ppxscal/embedding_network huggingface dataset:
Understanding intrinsic characteristics and system implications of flash memory based solid state drives
Authors: Feng Chen, David A. Koufaty, Xiaodong Zhang
Venue: Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems (2009)

Flash Memory based Solid State Drive (SSD) has been called a "pivotal technology" that could revolutionize data storage systems. Since SSD shares a common interface with the traditional hard disk drive (HDD), both physically and logically, an effective integration of SSD into the storage hierarchy is very important. However, details of SSD hardware implementations tend to be hidden behind such narrow interfaces. In fact, since sophisticated algorithms are usually, of necessity, adopted in SSD controller firmware, more complex performance dynamics are to be expected in SSD than in HDD systems. Most existing literature or product specifications on SSD just provide high-level descriptions and standard performance data, such as bandwidth and latency. In order to gain insight into the unique performance characteristics of SSD, we have conducted intensive experiments and measurements on different types of state-of-the-art SSDs, from low-end to high-end products. We have observed several unexpected performance issues and uncertain behavior of SSDs, which have not been reported in the literature. For example, we found that fragmentation could seriously impact performance -- by a factor of over 14 times on a recently announced SSD. Moreover, contrary to the common belief that accesses to SSD are uncorrelated with access patterns, we found a strong correlation between performance and the randomness of data accesses, for both reads and writes. In the worst case, average latency could increase by a factor of 89 and bandwidth could drop to only 0.025MB/sec. Our study reveals several unanticipated aspects in the performance dynamics of SSD technology that must be addressed by system designers and data-intensive application users in order to effectively place it in the storage hierarchy.
Code transformation and instruction set extension
Authors: Alastair C. Murray, Richard V. Bennett, Björn Franke, Nigel Topham
Venue: ACM Transactions on Embedded Computing Systems (TECS) (2009)

The demand for flexible embedded solutions and short time-to-market has led to the development of extensible processors that allow for customization through user-defined instruction set extensions (ISEs). These are usually identified from plain C sources. In this article, we propose a combined exploration of code transformations and ISE identification. The resulting performance of such a combination has been measured on two benchmark suites. Our results demonstrate that combined code transformations and ISEs can yield average performance improvements of 49&percnt;. This outperforms ISEs when applied in isolation, and in extreme cases yields a speed-up of 2.85.
Using Neural Networks for the Foreign Investment Management Decision Support System
Authors: Sihai Guo, Shan Feng, Yong Zhao, Kaibo Zhou
Venue: Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks (2009)

How to effectively anticipate the correct future trends of the foreign investment are the most important job and critical issue for the economic development. With data warehouse and neural network, the development states of foreign investment are predicted in foreign investment management decision support system. The paper illustrates the architecture and implementation of decision support system oriented to foreign capital, moreover, the method which multidimensional data-mining based on neural network is applied in data warehouse is presented. The experimental result shows that the method can be used to analysis and decision of foreign investment information
Learning the IPA market with individual and social rewards
Authors: Eduardo Rodrigues Gomes, Ryszard Kowalczyk
Venue: Web Intelligence and Agent Systems (2009)

Market-based mechanisms offer a promising approach for distributed resource allocation. In this paper we consider the Iterative Price Adjustment, a pricing mechanism that can be used in commodity-market resource allocation systems. We address the scenario where agents use utility functions to describe preferences in the allocation and learn demand functions optimized for the market by Reinforcement Learning. In particular, we investigate reward functions based on the individual utilities of the agents and the Social Welfare of the market. We also evaluate the quality of demand functions obtained throughout the learning process with the aim of analyzing its influence on the behavior of the agents and exploring how much learning is enough, so the amount required can be reduced. This investigation shows that both reward functions deliver similar results when the market consists of only learning agents. We further investigate this behavior and present its theoretical-experimental explanation.
A hierarchical approach to encoding medical concepts for clinical notes
Authors: Yitao Zhang
Venue: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Student Research Workshop (2008)

This paper proposes a hierarchical text categorization (TC) approach to encoding free-text clinical notes with ICD-9-CM codes. Preliminary experimental result on the 2007 Computational Medicine Challenge data shows a hierarchical TC system has achieved a micro-averaged F1 value of 86.6, which is comparable to the performance of state-of-the-art flat classification systems.
Embeddings and Ramsey numbers of sparse &#x03ba;-uniform hypergraphs
Authors: Oliver Cooley, Nikolaos Fountoulakis, Daniela K&#x00fc;hn, Deryk Osthus
Venue: Combinatorica (2009)

Chv&#x00e1;tal, R&#x00f6;dl, Szemer&#x00e9;di and Trotter [3] proved that the Ramsey numbers of graphs of bounded maximum degree are linear in their order. In [6,23] the same result was proved for 3-uniform hypergraphs. Here we extend this result to &#x03ba;-uniform hypergraphs for any integer &#x03ba; &#x2265; 3. As in the 3-uniform case, the main new tool which we prove and use is an embedding lemma for &#x03ba;-uniform hypergraphs of bounded maximum degree into suitable &#x03ba;-uniform &#x2018;quasi-random&#x2019; hypergraphs.
Optimal Coalition Structure Generation In Partition Function Games
Authors: Tomasz Michalak, Andrew Dowell, Peter McBurney, Michael Wooldridge
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

The authors are grateful for financial support received from the UK EP-SRC through the project Market-Based Control of Complex Computational Systems (GR/T10657/01). The authors are also thankful to Jennifer McManus, School of English, University of Liverpool for excellent editorial assistance. In multi-agent systems (MAS), coalition formation is typically studied using characteristic function game (CFG) representations, where the performance of any coalition is independent from co-existing coalitions in the system. However, in a number of environments, there are significant externalities from coalition formation where the effectiveness of one coalition may be affected by the formation of other distinct coalitions. In such cases, coalition formation can be modeled using partition function game (PFG) representations. In PFGs, to accurately generate an optimal division of agents into coalitions (so called CSG problem), one would have to search through the entire search space of coalition structures since, in a general case, one cannot predict the values of the coalitions affected by the externalities a priori. In this paper we consider four distinct PFG settings and prove that in such environments one can bound the values of every coalition. From this insight, which bridges the gap between PFG and CFG environments, we modify the existing state-of-the-art anytime CSG algorithm for the CFG setting and show how this approach can be used to generate the optimal CS in the PFG settings.
Basic and Intermediate NX5 Modeling, Drafting, and Assemblies
Authors: Stephen Samuel, Anuranjini Pragada, Eric Weeks
Venue:  (2007)

This NX5 book, a joint effort by the DV team, compiles the tips and techniques developed by DV President Stephen Samuel over the years he has been training the engineering community. The methods outlined in this Unigraphics book go beyond an academic use of NX5 modeling; they are tricks of the trade that come from thousands of hours of actual use of the software to design some of the most difficult products in the world. The exercises in this learning manual are classroom-tested, project-oriented, and guaranteed to give you the knowledge you need to learn NX5. Make NX5 work for YOU!
Open standards and cloud computing: KDD-2009 panel report
Authors: Michael Zeller, Robert Grossman, Christoph Lingenfelder, Michael R. Berthold, Erik Marcade, Rick Pechter, Mike Hoskins, Wayne Thompson, Rich Holada
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

At KDD-2009 in Paris, a panel on open standards and cloud computing addressed emerging trends for data mining applications in science and industry. This report summarizes the answers from a distinguished group of thought leaders representing key software vendors in the data mining industry. Supporting open standards and the Predictive Model Markup Language (PMML) in particular, the panel members discuss topics regarding the adoption of prevailing standards, benefits of interoperability for business users, and the practical application of predictive models. We conclude with an assessment of emerging technology trends and the impact that cloud computing will have on applications as well as licensing models for the predictive analytics industry.
Swarming along the evolutionary branches sheds light on genome rearrangement scenarios
Authors: Nikolay Vyahhi, Adrien Goëffon, Macha Nikolski, David James Sherman
Venue: Proceedings of the 11th Annual conference on Genetic and evolutionary computation (2009)

A genome rearrangement scenario describes a series of chromosome fusion, fission, and translocation operations that suffice to rewrite one genome into another. Exact algorithmic methods for this important problem focus on providing one solution, while the set of distance-wise equivalent scenarios is very large. Moreover, no criteria for filtering for biologically plausible scenarios is currently proposed. We present an original metaheuristic method that uses Ant Colony Optimization to randomly explore the space of optimal and suboptimal rearrangement scenarios. It improves on the state of the art both by permitting large-scale enumeration of optimal scenarios, and by labeling each with metrics that can be used for post-processing filtering based on biological constraints.
Toward a perceptual space for gloss
Authors: Josh Wills, Sameer Agarwal, David Kriegman, Serge Belongie
Venue: ACM Transactions on Graphics (TOG) (2009)

We design and implement a comprehensive study of the perception of gloss. This is the largest study of its kind to date, and the first to use real material measurements. In addition, we develop a novel multi-dimensional scaling (MDS) algorithm for analyzing pairwise comparisons. The data from the psychophysics study and the MDS algorithm is used to construct a low dimensional perceptual embedding of these bidirectional reflectance distribution functions (BRDFs). The embedding is validated by correlating it with nine gloss dimensions, fitted parameters of seven analytical BRDF models, and a perceptual parameterization of Ward's model. We also introduce a novel perceptual interpolation scheme that uses the embedding to provide the user with an intuitive interface for navigating the space of gloss and constructing new materials.
Fast communication: Local aggregation function learning based on support vector machines
Authors: Jun Zhang, Lei Ye
Venue: Signal Processing (2009)

In content-based image retrieval (CBIR), feature aggregation is an approach to obtain image similarity by combining multiple feature distances. Most existing feature aggregation methods focus on heuristic-based or linear combination functions, which cannot sufficiently explore the interdependencies between features. Instead, a single aggregation function is always applied to all query images without considering the special features of each query image. In this paper, aggregation is formulated as a classification problem in a feature similarity space and solved by support vector machines (SVMs). The new method can learn an aggregation function for each query image and extend the linear aggregation to a nonlinear one using the kernel trick. Experiments demonstrate that the image retrieval performance of the proposed method is superior.
Constrained Minkowski Sums: A Geometric Framework for Solving Interval Problems in&#x00a0;Computational Biology Efficiently
Authors: Thorsten Bernholt, Friedrich Eisenbrand, Thomas Hofmeister
Venue: Discrete Computational Geometry (2009)

In this paper, we introduce the notion of a constrained Minkowski sum: for two (finite) point-sets P,Q&#x2286;&#x211d;2 and a set of k inequalities Ax&#x2265;b, it is defined as the point-set (P &#x2295; Q) Ax&#x2265;b ={x=p+q&#x2223;p&#x2208;P,q&#x2208;Q,Ax&#x2265;b}. We show that typical interval problems from computational biology can be solved by computing a set containing the vertices of the convex hull of an appropriately constrained Minkowski sum. We provide an algorithm for computing such a set with running time O(Nlog&#x2009;N), where N=|P|+|Q| if k is fixed. For the special case $(P\oplus Q)_{x_{1}\geq \beta}$where P and Q consist of points with integer x 1-coordinates whose absolute values are bounded by O(N), we even achieve a linear running time O(N). We thereby obtain a linear running time for many interval problems from the literature and improve upon the best known running times for some of them. The main advantage of the presented approach is that it provides a general framework within which a broad variety of interval problems can be modeled and solved.
Ensemble: enriching communities and collections to support education in computing
Authors: Cassel Lillian (Boots), Hislop Gregory, Bharath Nadella
Venue: ACM SIGCSE Bulletin (2009)

Ensemble is a new NSF NSDL Pathways project working to establish a distributed digital library for computing education. This project is funded by the National Science Foundation. The project includes the CSTA portal for teaching materials for teachers in schools.
Nyoro-Nyoro San: Sprits in Your Belongings
Authors: Kazushi Mukaiyama
Venue: Proceedings of the 10th International Symposium on Smart Graphics (2009)

This is an interactive art work using with a table-top interface. "Nyoro-Nyoro" are sprits living in your belongings like a wallet, cell-phone and so on. They usually hide in your belongings. But, when you put your belongings on the magic table which I made, they will show you themselves like snails. But please carefully remember that they're so shy and they will hide quickly if you try to capture them.
On the power of the basic algorithmic design paradigms
Authors: Russell Impagliazzo
Venue:  (2008)

This dissertation formalizes the intuitive notion of the basic algorithmic paradigms. We present three formal models which aim to capture the intrinsic power of greedy, backtracking and dynamic programming algorithms. We develop lower bound techniques for proving negative results for all algorithms in all models, which allow us to make strong statements about the limitations of each paradigm. Borodin et al. (2003) designed the Priority algorithms, a formal model of greedy algorithms for scheduling problems. We generalized the priority model to arbitrary problem domain and in particular graph problems and develop a lower bound technique for proving negative results for the class of all priority algorithms. We use the lower bound technique to show that finding shortest path in graphs with negative weights cannot be solved by a priority algorithm. We also prove that Dijkstra's algorithm is inherently adaptive and cannot be made non-adaptive. We show inapproximability results within the model for minimum weighted vertex cover, minimum metric Steiner tree, and maximum independent set problems. We develop a new 1.8-approximation scheme for the Steiner(1,2) problem. Alekhnovich et al. (2005) presented a model of backtracking and dynamic programming algorithms called prioritized Branching Trees (pBT). We generalize their model to allow free branching and call this new model prioritized Free Branching Tree (pFBT) algorithms and developed a lower bound technique for proving negative results for randomized priority algorithms, pBT and pFBT algorithms. We use the technique to prove that pBT algorithms require exponential width to solve the 7-SAT problem and that pFBT algorithms require width 2W&parl0;n&parr0; to solve the 7-SAT problem. Bellman-Ford is a classical dynamic programming algorithm and we show that pBT algorithms require width of 2W&parl0;n1/9&parr0; to solve the shortest path problem in graphs with negative weights exactly. Next we develop a stronger model of dynamic programming algorithms called prioritized Branching Programs (pBP). pBP algorithms can simulate pBT algorithms at no additional cost but also capture the notion of memoization which we believe is an essential part of the dynamic programming paradigm. We show that this class of algorithms can solve the shortest paths in graphs with negative weights but no negative cycles efficiently. We also show that two pBP sub-models can be simulated by pBT algorithms.
Improving the comprehension of legal documentation: the case of patent claims
Authors: Nadjet Bouayad-Agha, Gerard Casamayor, Gabriela Ferraro, Simon Mille, Vanesa Vidal, Leo Wanner
Venue: Proceedings of the 12th International Conference on Artificial Intelligence and Law (2009)

With their abstract vocabulary and overly long sentences, patent claims, like several other genres of legal discourse, are notoriously difficult to read and comprehend. The enormous number of both native and non-native users reading patent claims on a daily basis raises the demand for means that make them easier and faster to understand. An obvious way to satisfy this demand is to paraphrase the original material, i.e., to rewrite it in a more appropriate style, or---even better---to summarize it in the language of preference of the reader such that the reader can rapidly grasp its essence. PATExpert is a patent processing service which incorporates, among other technologies, paraphrasing and multilingual summarization of patent claims. With the goal to offer the user the most suitable options and to evaluate alternative techniques that are based on different contextual and linguistic criteria, both paraphrasing and summarization implement "surface-oriented" strategies and "deep" strategies. The surface strategies make use of shallow linguistic criteria such as punctuation and syntactic and lexical markers. The deep strategies operate on deep-syntactic structures of the claims, using a full fledged text generator for synthesis of the paraphrase or summary, respectively.
Classifier quality definition on the basis of the estimation calculation approach
Authors: Vitaliy Tayanov, Oleksiy Lutsyk
Venue: Proceedings of the 10th WSEAS international conference on evolutionary computing (2009)

The generalized problem of classifiers training, using training sets has been considered. The accents on the principle moments, reasoning the classifiers overtraining, the ways of impact estimation of these effects degree and the methods of their control have been made. The approach, giving the possibility to estimate the effect of training set reduction on the basis of probabilistically combinatorial method has been considered. The training set reduction effect estimation gives the possibility to establish the redundancy of the training data and define the relative of the negative training objects such as spikes and spurious objects.
Automatic Database Creation and Object's Model Learning
Authors: Nguyen Dang Binh, Thuy Thi Nguyen
Venue: Knowledge Acquisition: Approaches, Algorithms and Applications: Pacific Rim Knowledge Acquisition Workshop, PKAW 2008, Hanoi, Vietnam, December 15-16, 2008, Revised Selected Papers (2009)

This paper proposes a new framework to automatically generate visual object database meanwhile efficiently learn the object's model. The system is of important need for the problems of object detection and recognition. Our main idea is to acquire the huge amount of video data actively, and seeks out opportunities to autonomously exploit information from object samples. We employ autonomous learning approach based on online boosting technique, which allows to combine an object detector trained on a single initialized input image with tracking to extract object samples for learning. The autonomous learning process with interactive learning strategy allows to adaptively improve the learning object model while generating informative samples. Our method allows to generate thousands of object samples within hours from large video databases or from live camera, thus saving time and labor's efforts. We will show that the proposed method can extracts well-localized, diverse appearances of object examples from video sequence through only one initialized input sample, and builds robust object model. In addition to requiring very little human intervention, a significant benefit of this method is that it does not require pre-training. In the experiments, the approach is evaluated in detail for creating data sets and learning for the problems of human hand gesture recognition and face detection. In addition, to show the generality, results for different objects are also presented.
Maintaining reference graphs of globally accessible objects in fully decentralized distributed systems
Authors: Björn Saballus, Thomas Fuhrmann
Venue: Proceedings of the 18th ACM international symposium on High performance distributed computing (2009)

Since the advent of electronic computing, the processors' clock speed has risen tremendously. Now that energy efficiency requirements have stopped that trend, the number of processing cores per machine started to rise. In near future, these cores will become more specialized, and their inter-connections will form complex networks, both on-chip and beyond. This trend opens new fields of applications for high performance computing: Heterogeneous architectures offer different functionalities and thus support a wider range of applications. The increased compute power of these systems allows more complex simulations and numerical computations. Falling costs enable even small companies to invest in multi-core systems and clusters. However, the growing complexity might impede this growth. Imagine a cluster of thousands of interconnected heterogeneous processor cores. A software developer will need a deep knowledge about the underlying infrastructure as well as the data and communication dependencies in her application to partition it optimally across the available cores. Moreover, a predetermined partitioning scheme cannot reflect failing processors or additionally provided resources. In our poster, we introduce J-Cell, a project that aims at simplifying high performance distributed computing. J-Cell offers a single system image, which allows applications to run transparently on heterogeneous multi-core machines. It distributes code, objects and threads onto the compute resources which may be added or removed at run-time. This dynamic property leads to an ad-hoc network of processors and cores. In this network, a fully decentralized object localization and retrieval algorithm guarantees the access to distributed shared objects.