nils-herrmann/clean_scirepeval huggingface dataset:
Deep GDashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks
Venue: ArXiv

Deep neural network (DNN) models have recently obtained state of the art prediction accuracy for the transcription factor binding (TFBS) site classification task. However, it remains unclear how these approaches identify meaningful DNA sequence signals and give insights as to why TFs bind to certain locations. In this paper, we propose a toolkit called the Deep Genomic Dashboard (Deep GDashboard) which provides a suite of visualization strategies to extract motifs, or sequence patterns from deep neural network models for TFBS classification. We demonstrate how to visualize and understand three important DNN models: convolutional, recurrent, and convolutional recurrent networks. Our first visualization method is finding a test sequence's saliency map which uses first order derivatives to describe the importance of each nucleotide in making the final prediction. Second, considering recurrent models make predictions in a temporal manner (from one end of a TFBS sequence to the other) we introduce temporal output values, indicating the prediction score of a model over time for a sequential input. Lastly, a class specific visualization strategy finds the optimal input sequence for a given TFBS positive class via stochastic gradient optimization. Our experimental results indicate that a convolutional recurrent architecture performs the best among the three architectures. The visualization techniques indicate that CNN RNN makes predictions by modeling both motifs as well as dependencies among them.
Coping strategies and anxiety in caregivers of palliative cancer patients.
Venue: European journal of cancer care

The study purpose was to determine the relationship between coping strategies and anxiety in primary family caregivers of palliative cancer patients. A cross sectional study was carried out in a Pain and Palliative Care Unit in Spain. Data were collected through interviews from fifty primary family caregivers of palliative cancer patients. Main research variables were: (1) dependent variable: anxiety (subscale of anxiety from Goldberg's scale) (2) independent variable: coping (Brief COPE) (3) control variables: functional capacity and perceived burden. Analyses comprised descriptive statistics, correlation coefficients and multiple linear regression. Anxiety was present in the majority of caregivers surveyed (76% Anxiety was related to the perception of perceived burden (b 0.42, P 0.001) the emotion focused coping (b 0.28, P 0.01) and dysfunctional coping (b 0.41, P 0.001) after adjusting for control variables. Thus, emotion focused coping is negatively associated with anxiety, while dysfunctional coping is positively associated with anxiety. Problem focused coping is not related to anxiety. Assessment of coping should be done in a systematic way in caregivers of palliative cancer patients.
Poly(boron dipyrromethene) A Redox Active Polymer Class for Polymer Redox Flow Batteries

The utilization of boron dipyrromethene (BODIPY) as active group for the charge storage process in a battery application is reported. Two BODIPY containing copolymers were synthesized and electrochemically characterized. The polymers feature redox processes at 0.7 V and 1.5 V vs AgNO3/Ag, which enable the application in a redox flow battery setup.
TEMPO/Phenazine Combi Molecule: A Redox Active Material for Symmetric Aqueous Redox Flow Batteries

The combination of 2,2,6,6 tetramethylpiperidinyl N oxyl and phenazine yields an organic redox active material for redox flow battery applications. This combined molecule (combi molecule) features a redox voltage of 1.2 V and facilitates the utilization of aqueous electrolytes. It was synthesized from cost efficient starting materials, electrochemically characterized and applied as charge storage material in a symmetric aqueous redox flow battery.
A probabilistic study of the robustness of an adaptive neural estimation method for hysteretic internal forces in nonlinear MDOF systems

The Volterra/Wiener neural network (VWNN) has been shown to be an effective tool for on line estimation of non linear restoring forces and responses. However, the power of the VWNN for on line identification has not been fully harnessed due to the high sensitivity of its parameters. This study adopts a probabilistic approach in examining the effects of the VWNN's parameters on the robustness and stability of its estimation capabilities. Large ensembles of simulations were conducted in which random (earthquake like) ground motions were used to excite representative non linear structures, and on line estimation of their acceleration responses was performed. The nonlinearity in the system was introduced via hysteretic restoring forces, and a variety of cases were tested, including softening and hardening. The results showed that each design parameter within the VWNN was linked to a certain type of performance sensitivity. The adaptive gain that controls the change in the weights of the VWNN was also directly linked to the stability of the estimates, as small increases in the gain led to the estimates diverging. Within the neural network, the weight within the transfer function was found to directly correlate with accuracy. The optimum set of parameters for a given excitation often produced unstable solutions for other excitations, but by understanding the relationships between the parameters and their sensitivities, a set of parameters could be carefully chosen to consistently produce accurate and stable on line estimates for all simulations. The knowledge gained from the relationships between VWNN parameters also allowed for informed decisions on parameter sets for simulations involving different classes of nonlinearities. Offering users a starting point provides a necessary and helpful feature so often missing from other non linear identification schemes that deal with non parametric identification of complex nonlinear systems.
Teachers' Perceptions of Contextual Influences on Instructional Decision Making Regarding The Use of Educational Technology in Secondary Social Studies

ix Half Title Page. 1 Chapter 1: Introduction. 2 Statement of the Problem. 4 Conceptual Lens. 5  Chapter 2: Review of the Literature.  Making in the Teaching Context. 43  Teacher Gatekeeping Decisions Regarding Educational Technology in the Classroom Space. 51  Theoretical Framework. 56 Design. 62 Ethical Considerations. 69  Study Environment. 77  Martha's Information Ecology. 81  Mary's Information Ecology.  Paul's Information Ecology. 111 Cross Case Analysis.
Thermal strain behavior and strength degradation of ultra high strength concrete

With the increasing application of high strength concrete (HSC) in high rise buildings for which structural safety performance is needed. However, the thermal mechanical behavior of HSC exposed to fire differs from that of normal strength concrete (NSC) HSC is known to show different thermal strain behavior and strength degradation from NSC. It is needed to consider the thermal strain of HSC at elevated temperature under loading condition. In this study, the thermal strain behavior and strength degradation of HSC when exposed to elevated temperatures under loading conditions were examined experimentally. The compressive strength, thermal expansion, total strain, and hightemperature creep at elevated temperatures were evaluated. To evaluate the thermal expansion of HSC at elevated temperatures, HSC with compressive strengths of 80,130, and 180 MPa concrete were heated to 700 degC at a rate of 1 degC/min. The total strain and high temperature creep were measured under the loading condition of 25 of the compressive strength at room temperature. The experimental results clearly showed that the strength degradation of HSC increased with the higher compressive strength at elevated temperature. Thermal expansion occurred consistently regardless of the strength level without loading. However, 180 MPa concrete failed while being heated to around 300 degC. The transient creep had a large influence on the high temperature creep as the HSC was heated at elevated temperature while under a load. It is considered that the reduced amount of aggregate and increased binder content make extremely density matrix in HSC, and it is particularly evident as the compressive strength increased.
Radio Resource Allocation for D2D Enabled Massive Machine Communication in the 5G Era
Venue: 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)

Device to Device (D2D) and Massive Machine Communication (MMC) are believed to be the cornerstones of future 5th generation (5G) cellular technologies. As a method to increase spectrum utilization, extend cellular coverage, and offload backhaul traffic, D2D has been recently incorporated into Release 12 of 3rd Generation Partnership Project (3GPP) Long Term Evolution Advanced (LTE A) specifications. Devices in physical proximity can thus discover each other and communicate via a direct path using licensed LTE spectrums. Leveraging on the ubiquity of cellular coverage and harnessing LTE A D2D for networks with massive number of machine type communications, such as large scale sensor networks and vehicular networks, introduces a paradigm shift and opens up new opportunities for proximity based services. In this paper, we propose a novel radio resource allocation method for D2D discovery in clustered MMC networks. With a large number of nodes, the proposed method can still maintain the signaling overhead at a reasonable level while achieving high discovery rate. Experimental results show that the proposed approach significantly outperforms the existing 3GPP random resource allocation mechanism.
Improving the Transition From Oncology to Primary Care Teams: A Case for Shared Leadership.
Venue: Journal of oncology practice

This article discusses the case of a 47 year old woman who underwent primary therapy with curative intent for breast cancer. The case illustrates a number of failure events in transferring information and responsibility from oncology to primary care teams. The article emphasizes the importance of shared leadership, as multiple team members, dispersed in time and space, pursue their own objectives while achieving the common goal of coordinating care for survivors of cancer transitioning across settings. Shared leadership is defined as a team property comprising shared responsibility and mutual influence between the patient and the patient's family, primary care providers, and oncology teams, whereby they lead each other toward quality and safety of care. Teams, including the patient family, should achieve leadership when their contribution is relevant in managing task interdependence during transition. Shared leadership fosters coordinated actions to enable functioning as an integrated team of teams. This article illustrates how shared leadership can make a difference to coordinate interfaces and pathways, from therapy with curative intent to the follow up and management of survivors of breast cancer. The detailed case is elaborated as a clinical vignette. It can be used by care providers and researchers to consider the need for new models of care for survivors of cancer by addressing the following questions. Who accepts shared leadership, how, with whom, and under what conditions? What is the evidence that supports the answers to these questions? The detailed case is also valuable for medical and allied health professional education.
Deeply Recursive Convolutional Network for Image Super Resolution
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

We propose an image super resolution method (SR) using a deeply recursive convolutional network (DRCN) Our network has a very deep recursive layer (up to 16 recursions) Increasing recursion depth can improve performance without introducing new parameters for additional convolutions. Albeit advantages, learning a DRCN is very hard with a standard gradient descent method due to exploding/ vanishing gradients. To ease the difficulty of training, we propose two extensions: recursive supervision and skip connection. Our method outperforms previous methods by a large margin.
Dedicated Industrial Oilseed Crops as Metabolic Engineering Platforms for Sustainable Industrial Feedstock Production
Venue: Scientific reports

Feedstocks for industrial applications ranging from polymers to lubricants are largely derived from petroleum, a non renewable resource. Vegetable oils with fatty acid structures and storage forms tailored for specific industrial uses offer renewable and potentially sustainable sources of petrochemical type functionalities. A wide array of industrial vegetable oils can be generated through biotechnology, but will likely require non commodity oilseed platforms dedicated to specialty oil production for commercial acceptance. Here we show the feasibility of three Brassicaceae oilseeds crambe, camelina, and carinata, none of which are widely cultivated for food use, as hosts for complex metabolic engineering of wax esters for lubricant applications. Lines producing wax esters >20% of total seed oil were generated for each crop and further improved for high temperature oxidative stability by down regulation of fatty acid polyunsaturation. Field cultivation of optimized wax ester producing crambe demonstrated commercial utility of these engineered crops and a path for sustainable production of other industrial oils in dedicated specialty oilseeds.
Integration of omics approaches to understand oil/protein content during seed development in oilseed crops
Venue: Plant Cell Reports

Oilseed crops, especially soybean (Glycine max) and canola/rapeseed (Brassica napus) produce seeds that are rich in both proteins and oils and that are major sources of energy and nutrition worldwide. Most of the nutritional content in the seed is accumulated in the embryo during the seed filling stages of seed development. Understanding the metabolic pathways that are active during seed filling and how they are regulated are essential prerequisites to crop improvement. In this review, we summarize various omics studies of soybean and canola/rapeseed during seed filling, with emphasis on oil and protein traits, to gain a systems level understanding of seed development. Currently, most (80 85% of the soybean and rapeseed reference genomes have been sequenced (950 and 850 megabases, respectively) Parallel to these efforts, extensive omics datasets from different seed filling stages have become available. Transcriptome and proteome studies have detected preponderance of starch metabolism and glycolysis enzymes to be the possible cause of higher oil in B. napus compared to other crops. Small RNAome studies performed during the seed filling stages have revealed miRNA mediated regulation of transcription factors, with the suggestion that this interaction could be responsible for transitioning the seeds from embryogenesis to maturation. In addition, progress made in dissecting the regulation of de novo fatty acid synthesis and protein storage pathways is described. Advances in high throughput omics and comprehensive tissue specific analyses make this an exciting time to attempt knowledge driven investigation of complex regulatory pathways.
Genetic engineering approaches to enhance oil content in oilseed crops
Venue: Plant Growth Regulation

Oilseed crops play an important role in the agricultural economy. Apart from being an integral component of human diet and industrial applications, they are also gaining importance as replacement to fossil fuels for meeting the energy needs. The last two decades have been marked by several important events in genetic engineering and identification of gene targets for enhancing seed oil content in oilseed crops, and will aid the successful development of new generation high yielding oil crops. Specifically, genetic engineering has shown real breakthrough in enhancing oil content in oilseed rape, camelina, soybean and maize. Moreover, ongoing research efforts to decipher the possibilities of genetic modifications of key regulators of oil accumulation along with physiological and biochemical studies to understand lipid biosynthesis will set a platform to produce transgenic oilseed crops with enhanced oil content. In this review, we briefly describe different genetic engineering approaches explored by different researchers for enhancing oil content. Further, we discuss a few promising and potential approaches and challenges for engineering oil content in oilseed crops.
Ultrasound assisted extraction for the determination of Cu, Mn, Ca, and Mg in alternative oilseed crops using flame atomic absorption spectrometry.
Venue: Food chemistry

An ultrasound assisted extraction procedure was evaluated for the multi element determination of Cu, Mn, Ca, and Mg in alternative oilseed crops using flame atomic absorption spectrometry. The best results were obtained when 0.3g of samples were used to extract the mineral content using 10 mL of a 1.40 mol L( 1) HNO3 solution for 10 min at 25 degC. The accuracy and precision of the analysis were evaluated using two oilseed reference materials, and the results were in agreement with reference values at 95% confidence level (paired t test) The method was used to analyze five oilseed samples and the results were in agreement with those obtained using a closed vessel microwave assisted acid digestion system for sample preparation. The relative standard deviations were 0.52 6.13% for all of the standard and sample measurements, and the limits of detection were 666.7, 416.7, 333.4 mg g( 1) and 3.5 mg g( 1) for Cu, Mn, Ca, and Mg, respectively.
Application of Convolutional Neural Network for Image Classification on Pascal VOC Challenge 2012 dataset
Venue: ArXiv

In this project we work on creating a model to classify images for the Pascal VOC Challenge 2012. We use convolutional neural networks trained on a single GPU instance provided by Amazon via their cloud service Amazon Web Services (AWS) to classify images in the Pascal VOC 2012 data set. We train multiple convolutional neural network models and finally settle on the best model which produced a validation accuracy of 85.6% and a testing accuracy of 85.24%
Residential power scheduling for demand response in smart grid

This paper studies the power scheduling problem for residential consumers in smart grid. In general, the consumers have two types of electric appliances. The first type of appliances have flexible starting time and work continuously with a fixed power. The second type of appliances work with a flexible power in a predefined working time. The consumers can adjust the starting time of the first type of appliances or reduce the power consumption of the second type of appliances to reduce the payments. However, this will also incur discomfort to the consumers. Assuming the electricity price is announced by the service provider ahead of time, we propose a power scheduling strategy for the residential consumers to achieve a desired trade off between the payments and the discomfort. The power scheduling is formulated as an optimization problem including integer and continuous variables. An optimal scheduling strategy is obtained by solving the optimization problem. Simulation results demonstrate that the scheduling strategy can achieve a desired tradeoff between the payments and the discomfort.
Residential Demand Response Scheduling With Multiclass Appliances in the Smart Grid
Venue: IEEE Transactions on Smart Grid

In this paper, we study an electricity load scheduling problem in a residence. Compared with previous works in which only limited sets of appliances are considered, we classify various appliances into five sets considering their different energy consumption and operation characteristics, and provide mathematical models for them. With these appliance models, we propose an electricity load scheduling algorithm that controls the operation time and energy consumption level of each appliance adapting to time of use pricing in order to maximize the overall net utility of the residence while satisfying its budget limit. The optimization problem is formulated as a mixed integer nonlinear programming (MINLP) problem, which is in general, difficult to solve. In order to solve the problem, we use the generalized Benders decomposition approach with which we can solve the MINLP problem easily with low computational complexity. By solving the problem, we provide an algorithm to obtain the optimal electricity load scheduling of various appliances with different energy consumption and operation characteristics in a unified way.
A demand response modeling for residential consumers in smart grid environment using game theory based energy scheduling algorithm

In this paper, demand response modeling scheme is proposed for residential consumers using game theory algorithm as Generalized Tit for Tat (GTFT) Dominant Game based Energy Scheduler. The methodology is established as a work flow domain model between the utility and the user considering the smart grid framework. It exhibits an algorithm which schedules load usage by creating several possible tariffs for consumers such that demand is never raised. This can be done both individually and among multiple users of a community. The uniqueness behind the demand response proposed is that, the tariff is calculated for all hours and the load during the peak hours which can be rescheduled is shifted based on the Peak Average Ratio. To enable the vitality of the work simulation results of a general case of three domestic consumers are modeled extended to a comparative performance and evaluation with other algorithms and inference is analyzed.
A Matlab Simulink model of network reactive power compensation based on binary switchable capacitors and thyristor controlled reactor
Venue: 2016 IEEE International Conference on the Science of Electrical Engineering (ICSEE)

This paper considers a Matlab Simulink Model of a system for a load reactive power compensation with consisting of a thyristor controlled binary switched capacitors (TBSC) and a thyristor controlled reactor (TCR) The capacitors ensure stepwise compensation of reactive power, while the controlled inductance makes it possible to achieve smooth changing of the resulting compensating power within the limits of the minimal capacity in the binary capacitors series. The model uses a very simple algorithm of capacitor control, provided with a logical system that prevents current surges in the moments when capacitors are being connected to the network. The switching of capacitors is set by a closed system for maintenance of the maximal power coefficient. To regulate the inductance in TCR, an invariant system of phase control is used, which maintains the unit value of the power coefficient in the network irrespective of load changes. The realization of the system in the model is maximally close to its possible hardware realization. The functioning of the model has been tested in a wide range of variation of the reactive load power, and the testing confirmed the efficiency of combining a switchable series of binary capacitors and a thyristor controlled inductance when using the proposed control algorithms.