Miracle-dz/UniFAR-FTUs huggingface dataset:
The Ly-edge paradox and the need for obscured QSOs

Based on the most recent QSO ultraviolet spectra, the covering factor of the clouds of the Broad Line Region (BLR) is about 30%, or larger. This value would imply that in at least 30% of the QSOs our line of sight crosses one, or more, BLR clouds and, in the latter case, the UV spectrum should show a sharp Ly-edge in absorption. This Ly-edge in absorption is never observed. This paradox is solved if, as suggested by various authors, the BLR is flattened and the dusty gas in the outer parts, on the same plane, prevents the observation along the lines of sight passing through the BLR clouds. The objects observed edge-on (with respect to the flattened BLR) would be classied as obscured QSOs or, within the framework of the unied model, type 2 QSOs. The covering factor of the BLR constrains the fraction of obscured QSOs to be QSO2/QSO1 > 0.5. This lower limit is already high with respect to the number of candidate type 2 QSOs claimed so far. We discuss this constraint in relation to recent AGN surveys.
On the training distortion of vector quantizers

The in-training-set performance of a vector quantizer as a function of its training set size is investigated. For squared error distortion and independent training data, worst-case type upper bounds are derived on the minimum training distortion achieved by an empirically optimal quantizer. These bounds show that the training distortion can underestimate the minimum distortion of a truly optimal quantizer by as much as a constant times n/sup -1/2/, where n is the size of the training data. Earlier results provide lower bounds of the same order.
Association of the MTHFR C677T (rs1801133) polymorphism with idiopathic male infertility in a local Pakistani population

Abstract The present study determined an association between idiopathic sperm disorders in a local Pakistani infertile male population and the MTHFR C677T polymorphism. After ruling out non genetic factors, a total of 437 idiopathic infertile men including 57 azoospermic, 66 oligospermic, 44 asthenozoospermic, 29 teratozoospermic, 20 oligoasthenospermic and 221 infertile normospermic men were recruited. Furthermore, 218 normospermic fertile men, who had two children (or more) were included as controls. The polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) technique was used to determine MTHFR C677T (rs1801133) polymorphism. A significant association of the minor MTHFR 677T allele with male infertility was observed (p <0.05). In addition, men with MTHFR 677 CT and TT genotypes were at a greater risk [odds ratio (OR): 1.81, 95% confidence interval (95% CI): 1.17-2.80, p = 0.008 and OR: 9.24, 95% CI: 1.20-70.92, p = 0.032, respectively] of infertility. All the subgroups of male infertility (azoospermic, oligospermic, asthenospermic, oligoasthenoteratospermic (OAT) and normospermic infertile) had significantly (p <0.05) higher frequencies of CT and TT genotypes when compared to fertile men. The combined genotypes (CT + TT) were also found significantly (OR: 2.01, 95% CI: 1.31-3.08, p <0.001) associated with male infertility. The results suggest that the polymorphism might be a factor of male infertility in the Pakistani population.
P HONE MAPPING AND PROSODIC TRANSFER IN SPEECH SYNTHESIS OF SIMILAR DIALECT PAIRS

: In this paper we describe a phone mapping based method that can be used to synthesize a new dialect with an existing dialect model of a similar dialect. The method only uses transcriptions of original dialect data, which are then mapped onto the phones in the model. We use prosodic transfer of original duration and F0 to evaluate how the basic mapping model can be improved. We show that the prosodically enhanced models can outperform the basic model in a pairwise comparison task and can also achieve a slightly higher score on dialect authenticity. The goal of the proposed systems is to realize a dialect synthesis system with a small amount of symbolic training data that can come from transcribed dialect utterances or from the literature
How social information can improve estimation accuracy in human groups

Significance Digital technologies deeply impact the way that people interact. Therefore, it is crucial to understand how social influence affects individual and collective decision-making. We performed experiments where subjects had to answer questions and then revise their opinion after knowing the average opinion of some previous participants. Moreover, unbeknownst to the subjects, we added a controlled number of virtual participants always giving the true answer, thus precisely controlling social information. Our experiments and data-driven model show how social influence can help a group of individuals collectively improve its performance and accuracy in estimation tasks depending on the quality and quantity of information provided. Our model also shows how giving slightly incorrect information could drive the group to a better performance. In our digital and connected societies, the development of social networks, online shopping, and reputation systems raises the questions of how individuals use social information and how it affects their decisions. We report experiments performed in France and Japan, in which subjects could update their estimates after having received information from other subjects. We measure and model the impact of this social information at individual and collective scales. We observe and justify that, when individuals have little prior knowledge about a quantity, the distribution of the logarithm of their estimates is close to a Cauchy distribution. We find that social influence helps the group improve its properly defined collective accuracy. We quantify the improvement of the group estimation when additional controlled and reliable information is provided, unbeknownst to the subjects. We show that subjects’ sensitivity to social influence permits us to define five robust behavioral traits and increases with the difference between personal and group estimates. We then use our data to build and calibrate a model of collective estimation to analyze the impact on the group performance of the quantity and quality of information received by individuals. The model quantitatively reproduces the distributions of estimates and the improvement of collective performance and accuracy observed in our experiments. Finally, our model predicts that providing a moderate amount of incorrect information to individuals can counterbalance the human cognitive bias to systematically underestimate quantities and thereby improve collective performance.
A Sex-Related Genetic Difference in Bipolar Disorder: Tryptophan Hydroxylase 1 Gene 218 A>C Polymorphism

Nobel T›p Merkezi, Psikiyatri Bolumu, Adana, Turkiye *Dunya T›p Merkezi, Psikiyatri Bolumu, Antalya, Turkiye **Mersin Universitesi T›p Fakultesi, Genetik Anabilim Dal›, Mersin, Turkiye ***Pamukkale Universitesi T›p Fakultesi, Psikiyatri Anabilim Dal›, Denizli, Turkiye ****Gaziantep Universitesi T›p Fakultesi, Psikiyatri Anabilim Dal›, Gaziantep, Turkiye A Sex-Related Genetic Difference in Bipolar Disorder: Tryptophan Hydroxylase 1 Gene 218 A>C Polymorphism
Typical Peers in School Settings Using Perseverative Interests to Improve Interactions Between Adolescents With Autism and Their-Jun

The literature suggests that adolescents with Autism Spectrum Disorders (ASD) typically are not socially engaged during unstructured school activities and do not initiate social activities with typically developing peers. This study assessed whether implementing socialization opportunities in the form of lunch clubs based around aspects of the adolescents with ASD’s perseverative interests would promote positive and direct social interaction between the target adolescent and their typically developing peers. A repeated measures multiple baseline experimental design (with two reversals) was implemented across participants. During baseline measures, the participants did not show social engagement or initiations. During intervention, results showed large increases in both social engagement and initiations. These results have implications for understanding variables related to social development in autism.
Characterizing the Clinical Benefit of Ipilimumab in Patients Who Progressed on High-dose IL-2

Ipilimumab improves overall survival (OS) in patients with metastatic melanoma including those previously treated with high-dose interleukin-2 (HD IL-2). The primary objective of this study was to determine if clinical response or progression-free survival (PFS) to HD IL-2 could predict benefit to subsequent ipilimumab. The secondary objective was to further characterize the clinical benefit of ipilimumab in patients who have progressed on HD IL-2. We reviewed the records of all patients with metastatic melanoma who received HD IL-2 at MD Anderson Cancer Center or Beth Israel Deaconess Medical Center from 2003 to 2009 and further identified patients who also received ipilimumab after progressing on HD IL-2. OS to ipilimumab was calculated from the first dose of ipilimumab, determined by Kaplan-Meier analysis, and was compared in patients based on their prior clinical response and PFS to HD IL-2 using the log-rank test. Patients were grouped based on their prior response to HD IL-2 as follows: complete response and partial response, stable disease, and progressive disease. Patients were also grouped and compared by prior PFS to HD IL-2 in >60 days versus ⩽60 days. A total of 208 patients with melanoma were treated with HD IL-2, 130 (63%) received additional systemic therapy after confirmed disease progression, and 48 (23%) received ipilimumab. The clinical benefit of ipilimumab was similar to previously published results (OS, 12.0 mo; PFS, 2.5 mo; response rate, 16.7%). Prior clinical response or PFS to HD IL-2 did not predict benefit to subsequent ipilimumab. Prospective trials of HD IL-2 followed by ipilimumab could potentially identify patients most likely to benefit from a sequential approach of HD IL-2 followed by ipilimumab.
Measuring the Mental Effort of Blind Mobility

A comparative analysis of the information processing associated with sighted and blind mobility indicates that the performance of blind pedestrians may be limited by the excessive “mental effort” demands of the task. Describes a group of experiments conducted to test the effectiveness of a secondary task as a measure of the blind mobility information processing workload. Secondary task performance is shown to be a useful index of the effects of preview and route difficulty, as well as the perceptual and cognitive demands placed on users of different mobility aids.
Unscented Bayesian optimization for safe robot grasping

Safe and robust grasping of unknown objects is a major challenge in robotics, which has no general solution yet. A promising approach relies on haptic exploration, where active optimization strategies can be employed to reduce the number of exploration trials. One critical problem is that certain optimal grasps discoverd by the optimization procedure may be very sensitive to small deviations of the parameters from their nominal values: we call these unsafe grasps because small errors during motor execution may turn optimal grasps into bad grasps. To reduce the risk of grasp failure, safe grasps should be favoured. Therefore, we propose a new algorithm, unscented Bayesian optimization, that performs efficient optimization while considering uncertainty in the input space, leading to the discovery of safe optima. The results highlight how our method outperforms the classical Bayesian optimization both in synthetic problems and in realistic robot grasp simulations, finding robust and safe grasps after a few exploration trials.
Neuroendocrine Disruption: More than Hormones are Upset

Only a small proportion of the published research on endocrine-disrupting chemicals (EDC) directly examined effects on neuroendocrine processes. There is an expanding body of evidence that anthropogenic chemicals exert effects on neuroendocrine systems and that these changes might impact peripheral organ systems and physiological processes. Neuroendocrine disruption extends the concept of endocrine disruption to include the full breadth of integrative physiology (i.e., more than hormones are upset). Pollutants may also disrupt numerous other neurochemical pathways to affect an animal's capacity to reproduce, develop and grow, or deal with stress and other challenges. Several examples are presented in this review, from both vertebrates and invertebrates, illustrating that diverse environmental pollutants including pharmaceuticals, organochlorine pesticides, and industrial contaminants have the potential to disrupt neuroendocrine control mechanisms. While most investigations on EDC are carried out with vertebrate models, an attempt is also made to highlight the importance of research on invertebrate neuroendocrine disruption. The neurophysiology of many invertebrates is well described and many of their neurotransmitters are similar or identical to those in vertebrates; therefore, lessons learned from one group of organisms may help us understand potential adverse effects in others. This review argues for the adoption of systems biology and integrative physiology to address the effects of EDC. Effects of pulp and paper mill effluents on fish reproduction are a good example of where relatively narrow hypothesis testing strategies (e.g., whether or not pollutants are sex steroid mimics) have only partially solved a major problem in environmental biology. It is clear that a global, integrative physiological approach, including improved understanding of neuroendocrine control mechanisms, is warranted to fully understand the impacts of pulp and paper mill effluents. Neuroendocrine disruptors are defined as pollutants in the environment that are capable of acting as agonists/antagonists or modulators of the synthesis and/or metabolism of neuropeptides, neurotransmitters, or neurohormones, which subsequently alter diverse physiological, behavioral, or hormonal processes to affect an animal's capacity to reproduce, develop and grow, or deal with stress and other challenges. By adopting a definition of neuroendocrine disruption that encompasses both direct physiological targets and their indirect downstream effects, from the level of the individual to the ecosystem, a more comprehensive picture of the consequences of environmentally relevant EDC exposure may emerge.
Role of the pharmacist in parenteral nutrition therapy: challenges and opportunities to implement pharmaceutical care in Kuwait

Background: Pharmacists can provide beneficial pharmaceutical care services to patients receiving Parenteral Nutrition (PN) therapy by working within Nutrition Support Teams (NSTs). Objective: This study was designed to explore pharmacists’ role in PN therapy in hospitals of Kuwait, sources of PN-related information, opinions on NSTs, perceptions about the barriers to pharmaceutical care implementation and views on how to enhance their practices. Methods: Data were collected via face-to-face semi-structured interviews with the senior Total Parenteral Nutrition (TPN) pharmacists at all the hospitals which provide TPN preparation services (six governmental hospitals and one private hospital) in Kuwait. Descriptive statistics were used to describe pharmacists’ demographic details and practice site characteristics. The interviews were audio-recorded, transcribed verbatim and analysed using thematic analysis. Results: The pharmacists mainly performed technical tasks such as TPN compounding with minimal role in providing direct patient care. They used multiple different sources of TPN-related information to guide their practice. They reported positive and negative experiences with physicians depending on their practice environment. None of the hospitals had a functional NST. However, pharmacists expressed preference to work within NSTs due to the potential benefits of enhanced communication and knowledge exchange among practitioners and to improve service. Pharmacists perceived several barriers to providing pharmaceutical care including lack of reliable sources of TPN-related information, lack of a standard operating procedure for TPN across hospitals, insufficient staff, time constraints and poor communication between TPN pharmacists. To overcome these barriers, they recommended fostering pharmacists’ education on TPN, establishing national standards for TPN practices, provision of pharmacy staff, development of NSTs, enhancing TPN pharmacists’ communication and conducting TPN-research research. Conclusion: TPN pharmacists in Kuwait are confined to performing TPN manufacturing processes. There are promising avenues for future development of their role in patient care. This can be achieved by overcoming the barriers to pharmaceutical care practice and providing pharmacists with educational opportunities to equip them with the clinical competencies needed to practise as nutrition support pharmacists with patient-centred roles.
Grid Long Short-Term Memory

This paper introduces Grid Long Short-Term Memory, a network of LSTM cells arranged in a multidimensional grid that can be applied to vectors, sequences or higher dimensional data such as images. The network differs from existing deep LSTM architectures in that the cells are connected between network layers as well as along the spatiotemporal dimensions of the data. The network provides a unified way of using LSTM for both deep and sequential computation. We apply the model to algorithmic tasks such as 15-digit integer addition and sequence memorization, where it is able to significantly outperform the standard LSTM. We then give results for two empirical tasks. We find that 2D Grid LSTM achieves 1.47 bits per character on the Wikipedia character prediction benchmark, which is state-of-the-art among neural approaches. In addition, we use the Grid LSTM to define a novel two-dimensional translation model, the Reencoder, and show that it outperforms a phrase-based reference system on a Chinese-to-English translation task.
Race, Ethnicity, and Sexuality: Intimate Intersections, Forbidden Frontiers

Introduction Sex Matters: Racing Sex and Sexing Race 1. Ethnosexual Frontiers: Cruising and Crossing Intimate Intersections 2. Constructing Ethnicity and Sexuality: Building Boundaries and Identities 3. Sex and Conquest: Domination and Desire on Ethnosexual Frontiers 4. Sex and Race: The Color of Sex in America 5. Sex and Nationalism: Sexually Imagined Communities 6. Sex and War: Fighting Men, Comfort Women, and the Military-Sexual Complex 7. Sex and Tourism: Travel and Romance in Ethnosexual Destinations 8. Sex and Globalization: The Global Economy of Desire Conclusion Sex-Baiting and Race-Baiting: The Politics of Ethnosexuality
ACCURACY COMPARISON OF LAND COVER MAPPING USING THE OBJECT-ORIENTED IMAGE CLASSIFICATION WITH MACHINE LEARNING ALGORITHMS

Land cover mapping provides basic information for advanced science such as ecological management, biodiversity conservation, forest planning and so on. In remote sensing research, the process of creating an accurate land cover map is an important subject. Recently, there has been growing research interest in the object-oriented image classification techniques. The object-oriented image classification consists of multidimensional features including object features and thus requires multi-dimensional image classification approaches. For example, a linear model such as the maximum likelihood method of pixel-based classification cannot characterize the patterns or relations of multi-dimensional data. In multi-dimensional image classification, data mining and ensemble learning have been shown to increase accuracy and flexibility. This study examined the use of the object-oriented image classification by the multiple machine learning algorithms for land cover mapping. We applied four classifiers: Classification and regression tree (CART), Decision tree with Boosting, Decision tree with Bagging, and Random Forest. The study area was Sado Island in Niigata Prefecture, Japan. Pan-sharpened SPOT/HRG imagery (June 2007) was used and classified into the following eight classes: broad-leaved deciduous forest, Japanese cedar, Japanese red pine, bamboo forest, paddy field, urban area, road, and bare land. We prepared four data sets with the object based features including textural information. The number of features is increased from data set I through IV. As the result, CART was unsuitable for multi-dimensional classification. Random Forest and Decision tree with Boosting showed high classification accuracies. Furthermore, in the data set with the limited features, Decision tree with Boosting was the accurate classifier. Finally, we propose two machine learning algorithms to every datasets. Random Forest is effective in the case of the multi-dimensional image classification such as data set II, III, and IV. Decision tree with Boosting is effective in the case of the image classification with the limited features such as data set I.
The push and pull of work: The differences between workaholism and work engagement

The term workaholism has been part of our everyday vocabulary for almost four decades now. Given the widespread use of this term among lay people, it is quite remarkable that our scientific understanding of workaholism is as yet quite limited: even the correct conceptualization of this concept is still heavily debated (Burke, 2006). Is workaholism more than just devoting too much time to work? Should workaholics be considered with compassion as they suffer from a serious disorder that requires treatment, or is workaholism a desirable state that has positive consequences, for both the individual workers and their employers? And, most importantly in the present context, how does workaholism relate to the concept of work engagement, which also involves notions of hard work, high work involvement, and superior performance? The current chapter first discusses the origins and conceptualization of workaholism. After addressing the conceptual distinctions and similarities between workaholism and work engagement, we explore the empirical evidence on the differentiation between the two concepts – are they really different? Finally, we draw conclusions regarding the distinction between work engagement and work addiction, and provide a short
Uplift Modeling with Multiple Treatments and General Response Types

Randomized experiments have been used to assist decision-making in many areas. They help people select the optimal treatment for the test population with certain statistical guarantee. However, subjects can show significant heterogeneity in response to treatments. The problem of customizing treatment assignment based on subject characteristics is known as uplift modeling, differential response analysis, or personalized treatment learning in literature. A key feature for uplift modeling is that the data is unlabeled. It is impossible to know whether the chosen treatment is optimal for an individual subject because response under alternative treatments is unobserved. This presents a challenge to both the training and the evaluation of uplift models. In this paper we describe how to obtain an unbiased estimate of the key performance metric of an uplift model, the expected response. We present a new uplift algorithm which creates a forest of randomized trees. The trees are built with a splitting criterion designed to directly optimize their uplift performance based on the proposed evaluation method. Both the evaluation method and the algorithm apply to arbitrary number of treatments and general response types. Experimental results on synthetic data and industry-provided data show that our algorithm leads to significant performance improvement over other applicable methods.