nils-herrmann/clean_scirepeval huggingface dataset:
Insecticide Resistance Alleles Affect Vector Competence of Anopheles gambiae s.s. for Plasmodium falciparum Field Isolates

The widespread insecticide resistance raises concerns for vector control implementation and sustainability particularly for the control of the main vector of human malaria, Anopheles gambiae sensu stricto. However, the extent to which insecticide resistance mechanisms interfere with the development of the malignant malaria parasite in its vector and their impact on overall malaria transmission remains unknown. We explore the impact of insecticide resistance on the outcome of Plasmodium falciparum infection in its natural vector using three An. gambiae strains sharing a common genetic background, one susceptible to insecticides and two resistant, one homozygous for the ace-1(R) mutation and one for the kdr mutation. Experimental infections of the three strains were conducted in parallel with field isolates of P. falciparum from Burkina Faso (West Africa) by direct membrane feeding assays. Both insecticide resistant mutations influence the outcome of malaria infection by increasing the prevalence of infection. In contrast, the kdr resistant allele is associated with reduced parasite burden in infected individuals at the oocyst stage, when compared to the susceptible strain, while the ace-1 (R) resistant allele showing no such association. Thus insecticide resistance, which is particularly problematic for malaria control efforts, impacts vector competence towards P. falciparum and probably parasite transmission through increased sporozoite prevalence in kdr resistant mosquitoes. These results are of great concern for the epidemiology of malaria considering the widespread pyrethroid resistance currently observed in Sub-Saharan Africa and the efforts deployed to control the disease.
Motorization for an Electric Scooter by Using Permanent-Magnet Machines Optimized Based on a Hybrid Metaheuristic Algorithm

This paper deals with the proposition of a permanent-magnet (PM) machine used to motorize an electric scooter. Several electrical machine configurations are discussed, and the best solution is chosen. The power density and the energetic performances of the chosen machine (with a fractional-slot configuration) are improved using a hybrid optimization algorithm, which combines simulated annealing (SA) and particle swarm optimization (PSO) techniques. The obtained results are numerically and experimentally validated to prove that the proposed solution suites the scooter application.
An improved wavelet based shock wave detector

In this paper, the detection of shock wave that generated by supersonic bullet is considered. A wavelet based multi-scale products method has been widely used for detection. However, the performance of method decreased at low signal-to-noise ratio (SNR). It is noted that the method does not consider the distribution of the signal and noise. Thus we analyze the method under the standard likelihood ratio test in this paper. It is found that the multi-scale product method is made in an assumption that is extremely restricted, just hold for a special noise condition. Based on the analysis, a general condition is considered for the detection. An improved detector under the standard likelihood ratio test is proposed. Monte Carlo simulations is conducted with simulated shock waves under additive white Gaussian noise. The result shows that this new detection algorithm outperforms the conventional detection algorithm.
Knapp: A Packet Processing Framework for Manycore Accelerators

High-performance network packet processing benefits greatly from parallel-programming accelerators such as Graphics Processing Units (GPUs). Intel Xeon Phi, a relative newcomer in this market, is a distinguishing platform because its x86-compatible vectorized architecture offers additional optimization opportunities. Its software stack exposes low-level communication primitives, enabling fine-grained control and optimization of offloading processes. Nonetheless, our microbenchmarks show that offloading APIs for Xeon Phi comes in short for combining low latency and high throughput for both I/O and computation. In this work, we exploit Xeon Phi's low-level threading mechanisms to design a new offloading framework, Knapp, and evaluate it using simplified IP routing applications. Knapp lays the ground for full exploitation of Xeon Phi as a packet processing framework.
Early androgen exposure and human gender development

During early development, testosterone plays an important role in sexual differentiation of the mammalian brain and has enduring influences on behavior. Testosterone exerts these influences at times when the testes are active, as evidenced by higher concentrations of testosterone in developing male than in developing female animals. This article critically reviews the available evidence regarding influences of testosterone on human gender-related development. In humans, testosterone is elevated in males from about weeks 8 to 24 of gestation and then again during early postnatal development. Individuals exposed to atypical concentrations of testosterone or other androgenic hormones prenatally, for example, because of genetic conditions or because their mothers were prescribed hormones during pregnancy, have been consistently found to show increased male-typical juvenile play behavior, alterations in sexual orientation and gender identity (the sense of self as male or female), and increased tendencies to engage in physically aggressive behavior. Studies of other behavioral outcomes following dramatic androgen abnormality prenatally are either too small in their numbers or too inconsistent in their results, to provide similarly conclusive evidence. Studies relating normal variability in testosterone prenatally to subsequent gender-related behavior have produced largely inconsistent results or have yet to be independently replicated. For studies of prenatal exposures in typically developing individuals, testosterone has been measured in single samples of maternal blood or amniotic fluid. These techniques may not be sufficiently powerful to consistently detect influences of testosterone on behavior, particularly in the relatively small samples that have generally been studied. The postnatal surge in testosterone in male infants, sometimes called mini-puberty, may provide a more accessible opportunity for measuring early androgen exposure during typical development. This approach has recently begun to be used, with some promising results relating testosterone during the first few months of postnatal life to later gender-typical play behavior. In replicating and extending these findings, it may be important to assess testosterone when it is maximal (months 1 to 2 postnatal) and to take advantage of the increased reliability afforded by repeated sampling.
Correction of Location and Orientation Errors in Electromagnetic Motion Tracking

We describe a method for calibrating an electromagnetic motion tracking device. Algorithms for correcting both location and orientation data are presented. In particular, we use a method for interpolating rotation corrections that has not previously been used in this context. This method, unlike previous methods, is rooted in the geometry of the space of rotations. This interpolation method is used in conjunction with Delaunay tetrahedralization to enable correction based on scattered data samples. We present measurements that support the assumption that neither location nor orientation errors are dependent on sensor orientation. We give results showing large improvements in both location and orientation errors. The methods are shown to impose a minimal computational burden.
Online analysis of information diffusion in twitter

The advent of social media has facilitated the study of information diffusion, user interaction and user influence over social networks. The research on analyzing information spreading focuses mostly on modeling, while analyses of real-life data have been limited to small, carefully cleaned datasets that are analyzed in an offline fashion. In this paper, we present an approach for online analysis of information diffusion in Twitter. We reconstruct so-called information cascades that model how information is being propagated from user to user from the stream of messages and the social graph. The results show that such an inference is feasible even on noisy, large-scale, rapidly produced data. We provide insights into the impact of incomplete data and the effect of different influence models on the cascades. The observed cascades show a significant amount of variety in scale and structure.
Believable Social and Emotional Agents.

One of the key steps in creating quality interactive drama is the ability to create quality interactive characters (or believable agents). Two important aspects of such characters will be that they appear emotional and that they can engage in social interactions. My basic approach to these problems has been to use a broad agent architecture and minimal amounts of modeling of other agent in the environment. This approach is based on an understanding of the artistic nature of the problem. To enable agent-builders (artists) to create emotional agents, I provide a general framework for building emotional agents, default emotion-processing rules, and discussion about how to create quality, emotional characters. My framework gets a lot of its power from being part of a broad agent architecture. The concept is simple: the agent will be emotionally richer if there are more things to have emotions about and more ways to express them. This reliance on breadth has also meant that I have been able to create simple emotion models that rely on perception and motivation instead of deep modeling of other agents and complex cognitive processing. To enable agent builders to create social behaviors for believable agents, I have designed a methodology that provides heuristics for incorporating personality into social behaviors and suggests how to model other agents in the environment. I propose an approach to modeling other agents that calls for limiting the amount of modeling of other agents to that which is sufficient to create the desired behavior. Using this technique, I have been able to build robust social behaviors that use surprisingly little representation. I have used this methodology to build a number of social behaviors, like negotiation and making friends. I have built three simulations containing seven agents to drive and test this work. I have also conducted user studies to demonstrate that these agents appear to be emotional and can engage in non-trivial social interactions while also being good characters with distinct personalities.
Insider Threat Security Reference Architecture

The Insider Threat Security Reference Architecture, ITSRA, provides an enterprise wide solution to insider threat. The architecture consists of four security layers - Business, Information, Data, and Application. Organizations should deploy and enforce controls at each layer in order to address insider attacks. Each layer does not function in isolation or independently of other layers. Rather, the correlation of indicators and application of controls across all four layers forms the crux of this approach. Based on empirical data consisting of over 600 cases of insider crimes, insider attacks proved successful in inflicting damage when an organization failed to implement adequate controls in any of three security principles - authorized access, acceptable use, and continuous monitoring. The ITSRA draws from existing best practices and standards as well as from analysis of these cases to provide actionable guidance for organizations to improve their posture against the insider threat.
A Critical Review of Recent Progress in Mid-Range Wireless Power Transfer

Starting from Tesla's principles of wireless power transfer a century ago, this critical review outlines recent magneto-inductive research activities on wireless power transfer with the transmission distance greater than the transmitter coil dimension. It summarizes the operating principles of a range of wireless power research into 1) the maximum power transfer and 2) the maximum energy efficiency principles. The differences and the implications of these two approaches are explained in terms of their energy efficiency and transmission distance capabilities. The differences between the system energy efficiency and the transmission efficiency are also highlighted. The review covers the two-coil systems, the four-coil systems, the systems with relay resonators and the wireless domino-resonator systems. Related issues including human exposure issues and reduction of winding resistance are also addressed. The review suggests that the use of the maximum energy efficiency principle in the two-coil systems is suitable for short-range rather than mid-range applications, the use of the maximum power transfer principle in the four-coil systems is good for maximizing the transmission distance, but is under a restricted system energy efficiency (<;50%); the use of the maximum energy efficiency principle in relay or domino systems may offer a good compromise for good system energy efficiency and transmission distance on the condition that relay resonators can be placed between the power source and the load.
Rating Image Aesthetics Using Deep Learning

This paper investigates unified feature learning and classifier training approaches for image aesthetics assessment . Existing methods built upon handcrafted or generic image features and developed machine learning and statistical modeling techniques utilizing training examples. We adopt a novel deep neural network approach to allow unified feature learning and classifier training to estimate image aesthetics. In particular, we develop a double-column deep convolutional neural network to support heterogeneous inputs, i.e., global and local views, in order to capture both global and local characteristics of images . In addition, we employ the style and semantic attributes of images to further boost the aesthetics categorization performance . Experimental results show that our approach produces significantly better results than the earlier reported results on the AVA dataset for both the generic image aesthetics and content -based image aesthetics. Moreover, we introduce a 1.5-million image dataset (IAD) for image aesthetics assessment and we further boost the performance on the AVA test set by training the proposed deep neural networks on the IAD dataset.
Penile lengthening and widening without grafting according to a modified 'sliding' technique.

To present the feasibility and safety of penile length and girth restoration based on a modified 'sliding' technique for patients with severe erectile dysfunction (ED) and significant penile shortening, with or without Peyronie's disease (PD).



Between January 2013 and January 2014, 143 patients underwent our modified 'sliding' technique for penile length and girth restoration and concomitant penile prosthesis implantation. It is based on three key elements: (i) the sliding manoeuvre for penile length restoration; (ii) potential complementary longitudinal ventral and/or dorsal tunical incisions for girth restoration; and (iii) closure of the newly created rectangular bow-shaped tunical defects with Buck's fascia only.



In all, 143 patients underwent the procedure. The causes of penile shortening and narrowing were: PD in 53.8%; severe ED with unsuccessful intracavernosal injection therapy in 21%; post-radical prostatectomy 14.7%; androgen-deprivation therapy, with or without brachytherapy or external radiotherapy, for prostate cancer in 7%; post-penile fracture in 2.1%; post-redo-hypospadias repair in 0.7%; and post-priapism in 0.7%. In patients with ED and PD, the mean (range) deviation of the penile axis was 45 (0-100)°. The mean (range) subjective penile shortening reported by patients was 3.4 (1-7) cm and shaft constriction was present in 53.8%. Malleable penile prostheses were used in 133 patients and inflatable penile prostheses were inserted in 10 patients. The median (range) follow-up was 9.7 (6-18) months. The mean (range) penile length gain was 3.1 (2-7) cm. No penile prosthesis infection caused device explantation. The average International Index of Erectile Function (IIEF) score increased from 24 points at baseline to 60 points at the 6-month follow-up.



Penile length and girth restoration based on our modified sliding technique is a safe and effective procedure. The elimination of grafting saves operative time and, consequently, decreases the infection risk and costs associated with surgery.
Analyzing User Preference for Social Image Recommendation

With the incredibly growing amount of multimedia data shared on the social media platforms, recommender systems have become an important necessity to ease users’ burden on the information overload. In such a scenario, extensive amount of heterogeneous information such as tags, image content, in addition to the user-to-item preferences, is extremely valuable for making effective recommendations. In this paper, we explore a novel hybrid algorithm termed STM, for image recommendation. STM jointly considers the problem of image content analysis with the users’ preferences on the basis of sparse representation. STM is able to tackle the challenges of highly sparse user feedbacks and cold-start problmes in the social network scenario. In addition, our model is based on the classical probabilistic matrix factorization and can be easily extended to incorporate other useful information such as the social relationships. We evaluate our approach with a newly collected 0.3 million social image data set from Flickr. The experimental results demonstrate that sparse topic modeling of the image content leads to more effective recommendations, , with a significant performance gain over the state-of-the-art alternatives.
Weight-averaged consistency targets improve semi-supervised deep learning results

The recently proposed Temporal Ensembling has achieved state-of-the-art results in several semi-supervised learning benchmarks. It maintains an exponential moving average of label predictions on each training example, and penalizes predictions that are inconsistent with this target. However, because the targets change only once per epoch, Temporal Ensembling becomes unwieldy when learning large datasets. To overcome this problem, we propose Mean Teacher, a method that averages model weights instead of label predictions. As an additional benefit, Mean Teacher improves test accuracy and enables training with fewer labels than Temporal Ensembling. Without changing the network architecture, Mean Teacher achieves an error rate of 4.35% on SVHN with 250 labels, outperforming Temporal Ensembling trained with 1000 labels. We also show that a good network architecture is crucial to performance. Combining Mean Teacher and Residual Networks, we improve the state of the art on CIFAR-10 with 4000 labels from 10.55% to 6.28%, and on ImageNet 2012 with 10% of the labels from 35.24% to 9.11%.
Real-time lane detection and tracking system using simple filter and Kalman filter

In this paper, we propose a real-time lane detection and tracking algorithm using a simple filter and Kalman filter to develop a Lane Departure Warning System(LDWS) that can be implemented in an embedded system. Our LDWS was realized on the I.MX6Q board, mounted on a test vehicle, and traveled about 1,000km to derive the experimental results. Experimental results show that our LDWS operates at 97% detection rate in the day time and 95% in the night time. The average processing time of the our LDWS is about 15frame per seconds.
A Review of Feature Selection Methods for Classification Problem

The Classification are carried out using various feature selection technique. The feature selection methods allows the classification to be carried out more accurately and efficiently. Feature selection is one of the leading trends in the research work going on. There are various feature selection methods which are used along with the classification methods. According to the application the most appropriate feature selection method is selection for selection the feature. The selected feature is then supplied to the classifier to carry out the classification of data. Here we study 6 different Feature selection method which are Document Frequency (DF), Mutual Information (MI), Information Gain (IG), CHI Square Statistics, and Bi – Normal Separation. These methods are used separately for the text classification or a combination of methods are used. Index Terms Feature Selection, Classification, Mutual Information, Information gain, CHI Square Statistics, Bi – Normal Separation, Text Classification. ________________________________________________________________________________________________________
Optimal Expectations and the Welfare Cost of Climate Variability

Uncertainty about the future is an important determinant of well-being, especially in developing countries where financial markets and other market failures result in ineffective insurance mechanisms. However, separating the effects of future uncertainty from realised events, and then measuring the impact of uncertainty on utility, presents a number of empirical challenges. This paper aims to address these issues and provides supporting evidence to show that increased climate variability (a proxy for future income uncertainty) reduces farmers’ subjective well-being, consistent with the theory of optimal expectations (Brunnermeier & Parker, 2005 AER), using panel data from rural Ethiopia and a new data set containing daily atmospheric parameters. The magnitude of our result indicates that a one standard deviation (7%) increase in climate variability has an equivalent effect on life satisfaction to a two standard deviation (1-2%) decrease in consumption. This effect is one of the largest determinants of life satisfaction in rural Ethiopia. . We are grateful to Allen Blackman, Jeffery Bookwalter, Gharad Bryan, Douglas Dalenberg, Paul Dolan, John Feddersen, Greer Gosnell, Cameron Hepburn, Derek Kellenberg, Peter Martinsson, Kyle Meng, Rob Metcalfe, Eric Neumayer, Jonathan Parker, Hendrik Wolff and seminar participants at the EfD 6th Annual Meeting, EAERE, the University of Oxford, London School of Economics, University of Montana, and Resources for the Future for helpful thoughts, comments and discussions. The first author would like to thank the Swedish International Development Agency (Sida) through the Environment for Development Initiative (EfD) at the University of Gothenburg, and the Swedish Research Council Formas through the Human Cooperation to Manage Natural Resources (COMMONS) programme for financial support. Part of this research was done while Alem was a Visiting Scholar at the Department of Agricultural and Resource Economics, University of California Berkeley. The second author would like to thank the ESRC Centre for Climate Change Economics and Policy and the Grantham Foundation for financial support. The data used in this article were collected by the University of Addis Ababa, the International Food Policy Research Institute (IFPRI), and the Centre for the Study of African Economies (CSAE). Funding for the ERHS survey was provided by the Economic and Social Research Council (ESRC), the Swedish International Development Agency (SIDA) and the United States Agency for International Development (USAID). All errors and omissions are our own.