ppxscal/embedding_network huggingface dataset:
Predictions of Two Photon Absorption Profiles Using Time-Dependent Density Functional Theory Combined with SOS and CEO Formalisms
Authors: Sergio Tafur, Ivan A. Mikhailov, Kevin D. Belfield, Artëm E. Masunov
Venue: Proceedings of the 9th International Conference on Computational Science (2009)

Two-photon absorption (2PA) and subsequent processes may be localized in space with a tightly focused laser beam. This property is used in a wide range of applications, including three dimensional data storage. We report theoretical studies of 5 conjugated chromophores experimentally shown to have large 2PA cross-sections. We use the Time Dependent Density Functional Theory (TD-DFT) to describe the electronic structure. The third order coupled electronic oscillator formalism is applied to calculate frequency-dependent second order hyperpolarizability. Alternatively, the sum over states formalism using state-to-state transition dipoles provided by the a posteriori Tamm-Dancoff approximation is employed. It provides new venues for qualitative interpretation and rational design of 2PA chromophores.
Telemedicine and ethical dilemmas
Authors: Liliana Rogozea, Luciana Cristea, Mihaela Baritz, Victoria Burtea
Venue: Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases (2009)

Telemedicine is still under development and the main goal is to deliver medical care at distance. The application of telemedicine and telecommunication - telematics - in the healthcare system is the privilege of the rich countries, where reliable and secure satellite connections are available at high speed transfer rate, and its use is limited by the nec?ssity to exist the highly sophisticated equipment. To facilitate telemedicine applications for all countries is one of the goals for the next years. In the same time, educational programs in the field and ethical analyzes are not only a trend for the next years but a real necessity. In this respect, in this paper, we will consecutively pay attention to the ethical aspects of telemedicine correlate with patients, health care services, communication or biomedical literature.
Cooperative Object Segmentation and Behavior Inference in Image Sequences
Authors: Laura Gui, Jean-Philippe Thiran, Nikos Paragios
Venue: International Journal of Computer Vision (2009)

In this paper, we propose a general framework for fusing bottom-up segmentation with top-down object behavior inference over an image sequence. This approach is beneficial for both tasks, since it enables them to cooperate so that knowledge relevant to each can aid in the resolution of the other, thus enhancing the final result. In particular, the behavior inference process offers dynamic probabilistic priors to guide segmentation. At the same time, segmentation supplies its results to the inference process, ensuring that they are consistent both with prior knowledge and with new image information. The prior models are learned from training data and they adapt dynamically, based on newly analyzed images. We demonstrate the effectiveness of our framework via particular implementations that we have employed in the resolution of two hand gesture recognition applications. Our experimental results illustrate the robustness of our joint approach to segmentation and behavior inference in challenging conditions involving complex backgrounds and occlusions of the target object.
Global and componentwise extrapolations for accelerating training of Bayesian networks and conditional random fields
Authors: Han-Shen Huang, Bo-Hou Yang, Yu-Ming Chang, Chun-Nan Hsu
Venue: Data Mining and Knowledge Discovery (2009)

The triple jump extrapolation method is an effective approximation of Aitken's acceleration that can accelerate the convergence of many algorithms for data mining, including EM and generalized iterative scaling (GIS). It has two options--global and componentwise extrapolation. Empirical studies showed that neither can dominate the other and it is not known which one is better under what condition. In this paper, we investigate this problem and conclude that, when the Jacobian is (block) diagonal, componentwise extrapolation will be more effective. We derive two hints to determine the block diagonality. The first hint is that when we have a highly sparse data set, the Jacobian of the EM mapping for training a Bayesian network will be block diagonal. The second is that the block diagonality of the Jacobian of the GIS mapping for training CRF is negatively correlated with the strength of feature dependencies. We empirically verify these hints with controlled and real-world data sets and show that our hints can accurately predict which method will be superior. We also show that both global and componentwise extrapolation can provide substantial acceleration. In particular, when applied to train large-scale CRF models, the GIS variant accelerated by componentwise extrapolation not only outperforms its global extrapolation counterpart, as our hint predicts, but can also compete with limited-memory BFGS (L-BFGS), the de facto standard for CRF training, in terms of both computational efficiency and F-scores. Though none of the above methods are as fast as stochastic gradient descent (SGD), careful tuning is required for SGD and the results given in this paper provide a useful foundation for automatic tuning.
Meme Media for the Federation of Intellectual Resources over the Web by Clipping and Combining Them
Authors: Yuzuru Tanaka
Venue: Proceeding of the 2005 conference on New Trends in Software Methodologies, Tools and Techniques: Proceedings of the fourth SoMeT_W05 (2005)

The publication and reuse of intellectual resources using the Web technologies provide no support for us to clip out any portion of Web pages, to combine them together for their local reuse, nor to distribute the newly composed object for its reuse by other people. Such a composition requires both a layout composition and a functional composition. Both of them should be performed only through direct manipulation. This paper shows how the meme-media architecture is applied to the Web to provide such support for us. This makes the Web work as a shared repository not only for publishing intellectual resources, but also for their collaborative reediting, and furthermore for the flexible federation of intellectual resources. Federation here denotes ad hoc definition and/or execution of interoperation among intellectual resources. We will propose a general framework for clipping arbitrary Web contents as live objects, and for the recombination and linkage of such clips based on both the original and some user-defined relationships among them. This framework allows us to define federations of intellectual resources over the Web dynamically through direct manipulation.
DRAM Circuit Design: Fundamental and High-Speed Topics, 2nd edition
Authors: Brent Keeth, R. Jacob Baker, Brian Johnson, Feng Lin
Venue:  (2007)

A modern, comprehensive introduction to DRAM for students and practicing chip designers Dynamic Random Access Memory (DRAM) technology has been one of the greatestdriving forces in the advancement of solid-state technology. With its ability to produce high product volumes and low pricing, it forces solid-state memory manufacturers to work aggressively to cut costs while maintaining, if not increasing, their market share. As a result, the state of the art continues to advance owing to the tremendous pressure to get more memory chips from each silicon wafer, primarily through process scaling and clever design. From a team of engineers working in memory circuit design, DRAM Circuit Design gives students and practicing chip designers an easy-to-follow, yet thorough, introductory treatment of the subject. Focusing on the chip designer rather than the end user, this volume offers expanded, up-to-date coverage of DRAM circuit design by presenting both standard and high-speed implementations. Additionally, it explores a range of topics: the DRAM array, peripheral circuitry, global circuitry and considerations, voltage converters, synchronization in DRAMs, data path design, and power delivery. Additionally, this up-to-date and comprehensive book features topics in high-speed design and architecture and the ever-increasing speed requirements of memory circuits. The only book that covers the breadth and scope of the subject under one cover, DRAM Circuit Design is an invaluable introduction for students in courses on memory circuit design or advanced digital courses in VLSI or CMOS circuit design. It also serves as an essential, one-stop resource for academics, researchers, and practicing engineers.
Advances in automated diagnostic systems
Authors: Elif Derya Übeyli
Venue: Proceedings of the 10th WSEAS international conference on Neural networks (2009)

This paper intends to an integrated view of the advances in automated diagnostic systems. The paper includes illustrative and detailed information about automated diagnostic systems and feature extraction/selection from biomedical signals. In this respect, the paper satisfies the automated diagnostic systems, which includes the spectral analysis techniques, feature extraction and/or selection methods and decision support systems. The paper includes spectral analysis techniques used in the feature extraction stage, experiments for implementation of automated diagnostic systems and measuring performance of automated diagnostic systems. The objective of the paper is to be a guide for the readers, who want to develop an automated diagnostic systems for detection of any diseases.
ContextAggregator: A heuristic-based approach for automated feature construction and selection
Authors: Robert Lokaiczyk, Manuel Goertz
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

Our research goal is to work towards a personal contextaware assistance and retrieval of relevant resources to computer users during a certain work task. This paper presents a general-purpose, algorithmic approach for automated context aggregation by heuristic-based feature construction. Our implementation of the context reasoning layer combines lower-level context features to new aggregated higher-level context features. Our approach allows --in contrast to most other approaches --an automated feature combination to achieve a high prediction accuracy of the user's work task.
Cost-Optimal Symbolic Planning with State Trajectory and Preference Constraints
Authors: Stefan Edelkamp
Venue: Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy (2006)

State trajectory and plan preference constraints are the two language features recently introduced to PDDL in the context of the 5th international planning competition. For planning with soft constraints, an objective function monitors their violation. This paper introduces a symbolic approach for finding cost-optimal plans. The set-based branch-and-bound algorithm exploits an efficient symbolic representation of the objective function. State trajectory constraints are compiled into automata, while ordinary preferences are evaluated on-line for the intersection of the search frontier with the goal.
An algorithm to bound the regularity and nonemptiness of linear systems in Pn
Authors: Marcin Dumnicki
Venue: Journal of Symbolic Computation (2009)

The main goal of this paper is to present a new algorithm bounding the regularity and ''alpha'' (the lowest degree of existing hypersurface) of a linear system of hypersurfaces in P^n passing through multiple points in a general position. To do the above we formulate and prove a new theorem, which allows us to show the non-speciality of a linear system by splitting it into non-special and simpler systems. As a result we give new bounds for multiple point Seshadri constants on P^2.
Decentralized multi-dimensional alert correlation for collaborative intrusion detection
Authors: Chenfeng Vincent Zhou, Christopher Leckie, Shanika Karunasekera
Venue: Journal of Network and Computer Applications (2009)

The growth in coordinated network attacks such as scans, worms and distributed denial-of-service (DDoS) attacks is a profound threat to the security of the Internet. Collaborative intrusion detection systems (CIDSs) have the potential to detect these attacks, by enabling all the participating intrusion detection systems (IDSs) to share suspicious intelligence with each other to form a global view of the current security threats. Current correlation algorithms in CIDSs are either too simple to capture the important characteristics of attacks, or too computationally expensive to detect attacks in a timely manner. We propose a decentralized, multi-dimensional alert correlation algorithm for CIDSs to address these challenges. A multi-dimensional alert clustering algorithm is used to extract the significant intrusion patterns from raw intrusion alerts. A two-stage correlation algorithm is used, which first clusters alerts locally at each IDS, before reporting significant alert patterns to a global correlation stage. We introduce a probabilistic approach to decide when a pattern at the local stage is sufficiently significant to warrant correlation at the global stage. We then implement the proposed two-stage correlation algorithm in a fully distributed CIDS. Our experiments on a large real-world intrusion data set show that our approach can achieve a significant reduction in the number of alert messages generated by the local correlation stage with negligible false negatives compared to a centralized scheme. The proposed probabilistic threshold approach gains a significant improvement in detection accuracy in a stealthy attack scenario, compared to a naive scheme that uses the same threshold at the local and global stages. A large scale experiment on PlanetLab shows that our decentralized architecture is significantly more efficient than a centralized approach in terms of the time required to correlate alerts.
A Causality Computation Retrieval Method with Context Dependent Dynamics and Causal-Route Search Functions
Authors: Kosuke Takano, Yasushi Kiyoki
Venue: Proceeding of the 2007 conference on Information Modelling and Knowledge Bases XVIII (2007)

In this paper, we present causality computation methods and its application of a semantic associative search. We propose two essential methods for causality search, which are a causality computation method with context dependent dynamics and a causality route search method. The causality computation method with context dependent dynamics makes it possible to retrieve documents describing causal events in the context that specifies each situation of occurrent events. The causality route search method realizes to search respectively set of documents related to each generation of causal events from query events. We define three types of vector for each event data, that is, cause vector, effect vector and cause-effect vector that are characterized respectively with cause, effect and “cause and effect” event data. Applying a set of these vectors, our search method makes it possible to retrieve respectively “the document data describing cause events” and “the document data describing effect events” according to the context specified. Also, for realizing a causality route search, we construct query that represent sequential generations of causal events from a query event. Using the query constructed, we can retrieve documents about each generation of causal events. We have implemented a search system for an aerospace engineering field and clarified the effectiveness and the feasibility of our search method by several experiments.
An online blog reading system by topic clustering and personalized ranking
Authors: Xin Li, Jun Yan, Weiguo Fan, Ning Liu, Shuicheng Yan, Zheng Chen
Venue: ACM Transactions on Internet Technology (TOIT) (2009)

There is an increasing number of people reading, writing, and commenting on blogs. According to a recent survey made by Technorati, there are about 75,000 new blogs and 1.2 million new posts everyday. However, it is difficult and time consuming for a blog reader to find the most interesting posts in the huge and dynamic blog world. In this article, an online Personalized Blog Reader (PBR) system is proposed, which facilitates blog readers in browsing the coolest and newest blog posts of their interests by automatically clustering the most relevant stories. PBR aims to make a user's potential favorite topics always ranked higher than those nonfavorite ones. This is accomplished in the following steps. First, the system collects and provides a unified incremental index of posts coming from different blogs. Then, an incremental clustering algorithm with a flexible half-bounded window of observation is proposed to satisfy the requirements of online processing. It learns people's personalized reading preferences to present a user with a final reading list. The experimental results show that the proposed incremental clustering algorithm is effective and efficient, and the personalization of the PBR performs well.
GP-GPU Implementation of the "Local Rank Differences" Image Feature
Authors: Adam Herout, Radovan Josth, Pavel Zemcik, Michal Hradis
Venue: Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers (2008)

A currently popular trend in object detection and pattern recognition is usage of statistical classifiers, namely AdaBoost and its modifications. The speed performance of these classifiers largely depends on the low level image features they are using: both on the amount of information the feature provides and the processor time of its evaluation. Local Rank Differences is an image feature that is alternative to commonly used haar wavelets. It is suitable for implementation in programmable (FPGA) or specialized (ASIC) hardware, but -as this paper shows -it performs very well on graphics hardware (GPU) used in general purpose manner (GPGPU, namely CUDA in this case) as well. The paper discusses the LRD features and their properties, describes an experimental implementation of the LRD in graphics hardware using CUDA, presents its empirical performance measures compared to alter native approaches, suggests several notes on practical usage of LRD and proposes directions for future work.
Maximum reliable path under multiple failures
Authors: Shengli Yuan
Venue: Proceedings of the international conference on Computational and information science 2009 (2009)

In this work, we study the problem of maximizing path reliability in mesh networks against simultaneous failures of multiple network links. The links belong to shared risk link groups (SRLGs) that have arbitrary failure probabilities. This problem is NP-hard and we propose a heuristic algorithm for networks with large numbers of SRLGs as well as optimal solutions for networks with smaller numbers of SRLGs. The solutions are evaluated through computer simulations.
International Workshop on Applications of Semantic Web Technologies for E-Learning (SW-EL'05)
Authors: Lora Aroyo, Darina Dicheva
Venue: Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (2005)

The SW-EL'05 workshop at AIED'05 covers topics related to the use of ontologies for knowledge representation in intelligent educational systems, modularised and standardized architectures, achievement of interoperability between intelligent learning applications, sharable user models and knowledge components and support for authoring of intelligent educational systems. Two focus-sessions are included in the workshop: 1) Application of Semantic Web technologies for Adaptive Learning Systems, which focuses on personalization and adaptation in educational systems (flexible user models), ontology-based reasoning for personalising the educational Semantic Web, and on techniques and methods to capture and employ learner semantics. 2) Application of Semantic Web technologies for Educational Information Systems, which focuses on Semantic Web-based indexing/annotation of educational content (incl. individual and community based), on ontology-based information browsing and retrieval and Semantic Web/ontology based recommender systems. Papers presented in the workshop illustrate Semantic Web-based methods, techniques, and tools for building and sharing educational content, models of users, and personalisation components; services in the context of intelligent educational systems (i.e. authoring service, user modelling service, etc.) and ontology evolution, versioning and consistency. A key part of the reported results are related to empirical research on Intelligent Educational Systems presenting real-world systems and case studies and providing community and individual support by using Semantic Web-technologies and ontologies. The workshop is also a forum for presenting research performed within the context of the KALEIDOSCOPE and PROLEARN network of excellences. Other editions of the SW-EL workshop include: • SW-EL'05 at ICALT'05, Kaohsiung, Taiwan • SW-EL'05 at AIED'05, Amsterdam, The Netherlands • SW-EL'05 at K-CAP'05, Banff, Canada • SW-EL'04 at AH'04, Eindhoven, The Netherlands • SW-EL'04 at ITS'04, Maceio, Brazil • SW-EL'04 at ISWC'04, Hiroshima, Japan General workshop web site: http://www.win.tue.nl/SW-EL/index.html
On copulas and their diagonals
Authors: Piotr Jaworski
Venue: Information Sciences: an International Journal (2009)

We formulate the necessary and sufficient conditions for a function @d:[0,1]->[0,1] to be the diagonal section of a multivariate absolutely continuous copula. Moreover we provide some simple analytic formulas for copulas having given diagonal section or given distribution functions of order statistics.
Adobe Flash CS3 Professional Bible
Authors: Robert Reinhardt, Snow Dowd
Venue:  (2007)

Two of the savviest Flash experts in the industry show you how to add impact to your Web content. Robert Reinhardt and Snow Dowd know all the techniques and tricks, and they take you under the hood in this one-stop total reference guide to Flash CS3 Professional. Follow their tutorials, learn from cool examples, and discover insider secrets you won't find in any other book. Don't miss the special 16-page color insert loaded with spectacular examples. Also includes a CD-ROM.
A System that Generates Word Problems Using Problem Generation Episodes
Authors: Kazuaki Kojima, Kazuhisa Miwa
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)

In mathematical learning, it is important to give learners problems that have various features in both surface problem situations and deep mathematical structures. In this study, we implement a system that can generate various problems by altering those surface and structural features for mathematical word problems using problem generation episodes. Each problem generation episode is knowledge comprised of a base example problem and a new analogical instance, which is regarded as a past case where an analogical instance was generated from an example problem. Our system generates problems applying problem generation episodes to initial problems stored in the system. In this paper, we describe our approach for generating mathematical word problems, and perform preliminary experiments to verify whether our system can appropriately expand the variety of problems.