Miracle-dz/UniFAR-FTUs huggingface dataset:
Taking Charge of ADHD: The Complete Authoritative Guide for Parents. Revised Edition.

Once again, Dr. Barkley, a recognized authority in the field of Attention-deficit/Hyperactivity Disorder (ADHD), has written an eloquent, easily digestible and thorough expose on a complex topic. Although written primarily for parents, as a child and adolescent psychiatrist I found it filled with useful information well supported by the scientific literature. This background is, of course, the foundation on which our clinical practice is based and is our best defense against, what sometimes seems like, an endless onslaught against our profession. The book is easy to read and, in spite of its length at 320 pages, is well apportioned in its content and practical in its advice. Rich in anecdote and warm in understanding of the human experience of ADHD, this book should be a must for most parents. The book is neatly divided into four parts. Part I, which occupies a third of the book provides an in depth understanding of ADHD. A history of the disorder is followed by a clinical description that leads into the central thesis that ADHD is much more than the list of criteria in DSM IV. ADHD is posited to be about an inability to inhibit, a failure in self-control and is related to higher executive frontal lobe functioning. This broader understanding invites a greater appreciation of the many academic, social, behavioral, psychological, emotional and family problems accompanying these children. Part II deals with taking charge as parents. There is advice on how to seek a professional assessment, managing the emotional reactions to the diagnosis, guiding principles, including those of personal guru Stephen Covey, and advice on parental self care. Part III offers assistance on behavioral management, solving peer problems, and managing adolescent and school issues. The behavioral strategies are targeted at a younger age group and, although repetitious of the basics, are, nonetheless, an important reminder that first principles come first. Part IV, on medications was somewhat disappointing in breadth. While the stimulants were well covered, only six pages were devoted to the other agents. An obvious error is the reference to tricyclics sometimes being used for the treatment of depression in children. No mention is made of venlafaxine although bupropion is included. Given the presence of a number of excellent psychopharmacology books on the market at the time of publication and the need for parents to have an excellent source of information, the medication chapter did not live up to the rest of the book. An excellent section on tapes, videos, books, academic references, support groups and websites rounds out a memorable read. Dr. Barkley has produced a book that will be added to the list of practical, reasoned, well thought out and helpful resources for parents who are looking for it all in one place.
Exercise and Alcohol Consumption: What We Know, What We Need to Know, and Why it is Important

Exercise provides a wealth of benefits to brain and body, and is regarded as a protective factor against disease. Protective factors tend to cluster together – that is, people who engage in one healthy behavior, such as exercise, also engage in other healthy behaviors, such as maintaining a nutritious diet and getting sufficient sleep. In contrast to exercise, alcohol consumption is not typically regarded as a health-promoting behavior, although moderate intake has been associated with a lower risk of cardiovascular disease. Surprisingly, several large, population-based studies have shown a positive association between physical activity and alcohol intake. The present review focuses on what is known about this relationship, including potential neural bases as well as moderating factors, and discusses important directions for further study, such as a more thorough characterization of people who both drink and exercise. We focus on ramifications for intervening with people who have alcohol use disorders, as exercise has been assessed as both a treatment and preventive measure, with mixed results. We believe that, in order for such interventions to be effective, clinical trials must distinguish treatment-seeking populations from non-treatment-seeking ones, as well as ensure that the use of exercise as a tool to decrease alcohol consumption is made explicit. We posit that a better understanding of the relationship between physical activity and alcohol intake will maximize intervention efforts by informing the design of clinical trials and research-driven prevention strategies, as well as enable individuals to make educated decisions about their health behaviors.
DERIUNLP: A Context Based Approach to Automatic Keyphrase Extraction

The DERI UNLP team participated in the SemEval 2010 Task #5 with an unsupervised system that automatically extracts keyphrases from scientific articles. Our approach does not only consider a general description of a term to select keyphrase candidates but also context information in the form of "skill types". Even though our system analyses only a limited set of candidates, it is still able to outperform baseline unsupervised and supervised approaches.
Diffracting trees

Shared counters are among the most basic coordination structures in multiprocessor conputation, with applications ranging from barrier synchronization to concurrent-data-structure design. This article introduces diffracting trees, novel data structures for share counting and load balancing in a distributed/parallel environment. Empirical evidence, collected on a simulated distributed shared-memory machine and several simulated message-passing architectures, shows that diffracting trees scale better and are more robust than both combining trees and counting networks, currently the most effective known methods for implementing concurrent counters in software. The use of a randomized coordination method together with a combinatorial data structure overcomes the resiliency drawbacks of combining trees. Our simulations show that to handle the same load, diffracting trees and counting networks should have a similar width w, yet the depth of a diffracting tree is O(log w), whereas counting networks have depth O(log2 w). Diffracting trees have already been used to implement highly efficient producer/consumer queues, and we believe diffraction will prove to be an effective alternative paradigm to combining and queue-locking in the design of many concurrent data structures.
Computational Lexicons: the Neat Examples and the Odd Exemplars

When implementing computational lexicons it is important to keep in mind the texts that a NLP system must deal with. Words relate to each other in many different, often queer, ways: this information is rarely found in dictionaries, and it is quite hard to be invented a priori, despite the imagination that linguists exhibit at inventing esoteric examples.In this paper we present the results of an experiment in learning from corpora the frequent selectional restrictions holding between content words. The method is based on the analysis of word associations augmented with syntactic markers and semantic tags. Word pairs are extracted by a morphosyntactic analyzer and clustered according to their semantic tags. A statistical measure is applied to the data to evaluate the significance of a detected relation. Clustered association data render the study of word associations more interesting with several respects: data are more reliable even for smaller corpora, more easy to interpret, and have many practical applications in NLP.
IO performance prediction in consolidated virtualized environments

We propose a trace-driven approach to predict the performance degradation of disk request response times due to storage device contention in consolidated virtualized environments. Our performance model evaluates a queueing network with fair share scheduling using trace-driven simulation. The model parameters can be deduced from measurements obtained inside Virtual Machines (VMs) from a system where a single VM accesses a remote storage server. The parameterized model can then be used to predict the effect of storage contention when multiple VMs are consolidated on the same virtualized server. The model parameter estimation relies on a search technique that tries to estimate the splitting and merging of blocks at the the Virtual Machine Monitor (VMM) level in the case of multiple competing VMs. Simulation experiments based on traces of the Postmark and FFSB disk benchmarks show that our model is able to accurately predict the impact of workload consolidation on VM disk IO response times.
Glucose-Resistant Sporulation in Bacillus subtilis crsA47 Mutants Does Not Depend on Promoter Switching at the spo0A Gene

ABSTRACT We have found that sporulation in Bacillus subtilis crsA47 mutants does not require the σH-dependent promoter of the spo0A gene. This implies that the glucose-resistant sporulation phenotype of this strain is not related to the switch from the vegetative-stage σA-dependent promoter to the σH-dependent promoter at the spo0A gene.
Predictive network with leveraging clinical measures as auxiliary task

Deep neural networks (DNNs) have made impressive improvements for predictive modeling in various fields. Successful network building for predictive tasks usually requires abundant data, for effectively learning high-level, non-additive information from raw input features. The merit of high-level learning makes DNN promising in clinical research, but the need for abundant data hampers DNN applications. To address this challenge, we propose the use of Auxiliary-Task-Augmented Network (ATAN), a predictive model (for primary target) with introducing auxiliary tasks as regularization. ATAN leverages clinically relevant measures as auxiliary targets and learns the clinical relevance explicitly. We focused on applying ATAN in a clinical dataset of hypertension collected from a vulnerable demographic population (African-American). Performance of ATAN was compared with various popular methods to demonstrate its effectiveness. In addition, we analyzed the learned parameters for finding risk factors and interpreting the model.
Calcium Channel Permeation: A Field in Flux

In 1955, Alan Hodgkin and Richard Keynes published a study whose intent was to answer a rather technical question, but whose result triggered the modern analysis of ion channel permeation ([Hodgkin and Keynes, 1955][1]). They aimed to determine whether the K+ fluxes in giant axons followed the “
Variations in the Prevalence of Risk Factors for Coronary Artery Disease in Rio Grande do Sul-Brazil: A Comparative Analysis between 2002 and 2014

Background Due to the importance of coronary artery disease (CAD), continuous investigation of the risk factors (RFs) is needed. Objective To evaluate the prevalence of RFs for CAD in cities in Rio Grande do Sul State, and compare it with that reported in a similar study conducted in the same cities in 2002. Methods Cross-sectional study on 1,056 healthy adults, investigating the prevalence and absolute and relative frequencies of the following RFs for CAD: obesity, systemic arterial hypertension (SAH), dyslipidemias, smoking, sedentary lifestyle, diabetes mellitus, and family history, as well as age and sex. Data was collected in 19 cities, host of the Offices of the Regional Coordinators of Health, as in the 2002 study. Results Twenty-six percent of the sample consisted of older adults and 57% were women. The prevalence of sedentary lifestyle was 44%, history family 50%, smoking 23%, overweight/obesity 68%, dyslipidemia (high cholesterol levels) 43%, SAH 40%, and diabetes 11%. When compared to the 2002 study, the prevalence of active smoking and sedentary behavior decreased, whereas the prevalence of hypertension, dyslipidemia and obesity increased. Obesity is the most prevalent RF in women, and SAH the most prevalent in men. Conclusions The prevalence of RFs for CAD in Rio Grande do Sul State remains high. Hypertension, obesity and dyslipidemia are still prevalent and require major prevention programs. Smoking and physical inactivity have decreased in the state, suggesting the efficacy of related campaigns.
Synergies in experimental psychology, artificial intelligence, and cognitive neuroscience

Part 1 Association Lecture: 1. Learning and Connectionist Representations David E. Rumelhart and Peter M. Todd. Part 2 Visual Information Processing: 2. Visual Information Processing: A Perspective Michael H. Van Kieeck and Stephm M. Kasslyn 3. Colour Constancy and Colour Perception - The Linear-Models Framework Lawrence T. Maloney 4. The Visual Recognition of Three-dimensional Objects Shimon Ullman 5. Five Hunches about Perceptual Processes and Dynamic Representations Jennifer J. Freyd 6. Perceiving an Integrated Visual World David E. Irwin 7. Interactions between Object and Space Systems Revealed through Neuropsychology Glyn W. Humphreys and M. Jane Riddoch 8. Representing Visual Objects Anne Treisman. Part 3 Attention: 9. Attention and Control, Have We Been Asking the Wrong Questions? A Critical Review of Twenty-Five Years Alan Allport 10 . Electrocortical Substrates of Visual Selective Attention George R. Mangun, Steven A. Hillyard, and Steven J.Luck 11. Dual-task Interference and Elementary Metal Mechanisms Harold Pashier 12. Using Repetition Detection to Define and Localize the Processes 13. The Skill of Attention Control - Acquistion and Execution of Attention Strategies Daniel Gopher 14. The Control of Skilled Behavior - Learning, Intelligence, and Distraction John Duncan et al 15. Attention before and during the Decade of the Brain Michael I. Posner Part 4 Learning and Memory: 16. Twenty-Five Years of learning and Memory: Was the cognitive Revolution a Mistake? Douglas L. Hintzman 17. Memory: Organization of Brain Systems and Cognition Larry R. Squire et al 18. Dissociations between Component Mechanisms in Short-Term Memory: Evidence from Brain-Damaged Patients David Howard and Sue Franklin 19. The Organization of object Knowledge: Evidence from Neuropsychology Giuseppe Sartorii, Remo Job, and Max Coltheart 20. The Story of the Two-Store Model of Memory: Past Criticisms, Current Status, and Future Directions Jeroen G. W. Raaijmakers 21. Memory Retrieval with Two Cues: Think of Intersecting Sets Michael S. Humphreys, Janet Wiles, and John D. Bain 22. Learning and Memory: Progress and Challenge Henry L. Roediger III. Part 5 Mental Chronometry and Processing Dynamics: 23. Information, Time, and the Structure of Mental Events: A Twenty-Five-Year Review Richard Schweickert 24. On the Transfer of Partial Information between Perception and Action Gijsbertus Mulder, H.G. 0.M. Smid, and L.J.M. Mulder 25. Use of Independent and Correlated Measures to Divide a Time-Discrimination Mechanism into Parts Seth Roberts 26. The Meaning of Additive Reaction-Time Effects: Tests of Three Alternatives Seth Roberts and Saul Sternberg 27. Toward a Theory of Information Processing in Graded, Random, and interactive networks, James L. McClelland performance theory and measurement through chronometric analysis, Andries F. Sanders. Part 6 Motor control and action. (Part contents).
Diaquabis[2-(2-pyridylmethoxy)pyrazine-κN 4]bis(thiocyanato-κN)cobalt(II)

In the title complex, [Co(NCS)2(C10H9N3O)2(H2O)2], the CoII ion is located on a crystallographic twofold rotation axis and is in a slightly distorted octahedral CoN4O2 coordination environment. The dihedral angle between the pyridine and pyrazine rings is 85.86 (10)°. In the crystal structure, intermolecular O—H⋯N and O—H⋯S hydrogen bonds link complex molecules into a three-dimensional network.
Multiobjective Neural Network Ensembles Based on Regularized Negative Correlation Learning

Negative Correlation Learning (NCL) [CHECK END OF SENTENCE], [CHECK END OF SENTENCE] is a neural network ensemble learning algorithm which introduces a correlation penalty term to the cost function of each individual network so that each neural network minimizes its mean-square-error (MSE) together with the correlation. This paper describes NCL in detail and observes that the NCL corresponds to training the entire ensemble as a single learning machine that only minimizes the MSE without regularization. This insight explains that NCL is prone to overfitting the noise in the training set. The paper analyzes this problem and proposes the multiobjective regularized negative correlation learning (MRNCL) algorithm which incorporates an additional regularization term for the ensemble and uses the evolutionary multiobjective algorithm to design ensembles. In MRNCL, we define the crossover and mutation operators and adopt nondominated sorting algorithm with fitness sharing and rank-based fitness assignment. The experiments on synthetic data as well as real-world data sets demonstrate that MRNCL achieves better performance than NCL, especially when the noise level is nontrivial in the data set. In the experimental discussion, we give three reasons why our algorithm outperforms others.
Performance of middle-aged and elderly European minority and majority populations on a Cross-Cultural Neuropsychological Test Battery (CNTB)

Abstract Objective: The aim of this study was to examine test performance on a cross-cultural neuropsychological test battery for assessment of middle-aged and elderly ethnic minority and majority populations in western Europe, and to present preliminary normative data. Method: The study was a cross-sectional multi-center study. Tests in the European Cross-Cultural Neuropsychological Test Battery (CNTB) cover several cognitive domains, including global cognitive function, memory, executive functions, and visuospatial functions. Results: A total of 330 participants were included: 14 Moroccan, 45 Pakistani/Indian Punjabi, 41 Polish, 66 Turkish, and 19 former Yugoslavian minority participants, and 145 western European majority participants. Significant differences between ethnic groups were found on most CNTB measures. However, ethnic groups differed greatly in demographic characteristics and differences in test scores were mainly related to educational differences, explaining an average of 15% of the variance. Preliminary multicultural CNTB normative data dichotomized by education and age were constructed using overlapping cells. Applying this normative data across the whole sample resulted in an acceptable number of participants scoring in the impaired range across all ethnic groups. Factor analyses found the CNTB to have a stable and clinically meaningful factor structure. Conclusions: The CNTB represents the first European joint effort to establish neuropsychological measures appropriate for ethnic minority populations in western Europe. The CNTB can be applied in approximately 60 min, covers several cognitive domains, and appears appropriate for assessment of the targeted populations. However, due to the small sample size in some ethnic groups further studies are needed replicate and support this.
An adaptive low-power transmission scheme for on-chip networks

Systems-on- Chip (SoC) are evolving toward complex heterogeneous multiprocessors made of many predesigned macrocells or subsystems with application-specific interconnections. Intra-chip interconnects are thus becoming one of the central elements of SoC design and pose conflicting goals in terms of low energy per transmitted bit, guaranteed signal integrity, and ease of design. This work introduces and shows first results on a novel interconnect system which uses low-swing signalling, error detection codes, and a retransmission scheme; it minimises the interconnect voltage swing and frequency subject to workload requirements and S/N conditions. Simulation results show that tangible savings in energy can be attained while achieving at the same time more robustness to large variations in actual workload, noise, and technology quality (all quantities easily mispredicted in very complex systems and advanced technologies). It can be argued that traditional worst-case correct-by-design paradigm will be less and less applicable in future multibillion transistor SoC and deep sub-micron technologies; this work represents a first example towards robust adaptive designs.
Enriching temporal query understanding through date identification: how to tag implicit temporal queries?

Generically, search engines fail to understand the user's temporal intents when expressed as implicit temporal queries. This causes the retrieval of less relevant information and prevents users from being aware of the possible temporal dimension of the query results. In this paper, we aim to develop a language-independent model that tackles the temporal dimensions of a query and identifies its most relevant time periods. For this purpose, we propose a temporal similarity measure capable of associating a relevant date(s) to a given query and filtering out irrelevant ones. Our approach is based on the exploitation of temporal information from web content, particularly within the set of k-top retrieved web snippets returned in response to a query. We particularly focus on extracting years, which are a kind of temporal information that often appears in this type of collection. We evaluate our methodology using a set of real-world text temporal queries, which are clear concepts (i.e. queries which are non-ambiguous in concept and temporal in their purpose). Experiments show that when compared to baseline methods, determining the most relevant dates relating to any given implicit temporal query can be improved with a new temporal similarity measure.
A New Approach of Rotated Staggered Grid FD Method With Unsplit Convolutional PML for GPR

We introduce a novel implementation of a rotated staggered grid (RSG) finite difference (FD) method based on the unsplit convolutional perfectly matched layer (UCPML). The method is carried out using rotated staggered FD operators and a general complex frequency shifted stretching factor. It can offer a number of advantages over the RSG with split convolutional perfectly matched layer (SCPML), such as improving computational efficiency, overcoming the numerical dispersions resulting from the high-frequency propagation and strong contrasts in material, and specifically absorbing the evanescent modes at grazing incidence. A half-space numerical example with a lossy medium attached demonstrates that large-scale spurious reflections and evanescent modes at grazing incidence are absorbed efficiently by the UCPML in RSG scheme. Meanwhile, the comparison of the reflection error between the proposed algorithm and traditional approaches [using time-dependent reflection error (TDRE) and frequency-dependent reflection error (FDRE)] suggests that the proposed algorithm of RSG provides a preferable improvement in absorbing low-frequency coda wave of evanescent modes, particularly in the lossy medium that possesses inhomogeneous discontinuities.