nils-herrmann/clean_scirepeval huggingface dataset:
The paradoxical success of fuzzy logic

Fuzzy logic methods have been used successfully in many real-world applications, but the foundations of fuzzy logic remain under attack. Taken together, these two facts constitute a paradox. A second paradox is that almost all of the successful fuzzy logic applications are embedded controllers, while most of the theoretical papers on fuzzy methods deal with knowledge representation and reasoning. I hope to resolve these paradoxes by identifying which aspects of fuzzy logic render it useful in practice, and which aspects are inessential. My conclusions are based on a mathematical result, on a survey of literature on the use of fuzzy logic in heuristic control and in expert systems, and on practical experience in developing expert systems.<<ETX>>
Graph Coloring with Adaptive Evolutionary Algorithms

This paper presents the results of an experimental investigation on solving graph coloring problems with Evolutionary Algorithms (EAs). After testing different algorithm variants we conclude that the best option is an asexual EA using order-based representation and an adaptation mechanism that periodically changes the fitness function during the evolution. This adaptive EA is general, using no domain specific knowledge, except, of course, from the decoder (fitness function). We compare this adaptive EA to a powerful traditional graph coloring technique DSatur and the Grouping Genetic Algorithm (GGA) on a wide range of problem instances with different size, topology and edge density. The results show that the adaptive EA is superior to the Grouping (GA) and outperforms DSatur on the hardest problem instances. Furthermore, it scales up better with the problem size than the other two algorithms and indicates a linear computational complexity.
A visual and interactive automata theory course with JFLAP 4.0

We describe the instructional software JFLAP 4.0 and how it can be used to provide a hands-on formal languages and automata theory course. JFLAP 4.0 doubles the number of chapters worth of material from JFLAP 3.1, now covering topics from eleven of thirteen chapters for a semester course. JFLAP 4.0 has easier interactive approaches to previous topics and covers many new topics including three parsing algorithms, multi-tape Turing machines, L-systems, and grammar transformations.
BHPMF – a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography

Max Planck Institute for Biogeochemistry, Hans-Knöll-Strasse 10, 07745 Jena, Germany, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5, 04103 Leipzig, Germany, Department of Forest Resources, University of Minnesota, St Paul, MN 55108, USA, Department of Computer Science and Engineering, University of Minnesota, Twin Cities, USA, Microsoft Corporation, One Microsoft Way, Redmond, WA 98052, USA, Instituto Multidisciplinario de Biología Vegetal (IMBIV – CONICET) and Departamento de Diversidad Biológica y Ecología, FCEFyN, Universidad Nacional de Córdoba, CC 495, 5000, Córdoba, Argentina, Royal Botanic Gardens Kew, Wakehurst Place, RH17 6TN, UK, Center for Biodiversity Management, Yungaburra 4884, Queensland, Australia, Centre National de la Recherche Scientifique, Grenoble, France, Laboratoire ESE, Université Paris-Sud, UMR 8079 CNRS, UOS, AgroParisTech, 91405 Orsay, France, University of Leipzig, Leipzig, Germany, Department of Biological Sciences, Macquarie University, NSW 2109, Australia, Smithsonian Tropical Research Institute, Apartado 0843-03092, Balboa, Republic of Panama, Hawkesbury Institute for the Environment, University of Western Sydney, Locked Bag 1797, Penrith, NSW 2751
Clinical trials in neonatal sepsis.

Antibiotic licensing studies remain a problem in neonates. The classical adult clinical syndrome-based licensing studies do not apply to neonates, where sepsis is the most common infection. The main obstacle to conducting neonatal antibiotic trials is a lack of consensus on the definition of neonatal sepsis itself and the selection of appropriate endpoints. This article describes the difficulties of the clinical and laboratory definitions of neonatal sepsis and reviews the varying designs of previous neonatal sepsis trials. The optimal design of future trials of new antibiotics will need to be based on pharmacokinetic/pharmacodynamic parameters, combined with adequately powered clinical studies to determine safety and efficacy.
Long-term Causal Effects via Behavioral Game Theory

Planned experiments are the gold standard in reliably comparing the causal effect of switching from a baseline policy to a new policy. One critical shortcoming of classical experimental methods, however, is that they typically do not take into account the dynamic nature of response to policy changes. For instance, in an experiment where we seek to understand the effects of a new ad pricing policy on auction revenue, agents may adapt their bidding in response to the experimental pricing changes. Thus, causal effects of the new pricing policy after such adaptation period, the long-term causal effects, are not captured by the classical methodology even though they clearly are more indicative of the value of the new policy. Here, we formalize a framework to define and estimate long-term causal effects of policy changes in multiagent economies. Central to our approach is behavioral game theory, which we leverage to formulate the ignorability assumptions that are necessary for causal inference. Under such assumptions we estimate long-term causal effects through a latent space approach, where a behavioral model of how agents act conditional on their latent behaviors is combined with a temporal model of how behaviors evolve over time.
Revisiting the PnP Problem: A Fast, General and Optimal Solution

In this paper, we revisit the classical perspective-n-point (PnP) problem, and propose the first non-iterative O(n) solution that is fast, generally applicable and globally optimal. Our basic idea is to formulate the PnP problem into a functional minimization problem and retrieve all its stationary points by using the Gr"obner basis technique. The novelty lies in a non-unit quaternion representation to parameterize the rotation and a simple but elegant formulation of the PnP problem into an unconstrained optimization problem. Interestingly, the polynomial system arising from its first-order optimality condition assumes two-fold symmetry, a nice property that can be utilized to improve speed and numerical stability of a Grobner basis solver. Experiment results have demonstrated that, in terms of accuracy, our proposed solution is definitely better than the state-of-the-art O(n) methods, and even comparable with the reprojection error minimization method.
Teaching Multiple Tasks to an RL Agent using LTL

This paper examines the problem of how to teach multiple tasks to a Reinforcement Learning (RL) agent. To this end, we use Linear Temporal Logic (LTL) as a language for specifying multiple tasks in a manner that supports the composition of learned skills. We also propose a novel algorithm that exploits LTL progression and off-policy RL to speed up learning without compromising convergence guarantees, and show that our method outperforms the state-of-the-art approach on randomly generated Minecraft-like grids.
Aggregate Query Answering under Uncertain Schema Mappings

Recent interest in managing uncertainty in data integration has led to the introduction of probabilistic schema mappings and the use of probabilistic methods to answer queries across multiple databases using two semantics: by-table and by-tuple. In this paper, we develop three possible semantics for aggregate queries: the range, distribution, and expected value semantics, and show that these three semantics combine with the by-table and by-tuple semantics in six ways. We present algorithms to process COUNT, AVG, SUM, MIN, and MAX queries under all six semantics and develop results on the complexity of processing such queries under all six semantics. We show that computing COUNT is in PTIME for all six semantics and computing SUM is in PTIME for all but the by-tuple/distribution semantics. Finally, we show that AVG, MIN, and MAX are PTIME computable for all by-table semantics and for the by-tuple/range semantics.We developed a prototype implementation and experimented with both real-world traces and simulated data. We show that, as expected, naive processing of aggregates does not scale beyond small databases with a small number of mappings. The results also show that the polynomial time algorithms are scalable up to several million tuples as well as with a large number of mappings.
Stereo Depth Estimation: A Confidence Interval Approach

We describe an estimation technique which, given a measurement of the depth of a target from a wide-fieldof-view (WFOV) stereo camera pair, produces a minimax risk fixed-size confidence interval estimate for the target depth. This work constitutes the first application to the computer vision domain of optimal fixed-size confidenceinterval decision theory. The approach is evaluated in terms of theoretical capture probability and empirical cap ture frequency during actual experiments with a target on an optical bench. The method is compared to several other procedures including the Kalman Filter. The minimax approach is found to dominate all the other methods in performance. In particular, for the minimax approach, a very close agreement is achieved between theoreticalcapture probability andempiricalcapture frequency. This allows performance to be accurately predicted, greatly facilitating the system design, and delineating the tasks that may be performed with a given system.
Heterogeneous Agent Models in Economics and Finance Cars

This chapter surveys work on dynamic heterogeneous agent models (HAMs) in economics and finance. Emphasis is given to simple models that, at least to some extent, are tractable by analytic methods in combination with computational tools. Most of these models are behavioral models with boundedly rational agents using different heuristics or rule of thumb strategies that may not be perfect, but perform reasonably well. Typically these models are highly nonlinear, e.g. due to evolutionary switching between strategies, and exhibit a wide range of dynamical behavior ranging from a unique stable steady state to complex, chaotic dynamics. Aggregation of simple interactions at the micro level may generate sophisticated structure at the macro level. Simple HAMs can explain important observed stylized facts in financial time series, such as excess volatility, high trading volume, temporary bubbles and trend following, sudden crashes and mean reversion, clustered volatility and fat tails in the returns distribution.
The neural bases of social pain: evidence for shared representations with physical pain.

Experiences of social rejection or loss have been described as some of the most "painful" experiences that we, as humans, face and perhaps for good reason. Because of our prolonged period of immaturity, the social attachment system may have co-opted the pain system, borrowing the pain signal to prevent the detrimental consequences of social separation. This review summarizes a program of research that has explored the idea that experiences of physical pain and social pain rely on shared neural substrates. First, evidence showing that social pain activates pain-related neural regions is reviewed. Then, studies exploring some of the expected consequences of such a physical pain-social pain overlap are summarized. These studies demonstrate that a) individuals who are more sensitive to one kind of pain are also more sensitive to the other and b) factors that increase or decrease one kind of pain alter the other in a similar manner. Finally, what these shared neural substrates mean for our understanding of socially painful experience is discussed.
Help seeking, learning and contingent tutoring

The focus of this paper is the application of the theory of contingent tutoring to the design of a computer-based system designed to support learning in aspects of algebra. Analyses of interactions between a computer-based tutoring system and 42, 14and 15-year-old pupils are used to explore and explain the relations between individual dierences in learner±tutor interaction, learners' prior knowledge and learning outcomes. Parallels between the results of these analyses and empirical investigations of help seeking in adult±child tutoring are drawn out. The theoretical signi®cance of help seeking as a basis for studying the impact of individual learner dierences in the collaborative construction of `zones of proximal development' is assessed. In addition to demonstrating the signi®cance of detailed analyses of learner±system interaction as a basis for inferences about learning processes, the investigation also attempts to show the value of exploiting measures of on-line help seeking as a means of assessing learning transfer. Finally, the implications of the ®ndings for contingency theory are discussed, and the theoretical and practical bene®ts of integrating psychometric assessment, interaction process analyses, and knowledge-based learner modelling in the design and evaluation of computer-based tutoring are explored.
Linked education: interlinking educational resources and the Web of data

Research on interoperability of technology-enhanced learning (TEL) repositories throughout the last decade has led to a fragmented landscape of competing approaches, such as metadata schemas and interface mechanisms. However, so far Web-scale integration of resources is not facilitated, mainly due to the lack of take-up of shared principles, datasets and schemas. On the other hand, the Linked Data approach has emerged as the de-facto standard for sharing data on the Web and offers a large potential to solve interoperability issues in the field of TEL. In this paper, we describe a general approach to exploit the wealth of already existing TEL data on the Web by allowing its exposure as Linked Data and by taking into account automated enrichment and interlinking techniques to provide rich and well-interlinked data for the educational domain. This approach has been implemented in the context of the mEducator project where data from a number of open TEL data repositories has been integrated, exposed and enriched by following Linked Data principles.
Promoting apoptosis as a strategy for cancer drug discovery

Apoptosis is deregulated in many cancers, making it difficult to kill tumours. Drugs that restore the normal apoptotic pathways have the potential for effectively treating cancers that depend on aberrations of the apoptotic pathway to stay alive. Apoptosis targets that are currently being explored for cancer drug discovery include the tumour-necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL) receptors, the BCL2 family of anti-apoptotic proteins, inhibitor of apoptosis (IAP) proteins and MDM2.
Shared Sampling for Real-Time Alpha Matting

Image matting aims at extracting foreground elements from an image by mean s of color and opacity (alpha) estimation. While a lot of progress has been made in recent years on improv ing the accuracy of matting techniques, one common problem persisted: the low speed of matte computation. We pre sent the first real-time matting technique for natural images and videos. Our technique is based on the obser vation that, for small neighborhoods, pixels tend to share similar attributes. Therefore, independently treating eac h pixel in the unknown regions of a trimap results in a lot of redundant work. We show how this computation can be significantly and safely reduced by means of a careful selection of pairs of background and foreground s amples. Our technique achieves speedups of up to two orders of magnitude compared to previous ones, while producin g high-quality alpha mattes. The quality of our results has been verified through an independent benchmark. The speed of our technique enables, for the first time, real-time alpha matting of videos, and has the potential to enable a n ew class of exciting applications.
Computer-Aided Biomimetics

The interdisciplinary character of Bio-Inspired Design (BID) has resulted in a plethora of approaches and methods that propose different types of design processes. Although sustainable, creative and complex system design processes are not mutually incompatible they do focus on different aspects of design. This research defines areas of focus for the development of computational tools to support biomimetics, technical problem solving through abstraction, transfer and application of knowledge from biological models. An overview of analysed literature is provided as well as a qualitative analysis of the main themes found in BID literature. The result is a set of recommendations for further research on Computer-Aided Biomimetics (CAB).
Textual description-based video summarization for video blogs

Recent popularization of camera devices, including action cams and smartphones, enables us to record videos in everyday life and share them through the Internet. Video blog is a recent approach for sharing videos, in which users enjoy expressing themselves in blog posts with attractive videos. Generating such videos, however, requires users to review vast amount of raw videos and edit them appropriately, which keeps users away from doing so. In this paper, we propose a novel video summarization method for helping users to create a video blog post. Unlike typical video summarization methods, the proposed method utilizes the text, which is written for a video blog post, and makes the video summary consistent with the content of the text. For this, we perform video summarization by solving an optimization problem, in which an objective function involves the content similarity between the summarized video and the text. Our user study with 20 participants has demonstrated that our proposed method is suitable to create video blog posts compared with conventional methods for video summarization.
CosFace: Large Margin Cosine Loss for Deep Face Recognition

Face recognition has made extraordinary progress owing to the advancement of deep convolutional neural networks (CNNs). The central task of face recognition, including face verification and identification, involves face feature discrimination. However, the traditional softmax loss of deep CNNs usually lacks the power of discrimination. To address this problem, recently several loss functions such as center loss, large margin softmax loss, and angular softmax loss have been proposed. All these improved losses share the same idea: maximizing inter-class variance and minimizing intra-class variance. In this paper, we propose a novel loss function, namely large margin cosine loss (LMCL), to realize this idea from a different perspective. More specifically, we reformulate the softmax loss as a cosine loss by L2 normalizing both features and weight vectors to remove radial variations, based on which a cosine margin term is introduced to further maximize the decision margin in the angular space. As a result, minimum intra-class variance and maximum inter-class variance are achieved by virtue of normalization and cosine decision margin maximization. We refer to our model trained with LMCL as CosFace. Extensive experimental evaluations are conducted on the most popular public-domain face recognition datasets such as MegaFace Challenge, Youtube Faces (YTF) and Labeled Face in the Wild (LFW). We achieve the state-of-the-art performance on these benchmarks, which confirms the effectiveness of our proposed approach.
A Reflection on Call-by-Value

A number of compilers exploit the following strategy: translate a term to continuation-passing style (CPS) and optimize the resulting term using a sequence of reductions. Recent work suggests that an alternative strategy is superior: optimize directly in an extended source calculus. We suggest that the appropriate relation between the source and target calculi may be captured by a special case of a Galois connection known as a reflection. Previous work has focused on the weaker notion of an equational correspondence, which is based on equality rather than reduction. We show that Moggi's monad translation and Plotkin's CPS translation can both be regarded as reflections, and thereby strengthen a number of results in the literature.
Computer recognition of unconstrained handwritten numerals

This paper proposes a simple yet highly accurate system for the recognition or unconstrained handwritten numerals. It starts with an examination of the basic characteristic loci (CL) features used along with a nearest neighbor classifier achieving a recognition rate of 90.5%. We then illustrate how the basic CL implementation can be extended and used in conjunction with a multilayer perception neural network classifier to increase the recognition rate to 98%. This proposed recognition system was tested on a totally unconstrained handwritten numeral database while training it with only 600 samples exclusive from the test set. An accuracy exceeding 98% is also expected if a larger training set is used. Lastly, to demonstrate the effectiveness of the system its performance is also compared to that of some other common recognition schemes. These systems use moment Invariants as features along with nearest neighbor classification schemes.
A prototype of multistage dynamic braking of three phase squirrel cage induction motor

This paper discussed a fast dynamic braking method of three phase induction motor. This braking method consists of two conventional braking methods i.e. direct current injection braking and capacitor self excitation braking. Those mathods were arranged in a such grading time to become a multistage dynamic braking. Simulation was done using MATLAB/Simulink software for design and predicting the behaviour. The results showed that the propossed method gave faster braking than the other two methods carried out separately.
An ultra low-power capacitor-less LDO with high PSR

A compact high PSR Low Drop-Out (LDO) voltage regulator providing a peak load-current (IL) of 100μA is realized in 0.13μm CMOS 1P6M process. Ultra low-power operation is achieved for the power block by realizing a nano-power bandgap reference circuit whose total power consumption including LDO is only just 95nW for 1.2Vsupply. The resistor-less reference circuit with no external capacitor for LDO stability results in a very compact design occupying just 0.033 mm2. The proposed post-layout reference and LDO block consumes only 38nA and 41nA respectively, regulating output at 0.9V with a 1.2V supply.