nils-herrmann/clean_scirepeval huggingface dataset:
A Novel Spatio Temporal FCN LSTM Network for Recognizing Various Crop Types Using Multi Temporal Radar Images
Venue: Remote. Sens.
Year: 2019

In recent years, analyzing Synthetic Aperture Radar (SAR) data has turned into one of the challenging and interesting topics in remote sensing. Radar sensors are capable of imaging Earth's surface independently of the weather conditions, local time of day, penetrating of waves through clouds, and containing spatial information on agricultural crop types. Based on these characteristics, the main goal sought in this research is to reveal the SAR imaging data capability in recognizing various agricultural crops in the main growth season in a more clarified and detailed way by using a deep learning based method. In the present research, the multi temporal C band Sentinel 1 images were used to classify 14 major classes of agricultural crops plus background in Denmark. By considering the capability of a deep learning method in analyzing satellite images, a novel, optimal, and lightweight network structure was developed and implemented based on a combination of a fully convolutional network (FCN) and a convolutional long short term memory (ConvLSTM) network. The average pixel based accuracy and Intersection over Union obtained from the proposed network were 86% and 0.64, respectively. Winter rapeseed, winter barley, winter wheat, spring barley, and sugar beet had the highest pixel based accuracies of 95% 94% 93% 90% and 90% respectively. The pixel based accuracies for eight crop types and the background class were more than 84% The network prediction showed that in field borders the classification confidence was lower than the center regions of the fields. However, the proposed structure has been able to identify different crops in multi temporal Sentinel 1 data of a large area of around 254 thousand hectares with high performance.
Walking is more effective than yoga at reducing sleep disturbance in cancer patients: A systematic review and meta analysis of randomized controlled trials.
Venue: Sleep medicine reviews
Year: 2019

This review aimed to determine whether walking is more effective than yoga at improving sleep disturbance in cancer patients. A systematic search of randomized controlled trials was performed in the PubMed, EMBASE, CINAHL, Cochrane Library, CNKI, Airiti Library, and other health related databases. Twenty five studies were identified with a total of 1918 participants. The Pittsburgh sleep quality index was the most commonly used outcome measurement tool, and moderate intensity walking was the most frequently used intervention. The majority of the included subjects were breast cancer patients. Overall, walking significantly improved sleep disturbance compared to yoga (p 0.01) Statistically significant moderators included adherence rate for walking (p 0.001) and allocation concealment and outcome measurement tool for yoga (p 0.04; p 0.03) We concluded that walking is more effective than yoga in improving sleep disturbance in cancer patients. Thus, moderate intensity walking is recommended for cancer patients with sleep disturbance.
End to End People Detection in Crowded Scenes
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2016

Current people detectors operate either by scanning an image in a sliding window fashion or by classifying a discrete set of proposals. We propose a model that is based on decoding an image into a set of people detections. Our system takes an image as input and directly outputs a set of distinct detection hypotheses. Because we generate predictions jointly, common post processing steps such as nonmaximum suppression are unnecessary. We use a recurrent LSTM layer for sequence generation and train our model end to end with a new loss function that operates on sets of detections. We demonstrate the effectiveness of our approach on the challenging task of detecting people in crowded scenes1.
Attribute Based Detection of Unfamiliar Classes with Humans in the Loop
Venue: 2013 IEEE Conference on Computer Vision and Pattern Recognition
Year: 2013

Recent work in computer vision has addressed zero shot learning or unseen class detection, which involves categorizing objects without observing any training examples. However, these problems assume that attributes or defining characteristics of these unobserved classes are known, leveraging this information at test time to detect an unseen class. We address the more realistic problem of detecting categories that do not appear in the dataset in any form. We denote such a category as an unfamiliar class, it is neither observed at train time, nor do we possess any knowledge regarding its relationships to attributes. This problem is one that has received limited attention within the computer vision community. In this work, we propose a novel approach to the unfamiliar class detection task that builds on attribute based classification methods, and we empirically demonstrate how classification accuracy is impacted by attribute noise and dataset "difficulty," as quantified by the separation of classes in the attribute space. We also present a method for incorporating human users to overcome deficiencies in attribute detection. We demonstrate results superior to existing methods on the challenging CUB 200 2011 dataset.
Biofuel types and membrane separation
Venue: Environmental Chemistry Letters
Year: 2018

Global warming induced by greenhouse gases is major issue worldwide. There is therefore a need to develop renewable sources of energy, such as biofuels. Here, we review the various types of biofuels such as biodiesel, bioethanol, biomethane, hydrotreated vegetable oils and fats, and lignocellulosic based fuels. First, second, and third generations of biofuels are compared in terms of economics, environmental aspects and energy yield. Economically, raw materials account for 60 75% of the final price of produced biofuels. The high cost of biodiesel compared to the lower price of diesel fuel is a major challenge toward commercializing biodiesel production from vegetable oils. Environmentally, biofuels can reduce carbon emissions and are more biodegradable compared to fossil fuels. For instance, biodiesel and diesel fuels are degraded by 95% and 40% respectively, during one month in water. Among liquid biofuels, biodiesel has the best energy yield, such that the amount of net biodiesel energy production is more than three times than that of diesel fuel. We also review membrane technologies for the purification and separation of biofuels such as bioethanol, biobutanol, biodiesel, and biogas. Commonly used membrane processes are ultrafiltration, microfiltration, nanofiltration, pervaporation, membrane distillation and reverse osmosis. Reverse osmosis is used for water treatment due to the very small pore size of membranes, which allow the water molecules to get through. Membrane bioreactors can be used for wastewater treatment with a combination of ultrafiltration and reverse osmosis. Ultrafiltration and nanofiltration membranes have applications in the production of biomass from olive mill wastewaters. Pervaporation and membrane distillation are efficient in the third generation of bioethanol production plants.
Assessment of Reliability and Validity of SF 12v2 among a Diabetic Population.
Venue: Value in health the journal of the International Society for Pharmacoeconomics and Outcomes Research
Year: 2018

To validate the Medical Outcomes Study Short Form version 2 (SF 12v2) in diabetic patients.  Adults with self reported diabetes from the Medical Expenditure Panel Survey (2011 2013) were identified. Reliability (internal consistency and test retest) and validity (construct, concurrent, criterion, and predictive) of the SF 12v2 were assessed. The SF 12v2 consists of two normalized composite scores: the physical component summary score (PCS12) and the mental component summary score (MCS12) Confirmatory factor analysis was conducted to assess the instrument structure. Concurrent (convergent and discriminant) validity was assessed by a multitrait multimethod matrix using the Patient Health Questionnaire, the Kessler Scale, and perceived health and mental health questions. The predictive validity was assessed by estimating future limitations. The concurrent validity was tested by comparing the MCS12, PCS12, and utility scores (six dimensional health state short form) across comorbidity scores.  The final sample comprised 2214 diabetic patients with mean normalized (population mean 50; range 0 100) PCS12 and MCS12 scores of 40.81 (standard error 0.33) and 49.82 (standard error 0.26) respectively. The PCS12 and MCS12 scores showed good internal consistency (Cronbach a: PCS12 0.85; MCS12 0.83) and acceptable test retest reliability (intraclass correlation coefficient: PCS12 0.72; MCS12 0.63) and produced acceptable goodness of fit indices (normed fit index 0.95; comparative fit index 0.95; root mean square error of approximation 0.11 [95% confidence interval 0.1017 0.1188] The PCS12 and MCS12 were moderately correlated with perceived health and perceived mental health. The MCS12 was highly correlated with the Patient Health Questionnaire and the Kessler Scale. Both the PCS12 and the MCS12 could predict the future health limitations. The PCS12, MCS12, and utility scores demonstrated sensitivity to the presence of comorbidity scores.  The SF 12v2 is a valid generic instrument for measuring quality of life in diabetic patients.
Improving communication skills of children with autism through support of applied behavioral analysis treatments using multimedia computing: a survey
Venue: Universal Access in the Information Society
Year: 2020

Naturalistic applied behavior analysis (ABA) techniques have been shown to help children with autism improve their communication skills. Recognizing that individuals who interact with children regularly are in the position to utilize treatments with profound effects, researchers have examined methodologies for training parents, teachers, and peers to implement treatments. These programs are time intensive and often unable to support trainees after training. Technologies need to be examined to determine how they can aid in the educational and support process. Academic publications and publicly available training programs were reviewed to determine the types of participants, methodologies, and training durations that have been reported for instructing interventionists. These resources illustrate a need to make programs more accessible. To address this, selected computer science research is applied to methods of evaluating ABA implementations in order to recommend how the technologies could be utilized to make training and support programs more accessible. Review results of instructional programs, both in research and available in the community, illustrate the challenges in providing training in ABA methodologies. Modern research in multimedia data processing and machine learning could be applied to reduce the human cost of training and support individuals implementing ABA techniques. Utilizing machine learning techniques to analyze video probes of naturalistic ABA treatment implementation could alleviate the human cost of evaluating fidelity, allowing for greater support for individuals interested in the treatments. These technologies could be used in the future to expand data collection to provide more perspective on the treatments.
Neural Networks: Tricks of the Trade
Venue: Lecture Notes in Computer Science
Year: 2012

The twenty last years have been marked by an increase in available data and computing power. In parallel to this trend, the focus of neural network research and the practice of training neural networks has undergone a number of important changes, for example, use of deep learning machines. The second edition of the book augments the first edition with more tricks, which have resulted from 14 years of theory and experimentation by some of the world's most prominent neural network researchers. These tricks can make a substantial difference (in terms of speed, ease of implementation, and accuracy) when it comes to putting algorithms to work on real problems.
Changing Trends in oral cancer a global scenario
Venue: Nepal journal of epidemiology
Year: 2016

Oral cancer is one of the highly prevalent cancers worldwide and a leading cause of mortality in certain regions like South Central Asia. It is a major public health problem. Late diagnosis, high mortality rates and morbidity are characteristics of the disease worldwide. For control of oral cancer an idea of the coverage of the same in the various regions is necessary. The estimated incidence, mortality and 5 year survival due to lip, oral cavity cancer in world is 3, 00, 373(2.1% 1, 45, 328(1.8% and 7, 02, 149(2.2% respectively according to data of GLOBOCAN 2012. A changing trend in incidence and prevalence of oral cancer has been observed with more women and youngsters being affected by oral cancer.
Demonstration abstract: BioWatch A wrist watch based physiological signal acquisition system
Venue: IPSN 14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks
Year: 2014

A wrist watch based system, which can measure electrocardiogram (ECG) and photoplethysmogram (PPG) is presented in this work. By using both ECG and PPG we measure pulse transit time (PTT) which is known to correlate well with the blood pressure (BP)  This system, called BioWatch, can potentially facilitate continuous and ubiquitous monitoring of ECG, PPG and BP.
The Flower Industry in Tanzania: Production Performance and Costs
Year: 2006

A survey was conducted to assess production levels, costs, marketing and policy regulations affecting the flower industry in Tanzania. Results indicated that rose and chrysanthemum were the main ornamental crops produced for export with yield of 908 and 145.6 million flower stems and shoot cuttings per year in 2003, respectively. The surface areas under rose and chrysanthemum cultivation increased by 328.9 and 658.3% and the corresponding production by 316.84 and 666.33% between 1997 and 2003, respectively. Greenhouse construction accounted for 50% while purchase of land and rose planting materials represented 17.7 and 12.3% of the total investment cost, respectively. Interest on loans and freight charges accounted for 19 and 16.0% of the total operational cost, respectively. All chrysanthemum shoot cuttings were directly exported to The Netherlands whereas 90% of total rose flowers were exported to Europe through European middlemen with a marketing commission of 18 30% of sales and 52% of total operational cost. The average selling prices of rose and chrysanthemum were 0.23 and 0.11 US$ per flower stem and shoot tip cutting, respectively. Foreign growers appreciated the provision of tax exemption of five years but considered the National Agricultural Policy of 1997 being too general and not favourable to the flower industry.
Super Resolution reconstruction of Bone Micro Structure Micro CT Image based on Auto Encoder Structure*
Venue: 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO)
Year: 2019

Computed Tomography (CT) is widely used for screening, diagnostic and image guided therapy for clinical and research purposes. The current Finite Element Analysis for Research Microstructure based on Micro CT scan data has been considered the gold standard for non invasive studies of bone microstructure. In medical diagnosis and treatment, the resolution of clinical medical images is low, and it is impossible to directly observe the fine structure of bone through Micro CT. However, there are many problems in high resolution image, such as long scan time, high radiation dose and complicated processing steps. Therefore, the research goal of this paper is to propose a new network structure which can reconstruct high resolution bone microstructure images with fine structure while reducing X ray radiation. The network model proposed in this paper combines the auto encoder structure and adopts a staged up sampling strategy to accurately reconstruct high resolution images from low resolution images.Moreover, in order to be more realistic, this paper constructs a real training data set according to strict operation. In the data set of this paper, a variety of existing super resolution reconstruction methods are used for comparison test. The results show that the network model proposed in this paper achieves better reconstruction results. We found in the experiment that the noise of the sample itself has a certain influence on the evaluation of the reconstruction effect and the impact of noise reduction is also discussed on super resolution reconstruction.
Building Expression into Virtual Characters
Venue: Eurographics
Year: 2006

Virtual characters are an important part of many 3D graphical simulations. In entertainment or training applications, virtual characters might be one of the main mechanisms for creating and developing content and scenarios. In such applications the user may need to interact with a number of different characters that need to invoke specific responses in the user, so that the user interprets the scenario in the way that the designer intended. Whilst representations of virtual characters have come a long way in recent years, interactive virtual characters tend to be a bit "wooden" with respect to their perceived behaviour. In this STAR we give an overview of work on expressive virtual characters. In particular, we assume that a virtual character representation is already available, and we describe a variety of models and methods that are used to give the characters more "depth" so that they are less wooden and more plausible. We cover models of individual characters' emotion and personality, models of interpersonal behaviour and methods for generating expression.
The Redox Role of G6PD in Cell Growth, Cell Death, and Cancer
Venue: Cells
Year: 2019

The generation of reducing equivalent NADPH via glucose 6 phosphate dehydrogenase (G6PD) is critical for the maintenance of redox homeostasis and reductive biosynthesis in cells. NADPH also plays key roles in cellular processes mediated by redox signaling. Insufficient G6PD activity predisposes cells to growth retardation and demise. Severely lacking G6PD impairs embryonic development and delays organismal growth. Altered G6PD activity is associated with pathophysiology, such as autophagy, insulin resistance, infection, inflammation, as well as diabetes and hypertension. Aberrant activation of G6PD leads to enhanced cell proliferation and adaptation in many types of cancers. The present review aims to update the existing knowledge concerning G6PD and emphasizes how G6PD modulates redox signaling and affects cell survival and demise, particularly in diseases such as cancer. Exploiting G6PD as a potential drug target against cancer is also discussed.
A new single end wideband impedance based fault location scheme for distribution systems
Venue: Electric Power Systems Research
Year: 2019

This paper proposes an improved impedance based fault location scheme based on system analysis at non fundamental frequencies. The fault is treated as a voltage source that injects high frequency components into the system and the analysis is carried out using these injected components. The proposed method only requires local measurements at the substation and therefore is classified as a single end method. The new contribution is that the proposed method uses the distributed parameter line model to account for inductive and capacitive effects of the line. It has been evaluated on the IEEE 34 bus feeder which is based on an actual distribution system which has the typical features such as non homogeneous feeder sections, asymmetrical line configurations, unbalanced loads and single and three phase laterals. The fault point, fault resistance and fault inception angle have been varied to check their influence on the accuracy of the method. The simulation results demonstrate the accuracy of the proposed method where for most cases, the error in fault location is less than 50 m.
Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans
Venue: IEEE Signal Processing Magazine
Year: 2018

Recent developments in deep convolutional neural networks (DCNNs) have shown impressive performance improvements on various object detection/recognition problems. This has been made possible due to the availability of large annotated data and a better understanding of the nonlinear mapping between images and class labels, as well as the affordability of powerful graphics processing units (GPUs) These developments in deep learning have also improved the capabilities of machines in understanding faces and automatically executing the tasks of face detection, pose estimation, landmark localization, and face recognition from unconstrained images and videos. In this article, we provide an overview of deep learning methods used for face recognition. We discuss different modules involved in designing an automatic face recognition system and the role of deep learning for each of them. Some open issues regarding DCNNs for face recognition problems are then discussed. This article should prove valuable to scientists, engineers, and end users working in the fields of face recognition, security, visual surveillance, and biometrics.
CNN based Stochastic Regression for IDDQ Outlier Identification
Venue: 2020 IEEE 38th VLSI Test Symposium (VTS)
Year: 2020

In order to reduce DPPM (defect parts per million) IDDQ testing methodology can be exploited for identifying "outliers" which are potentially defective but not detected by signoff functional and parametric tests. Conventional IDDQ testing paradigms depending on a simple statistical 6s rule or engineers' experience are usually too conservative to effectively identify non trivial outliers, especially when spatial correlations are of great concern/influence. In this paper, by employing a stochastic regression model, the mean as well as the variance of the IDDQ of a die under test (DUT) can be predicted. According to the predicted mean and variance, we derive an expected IDDQ range and identify the DUT as an outlier if its actual IDDQ measurement is beyond the expected range. The proposed stochastic regression model is obtained by training a convolutional neural network (CNN) and, based on its primitive property of convolutional kernel mapping with large volume of industrial data, spatial correlations (due to spatially correlated process variations, etc) can be considered/captured. The trained data driven CNN is highly accurate in terms of R square (0.958) and RMSE (0.783) and the percentage of identified outliers (0.047% is very close to the theoretical reference (0.050% which validates the efficacy of our proposed methodology.