nils-herrmann/clean_scirepeval huggingface dataset:
The electron cyclotron maser

The electron cyclotron maser (ECM) is based on a stimulated cyclotron emission process involving energetic electrons in gyrational motion. It constitutes a cornerstone of relativistic electronics, a discipline that has emerged from our understanding and utilization of relativistic effects for the generation of coherent radiation from free electrons. Over a span of four decades, the ECM has undergone a remarkably successful evolution from basic research to device implementation while continuously being enriched by new physical insights. By delivering unprecedented power levels, ECM-based devices have occupied a unique position in the millimeter and submillimeter regions of the electromagnetic spectrum, and find use in numerous applications such as fusion plasma heating, advanced radars, industrial processing, materials characterization, particle acceleration, and tracking of space objects. This article presents a comprehensive review of the fundamental principles of the ECM and their embodiment in practical devices.
Holographic Feature Representations of Deep Networks

It is often asserted that deep networks learn “features”, traditionally expressed by the activations of intermediate nodes. We explore an alternative concept by defining features as partial derivatives of model output with respect to model parameters—extending a simple yet powerful idea from generalized linear models. The resulting features are not equivalent to node activations, and we show that they can induce a holographic representation of the complete model: the network’s output on given data can be exactly replicated by a simple linear model over such features extracted from any ordered cut. We demonstrate useful advantages for this feature representation over standard representations based on node activations.
Fatigue analysis of connecting rod of universal tractor through finite element method ( ANSYS )

The connecting rod fatigue of universal tractor (U650) was investigated through the ANSYS software application and its lifespan was estimated. The reason for performing this research showed the connecting rod behavior affected by fatigue phenomenon due to the cyclic loadings and to consider the results for more savings in time and costs, as two very significant parameters relevant to manufacturing. The results indicate that with fully reverse loading, one can estimate longevity of a connecting rod and also find the critical points that more possibly the crack growth initiate from. Furthermore, the allowable number of load cycles and using fully reverse loading was gained 10. It is suggested that the results obtained can be useful to bring about modifications in the process of connecting rod manufacturing.
Reengineering, Simulation and Data Analysis of an RFID System

We present a discrete event simulation model reproducing the adoption of Radio Frequency Identification (RFID) technology for the optimal management of common logistics processes of a Fast Moving Consumer Goods (FMCG) warehouse. In this study, simulation is exploited as a powerful tool to replicate both the reengineered RFID logistics processes and the flows of Electronic Product Code (EPC) data generated by such processes. Moreover, a complex tool has been developed to analyze data resulting from the simulation runs, thus addressing the issue of how the flows of EPC data generated by RFID technology can be exploited to provide value-added information for optimally managing the logistics processes. Specifically, an EPCIS compliant Data Warehouse has been designed to act as EPCIS Repository and store EPC data resulting from simulation. Starting from EPC data, properly designed tools, referred to as Business Intelligence Modules, provide value-added information for processes optimization. Due to the newness of RFID adoption in the logistics context and to the lack of real case examples that can be examined, we believe that both the model and the data management system developed can be very useful to understand the practical implications of the technology and related information flow, as well as to show how to leverage EPC data for process management. Results of the study can provide a proof-of-concept to substantiate the adoption of RFID technology in the FMCG industry.
Template Matching Advances and Applications in Image Analysis

In most computer vision and image analysis problems, it is necessary to define a similarity measure between two or more different objects or images. Template matching is a classic and fundamental method used to score similarities between objects using certain mathematical algorithms. In this paper, we reviewed the basic concept of matching, as well as advances in template matching and applications such as invariant features or novel applications in medical image analysis. Additionally, deformable models and templates originating from classic template matching were discussed. These models have broad applications in image registration, and they are a fundamental aspect of novel machine vision or deep learning algorithms, such as convolutional neural networks (CNN), which perform shift and scale invariant functions followed by classification. In general, although template matching methods have restrictions which limit their application, they are recommended for use with other object recognition methods as preor post-processing steps. Combining a template matching technique such as normalized cross-correlation or dice coefficient with a robust decision-making algorithm yields a significant improvement in the accuracy rate for object detection and recognition.
Fabrication of omni-directional driving system using unconstrained steel ball

In this paper, a new omni-directional driving system with one spherical wheel is proposed. This system is able to overcome the existing driving systems with structural limitations in vertical, horizontal and diagonal movement. This driving system was composed of two stepping motors, a spherical wheel covered by a ball bearing, a weight balancer for the elimination of eccentricity, and ball plungers for balance. All parts of this structure is located at same distance on the center because the center of gravity of this system must be placed at the center of the system. An own ball bearing was designed for settled rotation and smooth direction change of a spherical wheel. The principle of an own ball bearing is the reversal of the ball mouse. Steel as the material of ball in the own ball bearing, was used for the prevention the slip with ground. One of the stepping motors is used for driving the spherical wheel. This spherical wheel is stable because of the support of ball bearing. And the other enables to move in a wanted direction while it rotates based on the central axis. The ATmega128 chip is used for the control of two stepping motors. To verify the proposed system, driving experiments was executed in variety of environments. Finally, the performance and the validity of the omni-directional driving system were confirmed.
Accessible Maps for the Blind: Comparing 3D Printed Models with Tactile Graphics

Tactile maps are widely used in Orientation and Mobility (O&M) training for people with blindness and severe vision impairment. Commodity 3D printers now offer an alternative way to present accessible graphics, however it is unclear if 3D models offer advantages over tactile equivalents for 2D graphics such as maps. In a controlled study with 16 touch readers, we found that 3D models were preferred, enabled the use of more easily understood icons, facilitated better short term recall and allowed relative height of map elements to be more easily understood. Analysis of hand movements revealed the use of novel strategies for systematic scanning of the 3D model and gaining an overview of the map. Finally, we explored how 3D printed maps can be augmented with interactive audio labels, replacing less practical braille labels. Our findings suggest that 3D printed maps do indeed offer advantages for O&M training.
An Object-Oriented Software Architecture for 3 D Mixed Reality Applications

This paper presents a new software architecture for 3D mixed reality applications, named Tinmith-evo5. Currently there are a limited number of existing toolkits for the development of 3D mixed reality applications, each optimised for a particular feature but at the detriment of others. Complex interactive user interfaces and applications require extensive supporting infrastructure, and can be hampered by inadequate support. The Tinmith-evo5 architecture is optimised to develop mobile augmented reality and other interactive 3D applications on portable platforms with limited resources. This architecture is implemented in C++ with an object-oriented data flow design, an object store based on the Unix file system model, and uses other ideas from existing previous work.
Uncertainty Visualization Methods in Isosurface Rendering

We describe two techniques for rendering isosurfaces in multiresolution volume data such that the uncertainty (error) in the data is shown in the resulting visualization. In general the visualization of uncertainty in data is difficult, but the nature of isosurface rendering makes it amenable to an effective solution. In addition to showing the error in the data used to generate the isosurface, we also show the value of an additional data variate on the isosurface. The results combine multiresolution and uncertainty visualization techniques into a hybrid approach. Our technique is applied to multiresolution examples from the medical domain.
Warming and eutrophication combine to restructure diatoms and dinoflagellates.

Temperature change and eutrophication are known to affect phytoplankton communities, but relatively little is known about the effects of interactions between simultaneous changes of temperature and nutrient loading in coastal ecosystems. Here we show that such interaction is key in driving diatom-dinoflagellate dynamics in the East China Sea. Diatoms and dinoflagellates responded differently to temperature, nutrient concentrations and ratios, and their interactions. Diatoms preferred lower temperature and higher nutrient concentrations, while dinoflagellates were less sensitive to temperature and nutrient concentrations, but tended to prevail at low phosphorus and high N:P ratio conditions. These different traits of diatoms and dinoflagellates resulted in the fact that both the effect of warming resulting in nutrients decline as a consequence of increasing stratification and the effect of increasing terrestrial nutrient input as a result of eutrophication might promote dinoflagellates over diatoms. We predict that conservative forecasts of environmental change by the year 2100 are likely to result in the decrease of diatoms in 60% and the increase of dinoflagellates in 70% of the surface water of the East China Sea, and project that mean diatoms should decrease by 19% while mean dinoflagellates should increase by 60% in the surface water of the coastal East China Sea. This analysis is based on a series of statistical niche models of the consequences of multiple environmental changes on diatom and dinoflagellate biomass in the East China Sea based on 2815 samples randomly collected from 23 cruises spanning 14 years (2002-2015). Our findings reveal that dinoflagellate blooms will be more frequent and intense, which will affect coastal ecosystem functioning.
Unfolding Temporal Dynamics: Predicting Social Media Popularity Using Multi-scale Temporal Decomposition

Time information plays a crucial role on social media popularity. Existing research on popularity prediction, effective though, ignores temporal information which is highly related to user-item associations and thus often results in limited success. An essential way is to consider all these factors (user, item, and time), which capture the dynamic nature of photo popularity. In this paper, we present a novel approach to factorize the popularity into user-item context and time-sensitive context for exploring the mechanism of dynamic popularity. The user-item context provides a holistic view of popularity, while the time-sensitive context captures the temporal dynamics nature of popularity. Accordingly, we develop two kinds of time-sensitive features, including user activeness variability and photo prevalence variability. To predict photo popularity, we propose a novel framework named Multi-scale Temporal Decomposition (MTD), which decomposes the popularity matrix in latent spaces based on contextual associations. Specifically, the proposed MTD models time-sensitive context on different time scales, which is beneficial to automatically learn temporal patterns. Based on the experiments conducted on a real-world dataset with 1.29M photos from Flickr, our proposed MTD can achieve the prediction accuracy of 79.8% and outperform the best three state-of-the-art methods with a relative improvement of 9.6% on average.
ARmatika: 3D game for arithmetic learning with Augmented Reality technology

Learning mathematics is one of the most important aspects that determine the future of learners. However, mathematics as one of the subjects is often perceived as being complicated and not liked by the learners. Therefore, we need an application with the use of appropriate technology to create visualization effects which can attract more attention from learners. The application of Augmented Reality technology in digital game is a series of efforts made to create a better visualization effect. In addition, the system is also connected to a leaderboard web service in order to improve the learning motivation through competitive process. Implementation of Augmented Reality is proven to improve student's learning motivation moreover implementation of Augmented Reality in this game is highly preferred by students.
Analysis of a Multilevel Dual Active Bridge ( ML-DAB ) DC-DC Converter Using Symmetric Modulation

Dual active bridge (DAB) converters have been popular in high voltage, low and medium power DC-DC applications, as well as an intermediate high frequency link in solid state transformers. In this paper, a multilevel DAB (ML-DAB) has been proposed in which two active bridges produce two-level (2L)-5L, 5L-2L and 3L-5L voltage waveforms across the high frequency transformer. The proposed ML-DAB has the advantage of being used in high step-up/down converters, which deal with higher voltages, as compared to conventional two-level DABs. A three-level neutral point diode clamped (NPC) topology has been used in the high voltage bridge, which enables the semiconductor switches to be operated within a higher voltage range without the need for cascaded bridges or multiple two-level DAB converters. A symmetric modulation scheme, based on the least number of angular parameters rather than the duty-ratio, has been proposed for a different combination of bridge voltages. This ML-DAB is also suitable for maximum power point tracking (MPPT) control in photovoltaic applications. Steady-state analysis of the converter with symmetric phase-shift modulation is presented and verified using simulation and hardware experiments.
Improving User Topic Interest Profiles by Behavior Factorization

Many recommenders aim to provide relevant recommendations to users by building personal topic interest profiles and then using these profiles to find interesting contents for the user. In social media, recommender systems build user profiles by directly combining users' topic interest signals from a wide variety of consumption and publishing behaviors, such as social media posts they authored, commented on, +1'd or liked. Here we propose to separately model users' topical interests that come from these various behavioral signals in order to construct better user profiles. Intuitively, since publishing a post requires more effort, the topic interests coming from publishing signals should be more accurate of a user's central interest than, say, a simple gesture such as a +1. By separating a single user's interest profile into several behavioral profiles, we obtain better and cleaner topic interest signals, as well as enabling topic prediction for different types of behavior, such as topics that the user might +1 or comment on, but might never write a post on that topic.
 To do this at large scales in Google+, we employed matrix factorization techniques to model each user's behaviors as a separate example entry in the input user-by-topic matrix. Using this technique, which we call "behavioral factorization", we implemented and built a topic recommender predicting user's topical interests using their actions within Google+. We experimentally showed that we obtained better and cleaner signals than baseline methods, and are able to more accurately predict topic interests as well as achieve better coverage.
Empathy and Its Modulation in a Virtual Human

Endowing artificial agents with the ability to empathize is believed to enhance their social behavior and to make them more likable, trustworthy, and caring. Neuropsychological findings substantiate that empathy occurs to different degrees depending on several factors including, among others, a person’s mood, personality, and social relationships with others. Although there is increasing interest in endowing artificial agents with affect, personality, and the ability to build social relationships, little attention has been devoted to the role of such factors in influencing their empathic behavior. In this paper, we present a computational model of empathy which allows a virtual human to exhibit different degrees of empathy. The presented model is based on psychological models of empathy and is applied and evaluated in the context of a conversational agent scenario.
Taverna: lessons in creating a workflow environment for the life sciences

Life sciences research is based on individuals, often with diverse skills, assembled into research groups. These groups use their specialist expertise to address scientific problems. The in silico experiments undertaken by these research groups can be represented as workflows involving the co-ordinated use of analysis programs and information repositories that may be globally distributed. With regards to Grid computing, the requirements relate to the sharing of analysis and information resources rather than sharing computational power. The Grid project has developed the Taverna workbench for the composition and execution of workflows for the life sciences community. This experience paper describes lessons learnt during the development of Taverna. A common theme is the importance of understanding how workflows fit into the scientists’ experimental context. The lessons reflect an evolving understanding of life scientists’ requirements on a workflow environment, which is relevant to other areas of data intensive and exploratory science.
Eruptive syringomas: unresponsiveness to oral isotretinoin.

Eruptive syringomas: unresponsiveness to oral isotretinoin A 22-year-old man of Egyptian origin was referred to our department due to exacerbation of pruritic pre-existing papular dermatoses. The skin lesions had been present since childhood. The family history was negative for a similar condition. The patient complained of exacerbation of the pruritus during physical activity under a hot climate and had moderate to severe pruritus during his work. Physical examination revealed multiple reddish-brownish smooth-surfaced, symmetrically distributed papules 2–4 mm in diameter on the patient’s trunk, neck, axillae, and limbs . The rest of the physical examination was unremarkable. The Darier sign was negative. A skin biopsy was obtained from a representative lesion on the trunk. Histopathologic examination revealed a small, wellcircumscribed neoplasm confined to the upper dermis, composed of small solid and ductal structures relatively evenly distributed in a sclerotic collagenous stroma. The solid elements were of various shapes (round, oval, curvilinear, “comma-like,” or “tadpole-like”) . These microscopic features and the clinical presentation were consistent with the diagnosis of eruptive syringomas. Our patient was treated with a short course of oral antihistamines without any effect and subsequently with low-dose isotretinoin (10 mg/day) for 5 months. No improvement of the skin eruption was observed while cessation of the pruritus was accomplished. Syringoma is a common adnexal tumor with differentiation towards eccrine acrosyringium composed of small solid and ductal elements embedded in a sclerotic stroma and restricted as a rule to the upper to mid dermis, usually presenting clinically as multiple lesions on the lower eyelids and cheeks of adolescent females. A much less common variant is the eruptive or disseminated syringomas, which involve primarily young women. Eruptive syringomas are characterized by rapid development during a short period of time of hundreds of small (1–5 mm), ill-defined, smooth surfaced, skin-colored, pink, yellowish, or brownish papules typically involving the face, trunk, genitalia, pubic area, and extremities but can occur principally in any site where eccrine glands are found. The pathogenesis of eruptive syringoma remains unclear. Some authors have recently challenged the traditional notion that eruptive syringomas are neoplastic lesions. Chandler and Bosenberg presented evidence that eruptive syringomas result from autoimmune destruction of the acrosyringium and proposed the term autoimmune acrosyringitis with ductal cysts. Garrido-Ruiz et al. support the theory that eruptive syringomas may represent a hyperplastic response of the eccrine duct to an inflammatory reaction. In a recent systematic review by Williams and Shinkai the strongest association of syringomas was with Down’s syndrome (183 reported cases, 22.2%). Syringomas are also associated with diabetes mellitus (17 reported cases, 2.1%), Ehlers–Danlos
Wild and commercial mushrooms as source of nutrients and nutraceuticals.

In order to promote the use of mushrooms as source of nutrients and nutraceuticals, several experiments were performed in wild and commercial species. The analysis of nutrients included determination of proteins, fats, ash, and carbohydrates, particularly sugars by HPLC-RI. The analysis of nutraceuticals included determination of fatty acids by GC-FID, and other phytochemicals such as tocopherols, by HPLC-fluorescence, and phenolics, flavonoids, carotenoids and ascorbic acid, by spectrophotometer techniques. The antimicrobial properties of the mushrooms were also screened against fungi, Gram positive and Gram negative bacteria. The wild mushroom species proved to be less energetic than the commercial sp., containing higher contents of protein and lower fat concentrations. In general, commercial species seem to have higher concentrations of sugars, while wild sp. contained lower values of MUFA but also higher contents of PUFA. alpha-Tocopherol was detected in higher amounts in the wild species, while gamma-tocopherol was not found in these species. Wild mushrooms revealed a higher content of phenols but a lower content of ascorbic acid, than commercial mushrooms. There were no differences between the antimicrobial properties of wild and commercial species. The ongoing research will lead to a new generation of foods, and will certainly promote their nutritional and medicinal use.