nils-herrmann/clean_scirepeval huggingface dataset:
UAV autopilot controllers test platform using Matlab/Simulink and X-Plane

Presently there is vast interest in UAV (Unmanned Aerial Vehicle) development given its civilian and military applications. One of the main UAV components is the autopilot system. Its development invariably demands several lab simulations and field tests. Generally after an UAV crash few parts remain usable. Thus, before embedding an autopilot system, it has to be exhaustively lab tested. With educational and research purposes in autopilot control systems development area, a test platform is herein proposed. It employs Matlab/Simulink to run the autopilot controller under test, the flight simulator X-Plane with the aircraft to be commanded, a microcontroller to command model aircraft flight control surfaces and a servo to drive these control surfaces. These resources are interconnected through data communication buses. So that, the autopilot controller designed on Matlab/Simulink is tested by controlling an aircraft on X-Plane. The inputs given to the aircraft flight control surfaces in the X-Plane are simultaneously sent to the microcontroller which translates these commands into effective servo movement control. Through this platform, designed autopilot systems can be applied into models similar to real aircraft minimizing risks and increasing flexibility for design changes. As study case, tests results from a roll attitude autopilot system are presented.
A Photovoltaic Array Simulation Model for Matlab-Simulink GUI Environment

A photovoltaic array (PVA) simulation model to be used in Matlab-Simulink GUI environment is developed and presented in this paper. The model is developed using basic circuit equations of the photovoltaic (PV) solar cells including the effects of solar irradiation and temperature changes. The new model was tested using a directly coupled dc load as well as ac load via an inverter. Test and validation studies with proper load matching circuits are simulated and results are presented here.
Dual n-back training increases the capacity of the focus of attention.

Working memory (WM) training has been reported to benefit abilities as diverse as fluid intelligence (Jaeggi et al.,  2008) and reading comprehension (Chein & Morrison, Psychonomic Bulletin & Review, 17:193-199, 2010), but transfer is not always observed (for reviews, see Morrison & Chein, Psychonomics Bulletin & Review, 18:46-60, 2011; Shipstead et al.,  Bulletin, 138:628-654, 2012). In contrast, recent WM training studies have consistently reported improvement on the trained tasks. The basis for these training benefits has received little attention, however, and it is not known which WM components and/or processes are being improved. Therefore, the goal of the present study was to investigate five possible mechanisms underlying the effects of adaptive dual n-back training on working memory (i.e., improvements in executive attention, updating, and focus switching, as well as increases in the capacity of the focus of attention and short-term memory). In addition to a no-contact control group, the present study also included an active control group whose members received nonadaptive training on the same task. All three groups showed significant improvements on the n-back task from pretest to posttest, but adaptive training produced larger improvements than did nonadaptive training, which in turn produced larger improvements than simply retesting. Adaptive, but not nonadaptive, training also resulted in improvements on an untrained running span task that measured the capacity of the focus of attention. No other differential improvements were observed, suggesting that increases in the capacity of the focus of attention underlie the benefits of adaptive dual n-back training.
TimeWarp: interactive time travel with a mobile mixed reality game

Mobile location-aware applications have become quite popular across a range of new areas such as pervasive games and mobile edutainment applications. However it is only recently, that approaches have been presented which combine gaming and education with mobile Augmented Reality systems. However they typically lack a close crossmedia integration of the surroundings, and often annotate or extend the environment rather than modifying and altering it.
 In this paper we present a mobile outdoor mixed reality game for exploring the history of a city in the spatial and the temporal dimension. We introduce the design and concept of the game and present a universal mechanism to define and setup multi-modal user interfaces for the game challenges. Finally we discuss the results of the user tests.
Update on nasopharyngeal carcinoma.

The most common type of nasopharyngeal tumor is nasopharyngeal carcinoma. The etiology is multifactorial with race, genetics, environment and Epstein-Barr virus (EBV) all playing a role. While rare in Caucasian populations, it is one of the most frequent nasopharyngeal cancers in Chinese, and has endemic clusters in Alaskan Eskimos, Indians, and Aleuts. Interestingly, as native-born Chinese migrate, the incidence diminishes in successive generations, although still higher than the native population. EBV is nearly always present in NPC, indicating an oncogenic role. There are raised antibodies, higher titers of IgA in patients with bulky (large) tumors, EBERs (EBV encoded early RNAs) in nearly all tumor cells, and episomal clonal expansion (meaning the virus entered the tumor cell before clonal expansion). Consequently, the viral titer can be used to monitor therapy or possibly as a diagnostic tool in the evaluation of patients who present with a metastasis from an unknown primary. The effect of environmental carcinogens, especially those which contain a high levels of volatile nitrosamines are also important in the etiology of NPC. Chinese eat salted fish, specifically Cantonese-style salted fish, and especially during early life. Perhaps early life (weaning period) exposure is important in the ‘‘two-hit’’ hypothesis of cancer development. Smoking, cooking, and working under poor ventilation, the use of nasal oils and balms for nose and throat problems, and the use of herbal medicines have also been implicated but are in need of further verification. Likewise, chemical fumes, dusts, formaldehyde exposure, and radiation have all been implicated in this complicated disorder. Various human leukocyte antigens (HLA) are also important etiologic or prognostic indicators in NPC. While histocompatibility profiles of HLA-A2, HLA-B17 and HLA-Bw46 show increased risk for developing NPC, there is variable expression depending on whether they occur alone or jointly, further conferring a variable prognosis (B17 is associated with a poor and A2B13 with a good prognosis, respectively).
Automation of the CHARMM General Force Field (CGenFF) II: Assignment of Bonded Parameters and Partial Atomic Charges

Molecular mechanics force fields are widely used in computer-aided drug design for the study of drug candidates interacting with biological systems. In these simulations, the biological part is typically represented by a specialized biomolecular force field, while the drug is represented by a matching general (organic) force field. In order to apply these general force fields to an arbitrary drug-like molecule, functionality for assignment of atom types, parameters, and partial atomic charges is required. In the present article, algorithms for the assignment of parameters and charges for the CHARMM General Force Field (CGenFF) are presented. These algorithms rely on the existing parameters and charges that were determined as part of the parametrization of the force field. Bonded parameters are assigned based on the similarity between the atom types that define said parameters, while charges are determined using an extended bond-charge increment scheme. Charge increments were optimized to reproduce the charges on model compounds that were part of the parametrization of the force field. A "penalty score" is returned for every bonded parameter and charge, allowing the user to quickly and conveniently assess the quality of the force field representation of different parts of the compound of interest. Case studies are presented to clarify the functioning of the algorithms and the significance of their output data.
Environmentally friendly fertilizers: A review of materials used and their effects on the environment.

Fertilizer plays an important role in maintaining soil fertility, increasing yields and improving harvest quality. However, a significant portion of fertilizers are lost, increasing agricultural cost, wasting energy and polluting the environment, which are challenges for the sustainability of modern agriculture. To meet the demands of improving yields without compromising the environment, environmentally friendly fertilizers (EFFs) have been developed. EFFs are fertilizers that can reduce environmental pollution from nutrient loss by retarding, or even controlling, the release of nutrients into soil. Most of EFFs are employed in the form of coated fertilizers. The application of degradable natural materials as a coating when amending soils is the focus of EFF research. Here, we review recent studies on materials used in EFFs and their effects on the environment. The major findings covered in this review are as follows: 1) EFF coatings can prevent urea exposure in water and soil by serving as a physical barrier, thereby reducing the urea hydrolysis rate and decreasing nitrogen oxide (NOx) and dinitrogen (N2) emissions, 2) EFFs can increase the soil organic matter content, 3) hydrogel/superabsorbent coated EFFs can buffer soil acidity or alkalinity and lead to an optimal pH for plants, and 4) hydrogel/superabsorbent coated EFFs can improve water-retention and water-holding capacity of soil. In conclusion, EFFs play an important role in enhancing nutrients efficiency and reducing environmental pollution.
Filter technologies for converged RF-frontend architectures: SAW, BAW and beyond

Multi-band/multi-system mobile phones require a complex RF-frontend architecture. Complexity has increased to a point where adding switches and whole signal branches for an additional band is no longer cost effective. Alternative concepts involve ‘converged’ power amplifiers and switching concepts supporting those. Filters and duplexers play a key role in converged architectures and their requirements will be reviewed. Challenges that arise from combining filter functions for different wireless standards will be pointed out. Existing RF-filter technologies are based on high-Q acoustic resonators realized either in Surface Acoustic Wave (SAW) or Bulk-Acoustic-Wave (BAW) technology and do not allow changing the frequency characteristics on the fly. Concepts for tunable RF-filters - pursuing the ‘holy grail’ - will be discussed and an overview on the status of this matter will be presented.
Intent Communication between Autonomous Vehicles and Pedestrians

When pedestrians encounter vehicles, they typically stop and wait for a signal from the driver to either cross or wait. What happens when the car is autonomous and there isn’t a human driver to signal them? This paper seeks to address this issue with an intent communication system (ICS) that acts in place of a human driver. This intent system has been developed to take into account the psychology behind what pedestrians are familiar with and what they expect from machines. The system integrates those expectations into the design of physical systems and mathematical algorithms. The goal of the system is to ensure that communication is simple, yet effective without leaving pedestrians with a sense of distrust in autonomous vehicles. To validate the ICS, two types of experiments have been run: field tests with an autonomous vehicle to determine how humans actually interact with the ICS and simulations to account for multiple potential behaviors.The results from both experiments show that humans react positively and more predictably when the intent of the vehicle is communicated compared to when the intent of the vehicle is unknown. In particular, the results from the simulation specifically showed a 142 percent difference between the pedestrian’s trust in the vehicle’s actions when the ICS is enabled and the pedestrian has prior knowledge of the vehicle than when the ICS is not enabled and the pedestrian having no prior knowledge of the vehicle.
Theory of Planned Behavior in School-Based Adolescent Problem Gambling Prevention: A Conceptual Framework

Given its serious implications for psychological and socio-emotional health, the prevention of problem gambling among adolescents is increasingly acknowledged as an area requiring attention. The theory of planned behavior (TPB) is a well-established model of behavior change that has been studied in the development and evaluation of primary preventive interventions aimed at modifying cognitions and behavior. However, the utility of the TPB has yet to be explored as a framework for the development of adolescent problem gambling prevention initiatives. This paper first examines the existing empirical literature addressing the effectiveness of school-based primary prevention programs for adolescent gambling. Given the limitations of existing programs, we then present a conceptual framework for the integration of the TPB in the development of effective problem gambling preventive interventions. The paper describes the TPB, demonstrates how the framework has been applied to gambling behavior, and reviews the strengths and limitations of the model for the design of primary prevention initiatives targeting adolescent risk and addictive behaviors, including adolescent gambling.
A Novel Multi-Task Tensor Correlation Neural Network for Facial Attribute Prediction

Face multi-attribute prediction benefits substantially from multi-task learning (MTL), which learns multiple face attributes simultaneously to achieve shared or mutually related representations of different attributes. The most widely used MTL convolutional neural network is heuristically or empirically designed by sharing all of the convolutional layers and splitting at the fully connected layers for task-specific losses. However, it is improper to view all low and midlevel features for different attributes as being the same, especially when these attributes are only loosely related. In this paper, we propose a novel multi-attribute tensor correlation neural network (MTCN) for face attribute prediction. The structure shares the information in low-level features (e.g., the first two convolutional layers) but splits that in high-level features (e.g., from the third convolutional layer to the fully connected layer). At the same time, during high-level feature extraction, each subnetwork (e.g., AgeNet, Gender-Net, ..., and Smile-Net) excavates closely related features from other networks to enhance its features. Then, we project the features of the C9 layers of the finetuned subnetworks into a highly correlated space by using a novel tensor correlation analysis algorithm (NTCCA). The final face attribute prediction is made based on the correlation matrix. Experimental results on benchmarks with multiple face attributes (CelebA and LFWA) show that the proposed approach has superior performance compared to state-of-the-art methods.
A note on element-wise matrix sparsification via a matrix-valued Bernstein inequality

Given a matrix A ∈ R, we present a simple, element-wise sparsification algorithm that zeroes out all sufficiently small elements of A and then retains some of the remaining elements with probabilities proportional to the square of their magnitudes. We analyze the approximation accuracy of the proposed algorithm using a recent, elegant non-commutative Bernstein inequality, and compare our bounds with all existing (to the best of our knowledge) elementwise matrix sparsification algorithms.
Fast recommendation on latent collaborative relations

One important property of collaborative filtering recommender systems is that popular items are recommended disproportionately often because they provide extensive usage data and, thus, can be recommended to more users. Compared to popular products, the niches can be as economically attractive as mainstream fare for online retailers. The online retailers can stock virtually everything, and the number of available niche products exceeds the hits by several orders of magnitude. This work addresses accuracy, coverage and prediction time issues to propose a novel latent factor model called latent collaborative relations (LCR), which transforms the recommendation problem into a nearest neighbor search problem by using the proposed scoring function. We project users and items to the latent space, and calculate their similarities based on Euclidean metric. Additionally, the proposed model provides an elegant way to incorporate with locality sensitive hashing (LSH) to provide a fast recommendation while retaining recommendation accuracy and coverage. The experimental results indicate that the speedup is significant, especially when one is confronted with large-scale data sets. As for recommendation accuracy and coverage, the proposed method is competitive on three data sets.
Optimal Design of an Outer-Stator Magnetically Geared Permanent Magnet Machine

In this paper, a magnetically geared permanent magnet (MGPM) machine is proposed for the use in wind power generation. The structure layout, operating principles, and equivalent circuit of the MGPM machine are first described. Then, an efficient finite-element-method-based design optimization approach is formulated and implemented in the design of the machine. The optimized machine shows that a rated torque density close to 70 kNm/m3 can be achieved, which is significantly higher than that of the conventional electrical machines. Furthermore, a prototype machine based on the design is constructed and experimentally evaluated. The measured torque performance shows good agreement with the predicted one, but a significant difference exists between the measured and calculated efficiencies. This is mainly due to higher than the expected mechanical losses in the MGPM machine. Relevant conclusions are drawn based on the analysis of presented results and some key design-related aspects.
STRONGER LASOTA-YORKE INEQUALITY FOR ONE-DIMENSIONAL PIECEWISE EXPANDING TRANSFORMATIONS

For a large class of piecewise expanding C1,1 maps of the interval we prove the Lasota-Yorke inequality with a constant smaller than the previously known 2/ inf |τ ′|. Consequently, the stability results of Keller-Liverani [7] apply to this class and in particular to maps with periodic turning points. One of the applications is the stability of acim’s for a class of W-shaped maps. Another application is an affirmative answer to a conjecture of Eslami-Misiurewicz  regarding acim-stability of a family of unimodal maps.
ResilientFlow: Deployments of distributed control channel maintenance modules to recover SDN from unexpected failures

To protect SDN-enabled networks under large-scale, unexpected link failures, we propose ResilientFlow that deploys distributed modules called Control Channel Maintenance Module (CCMM) for every switch and controllers. The CCMMs makes switches able to maintain their own control channels, which are core and fundamental part of SDN. In this paper, we design, implement, and evaluate the ResilientFlow.
Phylogenetic analyses and comparative genomics of vitamin B6 (pyridoxine) and pyridoxal phosphate biosynthesis pathways.

Vitamin B6 in its active form pyridoxal phosphate is an essential coenzyme of many diverse enzymes. Biochemistry, enzymology and genetics of de novo vitamin B6 biosynthesis have been primarily investigated in Escherichia coli. Database searches revealed that the key enzymes involved in ring closure of the aromatic pyridoxin ring (PdxA; PdxJ) are present mainly in genomes of bacteria constituting the gamma subdivision of proteobacteria. The distribution of DXS, a transketolase-like enzyme involved in vitamin B6 biosynthesis as well as in thiamine and isoprenoid biosynthesis and the distribution of vitamin B6 modifying enzymes (PdxH: oxidase; PdxK: kinase) was also analyzed. These enzymes are also present in the genomes of animals. Two recent papers (Ehrenshaft et al., 1999, ; Osmani et al., 1999, ) show the involvement of an extremely conserved protein (a member of the UPF0019 or SNZ family) found in all three domains of life (bacteria, archaea, eukarya) in an alternative vitamin B6 biosynthesis pathway. Members of this family were previously identified as a stationary phase inducible protein in yeast, as an ethylene responsible protein in plants and in a marine sponge, as a singlet oxygen resistance protein in Cercospora nicotianae and as a cumene hydroperoxide and H2O2 inducible protein in Bacillus subtilis. In yeast, the SNZ protein interacts with another protein called SNO which also represents a member of a highly conserved protein family (called UPF0030 or SNO family). Phylogenetic trees for the DXS, PdxA, PdxJ, PdxH, PdxK, SNZ and SNO protein families are presented and possible implications of the two different vitamin B6 biosynthesis pathways in cellular metabolism are discussed. A radically different view of bacterial evolution (Gupta, 2000,  111-131) which proposes a linear rather than a treelike evolutionary relationship between procaryotic species indicates that the gamma subdivision of proteobacteria represents the most recently evolved bacterial lineage. This proposal might help to explain why the PdxA/PdxJ pathway is largely restricted to this subdivision.
A Comparative Analysis on indoor positioning Techniques and Systems

Positioning objects has been an important topic since it is needed to locate people, guide them to a certain place, and assist companies and organizations with their assets management. Several techniques and algorithms were proposed to solve the positioning problem and to enhance existing systems. In general, one of the several existing wireless standards such as Infrared, RFID, Bluetooth or Wi-Fi is chosen as the target standard for indoor positioning. This paper provides an overview of the existing wireless indoor positioning solutions and attempts to classify different techniques and systems. Comprehensive performance comparisons including accuracy, precision complexity, scalability, robustness, and cost are presented. Indoor Localization, Positioning techniques, Wireless Positioning Technologies.
Child face detection using age specific luminance invariant geometric descriptor

While considerable research have been conducted on age-wise age estimation using skin detection most often with other visual cues, relatively little research has looked closely at the subject. In this paper, we present a new framework for interpreting facial image patterns that can be employed in categorical age estimation. The aim is to propose a novel approach to investigate and implement a child face detection technique that is able to estimate age categorically — adult or child based on a new hybrid feature descriptor. The novel hybrid feature descriptor LIGD (the luminance invariant geometric descriptor) is composed of some low and high level features, which are found to be effective in characterizing the local appearance. In local appearance estimation, chromaticity, texture, and positional information of few facial visual cues can be employed simultaneously. Compared to the results published in a recent work, our proposed approach yields the highest precision and recall, and overall accuracy in recognition.