nils-herrmann/clean_scirepeval huggingface dataset:
Global Supply Chain Management at Digital Equipment Corporation

Digital Equipme nt Corporation eva luates globa l supply chain alternatives and determines worldwide manufacturing and distribution strategy , using the Global Supply Cha in Model (GSCM) which recommends a production, distribution , and vendor network, GSCM minimizes cost or weighted cumulative production and distribution times or both sub ject to meeting estimated demand and restrictions on local content, offset trade, and joint capacity for multiple products, eche lons, and time periods. Cos t factors include fi xed and variable production cha rges, inventory charges, distribution expenses via multiple mod es, taxes, duties, and duty drawback. GSCM is a large mixed-integer linear program that incorporates a global, multi product bill of materials for supply chains with arbitrary eche lon structure and a comprehensive model of integrated globa l manufacturing and distribution decisions. The supply chain restructuring has saved over $100 million (US ),
L0 REGULARIZATION

We propose a practical method for L0 norm regularization for neural networks: pruning the network during training by encouraging weights to become exactly zero. Such regularization is interesting since (1) it can greatly speed up training and inference, and (2) it can improve generalization. AIC and BIC, well-known model selection criteria, are special cases of L0 regularization. However, since the L0 norm of weights is non-differentiable, we cannot incorporate it directly as a regularization term in the objective function. We propose a solution through the inclusion of a collection of non-negative stochastic gates, which collectively determine which weights to set to zero. We show that, somewhat surprisingly, for certain distributions over the gates, the expected L0 regularized objective is differentiable with respect to the distribution parameters. We further propose the hard concrete distribution for the gates, which is obtained by “stretching” a binary concrete distribution and then transforming its samples with a hard-sigmoid. The parameters of the distribution over the gates can then be jointly optimized with the original network parameters. As a result our method allows for straightforward and efficient learning of model structures with stochastic gradient descent and allows for conditional computation in a principled way. We perform various experiments to demonstrate the effectiveness of the resulting approach and regularizer.
Multi-Task Learning as Multi-Objective Optimization

In multi-task learning, multiple tasks are solved jointly, sharing inductive bias between them. Multi-task learning is inherently a multi-objective problem because different tasks may conflict, necessitating a trade-off. A common compromise is to optimize a proxy objective that minimizes a weighted linear combination of pertask losses. However, this workaround is only valid when the tasks do not compete, which is rarely the case. In this paper, we explicitly cast multi-task learning as multi-objective optimization, with the overall objective of finding a Pareto optimal solution. To this end, we use algorithms developed in the gradient-based multiobjective optimization literature. These algorithms are not directly applicable to large-scale learning problems since they scale poorly with the dimensionality of the gradients and the number of tasks. We therefore propose an upper bound for the multi-objective loss and show that it can be optimized efficiently. We further prove that optimizing this upper bound yields a Pareto optimal solution under realistic assumptions. We apply our method to a variety of multi-task deep learning problems including digit classification, scene understanding (joint semantic segmentation, instance segmentation, and depth estimation), and multilabel classification. Our method produces higher-performing models than recent multi-task learning formulations or per-task training.
The Core Components of RTI : A Closer Look at Evidence-based Core Curriculum , Assessment and Progress Monitoring , and Data-based Decision Making

The recent special report, “Indiana’s Vision of Response to Intervention” issued by the Center for Evaluation & Education Policy (CEEP) was the first of a three-part series aimed to build a fundamental understanding of a Response-to-Intervention (RTI) framework in Indiana’s schools to aid in the prevention and intervention of both academic and behavioral problems for all students. The report also discussed the impetus for implementation of RTI, as well as what the state of Indiana is currently doing to respond to and guide schools through this new initiative. Specifically, Indiana’s Department of Education (IDOE) has developed a framework of RTI that addresses six core components on which to focus: (1) evidence-based curriculum, instruction, intervention and extension; (2) assessment and progress monitoring; (3) data-based decision making; (4) leadership; (5) family, school, and community partnerships; and (6) cultural responsivity.
Security Analysis for Online Banking System Using Hierarchical Stochastic Game Nets Model

In this paper, we propose hierarchical Stochastic Game Nets, and use it to model and analyze the security issues in online banking system. Firstly, the definition and modeling algorithm of hierarchical Stochastic Game Nets are given. In which, some theorems are proved. Then we apply hierarchical Stochastic Game Nets to describe the attack and defense course in the online banking. Finally we analyze the attack time and attack probability in the online banking quantitatively based on the model successfully.
On the development of magnetic induction heating for hyperthermia and ablation of tumors

The present report is on the recent progress of the development of Magnetic Induction Heating (MIH) for treating tumors by electromagnetic energy, namely, hyperthermia or ablation. New concept of system design and new material usage are introduced in the recent published results. The theoretic concept, previous study in literature and constant challenge of MIH are reviewed and discussed. Some animal experimental results are presented as well.
Non-Projective Dependency Parsing using Spanning Tree Algorithms

We formalize weighted dependency parsing as searching for maximum spanning trees (MSTs) in directed graphs. Using this representation, the parsing algorithm of Eisner (1996) is sufficient for searching over all projective trees in O(n3) time. More surprisingly, the representation is extended naturally to non-projective parsing using Chu-Liu-Edmonds (Chu and Liu, 1965; Edmonds, 1967) MST algorithm, yielding anO(n2) parsing algorithm. We evaluate these methods on the Prague Dependency Treebank using online large-margin learning techniques (Crammer et al., 2003; McDonald et al., 2005) and show that MST parsing increases efficiency and accuracy for languages with non-projective dependencies.
A flexible tensor block coordinate ascent scheme for hypergraph matching

The estimation of correspondences between two images resp. point sets is a core problem in computer vision. One way to formulate the problem is graph matching leading to the quadratic assignment problem which is NP-hard. Several so called second order methods have been proposed to solve this problem. In recent years hypergraph matching leading to a third order problem became popular as it allows for better integration of geometric information. For most of these third order algorithms no theoretical guarantees are known. In this paper we propose a general framework for tensor block coordinate ascent methods for hypergraph matching. We propose two algorithms which both come along with the guarantee of monotonic ascent in the matching score on the set of discrete assignment matrices. In the experiments we show that our new algorithms outperform previous work both in terms of achieving better matching scores and matching accuracy. This holds in particular for very challenging settings where one has a high number of outliers and other forms of noise.
Ontology-Based Extraction and Structuring of Information from Data-Rich Unstructured Documents

We present a new approach to extracting information from unstructured documents based on an application ontology that describes a domain of interest. Starting with such an ontology, we formulate rules to extract constants and context keywords from unstructured documents. For each unstructured document of interest, we extract its constants and keywords and apply a recognizer to organize extracted constants as attribute values of tuples in a generated database schema. To make our approach general, we fix all the processes and change only the ontological description for a different application domain. In experiments we conducted on two different types of unstructured documents taken from the Web, our approach attained recall ratios in the 80% and 90% range and precision ratios near 98%.
Validity, reliability, and generalizability in qualitative research

In general practice, qualitative research contributes as significantly as quantitative research, in particular regarding psycho-social aspects of patient-care, health services provision, policy setting, and health administrations. In contrast to quantitative research, qualitative research as a whole has been constantly critiqued, if not disparaged, by the lack of consensus for assessing its quality and robustness. This article illustrates with five published studies how qualitative research can impact and reshape the discipline of primary care, spiraling out from clinic-based health screening to community-based disease monitoring, evaluation of out-of-hours triage services to provincial psychiatric care pathways model and finally, national legislation of core measures for children's healthcare insurance. Fundamental concepts of validity, reliability, and generalizability as applicable to qualitative research are then addressed with an update on the current views and controversies.
Improving Conditional Sequence Generative Adversarial Networks by Stepwise Evaluation

Sequence generative adversarial networks SeqGAN have been used to improve conditional sequence generation tasks, for example, chit-chat dialogue generation. To stabilize the training of SeqGAN, Monte Carlo tree search MCTS or reward at every generation step REGS is used to evaluate the goodness of a generated subsequence. MCTS is computationally intensive, but the performance of REGS is worse than MCTS. In this paper, we propose stepwise GAN StepGAN, in which the discriminator is modified to automatically assign scores quantifying the goodness of each subsequence at every generation step. StepGAN has significantly less computational costs than MCTS. We demonstrate that StepGAN outperforms previous GAN-based methods on both synthetic experiment and chit-chat dialogue generation.
Informed Trading in Stock and Option Markets

We investigate the contribution of option markets to price discovery, using a modification of Hasbrouck’s (1995) “information share” approach. Based on five years of stock and options data for 60 firms, we estimate the option market’s contribution to price discovery to be about 17 percent on average. Option market price discovery is related to trading volume and spreads in both markets, and stock volatility. Price discovery across option strike prices is related to leverage, trading volume, and spreads. Our results are consistent with theoretical arguments that informed investors trade in both stock and option markets, suggesting an important informational role for options. ∗Chakravarty is from Purdue University; Gulen is from the Pamplin College of Business, Virginia Tech; and Mayhew is from the Terry College of Business, University of Georgia and the U.S. Securities and Exchange Commission. We would like to thank the Institute for Quantitative Research in Finance (the Q-Group) for funding this research. Gulen acknowledges funding from a Virginia Tech summer grant and Mayhew acknowledges funding from the TerrySanford Research Grant at the Terry College of Business and from the University of Georgia Research Foundation. We would like to thank the editor, Rick Green; Michael Cliff; Joel Hasbrouck; Raman Kumar; an anonymous referee; and seminar participants at Purdue University, the University of Georgia, Texas Christian University, the University of South Carolina, the Securities and Exchange Commission, the University of Delaware, George Washington University, the Commodity Futures Trading Commission, the Batten Conference at the College of William and Mary, the 2002 Q-Group Conference, and the 2003 INQUIRE conference. The U.S. Securities and Exchange Commission disclaims responsibility for any private publication or statement of any SEC employee or Commissioner. This study expresses the author’s views and does not necessarily reflect those of the Commission, the Commissioners, or other members of the staff.
Multimodal Trip Hazard Affordance Detection on Construction Sites

Trip hazards are a significant contributor to accidents on construction and manufacturing sites. Current safety inspections are labor intensive and limited by human fallibility, making automation of trip hazard detection appealing from both a safety and economic perspective. Trip hazards present an interesting challenge to modern learning techniques because they are defined as much by affordance as by object type, for example, wires on a table are not a trip hazard, but can be if lying on the ground. To address these challenges, we conduct a comprehensive investigation into the performance characteristics of 11 different colors and depth fusion approaches, including four fusion and one nonfusion approach, using color and two types of depth images. Trained and tested on more than 600 labeled trip hazards over four floors and 2000 m2 in an active construction site, this approach was able to differentiate between identical objects in different physical configurations. Outperforming a color-only detector, our multimodal trip detector fuses color and depth information to achieve a 4% absolute improvement in F1-score. These investigative results and the extensive publicly available dataset move us one step closer to assistive or fully automated safety inspection systems on construction sites.
The future of sex and gender in psychology: Five challenges to the gender binary.

The view that humans comprise only two types of beings, women and men, a framework that is sometimes referred to as the "gender binary," played a profound role in shaping the history of psychological science. In recent years, serious challenges to the gender binary have arisen from both academic research and social activism. This review describes 5 sets of empirical findings, spanning multiple disciplines, that fundamentally undermine the gender binary. These sources of evidence include neuroscience findings that refute sexual dimorphism of the human brain; behavioral neuroendocrinology findings that challenge the notion of genetically fixed, nonoverlapping, sexually dimorphic hormonal systems; psychological findings that highlight the similarities between men and women; psychological research on transgender and nonbinary individuals' identities and experiences; and developmental research suggesting that the tendency to view gender/sex as a meaningful, binary category is culturally determined and malleable. Costs associated with reliance on the gender binary and recommendations for future research, as well as clinical practice, are outlined.
On the concept of e-maintenance: Review and current research

The importance of the maintenance function has increased because of its role in keeping and improving system availability and safety, as well as product quality. To support this role, the development of the communication and information technologies has allowed the emergence of the concept of e-maintenance. Within the era of e-manufacturing and e-business, e-maintenance provides the opportunity for a new maintenance generation. As we will discuss later in this paper, e-maintenance integrates existing telemaintenance principles, with Web services and modern e-collaboration principles. Collaboration allows to share and exchange not only information but also knowledge and (e)-intelligence. By means of a collaborative environment, pertinent knowledge and intelligence become available and usable at the right place and time, in order to facilitate reaching the best maintenance decisions. This paper outlines the basic ideas within the e-maintenance concept and then provides an overview of the current research and challenges in this emerging field. An underlying objective is to identify the industrial/academic actors involved in the technological, organizational or management issues related to the development of e-maintenance. Today, this heterogeneous community has to be federated in order to bring up e-maintenance as a new scientific discipline.
Interpretable and Compositional Relation Learning by Joint Training with an Autoencoder

Embedding models for entities and relations are extremely useful for recovering missing facts in a knowledge base. Intuitively, a relation can be modeled by a matrix mapping entity vectors. However, relations reside on low dimension sub-manifolds in the parameter space of arbitrary matrices – for one reason, composition of two relations M1,M2 may match a third M3 (e.g. composition of relations currency of country and country of film usually matches currency of film budget), which imposes compositional constraints to be satisfied by the parameters (i.e. M1 ·M2 ≈ M3). In this paper we investigate a dimension reduction technique by training relations jointly with an autoencoder, which is expected to better capture compositional constraints. We achieve state-of-the-art on Knowledge Base Completion tasks with strongly improved Mean Rank, and show that joint training with an autoencoder leads to interpretable sparse codings of relations, helps discovering compositional constraints and benefits from compositional training. Our source code is released at github.com/tianran/glimvec.
On analyzing of fingerprint direct-access strategies

This paper analyzed several aspect of publicly available fingerprint direct-access strategies over three public data sets, including their advantages and drawbacks, related works, and accuracy performance comparison on the graph of error rate and penetration rate. On recent research, smaller penetration rate at certain error rate (smaller area under curve) could be obtained by certain strategy. Penetration rate equal to 5% could be produced at error rate equal to 4%, 10%, and 1% on FVC2000 DB2A, FVC2000 DB3A, and FVC2002 DB1A, respectively. Through this analysis, hopely it could be used as a foundation for better future developments over new strategies or future improvements over existing strategies in area of fingerprint identification, especially to obtain more accurate result with smaller area under curve and achieved with faster search time.
Combining Graph Degeneracy and Submodularity for Unsupervised

We present a fully unsupervised, extractive text summarization system that leverages a submodularity framework introduced by past research. The framework allows summaries to be generated in a greedy way while preserving near-optimal performance guarantees. Our main contribution is the novel coverage reward term of the objective function optimized by the greedy algorithm. This component builds on the graph-of-words representation of text and the k-core decomposition algorithm to assign meaningful scores to words. We evaluate our approach on the AMI and ICSI meeting speech corpora, and on the DUC2001 news corpus. We reach state-of-the-art performance on all datasets. Results indicate that our method is particularly well-suited to the meeting domain.
Solving Sudoku with Boolean Algebra

Sudoku is a very popular puzzle which consists of placing several numbers in a squared grid according to some simple rules. In this paper, we present a Sudoku solving technique named Boolean Sudoku Solver (BSS) using only simple Boolean algebras. Use of Boolean algebra increases the execution speed of the Sudoku solver. Simulation results show that our method returns the solution of the Sudoku in minimum number of iterations and outperforms the existing popular approaches.
Vein detection using infrared imaging system

Infrared vein detection is one of the newest biomedical techniques researched today. Basic principal behind this is, when IR light transmitted on palm it passes through tissue and veins absorbs that light and the vein appears darker than surrounding tissue. This paper presents vein detection system using strong IR light source, webcam, Matlab based image processing algorithm. Using the Strong IR light source consisting of high intensity led and webcam camera we captured transillumination image of palm. Image processing algorithm is used to separate out the veins from palm.
Learning to Linearize Under Uncertainty

Training deep feature hierarchies to solve supervised learning tasks has achieved state of the art performance on many problems in computer vision. However, a principled way in which to train such hierarchies in the unsupervised setting has remained elusive. In this work we suggest a new architecture and loss for training deep feature hierarchies that linearize the transformations observed in unlabeled natural video sequences. This is done by training a generative model to predict video frames. We also address the problem of inherent uncertainty in prediction by introducing latent variables that are non-deterministic functions of the input into the network architecture.
Improving Cross-Topic Authorship Attribution: The Role of Pre-Processing

The effectiveness of character n-gram features for representing the stylistic properties of a text has been demonstrated in various independent Authorship Attribution (AA) studies. Moreover, it has been shown that some categories of character n-grams perform better than others both under single and cross-topic AA conditions. In this work, we present an improved algorithm for cross-topic AA. We demonstrate that the effectiveness of character n-grams representation can be significantly enhanced by performing simple pre-processing steps and appropriately tuning the number of features, especially in cross-topic conditions.
An IoT-cloud Based Wearable ECG Monitoring System for Smart Healthcare

Public healthcare has been paid an increasing attention given the exponential growth human population and medical expenses. It is well known that an effective health monitoring system can detect abnormalities of health conditions in time and make diagnoses according to the gleaned data. As a vital approach to diagnose heart diseases, ECG monitoring is widely studied and applied. However, nearly all existing portable ECG monitoring systems cannot work without a mobile application, which is responsible for data collection and display. In this paper, we propose a new method for ECG monitoring based on Internet-of-Things (IoT) techniques. ECG data are gathered using a wearable monitoring node and are transmitted directly to the IoT cloud using Wi-Fi. Both the HTTP and MQTT protocols are employed in the IoT cloud in order to provide visual and timely ECG data to users. Nearly all smart terminals with a web browser can acquire ECG data conveniently, which has greatly alleviated the cross-platform issue. Experiments are carried out on healthy volunteers in order to verify the reliability of the entire system. Experimental results reveal that the proposed system is reliable in collecting and displaying real-time ECG data, which can aid in the primary diagnosis of certain heart diseases.
Adaptation to Climate Change in the Context of Sustainable Development and Equity 18

Adaptation and Adaptive Capacity 8 8 1 1 8. 2. Adaptation Characteristics and Pro c e s s e s 8 8 2 1 8. 2. 1. Components and Forms of A d a p t a t i o n 8 8 2 1 8. 2. 2. Climate Stimuli for A d a p t a t i o n A d a p t a t i o n s 8 8 4 1 8. 3. F u t u re A d a p t a t i o n s