nils-herrmann/clean_scirepeval huggingface dataset:
Accelerating magnetic resonance imaging via deep learning
Venue: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)

This paper proposes a deep learning approach for accelerating magnetic resonance imaging (MRI) using a large number of existing high quality MR images as the training datasets. An off line convolutional neural network is designed and trained to identify the mapping relationship between the MR images obtained from zero filled and fully sampled k space data. The network is not only capable of restoring fine structures and details but is also compatible with online constrained reconstruction methods. Experimental results on real MR data have shown encouraging performance of the proposed method for efficient and accurate imaging.
BOARD LEVEL RELIABILITY OF FINE PITCH FLIP CHIP BGA PACKAGES FOR AUTOMOTIVE APPLICATIONS

The drive for higher performance and smaller electronic components make flip chip ball grid array (FCBGA) packages attractive for automotive applications. As automobiles rely more heavily on electronics for passenger safety, efficiency, and comfort, component reliability has become increasingly important. Since automotive components operate in extreme environmental conditions, with a temperature range of 40degC to +125degC, solder joint reliability of ball grid array packages is a major concern in automotive systems. Because of the difference in thermal expansion of the different packaging materials and the printed circuit board (PCB) temperature variations create thermal mismatch, resulting in solder joint stress. After repeated thermal cycling, the solder joints can crack and lead to system failures. In our study, the board level reliability of different FCBGA packages was evaluated in the automotive thermal cycling environment. The effect of different factors, such as package construction, underfill type, and ball pitch, have on board level reliability of flip chip packages will be discussed. Our studies showed FCBGA packages using organic substrates can achieve high board level reliability under automotive operating conditions.
Decidability and Complexity of Fuzzy Description Logics
Venue: KI Kunstliche Intelligenz

Fuzzy description logics (FDLs) have been introduced to represent concepts for which membership cannot be determined in a precise way, i.e. where instead of providing a strict border between being a member and not being a member, it is more appropriate to model a gradual change from membership to non membership. First approaches for reasoning in FDLs where based either on a reduction to reasoning in classical description logics (DLs) or on adaptations of reasoning approaches for DLs to the fuzzy case. However, it turned out that these approaches in general do not work if expressive terminological axioms, called general concept inclusions (GCIs) are available in the FDL. The goal of this project was a comprehensive study of the border between decidability and undecidability for FDLs with GCIs, as well as determining the exact complexity of the decidable logics. As a result, we have provided an almost complete classification of the decidability and complexity of FDLs with GCIs.
Reasoning in Fuzzy Description Logics using Automata
Venue: Fuzzy Sets Syst.

Automata based methods have been successfully employed to prove tight complexity bounds for reasoning in many classical logics, and in particular in Description Logics (DLs) Very recently, the ideas behind these automata based approaches were adapted for reasoning also in fuzzy extensions of DLs, with semantics based either on finitely many truth degrees or the Godel t norm over the interval 0 1 Clearly, due to the different semantics in these logics, the construction of the automata for fuzzy DLs is more involved than for the classical case. In this paper we provide an overview of the existing automata based methods for reasoning in fuzzy DLs, with a special emphasis on explaining the ideas and the requirements behind them. The methods vary from deciding emptiness of automata on infinite trees to inclusions between automata on finite words. Overall, we provide a comprehensive perspective on the automata based methods currently in use, and the many complexity results obtained through them.
Influence of Various Lifestyle and Psychosocial Factors on Sleep Disturbances among the College Students A Cross Sectional Study from an Urban Area of India

Sleep occupies nearly 1/3rd of our life and is essential for overall growth and stability. Sleep deprivation results weakening of physical functions, mental health problems like depression and lowering of productivity, thus resulting in loss to an individual and society.  Sleep is essential for physical and mental stability. Its deprivation lowers work productivity and results in mental problem like depression. Various lifestyle and psychosocial factors may have impact on the sleep. In the western countries the subject is amply explored; however studies on student from developing countries like India are limited. Our objective was to study the extent of sleep disturbance and associated factors among the graduating college students.  It is a cross sectional study conducted in Arts, Commerce and Science graduating college students from an urban area. The sampling technique was cluster random sampling with the sample size of 890. A pretested, selfadministered questionnaire was used as a study tool. Statistical Analysis was done using percentages, chisquare test and bi variate logistic regression.  The mean duration of sleep reported by the 900 study subjects was 7.3 hours (std. deviation 1 hour) Any sleep disturbance was reported by 826 (91.8% subjects; with day time sleepiness (77.5% and difficulty in falling asleep (65.4% being the commonest complaint. Sleep disturbance score was associated with exercise, outdoor games and tea coffee intake. It was also associated with nocturnal use of mobiles and feeling depressed.  Sleep disturbances were present in majority of college students with day time sleepiness as its commonest manifestation. Various lifestyle and psychosocial factors had impact on the sleep. Proper lifestyle modification and good family environment are Influence of Various Lifestyle and Psychosocial Factors on Sleep Disturbances among the College Students: A Cross Sectional Study from an Urban Area of India 1* 1 1* 1 Yugantara R. Kadam Saket R. Patil Vivek Waghachavare Alka D. Gore Department of Preventive and Social Medicine, Bharati Vidyapeeth Deemed University Medical College Hospital, Sangli 416416 (Maharashtra) India important to avoid sleep disturbances among the college students.
Smart and Multifunctional Core Shell Nanoparticles (NPs) for Drug Delivery

Fe@Au core shell nanoparticles (NPs) exhibit multiple functionalities enabling their effective use in applications such as medical imaging and drug delivery. In this work, a novel synthetic method was developed and optimized for the synthesis of highly stable, monodisperse Fe@Au NPs of average diameter ~24 nm exhibiting magneto plasmonic characteristics. Fe@Au NPs were characterized by a wide range of exper
Self compassion, self criticism, parent child attachment moderate the relation between anxious solitude and psychosocial adjustment in early adolescence

xiv CHAPTER 1: INTRODUCTION 1 CHAPTER 2: SELF COMPASSION AND SELF CRITICISM AS MODERATORS OF THE RELATION BETWEEN ANXIOUS SOLITUDE AND DEPRESSIVE SYMPTOM TRAJECTORIES FROM 5TH THROUGH 7TH GRADE 55 CHAPTER 3: ALTERNATE MODELS WITH PEER REPORTED VARIABLES 100 CHAPTER 4: ORIGINS OF SELF COMPASSION AND SELF CRITICISM 132 CHAPTER 5: PARENTAL ATTACHMENT SECURITY AS A MODERATOR OF SELFCOMPASSION AND SELF CRITICISM TRAJECTORIES IN ANXIOUS SOLITARY YOUTH 142 CHAPTER 6: ALTERNATE MODELS PEER REPORTED ANXIOUS SOLITUDE 169 CHAPTER 7: GENERAL DISCUSSION 189
Rain or Snow: Hydrologic Processes, Observations, Prediction, and Research Needs

The phase of precipitation when it reaches the ground is a first order driver of hydrologic processes in a watershed. The presence of snow, rain, or mixed phase precipitation affects the initial and boundary conditions that drive hydrological models. Despite their foundational importance to terrestrial hydrology, typical phase partitioning methods (PPMs) specify the phase based on near surface air temperature only. Our review conveys the diversity of tools available for PPMs in hydrological modeling and the advancements needed to improve predictions in complex terrain with large spatiotemporal variations in precipitation phase. Initially, we review the processes and physics that control precipitation phase as relevant to hydrologists, focusing on the importance of processes occurring aloft. There is a wide range of options for field observations of precipitation phase, but there is a lack of a robust observation networks in complex terrain. New remote sensing observations have the potential to increase PPM fidelity, but generally require assumptions typical of other PPMs and field validation before they are operational. We review common PPMs and find that accuracy is generally increased at finer measurement intervals and by including humidity information. One important tool for PPM development is atmospheric modeling, which includes microphysical schemes that have not been effectively linked to hydrological models or validated against near surface precipitation phase observations. The review concludes by describing key research gaps and recommendations to improve PPMs, including better incorporation of atmospheric information, improved validation datasets, and regional scale gridded data products. Two key points emerge from this synthesis for the hydrologic community: (1) current PPMs are too simple to capture important processes and are not well validated for most locations, (2) lack of sophisticated PPMs increases the uncertainty in estimation of hydrological sensitivity to changes in precipitation phase at local to regional scales. The advancement of PPMs is a critical research frontier in hydrology that requires scientific cooperation between hydrological and atmospheric modelers and field scientists.
Optimal Design of an Outer Stator Magnetically Geared Permanent Magnet Machine
Venue: IEEE Transactions on Magnetics

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.
The Eye As a Biomarker for Alzheimer's Disease
Venue: Frontiers in neuroscience

Alzheimer's disease (AD) is a progressive neurodegenerative disorder resulting in dementia and eventual death. It is the leading cause of dementia and the number of cases are projected to rise in the next few decades. Pathological hallmarks of AD include the presence of hyperphosphorylated tau and amyloid protein deposition. Currently, these pathological biomarkers are detected either through cerebrospinal fluid analysis, brain imaging or post mortem. Though effective, these methods are not widely available due to issues such as the difficulty in acquiring samples, lack of infrastructure or high cost. Given that the eye possesses clear optics and shares many neural and vascular similarities to the brain, it offers a direct window to cerebral pathology. These unique characteristics lend itself to being a relatively inexpensive biomarker for AD which carries the potential for wide implementation. The development of ocular biomarkers can have far implications in the discovery of treatments which can improve the quality of lives of patients. In this review, we consider the current evidence for ocular biomarkers in AD and explore potential future avenues of research in this area.
Imaging Biomarkers in Immunotherapy
Venue: Biomarkers in cancer

Immune based therapies have been in use for decades but recent work with immune checkpoint inhibitors has now changed the landscape of cancer treatment as a whole. While these advances are encouraging, clinicians still do not have a consistent biomarker they can rely on that can accurately select patients or monitor response. Molecular imaging technology provides a noninvasive mechanism to evaluate tumors and may be an ideal candidate for these purposes. This review provides an overview of the mechanism of action of varied immunotherapies and the current strategies for monitoring patients with imaging. We then describe some of the key researches in the preclinical and clinical literature on the current uses of molecular imaging of the immune system and cancer.
Fully Convolutional Attention Localization Networks: Efficient Attention Localization for Fine Grained Recognition
Venue: ArXiv

Fine grained recognition is challenging mainly because the inter class differences between fine grained classes are usually local and subtle while intra class differences could be large due to pose variations. In order to distinguish them from intra class variations, it is essential to zoom in on highly discriminative local regions. In this work, we introduce a reinforcement learning based fully convolutional attention localization network to adaptively select multiple task driven visual attention regions. We show that zooming in on the selected attention regions significantly improves the performance of fine grained recognition. Compared to previous reinforcement learning based models, the proposed approach is noticeably more computationally efficient during both training and testing because of its fully convolutional architecture, and it is capable of simultaneous focusing its glimpse on multiple visual attention regions. The experiments demonstrate that the proposed method achieves notably higher classification accuracy on three benchmark fine grained recognition datasets: Stanford Dogs, Stanford Cars, and CUB 200 2011.
Fully Convolutional Attention Networks for Fine Grained Recognition

Fine grained recognition is challenging due to its subtle local inter class differences versus large intra class variations such as poses. A key to address this problem is to localize discriminative parts to extract pose invariant features. However, ground truth part annotations can be expensive to acquire. Moreover, it is hard to define parts for many fine grained classes. This work introduces Fully Convolutional Attention Networks (FCANs) a reinforcement learning framework to optimally glimpse local discriminative regions adaptive to different fine grained domains. Compared to previous methods, our approach enjoys three advantages: 1) the weakly supervised reinforcement learning procedure requires no expensive part annotations; 2) the fully convolutional architecture speeds up both training and testing; 3) the greedy reward strategy accelerates the convergence of the learning. We demonstrate the effectiveness of our method with extensive experiments on four challenging fine grained benchmark datasets, including CUB 200 2011, Stanford Dogs, Stanford Cars and Food 101.
Fine Grained Recognition with Automatic and Efficient Part Attention

Fine grained recognition is challenging due to the subtle local inter class differences versus the large intra class variations such as poses. A key to address this problem is to localize discriminative parts to extract pose invariant features. However, ground truth part annotations can be expensive to acquire. Moreover, it is hard to define parts for many fine grained classes. This work introduces Fully Convolutional Attention Networks (FCANs) a reinforcement learning framework to optimally glimpse local discriminative regions adaptive to different fine grained domains. Compared to previous methods, our approach enjoys four advantages: 1) the three components including feature extraction, visual attention and fine grained classification are unified in an end to end system; 2) the weaklysupervised reinforcement learning procedure requires no expensive part annotations; 3) the fully convolutional architecture speeds up both training and testing; 4) the greedy reward strategy accelerates the convergence of the learning. We demonstrate the effectiveness of our method with extensive experiments on four challenging fine grained benchmark datasets, including Stanford Dogs, Stanford Cars, CUB 200 2011 and Food 101.
Self efficacy configurations and wellbeing in the academic context: A person centred approach

The aim of the present study was to identify self efficacy configurations in different domains (i.e. emotional, social, and self regulated learning) in a sample of university students using a person centred approach. Results from a two cohort sample (N 1650) assessed at the beginning of their first year supported a 4 cluster solution: 1) Highly Self Efficacious students, with high levels of self efficacy in all domains; 2) Low Self Efficacious students, with low levels of self efficacy in all domains; 3) Learning and Socially Self Efficacious students, with a medium high level of self regulated learning, medium level of social, and medium low level of emotional self efficacies; and 4) Emotionally Self Efficacious students, with a medium high level of emotional, medium low level of social, and low level of self regulated learning self efficacies. The association of these configurations with wellbeing indicators, concurrently and one year later, provides support for the validity of the cluster solution. Specifically, by adopting the informative hypothesis testing approach, results showed that the first and second groups have the best and the worst wellbeing levels, respectively. Furthermore, whereas the other two groups did not differ with respect to depression, Learning and Socially Self Efficacious students have higher life satisfaction than the last group. These results were confirmed both concurrently and over time.
Mode of action uncovered for the specific reduction of methane emissions from ruminants by the small molecule 3 nitrooxypropanol
Venue: Proceedings of the National Academy of Sciences

Methane emission from the ruminant livestock sector a by product from enteric fermentation of plant biomass in the ruminant digestive system is produced by methanogenic archaea and represents not only a significant amount of anthropogenic greenhouse gases contributing to climate change but also an energy loss and a reduction in feed efficacy. The present study elucidates the development and the unique mode of action of the highly specific inhibitor 3 nitrooxypropanol (3 NOP) which is targeting the nickel enzyme methyl coenzyme M reductase in rumen archaea that catalyzes the methane forming reaction. At the very low effective concentrations recently applied in vivo (dairy and beef cattle) 3 NOP appears to inhibit only methanogens and thus to be attractive for development as a feed supplement. Ruminants, such as cows, sheep, and goats, predominantly ferment in their rumen plant material to acetate, propionate, butyrate, CO2, and methane. Whereas the short fatty acids are absorbed and metabolized by the animals, the greenhouse gas methane escapes via eructation and breathing of the animals into the atmosphere. Along with the methane, up to 12% of the gross energy content of the feedstock is lost. Therefore, our recent report has raised interest in 3 nitrooxypropanol (3 NOP) which when added to the feed of ruminants in milligram amounts persistently reduces enteric methane emissions from livestock without apparent negative side effects [Hristov AN, et al. (2015) Proc Natl Acad Sci USA 112(34):10663 10668] We now show with the aid of in silico, in vitro, and in vivo experiments that 3 NOP specifically targets methyl coenzyme M reductase (MCR) The nickel enzyme, which is only active when its Ni ion is in the +1 oxidation state, catalyzes the methane forming step in the rumen fermentation. Molecular docking suggested that 3 NOP preferably binds into the active site of MCR in a pose that places its reducible nitrate group in electron transfer distance to Ni(I) With purified MCR, we found that 3 NOP indeed inactivates MCR at micromolar concentrations by oxidation of its active site Ni(I) Concomitantly, the nitrate ester is reduced to nitrite, which also inactivates MCR at micromolar concentrations by oxidation of Ni(I) Using pure cultures, 3 NOP is demonstrated to inhibit growth of methanogenic archaea at concentrations that do not affect the growth of nonmethanogenic bacteria in the rumen.
An Underground Revolution: Biodiversity and Soil Ecological Engineering for Agricultural Sustainability.
Venue: Trends in ecology evolution

Soil organisms are an integral component of ecosystems, but their activities receive little recognition in agricultural management strategies. Here we synthesize the potential of soil organisms to enhance ecosystem service delivery and demonstrate that soil biodiversity promotes multiple ecosystem functions simultaneously (i.e. ecosystem multifunctionality) We apply the concept of ecological intensification to soils and we develop strategies for targeted exploitation of soil biological traits. We compile promising approaches to enhance agricultural sustainability through the promotion of soil biodiversity and targeted management of soil community composition. We present soil ecological engineering as a concept to generate human land use systems, which can serve immediate human needs while minimizing environmental impacts.
Sustainable intensification of agriculture for human prosperity and global sustainability
Venue: Ambio

There is an ongoing debate on what constitutes sustainable intensification of agriculture (SIA) In this paper, we propose that a paradigm for sustainable intensification can be defined and translated into an operational framework for agricultural development. We argue that this paradigm must now be defined at all scales in the context of rapidly rising global environmental changes in the Anthropocene, while focusing on eradicating poverty and hunger and contributing to human wellbeing. The criteria and approach we propose, for a paradigm shift towards sustainable intensification of agriculture, integrates the dual and interdependent goals of using sustainable practices to meet rising human needs while contributing to resilience and sustainability of landscapes, the biosphere, and the Earth system. Both of these, in turn, are required to sustain the future viability of agriculture. This paradigm shift aims at repositioning world agriculture from its current role as the world's single largest driver of global environmental change, to becoming a key contributor of a global transition to a sustainable world within a safe operating space on Earth.