ppxscal/embedding_network huggingface dataset:
Pipelined successive approximation conversion (PSAC) with error correction for a CMOS ophthalmic sensor
Authors: Frank Sill, Davies W. de Lima Monteiro
Venue: Proceedings of the 22nd Annual Symposium on Integrated Circuits and System Design: Chip on the Dunes (2009)

The purpose of this work is the proposal of a 10-Bit / 1 MSPS Analog to Digital Converter (ADC) with error correction to match the requirements of a CMOS wavefront sensor for ophthalmic applications. The developed ADC is a combination of different techniques for integrated converters. It unifies the advantages of successive approximation converters and pipelined converters. The result of this combination is a very good tradeoff between performance and power dissipation. In a second step, the problem of parameter variations is examined. Therefore, the developed ADC is enhanced by two error correction techniques to improve its reliability. Transistor level simulations indicate that in an environment with varying design parameters the maximum (Differential Non-Linearity) DNL error could be reduced from more than 130 VLSB to 0.7 VLSB. The ADC selection assessment and error correction presented herein can be adopted for other sensors within other signal domains as well.
Identifying financial time series with similar dynamic conditional correlation
Authors: Edoardo Otranto
Venue: Computational Statistics Data Analysis (2010)

One of the main problems in modelling multivariate conditional covariance time series is the parameterization of the correlation structure. If no constraints are imposed, it implies a large number of unknown coefficients. The most popular models propose parsimonious representations, imposing similar correlation structures to all the series or to groups of time series, but the choice of these groups is quite subjective. A statistical approach is proposed to detect groups of homogeneous time series in terms of correlation dynamics for one of the widely used models: the Dynamic Conditional Correlation model. The approach is based on a clustering algorithm, which uses the idea of distance between dynamic conditional correlations, and the classical Wald test, to compare the coefficients of two groups of dynamic conditional correlations. The proposed approach is evaluated in terms of simulation experiments and applied to a set of financial time series.
A Theory of Kernel Extreme Energy Difference for Feature Extraction of EEG Signals
Authors: Shiliang Sun, Jinbo Li
Venue: Proceedings of the 5th International Conference on Advanced Data Mining and Applications (2009)

Energy features are of great importance for electroencephalogram (EEG) signal classification in the application of brain-computere interfaces (BCI). In this paper, we address the problem of extracting energy features of EEG signals in a feature space induced by kernel functions. The devised method, based on a recently proposed technique extreme energy difference (EED), is called kernel extreme energy difference (KEED). This paper derives solutions which optimize the KEED criterion by the method of Lagrange multipliers.
Cyberpower and National Security, 1st edition
Authors: Franklin D. Kramer, Stuart H. Starr, Larry Wentz
Venue:  (2009)

The cyber domain is undergoing extraordinary changes that present both exceptional opportunities to and major challenges for users of cyberspace. The challenges arise from the malevolent actors who use cyberspace and the many security vulnerabilities that plague this sphere. Exploiting opportunities and overcoming challenges will require a balanced body of knowledge on the cyber domain. Cyberpower and National Security assembles a group of experts and discusses pertinent issues in five areas.The first section provides a broad foundation and overview of the subject by identifying key policy issues, establishing a common vocabulary, and proposing an initial version of a theory of cyberpower. The second section identifies and explores possible changes in cyberspace over the next fifteen years by assessing cyber infrastructure and security challenges. The third section analyzes the potential impact of changes in cyberspace on the military and informational levers of power. The fourth section addresses the extent to which changes in cyberspace serve to empower key entities such as transnational criminals, terrorists, and nation-states. The final section examines key institutional factors, which include issues concerning governance, legal dimensions, critical infrastructure protection, and organization.Cyberpower and National Security frames the key issues concerned and identifies the important questions involved in building the human capacity to address cyber issues, balancing civil liberties with national security considerations, and developing the international partnerships needed to address cyber challenges. With more than two dozen contributors, Cyberpower and National Security covers it all.
Counting CSP solutions using generalized XOR constraints
Authors: Carla P. Gomes, Willem-Jan Van Hoeve, Ashish Sabharwal, Bart Selman
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 1 (2007)

We present a general framework for determining the number of solutions of constraint satisfaction problems (CSPs) with a high precision. Our first strategy uses additional binary variables for the CSP, and applies an XOR or parity constraint based method introduced previously for Boolean satisfiability (SAT) problems. In the CSP framework, in addition to the naive individual filtering of XOR constraints used in SAT, we are able to apply a global domain filtering algorithm by viewing these constraints as a collection of linear equalities over the field of two elements. Our most promising strategy extends this approach further to larger domains, and applies the so-called generalized XOR constraints directly to CSP variables. This allows us to reap the benefits of the compact and structured representation that CSPs offer. We demonstrate the effectiveness of our counting framework through experimental comparisons with the solution enumeration approach (which, we believe, is the current best generic solution counting method for CSPs), and with solution counting in the context of SAT and integer programming.
Maximum weight bipartite matching in matrix multiplication time
Authors: Piotr Sankowski
Venue: Theoretical Computer Science (2009)

In this paper we consider the problem of finding maximum weight matchings in bipartite graphs with nonnegative integer weights. The presented algorithm for this problem works in O@?(Wn^@w) time, where @w is the matrix multiplication exponent, and W is the highest edge weight in the graph. As a consequence of this result we obtain O@?(Wn^@w) time algorithms for computing: minimum weight bipartite vertex cover, single source shortest paths and minimum weight vertex disjoint s-t paths. All of the presented algorithms are randomized and with small probability can return suboptimal solutions.
A Design of Segmented Data Rates for Recurrent Channel Estimation
Authors: Yuli Yang, Meng Ma, Bingli Jiao, William C. Lee
Venue: Wireless Personal Communications: An International Journal (2009)

In a conventional pilot-symbol assisted modulation design, the signals of a short period are composed of the pilot tones and an information symbol block. For improving the accuracy of the channel estimate, some recurrent methods use the detected information symbols as the additional pilots. Working on an orthogonal frequency division multiplexing system, we divide the information symbol block into several sub-blocks and assign them with different data rates. Then the recurrent channel estimations can exploit the potential strength of the new approach, and the averaged data rate can be increased with the minimum mean-square error channel estimator. The simulation results confirm the merit of the proposed approach.
Delay-distribution-dependent stability of stochastic discrete-time neural networks with randomly mixed time-varying delays
Authors: Yang Tang, Jian-an Fang, Min Xia, Dongmei Yu
Venue: Neurocomputing (2009)

In this paper, the stability analysis problem for a new class of discrete-time neural networks with randomly discrete and distributed time-varying delays has been investigated. Compared with the previous work, the distributed delay is assumed to be time-varying. Moreover, the effects of both variation range and probability distribution of mixed time-delays are taken into consideration in the proposed approach. The distributed time-varying delays and coupling term in complex networks are considered by introducing two Bernoulli stochastic variables. By using some novel analysis techniques and Lyapunov-Krasovskii function, some delay-distribution-dependent conditions are derived to ensure that the discrete-time complex network with randomly coupling term and distributed time-varying delay is synchronized in mean square. A numerical example is provided to demonstrate the effectiveness and the applicability of the proposed method.
A tempora mereology for distinguishing between integral objects and portions of stuff
Authors: Thomas Bittner, Maureen Donnelly
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 1 (2007)

We develop a formal theory of mereology that includes relations that change over time. We show how this theory formalizes reasoning over domains of material objects, which include not only integral objects (my computer, your liver) but also portions of stuff (the water in your glass, the blood in a vial). In particular, we use different mereological summation relations to distinguish between the ways in which i) integral objects, ii) portions of unstructured, homogenous stuffs (e.g. the water in your glass), and iii) mixtures (the blood in a vial) are linked to their parts over time.
Classification of symbolic objects: A lazy learning approach
Authors: Annalisa Appice, Claudia D'Amato, Floriana Esposito, Donato Malerba
Venue: Intelligent Data Analysis (2006)

Symbolic data analysis aims at generalizing some standard statistical data mining methods, such as those developed for classification tasks, to the case of symbolic objects (SOs). These objects synthesize information concerning a group of individuals of a population, eventually stored in a relational database, and ensure confidentiality of original data. Classifying SOs is an important task in symbolic data analysis. In this paper a lazy-learning approach that extends a traditional distance weighted k-Nearest Neighbor classification algorithm to SOs, is presented. The proposed method has been implemented in the system SO-NN (Symbolic Objects Nearest Neighbor) and evaluated on symbolic datasets.
Neural discrimination of nonprototypical chords in music experts and laymen: An meg study
Authors: Elvira Brattico, Karen Johanne Pallesen, Olga Varyagina, Christopher Bailey, Irina Anourova, Miika Järvenpää, Tuomas Eerola, Mari Tervaniemi
Venue: Journal of Cognitive Neuroscience (2009)

At the level of the auditory cortex, musicians discriminate pitch changes more accurately than nonmusicians. However, it is not agreed upon how sound familiarity and musical expertise interact in the formation of pitch-change discrimination skills, that is, whether musicians possess musical pitch discrimination abilities that are generally more accurate than in nonmusicians or, alternatively, whether they may be distinguished from nonmusicians particularly with respect to the discrimination of nonprototypical sounds that do not play a reference role in Western tonal music. To resolve this, we used magnetoencephalography (MEG) to measure the change-related magnetic mismatch response (MMNm) in musicians and nonmusicians to two nonprototypical chords, a "dissonant" chord containing a highly unpleasant interval and a "mistuned" chord including a mistuned pitch, and a minor chord, all inserted in a context of major chords. Major and minor are the most frequently used chords in Western tonal music which both musicians and nonmusicians are most familiar with, whereas the other chords are more rarely encountered in tonal music. The MMNm was stronger in musicians than in nonmusicians in response to the dissonant and mistuned chords, whereas no group difference was found in the MMNm strength to minor chords. Correspondingly, the length of musical training correlated with the MMNm strength for the dissonant and mistuned chords only. Our findings provide evidence for superior automatic discrimination of nonprototypical chords in musicians. Most likely, this results from a highly sophisticated auditory system in musicians allowing a more efficient discrimination of chords deviating from the conventional categories of tonal music.
On the Energy Consumption of Fast Convergecast in Wireless Networks
Authors: Gruia Calinescu
Venue: Proceedings of the 2009 Third International Conference on Sensor Technologies and Applications (2009)

Kesselman and Kowalski (JPDC '06) have proposed a simple distributed randomized algorithm for fast convergecast in wireless networks.The Kesselman-Kowalski algorithm works in a synchronized mode under the assumptions that all wireless nodes have variable transmission range, each can learn the distance to the closest active neighbor at any time, and all have a special collision detection (CD) capability so that a transmitting node can detect a collision within its transmission range.They prove that their algorithm finishes in O(log n) expected timeslots and that, assuming the nodes are located in a Euclidean plane, the total energy used is O(n log n) times the minimum energy of a (not necessarily fast) convergecast. They also provide instances where an Omega(n/log n) gap exists between the energy of any fast (that is, with O(\log n) timeslots) convergecast, and the minimum energy convergecast. We prove that, when compared to the most energy efficient fast convergecast, the Kesselman-Kowalski algorithm uses O(log^2 n) times the optimum energy.With the additional assumption that n, or a good estimate of it, is known, we modify the Kesselman-Kowalski algorithm such that it also finishes in O(log n) expected timeslots, and uses O(n) times the energy of a minimum energy convergecast.We also provide a centralized algorithm which produces a fast convergecast using n/log n times the minimum energy convergecast.All the above bounds (including those from Kesselman-Kowalski)assumethat the energy spent for sending a packet from a node to another is proportional to the square of the Euclidean distance between the two nodes.
The Research on Chinese Taxation Policies Based on E-commerce Model Classification
Authors: Yiming Xiang, Yun Ling
Venue: Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 01 (2009)

The formulation of a country’s e-commerce taxation policies needs the combination of this country’s developing status quo of e-commerce and its international economy status. By comparing with the e-commerce taxation policies of US, EU and India, analyzing the similarities and differences of various countries' policies as well as the reasons, this paper suggests China should impose the taxation to the e-commerce. According to the thought of classification, analyzing the characteristics of different models of e-commerce, based on comparing the proportion of trading amount of Chinese different e-commerce in the recent years and analyzing its development, this paper proposes different models of e-commerce should adopt different taxation policies; On the basis of referring to overseas experience and unifying the actual developing situation of Chinese e-commerce, this paper proposes the taxation policies that suit the development of Chinese e-commerce.
Design-for-testability techniques for CORDIC design
Authors: Bo-Yuan Ye, Po-Yu Yeh, Sy-Yen Kuo, Ing-Yi Chen
Venue: Microelectronics Journal (2009)

In this paper, we propose two C-testable design-for-testability (DFT) architectures for coordinate rotation digital computer (CORDIC) design. The first design is achieved by using scalable cells. A scalable cell consists of n bit-level cells and has both hardware and bijective scalability. These simple scalable cells establish the relationship between hardware overhead (HO) and number of test patterns (NTP). Both HO and NTP change as n varies. By adjusting the value of n, we can obtain an optimal balance between HO and NTP. Based on these scalable cells, the iterative logic array (ILA) will be still C-testable. For the first proposed design, the HO and NTP for n=2 are 5.37% and 74, respectively. The second one is achieved by the reorganized test sequences, where the HO and NTP are only 3.15% and 18, respectively. The first design can be connected into a non-homogenous ILA for saving lot of test pins and built-in self-test (BIST) area; in the second one, the special properties of the sequences reduce HO/NTP significantly.
Automatically identifying relations in privacy policies
Authors: John W. Stamey, Ryan A. Rossi
Venue: Proceedings of the 27th ACM international conference on Design of communication (2009)

E-commerce privacy policies tend to consist of many ambiguities in language that protects companies more than the customers. Types of ambiguities found are currently divided into four patterns: mitigation (downplaying frequency), enhancement (emphasizing nonessential qualities), obfuscation (hedging claims and obscuring causality), and omission (removing agents). A number of phrases have been identified as creating ambiguities within these four categories. When a customer accepts the terms and conditions of a privacy policy, words and phrases (from the category of mitigation) such as "occasionally" or "from time to time" actually give the e-commerce vendor permission to send as many spamming email offers as they deem necessary . Our study uses techniques based on Latent Semantic Analysis to discover the underlying semantic relations between words in privacy policies. Additional potential ambiguities and other word relations are found automatically. Words are clustered according to their topic in privacy policies using principal directions. This provides us with a ranking of the most significant words from each clustered topic as well as a ranking of the privacy policy topics. We also extract a signature that forms the basis of a typical privacy policy. These results lead to the design of a system used to analyze privacy policies called Hermes. Given an arbitrary privacy policy our system provides a list of the potential ambiguities along with a score that represents the similarity to a typical privacy policy.
Quantum error correction via convex optimization
Authors: Robert L. Kosut, Daniel A. Lidar
Venue: Quantum Information Processing (2009)

We show that the problem of designing a quantum information processing error correcting procedure can be cast as a bi-convex optimization problem, iterating between encoding and recovery, each being a semidefinite program. For a given encoding operator the problem is convex in the recovery operator. For a given method of recovery, the problem is convex in the encoding scheme. This allows us to derive new codes that are locally optimal. We present examples of such codes that can handle errors which are too strong for codes derived by analogy to classical error correction techniques.
An Algorithm of Glass-Image Recognition Based on Wavelet Packet Decomposition
Authors: Hu Liang, Wei ZhiHua
Venue: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 01 (2009)

Wavelet packet decomposition not only has the decompose effect at low-frequency by using wavelet decomposition, but also has the decompose effect at high-frequency where can not do by using wavelet decomposition. In this paper, the wavelet packet decomposition algorithm was proposed and applied to glass-image recognition. Compared with other feature extracting technologies such as Zernike’s moments and wavelet transformation, the experiments proved that the wavelet packet decomposition was the best on both precision and efficiency
The study and handling of program inputs in the selection of garbage collectors
Authors: Xipeng Shen, Feng Mao, Kai Tian, Eddy Zheng Zhang
Venue: ACM SIGOPS Operating Systems Review (2009)

Many studies have shown that the best performer among a set of garbage collectors tends to be different for different applications. Researchers have proposed applicationspecific selection of garbage collectors. In this work, we concentrate on a second dimension of the problem: the influence of program inputs on the selection of garbage collectors. We collect tens to hundreds of inputs for a set of Java benchmarks, and measure their performance on Jikes RVM with different heap sizes and garbage collectors. A rigorous statistical analysis produces four-fold insights. First, inputs influence the relative performance of garbage collectors significantly, causing large variations to the top set of garbage collectors across inputs. Profiling one or few runs is thus inadequate for selecting the garbage collector that works well for most inputs. Second, when the heap size ratio is fixed, one or two types of garbage collectors are enough to stimulate the top performance of the program on all inputs. Third, for some programs, the heap size ratio significantly affects the relative performance of different types of garbage collectors. For the selection of garbage collectors on those programs, it is necessary to have a cross-input predictive model that predicts the minimum possible heap size of the execution on an arbitrary input. Finally, by adoptingstatistical learning techniques, we investigate the cross-input predictability of the influence. Experimental results demonstrate that with regression and classification techniques, it is possible to predict the best garbage collector (along with the minimum possible heap size) with reasonable accuracy given an arbitrary input to an application. The exploration opens the opportunities for tailoring the selection of garbage collectors to not only applications but also their inputs.
A review of methods for automatic understanding of natural language mathematical problems
Authors: Anirban Mukherjee, Utpal Garain
Venue: Artificial Intelligence Review (2008)

This article addresses the problem of understanding mathematics described in natural language. Research in this area dates back to early 1960s. Several systems have so far been proposed to involve machines to solve mathematical problems of various domains like algebra, geometry, physics, mechanics, etc. This correspondence provides a state of the art technical review of these systems and approaches proposed by different research groups. A unified architecture that has been used in most of these approaches is identified and differences among the systems are highlighted. Significant achievements of each method are pointed out. Major strengths and weaknesses of the approaches are also discussed. Finally, present efforts and future trends in this research area are presented.