ppxscal/embedding_network huggingface dataset:
Modeling additive structure and detecting interactions with groves of trees
Authors: Richard Caruana
Venue:  (2008)

Discovery of additive structure is an important step towards understanding a complex multi-dimensional function, because it allows for expressing this function as the sum of lower-dimensional or otherwise simpler components. Modeling additive structure also opens up opportunities for learning better regression models. The term statistical interaction is used to describe the presence of non-additive effects among two or more variables in a function. When variables interact, their effects must be modeled and interpreted simultaneously. Thus, detecting statistical interactions can be critical for an understanding of processes by domain researchers. This dissertation analyzes benefits of modelling additive structure for prediction and interaction detection problems. It describes a new learning algorithm called Groves, which is an ensemble of additive regression trees. Groves is based on such existing techniques as bagging and additive models; their combination allows us to use large trees in the ensemble and at the same time model additive structure of the response function. Regression version of the algorithm, Additive Groves, and its classification counterpart, Gradient Groves, yield consistently high performance across a variety of problems, outperforming on average a large number of other algorithms. Additive nature of Groves makes it particularly useful for interaction detection. This dissertation introduces a new approach to interaction detection: it is based on comparing the performance of restricted and unrestricted predictive models. Groves of trees allow variable interactions to be carefully controlled and therefore are especially useful for this framework. The details of proposed practical approach to interaction detection analysis are demonstrated on real data describing the abundance of different species of birds in the prairies east of the southern Rocky Mountains.
Adaptive authentication system for behavior biometrics using supervised pareto self organizing maps
Authors: Masanori Nakakuni, Hiroshi Dozono, Shinsuke Itou
Venue: Proceedings of the 10th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems (2008)

The biometrics authentication systems take attentions to cover the weakness of password authentication system. In this paper, we focus attention on the multi modal-biometrics of behavior characteristics. For the integration of multi modal biometrics Supervised Pareto learning SOM(SP-SOM) and its incremental learning method for implementing adaptive authentication system are proposed.
Approximation of Signals by Predict Wavelet Transform
Authors: Marcin Jaromin
Venue: Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers (2008)

The article presents a general outline of the signal theory using predict wavelet transform. The predict wavelet transform (1) is a new attitude to a multiresolution signal analysis by discrete wavelet transform [8]. It is implemented in accordance with a lifting scheme, which allows realizing a signal filtration by biorthogonal wavelets [10,9]. Thanks to it, there exists a possibility to receive an optimal, biorthogonal filter for analysis of chosen signal characteristic. The article describes the signal approximation method by lifting scheme (9). This method is a generalization of the predict interpolation proposed by Wim Sweldens [6,10]. It allows to predict odd signal samples using a polynomial degree much lower than an interpolation polynomial degree. This solution not only enables approximating by algebraic polynomial but also by some base functions. Thus, this method is more flexible and optimal. Using an orthogonal Gram's polynomial (23) or a trigonometric polynomial for approximation eliminates a problem of ill conditioning of matrix coefficients (19). Such conditioning can cause large rounding errors during performing computer operation, so it leads to incorrect results. The article enables to get acquainted with the relation between classical and predict wavelet transform (6). The method of obtaining a biorthogonal highpass filter and lowpass filter (analysis and synthesis signal) is shown on the base of a second rank predictor example. The corresponding filters are received by summation of flow-ways between Predict and Update coefficients. The calculated filter coefficients are described by wavelet and scaling functions in a graphical form (7, 8). The summary chapter presents the use of lifting scheme during multiresolution image analysis (9) and irregular meshes analysis 3D (10). The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
A multivariate IFR notion based on the multivariate dispersive ordering
Authors: José Pablo Arias-Nicolás, Félix Belzunce, Olga Núñez-Barrera, Alfonso Suárez-Llorens
Venue: Applied Stochastic Models in Business and Industry (2009)

In this paper we present a definition of multivariate increasing failure rate based on the concept of multivariate dispersion. This new definition is an extension of the univariate characterization of increasing failure rate distributions under dispersive ordering of the residual lives. We study this definition in the Clayton&ndash;Oakes model and the family of generalized order statistics. Copyright &copy; 2009 John Wiley & Sons, Ltd.
An Assertion Mechanism for Software Unit Testing to Remain Unaffected by Program ModificationThe Mechanism to Eliminate Dependency from/to Unnecessary Object
Authors: Masanori Sakakibara, Kohei Sakurai, Seiichi Komiya
Venue: Proceeding of the 2008 conference on Knowledge-Based Software Engineering: Proceedings of the Eighth Joint Conference on Knowledge-Based Software Engineering (2008)

Assertions are used for unit testing in Object-Oriented Programming. With assertions, a test programmer compares with an expected value the result of a test execution which is obtained by referring to the object containing its result in the object being tested. Therefore, there is a problem that test classes described as testing code depend strongly on the unnecessary objects which are not of concern to unit testing. In this work we developed an assertion mechanism which traverses objects automatically in order to eliminate the dependency from/to such unnecessary objects. Furthermore, we performed experiments on open source products, and confirmed that using this assertion mechanism decreases the coupling between a test class and other classes.
A partly deterministic and partly stochastic scheme for the identification of fractional-order processes
Authors: Deepyaman Maiti, Mithun Chakraborty, Ayan Acharya, Amit Konar
Venue: International Journal of Advanced Intelligence Paradigms (2009)

This contribution proposes a simple yet elegant scheme for identifying a fractional-order dynamic system based on its observed response to a standard excitation. If the fractional powers in the transfer function are precisely known, a set of simultaneous linear equations connecting the unknown coefficient values is obtained and then solved deterministically to yield the desired estimates. In case the fractional powers are uncertain, these are estimated by formulating an optimisation problem and employing a stochastic search algorithm. Results show that the proposed method offers a high degree of accuracy even for data that are intentionally corrupted to simulate real-life conditions.
On a measurement-free quantum lambda calculus with classical control
Authors: Ugo dal Lago, Andrea Masini, Margherita Zorzi
Venue: Mathematical Structures in Computer Science (2009)

We study a measurement-free, untyped λ-calculus with quantum data and classical control. This work arises from previous proposals by Selinger and Valiron, and Van Tonder. We focus on operational and expressiveness issues, rather than (denotational) semantics. We prove subject reduction and confluence, and a standardisation theorem. Moreover, we prove the computational equivalence of the proposed calculus with a suitable class of quantum circuit families.
Exact Geodesics and Shortest Paths on Polyhedral Surfaces
Authors: Mukund Balasubramanian, Jonathan R. Polimeni, Eric L. Schwartz
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2009)

We present two algorithms for computing distances along convex and non-convex polyhedral surfaces. The first algorithm computes exact minimal-geodesic distances and the second algorithm combines these distances to compute exact shortest-path distances along the surface. Both algorithms have been extended to compute the exact minimal-geodesic paths and shortest paths. These algorithms have been implemented and validated on surfaces for which the correct solutions are known, in order to verify the accuracy and to measure the run-time performance, which is cubic or less for each algorithm. The exact-distance computations carried out by these algorithms are feasible for large-scale surfaces containing tens of thousands of vertices, and are a necessary component of near-isometric surface flattening methods that accurately transform curved manifolds into flat representations.
Antecedents of IS Strategic Alignment: A Nomological Network
Authors: David S. Preston, Elena Karahanna
Venue: Information Systems Research (2009)

Alignment of information systems (IS) strategy with business strategy is a top concern of both the chief information officer (CIO) and the top management team (TMT) of organizations. Even though researchers and key decision makers in organizations recognize the importance of IS strategic alignment, they often struggle to understand how this alignment is created. In this paper, we develop a nomological network in which shared understanding between the CIO and TMT about the role of IS in the organization (which represents the social dimension of IS strategic alignment) is posited to be a proximal antecedent of the intellectual dimension of IS strategic alignment. We further posit that shared language, shared domain knowledge manifest in the CIO's business knowledge and the TMT's strategic IS knowledge, systems of knowing (structural and social), and CIO-TMT experiential similarity are important determinants of this shared understanding. Data were collected from 243 matched CIO-TMT pairs. Results largely support the proposed nomological network. Specifically, shared understanding between the CIO and TMT is a significant antecedent of IS strategic alignment. Furthermore, shared language, shared domain knowledge, and structural systems of knowing influence the development of shared understanding between the CIO and the TMT. Contrary to expectations and to findings of prior research, social systems of knowing, representing informal social interactions between the CIO and TMT, and experiential similarity did not have a significant effect on shared understanding.
Design of A 100MHz 64-point FFT processor in 0.35µm standard CMOS technology
Authors: Hojat Mojarad, Hassan Hajghassem
Venue: Proceedings of the 8th WSEAS international conference on Microelectronics, nanoelectronics, optoelectronics (2009)

Applications such as digital spectrum analyzers, digital filtering, image processing, and video transmission need to compute the Discrete Fourier Transform (DFT). A Chip architecture to compute a 64- point DFT using radix-4 algorithm every 18.87 µs at 100MHz clock rate is designed. This processor incorporates static memory, controller, and combinational operating unit (COU). Input data and output data are 10-bit and 54-bit words, respectively. A 10-bit × 27-bit multiplier is used inside the processor. Two's complement is used to present negative data. This processor is implemented in 0.35µm tsmc standard CMOS process.
Preserving the Fault-Containment of Ring Protocols Executed on Trees
Authors: Yukiko Yamauchi, Toshimitsu Masuzawa, Doina Bein
Venue: The Computer Journal (2009)

Reliable and fault-tolerant distributed systems have been attracting more and more attention (see Autonomic Computing Project by IBM, http://www-03.ibm.com/autonomic/). A self-stabilizing protocol is a fault-tolerant protocol that guarantees autonomous recovery from any number of and any type of faults that can affect the data stored locally at some process(es). If the impact of the faults can be contained to the affected process(es) and some of its immediate neighbors, then the protocol is also fault-containing. We present a new method, called causal simulation, which preserves the fault-containing property of ring protocols executed on trees.
An initialization method for the K-Means algorithm using neighborhood model
Authors: Fuyuan Cao, Jiye Liang, Guang Jiang
Venue: Computers Mathematics with Applications (2009)

As a simple clustering method, the traditional K-Means algorithm has been widely discussed and applied in pattern recognition and machine learning. However, the K-Means algorithm could not guarantee unique clustering result because initial cluster centers are chosen randomly. In this paper, the cohesion degree of the neighborhood of an object and the coupling degree between neighborhoods of objects are defined based on the neighborhood-based rough set model. Furthermore, a new initialization method is proposed, and the corresponding time complexity is analyzed as well. We study the influence of the three norms on clustering, and compare the clustering results of the K-means with the three different initialization methods. The experimental results illustrate the effectiveness of the proposed method.
A Classifier-based approach to identify genetic similarities between diseases
Authors: Marc A. Schaub, Irene M. Kaplow, Marina Sirota, Chuong B. Do, Atul J. Butte, Serafim Batzoglou
Venue: Bioinformatics (2009)

Motivation: Genome-wide association studies are commonly used to identify possible associations between genetic variations and diseases. These studies mainly focus on identifying individual single nucleotide polymorphisms (SNPs) potentially linked with one disease of interest. In this work, we introduce a novel methodology that identifies similarities between diseases using information from a large number of SNPs. We separate the diseases for which we have individual genotype data into one reference disease and several query diseases. We train a classifier that distinguishes between individuals that have the reference disease and a set of control individuals. This classifier is then used to classify the individuals that have the query diseases. We can then rank query diseases according to the average classification of the individuals in each disease set, and identify which of the query diseases are more similar to the reference disease. We repeat these classification and comparison steps so that each disease is used once as reference disease. Results: We apply this approach using a decision tree classifier to the genotype data of seven common diseases and two shared control sets provided by the Wellcome Trust Case Control Consortium. We show that this approach identifies the known genetic similarity between type 1 diabetes and rheumatoid arthritis, and identifies a new putative similarity between bipolar disease and hypertension. Contact: serafim@cs.stanford.edu
Coreference-inspired coherence modeling
Authors: Micha Elsner, Eugene Charniak
Venue: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers (2008)

Research on coreference resolution and summarization has modeled the way entities are realized as concrete phrases in discourse. In particular there exist models of the noun phrase syntax used for discourse-new versus discourse-old referents, and models describing the likely distance between a pronoun and its antecedent. However, models of discourse coherence, as applied to information ordering tasks, have ignored these kinds of information. We apply a discourse-new classifier and pronoun coreference algorithm to the information ordering task, and show significant improvements in performance over the entity grid, a popular model of local coherence.
A general computational method for robustness analysis with applications to synthetic gene networks
Authors: Aurélien Rizk, Gregory Batt, François Fages, Sylvain Soliman
Venue: Bioinformatics (2009)

Motivation: Robustness is the capacity of a system to maintain a function in the face of perturbations. It is essential for the correct functioning of natural and engineered biological systems. Robustness is generally defined in an ad hoc, problem-dependent manner, thus hampering the fruitful development of a theory of biological robustness, recently advocated by Kitano. Results: In this article, we propose a general definition of robustness that applies to any biological function expressible in temporal logic LTL (linear temporal logic), and to broad model classes and perturbation types. Moreover, we propose a computational approach and an implementation in BIOCHAM 2.8 for the automated estimation of the robustness of a given behavior with respect to a given set of perturbations. The applicability and biological relevance of our approach is demonstrated by testing and improving the robustness of the timed behavior of a synthetic transcriptional cascade that could be used as a biological timer for synthetic biology applications. Availability: Version 2.8 of BIOCHAM and the transcriptional cascade model are available at http://contraintes.inria.fr/BIOCHAM/ Contact: gregory.batt@inria.fr
Coupling Analysis of Manufacturing Characteristics and Mechanics Property of Microminiature Gear Mechanism Based on Neural Network
Authors: Xin Jin, Zhijing Zhang, Fuchang Zuo, Zhongxin Li
Venue: Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks (2009)

A coupling analysis method of manufacturing characteristics and mechanics property of microminiature gear mechanism based on BP neural network was proposed. By use of the existing finite element model with manufacturing characteristics, output data as BP neural network training set of samples was obtained. Through a comparative study of the effects of different network parameters settings on the precision of network model, the optimal network structure and parameters were determined and the neural network model which can approximate the mechanics property microminiature gear mechanism with high precision. This shows the nonlinear coupling relationships between input manufacturing characteristics and the output mechanical characteristics, and verifies the accuracy of the model.
Anchor garden: an interactive workbenchfor basic data concept learningin object oriented programming languages
Authors: Motoki Miura, Taro Sugihara, Susumu Kunifuji
Venue: ACM SIGCSE Bulletin (2009)

We propose Anchor Garden (AG), an interactive workbench software for learning fundamentals of data structures with the concepts of type, variable, object, and their relations in a strongly typed object-oriented programming language (OOPL) such as Java or C#. Learners can approach the basic data-handling concepts of OOPLs by direct manipulation of graphical models in AG. In addition, the learner can correlate his/her manipulation and notation of source code because AG automatically generates appropriate source-code corresponding to this manipulation. Experimental results showed a tendency of a learning effect with AG and high correlations between concept understanding and programming ability. Thus, AG has the potential to enhance the programming ability of novice programmers.
Grammatical inference as a principal component analysis problem
Authors: Raphaël Bailly, François Denis, Liva Ralaivola
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

One of the main problems in probabilistic grammatical inference consists in inferring a stochastic language, i.e. a probability distribution, in some class of probabilistic models, from a sample of strings independently drawn according to a fixed unknown target distribution p. Here, we consider the class of rational stochastic languages composed of stochastic languages that can be computed by multiplicity automata, which can be viewed as a generalization of probabilistic automata. Rational stochastic languages p have a useful algebraic characterization: all the mappings up: v &rarr; p(uv) lie in a finite dimensional vector subspace Vp* of the vector space &#x211D; &lang;&lang;&Sigma;&rang;&rang; composed of all real-valued functions defined over &Sigma;*. Hence, a first step in the grammatical inference process can consist in identifying the subspace Vp*. In this paper, we study the possibility of using Principal Component Analysis to achieve this task. We provide an inference algorithm which computes an estimate of this space and then build a multiplicity automaton which computes an estimate of the target distribution. We prove some theoretical properties of this algorithm and we provide results from numerical simulations that confirm the relevance of our approach.
Finding a team of experts in social networks
Authors: Theodoros Lappas, Kun Liu, Evimaria Terzi
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

Given a task T, a pool of individuals X with different skills, and a social network G that captures the compatibility among these individuals, we study the problem of finding X, a subset of X, to perform the task. We call this the TEAM FORMATION problem. We require that members of X' not only meet the skill requirements of the task, but can also work effectively together as a team. We measure effectiveness using the communication cost incurred by the subgraph in G that only involves X'. We study two variants of the problem for two different communication-cost functions, and show that both variants are NP-hard. We explore their connections with existing combinatorial problems and give novel algorithms for their solution. To the best of our knowledge, this is the first work to consider the TEAM FORMATION problem in the presence of a social network of individuals. Experiments on the DBLP dataset show that our framework works well in practice and gives useful and intuitive results.
Fabrication of bio-microelectrodes for deep-brain stimulation using microfabrication and electroplating process
Authors: Ge Qin, Yaxiong Liu, Hongzhong Liu, Yucheng Ding, Xiaping Qi, Rukun Du
Venue: Microsystem Technologies (2009)

This paper presents a novel bio-microelectrode structure for deep-brain stimulation in neural disorder treatment and a method for connecting PtIr10 microwires onto this microelectrode. The microelectrode is consisted of four stimulation sites fabricated by microfabrication process and two recording sites formed by encapsulation technique. After Si-based microelectrode is fabricated through microfabrication process, electroplating is used to connect microwires to the microelectrode through a Pt film electroplated on the microwires that is fixed in the grooves of the microelectrode. Scanning electron microscopy revealed that the microwire was connected tightly to the microelectrode by electroplating, and the tensile strength of the microwire connection reached up to 0.375&#x00a0;MPa when the thickness of the Pt film was 3&#x00a0;&#x03bc;m and above. Cyclic voltammograms showed that the microelectrode and the electroplated connection had fine electrochemical stability. The results indicated that the electroplating could be used as an effective method for connecting PtIr10 microwires with Pt film microelectrode in deep-brain stimulation.