nils-herrmann/clean_scirepeval huggingface dataset:
Low-Cost Ku-Band Waveguide Devices Using 3-D Printing and Liquid Metal Filling

This paper presents a simple, low-cost, hours-long fabrication method for microwave waveguide components of high RF performance. The technique combines 3-D-printed configurations with liquid metal waveguide structures. As a demonstration, a fused deposition modeling multimaterial 3-D consumer-grade printer and liquid gallium were used. A conductive polylactic acid (PLA) waveguide flange was 3-D printed along, in-one-go, with standard PLA for the rectangular waveguide liquid metal enclosures. Microwave WR62 waveguides, resonators, and filters operating in Ku-band were designed, fabricated, and tested. The RF performance of the fabricated waveguide devices is in agreement with the simulations demonstrating better than 1.29 dB/m attenuation in the waveguide and better than 1000  $Q$ -factor for the resonator and the filter at 13 GHz. The fabricated devices demonstrate a new option of an economical fabrication technology for high RF performance microwave waveguide-based devices that can be delivered in hours-time, anywhere, anytime with minimal equipment deployment and investment.
Fault diagnosis of engine lubrication system

The techniques used in fault diagnosis of automotive engine oils are discussed. The importance of Oil change at the right time and the effect of parameters like water contamination, particle contamination, oxidation, viscosity, fuel content in oil are also discussed. Analysis is carried out on MATLAB with reference to the variation of Dielectric constant of lubrication oil over the use period. The program is designed to display the values of Iron content (particles), water content, density and Acid value at particular instant and display the condition of oil in terms of parameter.
Agent-based computational investing recommender system

The fast development of computing and communication has reformed the financial markets' dynamics. Nowadays many people are investing and trading stocks through online channels and having access to real-time market information efficiently. There are more opportunities to lose or make money with all the stocks information available throughout the World; however, one should spend a lot of effort and time to follow those stocks and the available instant information. This paper presents a preliminary regarding a multi-agent recommender system for computational investing. This system utilizes a hybrid filtering technique to adaptively recommend the most profitable stocks at the right time according to investor's personal favour. The hybrid technique includes collaborative and content-based filtering. The content-based model uses investor preferences, influencing macro-economic factors, stocks profiles and the predicted trend to tailor to its advices. The collaborative filter assesses the investor pairs' investing behaviours and actions that are proficient in economic market to recommend the similar ones to the target investor.
Mitochondrial proteostasis in the control of aging and longevity.

Mitochondria play a central role in the aging process. Studies in model organisms have started to integrate mitochondrial effects on aging with the maintenance of protein homeostasis. These findings center on the mitochondrial unfolded protein response (UPR(mt)), which has been implicated in lifespan extension in worms, flies, and mice, suggesting a conserved role in the long-term maintenance of cellular homeostasis. Here, we review current knowledge of the UPR(mt) and discuss its integration with cellular pathways known to regulate lifespan. We highlight how insight into the UPR(mt) is revolutionizing our understanding of mitochondrial lifespan extension and of the aging process.
Groupwise Maximin Fair Allocation of Indivisible Goods

We study the problem of allocating indivisible goods among n agents in a fair manner. For this problem, maximin share (MMS) is a well-studied solution concept which provides a fairness threshold. Specifically, maximin share is defined as the minimum utility that an agent can guarantee for herself when asked to partition the set of goods into n bundles such that the remaining (n−1) agents pick their bundles adversarially. An allocation is deemed to be fair if every agent gets a bundle whose valuation is at least her maximin share. Even though maximin shares provide a natural benchmark for fairness, it has its own drawbacks and, in particular, it is not sufficient to rule out unsatisfactory allocations. Motivated by these considerations, in this work we define a stronger notion of fairness, called groupwise maximin share guarantee (GMMS). In GMMS, we require that the maximin share guarantee is achieved not just with respect to the grand bundle, but also among all the subgroups of agents. Hence, this solution concept strengthens MMS and provides an ex-post fairness guarantee. We show that in specific settings, GMMS allocations always exist. We also establish the existence of approximate GMMS allocations under additive valuations, and develop a polynomial-time algorithm to find such allocations. Moreover, we establish a scale of fairness wherein we show that GMMS implies approximate envy freeness. Finally, we empirically demonstrate the existence of GMMS allocations in a large set of randomly generated instances. For the same set of instances, we additionally show that our algorithm achieves an approximation factor better than the established, worst-case bound.
Real-time Coherent Video Style Transfer

With the increasing popularity of deep learning and computer vision, the field of image style transfer using convolutional neural networks has gained interests of researchers globally. Although there have been mature solutions for style transfer on images, they usually suffer from high temporal inconsistency when applied to videos. Some video style transfer models have recently been proposed to improve temporal consistency, yet they fail to guarantee fast processing speed, nice perceptual style quality and high temporal consistency at the same time. In this project, we propose a novel real-time video style transfer model, ReCoNet, as a solution to this problem. ReCoNet is a feed-forward convolutional neural network which can generate temporally coherent style transfer videos in real-time speed. In addition to the loss functions used in previous video style transfer models, it also adopts a novel luminance warping constraint to increase stylization stability under illumination effects and a novel feature-maplevel temporal loss to further enhance temporal consistency on traceable objects. Experimental results indicate that ReCoNet exhibits outstanding performance both qualitatively and quantitatively. A corresponding computer application has also been developed to stylize streaming videos using ReCoNet models pre-trained on different style images.
Predicting potential ranges of primary malaria vectors and malaria in northern South America based on projected changes in climate, land cover and human population

Changes in land use and land cover (LULC) as well as climate are likely to affect the geographic distribution of malaria vectors and parasites in the coming decades. At present, malaria transmission is concentrated mainly in the Amazon basin where extensive agriculture, mining, and logging activities have resulted in changes to local and regional hydrology, massive loss of forest cover, and increased contact between malaria vectors and hosts. Employing presence-only records, bioclimatic, topographic, hydrologic, LULC and human population data, we modeled the distribution of malaria and two of its dominant vectors, Anopheles darlingi, and Anopheles nuneztovari s.l. in northern South America using the species distribution modeling platform Maxent. Results from our land change modeling indicate that about 70,000 km2 of forest land would be lost by 2050 and 78,000 km2 by 2070 compared to 2010. The Maxent model predicted zones of relatively high habitat suitability for malaria and the vectors mainly within the Amazon and along coastlines. While areas with malaria are expected to decrease in line with current downward trends, both vectors are predicted to experience range expansions in the future. Elevation, annual precipitation and temperature were influential in all models both current and future. Human population mostly affected An. darlingi distribution while LULC changes influenced An. nuneztovari s.l. distribution. As the region tackles the challenge of malaria elimination, investigations such as this could be useful for planning and management purposes and aid in predicting and addressing potential impediments to elimination.
Policy review: Japan-Philippines Economic Partnership Agreement (JPEPA)--analysis of a failed nurse migration policy.

In 2008, the bilateral Japan-Philippines Economic Partnership Agreement took effect. Contained within this regional free trade agreement are unique provisions allowing exchange of Filipino nurses and healthcare workers to work abroad in Japan. Japan's increasing need for healthcare workers due to its aging demographic and the Philippines need for economic development could have led to shared benefits under the Japan-Philippines Economic Partnership Agreement. However, 4 years following program implementation, results have been disappointing, e.g., only 7% of candidates passing the programs requirements since 2009. These disappointing results represent a policy failure within the current Japan-Philippines Economic Partnership Agreement framework, and point to the need for reform. Hence, amending the current Japan-Philippines Economic Partnership Agreement structure by potentially adopting a USA based approach to licensure examinations and implementing necessary institutional and governance reform measures may be necessary to ensure beneficial healthcare worker migration for both countries.
Changes in satellite-derived spring vegetation green-up date and its linkage to climate in China from 1982 to 2010: a multimethod analysis.

The change in spring phenology is recognized to exert a major influence on carbon balance dynamics in temperate ecosystems. Over the past several decades, several studies focused on shifts in spring phenology; however, large uncertainties still exist, and one understudied source could be the method implemented in retrieving satellite-derived spring phenology. To account for this potential uncertainty, we conducted a multimethod investigation to quantify changes in vegetation green-up date from 1982 to 2010 over temperate China, and to characterize climatic controls on spring phenology. Over temperate China, the five methods estimated that the vegetation green-up onset date advanced, on average, at a rate of 1.3 ± 0.6 days per decade (ranging from 0.4 to 1.9 days per decade) over the last 29 years. Moreover, the sign of the trends in vegetation green-up date derived from the five methods were broadly consistent spatially and for different vegetation types, but with large differences in the magnitude of the trend. The large intermethod variance was notably observed in arid and semiarid vegetation types. Our results also showed that change in vegetation green-up date is more closely correlated with temperature than with precipitation. However, the temperature sensitivity of spring vegetation green-up date became higher as precipitation increased, implying that precipitation is an important regulator of the response of vegetation spring phenology to change in temperature. This intricate linkage between spring phenology and precipitation must be taken into account in current phenological models which are mostly driven by temperature.
ImVerde: Vertex-Diminished Random Walk for Learning Network Representation from Imbalanced Data

Imbalanced data widely exist in many high-impact applications. An example is in air traffic control, where among all three types of accident causes, historical accident reports with ‘personnel issues’ are much more than the other two types (‘aircraft issues’ and ‘environmental issues’) combined. Thus, the resulting data set of accident reports is highly imbalanced. On the other hand, this data set can be naturally modeled as a network, with each node representing an accident report, and each edge indicating the similarity of a pair of accident reports. Up until now, most existing work on imbalanced data analysis focused on the classification setting, and very little is devoted to learning the node representations for imbalanced networks. To bridge this gap, in this paper, we first propose Vertex-Diminished Random Walk (VDRW) for imbalanced network analysis. It is significantly different from the existing Vertex Reinforced Random Walk by discouraging the random particle to return to the nodes that have already been visited. This design is particularly suitable for imbalanced networks as the random particle is more likely to visit the nodes from the same class, which is a desired property for learning node representations. Furthermore, based on VDRW, we propose a semi-supervised network representation learning framework named ImVerde for imbalanced networks, where context sampling uses VDRW and the limited label information to create node-context pairs, and balanced-batch sampling adopts a simple under-sampling method to balance these pairs from different classes. Experimental results demonstrate that ImVerde based on VDRW outperforms stateof-the-art algorithms for learning network representations from imbalanced data.
Exploiting both Vertical and Horizontal Dimensions of Feature Hierarchy for Effective Recommendation

Feature hierarchy (FH) has proven to be effective to improve recommendation accuracy. Prior work mainly focuses on the influence of vertically affiliated features (i.e. child-parent) on user-item interactions. The relationships of horizontally organized features (i.e. siblings and cousins) in the hierarchy, however, has only been little investigated. We show in real-world datasets that feature relationships in horizontal dimension can help explain and further model user-item interactions. To fully exploit FH, we propose a unified recommendation framework that seamlessly incorporates both vertical and horizontal dimensions for effective recommendation. Our model further considers two types of semantically rich feature relationships in horizontal dimension, i.e. complementary and alternative relationships. Extensive validation on four real-world datasets demonstrates the superiority of our approach against the state of the art. An additional benefit of our model is to provide better interpretations of the generated recommendations.
A motivated definition of exploitation and exploration

This paper can be seen as an exercise in formal reasoning to provide a well-understood, unambiguous definition of the terms exploitation and exploration. It is not our aim to distill a common denominator from the current usage of the two terms in the literature — we refer to  for a detailed overview. Instead, we start from a simple algorithm framework and try to give a precise definition of exploitation and exploration in this context. Then we discuss these definitions in great depth. Population-based algorithms and algorithms with any sort of memory remain out of the scope of this paper, and will be considered in further contributions.
Zipf's and Benford's laws in Twitter hashtags

Social networks have transformed communication dramatically in recent years through the rise of new platforms and the development of a new language of communication. This landscape requires new forms to describe and predict the behaviour of users in networks. This paper presents an analysis of the frequency distribution of hashtag popularity in Twitter conversations. Our objective is to determine if these frequency distribution follow some well-known frequency distribution that many real-life sets of numerical data satisfy. In particular, we study the similarity of frequency distribution of hashtag popularity with respect to Zipf’s law, an empirical law referring to the phenomenon that many types of data in social sciences can be approximated with a Zipfian distribution. Additionally, we also analyse Benford’s law, is a special case of Zipf’s law, a common pattern about the frequency distribution of leading digits. In order to compute correctly the frequency distribution of hashtag popularity, we need to correct many spelling errors that Twitter’s users introduce. For this purpose we introduce a new filter to correct hashtag mistake based on string distances. The experiments obtained employing datasets of Twitter streams generated under controlled conditions show that Benford’s law and Zipf’s law can be used to model hashtag frequency distribution.
A discrete robust adaptive control of a tilt-rotor UAV for an enlarged flight envelope

This work presents the modeling and control of a tilt-rotor UAV with tail controlled surfaces for path tracking with improved forward flight performance. A nonlinear dynamic model is obtained through Euler-Lagrange formulation and linearized around a reference trajectory in order to obtain a linear parameter-varying model. The forward velocity is treated as an uncertain parameter, and the linearized system is represented as a set of polytopes with nonempty intersection regarding the forward velocity. Feedback gains are computed for each of the vertices of the polytopes using a discrete mixed control approach with pole placement constraints strategy. The resultant feedback gain, which is able to control the system inside a given polytope, is obtained using an adaptive law through an optimal convex combination of the vertices' gains. Finally, an adaptive mixing scheme is used to smoothly schedule the feedback gains between the polytopes.
Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling

Long-term situation prediction plays a crucial role for intelligent vehicles. A major challenge still to overcome is the prediction of complex downtown scenarios with multiple road users, e.g., pedestrians, bikes, and motor vehicles, interacting with each other. This contribution tackles this challenge by combining a Bayesian filtering technique for environment representation, and machine learning as long-term predictor. More specifically, a dynamic occupancy grid map is utilized as input to a deep convolutional neural network. This yields the advantage of using spatially distributed velocity estimates from a single time step for prediction, rather than a raw data sequence, alleviating common problems dealing with input time series of multiple sensors. Furthermore, convolutional neural networks have the inherent characteristic of using context information, enabling the implicit modeling of road user interaction. Pixel-wise balancing is applied in the loss function counteracting the extreme imbalance between static and dynamic cells. One of the major advantages is the unsupervised learning character due to fully automatic label generation. The presented algorithm is trained and evaluated on multiple hours of recorded sensor data and compared to Monte-Carlo simulation. Experiments show the ability to model complex interactions.
INDIAN MUSTARD BRASSICA JUNCEA L . MEDIATED PHYTOREMEDIATION OF LEAD

The contamination from heavy metals has risen during the last decade due to increase in Industrialization. This has led to a significant increase in health problems. Many of the known remediation techniques to remove heavy metal from soil are expensive, time consuming and environmentally destructive. Phytoremediation is an emerging technology for removal of heavy metals which is cost effective, and has aesthetic advantages and long term applicability. The present study aims at efficiently utilizing Brassica juncea L. to remove lead (Pb). The result of our study indicate that amount of lead in Indian mustard is increased with the amount of EDTA applied to the soil and maximum accumulation was achieved with 5mmol/kg of EDTA. On further increase in EDTA resulted in leaf necrosis and early shedding of leaves. Therefore EDTA at a concentration of 5mmol/kg was considered optimum for lead accumulation by Brassica juncea L.
PERCEPTION OF FACTORS THAT INFLUENCE STUDENTS' VOCATIONAL CHOICE OF SECRETARIAL STUDIES IN TERTIARY INSTITUTIONS IN EDO STATE OF NIGERIA.

This paper examines the perceived factors that influence students’ vocational choice of secretarial studies in tertiary institutions in Edo State of Nigeria. The factors investigated included parental, peer group, gender and interest. This is informed by the low enrolment into secretarial education programmes across the universities and college of education and the poor attitude of students with regard to their self worth in spite of the many job opportunities and career satisfaction offered by this all pervasive skill oriented vocational academic programme. The design of the study was descriptive with a stratified sample of 191 subjects randomly selected from a population of 447 students enrolled in secretarial studies programmes in public tertiary institutions in Edo State during the 2006/2007 academic session. A questionnaire was the instrument used to gather data from the field for analysis. Two research questions were raised and answered; while two hypotheses were formulated and tested. The results showed that there were variations in the perceived factors that influence students’ vocational choice of secretarial studies between male and female students; and also students from the universities and college of education differed significantly with regard to some of the factors that influence their choice. Based on this conclusions were drawn and recommendation made.
The Impact of Healthcare Workers Job Environment on Their Mental-emotional Health. Coping Strategies: The Case of a Local General Hospital

Workplace stress can influence healthcare professionals' physical and emotional well-being by curbing their efficiency and having a negative impact on their overall quality of life. The aim of the present study was to investigate the impact that work environment in a local public general hospital can have on the health workers' mental-emotional health and find strategies in order to cope with negative consequences. The study took place from July 2010 to October 2010. Our sample consisted of 200 healthcare professionals aged 21-58 years working in a 240-bed general hospital and the response rate was 91.36%). Our research protocol was first approved by the hospital's review board. A standardized questionnaire that investigates strategies for coping with stressful conditions was used. A standardized questionnaire was used in the present study Coping Strategies for Stressful Events, evaluating the strategies that persons employ in order to overcome a stressful situation or event. The questionnaire was first tested for validity and reliability which were found satisfactory (Cronbach's α=0.862). Strict anonymity of the participants was guaranteed. The SPSS 16.0 software was used for the statistical analysis. Regression analysis showed that health professionals' emotional health can be influenced by strategies for dealing with stressful events, since positive re-assessment, quitting and seeking social support are predisposing factors regarding the three first quality of life factors of the World Health Organization Quality of Life - BREF. More specifically, for the physical health factor, positive re-assessment (t=3.370, P=0.001) and quitting (t=-2.564, P=0.011) are predisposing factors. For the 'mental health and spirituality' regression model, positive re-assessment (t=5.528, P=0.000) and seeking social support (t=-1.991, P=0.048) are also predisposing factors, while regarding social relationships positive re-assessment (t=4.289, P=0.000) is a predisposing factor. According to our findings, there was a notable lack of workplace stress management strategies, which the participants usually perceive as a lack of interest on behalf of the management regarding their emotional state. Some significant factors for lowering workplace stress were found to be the need to encourage and morally reward the staff and also to provide them with opportunities for further or continuous education.