ppxscal/embedding_network huggingface dataset:
Grammatical inference methodology for control systems
Authors: Aboubekeur Hamdi-Cherif, Chafia Kara-Mohammed
Venue: WSEAS Transactions on Computers (2009)

Machine Learning is a computational methodology that provides automatic means of improving programmed tasks from experience. As a subfield of Machine Learning, Grammatical Inference (GI) attempts to learn structural models, such as grammars, from diverse data patterns, such as speech, artificial and natural languages, sequences provided by bioinformatics databases, amongst others. Here we are interested in identifying artificial languages from sets of positive and eventually negative samples of sentences. The present research intends to evaluate the effectiveness and usefulness of grammatical inference (GI) in control systems. The ultimate far-reaching goal addresses the issue of robots for self-assembly purposes. At least two benefits are to be rawn. First, on the epistemological level, it unifies two apparently distinct scientific communities, namely formal languages theory and robot control communities. Second, on the technological level, blending research from both fields results in the appearance of a richer community, as has been proven by the emergence of many multidisciplinary fields. Can we integrate diversified works dealing with robotic self-assembly while concentrating on grammars as an alternative control methodology? Our aim is to answer positively this central question. As far as this paper is concerned, we set out the broad methodological lines of the research while stressing the integration of these different approaches into one single unifying entity.
Online Feedback and the Real-Time Evaluation: Integrating Wireless Technology into Instructional Strategies
Authors: Lyna, Lian Sai Chia, Ngoh Khang Goh, Widya Andyardja Weliamto
Venue: Proceeding of the 2005 conference on Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences: Sharing Good Practices of Research, Experimentation and Innovation (2005)

The tremendous development of technology has significant contributions in the educational field. Education becomes ubiquitous, as the interconnectivity between teacher and students across geographical and time constraints is plausible. Another essential part of instructional strategy is evaluation. It is important for the teacher to get feedback from students to evaluate whether the materials explained are understood. A system called “PDA-Based Real-Time Evaluation Media”, is proposed in this paper to permit the teacher to solicit each student's online-feedback during class and evaluate them in real-time. Because of their affordability, versatility, and mobility, it is predicted that PDAs will be very popular tools that most of students can have.
Teaching about Dynamic Processes A Teachable Agents Approach
Authors: Ruchie Gupta, Yanna Wu, Gautam Biswas
Venue: Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (2005)

This paper discusses the extensions that we have made to Betty's Brain teachable agent system to help students learn about dynamic processes in a river ecosystem. Students first learn about dynamic behavior in a simulation environment, and then teach Betty by introducing cycles into the concept map representation. Betty's qualitative reasoning mechanisms have been extended so that she can reason about cycles and determine how entities change over time. Preliminary experiments were conducted to study and analyze the usefulness of the simulation. Analysis of the students' protocols was very revealing, and the lessons learnt have led to redesigned simulation interfaces. A new study with the system will be conducted in a fifth grade science classroom in May, 2005.
Adobe Photoshop CS3 Studio Techniques
Authors: Ben Willmore
Venue:  (2007)

Adobe Photoshop CS3 Studio Techniques has been completely updated to cover the new features in CS3. In a friendly, easy-going style that's long on information and short on techno-babble, Photoshop Hall-of-Famer Ben Willmore guides you through the concepts, features that will truly make a difference in how you use Photoshop every day. He takes you from blindly following step-by-step instructions to an in-depth understanding of how Photoshop works, cutting through the fat to focus on what he considers to be Photoshop's essential features. This full-color book delivers the content in three easily digestible sections: Working Foundations, Production Essentials, and Creative Explorations, and includes a companion CD with bonus chapters and practice images so you can quickly and easily apply the techniques covered throughout. Beginning with the working foundations of Photoshop--the basic tools, palettes, layers, and masks, you'll quickly move on to real-world production techniques, such as how to sharpen scans, correct and optimize images, and use the powerful Curves and Channels features to your advantage. Finally, you'll get to fully explore Photoshop's creative potential by blending and enhancing images, creating collages, retouching photographs, colorizing, and working with filters and layer masks. By the time you finish this book, your creativity and efficiency levels should soar and you'll start to feel like you finally "get" Photoshop.
Pose Invariant Shape Prior Segmentation Using Continuous Cuts and Gradient Descent on Lie Groups
Authors: Niels Chr. Overgaard, Ketut Fundana, Anders Heyden
Venue: Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision (2009)

This paper proposes a novel formulation of the Chan-Vese model for pose invariant shape prior segmentation as a continuous cut problem. The model is based on the classic L 2 shape dissimilarity measure and with pose invariance under the full (Lie-) group of similarity transforms in the plane. To overcome the common numerical problems associated with step size control for translation, rotation and scaling in the discretization of the pose model, a new gradient descent procedure for the pose estimation is introduced. This procedure is based on the construction of a Riemannian structure on the group of transformations and a derivation of the corresponding pose energy gradient. Numerically, this amounts to an adaptive step size selection in the discretization of the gradient descent equations. Together with efficient numerics for TV-minimization we get a fast and reliable implementation of the model. Moreover, the theory introduced is generic and reliable enough for application to more general segmentation- and shape-models.
Analysis of pattern height development in hot embossing process
Authors: Yong He, Jian-Zhong Fu, Zi-Chen Chen
Venue: Microsystem Technologies (2009)

The pattern height of hot embossing was analyzed based on the polymer power&#x2013;law material assumption for better controlling product quality. The time development of the polymer substrate height, pattern height and the imprint pressure were obtained. The imprint temperature with respect to the pattern height was discussed. Theory analysis shows that in a constant imprint pressure, the pattern height has a power relationship with the imprint time. At the beginning imprint stage, as for the elastic response of polymer, the pattern height increases rapidly. With the time increasing, the polymer deform is mainly caused by creep and this results in low imprint speed. On the other hand, the increasing friction force between the polymer and the mold will further reduce the imprint speed. The theoretical data were compared with experiments and the results show that this model can well predict the rule of polymer substrate thickness variation.
Norm Modifications in Defeasible Logic
Authors: Guido Governatori, Monica Palmirani, Regis Riveret, Antonio Rotolo, Giovanni Sartor
Venue: Proceeding of the 2005 conference on Legal Knowledge and Information Systems: JURIX 2005: The Eighteenth Annual Conference (2005)

This paper proposes a framework based on Defeasible Logic (DL) to reason about normative modifications. We show how to express them in DL and how the logic deals with conflicts between temporalised normative modifications. Some comments will be given with regard to the phenomenon of retroactivity.
Distance computation in the space of phylogenetic trees
Authors: Louis Billera
Venue:  (2008)

A phylogenetic tree represents the evolutionary history of a set of organisms. There are many different methods to construct phylogenetic trees from biological data. To either compare one such algorithm with another, or to find the likelihood that a certain tree is generated from the data, researchers need to be able to compute the distance between trees. In 2001, Billera, Holmes, and Vogtmann introduced a space of phylogenetic trees, and defined the distance between two trees to be the length of the shortest path between them in that space. We use the combinatorial and geometric properties of the tree space to develop two algorithms for computing this geodesic distance. In doing so, we show that the possible shortest paths between two trees can be compactly represented by a partially ordered set. We calculate the shortest distance between the start and target trees for each potential path by converting the problem into one of finding the shortest path through a certain subspace of Euclidean space. In particular, we show there is a linear time algorithm for finding the shortest path between a point in the all positive orthant and a point in the all negative orthant of Rk contained in the subspace of Rk consisting of all orthants with the first i coordinates non-positive and the remaining coordinates non-negative for 0 < i < k. This case is of interest, because the general problem of finding a shortest path through higher dimensional Euclidean space with obstacles is NP-hard. The resulting algorithms for computing the geodesic distance appear to be the best available to date.
Designing Digital Computer Systems with Verilog, 1st edition
Authors: David J. Lilja, Sachin S. Sapatnekar
Venue:  (2007)

Using Verilog, a leading commercial hardware description language, this text describes how to specify, design, and test a complete digital system. After a brief introduction to the Verilog language, the instruction set architecture (ISA) for the simple VeSPA (Very Small Processor Architecture) processor is defined. The remainder of the book demonstrates how both behavioral and structural models can be developed and intermingled in Verilog.
Microsoft Office Publisher 2007 For Dummies
Authors: Jim McCarter, Jacqui Salerno Mabin
Venue:  (2007)

This is supposed to be the age of instant and constant communication, right? And if you have a business, belong to an organization, or have a cause you want to promote, a great-looking flyer or brochure can say a lot. If you have Microsoft Office Professional, Small Business, or Ultimate on your PC, you already have a great communication tool hidden insidePublisher 2007. Use it to promote your organization with newsletters, cards, and brochures. This book gets you started with Publisher basics so you can start communicating with your public. Chances are youre not planning to become a Publisher guru; you just want to use Publisher to get some things done. Then Microsoft Office Publisher 2007 For Dummies is just the book for you! It has just what you need to know to Understand design basics and plan a page Set up a flyer or publication and place text and pictures where they work best Use various Publisher templates Incorporate images and files from other programs Build Web sites with Publisher Prepare your creations for printing or posting online Whether youre selling a product or service, getting the word out about a not-for-profit organization, or helping out your church, synagogue, or school, Microsoft Office Publisher 2007 For Dummies makes it easy.
Non-blocking programming on multi-core graphics processors: (extended asbtract)
Authors: Phuong Hoai Ha, Philippas Tsigas, Otto J. Anshus
Venue: ACM SIGARCH Computer Architecture News (2009)

This paper investigates the synchronization power of coalesced memory accesses, a family of memory access mechanisms introduced in recent large multicore architectures like the CUDA graphics processors. We first design three memory access models to capture the fundamental features of the new memory access mechanisms. Subsequently, we prove the exact synchronization power of these models in terms of their consensus numbers. These tight results show that the coalesced memory access mechanisms can facilitate strong synchronization between the threads of multicore processors, without the need of synchronization primitives other than reads and writes. Moreover, based on the intrinsic features of recent GPU architectures, we construct strong synchronization objects like wait-free and t-resilient read-modify-write objects for a general model of recent GPU architectures without strong hardware synchronization primitives like test-and-set and compare-and-swap. Accesses to the wait-free objects have time complexity O(N), where N is the number of processes. Our result demonstrates that it is possible to construct waitfree synchronization mechanisms for GPUs without the need of strong synchronization primitives in hardware and that wait-free programming is possible for GPUs.
Cross domain distribution adaptation via kernel mapping
Authors: Erheng Zhong, Wei Fan, Jing Peng, Kun Zhang, Jiangtao Ren, Deepak Turaga, Olivier Verscheure
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

When labeled examples are limited and difficult to obtain, transfer learning employs knowledge from a source domain to improve learning accuracy in the target domain. However, the assumption made by existing approaches, that the marginal and conditional probabilities are directly related between source and target domains, has limited applicability in either the original space or its linear transformations. To solve this problem, we propose an adaptive kernel approach that maps the marginal distribution of target-domain and source-domain data into a common kernel space, and utilize a sample selection strategy to draw conditional probabilities between the two domains closer. We formally show that under the kernel-mapping space, the difference in distributions between the two domains is bounded; and the prediction error of the proposed approach can also be bounded. Experimental results demonstrate that the proposed method outperforms both traditional inductive classifiers and the state-of-the-art boosting-based transfer algorithms on most domains, including text categorization and web page ratings. In particular, it can achieve around 10% higher accuracy than other approaches for the text categorization problem. The source code and datasets are available from the authors.
A Bayesian segmentation approach to ascertain copy number variations at the population level
Authors: Long Yang Wu, Hugh A. Chipman, Shelley B. Bull, Laurent Briollais, Kesheng Wang
Venue: Bioinformatics (2009)

Motivation: Efficient and accurate ascertainment of copy number variations (CNVs) at the population level is essential to understand the evolutionary process and population genetics, and to apply CNVs in population-based genome-wide association studies for complex human diseases. We propose a novel Bayesian segmentation approach to identify CNVs in a defined population of any size. It is computationally efficient and provides statistical evidence for the detected CNVs through the Bayes factor. This approach has the unique feature of carrying out segmentation and assigning copy number status simultaneously—a desirable property that current segmentation methods do not share. Results: In comparisons with popular two-step segmentation methods for a single individual using benchmark simulation studies, we find the new approach to perform competitively with respect to false discovery rate and sensitivity in breakpoint detection. In a simulation study of multiple samples with recurrent copy numbers, the new approach outperforms two leading single sample methods. We further demonstrate the effectiveness of our approach in population-level analysis of previously published HapMap data. We also apply our approach in studying population genetics of CNVs. Availability: R programs are available at http://www.mshri.on.ca/mitacs/software/SOFTWARE.HTML Contact: lwu@math.uwaterloo.ca Supplementary information:Supplementary data are available at Bioinformatics online.
Evolutionary p2p network adaptively changing its topologies for reliable searching
Authors: Kei Ohnishi, Kento Ichikawa, Yuji Oie
Venue: Proceedings of the 2009 workshop on Bio-inspired algorithms for distributed systems (2009)

In the present paper, through simulations, we examine whether the evolutionary peer-to-peer (P2P) networking technique can provide reliable search capability in dynamic P2P environments. The evolutionary P2P networking technique optimizes several P2P network topologies, to which all of the nodes belong at the same time, in an evolutionary manner according to given evaluation criteria. In simulations, we assume dynamic P2P environments in which each node leaves and joins the network with its own probability and in which search objects vary with time. The simulation results show (1) that random topology reconstruction is better than topology reconstruction by the evolutionary P2P networking technique in terms of reliable search capability when several types of search objects are present in the network at any moment and replicas of these search objects are created in the nodes and (2) that topology reconstruction by the evolutionary P2P networking technique is better than random topology reconstruction when only a few types of search objects are present in the network at any moment and these search objects are not replicated.
Content-Based Social Network Analysis
Authors: Paola Velardi, Roberto Navigli, Alessandro Cucchiarelli, Mirco Curzi
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

Relationships among actors in traditional social network analysis are modelled as a function of the quantity of relations (co-authorships, business relations, friendship, etc.). In contrast, within a business, social or research community, network analysts are interested in the communicative content exchanged by the community members, not merely in the number of relationships. In order to meet this need, this paper presents a novel social network model, in which the actors are not simply represented through the intensity of their mutual relationships, but also through the analysis and evolution of their shared interests. Text mining and clustering techniques are used to capture the content of communication and to identify the most popular topics.
Computer-aided detection of malignant lesions in dynamic contrast enhanced mri breast and prostate cancer datasets
Authors: Bradley Clymer
Venue:  (2008)

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is considered to have great potential in cancer diagnosis and monitoring. During the DCE-MRI procedure, repeated MRI scans are used to monitor contrast agent movement through the vascular system and into tissue. By observing the vascular permeability characteristics, radiologists can detect and classify malignant tissues. When used for diagnostic purposes, the DCE-MRI procedure often requires manual detection, classification, and marking of tumor tissues. This process can be time consuming and fatiguing especially when multiple DCE-MRI procedures must be processed to monitor the progress of a cancer therapy. Manual analysis also suffers from inter- and intra-observer variations which can lead to lesion segmentation inconsistencies.The goal of this dissertation research is to design and develop a tool to aid radiologists, researchers, and clinicians in the detection, segmentation, and analysis of malignant lesions from DCE-MRI datasets. The diagnostic tool presented in this research is model independent, speeds analysis, and provides more consistent segmentations. The approach of the project is to apply statistical 4-D image texture analysis features along with a classifier (such as a neural network) to analyze DCE-MRI datasets.Performance of the computer aided diagnosis (CAD) tool for this project is demonstrated with breast and prostate DCE-MRI data. Training methodology is reported so that extension to other types of cancers and anatomical regions is made possible. Results from the computer assisted diagnostic tool are compared with manual analysis performed by radiologists. The specific research aims of this dissertation are: (a) provide a tool for quantitative and quick DCE-MRI analysis by providing radiologists a segmentation (for semi-automatic or automatic application), (b) quantify inter- and intra-observer variations that occur during manual lesion segmentation and compare performance with computer-based segmentations, and (c) explore the performance of the CAD system in different anatomic regions (including breast and prostate cancer datasets).
On the design of efficient resource allocation mechanisms for grids
Authors: George Rouskas, Khaled Harfoush
Venue:  (2008)

In this thesis we consider the problem of providing QoS guarantees to Grid users through advance reservation. Advance reservation mechanisms provide the ability to allocate resources to users based on agreed-upon QoS requirements and increase the predictability of a Grid system, yet incorporating such mechanisms into current Grid environments has proven to be a challenging task due to the resulting resource fragmentation. In view of these observations we have devised efficient scheduling algorithms that support advance reservations. We can organize this thesis in two parts. We first use concepts from computational geometry and efficient data structures to present a framework for tackling the resource fragmentation, and for formulating a suite of scheduling strategies. We also develop efficient implementations of the scheduling algorithms that scale to large Grids. We conduct a comprehensive performance evaluation study using simulation, and we present numerical results to demonstrate that our algorithms perform well across several metrics that reflect both user- and system-specific goals. Advance reservations has also been proposed as one mechanisms to provide Grid resource managers with the ability to co-allocate resources. Co-allocation of resources is one problem that has gained increasing attention due to the emergence of complex applications that require orchestration of resources never envisioned before. In practice, a co-allocation request can be handled manually as a set of individual advance reservations requests. However, such a solution can be computationally expensive and inappropriate for time-sensitive applications. Furthermore, the trend towards more dynamic solutions has emphasized the need for more automatic solutions. As a second contribution, in this thesis we design and develop a co-allocation algorithm that is efficient in co-allocating resources while respecting the atomicity of the co-allocation request and improving user and system performance. This is achieved by quantizing the temporal space and using efficient 2-dimensional balanced search trees. We perform a comparative analysis of our algorithm by means of simulations driven by workloads from real systems and show that our algorithm scales to Grid systems with large number of resources and heavy workloads.