nils-herrmann/clean_scirepeval huggingface dataset:
The prepuce: preservation and reconstruction.

To analyze the role of prepuce preservation in various disorders and discuss options available to reconstruct the prepuce.



The prepuce can be preserved in selected cases of penile degloving procedures, phimosis or hypospadias repair, and penile cancer resection. There is no clear evidence that debilitating and persistent preputial lymphedema develops after a prepuce-sparing penile degloving procedure. In fact, the prepuce can at times be preserved even if lymphedema develops. The prepuce can potentially be preserved in both phimosis and hypospadias repair. Penile cancer localized to the prepuce can be excised using Mohs' micrographic surgery without compromising survival. Reconstruction of the prepuce still remains a theoretical topic. There has been no study that has systematically evaluated efficacy of any reconstructive procedures.



The standard practice for preputial disorders remains circumcision. However, prepuce preservation is often technically feasible without compromising treatment. Preservative surgery combined with reconstruction may lead to better patient satisfaction and quality of life.
Fruits Sorting and Grading using Fuzzy Logic

All Rights Reserved  Now a days, the classification and grading is performed based on observations and through experience. The system utilizes image-processing techniques to classify and grade fruits. The developed system starts the process by capturing the fruit’s image using a regular digital camera. Then, the image is transmitted to the processing level where feature extraction, classification and grading is done using MATLAB. The fruits are classified based on color and graded based on size. Both classification and grading are realized by Fuzzy Logic approach. The results obtained are very promising.
Research proposal Information Visualization for Information Fusion

Information fusion is a field of research that strives to establish theories, techniques and tools that exploit synergies in data retrieved from multiple sources. In many real-world applications huge amounts of data need to be gathered, evaluated and analyzed in order to make the right decisions. An important key element of information fusion is the adequate presentation of the data that guides decision-making processes efficiently. This is where theories and tools developed in information visualization, visual data mining and human computer interaction (HCI) research can be of great support. This report presents an overview of information fusion and information visualization, highlighting the importance of the latter in information fusion research. Information visualization techniques that can be used in information fusion are presented and analyzed providing insights into its strengths and weakness. Problems and challenges regarding the presentation of information that the decision maker faces in the ground situation awareness scenario (GSA) lead to open questions that are assumed to be the focus of further research.
"How Many Silences Are There?" Men's Experience of Victimization in Intimate Partner Relationships.

There is a substantive body of research focusing on women's experiences of intimate partner violence (IPV), but a lack of qualitative studies focusing on men's experiences as victims of IPV. This article addresses this gap in the literature by paying particular attention to hegemonic masculinities and men's perceptions of IPV. Men ( N = 9) participated in in-depth interviews. Interview data were rigorously subjected to thematic analysis, which revealed five key themes in the men's narratives: fear of IPV, maintaining power and control, victimization as a forbidden narrative, critical understanding of IPV, and breaking the silence. Although the men share similar stories of victimization as women, the way this is influenced by their gendered histories is different. While some men reveal a willingness to disclose their victimization and share similar fear to women victims, others reframe their victim status in a way that sustains their own power and control. The men also draw attention to the contextual realities that frame abuse, including histories of violence against the women who used violence and the realities of communities suffering intergenerational affects of colonized histories. The findings reinforce the importance of in-depth qualitative work toward revealing the context of violence, understanding the impact of fear, victimization, and power/control on men's mental health as well as the outcome of legal and support services and lack thereof. A critical discussion regarding the gendered context of violence, power within relationships, and addressing men's need for support without redefining victimization or taking away from policies and support for women's ongoing victimization concludes the work.
Aircraft Fuselage Defect Detection using Deep Neural Networks

To ensure flight safety of aircraft structures, it is necessary to have regular maintenance using visual and nondestructive inspection (NDI) methods. In this paper, we propose an automatic image-based aircraft defect detection using Deep Neural Networks (DNNs). To the best of our knowledge, this is the first work for aircraft defect detection using DNNs. We perform a comprehensive evaluation of state-of-the-art feature descriptors and show that the best performance is achieved by vgg-f DNN as feature extractor with a linear SVM classifier. To reduce the processing time, we propose to apply SURF key point detector to identify defect patch candidates. Our experiment results suggest that we can achieve over 96% accuracy at around 15s processing time for a high-resolution (20-megapixel) image on a laptop.
NMDA receptors: linking physiological output to biophysical operation

NMDA receptors are preeminent neurotransmitter-gated channels in the CNS, which respond to glutamate in a manner that integrates multiple external and internal cues. They belong to the ionotropic glutamate receptor family and fulfil unique and crucial roles in neuronal development and function. These roles depend on characteristic response kinetics, which reflect the operation of the receptors. Here, we review biologically salient features of the NMDA receptor signal and its mechanistic origins. Knowledge of distinctive NMDA receptor biophysical properties, their structural determinants and physiological roles is necessary to understand the physiological and neurotoxic actions of glutamate and to design effective therapeutics.
Primo-Vascular System as Presented by Bong Han Kim

In the 1960s Bong Han Kim discovered and characterized a new vascular system. He was able to differentiate it clearly from vascular blood and lymph systems by the use of a variety of methods, which were available to him in the mid-20th century. He gave detailed characterization of the system and created comprehensive diagrams and photographs in his publications. He demonstrated that this system is composed of nodes and vessels, and it was responsible for tissue regeneration. However, he did not disclose in detail his methods. Consequently, his results are relatively obscure from the vantage point of contemporary scientists. The stains that Kim used had been perfected and had been in use for more than 100 years. Therefore, the names of the stains were directed to the explicit protocols for the usage with the particular cells or molecules. Traditionally, it was not normally necessary to describe the method used unless it is significantly deviated from the original method. In this present work, we have been able to disclose staining methods used by Kim.
Intraradicular bacteria and fungi in root-filled, asymptomatic human teeth with therapy-resistant periapical lesions: a long-term light and electron microscopic follow-up study.

Light and electron microscopy were used to analyze nine therapy-resistant and asymptomatic human periapical lesions, which were removed as block biopsies during surgical treatment of the affected teeth. The cases that required surgery represented about 10% of all of the cases which received endodontic treatment and root fillings during the period 1977 to 1984. These cases revealed periapical lesions when they were examined 4 to 10 yr after treatment. The biopsies were processed for correlated light and electron microscopy. Six of the nine biopsies revealed the presence of microorganisms in the apical root canal. Four contained one or more species of bacteria and two revealed yeasts. Of the four cases in which bacteria were found, only in one biopsy could they be found by light microscope. In the other three specimens, the bacterial presence could be confirmed only after repeated electron microscopic examination of the apical root canal by serial step-cutting technique. Among the three cases in which no microorganisms could be encountered, one showed histopathological features of a foreign body giant cell granuloma. These findings suggest that in the majority of root-filled human teeth with therapy-resistant periapical lesions, microorganisms may persist and may play a significant role in endodontic treatment failures. In certain instances such lesions may also be sustained by foreign body giant cell type of tissue responses at the periapex of root-filled teeth.
Glycemic control and excess mortality in type 1 diabetes.

n engl j med 371;21 nejm.org November 20, 2014 1972 From the Department of Medicine, NUHospital Organization, Uddevalla (M.L., S.D.), Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg (M.L., S.G., A.R.), Center of Registers in Region Västra Götaland (A.-M.S.), Statistiska Konsultgruppen (A.P.), Nordic School of Public Health (H.W.), and Sahlgrenska University Hospital (A.R.), Gothenburg — all in Sweden; Saint Luke’s Mid America Heart Institute (M.K.), University of Missouri– Kansas City School of Medicine (M.K., M.C.), and Children’s Mercy Hospital (M.C.), Kansas City, MO; and the University of Kansas School of Medicine, Kansas City, KS (M.C.). Address reprint requests to Dr. Lind at the Department of Medicine, Uddevalla Hospital, 451 80 Uddevalla, Uddevalla, Sweden, or at lind . marcus@ telia . com.
3 D Sketching using MultiView Deep Volumetric Prediction

Sketch-based modeling strives to bring the ease and immediacy of drawing to the 3D world. However, while drawings are easy for humans to create, they are very challenging for computers to interpret due to their sparsity and ambiguity. We propose a data-driven approach that tackles this challenge by learning to reconstruct 3D shapes from one or more drawings. At the core of our approach is a deep convolutional neural network (CNN) that predicts occupancy of a voxel grid from a line drawing. This CNN provides an initial 3D reconstruction as soon as the user completes a single drawing of the desired shape. We complement this single-view network with an updater CNN that refines an existing prediction given a new drawing of the shape created from a novel viewpoint. A key advantage of our approach is that we can apply the updater iteratively to fuse information from an arbitrary number of viewpoints, without requiring explicit stroke correspondences between the drawings. We train both CNNs by rendering synthetic contour drawings from hand-modeled shape collections as well as from procedurally-generated abstract shapes. Finally, we integrate our CNNs in an interactive modeling system that allows users to seamlessly draw an object, rotate it to see its 3D reconstruction, and refine it by re-drawing from another vantage point using the 3D reconstruction as guidance. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). Conference’17, July 2017, Washington, DC, USA   This is the authors version of the work. It is posted by permission of ACM for your personal use. Not for redistribution. The definite version will be published in PACMCGIT.
DeFT : A conceptual framework for considering learning with multiple representations

Multiple (external) representations can provide unique benefits when people are learning complex new ideas. Unfortunately, many studies have shown this promise is not always achieved. The DeFT (Design, Functions, Tasks) framework for learning with multiple representations integrates research on learning, the cognitive science of representation and constructivist theories of education. It proposes that the effectiveness of multiple representations can best be understood by considering three fundamental aspects of learning: the design parameters that are unique to learning with multiple representations; the functions that multiple representations serve in supporting learning and the cognitive tasks that must be undertaken by a learner interacting with multiple representations. The utility of this framework is proposed to be in identifying a broad range of factors that influence learning, reconciling inconsistent experimental findings, revealing under-explored areas of multi-representational research and pointing forward to potential design heuristics for learning with multiple representations.
A new grouping genetic algorithm approach to the multiple traveling salesperson problem

The multiple traveling salesperson problem (MTSP) is an extension of the well known traveling salesperson problem (TSP). Given m > 1 salespersons and n > m cities to visit, the MTSP seeks a partition of cities into m groups as well as an ordering among cities in each group so that each group of cities is visited by exactly one salesperson in their specified order in such a way that each city is visited exactly once and sum of total distance traveled by all the salespersons is minimized. Apart from the objective of minimizing the total distance traveled by all the salespersons, we have also considered an alternate objective of minimizing the maximum distance traveled by any one salesperson, which is related with balancing the workload among salespersons. In this paper, we have proposed a new grouping genetic algorithm based approach for the MTSP and compared our results with other approaches available in the literature. Our approach outperformed the other approaches on both the objectives.
VIRTUAL CRIME SCENE RECONSTRUCTION WITH INTEGRATED ANIMATED CHARACTERS

This paper proposes some new techniques for the development of virtual 3D environments for crime scene reconstruction. Current techniques were investigated, which concentrate on the production of photo-realistic scenes. However, these were considered to be too specialist and expensive. The Crime Scene Creator developed here provides a cheaper and quicker alternative that allows quasiaccurate scenes to be constructed and populated with animated characters to act out crimes events, with some very encouraging results. The paper also describes some initial investigations for implementing voice recognition and natural language processing as a basis for further development.
Novel Surgical Method for Pincer Nail Treatment: Partial Matricectomy and Triple Flap Technique.

Transverse overcurvature of the nail plate that pinches distal nail bed progressively is called as pincer nail deformity. This condition is more frequent on toes, but it is rare on fingers. The major indications for treatment are pain, inflammation, interference with wearing shoes, and cosmetic embarrassment. There are various surgical treatment methods described for the correction of pincer nail deformity. Here, a novel surgical treatment method for pincer nail deformity using triple flap technique is presented.
A Comparison Between the Firefly Algorithm and Particle Swarm Optimization

When a problem is large or difficult to solve, computers are often used to find the solution. But when the problem becomes too large, traditional methods of finding the answer may not be enough. It is in turning to nature that inspiration can be found to solve these difficult problems. Artificial intelligence seeks to emulate creatures and processes found in nature, and turn their techniques for solving a problem into an algorithm. Many such metaheuristic algorithms have been developed, but there is a continuous search for better, faster algorithms. The recently developed Firefly Algorithm has been shown to outperform the longstanding Particle Swarm Optimization, and this work aims to verify those results and improve upon them by comparing the two algorithms with a large scale application. A direct hardware implementation of the Firefly Algorithm is also proposed, to speed up performance in embedded systems applications.
Effect of Facebook on the life of Medical University students

Facebook is a social networking service launched in February 2004, owned and operated by Facebook, Inc. As of June 2012, Facebook reports more than 1 billion active users. Objective of study was to evaluate the effect of Facebook on the social life, health and behavior of medical students.



It was a cross sectional, observational and questionnaire based study conducted in Dow University OF Health Sciences during the period of January 2012 to November 2012. We attempted to interview all the participants who could be approached during the period of the study. Participants were MBBS students, while all students of other courses and programs were taken as exclusion criteria. Approximately 1050 questionnaires were distributed to participants. Fifty questionnaires were rejected due to incomplete answers, yielding 1000 usable responses for an approximate 95% response rate. Informed verbal consent was taken from each participant. Study was ethically approved by Institutional Review Board of Dow University of Health Sciences. All the data was entered and analyzed through SPSS 19.



Out of total 1000 participants, males were 400 (40%) and females were 600 (60%). Participants were in the age group of 18-25 years with a mean age of 20.08 years. Most of the participants were using Facebook daily (N = 640, 64%) for around 3-4 hours (N = 401, 40.1%). Majority of them (N = 359, 35.9%) believed that they were equally active on Facebook and in real life while few believed their social life became worse after start using Facebook (N = 372, 37.2%). Most of the participants admitted that they were considered as shy in real world (N = 390, 39.0%) while in the world of Facebook they were considered as fun loving by their friends (N = 603, 60.3%). A large number of participants (N = 715, 75%) complained of mood swings.



Youngsters are willing to compromise their health, social life, studies for the sake of fun and entertainment or whatever satisfaction they get after using Facebook. What we observed in our study was that although majority of our subjects showed multiple signs of Facebook addiction, they don't realize it and if even they realize it they don't want to quit Facebook and even if they want to quit, they can't. Our observance concluded that majority of the users are highly addicted.
An ultra-low-power FPGA for IoT applications

The rapid development of the Internet-of-Things requires hardware that is both energy-efficient and flexible, and an ultra-low-power Field-Programmable-Gate-Array (FPGA) is a very promising solution. This paper presents a near/sub-threshold FPGA with low-swing global interconnect, folded switch box (SB), per-path voltage scaling, and power-gating. A fully programmable 512-look-up-table FPGA chip is fabricated in 130nm CMOS. When implementing a 4bit-adder, the measured energy of the proposed FPGA is 15% less than the normalized energy of the state-of-the-art. When implementing fifteen selected low-power applications, the estimated energy of the proposed FPGA is on average 75x lower than Microsemi IGLOO.
Random Shuffling and Resets for the Non-stationary Stochastic Bandit Problem

We consider a non-stationary formulation of the stochastic multi-armed bandit where the rewards are no longer assumed to be identically distributed. For the best-arm identification task, we introduce a version of SUCCESSIVE ELIMINATION based on random shuffling of the K arms. We prove that under a novel and mild assumption on the mean gap ∆, this simple but powerful modification achieves the same guarantees in term of sample complexity and cumulative regret than its original version, but in a much wider class of problems, as it is not anymore constrained to stationary distributions. We also show that the original SUCCESSIVE ELIMINATION fails to have controlled regret in this more general scenario, thus showing the benefit of shuffling. We then remove our mild assumption and adapt the algorithm to the best-arm identification task with switching arms. We adapt the definition of the sample complexity for that case and prove that, against an optimal policy with N −1 switches of the optimal arm, this new algorithm achieves an expected sample complexity ofO(∆−2 √ NKδ−1 log(Kδ−1)), where δ is the probability of failure of the algorithm, and an expected cumulative regret of O(∆−1 √ NTK log(TK)) after T time steps.
Magnetic skyrmion-based synaptic devices

Magnetic skyrmions are promising candidates for next-generation information carriers, owing to their small size, topological stability, and ultralow depinning current density. A wide variety of skyrmionic device concepts and prototypes have recently been proposed, highlighting their potential applications. Furthermore, the intrinsic properties of skyrmions enable new functionalities that may be inaccessible to conventional electronic devices. Here, we report on a skyrmion-based artificial synapse device for neuromorphic systems. The synaptic weight of the proposed device can be strengthened/weakened by positive/negative stimuli, mimicking the potentiation/depression process of a biological synapse. Both short-term plasticity and long-term potentiation functionalities have been demonstrated with micromagnetic simulations. This proposal suggests new possibilities for synaptic devices in neuromorphic systems with adaptive learning function.
Multilevel Cloud Detection in Remote Sensing Images Based on Deep Learning

Cloud detection is one of the important tasks for remote sensing image processing. In this paper, a novel multilevel cloud detection method based on deep learning is proposed for remote sensing images. First, the simple linear iterative clustering (SLIC) method is improved to segment the image into good quality superpixels. Then, a deep convolutional neural network (CNN) with two branches is designed to extract the multiscale features from each superpixel and predict the superpixel as one of three classes including thick cloud, thin cloud, and noncloud. Finally, the predictions of all the superpixels in the image yield the cloud detection result. In the proposed cloud detection framework, the improved SLIC method can obtain accurate cloud boundaries by optimizing initial cluster centers, designing dynamic distance measure, and expanding search space. Moreover, different from traditional cloud detection methods that cannot achieve multilevel detection of cloud, the designed deep CNN model can not only detect cloud but also distinguish thin cloud from thick cloud. Experimental results indicate that the proposed method can detect cloud with higher accuracy and robustness than compared methods.