Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on the Potential Development of Rainwater Utilization in the Hilly City of Southern China

Aimed at the current flood problems and the contradiction between supply and demand of water resources in the southern cities of China, the comprehensive utilization of Urban Rainwater Resources (URRs) is a significant solution. At present, the research on the comprehensive utilization system of urban rainwater resources in China is still immature, especially the lack of a comprehensive method for the comprehensive utilization of the rainwater and flood resources in the south. Based on the current mode for utilization of URRs at home and abroad, Fenghuang County in Hunan Province was taken as a case of study, which is a typical mountainous city in the southern China. And the potential development of URRs was simulated and evaluated with a comparison of before and after the exploitation and utilization of URRs in this paper. The reduction effect of flood and waterlogging on the ancient city area is analyzed from SWMM. The simulation results show that the potential of exploitation and utilization of URRs in Fenghuang county is remarkable under the mode of exploitation and utilization which is given priority to flood prevention and control, and the annual development potential is 4.865×105 m3. The rainwater utilization measures of flood control effect is obvious with this mode, and the relevant research results can provide theoretical and technical support for enhancing urban water security capability, water conservation capacity, and disaster mitigation of urban flood.
Accuracy of Global Upper Ocean Heat Content Estimation Expected from Present Observational Data Sets

The simplest global mapping method and dense data coverage for the global oceans by the latest observation network ensure an estimate of global ocean heat content (OHC) within a satisfac- tory uncertainty for the last 60 years. The observational database conditionally presented a level high enough for practical use for the global OHC estimation when applying bias corrections of expendable bathythermograph, assuming that the other severe observational biases are not included in the database. Uncertainties in annual global mean temperatures averaged vertically from the surface to 1,500 m are within 0.01 K for the period from 1955 onward, when only sampling errors are taken into account. Those in annual mean global OHC of an improved objective analysis for 0−1,500 m depth is 16 ZJ on average throughout the period. Com- pared to previous studies, the new objective analysis provides a higher estimation of the global 0−1,500 m OHC trend for a lon - ger period from 1955 to 2015, which is an increase of 350 ± 57 ZJ with a 95% confidence interval. ( Citation M., T. Suzuki, and K. Sato, 2017: Accuracy of global upper ocean heat content estimation expected from present observational data sets. Profile Program (GTSPP), and the Argo Project, were used for estimating global OHC. Observations in the latter two data sets were used prior to WOD13, as they were updated later than WOD13. In addition, the duplicates in WOD13 were discarded in the following calculations. For easy handling and analyzing observed data distributed irregularly in space and time, observed anomalies relative to the climatology were arithmetically aver- aged monthly from 1945 to 2015 in 5°-longitude and 5°-latitude boxes allocated over the global oceans. A typical spatial decor- relation scale ranges from 300 to 900 km among literature (Lev-itus et al. 2000; Ishii et al. 2003; Willis et al. 2003; Ingleby and Huddleston 2007), and therefore, the 5°-box mean values are regarded as locally representative. The monthly box mean val- ues of observed profiles were computed after taking averages of data observed by each ship or buoy in each 10-day interval and in each 5°-longitude and 5°-latitude square at 28 levels from surface to 3,000 m depth; biases from localized data in space and time and from specific ships or buoys are minimized. The climatology used here is mean temperature fields of IK09 for the period from 1961 to 2005. The climatology is defined on a 1-degree grid. This minimizes spatial interpolation errors in computing 5-degree box mean values rather than the case of 5-degree climatology, particularly in areas where the horizontal temperature gradients are large. Vertically, the boxes were placed in the upper 3,000 m and the box heights, i.e., thickness, varied from 5 to 50 m in the upper
ScholarWorks ScholarWorks

When interacting with robots in a situated spoken dialogue setting, human dialogue partners tend to assign anthropomorphic and social characteristics to those robots. In this paper, we explore the age and educational level that human dialogue partners assign to three different robotic systems, including an un-embodied spoken dialogue system. We found that how a robot speaks is as important to human perceptions as the way the robot looks. Using the data from our experiment, we derived prosodic, emotional, and linguistic features from the participants to train and evaluate a classiﬁer that predicts perceived intelligence, age, and education level.
A new difference image creation method based on deep neural networks for change detection in remote-sensing images

ABSTRACT In this article, we propose a novel difference image (DI) creation method for unsupervised change detection in multi-temporal multi-spectral remote-sensing images based on deep learning theory. First, we apply deep belief network to learn local and high-level features from the local neighbour of a given pixel in an unsupervised manner. Second, a back propagation algorithm is improved to build a DI based on selected training samples, which can highlight the difference on changed regions and suppress the false changes on unchanged regions. Finally, we get the change trajectory map using simple clustering analysis. The proposed scheme is tested on three remote-sensing data sets. Qualitative and quantitative evaluations show its superior performance compared to the traditional pixel-level and texture-level-based approaches.
A tetrahedra-based stream surface algorithm

This paper presents a new algorithm for the calculation of stream surfaces for tetrahedral grids. It propagates the surface through the tetrahedra, one at a time, calculating the intersections with the tetrahedral faces. The method allows us to incorporate topological information from the cells, e.g. critical points. The calculations are based on barycentric coordinates, since this simplifies the theory and the algorithm. The stream surfaces are ruled surfaces inside each cell, and their construction starts with line segments on the faces. Our method supports the analysis of velocity fields resulting from computational fluid dynamics (CFD) simulations.
1 Equivalence in Functional Languages with E ectsIan

Traditionally the view has been that direct expression of control and store mechanisms and clear mathematical semantics are incompatible requirements. This paper shows that adding objects with memory to the call-by-value lambda calculus results in a language with a rich equational theory, satisfying many of the usual laws. Combined with other recent work this provides evidence that expressive, mathematically clean programming languages are indeed possible. 1. Overview Real programs have eeects|creating new structures, examining and modifying existing structures, altering ow of control, etc. Such facilities are important not only for optimization, but also for communication, clarity, and simplicity in programming. Thus it is important to be able to reason both informally and formally about programs with eeects, and not to sweep eeects either to the side or under the store parameter rug. Recent work of Talcott, Mason, Felleisen, and Moggi establishes a mathematical foundation for studying notions of program equivalence for programming languages with function and control abstractions operating on objects with memory. This work extends work of Landin, Reynolds, Morris and Plotkin. Landin 14] and Reynolds 35] describe high-level abstract machines for deening language semantics. Morris 29] deenes an extensional equivalence relation for the classical lambda calculus. Plotkin 33] extends these ideas to the call-by-value lambda calculus and deenes the operational equivalence relation. Operational approximation is the pre-ordering induced by an operational semantics. Operational equivalence is the equivalence naturally associated with this pre-ordering. One expression operationally approximates another if for all closing program contexts either the rst expression is undeened or both expressions are deened and their values are indistinguishable (with respect to some primitive means of testing equality). Operational approximation and equivalence are congruence relations on expressions and hence closed under substitution and abstraction. Mason and Talcott in 39, 17, 18, 40] study operational approximation and equivalence for subsets of a language with function and control abstractions and objects with memory. Felleisen 8] deenes reduction calculi extending the call-by-value lambda calculus to languages with control and assignment abstractions. These calculi are simpliied and extended by Felleisen and Hieb in 9]. Talcott, Mason, and Felleisen all apply their theories to expressing and proving properties of program constructs and of particular programs. Moggi 27, 28] introduces the notion of computational monad as a framework for axiomatizing features of programming languages. Computational monads are categories with certain additional structure that accommodate a wide variety of language features including assignment, exceptions, and …
Nearest neighbor search

Definition 1.1. Nearest Neighbor Search: Given a set of points {x1, . , xn} ∈ R preprocess them into a data structure X of size poly(n, d) in time poly(n, d) such that nearest neighbor queries can be performed in logarithmic time. In other words, given a search point q a radius r and X one can return an xi such the ||q − xi|| ≤ r or nothing if no such point exists. The search for xi should require at most poly(d, log(n)) time.
Social , Group and Individual Mind extracted from Rule Bases of Multiple Agents

This paper shows possibility of extraction Social, Group and Individual Mind from Multiple Agents Rule Bases. Types those Rule bases are selected as two fuzzy systems, namely Mambdani and Takagi-Sugeno fuzzy system. Their rule bases are describing (modeling) agent behavior. Modifying of agent behavior in the time varying environment will be provided by learning fuzzyneural networks and optimization of their parameters with using genetic algorithms in development system FUZNET. Finally, extraction Social, Group and Individual Mind from Multiple Agents Rule Bases are provided by Cognitive analysis and Matching criterion. Keywords—Mind, Multi-agent system, Cognitive analysis, Fuzzy system, Neural network, Genetic algorithm, Rule base.
Estimating Information-Theoretic Quantities with Decision Forests

Information-theoretic quantities, such as mutual information and conditional entropy, are useful statistics for measuring the dependence between two random variables. However, estimating these quantities in a non-parametric fashion is difficult, especially when the variables are high-dimensional, a mixture of continuous and discrete values, or both. In this paper, we propose a decision forest method, Conditional Forests (CF), to estimate these quantities. By combining quantile regression forests with honest sampling, and introducing a finite sample correction, CF improves finite sample bias in a range of settings. We demonstrate through simulations that CF achieves smaller bias and variance in both lowand high-dimensional settings for estimating posteriors, conditional entropy, and mutual information. We then use CF to estimate the amount of information between neuron class and other cellular features in a connectome dataset.
Bootstrapping A Statisti al Spee h Translator From A Rule-Based One

Manny Rayner University of Geneva, TIM/ISSCO, 40 bvd du Pont-d'Arve CH-1211 Gen eve, Switzerland Emmanuel.Rayner unige. h Paula Estrella FaMAF, U. Na ional de Córdoba 5000 Córdoba, Argentine pestrella famaf.un . edu.ar Pierrette Bouillon University of Geneva, TIM/ISSCO, 40 bvd du Pont-d'Arve CH-1211 Gen eve, Switzerland Pierrette.Bouillon unige. h Abstra t We des ribe a series of experiments in whi h we start with English ! Fren h and English ! Japanese versions of an Open Sour e rule-based spee h translation system for a medi al domain, and bootstrap orresponding statisti al systems. Comparative evaluation reveals that the rule-based systems are still signi antly better than the statisti al ones, despite the fa t that onsiderable effort has been invested in tuning both the re ognition and translation omponents; also, a hybrid system only marginally improved re all at the ost of a loss in pre ision. The result suggests that rule-based ar hite tures may still be preferable to statisti al ones for safetyriti al spee h translation tasks.
Safety-Critical Java on a Time-Predictable Processor

For real-time systems the whole execution stack needs to be time-predictable and analyzable for the worst-case execution time (WCET). This paper presents a time-predictable platform for safety-critical Java. The platform consists of (1) the Patmos processor, which is a time-predictable processor; (2) a C compiler for Patmos with support for WCET analysis; (3) the HVM, which is a Java-to-C compiler; (4) the HVM-SCJ implementation which supports SCJ Level 0, 1, and 2 (for both single and multicore platforms); and (5) a WCET analysis tool. We show that real-time Java programs translated to C and compiled to a Patmos binary can be analyzed by the AbsInt aiT WCET analysis tool. To the best of our knowledge the presented system is the second WCET analyzable real-time Java system; and the first one on top of a RISC processor.
Prevalence and Cumulative Incidence of Mortality Associated with Cardiovascular-Kidney-Metabolic Syndrome in the United States

Background: To inform public health policies regarding disease management, it is crucial to comprehend the prevalence and mortality rates linked to various stages of Cardiovascular-Kidney-Metabolic (CKM) syndrome. Methods: A longitudinal study was conducted using National Health and Nutrition Examination Survey (NHANES) data (1999-2018) from 50,624 U.S. adults, defining CKM syndrome stages based on the 2023 American Heart Association (AHA) Presidential Advisory Statement. The investigation aimed to assess changes in 10-and 15-year adjusted cumulative incidences of cardiovascular mortality with each CKD syndrome stage and describe the cross-sectional prevalence of CKM syndrome from 1999 to 2020. Results: CKM syndrome prevalence by stage was as follows: Stage 0, 12.5% (95% CI, 12.0-12.9); Stage 1, 16.7% (95% CI, 16.2-17.2); Stage 2, 40.0% (95% CI, 38.4-39.6); Stage 3, 22.9% (95% CI, 22.5-23.4); Stage 4, 8.9% (95% CI, 8.6-9.2). Over a median 9.5-year follow-up, 2,557 participants experienced cardiovascular death. The proportion of participants in Stages 0 and 3 decreased, while Stage 1 increased. The 15-year adjusted cumulative incidences of cardiovascular mortality were: Stage 0, 4.8% (95% CI 3.1-6.6); Stage 1, 5.3% (95% CI 4.0-6.6); Stage 2, 7.9% (95% CI 8.1-10.3); Stage 3, 9.2% (95% CI 8.1-10.3); Stage 4, 15.6% (95% CI 14.7-16.6). The absolute risk difference between CKM Stage 4 and Stage 0 at 15 years was 10.8% (95% CI 8.8-12.8). Conclusions: Our findings showed a graded increase in cardiovascular mortality associated with each CKM stage. The trends observed by stage prevalence emphasize critical opportunities for stabilizing risk factors, thereby preventing adverse cardiovascular outcomes.
Quality Function Deployment (QFD)

Quality function deployment (QFD) is a product-development methodology that “deploys” the Voice of the Customer (VOC) throughout the product-development process. A cross-functional team implements QFD by creating a series of one or more matrices, the first of which is referred to as the House of Quality (HOQ). These matrices relate customer wants and needs to an extensive set of product features. A set of metrics is developed to measure how well sets of product features are meeting customer needs, and then the product's design specifications are identified and prioritized. QFD enables the entire product-development team to prioritize their development activities in a systematic, analytical way. It helps them work together to achieve a common view of the relationship between customer needs and product design. It provides an “audit trail” that reminds people, both new and old to the project, as to why past decisions were made. The QFD process stretches the product-development team's thinking as to which activities are most critical toward creating a winning product or service. It also improves communication among members of the product-development team. The result is a product that is thoughtfully designed right from the beginning, cutting down the need for later rework and reducing development time and costs. Keywords: quality function deployment; House of Quality; new-product design; product development; customer needs; performance measures; Voice of the Customer; market research
International space station image extraction from a dynamic environment using deep learning

This paper investigates the use of convolutional neural networks for the purpose of image foreground extraction from a dynamic environment. The proposed solution utilises the latest developments in image segmentation using pixel-wise classification to produce foreground target extraction for real-time operations. A collection of spacecraft images were assembled for network training and evaluation. The proposed technique takes advantage of transfer learning for the stable training of a convolutional neural network classifier. The image extraction software was applied to a thermal camera video, taken by an undocking spacecraft from the International Space Station. The results show the proposed deep learning-based image extraction has advantages over traditional background subtraction methods. This investigation provides evidence that semantic segmentation using convolutional neural network can be an effective tool for spacecraft image isolation and extraction from a dynamically cluttered scene.
WordNet : an electronic lexical database

Part 1 The lexical database: nouns in WordNet, George A. Miller modifiers in WordNet, Katherine J. Miller a semantic network of English verbs, Christiane Fellbaum design and implementation of the WordNet lexical database and searching software, Randee I. Tengi. Part 2: automated discovery of WordNet relations, Marti A. Hearst representing verb alterations in WordNet, Karen T. Kohl et al the formalization of WordNet by methods of relational concept analysis, Uta E. Priss. Part 3 Applications of WordNet: building semantic concordances, Shari Landes et al performance and confidence in a semantic annotation task, Christiane Fellbaum et al WordNet and class-based probabilities, Philip Resnik combining local context and WordNet similarity for word sense identification, Claudia Leacock and Martin Chodorow using WordNet for text retrieval, Ellen M. Voorhees lexical chains as representations of context for the detection and correction of malapropisms, Graeme Hirst and David St-Onge temporal indexing through lexical chaining, Reem Al-Halimi and Rick Kazman COLOR-X - using knowledge from WordNet for conceptual modelling, J.F. M. Burg and R.P. van de Riet knowledge processing on an extended WordNet, Sanda M. Harabagiu and Dan I Moldovan appendix - obtaining and using WordNet.
Representational Deficiency of Process Modelling Languages: Measures and Implications

The large number of available process modelling languages has given rise to the need for evaluation and comparison of their representational capabilities. Over the last few years, the research community has risen to the challenge by carrying out a significant amount of work in the area of such analysis. Much of this effort is based on the Bunge-Wand-Weber representation model, a common benchmark used for the analysis of grammars that purport to model the real world and the interactions within it. However, the carried out BWW analyses of various process modelling languages exist largely separately from each other, with no comprehensive effort at the comparative measurement of their representational capability. This paper introduces four measures that, together, reflect the representational capacity and clarity of a process modelling language. These four measures are used in this paper to compare seven popular process modelling languages. The work provides insights into the representational deficiency similarities and differences between process modelling languages and also predicts some of their implications for practice.
AUTOMATED KEYWORD GENERATION IN THE PUBLIC RADIO SECTOR USING WORD EMBEDDINGS 1

Public broadcasters find themselves in a difficult situation when it comes to digital transformation. In more and more use cases, metadata is needed, e.g. to allow radio editors to search for content pieces, to provide short summaries of items in the media library, to set up content-based recommendation services for listeners, to allow users to browse by categories or tags, or to optimize content for search engines on the internet. Public broadcasters are in need of proper metadata to survive the digital transformation and to offer new and timely services.
Defocus Video Matting

Video matting is the process of pulling a high-quality alpha matte and foreground from a video sequence. Current techniques require either a known backbround (e.g., a blue screen) or extensive user interaction (e.g., to specify known foreground and background elements). The matting problem is generally under-constrained, since not enough information has been collected at capture time. We propose a novel, fully autonomous method for pulling a matte using multiple synchronized video streams that share a point of view but differ in their plane of focus. The solution is obtained by directly minimizing the error in filter-based image formation equations, which are over-constrained by our rich data stream. Our system solves the fully dynamic video matting problem without user assistance: both the foreground and background may be high frequency and have dynamic content, the foreground may resemble the background, and the scene is lit by natural (as opposed to polarized or collimated) illumination.
in Computer

—In most cases, different parties use different techniques for conducting privacy-preserving data analysis (PPDA) to know about the final analysis result. Current PPDA techniques reveal only the final analysis results and cannot guarantee the truthfulness of the input data given by the participating parties for conducting PPDA unless proper incentives are used. In this paper we have proposed the non -corporative computation (NCC) for optimizing the faulty in the input data given by the participating parties and shown how tools from theoretical computer science in general and non-cooperative computation in particular could be used to analyze the incentive issues in distributed data analysis framework.