nils-herrmann/clean_scirepeval huggingface dataset:
Barriers to NP Practice that Impact Healthcare Redesign.

As healthcare reform evolves, nurse practitioners (NP) will play key roles in improving health outcomes of diverse populations. According to the Institute of Medicine (IOM) 2011 report, The Future of Nursing: Leading Change Advancing Health, nurses should be change advocates by caring for populations within complex healthcare systems. The IOM reports asserts, "advanced practice registered nurses (APRNs) should be able to practice to the fullest extent of their education and training" . However, existing barriers in the healthcare arena limit APRN practice. This article will discuss some of these barriers and provide suggestions for possible ways to decrease the barriers.
Resampling-based Multiple Testing for Microarray Data Analysis

The burgeoning field of genomics has revived interest in multiple testing procedures by raising new methodological and computational challenges. For example, microarray experiments generate large multiplicity problems in which thousands of hypotheses are tested simultaneously. Westfall and Young (1993) propose resampling-based p-value adjustment procedures which are highly relevant to microarray experiments. This article discusses different criteria for error control in resampling-based multiple testing, including (a) the family wise error rate of Westfall and Young (1993) and (b) the false discovery rate developed by Benjamini and Hochberg (1995), both from a frequentist viewpoint; and (c) the positive false discovery rate of Storey (2002a), which has a Bayesian motivation. We also introduce our recently developed fast algorithm for implementing the minP adjustment to control family-wise error rate. Adjusted p-values for different approaches are applied to gene expression data from two recently published microarray studies. The properties of these procedures for multiple testing are compared.
1 Quality Changes in Food Materials as Influenced by Drying Processes

Drying and dewatering plays a major role in food manufacturing or food processing activities worldwide. Often one of the last operations in the food processing, it controls to a large extent the quality of the final product. Drying is applied to a wide variety of food products, from cereals to finished goods, from raw materials to byproducts. The processes used are numerous, according to the type and quantity of product to dry, the amount of water to eliminate, the final desired quality or functionality of the dried product (Tab. 1.1). Drying and dewatering impact the mechanical, sensory and nutritional properties of food products, and can be used to create new functionalities (Bonazzi and Bimbenet, 2003, 2008). Drying is one of the main techniques for preserving agricultural and food products; it takes place in the processing of many products, as the main operation or as a consequence of other processing steps. Heat and mass transfer phenomena which are typical of drying also appear during other processes, as in cooking, baking, roasting, smoking, refrigeration, freezing, during storage, and during pneumatic transportation. The main objective of drying is to decrease the water activity (aw) of various perishable materials to values <0.5, in order to enable their storage at ambient temperature.The importanceofaw incontrolling theshelf-lifeof foodsbysuppressing the growth of micro-organisms, by reducing the rates of chemical reactions, and by inhibiting enzymatic deterioration is well established. The respective relationships have been summarized by Labuza et al. (1970) in a diagram similar to that of Fig. 1.1. Water activity is more important to the stability of a food than the total amount of water present, and it makes it possible to develop generalized rules or limits for the stability of foods. For most foods, the critical point below which no micro-organism can grow is in the 0.6–0.7 water activity range. A food product is most stable at its monolayer moisture content, which varies with the chemical composition and structure. Water activity is also useful for predicting the final moisture content at equilibrium versus drying conditions (temperature and relative humidity), and for
Body weight loss and weight maintenance in relation to habitual caffeine intake and green tea supplementation.

Investigation of the effect of a green tea-caffeine mixture on weight maintenance after body weight loss in moderately obese subjects in relation to habitual caffeine intake.



A randomized placebo-controlled double blind parallel trial in 76 overweight and moderately obese subjects, (BMI, 27.5 +/- 2.7 kg/m2) matched for sex, age, BMI, height, body mass, and habitual caffeine intake was conducted. A very low energy diet intervention during 4 weeks was followed by 3 months of weight maintenance (WM); during the WM period, the subjects received a green tea-caffeine mixture (270 mg epigallocatechin gallate + 150 mg caffeine per day) or placebo.



Subjects lost 5.9 +/-1.8 (SD) kg (7.0 +/- 2.1%) of body weight (p < 0.001). At baseline, satiety was positively, and in women, leptin was inversely, related to subjects' habitual caffeine consumption (p < 0.01). High caffeine consumers reduced weight, fat mass, and waist circumference more than low caffeine consumers; resting energy expenditure was reduced less and respiratory quotient was reduced more during weight loss (p < 0.01). In the low caffeine consumers, during WM, green tea still reduced body weight, waist, respiratory quotient and body fat, whereas resting energy expenditure was increased compared with a restoration of these variables with placebo (p < 0.01). In the high caffeine consumers, no effects of the green tea-caffeine mixture were observed during WM.



High caffeine intake was associated with weight loss through thermogenesis and fat oxidation and with suppressed leptin in women. In habitual low caffeine consumers, the green tea-caffeine mixture improved WM, partly through thermogenesis and fat oxidation.
Cultural meaning of perceived control: a meta-analysis of locus of control and psychological symptoms across 18 cultural regions.

Integrating more than 40 years of studies on locus of control (LOC), this meta-analysis investigated whether (a) the magnitude of the relationship between LOC and psychological symptoms differed among cultures with distinct individualist orientations and (b) depression and anxiety symptoms yielded different patterns of cultural findings with LOC. We included studies that examined global self-ratings of LOC and at least 1 of the criterion variables in nonclinical samples (age range: 18-80 years). Data were analyzed on the basis of 152 independent samples, representing the testing of 33,224 adults across 18 cultural regions. Results revealed moderately strong relationships for external LOC with depression symptoms (k = 123, N = 28,490, r = .30, 95% confidence interval [CI] [.27, .32]) and anxiety symptoms (k = 65, N = 13,208, r = .30, 95% CI [.27, .33]). Individualism explained 20% of unique variance only in the external LOC-anxiety relationship: The link between external LOC and anxiety symptoms was weaker for collectivist societies (k = 8, N = 2,297, r = .20, 95% CI [.13, .28]) compared with individualist societies (k = 54, N = 9,887, r = .32, 95% CI [.29, .34]). Such cultural differences were attributed to the reduced emphasis on agentic goals in more collectivist societies. It is noteworthy that external LOC does not carry the same negative connotations across cultures, and members of collectivist societies may be more ready to endorse such items. Culture has been examined at the country level, and the findings may not be applicable to any particular person in a cultural region. Implications for integrating cultural meaning of perceived control into formulation of theories, research design, and intervention programs are discussed.
The Design Space for Robot Appearance and Behaviour for Social Robot Companions

To facilitate necessary task-based interactions and to avoid annoying or upsetting people a domestic robot will have to exhibit appropriate non-verbal social behaviour. Most current robots have the ability to sense and control for the distance of people and objects in their vicinity. An understanding of human robot proxemic and associated non-verbal social behaviour is crucial for humans to accept robots as domestic or servants. Therefore, this thesis addressed the following hypothesis: Attributes of robot appearance, behaviour, task context and situation will affect the distances that people will find comfortable between themselves and a robot.. Initial exploratory Human-Robot Interaction (HRI) experiments replicated human-human studies into comfortable approach distances with a mechanoid robot in place of one of the human interactors. It was found that most human participants respected the robot's interpersonal space and there were systematic differences for participants' comfortable approach distances to robots with different voice styles. It was proposed that greater initial comfortable approach distances to the robot were due to perceived inconsistencies between the robots overall appearance and voice style. To investigate these issues further it was necessary to develop HRI experimental set-ups, a novel Video-based HRI (VHRI) trial methodology, trial data collection methods and analytical methodologies. An exploratory VHRI trial then investigated human perceptions and preferences for robot appearance and non-verbal social behaviour. The methodological approach highlighted the holistic and embodied nature of robot appearance and behaviour. Findings indicated that people tend to rate a particular behaviour less favourably when the behaviour is not consistent with the robot’s appearance. A live HRI experiment finally confirmed and extended from these previous findings that there were multiple factors which significantly affected participants preferences for robot to human approach distances. There was a significant general tendency for participants to prefer either a tall humanoid robot or a
Selection of robot pre-grasps using box-based shape approximation

Grasping is a central issue of various robot applications, especially when unknown objects have to be manipulated by the system. In earlier work, we have shown the efficiency of 3D object shape approximation by box primitives for the purpose of grasping. A point cloud was approximated by box primitives . In this paper, we present a continuation of these ideas and focus on the box representation itself. On the number of grasp hypotheses from box face normals, we apply heuristic selection integrating task, orientation and shape issues. Finally, an off-line trained neural network is applied to chose a final best hypothesis as the final grasp. We motivate how boxes as one of the simplest representations can be applied in a more sophisticated manner to generate task-dependent grasps.
Independent motion detection with event-driven cameras

Unlike standard cameras that send intensity images at a constant frame rate, event-driven cameras asynchronously report pixel-level brightness changes, offering low latency and high temporal resolution (both in the order of micro-seconds). As such, they have great potential for fast and low power vision algorithms for robots. Visual tracking, for example, is easily achieved even for very fast stimuli, as only moving objects cause brightness changes. However, cameras mounted on a moving robot are typically non-stationary and the same tracking problem becomes confounded by background clutter events due to the robot ego-motion. In this paper, we propose a method for segmenting the motion of an independently moving object for event-driven cameras. Our method detects and tracks corners in the event stream and learns the statistics of their motion as a function of the robot's joint velocities when no independently moving objects are present. During robot operation, independently moving objects are identified by discrepancies between the predicted corner velocities from ego-motion and the measured corner velocities. We validate the algorithm on data collected from the neuromorphic iCub robot. We achieve a precision of ∼ 90% and show that the method is robust to changes in speed of both the head and the target.
High resolution automotive radar data clustering with novel cluster method

Clustering of measurement data is an important task in digital signal processing. Especially in the case of radar signal processing the need of clustering detection points becomes obvious when high-resolution radar sensor systems are used. Clustering is usually used as a preprocessing step for classification of the measured data. In this paper a new approach for automotive radar data clustering is presented. A shape finding technique from image signal processing, called border following, is used to perform this task. Some adjustments and modifications of the method are required to get it working with radar measurements. The adapted algorithm is proven in three different measurement spaces and rated for the best performance by focusing on clustering of cyclists. It is showed, that the technique produces clustered radar data appropriate to their physical appearance.
A Survey of Learning Causality with Data: Problems and Methods

The era of big data provides researchers with convenient access to copious data. However, we often have little knowledge of such data. The increasing prevalence of massive data is challenging the traditional methods of learning causality because they were developed for the cases with limited amount of data and strong prior causal knowledge. This survey aims to close the gap between big data and learning causality with a comprehensive and structured review of both traditional and frontier methods followed by a discussion about some open problems of learning causality. We begin with preliminaries of learning causality. Then we categorize and revisit methods of learning causality for the typical problems and data types. After that, we discuss the connections between learning causality and machine learning. At the end, some open problems are presented to show the great potential of learning causality with data.
An HTTP/2 Push-Based Approach for Low-Latency Live Streaming with Super-Short Segments

Over the last years, streaming of multimedia content has become more prominent than ever. To meet increasing user requirements, the concept of HTTP Adaptive Streaming (HAS) has recently been introduced. In HAS, video content is temporally divided into multiple segments, each encoded at several quality levels. A rate adaptation heuristic selects the quality level for every segment, allowing the client to take into account the observed available bandwidth and the buffer filling level when deciding the most appropriate quality level for every new video segment. Despite the ability of HAS to deal with changing network conditions, a low average quality and a large camera-to-display delay are often observed in live streaming scenarios. In the meantime, the HTTP/2 protocol was standardized in February 2015, providing new features which target a reduction of the page loading time in web browsing. In this paper, we propose a novel push-based approach for HAS, in which HTTP/2’s push feature is used to actively push segments from server to client. Using this approach with video segments with a sub-second duration, referred to as super-short segments, it is possible to reduce the startup time and end-to-end delay in HAS live streaming. Evaluation of the proposed approach, through emulation of a multi-client scenario with highly variable bandwidth and latency, shows that the startup time can be reduced with 31.2% compared to traditional solutions over HTTP/1.1 in mobile, high-latency networks. Furthermore, the end-to-end delay in live streaming scenarios can be reduced with 4 s, while providing the content at similar video quality.
Development and evaluation of interactive humanoid robots

We report the development and evaluation of a new interactive humanoid robot that communicates with humans and is designed to participate in human society as a partner. A human-like body will provide an abundance of nonverbal information and enable us to smoothly communicate with the robot. To achieve this, we developed a humanoid robot that autonomously interacts with humans by speaking and gesturing. Interaction achieved through a large number of interactive behaviors, which are developed by using a visualizing tool for understanding the developed complex system. Each interactive behavior is designed by using knowledge obtained through cognitive experiments and implemented by using situated recognition. The robot is used as a testbed for studying embodied communication. Our strategy is to analyze human-robot interaction in terms of body movements using a motion-capturing system that allows us to measure the body movements in detail. We performed experiments to compare the body movements with subjective evaluation based on a psychological method. The results reveal the importance of well-coordinated behaviors as well as the performance of the developed interactive behaviors and suggest a new analytical approach to human-robot interaction.
Autoencoding Variational Inference For Topic Models

Topic models are one of the most popular methods for learning representations of text, but a major challenge is that any change to the topic model requires mathematically deriving a new inference algorithm. A promising approach to address this problem is autoencoding variational Bayes (AEVB), but it has proven difficult to apply to topic models in practice. We present what is to our knowledge the first effective AEVB based inference method for latent Dirichlet allocation (LDA), which we call Autoencoded Variational Inference For Topic Model (AVITM). This model tackles the problems caused for AEVB by the Dirichlet prior and by component collapsing. We find that AVITM matches traditional methods in accuracy with much better inference time. Indeed, because of the inference network, we find that it is unnecessary to pay the computational cost of running variational optimization on test data. Because AVITM is black box, it is readily applied to new topic models. As a dramatic illustration of this, we present a new topic model called ProdLDA, that replaces the mixture model in LDA with a product of experts. By changing only one line of code from LDA, we find that ProdLDA yields much more interpretable topics, even if LDA is trained via collapsed Gibbs sampling.
Molecular pathological classification of colorectal cancer

Colorectal cancer (CRC) shows variable underlying molecular changes with two major mechanisms of genetic instability: chromosomal instability and microsatellite instability. This review aims to delineate the different pathways of colorectal carcinogenesis and provide an overview of the most recent advances in molecular pathological classification systems for colorectal cancer. Two molecular pathological classification systems for CRC have recently been proposed. Integrated molecular analysis by The Cancer Genome Atlas project is based on a wide-ranging genomic and transcriptomic characterisation study of CRC using array-based and sequencing technologies. This approach classified CRC into two major groups consistent with previous classification systems: (1) ∼16 % hypermutated cancers with either microsatellite instability (MSI) due to defective mismatch repair (∼13 %) or ultramutated cancers with DNA polymerase epsilon proofreading mutations (∼3 %); and (2) ∼84 % non-hypermutated, microsatellite stable (MSS) cancers with a high frequency of DNA somatic copy number alterations, which showed common mutations in APC, TP53, KRAS, SMAD4, and PIK3CA. The recent Consensus Molecular Subtypes (CMS) Consortium analysing CRC expression profiling data from multiple studies described four CMS groups: almost all hypermutated MSI cancers fell into the first category CMS1 (MSI-immune, 14 %) with the remaining MSS cancers subcategorised into three groups of CMS2 (canonical, 37 %), CMS3 (metabolic, 13 %) and CMS4 (mesenchymal, 23 %), with a residual unclassified group (mixed features, 13 %). Although further research is required to validate these two systems, they may be useful for clinical trial designs and future post-surgical adjuvant treatment decisions, particularly for tumours with aggressive features or predicted responsiveness to immune checkpoint blockade.
Understanding transportation modes based on GPS data for web applications

User mobility has given rise to a variety of Web applications, in which the global positioning system (GPS) plays many important roles in bridging between these applications and end users. As a kind of human behavior, transportation modes, such as walking and driving, can provide pervasive computing systems with more contextual information and enrich a user's mobility with informative knowledge. In this article, we report on an approach based on supervised learning to automatically infer users' transportation modes, including driving, walking, taking a bus and riding a bike, from raw GPS logs. Our approach consists of three parts: a change point-based segmentation method, an inference model and a graph-based post-processing algorithm. First, we propose a change point-based segmentation method to partition each GPS trajectory into separate segments of different transportation modes. Second, from each segment, we identify a set of sophisticated features, which are not affected by differing traffic conditions (e.g., a person's direction when in a car is constrained more by the road than any change in traffic conditions). Later, these features are fed to a generative inference model to classify the segments of different modes. Third, we conduct graph-based postprocessing to further improve the inference performance. This postprocessing algorithm considers both the commonsense constraints of the real world and typical user behaviors based on locations in a probabilistic manner. The advantages of our method over the related works include three aspects. (1) Our approach can effectively segment trajectories containing multiple transportation modes. (2) Our work mined the location constraints from user-generated GPS logs, while being independent of additional sensor data and map information like road networks and bus stops. (3) The model learned from the dataset of some users can be applied to infer GPS data from others. Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments. As a result, based on the change-point-based segmentation method and Decision Tree-based inference model, we achieved prediction accuracy greater than 71 percent. Further, using the graph-based post-processing algorithm, the performance attained a 4-percent enhancement.
Foundations for Model-Based Engineering of Systems of Systems

The engineering of Systems of Systems presents daunting challenges. In this paper it is argued that rigorous semantic foundations for model-based techniques are potentially beneficial in addressing these. Three priorities for current research are identified: contractual interface definition, the verification of emergent behaviour, and the need to deal with semantic heterogeneity of constituent systems and their models. We describe the foundations of an approach in which architectural models in SysML may be analysed in a formal modelling language that has an extensible semantics given using the Unifying Theories of Programming (UTP).