ppxscal/embedding_network huggingface dataset:
A 4-in.-based single-step UV-NIL tool using a low vacuum environment and additive air pressure
Authors: Ki-don Kim, Ali Altun, Dae-geun Choi, Jun-ho Jeong
Venue: Microelectronic Engineering (2008)

Ultraviolet nanoimprint lithography (UV-NIL) is a promising technology for the fabrication of sub-10-nm features. Research has focused on employing a large-area stamp to improve UV-NIL throughput, but a large-area stamp makes it difficult to obtain an acceptable uniform residual layer thickness and/or avoid defects such as air entrapment. This paper presents the development of a single-step UV-NIL tool in which a 4-in. Pyrex stamp is first used to imprint coated resin against a 4-in. Si wafer in a low vacuum environment. Pressurized N"2 is subsequently applied to the wafer bottom to improve the quality of imprint results. This UV-NIL tool was used to successfully imprint a 4-in. stamp with recessed patterns engraved over the entire stamp areas onto a 4-in. Si wafer.
Pipelined Architecture for Additive Range Reduction
Authors: Francisco J. Jaime, Julio Villalba, Javier Hormigo, Emilio L. Zapata
Venue: Journal of Signal Processing Systems (2008)

Range reduction is a crucial step for the accuracy in trigonometric functions evaluation. A new pipelined architecture to deal with range reduction for floating point representation is presented in this paper. The algorithm is based on a look-up table storing the corresponding powers of 2 mod A. The overall design has been optimized for a modulo equal to 2¿, which is the most widely used due to trigonometric functions requirements. We provide an evaluation of different configurations and a full error propagation study which ensures an accuracy of one unit in the last place.
On mentoring, social mentoring and befriending
Authors: Bill McGowan, Patrick Saintas, Karamjit S. Gill
Venue: AI Society (2008)

This is an account of the origins of the concept of social mentoring and development of a social mentoring project in a local community in the city of Brighton and Hove in the UK. The account highlights the potential of social intervention for investigating the gaps and opportunities for mentoring services for people living with Asperger Syndrome. This initiative provides a model for supporting a parallel dialogue alongside and beyond the practice initiative so as to create a space for reflection on the impact and the broader application of the intervention. This project was made possible by the commitment and tenacity of a number of local community activists and welfare entrepreneur&#x2019;s on the ground who were able to set in train a number of events. These events are mapped out starting with community concerns, campaign initiatives, survey of the area of need and the use of evidence to secure funding and establish a client specific city-wide initiative. The role and importance of establishing alliances between the community and the local university academic community through a participatory research approach is highlighted and the benefits of creating space for the sharing of evidence from the literature and squaring this with evidence from practice through the development of a wider community of practice network is discussed. The resulting knowledge synthesis regarding the differentiation between mentoring, social mentoring and befriending is presented.
An improved data mining approach using predictive itemsets
Authors: Tzung-Pei Hong, Chyan-Yuan Horng, Chih-Hung Wu, Shyue-Liang Wang
Venue: Expert Systems with Applications: An International Journal (2009)

In this paper, we present a mining algorithm to improve the efficiency of finding large itemsets. Based on the concept of prediction proposed in the (n,p) algorithm, our method considers the data dependency in the given transactions to predict promising and non-promising candidate itemsets. Our method estimates for each level a different support threshold that is derived from a data dependency parameter and determines whether an item should be included in a promising candidate itemset directly. In this way, we maintain the efficiency of finding large itemsets by reducing the number of scanning the input dataset and the number candidate items. Experimental results show our method has a better efficiency than the apriori and the (n,p) algorithms when the minimum support value is small.
Simulations of nanoscale thermal conduction
Authors: Pekka Heino
Venue: Microsystem Technologies (2008)

In nanoscale, thermal conduction is affected by system size. Reasons are increased phonon scattering and changes in phonon group velocity. Computational modeling tools for thermal analysis have evolved in the past two decades. A brief overview of the state of the art is given. Nanoscale models can supply microscale computations with important parameters including relaxation times and group velocity. This work concentrates on group velocity and thermal conduction in thin silicon films. Changes in group velocity are addressed by calculating the dispersion relation with the molecular dynamics method, and for comparison, the conductivity is also directly computed. In all cases, the thermal resistivity increases as the film becomes thinner. Thermal conductivity is affected both by surface scattering and decreased group velocity. The group velocity changes both due to band folding and phonon confinement. The results indicate that in very thin films, the conductivity becomes highly anisotropic due to differences in surface scattering. In two low-index surfaces, the role of dispersion is only minor as compared to the role of surface scattering, while in one case, evidence of almost specular scattering was seen.
Similarity computing model of high dimension data for symptom classification of Chinese traditional medicine
Authors: Jing Peng, Chang-jie Tang, Dong-qing Yang, Jing Zhang, Jian-jun Hu
Venue: Applied Soft Computing (2009)

In recent years, researchers have paid more and more attention on data mining of practical applications. Aimed to the problem of symptom classification of Chinese traditional medicine, this paper proposes a novel computing model based on the similarities among attributes of high dimension data to compute the similarity between any tuples. This model assumes data attributes as basic vectors of m dimensions and each tuple as a sum vector of all the attribute-vectors. Based on the transcendental concept similarity information among attributes, it suggests a novel distance algorithm to compute the similarity distance of any pair of attribute-vectors. In this method, the computing of similarity between any tuples are turned to the formulas of attribute-vectors and their projections of each other, and the similarity between any pair of tuples can be worked out by computing these vectors and formulas. This paper also presents a novel classification algorithm based on the similarity computing model and successfully applies the algorithm into the symptom classification of Chinese traditional medicine. The efficiency of the algorithm is proved by extensive experiments.
A neural network ensemble-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes
Authors: Jian-bo Yu, Li-feng Xi
Venue: Expert Systems with Applications: An International Journal (2009)

In multivariate statistical process control, most multivariate quality control charts are shown to be effective in detecting out-of-control signals based upon an overall statistics. But these charts do not relieve the need for pinpointing source(s) of the out-of-control signals, which would be very useful for quality practitioners to locate the assignable causes that give rise to the out-of-control situation. In this study, a learning-based model has been investigated for monitoring and diagnosing out-of-control signals in a bivariate process. In this model, a selective neural network (NN) ensemble approach (DPSOEN, Discrete Particle Swarm Optimization) was developed for performing these tasks. The simulation results demonstrate that the proposed model outperforms the conventional multivariate control scheme in terms of average run length (ARL), and can accurately classify source(s) of out-of-control signals. Extensive experiment is also carried out to examine the effects of six statistical features on the performance of DPSOEN. Analysis from this study provides guidelines in developing NN ensemble-based Statistical process control recognition systems in multivariate processes.
A Logic Programming Approach to Scientific Workflow Provenance Querying
Authors: Yong Zhao, Shiyong Lu
Venue: Provenance and Annotation of Data and Processes: Second International Provenance and Annotation Workshop, IPAW 2008, Salt Lake City, UT, USA, June 17-18, 2008. Revised Selected Papers (2008)

Scientific workflows have become increasingly important for enabling and accelerating many scientific discoveries. More and more scientists and researchers rely on workflow systems to integrate and structure various local and remote heterogeneous data and services to perform in silico experiments. In order to support understanding, validation, and reproduction of scientific results, provenance querying and management has become a critical component in scientific workflows. In this paper, we propose a logic programming approach to scientific workflow provenance querying and management with the following contributions: i) We identify a set of characteristics that are desirable for a scientific workflow provenance query language; ii) Based on these requirements, we propose FLOQ, a Frame Logic based query language for scientific workflow provenance, iii) We demonstrate that our previous relational database based provenance model, virtual data schema, can be easily mapped to the FLOQ model; and iv) We show by examples that FLOQ is expressive enough to formulate common provenance queries, including all the provenance challenge queries proposed in the provenance challenge series.
On the design and exploitation of user's personal and public information for semantic personal digital photograph annotation
Authors: Supheakmungkol Sarin, Toshinori Nagahashi, Tadashi Miyosawa, Wataru Kameyama
Venue: Advances in Multimedia (2008)

Automating the process of semantic annotation of digital personal photographs is a crucial step towards efficient and effective management of this increasingly high volume of content. However, this is still a highly challenging task for the research community. This paper proposes a novel solution. Our solution integrates all contextual information available to and from the users, such as their daily emails, schedules, chat archives, web browsing histories, documents, online news, Wikipedia data, and so forth. We then analyze this information and extract important semantic terms, using them as semantic keyword suggestions for their photos. Those keywords are in the form of named entities, such as names of people, organizations, locations, and date/time as well as high frequency terms. Experiments conducted with 10 subjects and a total of 313 photos proved that our proposed approach can significantly help users with the annotation process. We achieved a 33% gain in annotation time as compared to manual annotation. We also obtained very positive results in the accuracy rate of our suggested keywords.
Modularizing Monitoring Rules in Business Processes Models
Authors: Oscar González, Rubby Casallas, Dirk Deridder
Venue: Proceedings of the OTM Confederated International Workshops and Posters on On the Move to Meaningful Internet Systems: 2008 Workshops: ADI, AWeSoMe, COMBEK, EI2N, IWSSA, MONET, OnToContent + QSI, ORM, PerSys, RDDS, SEMELS, and SWWS (2008)

Business process management systems contain monitoring, measurement and control (MMC) specifications to enable the identification of problems and solutions to improve business processes. Business processes facilitate the integration of human and technological resources in an organization, according to a set of activities that fulfil a policy goal [1].Currently, several business-process monitoring, measurement and control solutions are available [2]. However, MMC specifications typically are implicitly encoded in the low-level implementation of the workflow system. This results in tangled and scattered MMC knowledge in the underlying process code. It is clear that this decreases the maintainability and reusability of the MMC specifications since they are not specified in a modular fashion. Furthermore, specific knowledge about the overall system implementation is required if the MMC requirements evolve. Due to the entanglement with the low-level implementation, this requires a level of expertise that is normally available to technical developers. This is unfortunate since business experts typically express the MMC requirements at a high level and in terms of the business domain (as opposed to the technical implementation). The task becomes even more complicated because the MMC specifications involve data that is not readily available in one location of the process code. In addition the majority of existing approaches focus on describing the MMC specification in terms of the process execution instead of the data flow of the process. Consequently, when existing MMC specifications need to be adapted, the different pieces of process code must be manually localized after which adaptations will occur at several places.
A circulatory system approach for wireless sensor networks
Authors: Vasileios Pappas, Dinesh Verma, Bong-Jun Ko, Ananthram Swami
Venue: Ad Hoc Networks (2009)

One of the challenges in a military wireless sensor network is the determination of an information collection infrastructure which minimizes battery power consumption. The problem of determining the right information collection infrastructure can be viewed as a variation of the network design problem, with the additional constraints related to battery power minimization and redundancy. The problem in its generality is NP-hard and various heuristics have been developed over time to address various issues associated with it. In this paper, we propose a heuristic based on the mammalian circulatory system, which results in a better solution to the design problem than the state of the art alternatives.
Global SAP Systems - Design and Architecture, 1st edition
Authors: Alexander Davidenkoff
Venue:  (2008)

When designing your global SAP systems, you must consider the key question of whether you should implement one global system or several distributed local systems. This book helps you to make a sound decision and successfully implement the most appropriate system architecture. In this book, you'll learn about the business process challenges faced by a global enterprise, and you'll explore the technical requirements for carrying out global implementation projects. All relevant subject areas are covered, including the many different requirements global systems must meet, factors influencing the architecture, and available architecture options. In addition, you'll find valuable information and best practices on how to optimally map system topologies through IT. Extensive customer examples and an invaluable decision matrix serve to round off this comprehensive guide. Highlights Global systems challenges: regional requirements, time zones, data transfer Factors influencing the system architecture: product-related and technical aspects, business processes Various architectures: distributed, regional, and global architecture, combined architectures Technical issues: IT platform, network aspects, tools and central components, special SAP solutions, release management, maintenance Customer scenarios Decision matrix for an optimal system topology
Long-term monitoring of land cover changes based on Landsat imagery to improve hydrological modelling in West Africa
Authors: D. Ruelland, A. Dezetter, C. Puech, S. Ardoin-Bardin
Venue: International Journal of Remote Sensing (2008)

The spatial and temporal variability of land cover changes is a fundamental parameter to integrate when modelling water resources in order to reproduce the relations between rainfall and surface flow more precisely. This is particularly important in West Africa, where the land cover has been changing for more than 40 years under the combined impact of climatic effects and human activities. In this study, we evaluated the potential of Landsat imagery to monitor the vegetation cover in the upper Niger watershed (120 000 km2) using archive images from MSS, TM and ETM+ sensors covering three periods of time around 1975, 1985, and 2000. Because of the heterogeneity of the acquisition dates, the spatial and spectral resolution of the images, and the scale of analysis, we chose a simple system of classification. Pretreatments were applied to reduce variations between the images. Vegetation indices (NDVI) were then calculated and subsequently thresholded using the same land-cover classification system. The thresholds were then optimized by automated recursive calculations of confusion matrices and control parcels. Our results revealed that although the accuracy was not perfect, it was nevertheless possible to estimate changes using an unconventional spatio-temporal scale. The resulting changes were characterized by a moderate trend to deforestation with a corresponding increase in bare soils, soils with sparse vegetation, and shrublands. The spatial layers produced were then combined with a soil map to incorporate changes in surface conditions in the hydrological modelling of the Niger River.
A workflow model for heterogeneous computing environments
Authors: Oisín Curran, Andy Shearer
Venue: Future Generation Computer Systems (2009)

This work presents the details of a model for fully decentralised scientific workflow management designed to support the efficient execution of applications in heterogeneous and unpredictable computing environments. The proposed model involves a novel application of the overlay metacomputer concept, built on a graph-oriented peer to peer framework. The use of peer to peer overlay on both batch and cycle scavenging systems simultaneously enables the aggregation and virtual homogenisation of heterogeneous resource collectives, and makes possible the execution of complex scientific workflow applications on otherwise batch oriented processors. Overlaying a peer to peer workflow management system on multiple independent sites decouples task expression from resource allocation, i.e., the host resource managers allocate processing elements to host the peers, allowing the peers execute multiple workflow tasks. This facilitates the efficient execution of very fine grained workflow expressions, which is shown to reduce both application makespan and wasted cycles when using processing elements allocated by a pre-emptive resource manager. Quantitative results are presented based on performance observed in a large scale heterogeneous multi-user environment.
Apparently low reproducibility of true differential expression discoveries in microarray studies
Authors: Min Zhang, Chen Yao, Zheng Guo, Jinfeng Zou, Lin Zhang, Hui Xiao, Dong Wang, Da Yang, Xue Gong, Jing Zhu, Yanhui Li, Xia Li
Venue: Bioinformatics (2008)

Motivation: Differentially expressed gene (DEG) lists detected from different microarray studies for a same disease are often highly inconsistent. Even in technical replicate tests using identical samples, DEG detection still shows very low reproducibility. It is often believed that current small microarray studies will largely introduce false discoveries. Results: Based on a statistical model, we show that even in technical replicate tests using identical samples, it is highly likely that the selected DEG lists will be very inconsistent in the presence of small measurement variations. Therefore, the apparently low reproducibility of DEG detection from current technical replicate tests does not indicate low quality of microarray technology. We also demonstrate that heterogeneous biological variations existing in real cancer data will further reduce the overall reproducibility of DEG detection. Nevertheless, in small subsamples from both simulated and real data, the actual false discovery rate (FDR) for each DEG list tends to be low, suggesting that each separately determined list may comprise mostly true DEGs. Rather than simply counting the overlaps of the discovery lists from different studies for a complex disease, novel metrics are needed for evaluating the reproducibility of discoveries characterized with correlated molecular changes. Contact:guoz@ems.hrbmu.edu.cn; lixia@ems.hrbmu.edu.cn Supplementaty information: Supplementary data are available at Bioinformatics online.
Optimizing amino acid groupings for GPCR classification
Authors: Matthew N. Davies, Andrew Secker, Alex A. Freitas, Edward Clark, Jon Timmis, Darren R. Flower
Venue: Bioinformatics (2008)

Motivation: There is much interest in reducing the complexity inherent in the representation of the 20 standard amino acids within bioinformatics algorithms by developing a so-called reduced alphabet. Although there is no universally applicable residue grouping, there are numerous physiochemical criteria upon which one can base groupings. Local descriptors are a form of alignment-free analysis, the efficiency of which is dependent upon the correct selection of amino acid groupings. Results: Within the context of G-protein coupled receptor (GPCR) classification, an optimization algorithm was developed, which was able to identify the most efficient grouping when used to generate local descriptors. The algorithm was inspired by the relatively new computational intelligence paradigm of artificial immune systems. A number of amino acid groupings produced by this algorithm were evaluated with respect to their ability to generate local descriptors capable of providing an accurate classification algorithm for GPCRs. Contact: m.davies@mail.cryst.bbk.ac.uk
Scheduling of parallel machines to minimize total completion time subject to s-precedence constraints
Authors: Eun-Seok Kim, Chang-Sup Sung, Ik-Sun Lee
Venue: Computers and Operations Research (2009)

This paper considers a deterministic scheduling problem where multiple jobs with s-precedence relations are processed on multiple identical parallel machines. The objective is to minimize the total completion time. The s-precedence relation between two jobs i and j represents the situation where job j is constrained from processing until job i starts processing, which is different from the standard definition of a precedence relation where j cannot start until i completes. The s-precedence relation has wide applicability in the real world such as first-come-first-served processing systems. The problem is shown to be intractable, for which a heuristic procedure is derived. Numerical experiments are conducted to show that the derived heuristic provides effective solutions.
XAV: a fast and flexible tracing framework for network simulation
Authors: Ryad Ben-El-Kezadri, Farouk Kamoun, Guy Pujolle
Venue: Proceedings of the 11th international symposium on Modeling, analysis and simulation of wireless and mobile systems (2008)

With current network simulators, simulation results are obtained from flat trace files via sequential data access. To speed up simulation output analysis, we propose to store the simulation traces into data warehouse. Our proposed framework relies on a three-layer architecture, with the intermediary database layer serving the trace file post processing services. The trace files are accessed through SQL-like queries which greatly enhances the flexibility of the post processing tools. Furthermore, the database paradigm allows to separate the packets data from the nodes send and receive operations and thus to remove redundant information between simulation events. The global system relies on a new trace file format based on the popular XML language. XML pointers are used to create relationships between data so that post processing tools can navigate into simulation outputs. The tests are carried out under the MonetDB database. The obtained results show considerable response time improvement compared to traditional sequential access mode.