nils-herrmann/clean_scirepeval huggingface dataset:
Alcohol Use, Misuse, and Abuse Among Nursing Students: A Photovoice Study.

Rates and frequencies of alcohol consumption remain unknown among nursing students, and risk and protective factors associated with alcohol misuse are poorly understood. Nursing curricula often lack content on substance use disorders among nurses, which is reported to begin prior to or during college.



The aims of the study were to examine nursing students' perceptions of the risk and protective factors associated with alcohol behaviors among themselves and their peers and to identify substance use policies and their influence.



We used the Photovoice method, which employs participatory action research. Participants were screened for alcohol misuse via AUDIT-C, and thoughts and perceptions were obtained regarding their alcohol consumption behaviors through self-expression and group advocacy. Data were collected from four focus groups that included nine undergraduate nursing students.



Data reflected the following key issues: stress, environmental influences, societal acceptance, and availability of alcohol. Participants identified that the following problems place them at risk for alcohol misuse: lack of addiction/alcohol education; nursing program expectations increase stress/anxiety; unhealthy habits, social isolation, and individual influences; peer influence/the college experience; and ineffective and unenforced campus policies. Protective factors included university policies; life experiences; and nursing program policies, responsibilities, peer influences, and perceived reputation.



This action research informed a dialogue with colleagues regarding nursing students' stressors and resulting professional ramifications. Recommendations for future work include investigation of expressed social isolation from university peers and its effects on their alcohol consumption behaviors and increase alcohol education with emphasis on adaptive coping strategies in a stressful professional role in Bachelor of Science in Nursing curricula.
The molecular hallmarks of epigenetic control

Over the past 20 years, breakthrough discoveries of chromatin-modifying enzymes and associated mechanisms that alter chromatin in response to physiological or pathological signals have transformed our knowledge of epigenetics from a collection of curious biological phenomena to a functionally dissected research field. Here, we provide a personal perspective on the development of epigenetics, from its historical origins to what we define as 'the modern era of epigenetic research'. We primarily highlight key molecular mechanisms of and conceptual advances in epigenetic control that have changed our understanding of normal and perturbed development.
NISTIR 7908 A Sensor Ontology Literature Review

The purpose of this paper is to review existing sensor and sensor network ontologies to understand whether they can be reused as a basis for a manufacturing perception sensor ontology, or if the existing ontologies hold lessons for the development of a new ontology. We develop an initial set of requirements that should apply to a manufacturing perception sensor ontology. These initial requirements are used in reviewing selected existing sensor ontologies. This paper describes the steps for 1) extending and refining the requirements; 2) proposing hierarchical structures for verifying the purposes of the ontology; and 3) choosing appropriate tools and languages to support such an ontology. Some languages could include OWL (Web Ontology Language)  and SensorML (Sensor Markup Language) . This work will be proposed as a standard within the IEEE Robotics and Automation Society (RAS) Ontologies for Robotics Automation (ORA) Working Group . 1. Overview of Sensor Ontology Effort Next generation robotic systems for manufacturing must perform highly complex tasks in dynamic environments. To improve return on investment, manufacturing robots and automation must become more flexible and adaptable, and less dependent on blind, repetitive motions in a structured, fixed environment. To become more adaptable, robots need both precise sensing for parts and assemblies, so they can focus on specific tasks in which they must interact with and manipulate objects; and situational awareness, so they can robustly sense their entire environment for long-term planning and short-term safety. Meeting these requirements will need advances in sensing and perception systems that can identify and locate objects, can detect people and obstacles, and, in general, can perceive as many elements of the manufacturing environment as needed for operation. To robustly and accurately perceive many elements of the environment will require a wide range of collaborating smart sensors such as cameras, laser scanners, stereo cameras, and others. In many cases these sensors will need to be integrated into a distributed sensor network that offers extensive coverage of a manufacturing facility by sensors of complementary capabilities. To support the development of these sensors and networks, the National Institute of Standards and Technology (NIST) manufacturing perception sensor ontology effort looks to create an ontology of sensors, sensor networks, sensor capabilities, environmental objects, and environmental conditions so as to better define and anticipate the wide range of perception systems needed. The ontology will include:
Autoamputation of diabetic toe with dry gangrene: a myth or a fact?

Diabetes is associated with various complications and reduced quality of life. Of the many complications, some are life-threatening. Among these, foot complications remain an important concern. The major foot complications include foot ulceration, cellulitis, abscess, wet gangrene, dry gangrene, and necrotizing fasciitis, with different pathophysiological concepts behind each of them. Gangrene occurs due to reduced blood supply in the body tissues that leads to necrosis. This condition may arise because of an injury, infection, or other health conditions, majorly diabetes. Gangrene is classified as dry, wet, and gas gangrene. In case of wet and gas gangrene, surgical amputation is usually performed to prevent the spread of infection to other tissues. In dry gangrene, due to the presence of clear demarcation, autoamputation is preferred in certain parts of the globe. The present review aims to analyze the mode of dry gangrene management in diabetic patients based on previous evidence and plans to highlight various management strategies available for dry gangrene and the advantages/disadvantages of different treatments with special consideration to autoamputation.
An Alternative Prior Process for Nonparametric Bayesian Clustering

Prior distributions play a crucial role in Bayesian approaches to clustering. Two commonly-used prior distributions are the Dirichlet and Pitman-Yor processes. In this paper, we investigate the predictive probabilities that underlie these processes, and the implicit “rich-get-richer” characteristic of the resulting partitions. We explore an alternative prior for nonparametric Bayesian clustering—the uniform process—for applications where the “rich-get-richer” property is undesirable. We also explore the cost of this process: partitions are no longer exchangeable with respect to the ordering of variables. We present new asymptotic and simulation-based results for the clustering characteristics of the uniform process and compare these with known results for the Dirichlet and Pitman-Yor processes. We compare performance on a real document clustering task, demonstrating the practical advantage of the uniform process despite its lack of exchangeability over orderings.
A stochastic approach to simulate artists behaviour for automatic felt-tipped stippling

Nowadays, non-photorealistic rendering is an area in computer graphics that tries to simulate what artists do and the tools they use. Stippling illustrations with felt-tipped colour pen is not a commonly used technique by artists due to its complexity. In this paper we present a new method to simulate stippling illustrations with felt-tipped colour pen from a photograph or an image. This method infers a probability function with an expert system from some rules given by the artist and then simulates the behaviour of the artist when placing the dots on the illustration by means of a stochastic algorithm.
A reliable gyroscope-based gait-phase detection sensor embedded in a shoe insole

This paper presents results of patient experiments using a new gait-phase detection sensor (GPDS) together with a programmable functional electrical stimulation (FES) system for subjects with a dropped-foot walking dysfunction. The GPDS (sensors and processing unit) is entirely embedded in a shoe insole and detects in real time four phases (events) during the gait cycle: stance, heel off, swing, and heel strike. The instrumented GPDS insole consists of a miniature gyroscope that measures the angular velocity of the foot and three force sensitive resistors that measure the force load on the shoe insole at the heel and the metatarsal bones. The extracted gait-phase signal is transmitted from the embedded microcontroller to the electrical stimulator and used in a finite state control scheme to time the electrical stimulation sequences. The electrical stimulations induce muscle contractions in the paralyzed muscles leading to a more physiological motion of the affected leg. The experimental results of the quantitative motion analysis during walking of the affected and nonaffected sides showed that the use of the combined insole and FES system led to a significant improvement in the gait-kinematics of the affected leg. This combined sensor and stimulation system has the potential to serve as a walking aid for rehabilitation training or permanent use in a wide range of gait disabilities after brain stroke, spinal-cord injury, or neurological diseases.
Anchor Cascade for Efficient Face Detection

Face detection is essential to facial analysis tasks, such as facial reenactment and face recognition. Both cascade face detectors and anchor-based face detectors have translated shining demos into practice and received intensive attention from the community. However, cascade face detectors often suffer from a low detection accuracy, while anchor-based face detectors rely heavily on very large neural networks pre-trained on large-scale image classification datasets such as ImageNet, which is not computationally efficient for both training and deployment. In this paper, we devise an efficient anchor-based cascade framework called anchor cascade. To improve the detection accuracy by exploring contextual information, we further propose a context pyramid maxout mechanism for anchor cascade. As a result, anchor cascade can train very efficient face detection models with a high detection accuracy. Specifically, compared with a popular convolutional neural network (CNN)-based cascade face detector MTCNN, our anchor cascade face detector greatly improves the detection accuracy, e.g., from 0.9435 to 0.9704 at  $1k$  false positives on FDDB, while it still runs in comparable speed. Experimental results on two widely used face detection benchmarks, FDDB and WIDER FACE, demonstrate the effectiveness of the proposed framework.
Large-Scale Weakly Supervised Audio Classification Using Gated Convolutional Neural Network

In this paper, we present a gated convolutional neural network and a temporal attention-based localization method for audio classification, which won the 1st place in the large-scale weakly supervised sound event detection task of Detection and Classification of Acoustic Scenes and Events (DCASE) 2017 challenge. The audio clips in this task, which are extracted from YouTube videos, are manually labelled with one or more audio tags, but without time stamps of the audio events, hence referred to as weakly labelled data. Two subtasks are defined in this challenge including audio tagging and sound event detection using this weakly labelled data. We propose a convolutional recurrent neural network (CRNN) with learnable gated linear units (GLUs) non-linearity applied on the log Mel spectrogram. In addition, we propose a temporal attention method along the frames to predict the locations of each audio event in a chunk from the weakly labelled data. The performances of our systems were ranked the 1st and the 2nd as a team in these two sub-tasks of DCASE 2017 challenge with F value 55.6% and Equal error 0.73, respectively.
Branching Path Following for Graph Matching

 Existing path following algorithms for graph matching can be viewed as special cases of the numerical continuation method (NCM), and correspond to particular implementation named generic predictor corrector (GPC).  The GPC approach succeeds at regular points, but may fail at singular points. Illustration of GPC and the proposed method is shown in Fig. 1.  This paper presents a branching path following (BPF) method to exploring potentially better paths at singular points to improve matching performance. Tao Wang , Haibin Ling 1,3, Congyan Lang , Jun Wu 1Meitu HiScene Lab, HiScene Information Technologies, Shanghai, China 2 School of Computer & Information Technology, Beijing Jiaotong University, Beijing 100044, China 3 Computer & Information Sciences Department, Temple University, Philadelphia 19122, USA Email: twang@bjtu.edu.cn, hbling@temple.edu, cylang@bjtu.edu.cn, wuj@bjtu.edu.cn Branching Path Following for Graph Matching
GSLAM: Initialization-Robust Monocular Visual SLAM via Global Structure-from-Motion

Many monocular visual SLAM algorithms are derived from incremental structure-from-motion (SfM) methods. This work proposes a novel monocular SLAM method which integrates recent advances made in global SfM. In particular, we present two main contributions to visual SLAM. First, we solve the visual odometry problem by a novel rank-1 matrix factorization technique which is more robust to the errors in map initialization. Second, we adopt a recent global SfM method for the pose-graph optimization, which leads to a multi-stage linear formulation and enables L1 optimization for better robustness to false loops. The combination of these two approaches generates more robust reconstruction and is significantly faster (4X) than recent state-of-the-art SLAM systems. We also present a new dataset recorded with ground truth camera motion in a Vicon motion capture room, and compare our method to prior systems on it and established benchmark datasets
TAPAS: Train-less Accuracy Predictor for Architecture Search

In recent years an increasing number of researchers and practitioners have been suggesting algorithms for large-scale neural network architecture search: genetic algorithms, reinforcement learning, learning curve extrapolation, and accuracy predictors. None of them, however, demonstrated high-performance without training new experiments in the presence of unseen datasets. We propose a new deep neural network accuracy predictor, that estimates in fractions of a second classification performance for unseen input datasets, without training. In contrast to previously proposed approaches, our prediction is not only calibrated on the topological network information, but also on the characterization of the dataset-difficulty which allows us to re-tune the prediction without any training. Our predictor achieves a performance which exceeds 100 networks per second on a single GPU, thus creating the opportunity to perform large-scale architecture search within a few minutes. We present results of two searches performed in 400 seconds on a single GPU. Our best discovered networks reach 93.67% accuracy for CIFAR-10 and 81.01% for CIFAR-100, verified by training. These networks are performance competitive with other automatically discovered state-of-the-art networks however we only needed a small fraction of the time to solution and computational resources.
Allergic contact dermatitis caused by 3-o-ethyl-L-ascorbic acid (vitamin C ethyl).

Skin-lightening agents such as kojic acid, arbutin, ellagic acid, lucinol and 5,5′-dipropylbiphenyl-2,2′-diol are used in ‘anti-ageing’ cosmetics. Cases of allergic contact dermatitis caused by these skin-lightening agents have been reported (1, 2). Vitamin C and its derivatives have also been used in cosmetics as skin-lightening agents for a long time. Vitamin C in topical agents is poorly absorbed through the skin, and is easily oxidized after percutaneous absorption. Recently, ascorbic acid derivatives have been developed with enhanced properties. The ascorbic acid derivative 3-o-ethyl-l-ascorbic acid (CAS no. 86404-048, molecular weight 204.18; , also known as vitamin C ethyl, is chemically stable and is more easily absorbed through the skin than the other vitamin C derivatives. Moreover, 3-o-ethyl-l-ascorbic acid has skinlightening properties. Here, we report a case of allergic contact dermatitis caused by a skin-lightening lotion containing 3-o-ethyl-l-ascorbic acid.
A comparative evaluation of open-source graph processing platforms

In recent years, advances in digital sensors, communication, computation and storage have created huge collections of data. Many real applications in various fields require efficient and effective management of these large-scale, graph-structured data, demanding the design of new techniques and platforms for analyzing, processing and mining these large-scale graphs. There are distributed graph processing platforms running on a cluster of machines as well as non-distributed platforms working on a single machine. Most of the platforms use homogeneous processors such as multi-core CPUs while several platforms utilize both multi-core CPUs and many-core GPUs. The diversities of the available graphs, the processing algorithms, and the graph-processing platforms make the selection of a platform a difficult task. In this paper, we provide a comparative study on a selection of open-source graph processing platforms. We evaluate their performance, scalability and energy efficiency and discuss the reasons behind for designers or users of graph processing platforms.
A Modular Dielectric Elastomer Actuator to Drive Miniature Autonomous Underwater Vehicles

In this paper we present the design of a fin-like dielectric elastomer actuator (DEA) that drives a miniature autonomous underwater vehicle (AUV). The fin-like actuator is modular and independent of the body of the AUV. All electronics required to run the actuator are inside the 100 mm long 3D-printed body, allowing for autonomous mobility of the AUV. The DEA is easy to manufacture, requires no pre-stretch of the elastomers, and is completely sealed for underwater operation. The output thrust force can be tuned by stacking multiple actuation layers and modifying the Young's modulus of the elastomers. The AUV is reconfigurable by a shift of its center of mass, such that both planar and vertical swimming can be demonstrated on a single vehicle. For the DEA we measured thrust force and swimming speed for various actuator designs ran at frequencies from 1 Hz to 5 Hz. For the AUV we demonstrated autonomous planar swimming and closed-loop vertical diving. The actuators capable of outputting the highest thrust forces can power the AUV to swim at speeds of up to 0.55 body lengths per second. The speed falls in the upper range of untethered swimming robots powered by soft actuators. Our tunable DEAs also demonstrate the potential to mimic the undulatory motions of fish fins.
Features and classifiers for replay spoofing attack detection

Automatic speaker verification (ASV) systems are known to be highly vulnerable against spoofing attacks. Various successful countermeasures have recently been proposed to detect spoofing attacks originating from speech synthesis (SS) and voice conversion (VC). However, detecting replay attacks, the most easily implementable spoofing attacks against ASV systems, has gained less attention. Thus, in this paper we present an experimental comparison of various feature extraction techniques and classifiers for replay attack detection. In total, six magnitude spectrum and three phase spectrum based features are used for feature extraction. For classification in turn, four different techniques are utilized. Experiments are conducted on recently released ASVspoof 2017 replay attack detection challenge. Experimental results reveals that magnitude spectrum features considerably outperform phase based features independent of the classifier. Comparative results using four different classifiers indicate that i-vector cosine scoring yields lower equal error rates (EERs) than other methods.
Demo: I am a smartphone and i can tell my user's walking direction

We present a demonstration of WalkCompass, a system to appear in the MobiSys 2014 main conference. WalkCompass exploits smartphone sensors to estimate the direction in which a user is walking. We find that several smartphone localization systems in the recent past, including our own, make a simplifying assumption that the user's walking direction is known. In trying to relax this assumption, we were not able to find a generic solution from past work. While intuition suggests that the walking direction should be detectable through the accelerometer, in reality this direction gets blended into various other motion patterns during the act of walking, including up and down bounce, side-to-side sway, swing of arms or legs, etc. WalkCompass analyzes the human walking dynamics to estimate the dominating forces and uses this knowledge to find the heading direction of the pedestrian. In the demonstration we will show the performance of this system when the user holds the smartphone on the palm. A collection of YouTube videos of the demo is posted at http://synrg.csl.illinois.edu/projects/ localization/walkcompass.
VEHICLE ROUTING PROBLEM WITH TIME WINDOWS

The topic of this paper is a Genetic Algorithm solution to the Vehicle Routing Problem with Time Windows, a variant of one of the most common problems in contemporary operations research. The paper will introduce the problem starting with more general Traveling Salesman and Vehicle Routing problems and present some of the prevailing strategies for solving them, focusing on Genetic Algorithms. At the end, it will summarize the Genetic Algorithm solution proposed by K.Q. Zhu which was used in the programming part of the project.
Leoligin, the major lignan from Edelweiss, inhibits intimal hyperplasia of venous bypass grafts

Despite the lower patency of venous compared with arterial coronary artery bypass grafts, approximately 50% of grafts used are saphenous vein conduits because of their easier accessibility. In a search for ways to increase venous graft patency, we applied the results of a previous pharmacological study screening for non-toxic compounds that inhibit intimal hyperplasia of saphenous vein conduits in organ cultures. Here we analyse the effects and mechanism of action of leoligin [(2S,3R,4R)-4-(3,4-dimethoxybenzyl)-2-(3,4-dimethoxyphenyl)tetrahydrofuran-3-yl]methyl (2Z)-2-methylbut-2-enoat, the major lignan from Edelweiss (Leontopodium alpinum Cass.).



We found that leoligin potently inhibits vascular smooth muscle cell (SMC) proliferation by inducing cell cycle arrest in the G1-phase. Leoligin induced cell death neither in SMCs nor, more importantly, in endothelial cells. In a human saphenous vein organ culture model for graft disease, leoligin potently inhibited intimal hyperplasia, and even reversed graft disease in pre-damaged vessels. Furthermore, in an in vivo mouse model for venous bypass graft disease, leoligin potently inhibited intimal hyperplasia.



Our data suggest that leoligin might represent a novel non-toxic, non-thrombogenic, endothelial integrity preserving candidate drug for the treatment of vein graft disease.