Miracle-dz/UniFAR-FTUs huggingface dataset:
Salivary Oxytocin Concentration Associates with the Subjective Feeling of Body Ownership during the Rubber Hand Illusion

Oxytocin is a hormone of the posterior pituitary that promotes lactation, maternal bonding, and birth. Recent studies have shown that oxytocin may modulate social recognition in both sexes, and thus it may be related to empathy. Brain regions that are associated with social recognition and empathy (e.g., the insular cortex) are activated in the rubber hand illusion (RHI), which involves illusory ownership of a rubber hand caused by brush strokes applied synchronously to both a rubber hand and one of the participant's hand, which is hidden from view. It is intriguing to examine whether oxytocin modulates plastic changes in body representation, such as the changes occurring in the RHI. In the present study, we investigated the relationship between salivary oxytocin concentration and the feeling of rubber hand ownership. Brush strokes were applied synchronously or asynchronously to the participant's hand and a rubber hand on different days. Salivary oxytocin was measured before and after the behavioral tasks. We found that participants who had high concentrations of salivary oxytocin tended to feel strong ownership of the rubber hand. We also found that the participants with a high autism spectrum quotient (AQ) score who particularly felt difficulties in social skills and communications tended to feel weak rubber hand ownership. We observed that illusory body ownership was closely linked to social communications and a related neuroendocrine basis. The results of the present study suggest that an individual's salivary oxytocin concentration can predict the extent to which the individual experiences the RHI; furthermore, oxytocin might modulate the sensation of body ownership.
Estimating a Dynamic Oligopolistic Game with Serially Correlated Unobserved Production Costs∗

We propose a likelihood based method that relies on sequential importance sampling to estimate dynamic discrete games of complete information with serially correlated unobserved state variables. Our method is applicable to similar games that have a Markovian representation of the latent dynamics and an algorithm to solve the game. We apply the method to a dynamic oligopolistic game of entry for the generic pharmaceutical industry in which the production costs of firms are the serially correlated unobserved state variable. Costs evolve dynamically and endogenously in response to past entry decisions, leading to heterogeneity among firms regardless of whether they are ex ante identical or heterogeneous. We find that there are significant spillovers of entry on costs. Each entry on average reduces costs by 7% at the next market opportunity; the average annual cumulative reduction is 51%. Our results provide evidence on the dynamic spillover effects of industry experience on subsequent market performance. The dynamic evolution of production cost plays an important role in the equilibrium path of the structure of the generic pharmaceutical industry.
Speech and Language Processing: an Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. 8 Speech Synthesis

And computers are getting smarter all the time: Scientists tell us that soon they will be able to talk to us. (By 'they' I mean 'comput-ers': I doubt scientists will ever be able to talk to us.) In Vienna in 1769, Wolfgang von Kempelen built for the Empress Maria Theresa the famous Mechanical Turk, a chess-playing automaton consisting of a wooden box filled with gears, and a mannequin sitting behind the box who played chess by moving pieces with his mechanical arm. The Turk toured Europe and the Americas for decades, defeating Napolean Bonaparte and even playing Charles Babbage. The Mechanical Turk might have been one of the early successes of artificial intelligence if it were not for the fact that it was, alas, a hoax, powered by a chessplayer hidden inside the box. What is perhaps less well-known is that von Kempelen, an extraordinarily prolific inventor, also built between 1769 and 1790 what is definitely not a hoax: the first full-sentence speech synthesizer. His device consisted of a bellows to simulate the lungs, a rubber mouthpiece and a nose aperature, a reed to simulate the vocal folds, various whistles for each of the fricatives. and a small auxiliary bellows to provide the puff of air for plosives. By moving levers with both hands, opening and closing various openings, and adjusting the flexible leather 'vocal tract', different consonants and vowels could be produced. More than two centuries later, we no longer build our speech synthesizers out of wood, leather, and rubber, nor do we need trained human operators. The modern task of speech synthesis, also called text-to-speech or TTS, is to produce speech (acoustic SPEECH SYNTHESIS TEXT-TO-SPEECH TTS waveforms) from text input. Modern speech synthesis has a wide variety of applications. Synthesizers are used, together with speech recognizers, in telephone-based conversational agents that conduct dialogues with people (see Ch. 23). Synthesizer are also important in non-conversational applications that speak to people, such as in devices that read out loud for the blind, or in video games or children's toys. Finally, speech synthesis can be used to speak for sufferers of neurological disorders, such as astrophysicist Steven Hawking who, having lost the use of his voice due to ALS, speaks by typing to a speech synthesizer and having the synthesizer speak out the words. State of the art
A comparison of computational color constancy algorithms. I: Methodology and experiments with synthesized data

We introduce a context for testing computational color constancy, specify our approach to the implementation of a number of the leading algorithms, and report the results of three experiments using synthesized data. Experiments using synthesized data are important because the ground truth is known, possible confounds due to camera characterization and pre-processing are absent, and various factors affecting color constancy can be efficiently investigated because they can be manipulated individually and precisely. The algorithms chosen for close study include two gray world methods, a limiting case of a version of the Retinex method, a number of variants of Forsyth's gamut-mapping method, Cardei et al.'s neural net method, and Finlayson et al.'s color by correlation method. We investigate the ability of these algorithms to make estimates of three different color constancy quantities: the chromaticity of the scene illuminant, the overall magnitude of that illuminant, and a corrected, illumination invariant, image. We consider algorithm performance as a function of the number of surfaces in scenes generated from reflectance spectra, the relative effect on the algorithms of added specularities, and the effect of subsequent clipping of the data. All data is available on-line at http://www.cs. sfu.ca/(tilde)color/data, and implementations for most of the algorithms are also available (http://www.cs.sfu.ca/(tilde)color/code).
A comparative study of acceptance and use of ICT among university academic staff of ADSU and LASU: Nigeria

This research is on the comparative study of the intention to accept and use ICT among university academic staff of Adamawa State University (ADSU) and Lagos state University (LASU). 100 UTAUT survey questionnaires were administered to each of the university. The data collected was runned by SPSS version 17. The Unified Theory of Acceptance and Use of Technology (UTAUT) model theory was verified using regression analysis to understand the behavioral intention of ADSU and LASU academic staff to accept and use ICT in their workplace. By comparing ADSU and LASU, the study confirms that UTAUT model predict successful acceptance of ICT usage in both universities. The regression results show that the most influential predictor of academic staff intention to accept and use ICT in ADSU is Effort expectancy (EE), while that of LASU is Performance expectancy (PE). The study further confirms that in both institutions some of the academicians are still having the fear of using ICT for their teaching and learning. The comparison also shows that both ADSU and LASU do not have intention to reject ICT acceptance and usage in their workplace. Again the findings reveals that, the most influential construct outside UTAUT model influencing the behavioral intention of the academic staff to accept and use ICT in ADSU and LASU is attitudes towards use of technology. The knowledge gained from this study is beneficiary to the University administrators, academic staff and the Nigerian ICT policy makers.
‘So what is the problem this book addresses?’: Interactions in academic book reviews

Abstract Metadiscourse is the term used for self-reflective linguistic expressions that refer to the evolving text, to the writer, and to the imagined readers of that text. It is based on a view of writing as social engagement and in academic contexts reveals the ways writers project themselves into their discourse to signal their attitudes and commitments to matters in the text and to their disciplinary communities. This paper examines the frequencies and pragmatic purposes of metadiscourse in the relatively neglected academic genre of the book review. On the basis of a corpus of 84 reviews from three contrasting disciplines and interviews with journal editors and reviewers, we describe the ways these writers use metadiscourse to offer a credible representation of themselves and their work in different fields. The analysis shows how metadiscourse use can be seen as pragmatic strategies through which writers shape their social purposes to the formal constraints of the genre and the preferred practices of their disciplines. It therefore suggests how this genre not only draws on readers' familiarity with disciplinary knowledge of the field, but also an interpretive framework that includes appropriate social interactions.
Feelings of control, unconditional self-acceptance and maternal self-esteem in women who had delivered by caesarean

Objective: the objective of this study is to establish a greater understanding of the emotional and cognitive mechanisms associated with caesarean delivery. Method: 201 women who had given birth in the previous year (58 by caesarean) responded to 3 scales: the Labor Agentry Scale (LAS) evaluating sense of control during the delivery, the Maternal Self Report Inventory (MSRI) measuring the level of maternal self-esteem and the Unconditional Self-Acceptance Questionnaire (USAQ) assessing the degree of unconditional self-acceptance. Results: women who had undergone a planned or an emergency caesarean had a lower sense of control and reduced maternal self-esteem when compared to women who had spontaneous vaginal delivery. The sense of control at the delivery had a mediating role in the relationship between the delivery mode and the level of maternal self-esteem. No significant difference was found for unconditional self-acceptance concerning the type of delivery. Conclusions: delivery by caesarean has repercussions regarding women’s beliefs. Giving a sense of control at the birth and unconditional self-acceptance should be of central importance in improving maternal self-esteem.
The Pros and Cons of Getting Engaged in an Online Social Community Embedded Within Digital Cognitive Behavioral Therapy for Insomnia: Survey Among Users

Background Sleepio is a proven digital sleep improvement program based on cognitive behavioral therapy techniques. Users have the option to join an online community that includes weekly expert discussions, peer-to-peer discussion forums, and personal message walls. Objective The aim of this study was to conduct an online survey to (1) explore the reasons for deciding to engage with the Sleepio online community, (2) explore the potential benefits arising from engagement with the online community, and (3) identify and describe any problematic issues related to use of the online community. Methods We developed an online survey and posted an invitation to the community discussion forum inviting users to participate. In addition, we sent an email invitation to 970 individuals who had previously or were currently working through the Sleepio program to participate in this study. Results In total, 100 respondents (70/100, 70% female; mean age 51 years, range 26–82 years) completed the online survey. Most respondents had started Sleepio with chronic sleep problems (59/100, 59% up to 10 years; 35/100, 35% >10 years) and had actively engaged with the online community (85/100, 85%) had made a discussion or wall post). At the time of the survey, respondents had used Sleepio for a median of 12 weeks (range from 3 weeks to 2 years). We analyzed responses to the open-ended questions using thematic analysis. This analysis revealed 5 initial drivers for engagement: (1) the desire to connect with people facing similar issues, (2) seeking personalized advice, (3) curiosity, (4) being invited by other members, and (5) wanting to use all available sleep improvement tools. Advantages of engagement included access to continuous support, a reduced sense of isolation, being part of a nonjudgmental community, personalized advice, positive comparisons with others, encouragement to keep going, and altruism. We found 5 potential disadvantages: design and navigation issues, uncertain quality of user-generated content, negative comparisons with others, excessive time commitments, and data privacy concerns. Participants related their community experiences to engagement with the Sleepio program, with many stating it had supported their efforts to improve their sleep, as well as helping with adherence and commitment to the program. Despite some concerns, members regarded the Sleepio community as a valuable resource. Conclusions Online communities may be a useful means through which to support long-term engagement with Web-based therapy for insomnia.
Determination of Carbonate Rock Chemistry Using Laboratory-Based Hyperspectral Imagery

The development of advanced laboratory-based imaging hyperspectral sensors, such as SisuCHEMA, has created an opportunity to extract compositional information of mineral mixtures from spectral images. Determining proportions of minerals on rock surfaces based on spectral signature is a challenging approach due to naturally-occurring minerals that exist in the form of intimate mixtures, and grain size variations. This study demonstrates the application of SisuCHEMA hyperspectral data to determine mineral components in hand specimens of carbonate rocks. Here, we applied wavelength position, spectral angle mapper (SAM) and linear spectral unmixing (LSU) approaches to estimate the chemical composition and the relative abundance of carbonate minerals on the rock surfaces. The accuracy of these classification methods and correlation between mineral chemistry and mineral spectral characteristics in determining mineral constituents of rocks are also analyzed. Results showed that chemical composition (Ca-Mg ratio) of carbonate minerals at a pixel (e.g., sub-grain) level can be extracted from the image pixel spectra using these spectral analysis methods. The results also indicated that the spatial distribution and the proportions of calcite-dolomite mixtures on the rock surfaces vary between the spectral methods. For the image shortwave infrared (SWIR) spectra, the wavelength position approach was found to be sensitive to all compositional variations of carbonate mineral mixtures when compared to the SAM and LSU approaches. The correlation between geochemical elements and spectroscopic parameters also revealed the presence of these carbonate mixtures with various chemical compositions in the rock samples. This study concludes that the wavelength position approach is a stable and reproducible technique for estimating carbonate mineral chemistry on the rock surfaces using laboratory-based hyperspectral data.
Institutional and Rational Determinants of Organizational Practices: Human Resource Management in European Firms

This paper tests predictions from institutional and rational perspectives about the adoption of organizational practices through a comparative study of human resource management in firms located in six European countries. Distinguishing between calculative practices—aimed at efficient use of human resources—and collaborative practices—aimed at promoting the goals of both employees and employer—the paper predicts differences in adoption across countries. Results show that institutional determinants, as indicated by the national embeddedness of firms, have a strong effect on the application of both calculative and collaborative human resource management practices. Firm size, a rational determinant, has a considerable impact on calculative practices, whereas the effect of industrial embeddedness is quite modest for both practices.
Augmented Telepresence as a Tool for Immersive Simulated Dancing in Experience and Learning

The paper explores the use of interaction technologies in the domain of dance and attempts to visualize a future tool to complement current applications. It begins with a review of various tools and technologies that have been used within the domain in the past and make a projection for how interaction technologies could develop in the coming decade. It then presents a conceptual tool for simulated dancing - 'disDans', which utilizes the modalities of touch, vision and hearing in order to provide an immersive experience. It allows multiple users to touch and feel each other while dancing together, without having to be physically present in the same space. In the end the paper discusses some challenges and limitations to the proposal.
Privacy and the expression of white racial attitudes : A comparison across three contexts

Sample survey data on white racial attitudes show a dramatic increase over time and by educational level in support for principles of racial equality, but lower levels of support, less change, and little relation to education for government policies to implement these principles. A frequent explanation is that the normative climate presently salient in the semipublic setting of a survey interview creates social desirability pressures, resulting in an overstatement of liberal racial attitudes, especially by more educated respondents. Using a tripartite survey-based experiment, the A. compares answers to racial attitude questions under three conditions of privacy: a standard survey condition where interviewers asked all questions; a modified face-to-face condition where respondents answered a subset of racial questions in a self-administered form; and a completely noninterviewer condition where questionnaires were mailed to and returned by respondents. Three hypotheses are investigated: (1) white respondents will express less liberal racial attitudes as privacy increases; (2) privacy effects are greatest for questions about the principles of racial equality and other traditional racial attitudes and least for questions about racial policies and symbolic racism; and (3) privacy effects are stronger among the highly educated, who are more aware of current norms and thus feel social desirability pressures with greater force. Results offer some support for the social desirability hypothesis, especially among more educated respondents. However, contrary to expectations, the effects occur more consistently for racial policies than traditional racial attitudes. Instead of treating privacy effects as errors in a simple sense, the A. draws on supplementary qualitative interviews to connect the survey results to the larger normative change in white racial attitudes. Other complicating factors, such as acquiescent tendencies among less educated respondents, are also considere
Daehr

Electronic health records (EHR) provide a rich source of temporal data that present a unique opportunity to characterize disease patterns and risk of imminent disease. While many data-mining tools have been adopted for EHR-based disease early detection, linear discriminant analysis (LDA) is one of the most commonly used statistical methods. However, it is difficult to train an accurate LDA model for early disease diagnosis when too few patients are known to have the target disease. Furthermore, EHR data are heterogeneous with significant noise. In such cases, the covariance matrices used in LDA are usually singular and estimated with a large variance. This article presents Daehr, an extension of the LDA framework using electronic health record data to address these issues. Beyond existing LDA analyzers, we propose Daehr to (1) eliminate the data noise caused by the manual encoding of EHR data and (2) lower the variance of parameter (covariance matrices) estimation for LDA models when only a few patients’ EHR are available for training. To achieve these two goals, we designed an iterative algorithm to improve the covariance matrix estimation with embedded data-noise/parameter-variance reduction for LDA. We evaluated Daehr extensively using the College Health Surveillance Network, a large, real-world EHR dataset. Specifically, our experiments compared the performance of LDA to three baselines (i.e., LDA and its derivatives) in identifying college students at high risk for mental health disorders from 23 U.S. universities. Experimental results demonstrate Daehr significantly outperforms the three baselines by achieving 1.4%--19.4% higher accuracy and a 7.5%--43.5% higher F1-score.
Arengiya Anyainya Aidmeniya Aburliya Adardiya Agngiya Aweniya Amaidya Abmarliya Awaadya Anguriya Adiaya Angeliya Algyeliya Adniadya Aleriya Umbaidya Anowadya Muriya Angenduriya Amburniya Andungiya Aneriya Aiyenga Undyaidya Gnaldena

© 2 0 0 4 m a s s a c h u s e t t s i n s t i t u t e o f t e c h n o l o g y, c a m b r i d g e , m a 0 213 9 u s a — w w w. c s a i l. m i t. e d u massachusetts institute of technology — computer science and artificial intelligence laboratory We present a framework for learning abstract relational knowledge with the aim of explaining how people acquire intuitive theories of physical, biological, or social systems. Our approach is based on a generative relational model with latent classes, and simultaneously determines the kinds of entities that exist in a domain, the number of these latent classes, and the relations between classes that are possible or likely. This model goes beyond previous psychological models of category learning, which consider attributes associated with individual categories but not relationships between categories. We apply this domain-general framework to two specific problems: learning the structure of kinship systems and learning causal theories.
Local Partitioning for Directed Graphs Using PageRank

A local partitioning algorithm finds a set with small conductance near a specified seed vertex. In this paper, we present a generalization of a local partitioning algorithm for undirected graphs to strongly connected directed graphs. In particular, we prove that by computing a personalized PageRank vector in a directed graph, starting from a single seed vertex within a set S that has conductance at most α, and by performing a sweep over that vector, we can obtain a set of vertices S′ with conductance . Here, the conductance function Φ M is defined in terms of the stationary distribution of a random walk in the directed graph. In addition, we describe how this algorithm may be applied to the PageRank Markov chain of an arbitrary directed graph, which provides a way to partition directed graphs that are not strongly connected.