nils-herrmann/clean_scirepeval huggingface dataset:
Learning Classifiers from Synthetic Data Using a Multichannel Autoencoder

We propose a method for using synthetic data to help learning classifiers. Synthetic data, even is generated based on real data, normally results in a shift from the distribution of real data in feature space. To bridge the gap between the real and synthetic data, and jointly learn from synthetic and real data, this paper proposes a Multichannel Autoencoder(MCAE). We show that by suing MCAE, it is possible to learn a better feature representation for classification. To evaluate the proposed approach, we conduct experiments on two types of datasets. Experimental results on two datasets validate the efficiency of our MCAE model and our methodology of generating synthetic data.
Multi-Agent Systems for the Simulation of Land-Use and LandCover Change : A Review

form to demonstrate potential theoretical causes for qualitatively assessed real-world phenomena? Alternatively, can they be used to create wellparameterized empirical simulations appropriate for scenario and policy analysis? How can these models be empirically parameterized, verified, and validated? What are some remaining challenges and open questions in this research area? By providing answers to these questions, we hope to offer guidance to researchers considering the utility of this new modeling approach. We also hope to spark a healthy debate among researchers as to the potential advantages, limitations, andmajor research challenges ofMAS/LUCC modeling.AsMASmodeling studies are beingundertaken by geographers in other research fields—including transportation, integrated assessment, recreation, and resource management—many of the issues raised in this articlemay be relevant for other applications as well. The remainder of this article sequentially addresses the questions outlined above. Approaches to Modeling Land-Use/Cover Change This section examines myriad LUCC modeling approaches and offers MAS as a means of complementing other techniques. We briefly discuss the strengths and weaknesses of seven broad, partly overlapping categories of models: mathematical equation-based, system dynamics, statistical, expert system, evolutionary, cellular, and hybrid. This review is not exhaustive and only serves to highlight ways in which present techniques are complemented by MAS/LUCC models that combine cellular and agent-based models. More comprehensive overviews of LUCCmodeling techniques focus on tropical deforestation (Lambin 1994; Kaimowitz and Angelsen 1998), economic models of land use (Plantinga 1999), ecological landscapes (Baker 1989), urban and regional community planning (U.S. EPA 2000), and LUCC dynamics (Briassoulis 2000; Agarwal et al. 2002; Veldkamp and Lambin 2001; Verburg et al. forthcoming). Based Models Most models are mathematical in some way, but some are especially so, in that they rely on equations that seek a static or equilibrium solution. The most common mathematical models are sets of equations based on theories of population growth and diffusion that specify cumulative LUCC over time (Sklar and Costanza 1991). More complex models, often grounded in economic theory, employ simultaneous joint equations (Kaimowitz and Angelsen 1998). One variant of such models is based on linear programming (Weinberg, Kling, and Wilen 1993; Howitt 1995), potentially linked to GIS information on land parcels (Chuvieco 1993; Longley, Higgs, and Martin 1994; Cromley and Hanink 1999). A major drawback of such models is that a numerical or analytical solution to the system of equations must be obtained, limiting the level of complexity that may practically be built into such models. Simulation models that combine mathematical equationswith other data structures are considered below.
THE ACUTE EFFECTS OF DIFFERENT FOAM ROLLING TIMING DURATIONS ON HAMSTRING FLEXIBILITY By Chloe

.................................................................................................................................. iii ACKNOWLEDGEMENTS ............................................................................................................ v TABLE OF CONTENTS .............................................................................................................. vii LIST OF TABLES ....................................................................................................................... viii LIST OF FIGURES .......................................................................................................................  ................................................................................................... 1 CHAPTER 2: REVIEW OF RELATED LITERATURE ............................................................... 4 Flexibility Interventions .............................................................................................................. 4 Athletic Performance Interventions .......................................................................................... 18 Recovery Interventions ............................................................................................................. 29 Methodology & Supporting Arguments ................................................................................... 35 CHAPTER 3: METHODOLOGY ................................................................................................ 37 CHAPTER 4: RESULTS .............................................................................................................. 43 CHAPTER 5: DISCUSSION ........................................................................................................ 48 APPENDIX A: PRE-RESEARCH QUESTIONNAIRE .............................................................. 54 APPENDIX B: NUMERIC PRESSURE SCALE ........................................................................ 55  56 REFERENCES ............................................................................................................................. 58 CURRICULUM VITAE ............................................................................................................... 61
Rainfall-runoff models using artificial neural networks for ensemble streamflow prediction

Previous ensemble streamflow prediction (ESP) studies in Korea reported that modelling error significantly affects the accuracy of the ESP probabilistic winter and spring (i.e. dry season) forecasts, and thus suggested that improving the existing rainfall-runoff model, TANK, would be critical to obtaining more accurate probabilistic forecasts with ESP. This study used two types of artificial neural network (ANN), namely the single neural network (SNN) and the ensemble neural network (ENN), to provide better rainfall-runoff simulation capability than TANK, which has been used with the ESP system for forecasting monthly inflows to the Daecheong multipurpose dam in Korea. Using the bagging method, the ENN combines the outputs of member networks so that it can control the generalization error better than an SNN. This study compares the two ANN models with TANK with respect to the relative bias and the root-mean-square error. The overall results showed that the ENN performed the best among the three rainfall-runoff models. The ENN also considerably improved the probabilistic forecasting accuracy, measured in terms of average hit score, half-Brier score and hit rate, of the present ESP system that used TANK. Therefore, this study concludes that the ENN would be more effective for ESP rainfall-runoff modelling than TANK or an SNN.
Sources of nitrate yields in the Mississippi River Basin.

Riverine nitrate N in the Mississippi River leads to hypoxia in the Gulf of Mexico. Several recent modeling studies estimated major N inputs and suggested source areas that could be targeted for conservation programs. We conducted a similar analysis with more recent and extensive data that demonstrates the importance of hydrology in controlling the percentage of net N inputs (NNI) exported by rivers. The average fraction of annual riverine nitrate N export/NNI ranged from 0.05 for the lower Mississippi subbasin to 0.3 for the upper Mississippi River basin and as high as 1.4 (4.2 in a wet year) for the Embarras River watershed, a mostly tile-drained basin. Intensive corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] watersheds on Mollisols had low NNI values and when combined with riverine N losses suggest a net depletion of soil organic N. We used county-level data to develop a nonlinear model ofN inputs and landscape factors that were related to winter-spring riverine nitrate yields for 153 watersheds within the basin. We found that river runoff times fertilizer N input was the major predictive term, explaining 76% of the variation in the model. Fertilizer inputs were highly correlated with fraction of land area in row crops. Tile drainage explained 17% of the spatial variation in winter-spring nitrate yield, whereas human consumption of N (i.e., sewage effluent) accounted for 7%. Net N inputs were not a good predictor of riverine nitrate N yields, nor were other N balances. We used this model to predict the expected nitrate N yield from each county in the Mississippi River basin; the greatest nitrate N yields corresponded to the highly productive, tile-drained cornbelt from southwest Minnesota across Iowa, Illinois, Indiana, and Ohio. This analysis can be used to guide decisions about where efforts to reduce nitrate N losses can be most effectively targeted to improve local water quality and reduce export to the Gulf of Mexico.
Algorithms for Reinforcement Learning

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner’s predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms’ merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems,
Novel High Step-Up DC–DC Converter With Coupled-Inductor and Voltage-Doubler Circuits

In this paper, a novel high step-up dc-dc converter with coupled-inductor and voltage-doubler circuits is proposed. The converter achieves high step-up voltage gain with appropriate duty ratio and low voltage stress on the power switches. Also, the energy stored in the leakage inductor of the coupled inductor can be recycled to the output. The operating principles and the steady-state analyses of the proposed converter are discussed in detail. Finally, a prototype circuit of the proposed converter is implemented in the laboratory to verify the performance of the proposed converter.
Count Models Based on Weibull Interarrival Times

The widespread popularity and use of both the Poisson and the negative binomial models for count data arise, in part, from their derivation as the number of arrivals in a given time period assuming exponentially distributed interarrival times (without and with heterogeneity in the underlying base rates, respectively). However, with that clean theory come some limitations including limited flexibility in the assumed underlying arrival rate distribution and the inability to model underdispersed counts (variance less than the mean). Although extant research has addressed some of these issues, there still remain numerous valuable extensions. In this research, we present a model that, due to computational tractability, was previously thought to be infeasible. In particular, we introduce here a generalized model for count data based upon an assumed Weibull interarrival process that nests the Poisson and negative binomial models as special cases. The computational intractability is overcome by deriving the Weibull count model using a polynomial expansion which then allows for closed-form inference (integration term-by-term) when incorporating heterogeneity due to the conjugacy of the expansion and a commonly employed gamma distribution. In addition, we demonstrate that this new Weibull count model can (1) model both overand underdispersed count data, (2) allow covariates to be introduced in a straightforward manner through the hazard function, and (3) be computed in standard software.
Activity-Based Management for Electronic Commerce: A Structured Implementation Procedure

This paper explores the application of Activity-Based Costing and Activity-Based Management in ecommerce. The proposed application may lead to better firm performance of many companies in offering their products and services over the Internet. A case study of a fictitious Business-to-Customer (B2C) company is used to illustrate the proposed structured implementation procedure and effects of an Activity-Based Costing analysis. The analysis is performed by using matrixes in order to trace overhead. The Activity-Based Costing analysis is then used to demonstrate operational and strategic Activity-Based Management in e-commerce.
27 Critical Thinking about Critical Periods : Perspectives on a Critical Period for Language Acquisition

Over many years Jacques Mehler has provided us all with a wealth of surprising and complex results on both nature and nurture in language acquisition. He has shown that there are powerful and enduring effects of early (and even prenatal) experience on infant language perception, and also considerable prior knowledge that infants bring to the language acquisition task. He has shown strong age effects on second-language acquisition and its neural organization, and has also shown that profi­ ciency predicts cerebral organization for the second language. In his honor, we focus here on one of the problems he has addressed-the no­ tion of a critical period for language acquisition-and attempt to sort out the current state of the evidence. In recent years there has been much discussion about whether there is a critical, or sensitive, period for language acquisition. Two issues are implicit in this discussion: First, what would constitute evidence for a critical period, particularly in humans, where the time scale for develop­ ment is greater than that in the well-studied nonhuman cases, and where proficient behavioral outcomes might be achieved by more than one route? Second, what is the import of establishing, or failing to establish, such a critical period? What does this mean for our understanding of the computational and neural mechanisms underLying language acquisition? In this chapter we address these issues explicitly, by briefly reviewing the available evidence on a critical period for human language acquisi­ tion, and then by asking whether the evidence meets the expected criteria for critical or sensitive periods seen in other well-studied domains in hu­ man and nonhuman development. We conclude by stating what we think 482 Neuport, Bave/ier & Neville the outcome of this issue means (and does not mean) for our understand­ ing of language acquisition. What Is a Critical or Sensitive Period? Before beginning, we should state briefly what we (and others) mean by a critical or sensitive period. A critical or sensitive period for learning is shown when there is a relationship between the age (more technically, the developmental state of the organism) at which some crucial experience is presented to the organism and the amount of learning which results. ill most domains with critical or sensitive periods, the privileged time for learning occurs during early development, but this is not necessarily the case (ef. bonding in sheep, which occurs immediately surrounding partu­ rition). The important feature is …
A Behavioral Approach to Law and Economics

Economic analysis of law usually proceeds under the assumptions of neoclassical economics. But empirical evidence gives much reason to doubt these assumptions; people exhibit bounded rationality, bounded self-interest, and bounded willpower. This article offers a broad vision of how law and economics analysis may be improved by increased attention to insights about actual human behavior. It considers specific topics in the economic analysis of law and proposes new models and approaches for addressing these topics. The analysis of the article is organized into three categories: positive, prescriptive, and normative. Positive analysis of law concerns how agents behave in response to legal rules and how legal rules are shaped. Prescriptive analysis concerns what rules should be adopted to advance specified ends. Normative analysis attempts to assess more broadly the ends of the legal system: Should the system always respect people’s choices? By drawing attention to cognitive and motivational problems of both citizens and government, behavioral law and economics offers answers distinct from those offered by the standard analysis.
Laparoscopic Transabdominal Pre-Peritoneal (TAPP) procedure - step-by-step tips and tricks.

Minimally invasive approach for groin hernia treatment is still controversial, but in the last decade, it tends to become the standard procedure for one day surgery. We present herein the technique of laparoscopic Trans Abdominal Pre Peritoneal approach (TAPP). The surgical technique is presented step-by step;the different procedures key points (e.g. anatomic landmarks recognition, diagnosis of "occult" hernias, preperitoneal and hernia sac dissection, mesh placement and peritoneal closure) are described and discussed in detail, several tips and tricks being noted and highlighted.



TAPP is a feasible method for treating groin hernia associated with low rate of postoperative morbidity and recurrence. The anatomic landmarks are easily recognizable. The laparoscopic exploration allows for the treatment of incarcerated strangulated hernias and the intraoperative diagnosis of occult hernias.
HATE CRIME VICTIMIZATION IN WALES Psychological and Physical Impacts Across Seven Hate Crime Victim Types

This paper presents findings from the All Wales Hate Crime Project. Most hate crime research has focused on discrete victim types in isolation. For the first time, internationally, this paper examines the psychological and physical impacts of hate crime across seven victim types drawing on quantitative and qualitative data. It contributes to the hate crime debate in two significant ways: (1) it provides the first look at the problem in Wales and (2) it provides the first multi-victim-type analysis of hate crime, showing that impacts are not homogenous across victim groups. The paper provides empirical credibility to the impacts felt by hate crime victims on the margins who have routinely struggled to gain support.
Intelligent Data Mining and Decision System for Commercial Decision Making

Information system and data mining are important resources for the investors to make decisions. Information theory pointed that the information is increasing all the time, when the corporations build their millions of databases in order to improve the efficiency. Database technology caters to the needs of fully developing the information resources. This essay discusses the problem of decision making support system and the application of business data mining in commercial decision making. It is recommended that the intelligent decision support system should be built. Besides, the business information used in the commercial decision making must follow the framework of a whole system under guideline, which should be designed by the company.
To Centralize or to Distribute: That Is the Question: A Comparison of Advanced Microgrid Management Systems

The advanced microgrid is envisioned to be a critical part of the future smart grid because of its local intelligence, automation, interoperability, and distributed energy resources (DER) hosting capability. The enabling technology of advanced microgrids is the microgrid management system (MGMS). In this article, we discuss and review the concept of the MGMS and state-of-the-art solutions regarding centralized and distributed MGMSs in the primary, secondary, and tertiary levels, from which we observe a general tendency toward decentralization.
Cross-Agent Action Recognition

An action is something which is done by an agent. Most action recognition researchers merely focus on the actions to be recognized, and ignore the differences of agents. Philosophers and behaviorists discover that actions are common among many species, but are performed in different ways and with different levels of sophistication. In this paper, in order to bridge action recognition tasks between different agents, we introduce a new problem, cross-agent action recognition, i.e., recognizing action for one particular agent (target) while training from other agents (source). We model this problem under three different scenarios: single source and single target, multiple sources and single target, and multiple sources and multiple targets. To this end, corresponding methods based on transfer learning are proposed to address these problems. We further design three different strategies to model the situation when a partial labeled data is provided for the target. Experimental results show that the performances of the transfer method are generally better than those of the comparative method without transfer learning, especially when we have multiple sources. Particularly, the transfer method outperforms the others significantly when the source is a human adult. In addition, cross-agent method significantly improves the results when partially labeled data is provided for the target. These demonstrate that for action recognition, knowledge can be transferred across different agents. A straightforward application of this finding is to use human action (training data is abundant) data to enhance animal action recognition.
Second-order Temporal Pooling for Action Recognition

Deep learning models for video-based action recognition usually generate features for short clips (consisting of a few frames); such clip-level features are aggregated to video-level representations by computing statistics on these features. Typically zero-th (max) or the first-order (average) statistics are used. In this paper, we explore the benefits of using second-order statistics.Specifically, we propose a novel end-to-end learnable feature aggregation scheme, dubbed temporal correlation pooling that generates an action descriptor for a video sequence by capturing the similarities between the temporal evolution of clip-level CNN features computed across the video. Such a descriptor, while being computationally cheap, also naturally encodes the co-activations of multiple CNN features, thereby providing a richer characterization of actions than their first-order counterparts. We also propose higher-order extensions of this scheme by computing correlations after embedding the CNN features in a reproducing kernel Hilbert space. We provide experiments on benchmark datasets such as HMDB-51 and UCF-101, fine-grained datasets such as MPII Cooking activities and JHMDB, as well as the recent Kinetics-600. Our results demonstrate the advantages of higher-order pooling schemes that when combined with hand-crafted features (as is standard practice) achieves state-of-the-art accuracy.