Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Hydraulic Responses to Meteorological Forcing: From Canopy to Aquifer

An understanding of the hydrologic interactions among atmosphere, land surface, and subsurface is one of the keys to understanding the water cycling system that supports our life system on earth. Properly modeling such interactions is a difficult task because of the inherent coupled processes and complex feedback structures among subsystems. In this paper, we present a model that simulates the land‐surface and subsurface hydrologic response to meteorological forcing. This model combines a state‐of‐the‐art land‐surface model, the National Center for Atmospheric Research (NCAR) Community Land Model version 3 (CLM3), with a variably saturated groundwater model, TOUGH2, through an internal interface that includes flux and state variables shared by the two submodels. Specifically, TOUGH2 in its simulation uses infiltration, evaporation, and root‐uptake rates, calculated by CLM3, as source–sink terms; CLM3 in its simulation uses saturation and capillary pressure profiles, calculated by TOUGH2, as state variables. This new model, CLMT2, preserves the best aspects of both submodels: the state‐of‐the‐art modeling capability of surface energy and hydrologic processes from CLM3 and the more realistic physical process–based modeling capability of subsurface hydrologic processes from TOUGH2. The preliminary simulation results show that the coupled model greatly improves the predictions of the water table, evapotranspiration, surface temperature, and moisture in the top 20 cm of soil at a real watershed, as evaluated from 18 yr of observed data. The new model is also ready to be coupled with an atmospheric simulation model, representing one of the first models capable of simulating hydraulic processes from the top of the atmosphere to deep ground.
Reverse engineering time-series interaction data from screen-captured videos

In recent years the amount of research on human aspects of software engineering has increased. Many studies use screen-capture software (e.g., Snagit) to record developers' behavior as they work on software development tasks. The recorded task videos capture direct information about which activities the developers carry out with which content and in which applications during the task. Such behavioral data can help researchers and practitioners understand and improve software engineering practices from human perspective. However, extracting time-series interaction data (software usage and application content) from screen-captured videos requires manual transcribing and coding of videos, which is tedious and error-prone. In this paper we present a computer-vision based video scraping technique to automatically reverse-engineer time-series interaction data from screen-captured videos. We report the usefulness, effectiveness and runtime performance of our video scraping technique using a case study of the 29 hours task videos of 20 developers in the two development tasks.
Application of Modern Technology in New Insurance Product Development

Development of new technologies brings new opportunities to all business sectors. The work presented here represents a connection of the ubiquitous computing with the real world business opportunities. We are namely focusing on some changes in the insurance industry. We address the more complex problem of business changes of introducing new insurance products connected with mobile phones, GPS and other technologies development. We suppose new risk model that integrates the net premium calculation with some methods of multivariate analysis. Thus, aim of this work is primary situated to predict some business and risk opportunities from ubiquitous computing and to analyze some important factors for successful implementation.
Classification trees as an alternative to linear discriminant analysis.

Linear discriminant analysis (LDA) is frequently used for classification/prediction problems in physical anthropology, but it is unusual to find examples where researchers consider the statistical limitations and assumptions required for this technique. In these instances, it is difficult to know whether the predictions are reliable. This paper considers a nonparametric alternative to predictive LDA: binary, recursive (or classification) trees. This approach has the advantage that data transformation is unnecessary, cases with missing predictor variables do not require special treatment, prediction success is not dependent on data meeting normality conditions or covariance homogeneity, and variable selection is intrinsic to the methodology. Here I compare the efficacy of classification trees with LDA, using typical morphometric data. With data from modern hominoids, the results show that both techniques perform nearly equally. With complete data sets, LDA may be a better choice, as is shown in this example, but with missing observations, classification trees perform outstandingly well, whereas commercial discriminant analysis programs do not predict classifications for cases with incompletely measured predictor variables and generally are not designed to address the problem of missing data. Testing of data prior to analysis is necessary, and classification trees are recommended either as a replacement for LDA or as a supplement whenever data do not meet relevant assumptions. It is highly recommended as an alternative to LDA whenever the data set contains important cases with missing predictor variables.
Intuitive geometry and visuospatial working memory in children showing symptoms of nonverbal learning disabilities

Visuospatial working memory (VSWM) and intuitive geometry were examined in two groups aged 11–13, one with children displaying symptoms of nonverbal learning disability (NLD; n = 16), and the other, a control group without learning disabilities (n = 16). The two groups were matched for general verbal abilities, age, gender, and socioeconomic level. The children were presented with simple storage and complex-span tasks involving VSWM and with the intuitive geometry task devised by Dehaene, Izard, Pica, and Spelke (2006). Results revealed that the two groups differed in the intuitive geometry task. Differences were particularly evident in Euclidean geometry and in geometrical transformations. Moreover, the performance of NLD children was worse than controls to a larger extent in complex-span than in simple storage tasks, and VSWM differences were able to account for group differences in geometry. Finally, a discriminant function analysis confirmed the crucial role of complex-span tasks involving VSWM in distinguishing between the two groups. Results are discussed with reference to the relationship between VSWM and mathematics difficulties in nonverbal learning disabilities.
A time to change? The supply of climate mitigation products from land-use change in northern NSW

With the impending introduction of an Australian Carbon Pollution Reduction Scheme, farmers and landholders in rural Australia have increased opportunities to participate in the market. This includes the adoption of land-use change to sequester additional carbon in exchange for carbon credits and the production of a renewable energy source (biofuels). However, these land-use changes compete with existing farm enterprises and may contain significant transaction costs. Therefore it is necessary for the institutional arrangements to provide adequate incentives for landholders to adopt these land-use changes. This paper examines the potential supply of these land-use changes for climate mitigation from landholders in a northern NSW catchment. These results will allow further investigation of how incentive structures and policy instruments may be developed to increase the supply of these goods from landholders.
EM-based angle-domain channel estimation for MIMO-OFDM systems

This paper presents an expectation-maximization (EM) based angle-domain algorithm (EMA) channel estimation for multiple-input multiple-output with orthogonal frequency division multiplexing (MIMO-OFDM) systems. Although EM algorithm combined with maximum a posteriori (MAP) estimator could obtain good performance, its high complexity makes this algorithm unpractical. The proposed algorithm do not require prior knowledge of the channel correlation, avoids inversion induced by LS algorithm and exploits channel coefficients irrelevance in angle domain for ignoring the small-valued channel taps and retaining only the most significant taps (MST) to reduce the effect of noise on the estimates. Simulation results indicate that the proposed algorithm gets better performance than phase shifted orthogonal LS (PSO-LS) and EM algorithms.
Sequential decoding of a general classical-quantum channel

Because a quantum measurement generally disturbs the state of a quantum system, one might think that it should not be possible for a sender and receiver to communicate reliably when the receiver performs a large number of sequential measurements to determine the message of the sender. We show here that this intuition is not true, by demonstrating that a sequential decoding strategy works well even in the most general ‘one-shot’ regime, where we are given a single instance of a channel and wish to determine the maximal number of bits that can be communicated up to a small failure probability. This result follows by generalizing a non-commutative union bound to apply for a sequence of general measurements. We also demonstrate two ways in which a receiver can recover a state close to the original state after it has been decoded by a sequence of measurements that each succeed with high probability. The second of these methods will be useful in realizing an efficient decoder for fully quantum polar codes, should a method ever be found to realize an efficient decoder for classical-quantum polar codes.
Pricing Perpetual Options for Jump Processes

Abstract We consider two models in which the logarithm of the price of an asset is a shifted compound Poisson process. Explicit results are obtained for prices and optimal exercise strategies of certain perpetual American options on the asset, in particular for the perpetual put option. In the first model in which the jumps of the asset price are upwards, the results are obtained by the martingale approach and the smooth junction condition. In the second model in which the jumps are downwards, we show that the value of the strategy corresponding to a constant option-exercise boundary satisfies a certain renewal equation. Then the optimal exercise strategy is obtained from the continuous junction condition. Furthermore, the same model can be used to price certain reset options. Finally, we show how the classical model of geometric Brownian motion can be obtained as a limit and also how it can be integrated in the two models.
Mg/Ti multilayers: structural, optical and hydrogen absorption properties

Mg-Ti alloys have uncommon optical and hydrogen absorbing properties, originating from a “spinodal-like” microstructure with a small degree of chemical short-range order in the atoms distribution. In the present study we artificially engineer shortrange order by depositing Pd-capped Mg/Ti multilayers with different periodicities and characterize them both structurally and optically. Notwithstanding the large lattice parameter mismatch between Mg and Ti, the as-deposited metallic multilayers show good structural coherence. Upon exposure to H2 gas a two-step hydrogenation process occurs, with the Ti layers forming
Child labor, education, and children's rights

Child labor is widespread, and bad for development, both that of the individual child, and of the society and economy in which she, or he lives. If allowed to persist to the current extent, child labor will prevent the achievement of the Millennium Development Goals of halving poverty, and achieving Education for All. Nearly all of the world's governments have ratified international human rights conventions, which call for the elimination of child labor, and the provision of universal primary education. Fulfilling these commitments is of critical importance for development. This paper reviews the international legal framework relating to child labor, and access to education, and, provides a statistical portrait of child labor and education participation. It looks at why children work from the perspective of household decision-making. Various policy options are considered, including those which improve the incentives to education relative to labor, remove constraints to schooling, and increase education participation through legislation. Conclusions are drawn in the final section.
The New Theory of Discriminating and Generating Optimal-face

In this paper, a new theory of optimal-face generating is proposed to improve recognition rate of face image. Its aim is to solve how to select a relatively better face image to be recognized in dynamic face recognition system. The proposed theory improves the system performance by integrating the effects of multiple factors including pose, illuminating, expression and resolution. A new system is built between face detecting system and face recognition system which is called the optimal-face generating system. The method of optimal-face generating is to generate a face image suit for recognition through searching for or synthesizing or transforming images. The optimal-face is the one which has frontal view or small rotations, neutral expression, even illumination and high resolution. The recognition rate in dynamic recognition system is greatly improved after applied the proposed theory and new system
Cultural transmission of predator recognition in fishes: intraspecific and interspecific learning

Individuals that live in groups may have the opportunity to learn to recognize unfamiliar predators by observing the fright responses of experienced individuals in the group. In intraspecific trials, naive fathead minnows, Pimephales promelas, gave fright responses to chemical stimuli from predatory northern pike, Esox lucius, when paired with pike-experienced conspecifics but not when paired with pike-naive conspecifics. These pike-conditioned minnows retained the fright responses to pike odour when tested alone, indicating that learning had occurred, and transmitted their fright responses to pike-naive minnows in subsequent trials. Brook stickleback, Culaea inconstans, are found in mixed-species aggregations with fathead minnows and are also vulnerable to predation by northern pike. In a series of interspecific tests, pike-naive brook stickleback gave fright responses to chemical stimuli from northern pike when paired with pike-experienced minnows but not when paired with pike-naive minnows. Pike-conditioned stickleback also retained the fright responses when tested alone and subsequently also transmitted the fright responses to pike-naive minnows. Individuals may benefit from observations of the fright responses of conspecifics or heterospecifics by (1) being alerted to the immediate presence of unfamiliar predators and (2) learning to recognize unfamiliar predators as a potential threat.
A Unified Approach to the Change of Resolution: Space and Gray-Level

It is shown that by defining a suitable measure for the comparison of images, changes in resolution can be treated with the same tool as changes in color resolution. A gray-tone image, for example, can be compared to a half-tone image having only two colors (black and white), but of higher spatial resolution. A graph-theoretical definition of the basic measure used is introduced. This is followed by application to spatial resampling and gray-level requantization. This results in a hybrid treatment of resolution, and the possibility of trading spatial for gray-level resolution and vice versa. >
Development of Condition Monitoring Instrumentation for Sensing Power Transformers

The objective of this work is the design and development of the Data Acquisition and Storage parts of a measurement system that will be used for the Condition Monitoring (CM) of Power Transformers. The data captured from the sensors and stored in a database will be used by an application that will predict the behavior of the Power Transformer. Data Store and Data Acquisition were developed using Data Warehouse techniques and Virtual Instruments. This paper focuses in the description of the Data Acquisition subsystem, although a brief introduction to the other subsystems will be made. The CM system is currently installed and working properly in four electric Power Transformers.
Stable and efficient reduction of large, multiport RC networks by pole analysis via congruence transformations

A novel technique is presented which employs Pole Analysis via Congruence Transformations (PACT) to reduce RC networks in a well-conditioned manner. Pole analysis is shown to be more efficient than Pade approximations when the number of network ports is large, and congruence transformations preserve the passivity (and thus absolute stability) of the networks. Networks are represented by admittance matrices throughout the analysis, and this representation simplifies interfacing the reduced networks with circuit simulators as well as facilitates realization of the reduced networks using RC elements, A prototype SPICE-in, SPICE-out, network reduction CAD tool called RCFIT is detailed, and examples are presented which demonstrate the accuracy and efficiency of the PACT algorithm.
Tropical–Extratropical Teleconnections in Boreal Summer: Observed Interannual Variability*

AbstractMaximum covariance analysis is performed on the fields of boreal summer, tropical rainfall, and Northern Hemisphere (NH) 200-hPa height for the 62-yr period of record of 1948–2009. The leading mode, which appears preferentially in summers preceding the peak phases of the El Nino–Southern Oscillation (ENSO) cycle, involves a circumglobal teleconnection (CGT) pattern in the NH extratropical 200-hPa height field observed in association with Indian monsoon rainfall anomalies. The second mode, which tends to occur in summers following ENSO peak phases, involves a western Pacific–North America (WPNA) teleconnection pattern in the height field observed in association with western North Pacific summer monsoon rainfall anomalies. The CGT pattern is primarily a zonally oriented wave train along the westerly waveguide, while the WPNA pattern is a wave train emanating from the western Pacific monsoon trough and following a great circle. The CGT is accompanied by a pronounced tropical–extratropical seesaw in t.
The Search for Matter with Gravitational Lensing

Gravitational lensing is a powerful tool to detect compact matter on very different mass scales. Of particular importance is the fact that lensing is sensitive to both luminous and dark matter alike. Depending on the mass scale, all lensing effects are used in the search for matter: offset in position, image distortion, magnification, and multiple images. Gravitational lens detections cover three main mass ranges: roughly stellar mass, galaxy mass and galaxy cluster mass scales, i.e. well known classes of objects. Various searches based on different techniques explored the frequency of compact objects over more than 15 orders of magnitude, so far mostly providing null results in mass ranges different from the ones just mentioned. Combined, the lensing results offer some interesting limits on the cosmological frequency of compact objects in the mass interval 10^{-3} <= M/M_odot <= 10^{15}, unfortunately still with some gaps in between. In the near future, further studies along these lines promise to fill the gaps and to push the limits further down, or they might even detect new object classes.
Scene classification based on SIFT combined with GIST

To overcome the disadvantage of global GIST features that lose local information needed for scene classification tasks, a new scene feature description method that combines global GIST with local SIFT features is proposed in this paper. Firstly, local context information and global RGB color quantization information are introduced into the traditional SIFT and GIST features respectively, and then the similarity between the characteristics of the scene is measured based on BOW (Bag Of Words). Finally, the scene classification task is performed with SVM. The influence on classification accuracy of the combined features with different SVM match kernels and BOW is investigated in experiment, and based on three scene datasets, the classification results of the combined feature are compared with that of the methods in literature based on single feature of global GIST or local SIFT, the experimental results show the efficiency of the proposed feature construction method.
The Master Artisan: A Framework for Master Tradespeople in Australia. Occasional Paper.

This paper was funded through the Community of Practice program, part of the National Centre for Vocational Education Research (NCVER) Building Researcher Capacity initiative. The study reported here explores the prospect of improving pathways and opportunities for tradespeople in Australia through the introduction of a masters-level qualification. It investigates the views and opinions of senior industry representatives and professional educators to determine whether the introduction of a master trade or master artisan qualification will lead to greater prospects for tradespeople and an increased esteem for trade vocations within the community. Although the aim of this investigation was to examine the viability of introducing an educational framework for master artisans, what ultimately emerged from the research was a strong theme relating to the erosion of trade identity and trade structures in Australia. As a result of this unexpected direction, the paper also considers issues relevant to the impact of this erosion and its bearing on the implementation of the master trade concept.
Empirical Research on Critical Success Factors of Agile Software Process Improvement

In this paper, we discuss agile software process improvement in P company with their description of process management in current level and analysis of problems, design the P Company success factors model in organizational culture, systems, products, customers, markets, leadership, technology and other key dimensions, which is verified through questionnaire in P company. In the end, we apply knowledge creation theory to analyze the open source software community with successful application of the typical agile software method, propose ten principles of knowledge creation in open source software community: Self-organizing, Code sharing, Adaptation, Usability, Sustention, Talent, Interaction, Collaboration, Happiness, and Democracy.