Miracle-dz/UniFAR-FTUs huggingface dataset:
Extended Kalman Filter Methods for Tracking Weak GPS Signals

A combined phase-locked loop/delay-locked loop has been developed for tracking weak GPS C/A signals. This work enables the use of the weak side-lobe signals that are available at geosynchronous altitudes. The tracking algorithm is an extended Kalman filter (EKF) that estimates code phase, carrier phase, Doppler shift, rate of change of Doppler shift, carrier amplitude and data bit sign. It forms a likelihood function that depends on the errors between accumulations and their predicted values. It recursively minimizes this likelihood function in order to track the signal. It deals with data bit uncertainty using a Bayesian analysis that determines a posteriori probabilities for each bit sign. A second filter is used to initialize the EKF. This batch filter starts with coarse carrier frequency and code phase estimates and refines them using maximum likelihood techniques while estimating the carrier phase and the first PRN code period of a navigation data bit. The resulting system can acquire and maintain lock on a signal as weak as 15 dB Hz if the receiver clock is an ovenized crystal oscillator and if the line-of-sight acceleration variations are as mild as those seen by a geostationary user vehicle.
Social Skills: Laying the Foundation for Success.

Well-informed teachers of young children recognize the importance of children’s social development. The development of social skills lays a critical foundation for later academic achievement as well as work-related skills (McClelland & Morrison, 2003). Social development is such a key issue with young children that a number of methods to address social skills have been advocated. Some of these methods include  setting up classrooms to enhance social development,  providing play opportunities to promote social functioning, and  teaching social skills directly. Teaching social skills can incorporate a number of techniques, including direct instruction, learning from peers, prevention of problem behaviors, and children’s books. Many social behaviors are better learned among peers (Ladd, 2005), so teachers of young children are in a unique position to promote social learning in their classrooms. The purpose of this article is to provide teachers with several research-based strategies to promote young children’s social skills development.
The Association between Alcohol Consumption and β-Cell Function and Insulin Sensitivity in Korean Population

This cross-sectional study was performed to examine the association between alcohol consumption and insulin secretion and sensitivity using the Korean Genome and Epidemiology Study. Alcohol consumption levels were categorized into four groups: (i) abstainers, (ii) low (<5 g/day), (iii) intermediate (<30 g/day), and (iv) high (≥30 g/day) alcohol consumption. β-cell function and insulin sensitivity were estimated using the insulinogenic index (IGI60), and Matsuda insulin sensitivity index (ISI), respectively. IGI60 and ISI were dichotomized into high and low groups using median cut-off values and four groups were defined (G-I: high IGI60/high ISI; G-II: high IGI60/low ISI; G-III: low IGI60/high ISI; and G-IV: low IGI60/low ISI). Men consumed 26.5 g alcohol per day on average, whereas women only consumed 5.7 g/day, so women were excluded from subsequent analyses due to their low drinking levels. Alcohol consumption was positively associated with high-density lipoprotein (HDL) cholesterol, aspartate aminotransferase (AST), and triglycerides (TG) in men, but was negatively associated with IGI60 (p < 0.05). TG levels were only increased in individuals with decreased insulin sensitivity (G-II) or decreased β-cell function (G-III) with high alcohol consumption. In addition, alcohol consumption increased HDL cholesterol in the four groups (p < 0.001). In subjects with decreased insulin sensitivity (G-II), intermediate and high alcohol consumption increased the risk of high cholesterol and TG. In individuals with decreased β-cell function (G-III), alcohol consumption increased the risk of high TG and high AST levels. High alcohol consumption was significantly associated with reduced insulin secretion. In addition, alcohol consumption was related to some metabolic risk factors depending on insulin secretion or sensitivity.
Studying the Internet through the Ages

As a tribal elder, I often think back to the state of Internet and society scholarship before the dawning of the Internet. Although sociologist Roxanne Hiltz and computer scientist Murray Turoff had published their prophetic Network Nation in 1978, linking social science with computerized communication, the word “Internet” hadn’t been invented. As one of the first social scientists to be involved in research studying how people communicate online, I started going in 1990 to biannual gatherings of the then-tribe: CSCW (computer supported cooperative work) conferences that were dominated by computer scientists writing “groupware” applications. Lotus Notes applications were in vogue. Lab studies were the predominant research method of choice, summarized in Lee Sproull and Sara Kiesler’s Connections (1991). But all that people wanted to deal with were small closed groups. I remember standing lonely and forlorn at the microphone during a comments period at the CSCW 1992 conference. Feeling extremely frustrated (and now prophetic), I exclaimed:
Incorporation of Spatial Interactions in Location Networks to Identify Critical Geo-Referenced Routes for Assessing Disease Control Measures on a Large-Scale Campus

Respiratory diseases mainly spread through interpersonal contact. Class suspension is the most direct strategy to prevent the spread of disease through elementary or secondary schools by blocking the contact network. However, as university students usually attend courses in different buildings, the daily contact patterns on a university campus are complicated, and once disease clusters have occurred, suspending classes is far from an efficient strategy to control disease spread. The purpose of this study is to propose a methodological framework for generating campus location networks from a routine administration database, analyzing the community structure of the network, and identifying the critical links and nodes for blocking respiratory disease transmission. The data comes from the student enrollment records of a major comprehensive university in Taiwan. We combined the social network analysis and spatial interaction model to establish a geo-referenced community structure among the classroom buildings. We also identified the critical links among the communities that were acting as contact bridges and explored the changes in the location network after the sequential removal of the high-risk buildings. Instead of conducting a questionnaire survey, the study established a standard procedure for constructing a location network on a large-scale campus from a routine curriculum database. We also present how a location network structure at a campus could function to target the high-risk buildings as the bridges connecting communities for blocking disease transmission.
DEEP BRAIN STIMULATION IN MOVEMENT DISORDERS

After more than 2 decades since it was introduced, deep brain stimulation (DBS) has gained widespread popularity as a surgical treatment for medically refractory symptoms of Parkinson disease, essential tremor, and dystonia. In this chapter, authors review the benefits, risks, indications, limitations, and targets for DBS in patients with Parkinson disease, essential tremor, and dystonia. A brief outline of the DBS procedure and programming is also given.
Auto-Programming for Numerical Data Based on Remnant-Standard-Deviation-Guided Gene Expression Programming

Automatically numerical data modeling and computer code generation is significant for data mining, data reverse engineering, engineering applications, etc. On auto-programming for numerical data, a new approach, Remnant-standard-Deviation-guided Gene Expression Programming (RD-GEP), was proposed. New individual structure, the K-expression to Reverse Polish Notation code generation without expression tree construction algorithm (K2RPN), and remnant-standard-deviation based fitness evaluation method in RD-GEP were presented and studied. New individual structure makes easy to I/O or storage the candidate solution. New decoding algorithm with linear-time complexity can simplify system operation and unify I/O format. New evaluation mechanism can reduce hypothesis solution space to improve system performance and precision. Feasibility and usability of RD-GEP were verified on various synthetic data sets and real “Fishcatch” data set. Experimental results showed RD-GEP is good at automatically modeling numerical data and generating reverse polish notation for target model.
A mixed dataflow algorithm for ray tracing on the CRAY T3E

The ray tracing scheme is one of the most complete and efficient rendering methods. A major drawback of this model is its high computational cost which limits its practical use. Moreover, the quest for realistic rendering requires larger and larger databases to describe scenes. With the development of distributed memory parallel computers such as the CRAY T3E, the most promising way to improve ray traced pictures productions seems to be parallelization which offers both increased CPU power and memory facilities. In the ray tracing algorithm, each pixel of the screen is processed independently. A natural way of parallelization is to distribute pixels over the machine nodes. However, since we want to deal with large scenes, objects also have to be distributed among processors, so a modified parallel algorithm is necessary. Strategies based on object dataflow have been proposed, but their communication load is too high. More efficient algorithms have to reduce the number of messages. Therefore we propose a mixed dataflow approach : each message will contain several pieces of information on both objects and rays. By this way, we hope to limit the communication load and to ensure a dynamic load balancing. A parallel ray tracing algorithm on the T3E using our mixed dataflow approach is implemented. The results are very encouraging since the computation time and the communication flows may be reduced by significant factors. Scalability is globally improved, mainly because saturation occurs for larger problem sizes.
Workbook 8 · Economic Evaluations Economic Evaluations Workbook 8 2 Evaluation of Psychoactive Substance Use Disorder Treatment Who World Health Organization Undcp United Nations International Drug Control Programme Emcdda European Monitoring Center on Drugs and Drug Addiction World Health Organizat

c This document is not a formal publication of the World Health Organization (WHO) and all rights are reserved by the Organization. The document may, however, be freely reviewed, abstracted, reproduced and translated, in part or in whole but not for sale nor for use in conjunction with commercial purposes. The views expressed in documents by named authors are solely the responsibility of those authors.
Active learning for the prediction of prosodic phrase boundaries in Chinese speech synthesis systems using conditional random fields

Prosodic structure contributes to speech production and comprehension. One of the crucial problems in achieving natural-sounding synthesized speech is the prediction of appropriate phrase boundaries. Unfortunately, obtaining human annotations of prosodic phrases to train a supervised system can be laborious and costly. Active learning has been proven effective in reducing labeling efforts for supervised learning. This study explores active learning techniques with the objective to reduce the amount of human-annotated data needed to attain a given level of performance. It presents an approach based on active learning to predict the Chinese prosodic phrase boundaries in unrestricted Chinese text. Experiments show that for most of the cases considered, the active selection strategies for labeling the prosodic phrase boundaries are as good as or exceed the performance of random data selection.
Semi-Supervised Regression with Co-Training Style Algorithms

The traditional setting of supervised learning requires a large amount of labeled training examples in order to achieve good generalization. However, in many practical applications, unlabeled training examples are readily available but labeled ones are fairly expensive to obtain. Therefore, semi-supervised learning has attracted much attention. Previous research on semi-supervised learning mainly focuses on semi-supervised classification. Although regression is almost as important as classification, semisupervised regression is largely understudied. In particular, although co-training is a main paradigm in semi-supervised learning, few works has been devoted to co-training style semi-supervised regression algorithms. In this paper, a co-training style semi-supervised regression algorithm, i.e. COREG, is proposed. This algorithm uses two regressors each labels the unlabeled data for the other regressor, where the confidence in labeling an unlabeled example is estimated through the amount of reduction in mean square error over the labeled neighborhood of that example. Analysis and experiments show that COREG can effectively exploit unlabeled data to improve regression estimates.
Use of immediate-early gene expression to map relationships between limbic structures supporting memory

This thesis explores the influence of brain regions within the "extended hippocampal memory system" on the activity of the retrosplenial cortex in the rat. One of the first goals was to use lesion studies to improve the understanding of the vulnerability of the retrosplenial cortex, especially in the context of diencephalic and temporal lobe amnesia. The second was to assess what are the brain areas within the temporal lobe involved in object recognition and how they interact. These two objectives were made possible by visualising immediate-early gene expression. By combining this technique with lesions, distal effects of different lesions (hippocampus, mammillothalamic tract and fornix) on the activity of the retrosplenial cortex were measured. For object recognition, the immediate-early gene imaging enabled the assessment of normal brain activity in rats associated with behavioural discrimination of novelty. The lesion studies provide information about the specific and common vulnerability of the retrosplenial cortex, as all three distal lesions resulted in a decrease of immediate-early gene activity in the retrosplenial cortex. In addition, these findings unify diencephalic amnesia with temporal amnesia, and emphasize the need to study networks or systems instead of individual structure. The immediate-early gene/object recognition experiment implicated the caudal part of the perirhinal cortex (and Te2) and of the hippocampus in object recognition, and highlighted the importance of mapping brain region relationships within a connected system. Taken together, these experiments provide clear support for the concept of an extended hippocampal memory system, but also show how this system may interact with other structures involved in different forms of memory. The findings underlie the potential afforded by use of immediate-early gene expression techniques in animal studies.
Binary Decision Diagrams for Affine Approximation

Selman and Kautz's work on ``knowledge compilation'' established how approximation (strengthening and/or weakening) of a propositional knowledge-base can be used to speed up query processing, at the expense of completeness. In this classical approach, querying uses Horn over- and under-approximations of a given knowledge-base, which is represented as a propositional formula in conjunctive normal form (CNF). Along with the class of Horn functions, one could imagine other Boolean function classes that might serve the same purpose, owing to attractive deduction-computational properties similar to those of the Horn functions. Indeed, Zanuttini has suggested that the class of affine Boolean functions could be useful in knowledge compilation and has presented an affine approximation algorithm. Since CNF is awkward for presenting affine functions, Zanuttini considers both a sets-of-models representation and the use of modulo 2 congruence equations. In this paper, we propose an algorithm based on reduced ordered binary decision diagrams (ROBDDs). This leads to a representation which is more compact than the sets of models and, once we have established some useful properties of affine Boolean functions, a more efficient algorithm.
‘Authoring’ Open Innovation: The Managerial Practices of an Open Innovation Director

Abstract This chapter asks how we can understand the managerial practices in open innovation, a recently popularized way of organizing innovative work. Open innovation implies opening up the borders of the organization, creating a context where conventional steering and managerial tools no longer apply. Utilizing a collaborative research approach, following an open innovation collaboration over 8 years, this chapter outlines the managerial practices that direct the collaboration. These practices are important for meaning making and identity creation in the collaboration and can be understood as a form of authorship, a continuous intervention strategy to manage, develop and change the organizational context.
Conceptual Modeling of Data-Intensive Web Applications

Many of the Web applications around us are data-intensive; their main purpose is to present a large amount of data to their users. Most online trading and e-commerce sites fall into this category, as do digital libraries and institutional sites describing private and public organizations. Several commercial Web development systems aid rapid creation of data-intensive applications by supporting semiautomatic data resource publishing. Automatic publishing is typically subject to the constraints of database schemas, which limit an application designer's choices. Thus, Web application development often requires adaptation through programming, and programs end up intricately mixing data, navigation, and presentation semantics. Presentation is often a facade for elements of structure, composition, and navigation. Despite this frequently unstructured development process, data-intensive applications, based on large data sets organized within a repository or database, generally follow some typical patterns and rules. We describe these patterns and rules using WebML as a conceptual tool to make such notions explicit. WebML is a conceptual Web modeling language that uses the entity-relationship (ER) model for describing data structures and an original, high-level notation for representing Web content composition and navigation in hypertext form.
A randomized, double-blind, placebo-controlled study of an RNAi-based therapy directed against respiratory syncytial virus

RNA interference (RNAi) is a natural mechanism regulating protein expression that is mediated by small interfering RNAs (siRNA). Harnessing RNAi has potential to treat human disease; however, clinical evidence for the effectiveness of this therapeutic approach is lacking. ALN-RSV01 is an siRNA directed against the mRNA of the respiratory syncytial virus (RSV) nucleocapsid (N) protein and has substantial antiviral activity in a murine model of RSV infection. We tested the antiviral activity of ALN-RSV01 in adults experimentally infected with wild-type RSV. Eighty-eight healthy subjects were enrolled into a randomized, double-blind, placebo-controlled trial. A nasal spray of ALN-RSV01 or saline placebo was administered daily for 2 days before and for 3 days after RSV inoculation. RSV was measured serially in nasal washes using several different viral assays. Intranasal ALN-RSV01 was well tolerated, exhibiting a safety profile similar to saline placebo. The proportion of culture-defined RSV infections was 71.4 and 44.2% in placebo and ALN-RSV01 recipients, respectively (P = 0.009), representing a 38% decrease in the number of infected and a 95% increase in the number of uninfected subjects. The acquisition of infection over time was significantly lower in ALN-RSV01 recipients (P = 0.007 and P = 0.03, viral culture and PCR, respectively). Multiple logistic regression analysis showed that the ALN-RSV01 antiviral effect was independent of other factors, including preexisting RSV antibody and intranasal proinflammatory cytokine concentrations. ALN-RSV01 has significant antiviral activity against human RSV infection, thus establishing a unique proof-of-concept for an RNAi therapeutic in humans and providing the basis for further evaluation in naturally infected children and adults.
Distributed graph query processing in dynamic networks

In this paper we examine a popular network computational model (BSP: Bulk Synchronous Parallel) that has been adopted by the Google Pregel system to support large scale graph processing. We show that the synchronicity assumption made by the BSP model, while acceptable in data center like environments with strong and persistent network connectivity, can result in severe performance penalties in the context of dynamic networks. We introduce a new computational model (BAP: Bulk Asynchronous Parallel) that preserves the bulk and parallel nature of the BSP model but extends the model to asynchronous network communication. We consider two popular classes of graph queries (random walk queries and shortest path queries), present both BSP and BAP algorithms for these queries and evaluate their performance using realistic graphs datasets (DBLP and Flickr) and dynamic network datasets (Infocom06 and MIT Reality dataset). Our initial results show that in dynamic networks BAP algorithms can achieve several orders of magnitude in improvement for various QoI metrics such as accuracy and latency of (partial and complete) query evaluation.
Etkili Matematik Öğretiminin Gerçekleştirilmesindeki Engellerden Biri:Kaygı ve Nedenleri

Bu arastirmada, ogrencilerin algisina gore etkili matematik ogretiminin gerceklestirilmesindeki engellerden biri olan matematik kaygisina yol acan nedenleri belirlemek amaclanmistir. Bu amac dogrultusunda, nitel arastirma yontemi kullanilmistir. Arastirmanin orneklemi, amaca yonelik ve tipik ornekleme yontemleri kullanilarak belirlenmistir. Arastirmada, Denizli ilinde bulunan uc resmi ilkogretim okulundan ikiser sinif tespit edilmis ve bu siniflardan ucer ogrenci secilerek 18 ogrenciden olusan orneklem olusturulmustur. Yeterli ve etkili veri elde edebilmek icin yari-yapilandirilmis gorusme teknigi kullanilmistir. Orneklemdeki ogrenciler, matematik dersinde kaygili olduklarinin farkinda olduklarini vurgulamislar ve bu kayginin nedenini kendilerine, ogretmenlerine, ailelerine ve arkadaslarina baglamislardir. Bu arastirmanin sonuclari ile bu alana akademik anlamda katki saglama amacinin yani sira ogretmen, ogrenci ve ailelere matematik kaygisinin nedenleri sunulmustur. Ayrica, bu arastirmanin sonuclarinin matematik kaygisi ile basa cikabilmek icin yol gosterici olacagi dusunulmektedir.