nils-herrmann/clean_scirepeval huggingface dataset:
Chapter 10* -data Handling and Presentation 10.1. Introduction

Data analysis and presentation, together with interpretation of the results and report writing, form the last step in the water quality assessment process . It is this phase that shows how successful the monitoring activities have been in attaining the objectives of the assessment. It is also the step that provides the information needed for decision making, such as choosing the most appropriate solution to a water quality problem, assessing the state of the environment or refining the water quality assessment process itself. Although computers now help the process of data analysis and presentation considerably, these activities are still very labour intensive. In addition, they require a working knowledge of all the preceding steps of the water quality assessment (see Figure 2.2), as well as a good understanding of statistics as it applies to the science of water quality assessment. This is perhaps one of the reasons why data analysis and interpretation do not always receive proper attention when water quality studies are planned and implemented. Although the need to integrate this activity with all the other activities of the assessment process seems quite obvious, achievement of this is often difficult. The " data rich, information poor " syndrome is common in many agencies, both in developed and developing countries. This chapter gives some guidelines and techniques for water quality data analysis and presentation. Emphasis is placed on the simpler methods, although the more complex procedures are mentioned to serve as a starting point for those who may want to use them, or to help in understanding published material which has used these techniques. For those individuals with limited knowledge of statistical procedures, caution is recommended before applying some of the techniques described in this chapter. With the advent of computers and the associated statistical software, it is often too easy to invoke techniques which are inappropriate to the data, without considering whether they are actually suitable, simply because the statistical tests are readily available on the computer and involve no computational effort. If in doubt, consult a statistician, preferably before proceeding too far with the data collection, i.e. at the planning and design phase of the assessment programme. The collection of appropriate numbers of samples from representative locations is particularly important for the final stages of data analysis and interpretation of results. The subject of statistical sampling and programme design is complex and cannot be discussed in …
DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networks

Network embedding algorithms to date are primarily designed for static networks, where all nodes are known before learning. How to infer embeddings for out-of-sample nodes, i.e. nodes that arrive after learning, remains an open problem. The problem poses great challenges to existing methods, since the inferred embeddings should preserve intricate network properties such as high-order proximity, share similar characteristics (i.e. be of a homogeneous space) with in-sample node embeddings, and be of low computational cost. To overcome these challenges, we propose a Deeply Transformed High-order Laplacian Gaussian Process (DepthLGP) method to infer embeddings for out-of-sample nodes. DepthLGP combines the strength of nonparametric probabilistic modeling and deep learning. In particular, we design a high-order Laplacian Gaussian process (hLGP) to encode network properties, which permits fast and scalable inference. In order to further ensure homogeneity, we then employ a deep neural network to learn a nonlinear transformation from latent states of the hLGP to node embeddings. DepthLGP is general, in that it is applicable to embeddings learned by any network embedding algorithms. We theoretically prove the expressive power of DepthLGP, and conduct extensive experiments on real-world networks. Empirical results demonstrate that our approach can achieve significant performance gain over existing approaches.
Knowledge and new product development: problem solving strategy and organisation

Viewing new product development as a problem-solving activity, the focus in the article is on abduction as a problem-solving strategy. We identify the advantages of problem-solving by way of abduction, as compared to induction and deduction, present a rationale of how to decide between the different problem-solving strategies, and finally, draw on a case study of a main European car manufacturer to analyze how problem-solving by abduction is implemented in practice.
3D Selective Search for obtaining object candidates

We propose a new method for obtaining object candidates in 3D space. Our method requires no learning, has no limitation of object properties such as compactness or symmetry, and therefore produces object candidates using a completely general approach. This method is a simple combination of Selective Search, which is a non-learning-based objectness detector working in 2D images, and a supervoxel segmentation method, which works with 3D point clouds. We made a small but non-trivial modification to supervoxel segmentation; it brings better “seeding” for supervoxels, which produces more proper object candidates as a result. Our experiments using a couple of publicly available RGB-D datasets demonstrated that our method outperformed state-of-the-art methods of generating object proposals in 2D images.
Stochastic radial basis function algorithms for large-scale optimization involving expensive black-box objective and constraint functions

Involving Expensive Black-Box Objective and Constraint Functions Rommel G. Regis Mathematics Department, Saint Joseph’s University, Philadelphia, PA 19131, USA, rregis@sju.edu August 23, 2010 Abstract. This paper presents a new algorithm for derivative-free optimization of expensive black-box objective functions subject to expensive black-box inequality constraints. The proposed algorithm, called ConstrLMSRBF, uses radial basis function (RBF) surrogate models and is an extension of the Local Metric Stochastic RBF (LMSRBF) algorithm by Regis and Shoemaker (2007a) that can handle black-box inequality constraints. Previous algorithms for the optimization of expensive functions using surrogate models have mostly dealt with bound constrained problems where only the objective function is expensive, and so, the surrogate models are used to approximate the objective function only. In contrast, ConstrLMSRBF builds RBF surrogate models for the objective function and also for all the constraint functions in each iteration, and uses these RBF models to guide the selection of the next point where the objective and constraint functions will be evaluated. Computational results indicate that ConstrLMSRBF is better than alternative methods on 9 out of 14 test problems and on the MOPTA08 problem from the automotive industry (Jones 2008). The MOPTA08 problem has 124 decision variables and 68 inequality constraints and is considered a large-scale problem in the area of expensive black-box optimization. The alternative methods include a Mesh Adaptive Direct Search (MADS) algorithm (Abramson and Audet 2006, Audet and Dennis 2006) that uses a kriging-based surrogate model, the Multistart LMSRBF algorithm by Regis and Shoemaker (2007a) modified to handle black-box constraints via a penalty approach, a genetic algorithm, a pattern search algorithm, a sequential quadratic programming algorithm, and COBYLA (Powell 1994), which is a derivative-free trust-region algorithm. Based on the results of this study, the results in Jones (2008) and other approaches presented at the ISMP 2009 conference, ConstrLMSRBF appears to be among the best, if not the best, known algorithm for the MOPTA08 problem in the sense of providing the most improvement from an initial feasible solution within a very limited number of objective and constraint function evaluations.
Meta-knowledge in systems design: panacea ... or undelivered promise?

In this study we present a review of the emerging eld of meta-knowledge components as practised over the past decade among a variety of practitioners. We use the arti cially-de ned term `meta-knowledge' to encompass all those di erent but overlapping notions used by the Arti cial Intelligence and Software Engineering communities to represent reusable modelling frameworks: ontologies, problem-solving methods, experience factories and experience bases, patterns, to name a few. We then elaborate on how meta-knowledge is deployed in the context of system's design to improve its reliability by consistency checking, enhance its reuse potential, and manage its knowledge sharing. We speculate on its usefulness and explore technologies for supporting deployment of meta-knowledge. We argue that, despite the di erent approaches being followed in systems design by divergent communities, meta-knowledge is present in all cases, in a tacit or explicit form, and its utilisation depends on pragmatic aspects which we try to identify and critically review on criteria of e ectiveness.  Ontologies, Problem-Solving Methods, Experienceware, Patterns, Design Types, Cost-E ective Analysis.
Discovery Through Constraints: Imposing Constraints on Autoencoders for Data Representation and Dictionary Learning

Without the proper choice of constraints, autoencoders (AEs) are capable of learning identity mapping or overcomplete representations. The features learned by this architecture may be local, isolated, or primitive. The extraction of features, however, can be controlled by judiciously enforcing some desired attributes in the form of constraints on its parameters. This article gives an overview of AEs and such constraints for data representation. It also puts AE learning in the broader context of dictionary learning.
Nonlinear Optimal Line-Of-Sight Stabilization with Fuzzy Gain-Scheduling

A nonlinear optimal controller with a fuzzy gain scheduler has been designed and applied to a Line-Of-Sight (LOS) stabilization system. Use of Linear Quadratic Regulator (LQR) theory is an optimal and simple manner of solving many control engineering problems. However, this method cannot be utilized directly for multigimbal LOS systems since they are nonlinear in nature. To adapt LQ controllers to nonlinear systems at least a linearization of the model plant is required. When the linearized model is only valid within the vicinity of an operating point a gain scheduler is required. Therefore, a Takagi-Sugeno Fuzzy Inference System gain scheduler has been implemented, which keeps the asymptotic stability performance provided by the optimal feedback gain approach. The simulation results illustrate that the proposed controller is capable of overcoming disturbances and maintaining a satisfactory tracking performance. Fuzzy Gain-Scheduling, Gimbal, Line-Of-Sight Stabilization, LQR, Optimal Control
Learning to Embed Words in Context for Syntactic Tasks

We present models for embedding words in the context of surrounding words. Such models, which we refer to as token embeddings, represent the characteristics of a word that are specific to a given context, such as word sense, syntactic category, and semantic role. We explore simple, efficient token embedding models based on standard neural network architectures. We learn token embeddings on a large amount of unannotated text and evaluate them as features for part-of-speech taggers and dependency parsers trained on much smaller amounts of annotated data. We find that predictors endowed with token embeddings consistently outperform baseline predictors across a range of context window and training set sizes.
Cash Transfers to Increase Antenatal Care Utilization in Kisoro, Uganda: A Pilot Study.

The World Health Organization recommends four antenatal visits for pregnant women in developing countries. Cash transfers have been used to incentivize participation in health services. We examined whether modest cash transfers for participation in antenatal care would increase antenatal care attendance and delivery in a health facility in Kisoro, Uganda. Twenty-three villages were randomized into four groups: 1) no cash; 2) 0.20 United States Dollars (USD) for each of four visits; 3) 0.40 USD for a single first trimester visit only; 4) 0.40 USD for each of four visits. Outcomes were three or more antenatal visits and delivery in a health facility. Chi-square, analysis of variance, and generalized estimating equation analyses were performed to detect differences in outcomes. Women in the 0.40 USD/visit group had higher odds of three or more antenatal visits than the control group (OR 1.70, 95% CI: 1.13-2.57). The odds of delivering in a health facility did not differ between groups. However, women with more antenatal visits had higher odds of delivering in a health facility (OR 1.21, 95% CI: 1.03-1.42). These findings are important in an area where maternal mortality is high, utilization of health services is low, and resources are scarce.
Deep Learning Autoencoder Approach for Handwritten Arabic Digits Recognition

This paper presents a new unsupervised learning approach with stacked autoencoder (SAE) for Arabic handwritten digits categorization. Recently, Arabic handwritten digits recognition has been an important area due to its applications in several fields. This work is focusing on the recognition part of handwritten Arabic digits recognition that face several challenges, including the unlimited variation in human handwriting and the large public databases. Arabic digits contains ten numbers that were descended from the Indian digits system. Stacked autoencoder (SAE) tested and trained the MADBase database (Arabic handwritten digits images) that contain 10000 testing images and 60000 training images. We show that the use of SAE leads to significant improvements across different machine-learning classification algorithms. SAE is giving an average accuracy of 98.5%.
Simple Heuristics That Make Us Smart

How can anyone be rational in a world where knowledge is limited, time is pressing, and deep thought is often an unattainable luxury? Traditional models of unbounded rationality and optimization in cognitive science, economics, and animal behavior have tended to view decision-makers as possessing supernatural powers of reason, limitless knowledge, and endless time. But understanding decisions in the real world requires a more psychologically plausible notion of bounded rationality. In Simple heuristics that make us smart, we explore fast and frugal heuristics--simple rules in the mind's adaptive toolbox for making decisions with realistic mental resources. These heuristics can enable both living organisms and artificial systems to make smart choices quickly and with a minimum of information by exploiting the way that information is structured in particular environments. In this precis, we show how simple building blocks that control information search, stop search, and make decisions can be put together to form classes of heuristics, including: ignorance-based and one-reason decision making for choice, elimination models for categorization, and satisficing heuristics for sequential search. These simple heuristics perform Simple Heuristics That Make Us Smart http://www.bbsonline.org/documents/a/00/00/04/69/bbs00000469-00/... 2 von 21 23.05.2005 18:16 comparably to more complex algorithms, particularly when generalizing to new data--that is, simplicity leads to robustness. We present evidence regarding when people use simple heuristics and describe the challenges to be addressed by this research program.
Causality, Feedback and Directed Information

It is shown that the "usual definition" of a discrete memoryless channel (DMC) in fact prohibits the use of feedback. The difficulty stems from the confusion of causality and statistical dependence. An adequate definition of a DMC is given, as well as a definition of using a channel without feedback. A definition, closely based on an old idea of Marko, is given for the directed information flowing from one sequence to another. This directed information is used to give a simple proof of the well-known fact that the use of feedback cannot increase the capacity of a DMC. It is shown that, when feedback is present, directed information is a more useful quantity than the traditional mutual information.
Application of Perceived Maternal Parenting Self-Efficacy (PMP S-E) Questionnaire in a Mid-West Community Medical Center NICU in the United States

Preterm birth and infants’ admission to neonatal intensive care units (NICU) are associated with significant emotional and psychological stresses on mothers that interfere with normal mother-infant relationship. Maternal selfefficacy in parenting ability may predict long-term outcome of mother-infant relationship as well as neurodevelopmental and behavioral development of preterm infants. The Perceived Maternal Parenting Self-Efficacy (PMP S-E) tool was developed to measure self-efficacy in mothers of premature infants in the United Kingdom. The present study determined if maternal and neonatal characteristics could predict PMP S-E scores of mothers who were administered to in a mid-west community medical center NICU. Mothers whose infants were born less than 37 weeks gestational age and admitted to a level III neonatal intensive care unit participated. Participants completed the PMP S-E and demographic survey prior to discharge. A logistic regression analysis was conducted from PMP SE scores involving 103 dyads using maternal education, race, breast feeding, maternal age, infant’s gestational age, Apgar 5-minute score, birth weight, mode of delivery and time from birth to completion of PMP S-E questionnaire. Time to completion of survey and gestational age were the significant predictors of PMP S-E scores. The finding of this study concerning the utilization of the PMP S-E in a United States mid-west tertiary neonatal center suggest that interpretation of the score requires careful consideration of these two variables.
Coverage path planning based on a multiple sweep line decomposition

Coverage path planning determines a path that guides an autonomous vehicle to pass every part of a workspace completely and efficiently. Since turns are often costly for autonomous vehicles, minimizing the number of turns usually produces more working efficiency. This paper presents an optimization approach to minimize the number of turns of autonomous vehicles in coverage path planning. For complex polygonal fields, the problem is reduced to finding the optimal decomposition of the original field into simple subfields. The optimization criterion is minimization of the sum of widths of these decomposed subfields. Here, a new algorithm is designed based on a multiple sweep line decomposition. The time complexity of the proposed algorithm is O(n2 log n). Experiments show that the proposed algorithm can provide nearly optimal solutions very efficiently when compared against recent state-of-the-art. The proposed algorithm can be applied for both convex and non-convex fields.
A new look at cerebrospinal fluid circulation

According to the traditional understanding of cerebrospinal fluid (CSF) physiology, the majority of CSF is produced by the choroid plexus, circulates through the ventricles, the cisterns, and the subarachnoid space to be absorbed into the blood by the arachnoid villi. This review surveys key developments leading to the traditional concept. Challenging this concept are novel insights utilizing molecular and cellular biology as well as neuroimaging, which indicate that CSF physiology may be much more complex than previously believed. The CSF circulation comprises not only a directed flow of CSF, but in addition a pulsatile to and fro movement throughout the entire brain with local fluid exchange between blood, interstitial fluid, and CSF. Astrocytes, aquaporins, and other membrane transporters are key elements in brain water and CSF homeostasis. A continuous bidirectional fluid exchange at the blood brain barrier produces flow rates, which exceed the choroidal CSF production rate by far. The CSF circulation around blood vessels penetrating from the subarachnoid space into the Virchow Robin spaces provides both a drainage pathway for the clearance of waste molecules from the brain and a site for the interaction of the systemic immune system with that of the brain. Important physiological functions, for example the regeneration of the brain during sleep, may depend on CSF circulation.
Pathophysiology, epidemiology, and assessment of obesity in adults.

Obesity is a multifactorial disease that results from a combination of both physiological, genetic, and environmental inputs. Obesity is associated with adverse health consequences, including T2DM, cardiovascular disease, musculoskeletal disorders, obstructive sleep apnea, and many types of cancer. The probability of developing adverse health outcomes can be decreased with maintained weight loss of 5% to 10% of current body weight. Body mass index and waist circumference are 2 key measures of body fat. A wide variety of tools are available to assess obesity-related risk factors and guide management.
Simplified real-time heartbeat detection in ballistocardiography using a dispersion-maximum method

Ballistocardiography (BCG) enables the recording of heartbeat, respiration, and body movement data from an unconscious human subject. In this paper, we propose a new heartbeat detection algorithm for calculating heart rate (HR) and heart rate variability (HRV) from the BCG signal. The proposed algorithm consists of a moving dispersion calculation method to effectively highlight the respective heartbeat locations and an adaptive heartbeat peak detection method that can set a heartbeat detection window by automatically predicting the next heartbeat location. To evaluate the proposed algorithm, we compared it with other reference algorithms using a filter, waveform analysis and envelope calculation of signal by setting the ECG lead I as the gold standard. The heartbeat detection in BCG should be able to measure sensitively in the regions for lower and higher HR. However, previous detection algorithms are optimized mainly in the region of HR range (60~90 bpm) without considering the HR range of lower (40~60 bpm) and higher (90~110 bpm) HR. Therefore, we proposed an improved method in wide HR range that 40~110 bpm. The proposed algorithm detected the heartbeat greater stability in varying and wider heartbeat intervals as comparing with other previous algorithms. Our proposed algorithm achieved a relative accuracy of 98.29% with a root mean square error (RMSE) of 1.83 bpm for HR, as well as coverage of 97.63% and relative accuracy of 94.36% for HRV. And we obtained the root mean square (RMS) value of 1.67 for separated ranges in HR.
A Greedy Approach for Budgeted Maximum Inner Product Search

Maximum Inner Product Search (MIPS) is an important task in many machine learning applications such as the prediction phase of low-rank matrix factorization models and deep learning models. Recently, there has been substantial research on how to perform MIPS in sub-linear time, but most of the existing work does not have the flexibility to control the trade-off between search efficiency and search quality. In this paper, we study the important problem of MIPS with a computational budget. By carefully studying the problem structure of MIPS, we develop a novel Greedy-MIPS algorithm, which can handle budgeted MIPS by design. While simple and intuitive, Greedy-MIPS yields surprisingly superior performance compared to state-of-the-art approaches. As a specific example, on a candidate set containing half a million vectors of dimension 200, Greedy-MIPS runs 200x faster than the naive approach while yielding search results with the top-5 precision greater than 75%.