nils-herrmann/clean_scirepeval huggingface dataset:
Point-of-care testing: where is the evidence? A systematic survey.

Point-of-care testing (POCT) has had rapid technological development and their use is widespread in clinical laboratories to assure reduction of turn-around-time and rapid patient management in some clinical settings where it is important to make quick decisions. Until now the papers published about the POCT have focused on the reliability of the technology used and their analytical accuracy. We aim to perform a systematic survey of the evidence of POCT efficacy focused on clinical outcomes, selecting POCT denoted special analytes characterized by possible high clinical impact. We searched in Medline and Embase. Two independent reviewers assessed the eligibility, extracted study details and assessed the methodological quality of studies. We analyzed 84 studies for five POCT instruments: neonatal bilirubin, procalcitonin, intra-operative parathyroid hormone, troponin and blood gas analysis. Studies were at high risk of bias. Most of the papers (50%) were studies of correlation between the results obtained by using POCT instruments and those obtained by using laboratory instruments. These data showed a satisfactory correlation between methods when similar analytical reactions were used. Only 13% of the studies evaluated the impact of POCT on clinical practice. POCT decreases the time elapsed for making decisions on patient management but the clinical outcomes have never been adequately evaluated. Our work shows that, although POCT has the potential to provide beneficial patient outcome, further studies may be required, especially for defining its real utility on clinical decision making.
Deploying Fog Applications: How Much Does It Cost, By the Way?

Deploying IoT applications through the Fog in a QoS-, context-, and cost-aware manner is challenging due to the heterogeneity, scale and dynamicity of Fog infrastructures. To decide how to allocate app functionalities over the continuum from the IoT to the Cloud, app administrators need to find a trade-off among QoS, resource consumption and cost. In this paper, we present a novel cost model for estimating the cost of deploying IoT applications to Fog infrastructures. We show how the inclusion of the cost model in the FogTorchΠ open-source prototype permits to determine eligible deployments of multi-component applications to Fog infrastructures and to rank them according to their QoS-assurance, Fog resource consumption and cost. We run the extended prototype on a motivating scenario, showing how it can support IT experts in choosing the deployments that best suit their desiderata.
Attention-Gated Networks for Improving Ultrasound Scan Plane Detection

In this work, we apply an attention-gated network to real-time automated scan plane detection for fetal ultrasound screening. Scan plane detection in fetal ultrasound is a challenging problem due the poor image quality resulting in low interpretability for both clinicians and automated algorithms. To solve this, we propose incorporating self-gated soft-attention mechanisms. A soft-attention mechanism generates a gating signal that is end-to-end trainable, which allows the network to contextualise local information useful for prediction. The proposed attention mechanism is generic and it can be easily incorporated into any existing classification architectures, while only requiring a few additional parameters. We show that, when the base network has a high capacity, the incorporated attention mechanism can provide efficient object localisation while improving the overall performance. When the base network has a low capacity, the method greatly outperforms the baseline approach and significantly reduces false positives. Lastly, the generated attention maps allow us to understand the model’s reasoning process, which can also be used for weakly supervised object localisation.
The continuum of psychotic symptoms in the general population: a cross-national study.

To identify the cross-national prevalence of psychotic symptoms in the general population and to analyze their impact on health status.



The sample was composed of 256,445 subjects (55.9% women), from nationally representative samples of 52 countries worldwide participating in the World Health Organization's World Health Survey. Standardized and weighted prevalence of psychotic symptoms were calculated in addition to the impact on health status as assessed by functioning in multiple domains.



Overall prevalences for specific symptoms ranged from 4.80% (SE = 0.14) for delusions of control to 8.37% (SE = 0.20) for delusions of reference and persecution. Prevalence figures varied greatly across countries. All symptoms of psychosis produced a significant decline in health status after controlling for potential confounders. There was a clear change in health impact between subjects not reporting any symptom and those reporting at least one symptom (effect size of 0.55).



The prevalence of the presence of at least one psychotic symptom has a wide range worldwide varying as much as from 0.8% to 31.4%. Psychotic symptoms signal a problem of potential public health concern, independent of the presence of a full diagnosis of psychosis, as they are common and are related to a significant decrement in health status. The presence of at least one psychotic symptom is related to a significant poorer health status, with a regular linear decrement in health depending on the number of symptoms.
Deep Region Hashing for Efficient Large-scale Instance Search from Images

Instance Search (INS) is a fundamental problem for many applications, while it is more challenging comparing to traditional image search since the relevancy is defined at the instance level. Existing works have demonstrated the success of many complex ensemble systems that are typically conducted by firstly generating object proposals, and then extracting handcrafted and/or CNN features of each proposal for matching. However, object bounding box proposals and feature extraction are often conducted in two separated steps, thus the effectiveness of these methods collapses. Also, due to the large amount of generated proposals, matching speed becomes the bottleneck that limits its application to large-scale datasets. To tackle these issues, in this paper we propose an effective and efficient Deep Region Hashing (DRH) approach for large-scale INS using an image patch as the query. Specifically, DRH is an end-toend deep neural network which consists of object proposal, feature extraction, and hash code generation. DRH shares full-image convolutional feature map with the region proposal network, thus enabling nearly cost-free region proposals. Also, each high-dimensional, real-valued region features are mapped onto a low-dimensional, compact binary codes for the efficient object region level matching on large-scale dataset. Experimental results on four datasets show that our DRH can achieve even better performance than the state-of-the-arts in terms of MAP, while the efficiency is improved by nearly 100 times.
Deep Reinforcement Learning For Sequence to Sequence Models

In recent years, sequence-to-sequence (seq2seq) models are used in a variety of tasks from machine translation, headline generation, text summarization, speech to text, to image caption generation. The underlying framework of all these models are usually a deep neural network which contains an encoder and decoder. The encoder processes the input data and a decoder receives the output of the encoder and generates the final output. Although simply using an encoder/decoder model would, most of the time, produce better result than traditional methods on the above-mentioned tasks, researchers proposed additional improvements over these sequence to sequence models, like using an attention-based model over the input, pointer-generation models, and self-attention models. However, all these seq2seq models suffer from two common problems: 1) exposure bias and 2) inconsistency between train/test measurement. Recently a completely fresh point of view emerged in solving these two problems in seq2seq models by using methods in Reinforcement Learning (RL). In these new researches, we try to look at the seq2seq problems from the RL point of view and we try to come up with a formulation that could combine the power of RL methods in decision-making and sequence to sequence models in remembering long memories. In this paper, we will summarize some of the most recent frameworks that combines concepts from RL world to the deep neural network area and explain how these two areas could benefit from each other in solving complex seq2seq tasks. In the end, we will provide insights on some of the problems of the current existing models and how we can improve them with better RL models. We also provide the source code for implementing most of the models that will be discussed in this paper on the complex task of abstractive text summarization.
Computational plenoptic imaging

A new generation of computational cameras is emerging, spawned by the introduction of the Lytro light-field camera to the consumer market and recent accomplishments in the speed at which light can be captured. By exploiting the co-design of camera optics and computational processing, these cameras capture unprecedented details of the plenoptic function: a ray-based model for light that includes the color spectrum as well as spatial, temporal, and directional variation. Although digital light sensors have greatly evolved in the last years, the visual information captured by conventional cameras has remained almost unchanged since the invention of the daguerreotype. All standard CCD and CMOS sensors integrate over the dimensions of the plenoptic function as they convert photons into electrons. In the process, all visual information is irreversibly lost, except for a two-dimensional, spatially varying subset: the common photograph.
 This course reviews the plenoptic function and discusses approaches for optically encoding high-dimensional visual information that is then recovered computationally in post-processing. It begins with an overview of the plenoptic dimensions and shows how much of this visual information is irreversibly lost in conventional image acquisition. Then it discusses the state of the art in joint optical modulation and computation reconstruction for acquisition of high-dynamic-range imagery and spectral information. It unveils the secrets behind imaging techniques that have recently been featured in the news and outlines other aspects of light that are of interest for various applications before concluding with question, answers, and a short discussion.
ACCELERATED VIBRATION TESTING BASED ON FATIGUE DAMAGE SPECTRA

Failure of an aerospace component can arise through the long term exposure to fatigue damaging events such as large numbers of low amplitude random events and/or relatively fewer high amplitude events. Mission profiling and test synthesis is a process for deriving a simple laboratory test that has at least the same damage potential as the real environment but in a fraction of the real time. In this paper we introduce the technical concepts and present a case study showing how new technology has dramatically reduced the time it takes to prepare and reduce the original test data.
FIGURE 2 The KMV Model of Relationship Marketing

Relationship marketing—establishing, developing, and maintaining successful relational exchanges—constitutes a major shift in marketing theory and practice. After conceptualizing relationship marketing and discussing its ten forms, the authors (1) theorize that successful relationship marketing requires relationship commitment and tnjst, (2) model relationship commitment and trust as key mediating variables, (3) test this key mediating variable model using data from automobile tire retailers, and (4) compare their model with a rival that does not allow relationship commitment and trust to function as mediating variables. Given the favorable test results for the key mediating variable model, suggestions for further explicating and testing it are offered.
Repairing Reed-Solomon Codes

A fundamental fact about polynomial interpolation is that k evaluations of a degree-(k-1) polynomial f are sufficient to determine f. This is also necessary in a strong sense: given k-1 evaluations, we learn nothing about the value of f on any k'th point. In this paper, we study a variant of the polynomial interpolation problem. Instead of querying entire evaluations of f (which are elements of a large field F), we are allowed to query partial evaluations; that is, each evaluation delivers a few elements from a small subfield of F, rather than a single element from F. We show that in this model, one can do significantly better than in the traditional setting, in terms of the amount of information required to determine the missing evaluation. More precisely, we show that only O(k) bits are necessary to recover a missing evaluation. In contrast, the traditional method of looking at k evaluations requires Omega(k log(k)) bits. We also show that our result is optimal for linear methods, even up to the leading constants. Our motivation comes from the use of Reed-Solomon (RS) codes for distributed storage systems, in particular for the exact repair problem. The traditional use of RS codes in this setting is analogous to the traditional interpolation problem. Each node in a system stores an evaluation of f, and if one node fails we can recover it by reading k other nodes. However, each node is free to send less information, leading to the modified problem above. The quickly-developing field of regenerating codes has yielded several codes which take advantage of this freedom. However, these codes are not RS codes, and RS codes are still often used in practice; in 2011, Dimakis et al. asked how well RS codes could perform in this setting. Our results imply that RS codes can also take advantage of this freedom to download partial symbols. In some parameter regimes---those with small levels of sub-packetization---our scheme for RS codes outperforms all known regenerating codes. Even with a high degree of sub-packetization, our methods give non-trivial schemes, and we give an improved repair scheme for a specific (14,10)-RS code used in the Facebook Hadoop Analytics cluster.
Bedroom Light Exposure at Night and the Incidence of Depressive Symptoms: A Longitudinal Study of the HEIJO-KYO Cohort.

Previous studies have indicated that minimal exposure to light at night (LAN) increases depression risk, even at 5 lux, in nocturnal and diurnal mammals. Although such low-level LAN may affect human circadian physiology, the association between exposure to LAN and depressive symptoms remains uncertain. In the present study, bedroom light intensity was measured objectively, and depressive symptoms were assessed, during 2010-2014 in Nara, Japan. Of 863 participants (mean age = 71.5 years) who did not have depressive symptoms at baseline, 73 participants reported development of depressive symptoms during follow-up (median, 24 months). Compared with the "dark" group (average of <5 lux; n = 710), the LAN group (average of ≥5 lux; n = 153) exhibited a significantly higher depression risk (hazard ratio = 1.89; 95% CI: 1.13, 3.14), according to a Cox proportional hazards model adjusting for age, sex, body mass index, and economic status. Further, the significance remained in a multivariable model adjusting for hypertension, diabetes, and sleep parameters (hazard ratio = 1.72; 95% CI: 1.03, 2.89). Sensitivity analyses using bedroom light data with a cutoff value of ≥10 lux suggested consistent results. In conclusion, these results indicated that exposure to LAN in home settings was independently associated with subsequent depression risk in an elderly general population.
Mobile learning in resource-constrained environments: a case study of medical education.

The achievement of the millennium development goals may be facilitated by the use of information and communication technology in medical and health education.



This study intended to explore the use and impact of educational technology in medical education in resource-constrained environments.



A multiple case study was conducted in two Nepalese teaching hospitals. The data were analysed using activity theory as an analytical basis.



There was little evidence for formal e-learning, but the findings indicate that students and residents adopted mobile technologies, such as mobile phones and small laptops, as cultural tools for surprisingly rich 'informal' learning in a very short time. These tools allowed learners to enhance (a) situated learning, by immediately connecting virtual information sources to their situated experiences; (b) cross-contextual learning by documenting situated experiences in the form of images and videos and re-using the material for later reflection and discussion and (c) engagement with educational content in social network communities.



By placing the students and residents at the centre of the new learning activities, this development has begun to affect the overall educational system. Leveraging these tools is closely linked to the development of broad media literacy, including awareness of ethical and privacy issues.
A Novel Discriminative Framework for Sentence-Level Discourse Analysis

We propose a complete probabilistic discriminative framework for performing sentencelevel discourse analysis. Our framework comprises a discourse segmenter, based on a binary classifier, and a discourse parser, which applies an optimal CKY-like parsing algorithm to probabilities inferred from a Dynamic Conditional Random Field. We show on two corpora that our approach outperforms the state-of-the-art, often by a wide margin.
Software-Defined Networking Using OpenFlow: Protocols, Applications and Architectural Design Choices

We explain the notion of software-defined networking (SDN), whose southbound interface may be implemented by the OpenFlow protocol. We describe the operation of OpenFlow and summarize the features of specification versions 1.0–1.4. We give an overview of existing SDN-based applications grouped by topic areas. Finally, we point out architectural design choices for SDN using OpenFlow and discuss their performance implications.
Creating Autonomous Vehicle Systems

This book is the first technical overview of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences of creating autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions about its actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, we are able to test new algorithms and update the HD map—plus, train better recognition, tracking, and decision models. This book consists of nine chapters. Chapter 1 provides an overview of autonomous vehicle systems; Chapter 2 focuses on localization technologies; Chapter 3 discusses traditional techniques used for perception; Chapter 4 discusses deep learning based techniques for perception; Chapter 5 introduces the planning and control sub-system, especially prediction and routing technologies; Chapter 6 focuses on motion planning and feedback control of the planning and control subsystem; Chapter 7 introduces reinforcement learning-based planning and control; Chapter 8 delves into the details of client systems design; and Chapter 9 provides the details of cloud platforms for autonomous driving. This book should be useful to students, researchers, and practitioners alike. Whether you are an undergraduate or a graduate student interested in autonomous driving, you will find herein a comprehensive overview of the whole autonomous vehicle technology stack. If you are an autonomous driving practitioner, the many practical techniques introduced in this book will be of interest to you. Researchers will also find plenty of references for an effective, deeper exploration of the various technologies.
Learning Word Importance with the Neural Bag-of-Words Model

The Neural Bag-of-Words (NBOW) model performs classification with an average of the input word vectors and achieves an impressive performance. While the NBOW model learns word vectors targeted for the classification task it does not explicitly model which words are important for given task. In this paper we propose an improved NBOW model with this ability to learn task specific word importance weights. The word importance weights are learned by introducing a new weighted sum composition of the word vectors. With experiments on standard topic and sentiment classification tasks, we show that (a) our proposed model learns meaningful word importance for a given task (b) our model gives best accuracies among the BOW approaches. We also show that the learned word importance weights are comparable to tf-idf based word weights when used as features in a BOW SVM classifier.
Muscle hypertrophy and a syndrome of continuous motor unit activity in prednisone-responsive Guillain-Barré polyneuropathy

The clinical, electrophysiological and morphological findings (light and electron microscopy of the sural nerve and gastrocnemius muscle) are reported in an unusual case of Guillain-Barré polyneuropathy with an association of muscle hypertrophy and a syndrome of continuous motor unit activity. Fasciculation, muscle stiffness, cramps, myokymia, impaired muscle relaxation and percussion myotonia, with their electromyographic accompaniments, were abolished by peripheral nerve blocking, carbamazepine, valproic acid or prednisone therapy. Muscle hypertrophy, which was confirmed by morphometric data, diminished 2 months after the beginning of prednisone therapy. Electrophysiological and nerve biopsy findings revealed a mixed process of axonal degeneration and segmental demyelination. Muscle biopsy specimen showed a marked predominance and hypertrophy of type-I fibres and atrophy, especially of type-II fibres.
A Data-Driven Stochastic Method for Elliptic PDEs with Random Coefficients

We propose a data-driven stochastic method (DSM) to study stochastic partial differential equations (SPDEs) in the multiquery setting. An essential ingredient of the proposed method is to construct a data-driven stochastic basis under which the stochastic solutions to the SPDEs enjoy a compact representation for a broad range of forcing functions and/or boundary conditions. Our method consists of offline and online stages. A data-driven stochastic basis is computed in the offline stage using the Karhunen–Loève (KL) expansion. A two-level preconditioning optimization approach and a randomized SVD algorithm are used to reduce the offline computational cost. In the online stage, we solve a relatively small number of coupled deterministic PDEs by projecting the stochastic solution into the data-driven stochastic basis constructed offline. Compared with a generalized polynomial chaos method (gPC), the ratio of the computational complexities between DSM (online stage) and gPC is of order O((m/Np) ). Herem andNp are the numbers of elements in the basis used in DSM and gPC, respectively. Typically we expect m Np when the effective dimension of the stochastic solution is small. A timing model, which takes into account the offline computational cost of DSM, is constructed to demonstrate the efficiency of DSM. Applications of DSM to stochastic elliptic problems show considerable computational savings over traditional methods even with a small number of queries. We also provide a method for an a posteriori error estimate and error correction.