ppxscal/embedding_network huggingface dataset:
Efficient Genome Wide Tagging by Reduction to SAT
Authors: Arthur Choi, Noah Zaitlen, Buhm Han, Knot Pipatsrisawat, Adnan Darwiche, Eleazar Eskin
Venue: Proceedings of the 8th international workshop on Algorithms in Bioinformatics (2008)

Whole genome association has recently demonstrated some remarkable successes in identifying loci involved in disease. Designing these studies involves selecting a subset of known single nucleotide polymorphisms (SNPs) or tag SNPs to be genotyped. The problem of choosing tag SNPs is an active area of research and is usually formulated such that the goal is to select the fewest number of tag SNPs which "cover" the remaining SNPs where "cover" is defined by some statistical criterion. Since the standard formulation of the tag SNP selection problem is NP-hard, most algorithms for selecting tag SNPs are either heuristics which do not guarantee selection of the minimal set of tag SNPs or are exhaustive algorithms which are computationally impractical. In this paper, we present a set of methods which guarantee discovering the minimal set of tag SNPs, yet in practice are much faster than traditional exhaustive algorithms. We demonstrate that our methods can be applied to discover minimal tag sets for the entire human genome. Our method converts the instance of the tag SNP selection problem to an instance of the satisfiability problem, encoding the instance into conjunctive normal form (CNF). We take advantage of the local structure inherent in human variation, as well as progress in knowledge compilation, and convert our CNF encoding into a representation known as DNNF, from which solutions to our original problem can be easily enumerated. We demonstrate our methods by constructing the optimal tag set for the whole genome and show that we significantly outperform previous exhaustive search-based methods. We also present optimal solutions for the problem of selecting multi-marker tags in which some SNPs are "covered" by a pair of tag SNPs. Multi-marker tags can significantly decrease the number of tags we need to select, however discovering the minimal number of multi-marker tags is much more difficult. We evaluate our methods and perform benchmark comparisons to other methods by choosing tag sets using the HapMap data.
Hierarchical Latent Class Models and Statistical Foundation for Traditional Chinese Medicine
Authors: Nevin L. Zhang, Shihong Yuan, Tao Chen, Yi Wang
Venue: Proceedings of the 11th conference on Artificial Intelligence in Medicine (2007)

The theories of traditional Chinese medicine (TCM) originated from experiences doctors had with patients in ancient times. We ask the question whether aspects of TCM theories can be reconstructed through modern day data analysis. We have recently analyzed a TCM data set using a machine learning method and found that the resulting statistical model matches the relevant TCM theory well. This is an exciting discovery because it shows that, contrary to common perception, there are scientific truths in TCM theories. It also suggests the possibility of laying a statistical foundation for TCM through data analysis and thereby turning it into a modern science.
Error analysis of a parameter expansion method for corrosion detection in a pipe
Authors: Xuehai Huang, Jianguo Huang, Yu Chen
Venue: Computers Mathematics with Applications (2008)

Error analysis is developed for a parameter expansion method of determining the corrosion coefficient in a pipe. For the two approximate solutions proposed, the magnitude of their errors is shown to be O(a) and O(a^2), respectively, where a stands for the thickness of the pipe.
Observational Versus Trial and Error Effects in a Model of an Infant Learning Paradigm
Authors: Matthew Hartley, Jacqueline Fagard, Rana Esseily, John Taylor
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008)

We present results from a paradigm of infant motor skill learning, involving selecting the correct approach to grasp a ball placed on a low stability stand. We also construct a theoretical model, based on the concept of observational learning as the discovery of non-perceived affordances and the transfer of actions on affordances, which can explain some of the experimental data presented. We discuss the underlying concepts of the model (including its basis in neuroanatomy) and how they relate to the paradigm, and suggest possible model extensions.
Decoding Code on a Sensor Node
Authors: Pascal Rickenbach, Roger Wattenhofer
Venue: Proceedings of the 4th IEEE international conference on Distributed Computing in Sensor Systems (2008)

Wireless sensor networks come of age and start moving out of the laboratory into the field. As the number of deployments is increasing the need for an efficient and reliable code update mechanism becomes pressing. Reasons for updates are manifold ranging from fixing software bugs to retasking the whole sensor network. The scale of deployments and the potential physical inaccessibility of individual nodes asks for a wireless software management scheme. In this paper we present an efficient code update strategy which utilizes the knowledge of former program versions to distribute mere incremental changes. Using a small set of instructions, a delta of minimal size is generated. This delta is then disseminated throughout the network allowing nodes to rebuild the new application based on their currently running code. The asymmetry of computational power available during the process of encoding (PC) and decoding (sensor node) necessitates a careful balancing of the decoder complexity to respect the limitations of today's sensor network hardware. We provide a seamless integration of our work into Deluge, the standard TinyOS code dissemination protocol. The efficiency of our approach is evaluated by means of testbed experiments showing a significant reduction in message complexity and thus faster updates.
Integration of high-performance RF passive modules (MIM capacitors and inductors) in advanced BEOL
Authors: A. Farcy, J. -F. Carpentier, M. Thomas, J. Torres, P. Ancey
Venue: Microelectronic Engineering (2008)

Passive components are key elements for the integration of system-on-chips, fixing circuit performance and down-scaling capability. The integration of MIM capacitors and inductors among Cu BEOL of CMOS technologies faces many challenges and requires technological innovations in the field of material, process, and architecture developments. An overview of the main challenges and investigated solutions is presented in this paper.
Creating a Secure Infrastructure for Wireless Diagnostics and Software Updates in Vehicles
Authors: Dennis K. Nilsson, Ulf E. Larson, Erland Jonsson
Venue: Proceedings of the 27th international conference on Computer Safety, Reliability, and Security (2008)

A set of guidelines for creating a secure infrastructure for wireless diagnostics and software updates in vehicles is presented. The guidelines are derived from a risk assessment for a wireless infrastructure. From the outcome of the risk assessment, a set of security requirements to counter the identified security risks were developed. The security requirements can be viewed as guidelines to support a secure implementation of the wireless infrastructure. Moreover, we discuss the importance of defining security policies.
Comparison of RBF Network Learning and Reinforcement Learning on the Maze Exploration Problem
Authors: Stanislav Slušný, Roman Neruda, Petra Vidnerová
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part I (2008)

An emergence of intelligent behavior within a simple robotic agent is studied in this paper. Two control mechanisms for an agent are considered -- a radial basis function neural network trained by evolutionary algorithm, and a traditional reinforcement learning algorithm over a finite agent state space. A comparison of these two approaches is presented on the maze exploration problem.
Computational Challenges of Systems Biology
Authors: Anthony Finkelstein, James Hetherington, Linzhong Li, Ofer Margoninski, Peter Saffrey, Rob Seymour, Anne Warner
Venue: Computer (2004)

By posing novel computational challenges and stretching the state of the art, bioinformatics has become the computing response to the molecular revolution in biology. But bioinformatics is only the first step in reshaping the life sciences. For further progress, we must return to the study of whole biological systems: the heart, cardiovascular system, brain, and liver.Progress in systems biology will require computer scientists to work closely with life scientists and mathematicians. In contrast to the molecular biology revolution, computer science will actively engage in shaping systems biology. The prize to be attained is immense, ranging from in silico drug design and testing to individualized medicine that takes into account physiology and genetic profiles.
Robust control applied to improve the performance of a buck-boost converter
Authors: Wilmar Hernandez
Venue: Proceedings of the 7th WSEAS International Conference on Application of Electrical Engineering (2008)

In this paper, H∞ loop-shaping control is applied to improve the performance of a buck-boost dc-dc power converter based on pulse-width-modulation (PWM) techniques. Here, classical control techniques (i.e., proportional-integral-derivative (PID) control) and post-modern control techniques (i.e., H∞ control), are used to design the feedback loop of a buck-boost dc-dc power converter. The results of the experiment are satisfactory and show that robust controllers do not depend strongly on the operating point, and that H∞ loop-shaping control performs better than PID control.
Tracking Moving Objects in Anonymized Trajectories
Authors: Nikolay Vyahhi, Spiridon Bakiras, Panos Kalnis, Gabriel Ghinita
Venue: Proceedings of the 19th international conference on Database and Expert Systems Applications (2008)

Multiple target tracking (MTT) is a well-studied technique in the field of radar technology, which associates anonymized measurements with the appropriate object trajectories. This technique, however, suffers from combinatorial explosion, since each new measurement may potentially be associated with any of the existing tracks. Consequently, the complexity of existing MTT algorithms grows exponentially with the number of objects, rendering them inapplicable to large databases. In this paper, we investigate the feasibility of applying the MTT framework in the context of large trajectory databases. Given a history of object movements, where the corresponding object ids have been removed, our goal is to track the trajectory of every object in the database in successive timestamps. Our main contribution lies in the transition from an exponential solution to a polynomial one. We introduce a novel method that transforms the tracking problem into a min-cost max-flow problem. We then utilize well-known graph algorithms that work in polynomial time with respect to the number of objects. The experimental results indicate that the proposed methods produce high quality results that are comparable with the state-of-the-art MTT algorithms. In addition, our methods reduce significantly the computational cost and scale to a large number of objects.
Bridging the gap between the digital and the physical: design and evaluation of a mobile augmented reality guide for the museum visit
Authors: Areti Damala, Pierre Cubaud, Anne Bationo, Pascal Houlier, Isabelle Marchal
Venue: Proceedings of the 3rd international conference on Digital Interactive Media in Entertainment and Arts (2008)

Can Augmented Reality (AR) techniques inform the design and implementation of a mobile multimedia guide for the museum setting? Drawing from our experience both on previous mobile museum guides projects and in AR technology, we present a fully functional prototype of an AR-enabled mobile multimedia museum guide, designed and implemented for the Museum of Fine Arts in Rennes, France. We report on the life cycle of the prototype and the methodology employed for the AR approach as well as on the selected mixed method evaluation process; finally, the first results emerging from quantitative evaluation are discussed, supported by evidence and findings from the qualitative part of the assessment process. We conclude with lessons learned during the full circle of conception, implementation, testing and assessment of the guide.
A Web-Based Automated System for Industry and Occupation Coding
Authors: Yuchul Jung, Jihee Yoo, Sung-Hyon Myaeng, Dong-Cheol Han
Venue: Proceedings of the 9th international conference on Web Information Systems Engineering (2008)

This paper describes our newly developed Automated Industry and Occupation Coding System (AIOCS). The main function of the system is to classify natural language responses of survey questionnaires into equivalent numeric codes according to the standard code book from the Korean National Statistics Office (KNSO). We implemented the system using a range of automated classification techniques, including hand-crafted rules, a maximum entropy model, and information retrieval techniques, to enhance the performance of automated industry/occupation coding task. The result is a Web-based AIOCS available for public services via the Web site of KNSO. Compared with the previous system developed in 2005, the new Web-based system decreases coding cost with a higher speed and shows significant performance enhancement in production rate and accuracy. Furthermore, it facilitates practical uses through an easy Web user interface.
Conversations Amidst Computing: A Study of Interruptions and Recovery of Task Activity
Authors: Shamsi T. Iqbal, Eric Horvitz
Venue: Proceedings of the 11th international conference on User Modeling (2007)

We present results from a field study investigating the influence of conversations on the multitasking behavior of computer users. We report on several findings, including the timing of the resumption of tasks following conversational interruptions and on the nature and rate of computing activities that are performed concurrently with conversation.
Quality of Service Specification in Video Databases
Authors: Elisa Bertino, Tiziana Catarci, Ahmed K. Elmagarmid, Mohand-Said Hacid
Venue: IEEE MultiMedia (2003)

Multimedia database systems require not only that correct results are delivered with acceptable delay, but also that they're delivered in real time with acceptable quality. A constraint-based rule language can serve as the foundation for providing this level of quality of service management in video databases.
H ∞ Tracking Control of Descriptor Nonlinear System for Output PDFs of Stochastic Systems Based on B-Spline Neural Networks
Authors: Haiqin Sun, Huiling Xu, Chenglin Wen
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

For stochastic systems with non-Gaussian variables, a descriptor nonlinear system model based on linear B-spline approximation is first established. A new tracking strategy based on H ∞ state feedback control for the descriptor nonlinear system is proposed, with which the probability density functions (PDFs) tracking control problem of the non-Gaussian stochastic systems can be solved. Necessary and sufficient condition for the existence of H ∞ state feedback controller of the problem is presented by linear-matrix-inequality (LMI). Furthermore, simulations on particle distribution control problems are given to demonstrate the efficiency of the proposed approach and encouraging results have been obtained.
An active distributed medical advisory system
Authors: Kim Le
Venue: Proceedings of the 7th WSEAS International Conference on Software Engineering, Parallel and Distributed Systems (2008)

For the last few decades, medical practitioners and specialists have been waiting for efficient medical advisory systems. Most of current computer-aided diagnosis systems are rather passive. Health data, a rich source of medical knowledge, often have not been used efficiently for medical treatment as well as financial management. Their accesses are very limited. This paper introduces the design of a well-structured medical advisory system based on the advanced Internet. The system is also equipped with data mining and AI techniques to make it becoming an active distributed medical advisory system.
Minimizing Energy Consumption in Heating Systems under Uncertainty
Authors: José Ramón Villar, Enrique Cal, Javier Sedano
Venue: Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems (2008)

Energy saving systems are needed to reduce the energy taxes, so the electric energy remains balanced. In Spain, a local company that produces electric heaters needs an energy saving device to be integrated with the heaters. The building regulations in Spain introduce five different climate zones, so such device must meet all of them. In previous works the uncertainty in the process was shown, and the different configurations that must be included to accomplish the Spanish regulations were established. It was proven that a hybrid artificial intelligent systems (HAIS) could afford the energy saving reasonably, even though some improvements must be introduced. This work proposes a modified solution to relax the hardware restrictions and to solve the lack of distribution observed. The modified energy saving HAIS is detailed and compared with that obtained in previous works.
Runtime Analyses for Using Fairness in Evolutionary Multi-Objective Optimization
Authors: Tobias Friedrich, Christian Horoba, Frank Neumann
Venue: Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X (2008)

It is widely assumed that evolutionary algorithms for multi-objective optimization problems should use certain mechanisms to achieve a good spread over the Pareto front. In this paper, we examine such mechanisms from a theoretical point of view and analyze simple algorithms incorporating the concept of fairness introduced by Laumanns et al.[7]. This mechanism tries to balance the number of offspring of all individuals in the current population. We rigorously analyze the runtime behavior of different fairness mechanisms and present showcase examples to point out situations where the right mechanism can speed up the optimization process significantly.
A High-Level Recognizer for Schematic Diagrams
Authors: Douglas Tudhope, John Oldfield
Venue: IEEE Computer Graphics and Applications (1983)

This recognizer uses high-level knowledge stored in a global, modifiable database. It is adaptable to a variety of diagrams.
Using New Tools for Certificate Repositories Generation in MANETs
Authors: Candelaria Hernández-Goya, Pino Caballero-Gil, Oscar Delgado-Mohatar, Jezabel Molina-Gil, Cándido Caballero-Gil
Venue: Proceeedings of the 22nd annual IFIP WG 11.3 working conference on Data and Applications Security (2008)

This paper includes a new proposal for the generation of certificates repositories in MANETs. The described process is based on the combination of the self-organized key management model together with the MultiPoint Relay (MPR) technique, generally used in the Optimized Link State Routing protocol. The main objective is to reduce the cost of generating and updating local certificate repositories by selecting those certificates that allow to reach the maximum number of nodes. This goal is just achieved by applying low-cost operations carried out locally by the nodes themselves.
New Point Addition Formulae for ECC Applications
Authors: Nicolas Meloni
Venue: Proceedings of the 1st international workshop on Arithmetic of Finite Fields (2007)

In this paper we propose a new approach to point scalar multiplication on elliptic curves defined over fields of characteristic greater than 3. It is based on new point addition formulae that suit very well to exponentiation algorithms based on Euclidean addition chains. However finding small chains remains a very difficult problem, so we also develop a specific exponentiation algorithm, based on Zeckendorf representation (i.e. representing the scalar k using Fibonacci numbers instead of powers of 2), which takes advantage of our formulae.
Visual and Auditory Interfaces of Advanced Driver Assistant Systems for Older Drivers
Authors: Martina Ziefle, Preethy Pappachan, Eva-Maria Jakobs, Henning Wallentowitz
Venue: Proceedings of the 11th international conference on Computers Helping People with Special Needs (2008)

Advanced Driver Assistant Systems (ADAS) are assumed to support drivers in critical traffic situations. This is especially important for older drivers and also drivers with disabilities, whose physical and cognitive resources are limited. An electronic intersection assistant was developed and implemented in a driving simulation setting. Independent variables were users' age and output modality. The utility of visual and auditory interfaces was examined and com pared to a control group which was not assisted. Dependent variables were speed control, accuracy of lane tracking and users' acceptance. Older adults drove significantly slower, but equally accurate than younger drivers. When no assistance was present, driving performance was superior than in both assis tance conditions. The visual interface had a lower detrimental effect than the auditory ADAS which had the strongest distracting effect. In contrast to per formance outcomes, the auditory interface was rated as more helpful by older drivers compared to the visual interface.
Neural Noise Induces the Evolution of Robust Behaviour by Avoiding Non-functional Bifurcations
Authors: Jose A. Fernandez-Leon, Ezequiel A. Paolo
Venue: Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats (2008)

Continuous-time recurrent neural networks affected by random additive noise are evolved to produce phototactic behaviour in simulated mobile agents. The resulting neurocontrollers are evaluated after evolution against perturbations and for different levels of neural noise. Controllers evolved with neural noise are more robust and may still function in the absence of noise. Evidence from behavioural tests indicates that robust controllers do not undergo noise-induced bifurcations or if they do, the transient dynamics remain functional. A general hypothesis is proposed according to which evolution implicitly selects neural systems that operate in noise-resistant landscapes which are hard to bifurcate and/or bifurcate while retaining functionality.
Information Fusion for Uncertainty Determination in Video and Infrared Cameras System
Authors: Cornel Barna
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III (2008)

In the paper I present a new method for measuring uncertainty based on multiple information fusion. A system composed of video and infrared cameras is used. From this system, the features of the observed objects are extracted, separate for the video and infrared cameras. Based on these characteristics, the uncertainty value for video and infrared observations are determined. Three information fusion methods are applied, and for each of them the aggregated uncertainty measure is determined. Finally, a global uncertainty estimation is obtained from the previous results, which can be used for the labeling decision process of recognizing an object.