Miracle-dz/UniFAR-FTUs huggingface dataset:
Mathematical Evaluation of Environmental Monitoring Estimation Error through Energy-Efficient Wireless Sensor Networks

In this paper, the estimation of a scalar field over a bidimensional scenario (e.g., the atmospheric pressure in a wide area) through a self-organizing wireless sensor network (WSN) with energy constraints is investigated. The sensor devices (denoted as nodes) are randomly distributed; they transmit samples to a supervisor by using a clustered network. This paper provides a mathematical framework to analyze the interdependent aspects of WSN communication protocol and signal processing design. Channel modelling and connectivity issues, multiple access control and routing, and the role of distributed digital signal processing (DDSP) techniques are accounted for. The possibility that nodes perform DDSP is studied through a distributed compression technique based on signal resampling. The DDSP impact on network energy efficiency is compared through a novel mathematical approach to the case where the processing is performed entirely by the supervisor. The trade-off between energy conservation (i.e., network lifetime) and estimation error is discussed and a design criterion is proposed as well. Comparison to simulation outcomes validates the model. As an example result, the required node density is found as a trade-off between estimation quality and network lifetime for different system parameters and scalar field characteristics. It is shown that both the DDSP technique and the MAC protocol choice have a relevant impact on the performance of a WSN.
Marx's economics : a dual theory of value and growth

Preface Introduction Part I. The Labour Theory of Value: 1. Dual definition of value 2. Hidden assumptions 3. Quantitative determination of relative value 4. Value, use-value and exchange-value Part II. The Theory of Exploitation: 5. Surplus value and exploitation 6. The rate of profit Part III. The Transformation Problem: 7. The static transformation problem 8. The aggregation problem Part IV. The Reproduction Scheme: 9. Simple reproduction 10. Extended reproduction 11. The reserve army and the falling rate of profit 12. The dynamic transformation problem Part V. Capital and Value: 13. The turnover of capital 14. The labour theory of value revisited Index.
Automatic Classification of Cognitive States November 2010

Functional Magnetic Resonance Imaging has established itself as the most powerful technique available today to measure brain activity induced by a perceptual or cognitive state. The inverse problem is considered in this study; given the measured brain activity, our goal is to predict the perceptual state. Machine Learning algorithms were used to address this problem in this work. Multi-subject fMRI data analysis poses a great challenge for the machine learning paradigm, by its characteristics: the low Signal to Noise Ratio (SNR), high dimensionality, small number of examples and inter-subject variability. To address this problem, several methods of classification and feature selection were tested. The main criterion of feature selection was mutual information in a univariate method, but a multivariate feature selection was also proposed. Both a single classifier and an ensemble of classifiers were tested. The ensemble of classifiers approach consisted on training an optimized classifier for each class and then the combination was made. The data analysed was obtained from three multi-subject experiments of visual stimulation with 4 classes of stimuli, at different magnetic field strengths. The ensemble of classifiers performs best for most data sets and methods of feature selection. The multivariate method does not show overall improvement in the classification. In summary, the results suggest that a combination of classifiers can perform better than a single classifier, particularly when decoding stimuli associated with specific brain areas.
Linking Daily Stress Processes and Laboratory-Based Heart Rate Variability in a National Sample of Midlife and Older Adults

Objective This study evaluates the associations between people's trait-like patterns of stress in daily life (stressor frequency, perceived stressor severity, affective reactivity to stressors, and negative affect) and laboratory-assessed heart rate variability (HRV). Methods Data were collected from 909 participants aged 35 to 85 years in the Midlife in the United States Study. Participants reported negative affect and minor stressful events during telephone interviews on 8 consecutive evenings. On a separate occasion, HRV was measured from electrocardiograph recordings taken at rest during a laboratory-based psychophysiology protocol. Regression models were used to evaluate the associations between daily stress processes and three log-transformed HRV indices: standard deviation of R-R intervals (SDRR), root mean square of successive differences (RMSSD), and high-frequency power (high-frequency HRV [HF-HRV]). Analyses were adjusted for demographics, body mass index, comorbid conditions, medications, physical activity, and smoking. Results Stressor frequency was unrelated to HRV (r values ranging from −0.04 to −0.01, p values >.20). However, people with greater perceived stressor severity had lower resting SDRR (fully adjusted B [standard error {SE}] = −0.05 [0.02]), RMSSD (−0.08 [0.03]), and HF-HRV (−0.16 [0.07]). Individuals with more pronounced affective reactivity to stressors also had lower levels of all three HRV indices (SDRR: B [SE] = −0.28 [0.14]; RMSSD: −0.44 [0.19]; HF-HRV: −0.96 [0.37]). Furthermore, aggregated daily negative affect was linked to reduced RMSSD (B [SE] = −0.16 [0.08]) and HF-HRV (−0.35 [0.15]). Conclusions In a national sample, individual differences in daily negative affect and responses to daily stressors were more strongly related to cardiovascular autonomic regulation than the frequency of such stressors.
An Empirical Analysis of Online Consumer Initial Trust Building Based on ELM

Due to perceived high risk in the first transaction and Internet low switching cost, building consumer initial trust is crucial for online vendors. Extant research has often examined determinants of initial trust from the perspectives of TAM and trust transference, and seldom considered the initial trust building from dual-routes. Drawing on ELM, we analyzed the different effect of argument quality via the central route and source credibility via the peripheral route on initial trust. We collected 193 valid papers and conducted data analysis with LISREL. The results show that argument quality, source credibility and self-efficacy have significant effect on initial trust. Moreover, self-efficacy positively moderates the effect of argument quality on initial trust, whereas it negatively moderates the effect of source credibility on initial trust.
Score-Informed Analysis of Intonation and Pitch Modulation in Jazz Solos

The paper presents new approaches for analyzing the characteristics of intonation and pitch modulation of woodwind and brass solos in jazz recordings. To this end, we use score-informed analysis techniques for source separation and fundamental frequency tracking. After splitting the audio into a solo and a backing track, a reference tuning frequency is estimated from the backing track. Next, we compute the fundamental frequency contour for each tone in the solo and a set of features describing its temporal shape. Based on this data, we first investigate, whether the tuning frequencies of jazz recordings changed over the decades of the last century. Second, we analyze whether the intonation is artist-specific. Finally, we examine how the modulation frequency of vibrato tones depends on contextual parameters such as pitch, duration, and tempo as well as the performing artist.
Semantic approaches to domain template construction and opinion mining from natural language

Most of the text mining algorithms in use today are based on lexical representation of input texts, for example bag of words. A possible alternative is to first convert text into a semantic representation, one that captures the text content in a structured way and using only a set of pre-agreed labels. This thesis explores the feasibility of such an approach to two tasks on collections of documents: identifying common structure in input documents (»domain template construction«), and helping users find differing opinions in input documents (»opinion mining«). We first discuss ways of converting natural text to a semantic representation. We propose and compare two new methods with varying degrees of target representation complexity. The first method, showing more promise, is based on dependency parser output which it converts to lightweight semantic frames, with role fillers aligned to WordNet. The second method structures text using Semantic Role Labeling techniques and aligns the output to the Cyc ontology. Based on the first of the above representations, we next propose and evaluate two methods for constructing frame-based templates for documents from a given domain (e.g. bombing attack news reports). A template is the set of all salient attributes (e.g. attacker, number of casualties, \ldots). The idea of both methods is to construct abstract frames for which more specific instances (according to the WordNet hierarchy) can be found in the input documents. Fragments of these abstract frames represent the sought-for attributes. We achieve state of the art performance and additionally provide detailed type constraints for the attributes, something not possible with competing methods. Finally, we propose a software system for exposing differing opinions in the news. For any given event, we present the user with all known articles on the topic and let them navigate them by three semantic properties simultaneously: sentiment, topical focus and geography of origin. The result is a dynamically reranked set of relevant articles and a near real time focused summary of those articles. The summary, too, is computed from the semantic text representation discussed above. We conducted a user study of the whole system with very positive results.
Design and Characterization of Planar Antennas on the Semi-Insulated GaAs for Rectenna Devices

This paper presents the design and characterization of the planar type antennas. A dipole type antenna has been designed to be used in the rectifying antenna (rectenna) devices. Normally, a rectenna consists of an antenna and a diode which can rectify microwave power to dc power. We present the novel coplanar waveguide (CPW) facilitated with antenna for future applications such as Intelligent Quantum (IQ) Chips. The return loss characteristic of antenna has been investigated by simulation. From our preliminary results, it is shown that an antenna with length, L=4 mm and width, W=20 mum showed high return loss characteristics up to -47 dB at 15 GHz. The obtain characteristics also showed that the dipole antenna can be directly integrated with other devices without any matching circuits for use like in approximity communication system.
Weighted window method for TCP fairness in WLANs

Distributed Coordination Function (DCF) in IEEE 802.11 WLANs cannot guarantee per-station TCP fairness among wireless stations. In order to satisfy users, and guarantee to allocate fair bandwidth among wireless users regardless of the number and direction of flows, the Weighted Window method is proposed. This method can be simply implemented in mobile stations without having to modify the MAC layer. In this paper, the proposed method and its properties will be presented in detail using ns2 simulation tool under various network conditions. The simulation results are shown that the proposed method achieves fair throughput among stations as compared with previous methods.
Gene Ranking Using R-Metric

Selecting a small number of genes from thousands of genes is useful in disease diagnosis. We propose in this study a new metric termed R-test to analyze how the instances of two classes distribute against each other. We validated the proposed scheme through two well-known benchmarks. R-test outperforms two-well known methods on the two benchmarks
Learning particle filter for multiple target tracking

This paper presents a learning particle filter (LPF) to solve the problems of uncertainty, varying number, overlap, ambiguous, non-rigid, nonlinear, and non-Gaussian in tracking multiple visual targets. Evolution learning obtains a detector that guides proposal distribution originally, and then online learning renders the detector adaptive to pose altering and proposal distribution closer to posterior distribution. Simulation results demonstrate the performance of LPF algorithm.
Large-scale Computer-Assisted Assessment in Computer Science Education New Possibilities , New Questions

Since 2003 we have successively introduced the use of e-learning and computer-assisted assessment (CAA) components into all of our courses, namely online multiple-choice tests, electronic submission of assignments, and automatic testing of programs. We originally did not intend to make major changes to the courses; our primary motivation was just to make them more efficient and more effective by freeing teachers from administrative burdens and by offering more flexibility and interactivity for students. After several semesters of usage we have noticed, however, that the courses have changed much more radically than originally envisaged. The electronic support of faceto-face courses offers many new possibilities, but it also opens up new questions. This paper describes our system and our experience, and discusses some of the questions we have encountered.
Radix-b extensions to some common empirical tests for pseudorandom number generators

Empirical testing of computer generated pseudo-random sequences is widely practiced. Extensions to the <italic>coupon collector's</italic> and <italic>gap</italic> tests are presented that examine the distribution and independence of radix-<italic>b</italic> digit patterns in sequences with modulo of the form <italic>b<supscrpt>w</supscrpt></italic>. An algorithm is given and the test is applied to a number of popular generators. Theoretical expected values are derived for a number of defects that may be present in a pseudorandom sequence and additional empirical evidence is given to support these values. The test has a simple model and a known distribution function. It is easily and efficiently implemented and easily adaptable to testing only the bits of interest, griven a certain application.
Trans-dimensional Markov chains : A decade of progress and future perspectives

The last ten years have witnessed the development of sampling frameworks that permit the construction of Markov chains which simultaneously traverse both parameter and model space. In this time substantial methodological progress has been made. In this article we present a survey of the current state of the art and evaluate some of the most recent advances in this field. We also discuss future research perspectives in the context of the drive to develop sampling mechanisms with high degrees of both efficiency and automation.
Optical and mechanical characterization of thin membranes for X-ray lithography

An optical transmission test and a bulge test were designed and tested as part of an endeavor to improve x-ray mask manufacturing. The thicknesses and indices of refraction of silicon nitride membranes were measured with the optical transmission test, and the results were explained using Male's graphical method. The residual stress and bulk modu-lus of silicon nitride membranes and PMMA were measured with the bulge test. The bulge test showed a repeatability error of 3% in residual stress measurements and 6% in bulk modulus measurements. A novel in-situ bulge test for a tungsten sputtering system is proposed for controlling the value of the residual stress of sputtered tungsten. Abstract An optical transmission test and a bulge test were designed and tested as part of an endeavor to improve x-ray mask manufacturing. The thicknesses and indices of refraction of silicon nitride membranes were measured with the optical transmission test, and the results were explained using Mal6's graphical method. The residual stress and bulk modu-lus of silicon nitride membranes and PMMA were measured with the bulge test. The bulge test showed a repeatability error of 3% in residual stress measurements and 6% in bulk modulus measurements. A novel in-situ bulge test for a tungsten sputtering system is proposed for controlling the value of the residual stress of sputtered tungsten. Acknowledgements There are many people who have helped in the production of this thesis. I am indebted to all of my teachers at M.I. T. and at previous schools for helping me to develop my technical skills. I am grateful to my advisor Professor Henry I. Smith for his superb technical guidance and financial support. While busy designing his new lab, Dr. Mark Schattenburg has also enthusiastically guided this research. I would like to thank Scott Hector for his patience in answering my many questions and for his help in preparing a multilayer bulge sample. The technical assistance in the NanoStructures Laboratory also deserves thanks for their outstanding work. Mark Mondol's expert assistance in building the bulge test measurement system was critical to this research. Bob Sisson has been key in fabricating the x-ray masks, and Jimmy Carter has kept the lab running through the many power out-ages. Many thanks to all of the students in the NanoStructures Lab! I would finally like to thank my family and all of my friends for their continuous support, humor, and understanding. Thanks a bunch everyone!
Capacity of the State-Dependent Wiretap Channel: Secure Writing on Dirty Paper

In this paper we consider the State-Dependent Wiretap Channel (SD-WC). As the main idea, we model the SD-WC as a Cognitive Interference Channel (CIC), in which the primary receiver acts as an eavesdropper for the cognitive transmitter's message. By this point of view, the Channel State Information (CSI) in SD-WC plays the role of the primary user's message in CIC which can be decoded at the eavesdropper. This idea enables us to use the main achievability approaches of CIC, i. ~e. , Gel'fand-Pinsker Coding (GPC) and Superposition Coding (SPC), to find new achievable equivocation-rates for the SD-WC. We show that these approaches meet the capacity under some constraints on the rate of the channel state. Similar to the dirty paper channel, extending the results to the Gaussian case shows that the GPC lead to the capacity of the Gaussian SD-WC which is equal to the capacity of the wiretap channel without channel state. Hence, we achieve the capacity of the Gaussian SD-WC using the dirty paper technique. Moreover, our proposed approaches provide the capacity of the Binary SD-WC. It is shown that the capacity of the Binary SD-WC is equal to the capacity of the Binary wiretap channel without channel state.
Developing a CSF Causal Loop Model for Managing IT Projects: A Case Study of an Inter-Organisational Healthcare Pathology IT System

This research aims to develop a causal loop model using a Critical Success Factors (CSF) approach to investigate the organisational and IT project management issues concerning the planning and adoption of inter-organisational IT systems. The researchers adopted an interpretive research approach utilising template analysis. Categories were initially derived from a review of the literature based on CSF analysis from Enterprise Resource Planning (ERP) Projects. Causal loop modelling, using CSFs, was examined in order to assess the process of adoption of large scale integrated IT systems. The discussion and conclusions assess the appropriateness and utility of using a CSF approach, and an adapted causal loop model, to inform more effective approaches to IT planning and adoption. The findings indicate that CSF analyses may be combined with causal loop modelling and has the potential to be used for future research studies and also as a practical heuristic for IT project teams.
00 ITEP-2000 / On Exact Tachyon Potential in Open String Field Theory

In these notes we revisit the tachyon lagrangian in the open string field theory using background independent approach of Witten from 1992. We claim that the tree level lagrangian (up to second order in derivatives and modulo some class of field redefinitions) is given by L = e (∂T ) + (1 + T )e . Upon obvious change of variables this leads to the potential energy −φ2 log φ 2 e with canonical kinetic term. This lagrangian may be also obtained from the effective tachyon lagrangian of the p-adic strings in the limit p → 1. Applications to the problem of tachyon condensation are discussed.
Comparative study on dynamic identification of parallel motion platform for a novel flight simulator

This paper investigates theoretical and experimental comparison of LS estimation and Bayesian type filters such as EKF, UKF, PF and GMSPPF (Gaussian Mixture Sigma Point Particle Filter) methods for dynamic identification of a 6-DOF parallel motion platform. Comparison results show that the UKF method and the GMSPPF method are most efficient and easy-to-use parameter identification approaches for highly nonlinear system.
Toxin-Induced Necroptosis Is a Major Mechanism of Staphylococcus aureus Lung Damage

Staphylococcus aureus USA300 strains cause a highly inflammatory necrotizing pneumonia. The virulence of this strain has been attributed to its expression of multiple toxins that have diverse targets including ADAM10, NLRP3 and CD11b. We demonstrate that induction of necroptosis through RIP1/RIP3/MLKL signaling is a major consequence of S. aureus toxin production. Cytotoxicity could be prevented by inhibiting either RIP1 or MLKL signaling and S. aureus mutants lacking agr, hla or Hla pore formation, lukAB or psms were deficient in inducing cell death in human and murine immune cells. Toxin-associated pore formation was essential, as cell death was blocked by exogenous K+ or dextran. MLKL inhibition also blocked caspase-1 and IL-1β production, suggesting a link to the inflammasome. Rip3 -/- mice exhibited significantly improved staphylococcal clearance and retained an alveolar macrophage population with CD200R and CD206 markers in the setting of acute infection, suggesting increased susceptibility of these leukocytes to necroptosis. The importance of this anti-inflammatory signaling was indicated by the correlation between improved outcome and significantly decreased expression of KC, IL-6, TNF, IL-1α and IL-1β in infected mice. These findings indicate that toxin-induced necroptosis is a major cause of lung pathology in S. aureus pneumonia and suggest the possibility of targeting components of this signaling pathway as a therapeutic strategy.
Traffic grooming algorithms for power efficiency in IP over WDM networks

In IP over WDM networks, lightpath bypass is an effective method of decreasing the power consumed by IP routers since this technology can multiplex many requests into one high-capacity lightpath and switch these requests as a single entity in all-optical layer. However, the power consumed by individual lightpaths should also be considered in IP over WDM networks. Therefore, the power efficiency has become an important issue. In this paper, we propose two heuristic algorithms named Power-aware Grooming with Single-hop based on Wavelength Integrated Auxiliary Graph (PGSWIAG) and Power-aware Grooming with Multi-hop based on Wavelength Integrated Auxiliary Graph (PGMWIAG). Simulation results show that, compared with PGSWIAG, PGMWIAG can obtain better performance of power efficiency.
Market Trends Study Based on Moving Average Slope and Support Vector Machine

Securities investment is a high risk, high-yield financial management, while the good operation of the market trends help investors avoid risk and seize the opportunity. In this paper, we propose a method based on several moving average lines slope and support vector machine algorithm to determine the current market trends in order to take the appropriate investment strategy. Simulations show that the method achieved good results.