nils-herrmann/clean_scirepeval huggingface dataset:
Revisiting the Marshmallow Test: A Conceptual Replication Investigating Links Between Early Delay of Gratification and Later Outcomes
Venue: Psychological science
Year: 2018

We replicated and extended Shoda, Mischel, and Peake's (1990) famous marshmallow study, which showed strong bivariate correlations between a child's ability to delay gratification just before entering school and both adolescent achievement and socioemotional behaviors. Concentrating on children whose mothers had not completed college, we found that an additional minute waited at age 4 predicted a gain of approximately one tenth of a standard deviation in achievement at age 15. But this bivariate correlation was only half the size of those reported in the original studies and was reduced by two thirds in the presence of controls for family background, early cognitive ability, and the home environment. Most of the variation in adolescent achievement came from being able to wait at least 20 s. Associations between delay time and measures of behavioral outcomes at age 15 were much smaller and rarely statistically significant.
Degradation of 4 chloro 2 aminophenol using combined strategies based on ultrasound, photolysis and ozone.
Venue: Ultrasonics sonochemistry
Year: 2016

The present work investigates the degradation of 4 chloro 2 aminophenol (4C2AP) a highly toxic organic compound, using ultrasonic reactors and combination of ultrasound with photolysis and ozonation for the first time. Two types of ultrasonic reactors viz. ultrasonic horn and ultrasonic bath operating at frequency of 20 kHz and 36 kHz respectively have been used in the work. The effect of initial pH, temperature and power dissipation of the ultrasonic horn on the degradation rate has been investigated. The established optimum parameters of initial pH as 6 (natural pH of the aqueous solution) and temperature as 30 2degC were then used in the degradation studies using the combined approaches. Kinetic study revealed that degradation of 4C2AP followed first order kinetics for all the treatment approaches investigated in the present work. It has been established that US+UV+O3 combined process was the most promising method giving maximum degradation of 4C2AP in both ultrasonic horn (complete removal) and bath (89.9% with synergistic index as 1.98 and 1.29 respectively. The cavitational yield of ultrasonic bath was found to be eighteen times higher as compared to ultrasonic horn implying that configurations with higher overall areas of transducers would be better selection for large scale treatment. Overall, the work has clearly demonstrated that combined approaches could synergistically remove the toxic pollutant (4C2AP)
Discriminative Regularized Deep Generative Models for Semi Supervised Learning
Venue: 2019 IEEE International Conference on Data Mining (ICDM)
Year: 2019

Deep generative models (DGMs) have shown strong performance in semi supervised learning (SSL) which incorporate discrete class information into the learning process. Yet existing methods generally overfit to the given labeled data, for only considering the conditional probability of labels. In this paper, we propose a novel discriminative regularized deep generative method for SSL, which fully exploits the discriminative and geometric information of data to address the aforementioned issue. Our method introduces the cluster and manifold assumption that maximizes the classification margin between clusters and simultaneously smooths the predictions of the data which is close in the sub manifold of each cluster, to regularize the learning of the classifier in DGMs. To derive the regularization based on introduced assumptions, we adopt the generated data of DGMs along with labelled and unlabelled data, to model the data manifold and yield clusters based on the Gumbel softmax distribution. Experimental results on both text and image datasets demonstrate the effectiveness and flexibility of our method, and prove that two introduced assumptions are complementary in guiding the classification boundary, thus improving the discriminative ability of the classifier.
Visualizing Dimension Coverage to Support Exploratory Analysis
Venue: IEEE Transactions on Visualization and Computer Graphics
Year: 2017

Data analysis involves constantly formulating and testing new hypotheses and questions about data. When dealing with a new dataset, especially one with many dimensions, it can be cumbersome for the analyst to clearly remember which aspects of the data have been investigated (i.e. visually examined for patterns, trends, outliers etc. and which combinations have not. Yet this information is critical to help the analyst formulate new questions that they have not already answered. We observe that for tabular data, questions are typically comprised of varying combinations of data dimensions (e.g. what are the trends of Sales and Profit for different Regions? We propose representing analysis history from the angle of dimension coverage (i.e. which data dimensions have been investigated and in which combinations) We use scented widgets to incorporate dimension coverage of the analysts' past work into interaction widgets of a visualization tool. We demonstrate how this approach can assist analysts with the question formation process. Our approach extends the concept of scented widgets to reveal aspects of one's own analysis history, and offers a different perspective on one's past work than typical visualization history tools. Results of our empirical study showed that participants with access to embedded dimension coverage information relied on this information when formulating questions, asked more questions about the data, generated more top level findings, and showed greater breadth of their analysis without sacrificing depth.
Effectiveness of Deep Transcranial Magnetic Stimulation Combined with a Brief Exposure Procedure in Post Traumatic Stress Disorder A Pilot Study
Venue: Brain Stimulation
Year: 2013

Post traumatic stress disorder (PTSD) is a debilitating anxiety disorder induced by traumatic experiences. To date, psychotherapy and drug treatment achieve only partial success, indicating need for further development of treatment strategies. Recent research has found that impaired acquired fear extinction capability serves as an important factor at the pathogenesis of the disorder. Medial prefrontal cortex (mPFC) hypo activity has been implicated in this extinction impairment, providing insight as to why some trauma exposed individuals will develop PTSD.  To test whether fear extinction can be facilitated and therapeutic effect achieved by repeated mPFC deep transcranial magnetic stimulation (DTMS) of PTSD patients resistant to standard treatment.  In a double blind study, 30 PTSD patients were enrolled and randomly assigned into 3 treatment groups: A) DTMS after brief exposure to the traumatic event with the script driven imagery procedure; B) DTMS after brief exposure to a non traumatic event; C) sham stimulation after brief exposure to the traumatic event.  Significant improvement was demonstrated in the intrusive component of the CAPS scale in patients administered DTMS after exposure to the traumatic event script, while patients in the control groups showed no significant improvement. Similar trend was demonstrated in the Total CAPS score as in the other rating scales. A significant reduction in the HR response to the traumatic script was evident in group A, further supporting the above results.  Combining brief script driven exposure with DTMS can induce therapeutic effects in PTSD patients. A wide multi center study is suggested to substantiate these findings.
Hydration and Durability of Ternary Binders Based on Metakaolin and Limestone Filler
Year: 2020

The combination of Portland cement and mineral additions allows reducing the carbon footprint of cement based materials and improving their resistance to several environmental actions. The development of binary binders is limited by the reactivity of pozzolanic materials at high substitution levels and the availability of industrial by products such as slags and fly ash. Thus, it is necessary to develop new cements from natural raw materials such as clay and limestone and to combine them to design ternary binders with higher hydraulic activity. The study is focused on mortar based on binary and ternary binders (Portland cement, metakaolin, limestone filler) with a maximum substitution level of 45% Two sets of mortar mixtures with different water to cement ratios were designed. The experimental program includes the determination of strength, porosity, hydration heat, portlandite content, shrinkage and natural carbonation. The analysis of data aimed at correlating the evolution of mechanical properties with hydration degree and reactivity of calcined clays. The results actually showed that the performances of ternary binders closely depend on the properties of the three studied metakaolins, especially their production process and physical properties. For a given substitution level, the studied ternary binders clearly showed better performances than other mineral additions. The reduction of water to cement ratio resulted in an acceleration of pozzolanic reaction. This allowed an improvement of short term strength as well as potential durability.
The effect of procedural fairness and supervisor support in the relationship between job insecurity and organizational citizenship behavior
Year: 2017

The aim of this paper is to examine whether the perception of job insecurity negatively affects procedural fairness, which may relate to the Organizational Citizenship Behavior (OCB) Finally, we have tested whether perceived supervisor support has a moderating effect in the relationship between procedural fairness and OCB.  Data were collected from a sample of 707 employees in a metal mechanic company in Chile. Regression analysis was used to analyze the mediating and moderating effects.  It was found that procedural fairness fully mediates the relationship between Job Insecurity and OCB, such that Job Insecurity is negatively related to procedural fairness, which in turn is positively related to OCB. Also, perceived supervisor support moderates the relationship between procedural fairness and OCB.  The paper provides new theoretical insights on the effects that perceived job insecurity has on OCB, giving an empirical insight of the mediator and moderator effects that aid in the comprehension of this relationship.
Plant phenology and global climate change: Current progresses and challenges.
Venue: Global change biology
Year: 2019

Plant phenology, the annually recurring sequence of plant developmental stages, is important for plant functioning and ecosystem services and their biophysical and biogeochemical feedbacks to the climate system. Plant phenology depends on temperature, and the current rapid climate change has revived interest in understanding and modeling the responses of plant phenology to the warming trend and the consequences thereof for ecosystems. Here, we review recent progresses in plant phenology and its interactions with climate change. Focusing on the start (leaf unfolding) and end (leaf coloring) of plant growing seasons, we show that the recent rapid expansion in ground and remote sensing based phenology data acquisition has been highly beneficial and has supported major advances in plant phenology research. Studies using multiple data sources and methods generally agree on the trends of advanced leaf unfolding and delayed leaf coloring due to climate change, yet these trends appear to have decelerated or even reversed in recent years. Our understanding of the mechanisms underlying the plant phenology responses to climate warming is still limited. The interactions between multiple drivers complicate the modeling and prediction of plant phenology changes. Furthermore, changes in plant phenology have important implications for ecosystem carbon cycles and ecosystem feedbacks to climate, yet the quantification of such impacts remains challenging. We suggest that future studies should primarily focus on using new observation tools to improve the understanding of tropical plant phenology, on improving process based phenology modeling, and on the scaling of phenology from species to landscape level.
Oxidative stress indicators in Chinese women with PCOS and correlation with features of metabolic syndrome and dependency on lipid patterns
Venue: Archives of Gynecology and Obstetrics
Year: 2019

The aim was to investigate oxidative stress indicators in the blood of women with PCOS without and with metabolic syndrome (MS) and their dependency on lipids, comparing with healthy women. To our knowledge, this is the first study on this topic. This was a cross sectional study, and blood tests performed were double blind. Within 3 months, 205 PCOS patients, from whom 55 also had MS, and 65 healthy women (control) were recruited. Malondialdehyde (MDA) was assessed as an important oxidative indicator, and superoxide dismutase (SOD) total antioxidant activity (TAA) vitamin C (VC) vitamin E (VE) and retinol (RET) as antioxidative indicators. Their correlation with features of MS was analyzed including their dependency on lipid pattern. SOD, TAA, VE and RET in the PCOS group and PCOS MS group were lower and MDA higher than in the control group (p 0.05) SOD, VE and RET were the lowest in PCOS MS group (p 0.05) Thus, patients in this group had the highest oxidative stress levels but the lowest antioxidative capacity. SOD and TAA significantly decreased with increase of triglycerides (TG) and LDL C in the PCOS MS group (p 0.05) but without dependency on HDL C. Stepwise multiple linear regression analysis confirmed the different expression of oxidative stress in the three groups and decrease of SOD from control to PCOS group to PCOS MS group, being associated with an increase of TG. MS can accelerate the oxidative stress process in patients with PCOS and decrease the antioxidative capacity. The decreased antioxidant capacity in PCOS with MS is related to increased TG and LDL C.
Evaluation of the energetic potential of sewage sludge by characterization of its organic composition.
Venue: Water science and technology a journal of the International Association on Water Pollution Research
Year: 2016

The composition of sewage sludge and, thus, its energetic potential is influenced by wastewater and wastewater treatment processes. Higher or lower heating values (HHV or LHV) are decisive factors for the incineration/gasification/pyrolysis of sewage sludge. The HHV is analyzed with a bomb calorimeter and converted to the LHV. It is also possible to calculate the heating value via chemical oxygen demand (COD) total volatile solids (TVS) and elemental composition. Calculating the LHV via the COD provides a suitable method. In contrast, the correlation of the HHV or LHV with the TVS is limited. One prerequisite here is a constant specific energy density; this was given with the types of sewage sludge (primary, surplus/excess, and digested sludge) investigated. If the energy density is not comparable with sewage sludge, for instance with the co substrate (bio waste, grease, etc. the estimation of the heating value using TVS will fail. When calculating the HHV or LHV via the elemental composition, one has to consider the validity of the coefficients of the calculation equation. Depending on the organic composition, it might be necessary to adjust the coefficients, e.g. when adding co substrates.
Aligning sequence reads, clone sequences and assembly contigs with BWA MEM
Year: 2013

BWA MEM is a new alignment algorithm for aligning sequence reads or long query sequences against a large reference genome such as human. It automatically chooses between local and end to end alignments, supports paired end reads and performs chimeric alignment. The algorithm is robust to sequencing errors and applicable to a wide range of sequence lengths from 70bp to a few megabases. For mapping 100bp sequences, BWA MEM shows better performance than several state of art read aligners to date. Availability and implementation: BWA MEM is implemented as a component of BWA, which is available at this http URL.
WebNLG 2020 Challenge: Semantic Template Mining for Generating References from RDF
Venue: WEBNLG
Year: 2020

We present in this paper our mining system for shared task WebNLG Challenge 2020. The general idea of the system is that we generate the semantic template of the output reference from the input RDF XML structure. In the training process, we perform the following subtasks: (i) extract the core information from input RDF; (ii) generate semantic templates from corresponding references. With new RDF XML data, we detect the core information, in turn add the new template into the warehouse and determine the output semantic template. We will evaluate the output natural language references in two processes: automatic and human evaluations. The results of the first tested process show that our system generates the high quality English descriptions from testing RDF XML structures and has a good contribution to the NLG state of the art.
A simple multifunctional PNIPAM GO/PANI hydrogel preparation strategy and its application in dye adsorption and infrared switching
Year: 2020

Therefore, the development of hydrogels with multi stimulus response characteristics will greatly broaden its application, such as smart appliances and dye adsorption aspects. In this study, we designed a simple two step radical polymerization method to fabricate a Poly(N isopropylacrylanide)(PNIPAM) graphene oxide (GO)/Polyaniline (PANI) thermosensitive conductive composite hydrogel. The synthetic material was characterized by scanning electron microscopy (SEM) thermogravimetric analysis (TGA) and Brunauer Emmett Teller (BET) The tensile/compression test and infrared light response analysis show that the material has good mechanical properties and good thermal response. The as prepared hydrogels show good applications in infrared light switching and dye selective adsorption. It provides some reference for the following hydrogel materials in the application of infrared light control switch in farmland environment and the adsorption and separation of anionic dye in sewage.
Adaptive Primal Dual Stochastic Gradient Method for Expectation constrained Convex Stochastic Programs
Year: 2020

Stochastic gradient methods (SGMs) have been widely used for solving stochastic optimization problems. A majority of existing works assume no constraints or easy to project constraints. In this paper, we consider convex stochastic optimization problems with expectation constraints. For these problems, it is often extremely expensive to perform projection onto the feasible set. Several SGMs in the literature can be applied to solve the expectation constrained stochastic problems. We propose a novel primal dual type SGM based on the Lagrangian function. Different from existing methods, our method incorporates an adaptiveness technique to speed up convergence. At each iteration, our method inquires an unbiased stochastic subgradient of the Lagrangian function, and then it renews the primal variables by an adaptiveSGM update and the dual variables by a vanilla SGM update. We show that the proposed method has a convergence rate of O(1/ k) in terms of the objective error and the constraint violation. Although the convergence rate is the same as those of existing SGMs, we observe its significantly faster convergence than an existing non adaptive primal dual SGM and a primal SGM on solving the Neyman Pearson classification and quadratically constrained quadratic programs. Furthermore, we modify the proposed method to solve convex concave stochastic minimax problems, for which we perform adaptive SGM updates to both primal and dual variables. A convergence rate of O(1/ k) is also established to the modified method for solving minimax problems in terms of primal dual gap.
Visual Marketing: From Attention to Action
Year: 2007

M. Wedel, R. Pieters,  Attention and Perception. K. Rayner, M.S. Castelhano, Eye Movements During Reading, Scene Perception, Visual Search and While Looking at Print Advertisements. R. Pieters, M. Wedel, The Informativeness of Eye Movements for Visual  Six Cornerstones. N.T. Tavassoli, The Effect of Selecting and Ignoring on Liking. Part 2. Visual Cognition. E.F. McQuarrie, Differentiating the Pictorial Element in  E. Greenleaf, P. Raghubir, Geometry in the Market Place. P. Raghubir, Are Visual Perception Biases Hard Wired? A. Krishna, Spatial Perception Research: An Integrative Review of Length, Area, Volume, and Number Perception. Part 3. Action and Choice. J. Meyers Levy, R. Zhu, Perhaps the Store Made You  Toward an Understanding of Structural Aspects of Indoor Shopping Environments. P. Chandon, J.W. Hutchinson, E.T. Bradlow, S.H. Young, Measuring the Value of Point of Purchase Marketing with Commercial Eye Tracking Data. H. Cho, N. Schwarz, H. Song, Images and Preference: A Feeling as Information Analysis.  Updating Old Constructs and Considering New Metaphors.
Anisotropic EM Segmentation by 3D Affinity Learning and Agglomeration
Venue: ArXiv
Year: 2017

The field of connectomics has recently produced neuron wiring diagrams from relatively large brain regions from multiple animals. Most of these neural reconstructions were computed from isotropic (e.g. FIBSEM) or near isotropic (e.g. SBEM) data. In spite of the remarkable progress on algorithms in recent years, automatic dense reconstruction from anisotropic data remains a challenge for the connectomics community. One significant hurdle in the segmentation of anisotropic data is the difficulty in generating a suitable initial over segmentation. In this study, we present a segmentation method for anisotropic EM data that agglomerates a 3D over segmentation computed from the 3D affinity prediction. A 3D U net is trained to predict 3D affinities by the MALIS approach. Experiments on multiple datasets demonstrates the strength and robustness of the proposed method for anisotropic EM segmentation.
Senti N Gram: An n gram lexicon for sentiment analysis
Venue: Expert Syst. Appl.
Year: 2018

Sentiment analysis helps evaluating the performance of products or services from user generated contents. Lexicon based sentiment analysis approaches are preferred over learning based ones when training data is not adequate. Existing lexicons contain only unigrams along with their sentiment scores. It is observed that sentiment n grams formed by combining unigrams with intensifiers or negations show improved results. Such sentiment n gram lexicons are not publicly available. This paper presents a methodology to create such a lexicon called Senti N Gram. Proposed rule based approach extracts the n grams sentiment scores from a random corpus containing product reviews and corresponding numeric rating in five point scale. The scores from this automated procedure are compared with that of the human annotators using t test and found to be statistically equivalent. The paper also proposes a sentiment classification methodology by using a ratio based approach based on counts of positive and negative sentences of a document. When used Senti N Gram lexicon, proposed method outperforms well known unigram lexicon based approach using VADER and an n gram sentiment analysis approach SO CAL.