Miracle-dz/UniFAR-FTUs huggingface dataset:
Make Platform Innovation Drive Enterprise Growth

OVERVIEW: Most corporations lack a precise, operational definition of their product platforms, without which progress is difficult to achieve. With that definition, new product strategy must be recast to capture the degree to which common architectures and subsystems will be leveraged across existing and new market applications, as well as the timing of product introductions. Managers must also reconsider the financial model of the business, including investments in the development of platforms versus products, and the changes that are possible in the margins and contribution of platform-based products to profits. Perhaps most pressing are the organizational questions: How can the R&D executive best structure the organization and the processes by which groups plan and interact so as to facilitate platform development and platform-based product development? Recent efforts of IBM and other corporations to embrace platforms provide both a management and a technology paradigm.
Monte Carlo Methods : Overview and Basics

c © 2002 by John von Neumann Institute for Computing Permission to make digital or hard copies of portions of this work for personal or classroom use is granted provided that the copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise requires prior specific permission by the publisher mentioned above.
Automatic detection of task-incompleted dialog for spoken dialog system based on dialog act n-gram

In this paper, we propose a method of detecting taskincompleted users for a spoken dialog system using an N-grambased dialog history model. We collected a large amount of spoken dialog data accompanied by usability evaluation scores by users in real environments. The database was made by a field test in which naive users used a client-server music retrieval system with a spoken dialog interface on their own PCs. An N-gram model was trained from sequences that consist of user dialog acts and/or system dialog acts for two dialog classes, that is, the dialog completed the music retrieval task or the dialog incompleted the task. Then the system detects unknown dialogs that is not completed the task based on the N-gram likelihood. Experiments were conducted on large real data, and the results show that our proposed method achieved good classification performance. When the classifier correctly detected all of the task-incompleted dialogs, our proposed method achieved a false detection rate of 6%.
A Framework for Social Semantic Journalism

Increasingly, news breaks on social media, where ordinary citizens post images and videos and their own commentary in the form of text. This user-generated content (UGC) is newsworthy information and invaluable for newsrooms. In order to incorporate this data into a news story, the journalist needs to process, compile and verify information on the social web within a very short timespan. This is done mostly manually and is a time-consuming and labour-intensive process for media organisations. This paper proposes Social Semantic Journalism framework as an assistant to journalists for breaking news production, and as solution to the above problem.
New Companions for the lonely Crab? VHE emission from young pulsar wind nebulae revealed by H.E.S.S

The deeper and more extended survey of the central parts of the Galactic Plane by H.E. S.S. during 2005-2007 has revealed a number of new point-like, as well as, extended sources. Two point-like sources can be associated to two remarkable objects around "Crab-like" young and energetic pulsars in our Galaxy: G21.5-0.9 and Kes 75. The characteristics of each of the sources are presented and possible interpretations are briefly discussed.
Lognormal-Mixture Dynamics and Calibration to Volatility Smiles and Skews

We introduce a general class of analytically tractable models for the dynamics of an asset price based on the assumption that the asset-price density is given by the mixture of known basic densities. We consider the lognormal-mixture model as a fundamental example, and for the first time we derive the related explicit dynamics and show that it leads to a stochastic differential equation admitting a unique strong solution. We also provide closed form formulas for option prices and analytical approximations for the implied volatility function. We then introduce the asset-price model that is obtained by shifting the previous lognormal-mixture dynamics and investigate its analytical tractability. We finally consider a specific example of calibration to real market option data.
Understanding security goals provided by crypto-protocol implementations

Understanding the security goals provided by cryptographic protocol implementations is known to be difficult, since security requirements such as secrecy, integrity and authenticity of data are notoriously hard to establish, especially in the presence of cryptographic interactions. A lot of research has been devoted to develop formal techniques to analyze abstract specifications of cryptographic protocols. Less attention has been paid to the source code analysis of legacy crypto-protocol implementations, for which specifications are often not available. This is an important challenge since it is non-trivial to determine from a given protocol implementation exactly which security goals are achieved, which is necessary for a reliable maintenance of security-critical systems. In this paper, we propose an approach to determine security goals provided by an implemented protocol based on control flow graphs and automated theorem provers for first-order logic.
Saxion Emission from SN 1987 A

We study the possibility of emission of the saxion, a superpartner of the axion, from SN1987A. The fact that the observed neutrino pulse from SN1987A is in excellent agreement with the current theory of supernovae places a strong bound on the energy loss into any non-standard model channel, therefore enabling bounds to be placed on the decay constant, fa, of a light saxion. The low-energy coupling of the saxion, which couples at high energies to the QCD gauge field strength, is expected to be enhanced from QCD scaling, making it interesting to investigate if the saxion could place stronger bounds on fa than the axion itself. Moreover, since the properties of the saxion are determined by fa, a constraint on this parameter can be translated into a constraint on the supersymmetry breaking scale. We find that the bound on fa from saxion emission is comparable with the one derived from axion emission due to a cancellation of leading-order terms in the soft-radiation expansion. arndt@phys.washington. edu pjfox@phys.washington. edu
On the Parametric Instability Caused by Step Size Variation in Runge-Kutta-Nyström Methods

The parametric instability arising when ordinary differential equations (ODEs) are numerically integrated with Runge-Kutta-Nystr\"om (RKN) methods with varying step sizes is investigated. It is shown that when linear constant coefficient ODEs are integrated with RKN methods that are based on A-stable Runge-Kutta methods, the solution is nonincreasing in some norm for all positive step sizes, constant or varying. Perturbation methods are used to quantify the critical step sizes associated with parametric instability.
Scanning laser entoptic perimetry for the detection of age-related macular degeneration.

OBJECTIVE To determine the sensitivity and specificity of scanning laser entoptic perimetry for detecting visual function damage due to age-related macular degeneration (ARMD). METHODS We measured the presence or absence of visual field disturbances by entoptic perimetry and determined the severity of ARMD based on masked readings of fundus photographs. A prospective masked study comparing the findings of entoptic perimetry with fundus photographs was performed. We recruited 91 patients with ARMD and 24 patients without ARMD during ophthalmologic visits. An appropriate institutional review board approval was obtained for the project. The main outcome measure was the detection of visual scotomata. RESULTS Scanning laser entoptic perimetry had an overall sensitivity of 82% and a specificity of 100% for the detection of ARMD. The sensitivity for early stages of the disease is greater than 70%, and increases to above 90% for moderate to late stages. CONCLUSION Scanning laser entoptic perimetry is a specific and sensitive test for detecting ARMD, even at the earliest stages when patients are typically asymptomatic.
A unified formation control policy of multi-robot system

A unified formation control policy is presented. From the trajectory and shape aspects, the group formation control problem is decoupled into the desired trajectory tracking problem of each individual, and the consistency of stability and optimality between these two problems are proved. The overall criterion and decomposed criterions to evaluate the formation performance are designed. The decomposed criterions reflect the trajectory tracking precision and shape distortion degree, and they are an orthogonal decomposition of the overall criterion. Analysis show the control policy can be extended into the flexible formation applications.
Janitor Bot-Detecting Light Switches

The demand for janitorial robots has gone up with the rising affluence and increasingly busy lifestyles of people [1], both in offices and personal homes. All too often people forget to switch off lights, close doors, throw away garbage and so on; this is where a janitorial robot would come in handy. This kind of robot must have the necessary characteristics of being able to autonomously navigate through its surroundings, and also detect and recognize objects of interest for its assigned tasks. In this project, we focus on the latter problem for the specific case of turning off lights. The robot needs to know where the light switch is in order to turn the lights off, so the visual system must be able to precisely detect the 2D location of the light switch on the wall.
Numerical Linear Algebra Internet and Large Scale Applications

Automated approaches for the identiﬂcation and clustering of semantic features or topics are highly desired for text mining applications. Using a low rank non-negative matrix factorization (NNMF) algorithm to retain natural data non-negativity, we eliminate the need to use subtractive basis vector and encoding calculations present in techniques such as principal component analysis for semantic feature abstraction. Using non-negative tensor factorization (NNTF), temporal and semantic proximity can be exploited to enable tracking of focused discussions as well as latent (unknown) communication patterns. Demonstrations of NNMF and NNTF algorithms for topic (or discussion) detection and tracking using the Enron Email Collection is presented.
Haitian Creole: How to Build and Ship an MT Engine from Scratch in 4 days, 17 hours, & 30 minutes

We describe the effort of the Microsoft Translator team to develop a Haitian Creole statistical machine translation engine from scratch in a matter of days. Haitian Creole presents a number of difficulties for devleoping an SMT system, principal among these is the lack of significant amounts of parallel training data and an inconsistent orthography, both of which lead to data sparseness. We demonstrate, however, that it is possible to build a translation engine of reasonable quality over very little data by engaging with the native language community and reducing data sparseness in creative ways. As such, we show that MT as a technology and as a service can be deployed rapidly in crisis situations.
A three-dimensional locating method for underwater robot based on sensors fusion

Among various vision sensors used in localizing targets in robotics, the locating precision of monocular vision is low, and binocular vision requires difficult stereo matching and a heavy computation. Hence a novel application of sensors fusion for underwater robot locating is presented in this paper, which is combined with a linear laser emitter and an underwater camera to obtain 3D coordinates of targets. Due to the distortions especially the edge of the images contributing significantly to the accurate locating, camera radial aberration is considered when calibrating camera parameters to weaken the image distortion. Considering refraction affects locating precision in water, a method of modifying underwater refraction based on sensors fusion is presented to reduce locating error of depth. Experiment results verify the method based on sensors fusion is better than the secondary focus method. Also the camera calibration method and refraction influence modifying method proposed in this paper are proved to be able to improve locating precision obviously.
Aerial obstacle estimation using RSSI observations based on OHLOSS diffraction model

This paper presents a new approach to estimate the location and height of the obstacle located between two unmanned aerial vehicles (UAVs) using Received Signal Strength Indication (RSSI) observations. The goal of developing this approach is to improve the localization of a radio frequency (RF) source by estimating the effect of obstacles on the signal attenuation. The effect of an obstacle on signal strength attenuation is the most important source of error in distance estimation based on general or empirical path loss model. Therefore, mapping the primary obstacle, which has the greatest effect on the signal attenuation, can improve the distance estimation. The main idea of the proposed approach is in the distinction between the effects of obstacle(s) on the signal attenuation, i.e. the diffraction loss, and the path loss. The proposed approach uses a model of diffraction loss to estimate the height and position of the primary knife-edge obstacle. The observations include the diffraction losses which are collected on the UAV's paths. Due to the Gaussian distribution of the diffraction loss observations and nonlinearity of the observation function, extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter are implemented and compared with each other. The results of the simulations show that the proposed approach is able to map an obstacle between two RF sources. Furthermore, the detected and estimated obstacle can be used for better RF source localization.
Evaluation of the cognitive-behavioral model of generalized and problematic Internet use in Spanish adolescents.

BACKGROUND Problematic use of Internet is a growing concern that interferes in the family and academic life of adolescents. This study had three related objectives: 1) to analyze the psychometric properties of the Revised Generalized and Problematic Internet Use Scale (GPIUS2) among Spanish adolescents; 2) to examine the theoretical cognitive-behavioral model of generalized and problematic Internet use; and 3) to study the relationship between the type of Internet use and generalized and problematic Internet use. METHOD Participants were 1,021 adolescents in Bizkaia (55.7% girls, mean age = 14.95 years, SD = 1.71). RESULTS Factor analyses confirmed the internal structure of four factors originally proposed for GPIUS2, called Preference for online social interaction, Mood regulation, Deficient self-regulation, and Negative consequences. Consistent with the theoretical model, preference for online social interaction and mood regulation increased the likelihood of reporting deficient self-regulation, which, in turn, was associated with negative consequences for the adolescents. CONCLUSIONS The GPIUS2 is a reliable and valid instrument that can be used in adolescent samples. Furthermore, the results support the cognitive behavioral model of problematic Internet use in adolescents.
The hyper Dirichlet process and its discrete approximations: the single conditional independence model

The aim of this paper is the study of some laws of random probability distributions, called hyper Dirichlet processes, charging the product of three sample spaces, with the property that the first and the third components are independent conditional to the second one. The law of the marginals on the first two and on the last two components are specified to be Dirichlet processes with the same marginal parameter measure on the common second component. The joint law is then obtained as the hyper Markov combination of these two laws, as introduced in [3], and in fact these laws are generalizations of the hyper Dirichlet laws on contingency tables considered in the above paper. Our main result is the convergence to the law of a hyper Dirichlet process of the sequence of hyper Dirichlet laws associated to finer and finer ”discretizations” of the two parameter measures, which is proved by means of a suitable coupling construction. MSC: 60G57; 62G99; 62F15; 60F99.
Potential Utility of Novel Biomarkers in Active Surveillance of Low-Risk Prostate Cancer

Active surveillance (AS) is now an accepted management strategy for men with low-risk localized prostate cancer (PCa). However, detecting disease progression in a patient selected for AS remains a challenge. It is crucial to know what will serve as the best parameter to correctly identify tumors that progress to a more aggressive phenotype so as not to miss the window of curability. Several biomarkers are now being actively investigated as novel tools to improve PCa risk assessments. To date, several serum, urinary, and tissue biomarkers have shown promising prognostic value. %[−2]proPSA and PHI showed improved predictive value for an unfavorable biopsy conversion at annual surveillance biopsy in the AS program. PCA3 and TMPRSS2:ERG had additional independent predictive value for the prediction of PCa detection and progression, although PCA3 was limited in predicting aggressive cancer. Other tissue biomarkers also showed promising ability to predict disease progression. Although several of these novel biomarkers have an improved predictive accuracy that is better than classical parameters, there is still a need for further well-designed, large, multicenter, prospective trials to avoid common bias and clinical validation.
A survey of the main line of the polish pioneering power control systems/networks

In spite of the ethical, political, social and cultural issues and differences in the country development level, especially in Information and Communication Technology (ICT) and the Automatic Control Technology (ACT) between the case-study country (Poland) and well developed countries, the history of computer control system/network applications in the Polish power industry is rather interesting. Of a special importance here were the successful, effective, very profitable and low cost large-scale communication system/network home solutions that lead the whole case-study-country into the XXI-st Century. The paper presents a survey of the main line of successful power industry control communication systems/networks implemented in the case-study country.
Top-Down Control in Contour Grouping

Human observers tend to group oriented line segments into full contours if they follow the Gestalt rule of 'good continuation'. It is commonly assumed that contour grouping emerges automatically in early visual cortex. In contrast, recent work in animal models suggests that contour grouping requires learning and thus involves top-down control from higher brain structures. Here we explore mechanisms of top-down control in perceptual grouping by investigating synchronicity within EEG oscillations. Human participants saw two micro-Gabor arrays in a random order, with the task to indicate whether the first (S1) or the second stimulus (S2) contained a contour of collinearly aligned elements. Contour compared to non-contour S1 produced a larger posterior post-stimulus beta power (15–21 Hz). Contour S2 was associated with a pre-stimulus decrease in posterior alpha power (11–12 Hz) and in fronto-posterior theta (4–5 Hz) phase couplings, but not with a post-stimulus increase in beta power. The results indicate that subjects used prior knowledge from S1 processing for S2 contour grouping. Expanding previous work on theta oscillations, we propose that long-range theta synchrony shapes neural responses to perceptual groupings regulating lateral inhibition in early visual cortex.
Pedestrian tracking in video sequences: A particle filtering approach

In this work we study the methods for pedestrian tracking in video sequences and indicate various applications of these methods ranging from surveillance systems to aiding the visually impaired persons. First, we define the general problem of object tracking that comprises the tasks of object detection, identifying the flow of object location in consecutive video images and finally analysis of the tracked trajectory data. We review the well known object tracking techniques i.e. the Mean-Shift and the CAMSHIFT algorithm and discuss their properties. Then we introduce the computational technique known as particle filtering (PF) and explain how we have applied it to the tasks of pedestrian tracking. We compare the PF approach against the Mean-Shift and the CAMSHIFT algorithms in terms of tracking robustness and the required computational demand. We conclude, that on the tested video sequences, the PF tracker outperforms the Mean-Shift and by a small margin the CAMSHIFT algorithm. The PF tracker requires more computational power, however, its tracking performance can be flexibly adjusted to the application requirements.
Improving mobile gaming performance through cooperative CPU-GPU thermal management

State-of-the-art thermal management techniques independently throttle the frequencies of high-performance multi-core CPU and powerful graphics processing units (GPU) on heterogeneous multiprocessor system-on-chips deployed in latest mobile devices. For graphics-intensive gaming applications, this approach is inadequate because both the CPU and the GPU contribute towards the overall application performance (frames per second or FPS) as well as the on-chip temperature. The lack of coordination between CPU and GPU induces recurrent frequency throttling to maintain on-chip temperature below the permissible limit. This leads to significantly degraded application performance and large variation in temperature over time. We propose a control-theory based dynamic thermal management technique that cooperatively scales CPU and GPU frequencies to meet the thermal constraint while achieving high performance for mobile gaming. Experimental results with six popular Android games on a commercial mobile platform show an average 19% performance improvement and over 90% reduction in temperature variance compared to the original Linux approach.
RELACS for systolic programming

The RELACS language is a systolic programming language, which simplifies the programmer's task by making explicit the data-flow of systolic algorithms, and by exposing the data delivery mechanism. The underlying architecture model is different from other SIMD architectures in that it physically separates computation and data management. The authors introduce the RELACS language as a syntaxic and a sermantic extension of the C language. It is shown that the RELACS programming model provides a simple programming method for systolic algorithms, which is applicable to a variety of parallel machines. <<ETX>>