nils-herrmann/clean_scirepeval huggingface dataset:
Systematic review of clinical trials on dietary interventions to prevent excessive weight gain during pregnancy among normal weight, overweight and obese women

Excessive weight gain during pregnancy and subsequent postpartum weight retention may contribute to the epidemic of obesity among women of childbearing age. Preventing excessive gestational weight gain (GWG) to optimize maternal, fetal and infant wellbeing is therefore of great importance. A number of dietary interventions in this area has been conducted with inconsistent results, which has made it difficult to identify effective strategies to prevent excessive weight gain during pregnancy among normal weight, overweight and obese women. The primary objective of this review was to evaluate the effect of dietary interventions for reducing GWG. The secondary objective was to examine the impact of these interventions on different child and maternal health outcomes.



The PUBMED, the Cochrane Central Register of Controlled Trials (CENTRAL) and the LILACS databases were searched for relevant articles. All published randomized controlled trials (RCT) and quasi-randomized controlled trials (QCT), with concurrent controls, on dietary interventions during pregnancy were considered. Results were presented using relative risk (RR) for categorical data and weighted mean difference (WMD) for continuous data. Data were primarily analyzed with a fixed-effect model and a random-effects model was used in the presence of heterogeneity. No date and language restrictions were applied.



In total, 13 studies were included in this review and 10 trials contributed data on total GWG. Dietary intervention significantly reduced total GWG (n = 1434; WMD = -1.92 kg; 95% CI = -3.65/-0.19; p = 0.03), weight retention at six months postpartum (n = 443; WMD = -1.90 kg; 95% CI = -2.69/-1.12; p < 0.0001) and incidence of cesarean section (n = 609; RR = 0.75; 95% CI = 0.60/0.94; p = 0.013). However, dietary intervention had no significant effect on weight retention at six weeks postpartum, birth weight, preeclampsia, gestational diabetes and preterm birth.



Dietary advice during pregnancy appears effective in decreasing total GWG and long-term postpartum weight retention, but so far there is limited evidence for further benefits on infant and maternal health.
Confidence-based policy learning from demonstration using Gaussian mixture models

We contribute an approach for interactive policy learning through expert demonstration that allows an agent to actively request and effectively represent demonstration examples. In order to address the inherent uncertainty of human demonstration, we represent the policy as a set of Gaussian mixture models (GMMs), where each model, with multiple Gaussian components, corresponds to a single action. Incrementally received demonstration examples are used as training data for the GMM set. We then introduce our confident execution approach, which focuses learning on relevant parts of the domain by enabling the agent to identify the need for and request demonstrations for specific parts of the state space. The agent selects between demonstration and autonomous execution based on statistical analysis of the uncertainty of the learned Gaussian mixture set. As it achieves proficiency at its task and gains confidence in its actions, the agent operates with increasing autonomy, eliminating the need for unnecessary demonstrations of already acquired behavior, and reducing both the training time and the demonstration workload of the expert. We validate our approach with experiments in simulated and real robot domains.
Architecting Dependable Systems

Find the secret to improve the quality of life by reading this architecting dependable systems. This is a kind of book that you need now. Besides, it can be your favorite book to read after having this book. Do you ask why? Well, this is a book that has different characteristic with others. You may not need to know who the author is, how wellknown the work is. As wise word, never judge the words from who speaks, but make the words as your good value to your life.
SkyLens: Visual Analysis of Skyline on Multi-Dimensional Data

Skyline queries have wide-ranging applications in fields that involve multi-criteria decision making, including tourism, retail industry, and human resources. By automatically removing incompetent candidates, skyline queries allow users to focus on a subset of superior data items (i.e., the skyline), thus reducing the decision-making overhead. However, users are still required to interpret and compare these superior items manually before making a successful choice. This task is challenging because of two issues. First, people usually have fuzzy, unstable, and inconsistent preferences when presented with multiple candidates. Second, skyline queries do not reveal the reasons for the superiority of certain skyline points in a multi-dimensional space. To address these issues, we propose SkyLens, a visual analytic system aiming at revealing the superiority of skyline points from different perspectives and at different scales to aid users in their decision making. Two scenarios demonstrate the usefulness of SkyLens on two datasets with a dozen of attributes. A qualitative study is also conducted to show that users can efficiently accomplish skyline understanding and comparison tasks with SkyLens.
New cytotoxic alkyl phloroglucinols from Protorhus thouvenotii.

Two new cytotoxic alkylene phloroglucinols and one known compound were isolated through chromatographic separation from the methanolic extract of the dried fruits of Protorhus thouvenotii (Anacardiaceae). The compounds showed marginal in vitro cytotoxicity in the A2780 ovarian cancer cell line assay with an IC50 of 11 microg/mL.
Role of Awareness and Universal Context in a Spiking Conscious Neural Network (SCNN): A New Perspective and Future Directions

Awareness plays a major role in human cognition and adaptive behaviour, though mechanisms involved remain unknown. Awareness is not an objectively established fact, therefore, despite extensive research, scientists have not been able to fully interpret its contribution in multisensory integration and precise neural firing, hence, questions remain: (1) How the biological neuron integrates the incoming multisensory signals with respect to different situations? (2) How are the roles of incoming multisensory signals defined (selective amplification or attenuation) that help neuron(s) to originate a precise neural firing complying with the anticipated behavioural-constraint of the environment? (3) How are the external environment and anticipated behaviour integrated? Recently, scientists have exploited deep learning architectures to integrate multimodal cues and capture context-dependent meanings. Yet, these methods suffer from imprecise behavioural representation and a limited understanding of neural circuitry or underlying information processing mechanisms with respect to the outside world. In this research, we introduce a new theory on the role of awareness and universal context that can help answering the aforementioned crucial neuroscience questions. Specifically, we propose a class of spiking conscious neuron in which the output depends on three functionally distinctive integrated input variables: receptive field (RF), local contextual field (LCF), and universal contextual field (UCF) a newly proposed dimension. The RF defines the incoming ambiguous sensory signal, LCF defines the modulatory sensory signal coming from other parts of the brain, and UCF defines the awareness. It is believed that the conscious neuron inherently contains enough knowledge about the situation in which the problem is to be solved based on past learning and reasoning and it defines the precise role of incoming multisensory signals (amplification or attenuation) to originate a precise neural firing (exhibiting switch-like behaviour). It is shown, when implemented within an SCNN, the conscious neuron helps modelling a more precise human behaviour e.g., when exploited to model human audiovisual speech processing, the SCNN performed comparably to deep long-short-term memory (LSTM) network. We believe that the proposed theory could be applied to address a range of real-world problems including elusive neural disruptions, explainable artificial intelligence, human-like computing, low-power neuromorphic chips etc.
Indefinite Probabilities for General Intelligence

The creation of robust mechanisms for uncertain inference is central to the development of Artificial General Intelligence systems. While probability theory provides a principled foundation for uncertain inference, the mathematics of probability theory has not yet been developed to the point where it is possible to handle every aspect of the uncertain inference process in practical situations using rigorous probabilistic calculations. Due to the need to operate within realistic computational resources, probability theory presently requires augmentation with heuristics in order to be pragmatic for general intelligence (as well as for other purposes such as large-scale data analysis). The authors have been involved with the creation of a novel, general framework for pragmatic probabilistic inference in an AGI context, called Probabilistic Logic Networks (PLN). PLN integrates probability theory with a variety of heuristic inference mechanisms; it encompasses a rich set of first-order and higher-order inference rules, and it is highly flexible and adaptive, and easily configurable. This paper describes a single, critical aspect of the PLN framework, which has to with the quantification of uncertainty. In short, it addresses the question: What should an uncertain truth value be, so that a general intelligence may use it for pragmatic reasoning? We propose a new approach to quantifying uncertainty via a hybridization of Walley’s theory of imprecise probabilities and Bayesian credible intervals. This “indefinite probability” approach provides a general method for calculating the “weight-of-evidence” underlying the conclusions of uncertain inferences. Moreover, both Walley’s imprecise beta-binomial model and standard Bayesian inference can be viewed mathematically as special cases of the more general indefinite probability model. Via exemplifying the use of indefinite probabilities in a variety of PLN inference rules (including exact and heuristic ones), we argue that this mode of quantifying uncertainty may be adequate to serve as an ingredient of powerful artificial general intelligence.
Business Strategy Design Patterns The Porter Patterns

1  This paper describes five widely-applicable business strategy patterns. The initiate patterns where inspired Michael Porter's work on competitive strategy (1980). By applying the pattern form we are able to explore the strategies and consequences in a fresh light. The patterns form part of a larger endeavour to apply pattern thinking to the business domain. This endeavour seeks to map the business domain in patterns, this involves develop patterns, possibly based on existing literature, and mapping existing patterns into a coherent model of the business domain. If you find the paper interesting you might be interested in some more patterns that are currently (May 2005) in development. These describe in more detail how these strategies can be implemented: This paper is one of the most downloaded pieces on my website. I'd be interested to know more about who is downloading the paper, what use your making of it and any comments you have on it-allan@allankelly.net. Cost Leadership Build an organization that can produce your chosen product more cheaply than anyone else. You can then choose to undercut the opposition (and sell more) or sell at the same price (and make more profit per unit.) Differentiated Product Build a product that fulfils the same functions as your competitors but is clearly different, e.g. it is better quality, novel design, or carries a brand name. Customer will be prepared to pay more for your product than the competition. Market Focus You can't compete directly on cost or differentiation with the market leader; so, focus on a niche in the market. The niche will be smaller than the overall market (so sales will be lower) but the customer requirements will be different, serve these customers requirements better then the mass market and they will buy from you again and again. Sweet Spot Customers don't always want the best or the cheapest, so, produce a product that combines elements of differentiation with reasonable cost so you offer superior value. However, be careful, customer tastes
Selforganization of matter and the evolution of biological macromolecules

465 1.2. Prerequisites of Selforganization . . . . . . . 467 1.2.3. Evolut ion Must S ta r t f rom R andom Even ts 467 1.2.2. Ins t ruc t ion Requires In format ion . . . . 467 1.2.3. In format ion Originates or Gains Value by S e l e c t i o n . . . . . . . . . . . . . . . 469 1.2.4. Selection Occurs wi th Special Substances under Special Conditions . . . . . . . .
PyMedTermino: an open-source generic API for advanced terminology services

The integration of terminologies is still a challenging problem in medical informatics research and software applications, due to the high number of heterogeneous terminologies. In this paper, we present a generic API (Application Programming Interface) for a multi-terminology multilingual terminology service, and PyMedTermino, its open-source implementation in Python with 5 terminological resources (ICD10, SNOMED CT, MedDRA, CDF, VCM iconic language) and the UMLS compendium. This service has been designed for research and educational purpose. It offers various advanced functionalities rarely present in terminology services.
Balanced and Unbalanced Voltage Sag Mitigation Using DSTATCOM with Linear and Nonlinear Loads

DSTATCOM is one of the equipments for voltage sag mitigation in power systems. In this paper a new control method for balanced and unbalanced voltage sag mitigation using DSTATCOM is proposed. The control system has two loops in order to regulate compensator current and load voltage. Delayed signal cancellation has been used for sequence separation. The compensator should protect sensitive loads against different types of voltage sag. Performance of the proposed method is investigated under different types of voltage sags for linear and nonlinear loads. Simulation results show appropriate operation of the proposed control system. Custom power, power quality, voltage sag mitigation, current vector control.
Gastrointestinal food allergy in infants.

Food allergies are classified into three types, "IgE-mediated," "combined IgE- and cell-mediated" and "cell-mediated/non-IgE-mediated," depending on the involvement of IgE in their pathogenesis. Patients who develop predominantly cutaneous and/or respiratory symptoms belong to the IgE-mediated food allergy type. On the other hand, patients with gastrointestinal food allergy (GI allergy) usually develop gastrointestinal symptoms several hours after ingestion of offending foods; they belong to the cell-mediated/non-IgE-mediated or combined IgE- and cell-mediated food allergy types. GI allergies are also classified into a number of different clinical entities: food protein-induced enterocolitis syndrome (FPIES), food protein-induced proctocolitis (FPIP), food protein-induced enteropathy (Enteropathy) and eosinophilic gastrointestinal disorders (EGID). In the case of IgE-mediated food allergy, the diagnostic approaches and pathogenic mechanisms are well characterized. In contrast, the diagnostic approaches and pathogenic mechanisms of GI allergy remain mostly unclear. In this review, we summarized each type of GI allergy in regard to its historical background and updated clinical features, offending foods, etiology, diagnosis, examinations, treatment and pathogenesis. There are still many problems, especially in regard to the diagnostic approaches for GI allergy, that are closely associated with the definition of each disease. In addition, there are a number of unresolved issues regarding the pathogenic mechanisms of GI allergy that need further study and elucidation. Therefore, we discussed some of the diagnostic and research issues for GI allergy that need further investigation.
A wearable virtual guide for context-aware cognitive indoor navigation

In this paper, we explore a new way to provide context-aware assistance for indoor navigation using a wearable vision system. We investigate how to represent the cognitive knowledge of wayfinding based on first-person-view videos in real-time and how to provide context-aware navigation instructions in a human-like manner. Inspired by the human cognitive process of wayfinding, we propose a novel cognitive model that represents visual concepts as a hierarchical structure. It facilitates efficient and robust localization based on cognitive visual concepts. Next, we design a prototype system that provides intelligent context-aware assistance based on the cognitive indoor navigation knowledge model. We conducted field tests and evaluated the system's efficacy by benchmarking it against traditional 2D maps and human guidance. The results show that context-awareness built on cognitive visual perception enables the system to emulate the efficacy of a human guide, leading to positive user experience.
See Through Walls with COTS RFID System!

Through-wall tracking has gained a lot of attentions in civilian applications recently. Many applications would benefit from such device-free tracking, e.g. elderly people surveillance, intruder detection, gaming, etc. In this work, we present a system, named Tadar, for tracking moving objects without instrumenting them us- ing COTS RFID readers and tags. It works even through walls and behind closed doors. It aims to enable a see-through-wall technology that is low-cost, compact, and accessible to civilian purpose. In traditional RFID systems, tags modulate their IDs on the backscatter signals, which is vulnerable to the interferences from the ambient reflections. Unlike past work, which considers such vulnerability as detrimental, our design exploits it to detect surrounding objects even through walls. Specifically, we attach a group of RFID tags on the outer wall and logically convert them into an antenna array, receiving the signals reflected off moving objects. This paper introduces two main innovations. First, it shows how to eliminate the flash (e.g. the stronger reflections off walls) and extract the reflections from the backscatter signals. Second, it shows how to track the moving object based on HMM (Hidden Markov Model) and its reflections. To the best of our knowledge, we are the first to implement a through-wall tracking using the COTS RFID systems. Empirical measurements with a prototype show that Tadar can detect objects behind 5" hollow wall and 8" concrete wall, and achieve median tracking errors of 7.8cm and 20cm in the X and Y dimensions.
Literature survey of sarcasm detection

Sarcasm is a form of language in which individual convey their message in an implicit way i.e. the opposite of what is implied. Sarcasm detection is the task of predicting sarcasm in text. This is the crucial step in sentiment analysis due to inherently ambiguous nature of sarcasm. With this ambiguity, sarcasm detection has always been a difficult task, even for humans. Therefore sarcasm detection has gained importance in many Natural Language Processing applications. In this paper, we describe approaches, issues, challenges and future scopes in sarcasm detection.
Maximum Correntropy Kalman Filter

—Traditional Kalman filter (KF) is derived under the well-known minimum mean square error (MMSE) criterion, which is optimal under Gaussian assumption. However, when the signals are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises, the performance of KF will deteriorate seriously. To improve the robustness of KF against impulsive noises, we propose in this work a new Kalman filter, called the maximum correntropy Kalman filter (MCKF), which adopts the robust maximum correntropy criterion (MCC) as the optimality criterion, instead of using the MMSE. Similar to the traditional KF, the state mean and covariance matrix propagation equations are used to give prior estimations of the state and covariance matrix in MCKF. A novel fixed-point algorithm is then used to update the posterior estimations. A sufficient condition that guarantees the convergence of the fixed-point algorithm is given. Illustration examples are presented to demonstrate the effectiveness and robustness of the new algorithm.
Yeast Killer Toxin K28: Biology and Unique Strategy of Host Cell Intoxication and Killing

The initial discovery of killer toxin-secreting brewery strains of Saccharomyces cerevisiae (S. cerevisiae) in the mid-sixties of the last century marked the beginning of intensive research in the yeast virology field. So far, four different S. cerevisiae killer toxins (K28, K1, K2, and Klus), encoded by cytoplasmic inherited double-stranded RNA viruses (dsRNA) of the Totiviridae family, have been identified. Among these, K28 represents the unique example of a yeast viral killer toxin that enters a sensitive cell by receptor-mediated endocytosis to reach its intracellular target(s). This review summarizes and discusses the most recent advances and current knowledge on yeast killer toxin K28, with special emphasis on its endocytosis and intracellular trafficking, pointing towards future directions and open questions in this still timely and fascinating field of killer yeast research.
Music therapy in the assessment and treatment of autistic spectrum disorder: clinical application and research evidence.

Children and adolescents with autistic spectrum disorder (ASD) presenting with significant limitations in conventional forms of verbal and non-verbal communication are found to respond positively to music therapy intervention involving both active, improvizational methods and receptive music therapy approaches. Improvizational musical activity with therapeutic objectives and outcomes has been found to facilitate motivation, communication skills and social interaction, as well as sustaining and developing attention. The structure and predictability found in music assist in reciprocal interaction, from which tolerance, flexibility and social engagement to build relationships emerge, relying on a systematic approach to promote appropriate and meaningful interpersonal responses.



Published reports of the value and effectiveness of music therapy as an intervention for children with ASD range from controlled studies to clinical case reports. Further documentation has emphasized the role music therapy plays in diagnostic and clinical assessment. Music therapy assessment can identify limitations and weaknesses in children, as well as strengths and potentials. Research evidence from a systematic review found two randomized controlled trials that examined short-term effects of structured music therapy intervention. Significant effects were found in these studies even with extremely small samples, and the findings are important because they demonstrate the potential of the medium of music for autistic children. Case series studies were identified that examined the effects of improvizational music therapy where communicative behaviour, language development, emotional responsiveness, attention span and behavioural control improved over the course of an intervention of improvizational music therapy.