. Robyns
Venue: Mathematics and Computers in Simulation (2006)

In this paper, the modelling of an autonomous variable speed micro hydropower station is presented. It is composed of a doubly fed induction generator linked mechanically and electrically to a permanent magnet synchronous machine which may recover or supply the slip power and which feeds the DFIG with its magnetizing reactive power leading to classical capacitor removing. The model lies on a classical approach, based on a Park representation, and on an energetic macroscopic representation (EMR). In its first part, this paper develops the modelling principles and underlines the complementarity of both approaches. The control strategy is then depicted in the case of passive loads supplying. Finally, some simulation results are showed and commented.
A novel architecture to model non-linear systems
Authors: A. Pages-Zamora, M. A. Lagunas
Venue: Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04 (1994)

This paper shows a new architecture specially thought to model non-linear systems (NLSs). At first, it was applied only to memoryless systems but then it developed to solve a more general problem, NLSs with memory. The result is a new filter, based on the Fourier transform, that the authors have named "K-filter". Important features of the K-filter are its nonlinear behaviour and second, that it profits from a temporal diversity of the input signal in order to provide itself with memory. At the end of the paper, the K-filter is used to solve an identification problem of a communication system which behaves nonlinearly due to the response of the amplifiers and which also has memory introduced basically by the channel response. The simulation results will provide an evaluation of the K-filter.
An Adaptive Algorithm Based On The Sigmoidal Function
Authors: Ewaldo Santana, José C. Principe, Allan K. Barros, R. C. S. Freire
Venue: Proceedings of the Ninth Brazilian Symposium on Neural Networks (2006)

In this work, we show the development of an adaptive algorithm based on the Ln(cosh \varepsilon) as cost function applied upon the error, called Sigmoidal Algorithm (SA). That function generates a surface which yields fast convergence along with lower misadjustment. It is similar to the family of algorithms proposed by Walach and Widrow [1]. The later ones were shown to behave poorer than the LMS algorithm [2], when the noise was Gaussian. We study the SA algorithm convergence behavior and find equations for the misadjustment and the learning time. Results showed that the SA had a better performance than the LMS when the noise had a Gaussian distribution.
Interactions Between Forms of Memory: When Priming Hinders New Episodic Learning
Authors: Anthony D. Wagner, Anat Maril, Daniel L. Schacter
Venue: Journal of Cognitive Neuroscience (2000)

Human memory consists of multiple forms, including priming and explicit memory. Although considerable evidence indicates that priming and explicit memory are functionally and neuroanatomically distinct, little is know about when and how these different forms of memory interact. Here, behavioral and functional magnetic resonance imaging (fMRI) methods were used to examine a novel and counterintuitive hypothesis: Priming during episodic encoding may be negatively associated with subsequent explicit memory. Using an experimental design that exploited known properties of spacing or lag effects, the magnitudes of behavioral and neural priming during a second study episode were varied and the relation between these magnitudes of priming during re-encoding and performance on a subsequent explicit memory test was examined. Results revealed that greater behavioral priming (reduced reaction times) and neural priming (reduced left inferior prefrontal brain activation) during re-encoding were associated with lower levels of subsequent explicit memory. Moreover, those subjects who demonstrated greater behavioral and neural priming effects during re-encoding following a long lag tended to demonstrate the least benefit in subsequent explicit memory due to this second study episode. These findings suggest that priming for past experiences can hinder new episodic encoding.
An Optimal Adaptive Checkpoint Strategy for DMR with Energy-Aware
Authors: Zhongwen Li, Yefeng Jiang
Venue: Proceedings of the Seventh International Conference on Parallel and Distributed Computing, Applications and Technologies (2006)

A combination of DVS and CSCPs (CCPs or SCPs) can be used to satisfy system's DVS requirement and improve the performance of realtime systems. However, few papers addresses these issues in conjunction. In this paper, we present an integrated approach that facilitates fault tolerance through additional CCPs and SCPs checkpoints and power management through DVS. Further, an optimal checkpointing interval that minimizes the average times is analytically derived and is numerically computed. Numerical examples results show that compared to previous method, the proposed approach reduces task average execution time.
GMP: Distributed Geographic Multicast Routing in Wireless Sensor Networks
Authors: Shibo Wu, K. Selcuk Candan
Venue: Proceedings of the 26th IEEE International Conference on Distributed Computing Systems (2006)

In this paper, we propose a novel Geographic Multicast routing Protocol (GMP) for wireless sensor networks1. The proposed protocol is fully distributed and stateless. Given a set of the destinations, the transmitting node first constructs a virtual Euclidean Steiner tree rooted at itself and including the destinations, using a novel and highly efficient reduction ratio heuristic (called rrSTR). Based on this locally computed tree and the information regarding the locations of its immediate neighbors, the transmitting node then splits the destinations into a set of groups and calculates a next hop for each of these groups. A copy of the packet and the locations of the corresponding group of destination nodes are directed towards the corresponding hop. The simulation results on NS2 show that the average per-destination hop count obtained using GMP is comparable to the existing PBM [21] algorithm and significantly less than obtained by using LGS [5]. Most significantly, GMP requires 25% less hops and energy than alternative algorithms.