nils-herrmann/clean_scirepeval huggingface dataset:
Psychodynamic psychotherapy for children and adolescents : a critical review of the evidence base

For many years psychoanalytic and psychodynamic therapies have been considered to lack a credible evidence-base and have consistently failed to appear in lists of ‘empirically supported treatments’. This study systematically reviews the research evaluating the efficacy and effectiveness of psychodynamic psychotherapy for children and young people. The researchers identified 34 separate studies that met criteria for inclusion, including nine randomised controlled trials. While many of the studies reported are limited by sample size and lack of control groups, the review indicates that there is increasing evidence to suggest the effectiveness of psychoanalytic psychotherapy for children and adolescents. The article aims to provide as complete a picture as possible of the existing evidence base, thereby enabling more refined questions to be asked regarding the nature of the current evidence and gaps requiring further exploration.
Association between mortality and indicators of traffic-related air pollution in the Netherlands: a cohort study

Long-term exposure to particulate matter air pollution has been associated with increased cardiopulmonary mortality in the USA. We aimed to assess the relation between traffic-related air pollution and mortality in participants of the Netherlands Cohort study on Diet and Cancer (NLCS), an ongoing study.



We investigated a random sample of 5000 people from the full cohort of the NLCS study (age 55-69 years) from 1986 to 1994. Long-term exposure to traffic-related air pollutants (black smoke and nitrogen dioxide) was estimated for the 1986 home address. Exposure was characterised with the measured regional and urban background concentration and an indicator variable for living near major roads. The association between exposure to air pollution and (cause specific) mortality was assessed with Cox's proportional hazards models, with adjustment for potential confounders.



489 (11%) of 4492 people with data died during the follow-up period. Cardiopulmonary mortality was associated with living near a major road (relative risk 1.95, 95% CI 1.09-3.52) and, less consistently, with the estimated ambient background concentration (1.34, 0.68-2.64). The relative risk for living near a major road was 1.41 (0.94-2.12) for total deaths. Non-cardiopulmonary, non-lung cancer deaths were unrelated to air pollution (1.03, 0.54-1.96 for living near a major road).



Long-term exposure to traffic-related air pollution may shorten life expectancy.
Structured Connectivity in Cerebellar Inhibitory Networks

Defining the rules governing synaptic connectivity is key to formulating theories of neural circuit function. Interneurons can be connected by both electrical and chemical synapses, but the organization and interaction of these two complementary microcircuits is unknown. By recording from multiple molecular layer interneurons in the cerebellar cortex, we reveal specific, nonrandom connectivity patterns in both GABAergic chemical and electrical interneuron networks. Both networks contain clustered motifs and show specific overlap between them. Chemical connections exhibit a preference for transitive patterns, such as feedforward triplet motifs. This structured connectivity is supported by a characteristic spatial organization: transitivity of chemical connectivity is directed vertically in the sagittal plane, and electrical synapses appear strictly confined to the sagittal plane. The specific, highly structured connectivity rules suggest that these motifs are essential for the function of the cerebellar network.
Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension

We develop a technique for transfer learning in machine comprehension (MC) using a novel two-stage synthesis network (SynNet). Given a high-performing MC model in one domain, our technique aims to answer questions about documents in another domain, where we use no labeled data of question-answer pairs. Using the proposed SynNet with a pretrained model on the SQuAD dataset, we achieve an F1 measure of 46.6% on the challenging NewsQA dataset, approaching performance of in-domain models (F1 measure of 50.0%) and outperforming the out-ofdomain baseline by 7.6%, without use of provided
I NSNA Building a Network Theory of Social Capital '

In the past two decades, social capital in its various forms and contexts has emerged as one of the most salient concepts in social sciences. While much excitement has been generated, divergent views, perspectives, and expectations have also raised the serious question : is it a fad or does it have enduring qualities that will herald a new intellectual enterprise? This presentation's purpose is to review social capital as discussed in the literature, identify controversies and debates, consider some critical issues, and propose conceptual and research strategies in building a theory. I will argue that such a theory and the research enterprise must be based on the fundamental understanding that social capital is captured from embedded resources in social networks . Deviations from this understanding in conceptualization and measurement lead to confusion in analyzing causal mechanisms in the macroand microprocesses. It is precisely these mechanisms and processes, essential for an interactive theory about structure and action, to which social capital promises to make contributions .
Cannabis Vaporizer Combines Efficient Delivery of THC with Effective Suppression of Pyrolytic Compounds

Cannabis vaporization is a technology designed to deliver inhaled cannabinoids while avoiding the respiratory hazards of smoking by heating cannabis to a temperature where therapeutically active cannabinoid vapors are produced, but below the point of combustion where noxious pyrolytic byproducts are formed. This study was designed to evaluate the efficacy of an herbal vaporizer known as the Volcano®, produced by Storz & Bickel GmbH&Co. KG, Tuttlingen, Germany (http://www.storz-bickel.com). Three 200 mg samples of standard NIDA cannabis were vaporized at temperatures of 155°-218°C. For comparison, smoke from combusted samples was also tested. The study consisted of two phases: (1) a quantitative analysis of the solid phase of the vapor using HPLC-DAD-MS (High Performance LiqDale Gieringer is affiliated with California NORML, 2215-R Market Street  The authors offer thanks for insight and technical support to Jeff Jones, Elvy Musikka, Irvin Rosenfeld and Aidan Hampson. This research was supported by a grant from the Marijuana Policy Project. Additional support provided by the NORML Foundation, the Multidisciplinary Association for Psychedelic Studies and Storz & Bickel GmbH&Co. Journal of Cannabis Therapeutics, Vol. 4(1) 2004 http://www.haworthpress.com/web/JCANT  2004 by The Haworth Press, Inc. All rights reserved. Digital Object Identifier: 10.1300/J175v04n01_02 7 uid Chromatograph-Diode Array-Mass Spectrometry) to determine the amount of cannabinoids delivered; (2) a GC/MS (Gas Chromatograph/ Mass Spectrometer) analysis of the gas phase to analyze the vapor for a wide range of toxins, focusing on pyrene and other polynuculear aromatic hydrocarbons (PAHs). The HPLC analysis of the vapor found that the Volcano delivered 36%-61% of the THC in the sample, a delivery efficiency that compares favorably to that of marijuana cigarettes. The GC/MS analysis showed that the gas phase of the vapor consisted overwhelmingly of cannabinoids, with trace amounts of three other compounds. In contrast, over 111 compounds were identified in the combusted smoke, including several known PAHs. The results indicate that vaporization can deliver therapeutic doses of cannabinoids with a drastic reduction in pyrolytic smoke compounds. Vaporization therefore appears to be an attractive alternative to smoked marijuana for future medical cannabis studies.
BattRAE: Bidimensional Attention-Based Recursive Autoencoders for Learning Bilingual Phrase Embeddings

In this paper, we propose a bidimensional attention based recursive autoencoder (BattRAE) to integrate cues and source-target interactions at multiple levels of granularity into bilingual phrase representations. We employ recursive autoencoders to generate tree structures of phrase with embeddings at different levels of granularity (e.g., words, sub-phrases, phrases). Over these embeddings on the source and target side, we introduce a bidimensional attention network to learn their interactions encoded in a bidimensional attention matrix, from which we extract two soft attention weight distributions simultaneously. The weight distributions enable BattRAE to generate compositive phrase representations via convolution. Based on the learned phrase representations, we further use a bilinear neural model, trained via a max-margin method, to measure bilingual semantic similarity. In order to evaluate the effectiveness of BattRAE, we incorporate this semantic similarity as an additional feature into a state-of-the-art SMT system. Extensive experiments on NIST Chinese-English test sets show that our model achieves a substantial improvement of up to 1.82 BLEU points over the baseline.
Multilinear Spatial Discriminant Analysis for Dimensionality Reduction

In the last few years, great efforts have been made to extend the linear projection technique (LPT) for multidimensional data (i.e., tensor), generally referred to as the multilinear projection technique (MPT). The vectorized nature of LPT requires high-dimensional data to be converted into vector, and hence may lose spatial neighborhood information of raw data. MPT well addresses this problem by encoding multidimensional data as general tensors of a second or even higher order. In this paper, we propose a novel multilinear projection technique, called multilinear spatial discriminant analysis (MSDA), to identify the underlying manifold of high-order tensor data. MSDA considers both the nonlocal structure and the local structure of data in the transform domain, seeking to learn the projection matrices from all directions of tensor data that simultaneously maximize the nonlocal structure and minimize the local structure. Different from multilinear principal component analysis (MPCA) that aims to preserve the global structure and tensor locality preserving projection (TLPP) that is in favor of preserving the local structure, MSDA seeks a tradeoff between the nonlocal (global) and local structures so as to drive its discriminant information from the range of the non-local structure and the range of the local structure. This spatial discriminant characteristic makes MSDA have more powerful manifold preserving ability than TLPP and MPCA. Theoretical analysis shows that traditional MPTs, such as multilinear linear discriminant analysis, TLPP, MPCA, and tensor maximum margin criterion, could be derived from the MSDA model by setting different graphs and constraints. Extensive experiments on face databases (ORL, CMU PIE, and the extended Yale-B) and the Weizmann action database demonstrate the effectiveness of the proposed MSDA method.
Exploiting Sum of Submodular Structure for Inference in Very High Order MRF-MAP Problems

Several tasks in computer vision and machine learning can be modeled as MRF-MAP inference problems. Using higher order potentials to model complex dependencies can significantly improve the performance. The problem can often be modeled as minimizing a sum of submodular (SoS) functions. Since sum of submodular functions is also submodular, existing submodular function minimization (SFM) techniques can be employed for optimal inference in polynomial time , . These techniques, though oblivious to the clique sizes, have limited scalability in the number of pixels. On the other hand, state of the art algorithms in computer vision ,  can handle problems with a large number of pixels but fail to scale to large clique sizes. In this paper, we adapt two SFM algorithms , , to exploit the sum of submodular structure, thereby helping them scale to large number of pixels while maintaining scalability with large clique sizes. Our ideas are general enough and can be extended to adapt other existing SFM algorithms as well. Our experiments on computer vision problems demonstrate that our approach can easily scale up to clique sizes of 300, thereby unlocking the usage of really large sized cliques for MRF-MAP inference problems.
Trust in Automation: Designing for Appropriate Reliance

Automation is often problematic because people fail to rely upon it appropriately. Because people respond to technology socially, trust influences reliance on automation. In particular, trust guides reliance when complexity and unanticipated situations make a complete understanding of the automation impractical. This review considers trust from the organizational, sociological, interpersonal, psychological, and neurological perspectives. It considers how the context, automation characteristics, and cognitive processes affect the appropriateness of trust. The context in which the automation is used influences automation performance and provides a goal-oriented perspective to assess automation characteristics along a dimension of attributional abstraction. These characteristics can influence trust through analytic, analogical, and affective processes. The challenges of extrapolating the concept of trust in people to trust in automation are discussed. A conceptual model integrates research regarding trust in automation and describes the dynamics of trust, the role of context, and the influence of display characteristics. Actual or potential applications of this research include improved designs of systems that require people to manage imperfect automation.
Another look at dreaming: disentangling Freud's primary and secondary process theories.

The Interpretation of Dreams contains Freud's first and most complete articulation of the primary and secondary mental processes that serve as a framework for the workings of mind, conscious and unconscious. While it is generally believed that Freud proposed a single theory of dreaming, based on the primary process, a number of ambiguities, inconsistencies, and contradictions reflect an incomplete differentiation of the parts played by the two mental processes in dreaming. It is proposed that two radically different hypotheses about dreaming are embedded in Freud's work. The one implicit in classical dream interpretation is based on the assumption that dreams, like waking language, are representational, and are made up of symbols connected to latent unconscious thoughts. Whereas the symbols that constitute waking language are largely verbal and only partly unconscious, those that constitute dreams are presumably more thoroughly disguised and represented as arcane hallucinated hieroglyphs. From this perspective, both the language of the dream and that of waking life are secondary process manifestations. Interpretation of the dream using the secondary process model involves the assumption of a linear two-way "road" connecting manifest and latent aspects, which in one direction involves the work of dream construction and in the other permits the associative process of decoding and interpretation. Freud's more revolutionary hypothesis, whose implications he did not fully elaborate, is that dreams are the expression of a primary mental process that differs qualitatively from waking thought and hence are incomprehensible through a secondary process model. This seems more adequately to account for what is now known about dreaming, and is more consistent with the way dream interpretation is ordinarily conducted in clinical practice. Recognition that dreams are qualitatively distinctive expressions of mind may help to restore dreaming to its privileged position as a unique source of mental status information.
Tree-to-tree Neural Networks for Program Translation

Program translation is an important tool to migrate legacy code in one language into an ecosystem built in a different language. In this work, we are the first to employ deep neural networks toward tackling this problem. We observe that program translation is a modular procedure, in which a sub-tree of the source tree is translated into the corresponding target sub-tree at each step. To capture this intuition, we design a tree-to-tree neural network to translate a source tree into a target one. Meanwhile, we develop an attention mechanism for the tree-to-tree model, so that when the decoder expands one non-terminal in the target tree, the attention mechanism locates the corresponding sub-tree in the source tree to guide the expansion of the decoder. We evaluate the program translation capability of our tree-to-tree model against several state-of-the-art approaches. Compared against other neural translation models, we observe that our approach is consistently better than the baselines with a margin of up to 15 points. Further, our approach can improve the previous state-of-the-art program translation approaches by a margin of 20 points on the translation of real-world projects.
MLBCD: a machine learning tool for big clinical data

Predictive modeling is fundamental for extracting value from large clinical data sets, or "big clinical data," advancing clinical research, and improving healthcare. Machine learning is a powerful approach to predictive modeling. Two factors make machine learning challenging for healthcare researchers. First, before training a machine learning model, the values of one or more model parameters called hyper-parameters must typically be specified. Due to their inexperience with machine learning, it is hard for healthcare researchers to choose an appropriate algorithm and hyper-parameter values. Second, many clinical data are stored in a special format. These data must be iteratively transformed into the relational table format before conducting predictive modeling. This transformation is time-consuming and requires computing expertise.



This paper presents our vision for and design of MLBCD (Machine Learning for Big Clinical Data), a new software system aiming to address these challenges and facilitate building machine learning predictive models using big clinical data.



The paper describes MLBCD's design in detail.



By making machine learning accessible to healthcare researchers, MLBCD will open the use of big clinical data and increase the ability to foster biomedical discovery and improve care.
openXBOW - Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit

We introduce openXBOW, an open-source toolkit for the generation of bag-of-words (BoW) representations from multimodal input. In the BoW principle, word histograms were first used as features in document classification, but the idea was and can easily be adapted to, e. g., acoustic or visual low-level descriptors, introducing a prior step of vector quantisation. The openXBOW toolkit supports arbitrary numeric input features and text input and concatenates computed subbags to a final bag. It provides a variety of extensions and options. To our knowledge, openXBOW is the first publicly available toolkit for the generation of crossmodal bags-of-words. The capabilities of the tool are exemplified in two sample scenarios: time-continuous speech-based emotion recognition and sentiment analysis in tweets where improved results over other feature representation forms were observed.
A natural language bipolar argumentation approach to support users in online debate interactions†

With the growing use of the Social Web, an increasing number of applications for exchanging opinions with other people are becoming available online. These applications are widely adopted with the consequence that the number of opinions about the debated issues increases. In order to cut in on a debate, the participants need first to evaluate the opinions of the other users to detect whether they are in favour or against the debated issue. Bipolar argumentation proposes algorithms and semantics to evaluate the set of accepted arguments, given the support and the attack relations among them. Two main problems arise. First, an automated framework to detect the relations among the arguments represented by the natural language (NL) formulation of the users’ opinions is needed. Our paper addresses this open issue by proposing and evaluating the use of NL techniques to identify the arguments and their relations. In particular, we adopt the textual entailment (TE) approach, a generic framework for applied semantics, where linguistic objects are mapped by means of semantic inferences at a textual level. TE is then coupled together with an abstract bipolar argumentation system which allows to identify the arguments that are accepted in the considered online debate. Second, we address the problem of studying and comparing the different proposals put forward for modelling the support relation. The emerging scenario shows that there is not a unique interpretation of the support relation. In particular, different combinations of additional attacks among the arguments involved in a support relation are proposed. We provide an NL account of the notion of support based on online debates, by discussing and evaluating the support relation among arguments with respect to the more specific notion of TE in the NL processing field. Finally, we carry out a comparative evaluation of four proposals of additional attacks on a sample of NL arguments extracted from Debatepedia. The originality of the proposed framework lies in the following point: NL debates are analysed and the relations among the arguments are automatically extracted.
Multi-Class Object Detection from Aerial Images Using Mask R-CNN

Object detection, though gaining popularity, has largely been limited to detection from the ground or from satellite imagery. Aerial images, where the target may be obfuscated from the environmental conditions, angle-of-attack, and zoom level, pose a more significant challenge to correctly detect targets in. This paper describes the implementation of a regional convolutional neural network to locate and classify objects across several categories in complex, aerial images. Our current results show promise in detecting and classifying objects. Further adjustments to the network and data input should increase the localization and classification accuracies.
A Prototype Design for Enhancing Customer Trust in Online Payments

The adoption of mobile device technology can contri bu e significantly to enhance customers trust in online payment system s.  The perceptions and preferences of online shoppers are influenced by several key facto rs which serve to both enhance and compromise this trust and in turn affect a customer intentions and behavior in relation to use online payment systems. The first part of the research was a quant itative study to investigate these factors. In the second part of this research, a mobile payment mode l f r online payment systems was proposed. In this model, the customers do not need to trust merchants during the transaction because merchants will not act as an intermediary between customers and the ac quirer. Customers can therefore send their financial details without concern of disclosure, or potential misuse by the merchant.  In this study, the key factors influencing to adopt mobile payment systems were identify. The proposed model was developed and an analysis of the model architec ture against conventional online payment systems was discussed.  The significance of this research comes from providing a practical mobile payment model as a pos sible step towards increasing customer acceptance of online shopping and increasing their trust in on line payment systems. The new model focuses on enhancing the feeling of security of the use of an online payment system and satisfying the security requirements.