ppxscal/embedding_network huggingface dataset:
Modeling Multiple Time Units Delayed Gene Regulatory Network Using Dynamic Bayesian Network
Authors: Zhengzheng Xing, Dan Wu
Venue: Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops (2006)

Most of the current applications which use dynamic Bayesian network to model gene regulatory network assume that the time delay between regulators and their targets is one time unit in a time series gene expression dataset. In fact, multiple time units delay is indicated to exist in a gene regulation process. In this paper, we propose using higherorder Markov dynamic Bayesian network(DBN) to model multiple time units delayed gene regulatory network. A two steps heuristic learning framework is designed to learn higher-order Markov DBN from time series gene expression data. We apply the learning framework to a yeast cell cycle gene expression dataset. The predicted gene regulatory network is strongly supported by biological evidence and consistent with the yeast cell cycle phase information.
A Fuzzy Control System for Capturing Target of Intelligence Spraying
Authors: Zhang Fajun, Chai Cangxiu
Venue: Proceedings of the 16th International Conference on Artificial Reality and Telexistence--Workshops (2006)

In the PCO(Pest Control Operation) market, the intelligence spraying fog with truck-mounted machine is the most advanced method in its profession field currently. Facing with the characteristics of spray target and the require of operation apply, a methods of capturing target and control spray direction are proposed based on fuzzy theory in this paper. It is effectively solved the problem of environmental pollution and waste physic drug from blind sprayed, a new path has been provided which can be used in precision spraying for plant protection machinery study in-depth.
Efficiency of radix-K transforms on computers with cache
Authors: R. M. Stasinski
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 03 (1999)

The impact of the most critical part of the up-to-date computer memory hierarchy, the cache, on the efficiency of fast transform algorithms (e.g. FFT, DCT/DST, DWHT, including multidimensional generalizations) is analyzed. Cache misses can severely deteriorate the efficiency of a computer program, and indeed, it is shown that for large data vectors a modification of a fast transform algorithm realization may change their number dramatically. Several memory managing problems are pointed out, and suggestions for their amelioration are given. Formulae on the minimum number of data-related cache misses for radix-2/sup s/ transform realizations are given, s is an integer. 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
Human face verification by robust three-dimensional surface alignment
Authors: George Stockman
Venue:  (2006)

Traditional 2D face recognition systems are not tolerant to changes in pose, lighting and expression. This dissertation explores the use of 3D data to improve face recognition by accounting for these variations. A two step, fully automatic, 3D surface alignment algorithm is developed to correlate the surfaces of two 3D face scans. In the first step, key anchor points such as the tip of the nose are used to coarsely align two face scans. In the second step, the Iterative Closest Point (ICP) algorithm is used to finely align the scans. The quality of the face alignment is studied in depth using a Surface Alignment Measure (SAM). The SAM is the root mean squared error over all the control points used in the ICP algorithm, after trimming to account for noise in the data. This alignment algorithm is fast (<2 seconds on a 3.2GHz P4) and robust to noise in the data (<10% spike noise). Extensive experiments were conducted to show that the alignment algorithm can tolerate up to 15&deg; of variation in pose due to roll and pitch, and 30&deg; of variation in yaw. It is shown that this level of pose tolerance easily covers the normal pose variation of a database of over 275 cooperative subjects. By using the SAM as an initial matching score and automatically rejecting poor quality scans, an equal error rate of 1.2% is achieved on a database of 943 scans from 275 subjects. This surface alignment verification system is fast, fully automatic, and requires no additional training. By using the 3D face surface alignment algorithm, a Canonical Face Depth Map (CFDM) is defined to allow for automatic preprocessing of 3D face scans. The CFDM is shown to help in anchor point localization, reduce model memory requirements, and enables other algorithms, such as PCA or correlation, to be applied.
Fostering university/industry partnerships through sponsored undergraduate design
Authors: R. J. Coleman, J. W. Shelnutt
Venue: Proceedings of the Frontiers in Education Conference on 1995. Proceedings., 1995 vol 1. - Volume 01 (1995)

Describes a mechanism for fostering a partnership between the university and industrial communities, with the aim of sponsoring undergraduate design projects. This paper addresses the contributions and commitments of the university and industrial participants, including financial arrangements, legal agreements, matching contributions, and research and design supervision and evaluation. Industrial partners sponsor real-world design projects which are supervised and evaluated by faculty and industry facilitators. Students benefit not only from the technical aspects of the project but from exposure to market and economic aspects, design limitations and environmental and legal factors. Students work both in the university and industrial setting. The participation of engineering professionals with the student design teams ensures that the project realizes both the industrial partner's engineering and financial goals as well as assistance in evaluating the academic content of the project. Projects must be realizable in the academic time-frame, and this paper describes how projects such as alternative solutions, exploratory projects or lower-priority projects best fit the concept. A brief description of projects undertaken during the last two years for IBM, Duke Power Co., Ingersoll-Rand Compressor Division, Florida Steel Drum Co., and Siecor Corp. is presented. The financial obligations, legal boilerplate and intellectual property concerns are discussed. The final section presents lessons learned from the project and what future plans are for expanding the program.
Self-Adapting Resource Bounded Distributed Computations
Authors: Nadeem Jamali, Xinghui Zhao
Venue: Proceedings of the First International Conference on Self-Adaptive and Self-Organizing Systems (2007)

Self-adaptation is about computations adapting to their environments. The need for adaptation may dynamically arise as a result of evolving computations or the environment. An important part of the environment is the computational resources for which computations compete. The CyberOrgs model1 encapsulates distributed concurrent computations along with the computational and communication resources they require plus purchasing power for acquiring additional resources. Ownership of resources coupled with an effective control mechanism creates a predictable resource environment for computations to execute in in a coordinated manner. CyberOrgs create three opportunities for selfadaptation: algorithms may be chosen using resource knowledge, additional resources may be purchased to adapt to evolving needs, and computations may coordinate use of known computational and network resources for optimal results. The CyberOrgs model is presented and a prototype implementation is described. Our experience with using CyberOrgs' resource awareness for hierarchical coordination of distributed processor resource delivery is presented. Experimental results show that resource knowledge based reasoning leads to efficient distributed adaptation.
Localized Homology
Authors: Afra Zomorodian, Gunnar Carlsson
Venue: Proceedings of the IEEE International Conference on Shape Modeling and Applications 2007 (2007)

In this paper, we provide the theoretical foundation and an effective algorithm for localizing topological attributes such as tunnels and voids. Unlike previous work that fo- cused on 2-manifolds with restricted geometry, our theory is general and localizes arbitrary-dimensional attributes in arbitrary spaces. We implement our algorithm and present experiments to validate our approach in practice.
Performance evaluation of RSVP extensions for a guaranteed delivery scenario
Authors: Marcos Postigo-Boix, Jose L. Melús-Moreno
Venue: Computer Communications (2007)

In some guaranteed data delivery scenarios, servers can quickly and carefully allocate the available bandwidth among the requests of the users to reduce rejections. This is particularly important when reservations are higher than minimum required delivery rate (semi-elastic reservations). As an example, when semi-elastic reservations utilize all the available server bandwidth and a new flow reservation arrives, it is useful to reallocate current reservations to accept the new one. The RSVP protocol is receiver oriented and it is in charge of setting up these reservations. However, in some cases, to reallocate bandwidth in a receiver oriented way could delay the required sender reservation adjustments. This paper presents a new extension of the RSVP signaling messages that allows the server to adjust and modify these reservations. The performance evaluation of the extended and the native RSVP signaling protocols when used to manage the access bandwidth of a semi elastic flows server shows the benefits of the extensions. In particular, the use of resource reservation is reduced because of a more efficient bandwidth usage. In addition, the blocking probability is also reduced because of a more flexible bandwidth reallocation situation. The authors believe that this procedure is an intermediate step to improve the lack of flexibility that RSVP presents.
Towards Co-Design of Business Processes and Information Systems Using Web Services
Authors: Karthikeyan Umapathy
Venue: Proceedings of the 40th Annual Hawaii International Conference on System Sciences (2007)

A variety of forces are constantly transforming enterprises, thus, requiring them to constantly adapt to changing business environment. This emergent state requires enterprises to dynamically adjust both business processes and information systems as the environment shifts. Co-Design of business processes and information systems seems to provide a reasonable approach to meeting this need. This paper develops requirements for such an approach and assesses the applicability of web services against those requirements. Assessment suggests that, although web services facilitate Co-Design, they do not provide a comprehensive solution. Possible means to augment web services technologies to support missing requirements are provided.
A maximum entropy language model integrating N-grams and topic dependencies for conversational speech recognition
Authors: S. Khudanpur
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01 (1999)

A compact language model which incorporates local dependencies in the form of N-grams and long distance dependencies through dynamic topic conditional constraints is presented. These constraints are integrated using the maximum entropy principle. Issues in assigning a topic to a test utterance are investigated. Recognition results on the Switchboard corpus are presented showing that with a very small increase in the number of model parameters, reduction in word error rate and language model perplexity are achieved over trigram models. Some analysis follows, demonstrating that the gains are even larger on content-bearing words. The results are compared with those obtained by interpolating topic-independent and topic-specific N-gram models. The framework presented here extends easily to incorporate other forms of statistical dependencies such as syntactic word-pair relationships or hierarchical topic constraints.
pFFT in FastMaxwell: a fast impedance extraction solver for 3D conductor structures over substrate
Authors: Tarek Moselhy, Xin Hu, Luca Daniel
Venue: Proceedings of the conference on Design, automation and test in Europe (2007)

In this paper we describe the acceleration algorithm implemented in FastMaxwell, a program for wideband electromagnetic extraction of complicated 3D conductor structures over substrate. FastMaxwell is based on the integral domain mixed potential integral equation (MPIE) formulation, with 3-D full-wave substrate dyadic Green's function kernel. Two dyadic Green's functions are implemented. The pre-corrected Fast Fourier Transform (pFFT) algorithm is generalized and used to accelerate the translational invariant complex domain dyadic kernel. Computational results are given for a variety of structures to validate the accuracy and efficiency of FastMaxwell. O(NlogN) computational complexity is demonstrated by our results in both time and memory.
Sushi: an extensible human interface for NetBSD
Authors: Tim Rightnour
Venue: Proceedings of the BSD Conference 2002 on BSD Conference (2002)

This document describes Sushi, a menuing system designed for the administration and configuration of the NetBSD operating system. Included are detailed explanations of the reasons behind the creation and design of Sushi. In addition, this document includes descriptions of the file formats and examples of the menus provided by Sushi.
A composable data management architecture for scientific applications
Authors: Randall Bramley
Venue:  (2006)

Effectively managing vast data stores generated from instruments and simulations has become crucial for many scientific applications. This dissertation conducts a survey on major research efforts in scientific data management, and identifies the pros and cons of existing metadata categorization and management mechanism. Diversity and complexity of scientific datasets challenge the common approach of defining generic metadata schema, yet ad hoc customized data management systems can be obstacles for wide-area and cross-discipline collaborations. This dissertation presents the thesis that a composable architecture of simple and dedicated modules can address the concern. Data management needs from several application have been closely studied, representing a broad range of scientific disciplines including automated photometry, radiation oncology, computational biology, X-ray crystallography, and fusion physics. A coarse metadata categorization is proposed, and a composable architecture is derived, which provides a set of orthogonal components that each is dedicated to a simple data management task, and together can be rapidly composed to construct comprehensive customized solutions. A prototype toolkit implementation of the architecture called Obsidian is described in detail. Scientific Interface Definition Language (SIDL) interfaces are defined for core Obsidian modules, serving as the starting point of a standard and systematic procedure in building customized scientific data management systems, which can scale both down for personal computing and up for community collaborations worldwide. Obsidian has been used in all applications mentioned above, and customized data management solutions for each are demonstrated.
Signal processing strategies for better melody recognition and improved speech understanding in noise for cochlear implants
Authors: Philipos C. Loizou
Venue:  (2006)

Cochlear implants are prosthetic devices, consisting of implanted electrodes and a signal processor and are designed to restore partial hearing to the profoundly deaf community. Since their inception in early 1970s cochlear implants have gradually gained popularity and consequently considerable research has been done to advance and improve the cochlear implant technology. Most of the research conducted so far in the field of cochlear implants has been primarily focused on improving speech perception in quiet. Music perception and speech perception in noisy listening conditions with cochlear implants are still highly challenging problems. Many research studies have reported low recognition scores in the task of simple melody recognition. Most of the cochlear implant devices use envelope cues to provide electric stimulation. Understanding the effect of various factors on melody recognition in the context of cochlear implants is important to improve the existing coding strategies. In the present work we investigate the effect of various factors such as filter spacing, relative phase, spectral up-shifting, carrier frequency and phase perturbation on melody recognition in acoustic hearing. The filter spacing currently used in the cochlear implants is larger than the musical semitone steps and hence not all musical notes can be resolved. In the current work we investigate the use of new filter spacing techniques called the 'Semitone filter spacing techniques' in which filter bandwidths are varied in correspondence to the musical semitone steps. Noise reduction methods investigated so far for use with cochlear implants are mostly pre-processing methods. In these methods, the speech signal is first enhanced using the noise reduction method and the enhanced signal is then processed using the speech processor. A better and more efficient approach is to integrate the noise reduction mechanism into the cochlear implant signal processing. In this dissertation we investigate the use of two such embedded noise reduction methods namely, the 'SNR weighting method' and the 'S-shaped compression' to improve speech perception in noisy listening conditions. The SNR weighting noise reduction method is an exponential weighting method that uses the instantaneous signal to noise ratio (SNR) estimate to perform noise reduction in each frequency band that corresponds to a particular electrode in the cochlear implant. The S-shaped compression technique divides the compression curve into two regions based on the noise estimate. This method applies a different type of compression for the noise portion and the speech portion and hence better suppresses the noise compared to the regular power-law compression.
An integrated approach to feature invention and model construction for drug activity prediction
Authors: Jesse Davis, Vítor Santos Costa, Soumya Ray, David Page
Venue: Proceedings of the 24th international conference on Machine learning (2007)

We present a new machine learning approach for 3D-QSAR, the task of predicting binding affinities of molecules to target proteins based on 3D structure. Our approach predicts binding affinity by using regression on substructures discovered by relational learning. We make two contributions to the state-of-the-art. First, we use multiple-instance (MI) regression, which represents a molecule as a set of 3D conformations, to model activity. Second, the relational learning component employs the "Score As You Use" (SAYU) method to select substructures for their ability to improve the regression model. This is the first application of SAYU to multiple-instance, real-valued prediction. We evaluate our approach on three tasks and demonstrate that (i) SAYU outperforms standard coverage measures when selecting features for regression, (ii) the MI representation improves accuracy over standard single feature-vector encodings and (iii) combining SAYU with MI regression is more accurate for 3D-QSAR than either approach by itself.
Authentication anomaly detection: a case study on a virtual private network
Authors: Michael J. Chapple, Nitesh Chawla, Aaron Striegel
Venue: Proceedings of the 3rd annual ACM workshop on Mining network data (2007)

The authentication logs on a network can provide a trove of information for discovering potential anomalies in login attempts. Using such logs collected by a production Virtual Private Network device over a period of 15 months, we generate a diurnal model of network accesses. These models are used to detect anomalous authentications, which merit further investigation by a security analyst. We intend that this work will dramatically reduce the amount time spent by analysts identifying anomalous events and allow them to focus on in-depth analysis of these anomalies. Our work makes two contributions: a novel approach of mining authentication data, and the use of geographic distance as a metric to evaluate Virtual Private Network connections. We demonstrate the success of our model using real-world case analysis.
Enabling Large-Scale Peer-to-Peer Stored Video Streaming Service
Authors: Masaru Okuda, Taieb Znati
Venue: Proceedings of the 40th Annual Simulation Symposium (2007)

We aim to enable a large-scale, high-volume, peer-to-peer, stored-video streaming service over the Internet, such as on-line DVD rentals. This paper addresses the following three technical challenges associated with the distribution of streaming video over the P2P network: 1) allow users with limited transmit bandwidth capacity to become contributing sources, 2) support the advertisement and discovery of time-changing and time-bounded video frame availability, and 3) design the architecture of a streaming network that enables above two functionalities. To meet these requirements, we propose a video distribution network model based on the hybrid architecture between client-server and P2P. In this model, a video is divided into a sequence of small segments and each user retrieves the video segments according to the scheduling algorithm which determines the order, the timing, and the rate of segment retrievals from other users. The model also employs an advertisement and discovery scheme which incorporates parameters of the scheduling algorithm to allow users to share their life-time of video segment availability information in one advertisement and one query. The simulation study shows that the proposed model and associated schemes greatly alleviate the bandwidth requirement of the video distribution server, especially when the number of participating users grows large. As much as 90% of load reduction was observed in some experiments when compared to the traditional client-server based video distribution service.
Reducing the Frequent Pattern Set
Authors: Ronnie Bathoorn, Arne Koopman, Arno Siebes
Venue: Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops (2006)

One of the major problems in frequent pattern mining is the explosion of the number of results, making it difficult to identify the interesting frequent patterns. In a recent paper [7] we have shown that an MDL-based approach gives a dramatic reduction of the number of frequent item sets to consider. Here we show that MDL gives similarly good reductions for frequent patterns on other types of data, viz., on sequences and trees. Reductions of two to three orders of magnitude are easily attained on data sets from the web-mining field.
Effect of Gender Composition and Negotiation Support Systems in Dyadic Setting
Authors: Zhen Wang, John Lim
Venue: Proceedings of the 40th Annual Hawaii International Conference on System Sciences (2007)

Many empirical studies of negotiation support systems (NSS) have shown that NSS generally improve negotiation performance. While most research on NSS has focused on the functional and structural issues, the investigation of negotiator characteristics is largely limited. Gender has been considered as one with greater direct relevance in the context of negotiation. This paper examines the interaction effect of the NSS technology and gender composition. To address the black box involved in the negotiation process, we take negotiation strategy into account as a mediating construct. A research model with hypotheses is presented. Aspects pertaining to future study are discussed.