nils-herrmann/clean_scirepeval huggingface dataset:
Role of Biochar in Remediating Heavy Metals in Soil

Heavy metals are persistent pollutants in the environment which contaminate the food chain, pose risk to human health, and intimidate soil quality. Biochar has been proved efficient to reduce the heavy metal toxicity in contaminated soils. In this paper the properties of biochar and its ability to remediate heavy metals, i.e. Pb, Cd, Cr, As, Ni, Zn, and Cu, in soils are elaborated. Biochar application enhances soil fertility, crop yield, plant growth, carbon content, and nutrients availability. The different types of biochar, i.e. chicken manure, green waste, hardwood, oakwood, sewage sludge, rice straw, dairy manure, and cotton, can be applied by various methods for removal of heavy metals. pH is one of the main parameters among many which influence the function of biochar. Although heavy metal contaminated soils can be reclaimed effectively by application of biochar, further research is needed to explore its long term environmental and economic aspect to gain maximum benefits from this novel material.
Experimental and numerical investigations on the dynamic response of turbine blades with tip pin dampers

Friction dampers are used to reduce vibration amplitude of turbine blades. The dynamics of these assemblies (blades dampers) is nonlinear and the analysis is challenging from both the experimental and the numerical point of view. The study of the dynamics of blades with a tip damper is the aim of the present paper. The blades with axial entry fir tree attachment carry a damper in a pocket between the blade covers. Pin dampers significantly affect the resonance frequency of the first blade bending mode and introduces non linearity due to friction contacts. A test rig, made of two blades held in a fixture by an hydraulic press with one damper between the blades was used for the experimental activity. Three different types of dampers (cylindrical, asymmetrical, wedge) have been experimentally investigated and experiments have shown that asymmetrical damper performs better than the others. The response of the blades with the asymmetrical damper was then simulated with a non linear code based on the Harmonic Balance Method (HBM) In the analysis, both the blade and the damper are modelled with the Finite Elements and then the matrices reduced with the Craig Bampton Component Mode Synthesis (CB CMS) while the periodical contact forces are modelled with state of the art node to node contact elements. Numerical analysis has shown a strong influence of the actual extent of the contact area on the dynamics of the assembly. A model updating process was necessary. In the end, the numerical predictions match very well with the experimental curves.
Demosaicing and Denoising on Simulated Light Field Images

Light field cameras use an array of microlens to capture the 4D radiance of as scene. Standard image processing techniques with light field data do not utilize all four dimensions to demosaic or denoise captured images. In this paper, we formulate demosaicing as an optimization problem and enforce a TV prior on different dimensions of the light field. We apply our method on simulated light field data created from 3D virtual scenes. Because our data is simulated, we can use ground truth images to evaluate the effectiveness of our method. For certain combinations of dimensions, we achieve better overall PSNR values than the standard demosaicing technique described in Malvar et al.  Despite the improvement in PSNR, we introduce more color artifacts in areas of high frequency in the image. Our method also improves PNSR values for scenes with low illumination levels.
Application Analysis on Zhuzhe Hotel Management System in Chinese Hotel Chain
Venue: IEEE CSE 2016

Due to the advanced hotel management system, the Chinese hotel chains will efficiently manage a large number of information and introduce the advanced management concept to improve the efficiency and service quality. Only when the hotel improve the internal management system constantly, it could improve the level of the hotel's decision, economic interests and the relationship benefits, and finally enhance competitiveness. Zhuzhe hotel management system is a free hotel management system developed under the condition of the hotel informatization. This article mainly analyzed the application of Zhuzhe hotel management system in small and medium sized hotels. And through analysis, advantages and disadvantages are analyzed and evaluated. It could provide some guidance in the improvement and better application of the system.
GMAP and GSNAP for Genomic Sequence Alignment: Enhancements to Speed, Accuracy, and Functionality
Venue: Statistical Genomics

The programs GMAP and GSNAP, for aligning RNA Seq and DNA Seq datasets to genomes, have evolved along with advances in biological methodology to handle longer reads, larger volumes of data, and new types of biological assays. The genomic representation has been improved to include linear genomes that can compare sequences using single instruction multiple data (SIMD) instructions, compressed genomic hash tables with fast access using SIMD instructions, handling of large genomes with more than four billion bp, and enhanced suffix arrays (ESAs) with novel data structures for fast access. Improvements to the algorithms have included a greedy match and extend algorithm using suffix arrays, segment chaining using genomic hash tables, diagonalization using segmental hash tables, and nucleotide level dynamic programming procedures that use SIMD instructions and eliminate the need for F loop calculations. Enhancements to the functionality of the programs include standardization of indel positions, handling of ambiguous splicing, clipping and merging of overlapping paired end reads, and alignments to circular chromosomes and alternate scaffolds. The programs have been adapted for use in pipelines by integrating their usage into R/Bioconductor packages such as gmapR and HTSeqGenie, and these pipelines have facilitated the discovery of numerous biological phenomena.
Hardware Acceleration of Smith Waterman Algorithm for Short Read DNA Alignment Using FPGA
Venue: 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC)

The Field Programmable Gate Arrays (FPGAs) are highly attractive options for hardware implementations of Bioinformatics algorithms as they provide physical security and potentially higher performance than software solutions. The Smith Waterman algorithm is the most accurate local alignment algorithm for DNA and protein sequencing. However the existing parallel Smith Waterman algorithm needs extra memory space and this limits the size of a sequence to be aligned. As the data of biological banks expands exponentially, so we develop a new hardware parallel bi sequence and tri sequence alignment algorithm, using the strategy of divide and extend. This paper suggests hardware architecture for the Smith Waterman algorithm using Xilinx ZYNQ 7000 FPGA. Then we present the efficiency and utilization of our Parallel Smith Waterman over sequential algorithm. After that the simulated implementations of the algorithm presented to perform the most suitable hardware architecture within Xilinx ZYNQ 7000 FPGA which speeds up the algorithm by 28.4x with less Utilization and 29.4 more efficiency.
Robust Features of Finger Regions Based Hand Gesture Recognition Using Kinect Sensor
Venue: CCPR

Thanks to the emergence of commercial depth cameras, e.g. Kinect, hand gesture recognition has attracted great attention in recent years. In this context, we present a novel Kinect based hand gesture recognition system which focuses on the features of finger regions. A hand cropping approach is proposed to extract the useful finger regions from a noisy hand image including palm, wrist and arm obtained by Kinect. Furthermore, an original dissimilarity metric, called Balanced Finger Earth Movers Distance (BFEMD) is used to classify hand gestures along with the hierarchical recognition strategy. Finally, the 12 popular gestures recognition experiments have been done to illustrate the effectiveness of the proposed gesture recognition system, and the experimental results show that the proposed system can achieve high recognition accuracy at a high speed.
Cultural Indoctrination in Global Hypercompetition: A Conceptual Framework for International Management

This article examines the potential influence of cultural indoctrination CI on international management and corporate performance today in an era of global hypercompetition. The specific organizational function targeted in this work is international human resource management IHRM. As organizations are confronted with the need to engage with stakeholders from a variety of different cultural backgrounds, the need to understand the ways in which cultural imperatives play into individual and collective performances becomes increasingly important. Based on an encompassing literature review, this article examines the following seven factors which should be included in CI: Child Development, Cultural Institutionalization, Cultural Intelligence, Social Learning Theory, Religion, Social Capital, and Values Orientation Theory VOT. It is from these factors that a conceptual framework is developed for potential future application in IHRM theory and practice.
Error related negativity (ERN) and sustained threat: Conceptual framework and empirical evaluation in an adolescent sample.
Venue: Psychophysiology

The error related negativity (ERN) currently appears as a physiological measure in relation to three Research Domain Criteria (RDoC) constructs: Cognitive Control, Sustained Threat, and Reward Learning. We propose a conceptual model in which variance in the ERN reflects individual differences in the degree to which errors are evaluated as threatening. We also discuss evidence for the placement of the ERN in the "Sustained Threat" construct, as well as evidence that the ERN may more specifically reflect sensitivity to endogenous threat. Following this, we present data from a sample of 515 adolescent females demonstrating a larger ERN in relation to self reported checking behaviors, but only in older adolescents, suggesting that sensitivity to internal threat and the ERN checking relationship may follow a developmental course as adolescents develop behavioral control. In contrast, depressive symptoms were linked to a smaller ERN, and this association was invariant with respect to age. Collectively, these data suggest that the magnitude of the ERN is sensitive both to specific anxiety related processes and depression, in opposing directions that may reflect variation in internal threat sensitivity. We discuss directions for future research, as well as ways in which findings for the ERN complement and challenge aspects of the current RDoC matrix.
Highly efficient simultaneous ultrasonic assisted adsorption of brilliant green and eosin B onto ZnS nanoparticles loaded activated carbon: Artificial neural network modeling and central composite design optimization.
Venue: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

In this work, central composite design (CCD) combined with response surface methodology (RSM) and desirability function approach (DFA) gives useful information about operational condition and also to obtain useful information about interaction and main effect of variables concerned to simultaneous ultrasound assisted removal of brilliant green (BG) and eosin B (EB) by zinc sulfide nanoparticles loaded on activated carbon (ZnS NPs AC) Spectra overlap between BG and EB dyes was extensively reduced and/or omitted by derivative spectrophotometric method, while multi layer artificial neural network (ML ANN) model learned with Levenberg Marquardt (LM) algorithm was used for building up a predictive model and prediction of the BG and EB removal. The ANN efficiently was able to forecast the simultaneous BG and EB removal that was confirmed by reasonable numerical value i.e. MSE of 0.0021 and R(2) of 0.9589 and MSE of 0.0022 and R(2) of 0.9455 for testing data set, respectively. The results reveal acceptable agreement among experimental data and ANN predicted results. Langmuir as the best model for fitting experimental data relevant to BG and EB removal indicates high, economic and profitable adsorption capacity (258.7 and 222.2 mg g( 1) that supports and confirms its applicability for wastewater treatment.
Multiplicative Watermark Decoder in Contourlet Domain Using the Normal Inverse Gaussian Distribution
Venue: IEEE Transactions on Multimedia

In recent years, many works on digital image watermarking have been proposed all aiming at protection of the copyright of an image document or authentication of data. This paper proposes a novel watermark decoder in the contourlet domain It is known that the contourlet coefficients of an image are highly non Gaussian and a proper distribution to model the statistics of the contourlet coefficients is a heavy tailed PDF. It has been shown in the literature that the normal inverse Gaussian (NIG) distribution can suitably fit the empirical distribution. In view of this, statistical methods for watermark extraction are proposed by exploiting the NIG as a prior for the contourlet coefficients of images. The proposed watermark extraction approach is developed using the maximum likelihood method based on the NIG distribution. Closed form expressions are obtained for extracting the watermark bits in both clean and noisy environments. Experiments are performed to verify the robustness of the proposed decoder. The results show that the proposed decoder is superior to other decoders in terms of providing a lower bit error rate. It is also shown that the proposed decoder is highly robust against various kinds of attacks such as noise, rotation, cropping, filtering, and compression.
Sparse Word Embeddings Using l1 Regularized Online Learning
Venue: IJCAI

Recently, Word2Vec tool has attracted a lot of interest for its promising performances in a variety of natural language processing (NLP) tasks. However, a critical issue is that the dense word representations learned in Word2Vec are lacking of interpretability. It is natural to ask if one could improve their interpretability while keeping their performances. Inspired by the success of sparse models in enhancing interpretability, we propose to introduce sparse constraint into Word2Vec. Specifically, we take the Continuous Bag of Words (CBOW) model as an example in our study and add the ll regularizer into its learning objective. One challenge of optimization lies in that stochastic gradient descent (SGD) cannot directly produce sparse solutions with l1 regularizer in online training. To solve this problem, we employ the Regularized Dual Averaging (RDA) method, an online optimization algorithm for regularized stochastic learning. In this way, the learning process is very efficient and our model can scale up to very large corpus to derive sparse word representations. The proposed model is evaluated on both expressive power and interpretability. The results show that, compared with the original CBOW model, the proposed model can obtain state of the art results with better interpretability using less than 10% non zero elements.
Coordinated Resolved Motion Control of Dual Arm Manipulators with Closed Chain

When applied to some tasks, such as payload handling, assembling, repairing and so on, the two arms of a humanoid robot will form a closed kinematic chain. It makes the motion planning and control for dual arm coordination very complex and difficult. In this paper, we present three types of resolved motion control methods for a humanoid robot during coordinated manipulation. They are, respectively, position level, velocity level and acceleration level resolved motion control methods. The desired pose, velocity and acceleration of each end effector are then resolved according to the desired motion of the payload and the constraints on the closed chain system without consideration of the internal force. Corresponding to the three cases above, the joint variables of each arm are then calculated using the inverse kinematic equations, at position level, velocity level or acceleration level. Finally, a dynamic modelling and simulation platform is established based on ADAMS and Matlab software. The proposed methods are verified by typical cases. The simulation results show that the proposed control strategy can realize the dual arm coordinated operation and the internal force of the closed chain during the operation is controlled in a reasonable range at the same time.
Minimax Optimal Procedures for Locally Private Estimation
Venue: ArXiv

Working under a model of privacy in which data remain private even from the statistician, we study the tradeoff between privacy guarantees and the risk of the resulting statistical estimators. We develop private versions of classical information theoretical bounds, in particular those due to Le Cam, Fano, and Assouad. These inequalities allow for a precise characterization of statistical rates under local privacy constraints and the development of provably (minimax) optimal estimation procedures. We provide a treatment of several canonical families of problems: mean estimation and median estimation, generalized linear models, and nonparametric density estimation. For all of these families, we provide lower and upper bounds that match up to constant factors, and exhibit new (optimal) privacy preserving mechanisms and computationally efficient estimators that achieve the bounds. Additionally, we present a variety of experimental results for estimation problems involving sensitive data, including salaries, censored blog posts and articles, and drug abuse; these experiments demonstrate the importance of deriving optimal procedures.
Physiology Of Drowning: A Review.
Venue: Physiology

Drowning physiology relates to two different events: immersion (upper airway above water) and submersion (upper airway under water) Immersion involves integrated cardiorespiratory responses to skin and deep body temperature, including cold shock, physical incapacitation, and hypovolemia, as precursors of collapse and submersion. The physiology of submersion includes fear of drowning, diving response, autonomic conflict, upper airway reflexes, water aspiration and swallowing, emesis, and electrolyte disorders. Submersion outcome is determined by cardiac, pulmonary, and neurological injury. Knowledge of drowning physiology is scarce. Better understanding may identify methods to improve survival, particularly related to hot water immersion, cold shock, cold induced physical incapacitation, and fear of drowning.
Identification and characterization of naturally occurring inhibitors against human carboxylesterase 2 in White Mulberry Root bark.
Venue: Fitoterapia

White Mulberry Root bark (WMR) is an edible Chinese herbal used for the treatment of inflammation, nephritis and asthma. This study aimed to investigate the inhibitory effects of ethanol extract from WMR against human carboxylesterase 2 (hCE2) as well as to identity and character natural hCE2 inhibitors in this herbal. Our results demonstrated that the ethanol extract of WMR displayed potent inhibitory effects against hCE2, while three major bioactive constitutes in WMR were identified on the basis of LC fingerprinting combined with activity based screening of LC fractions. Three bioactive compounds including SD, KG and SC were efficiently identified by comparison of LC retention times, UV and MS spectral data, with the help of authentic standards. The inhibition potentials and inhibition types of these natural compounds against hCE2 were further investigated in human liver microsomes. The results demonstrated that these bioactive compounds are potent non competitive inhibitors against hCE2, with the Ki values ranging from 0.76mM to 1.09mM. All these findings suggested that three abundant natural compounds in WMR displayed potent inhibitory effects against hCE2, which could be used as lead compounds to develop more potent hCE2 inhibitors for the alleviation of hCE2 mediated severe delayed onset diarrhea.
Multicomponent Nonlinear Stochastic Dynamic Analysis by Tail Equivalent Linearization

AbstractThis paper extends the Tail Equivalent Linearization Method (TELM) to the case of a nonlinear mechanical system subjected to multiple stochastic excitations. Following the original formulation, the method employs a discrete representation of the stochastic inputs and the first order reliability method (FORM) Each component of the Gaussian excitation is expressed as a linear function of standard normal random variables. For a specified response threshold of the nonlinear system, the Tail Equivalent Linear System (TELS) is defined in the standard normal space by matching the design points of the equivalent linear and nonlinear systems. This leads to the identification of the TELS in terms of a frequency response function or, equivalently, an impulse response function relative to each component of the input excitation. The method is demonstrated through its application to an asymmetric, one story building with hysteretic behavior and subjected to bi component ground motion. The degree of asymmetry i.
Sequence to Sequence Learning as Beam Search Optimization
Venue: EMNLP

Sequence to Sequence (seq2seq) modeling has rapidly become an important general purpose NLP tool that has proven effective for many text generation and sequence labeling tasks. Seq2seq builds on deep neural language modeling and inherits its remarkable accuracy in estimating local, next word distributions. In this work, we introduce a model and beam search training scheme, based on the work of Daume III and Marcu (2005) that extends seq2seq to learn global sequence scores. This structured approach avoids classical biases associated with local training and unifies the training loss with the test time usage, while preserving the proven model architecture of seq2seq and its efficient training approach. We show that our system outperforms a highly optimized attention based seq2seq system and other baselines on three different sequence to sequence tasks: word ordering, parsing, and machine translation.
A molecular imprinting based turn on Ratiometric fluorescence sensor for highly selective and sensitive detection of 2,4 dichlorophenoxyacetic acid (2,4 D)
Venue: Biosensors bioelectronics

A novel molecular imprinting based turn on ratiometric fluorescence sensor was constructed via a facile sol gel polymerization for detection of 2,4 dichlorophenoxyacetic acid (2,4 D) on the basis of photoinduced electron transfer (PET) by using nitrobenzoxadiazole (NBD) as detection signal source and quantum dots (QDs) as reference signal source. With the presence and increase of 2,4 D, the amine groups on the surface of QDs@SiO2 could bind with 2,4 D and thereby the NBD fluorescence intensities could be significantly enhanced since the PET process was inhibited, while the QDs maintained constant intensities. Accordingly, the ratio of the dual emission intensities of green NBD and red QDs could be utilized for turn on fluorescent detection of 2,4 D, along with continuous color changes from orange red to green readily observed by the naked eye. The as prepared fluorescence sensor obtained high sensitivity with a low detection limit of 0.14mM within 5min, and distinguished recognition selectivity for 2,4 D over its analogs. Moreover, the sensor was successfully applied to determine 2,4 D in real water samples, and high recoveries at three spiking levels of 2,4 D ranged from 95.0% to 110.1% with precisions below 4.5% The simple, rapid and reliable visual sensing strategy would not only provide potential applications for high selective ultratrace analysis of complicated matrices, but also greatly enrich the research connotations of molecularly imprinted sensors.