nils-herrmann/clean_scirepeval huggingface dataset:
Comparative Effectiveness of Cancer Control and Survival after Robot-Assisted versus Open Radical Prostatectomy.

Robot-assisted surgery has been rapidly adopted in the U.S. for prostate cancer. Its adoption has been driven by market forces and patient preference, and debate continues regarding whether it offers improved outcomes to justify the higher cost relative to open surgery. We examined the comparative effectiveness of robot-assisted vs open radical prostatectomy in cancer control and survival in a nationally representative population.



This population based observational cohort study of patients with prostate cancer undergoing robot-assisted radical prostatectomy and open radical prostatectomy during 2003 to 2012 used data captured in the SEER (Surveillance, Epidemiology, and End Results)-Medicare linked database. Propensity score matching and time to event analysis were used to compare all cause mortality, prostate cancer specific mortality and use of additional treatment after surgery.



A total of 6,430 robot-assisted radical prostatectomies and 9,161 open radical prostatectomies performed during 2003 to 2012 were identified. The use of robot-assisted radical prostatectomy increased from 13.6% in 2003 to 2004 to 72.6% in 2011 to 2012. After a median followup of 6.5 years (IQR 5.2-7.9) robot-assisted radical prostatectomy was associated with an equivalent risk of all cause mortality (HR 0.85, 0.72-1.01) and similar cancer specific mortality (HR 0.85, 0.50-1.43) vs open radical prostatectomy. Robot-assisted radical prostatectomy was also associated with less use of additional treatment (HR 0.78, 0.70-0.86).



Robot-assisted radical prostatectomy has comparable intermediate cancer control as evidenced by less use of additional postoperative cancer therapies and equivalent cancer specific and overall survival. Longer term followup is needed to assess for differences in prostate cancer specific survival, which was similar during intermediate followup. Our findings have significant quality and cost implications, and provide reassurance regarding the adoption of more expensive technology in the absence of randomized controlled trials.
Aging of skeletal muscle: a 12-yr longitudinal study.

The present study examines age-related changes in skeletal muscle size and function after 12 yr. Twelve healthy sedentary men were studied in 1985-86 (T1) and nine (initial mean age 65.4 +/- 4.2 yr) were reevaluated in 1997-98 (T2). Isokinetic muscle strength of the knee and elbow extensors and flexors showed losses (P < 0.05) ranging from 20 to 30% at slow and fast angular velocities. Computerized tomography (n = 7) showed reductions (P < 0.05) in the cross-sectional area (CSA) of the thigh (12.5%), all thigh muscles (14.7%), quadriceps femoris muscle (16.1%), and flexor muscles (14. 9%). Analysis of covariance showed that strength at T1 and changes in CSA were independent predictors of strength at T2. Muscle biopsies taken from vastus lateralis muscles (n = 6) showed a reduction in percentage of type I fibers (T1 = 60% vs. T2 = 42%) with no change in mean area in either fiber type. The capillary-to-fiber ratio was significantly lower at T2 (1.39 vs. 1. 08; P = 0.043). Our observations suggest that a quantitative loss in muscle CSA is a major contributor to the decrease in muscle strength seen with advancing age and, together with muscle strength at T1, accounts for 90% of the variability in strength at T2.
A Unified Approach to Researcher Profiling

This article presents a characterization of user behavior on Flickr, a popular on-line photo sharing service that allows users to store, search, sort and share their photos. Based on a sub-set of photos being uploaded during a 10 day window, we track the interest of users in those photos over a period of 50 days. In particular we investigate the user behavior on temporal, social, and spatial dimensions. Results show that the users are able to discover new photos within hours after being uploaded and that 50% of the photo views are generated within the first two days. The social networking behavior of users, and photo pooling are identified as the two major indicators related to a photo's popularity. Finally we show that the geographic distribution is more focussed around a geographic location for the infrequently viewed photos, than for the photos that attract a large number of views.
Convolution engine: balancing efficiency & flexibility in specialized computing

This paper focuses on the trade-off between flexibility and efficiency in specialized computing. We observe that specialized units achieve most of their efficiency gains by tuning data storage and compute structures and their connectivity to the data-flow and data-locality patterns in the kernels. Hence, by identifying key data-flow patterns used in a domain, we can create efficient engines that can be programmed and reused across a wide range of applications.
 We present an example, the Convolution Engine (CE), specialized for the convolution-like data-flow that is common in computational photography, image processing, and video processing applications. CE achieves energy efficiency by capturing data reuse patterns, eliminating data transfer overheads, and enabling a large number of operations per memory access. We quantify the tradeoffs in efficiency and flexibility and demonstrate that CE is within a factor of 2-3x of the energy and area efficiency of custom units optimized for a single kernel. CE improves energy and area efficiency by 8-15x over a SIMD engine for most applications.
The Role of CM in Agile Development of Safety-Critical Software

Agile development is getting more and more used, also in the development of safety-critical software. For the sake of certification, it is necessary to comply with relevant standards – in this case IEC 61508 and EN 50128. In this paper we focus on two aspects of the need for configuration management and SafeScrum. First and foremost we need to adapt SafeScrum to the standards’ needs for configuration management. We show that this can be achieved by relative simple amendments to SafeScrum. In addition – in order to keep up with a rapidly changing set of development paradigms it is necessary to move the standards’ requirement in a goal based direction – more focus on what and not so much focus on how.
OOH4RIA Tool: Una Herramienta basada en el Desarrollo Dirigido por Modelos para las RIAs

Resumen. Debido a la necesidad de proporcionar aplicaciones Web con interfaces de usuario cada vez más usables y con mayor funcionalidad, en los últimos años han sugido un nuevo tipo de aplicaciones denominadas RIAs (Rich Internet Applications) que ofrecen interfaces tan interactivos como las aplicaciones de escritorio. Sin embargo, este tipo de aplicaciones son complejas y su desarrollo requiere de un gran esfuerzo de diseño e implementación. Con el objetivo de reducir trabajo y reducir los errores de desarrollo se ha definido una aproximación dirigida por modelos denominada OOH4RIA. Este artículo presenta la herramienta que da soporte a la aproximación denominada OOH4RIA Tool permitiendo representar los modelos y transformaciones para acelerar la obtención de una RIA implementada con el framework GWT.
Non-invasive Blood Glucose Monitoring and Data Analytics

Approaches for measuring blood glucose levels using noninvasive techniques also known as non-invasive glucose monitoring and minimally-invasive glucose monitoring techniques can help support easier and more frequent measurement of blood glucose levels and also lend themselves to support continuous glucose monitoring. This paper focuses on reviewing the emerging technologies for such monitoring, and their interrelationship with data analytics. The paper describes how these two areas of development together are contributing to the field of diabetes informatics, a more data-rich approach to understanding and managing diabetes.
Interaction+: Interaction enhancement for web-based visualizations

In this work, we present Interaction+, a tool that enhances the interactive capability of existing web-based visualizations. Different from the toolkits for authoring interactions during the visualization construction, Interaction+ takes existing visualizations as input, analyzes the visual objects, and provides users with a suite of interactions to facilitate the visual exploration, including selection, aggregation, arrangement, comparison, filtering, and annotation. Without accessing the underlying data or process how the visualization is constructed, Interaction+ is application-independent and can be employed in various visualizations on the web. We demonstrate its usage in two scenarios and evaluate its effectiveness with a qualitative user study.
Fast and robust text detection in images and video frames

Text in images and video frames carries important information for visual content understanding and retrieval. In this paper, by using multiscale wavelet features, we propose a novel coarse-to-fine algorithm that is able to locate text lines even under complex background. First, in the coarse detection, after the wavelet energy feature is calculated to locate all possible text pixels, a density-based region growing method is developed to connect these pixels into regions which are further separated into candidate text lines by structural information. Secondly, in the fine detection, with four kinds of texture features extracted to represent the texture pattern of a text line, a forward search algorithm is applied to select the most effective features. Finally, an SVM classifier is used to identify true text from the candidates based on the selected features. Experimental results show that this approach can fast and robustly detect text lines under various conditions. q 2005 Elsevier B.V. All rights reserved.
Controller Synthesis for String Stability of Vehicle Platoons

Cooperative adaptive cruise control (CACC) allows for short-distance automatic vehicle following using intervehicle wireless communication in addition to onboard sensors, thereby potentially improving road throughput. In order to fulfill performance, safety, and comfort requirements, a CACC-equipped vehicle platoon should be string stable, attenuating the effect of disturbances along the vehicle string. Therefore, a controller design method is developed that allows for explicit inclusion of the string stability requirement in the controller synthesis specifications. To this end, the notion of string stability is introduced first, and conditions for L2 string stability of linear systems are presented that motivate the development of an H∞ controller synthesis approach for string stability. The potential of this approach is illustrated by its application to the design of controllers for CACC for one- and two-vehicle look-ahead communication topologies. As a result, L2 string-stable platooning strategies are obtained in both cases, also revealing that the two-vehicle look-ahead topology is particularly effective at a larger communication delay. Finally, the results are experimentally validated using a platoon of three passenger vehicles, illustrating the practical feasibility of this approach.
Modern analogy, cultural theory and experimental replication: a merging point at the cutting edge of archaeology

An experimental approach to cut-mark investigation has proved particularly successful and should arguably be a prerequisite for individuals interested in developing standard methods to study butchery data. This paper offers a brief review of the criteria used to investigate cut marks and subsequently outlines recent research that has integrated results from replication studies of archaeological tools and cut marks with written resources to study historic butchery practices. The case is made for a degree of standardization to be incorporated into the recording of butchery data and for the integration of evidence from the analysis of cut marks and tool signatures. While the call for standardization is not without precedent the process would benefit from a suitable model: one is proposed herein based in large part on experimental replication and personal vocational experience gained in the modern butchery trade. Furthermore, the paper identifies issues that need to be kept at the forefront of an experimental approach to butchery investigation and places emphasis on the use of modern analogy and cultural theory as a means of improving our interpretation of cut-mark data.
SoK: Make JIT-Spray Great Again

Since the end of the 20th century, it has become clear that web browsers will play a crucial role in accessing Internet resources such as the World Wide Web. They evolved into complex software suites that are able to process a multitude of data formats. Just-In-Time (JIT) compilation was incorporated to speed up the execution of script code, but is also used besides web browsers for performance reasons. Attackers happily welcomed JIT in their own way, and until today, JIT compilers are an important target of various attacks. This includes for example JIT-Spray, JIT-based code-reuse attacks and JIT-specific flaws to circumvent mitigation techniques in order to simplify the exploitation of memory-corruption vulnerabilities. Furthermore, JIT compilers are complex and provide a large attack surface, which is visible in the steady stream of critical bugs appearing in them. In this paper, we survey and systematize the jungle of JIT compilers of major (client-side) programs, and provide a categorization of offensive techniques for abusing JIT compilation. Thereby, we present techniques used in academic as well as in non-academic works which try to break various defenses against memory-corruption vulnerabilities. Additionally, we discuss what mitigations arouse to harden JIT compilers to impede exploitation by skilled attackers wanting to abuse Just-In-Time compilers.
A fast face clustering method for indexing applications on mobile phones

Tagging of faces present in a photo or video at shot level has multiple applications related to indexing and retrieval. Face clustering, which aims to group similar faces corresponding to an individual, is a fundamental step of face tagging. We present a progressive method of applying easy-to-hard grouping technique that applies increasingly sophisticated feature descriptors and classifiers on reducing number of faces from each of the iteratively generated clusters. Our primary goal is to design a cost effective solution for deploying it on low-power devices like mobile phones. First, the method initiates the clustering process by applying K-Means technique with relatively large K value on simple LBP features to generate the first set of high precision clusters. Multiple clusters generated for each individual (low recall) are then progressively merged by applying linear and non-linear subspace modelling strategies on custom selected sophisticated features like Gabor filter, Gabor Jets, and Spin LGBP (Local Gabor Binary Patterns) with spatially spinning bin support for histogram computation. Our experiments on the standard face databases like YouTube Faces, YouTube Celebrities, Indian Movie Face database, eNTERFACE, Multi-Pie, CK+, MindReading and internally collected mobile phone samples demonstrate the effectiveness of proposed approach as compared to state-of-the-art methods and a commercial solution on a mobile phone.
BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning

Model-free Reinforcement Learning (RL) offers an attractive approach to learn control policies for highdimensional systems, but its relatively poor sample complexity often necessitates training in simulated environments. Even in simulation, goal-directed tasks whose natural reward function is sparse remain intractable for state-of-the-art model-free algorithms for continuous control. The bottleneck in these tasks is the prohibitive amount of exploration required to obtain a learning signal from the initial state of the system. In this work, we leverage physical priors in the form of an approximate system dynamics model to design a curriculum for a model-free policy optimization algorithm. Our Backward Reachability Curriculum (BaRC) begins policy training from states that require a small number of actions to accomplish the task, and expands the initial state distribution backwards in a dynamically-consistent manner once the policy optimization algorithm demonstrates sufficient performance. BaRC is general, in that it can accelerate training of any model-free RL algorithm on a broad class of goal-directed continuous control MDPs. Its curriculum strategy is physically intuitive, easy-to-tune, and allows incorporating physical priors to accelerate training without hindering the performance, flexibility, and applicability of the model-free RL algorithm. We evaluate our approach on two representative dynamic robotic learning problems and find substantial performance improvement relative to previous curriculum generation techniques and naı̈ve exploration strategies.
An X-band, 23.8-dBm fully integrated power amplifier with 25.8% PAE in 0.18-µm CMOS technology

An X-band high efficiency power amplifier with the highest PAE is presented in this letter. The single-stage power amplifier is implemented in TSMC standard bulk 0.18-µm 1P6M CMOS technology. In order to obtain wide bandwidth at PAE and output power, broadband output and input matching network are adopted in the design. From the measurements, the power amplifier obtained the best PAE of 25.8% and saturation output power of 23.8 dBm at 9.5 GHz. Besides, this PA demonstrates a 1-dB power bandwidth from 7.8 to 11 GHz and the PAE within the band all exceed 20%. To our knowledge, this power amplifier has the highest PAE, the smallest chip size, and wide bandwidth of output power and PAE in CMOS amplifiers at X-band to date.
A novel Bayesian network-based fault prognostic method for semiconductor manufacturing process

Fault prognostic in various levels of production of semiconductor chips is considered to be a great challenge. To reduce yield loss during the manufacturing process, tool abnormalities should be detected as early as possible during process monitoring. In this paper, we propose a novel fault prognostic method based on Bayesian networks. The network is designed such that it can process both discrete and continuous variables, to represent the correlations between critical deviations and to process quality control data based on divide-and-conquer strategy. Such a network enables us to perform high-precision multi-step prognostic on the status of the fabrication process given the current state of the sensory info. Additionally, we introduce a layer-wise approach for efficient learning of the Bayesian-network parameters. We evaluate the accuracy of our prognostic model on a wafer fabrication dataset where our model performs precise next-step fault prognostic by using the control sensory data.
Disgust: evolved function and structure.

Interest in and research on disgust has surged over the past few decades. The field, however, still lacks a coherent theoretical framework for understanding the evolved function or functions of disgust. Here we present such a framework, emphasizing 2 levels of analysis: that of evolved function and that of information processing. Although there is widespread agreement that disgust evolved to motivate the avoidance of contact with disease-causing organisms, there is no consensus about the functions disgust serves when evoked by acts unrelated to pathogen avoidance. Here we suggest that in addition to motivating pathogen avoidance, disgust evolved to regulate decisions in the domains of mate choice and morality. For each proposed evolved function, we posit distinct information processing systems that integrate function-relevant information and account for the trade-offs required of each disgust system. By refocusing the discussion of disgust on computational mechanisms, we recast prior theorizing on disgust into a framework that can generate new lines of empirical and theoretical inquiry.
Feature Selection and Dimension Reduction Techniques in SAS ®

In the field of predictive modeling, variable selection methods can significantly drive the final outcome. While the focus of the analysis may generally be to get the most accurate predictions, it is incomplete without key driver analysis. These drivers could be demographics, geography, credit worthiness, payments history, usage, pricing, and potentially a host of many other key characteristics. Due to a large number of dimensions, many features of these broad categories are bound to remain untested. A million dollar question is how to get to a subset of effects that must definitely be tested. In this paper, we highlight what we have found to be the most effective ways of feature selection along with illustrative applications and best practices on implementation in SAS®. These methods range from simple correlation procedure (PROC CORR) to more complex techniques involving variable clustering (PROC VARCLUS), decision tree importance list (PROC SPLIT) and EXL‟s proprietary process of random feature selection from models developed on bootstrapped samples. By applying these techniques, we have been able to deliver robust and high quality statistical models with the right mix of dimensions.
Orchestrating liver development

The liver is a central regulator of metabolism, and liver failure thus constitutes a major health burden. Understanding how this complex organ develops during embryogenesis will yield insights into how liver regeneration can be promoted and how functional liver replacement tissue can be engineered. Recent studies of animal models have identified key signaling pathways and complex tissue interactions that progressively generate liver progenitor cells, differentiated lineages and functional tissues. In addition, progress in understanding how these cells interact, and how transcriptional and signaling programs precisely coordinate liver development, has begun to elucidate the molecular mechanisms underlying this complexity. Here, we review the lineage relationships, signaling pathways and transcriptional programs that orchestrate hepatogenesis.
Geometric Alignment by Deep Learning for Recognition of Challenging License Plates

In this paper, we explore the problem of license plate recognition in-the-wild (in the meaning of capturing data in unconstrained conditions, taken from arbitrary viewpoints and distances). We propose a method for automatic license plate recognition in-the-wild based on a geometric alignment of license plates as a preceding step for holistic license plate recognition. The alignment is done by a Convolutional Neural Network that estimates control points for rectifying the image and the following rectification step is formulated so that the whole alignment and recognition process can be assembled into one computational graph of a contemporary neural network framework, such as Tensorflow. The experiments show that the use of the aligner helps the recognition considerably: the error rate dropped from 9.6 % to 2.1 % on real-life images of license plates. The experiments also show that the solution is fast - it is capable of real-time processing even on an embedded and low-power platform (Jetson TX2). We collected and annotated a dataset of license plates called CamCar6k, containing 6,064 images with annotated corner points and ground truth texts. We make this dataset publicly available.