nils-herrmann/clean_scirepeval huggingface dataset:
Leaching characteristics of EDTA enhanced phytoextraction of Cd and Pb by Zea mays L. in different particle size fractions of soil aggregates exposed to artificial rain
Venue: Environmental Science and Pollution Research

Chelator assisted phytoextraction is an alternative and effective technique for the remediation of heavy metal contaminated soils, but the potential for heavy metal leaching needs to be assessed. In the present study, a soil column cultivation leaching experiment was conducted to investigate the Cd and Pb leaching characteristics during assisted phytoextraction of metal contaminated soils containing different particle size soil aggregates. The columns were planted with Zea mays "Zhengdan 958" seedlings and treated with combined applications of EDTA and simulated rainfall (pH 4.5 or 6.5) The results were as follows: (1) The greatest uptake of Cd and Pb by Z. mays was observed after treatment with EDTA (2.5 mmol kg 1 soil) and soil aggregates of <1 mm; uptake decreased as the soil aggregate size increased. (2) Simulated rainfall, especially acid rain (pH 4.5) after EDTA applications led to the increasing metal concentrations in the leachate, and EDTA significantly increased the concentrations of both Cd and Pb in the leachate, especially with soil aggregates of <1 mm; metal leachate concentrations decreased as soil particle sizes increased. (3) Concentrations of Cd and Pb decreased with each continuing leachate collection, and data were fit to linear regression models with coefficients of determination (R2) above 0.90 and 0.87 for Cd and Pb, respectively. The highest total amounts of Cd (22.12% and Pb (19.29% were observed in the leachate of soils treated with EDTA and artificial acid rain (pH 4.5) with soil aggregates of <1 mm. The application of EDTA during phytoextraction method increased the leaching risk in the following order: EDTA2.5 1 (pH 4.5) EDTA2.5 1 (pH 6.5) EDTA2.5 2 (pH 4.5) EDTA2.5 4 (pH 4.5) EDTA2.5 2 (pH 6.5) EDTA2.5 4 (pH 6.5)
Linguistic Features Identify Alzheimer's Disease in Narrative Speech.
Venue: Journal of Alzheimer's disease JAD

Although memory impairment is the main symptom of Alzheimer's disease (AD) language impairment can be an important marker. Relatively few studies of language in AD quantify the impairments in connected speech using computational techniques.  We aim to demonstrate state of the art accuracy in automatically identifying Alzheimer's disease from short narrative samples elicited with a picture description task, and to uncover the salient linguistic factors with a statistical factor analysis.  Data are derived from the DementiaBank corpus, from which 167 patients diagnosed with "possible" or "probable" AD provide 240 narrative samples, and 97 controls provide an additional 233. We compute a number of linguistic variables from the transcripts, and acoustic variables from the associated audio files, and use these variables to train a machine learning classifier to distinguish between participants with AD and healthy controls. To examine the degree of heterogeneity of linguistic impairments in AD, we follow an exploratory factor analysis on these measures of speech and language with an oblique promax rotation, and provide interpretation for the resulting factors.  We obtain state of the art classification accuracies of over 81% in distinguishing individuals with AD from those without based on short samples of their language on a picture description task. Four clear factors emerge: semantic impairment, acoustic abnormality, syntactic impairment, and information impairment.  Modern machine learning and linguistic analysis will be increasingly useful in assessment and clustering of suspected AD.
Regionalized life cycle impact assessment of air pollution on the global scale: Damage to human health and vegetation

We developed regionalized characterization factors (CFs) for human health damage from particulate matter (PM2.5) and ozone, and for damage to vegetation from ozone, at the global scale. These factors can be used in the impact assessment phase of an environmental life cycle assessment. CFs express the overall damage of a certain pollutant per unit of emission of a precursor, i.e. primary PM2.5, nitrogen oxides (NOx) ammonia (NH3) sulfur dioxide (SO2) and non methane volatile organic compounds (NMVOCs) The global chemical transport model TM5 was used to calculate intake fractions of PM2.5 and ozone for 56 world regions covering the whole globe. Furthermore, region specific effect and damage factors were derived, using mortality rates, background concentrations and years of life lost. The emission weighted world average CF for primary PM2.5 emissions is 629 yr kton 1, varying up to 3 orders of magnitude over the regions. Larger CFs were obtained for emissions in central Asia and Europe, and smaller factors in Australia and South America. The world average CFs for PM2.5 from secondary aerosols, i.e. NOx, NH3, and SO2, is 67.2 to 183.4 yr kton 1. We found that the CFs for ozone human health damage are 2 4 orders of magnitude lower compared to the CFs for damage due to primary PM2.5 and PM2.5 precursor emissions. Human health damage due to the priority air pollutants considered in this study was 1.7*10 2 yr capita 1 worldwide in year 2010, with primary PM2.5 emissions as the main contributor (62% The emission weighted world average CF for ecosystem damage due to ozone was 2.5 km2 yr kton 1 for NMVOCs and 8.7 m2 yr kg 1 for NOx emissions, varying 2 3 orders of magnitude over the regions. Ecosystem damage due to the priority air pollutants considered in this study was 1.6*10 4 km2 capita 1 worldwide in 2010, with NOx as the main contributor (72% The spatial range in CFs stresses the importance of including spatial variation in life cycle impact assessment of priority air pollutants.
Heavy Hitter Estimation over Set Valued Data with Local Differential Privacy
Venue: CCS

In local differential privacy (LDP) each user perturbs her data locally before sending the noisy data to a data collector. The latter then analyzes the data to obtain useful statistics. Unlike the setting of centralized differential privacy, in LDP the data collector never gains access to the exact values of sensitive data, which protects not only the privacy of data contributors but also the collector itself against the risk of potential data leakage. Existing LDP solutions in the literature are mostly limited to the case that each user possesses a tuple of numeric or categorical values, and the data collector computes basic statistics such as counts or mean values. To the best of our knowledge, no existing work tackles more complex data mining tasks such as heavy hitter discovery over set valued data. In this paper, we present a systematic study of heavy hitter mining under LDP. We first review existing solutions, extend them to the heavy hitter estimation, and explain why their effectiveness is limited. We then propose LDPMiner, a two phase mechanism for obtaining accurate heavy hitters with LDP. The main idea is to first gather a candidate set of heavy hitters using a portion of the privacy budget, and focus the remaining budget on refining the candidate set in a second phase, which is much more efficient budget wise than obtaining the heavy hitters directly from the whole dataset. We provide both in depth theoretical analysis and extensive experiments to compare LDPMiner against adaptations of previous solutions. The results show that LDPMiner significantly improves over existing methods. More importantly, LDPMiner successfully identifies the majority true heavy hitters in practical settings.
Recurrent Highway Networks with Language CNN for Image Captioning
Venue: ArXiv

In this paper, we propose a Recurrent Highway Network with Language CNN for image caption generation. Our network consists of three sub networks: the deep Convolutional Neural Network for image representation, the Convolutional Neural Network for language modeling, and the Multimodal Recurrent Highway Network for sequence prediction. Our proposed model can naturally exploit the hierarchical and temporal structure of history words, which are critical for image caption generation. The effectiveness of our model is validated on two datasets MS COCO and Flickr30K. Our extensive experiment results show that our method is competitive with the state of the art methods.
Multimodal Memory Modelling for Video Captioning
Venue: ArXiv

Video captioning which automatically translates video clips into natural language sentences is a very important task in computer vision. By virtue of recent deep learning technologies, e.g. convolutional neural networks (CNNs) and recurrent neural networks (RNNs) video captioning has made great progress. However, learning an effective mapping from visual sequence space to language space is still a challenging problem. In this paper, we propose a Multimodal Memory Model (M3) to describe videos, which builds a visual and textual shared memory to model the long term visual textual dependency and further guide global visual attention on described targets. Specifically, the proposed M3 attaches an external memory to store and retrieve both visual and textual contents by interacting with video and sentence with multiple read and write operations. First, text representation in the Long Short Term Memory (LSTM) based text decoder is written into the memory, and the memory contents will be read out to guide an attention to select related visual targets. Then, the selected visual information is written into the memory, which will be further read out to the text decoder. To evaluate the proposed model, we perform experiments on two publicly benchmark datasets: MSVD and MSR VTT. The experimental results demonstrate that our method outperforms the state of theart methods in terms of BLEU and METEOR.
Generating Natural Video Descriptions via Multimodal Processing
Venue: INTERSPEECH

Generating natural language descriptions of visual content is an intriguing task which has wide applications such as assisting blind people. The recent advances in image captioning stimulate further study of this task in more depth including generating natural descriptions for videos. Most works of video description generation focus on visual information in the video. However, audio provides rich information for describing video contents as well. In this paper, we propose to generate video descriptions in natural sentences via multimodal processing, which refers to using both audio and visual cues via unified deep neural networks with both convolutional and recurrent structure. Experimental results on the Microsoft Research Video Description (MSVD) corpus prove that fusing audio information greatly improves the video description performance. We also investigate the impact of image amount vs caption amount on the image caption performance and see the trend that when limited amount of training is available, number of various captions is more important than number of various images. This will guide us to investigate in the future how to improve the video description system via increasing amount of training data.
Evaluation of pelvic floor muscle strength before and after robotic assisted radical prostatectomy and early outcomes on urinary continence
Venue: Journal of Robotic Surgery

The aim of the study was to evaluate the effect of pelvic floor muscle (PFM) assessment and training before and after robot assisted laparoscopic radical prostatectomy (RARP) in improving PFM strength and urinary continence. We performed an analysis of a database of patients who underwent robot assisted laparoscopic radical prostatectomy (RARP) performed by two urologists from 2011 to 2013. Pelvic floor muscle (PFM) activation and strength were graded by a trained pelvic floor physiotherapist. Patients were given an exercise program, grouped according to the strength of their pelvic floor as graded by assessment, to complete before and after surgery. PFM strength was recorded preoperatively, 4 days post catheter removal and 4 weeks post catheter removal. Continence was recorded at 4 weeks postop and was defined as the requirement of no continence aids. A total of 98 patients had RARP and a preoperative physiotherapy assessment plus postoperative appointments at around 1 and 4 weeks post RARP. The majority of men improved their PFM strength regardless of preoperative strength with no significant predictors of postoperative strength found. Age was the only significant predictor of postoperative incontinence. In this pilot study, a majority of patients increased their pelvic floor strength with time. Pelvic floor physiotherapy is an important modifiable patient factor, which does have an impact in improving patients' urinary continence by strengthening the pelvic floor muscles. Patient age influences response to pelvic floor physiotherapy.
Problem gambling worldwide: An update and systematic review of empirical research (2000 2015)
Venue: Journal of behavioral addictions

Problem gambling has been identified as an emergent public health issue, and there is a need to identify gambling trends and to regularly update worldwide gambling prevalence rates. This paper aims to review recent research on adult gambling and problem gambling (since 2000) and then, in the context of a growing liberalization of the gambling market in the European Union, intends to provide a more detailed analysis of adult gambling behavior across European countries.  A systematic literature search was carried out using academic databases, Internet, and governmental websites.  Following this search and utilizing exclusion criteria, 69 studies on adult gambling prevalence were identified. These studies demonstrated that there are wide variations in past year problem gambling rates across different countries in the world (0.12 5.8% and in Europe (0.12 3.4% However, it is difficult to directly compare studies due to different methodological procedures, instruments, cut offs, and time frames. Despite the variability among instruments, some consistent results with regard to demographics were found.  The findings highlight the need for continuous monitoring of problem gambling prevalence rates in order to examine the influence of cultural context on gambling patterns, assess the effectiveness of policies on gambling related harms, and establish priorities for future research.
Prevalence of Adolescent Problem Gambling: A Systematic Review of Recent Research
Venue: Journal of Gambling Studies

Previous research has shown that gambling is a popular activity among adolescents. Following a rapid expansion of legalized gambling opportunities and the emergence of new forms of gambling, many researchers have carried out studies on adolescent gambling and problem gambling. The present paper reviews studies that have been conducted worldwide since 2000, and then presents a more detailed picture of adolescent gambling research in Europe, by providing a country by country analysis. After an extensive search on academic databases and following an exclusion process, 44 studies were identified. The findings showed that 0.2 12.3 of youth meet criteria for problem gambling, notwithstanding differences among assessment instruments, cut offs, and timeframes. However, despite this variability, several demographic characteristics were associated with adolescent gambling involvement and problem gambling. It is concluded that a small but significant minority of adolescents have gambling related problems. Such findings will hopefully encourage more research into youth gambling to further understand the determinants of this phenomenon.
A Critical Review of the Harm Minimisation Tools Available for Electronic Gambling
Venue: Journal of Gambling Studies

The increasing sophistication of gambling products afforded by electronic technologies facilitates increased accessibility to gambling, as well as encouraging rapid and continuous play. This poses several challenges from a responsible gambling perspective, in terms of facilitating player self awareness and self control. The same technological advancements in gambling that may facilitate a loss of control may also be used to provide responsible gambling tools and solutions to reduce gambling related harm. Indeed, several harm minimisation strategies have been devised that aim to facilitate self awareness and self control within a gambling session. Such strategies include the use of breaks in play, 'pop up' messaging, limit setting, and behavioural tracking. The present paper reviews the theoretical argument underpinning the application of specific harm minimisation tools, as well as providing one of the first critical reviews of the empirical research assessing their efficacy, in terms of influencing gambling cognitions and behaviour.
Heuristics and Cognitive Biases: Can the Group Decision Making Avoid Them?

Bad decisions can be the result of occasional or cognitive errors. Occasional errors are related to information content and its validity, or may refer to errors in understanding the problem or situation. Cognitive distortions are systematic errors, repeated constantly and unconsciously. These too may be the result of errors in information interpretation or related to its reliability and accuracy. Heuristics and cognitive biases affect the different stages of the decision making process and have negative effects on human behavior and relationships, because they are not easily identifiable and preventable. However, the use of heuristics and cognitive biases makes easier the decision making process. The current study tries to understand and formulate some final conclusions regarding the presence of the cognitive distortions in the decision making process, comparing the individual level to the group level. The main reason for choosing this topic is the increased use of groups to make decisions due to the dynamic, uncertain and turbulent environment, the horizontal structure, the difficulty to establish universal rules which guide the decision making and the higher complexity. So, the aim of the current paper is to understand if the group can be a valid tool to avoid or reduce the use of heuristics and cognitive biases.
Entropic forces drive contraction of cytoskeletal networks.
Venue: BioEssays news and reviews in molecular, cellular and developmental biology

The cytoskeleton is a network of interconnected protein filaments, which provide a three dimensional scaffold for cells. Remodeling of the cytoskeleton is important for key cellular processes, such as cell motility, division, or morphogenesis. This remodeling is traditionally considered to be driven exclusively by processes consuming chemical energy, such as the dynamics of the filaments or the action of molecular motors. Here, we review two mechanisms of cytoskeletal network remodeling that are independent of the consumption of chemical energy. In both cases directed motion of overlapping filaments is driven by entropic forces, which arise from harnessing thermal energy present in solution. Entropic forces are induced either by macromolecular crowding agents or by diffusible crosslinkers confined to the regions where filaments overlap. Both mechanisms increase filament overlap length and lead to the contraction of filament networks. These force generating mechanisms, together with the chemical energy dependent mechanisms, need to be considered for the comprehensive quantitative picture of the remodeling of cytoskeletal networks in cells.
A comprehensive survey of augmented reality assembly research

In the past two decades, augmented reality (AR) has received a growing amount of attention by researchers in the manufacturing technology community, because AR can be applied to address a wide range of problems throughout the assembly phase in the lifecycle of a product, e.g. planning, design, ergonomics assessment, operation guidance and training. However, to the best of authors' knowledge, there has not been any comprehensive review of AR based assembly systems. This paper aims to provide a concise overview of the technical features, characteristics and broad range of applications of AR based assembly systems published between 1990 and 2015. Among these selected articles, two thirds of them were published between 2005 and 2015, and they are considered as recent pertinent works which will be discussed in detail. In addition, the current limitation factors and future trends in the development will also be discussed.
Real Time Single Image and Video Super Resolution Using an Efficient Sub Pixel Convolutional Neural Network
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super resolution (SR) operation is performed in HR space. We demonstrate that this is sub optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better +0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN based methods.
Extracellular cGMP Modulates Learning Biphasically by Modulating Glycine Receptors, CaMKII and Glutamate Nitric Oxide cGMP Pathway
Venue: Scientific reports

It has been proposed that extracellular cGMP modulates the ability to learn a Y maze task, but the underlying mechanisms remained unknown. Here we show that extracellular cGMP, at physiological concentrations, modulates learning in the Y maze in a biphasic way by modulating the glutamate nitric oxide cGMP pathway in cerebellum. Extracellular cGMP reduces glycine receptors activation inducing a voltage dependent calcium channels mediated increase of calcium in Purkinje neurons. This calcium increase modulates CaMKII phosphorylation in a biphasic way. When basal calcium concentration is low extracellular cGMP reduces CaMKII phosphorylation, increasing nitric oxide synthase activity, the glutamate NO cGMP pathway function and learning ability. When basal calcium is normal extracellular cGMP increases CaMKII phosphorylation, reducing nitric oxide synthase activity, the pathway function and learning. These data unveil new mechanisms modulating learning in the Y maze and likely other learning types which may be therapeutic targets to improve learning in pathological situations associated with altered cGMP levels.
Recent advances of sonodynamic therapy in cancer treatment
Venue: Cancer biology medicine

Sonodynamic therapy (SDT) is an emerging approach that involves a combination of low intensity ultrasound and specialized chemical agents known as sonosensitizers. Ultrasound can penetrate deeply into tissues and can be focused into a small region of a tumor to activate a sonosensitizer which offers the possibility of non invasively eradicating solid tumors in a site directed manner. In this article, we critically reviewed the currently accepted mechanisms of sonodynamic action and summarized the classification of sonosensitizers. At the same time, the breath of evidence from SDT based studies suggests that SDT is promising for cancer treatment.