nils-herrmann/clean_scirepeval huggingface dataset:
Cognitive and psychomotor effects in males after smoking a combination of tobacco and cannabis containing up to 69 mg delta-9-tetrahydrocannabinol (THC)

Δ9-Tetrahydrocannabinol (THC) is the main active constituent of cannabis. In recent years, the average THC content of some cannabis cigarettes has increased up to approximately 60 mg per cigarette (20% THC cigarettes). Acute cognitive and psychomotor effects of THC among recreational users after smoking cannabis cigarettes containing such high doses are unknown. The objective of this study was to study the dose–effect relationship between the THC dose contained in cannabis cigarettes and cognitive and psychomotor effects for THC doses up to 69.4 mg (23%). This double-blind, placebo-controlled, randomised, four-way cross-over study included 24 non-daily male cannabis users (two to nine cannabis cigarettes per month). Participants smoked four cannabis cigarettes containing 0, 29.3, 49.1 and 69.4 mg THC on four exposure days. The THC dose in smoked cannabis was linearly associated with a slower response time in all tasks (simple reaction time, visuo-spatial selective attention, sustained attention, divided attention and short-term memory tasks) and motor control impairment in the motor control task. The number of errors increased significantly with increasing doses in the short-term memory and the sustained attention tasks. Some participants showed no impairment in motor control even at THC serum concentrations higher than 40 ng/mL. High feeling and drowsiness differed significantly between treatments. Response time slowed down and motor control worsened, both linearly, with increasing THC doses. Consequently, cannabis with high THC concentrations may be a concern for public health and safety if cannabis smokers are unable to titrate to a high feeling corresponding to a desired plasma THC level.
Polymerase Epsilon Exonuclease Domain Mutations in Ovarian Endometrioid Carcinoma.

Polymerase epsilon (POLE) is a DNA polymerase with a proofreading (exonuclease) domain, responsible for the recognition and excision of mispaired bases, thereby allowing high-fidelity DNA replication to occur. The Cancer Genome Atlas research network recently identified an ultramutated group of endometrial carcinomas, characterized by mutations in POLE, and exceptionally high substitution mutation rates. These POLE mutated endometrial tumors were almost exclusively of the endometrioid histotype. The prevalence and patterns of POLE mutated tumors in endometrioid carcinomas of the ovary, however, have not been studied in detail.



In this study, we investigate the frequency of POLE exonuclease domain mutations in a series of 89 ovarian endometrioid carcinomas.



We found POLE mutations in 4 of 89 (4.5%) cases, occurring in 3 of 23 (13%) International Federation of Gynecology and Obstetrics (FIGO) grade 1, 1 of 43 (2%) FIGO grade 2, and 0 of 23 (0%) FIGO grade 3 tumors. All mutations were somatic missense point mutations, occurring at the commonly reported hotspots, P286R and V411L. All 3 POLE-mutated FIGO grade 1 tumors displayed prototypical histology, and the POLE-mutated FIGO grade 2 tumor displayed morphologic heterogeneity with focally high-grade features. All 4 patients with POLE-mutated tumors followed an uneventful clinical course with no disease recurrence; however, this finding was not statistically significant (P = 0.59).



The low rate of POLE mutations in ovarian endometrioid carcinoma and their predominance within the low FIGO grade tumors are in contrast to the findings in the endometrium.
Food and Nutrient Intake and Nutritional Status of Finnish Vegans and Non-Vegetarians

Vegetarian and vegan diets have become more popular among adolescents and young adults. However, few studies have investigated the nutritional status of vegans, who may be at risk of nutritional deficiencies.



To compare dietary intake and nutritional status of Finnish long-term vegans and non-vegetarians.



Dietary intake and supplement use were estimated using three-day dietary records. Nutritional status was assessed by measuring biomarkers in plasma, serum, and urine samples. Vegans' (n = 22) data was compared with those of sex- and age-matched non-vegetarians (n = 19).



All vegans adhered strictly to their diet; however, individual variability was marked in food consumption and supplementation habits. Dietary intakes of key nutrients, vitamins B12 and D, were lower (P < 0.001) in vegans than in non-vegetarians. Nutritional biomarker measurements showed lower concentrations of serum 25-hydroxyvitamin D3 (25(OH)D3), iodine and selenium (corrected for multiple comparisons, P < 0.001), Vegans showed more favorable fatty acid profiles (P < 0.001) as well as much higher concentrations of polyphenols such as genistein and daidzein (P < 0.001). Eicosapentaenoic acid proportions in vegans were higher than expected. The median concentration of iodine in urine was below the recommended levels in both groups.



Long-term consumption of a vegan diet was associated with some favorable laboratory measures but also with lowered concentrations of key nutrients compared to reference values. This study highlights the need for nutritional guidance to vegans.
Analysis and evaluation of unstructured data: text mining versus natural language processing

Nowadays, most of information saved in companies are as unstructured models. Retrieval and extraction of the information is essential works and importance in semantic web areas. Many of these requirements will be depend on the storage efficiency and unstructured data analysis. Merrill Lynch recently estimated that more than 80% of all potentially useful business information is unstructured data. The large number and complexity of unstructured data opens up many new possibilities for the analyst. We analyze both structured and unstructured data individually and collectively. Text mining and natural language processing are two techniques with their methods for knowledge discovery form textual context in documents. In this study, text mining and natural language techniques will be illustrated. The aim of this work comparison and evaluation the similarities and differences between text mining and natural language processing for extraction useful information via suitable themselves methods.
Gradient-based boosting for statistical relational learning: The relational dependency network case

Dependency networks approximate a joint probability distribution over multiple random variables as a product of conditional distributions. Relational Dependency Networks (RDNs) are graphical models that extend dependency networks to relational domains. This higher expressivity, however, comes at the expense of a more complex model-selection problem: an unbounded number of relational abstraction levels might need to be explored. Whereas current learning approaches for RDNs learn a single probability tree per random variable, we propose to turn the problem into a series of relational function-approximation problems using gradient-based boosting. In doing so, one can easily induce highly complex features over several iterations and in turn estimate quickly a very expressive model. Our experimental results in several different data sets show that this boosting method results in efficient learning of RDNs when compared to state-of-the-art statistical relational learning approaches.
Bone age estimation based on phalanx information with fuzzy constrain of carpals

The proposed automatic bone age estimation system was based on the phalanx geometric characteristics and carpals fuzzy information. The system could do automatic calibration by analyzing the geometric properties of hand images. Physiological and morphological features are extracted from medius image in segmentation stage. Back-propagation, radial basis function, and support vector machine neural networks were applied to classify the phalanx bone age. In addition, the proposed fuzzy bone age (BA) assessment was based on normalized bone area ratio of carpals. The result reveals that the carpal features can effectively reduce classification errors when age is less than 9 years old. Meanwhile, carpal features will become less influential to assess BA when children grow up to 10 years old. On the other hand, phalanx features become the significant parameters to depict the bone maturity from 10 years old to adult stage. Owing to these properties, the proposed novel BA assessment system combined the phalanxes and carpals assessment. Furthermore, the system adopted not only neural network classifiers but fuzzy bone age confinement and got a result nearly to be practical clinically.
Webpage segmentation for extracting images and their surrounding contextual information

Web images come in hand with valuable contextual information. Although this information has long been mined for various uses such as image annotation, clustering of images, inference of image semantic content, etc., insufficient attention has been given to address issues in mining this contextual information. In this paper, we propose a webpage segmentation algorithm targeting the extraction of web images and their contextual information based on their characteristics as they appear on webpages. We conducted a user study to obtain a human-labeled dataset to validate the effectiveness of our method and experiments demonstrated that our method can achieve better results compared to an existing segmentation algorithm.
The effect of a patient centred care bundle intervention on pressure ulcer incidence (INTACT): A cluster randomised trial.

Hospital-acquired pressure ulcers are a serious patient safety concern, associated with poor patient outcomes and high healthcare costs. They are also viewed as an indicator of nursing care quality.



To evaluate the effectiveness of a pressure ulcer prevention care bundle in preventing hospital-acquired pressure ulcers among at risk patients.



Pragmatic cluster randomised trial.



Eight tertiary referral hospitals with >200 beds each in three Australian states.



1600 patients (200/hospital) were recruited. Patients were eligible if they were: ≥18 years old; at risk of pressure ulcer because of limited mobility; expected to stay in hospital ≥48h and able to read English.



Hospitals (clusters) were stratified in two groups by recent pressure ulcer rates and randomised within strata to either a pressure ulcer prevention care bundle or standard care. The care bundle was theoretically and empirically based on patient participation and clinical practice guidelines. It was multi-component, with three messages for patients' participation in pressure ulcer prevention care: keep moving; look after your skin; and eat a healthy diet. Training aids for patients included a DVD, brochure and poster. Nurses in intervention hospitals were trained in partnering with patients in their pressure ulcer prevention care. The statistician, recruiters, and outcome assessors were blinded to group allocation and interventionists blinded to the study hypotheses, tested at both the cluster and patient level. The primary outcome, incidence of hospital-acquired pressure ulcers, which applied to both the cluster and individual participant level, was measured by daily skin inspection.



Four clusters were randomised to each group and 799 patients per group analysed. The intraclass correlation coefficient was 0.035. After adjusting for clustering and pre-specified covariates (age, pressure ulcer present at baseline, body mass index, reason for admission, residence and number of comorbidities on admission), the hazard ratio for new pressure ulcers developed (pressure ulcer prevention care bundle relative to standard care) was 0.58 (95% CI: 0.25, 1.33; p=0.198). No adverse events or harms were reported.



Although the pressure ulcer prevention care bundle was associated with a large reduction in the hazard of ulceration, there was a high degree of uncertainty around this estimate and the difference was not statistically significant. Possible explanations for this non-significant finding include that the pressure ulcer prevention care bundle was effective but the sample size too small to detect this.
MSnet: Mutual Suppression Network for Disentangled Video Representations

The extraction of meaningful features from videos is important as they can be used in various applications. Despite its importance, video representation learning has not been studied much, because it is challenging to deal with both content and motion information. We present a Mutual Suppression network (MSnet) to learn disentangled motion and content features in videos. The MSnet is trained in such way that content features do not contain motion information and motion features do not contain content information; this is done by suppressing each other with adversarial training. We utilize the disentangled features from the MSnet for several tasks, such as frame reproduction, pixel-level video frame prediction, and dense optical flow estimation, to demonstrate the strength of MSnet. The proposed model outperforms the state-of-the-art methods in pixel-level video frame prediction. The source code will be publicly available.
The three R's of computer vision: Recognition, reconstruction and reorganization

Over the last two decades, we have seen remarkable progress in computer vision with demonstration of capabilities such as face detection, handwritten digit recognition, reconstructing three-dimensional models of cities, automated monitoring of activities, segmenting out organs or tissues in biological images, and sensing for control of robots and cars. Yet there are many problems where computers still perform significantly below human perception. For example, in the recent PAS. CAL benchmark challenge on visual object detection, the average precision for most 3D object categories was under 50%.
I Said it First: Topological Analysis of Lyrical Influence Networks

We present an analysis of musical influence using intact lyrics of over 550,000 songs, extending existing research on lyrics through a novel approach using directed networks. We form networks of lyrical influence over time at the level of three-word phrases, weighted by tf-idf. An edge reduction analysis of strongly connected components suggests highly central artist, songwriter, and genre network topologies. Visualizations of the genre network based on multidimensional scaling confirm network centrality and provide insight into the most influential genres at the heart of the network. Next, we present metrics for influence and self-referential behavior, examining their interactions with network centrality and with the genre diversity of songwriters. Here, we uncover a negative correlation between songwriters’ genre diversity and the robustness of their connections. By examining trends among the data for top genres, songwriters, and artists, we address questions related to clustering, influence, and isolation of nodes in the networks. We conclude by discussing promising future applications of lyrical influence networks in music information retrieval research. The networks constructed in this study are made publicly available for research purposes.
Design and analysis of a completely decoupled compliant parallel XY micro-motion stage

With the purpose of designing a completely decoupled XY micro-motion stage, a novel flexure hinge-based compliant parallel mechanism driven by piezoelectric actuators (PZT) is presented in this paper. The mechanism with a double symmetric structure is constructed by employing double fourbar flexures as prismatic joints due to their better stiffness performance. To obtain an accurate model, matrix method is applied for the compliance analysis. Then the dynamics model is derived via Lagrange equation. Finally, finite element analysis (FEA) is carried out using ANSYS software to validate the models and evaluate the performance of the mechanism. The simulation results also reveal that the mechanism has ideal linearity in terms of the static properties.
Feedback directed implicit parallelism

In this paper we present an automated way of using spare CPU resources within a shared memory multi-processor or multi-core machine. Our approach is (i) to profile the execution of a program, (ii) from this to identify pieces of work which are promising sources of parallelism, (iii) recompile the program with this work being performed speculatively via a work-stealing system and then (iv) to detect at run-time any attempt to perform operations that would reveal the presence of speculation.
 We assess the practicality of the approach through an implementation based on GHC 6.6 along with a limit study based on the execution profiles we gathered. We support the full Concurrent Haskell language compiled with traditional optimizations and including I/O operations and synchronization as well as pure computation. We use 20 of the larger programs from the 'nofib' benchmark suite. The limit study shows that programs vary a lot in the parallelism we can identify: some have none, 16 have a potential 2x speed-up, 4 have 32x. In practice, on a 4-core processor, we get 10-80% speed-ups on 7 programs. This is mainly achieved at the addition of a second core rather than beyond this.
 This approach is therefore not a replacement for manual parallelization, but rather a way of squeezing extra performance out of the threads of an already-parallel program or out of a program that has not yet been parallelized.
Formalising Traffic Rules for Accountability of Autonomous Vehicles

One significant barrier in introducing autonomous driving is the liability issue of a collision; e.g. when two autonomous vehicles collide, it is unclear which vehicle should be held accountable. To solve this issue, we view traffic rules from legal texts as requirements for autonomous vehicles. If we can prove that an autonomous vehicle always satisfies these requirements during its operation, then it cannot be held responsible in a collision. We present our approach by formalising a subset of traffic rules from the Vienna Convention on Road Traffic for highway scenarios in Isabelle/HOL.
An XFEM method for modeling geometrically elaborate crack propagation in brittle materials

We present a method for simulating quasistatic crack propagation in 2-D which combines the extended finite element method (XFEM) with a general algorithm for cutting triangulated domains, and introduce a simple yet general and flexible quadrature rule based on the same geometric algorithm. The combination of these methods gives several advantages. First, the cutting algorithm provides a flexible and systematic way of determining material connectivity, which is required by the XFEM enrichment functions. Also, our integration scheme is straightfoward to implement and accurate, without requiring a triangulation that incorporates the new crack edges or the addition of new degrees of freedom to the system. The use of this cutting algorithm and integration rule allows for geometrically complicated domains and complex crack patterns.
Fading Channels: Information-Theoretic and Communication Aspects

In this paper we review the most peculiar and interesting information-theoretic and communications features of fading channels. We first describe the statistical models of fading channels which are frequently used in the analysis and design of communication systems. Next, we focus on the information theory of fading channels, by emphasizing capacity as the most important performance measure. Both single-user and multiuser transmission are examined. Further, we describe how the structure of fading channels impacts code design, and finally overview equalization of fading multipath channels.
Enhancement of optic cup to disc ratio detection in glaucoma diagnosis

Glaucoma is a major global cause of blindness. An approach to automatically extract the main features in color fundus images is proposed in this paper. The optic cup-to-disc ratio (CDR) in retinal fundus images is one of the principle physiological characteristics in the diagnosis of glaucoma. The least square fitting algorithm aims to improve the accuracy of the boundary estimation. The technique used here is a core component of ARGALI (Automatic cup-to-disc Ratio measurement system for Glaucoma detection and AnaLysIs), a system for automated glaucoma risk assessment. The algorithm's effectiveness is demonstrated manually on segmented retina fundus images. By comparing the automatic cup height measurement to ground truth, we found that the method accurately detected neuro-retinal cup height. This work improves the efficiency of clinical interpretation of Glaucoma in fundus images of the eye. The tool utilized to accomplish the objective is MATLAB7.5.
4CeeD: Real-Time Data Acquisition and Analysis Framework for Material-Related Cyber-Physical Environments

In this paper, we present a data acquisition and analysis framework for materials-to-devices processes, named 4CeeD, that focuses on the immense potential of capturing, accurately curating, correlating, and coordinating materials-to-devices digital data in a real-time and trusted manner before fully archiving and publishing them for wide access and sharing. In particular, 4CeeD consists of novel services: a curation service for collecting data from microscopes and fabrication instruments, curating, and wrapping of data with extensive metadata in real-time and in a trusted manner, and a cloud-based coordination service for storing data, extracting meta-data, analyzing and finding correlations among the data. Our evaluation results show that our novel cloud framework can help researchers significantly save time and cost spent on experiments, and is efficient in dealing with high-volume and fast-changing workload of heterogeneous types of experimental data.
A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning Agents

This paper proposes a low-cost, easily realizable strategy to equip a reinforcement learning (RL) agent the capability of behaving ethically. Our model allows the designers of RL agents to solely focus on the task to achieve, without having to worry about the implementation of multiple trivial ethical patterns to follow. Based on the assumption that the majority of human behavior, regardless which goals they are achieving, is ethical, our design integrates human policy with the RL policy to achieve the target objective with less chance of violating the ethical code that human beings normally obey.
What Makes Live Events Engaging on Facebook Live, Periscope, and Snapchat

Live streaming platforms bring events from all around the world to people's computing devices. We conducted a mixed methods study including interviews (N = 42) and a survey (N = 223) to understand how people currently experience events using Facebook Live, Periscope, and Snapchat Live Stories. We identified four dimensions that make remote event viewing engaging: immersion, immediacy, interaction, and sociality. We find that both live streams and the more curated event content found on Snapchat are immersive and immediate, yet Snapchat Live Stories enable quickly switching among different views of the event. Live streams, on the other hand, offer real time interaction and sociality in a way that Snapchat Live Stories do not. However, the interaction's impact depends on comment volume, comment content, and relationship between viewer and broadcaster. We describe how people experience events remotely using these social media, and identify design opportunities around detecting exciting content, leveraging multiple viewpoints, and enabling interactivity to create engaging user experiences for remotely participating in events.