nils-herrmann/clean_scirepeval huggingface dataset:
Genetics of motor neuron disorders: new insights into pathogenic mechanisms
Venue: Nature Reviews Genetics
Year: 2009

The past few years have seen the identification of dozens of genes with causal roles in motor neuron diseases (MNDs) particularly for amyotrophic lateral sclerosis and hereditary spastic paraplegia. Although many additional MND genes remain to be identified, the accumulated genetic evidence has already provided new insights into MND pathogenesis, which adds to the well established involvement of superoxide dismutase 1 (SOD1) mutations. The pathways that have been recently implicated include those that affect RNA processing, axonal transport and mitochondrial function. The functional classes of MND genes identified so far are likely to aid the selection of high priority candidate genes for future investigation, including those for so called sporadic cases.
Energy aware Adaptive Multimedia for Game based e learning
Venue: 2014 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting
Year: 2014

Thanks to their motivational potential, video games have started to be increasingly used for e learning. However, as e learning gradually shifts towards mobile learning, there is a growing need for innovative techniques to deliver rich learning material such as educational games to resource constrained devices. In particular, the limited battery capacity of mobile devices stands out as a key issue that can significantly limit players' access to educational games. This paper proposes an Energy aware Adaptive Multimedia Game based E learning (EAMGBL) framework that aims to enable energy efficient educational games delivery to mobile devices over wireless networks. The framework builds on top of the idea to render the game on the server side and stream a recording of it to the player's device over the Internet. To reduce the mobile device energy consumption and enable the player to play for longer, the proposed framework proposes to adapt both the educational game elements as well as the game's recorded multimedia stream.
Scale for the Phase Diagram of Quantum Chromodynamics
Venue: Science
Year: 2011

The temperature scale for the breakdown of protons and neutrons can be determined from high energy ion collisions and calculations. Matter described by quantum chromodynamics (QCD) the theory of strong interactions, may undergo phase transitions when its temperature and the chemical potentials are varied. QCD at finite temperature is studied in the laboratory by colliding heavy ions at varying beam energies. We present a test of QCD in the nonperturbative domain through a comparison of thermodynamic fluctuations predicted in lattice computations with the experimental data of baryon number distributions in high energy heavy ion collisions. This study provides evidence for thermalization in these collisions and allows us to find the crossover temperature between normal nuclear matter and a deconfined phase called the quark gluon plasma. This value allows us to set a scale for the phase diagram of QCD.
Printed antenna design for cognitive radio application at 1.8 GHz and 2.35 GHz spectrum allocation
Venue: Asia Pacific Microwave Conference 2011
Year: 2011

Design of antenna for cognitive radio application is proposed in this paper. The design consists of two printed monopole antenna printed over FR4 substrate, sharing a common partial ground. The first antenna is the sensing antenna for spectrum sensing, having wideband characteristic and omnidirectional radiation pattern. The second one is the communicating antenna which is a frequency reconfigurable antenna with the existing switch on the antenna's structure. The measurement shows that the reconfigurable communicating antenna is working at 2.35 GHz when the switch is OFF and it is working at 1.8 GHz when the switch is ON.
A social approach of the individual decision making process by the search for a dominance structure: risk management by a business process manager
Year: 2007

We focus on the way a business process manager treats the risks affecting a supervised business process. A descriptive model of the individual decision making process is proposed. This model mainly results from the integration, in the model of decision making by the search for a dominance structure, of the social aspect of the business process manager activity. In order to do this, we adopt a constructive and constructivist approach concerning the definition by the business process manager of his representation of the risk treatment strategy that has to be implemented. RESUME. Nous nous interessons a la maniere dont le responsable d'un processus metier traite les risques affectant un processus metier non completement automatise. Un modele descriptif du processus individuel de decision est propose. Il resulte principalement de l'integration, dans le modele de decision base sur la recherche d'une structure de dominance, de la composante sociale de l'activite d'un responsable de processus. Nous adoptons pour cela une approche constructive et constructiviste concernant la definition par le responsable de processus de sa representation de la strategie de traitement du risque a mettre en oeuvre.
DESAFIOS DA LIDERANCA ESCOLAR DIANTE DA REFORMA DO ENSINO MEDIO.
Year: 2020

Este artigo apresenta o resultado da analise feita em documentos que abordam a Reforma do Ensino Medio, sua relacao com o decreto 2.208/17 e o desafio da lideranca frente a essa reforma para a educacao brasileira. Os dados foram obtidos a partir da analise documental e revisao bibliografica de autores que discutem acerca da origem e percursos da escola desde a antiguidade aos dias atuais, sua funcao como aparelho ideologico de dominacao e o possivel destino da Educacao Profissional e Tecnologica. Concluiu se que a atual conjuntura da educacao brasileira, esta a servico da classe dominante que por muitos anos detem o saber cientifico em posse da minoria. E que essa reforma constitui uma forma de manutencao e ampliacao da desigualdade na oferta do Ensino Medio e Profissional, pondo em risco o futuro do Ensino Medio Integrado e da formacao integral dos sujeitos pertencentes a classe popular.
Clinical performance of 68Ga PSMA 11 PET/MRI for the detection of recurrent prostate cancer following radical prostatectomy
Venue: European Journal of Nuclear Medicine and Molecular Imaging
Year: 2017

PurposeSensitive visualization of recurrent prostate cancer foci is a challenge in patients with early biochemical recurrence (EBR) The recently established 68Ga PSMA 11 PET/CT has significantly improved the detection rate with published values of up to 55% for patients with a serum PSA concentration between 0.2 0.5 ng/mL. The increased soft tissue contrast in the pelvis using simultaneous 68Ga PSMA 11 PET/MRI might further improve the detection rate in patients with EBR and low PSA values over PET/CT.MethodsWe retrospectively analyzed a cohort of 56 consecutive patients who underwent a 68Ga PSMA 11 PET/MRI for biochemical recurrence in our institution between April and December 2016 with three readers. Median PSA level was 0.99 ng/mL (interquartile range: 3.1 ng/mL) Detection of PSMA positive lesions within the prostate fossa, local and distant lymph nodes, bones, or visceral organs was recorded. Agreement among observers was evaluated with Fleiss's kappa (k).ResultsOverall, in 44 of 56 patients (78.6% PSMA positive lesions were detected. In four of nine patients (44.4% with a PSA 0.2 ng/mL, suspicious lesions were detected (two pelvic and one paraaortic lymph nodes, and two bone metastases) In eight of 11 patients (72.7% with a PSA between 0.2 and 0.5 ng/mL, suspicious lesions were detected (two local recurrences, six lymph nodes, and one bone metastasis) Five out of 20 patients with a PSA 0.5 ng/mL had extrapelvic disease. In 12 of 15 patients (80.0% with a PSA between 0.5 and 2.0 ng/mL, suspicious lesions were detected (four local recurrences, nine lymph nodes, and four bone metastases) In 20 of 21 patients (95.2% with a PSA >2.0 ng/mL, suspicious lesions were detected. The overall interreader agreement for cancer detection was excellent (k 0.796, CI 0.645 0.947).ConclusionsOur data show that 68Ga PSMA 11 PET/MRI has a high detection rate for recurrent prostate cancer even at very low PSA levels <0.5 ng/mL. Furthermore, even at those low levels extrapelvic disease can be localized in 25% of the cases and local recurrence alone is seen only in 10%
The use of web questionnaires in second language acquisition and bilingualism research
Year: 2010

The present article focuses on data collection through web questionnaires, as opposed to the traditional pen and paper method for research in second language acquisition and bilingualism. It is argued that web questionnaires, which have been used quite widely in psychology, have the advantage of reaching out to a larger and more diverse pool of potential participants, which may increase the ecological validity of the resulting database. After considering some issues raised in debates on the strengths and weaknesses of traditional approaches to data collection through questionnaires as opposed to web based questionnaires, we present two case studies of research designs based on online questionnaires, that is, the bilingualism and emotions questionnaire (Dewaele and Pavlenko, 2001/03) and the feelings questionnaire (Wilson, 2008) We reflect on the issue of participant self selection and conclude that the potential benefits of web based questionnaires can outweigh their limitations.
Taking Actions to Deal with Climate Change Risks and Opportunities: Harnessing Superordinate Identities to Promote Knowledge Transfer and Creation
Year: 2012

The reciprocal influence between climate change and corporations is undeniable. On the one hand, human actions have an irrefutable impact on the environment, one of the most dangerous examples being the correlation between increased carbon emissions and temperature extremes (Solomon, Plattner, Knutti, et al. 2009) As primary actors in the global economy, corporations represent a significant portion of human impact on the environment. Corporate carbon emissions, for example, result from activities including manufacturing, resource harvesting, business travel, and building occupancy (Environmental Information Administration, 2011) On the other hand, climate change influences corporations by increasing uncertainty, instability, and risk. Such risks include difficulties with crop production caused by temperature variation and fluctuations in water supply; shifts in regional weather patterns and heightened potential for natural disasters (Stern, 2007) and increased costs of heating, cooling, and maintaining air quality (Mickley, Jacob, and Field, 2004) The magnitude of this influence is so significant that the United States Securities and Exchange Commission has issued interpretive guidance for publicly held companies on disclosing to investors any serious risks that climate change poses to their operations (Securities and Exchange Commission, 2010)
Mediators of Improved Adherence to Infant Safe Sleep Using a Mobile Health Intervention
Venue: Pediatrics
Year: 2019

This mediation analysis was conducted to determine mediators of improvements in infant SS practices in an effective mHealth intervention.  To determine mediators of improvements in infant safe sleep (SS) practices in a mobile health intervention.  In a cluster randomized controlled trial, mothers received SS intervention or breastfeeding control videos for 60 days. Maternal responses about infant sleep position and location (outcomes) and mediators (attitudes, perceived social norms, and perceived control) from the theory of planned behavior were assessed. Intervention effects on mediators and association between mediators and outcomes were examined.  Of 1600 recruited, 1263 mothers participated. Mothers receiving SS videos were more likely to have positive attitudes and norms for supine sleep (attitudes: adjusted odds ratio [aOR] 2.35 [95% confidence interval (CI) 1.72 to 3.20] norms: aOR 1.75 [95% CI 1.27 to 2.42] and recommended sleep location (attitudes: aOR 1.91 [95% CI 1.54 to 2.36] norms: aOR 1.37 [95% CI 1.13 to 1.66] Positive attitudes and norms toward supine sleep and room sharing without bed sharing were associated with higher odds of both practices (supine: aOR 8.25 [95% CI 4.72 to 14.43] for positive attitudes and aOR 6.67 [95% CI 4.25 to 10.46] for norms; room sharing: aOR 7.14 [95% CI 5.35 to 9.53] for positive attitudes and aOR 4.44 [95% CI 3.03 to 6.51] for norms) Both positive attitudes and positive norms mediated the effect of the intervention.  The intervention achieved success in improving adherence to SS recommendations by changing maternal attitudes and norms about supine sleeping and room sharing without bed sharing. Recognition that these attitudes and norms appear to be the main drivers of mothers' choices regarding infant sleep practices should inform health messaging strategies to promote SS.
Deep learning for detecting robotic grasps
Venue: Int. J. Robotics Res.
Year: 2015

We consider the problem of detecting robotic grasps in an RGB D view of a scene containing objects. In this work, we apply a deep learning approach to solve this problem, which avoids time consuming hand design of features. This presents two main challenges. First, we need to evaluate a huge number of candidate grasps. In order to make detection fast and robust, we present a two step cascaded system with two deep networks, where the top detections from the first are re evaluated by the second. The first network has fewer features, is faster to run, and can effectively prune out unlikely candidate grasps. The second, with more features, is slower but has to run only on the top few detections. Second, we need to handle multimodal inputs effectively, for which we present a method that applies structured regularization on the weights based on multimodal group regularization. We show that our method improves performance on an RGBD robotic grasping dataset, and can be used to successfully execute grasps on two different robotic platforms.
Geomorphometric feature selection based on intrinsic dimension estimation
Year: 2019

The use of geomorphometric variables or, from a machine learning perspective, geomorphometric features, sometimes coupled with other remote sensing derived variables, is often adopted for the spatial prediction of geoenvironmental properties of interest (e.g. soil and geo engineering mapping) In other circumstances, geomorphometric features are analyzed for unsupervised approaches in the context of landscape classification and pattern recognition. The detection of the relevant features and the distinction between redundant and irrelevant features is crucial both for improving prediction accuracy as well as for reducing computational cost. Moreover, the detection of relevant features improves the interpretation of studied processes. In this short paper, the potentialities of a new feature selection algorithm are evaluated in a supervised learning problem, tested on ad hoc designed synthetic dataset. The feature selection algorithm adopted is a Sequential Forward Selection filter, based on a fractal measure of Intrinsic Dimension, relying on a generalization of the Morisita index. The synthetic data set, built from real topography, is characterized by challenging characteristics as for example a strong linear correlation between relevant features. The tests performed on the data set show that the algorithm correctly individuates the relevant features and the irrelevant ones. Moreover, the impact of subsampling on the feature selection algorithm has been tested, showing a stable response up to roughly the 10% of the original data set. The results of this preliminary study suggest that the algorithm is promising in the geomorphometric context and that it is worth to investigate further its applicability in geomorphometry.
Semi Paired Discrete Hashing: Learning Latent Hash Codes for Semi Paired Cross View Retrieval
Venue: IEEE Transactions on Cybernetics
Year: 2017

Due to the significant reduction in computational cost and storage, hashing techniques have gained increasing interests in facilitating large scale cross view retrieval tasks. Most cross view hashing methods are developed by assuming that data from different views are well paired, e.g. text image pairs. In real world applications, however, this fully paired multiview setting may not be practical. The more practical yet challenging semi paired cross view retrieval problem, where pairwise correspondences are only partially provided, has less been studied. In this paper, we propose an unsupervised hashing method for semi paired cross view retrieval, dubbed semi paired discrete hashing (SPDH) In specific, SPDH explores the underlying structure of the constructed common latent subspace, where both paired and unpaired samples are well aligned. To effectively preserve the similarities of semi paired data in the latent subspace, we construct the cross view similarity graph with the help of anchor data pairs. SPDH jointly learns the latent features and hash codes with a factorization based coding scheme. For the formulated objective function, we devise an efficient alternating optimization algorithm, where the key binary code learning problem is solved in a bit by bit manner with each bit generated with a closed form solution. The proposed method is extensively evaluated on four benchmark datasets with both fully paired and semi paired settings and the results demonstrate the superiority of SPDH over several other state of the art methods in term of both accuracy and scalability.
Modeling score distributions in information retrieval
Venue: Information Retrieval
Year: 2010

We review the history of modeling score distributions, focusing on the mixture of normal exponential by investigating the theoretical as well as the empirical evidence supporting its use. We discuss previously suggested conditions which valid binary mixture models should satisfy, such as the Recall Fallout Convexity Hypothesis, and formulate two new hypotheses considering the component distributions, individually as well as in pairs, under some limiting conditions of parameter values. From all the mixtures suggested in the past, the current theoretical argument points to the two gamma as the most likely universal model, with the normal exponential being a usable approximation. Beyond the theoretical contribution, we provide new experimental evidence showing vector space or geometric models, and BM25, as being 'friendly' to the normal exponential, and that the non convexity problem that the mixture possesses is practically not severe. Furthermore, we review recent non binary mixture models, speculate on graded relevance, and consider methods such as logistic regression for score calibration.
Deep Learning Based Joint Optimization of Renewable Energy Storage and Routing in Vehicular Energy Network
Venue: IEEE Internet of Things Journal
Year: 2020

Recent development in renewable energy enabled electric vehicles (EVs) has posed challenges to the stability and efficiency of the vehicular energy network (VEN) which is a concrete implementation of Internet of Things (IoT) in energy and vehicular networks. In this article, we study a VEN with time varying point to point traffic flow and adjustable energy storage capacity at stations. The goal is to jointly optimize the routing and dynamic storage allocation of renewable energy so as to maximize the efficiency of plant to station energy transferring. We first adopt a time expanded topology graph to describe the scenario and model it as a maximum flow problem. Next, we incorporate routing in our methodology and derive a joint energy storage capacity and route planning method based on linear programming. Then, the problem is extended to a more general case, where the traffic pattern of the VEN is unknown. We apply the long short term memory model, a deep learning method, to predict the traffic pattern and utilize the concept of reinforcement learning to iteratively improve the prediction accuracy. To evaluate the performance, we implement our method first on regular buses, then extend to EVs based on the real trace data from the PeMS system in California. The simulation results show that the joint optimization can achieve near optimal performance and performs well even in case of high rate of missing traffic information, with the help of deep reinforcement learning.
Vitamin E, antioxidant and nothing more.
Venue: Free radical biology medicine
Year: 2007

All of the naturally occurring vitamin E forms, as well as those of synthetic all rac alpha tocopherol, have relatively similar antioxidant properties, so why does the body prefer alpha tocopherol as its unique form of vitamin E? We propose the hypothesis that all of the observations concerning the in vivo mechanism of action of alpha tocopherol result from its role as a potent lipid soluble antioxidant. The purpose of this review then is to describe the evidence for alpha tocopherol's in vivo function and to make the claim that alpha tocopherol's major vitamin function, if not only function, is that of a peroxyl radical scavenger. The importance of this function is to maintain the integrity of long chain polyunsaturated fatty acids in the membranes of cells and thus maintain their bioactivity. That is to say that these bioactive lipids are important signaling molecules and that changes in their amounts, or in their loss due to oxidation, are the key cellular events that are responded to by cells. The various signaling pathways that have been described by others to be under alpha tocopherol regulation appear rather to be dependent on the oxidative stress of the cell or tissue under question. Moreover, it seems unlikely that these pathways are specifically under the control of alpha tocopherol given that various antioxidants other than alpha tocopherol and various oxidative stressors can manipulate their responses. Thus, virtually all of the variation and scope of vitamin E's biological activity can be seen and understood in the light of protection of polyunsaturated fatty acids and the membrane qualities (fluidity, phase separation, and lipid domains) that polyunsaturated fatty acids bring about.
The sociocultural appraisals, values, and emotions (SAVE) framework of prosociality: core processes from gene to meme.
Venue: Annual review of psychology
Year: 2014

The study of prosocial behavior altruism, cooperation, trust, and the related moral emotions has matured enough to produce general scholarly consensus that prosociality is widespread, intuitive, and rooted deeply within our biological makeup. Several evolutionary frameworks model the conditions under which prosocial behavior is evolutionarily viable, yet no unifying treatment exists of the psychological decision making processes that result in prosociality. Here, we provide such a perspective in the form of the sociocultural appraisals, values, and emotions (SAVE) framework of prosociality. We review evidence for the components of our framework at four levels of analysis: intrapsychic, dyadic, group, and cultural. Within these levels, we consider how phenomena such as altruistic punishment, prosocial contagion, self other similarity, and numerous others give rise to prosocial behavior. We then extend our reasoning to chart the biological underpinnings of prosociality and apply our framework to understand the role of social class in prosociality.