nils-herrmann/clean_scirepeval huggingface dataset:
Surgical Correction of Gynecomastia in Thin Patients

Gynecomastia refers to a benign enlargement of the male breast. This article describes the authors’ method of using power-assisted liposuction and gland removal through a subareolar incision for thin patients. Power-assisted liposuction is performed for removal of fatty breast tissue in the chest area to allow skin retraction. The subareolar incision is used to remove glandular tissue from a male subject considered to be within a normal weight range but who has bilateral grade 1 or 2 gynecomastia. Gynecomastia correction was successfully performed for all the patients. The average volume of aspirated fat breast was 100–200 ml on each side. Each breast had 5–80 g of breast tissue removed. At the 3-month, 6-month, and 1-year follow-up assessments, all the treated patients were satisfied with their aesthetic results. Liposuction has the advantages of reducing the fat tissue where necessary to allow skin retraction and of reducing the traces left by surgery. The combination of surgical excision and power-assisted lipoplasty also is a valid choice for the treatment of thin patients.
High Precision Load Current Sensing Using On-Line Calibration of Trace Resistance

A method for the on-line calibration of a circuit board trace resistance at the output of a buck converter is described. The input current is measured with a precision resistor and processed to obtain a dc reference for the output current. The voltage drop across a trace resistance at the output is amplified with a gain that is adaptively adjusted to match the dc reference. This method is applied to obtain an accurate and high-bandwidth measurement of the load current in the modern microprocessor voltage regulator application (VRM), thus enabling an accurate dc load-line regulation as well as a fast transient response. Experimental results show an accuracy well within the tolerance band of this application, and exceeding all other popular methods.
An implantable low-power ultrasonic platform for the Internet of Medical Things

Wirelessly networked systems of implantable medical devices endowed with sensors and actuators will be the basis of many innovative, sometimes revolutionary therapies. The biggest obstacle in realizing this vision of networked implantable devices is posed by the dielectric nature of the human body, which strongly attenuates radio-frequency (RF) electromagnetic waves. In this paper we present the first hardware and software architecture of an Internet of Medical Things (IoMT) platform with ultrasonic connectivity for intra-body communications that can be used as a basis for building future IoT-ready medical implantable and wearable devices. We show that ultrasonic waves can be efficiently generated and received with low-power and mm-sized components, and that despite the conversion loss introduced by ultrasonic transducers the gap in attenuation between 2.4GHz RF and ultrasonic waves is still substantial, e.g., ultrasounds offer 70dB less attenuation over 10cm. We show that the proposed IoMT platform requires much lower transmission power compared to 2.4 GHz RF with equal reliability in tissues, e.g., 35 dBm lower over 12 cm for 10−3 Bit Error Rate (BEr) leading to lower energy per bit and longer device lifetime. Finally, we show experimentally that 2.4 GHz RF links are not functional at all above 12 cm, while ultrasonic links achieve a reliability of 10−6 up to 20 cm with less than 0 dBm transmission power.
Correlated Time Series Forecasting using Deep Neural Networks: A Summary of Results

Cyber-physical systems often consist of entities that interact with each other over time. Meanwhile, as part of the continued digitization of industrial processes, various sensor technologies are deployed that enable us to record time-varying attributes (a.k.a., time series) of such entities, thus producing correlated time series. To enable accurate forecasting on such correlated time series, this paper proposes two models that combine convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The first model employs a CNN on each individual time series, combines the convoluted features, and then applies an RNN on top of the convoluted features in the end to enable forecasting. The second model adds additional auto-encoders into the individual CNNs, making the second model a multi-task learning model, which provides accurate and robust forecasting. Experiments on two real-world correlated time series data set suggest that the proposed two models are effective and outperform baselines in most settings. This report extends the paper "Correlated Time Series Forecasting using Multi-Task Deep Neural Networks," to appear in ACM CIKM 2018, by providing additional experimental results.
Direct torque control, induction motor vector control without an encoder

The basic evolution of direct torque control from other drive types is explained. Qualitative comparisons with other drives are included. The basic concepts behind direct torque control are clarified. An explanation of direct self-control and the field orientation concepts implemented in the adaptive motor model block is presented. The reliance of the control method on fast processing techniques is stressed. The theoretical foundations for the control concept are provided in summary format. Information on the ancillary control blocks outside the basic direct torque control is given. The implementation of special functions directly related to the control approach is described. Finally, performance data from an actual system is presented.
Questionnaire on the perceptions of patients about total knee replacement.

We have developed a 12-item questionnaire for patients having a total knee replacement (TKR). We made a prospective study of 117 patients before operation and at follow-up six months later, asking them to complete the new questionnaire and the form SF36. Some also filled in the Stanford Health Assessment Questionnaire (HAQ). An orthopaedic surgeon completed the American Knee Society (AKS) clinical score. The single score derived from the new questionnaire had high internal consistency, and its reproducibility, examined by test-retest reliability, was found to be satisfactory. Its validity was established by obtaining significant correlations in the expected direction with the AKS scores and the relevant parts of the SF36 and HAQ. Sensitivity to change was assessed by analysing the differences between the preoperative scores and those at follow-up. We also compared change in scores with the patients' retrospective judgement of change in their condition. The effect size for the new questionnaire compared favourably with those for the relevant parts of the SF36. The change scores for the new knee questionnaire were significantly greater (p < 0.0001) for patients who reported the most improvement in their condition. The new questionnaire provides a measure of outcome for TKR that is short, practical, reliable, valid and sensitive to clinically important changes over time.
The Many Shades of Anonymity: Characterizing Anonymous Social Media Content

Recently, there has been a significant increase in the popularity of anonymous social media sites like Whisper and Secret. Unlike traditional social media sites like Facebook and Twitter, posts on anonymous social media sites are not associated with well-defined user identities or profiles. In this study, our goals are two-fold: (i) to understand the nature (sensitivity, types) of content posted on anonymous social media sites and (ii) to investigate the differences between content posted on anonymous and non-anonymous social media sites like Twitter. To this end, we gather and analyze extensive content traces from Whisper (anonymous) and Twitter (non-anonymous) social media sites. We introduce the notion of anonymity sensitivity of a social media post, which captures the extent to which users think the post should be anonymous. We also propose a human annotator based methodology to measure the same for Whisper and Twitter posts. Our analysis reveals that anonymity sensitivity of most whispers (unlike tweets) is not binary. Instead, most whispers exhibit many shades or different levels of anonymity. We also find that the linguistic differences between whispers and tweets are so significant that we could train automated classifiers to distinguish between them with reasonable accuracy. Our findings shed light on human behavior in anonymous media systems that lack the notion of an identity and they have important implications for the future designs of such systems.
Traffic Light Control System Based on FPGA

Traffic light control is a challenging problem in many cities. This is due to the large number of vehicles and the high dynamics of the traffic system. Poor traffic systems are the big reason for of accidents, time losses. This system proposed, in this paper aims at reducing waiting times of the vehicles at traffic signals. Traffic Light Control (TLC) system also based on microcontroller and microprocessor. But the disadvantage of with microcontroller or microprocessor is that it works on fixed time, which is functioning according to the program that does not have the flexibility of modification on real time basis. This proposed system using FPGA with traffic sensors to control traffic according requirement means designer can change the program if it require and thus reduces the waiting time. The hardware design has been developed using Verilog Hardware Description Language (HDL) programming. The output of system has been tested using Xilinx. The implementation of traffic Light Controller also through Application Specific Integrated Circuit. But implementation with FPGA is less expensive compared to ASIC design. This paper presents the FPGA implemented low cost advanced TLC system. Coding of the design is done using Verilog HDL and the design is tested and simulated on Spartan-3E FPGA development kit.
A UML-Based Approach to System Testing

System testing is concerned with testing an entire system based on its specifications. In the context of object-oriented, UML development, this means that system test requirements are derived from UML analysis artifacts such as use cases, their corresponding sequence and collaboration diagrams, class diagrams, and possibly Object Constraint Language (OCL) expressions across all these artifacts. Our goal here is to support the derivation of functional system test requirements, which will be transformed into test cases, test oracles, and test drivers once we have detailed design information. In this paper, we describe a methodology in a practical way and illustrate it with an example. In this context, we address testability and automation issues, as the ultimate goal is to fully support system testing activities with high-capability tools.
RISK ANALYSIS IN FEASIBILITY STUDY OF BUILDING CONSTRUCTION PROJECT : CASE STUDY-PT . PERUSAHAAN GAS NEGARA INDONESIA

Investment in construction project is able to give higher benefit beside of its high uncertainty. The uncertainty depends on many risk factors. The influence of the identified risk then has to be evaluated and calculated towards the project feasibility. Before investment, the feasibility of the project has to be done that gives figures of cash flow on the following years. This can be one of the considerations for making a decision whether this project is feasible or not. Risks that overshadow the construction project have to be calculated as an influential factor towards the failure of a project. This paper aims to know the feasibility of project investment by calculating the risk factors and treatment. Risk probability matrix is used to obtain the risk priority, which then continued with financial analysis for the feasibility study and also sensitivity analysis. The study shows that the parameter investment value will be increased when treatment is done on risk.
On Successful Team Formation: Statistical Analysis of a Multiplayer Online Game

Teamwork plays an important role in many areas of today's society, such as business activities. Thus, the question of how to form an effective team is of increasing interest. In this paper we use the team-oriented multiplayer online game Dota 2 to study cooperation within teams and the success of teams. Making use of game log data, we choose a statistical approach to identify factors that increase the chance of a team to win. The factors that we analyze are related to the roles that players can take within the game, the experiences of the players and friendship ties within a team. Our results show that such data can be used to infer social behavior patterns.
8th-order cavity filter design with co-simulation by HFSS and Designer

In this paper, an 8th-order cavity filter with two symmetrical transmission zeros in stopband is designedwith the method of generalized Chebyshev synthesis so as to satisfy the IMT-Advanced system demands. To shorten the development cycle of the filter from two or three days to several hours, a co-simulation with Ansoft HFSS and Designer is presented. The effectiveness of the co-simulation method is validated by the excellent consistency between the simulation and the experiment results.
Guided Proofreading of Automatic Segmentations for Connectomics

Automatic cell image segmentation methods in connectomics produce merge and split errors, which require correction through proofreading. Previous research has identified the visual search for these errors as the bottleneck in interactive proofreading. To aid error correction, we develop two classifiers that automatically recommend candidate merges and splits to the user. These classifiers use a convolutional neural network (CNN) that has been trained with errors in automatic segmentations against expert-labeled ground truth. Our classifiers detect potentially-erroneous regions by considering a large context region around a segmentation boundary. Corrections can then be performed by a user with yes/no decisions, which reduces variation of information 7.5Ã— faster than previous proofreading methods. We also present a fully-automatic mode that uses a probability threshold to make merge/split decisions. Extensive experiments using the automatic approach and comparing performance of novice and expert users demonstrate that our method performs favorably against state-of-the-art proofreading methods on different connectomics datasets.
Geographical Distribution , Effect of Season & Life Cycle of Scrub Typhus

Scrub typhus is an infectious disease caused by Orientia tsutsugamushi, a tiny parasite about the size of bacteria that belongs to the family Rickettsiaceae. Scrub typhus was first described by Hashimoto from Japan in 1899 (1). The term scrub is used because of the type of vegetation (terrain between woods and clearings) that harbours the vector; however, the name is not entirely correct because certain endemic areas can also be sandy, semi-arid and mountain deserts (2,3). It is reported most often from Southeast Asia and Japan and is the most commonly reported rickettsial infection in India. Globally over one billion people are at risk for scrub typhus and an estimated one million cases occur annually (1).Scrub typhus is also known as tsutsugamushi disease. The name tsutsugamushi is derived from two Japanese words: tsutsuga, meaning something small and dangerous, and mushi, meaning creature. The infection is called scrub typhus because it generally occurs after exposure to areas with secondary (scrub) vegetation. It has recently been found, however, that the disease can also be prevalent in such areas as sandy beaches, mountain deserts, and equatorial rain forests. Therefore, it has been suggested that the names miteborne typhus, or chigger-borne typhus, are more appropriate. Since the disease is limited to eastern and southeastern Asia, India, northern Australia and the adjacent islands, it is also commonly referred to as tropical typhus Fig 1. Tsutsugamushi Triangle
Static analysis for probabilistic programs: inferring whole program properties from finitely many paths

We propose an approach for the static analysis of probabilistic programs that sense, manipulate, and control based on uncertain data. Examples include programs used in risk analysis, medical decision making and cyber-physical systems. Correctness properties of such programs take the form of queries that seek the probabilities of assertions over program variables. We present a static analysis approach that provides guaranteed interval bounds on the values (assertion probabilities) of such queries. First, we observe that for probabilistic programs, it is possible to conclude facts about the behavior of the entire program by choosing a finite, adequate set of its paths. We provide strategies for choosing such a set of paths and verifying its adequacy. The queries are evaluated over each path by a combination of symbolic execution and probabilistic volume-bound computations. Each path yields interval bounds that can be summed up with a "coverage" bound to yield an interval that encloses the probability of assertion for the program as a whole. We demonstrate promising results on a suite of benchmarks from many different sources including robotic manipulators and medical decision making programs.
Acoustic identification of twelve species of echolocating bat by discriminant function analysis and artificial neural networks.

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.
An introduction to gene expression deconvolution and the CellMix package A Comprehensive Framework for Gene Expression Deconvolution

This vignette motivates and describes the functionalities of the CellMix package, an R package for performing gene expression deconvolution analysis. The package defines a general framework to apply, develop and test gene expression deconvolution methods. It incorporates, generalises and extends the set of tools we implemented when developing a semi-supervised approach to this problem, and includes several other previously published algorithms, hence facilitating their application and comparison. A special focus is drawn on lists of cell/tissue marker genes, which are very valuable resources as they may be used not only as prior knowledge or post-hoc independent validation data by deconvolution algorithms, but also as gene sets in classical enrichment analysis. We envisage that this package will provide the bioinformatics research community with an easy to use and flexible platform for working with deconvolution methods, and cell heterogeneity in omics data in general. This vignette aims at providing a general background on gene expression deconvolution, motivating the CellMix package, as well as describing its main features. It serves as supplementary material for the following article: Renaud Gaujoux et al. “ A Comprehensive Framework for Gene Expression Deconvolution”. In: accepted (2012) Documentation, practical examples, and sample analyses can be found online at: http://web.cbio.uct.ac.za/~renaud/CRAN/web/CellMix Note that to reproduce some of the examples in this vignettes, you will also need to have the GEOquery package package installed, as well as some Bioconductor annotation packages which will be installed when needed: # install biocLite install.packages("BiocInstaller") # or alternatively do: source(✬http://www.bioconductor.org/biocLite.R✬) # install GEOquery (NB: might ask you to update some of your packages) library(BiocInstaller) biocLite("GEOquery") http://www.bioconductor.org/packages/release/bioc/html/GEOquery.html In an interactive R session, their installation is automated, after the user gives permission to do so.
Child sexual abuse: report of 311 cases with review of literature

Child sexual abuse (CSA) is a global problem that has significant consequences for public health; it has been a prominent topic of public concern for more than a decade, but many basic facts about the problem remain unclear or in dispute. We conducted a study of 311 cases of CSA in order to highlight the epidemiological features and negative impact on victims' well-being and to emphasize the need for a multidisciplinary approach to the primary prevention and management of CSA. We noted an increase in cases number with male predominance. Most of our patients came from lower socioeconomic classes. The perpetrators were male in 100% of cases; acquaintances in 70% of cases and family members in 22 cases. Physical examination were normal in 61% of cases, however, a range of psychological and physical effects were identified with dramatic health consequences: three cases of attempted suicide, five pregnancies and one case of HIV virus infection.
An Assistive Reading System for Visually Impaired using OCR and TTS

Reading machines are mechatronic devices which use optical character recognition and text-to-speech technology in order to output synthetic voice from printed text. In this paper an assistive system has been proposed for visually impaired or blind persons. It reads textual information on papers and produces corresponding voice using OCR (Optical Character Recognition)and TTS (Text-to-speech) system. To localize text regions in images connected component labeling approach using histogram analysis is done on binarized image. TTS system using Concatenative synthesis based on SDK (Software Development Kit) platform is used. This system is operated via a voice-based user interface and also has a user friendly GUI (graphical user interface) to scan the text and to control various speech parameters. Speech signal produced can be saved and reproduced for later use.