nils-herrmann/clean_scirepeval huggingface dataset:
Psoriasis: guidance on assessment and referral.

This concise guideline summarises the key recommendations from the recent National Institute for Health and Care Excellence (NICE) clinical guideline on the assessment and management of psoriasis (CG153) that are relevant to the non-dermatologist. The aim is to highlight important considerations for assessment and referral of people with psoriasis, including identification of relevant comorbid conditions. Psoriasis is a common inflammatory skin condition and, especially when severe, can be associated with increased risk of cardiovascular disease, diabetes and depression. Functional, psychological and social morbidity can also be encountered, and the extent of the disability is frequently underestimated. Importantly, highly effective treatments are available. Appropriate assessment and referral of people with psoriasis therefore has the potential to improve outcomes by correctly identifying the appropriate treatment pathway. Assessment should involve not only disease severity but also the impact on patient well-being and whether the patient has any comorbid conditions, such as psoriatic arthritis, which requires rapid referral to a rheumatologist.
CLEF 15th Birthday: Past, Present, and Future

2014 marks the 15th birthday for CLEF, an evaluation campaign activity which has applied the Cranfield evaluation paradigm to the testing of multilingual and multimodal information access systems in Europe. This paper provides a summary of the motivations which led to the establishment of CLEF, and a description of how it has evolved over the years, the major achievements, and what we see as the next challenges.
Nanobolometers for THz Photon Detection

This paper reviews the state of rapidly emerging terahertz hot-electron nanobolometers (nano-HEB), which are currently among of the most sensitive radiation power detectors at submillimeter wavelengths. With the achieved noise equivalent power close to 10-19 W/Hz1/2 and potentially capable of approaching NEP ~ 10-20 W/Hz1/2, nano-HEBs are very important for future space astrophysics platforms with ultralow submillimeter radiation background. The ability of these sensors to detect single low-energy photons with high dynamic range opens interesting possibilities for quantum calorimetry in the midinfrared and even in the far-infrared parts of the electromagnetic spectrum. We discuss the competition in the field of ultrasensitive detectors, the physics and technology of nano-HEBs, recent experimental results, and perspectives for future development.
Segmentation of liver using marker watershed transform algorithm for CT scan images

The Liver is a largest gland in the body. Distinct diseases affected on the liver. Liver diseases is one of the most serious health problem worldwide. For detecting the liver diseases the Segmentation Technique is essential. Segmentation is used for the classification of liver diseases. The liver diseases are focal or diffused is easily understood by the physician using segmentation. We use CT scan image for segmentation but the noise is present in the image. Therefore preprocessing is applied on the image for the removal of noise. In this paper, Watershed Transform segmentation Algorithm is used because it produce complete division of images in separate region even if contrast is poor. Therefore this method could be achieved 92.1% accuracy.
High-speed surface permanent magnet machines - rotor design analysis, considerations, and challenges

High-speed electric machines are becoming increasingly important and utilized in many applications. This paper addresses the considerations and challenges of the rotor design of high-speed surface permanent magnet machines. The paper focuses particularly on mechanical aspects of the design. Special attention is given to the rotor sleeve design including thickness and material. Permanent magnet design parameters are discussed. Surface permanent magnet rotor dynamic considerations and challenges are also discussed.
Overview of the TECSAR Satellite Hardware and Mosaic Mode

TECSAR satellite is part of a spaceborne synthetic-aperture-radar (SAR) satellite technology demonstration program. The purpose of this program is to develop and evaluate the technologies required to achieve high-resolution images combined with large-area coverage. These requirements can be fulfilled by designing a satellite with multimode operation. The TECSAR satellite is developed by the MBT Space Division, , which develops the SAR payload. This paper reviews the TECSAR radar system design, which enables to perform a variety of operational modes. It also describes the unique hardware components: deployable parabolic mesh antenna, multitube transmitter, and data-link transmission unit. The unique mosaic mode is presented. It is shown that this mode is the spot version of the scan mode.
Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network

Depth estimation from a single image is a fundamental problem in computer vision. In this paper, we propose a simple yet effective convolutional spatial propagation network (CSPN) to learn the affinity matrix for depth prediction. Specifically, we adopt an efficient linear propagation model, where the propagation is performed with a manner of recurrent convolutional operation, and the affinity among neighboring pixels is learned through a deep convolutional neural network (CNN). We apply the designed CSPN to two depth estimation tasks given a single image: (1) Refine the depth output from existing state-of-the-art (SOTA) methods; (2) Convert sparse depth samples to a dense depth map by embedding the depth samples within the propagation procedure. The second task is inspired by the availability of LiDAR that provides sparse but accurate depth measurements. We experimented the proposed CSPN over the popular NYU v2  and KITTI  datasets, where we show that our proposed approach improves not only quality (e.g., 30% more reduction in depth error), but also speed (e.g., 2 to 5× faster) of depth maps than previous SOTA methods.
Energy-efficient image compression algorithm for high-frame rate multi-view wireless capsule endoscopy

The design, architecture and VLSI implementation of an image compression algorithm for high-frame rate, multi-view wireless endoscopy is presented. By operating directly on Bayer color filter array image the algorithm achieves both high overall energy efficiency and low implementation cost. It uses two-dimensional discrete cosine transform to decorrelate image values in each $$4\times 4$$ 4 × 4 block. Resulting coefficients are encoded by a new low-complexity yet efficient entropy encoder. An adaptive deblocking filter on the decoder side removes blocking effects and tiling artifacts on very flat image, which enhance the final image quality. The proposed compressor, including a 4 KB FIFO, a parallel to serial converter and a forward error correction encoder, is implemented in 180 nm CMOS process. It consumes 1.32 mW at 50 frames per second (fps) and only 0.68 mW at 25 fps at 3 MHz clock. Low silicon area 1.1 mm  $$\times$$ ×  1.1 mm, high energy efficiency (27  $$\upmu$$ μ J/frame) and throughput offer excellent scalability to handle image processing tasks in new, emerging, multi-view, robotic capsules.
Anxiety disorders and comorbid medical illness.

To provide an overview of the role of anxiety disorders in medical illness.



The Anxiety Disorders Association of America held a multidisciplinary conference from which conference leaders and speakers reviewed presentations and discussions, considered literature on prevalence, comorbidity, etiology and treatment, and made recommendations for research. Irritable bowel syndrome (IBS), asthma, cardiovascular disease (CVD), cancer and chronic pain were reviewed.



A substantial literature supports clinically important associations between psychiatric illness and chronic medical conditions. Most research focuses on depression, finding that depression can adversely affect self-care and increase the risk of incident medical illness, complications and mortality. Anxiety disorders are less well studied, but robust epidemiological and clinical evidence shows that anxiety disorders play an equally important role. Biological theories of the interactions between anxiety and IBS, CVD and chronic pain are presented. Available data suggest that anxiety disorders in medically ill patients should not be ignored and could be considered conjointly with depression when developing strategies for screening and intervention, particularly in primary care.



Emerging data offer a strong argument for the role of anxiety in medical illness and suggest that anxiety disorders rival depression in terms of risk, comorbidity and outcome. Research programs designed to advance our understanding of the impact of anxiety disorders on medical illness are needed to develop evidence-based approaches to improving patient care.
Data analysis of Li-Ion and lead acid batteries discharge parameters with Simulink-MATLAB

This paper intends to data analysis for Li-Ion and Lead Acid Batteries. The analysis based on discharge parameters input and output were processed in Simulink MATLAB. The input parameters are nominal voltage, rated capacity, and SOC, while the output parameters consist of maximum capacity, fully charged voltage, nominal discharge current, internal resistance, exponential zone voltage, and exponential zone capacity. Study and investigation of Li-Ion batteries were done by comparing them to the Lead Acids at the voltage and battery capacity of 3.7 V, 1400 mAh and 12V, 100Ah respectively. The result showed that the maximum capacity parameter of Lead Acid batteries equally 104.16% is better than Li-Ions of 100%, while Li-Ion batteries is good for almost all others parameters except internal resistance.
Automatic derivation of phonological rules for mispronunciation detection in a computer-assisted pronunciation training system

Computer-Assisted Pronunciation Training System (CAPT) has become an important learning aid in second language (L2) learning. Our approach to CAPT is based on the use of phonological rules to capture language transfer effects that may cause mispronunciations. This paper presents an approach for automatic derivation of phonological rules from L2 speech. The rules are used to generate an extended recognition network (ERN) that captures the canonical pronunciations of words, as well as the possible mispronunciations. The ERN is used with automatic speech recognition for mispronunciation detection. Experimentation with an L2 speech corpus that contains recordings from 100 speakers aims to compare the automatically derived rules with manually authored rules. Comparable performance is achieved in mispronunciation detection (i.e. telling which phone is wrong). The automatically derived rules also offer improved performance in diagnostic accuracy (i.e. identify how the phone is wrong).
Giant Congenital Melanocytic Nevi: Selected Aspects of Diagnostics and Treatment

Treatment of giant melanocytic nevi (GMN) remains a multidisciplinary challenge. We present analysis of diagnostics, treatment, and follow- up in children with GMN to establish obligatory procedures in these patients.



In 24 children with GMN, we analyzed: localization, main nevus diameter, satellite nevi, brain MRI, catecholamines concentrations in 24-h urine collection, surgery stages number, and histological examinations. The t test was used to compare catecholamines concentrations in patient subgroups.



Nine children had "bathing trunk" nevus, 7 had main nevus on the back, 6 on head/neck, and 2 on neck/shoulder and neck/thorax. Brain MRI revealed neurocutaneous melanosis (NCM) in 7/24 children (29.2%), symptomatic in 1. Among urine catecholamines levels from 20 patients (33 samples), dopamine concentration was elevated in 28/33, noradrenaline in 15, adrenaline in 11, and vanillylmandelic acid in 4. In 6 NCM children, all catecholamines concentrations were higher than in patients without NCM (statistically insignificant). In all patients, histological examination of excised nevi revealed compound nevus, with neurofibromatic component in 15 and melanoma in 2. They remain without recurrence/metastases at 8- and 3-year-follow-up. There were 4/7 NCM patients with more than 1 follow-up MRI; in 1 a new melanin deposit was found and in 3 there was no progression.



Early excision with histological examination speeds the diagnosis of melanoma. Brain MRI is necessary to confirm/rule-out NCM. High urine dopamine concentration in GMN children, especially with NCM, is an unpublished finding that can indicate patients with more serious neurological disease. Treatment of GMN children should be tailored individually for each case with respect to all medical/psychological aspects.
Food, cooking skills, and health: a literature review.

Over the past century, a major shift in North American food practices has been taking place. However, the literature on this topic is lacking in several areas. Some available research on food and cooking practices in the current context is presented, with a focus on how these are affecting health and how they might be contributing to health inequalities within the population. First, cooking and cooking skills are examined, along with the ambiguities related to terms associated with cooking in the research literature. Food choice, cooking, and health are described, particularly in relation to economic factors that may lead to health inequalities within the population. The importance of developing an understanding of factors within the wider food system as part of food choice and cooking skills is presented, and gaps in the research literature are examined and areas for future research are presented. Cooking practices are not well studied but are important to an understanding of human nutritional health as it relates to cultural, environmental, and economic factors.
Fatal child neglect: characteristics, causation, and strategies for prevention.

Research in child fatalities because of abuse and neglect has continued to increase, yet the mechanisms of the death incident and risk factors for these deaths remain unclear. The purpose of this study was to systematically examine the types of neglect that resulted in children's deaths as determined by child welfare and a child death review board. This case review study reviewed 22 years of data (n=372) of child fatalities attributed solely to neglect taken from a larger sample (N=754) of abuse and neglect death cases spanning the years 1987-2008. The file information reviewed was provided by the Oklahoma Child Death Review Board (CDRB) and the Oklahoma Department of Human Services (DHS) Division of Children and Family Services. Variables of interest were child age, ethnicity, and birth order; parental age and ethnicity; cause of death as determined by child protective services (CPS); and involvement with DHS at the time of the fatal event. Three categories of fatal neglect--supervisory neglect, deprivation of needs, and medical neglect--were identified and analyzed. Results found an overwhelming presence of supervisory neglect in child neglect fatalities and indicated no significant differences between children living in rural and urban settings. Young children and male children comprised the majority of fatalities, and African American and Native American children were over-represented in the sample when compared to the state population. This study underscores the critical need for prevention and educational programming related to appropriate adult supervision and adequate safety measures to prevent a child's death because of neglect.
On certain aspects of Kazakh part-of-speech tagging

We compare and discuss various approaches to the problem of part of speech (POS) tagging of texts written in Kazakh, an agglutinative and highly inflectional Turkic language. In Kazakh a single root may produce hundreds of word forms, and it is difficult, if at all possible, to label enough training data to account for a vast set of all possible word forms in the language. Thus, current state of the art statistical POS taggers may not be as effective for Kazakh as for morphologically less complex languages, e.g. English. Also the choice of a POS tag set may influence the informativeness and the accuracy of tagging.
Complex PTSD - a better description for borderline personality disorder?

To consider the use of the diagnostic category 'complex posttraumatic stress disorder' (c-PTSD) as detailed in the forthcoming ICD-11 classification system as a less stigmatising, more clinically useful term, instead of the current DSM-5 defined condition of 'borderline personality disorder' (BPD).



Trauma, in its broadest definition, plays a key role in the development of both c-PTSD and BPD. Given this current lack of differentiation between these conditions, and the high stigma faced by people with BPD, it seems reasonable to consider using the diagnostic term 'complex posttraumatic stress disorder' to decrease stigma and provide a trauma-informed approach for BPD patients.
A deployable sampling strategy for data race detection

Dynamic data race detection incurs heavy runtime overheads. Recently, many sampling techniques have been proposed to detect data races. However, some sampling techniques (e.g., Pacer) are based on traditional happens-before relation and incur a large basic overhead. Others utilize hardware to reduce their sampling overhead (e.g., DataCollider) and they, however, detect a race only when the race really occurs by delaying program executions. In this paper, we study the limitations of existing techniques and propose a new data race definition, named as Clock Races, for low overhead sampling purpose. The innovation of clock races is that the detection of them does not rely on concrete locks and also avoids heavy basic overhead from tracking happens-before relation. We further propose CRSampler (Clock Race Sampler) to detect clock races via hardware based sampling without directly delaying program executions, to further reduce runtime overhead. We evaluated CRSampler on Dacapo benchmarks. The results show that CRSampler incurred less than 5% overhead on average at 1% sampling rate. Whereas, Pacer and DataCollider incurred larger than 25% and 96% overhead, respectively. Besides, at the same sampling rate, CRSampler detected significantly more data races than that by Pacer and DataCollider.
Predicting mortgage default using convolutional neural networks

We predict mortgage default by applying convolutional neural networks to consumer transaction data. For each consumer we have the balances of the checking account, savings account, and the credit card, in addition to the daily number of transactions on the checking account, and amount transferred into the checking account. With no other information about each consumer we are able to achieve a ROC AUC of 0.918 for the networks, and 0.926 for the networks in combination with a random forests classifier.
ASTRO: A Datalog System for Advanced Stream Reasoning

The rise of the Internet of Things (IoT) and the recent focus on a gamut of 'Smart City' initiatives world-wide have pushed for new advances in data stream systems to (1) support complex analytics and evolving graph applications as continuous queries, and (2) deliver fast and scalable processing on large data streams. Unfortunately current continuous query languages (CQL) lack the features and constructs needed to support the more advanced applications. For example recursive queries are now part of SQL, Datalog, and other query languages, but they are not supported by most CQLs, a fact that caused a significant loss of expressive power, which is further aggravated by the limitation that only non-blocking queries can be supported. To overcome these limitations we have developed an a dvanced st ream r easo ning system ASTRO that builds on recent advances in supporting aggregates in recursive queries. In this demo, we will briefly elucidate the formal Streamlog semantics, which combined with the Pre-Mappability (PreM) concept, allows the declarative specification of many complex continuous queries, which are then efficiently executed in real-time by the portable ASTRO architecture. Using different case studies, we demonstrate (i) the ease-of-use, (ii) the expressive power and (iii) the robustness of our system, as compared to other state-of-the-art declarative CQL systems.
GLASSES: Relieving The Myopia Of Bayesian Optimisation

We present glasses: Global optimisation with Look-Ahead through Stochastic Simulation and Expected-loss Search. The majority of global optimisation approaches in use are myopic, in only considering the impact of the next function value; the non-myopic approaches that do exist are able to consider only a handful of future evaluations. Our novel algorithm, glasses, permits the consideration of dozens of evaluations into the future. This is done by approximating the ideal look-ahead loss function, which is expensive to evaluate, by a cheaper alternative in which the future steps of the algorithm are simulated beforehand. An Expectation Propagation algorithm is used to compute the expected value of the loss. We show that the far-horizon planning thus enabled leads to substantive performance gains in empirical tests.
Detecting Signals of Adverse Drug Reactions from Health Consumer Contributed Content in Social Media

Adverse drug reactions are causing a substantial amount of hospital admissions and deaths, which cannot be underestimated. Although a great effort has been put on the pre-marketing review during pharmaceutical product development, it cannot identify all possible adverse drug reactions. Currently, post-marketing surveillance is conducted through centralized volunteering reporting systems. However, the reporting rate is low, which makes it difficult to detect the adverse drug reactions signals in a timely manner. With the advance of Web 2.0 technologies and the popularity of social media, many health consumers are discussing and exchanging health-related information with their peers. Many of this online discussion involve adverse drug reactions. In this work, we propose to mine the associations between drugs and adverse reactions from the user contributed content in social media. We have conducted an experiment using five drugs and five adverse drug reactions. The FDA alerts are used as the gold standard to test the performance of the proposed techniques. The result shows that the proposed technique is promising to detect the adverse drug reactions reported by FDA, such as diarrhea, heart condition, depression, and suicidal thoughts. However, adverse drug reaction such as cancer cannot be detected effectively.