Miracle-dz/UniFAR-FTUs huggingface dataset:
A longitudinal twin study on the association between ADHD symptoms and reading.

BACKGROUND Attention deficit hyperactivity disorder (ADHD) and reading disability commonly co-occur because of shared genetic risk factors. However, the stability and change of these genetic influences and the predictive relationships underlying this association longitudinally remain unclear. METHODS ADHD symptoms and reading were assessed as continuous dimensions in a UK general population sample of approximately 7,000 twin pairs. Parent ratings of ADHD symptoms and teacher ratings of reading were obtained at two ages: middle childhood (ages 7-8 years) and early adolescence (ages 11-12 years). Cross-lagged quantitative genetic analyses were applied. RESULTS ADHD symptoms and reading significantly predicted each other over time. However, ADHD symptoms were a significantly stronger predictor of reading than vice versa. Inattentive and hyperactive-impulsive symptoms of ADHD both contributed to the prediction of reading, but inattentiveness was a significantly stronger predictor. Furthermore, ADHD symptoms and reading were highly heritable, and their association was primarily attributable to shared genetic influences. Despite notable genetic innovation for each trait, genetic factors involved in the association of ADHD symptoms and reading over time were highly stable. CONCLUSIONS ADHD symptoms may put children at increased risk for reading problems and vice versa. Moreover, enduring genetic mechanisms appear to be important in the association of ADHD symptoms and reading over time.
An empirical evaluation and comparison of manual and automated test selection

Regression test selection speeds up regression testing by re-running only the tests that can be affected by the most recent code changes. Much progress has been made on research in automated test selection over the last three decades, but it has not translated into practical tools that are widely adopted. Therefore, developers either re-run all tests after each change or perform manual test selection. Re-running all tests is expensive, while manual test selection is tedious and error-prone. Despite such a big trade-off, no study assessed how developers perform manual test selection and compared it to automated test selection. This paper reports on our study of manual test selection in practice and our comparison of manual and automated test selection. We are the first to conduct a study that (1) analyzes data from manual test selection, collected in real time from 14 developers during a three-month study and (2) compares manual test selection with an automated state-of-the-research test-selection tool for 450 test sessions. Almost all developers in our study performed manual test selection, and they did so in mostly ad-hoc ways. Comparing manual and automated test selection, we found the two approaches to select different tests in each and every one of the 450 test sessions investigated. Manual selection chose more tests than automated selection 73% of the time (potentially wasting time) and chose fewer tests 27% of the time (potentially missing bugs). These results show the need for better automated test-selection techniques that integrate well with developers' programming environments.
Personality and decision-making in public administration: the five-factor model in cultural perspective

Using a cross-sectional survey of 254 Turkish district governors, this study assesses the impact of personality on decision-making among public managers. The research evaluates self-reported results from the Five-Factor Model of personality and the Decision-Making Questionnaire of the psychology literature. The findings suggest that Openness, Conscientiousness, and Extraversion are important in the analysis of decision-making subjects, tasks, and contexts, respectively. Departing from previous research, the analysis here suggests that culture and structure are important factors in explaining personality and decision-making in public administration. Points for practitioners This study offers policy implications for the recruitment, performance appraisal, training, and assignment of practitioners in the public service. Personality assessments can be used as a valuable tool during the evaluation of public officials under consideration for recruitment, appointment, and promotion. Matching the personalities of potential candidates to positions requiring comparable qualifications has important organizational, personnel, and financial implications. Our research also suggests that decision-making and personality should also account for social, cultural, and organizational differences in their applications.
Adoption of Mobile Commerce: Role of Exposure

The increasingly high penetration rate of mobile phones and the consequent exposure of subscribers to mobile technology present high hopes for the adoption of mobile commerce. Are such hopes justified? In this study, we address this question. More specifically, we develop and empirically test a model for explaining the role of exposure to mobile technology in the adoption of mobile commerce. The proposed model extends well-established behavioral theories with new constructs representing various forms of exposure, i.e., trial, communication and observation. The empirical results show significant both indirect (mediated by other constructs) and moderating effects of exposure on the intention of adopting mobile commerce.
Red Blood Cell Distribution Width during the First Week Is Associated with Severity and Mortality in Septic Patients

Objective Higher values of red blood cell distribution width (RDW) have been found in non-surviving than in surviving septic patients. However, it is unknown whether RDW during the first week of sepsis evolution is associated with sepsis severity and early mortality, oxidative stress and inflammation states, and these were the aims of the study. Methods We performed a prospective, observational, multicenter study in six Spanish Intensive Care Units with 297 severe septic patients. We measured RDW, serum levels of malondialdehyde (MDA) to assess oxidative stress, and tumour necrosis factor (TNF)-α to assess inflammation at days 1, 4, and 8. The end-point was 30-day mortality. Results We found higher RDW in non-surviving (n = 104) than in surviving (n = 193) septic patients at day 1 (p = 0.001), day 4 (p = 0.001), and day 8 (p = 0.002) of ICU admission. Cox regression analyses showed that RDW at day 1 (p<0.001), 4 (p = 0.005) and 8 (p = 0.03) were associated with 30-day mortality. Receiver operating characteristic curves showed that RDW at day 1 (p<0.001), 4 (p<0.001), and 8 (p<0.001) could be used to predict 30-day mortality. RDW showed a positive correlation with serum MDA levels at day 1 and day 4, with serum TNF-α levels at days 4 and 8, and with SOFA score at days 1, 4 and 8. Conclusions The major findings of our study were that non-surviving septic patients showed persistently higher RDW during the first week of ICU stay than survivors, that RDW during the first week was associated with sepsis severity and mortality, that RDW during the first week could be used as biomarker of outcome in septic patients, and that there was an association between RDW, serum MDA levels, and serum TNF-α levels during the first week.
Iterated Belief Change and Exogeneous Actions in the Situation Calculus

We present a novel theory of action and change capable of dealing with discord between an agent's beliefs and the results of its sensing. Previous work by Scherl and Levesque [9] and Shapiro et al. [10] have given accounts of iterated belief update and revision, and the ability to deal with mistaken beliefs. However, they assume that all actions, including exogenous actions beyond the agent's control, are accessible to the agent for the purposes of reasoning. Our approach abandons this idealistic stance and allows the agent to hypothesise the occurrence of exogenous actions to account for any discrepancy between belief and sensing.
An experimental analysis on time series transductive classification on graphs

Graph-based semi-supervised learning (SSL) algorithms perform well when the data lie on a low-dimensional manifold. Although these methods achieved satisfactory performance on a variety of domains, they have not been effectively evaluated on time series classification. In this paper, we provide a comprehensive empirical comparison of state-of-the-art graph-based SSL algorithms combined with a variety of graph construction methods in order to evaluate them on time series transductive classification tasks. Through a detailed experimental analysis using recently proposed empirical evaluation models, we show strong and weak points of these classifiers concerning both performance and stability with respect to graph construction and parameter selection. Our results show that some hypotheses raised on previous work do not hold in the time series domain while others may only hold under mild conditions.
Measuring paternal discrepancy and its public health consequences

Paternal discrepancy (PD) occurs when a child is identified as being biologically fathered by someone other than the man who believes he is the father. This paper examines published evidence on levels of PD and its public health consequences. Rates vary between studies from 0.8% to 30% (median 3.7%, n = 17). Using information from genetic and behavioural studies, the article identifies those who conceive younger, live in deprivation, are in long term relationships (rather than marriages), or in certain cultural groups are at higher risk. Public health consequences of PD being exposed include family break up and violence. However, leaving PD undiagnosed means cases having incorrect information on their genetics and fathers continuing to suspect that children may not be theirs. Increasing paternity testing and use of DNA techniques in clinical and judicial procedures means more cases of PD will be identified. Given developing roles for individual’s genetics in decisions made by health services, private services (for example, insurance), and even in personal lifestyle decisions, the dearth of intelligence on how and when PD should be exposed urgently needs addressing.
Media Advertising and Ballot Initiatives: An Experimental Analysis

Spending on political advertising increases with every election cycle, not only for congressional or presidential candidates, but also for state-level ballot initiatives. There is little research in marketing, however, on the effectiveness of political advertising at this level. In this study, we conduct an experimental analysis of advertisements used during the 2008 campaign to mandate new animal welfare standards in California (Proposition 2). Using subjects' willingness to pay for cage-free eggs as a proxy for their likely voting behavior, we investigate whether advertising provides real information to likely voters, and thus sharpens their existing attitudes toward the issue, or whether advertising can indeed change preferences. We find that advertising in support of Proposition 2 was more effective in raising subjects' willingness to pay for cage-free eggs than ads in opposition were in reducing it, but we also find that ads in support of the measure reduce the dispersion of preferences and thus polarize attitudes toward the initiative. More generally, political ads are found to contain considerably more "hype" than "real information" in the sense of Johnson and Myatt (2006).
Working memory: the seat of learning and comprehension

Overview Working memory is the small amount of information kept in mind at any time. It is needed for various sorts of learning, comprehension, problem-solving and goal-directed thinking. Humans have a small working memory limit but it can be overcome using strategies such as grouping items together and rehearsing them. Children’s working memory capabilities grow with maturity, and educational practices should be based on an understanding of both the limitations and the educational possibilities. These limitations of working memory, and means to overcome the limitations, will be examined with emphasis on the developmental changes from the early elementary school years through adulthood. Educational principles will be proposed to make the most of working memory for optimal learning throughout development. The first two section of the chapter provides a definition of working memory and a description of how it is used in information processing. This is followed by a discussion of several types of working memory limitations (item limits, goal maintenance limits and time limits) and the strategies that are used to overcome the limits. Within this framework, the childhood development of working memory is examined. To gain a complete picture of development one must separately consider the growth of storage capacity, goal maintenance processes, memory persistence and strategy use. Finally, lessons for maximizing education are presented, focusing on the use of materials and instructions that are challenging and stimulating but not beyond the working memory capabilities of the child.
Unsupervised segmentation of conversational transcripts

Contact centers provide dialog‐based support to organizations to address various customer‐related issues. We have observed that calls at contact centers mostly follow well‐defined patterns. Such call flows could specify how an agent should proceed in a call, handle objections, persuade customers, follow compliance issues, etc., and could also help to structure the operational process of call handling. Automatically identifying such patterns in terms of distinct segments from a collection of transcripts of conversations would improve productivity of agents as well as enable easy verification of whether calls comply with guidelines. Call transcripts from call centers typically tend to be noisy owing to the noise arising from agent/caller distractions, and errors introduced by the speech recognition engine. Such noise makes classical text segmentation algorithms such as TextTiling, which work on each transcript in isolation, very inappropriate. But such noise effects become statistically insignificant over a corpus of similar calls. In this paper, we propose an algorithm to segment conversational transcripts in an unsupervised way utilizing corpus level information of similar call transcripts. We show that our approach outperforms the classical TextTiling algorithm and also describes ways to improve the segmentation using limited supervision. We discuss various ways of evaluating such an algorithm. We apply the proposed algorithm to a corpus of transcripts of calls from a car reservation call center and evaluate it using various evaluation measures. We apply segmentation to the problem of automatically checking the compliance of agents and show that our segmentation algorithm considerably improves the precision. Copyright © 2009 Wiley Periodicals, Inc., A Wiley Company
Impedance Models in Time Domain including the Extended Helmholtz Resonator Model

The problem of translating a frequency domain impedance boundary condition to time domain involves the Fourier transform of the impedance function. This requires extending the definition of the impedance not only to all real frequencies but to the whole complex frequency plane. Not any extension, however, is physically possible. The problemshouldremain causal, the variables real, andthe wall passive. This leads to necessary conditions for an impedance function. Various methods of extending the impedance that are available in the literature are discussed. A most promising one is the so-called z-transform by Ozyoruk & Long, which is nothing but an impedance that is functionally dependent on a suitable complex exponent e −iωκ . By choosing κ a multiple of the time step of the numerical algorithm, this approach fits very well with the underlying numerics, because the impedance becomes in time domain a delta-comb function and gives thus an exact relation on the grid points. An impedance function is proposed which is based on the Helmholtz resonator model, called Extended Helmholtz Resonator Model. This has the advantage that relatively easily the mentioned necessary conditions can be satisfied in advance. At a given frequency, the impedance is made exactly equal to a given design value. Rules of thumb are derived to produce an impedance which varies only moderately in frequency near design conditions. An explicit solution is given of a pulse reflecting in time domain at a Helmholtz resonator impedance wall that provides some insight into the reflection problem in time domain and at the same time may act as an analytical test case for numerical implementations, like is presented at this conference by the companion paper AIAA-2006-2569 by N. Chevaugeon, J.-F. Remacle and X. Gallez. The problem of the instability, inherent with the Ingard-Myers limit with mean flow, is discussed. It is argued that this instability is not consistent with the assumptions of the Ingard-Myers limit and may well be suppressed.
GPU programming in a high level language: compiling X10 to CUDA

GPU architectures have emerged as a viable way of considerably improving performance for appropriate applications. Program fragments (kernels) appropriate for GPU execution can be implemented in CUDA or OpenCL and glued into an application via an API. While there is plenty of evidence of performance improvements using this approach, there are many issues with productivity. Programmers must understand an additional programming model and API to program the accelerator; concurrency and synchronization in this programming model is typically expressed differently from the programming model for the host. On top of this, the languages used to write kernels are very low level and thus prone to the kinds of errors that one does not encounter in higher level languages. Programmers must explicitly deal with moving data back-and-forth between the host and the accelerator. These problems are compounded when the user code must be run across a cluster of accelerated nodes. Now the host programming model must further be extended with constructs to deal with scale-out and remote accelerators. We believe there is a critical need for a single source programming model that can be used to write clean, efficient code for heterogeneous, multi-core and scale-out architectures. The APGAS programming model has been developed for such architectures over the past six years. APGAS is based on four fundamental (and architecture-independent) notions: locality, asynchrony, conditional atomicity and order. X10 is an instantiation of the APGAS programming model on top of a base sequential language with Java-style productivity. Earlier work has shown that X10 can be used to write clean and efficient code for homogeneous multi-cores, SMPs, Cell-accelerated nodes, and clusters of such nodes. In this paper we show how X10 programmers can write code that can be compiled and run on GPUs. GPU programming idioms such as threads, blocks, barriers, constant memory, local registers, shared memory variables, etc. can be directly expressed in X10, and do not require new language extensions. We present the design of an extension of the X10-to-C++ compiler which recognizes such idioms and produces CUDA kernel code. We show several benchmarks written in this style. The performance of these kernels is within 80% of hand-written CUDA kernels. We believe these results establish X10 as a single-source programming language in which clean, efficient programs can be written for GPU-accelerated clusters.
Images of Scientists through the Eyes of the Children

The purpose of this study is to determine primary school students' images of scientists. As research approach, the survey method, which is frequently used to learn about people's attitudes, beliefs, values, demographics, behavior, opinions, habits, desires, ideas and other types of information, was used. The study was conducted in the spring semester of 2011- 2012 academic year with primary school students enrolling through the first grade to the fifth grade. The data of the study were collected by a Draw-A-Scientist-Test. When scoring a DAST drawing, the raters coded each indicator with either 1 or 0 points depending on the presence or absence of the feature under examination. Drawings revealed that although there are variations from individual to individual, children hold common stereotypical images of scientists. The overwhelming majority of drawings were of male scientists. Children also produced images that represent individuals with messy hair, wearing glasses, wearing a laboratory coat. Also children depicted that scientists usually work in indoors, usually in a laboratory, and perform experiments.
A Coalition-resistant Group SignatureGiuseppe Ateniese

A group signature scheme allows any member of a potentially large group to sign on behalf of the group. Group signatures are anonymous and unlinkable. Only a designated group manager can co-relate signatures and/or reveal the identity of the actual signer. At the same time, no one (including a group manager) can misattribute a valid signature. Group signatures have many practical applications such as e-commerce. Despite some important and elegant prior results, existing group signature schemes remain either ineecient or uncertain in terms of security. In this paper we construct a very eecient group signature scheme with security properties based on a set of well-studied (and widely believed to be hard) computational problems.
Dynamic Neural Network Reorganization Associated with Second Language Vocabulary Acquisition: A Multimodal Imaging Study

It remains unsettled whether human language relies exclusively on innately privileged brain structure in the left hemisphere or is more flexibly shaped through experiences, which induce neuroplastic changes in potentially relevant neural circuits. Here we show that learning of second language (L2) vocabulary and its cessation can induce bidirectional changes in the mirror-reverse of the traditional language areas. A cross-sectional study identified that gray matter volume in the inferior frontal gyrus pars opercularis (IFGop) and connectivity of the IFGop with the caudate nucleus and the superior temporal gyrus/supramarginal (STG/SMG), predominantly in the right hemisphere, were positively correlated with L2 vocabulary competence. We then implemented a cohort study involving 16 weeks of L2 training in university students. Brain structure before training did not predict the later gain in L2 ability. However, training intervention did increase IFGop volume and reorganization of white matter including the IFGop-caudate and IFGop-STG/SMG pathways in the right hemisphere. These “positive” plastic changes were correlated with the gain in L2 ability in the trained group but were not observed in the control group. We propose that the right hemispheric network can be reorganized into language-related areas through use-dependent plasticity in young adults, reflecting a repertoire of flexible reorganization of the neural substrates responding to linguistic experiences.
Women's Imprisonment

Incarceration of women in the United States is at a historic high, but understanding of women's experiences in prison, their responses to treatment, their lives after prison, and how changing prison regimes have affected these things remains limited. Individual attributes, preprison experiences, and prison conditions are associated with how women respond to incarceration, but assessments of their joint and conditional influences are lacking. Needs assessments abound, but systematic evaluations of interventions based on these assessments are rare, as are studies of the long-term consequences of imprisonment. Understanding of ways women negotiate power and construct their lives in prison is greater than in the past; new theoretical frameworks have provided important insights, but fundamental questions remain unanswered.