Miracle-dz/UniFAR-FTUs huggingface dataset:
Alcohol use affects sleep duration among military couples

ABSTRACT Alcohol misuse and sleep disorders are highly comorbid, prevalent among service members and their romantic partners, and affected by relationship interdependence. As most military health research focuses on either service members or their spouses, the current study examined dyadic effects of alcohol use on sleep cycle duration in dating and married military couples (N = 149 dyads), using data from the National Longitudinal Study of Adolescent Health. Person-level results from a series of multilevel path models showed partial support for our focal hypothesis implicating high alcohol use in shortened average sleep duration for the service members and their romantic partners. Specifically, partners of service members who drank more regularly had shorter average sleep durations, as did female service members who drank more alcohol per drinking occasion. At the couple-level, a partner effect indicated that service members’ lower depressive symptoms were associated with their partner’s shorter average sleep durations. In addition, when service members reported relatively high alcohol-related problems, their partner tended to have a shorter average sleep duration. In contrast, when service members’ partners reported relatively high alcohol-related problems, they had a longer average sleep duration. This suggests the consequences of problematic alcohol use for service members’ partners depended on each dyad member’s drinking patterns. Taken together, these findings underscore the importance of viewing military couples as a dyadic unit in research studies and clinical interventions.
Social signaling in decision making

Nonverbal communication is an important and often underestimated instrument in social interactions. The paralinguistic elements of speech, which are described in common speech as "tone of voice", are one channel of the nonverbal communication. They, together with conversational dynamics, are a very powerful measurement for interactions, without looking at the content of the conversation. A subset of these features, the social signaling measurements, are very useful when analyzing and quantifying conversation. The goal of this thesis is to better understand the framework of social signaling. We applied the social signaling measurements to negotiations as well as to small group interactions. For negotiation we were able to predict up to 30% of the variance in individual outcome. The use of automated algorithms enables to build real-time feedback mechanisms that can then help users to improve and achieve their objectives. Thesis Supervisor: Alex P. Pentland Title: Toshiba Professor of Media Arts and Sciences, Program in Media Arts and Sciences Thesis Supervisor: Dan Ariely Title: Luis Alvarez Renta Professor of Behavioral Economics, Program in Media Arts and Sciences Social Signaling in Decision Making by Ron Caneel The following people served as readers for this thesis: teader V -90 Nalini Ambady Professor of Psychology Tufts University Thesis F
Weights matter: Improving the predictive validity of risk assessments for criminal offenders

Abstract This article explored whether the use of optimal weights can improve the predictive validity of risk assessments for criminal offenders, an issue the literature has long been divided on. By analyzing the unweighted instrument Level of Service Inventory – Revised (LSI-R), the study provided evidence in favor of optimal weights, showing that a weighted instrument clearly outperforms an unweighted one in predicting recidivism. The magnitude of the difference in performance would not be expected by some of the literature. The study also showed that objective and easily measurable items of such instruments were associated with larger weights than subjective items.
Predicting Transmembrane Helix Packing Arrangements using Residue Contacts and a Force-Directed Algorithm

Alpha-helical transmembrane proteins constitute roughly 30% of a typical genome and are involved in a wide variety of important biological processes including cell signalling, transport of membrane-impermeable molecules and cell recognition. Despite significant efforts to predict transmembrane protein topology, comparatively little attention has been directed toward developing a method to pack the helices together. Here, we present a novel approach to predict lipid exposure, residue contacts, helix-helix interactions and finally the optimal helical packing arrangement of transmembrane proteins. Using molecular dynamics data, we have trained and cross-validated a support vector machine (SVM) classifier to predict per residue lipid exposure with 69% accuracy. This information is combined with additional features to train a second SVM to predict residue contacts which are then used to determine helix-helix interaction with up to 65% accuracy under stringent cross-validation on a non-redundant test set. Our method is also able to discriminate native from decoy helical packing arrangements with up to 70% accuracy. Finally, we employ a force-directed algorithm to construct the optimal helical packing arrangement which demonstrates success for proteins containing up to 13 transmembrane helices. This software is freely available as source code from http://bioinf.cs. ucl.ac. uk/memsat/mempack/.
Epidermal Growth Factor Removal or Tyrphostin AG1478 Treatment Reduces Goblet Cells & Mucus Secretion of Epithelial Cells from Asthmatic Children Using the Air-Liquid Interface Model

Rationale Epithelial remodelling in asthma is characterised by goblet cell hyperplasia and mucus hypersecretion for which no therapies exist. Differentiated bronchial air-liquid interface cultures from asthmatic children display high goblet cell numbers. Epidermal growth factor and its receptor have been implicated in goblet cell hyperplasia. Objectives We hypothesised that EGF removal or tyrphostin AG1478 treatment of differentiating air-liquid interface cultures from asthmatic children would result in a reduction of epithelial goblet cells and mucus secretion. Methods In Aim 1 primary bronchial epithelial cells from non-asthmatic (n = 5) and asthmatic (n = 5) children were differentiated under EGF-positive (10ng/ml EGF) and EGF-negative culture conditions for 28 days. In Aim 2, cultures from a further group of asthmatic children (n = 5) were grown under tyrphostin AG1478, a tyrosine kinase inhibitor, conditions. All cultures were analysed for epithelial resistance, markers of differentiation using immunocytochemistry, ELISA for MUC5AC mucin secretion and qPCR for MUC5AC mRNA. Results In cultures from asthmatic children the goblet cell number was reduced in the EGF negative group (p = 0.01). Tyrphostin AG1478 treatment of cultures from asthmatic children had significant reductions in goblet cells at 0. 2μg/ml (p = 0.03) and 2μg/ml (p = 0.003) as well as mucus secretion at 2μg/ml (p = 0.04). Conclusions We have shown in this preliminary study that through EGF removal and tyrphostin AG1478 treatment the goblet cell number and mucus hypersecretion in differentiating air-liquid interface cultures from asthmatic children is significantly reduced. This further highlights the epidermal growth factor receptor as a potential therapeutic target to inhibit goblet cell hyperplasia and mucus hypersecretion in asthma.
Temporal models for microblogs

Time information impacts relevance in retrieval for the queries that are sensitive to trends and events. Microblog services particularly focused on recent news and events so dealing with the temporal aspects of microblogs is essential for providing effective retrieval. Recent work on time-based retrieval has shown that selecting the relevant time period for query expansion is promising. In this paper, we suggest a method for selecting the time period for query expansion based on a user behavior (i.e., retweets) that can be collected easily. We then use these time periods for query expansion in a pseudo-relevance feedback setting. More specifically, we use the difference in the temporal distribution between the top retrieved documents and retweets. The experimental results based on the TREC Microblog collection show that our method for selecting periods for query expansion improves retrieval performance compared to another approach.
Real-time Facial Expression Recognition in Image Sequences Using an AdaBoost-based Multi-classifier

—In this paper, a highly automatic facial expression recognition system without choosing characteristic blocks in advance is presented. The system is able to detect and locate human faces in image sequences acquired in real environments. To achieve efficient facial expression recognition, we evaluate the performance of three different classifiers using multi-layer perceptrons (MLPs), support vector machines (SVMs), and Adaboost algorithms (ABAs). From the experimental outcomes, we can observe that the average recognition rates obtained from both ABAs and MLPs are better than that from SVMs, but the training of MLPs takes quite a long time. Comparatively, ABAs have an advantage of facilitating the speed of convergence, which are chosen as the core technique to implement our strong facial expression classifier. Through conducting many experiments, the statistics of performance reveals that the accuracy rate of our facial expression recognition system reaches more than 90% for a single kind or multiple kinds of expressions appearing in an image sequence.
Time Series Analysis of Demand Patterns in Supply Chains

of the Master's Thesis Instructor Professor Ahti Salo This thesis focuses on the application of analysis techniques for demand data analysis in a supply chain. The emphasis is on the time series approach but some other useful analyses are also presented. The thesis suggests a systematic approach at demand analysis and explores its importance in planning and forecasting. The study is performed in the context of a case company and accompanied by a literature review of common problems and best practices. The analysis part is carried out on actual demand data from the case company. The case environment serves also as a basis for the subsequent demand management discussion. The literature review examines topics such as demand amplification, systems structures and forecasting. The main ideas of the thesis are presented in two parts. First, an overview of analysis techniques applicable to supply chains is presented and their use is demonstrated with actual data from the case company. The results are interpreted and linked to the supply chain challenges discussed in the literature review. Second, a framework is formulated for using demand information in the planning process. The framework illustrates how the different sources of information can be used in planning during the product's life cycle. Data sources and their use, as well as forecasting based on these sources, are further discussed. Finally, recommendations are made on the further development of the analysis process and what other measures should be undertaken to facilitate planning. These include a life cycle based planning model and data processing and analysis procedures.
An Overview of Multigigabit Wireless through Millimeter Wave Technology: Potentials and Technical Challenges

This paper presents an overview of 60 GHz technology and its potentials to provide next generation multigigabit wireless communications systems. We begin by reviewing the state-of-art of the 60 GHz radio. Then, the current status of worldwide regulatory efforts and standardization activities for 60 GHz band is summarized. As a result of the worldwide unlicensed 60 GHz band allocation, a number of key applications can be identified using millimeter-wave technology. Despite of its huge potentials to achieve multigigabit wireless communications, 60 GHz radio presents a series of technical challenges that needs to be resolved before its full deployment. Specifically, we will focus on the link budget analysis from the 60 GHz radio propagation standpoint and highlight the roles of antennas in establishing a reliable 60 GHz radio.
Correlation Velocity Measurement Technology Based on Linear CCD

To measure the velocity with high precision, the correlation velocity measurement technology based on linear CCD is developed in this paper. The displacement of moving object between two adjacent moments is measured by detecting light intensity of the light-emitting particles from the surface of moving object. Then the velocity of the moving object can easily be calculated. Simulations are conducted to measure the velocity of the moving object in uniform linear motion under ideal conditions. On the basis of the principle of correlation velocity measurement, the velocity which is measured makes a comparison with its actual velocity of the moving object. The results show that the proposed method of velocity measurement has the advantages of high precision and wide range to meet the requirements for velocity measurement of the vehicles.
An efficient iterative detection scheme for coded MIMO systems

This paper presents a complexity reduced maximum likelihood (ML) or maximum a posteriori (MAP) based iterative MIMO detection scheme. The a priori loglikelihood ratios (LLRs) provided by the decoder have a Gaussian distribution. By using this statistical characteristics of the LLRs, we calculate the mean and variance of LLRs provided by the decoder. Subsequently, a threshold value is calculated, and on the basis of the threshold value the MIMO detector makes the decision about calculating the extrinsic LLRs or using the same LLRs values provided by the decoder for a particular symbol transmitted from each transmit antenna. Simulation results show that the proposed complexity reduction method produces nearly the same bit error rate (BER) performance as the full search MAP with about a 30% of the complexity reduction. Complexity reduction can be controlled by adjusting the parameters of the proposed method.
Research on Parallel Machine Architecture for Fifth-Generation Computer Systems

The Institute for New Generation Computer Technology in Tokyo, known as the ICOT, has been conducting studies related to the development of fifth-generation computer systems since 1982. The FGCS project is concerned with the implementation of a prototype for new computer systems which are able to support and improve various activities of human intelligence, taking into account activities related to production. As these activities depend on human knowledge and human reasoning capabilities, questions arise regarding the approaches which can be used to endow computers with these human-specific powers. The major research problem of the FGCS project is concerned with these questions. Attention is given to the study of four mechanisms for the inference machine, and the development of a knowledge base machine. 23 references.
ISCC ’ 04 Submission 1 Improving Wireless Link Throughput via Interleaved FEC

Wireless communication is inherently vulnerable to errors from the dynamic wireless environment. Link layer packets discarded due to these errors impose a serious limitation on the maximum achievable throughput in the wireless channel. To enhance the overall throughput of wireless communication, it is necessary to have a link layer transmission scheme that is robust to the errors intrinsic to the wireless channel. To this end, we present Interleaved-Forward Error Correction (I-FEC), a clever link layer coding scheme that protects link layer data against random and busty errors. We examine the level of data protection provided by I-FEC against other popular schemes. We also simulate I-FEC in Bluetooth, and compare the TCP throughput result with Bluetooth’s integrated FEC coding feature. We show that I-FEC consistently and significantly outperforms other link layer coding schemes by providing an impressive amount of protection against heavy channel burst errors. Keyword: Interleaving, FEC, Gilbert-Elliott model, Burst Error, Bluetooth
Beyond consensus and conflict in housing governance: Returning to the local state

This article contends that the de-politicizing tendencies in urban planning that are often interpreted through a post-political frame of analysis might alternatively be investigated via the analytical lens of a transforming local state. Examining the formation of entrepreneurial municipal housing strategies in Gateshead, northeast England, the article reveals a recent history of community consensus being manipulated, a technocratic steering of participatory planning and a de-amplifying of dissenting voices. Amid protracted conditions of austerity, the more recent strategy sees Gateshead municipal authority assuming an increasingly ‘promotional’ role, essentially as a housing developer. Placing critical decisions over housing futures within Gateshead Regeneration Partnership, a potentially rough road towards attaining democratic legitimacy is actively being smoothed. These are all trends that are emblematic of a post-political repertoire. However, a more forensic examination reveals how these anti-democratic processes might be more appropriately understood as political accomplishments on the part of those who have been newly incorporated into a local state in transition. The article thereby offers a conceptual antidote to the post-political narrative.
Informative Content Extraction By Using Eifce [Effective Informative Content Extractor]

: Internet web pages contain several items that cannot be classified as the ―informative content,‖ e.g., search and filtering p anel, navigation links, advertisements, and so on. Most clients and end-users search for the informative content, and largely do not seek the non-informative content. As a result, the need of Informative Content Extraction from web pages becomes evident. Two steps, Web Page Segmentation and Informative Content Extraction, are needed to be carried out for Web Informative Content Extraction. DOM-based Segmentation Approaches cannot often provide satisfactory results. Vision-based Segmentation Approaches also have some drawbacks. So this paper proposes Effective Visual Block Extractor (EVBE) Algorithm to overcome the problems of DOM-based Approaches and reduce the drawbacks of previous works in Web Page Segmentation. And it also proposes Effective Informative Content Extractor (EIFCE) Algorithm to reduce the drawbacks of previous works in Web Informative Content Extraction. Web Page Indexing System, Web Page Classification and Clustering System, Web Information Extraction System can achieve significant savings and satisfactory results by applying the Proposed Algorithms.
Prevalence of Pachinko-Pachislot Playing Disorder and the Characteristics of Individuals with the Disorder: Analysis of National Pachinko/Pachislot Survey Results

To understand the reality of gambling disorder caused by playing pachinko/pachislot, a national pachinko/pachislot survey was conducted. Based on the survey result, we examined the appearance ratio and the characteristics of individuals with a suspected pachinko-pachislot playing disorder. The analysis result indicated that approximately 0.4% of survey respondents were suspected to have the disorder. This appearance ratio was comparable to the prevalence of gambling disorder in the general population. Those who had a suspected disorder were likely to be divorced, and have no savings. Meanwhile, no distinctive characteristics were found in their gender, annual household income, or the highest educational attainment.
Single-channel dynamic exemplar-based speech enhancement

This paper proposes an exemplar-based speech enhancement method based on high-resolution STFT magnitude spectrograms, where a selection of the nonnegative training data is used as the dictionary to provide a holistic nonnegative representation of the test data. We discuss how this exemplar-based model ensures that the enhanced speech signal falls on the speech manifold, which improves the quality of the enhanced speech signal. To exploit the temporal continuity, a vector autoregressive model is used to model the activations where the model parameters are learned using a new NMF-based approach. Results from several supervised and semi-supervised speech enhancement experiments indicate that the proposed exemplar-based method outperforms the considered supervised and unsupervised denoising algorithms in terms of both segmental SNR and PESQ at different input SNRs. Index Terms: nonnegative matrix factorization, exemplarbased noise reduction, overcomplete dictionary
FastInf : A Fast Algorithm to Infer Social Networks from Cascades Altan

The structure of social networks, i.e., the edges among nodes, are the fundamental information we need for future studies. However it may not be easily observed in many cases, such as flu infection, shopping recommendation, etc. Therefore it is an interesting and important problem to infer the networks from the observed data, e.g., information propagation cascades. Pervious works have focused on accuracy and developed several complex models to solve this problem. However in the real world those solutions are not efficient nor scalable enough to be used in big data environment. For example, people generate billions of records every day on the biggest social media websites such as Twitter and Facebook. We develop a very efficient and scalable algorithm, FastInf, to infer the hidden networks from cascades. Although its speed is much faster than other algorithms, we still can keep its accuracy at least close to the state of the art, and in some cases it is even better. We also provide a Map-Reduce implementation of this algorithm, which makes it feasible in the real-world industry environment.
Hadoop Extensions for Distributed Computing on Reconfigurable Active SSD Clusters

In this article, we propose new extensions to Hadoop to enable clusters of reconfigurable active solid-state drives (RASSDs) to process streaming data from SSDs using FPGAs. We also develop an analytical model to estimate the performance of RASSD clusters running under Hadoop. Using the Hadoop RASSD platform and network simulators, we validate our design and demonstrate its impact on performance for different workloads taken from Stanford's Phoenix MapReduce project. Our results show that for a hardware acceleration factor of 20×, compute-intensive workloads processing 153MB of data can run up to 11× faster than a standard Hadoop cluster.
Design and System Integration for the Expo.02 Robot

This paper presents the genesis of the Expo.02 robot. RoboX the tour guide robot has been built from the scratch for this project based on the experience of the Autonomous Systems Lab. The production of 11 of those machines has been guaranteed by a spin-off of the lab: BlueBotics SA. The goal was to maximize the autonomy and interactivity of the mobile platform while ensuring high robustness, reliability and performance. The result is an interactive moving machine which can operate in human environments and interact by seeing humans, talking to and looking at them, showing icons and asking them to answer its questions. Here, the complete design of mechanics, electronics and software is presented first, followed by the statistics about the first two month of operation.