nils-herrmann/clean_scirepeval huggingface dataset:
Introducing the HOPE (Hypospadias Objective Penile Evaluation)-score: a validation study of an objective scoring system for evaluating cosmetic appearance in hypospadias patients.

To determine the reliability and internal validity of the Hypospadias  Penile Evaluation (HOPE)-score, a newly developed scoring system assessing the cosmetic outcome in hypospadias.



The HOPE scoring system incorporates all surgically-correctable items: position of meatus, shape of meatus, shape of glans, shape of penile skin and penile axis. Objectivity was established with standardized photographs, anonymously coded patients, independent assessment by a panel, standards for a "normal" penile appearance, reference pictures and assessment of the degree of abnormality. A panel of 13 pediatric urologists completed 2 questionnaires, each consisting of 45 series of photographs, at an interval of at least 1 week. The inter-observer reliability, intra-observer reliability and internal validity were analyzed.



The correlation coefficients for the HOPE-score were as follows: intra-observer reliability 0.817, inter-observer reliability 0.790, "non-parametric" internal validity 0.849 and "parametric" internal validity 0.842. These values reflect good reproducibility, sufficient agreement among observers and a valid measurement of differences and similarities in cosmetic appearance.



The HOPE-score is the first scoring system that fulfills the criteria of a valid measurement tool: objectivity, reliability and validity. These favorable properties support its use as an objective outcome measure of the cosmetic result after hypospadias surgery.
Printed graphene interdigitated capacitive sensors on flexible polyimide substrates

Interdigitated capacitors (IDC) are extensively used for a variety of chemical and biological sensing applications. Printing and functionalizing these IDC sensors on bendable substrates will lead to new innovations in healthcare and medicine, food safety inspection, environmental monitoring, and public security. The synthesis of an electrically conductive aqueous graphene ink stabilized in deionized water using the polymer Carboxymethyl Cellulose (CMC) is introduced in this paper. CMC is a nontoxic hydrophilic cellulose derivative used in food industry. The water-based graphene ink is then used to fabricate IDC sensors on mechanically flexible polyimide substrates. The capacitance and frequency response of the sensors are analyzed, and the effect of mechanical stress on the electrical properties is examined. Experimental results confirm low thin film resistivity (~6;.6×10-3 Ω-cm) and high capacitance (>100 pF). The printed sensors are then used to measure water content of ethanol solutions to demonstrate the proposed conductive ink and fabrication methodology for creating chemical sensors on thin membranes.
Taking perspective in conversation: the role of mutual knowledge in comprehension.

When people interpret language, they can reduce the ambiguity of linguistic expressions by using information about perspective: the speaker's, their own, or a shared perspective. In order to investigate the mental processes that underlie such perspective taking, we tracked people's eye movements while they were following instructions to manipulate objects. The eye fixation data in two experiments demonstrate that people do not restrict the search for referents to mutually known objects. Eye movements indicated that addressees considered objects as potential referents even when the speaker could not see those objects, requiring addressees to use mutual knowledge to correct their interpretation. Thus, people occasionally use an egocentric heuristic when they comprehend. We argue that this egocentric heuristic is successful in reducing ambiguity, though it could lead to a systematic error.
Computational Health Informatics in the Big Data Age: A Survey

The explosive growth and widespread accessibility of digital health data have led to a surge of research activity in the healthcare and data sciences fields. The conventional approaches for health data management have achieved limited success as they are incapable of handling the huge amount of complex data with high volume, high velocity, and high variety. This article presents a comprehensive overview of the existing challenges, techniques, and future directions for computational health informatics in the big data age, with a structured analysis of the historical and state-of-the-art methods. We have summarized the challenges into four Vs (i.e., volume, velocity, variety, and veracity) and proposed a systematic data-processing pipeline for generic big data in health informatics, covering data capturing, storing, sharing, analyzing, searching, and decision support. Specifically, numerous techniques and algorithms in machine learning are categorized and compared. On the basis of this material, we identify and discuss the essential prospects lying ahead for computational health informatics in this big data age.
A time-dependent topic model for multiple text streams

In recent years social media have become indispensable tools for information dissemination, operating in tandem with traditional media outlets such as newspapers, and it has become critical to understand the interaction between the new and old sources of news. Although social media as well as traditional media have attracted attention from several research communities, most of the prior work has been limited to a single medium. In addition temporal analysis of these sources can provide an understanding of how information spreads and evolves. Modeling temporal dynamics while considering multiple sources is a challenging research problem. In this paper we address the problem of modeling text streams from two news sources - Twitter and Yahoo! News. Our analysis addresses both their individual properties (including temporal dynamics) and their inter-relationships. This work extends standard topic models by allowing each text stream to have both local topics and shared topics. For temporal modeling we associate each topic with a time-dependent function that characterizes its popularity over time. By integrating the two models, we effectively model the temporal dynamics of multiple correlated text streams in a unified framework. We evaluate our model on a large-scale dataset, consisting of text streams from both Twitter and news feeds from Yahoo! News. Besides overcoming the limitations of existing models, we show that our work achieves better perplexity on unseen data and identifies more coherent topics. We also provide analysis of finding real-world events from the topics obtained by our model.
iDNA-Prot|dis: Identifying DNA-Binding Proteins by Incorporating Amino Acid Distance-Pairs and Reduced Alphabet Profile into the General Pseudo Amino Acid Composition

Playing crucial roles in various cellular processes, such as recognition of specific nucleotide sequences, regulation of transcription, and regulation of gene expression, DNA-binding proteins are essential ingredients for both eukaryotic and prokaryotic proteomes. With the avalanche of protein sequences generated in the postgenomic age, it is a critical challenge to develop automated methods for accurate and rapidly identifying DNA-binding proteins based on their sequence information alone. Here, a novel predictor, called "iDNA-Prot|dis", was established by incorporating the amino acid distance-pair coupling information and the amino acid reduced alphabet profile into the general pseudo amino acid composition (PseAAC) vector. The former can capture the characteristics of DNA-binding proteins so as to enhance its prediction quality, while the latter can reduce the dimension of PseAAC vector so as to speed up its prediction process. It was observed by the rigorous jackknife and independent dataset tests that the new predictor outperformed the existing predictors for the same purpose. As a user-friendly web-server, iDNA-Prot|dis is accessible to the public at http://bioinformatics.hitsz.edu.cn/iDNA-Prot_dis/. Moreover, for the convenience of the vast majority of experimental scientists, a step-by-step protocol guide is provided on how to use the web-server to get their desired results without the need to follow the complicated mathematic equations that are presented in this paper just for the integrity of its developing process. It is anticipated that the iDNA-Prot|dis predictor may become a useful high throughput tool for large-scale analysis of DNA-binding proteins, or at the very least, play a complementary role to the existing predictors in this regard.
Learning to Extract International Relations from Political Context

We describe a new probabilistic model for extracting events between major political actors from news corpora. Our unsupervised model brings together familiar components in natural language processing (like parsers and topic models) with contextual political information— temporal and dyad dependence—to infer latent event classes. We quantitatively evaluate the model’s performance on political science benchmarks: recovering expert-assigned event class valences, and detecting real-world conflict. We also conduct a small case study based on our model’s inferences. A supplementary appendix, and replication software/data are available online, at: http://brenocon.com/irevents [This paper is forthcoming in ; Sofia, Bulgaria.
Trunk muscle activities during abdominal bracing: comparison among muscles and exercises.

Abdominal bracing is often adopted in fitness and sports conditioning programs. However, there is little information on how muscular activities during the task differ among the muscle groups located in the trunk and from those during other trunk exercises. The present study aimed to quantify muscular activity levels during abdominal bracing with respect to muscle- and exercise-related differences. Ten healthy young adult men performed five static (abdominal bracing, abdominal hollowing, prone, side, and supine plank) and five dynamic (V- sits, curl-ups, sit-ups, and back extensions on the floor and on a bench) exercises. Surface electromyogram (EMG) activities of the rectus abdominis (RA), external oblique (EO), internal oblique (IO), and erector spinae (ES) muscles were recorded in each of the exercises. The EMG data were normalized to those obtained during maximal voluntary contraction of each muscle (% EMGmax). The % EMGmax value during abdominal bracing was significantly higher in IO (60%) than in the other muscles (RA: 18%, EO: 27%, ES: 19%). The % EMGmax values for RA, EO, and ES were significantly lower in the abdominal bracing than in some of the other exercises such as V-sits and sit-ups for RA and EO and back extensions for ES muscle. However, the % EMGmax value for IO during the abdominal bracing was significantly higher than those in most of the other exercises including dynamic ones such as curl-ups and sit-ups. These results suggest that abdominal bracing is one of the most effective techniques for inducing a higher activation in deep abdominal muscles, such as IO muscle, even compared to dynamic exercises involving trunk flexion/extension movements. Key PointsTrunk muscle activities during abdominal bracing was examined with regard to muscle- and exercise-related differences.Abdominal bracing preferentially activates internal oblique muscles even compared to dynamic exercises involving trunk flexion/extension movements.Abdominal bracing should be included in exercise programs when the goal is to improve spine stability.
Use of different thresholds in learning and recognition

The neural networks’ ability to robustly recognize patterns is influenced by the selectivity of feature-extracting cells in the networks. This selectivity can be controlled by the threshold values of the cells. This paper proposes to use different threshold values for feature-extracting cells in the learning and recognition phases, when an unsupervised learning with a winner-take-all process is used to train the network. During the recognition phase, better performance is achieved when the thresholds are set low enough to maintain the generahzation ability. The thresholds in the learning phase, however, should be kept higher than in the recognition phase. If the thresholds in the learning phase are made as low as in the recognition phase, a sufficient number of feature-extracting cells cannot be generated in the network because of the competition among the cells. The effectiveness of adopting two different threshold values is demonstrated by computer simulation, taking the ‘neocognitron’ as an example.
Practical Data Breakpoints: Design and Implementation

A data breakpoint associates debugging actions with programmer-specified conditions on the memory state of an executing program. Data breakpoints provide a means for discovering program bugs that are tedious or impossible to isolate using control breakpoints alone. In practice, programmers rarely use data breakpoints, because they are either unimplemented or prohibitively slow in available debugging software. In this paper, we present the design and implementation of a practical data breakpoint facility.
A data breakpoint facility must monitor all memory updates performed by the program being debugged. We implemented and evaluated two complementary techniques for reducing the overhead of monitoring memory updates. First, we checked write instructions by inserting checking code directly into the program being debugged. The checks use a segmented bitmap data structure that minimizes address lookup complexity. Second, we developed data flow algorithms that eliminate checks on some classes of write instructions but may increase the complexity of the remaining checks.
We evaluated these techniques on the SPARC using the SPEC benchmarks. Checking each write instruction using a segmented bitmap achieved an average overhead of 42%. This overhead is independent of the number of breakpoints in use. Data flow analysis eliminated an average of 79% of the dynamic write checks. For scientific programs such the NAS kernels, analysis reduced write checks by a factor of ten or more. On the SPARC these optimizations reduced the average overhead to 25%.
The Hierarchical Daisy Architecture for Causal Delivery

In this paper, we propose theierarchical daisy architecture , which provides causal delivery of messages sent to any subset of processes. The architecture provides fault tolerance and maintains the amount of control information within a reasonable size. It divides processes intological groups. Messages inside a logical group are sent directly, while messages that need to cross logical groups’ boundaries are forwarded by servers. We prove the correctness of the daisy architecture, discuss possible optimizations, and present simulation results.
Optimized ALU with BIST Implementation Using Cadence Encounter Platform

The aim of this project is to optimize an Arithmetic Logical Unit with BIST capability. Arithmetic Logical Unit is used in many processing and computing devices, due to rapid development of technology the faster arithmetic and logical unit which consume less power and area required.Due to the increasing integration complexities of IC‘s the Optimized Arithmetic Logical Unit implement sometimes may mal-function, so testing capability must be provide and this is accomplished by Built-In-Self-Test (BIST).So this project has been done with the help of Verilog Hardware Description Language, Simulated by Xilinx10.1 Software and is synthesized by cadence tool. After synthesis Area and power are reduced by 31% and 42% respectively. KeywordsOptimized ALU, Ripple Carry Adder, Vedic Multiplier, Built-In-Self-Test.
Web Pontoon: a method for reflective web applications

Network-based provisioning of custom-made and adaptive services offers unlimited opportunities for service development. Examples include ICT-based information, assistance, coordination, and remote monitoring services for senior citizens. Addressing diversity and unpredictable changeability requirements of such service platforms entails novel design solutions. I present Web Pontoon, a method tailored specifically for handling these requirements by a combination of web content management, client-side end-user programming, closed-loop management of object lifecycles, and domain-driven design. Opportunities for massive deployment of relevant applications are being studied.
Transduction of DNA information through water and electromagnetic waves.

The experimental conditions by which electromagnetic signals (EMS) of low frequency can be emitted by diluted aqueous solutions of some bacterial and viral DNAs are described. That the recorded EMS and nanostructures induced in water carry the DNA information (sequence) is shown by retrieval of that same DNA by classical PCR amplification using the TAQ polymerase, including both primers and nucleotides. Moreover, such a transduction process has also been observed in living human cells exposed to EMS irradiation. These experiments suggest that coherent long-range molecular interaction must be present in water to observe the above-mentioned features. The quantum field theory analysis of the phenomenon is presented in this article.
Past Visions of Artificial Futures: One Hundred and Fifty Years under the Spectre of Evolving Machines

The influence of Artificial Intelligence (AI) and Artificial Life (ALife) technologies upon society, and their potential to fundamentally shape the future evolution of humankind, are topics very much at the forefront of current scientific, governmental and public debate. While these might seem like very modern concerns, they have a long history that is often disregarded in contemporary discourse. Insofar as current debates do acknowledge the history of these ideas, they rarely look back further than the origin of the modern digital computer age in the 1940s–50s. In this paper we explore the earlier history of these concepts. We focus in particular on the idea of self-reproducing and evolving machines, and potential implications for our own species. We show that discussion of these topics arose in the 1860s, within a decade of the publication of Darwin’s The Origin of Species, and attracted increasing interest from scientists, novelists and the general public in the early 1900s. After introducing the relevant work from this period, we categorise the various visions presented by these authors of the future implications of evolving machines for humanity. We suggest that current debates on the co-evolution of society and technology can be enriched by a proper appreciation of the long history of the ideas involved.
Effective Use of Bidirectional Language Modeling for Transfer Learning in Biomedical Named Entity Recognition

Biomedical named entity recognition (NER) is a fundamental task in text mining of medical documents and has many applications. Deep learning based approaches to this task have been gaining increasing attention in recent years as their parameters can be learned endto-end without the need for hand-engineered features. However, these approaches rely on high-quality labeled data, which is expensive to obtain. To address this issue, we investigate how to use unlabeled text data to improve the performance of NER models. Specifically, we train a bidirectional language model (BiLM) on unlabeled data and transfer its weights to “pretrain” an NER model with the same architecture as the BiLM, which results in a better parameter initialization of the NER model. We evaluate our approach on four benchmark datasets for biomedical NER and show that it leads to a substantial improvement in the F1 scores compared with the state-of-the-art approaches. We also show that BiLM weight transfer leads to a faster model training and the pretrained model requires fewer training examples to achieve a particular F1 score.
A hybrid GaN/organic X-band transmitter module

The design and implementation of a compact, flexible and lightweight X-band transmitter (Tx) module based on high-power gallium nitride (GaN) transistor technology and a low-cost organic package made from liquid crystal polymer (LCP) is presented. In-package measurements of the power amplifier (PA) at 8 GHz show a P.A.E.max of >31%, P1dB of 20 dBm and gain of 11.42 dB. A 4×1 patch antenna array was also fabricated on the same platform. Though no thermal management was used, an effective isotropically radiated power (EIRP) in excess of 20 dBm at 10 GHz was measured for the transmitter module, consisting of only a single-stage PA and antenna array, thus demonstrating that even greater performance can be achieved in the future.
From the guest editors: how do experts learn?

In this themed issue of the Journal of Sport & Exercise Psychology, we bring together an eclectic mix of papers focusing on how expert performers learn the skills needed to compete at the highest level in sport. In the preface, we highlight the value of adopting the expert performance approach as a systematic framework for the evaluation and development of expertise and expert performance in sport. We then place each of the empirical papers published in this issue into context and briefly outline their unique contributions to knowledge in this area. Finally, we highlight several potential avenues for future research in the hope of encouraging others to scientifically study how experts acquire the mechanisms mediating superior performance in sport and how coaches can draw on this knowledge to guide their athletes toward the most effective training activities.
Blind Forensics of Median Filtering in Digital Images

Exposing the processing history of a digital image is an important problem for forensic analyzers and steganalyzers. As the median filter is a popular nonlinear denoising operator, the blind forensics of median filtering is particularly interesting. This paper proposes a novel approach for detecting median filtering in digital images, which can 1) accurately detect median filtering in arbitrary images, even reliably detect median filtering in low-resolution and JPEG compressed images; and 2) reliably detect tampering when part of a median-filtered image is inserted into a nonmedian-filtered image, or vice versa. The effectiveness of the proposed approach is exhaustively evaluated in five different image databases.
One-Two-One Networks for Compression Artifacts Reduction in Remote Sensing

Compression artifacts reduction (CAR) is a challenging problem in the field of remote sensing. Most recent deep learning based methods have demonstrated superior performance over the previous hand-crafted methods. In this paper, we propose an end-to-end one-two-one (OTO) network, to combine different deep models, i.e., summation and difference models, to solve the CAR problem. Particularly, the difference model motivated by the Laplacian pyramid is designed to obtain the high frequency information, while the summation model aggregates the low frequency information. We provide an in-depth investigation into our OTO architecture based on the Taylor expansion, which shows that these two kinds of information can be fused in a nonlinear scheme to gain more capacity of handling complicated image compression artifacts, especially the blocking effect in compression. Extensive experiments are conducted to demonstrate the superior performance of the OTO networks, as compared to the state-of-the-arts on remote sensing datasets and other benchmark datasets. The source code will be available here.
Artificial Neural Networks and Machine Learning – ICANN 2017

Ladder networks are a notable new concept in the field of semi-supervised learning by showing state-of-the-art results in image recognition tasks while being compatible with many existing neural architectures. We present the recurrent ladder network, a novel modification of the ladder network, for semi-supervised learning of recurrent neural networks which we evaluate with a phoneme recognition task on the TIMIT corpus. Our results show that the model is able to consistently outperform the baseline and achieve fully-supervised baseline performance with only 75% of all labels which demonstrates that the model is capable of using unsupervised data as an effective regulariser.