Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Perspective about Climate Change

With the Sun as a reference system, the rotation of the Earth causes a diurnal temperature variation thereon; while its revolution around the sun results in seasonal climatic variations. When referenced by the galactic system, the sun also rotates and revolves: the Earth changes cyclically. Moreover, the galactic system rotates and revolves, and moves along its orbit in the universe, which leads to longer climate change periods on Earth. By analyzing these phenomena, this research considered that terrestrial daily weather variations were due to the orbit changes of celestial bodies leading to a constant change in the Earth’s gravitational and magnetic fields. The Earth, which revolves and rotates at high-speed, constantly regulates itself to reach dynamic equilibrium. As a result, this self-regulation causes daily water cycles including atmospheric water vapour changes, rainfall, and ocean currents, magmatic changes in the Earth’s interior, etc. Periodical variations of the climate on Earth arose from the periodical movement of celestial bodies while extreme climates were caused by the effects of celestial bodies which influenced the Earth over longer periods. The variations of terrestrial weather and climate are regarded as astronomical phenomena. Extreme weather results from abrupt changes in the gravitational field direction of celestial bodies which exert little influence on the Earth. In fact, human beings only play a minor role in terrestrial systems. In view of further research on climate change, a weather-gravitational variation model with the solar or the galactic system as its centre is expected to be constructed by joining the efforts of scholars from different fields. On this basis, more research is needed to simulate the mechanism of the changes of celestial body gravitation which result in the motions of the Earth’s atmosphere and ocean currents, as well as the occurrence of earthquakes and volcanic eruption, so that the disasters can be predicted. Due to the long motion period of celestial bodies, current observational data and technologies are insufficient. The celestial body’s movement law can be back-calculated from the knowledge of the periodic changes in terrestrial climate extremes.
Reducing multiclass to binary: a unifying approach for margin classifiers

We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a margin-based binary learning algorithm. The proposed framework unifies some of the most popular approaches in which each class is compared against all others, or in which all pairs of classes are compared to each other, or in which output codes with error-correcting properties are used. We propose a general method for combining the classifiers generated on the binary problems, and we prove a general empirical multiclass loss bound given the empirical loss of the individual binary learning algorithms. The scheme and the corresponding bounds apply to many popular classification learning algorithms including support-vector machines, AdaBoost, regression, logistic regression and decision-tree algorithms. We also give a multiclass generalization error analysis for general output codes with AdaBoost as the binary learner. Experimental results with SVM and AdaBoost show that our scheme provides a viable alternative to the most commonly used multiclass algorithms.
High-performance model predictive control for process industry

Process industry requires now accurate, efficient and flexible operation of the plants. There is always a need for development of innovative technologies and integrated software tools for process modelling, dynamic trajectory optimization and high-performance industrial process control. Process dynamics tend to become too complex to be efficiently controlled by the current generation of control and optimization techniques. The main goal of research in this thesis was the development of advanced process control technology and its implementation in an integrated real-time software environment. The research was done as part of the international European project: “INtegration of process COntrol and plantwide OPtimization” (INCOOP). The only advanced control technology which made a significant impact on industrial control engineering is model predictive control (MPC). Specifically, the research in this thesis is focused on MPC for nonlinear processes. Nonlinear MPC optimizations become computationally expensive to be solved in realtime. This thesis presents various MPC algorithms for nonlinear plants using successive linearizations. The prediction equation is computed via nonlinear integration. Local linear approximation of the state equation is used to develop an optimal prediction of the future states. The output prediction is made linear with respect to the undecided control input moves, which allows to reduce the MPC optimization to a quadratic programming problem (QP). It is shown that the constrained QP problem can be solved in various ways. First of all, one can use the model equations to eliminate the states, thus reducing the number of variables in the optimization. However, this makes the problem formulation dense. Solving QPs with these methods typically requires a computational time that increases with the third power of the number of optimization variables. The constrained optimization programs tend to become too large to be solved in real-time when these standard QP solvers are used. Many industrial examples show that large-scale, usually stiff, nonlinear systems may require long prediction horizons to fulfill certain performance specifications. These requirements increase the number of variables in the optimization. Naive implementations of standard QP solvers could be inefficient for such MPC problems. A structured interior-point method (IPM) has been developed in this thesis to solve the MPC problem for large-scale nonlinear systems to reduce the computational complexity. The developed optimization algorithm explicitly takes the structure of the given problem into account such that the computational cost varies linearly with the number of optimization variables, compared with the cubic growth for the standard QP solvers. The algorithm also easily allows to introduce multiple linear models, thus making the control more flexible. The state elimination was not carried out and the structure given by the dynamics
Comprehensive Experimental Analyses of Automotive Attack Surfaces

Modern automobiles are pervasively computerized, and hence potentially vulnerable to attack. However, while previous research has shown that the internal networks within some modern cars are insecure, the associated threat model--requiring prior physical access--has justifiably been viewed as unrealistic. Thus, it remains an open question if automobiles can also be susceptible to remote compromise. Our work seeks to put this question to rest by systematically analyzing the external attack surface of a modern automobile. We discover that remote exploitation is feasible via a broad range of attack vectors (including mechanics tools, CD players, Bluetooth and cellular radio), and further, that wireless communications channels allow long distance vehicle control, location tracking, in-cabin audio exfiltration and theft. Finally, we discuss the structural characteristics of the automotive ecosystem that give rise to such problems and highlight the practical challenges in mitigating them.
Image Quality Evaluation Method Based on Human Visual System

MSE, PSNR, SSIM and other traditional objective image assessment metrics simply calculate sta- tistical average results between original images and recon- structed ones, which is incompatible with human visual system. To address this issue, a joint subjective and ob- jective image quality evaluation method is proposed. It has the following features: flrstly, the subjective feelings of re- constructed image are measured quantitatively in wavelet domain; secondly, only noticeable visual distortion is uti- lized in this model; lastly, based on difierent character- istics of spatio-temporal frequency response, eye-sensitive wavelet coe-cients are enhanced, while those non-sensitive ones are inhibited. Experimental results show that the pro- posed model has simple structure and is easy-to-operate; furthermore, it can re∞ect the visual efiects accurately and objectively.
The "New Rural Reconstruction" : movement and sustainable agricultural development in China

This doctoral thesis studies the New Rural Reconstruction (NRR) movement from a sustainable development perspective, through a concrete case of Sancha village, a rural community in China's Guangxi province. Initiated in 2003, the NRR is a grassroots network of development projects which unites intellectuals, students and organizations to experiment with different models of agricultural and rural development in China. As an alternative to agricultural industrialization, the NRR favors the cooperation of smallholder farmers, local knowledge and agro-ecology for sustainable agricultural development. In order to understand the NRR's institutional characteristics, functioning and impact, we conducted a survey in Sancha village to collect data on smallholder farmers' socio-economic behavior and performed three in-depth NRR case studies. Our empirical analysis suggests that the NRR has promoted the development of organic farming in the village. Social activities are cost-effective for social network building where organic farming is diffused rapidly. Nevertheless, without sufficient, ongoing technical training, farmers newly converted to organic farming tend to overuse nitrogen and lose their environmental advantage in rice production. To improve the performance of smallholder farmers, participatory social learning appears useful but limited because smallholder farmers are interested in economic performance rather than environmental protection. On basis of these results, we recommend a state-civil society partnership which combines the government's agricultural extension services and bottom-up rural reconstruction for the commonobjective of sustainable agriculture in China.
Modelling a ship performance in dynamic ice. Part I, Transforming data into information

For safe and efficient exploitation of ice-covered waters the knowledge about ship performance in ice is crucial. Although ice navigation has received substantial attention over recent decades, there is still no known modelling technique to predict ship’s speed in a dynamic ice field. In order to gain an insight into this process, we need to transform the available data into information first. Only then information can be used to develop new knowledge. This paper demonstrates how to transform still data into dynamic information about operation of maritime transportation system in ice-covered waters. For this purpose, the data from the Automatic Identification System about the performance of a selected ship is used along with a numerical ice forecast model describing the ice field in the analysed sea area.
Census Snapshot: Michigan

Author(s): Romero, Adam P; Baumle, Amanda; Badgett, M.V. Lee; Gates, Gary J | Abstract: Using data from the U.S. Census Bureau, this report provides demographic and economic information about same-sex couples and same-sex couples raising children in Michigan. We compare same-sex “unmarried partners,” which the Census Bureau defines as an unmarried couple who “shares living quarters and has a close personal relationship,” to different-sex married couples in Michigan.In many ways, the more than 22,000 same-sex couples living in Michigan are similar to married couples. According to Census 2000, they live throughout the state, are racially and ethnically diverse, have partners that depend upon one another financially, and actively participate in Michigan’s economy. Census data also show that 18% of same-sex couples in Michigan are raising children. However, same-sex couples in Michigan, particularly those with children, have fewer economic resources to provide for their families than do their married counterparts. They have lower household incomes, on average, and lower rates of home ownership.
Assessing the Tradecraft of Intelligence Analysis

this work. This electronic representation of RAND intellectual property is provided for non-commercial use only. Unauthorized posting of RAND PDFs to a non-RAND Web site is prohibited. RAND PDFs are protected under copyright law. Permission is required from RAND to reproduce, or reuse in another form, any of our research documents for commercial use. For information on reprint and linking permissions, please see RAND Permissions. Limited Electronic Distribution Rights This PDF document was made available from www.rand. org as a public service of the RAND Corporation.
Large-scale Graph Computation on Just a PC

Abstract : Current systems for graph computation require a distributed computing cluster to handle very large real-world problems, such as analysis on social networks or the web graph. While distributed computational resources have become more accessible developing distributed graph algorithms still remains challenging, especially to non-experts. In this work, we present GraphChi, a disk-based system for computing efficiently on graphs with billions of edges. By using a well-known method to break large graphs into small parts, and a novel Parallel Sliding Windows algorithm, GraphChi is able to execute several advanced data mining, graph mining and machine learning algorithms on very large graphs, using just a single consumer-level computer. We show, through experiments and theoretical analysis, that GraphChi performs well on both SSDs and rotational hard drives. We build on the basis of Parallel Sliding Windows to propose a new data structure Partitioned Adjacency Lists, which we use to design an online graph database GraphChi-DB.We demonstrate that, on a single PC, GraphChi-DB can process over one hundred thousand graph updates per second, while simultaneously performing computation. GraphChi-DB compares favorably to existing graph databases, particularly on data that is much larger than the available memory. We evaluate our work both experimentally and theoretically. Based on the Parallel Sliding Windows algorithm, we propose new I/O efficient algorithms for solving fundamental graph problems. We also propose a novel algorithm for simulating billions of random walks in parallel on a single computer. By repeating experiments reported for existing distributed systems we show that with only fraction of the resources, GraphChi can solve the same problems in a very reasonable time. Our work makes large-scale graph computation available to anyone with a modern PC.
Measuring the informal sector and informal employment: the experience drawn from 1-2-3 surveys in African countries

Measuring the informal sector and informal employment is of critical importance owing to the key role that this sector plays in African economies – a role that is set to expand in the years to come. This article presents an assessment of 1-2-3 surveys on the infor mal sector and infor mal employment, which have grown exponentially in Africa for more than 15 years. After presenting the fundamental principles of the modular mixed surveys adopted by 1-2-3 surveys and their properties in comparison to other types of surveys, we describe the generic schema of the 1-2-3 survey, its logic, and limitations. The focus of the article then shifts to national experiments in implementing surveys. We show how the gap between theory and practice has been bridged, the way in which certain technical problems have been solved on the ground, and the issues that remain to be adressed. Paradoxically, we conclude that today it is easier to obtain reliable data on the informal sector than on the formal sector. For each of the three phases of 1-2-3 survey, we illustrate our analyses with significant examples drawn from the African context. Finally, we delineate some perspectives for future developments.
Should we use logistic mixed model analysis for the effect estimation in a longitudinal RCT with a dichotomous outcome variable?

Background: Within epidemiology both mixed model analysis and Generalised Estimating Equations (GEE analysis) are frequently used to analyse longitudinal RCT data. With a continuous outcome, both methods lead to more or less the same results, but with a dichotomous outcome the results are totally different. The purpose of the present study is to evaluate the performance of a logistic mixed model analysis and a logistic GEE analysis and to give an advice which of the two methods should be used. Methods: Two real life RCT datasets with and without missing data were used to perform this evaluation. Regarding the logistic mixed model analysis also two different estimation procedures were compared to each other. Results: The regression coefficients obtained from the two logistic mixed model analyses were different from each other, but were always higher then the regression coefficients derived from a logistic GEE analysis. Because this also holds for the standard errors, the corresponding p-values were more or less the same. It was further shown that the effect estimates derived from a logistic mixed model analysis were an overestimation of the ‘real’ effect estimates. Conclusions: Although logistic mixed model analysis is widely used for the analysis of longitudinal RCT data, this article shows that logistic mixed model analysis should not be used when one is interested in the magnitude of the regression coefficients (i.e. effect estimates).
The Risks of the Rose City: Assessing the Social Vulnerability of Communities to Multiple Environmental Hazards in the Portland Metropolitan Area

i List of Figures . iii List of Tables . iv Introduction . 1 Study Area . 3 Literature Review . 7 Methodology . 10 Results: Hazard and Exposure Analysis . 16 Results: Vulnerability and Risk Analysis . 24 Discussion . 32 Conclusion . 38 References . 39 Appendix: Emergency Management Planning Guide . 42
KIER DISCUSSION PAPER SERIES KYOTO INSTITUTE OF ECONOMIC RESEARCH

We analyze ﬁscal transfer policies using a quantitative spatial general equilibrium model given heterogeneous local productivities and amenities, migration of young and elderly population, and inter-regional trade. We conﬁrm that ﬁscal transfers improve welfare by reducing congestion in urban areas and increasing public services and real wages in rural areas. Contrary to the literature, introducing mobility of elderly population indicates possibility of optimal transfers that enable the central government to accomplish welfare gains without sacriﬁcing national output. We calibrate the model to the Japanese economy and conduct some counterfactual simulations. The results show that Japans’ central government’s current ﬁscal transfers improve welfare of young and old population by 17 . 5% and 20 . 4%, respectively, compared with a zero-transfer case. However, they reduce national output by 12 . 5%. If the central government makes the transfers at a uniform rate across regions, it improves welfare of working and old population by 20 . 3% and 27 . 2%, respectively, compared with the zero-transfer case, without reducing the national output.
Array Calibration using Measured Data for Precise Angle-of-Arrival Estimation

In this paper, array calibration methods using the real measured data have been investigated. A set of measured data is collected from a single source whose angle of arrival (AOA) is varied. Several calibration techniques are presented. The first one is a simple calibration method to compensate amplitude and phase errors between array elements. However, from the AOA estimation result, it is found that the calibration data based on the amplitude and phase compensation are effective only to estimate AOA close to the angle of the signal used to calculate the calibration data. We found that the phase differences of calibration data change with AOA while the amplitude differences do not change with AOA. Therefore, for the second calibration technique, we propose the least square fitting to approximate the phase differences to a polynomial function of AOA and then modify the calibration data. The result illustrates the improvement of the calibration data when the least square function is applied to the phase differences. Moreover, the third calibration technique based on a signal subspace approach is proposed. From the result, it is shown that this technique is also effective in some extent and tends to improve the performance if the number of the received signal increases. To demonstrate the effectiveness of the calibration method, AOA is estimated by the MUSIC algorithm. Keywords—Antenna array, Array calibration, AOA estimation
Whole stream nitrogen uptake and denitrification in a restored stream of the Chesapeake Bay

Title of Document: WHOLE STREAM NITROGEN UPTAKE AND DENITRIFICATION IN A RESTORED STREAM OF THE CHESAPEAKE BAY Carolyn A. Klocker, Master of Science, 2007 Directed By: Assistant Professor Sujay S. Kaushal, Marine, Estuarine, and Environmental Science Little is currently known about the effects of stream restoration practices on in-stream processing and nitrogen removal. This study quantified nitrate retention in a survey of two restored and two unrestored streams in Baltimore, MD using unenriched nitrate additions, denitrification enzyme assays, and a N isotope tracer addition in one of the urban restored streams, Minebank Run. Denitrification potential in sediments was variable across streams, whereas nitrate uptake length was significantly correlated to surface water velocity, which was lowest in restored streams. In situ denitrification rates in Minbank Run were 153 mg NO3 N m d, and approximately 40% of the daily load of nitrate could be removed over a distance of 220.5 m. Stream restoration projects that decrease water velocity and increase residence time may lead to considerable rates of nitrate removal through denitrification. WHOLE STREAM NITROGEN UPTAKE AND DENITRIFICATION IN A RESTORED STREAM OF THE CHESAPEAKE BAY
Evaluating hurricane advisories using eye-tracking and biometric data

The cartography of hurricane advisories is challenged with communicating complex information regarding hazards and spatio-temporal uncertainty. This research presents an exploratory geovisualization study assessing how hurricane advisory maps are perceived. In an experimental laboratory setting study compared student responses to official National Hurricane Center advisory maps and alternative test map products. Research measured human behavioral response and environmental perception using eye-tracking electroencephalograms (EEG) electrocardiography (ECG) electromyography (EMG) and a survey questionnaire to support analysis of participants' objective and expressed responses to competing geovisualization products. This approach allows the investigation of biometric responses with digital precision in order to infer cartographic design effects on individual map readers.
Military Utility of Multispectral and Hyperspectral Sensors

Abstract : This State-of-the-Art Report (SOAR) reviews the science and technology related to detection and discrimination of militarily useful objects through the use of multiple regions of the optical and infrared electromagnetic spectrum. The report examines the phenomenological basis of many spectral features found in the reflected and emitted radiation from typical materials. Elements of sensor design important in multispectral detection are also reviewed, as a sensor components critical to multispectral operation. A key challenge in multispectral detection is the necessity of extracting the requisite information from a massive amount of data in a reasonably short time. An overview of the advanced signal processing algorithms required to perform the detection and discrimination tasks is furnished. A number of multispectral sensors are currently in use, in space-based and airborne environments. The characteristics of these sensors are summarized. Potential applications of multispectral sensing in both military and civilian contexts are also covered.