nils-herrmann/clean_scirepeval huggingface dataset:
Warm Glow or Extra Charge? The Ambivalent Effect of Corporate Social Responsibility Activities on Customers' Perceived Price Fairness
Year: 2016

Prior research has firmly established that consumers draw benefits from a firm's engagement in corporate social responsibility (CSR) especially the feeling of a "warm glow." These benefits positively affect several desirable outcomes, such as willingness to pay and customer loyalty. The authors propose that consumers do not blindly perceive benefits from a firm's CSR engagement but tend to suspect that a firm's prices include a markup to finance the CSR engagement. Taking customers' benefit perceptions and price markup inferences into account, the authors suggest that CSR engagement has mixed effects on consumers' evaluation of price fairness and, thus, on subsequent outcomes such as customer loyalty. The authors conduct one qualitative study and four quantitative studies leveraging longitudinal field and experimental data from more than 4,000 customers and show that customers indeed infer CSR price markups, entailing mixed effects of firms' CSR engagement on price fairness. The authors find that perception critically depends on customers' CSR attributions, and they explore the underlying psychological mechanisms. They propose communication strategies to optimize the effect of CSR engagement on perceived price fairness.
Establishing a model system for evaluating CAR T cell therapy using dogs with spontaneous diffuse large B cell lymphoma
Venue: Oncoimmunology
Year: 2020

Multiple rodent and primate preclinical studies have advanced CAR T cells into the clinic. However, no single model accurately reflects the challenges of effective CAR T therapy in human cancer patients. To evaluate the effectiveness of next generation CAR T cells that aim to overcome barriers to durable tumor elimination, we developed a system to evaluate CAR T cells in pet dogs with spontaneous cancer. Here we report on this system and the results of a pilot trial using CAR T cells to treat canine diffuse large B cell lymphoma (DLBCL) We designed and manufactured CD20 targeting, second generation canine CAR T cells for functional evaluation in vitro and in vivo using lentivectors to parallel human CAR T cell manufacturing. A first in species trial of five dogs with DLBCL treated with CAR T was undertaken. Canine CAR T cells functioned in an antigen specific manner and killed CD20+ targets. Circulating CAR T cells were detectable post infusion, however, induction of canine anti mouse antibodies (CAMA) was associated with CAR T cell loss. Specific selection pressure on CD20+ tumors was observed following CAR T cell therapy, culminating in antigen escape and emergence of CD20 disease. Patient survival times correlated with ex vivo product expansion. Altering product manufacturing improved transduction efficiency and skewed toward a memory like phenotype of canine CAR T cells. Manufacturing of functional canine CAR T cells using a lentivector is feasible. Comparable challenges to effective CAR T cell therapy exist, indicating their relevance in informing future human clinical trial design.
Examining the Anxiety and Attitudes of Secondary School Students towards Geography Lessons in terms of Some Variables
Year: 2018

The purpose of this study is to reveal the relation between anxiety and attitude scores of the secondary school students. In addition to this, in the present study, the anxiety and attitude scores of the students for Geography lesson were examined in terms of their achievement levels, gender, grade level and school type variables. The relational screening model was used in the present study and total of 2854 students attending to secondary schools in Karabuk city center joined the research within the 2016 2017 academic year. In the present study, it was determined that there is a moderate level, negative and significant relation between the anxiety and attitude scores of the secondary school students. In addition, it was also determined that the anxiety levels of the students about Geography lessons were low; however, their attitude levels were positive. In the present study, a significant difference was detected between the gender, achievement level, class level and type of school and Geography attitude averages of the students. It was also determined in the present study that as the success levels of the students increased, and the geography lesson anxiety scores decreased. In the present study, it was determined that there were no significant differences between the geography lesson anxiety scores and gender and grade levels of the students.
Explicit congruences modulo 2048 for overpartitions
Year: 2020

Let \bar{p}(n) p n denote the number of overpartitions of n Recently, a number of congruences modulo powers of 2, 3 and 5 have been discovered. The moduli for these congruences modulo powers of 2 discovered before ranged as high as 1024. In this paper, we establish explicit congruences modulo 2048 for \bar{p}(n) p n In particular, we deduce some strange congruences modulo 2048 for \bar{p}(n) p n For instance, we prove that for $n\ge 0$ n 0 \begin{aligned} \bar{p}(1820n^2+1820n+455)\equiv 0\\pmod {2048} \end{aligned} p 1820 n 2 1820 n 455 0 mod 2048
Pressure Tuneable Visible Range Band Gap in the Ionic Spinel Tin Nitride
Venue: Angewandte Chemie
Year: 2018

The application of pressure allows systematic tuning of the charge density of a material cleanly, that is, without changes to the chemical composition via dopants, and exploratory high pressure experiments can inform the design of bulk syntheses of materials that benefit from their properties under compression. The electronic and structural response of semiconducting tin nitride Sn3N4 under compression is now reported. A continuous opening of the optical band gap was observed from 1.3 eV to 3.0 eV over a range of 100 GPa, a 540 nm blue shift spanning the entire visible spectrum. The pressure mediated band gap opening is general to this material across numerous high density polymorphs, implicating the predominant ionic bonding in the material as the cause. The rate of decompression to ambient conditions permits access to recoverable metastable states with varying band gaps energies, opening the possibility of pressure tuneable electronic properties for future applications.
Mean Field Games and Applications: Numerical Aspects
Venue: Lecture Notes in Mathematics
Year: 2020

The theory of mean field games aims at studying deterministic or stochastic differential games (Nash equilibria) as the number of agents tends to infinity. Since very few mean field games have explicit or semi explicit solutions, numerical simulations play a crucial role in obtaining quantitative information from this class of models. They may lead to systems of evolutive partial differential equations coupling a backward Bellman equation and a forward Fokker Planck equation. In the present survey, we focus on such systems. The forward backward structure is an important feature of this system, which makes it necessary to design unusual strategies for mathematical analysis and numerical approximation. In this survey, several aspects of a finite difference method used to approximate the previously mentioned system of PDEs are discussed, including convergence, variational aspects and algorithms for solving the resulting systems of nonlinear equations. Finally, we discuss in details two applications of mean field games to the study of crowd motion and to macroeconomics, a comparison with mean field type control, and present numerical simulations.
A Comparison of Different Non linear Numerical Models for Pier Shaft Interaction Analysis for study of the Seismic Performance of Bridges
Year: 2013

The AASHTO code of practice for design of bridges shows that the ductility level for foundation is given equal to one. Bridges are classified in three classes. For class of essential and normal bridges a question for not to use ductility level due to the importance of bridges' costs is become an important item in different countries particularly in Middle east. This paper aimed to study on performance of bridges based on the effect of soil non linear behavior. A comparison between Finite Difference (F.D) Finite Element (F.E) methods and close form solutions has been carried out on single and group piles under lateral loads in multilayered soils. Winkler model with non linear springs, FLAC 3D, ANSYS5.4 and ABAQUS software were used for verifications. The Lagrangian method was considered in FLAC 3D and the Drucker Prager was used in ANSYS. Pile behavior in sandy soils, uniform clayey soils, clayey deposits including sandy lenses, and Sand deposits with thin clay layers were analyzed. Verification between traditional close form solution methods with those analysis methods were carried out. Results show the effectiveness of foundation ductility in compare with the expected ductility levels and structural performance for superstructure. The ductility level of substructure due to the pile soil interaction has found equal to 3.5 to 4.5 for different soil types. It show in "Pile and Deck (pier shaft)" model in bridges for the normal and essential bridges classes the use of ductility should be taken into account in accordance with soil structure performance. In conclusion the paper shows that for the pier shaft system it would be possible to use ductility level for foundations for normal and essential class of bridges and for those countries that they use the structural ductility for design of all components of structure including piles, the level of performance should be precisely investigated and using of structural ductility level is under question for design of pier shaft models.
NormFace: L2 Hypersphere Embedding for Face Verification
Venue: ACM Multimedia
Year: 2017

Thanks to the recent developments of Convolutional Neural Networks, the performance of face verification methods has increased rapidly. In a typical face verification method, feature normalization is a critical step for boosting performance. This motivates us to introduce and study the effect of normalization during training. But we find this is non trivial, despite normalization being differentiable. We identify and study four issues related to normalization through mathematical analysis, which yields understanding and helps with parameter settings. Based on this analysis we propose two strategies for training using normalized features. The first is a modification of softmax loss, which optimizes cosine similarity instead of inner product. The second is a reformulation of metric learning by introducing an agent vector for each class. We show that both strategies, and small variants, consistently improve performance by between 0.2% to 0.4% on the LFW dataset based on two models. This is significant because the performance of the two models on LFW dataset is close to saturation at over 98%
Numerical simulations of concrete processing: From standard formative casting to additive manufacturing
Venue: Cement and Concrete Research
Year: 2020

The concrete industry is facing new digital shaping processes that still have to be optimized and that require cement based materials, for which fresh properties requirements are yet to be defined. In this paper, we first present the state of the art in the field of numerical simulations of concrete flow. We then focus on the literature on numerical simulations of extrusion based additive manufacturing processes for concrete. Our review shows that the available numerical modelling tools are able to detect and predict various type of failures through the printing process, no matter if these happen at the scale of the nozzle, of one printed layer or of the entire object being printed. As they allow for a systematic evaluation of the influence of the entire range of material, geometric and process parameters, numerical modelling is a valid option to optimize both process and material.
Depth Wise Asymmetric Bottleneck With Point Wise Aggregation Decoder for Real Time Semantic Segmentation in Urban Scenes
Venue: IEEE Access
Year: 2020

Semantic segmentation is a process of linking each pixel in an image to a class label, and is widely used in the field of autonomous vehicles and robotics. Although deep learning methods have already made great progress for semantic segmentation, they either achieve great results with numerous parameters or design lightweight models but heavily sacrifice the segmentation accuracy. Because of the strict requirements of real world applications, it is critical to design an effective real time model with both competitive segmentation accuracy and small model capacity. In this paper, we propose a lightweight network named DABNet, which employs Depth wise Asymmetric Bottleneck (DAB) and Point wise Aggregation Decoder (PAD) module to tackle the challenging real time semantic segmentation in urban scenes. Specifically, the DAB module creates a sufficient receptive field and densely utilizes the contextual information, and the PAD module aggregates the feature maps of different scales to optimize performance through the attention mechanism. Compared with existing methods, our network substantially reduces the number of parameters but still achieves high accuracy with real time inference ability. Extensive ablation experiments on two challenging urban scene datasets (Cityscapes and CamVid) have proved the effectiveness of the proposed approach in real time semantic segmentation.
Synthesizing value sensitive design, responsible research and innovation, and energy justice: A conceptual review
Year: 2020

Many academic approaches that claim to consider the broad set of social and ethical issues relevant to energy systems sit side by side without conversation. This paper considers three such literatures: Value Sensitive Design, Responsible Research and Innovation and the Energy Justice framework. We argue that whilst definitions of these concepts appear, on face value, to be united by a common normative goal improving the social outcomes and mitigating sensitivities at the interface of technological energy systems and human livelihoods their existence in academic silos has obscured complementarities, which, once synthesized, might increase their overall academic and practical relevance. This paper fills the emergent gap of critically discussing the concepts and their strengths and challenges as well as how they could contribute to each other. It compares: (1) the things that they claim to tackle, (2) the solutions they claim to provide and (3) the points that clearly distinguish one approach from another (if any at all) Not only does this make this paper the first of its kind, but it also makes it an impactful one. With each concept gaining various degrees of support in academia and practice, our discussion reveals where tensions exist and where positive gains can be made. We identify five opportunities for collaboration and integration with implications for the achievement of energy systems that are acceptable from a societal and ethical perspective.
Realization on intelligent GUI automation testing based on .NET
Venue: 2010 3rd International Conference on Computer Science and Information Technology
Year: 2010

Points out the obvious deficiencies in capture/playback mechanism at present, aiming at difficulties of maintenance and extension in constantly altered GUI elements, presents a new GUI automation testing solution Building AUILibrary. It can search, identify all the controls, trigger all kinds of mouse and keyboard events, execute data driving verification roundly and accurately, trace and record execution process and save the locale when exception occurs, implement flexible and effective GUI automation testing indeed.
Tensor Canonical Correlation Analysis Networks for Multi view Remote Sensing Scene Recognition
Year: 2020

Convolutional neural network (CNN) has been proven an effective way to extract high level features from remote sensing (RS) images automatically. Many variants of the CNN model have been proposed, including principal component analysis network (PCANet) canonical correlation analysis network (CCANet) multiple scale CCANet (MS CCANet) and multiview CCANet (MCCANet) The PCANet is specialized for single view feature abstraction, while in many real world practices, the RS data are frequently observed from many more views. Although CCANet, MS CCANet and MCCANet can be applied to two or more view data, they consider only the pair wise correlation by calculating a series of two order covariance matrices. Moreover, it remains undiscovered how to handle high order consistencies which can only be explored by simultaneously examining all views. In this paper, we propose the tensor canonical correlation analysis network (TCCANet) to tackle this problem. Particularly, TCCANet learns filter banks by simultaneously maximizing arbitrary number of views with high order correlation and solves the optimization problem by decomposing a covariance tensor. After the convolutional stage, we utilize binarization and block wise histogram strategies to generate the final feature. Numerical experiment results on RSSCN7 and SAT 6 datasets demonstrate the advantages of TCCANet and MS TCCANet for RS scene recognition.
Classification of facility layout problems: a review study
Year: 2018

Facility layout problem (FLP) is defined as the placement of facilities in a plant area, with the aim of determining the most effective arrangement in accordance with some criteria or objectives under certain constraints, such as shape, size, orientation, and pick up/drop off point of the facilities. It has been over six decades since Koopmans and Beckmann published their seminal paper on modeling the FLP. Since then, there have been improvements to these researchers' original quadratic assignment problem. However, research on many aspects of the FLP is still in its initial stage; hence, the issue is an interesting field to work on. Here, a review of literature is made by referring to numerous papers about FLPs. The study is mainly motivated by the current and prospective trends of research on such points as layout evolution, workshop characteristics, problem formulation, and solution methodologies. It points to gaps in the literature and suggests promising directions for future research on FLP.
Co training Based on Multi type Text Features
Year: 2017

Sentiment classification is intended to classify the sentiment color categories expressed by the text. This paper illustrates the sentiment classification method based on the semi supervised algorithm that aims to improve performance by using unlabeled data. This paper proposes a novel co training style semi supervised learning algorithm in order to improve semi supervised learning ability. In our algorithm, there are three classifiers trained on the original labeled data, where the text representation for each classifier is unigram, bigram, and word2vec, respectively. And then these classifiers can use unlabeled data to update themselves. In detail, any of two classifiers have the same label, then add the new labeled data to a training set of the third classifier. By combining different types of features, our algorithm can extract text information from multiple views which contribute to sentiment classification. In addition, this algorithm doesn't require redundant and sufficient perspectives. Experiments show that our algorithm is superior to traditional co training algorithm and partial semi supervised learning algorithm.
Shall I Trust You? From Child Robot Interaction to Trusting Relationships
Venue: Frontiers in Psychology
Year: 2020

Studying trust in the context of human robot interaction is of great importance given the increasing relevance and presence of robotic agents in the social sphere, including educational and clinical. We investigated the acquisition, loss, and restoration of trust when preschool and school age children played with either a human or a humanoid robot in vivo. The relationship between trust and the representation of the quality of attachment relationships, Theory of Mind, and executive function skills was also investigated. Additionally, to outline children's beliefs about the mental competencies of the robot, we further evaluated the attribution of mental states to the interactive agent. In general, no substantial differences were found in children's trust in the play partner as a function of agency (human or robot) Nevertheless, 3 year olds showed a trend toward trusting the human more than the robot, as opposed to 7 year olds, who displayed the reverse pattern. These findings align with results showing that, for 3 and 7 year olds, the cognitive ability to switch was significantly associated with trust restoration in the human and the robot, respectively. Additionally, supporting previous findings, we found a dichotomy between attributions of mental states to the human and robot and children's behavior: while attributing to the robot significantly lower mental states than the human, in the Trusting Game, children behaved in a similar way when they related to the human and the robot. Altogether, the results of this study highlight that similar psychological mechanisms are at play when children are to establish a novel trustful relationship with a human and robot partner. Furthermore, the findings shed light on the interplay during development between children's quality of attachment relationships and the development of a Theory of Mind, which act differently on trust dynamics as a function of the children's age as well as the interactive partner's nature (human vs. robot)
Glucose forecasting combining Markov chain based enrichment of data, random grammatical evolution and Bagging
Venue: Appl. Soft Comput.
Year: 2020

Diabetes Mellitus is a disease affecting more and more people every year. Depending on the kind of diabetes and sometimes on the stage of the illness, diabetic patients have to inject some amount of artificial insulin, namely bolus, before the meals, to make up the absence or malfunctioning of their natural insulin. This decision is a difficult task since they need to estimate the number of carbohydrates they are going to ingest, take into account the past and future circumstances, know the past values of glucose, evaluate if the effect of previously injected insulin has already finished and any other relevant information. In this paper, we present and compare a set of methodologies to automate the decision of the insulin bolus, which reduces the number of dangerous predictions. We combine two different data enrichment techniques based on Markov chains with grammatical evolution engines to generate models of blood glucose, and univariate marginal distribution algorithms and bagging techniques to select the set of models to assemble. In particular, we propose the Random GE procedure, an adaptation of Random Forests to Grammatical Evolution, which leads to excellent prediction models, with a simple configuration and a reduced execution time. The ensemble gives the prediction of glucose for a duple of food and insulins, helping patients in the selection of the appropriate bolus to maintain healthy glucose levels after the meals. Experimental results show that our models get more accurate and robust predictions than previous approaches.