nils-herrmann/clean_scirepeval huggingface dataset:
Mid-arm lymph nodes dissection for melanoma.

An interval node in the upper limb termed the mid-arm node was recently identified. However, its surgical anatomy remains unclear. We report a patient with metastatic melanoma of the mid-arm node and the epitrochlear node at 10 years after removal of the primary tumour from the forearm and therapeutic axillary lymph node dissection. The mid-arm node is located halfway up the upper arm on the medial intermuscular septum, at the site where the brachial vessels, the median nerve and the ulnar nerve run adjacent to each other. The mid-arm node lies adjacent to the basilic vein where lymphatic vessels ascend and converge. This is the first report regarding the surgical anatomy of the mid-arm node.
Geometry Optimize of Printed Circuit Board Stator Winding in Coreless Axial Field Permanent Magnet Motor

Axial Field Permanent Magnet Motor (AFPM) can be easily integrated into wheel-hub motor of Electrical Vehicle (EV) as impact structure and light weight. Improved hexagonal asymmetry coil is presented in Printed Circuit Board (PCB) stator of coreless AFPM. Geometry parameters of coil shape is optimized. According to the structure characteristics of coreless AFPM, dimension formulas of PCB coil are derived. Analytical calculation models of back-EMF and winding co-efficiency are built up. To maximize ratio of torque to copper loss, coil parameters such as radial length, inclined angle of active conductors etc. are optimized. Finally comparison and verification are performed by means of 3D finite element method and prototype test. The ratio of torque to copper loss is increased 30%. As a result, motor rated torque is also increased at certain motor size, heating load and dissipation condition.
Non-specific effects of methylphenidate (Ritalin) on cognitive ability and decision-making of ADHD and healthy adults

The effect of a single dose of methylphenidate (MPH) on cognitive measures and decision-making processes was assessed in a sample of adults with ADHD and in a control sample. Thirty-two adults satisfying DSM-IV criteria for ADHD and 26 healthy controls performed several cognitive tasks. Half of the participants received MPH prior to performing the tasks, and the other half received placebo in a randomized, double-blind manner. The average digit-span test score was higher in the groups receiving MPH compared to the groups receiving placebo, while diagnosis did not have an effect upon scores. In decision-making tasks, however, MPH did not have an effect upon performance, whereas in one of the tasks the average proportion of risky choices was higher in ADHD adults compared to controls. Our data therefore demonstrates that (a) MPH is capable of enhancing specific aspects of cognitive performance and (b) this enhancement is not specific to ADHD.
Estimating mobile application energy consumption using program analysis

Optimizing the energy efficiency of mobile applications can greatly increase user satisfaction. However, developers lack viable techniques for estimating the energy consumption of their applications. This paper proposes a new approach that is both lightweight in terms of its developer requirements and provides fine-grained estimates of energy consumption at the code level. It achieves this using a novel combination of program analysis and per-instruction energy modeling. In evaluation, our approach is able to estimate energy consumption to within 10% of the ground truth for a set of mobile applications from the Google Play store. Additionally, it provides useful and meaningful feedback to developers that helps them to understand application energy consumption behavior.
HCCL at SemEval-2017 Task 2: Combining Multilingual Word Embeddings and Transliteration Model for Semantic Similarity

In this paper, we introduce an approach to combining word embeddings and machine translation for multilingual semantic word similarity, the task2 of SemEval2017. Thanks to the unsupervised transliteration model, our cross-lingual word embeddings encounter decreased sums of OOVs. Our results are produced using only monolingual Wikipedia corpora and a limited amount of sentence-aligned data. Although relatively little resources are utilized, our system ranked 3rd in the monolingual subtask and can be the 6th in the cross-lingual subtask.
Studying Online Social Networks

What is Social Network Analysis? The Social Network Approach Units of Analysis Relations Ties Multiplexity Composition Beyond the Tie Social Networks Two Views: Ego-Centered and Whole Networks Network Characteristics Partitioning Networks Groups STUDYING ONLINE SOCIAL NETWORKS, by Laura Garton... http://jcmc.indiana.edu/vol3/issue1/garton.html 1 of 32 9/21/11 11:24 AM Positional Analysis Networks of Networks Placing CMC in Context Collecting Network Data for CMC Studies Selecting a Sample Collecting Data How are Network Data Analyzed? Ego-Centered Analysis Whole Network Analysis Conclusions References About the Authors
Diagnostic assessment of the borg MOEA for many-objective product family design problems

The recently introduced Borg multiobjective evolutionary algorithm (MOEA) framework features auto-adaptive search that tailors itself to effectively explore different problem spaces. A key auto-adaptive feature of the Borg MOEA is the dynamic allocation of search across a suite of recombination and mutation operators. This study explores the application of the Borg MOEA on a real-world product family design problem: the severely constrained, ten objective General Aviation Aircraft (GAA) problem. The GAA problem represents a promising benchmark problem that strongly highlights the importance of using auto-adaptive search to discover how to exploit multiple recombination strategies cooperatively. The auto-adaptive behavior of the Borg MOEA is rigorously compared against its ancestor algorithm, the ε-MOEA, by employing global sensitivity analysis across each algorithm's feasible parameter ranges. This study provides the first Sobol' sensitivity analysis to determine the individual and interactive parameter sensitivities of MOEAs on a real-world many-objective problem.
Expression and regulation of lincRNAs during T cell development and differentiation

Although intergenic long noncoding RNAs (lincRNAs) have been linked to gene regulation in various tissues, little is known about lincRNA transcriptomes in the T cell lineages. Here we identified 1,524 lincRNA clusters in 42 T cell samples, from early T cell progenitors to terminally differentiated helper T cell subsets. Our analysis revealed highly dynamic and cell-specific expression patterns for lincRNAs during T cell differentiation. These lincRNAs were located in genomic regions enriched for genes that encode proteins with immunoregulatory functions. Many were bound and regulated by the key transcription factors T-bet, GATA-3, STAT4 and STAT6. We found that the lincRNA LincR-Ccr2-5′AS, together with GATA-3, was an essential component of a regulatory circuit in gene expression specific to the TH2 subset of helper T cells and was important for the migration of TH2 cells.
The myth of the visual word form area

Recent functional imaging studies have referred to a posterior region of the left midfusiform gyrus as the "visual word form area" (VWFA). We review the evidence for this claim and argue that neither the neuropsychological nor neuroimaging data are consistent with a cortical region specialized for visual word form representations. Specifically, there are no reported cases of pure alexia who have deficits limited to visual word form processing and damage limited to the left midfusiform. In addition, we present functional imaging data to demonstrate that the so-called VWFA is activated by normal subjects during tasks that do not engage visual word form processing such as naming colors, naming pictures, reading Braille, repeating auditory words, and making manual action responses to pictures of meaningless objects. If the midfusiform region has a single function that underlies all these tasks, then it does not correspond to visual word form processing. On the other hand, if the region participates in several functions as defined by its interactions with other cortical areas, then identifying the neural system sustaining visual word form representations requires identification of the set of regions involved. We conclude that there is no evidence that visual word form representations are subtended by a single patch of neuronal cortex and it is misleading to label the left midfusiform region as the visual word form area.
Finding Similar Files in a Large File System

We present a tool, called sif, for finding all similar files in a large file system. Files are considered similar if they have significant number of common pieces, even if they are very different otherwise. For example, one file may be contained, possibly with some changes, in another file, or a file may be a reorganization of another file. The running time for finding all groups of similar files, even for as little as 25% similarity, is on the order of 500MB to 1GB an hour. The amount of similarity and several other customized parameters can be determined by the user at a post-processing stage, which is very fast. Sif can also be used to very quickly identify all similar files to a query file using a preprocessed index. Application of sif can be found in file management, information collecting (to remove duplicates), program reuse, file synchronization, data compression, and maybe even plagiarism detection.
Predicting Users' Future Interests on Twitter

In this paper, we address the problem of predicting future interests of users with regards to a set of unobserved topics in microblogging services which enables forward planning based on potential future interests. Existing works in the literature that operate based on a known interest space cannot be directly applied to solve this problem. Such methods require at least a minimum user interaction with the topic to perform prediction. To tackle this problem, we integrate the semantic information derived from the Wikipedia category structure and the temporal evolution of user’s interests into our prediction model. More specifically, to capture the temporal behaviour of the topics and user’s interests, we consider discrete intervals and build user’s topic profile in each time interval separately. Then, we generalize users’ interests that have been observed over several time intervals by transferring them over the Wikipedia category structure. Our approach not only allows us to generalize users’ interests but also enables us to transfer users’ interests across different time intervals that do not necessarily have the same set of topics. Our experiments illustrate the superiority of our model compared to the state of the art.
Interactive Image Manipulation with Natural Language Instruction Commands

We propose an interactive image-manipulation system with natural language instruction, which can generate a target image from a source image and an instruction that describes the difference between the source and the target image. The system makes it possible to modify a generated image interactively and make natural language conditioned image generation more controllable. We construct a neural network that handles image vectors in latent space to transform the source vector to the target vector by using the vector of instruction. The experimental results indicate that the proposed framework successfully generates the target image by using a source image and an instruction on manipulation in our dataset.
Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks

We examine how firms can create word-of-mouth peer influence and social contagion by designing viral features into their products and marketing campaigns. Word-of-mouth (WOM) is generally considered to be more effective at promoting product contagion when it is personalized and active. Unfortunately, the relative effectiveness of different viral features has not been quantified, nor has their effectiveness been definitively established, largely because of difficulties surrounding econometric identification of endogenous peer effects. We therefore designed a randomized field experiment on a popular social networking website to test the effectiveness of a range of viral messaging capabilities in creating peer influence and social contagion among the 1.4 million friends of 9,687 experimental users. Overall, we find that viral product design features can indeed generate econometrically identifiable peer influence and social contagion effects. More surprisingly, we find that passive-broadcast viral messaging generates a 246% increase in local peer influence and social contagion effects, while adding active-personalized viral messaging only generates an additional 98% increase in contagion. Although active-personalized messaging is more effective in encouraging adoption per message and is correlated with more user engagement and sustained product use, passivebroadcast messaging is used more often enough to eclipse those benefits, generating more total peer adoption in the network. In addition to estimating the effects of viral product design on social contagion and product diffusion, our work also provides a model for how randomized trials can be used to identify peer influence effects in networks.
Distributed representations

physical network mirrors the structure of the knowledge it contains. The naturalness and simplicity of this relationship between the knowledge and the hardware that implements it have led many people to simply assume that local representations are the best way to use parallel hardware. There are , of course , a wide variety of more complicated implementations in which there is no one-to-one correspondence between concepts and hardware units, but these implementations are only worth considering if they lead to increased efficiency or , interesting emergent properties that cannot be conveniently achieved using local representations. This chapter describes one type of representation that is less familiar and harder to think about than local representations. Each entity is
From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews

Recommending products to consumers means not only understanding their tastes, but also understanding their level of experience. For example, it would be a mistake to recommend the iconic film Seven Samurai simply because a user enjoys other action movies; rather, we might conclude that they will eventually enjoy it---once they are ready. The same is true for beers, wines, gourmet foods---or any products where users have acquired tastes: the `best' products may not be the most 'accessible'. Thus our goal in this paper is to recommend products that a user will enjoy now, while acknowledging that their tastes may have changed over time, and may change again in the future. We model how tastes change due to the very act of consuming more products---in other words, as users become more experienced. We develop a latent factor recommendation system that explicitly accounts for each user's level of experience. We find that such a model not only leads to better recommendations, but also allows us to study the role of user experience and expertise on a novel dataset of fifteen million beer, wine, food, and movie reviews.
Image semantic segmentation based on FCN-CRF model

Image segmentation is a key point for analyzing and understanding image, which occupies an important position in image processing. Recent studies have attempted to tackle pixel level labeling tasks using deep learning. In our paper, we propose an approach of combining fully convolutional network and conditional random field for image semantic segmentation. We utilize FCN model to automatically learn features directly from original image data, and create local predictions and global structure consistency by combining fine layers and coarse layers. CRF is a probabilistic graph and used to fully exploit the context information. Our model train the whole deep network end-to-end with the back-propagation algorithm and maximum likelihood estimation. The key of jointing FCN and CRF is sensitivity of neurons, calculating the sensitivity by CRF and then transferring it to FCN. Experiments show our method achieves improved accuracy compared with several other methods on Pascal VOC 2012 dataset.
Cooperative Wiener-ICA for source localization and Separation by distributed microphone arrays

During the last decade, distributed microphone arrays have been proposed in order to increase accuracy and spatial coverage of speaker localization systems operating in large and reverberant rooms. In principle, the framework provided by a distributed microphone network can also be applied effectively when using Blind Source Separation (BSS). Separation is commonly performed by processing the signals sampled at closely spaced microphones in a single adaptation step, for example by means of Independent Component Analysis (ICA). When the microphone spacing or the distance between source and microphones increase, the separation performance reduces due to spatial aliasing effects and to a reduced spatial coherence at microphones. In this paper we propose a new method, here referred to as Cooperative Wiener ICA (CW-ICA), which is able to apply BSS to signals acquired by a network of distributed microphone arrays. Different ICA adaptations are applied to the signals recorded by each array and are interconnected in order to constrain each adaptation to converge to a solution related to the same physical interpretation. A preliminary analysis on a network of two arrays shows that the proposed method can be applied successfully to source separation and localization tasks.
Parameter tuning or default values? An empirical investigation in search-based software engineering

Many software engineering problems have been addressed with search algorithms. Search algorithms usually depend on several parameters (e.g., population size and crossover rate in genetic algorithms), and the choice of these parameters can have an impact on the performance of the algorithm. It has been formally proven in the No Free Lunch theorem that it is impossible to tune a search algorithm such that it will have optimal settings for all possible problems. So, how to properly set the parameters of a search algorithm for a given software engineering problem? In this paper, we carry out the largest empirical analysis so far on parameter tuning in search-based software engineering. More than one million experiments were carried out and statistically analyzed in the context of test data generation for object-oriented software using the EvoSuite tool. Results show that tuning does indeed have impact on the performance of a search algorithm. But, at least in the context of test data generation, it does not seem easy to find good settings that significantly outperform the “default” values suggested in the literature. This has very practical value for both researchers (e.g., when different techniques are compared) and practitioners. Using “default” values is a reasonable and justified choice, whereas parameter tuning is a long and expensive process that might or might not pay off in the end.
Analysis of Metastability Errors in Conventional, LSB-First, and Asynchronous SAR ADCs

A practical model for characterizing comparator metastability errors in SAR ADCs is presented, and is used to analyze not only the conventional SAR but also LSB-first and asynchronous versions. This work makes three main contributions: first, it is shown that for characterizing metastability it is more reasonable to use input signals with normal or Laplace distributions. Previous work used uniformly-distributed signals in the interest of making derivations easier, but this simplifying assumption overestimated SMR by as much as 18 dB compared to the more reasonable analysis presented here. Second, this work shows that LSB-first SAR ADCs achieve SMR performance equal to or better than conventional SARs with the same metastability window, depending on bandwidth. Finally, the analysis is used to develop a framework for calculating the maximum effective sample rate for asynchronous SAR ADCs, and in doing so demonstrates that proximity detectors are not effective solutions to improving metastability performance.
Recent Developments and Design Challenges of High-Performance Ring Oscillator CMOS Time-to-Digital Converters

Time-to-digital converters (TDCs) are increasingly used as building blocks in biomedical imaging, digital communication, and measurement instrumentation systems. When fabricated in deep-submicrometer (DSM) CMOS technology, TDCs have outstanding time stamping capability on the order of picoseconds. Typically, the timing resolution of a TDC directly determines the minimum resolvable spatial resolution in time-of-flight (ToF) measurements. It also limits the signal-to-noise ratio in ToF positron emission tomography and the in-band noise in an all-digital phase-locked-loop. In TDCs, good linearity and precision result in high measurement accuracy, while the detectable range is limited by its dynamic range. In addition, size and power consumption are of significant importance in large-scale array implementations such as image sensors. Here, we discuss the most recent developments in CMOS TDCs, with an emphasis on ring-oscillator-based TDC and its variants, due to their suitability for array designs with less area overhead. In addition, key performance metrics, and accurate cost-effective characterization methods will be discussed. Finally, future perspectives of CMOS TDCs will be highlighted.
Sentiment Analyzer: Extracting Sentiments about a Given Topic using Natural Language Processing Techniques

We present Sentiment Analyzer (SA) that extracts sentiment (or opinion) about a subject from online text documents. Instead of classifying the sentiment of an entire document about a subject, SA detects all references to the given subject, and determines sentiment in each of the references using natural language processing (NLP) techniques. Our sentiment analysis consists of 1) a topic specific feature term extraction, 2) sentiment extraction, and 3) (subject, sentiment) association by relationship analysis. SA utilizes two linguistic resources for the analysis: the sentiment lexicon and the sentiment pattern database. The performance of the algorithms was verified on online product review articles (“digital camera” and “music” reviews), and more general documents including general webpages and news articles.
Blockchain technology in healthcare: The revolution starts here

Blockchain technology has shown its considerable adaptability in recent years as a variety of market sectors sought ways of incorporating its abilities into their operations. While so far most of the focus has been on the financial services industry, several projects in other service related areas such as healthcare show this is beginning to change. Numerous starting points for Blockchain technology in the healthcare industry are the focus of this report. With examples for public healthcare management, user-oriented medical research and drug counterfeiting in the pharmaceutical sector, this report aims to illustrate possible influences, goals and potentials connected to this disruptive technology.