Miracle-dz/UniFAR-FTUs huggingface dataset:
Discrete Choice Analysis: Theory and Application to Travel Demand

This book, which is intended as a graduate level text and a general professional reference, presents the methods of discrete choice analysis and their applications in the modeling of transportation systems. The first seven chapters provide a basic introduction to discrete choice analysis that covers the material needed to apply basic binary and multiple choice models. The chapters are as follows: introduction; review of the statistics of model estimation; theories of individual choice behavior; binary choice models; multinomial choice; aggregate forecasting techniques; and tests and practical issues in developing discrete choice models. The rest of the chapters cover more advanced material and culminate in the development of a complete travel demand model system presented in chapter 11. The advanced chapters are as follows: theory of sampling; aggregation and sampling of alternatives; models of multidimensional choice and the nested logit model; and systems of models. The last chapter (12) presents an overview of current research frontiers.
Characteristics of WWW Client-based Traces

Abstract The explosion of WWW traffic necessitates an accurate picture of WWW use, and in particular requires a good understanding of client requests for WWW documents. To address this need, we have collected traces of actual executions of NCSA Mosaic, reflecting over half a million user requests for WWW documents. In this paper we present a descriptive statistical summary of the traces we collected, which identifies a number of trends and reference patterns in WWW use. In particular, we show that many characteristics of WWW use can be modelled using power-law distributions, including the distribution of document sizes, the popularity of documents as a function of size, the distribution of user requests for documents, and the number of references to documents as a function of their overall rank in popularity (Zipf''s law). In addition, we show how the power-law distributions derived from our traces can be used to guide system designers interested in caching WWW documents. --- Our client-based traces are available via FTP from http://www.cs. bu.edu/techreports/1995-010-www-client-traces.tar.gz http://www.cs. bu.edu/techreports/1995-010-www-client-traces.a. tar.gz
Towards embodied perspective: exploring first-person, stereoscopic, 4K, wall-sized rendering of embodied sculpting

The central goal of this thesis is creating and testing technology toproduce embodied interaction experiences. Embodied interaction is thesense that we inhabit a digital space with our minds operating on it asif it were our physical bodies, without conscious thought, but as naturalas reaching out with your ngers and touching the object in front of you.Traditional interaction techniques such as keyboard and mouse get in theway of achieving embodiment. In this thesis, we have created an embodiedperspective of virtual three-dimensional objects oating in front of a user.Users can see the object from a rst-person perspective without a headsupdisplay and can change the perspective of the object by shifting theirpoint of view. The technology and aordances to make this possible in aunobtrusive, practical and ecient way is the subject of this thesis.Using a depth sensor, Microsoft's Kinect [7], we track the user's positionin front of a screen in real-time, thus making it possible to changethe perspectives seen by each of the user's eyes to t their real point ofview, in order to achieve a 3D embodied interaction outside the screen.We combined the rst-person perspective into an embodied sculptingproject that includes a wireless haptic glove to allow the user to feel whentouching the model and a small one-hand remote controller used to rotatethe object around as the user desires when pressing its single button.We have achieved what we call Embodied Perspective, which involves anoutside-screen stereoscopic visualization, which reacts to body interactionas if the visualization was really where the user perceives it, thanks to thedata from the depth sensor. This method does not block the user's viewof their own body, but ts and matches their brain's perception.When applied to virtual sculpting (embodied sculpting), it gives theuser the ability to feel and understand much better their actions; wherethey are touching/sculpting and how they should move to reach wherethey want, since the movements are the same one would perform withtheir body in a real-world sculpting situation.A further study of the viability of this method, not only on singleperson interaction but on group visualization of a single user perspective,is discussed and proposed.
S-대역 텔레메트리용 표면일체형 신관 안테나

본 논문에서는 S-대역 텔레메트리 신호 전송을 위한 표면일체형 신관 안테나를 제안하였다. 제안된 안테나는 신관의 원통 금속표면 일체형의 사각패치와 이의 급전회로로 구성된다. 신관표면 0°와 180° 각도 위치에 2개의 패치를 배치하고 동일한 크기와 180° 위상차의 전력을 급전하였다. 각각의 패치는 100Ω 마이크로스트립 선로에 의해 급전되며 100Ω 선로와의 임피던스 정합을 위해 인셋 급전 구조를 적용하였다. 안테나는 0.5mm 두께의 기판에 급전회로와 일체형으로 구현하였으며 대역폭을 증가시키기 위해 신관표면과 기판 사이에 두께 2. 5mm의 저손실 유전체를 설치하였다. 제작된 안테나를 측정한 결과 제안한 안테나는 S-대역에서 43MHz의 대역폭, 3. 7dBi의 최대이득, 신관 축방향에서 영점을 가지는 다이폴과 유사한 방사패턴을 보였다.
On Predicting User Affiliations Using Social Features in Online Social Networks

User profiling such as user affiliation prediction in online social network is a challenging task with many important applications in targeted marketing and personalized recommendation. The research task here is to predict some user affiliation attributes that suggest user participation in different social groups. One of user profiling tasks is religion profiling. Religious belief plays an important role in determining the way people behave, form preferences, interpret events around them, and develop relationships with others. Traditionally, the religion labels of user population are obtained by conducting a large scale census study. Such an approach is both high cost and time consuming. In this paper, we study the problem of predicting users’ religion labels using their microblogging data. We formulate religion label prediction as a classification task, and identify content, structure and aggregate features considering their self or social variants for representing a user. We introduce the notion of representative user to identify users who show religious interest. We further define features using representative users. We first propose a supervised multiclass classification method using our proposed features can accurately assign Christian, Muslim, and Buddhist labels to a set of Twitter users with known religion labels. We further proposed collective classification method which make use of additional top classification score unlabeled users. Adding top classification score users provides a means to improve classification accuracy. We present a thorough experiment to show the effective of proposed method.
Adaptive power signal prediction by non-fixed neural network model with modified fuzzy back-propagation learning algorithm

The authors present a nonfixed artificial neural network (ANN) model with a modified fuzzy back-propagation (BP) learning rule for power signal prediction. This model is designed to avoid the ill-learning of ANN training caused by improper information. Taipower 1990-1993 loads and relevant weather data are implemented. The experiments of next day peak load forecasting and one-day-ahead hourly load forecasting are made in this study. Some experiments using conventional BP ANN approach are also performed as a comparison with the proposed model.
The Buccaneer software for automated model building

A new technique for the automated tracing of protein chains in experimental electron-density maps is described. The technique relies on the repeated application of an oriented electron-density likelihood target function to identify likely C positions. This function is applied both in the location of a few promising `seed' positions in the map and to grow those initial C positions into extended chain fragments. Techniques for assembling the chain fragments into an initial chain trace are discussed.
Adaptive algorithm for force/position control of flexible joint manipulators with holonomic constraints

The authors present an adaptive force/position control strategy for flexible joint robot manipulators subject to holonomic constraints. Convergence and stability results are guaranteed regardless of the joint flexibility value, in the non-adaptive as well as in the adaptive case. The joint flexibility matrix can be incorporated into the parameter vector of the adaptive controller. When the system physical parameters are unknown, an additional assumption is needed, i.e., the constraints are assumed to be linear. <<ETX>>
A valency dictionary of English: a corpus-based analysis of the complementation patterns of English verbs, nouns and adjectives

This dictionary provides a valency description of English verbs, nouns and adjectives. Each entry contains a comprehensive list of the complementation patterns identified on the basis of the largest corpus of English available at the present time. All examples are taken directly from the COBUILD/Birmingham corpus. The valency description comprises statements about the quantitative valency of the lexical units established, an inventory of their obligatory, contextually optional and purely optional complements as well as systematic information on the semantic and collocational properties of the complements. An outline of the model of valency theory used in this dictionary is provided in the introduction. Key Features unique reference work with no comparable publication on the market English is the world's most important lingua franca and the most frequently learned second language
Automatic call unfolding in a partial evaluator

Partial evaluation of a functional language may be done by specialization of functions, implemented by reduction of expressions and unfolding of function calls. Expression reduction is fairly straightforward, but it is difficult to decide when to unfold a function call and when to leave it in the program produced by partial evaluation. A call unfolding strategy must be adopted in order to make this decision in each particular case. In the partial evaluators reported in the literature, this strategy is not formalized, and the user of a partial evaluator must guide the call unfolding by his own informal criteria. This is an obstacle to many applications of partial evaluation. In the present paper we describe a simple two-phase call unfolding method which is fully automatic. The first phase is based on static call annotations, obtained by a local analysis to avoid infinite unfolding and a global analysis to avoid call duplication. The second phase does additional unfolding based on a global analysis. The method has been employed in a recent version of the partial evaluator mix, developed by Neil D. Jones, Harald S~ndergaard, and the author.
Graphical mapping of tropical cyclone forecast wind probabilities worldwide

The product maps the likelihood that a specific point on a map will be struck by hurricane (74 mph) and/or by tropical storm strength (39mph) 1-min sustained winds during different time periods out to 5 days ahead. Probabilities are displayed from 1% to 100% in colorcoded 5% bands. A Table relates these probabilities to simple descriptions that the event will happen. For example, a probability of 50% indicates a medium or evens-chance that the event will occur. Major cities are included on the maps to aid risk assessment.
Handwritten alphabet and digit character recognition using feature extracting neural network and modified self-organizing map

A new pattern recognition method is proposed for handwritten alphabet and digit characteristics. The feature point distribution of a standard pattern is mapped onto that of a distorted pattern, through a modified self-organizing map (SOM). The distorted pattern is recognized based on similarity between both feature point distributions. The modified SOM has the following advantages. First, the number of feature points is small, and these are classified into several groups. Second, the mapping is carried out in the variable ring shape region to find a suitable pairing of the feature points. Third, the feature points are selected from both the standard and the distorted patterns to avoid any vibration. Finally, neighborhoods are selected along lines of the patterns. These improvements can provide stable and fast feature point mapping. Computer simulations demonstrated that the proposed method can adapt to a variety of pattern distortions. <<ETX>>
Standardized Documentation for Verification, Validation, and Accreditation / Paper 08S-SIW-003

: This paper updates the Modeling and Simulation (M&S) community on the status of the Department of Defense M&S Steering Committee-sponsored project titled, "Standardized Documentation for Verification, Validation, and Accreditation," since the paper 07F-SIW-068, "Automating Standardized Information for the Verification, Validation, and Accreditation Process: An Acquisition Community Sponsored M&S Project," was presented in September 2007.
Robot Workspace Geometry For Trajectory Feasibility Study

Due to the physical constraints of a robot manipulator, the successfulness of robot executing a planned Cartesian trajectory depends on the feasibility of this planned trajectory. As the constraints of robot's work-space, configuration and singularity can be described by the geometry of robot's workspace, the kinematic feasibility of a planned trajectory call be tested through workspace's geometry. Due to the complexity of the general case a robot manipulator with six revolute joints (6R) is selected as the case study in this paper. As a result, any point along a planned Cartesian trajectory can be determined whether it is inside the robot's workspace, if it is in the region of singularity, and be provided with the suitable robot configurations.
Elements of Electromagnetics

Using a vectors-first approach, Elements of Electromagnetics, Sixth Edition, explains electrostatics, magnetostatics, fields, waves, and applications like transmission lines, waveguides, and antennas. The book also provides a balanced presentation of time-varying and static fields, preparing students for employment in today's industrial and manufacturing sectors. Streamlined to facilitate student understanding, Elements of Electromagnetics, Sixth Edition, features worked examples in every chapter that explain how to use the theory presented in the text to solve different kinds of problems. It also covers numerical methods, including MATLAB and vector analysis, to help students analyze situations that they are likely to encounter in industry practice. The fully revised and updated sixth edition now features: *Fifteen new "Application Notes" that explain the connections between concepts discussed in the text and the real world*A math assessment that enables instructors to gauge their students' mathematical knowledge and preparedness for the course *Coverage of wave polarization (Chapter 10) *New and updated end-of-chapter problems A companion website for the book is available at http: //www.oup. com/us/sadiku and features PowerPoints of every image in the text.
Peptide-PAINT using a transfected-docker enables live- and fixed-cell super-resolution imaging

Point accumulation for imaging in nanoscale topography (PAINT) is a single-molecule technique for super-resolution microscopy, achieving ∼5-25 nanometer resolution. Here we show that by transfecting the protein-of-interest with a docker-coil, rather than by adding the docker externally—as is the norm when using DNA tethers or antibodies as dockers—we can achieve similar localization, ∼10 nm. However, using a transfected docker has several experimental advances and simplifications. Most importantly, it allows Peptide-PAINT to be applied to transfected live cells, including surface proteins in mammalian cells and neurons under physiological conditions. The enhance resolution of Peptide-PAINT is also shown for organelles in fixed cells to unravel structural details including ≈40-nm and ≈60-nm axial repeats in vimentin filaments in the cytoplasm, and fiber shapes of sub-100-nm histone-rich regions in the nucleus.
Implementation of a quaternion-based Kalman filter for human body motion tracking using MARG sensors

Human body motion tracking using inertial sensors requires an attitude estimation filter capable of tracking in all orientations. One way to represent orientation is to use Euler angles, but they have singularities and therefore are not suitable for human body tracking applications. Quaternions can be used to represent orientation without incurring singularities. A quaternion-based Kalman filter has been designed for this purpose and implemented in this thesis. Also, a new suboptimal algorithm to compute the quaternions based on magnetometer and accelerometer data is implemented. This new algorithm called “Factored Quaternion Algorithm” is computationally simpler than previous methods and provides a decoupling property from magnetometer and accelerometer data.
The Use of Constrained Cellular Automata for High-Resolution Modelling of Urban Land-Use Dynamics

A cellular automaton is specified to give a spatially detailed representation of the evolution of urban land-use patterns. Cell states represent land uses, and transition rules express the likelihood of a change from one state to another as a function both of existing land use in the 113-cell neighbourhood of the cell and of the inherent suitability of the cell for each possible use. The model is used to simulate the land-use pattern of Cincinnati, Ohio. The simulation results are realistic and sensitivity analysis shows that the predictions of the model are relatively accurate and reproducible, thus suggesting that cellular ast;automata-based models may be useful in a planning context.
Density Ratio Estimation in Machine Learning

Machine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications of systems that learn. This book introduces theories, methods, and applications of density ratio estimation, which is a newly emerging paradigm in the machine learning community. Various machine learning problems such as non-stationarity adaptation, outlier detection, dimensionality reduction, independent component analysis, clustering, classification, and conditional density estimation can be systematically solved via the estimation of probability density ratios. The authors offer a comprehensive introduction of various density ratio estimators including methods via density estimation, moment matching, probabilistic classification, density fitting, and density ratio fitting as well as describing how these can be applied to machine learning. The book also provides mathematical theories for density ratio estimation including parametric and non-parametric convergence analysis and numerical stability analysis to complete the first and definitive treatment of the entire framework of density ratio estimation in machine learning.
Acoustic synthesis and methodology for improving cochlear implant speech processing techniques

The evaluation of the global chain combining the electrical processing (cochlear implant) and the physiological processing can be efficiently achieved using a reverse model. The advantage of such a method is to measure the quality of speech processing technique for cochlear implant without needing implanted patients. This paper deals with an inverse processing technique presented as a powerful methodology for improving speech processing.
Application of PCA and Hough Transform to classify features in optical images

Viewing fine features of object with optical instruments have become increasingly difficult as the dimensions of many features of interest have become smaller than the traditional optical resolution limit. Examples of these features can be found in semiconductor components and biological tissues. This has caused a move to non-optical methods such as scanning electron and atomic force microscopy techniques, or optical methods combined with signal processing techniques to provide clearer images of samples. This thesis presents a method to increase the resolution of an optical system. This is achieved by using principal component analysis (PCA). Once the PCA measured the object image parameters, the new clearer image can be reconstructed based on these parameters. This process works extremely well. Various aspects of samples measured by the PCA have been investigated, such as the shift of sample, the sample with different sizes, the orientation of sample and the impact of noise. These studies show that the technique is extremely robust, and has huge potential for general usage. The thesis also contains the detail of the Hough Transform technique which was used to provide the initial parameters to the PCA. From the analysis of the technique, it is concluded that the accurate measurement of the technique can be achieved by providing adequate templates of the object image for the system.
Multiple carrier transmission with time-frequency well-localized impulses

Multiple carrier transmission represents a way to cope with the time and frequency selectivity of mobile radio channels. The signals are pulse shaped and transmitted on multiple carriers to reduce the multipath effects. The usually used rectangular or Nyquist root impulses permit a transmission with nearly optimal spectral efficiency, but they are not well localized either in time or in frequency so that the residual intersymbol interference (ISI) and interchannel interference (ICI) lower the system performance. Therefore, we present a new impulse to minimize ISI and ICI.
Robust Statistics for Multivariate Methods

Robust statistics, as a concept, probably dates back to the prehistory of statistics. It has, however, been formalized in the sixties by the pioneering work of Huber and Hampel. Robust statistics is an extension of classical statistics, which takes into account the fact that models assumed to have generated the data at hand are only approximate. It provides tools to investigate the robustness properties of a statistic T (such as estimators, test statistics) as well as robust estimators and robust testing procedures.
Estimates of error rates for codes on burst-noise channels

The error structure on communication channels used for data transmission may be so complex as to preclude the feasibility of accurately predicting the performance of given codes when employed on these channels. Use of an approximate error rate as an estimate of performance allows the complex statistics of errors to be reduced to a manageable table of parameters and used in an economical evaluation of large collections of error detecting codes. Exemplary evaluations of error detecting codes on the switched telephone network are included in this paper. On channels which may be represented by Gilbert's model of a burst-noise channel, the probabilities of error or of retransmission may be calculated without approximations for both error correcting and error detecting codes
A drowsy driver detection system for heavy vehicles

Driver drowsiness/fatigue is an important cause of combination-unit truck crashes. Drowsy driver detection methods can form the basis of a system to potentially reduce accidents related to drowsy driving. We report on efforts performed at the Carnegie Mellon Driving Research Center to develop such in vehicle driver monitoring systems. Commercial motor vehicle truck drivers were studied in actual fleet operations. The drivers operated vehicles that were equipped to measure vehicle performance and driver psychophysiological data. Based on this work, two drowsiness detection methods are being considered. The first is a video-based system that measures PERCLOS, a scientifically supported measure of drowsiness associated with slow eye closure. The second detection method is based on a model to estimate PERCLOS based on vehicle performance data. A non-parametric (neural network) model was used to estimate PERCLOS using measures associated with lane keeping, steering wheel movements and lateral acceleration of the vehicle.