nils-herrmann/clean_scirepeval huggingface dataset:
Smallholder agriculture in Africa An overview and implications for policy

Acknowledgements This paper has benefited from conversations and collaborations with colleagues, including most notably Stefan Dercon, Cheryl Doss, and Chris Udry. None of them has read this manuscript, however, and they are not responsible for the views expressed here. Steve Wiggins provided critical comments on the first draft of the document and persuaded me to rethink a number of points. The aim of the Natural Resources Group is to build partnerships, capacity and wise decision-making for fair and sustainable use of natural resources. Our priority in pursuing this purpose is on local control and management of natural resources and other ecosystems. The Institute of Development Studies (IDS) is a leading global Institution for international development research, teaching and learning, and impact and communications, based at the University of Sussex. Its vision is a world in which poverty does not exist, social justice prevails and sustainable economic growth is focused on improving human wellbeing. The Overseas Development Institute (ODI) is a leading independent think tank on international development and humanitarian issues. Its mission is to inspire and inform policy and practice which lead to the reduction of poverty, the alleviation of suffering and the achievement of sustainable livelihoods. Smallholder agriculture has long served as the dominant economic activity for people in sub-Saharan Africa, and it will remain enormously important for the foreseeable future. But the size of the sector does not necessarily imply that investments in the smallholder sector will yield high social benefits in comparison to other possible uses of development resources. Large changes could potentially affect the viability of smallholder systems, emanating from shifts in technology, markets, climate and the global environment. The priorities for development policy will vary across and within countries due to the highly heterogeneous nature of the smallholder sector.
Learning Beyond Human Expertise with Generative Models for Dental Restorations

Computer vision has advanced significantly that many discriminative approaches such as object recognition are now widely used in real applications. We present another exciting development that utilizes generative models for the mass customization of medical products such as dental crowns. In the dental industry, it takes a technician years of training to design synthetic crowns that restore the function and integrity of missing teeth. Each crown must be customized to individual patients, and it requires human expertise in a time-consuming and laborintensive process, even with computer assisted design software. We develop a fully automatic approach that learns not only from human designs of dental crowns, but also from natural spatial profiles between opposing teeth. The latter is hard to account for by technicians but important for proper biting and chewing functions. Built upon a Generative Adversarial Network architecture (GAN), our deep learning model predicts the customized crown-filled depth scan from the crown-missing depth scan and opposing depth scan. We propose to incorporate additional space constraints and statistical compatibility into learning. Our automatic designs exceed human technicians’ standards for good morphology and functionality, and our algorithm is being tested for production use.
Seasonality of reproduction in sheep

Unlike most domestic livestock species, sheep are widely known as an animal with marked seasonality of breeding activity. The annual cycle of daily photoperiod has been identified as the determinant factor of this phenomenon, while environmental temperature, nutritional status, social interactions, lambing date and lactation period are considered to modulate it. The aim of this paper is to review the current state of knowledge of the reproductive seasonality in sheep. Following general considerations concerning the importance of seasonal breeding as a reproductive strategy for the survival of species, the paper describes the manifestations of seasonality in both the ram and the ewe. Both determinant and modulating factors are developed and special emphasis is given to the neuroendocrine base of photoperiodic regulation of seasonal breeding. Other aspects such as the role of melatonin, the involvement of thyroid hormones and the concept of photorefractoriness are also reviewed.
Sleep Paralysis in Brazilian Folklore and Other Cultures: A Brief Review

Sleep paralysis (SP) is a dissociative state that occurs mainly during awakening. SP is characterized by altered motor, perceptual, emotional and cognitive functions, such as inability to perform voluntary movements, visual hallucinations, feelings of chest pressure, delusions about a frightening presence and, in some cases, fear of impending death. Most people experience SP rarely, but typically when sleeping in supine position; however, SP is considered a disease (parasomnia) when recurrent and/or associated to emotional burden. Interestingly, throughout human history, different peoples interpreted SP under a supernatural view. For example, Canadian Eskimos attribute SP to spells of shamans, who hinder the ability to move, and provoke hallucinations of a shapeless presence. In the Japanese tradition, SP is due to a vengeful spirit who suffocates his enemies while sleeping. In Nigerian culture, a female demon attacks during dreaming and provokes paralysis. A modern manifestation of SP is the report of "alien abductions", experienced as inability to move during awakening associated with visual hallucinations of aliens. In all, SP is a significant example of how a specific biological phenomenon can be interpreted and shaped by different cultural contexts. In order to further explore the ethnopsychology of SP, in this review we present the "Pisadeira", a character of Brazilian folklore originated in the country's Southeast, but also found in other regions with variant names. Pisadeira is described as a crone with long fingernails who lurks on roofs at night and tramples on the chest of those who sleep on a full stomach with the belly up. This legend is mentioned in many anthropological accounts; however, we found no comprehensive reference on the Pisadeira from the perspective of sleep science. Here, we aim to fill this gap. We first review the neuropsychological aspects of SP, and then present the folk tale of the Pisadeira. Finally, we summarize the many historical and artistic manifestations of SP in different cultures, emphasizing the similarities and differences with the Pisadeira.
Bayesian Affect Control Theory

Affect Control Theory is a mathematical representation of the interactions between two persons, in which it is posited that people behave in a way so as to minimize the amount of deflection between their cultural emotional sentiments and the transient emotional sentiments that are created by each situation. Affect Control Theory presents a maximum likelihood solution in which optimal behaviours or identities can be predicted based on past interactions. Here, we formulate a probabilistic and decision theoretic model of the same underlying principles, and show this to be a generalisation of the basic theory. The model is more expressive than the original theory, as it can maintain multiple hypotheses about behaviours and identities simultaneously as a probability distribution. This allows the model to generate affectively believable interactions with people by learning about their identity and predicting their behaviours. We demonstrate this generalisation with a set of simulations. We then show how our model can be used as an emotional "plug-in" for systems that interact with humans. We demonstrate human-interactive capability by building a simple intelligent tutoring application and pilot-testing it in an experiment with 20 participants.
RemixNet: Generative Adversarial Networks for Mixing Multiple Inputs

Generative Adversarial Networks (GANs) are a class of Artificial Neural Network architectures for generating realistic data distributions from a small number of latent variables. The latent variables are typically drawn IID from a Normal distribution and fed as input. Empirically, it has been shown that GANs are able to produce realistic images from as few as 100 latent variables. While numerous studies have focused on modifying the original GAN architecture, few have explored the structure of the input latent variables. In this study we propose placing structure on the input for aiding GANs to converge faster, lead insight into the structure of the underlying data distribution, and produce potentially more creative outputs. To achieve a GAN with such constraints, we call our proposed architecture RemixNet. In RemixNet the latent variables are a result of multiple draws from our existing dataset and "remixed" into a new distribution. This idea of combining existing data into new data is inspired from the artistic and musical practice of collage making and remixes. For example, an artist may take an existing recording of music, modify each with a filter and combine to form a new composition. We show several experiments on the MNIST dataset to gain some intuition on various RemixNet architectures. We also compare empirically RemixNet with GAN and show that the RemixNet architecture converges significantly faster to realistic images.
ELEMENT DETECTION IN JAPANESE COMIC BOOK PANELS

iv ABSTRACT Element Detection in Japanese Comic Book Panels Toshihiro Kuboi Comic books are a unique and increasingly popular form of entertainment combining visual and textual elements of communication. This work pertains to making comic books more accessible. Specifically, this paper explains how we detect elements such as speech bubbles present in Japanese comic book panels. Some applications of the work presented in this paper are automatic detection of text and its transformation into audio or into other languages. Automatic detection of elements can also allow reasoning and analysis at a deeper semantic level than what's possible today. Our approach uses an expert system and a machine learning system. The expert system process information from images and inspires feature sets which help train the machine learning system. The expert system detects speech bubbles based on heuristics. The machine learning system uses machine learning algorithms. Specifically, Naive Bayes, Maximum Entropy, and support vector machine are used to detect speech bubbles. The algorithms are trained in a fully-supervised way and a semi-supervised way. Both the expert system and the machine learning system achieved high accuracy. We are able to train the machine learning algorithms to detect speech bubbles just as accurately as the expert system. We also applied the same approach to eye detection of characters in the panels, and are able to detect majority of the eyes but with low precision. However, we are able to improve the performance of our eye detection system significantly by combining the SVM and either the Naive Bayes or the AdaBoost classifiers. v ACKNOWLEDGMENTS This thesis is inspired by Eriq Augustine's original project on the machine translation of Japanese comic books. I would like to thank Eriq for his guidance and assistance on the selection of this thesis topic.
Modified rectifier circuit for high efficiency and low power RF energy harvester

In this work, a passive rectifier circuit is presented, which is operating at 868 MHz. It allows energy harvesting from low power RF waves with a high efficiency. It consists of a novel multiplier circuit design and high quality components to reduce parasitic effects, losses and reaches a low startup voltage. Using lower capacitor rises up the switching speed of the whole circuit. An inductor L serves to store energy in a magnetic field during the negative cycle wave and returns it during the positive one. A low pass filter is arranged in cascade with the rectifier circuit to reduce ripple at high frequencies and to get a stable DC signal. A 50 kΩ load is added at the output to measure the output power and to visualize the behavior of the whole circuit. Simulation results show an outstanding potential of this RF-DC converter witch has a relative high sensitivity beginning with -40 dBm.
A survey on wearable devices used to assist the visual impaired user navigation in outdoor environments

In this paper we introduce a comprehensive survey of wearable systems designed to assist the visual impaired users navigation in everyday life outdoor scenarios. We focus on presenting the main advantages and limitations of each technique in effort to inform the scientific community about the progress in the area of assistive devices and also offer users a review about the capabilities of each system. Various performance parameters are introduced in order to classify different systems by giving qualitative and quantitative measures for evaluation. At the end of the study conclusions are presented along with some perspectives for future work and development.
Automatic system for facial expression recognition based histogram of oriented gradient and normalized cross correlation

In this paper we built an automatic system for performance reviews of a new approach of facial expression recognition FER which is essentially based on Histogram of Oriented Gradient and Normalized Cross Correlation. The performance was evaluated by varying two parameters: face resolution and colour space of images. Results show that good recognition can be attained using low resolution, in particular 64 by 64 pixels in RGB images.
Explicit Reflection in Prolog-Tutor

This paper describes a reflection-based approach for open learner modeling (OLM). Tutoring dialogues are used by learners to explicitly reveal their own knowledge state to themselves. Dewey's theory of reflective thinking is used to create tutorial strategies which govern these dialogues. Drake's specification of critical thinking, associated to a defined set of skills, is used to define tutoring tactics implementing these strategies. The main contribution of this approach to OLM is that it provides a set of principled and reusable tutorial strategies and tactics to promote reflection, as they are based on domain independent theories. Furthermore, an evaluation of such a principled approach to OLM is straightforward in certain cases, as it refers to theories which already provide evaluation criteria. The approach is integrated in Prolog-Tutor, an existing intelligent tutoring system for Logic Programming. This paper presents a qualitative study of the resulting system, based on think-aloud protocols. A result analysis reveals that explicitly fostering reflection supports reflection based OLM and provides landmarks to explain its manifestations. However, the results also suggest that this openness may be less helpful when used by learners who have already honed a high level of proficiency in logic programming.
Bilhemia following transjugular intrahepatic portosystemic shunt placement (Tips): liver transplantation as a rescue procedure - case report.

e múltiplos episódios de sangramento de varizes esofágicas e gástricas, passou por um procedimento de TIPS para controlar a hemorragia gastroesophaneal refratária e como uma ponte para transplante de fígado. Na admissão, ele estava clinicamente estável e tinha estágio final da doença hepática pontuação de 13 e bilirrubina sérica total inicial de 3,7 mg/dl. O procedimento TIPS foi realizada através da veia jugular interna direita, usando a padronização9. O stent selecionado e disponível era de metal autoexpansível Wallstent stent 10 x 68 mm (Boston Scientific Corporation, MA, EUA), que foi devidamente implantado no fígado, criando um shunt entre a veia hepática direita e um dos ramos esquerdos da veia porta. O trajeto pós-stent foi dilatada com balão de 10 mm e venograma portal de controle demonstrou patência de shunt e não opacificação significativa da circulação colateral venosa. Houve redução da pressão venosa portal de 26-16 mm Hg, e do gradiente de pressão portosistêmico 19-9 mmHg. O procedimento transcorreu sem intercorrências e paciente permaneceu no hospital para observação. Três dias depois ele apresentou icterícia súbita sem quaisquer sinais de insuficiência hepática (encefalopatia) ou sepse (febre ou hipotensão). Neste momento, os exames mostraram bilurribina nível total de 41,6 mg/dl (bilirrubina direta de 28,1 mg/dl), a relação internacional de 1/2, fosfatase alcalina de 151 UI/l, alanina aminotransferase de 60 UI/l, de aspartato aminotransferase 104 UI/l, de creatinina de 1,0 mg/dl e contagem de leucócitos totais de 6,800/ml. Doppler do fígado mostrou stent adequado, permeabilidade e fluxo anterógrado, sem evidência de dilatação das vias biliares. Tomografia computadorizada e angiografia abdominais foram realizadas e não forneceram qualquer informação adicional. Uma semana depois, o paciente estava clinicamente inalterado, com exceção de icterícia piorada. Não havia nenhuma evidência de infecção, ou encefalopatia ou hemobilia. Apesar dos testes de laboratório não serem INTRODUÇÃO
A phenomenological model of EEG based on the dynamics of a stochastic Duffing-van der Pol oscillator network

In this work, we propose a novel phenomenological model of the EEG signal based on the dynamics of a coupled Duffing-van der Pol oscillator network. An optimization scheme is adopted to match data generated from the model with clinically obtained EEG data from subjects under resting eyes-open (EO) and eyes-closed (EC) conditions. It is shown that a coupled system of two Duffing-van der Pol oscillators with optimized parameters yields signals with characteristics that match those of the EEG in both the EO and EC cases. The results, which are reinforced using statistical analysis, show that the EEG recordings under EC and EO resting conditions are clearly distinct realizations of the same underlying model occurring due to parameter variations with qualitatively different nonlinear dynamic characteristics. In addition, the interplay between noise and nonlinearity is addressed and it is shown that, for appropriately chosen values of noise intensity in the model, very good agreement exists between the model output and the EEG in terms of the power spectrum as well as Shannon entropy. In summary, the results establish that an appropriately tuned stochastic coupled nonlinear oscillator network such as the Duffing-van der Pol system could provide a useful framework for modeling and analysis of the EEG signal. In turn, design of algorithms based on the framework has the potential to positively impact the development of novel diagnostic strategies for brain injuries and disorders.
Decoding sequential finger movements from preparatory activity in higher-order motor regions: a functional magnetic resonance imaging multi-voxel pattern analysis.

Performing a complex sequential finger movement requires the temporally well-ordered organization of individual finger movements. Previous behavioural studies have suggested that the brain prepares a whole sequence of movements as a single set, rather than the movements of individual fingers. However, direct neuroimaging support for this hypothesis is lacking and, assuming it to be true, it remains unclear which brain regions represent the information of a prepared sequence. Here, we measured brain activity with functional magnetic resonance imaging while 14 right-handed healthy participants performed two types of well-learned sequential finger movements with their right hands. Using multi-voxel pattern analysis, we examined whether the types of the forthcoming sequence could be predicted from the preparatory activities of nine regions of interest, which included the motor, somatosensory and posterior parietal regions in each hemisphere, bilateral visual cortices, cerebellum and basal ganglia. We found that, during preparation, the activity of the contralateral motor regions could predict which of the two sequences would be executed. Further detailed analysis revealed that the contralateral dorsal premotor cortex and supplementary motor area were the key areas that contributed to the prediction consistently across participants. These contrasted with results from execution-related brain activity where a performed sequence was successfully predicted from the activities in the broad cortical sensory-motor network, including the bilateral motor, parietal and ipsilateral somatosensory cortices. Our study supports the hypothesis that temporary well-organized sequences of movements are represented as a set in the brain, and that preparatory activity in higher-order motor regions represents information about upcoming motor actions.
Analysis of road accidents in India using data mining classification algorithms

Classification is a model finding process which is used for segmenting the data into different classes based on some constraints. This work analyzes the road accidents in India data set using classification algorithms namely linear regression, logistic regression, decision tree, SVM, Naïve Bayes, KNN, Random Forest and gradient boosting algorithm. Performance measures used are accuracy, error rate and execution time. This analysis is done in R data mining tool. The performance of KNN is better than other algorithms.
Bringing light into the dark: effects of compression clothing on performance and recovery.

To assess original research addressing the effect of the application of compression clothing on sport performance and recovery after exercise, a computer-based literature research was performed in July 2011 using the electronic databases PubMed, MEDLINE, SPORTDiscus, and Web of Science. Studies examining the effect of compression clothing on endurance, strength and power, motor control, and physiological, psychological, and biomechanical parameters during or after exercise were included, and means and measures of variability of the outcome measures were recorded to estimate the effect size (Hedges g) and associated 95% confidence intervals for comparisons of experimental (compression) and control trials (noncompression). The characteristics of the compression clothing, participants, and study design were also extracted. The original research from peer-reviewed journals was examined using the Physiotherapy Evidence Database (PEDro) Scale. Results indicated small effect sizes for the application of compression clothing during exercise for short-duration sprints (10-60 m), vertical-jump height, extending time to exhaustion (such as running at VO2max or during incremental tests), and time-trial performance (3-60 min). When compression clothing was applied for recovery purposes after exercise, small to moderate effect sizes were observed in recovery of maximal strength and power, especially vertical-jump exercise; reductions in muscle swelling and perceived muscle pain; blood lactate removal; and increases in body temperature. These results suggest that the application of compression clothing may assist athletic performance and recovery in given situations with consideration of the effects magnitude and practical relevance.
Learning deep representations for semantic image parsing: a comprehensive overview

Semantic image parsing, which refers to the process of decomposing images into semantic regions and constructing the structure representation of the input, has recently aroused widespread interest in the field of computer vision. The recent application of deep representation learning has driven this field into a new stage of development. In this paper, we summarize three aspects of the progress of research on semantic image parsing, i.e., category-level semantic segmentation, instance-level semantic segmentation, and beyond segmentation. Specifically, we first review the general frameworks for each task and introduce the relevant variants. The advantages and limitations of each method are also discussed. Moreover, we present a comprehensive comparison of different benchmark datasets and evaluation metrics. Finally, we explore the future trends and challenges of semantic image parsing.