nils-herrmann/clean_scirepeval huggingface dataset:
Off-target Effects in CRISPR/Cas9-mediated Genome Engineering

CRISPR/Cas9 is a versatile genome-editing technology that is widely used for studying the functionality of genetic elements, creating genetically modified organisms as well as preclinical research of genetic disorders. However, the high frequency of off-target activity (≥50%)-RGEN (RNA-guided endonuclease)-induced mutations at sites other than the intended on-target site-is one major concern, especially for therapeutic and clinical applications. Here, we review the basic mechanisms underlying off-target cutting in the CRISPR/Cas9 system, methods for detecting off-target mutations, and strategies for minimizing off-target cleavage. The improvement off-target specificity in the CRISPR/Cas9 system will provide solid genotype-phenotype correlations, and thus enable faithful interpretation of genome-editing data, which will certainly facilitate the basic and clinical application of this technology.
Collaborative filtering with weighted opinion aspects

Collaborative filtering (CF) aims to produce recommendations based on other users' ratings to an item. Most existing CF methods rely on the overall ratings an item has received. However, these ratings alone sometimes cannot provide sufficient information to understand users' behaviors. For example, a user giving a high rating may indicate that he loves the item as a whole; however, it is still likely that he dislikes some particular aspects at the same time. In addition, users tend to place different emphases on different aspects when reaching an overall rating. This emphasis on aspects may even vary from users to items, and has a significant impact on a user's final decision. To make a better understanding of a user' behavior and generate a more accurate recommendation, we propose a framework that incorporates both user opinions and preferences on different aspects. This framework is composed of three components, namely, an opinion mining component, an aspect weighting computing component, and a rating inference component. The first component exploits opinion mining techniques to extract and summarize opinions on multiple aspects from reviews, and generates ratings on various aspects. The second component applies a tensor factorization strategy to automatically infer weights of different aspects in reaching an overall rating. The last one infers the overall rating of an item based on both aspect ratings and weights. Experiments on two real datasets prove that our model performs better compared with the baseline methods. & 2016 Elsevier B.V. All rights reserved.
Three-dimensional finite element analysis of cartilaginous tissues in human temporomandibular joint during prolonged clenching.

Bruxism, the parafunctional habit of nocturnal grinding of the teeth and clenching, is associated with the onset of joint degeneration. Especially prolonged clenching is suggested to cause functional overloading in the temporomandibular joint (TMJ). In this study, the distributions of stresses in the cartilaginous TMJ disc and articular cartilage, were analysed during prolonged clenching. The purpose of this study was to examine if joint degradation due to prolonged clenching can be attributed to changes in stress concentration in the cartilaginous tissues.



Finite element model was developed on the basis of magnetic resonance images from a healthy volunteer. Condylar movements recorded during prolonged clenching were used as the loading condition for stress analysis.



At the onset of clenching (time=0s), the highest von Mises stresses were located in the middle and posterior areas (6.18MPa) of the inferior disc surface facing the condylar cartilage. The largest magnitude of the minimum principal stress (-6.72MPa) was found in the condylar cartilage. The stress concentrations were relieved towards the superior disc surface facing the temporal cartilage. On the surfaces of the temporal cartilage, relatively lower stresses were found. After 5-min clenching, both stress values induced in the TMJ components were reduced to 50-80% of the stress values at the onset of clenching, although the concomitant strains increased slightly during this period.



It is suggested that both the condylar and temporal cartilage layers along with the TMJ disc, play an important role in stress distribution and transmission during prolonged clenching due to tissue expansion. Furthermore, our study suggests that a development of stress concentrations in the TMJ during prolonged clenching and risk factors for the initiation of TMJ degeneration could not be confirmed.
Industrial Control System Network Intrusion Detection by Telemetry Analysis

Until recently, industrial control systems (ICSs) used “air-gap” security measures, where every node of the ICS network was isolated from other networks, including the Internet, by a physical disconnect. Attaching ICS networks to the Internet benefits companies and engineers who use them. However, as these systems were designed for use in the air-gapped security environment, protocols used by ICSs contain little to no security features and are vulnerable to various attacks. This paper proposes an approach to detect the intrusions into network attached ICSs by measuring and verifying data that is transmitted through the network but is not inherently the data used by the transmission protocol-network telemetry. Using simulated PLC units, the developed IDS was able to achieve 94.3 percent accuracy when differentiating between machines of an attacker and engineer on the same network, and 99.5 percent accuracy when differentiating between attacker and engineer on the Internet.
A partial transmit sequences based approach for the reduction of peak-to-average power ratio in FBMC system

In this paper, we propose a PTS-based approach to solve the high peak-to-average power ratio (PAPR) problem in filter bank multicarrier (FBMC) system with the consider of the prototype filter and the overlap feature of the symbols in time domain. In this approach, we improve the performance of the traditional PTS approach by modifying the choice of the best weighting factors with the consideration of the overlap between the present symbol and the past symbols. The simulation result shows this approach performs better than traditional PTS approach in the reduction of PAPR in FBMC system.
Factors associated with stress among medical students.

To determine the probable factors responsible for stress among undergraduate medical students.



The qualitative descriptive study was conducted at a public-sector medical college in Islamabad, Pakistan, from January to April 2014. Self-administered open-ended questionnaires were used to collect data from first year medical students in order to study the factors associated with the new environment.



There were 115 students in the study with a mean age of 19±6.76 years. Overall, 35(30.4%) students had mild to moderate physical problems, 20(17.4%) had severe physical problems and 60(52.2%) did not have any physical problem. Average stress score was 19.6±6.76. Major elements responsible for stress identified were environmental factors, new college environment, student abuse, tough study routines and personal factors.



Majority of undergraduate students experienced stress due to both academic and emotional factors.
A Deep Reinforcement Learning Network for Traffic Light Cycle Control

Existing inefficient traffic light cycle control causes numerous problems, such as long delay and waste of energy. To improve efficiency, taking real-time traffic information as an input and dynamically adjusting the traffic light duration accordingly is a must. Existing works either split the traffic signal into equal duration or only leverage limited traffic information. In this paper, we study how to decide the traffic signal duration based on the collected data from different sensors. We propose a deep reinforcement learning model to control the traffic light cycle. In the model, we quantify the complex traffic scenario as states by collecting traffic data and dividing the whole intersection into small grids. The duration changes of a traffic light are the actions, which are modeled as a high-dimension Markov decision process. The reward is the cumulative waiting time difference between two cycles. To solve the model, a convolutional neural network is employed to map states to rewards. The proposed model incorporates multiple optimization elements to improve the performance, such as dueling network, target network, double Q-learning network, and prioritized experience replay. We evaluate our model via simulation on a Simulation of Urban MObility simulator. Simulation results show the efficiency of our model in controlling traffic lights.
Maximum Entropy Models of Shortest Path and Outbreak Distributions in Networks

Properties of networks are often characterized in terms of features such as node degree distributions, average path lengths, diameters, or clustering coefficients. Here, we study shortest path length distributions. On the one hand, average as well as maximum distances can be determined therefrom; on the other hand, they are closely related to the dynamics of network spreading processes. Because of the combinatorial nature of networks, we apply maximum entropy arguments to derive a general, physically plausible model. In particular, we establish the generalized Gamma distribution as a continuous characterization of shortest path length histograms of networks or arbitrary topology. Experimental evaluations corroborate our theoretical results.
The role of nitrite and free nitrous acid (FNA) in wastewater treatment plants.

Nitrite is known to accumulate in wastewater treatment plants (WWTPs) under certain environmental conditions. The protonated form of nitrite, free nitrous acid (FNA), has been found to cause severe inhibition to numerous bioprocesses at WWTPs. However, this inhibitory effect of FNA may possibly be gainfully exploited, such as repressing nitrite oxidizing bacteria (NOB) growth to achieve N removal via the nitrite shortcut. However, the inhibition threshold of FNA to repress NOB (∼0.02 mg HNO2-N/L) may also inhibit other bioprocesses. This paper reviews the inhibitory effects of FNA on nitrifiers, denitrifiers, anammox bacteria, phosphorus accumulating organisms (PAO), methanogens, and other microorganisms in populations used in WWTPs. The possible inhibition mechanisms of FNA on microorganisms are discussed and compared. It is concluded that a single inhibition mechanism is not sufficient to explain the negative impacts of FNA on microbial metabolisms and that multiple inhibitory effects can be generated from FNA. The review would suggest further research is necessary before the FNA inhibition mechanisms can be more effectively used to optimize WWTP bioprocesses. Perspectives on research directions, how the outcomes may be used to manipulate bioprocesses and the overall implications of FNA on WWTPs are also discussed.
Analysis of SimpleKMeans with Multiple Dimensions using WEKA

Clustering techniques have more importance in data mining especially when the data size is very large. It is widely used in the fields including pattern recognition system, machine learning algorithms, analysis of images, information retrieval and bio-informatics. Different clustering algorithms are available such as Expectation Maximization (EM), Cobweb, FarthestFirst, OPTICS, SimpleKMeans etc. SimpleKMeans clustering is a simple clustering algorithm. It partitions n data tuples into k groups such that each entity in the cluster has nearest mean. This paper is about the implementation of the clustering techniques using WEKA interface. This paper includes a detailed analysis of various clustering techniques with the different standard online data sets. Analysis is based on the multiple dimensions which include time to build the model, number of attributes, number of iterations, number of clusters and error rate.
Validity of clinical outcome measures to evaluate ankle range of motion during the weight-bearing lunge test.

To determine the concurrent validity of standard clinical outcome measures compared to laboratory outcome measure while performing the weight-bearing lunge test (WBLT).



Cross-sectional study.



Fifty participants performed the WBLT to determine dorsiflexion ROM using four different measurement techniques: dorsiflexion angle with digital inclinometer at 15cm distal to the tibial tuberosity (°), dorsiflexion angle with inclinometer at tibial tuberosity (°), maximum lunge distance (cm), and dorsiflexion angle using a 2D motion capture system (°). Outcome measures were recorded concurrently during each trial. To establish concurrent validity, Pearson product-moment correlation coefficients (r) were conducted, comparing each dependent variable to the 2D motion capture analysis (identified as the reference standard). A higher correlation indicates strong concurrent validity.



There was a high correlation between each measurement technique and the reference standard. Specifically the correlation between the inclinometer placement at 15cm below the tibial tuberosity (44.9°±5.5°) and the motion capture angle (27.0°±6.0°) was r=0.76 (p=0.001), between the inclinometer placement at the tibial tuberosity angle (39.0°±4.6°) and the motion capture angle was r=0.71 (p=0.001), and between the distance from the wall clinical measure (10.3±3.0cm) to the motion capture angle was r=0.74 (p=0.001).



This study determined that the clinical measures used during the WBLT have a high correlation with the reference standard for assessing dorsiflexion range of motion. Therefore, obtaining maximum lunge distance and inclinometer angles are both valid assessments during the weight-bearing lunge test.
A Large Scale Study to Understand the Relation between Twitter and Financial Market

Twitter has transformed from an online platform for communication to a mega content generator for all kinds of topics. The topic of posts (or tweets) generated on Twitter cover diverse topics of interests. For example, politics, public personalities, events and corporate organizations. In this study, we analysed if tweets related to corporate organizations can predict the financial market. Our analysis is performed on a Twitter dataset which spans over more than two years and is related to more than 1723 stocks. To the best of our knowledge this amount of large and big dataset, specifically in terms of time length and stocks has not been studied in the past. Our quantitative analysis shows that on an average correlation between tweets and stocks' volume being traded is 0.29 on an average. In this empirical study, we also evaluated the stocks from Yahoo's sectors' categories perspective to find which sectors are more correlated than others. We also looked at the influence of important users, that is users with a large number of followers. Our results ally with the fact that important users contribute more in influencing the market  rather than the wisdom of the crowd [6]. The verification of our results using statistical approaches on a large dataset can be seen as a contribution in the area of financial studies using data from online platforms.
A Progressively-Trained Scale-Invariant and Boundary-Aware Deep Neural Network for the Automatic 3D Segmentation of Lung Lesions

Volumetric segmentation of lesions on CT scans is important for many types of analysis, including lesion growth kinetic modeling in clinical trials and machine learning of radiomic features. Manual segmentation is laborious, and impractical for large-scale use. For routine clinical use, and in clinical trials that apply the Response Evaluation Criteria In Solid Tumors (RECIST), clinicians typically outline the boundaries of a lesion on a single slice to extract diameter measurements. In this work, we have collected a large-scale database, named LesionVis, with pixel-wise manual 2D lesion delineations on the RECIST-slices. To extend the 2D segmentations to 3D, we propose a volumetric progressive lesion segmentation (PLS) algorithm to automatically segment the 3D lesion volume from 2D delineations using a scale-invariant and boundary-aware deep convolutional network (SIBA-Net). The SIBA-Net copes with the size transition of a lesion when the PLS progresses from the RECIST-slice to the edge-slices, as well as when performing longitudinal assessment of lesions whose size change over multiple time points. The proposed PLS-SiBA-Net (P-SiBA) approach is assessed on the lung lesion cases from LesionVis. Our experimental results demonstrate that the P-SiBA approach achieves mean Dice similarity coefficients (DSC) of 0.81, which significantly improves 3D segmentation accuracy compared with the approaches proposed previously (highest mean DSC at 0.78 on LesionVis). In summary, by leveraging the limited 2D delineations on the RECIST-slices, P-SiBA is an effective semi-supervised approach to produce accurate lesion segmentations in 3D.
OxIOD: The Dataset for Deep Inertial Odometry

Advances in micro-electro-mechanical (MEMS) techniques enable inertial measurements units (IMUs) to be small, cheap, energy efficient, and widely used in smartphones, robots, and drones. Exploiting inertial data for accurate and reliable navigation and localization has attracted significant research and industrial interest, as IMU measurements are completely ego-centric and generally environment agnostic. Recent studies have shown that the notorious issue of drift can be significantly alleviated by using deep neural networks (DNNs) . However, the lack of sufficient labelled data for training and testing various architectures limits the proliferation of adopting DNNs in IMU-based tasks. In this paper, we propose and release the Oxford Inertial Odometry Dataset (OxIOD), a first-of-its-kind data collection for inertial-odometry research, with all sequences having ground-truth labels. Our dataset contains 158 sequences totalling more than 42 km in total distance, much larger than previous inertial datasets. Another notable feature of this dataset lies in its diversity, which can reflect the complex motions of phone-based IMUs in various everyday usage. The measurements were collected with four different attachments (handheld, in the pocket, in the handbag and on the trolley), four motion modes (halting, walking slowly, walking normally, and running), five different users, four types of off-the-shelf consumer phones, and large-scale localization from office buildings. Deep inertial tracking experiments were conducted to show the effectiveness of our dataset in training deep neural network models and evaluate learning-based and model-based algorithms. The OxIOD Dataset is available at:
A Framework for Integrating Non-Functional Requirements into Conceptual Models

The development of complex information systems calls for conceptual models that describe aspects beyond entities and activities. In particular, recent research has pointed out that conceptual models need to model goals, in order to capture the intentions which underlie complex situations within an organisational context. This paper focuses on one class of goals, namely nonfunctional requirements (NFR), which need to be captured and analysed from the very early phases of the software development process. The paper presents a framework for integrating NFRs into the ER and OO models. This framework has been validated by two case studies, one of which is very large. The results of the case studies suggest that goal modelling during early phases can lead to a more productive and complete modelling activity.
Sleep and rest facilitate auditory learning

Sleep is superior to waking for promoting performance improvements between sessions of visual perceptual and motor learning tasks. Few studies have investigated possible effects of sleep on auditory learning. A key issue is whether sleep specifically promotes learning, or whether restful waking yields similar benefits. According to the "interference hypothesis," sleep facilitates learning because it prevents interference from ongoing sensory input, learning and other cognitive activities that normally occur during waking. We tested this hypothesis by comparing effects of sleep, busy waking (watching a film) and restful waking (lying in the dark) on auditory tone sequence learning. Consistent with recent findings for human language learning, we found that compared with busy waking, sleep between sessions of auditory tone sequence learning enhanced performance improvements. Restful waking provided similar benefits, as predicted based on the interference hypothesis. These findings indicate that physiological, behavioral and environmental conditions that accompany restful waking are sufficient to facilitate learning and may contribute to the facilitation of learning that occurs during sleep.
Underwater Target Recognition Using Artificial Fish-Swarm Algorithm

基金项目:863 基金资助项目(2008AA092301); 水下智能机器人技术国防 科技重点实验室开放课题研究基金资助(2008003) 作者简介:张赫(1982-),女,辽宁葫芦岛人,博士研究生. 主要研究方向:水下 图像处理与模式识别. 具体应用。它能很好地解决非线性函数优化等问题。 其主要特点是不需要了解问题的特殊信息,只需要对问 题进行优劣的比较,有着较快的收敛速度。本文应用人 工鱼群算法结合神经网络建立水下目标的识别系统。该 优化方法利于发挥神经网络分类器对于处理环境信息 复杂、背景知识模糊的分类问题的优越性,同时能够提 高网络收敛速度及获取全局极值的能力。文中识别系统 的输入向量为水下目标不变性特征,采用了基于区域的 组合不变矩进行特征提取。通过对水池试验获取的样本 图像(四类不同目标图像和一类干扰图像)进行特征提 取和模式识别实验,并对比其他优化算法的识别性能, 结果表明建立的系统具有较优的聚类性能和较高的识 别精度。
Measuring Requirements Testing

Requirements are written to specify the functionality of a completed software system. Software systems are often released in segments called builds, each one adding new functionality and satisfying an additional set of requirements. New software requirements tools are allowing quality assurance engineers to develop and use new metrics to assist them in evaluating the relationships of requirements to tests, thus ensuring the required functionality in the new system. NASA's Goddard Space Flight Center is applying new tools and technology to measure the effectiveness of requirements testing. This paper discusses an effort that uses project data to demonstrate metrics effectiveness.
On the Links Between Argumentation-Based Reasoning and Nonmonotonic Reasoning

In this paper we investigate the links between instantiated argumentation systems and the axioms for non-monotonic reasoning described in  with the aim of characterising the nature of argument based reasoning. In doing so, we consider two possible interpretations of the consequence relation, and describe which axioms are met by ASPIC under each of these interpretations. We then consider the links between these axioms and the rationality postulates. Our results indicate that argument based reasoning as characterised by ASPICis — according to the axioms of  — non-cumulative and non-monotonic, and therefore weaker than the weakest non-monotonic reasoning systems considered possible. This weakness underpins ASPIC’s success in modelling other reasoning systems. We conclude by considering the relationship between ASPICand other weak logical systems.
Assembling Furniture by Asking for Help from a Human Partner

Robots inevitably fail, often without the ability to recover autonomously. A human partner can help a robot recover from a failure, but it is challenging even for a willing and motivated helper to determine what actions will effectively aid the robot. To address this problem, we demonstrate an approach for enabling a robot to communicate its need for specific help to a human partner using language. Our approach automatically detects failures, then generates targeted spoken-language request such as “Hand me the white table leg.” Once the human partner has repaired the failure condition, the system resumes full autonomy. We present a novel algorithm for generating help requests by emulating the human’s ability to interpret a command, based on the Generalized Grounding Graph framework. We report preliminary results for an early version of our algorithm.