nils-herrmann/clean_scirepeval huggingface dataset:
A comparative study of the clinical use of motion analysis from Kinect skeleton data

The analysis of human motion as a clinical tool can bring many benefits such as the early detection of disease and the monitoring of recovery, so in turn helping people to lead independent lives. However, it is currently under used. Developments in depth cameras, such as Kinect, have opened up the use of motion analysis in settings such as GP surgeries, care homes and private homes. To provide an insight into the use of Kinect in the healthcare domain, we present a review of the current state of the art. We then propose a method that can represent human motions from time-series data of arbitrary length, as a single vector. Finally, we demonstrate the utility of this method by extracting a set of clinically significant features and using them to detect the age related changes in the motions of a set of 54 individuals, with a high degree of certainty (F1-score between 0.9–1.0). Indicating its potential application in the detection of a range of age-related motion impairments.
[Trastuzumab emtansine (Kadcyla(®)) approval in HER2-positive metastatic breast cancers].

HER2 (human epidermal growth factor receptor 2) is overexpressed in 15 to 20% of breast cancer. Anti-HER2 targeted therapies, notably trastuzumab, have transformed the natural history of this disease. Trastuzumab emtansine, consisting of trastuzumab coupled to a cytotoxic agent, emtansine (DM1), by a stable linker, has been approved in November 2013 by the European Medicine Agency. Trastuzumab emtansine targets and inhibits HER2 signaling, but also allows emtansine to be directly delivered inside HER2-positive cancer cells. It is indicated as single-agent in taxane and trastuzumab-pretreated HER2-positive breast cancer patients with metastatic and locally recurrent unresecable disease or relapsing within 6 months of the end of adjuvant therapy. This indication is based on the results of the EMILIA study, an open label phase III randomized trial comparing trastuzumab emtansine to lapatinib-capecitabine. The two primary endpoints were reached. The progression-free survival was 6.4 months in the lapatinib-capecitabine arm versus 9.6 months for the trastuzumab emtansine arm (HR=0.65; 95% CI=0.55-0.77, P<0.001). Overall survival at the second interim analysis was 25.1 months in the lapatinib-capecitabine arm versus 30.9 months in the trastuzumab emtansine arm (HR=0.68; 95% CI=0.55-0.85, P<0.001). Moreover, adverse events were more frequent in the lapatinib-capecitabine arm.
The Role of Focal Therapy in the Management of Localised Prostate Cancer: A Systematic Review

The incidence of localised prostate cancer is increasing worldwide. In light of recent evidence, current, radical, whole-gland treatments for organ-confined disease have being questioned with respect to their side effects, cancer control, and cost. Focal therapy may be an effective alternative strategy.



To systematically review the existing literature on baseline characteristics of the target population; preoperative evaluation to localise disease; and perioperative, functional, and disease control outcomes following focal therapy.



Medline (through PubMed), Embase, Web of Science, and Cochrane Review databases were searched from inception to 31 October 2012. In addition, registered but not yet published trials were retrieved. Studies evaluating tissue-preserving therapies in men with biopsy-proven prostate cancer in the primary or salvage setting were included.



A total of 2350 cases were treated to date across 30 studies. Most studies were retrospective with variable standards of reporting, although there was an increasing number of prospective registered trials. Focal therapy was mainly delivered to men with low and intermediate disease, although some high-risk cases were treated that had known, unilateral, significant cancer. In most of the cases, biopsy findings were correlated to specific preoperative imaging, such as multiparametric magnetic resonance imaging or Doppler ultrasound to determine eligibility. Follow-up varied between 0 and 11.1 yr. In treatment-naïve prostates, pad-free continence ranged from 95% to 100%, erectile function ranged from 54% to 100%, and absence of clinically significant cancer ranged from 83% to 100%. In focal salvage cases for radiotherapy failure, the same outcomes were achieved in 87.2-100%, 29-40%, and 92% of cases, respectively. Biochemical disease-free survival was reported using a number of definitions that were not validated in the focal-therapy setting.



Our systematic review highlights that, when focal therapy is delivered with intention to treat, the perioperative, functional, and disease control outcomes are encouraging within a short- to medium-term follow-up. Focal therapy is a strategy by which the overtreatment burden of the current prostate cancer pathway could be reduced, but robust comparative effectiveness studies are now required.
Risk versus Uncertainty in Deep Learning : Bayes , Bootstrap and the Dangers of Dropout

The “Big Data” revolution is spawning systems designed to make decisions from data. In particular, deep learning methods have emerged as the state of the art method in many important breakthroughs [18, 20, 28]. This is due to the statistical flexibility and computational scalability of large and deep neural networks which allows them to harness the information of large and rich datasets. At the same time, elementary decision theory shows that the only admissible decision rules are Bayesian [5, 30]. Colloquially, this means that any decision rule which is not Bayesian can be strictly improved (or even exploited) by some Bayesian alternative . The implication of these results is clear: combine deep learning with Bayesian inference for the best decisions from data.
Developed Forward and Inverse Modelling of Vertical Electrical Sounding ( VES ) Using MATLAB Implementation

We have implemented a MATLAB source code for VES forward modeling and its inversion using a genetic algorithm (GA) optimization technique. The codes presented here are applied to the Schlumberger electrode arrangement. In the forward modeling computation, we have developed code to generate theoretical apparent resistivity curves from a specified layered earth model. The input to this program consists of the number of layers, the layer resistivity and thickness. The output of this program is apparent resistivity versus electrode spacing incorporated in the inversion process as apparent resistivity data. For the inversion, we have developed a MATLAB code to invert (for layer resistivity and thickness) the apparent resistivity data by the genetic algorithm optimization technique. The code also has some function files involving the basic stages in the GA inversion. Our inversion procedure addressed calculates forward solutions from sets of random input, to find the apparent resistivity. Then, it evolves the models by better sets of inputs through processes that imitate natural mating, selection, crossover, and mutation in each generation. The aim of GA inversion is to find the best correlation between model and theoretical apparent resistivity curves. In this study, we present three synthetic examples that demonstrate the effectiveness and usefulness of this program. Our numerical modeling shows that the GA optimization technique can be applied for resolving layer parameters with reasonably low error values.
2.2 A scalable 28GHz coupled-PLL in 65nm CMOS with single-wire synchronization for large-scale 5G mm-wave arrays

Demonstrations of mm-Wave arrays with >50 elements in silicon has led to an interest in large-scale mm-Wave MIMO arrays for 5G networks, which promise substantial improvements in network capacity . Practical considerations result in such arrays being developed with a tiled approach, where N unit cells with M elements each are tiled to achieve large MIMO/phased arrays with NM elements . Achieving stringent phase-noise specifications and scalable LO distribution to maintain phase coherence across different unit cell ICs/PCBs are a critical challenge. In this paper, we demonstrate a scalable, single-wire-synchronization architecture and circuits for mm-Wave arrays that preserve the simplicity of daisy-chained LO distribution, compensate for phase offset due to interconnects, and provide phase-noise improvement with increasing number of PLLs . Measurements on a scalable 28GHz prototype demonstrate a 21% improvement in rms jitter and a 3.4dB improvement in phase noise at 10MHz offset when coupling 28GHz PLLs across three different ICs.
Bringing High Performance Computing to Big Data Algorithms

Many ideas of High Performance Computing are applicable to Big Data problems. The more so now, that hybrid, GPU computing gains traction in mainstream computing applications. This work discusses the differences between the High Performance Computing software stack and the Big Data software stack and then focuses on two popular computing workloads, the Alternating Least Squares algorithm and the Singular Value Decomposition, and shows how their performance can be maximized using hybrid computing techniques.
A Review on Moving Object Detection and Tracking

Detecting the objects in the video and tracking its motion to identify its characteristics has been emerging as a demanding research area in the domain of image processing and computer vision. It has applications of visual surveillance in real time tracking of interested objects, traffic monitoring etc. This paper presents a review on phases for video analysis i.e detection of moving objects of interest and tracking of such objects frame to frame. Generally visual surveillance can be classified into three phases of data processing: moving object recognition, object extraction & tracking and to extract temporal information about such objects. This literature presents the techniques available for detection and tracking, their fundamental study and comparative analysis of these techniques in visual surveillance. General Terms Moving object detection, Object tracking, object representation, visual surveillance.
Security Analysis of the edX Platform

Our team provided a security analysis of the edX platform. At MIT, the edX platform is used by a wide variety of classes through MITx, and is starting to be used by many other organizations, making it of great interest to us. In our security analysis, we first provide an overview of the modules of edX, as well as how the different users are intended to interact with these modules. We then outline the vulnerabilities we found in the platform and how users may exploit them. We conclude with possible changes to their system to protect against the given attacks, and where we believe there may exist other vulnerabilities worth future investigation.
Shape-From-Silhouette of Articulated Objects and its Use for Human Body Kinematics Estimation and Motion Capture

Shape-From-Silhouette (SFS), also known as Visual Hull (VH) construction, is a popular 3D reconstruction method which estimates the shape of an object from multiple silhouette images. The original SFS formulation assumes that all of the silhouette images are captured either at the same time or while the object is static. This assumption is violated when the object moves or changes shape. Hence the use of SFS with moving objects has been restricted to treating each time instant sequentially and independently. Recently we have successfully extended the traditional SFS formulation to refine the shape of a rigidly moving object over time. Here we further extend SFS to apply to dynamic articulated objects. Given silhouettes of a moving articulated object, the process of recovering the shape and motion requires two steps: (1) correctly segmenting (points on the boundary of) the silhouettes to each articulated part of the object, (2) estimating the motion of each individual part using the segmented silhouette. In this paper, we propose an iterative algorithm to solve this simultaneous assignment and alignment problem. Once we have estimated the shape and motion of each part of the object, the articulation points between each pair of rigid parts are obtained by solving a simple motion constraint between the connected parts. To validate our algorithm, we first apply it to segment the different body parts and estimate the joint positions of a person. The acquired kinematic (shape and joint) information is then used to track the motion of the person in new video sequences.
Chinese Word Spelling Correction Based on N-gram Ranked Inverted Index List

Spelling correction can assist individuals to input text data with machine using written language to obtain relevant information efficiently and effectively in. By referring to relevant applications such as web search, writing systems, recommend systems, document mining, typos checking before printing is very close to spelling correction. Individuals can input text, keyword, sentence how to interact with an intelligent system according to recommendations of spelling correction. This work presents a novel spelling error detection and correction method based on N-gram ranked inverted index is proposed to achieve this aim, spelling correction. According to the pronunciation and the shape similarity pattern, a dictionary is developed to help detect the possible spelling error detection. The inverted index is used to map the potential spelling error character to the possible corresponding characters either in character or word level. According to the N-gram score, the ranking in the list corresponding to possible character is illustrated. Herein, E-How net is used to be the knowledge representation of tradition Chinese words. The data sets provided by SigHan 7 bakeoff are used to evaluate the proposed method. Experimental results show the proposed methods can achieve accepted performance in subtask one, and outperform other approaches in subtask two.
Incorporating Topic Aspects for Online Comment Convincingness Evaluation

In this paper, we propose to incorporate topic aspects information for online comments convincingness evaluation. Our model makes use of graph convolutional network to utilize implicit topic information within a discussion thread to assist the evaluation of convincingness of each single comment. In order to test the effectiveness of our proposed model, we annotate topic information on top of a public dataset for argument convincingness evaluation. Experimental results show that topic information is able to improve the performance for convincingness evaluation. We also make a move to detect topic aspects automatically.
Fluxless reflow of eutectic solder bump using formic acid

This work elucidates the solder reflow of eutectic (63Sn/37Pb) solder bump using fluxless formic acid. The dependences of formic acid reflow on metallic oxide reduction are investigated experimentally for eutectic solder bump. Appropriate temperature profile and sufficient formic acid concentration are the key factors to optimize the metallic oxide reduction during thermal reflow. A positive pressure in process chamber is beneficial to control the variations of unwanted oxygen and the regrowth of metallic oxide during mechanical wafer switching. A reflowed solder joint degrades considerably under shear strength testing after several reflow times.
Applying Probabilistic Term Weighting to OCR Text in the Case of a Large Alphabetic Library Catalogue

We report on a probabilistic weighting approach to indexing the scanned images of very short documents. This fully automatic process copes with short and very noisy texts (67% word accuracy) derived from the images by Optical Character Recognition (OCR). The probabilistic term weighting approach is based on a theoretical proof explaining how the retrieval effectiveness is affected by recognition errors. We have evaluated our probabilistic weighting approach on a sample of index cards from an alphabetic library catalogue where, on the average, a card contains only 23 terms. We have demonstrated over 30% improvement in retrieval effectiveness over a conventional weighted retrieval method where the recognition errors are not taken into account, We also show how we can take advantage of the ordering information of the alphabetic library catalogue.
Growing cell structures--A self-organizing network for unsupervised and supervised learning

Alrstract-We present a new self-organizing neural network model that has two variants. The first variant performs unsupervised learning and can be used for data visualization, clustering, and vector quantization. The main advantage over existing approaches ( e.g., the Kohonen feature map) is the ability o f the model to automatically find a suitable network structure and size. This is achieved through a controlled growth process that also includes occasional removal o f units. The second variant of the model is a supervised learning method that results from the combination of the above-mentioned self-organizing network with the radial basis function (RBF) approach. In this model it is possible--in contrast to earlier approaches--to perform the positioning of the RBF units and the supervised training of the weights in parallel. Therefore, the current classification error can be used to determine where to insert new RBF units. This leads to small networks that generalize very well. Results on the two-spirals benchmark and a vowel classification problem are presented that are better than any results previously published.
The end of CLT : a context approach to language teaching

This article argues that the dominance of CLT has led to the neglect of one crucial aspect of language pedagogy, namely the context in which that pedagogy takes place. It argues that it is time to replace CLT as the central paradigm in language teaching with a Context Approach which places context at the heart of the profession. The article argues that such a shift is already taking place, and that eventually it will radically change our practice. It concludes by outlining the features of the Context Approach and discussing its implications.
Overview of the IWSLT 2017 Evaluation Campaign

The IWSLT 2017 evaluation campaign has organised three tasks. The Multilingual task, which is about training machine translation systems handling many-to-many language directions, including so-called zero-shot directions. The Dialogue task, which calls for the integration of context information in machine translation, in order to resolve anaphoric references that typically occur in human-human dialogue turns. And, finally, the Lecture task, which offers the challenge of automatically transcribing and translating real-life university lectures. Following the tradition of these reports, we will described all tasks in detail and present the results of all runs submitted by their participants.
Differences in physical fitness among indoor and outdoor elite male soccer players

This study compared anthropometric (body height, body mass, percent body fat, fat-free body mass) and physical fitness characteristics (vertical jump height, power-load curve of the leg, 5 and 15 m sprint running time and blood lactate concentrations ([La]b) at submaximal running velocities) among 15 elite male indoor soccer (IS) and 25 elite male outdoor soccer (OS) players. IS players had similar values in body height, body mass, fat-free body mass and endurance running than OS players. However, the IS group showed higher (P < 0.05–0.01) values in percent body fat (28%) and sprint running time (2%) but lower values in vertical jump (15%) and half-squat power (20%) than the OS group. Significant negative correlations (P < 0.05–0.01) were observed between maximal sprint running time, power production during half-squat actions, as well as [La]b at submaximal running velocities. Percent body fat correlated positively with maximal sprint time and [La]b, but correlated negatively with vertical jump height. The present results show that compared to elite OS players, elite IS players present clearly lower physical fitness (lower maximal leg extension power production) characteristics associated with higher values of percent body fat. This should give IS players a disadvantage during soccer game actions.
A survey of dynamic scheduling in manufacturing systems

The problem of scheduling is concerned with searching for optimal (or near-optimal) schedules subject to a number of constraints. A variety of approaches have been developed to solve the problem of scheduling. However, many of these approaches are often impractical in dynamic real-world environments where there are complex constraints and a variety of unexpected disruptions. In most real-world environments, scheduling is an ongoing reactive process where the presence of real-time information continually forces reconsideration and revision of pre-established schedules. Scheduling research has largely ignored this problem, focusing instead on optimisation of static schedules. This paper outlines the limitations of static approaches to scheduling in the presence of real-time information and presents a number of issues that have come up in recent years on dynamic scheduling. The paper defines the problem of dynamic scheduling and provides a review of the state of the art of currently developing research on dynamic scheduling. The principles of several dynamic scheduling techniques, namely, dispatching rules, heuristics, meta-heuristics, artificial intelligence techniques, and multi-agent systems are described in detail, followed by a discussion and comparison of their potential.
Should Algorithms for Random SAT and Max-SAT Be Different?

We analyze to what extent the random SAT and Max-SAT problems differ in their properties. Our findings suggest that for random k-CNF with ratio in a certain range, Max-SAT can be solved by any SAT algorithm with subexponential slowdown, while for formulae with ratios greater than some constant, algorithms under the random walk framework require substantially different heuristics. In light of these results, we propose a novel probabilistic approach for random Max-SAT called ProMS. Experimental results illustrate that ProMS outperforms many state-of-the-art local search solvers on random Max-SAT benchmarks.
A Semantic Approach with Decision Support for Safety Service in Smart Home Management

Research on smart homes (SHs) has increased significantly in recent years because of the convenience provided by having an assisted living environment. The functions of SHs as mentioned in previous studies, particularly safety services, are seldom discussed or mentioned. Thus, this study proposes a semantic approach with decision support for safety service in SH management. The focus of this contribution is to explore a context awareness and reasoning approach for risk recognition in SH that enables the proper decision support for flexible safety service provision. The framework of SH based on a wireless sensor network is described from the perspective of neighbourhood management. This approach is based on the integration of semantic knowledge in which a reasoner can make decisions about risk recognition and safety service. We present a management ontology for a SH and relevant monitoring contextual information, which considers its suitability in a pervasive computing environment and is service-oriented. We also propose a rule-based reasoning method to provide decision support through reasoning techniques and context-awareness. A system prototype is developed to evaluate the feasibility, time response and extendibility of the approach. The evaluation of our approach shows that it is more effective in daily risk event recognition. The decisions for service provision are shown to be accurate.