nils-herrmann/clean_scirepeval huggingface dataset:
On Engineered Cementitious Composites ( ECC ) A Review of the Material and Its Applications

This article surveys the research and development of Engineered Cementitious Composites (ECC) over the last decade since its invention in the early 1990’s. The importance of micromechanics in the materials design strategy is emphasized. Observations of unique characteristics of ECC based on a broad range of theoretical and experimental research are examined. The advantageous use of ECC in certain categories of structural, and repair and retrofit applications is reviewed. While reflecting on past advances, future challenges for continued development and deployment of ECC are noted. This article is based on a keynote address given at the International Workshop on Ductile Fiber Reinforced Cementitious Composites (DFRCC) – Applications and Evaluations, sponsored by the Japan Concrete Institute, and held in October 2002 at Takayama, Japan.
Forecasting of Commercial Sales with Large Scale Gaussian Processes

This paper argues that there has not been enough discussion in the field of applications of Gaussian Process for the fast moving consumer goods industry. Yet, this technique can be important as it e.g., can provide automatic feature relevance determination and the posterior mean can unlock insights on the data. Significant challenges are the large size and high dimensionality of commercial data at a point of sale. The study reviews approaches in the Gaussian Processes modeling for large data sets, evaluates their performance on commercial sales and shows value of this type of models as a decision-making tool for management.
The Economic Performance of Regions 551

PORTER M. E. (2003) The economic performance of regions, Reg. Studies 37, 549–578. This paper examines the basic facts about the regional economic performance, the composition of regional economies and the role of clusters in the US economy over period of 1990 to 2000. The performance of regional economies varies markedly in terms of wage, wage growth, employment growth and patenting rate. Based on the distribution of economic activity across geography, we classify US industries into traded, local and resource-dependent. Traded industries account for only about one-third of employment but register much higher wages, far higher rates of innovation and influence local wages. We delineate clusters of traded industries using co-location patterns across US regions. The mix of clusters differs markedly across regions. The performance of regional economies is strongly influenced by the strength of local clusters and the vitality and plurality of innovation. Regional wage differences are dominated by the relative performance of the region in the clusters in which it has positions, with the particular mix of clusters secondary. A series of regional policy implications emerge from the findings.
FPGA-accelerated deep convolutional neural networks for high throughput and energy efficiency

Recent breakthroughs in the deep convolutional neural networks (CNNs) have led to great improvements in the accuracy of both vision and auditory systems. Characterized by their deep structures and large numbers of parameters, deep CNNs challenge the computational performance of today. Hardware specialization in the form of field-programmable gate array offers a promising path towards major leaps in computational performance while achieving high-energy efficiency. In this paper, we focus on accelerating deep CNNs using the Xilinx Zynq-zq7045 FPGA SoC. As most of the computational workload can be converted to matrix multiplications, we adopt a matrix multiplier-based accelerator architecture. Dedicated units are designed to eliminate the conversion overhead. We also design a customized memory system according to the memory access pattern of CNNs. To make the accelerator easily usable by application developers, our accelerator supports Caffe, which is a widely used software framework of deep CNN. Different CNN models can be adopted by our accelerator, with good performance portability. The experimental results show that for a typical application of CNN, image classification, an average throughout of 77.8 GFLOPS is achieved, while the energy efficiency is 4.7 better than an Nvidia K20 GPGPU.
Deep convolutional neural networks for accelerated dynamic magnetic resonance imaging

Dynamic magnetic resonance imaging (MRI) scans can be accelerated by utilizing compressed sensing (CS) reconstruction methods that allow for diagnostic quality images to be generated from undersampled data. Unfortunately, CS reconstruction is time-consuming, requiring hours between a dynamic MRI scan and image availability for diagnosis. In this work, we train a convolutional neural network (CNN) to perform fast reconstruction of severely undersampled dynamic cardiac MRI data, and we explore the utility of CNNs for further accelerating dynamic MRI scan times. Compared to state-of-the-art CS reconstruction techniques, our CNN achieves reconstruction speeds that are 150x faster without significant loss of image quality. Additionally, preliminary results suggest that CNNs may allow scan times that are 2x faster than those allowed by CS.
ORIENTATIONS TO HAPPINESS AND LIFE SATISFACTION: THE FULL LIFE VERSUS THE EMPTY LIFE

Different orientations to happiness and their association with life satisfaction were investigated with 845 adults responding to Internet surveys. We measured life satisfaction and the endorsement of three different ways to be happy: through pleasure, through engagement, and through meaning. Each of these three orientations individually predicted life satisfaction. People simultaneously low on all three orientations reported especially low life satisfaction. These findings point the way toward a distinction between the full life
IS THE RESOURCE-BASED " VIEW " A USEFUL PERSPECTIVE FOR STRATEGIC MANAGEMENT RESEARCH ?

Here I examine each of the major issues raised by Priem and Butler (this issue) about my 1991 article and subsequent resource-based research. While it turns out that Priem and Butler's direct criticisms oi the 1991 article are unfounded, they do remind resource-based researchers of some important requirements of this kind of research. I also discuss some important issues not raised by Priem and Butler—the resolutions of which will be necessary if a more complete resource-based theory of strategic advantage is to be developed.
Do medical marijuana laws reduce addictions and deaths related to pain killers?

Recent work finds that medical marijuana laws reduce the daily doses filled for opioid analgesics among Medicare Part-D and Medicaid enrollees, as well as population-wide opioid overdose deaths. We replicate the result for opioid overdose deaths and explore the potential mechanism. The key feature of a medical marijuana law that facilitates a reduction in overdose death rates is a relatively liberal allowance for dispensaries. As states have become more stringent in their regulation of dispensaries, the protective value generally has fallen. These findings suggest that broader access to medical marijuana facilitates substitution of marijuana for powerful and addictive opioids.
Virtual reality and neuropsychology: upgrading the current tools.

Virtual reality (VR) is an evolving technology that has been applied in various aspects of medicine, including the treatment of phobia disorders, pain distraction interventions, surgical training, and medical education. These applications have served to demonstrate the various assets offered through the use of VR.



To provide a background and rationale for the application of VR to neuropsychological assessment.



A brief introduction to VR technology and a review of current ongoing neuropsychological research that integrates the use of this technology.



VR offers numerous assets that may enhance current neuropsychological assessment protocols and address many of the limitations faced by our traditional methods.
Evolution Strategies for Deep Neural Network Models Design

Deep neural networks have become the state-ofart methods in many fields of machine learning recently. Still, there is no easy way how to choose a network architecture which can significantly influence the network performance. This work is a step towards an automatic architecture design. We propose an algorithm for an optimization of a network architecture based on evolution strategies. The al gorithm is inspired by and designed directly for the Keras library  which is one of the most common implementations of deep neural networks. The proposed algorithm is tested on MNIST data set and the prediction of air pollution based on sensor measurements, and it is compared to several fixed architectures and support vector regression.
LawStats - Large-Scale German Court Decision Evaluation Using Web Service Classifiers

LawStats provides quantitative insights into court decisions from the Bundesgerichtshof – Federal Court of Justice (BGH), the Federal Court of Justice in Germany. Using Watson Web Services and approaches from Sentiment Analysis (SA), we can automatically classify the revision outcome and offer statistics on judges, senates, previous instances etc. via faceted search. These statistics are accessible through a open web interface to aid law professionals. With a clear focus on interpretability, users can not only explore statistics, but can also understand, which sentences in the decision are responsible for the machine’s decision; links to the original texts provide more context. This is the first largescale application of Machine Learning (ML) based Natural Language Processing (NLP) for German in the analysis of ordinary court decisions in Germany that we are aware of. We have analyzed over 50,000 court decisions and extracted the outcomes and relevant entities. The modular architecture of the application allows continuous improvements of the ML model as more annotations become available over time. The tool can provide a critical foundation for further quantitative research in the legal domain and can be used as a proof-of-concept for similar efforts.
Author Name Disambiguation for PubMed

Log analysis shows that PubMed users frequently use author names in queries for retrieving scientific literature. However, author name ambiguity may lead to irrelevant retrieval results. To improve the PubMed user experience with author name queries, we designed an author name disambiguation system consisting of similarity estimation and agglomerative clustering. A machine-learning method was employed to score the features for disambiguating a pair of papers with ambiguous names. These features enable the computation of pairwise similarity scores to estimate the probability of a pair of papers belonging to the same author, which drives an agglomerative clustering algorithm regulated by 2 factors: name compatibility and probability level. With transitivity violation correction, high precision author clustering is achieved by focusing on minimizing false-positive pairing. Disambiguation performance is evaluated with manual verification of random samples of pairs from clustering results. When compared with a state-of-the-art system, our evaluation shows that among all the pairs the lumping error rate drops from 10.1% to 2.2% for our system, while the splitting error rises from 1.8% to 7.7%. This results in an overall error rate of 9.9%, compared with 11.9% for the state-of-the-art method. Other evaluations based on gold standard data also show the increase in accuracy of our clustering. We attribute the performance improvement to the machine-learning method driven by a large-scale training set and the clustering algorithm regulated by a name compatibility scheme preferring precision. With integration of the author name disambiguation system into the PubMed search engine, the overall click-through-rate of PubMed users on author name query results improved from 34.9% to 36.9%.
Supplier Selection Problems in Fashion Business Operations with Sustainability Considerations

In recent years, sustainability has been a major focus of fashion business operations because fashion industry development causes harmful effects to the environment, both indirectly and directly. The sustainability of the fashion industry is generally based on several levels and this study focuses on investigating the optimal supplier selection problem for sustainable materials supply in fashion clothing production. Following the ground rule that sustainable development is based on the Triple Bottom Line (TBL), this paper has framed twelve criteria from the economic, environmental and social perspectives for evaluating suppliers. The well-established multi-criteria decision making tool Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is employed for ranking potential suppliers among the pool of suppliers. Through a real case study, the proposed approach has been applied and some managerial implications are derived.
Stock Price Prediction With Long Short-Term Memory Recurrent Neural Network

In this paper, we investigate the prediction of daily stock prices of the top five companies in the Thai SET50 index. A Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) is applied to forecast the next daily stock price (High, Low, Open, Close). Deep Belief Network (DBN) is applied to compare the result with LSTM. The test data are CPALL, SCB, SCC, KBANK, and PTT from the SET50 index. The purpose of selecting these five stocks is to compare how the model performs in different stocks with various volatility. There are two experiments of five stocks from the SET50 index. The first experiment compared the MAPE with different length of training data. The experiment is conducted by using training data for one, three, and five-year. PTT and SCC stock give the lowest median value of MAPE error for five-year training data. KBANK, SCB, and CPALL stock give the lowest median value of MAPE error for one-year training data. In the second experiment, the number of looks back and input are varied. The result with one look back and four inputs gives the best performance for stock price prediction. By comparing different technique, the result show that LSTM give the best performance with CPALL, SCB, and KTB with less than 2% error. DBN give the best performance with PTT and SCC with less than 2% error.
Knowledge Management Strategies and Processes in Agile Software Development: A Systematic Literature Review

Knowledge-intensive companies that adopt Agile Software Development (ASD) relay on efficient implementation of Knowledge Management (KM) strategies to promotes different Knowledge Processes (KPs) to gain competitive advantage. This study aims to explore how companies that adopt ASD implement KM strategies utilizing practices that promote the KPs in the different organizational layers. Through a systematic literature review, we analyzed 32 primary studies, selected by automated search and snowballing in the extant literature. To analyze the data, we applied narrative synthesis. Most of the identified KM practices implement personalization strategies (81%), supported by codification (19%). Our review shows that the primary studies do not report KM practices in the strategic layer and two of them in the product portfolio layer; on the other hand, in the project layer, the studies report 33 practices that implement personalization strategy, and seven practices that implement codification. KM strategies in ASD promote mainly the knowledge transfer process with practices that stimulates social interaction to share tacit knowledge in the project layer. As a result of using informal communication, a significant amount of knowledge can be lost or not properly transferred to other individuals and, instead of propagating the knowledge, it remains inside a few individuals’ minds.
Relation Extraction and Grounding

Previous open Relation Extraction (open RE) approaches mainly rely on linguistic patterns and constraints to extract important relational triples from large-scale corpora. However, they lack of abilities to cover diverse relation expressions or measure the relative importance of candidate triples within a sentence. It is also challenging to name the relation type of a relational triple merely based on context words, which could limit the usefulness of open RE in downstream applications. We propose a novel importancebased open RE approach by exploiting the global structure of a dependency tree to extract salient triples. We design an unsupervised method to name relation types by grounding relational triples to a large-scale Knowledge Base (KB) schema, leveraging KB triples and weighted context words associated with relational triples. Experiments on the English Slot Filling 2013 dataset demonstrate that our approach achieves 8.1% higher F-score over stateof-the-art open RE methods.
Forecasting of sales by using fusion of machine learning techniques

Forecasting is an integral part of any organization for their decision-making process so that they can predict their targets and modify their strategy in order to improve their sales or productivity in the coming future. This paper evaluates and compares various machine learning models, namely, ARIMA, Auto Regressive Neural Network(ARNN), XGBoost, SVM, Hy-brid Models like Hybrid ARIMA-ARNN, Hybrid ARIMA-XGBoost, Hybrid ARIMA-SVM and STL Decomposition (using ARIMA, Snaive, XGBoost) to forecast sales of a drug store company called Rossmann. Training data set contains past sales and supplemental information about drug stores. Accuracy of these models is measured by metrics such as MAE and RMSE. Initially, linear model such as ARIMA has been applied to forecast sales. ARIMA was not able to capture nonlinear patterns precisely, hence nonlinear models such as Neural Network, XGBoost and SVM were used. Nonlinear models performed better than ARIMA and gave low RMSE. Then, to further optimize the performance, composite models were designed using hybrid technique and decomposition technique. Hybrid ARIMA-ARNN, Hybrid ARIMA-XGBoost, Hybrid ARIMA-SVM were used and all of them performed better than their respective individual models. Then, the composite model was designed using STL Decomposition where the decomposed components namely seasonal, trend and remainder components were forecasted by Snaive, ARIMA and XGBoost. STL gave better results than individual and hybrid models. This paper evaluates and analyzes why composite models give better results than an individual model and state that decomposition technique is better than the hybrid technique for this application.
Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features

Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for pharmacovigilance, via the use of natural language processing (NLP) techniques. However, the language in social media is highly informal, and user-expressed medical concepts are often nontechnical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and thus far, advanced machine learning-based NLP techniques have been underutilized. Our objective is to design a machine learning-based approach to extract mentions of adverse drug reactions (ADRs) from highly informal text in social media.



We introduce ADRMine, a machine learning-based concept extraction system that uses conditional random fields (CRFs). ADRMine utilizes a variety of features, including a novel feature for modeling words' semantic similarities. The similarities are modeled by clustering words based on unsupervised, pretrained word representation vectors (embeddings) generated from unlabeled user posts in social media using a deep learning technique.



ADRMine outperforms several strong baseline systems in the ADR extraction task by achieving an F-measure of 0.82. Feature analysis demonstrates that the proposed word cluster features significantly improve extraction performance.



It is possible to extract complex medical concepts, with relatively high performance, from informal, user-generated content. Our approach is particularly scalable, suitable for social media mining, as it relies on large volumes of unlabeled data, thus diminishing the need for large, annotated training data sets.
FIXED POINTS AND STABILITY IN NONLINEAR NEUTRAL VOLTERRA INTEGRO-DIFFERENTIAL EQUATIONS WITH VARIABLE DELAYS

In this paper we use the contraction mapping theorem to obtain asymptotic stability results of the zero solution of a nonlinear neutral Volterra integro-differential equation with variable delays. Some conditions which allow the coefficient functions to change sign and do not ask the boundedness of delays are given. An asymptotic stability theorem with a necessary and sufficient condition is proved, which improve and extend the results in the literature. Two examples are also given to illustrate this work.
Forecasting Economic Time Series Using Targeted Predictors

This paper studies two refinements to the method of factor forecasting. First, we consider the method of quadratic principal components that allows the link function between the predictors and the factors to be non-linear. Second, the factors used in the forecasting equation are estimated in a way to take into account that the goal is to forecast a specific series. This is accomplished by applying the method of principal components to ‘targeted predictors’ selected using hard and soft thresholding rules. Our three main findings can be summarized as follows. First, we find improvements at all forecast horizons over the current diffusion index forecasts by estimating the factors using fewer but informative predictors. Allowing for non-linearity often leads to additional gains. Second, forecasting the volatile one month ahead inflation warrants a high degree of targeting to screen out the noisy predictors. A handful of variables, notably relating to housing starts and interest rates, are found to have systematic predictive power for inflation at all horizons. Third, the variables chosen as targeted predictors selected by both soft and hard thresholding changes with the forecast horizon and the sample period. Holding the set of predictors fixed as is the current practice of factor forecasting is unnecessarily restrictive. ∗Department of Economics, NYU, 269 Mercer St, New York, NY 10003 Email: Jushan.Bai@nyu.edu. †Department of Economics, University of Michigan, Ann Arbor, MI 48109  Serena.Ng@umich.edu We would like to thank Jeremy Piger (discussant) and conference participants for helpful comments. We also acknowledge financial support from the NSF (grants SES-0137084, SES-0136923, SES-0549978)
3D face reconstruction based on multi-view stereo algorithm

This paper presents a 3D face reconstruction method based on multi-view stereo algorithm, the proposed algorithm reconstructs 3D face model from videos captured around static human faces. Image sequence is processed as the input of shape from motion algorithm to estimate camera parameters and camera positions, 3D points with different denseness degree could be acquired by using a method named patch based multi-view stereopsis, finally, the proposed method uses a surface reconstruction algorithm to generate a watertight 3D face model. The proposed approach can automatically detect facial feature points; it does not need any initialization and special equipments; videos can be obtained with commonly used picture pick-up device such as mobile phones. Several groups of experiments have been conducted to validate the availability of the proposed method.