ppxscal/embedding_network huggingface dataset:
Stability of multi-path dual congestion control algorithms
Authors: Thomas Voice
Venue: Proceedings of the 1st international conference on Performance evaluation methodolgies and tools (2006)

This paper investigates fair, scalable, stable congestion controls which achieve high bandwidth utilisation over networks of operating multi-path routing. The aim is to take advantage of path diversity to achieve efficient bandwidth allocation without causing instability. Two multi-path extensions to the class of dual algorithms are considered. The first is a natural extension previously proposed in the literature, which we show to be similar to a continuous time sub-gradient method for solving a network-wide optimisation problem. We establish that the continuous time fluid model possesses a weak stability property. This analysis assumes the absence of propagation delays. We then show that when propagation delays are present, even the weak stability property disappearsWe develop an alternative multi-path extension of the dual algorithm, which considers path diversity when evaluating fairness. This algorithm is shown to be globally stable in the absence of propagation delays and a sufficient condition for local stability, when heterogeneous propagation delays are present, is found. The sufficient condition we present is decentralised in the following sense: the gain parameter for each dynamic variable is restricted by the average round-trip time of packets passing through the link or source it represents, but not by the round-trip times of any other packets. The delay stability analysis is an extension of results for single-path congestion control. It is obtained by treating possible routes that belong to a given origin-destination pair as behaving as separate sources which pass through a virtual link located at the origin.The models considered apply to networks consisting of arbitrary interconnections of sources and links, with arbitrary, heterogeneous propagation delays.
Nonlinear Dimension Reduction and Activation Detection for fMRI Dataset
Authors: Xilin Shen, Francois G. Meyer
Venue: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)

Functional magnetic resonance imaging (fMRI) has been established as a powerful method for brain mapping. Different physical phenomena contribute to the dynamical changes in the fMRI signal, the task-related hemodynamic responses, non-task-related physiological rhythms, machine and motion artifacts, etc. In this paper, we propose a new approach for fMRI data analysis. Each fMRI time series is viewed as a point in RT . We are interested in learning the organization of the points in high dimensions and extracting useful information for data classification. A nonlinear manifold learning technique is applied to obtain a low dimensional embedding of a dataset. The embedding differentiates time series with different temporal patterns. By assuming that the subset of activated time series forms a low dimensional structure, we partition the dataset and separate subsets of points with low dimensionality. The correspondence between low dimensional subsets and time series that contain task-related responses is verified and the activation maps are generated accordingly. The proposed approach is data-driven. It does not require a model for the hemodynamic response. We have conducted several experiments with synthetic and in-vivo datasets that demonstrate the performance of our approach.
Mirror Neurons Responding to Observation of Actions Made with Tools in Monkey Ventral Premotor Cortex
Authors: Pier Francesco Ferrari, Stefano Rozzi, Leonardo Fogassi
Venue: Journal of Cognitive Neuroscience (2005)

In the present study, we describe a new type of visuomotor neurons, named tool-responding mirror neurons, which are found in the lateral sector of monkey ventral premotor area F5. Tool-responding mirror neurons discharge when the monkey observes actions performed by an experimenter with a tool (a stick or a pair of pliers). This response is stronger than that obtained when the monkey observes a similar action made with a biological effector (the hand or the mouth). These neurons respond also when the monkey executes actions with both the hand and the mouth. The visual and the motor responses of each neuron are congruent in that they share the same general goal, that is, taking possession of an object and modifying its state. It is hypothesized that after a relatively long visual exposure to tool actions, a visual association between the hand and the tool is created, so that the tool becomes as a kind of prolongation of the hand. We propose that tool-responding mirror neurons enable the observing monkey to extend action-understanding capacity to actions that do not strictly correspond to its motor representations. Our findings support the notion that the motor cortex plays a crucial role in understanding action goals.
The Development and Implementation of Optimum Load Control Strategy of Air Conditioner Based High Voltage Customers
Authors: Ming Yuan Cho, Tsair Fwu Lee, Shih Wei Kau, Chien Nan Chen
Venue: Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1 (2006)

This paper applies optimization mathematical techniques of dynamic programming .. DP .. algorithm to solve the daily optimal air conditioner loads .. ACL .. control schedule. The regular control mode, which supports three control skills of demand control, cycling control and timer control to assist customer to save the electrical fee. The optimum load control (OLC) scheme which supports any combinations of these three control skills to optimally save the customer's electric fee in the dispatch period. Besides, in order to carry out the proposed strategies, the techniques of microprocessor hardware is applied in this paper. Visual C++ language is adopted as the developing tool to carry out the proposed work. The field test of controlling the air conditioner located in the campus is tested on-site to demonstrate the effectiveness of the proposed load controller
Probabilistic simulation analysis of risk potential in cost management programs
Authors: Irina Warthen, Kris Arvind
Venue: Proceedings of the 37th conference on Winter simulation (2005)

Today's business leaders recognize that advanced decision making capabilities using simulation techniques can directly improve their organization's performance. In this case study, CGN will describe how we helped a large equipment manufacturer identify areas of potential risk within their portfolio of cost management initiatives for a new product line. In order to define the interdependencies between multiple cost initiatives, their relevant business processes and their subsequent business drivers, a sophisticated understanding of prevailing market conditions was necessary. The case study will explain how the simulation model was designed to analyze the variation of individual business drivers such as steel prices, currencies, labor rates, machine reliability etc., and then predict the potential for success or failure of the overall cost management program using probability distributions. Finally, the paper will demonstrate how intelligent input and output reports can provide a degree of scalability to such simulation models and thus, allow for incorporation of future initiatives.
Collaborative Filtering Process in a Whole New Light
Authors: Panagiotis Symeonidis, Alexandros Nanopoulos, Apostolos Papadopoulos, Yannis Manolopoulos
Venue: Proceedings of the 10th International Database Engineering and Applications Symposium (2006)

Collaborative Filtering (CF) Systems are gaining widespread acceptance in recommender systems and ecommerce applications. These systems combine information retrieval and data mining techniques to provide recommendations for products, based on suggestions of users with similar preferences. Nearest-neighbor CF process is influenced by several factors, which were not examined carefully in past work. In this paper, we bring to surface these factors in order to identify existing false beliefs. Moreover, by being able to view the "big picture" from the CF process, we propose new approaches that substantially improve the performance of CF algorithms. For instance, we obtain more than 40% percent increase in precision in comparison to widely-used CF algorithms. We perform an extensive experimental evaluation, with several real data sets, and produce results that invalidate some existing beliefs and illustrate the superiority of the proposed extensions.
Diverse Topic Phrase Extraction through Latent Semantic Analysis
Authors: Jilin Chen, Jun Yan, Benyu Zhang, Qiang Yang, Zheng Chen
Venue: Proceedings of the Sixth International Conference on Data Mining (2006)

We propose a novel algorithm for extracting diverse topic phrases in order to provide summary for large corpora. Previous works often ignore the importance of diversity and thus extract phrases crowded on some hot topics while failing to cover other less obvious but important topics. We solve this problem through document re-weighting and phrase diversification by using latent semantic analysis (LSA). Experiments on various datasets show that our new algorithm can improve relevance as well as diversity over different topics for topic phrase extraction problems.
Nonlinear identification of pneumatic servo-drive
Authors: S. Refaat, S. Nahavandi
Venue: International Journal of Modelling and Simulation (2006)

Three nonlinear approaches to model the nonlinear pneumatic servo-drive are presented. The three nonlinear approaches are: (1) the multi input-single output (MISO) approach, which describes the single input-single output (SISO) nonlinear plant using a MISO linear representation which allows replacement of the nonlinear analysis by a linear one without approximation, and is studied in both time and frequency domains; (2) piecewise linearization, which systematically replaces, using artificial neural network, the nonlinear surface representing the plant in the hyper input-output space by a number of linear planes that are continuous over the boundaries between them; and (3) Adaptive Neuro-Fuzzy Inference System (ANFIS), in which the fuzzy rules are placed in a neural network structure, and which consequently utilizes neural networks learning rules to systematically tune the nonlinear fuzzy model. The superiority of these nonlinear models over the best model that can be developed using linear identification techniques is shown.
Attentional Modulation of Unconscious "Automatic" Processes: Evidence from Event-related Potentials in a Masked Priming Paradigm
Authors: Markus Kiefer, Doreen Brendel
Venue: Journal of Cognitive Neuroscience (2006)

Automatic processes are usually thought to occur independently of any cognitive resources. This traditional view has been recently challenged by showing that temporal attention to a target stimulus is a prerequisite for "automatic" response priming. The event-related potential (ERP) study reported here extends this research by pursuing a somewhat different approach. In two experiments, it was investigated whether masked semantic priming effects can be modulated by temporal attention to the prime using a cueing procedure. We hypothesized that masked priming is amplified when attention is directed to the stimulus stream in the time window of masked prime presentation, even in the absence of any prime awareness. ERPs were recorded while subjects performed a primed lexical decision task. Target words were preceded by semantically related or unrelated masked prime words, which were not consciously identified. A cue stimulus prompted subjects to direct their attention to the stimulus stream either shortly before the masked prime (short cue interval) or a long time interval before. Priming affected the amplitude of the N400 ERP component, an electrophysiological index of semantic processing. Unrelated prime&ndash;target pairs elicited a larger N400 than related pairs (N400 priming effect). Most importantly, this masked N400 priming effect was strongest when the cue interval and the stimulus onset asynchronies were short. The present results show that temporal attention to the prime is a prerequisite for obtaining masked N400 priming effects. They also demonstrate that unconscious automatic processes are susceptible to attentional modulation.
Knowing how to play: uniform choices in logics of agency
Authors: Andreas Herzig, Nicolas Troquard
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

Reasoning about capabilities, strategies and knowledge is important in the analysis of multiagent systems. Alternating-time Temporal Epistemic Logic (ATEL) was designed with this aim. Nevertheless, the original interpretation of the language suffered from some counterintuitive properties. These are due to the fact that the strategies the agent applies in worlds that he cannot distinguish may not be uniform, in the sense that the same action is applied in all indistinguishable worlds. Several refinements of the original ATEL semantics were proposed since then. In this paper we argue that the STIT framework can easily account for uniform strategies. STIT is a logic of agency that has been proposed in the 90ies in the domain of philosophy of action. It is the logic of constructions of the form "agent a sees to it that &phi;". To support our claim, we first present a straightforward solution in STIT logic augmented by a modal operator of knowledge. Then we offer a simplification, by introducing a modal logic of knowledge-based uniform agency, for one-step strategies, alias choices.
A Novel Through-Wall Imaging Method Using Ultra WideBand Pulse System
Authors: Lin Ma, Zhongzhao Zhang, Xuezhi Tan
Venue: Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia (2006)

Through-wall imaging is one of the applications of Ultra Wideband communication that attracts recent interests for its ability to penetrate the wall. However, the available references fail to provide a clear feasible imaging method. This paper proposes a novel virtual elliptic curve imaging method based on the UWB pulse system, which presents the detail imaging process and imaging results. The key factor of this method is to correctly obtain the propagation delay by the correlator, and it is achieved by the spread gain of the pseudo-noise sequences. The imaging results show the proposed method has very high imaging resolution. And the error analysis is investigated on account of misestimating wall parameters. Simulations illustrate the effects of the errors under different scenarios.
A Camera-Based Energy Relaxation Framework to Minimize Color Artifacts in a Projected Display
Authors: Nathaniel Sanders, Christopher Jaynes
Venue: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)

We introduce a technique to automatically correct color inconsistencies in a display composed of one or more digital light projectors (DLP). The method is agnostic to the source of error and can detect and address color problems from a number of sources. Examples include inter- and intra-projector color differences, display surface markings, and environmental lighting differences on the display. In contrast to methods that discover and map all colors into the greatest common color space, we minimize local color discontinuities to create color seamlessness while remaining tolerant to significant color error. The technique makes use of a commodity camera and high-dynamic range sensing to measure color gamuts at many different spatial locations. A differentiable energy function is defined that combines both a smoothness and data term. This energy function is globally minimized through the successive application of projective warps defined using gradient descent. At convergence the warps can be applied at runtime to minimize color defects in the display. The framework is demonstrated on displays that suffer from several sources of color
Optimal path selection for minimizing the differential delay in ethernet-over-SONET
Authors: Satyajeet Ahuja, Marwan Krunz, Turgay Korkmaz
Venue: Computer Networks: The International Journal of Computer and Telecommunications Networking (2006)

We consider the problem of minimizing the differential delay in a virtually concatenated Ethernet-over-SONET (EoS) system by suitable path selection. In such a system, a service provider can dynamically add virtual channels to or drop them from a Virtually Concatenated Group (VCG). A new virtual channel can be added to the VCG provided that the differential delays between the new channel and the existing ones are within a certain limit that reflects the available memory buffer of the EoS system. We model the problem of finding such a virtual channel as a constrained path selection problem, where the delay of the required (feasible) path is constrained not only by an upper bound but also by a lower bound. We consider two cases: exactly known link delays and imprecisely known link delays. For the first case, we propose two algorithms for finding a feasible path. The first is based on a link metric that linearly combines the original link weight (the link delay) and its inverse. The theoretical properties of such a metric are studied and used to develop a highly efficient heuristic for path selection. The second algorithm is a "backward-forward" heuristic in which the nodes in the graph are prelabeled during the backward phase. The labels are then used in the forward phase to identify a feasible path. For the imprecise-link-state case, the problem is modeled as one of finding the most probable feasible path, where link weights are random variables. A "backward-forward" heuristic is proposed which again uses prelabeling of the graph in the backward direction followed by a forward search that attempts to minimize an objective function. Simulations are conducted to evaluate the performance of the proposed algorithms and to demonstrate the advantages of the probabilistic path selection approach over the classic trigger-based approach.
Merging deductive and inductive reasoning for processing textual descriptions of inter-human conflicts
Authors: Boris Galitsky
Venue: Journal of Intelligent Information Systems (2006)

We report on a novel approach to modeling a dynamic domain with limited knowledge. A domain may include participating agents where we are uncertain about motivations and decision-making principles of some of these agents. Our reasoning setting for such domains includes deductive, inductive, and abductive components. The deductive component is based on situation calculus and describes the behavior of agents with complete information. The machine learning-based inductive and abductive components involve the previous experience with the agents, whose actions are uncertain to the system. Suggested reasoning machinery is applied to the problem of processing customer complaints in the form of textual messages that contain a multiagent conflict. The task is to predict the future actions of an opponent agent to determine the required course of action to resolve a multiagent conflict. This study demonstrates that the hybrid reasoning approach outperforms both stand-alone deductive and inductive components. Suggested methodology reflects the general situation of reasoning in dynamic domains in the conditions of uncertainty, merging analytical (rule-based) and analogy-based reasoning.
Good faith and fair dealing in contracts formed and performed by electronic agents
Authors: Emily M. Weitzenböck
Venue: Artificial Intelligence and Law (2004)

The development of electronic agents that increasingly play an active role in the contract formation and execution process has highlighted the need for the creation of law-abiding autonomous agent systems. The principle of good faith is an important guideline for contractual behaviour which permeates civil law systems. This paper examines how this principle is applied both during the negotiation of a contract and during its performance. Selected examples from civil law literature of precontractual duties of good faith, and of precontractual behaviour that is deemed to be contrary to good faith, are discussed. This is followed by a discussion of the extent to which such duties are recognised, or such behaviour proscribed, in common law jurisdictions. Some common standards for precontractual behaviour in civil and common law systems are identified. There is then a parallel analysis of the principle of good faith in contract performance with a view to identifying common traits or standards between civil and common law systems. These standards, in the situation where contracts are being negotiated and/or performed by or through electronic agents, would need to be reflected in the way such agents operate.
Cobot in LambdaMOO: An Adaptive Social Statistics Agent
Authors: Charles Lee Isbell,  Jr., Michael Kearns, Satinder Singh, Christian R. Shelton, Peter Stone, Dave Kormann
Venue: Autonomous Agents and Multi-Agent Systems (2006)

We describe our development of Cobot, a novel software agent who lives in LambdaMOO, a popular virtual world frequented by hundreds of users. Cobot's goal was to become an actual part of that community. Here, we present a detailed discussion of the functionality that made him one of the objects most frequently interacted with in LambdaMOO, human or artificial. Cobot's fundamental power is that he has the ability to collect social statistics summarizing the quantity and quality of interpersonal interactions. Initially, Cobot acted as little more than a reporter of this information; however, as he collected more and more data, he was able to use these statistics as models that allowed him to modify his own behavior. In particular, cobot is able to use this data to "self-program," learning the proper way to respond to the actions of individual users, by observing how others interact with one another. Further, Cobot uses reinforcement learning to proactively take action in this complex social environment, and adapts his behavior based on multiple sources of human reward. Cobot represents a unique experiment in building adaptive agents who must live in and navigate social spaces.
DATC Newsletter
Venue: IEEE Design Test (2006)

This newsletter provides news, events, and information related to the IEEE Computer Society's Design Automation Technical Committee and the EDA community.
Attainable error bounds in multirate adaptive lossless FIR filters
Authors: P. A. Regalia, Dong-Yan Huang
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1995. on International Conference - Volume 02 (1995)

We consider the problem of adaptively optimizing a two-channel lossless FIR filter bank, which finds application in subband coding or wavelet signal analysis. Instead of using a gradient descent procedure-with its inherent problem of possible convergence to local minima-we consider two eigenstructure algorithms. Both algorithms feature a priori bounds on the output error variance at any convergent point, and based on simulations lead to solutions that lie acceptably close to a global minimum point of an output error cost function.
Exploring Adaptation & Self-Adaptation in Autonomic Computing Systems
Authors: M. T. Ibrahim, R. J. Anthony, T. Eymann, A. Taleb-Bendiab, L. Gruenwald
Venue: Proceedings of the 17th International Conference on Database and Expert Systems Applications (2006)

This panel paper sets out to discuss what self-adaptation means, and to explore the extent to which current autonomic systems exhibit truly self-adaptive behaviour. Many of the currently cited examples are clearly adaptive, but debate remains as to what extent they are simply following prescribed adaptation rules within preset bounds, and to what extent they have the ability to truly learn new behaviour. Is there a standard test that can be applied to differentiate? Is adaptive behaviour sufficient anyway? Other autonomic computing issues are also discussed.