Miracle-dz/UniFAR-FTUs huggingface dataset:
A Unified Approach to Support Vector Machines

This chapter presents a unified introduction to support vector machine (SVM) methods for binary classification, one-class classification, and regression. The SVM method for binary classification (binary SVC) is introduced first, and then extended to encompass one-class classification (clustering). Next, using the regularized risk approach as a motivation, the SVM method for regression (SVR) is described. These methods are then combined to obtain a single unified SVM formulation that encompasses binary classification, one-class classification, and regression (as well as some extensions of these), and the dual formulation of this unified model is derived. A mechanical analogy for binary and one-class SVCs is given to give an intuitive explanation of the operation of these two formulations. Finally, the unified SVM is extended to implement general cost functions, and an application of SVM classifiers to the problem of spam e-mail detection is considered.
A Partially Inexact Alternating Direction Method of Multipliers and its Iteration-Complexity Analysis.

This paper proposes a partially inexact alternating direction method of multipliers for computing approximate solution of a linearly constrained convex optimization problem. This method allows its first subproblem to be solved inexactly using a relative approximate criterion, whereas a proximal term is added to its second subproblem in order to simplify it. A stepsize parameter is included in the updating rule of the Lagrangian multiplier to improve its computational performance. Pointwise and ergodic interation-complexity bounds for the proposed method are established. To the best of our knowledge, this is the first time that complexity results for an inexact ADMM with relative error criteria has been analyzed. Some preliminary numerical experiments are reported to illustrate the advantages of the new method.
A Review of Off-Line Handwritten Chinese Character Recognition

Off-line handwritten Chinese character recognition is one of the most challenging problems in pattern recognition field.This paper analyzed the latest developments of offline handwritten Chinese character recognition in recent years.Main methods of the key technologies such as Chinese characters segmentation,feature extraction and classifier design were discussed.This paper also introduced 3 typical off-line handwritten Chinese character recognition databases.Finally,remain difficult issues and future trends of off-line handwritten Chinese character recognition were proposed.This paper will guide researchers in this field and promote development of off-line handwritten Chinese character recognition technology.
When Training and Test Sets Are Different: Characterizing Learning Transfer

This chapter contains sections titled: Introduction, Conditional and Generative Models, Real-Life Reasons for Dataset Shift, Simple Covariate Shift, Prior Probability Shift, Sample Selection Bias, Imbalanced Data, Domain Shift, Source Component Shift, Gaussian Process Methods for Dataset Shift, Shift or No Shift? , Dataset Shift and Transfer Learning, Conclusions
Modified almost unbiased Liu estimator in logistic regression

Abstract This paper focuses on introducing a new parameter estimator to the logistic regression model when the multicollinearity presents. The proposed estimator called Modified almost unbiased logistic Liu estimator (MAULLE) is obtained by composing the Liu estimator and the almost unbiased Liu estimator. Further, conditions for the superiority of the new estimator over some existing estimators were derived with respect to mean square error (MSE) and scalar mean square error (SMSE) sense. A Monte Carlo simulation study was carried out to compare the performance of the proposed estimator with some existing estimators in the scalar mean square error sense, and a real data example was discussed to illustrate the theoretical results.
Representing Nitrogen, Phosphorus, and Carbon Interactions in the E3SM Land Model: Development and Global Benchmarking

Over the past several decades, the land modeling community has recognized the importance of nutrient regulation on the global terrestrial carbon cycle. Implementations of nutrient limitation in land models are diverse, varying from applying simple empirical down‐regulation of potential gross primary productivity under nutrient deficit conditions to more mechanistic treatments. In this study, we introduce a new approach to model multinutrient (nitrogen [N] and phosphorus [P]) limitations in the Energy Exascale Earth System Model (E3SM) Land Model version 1 (ELMv1‐ECA). The development is grounded on (1) advances in representing multiple‐consumer, multiple‐nutrient competition; (2) a generic dynamic allocation scheme based on water, N, P, and light availability; (3) flexible plant CNP stoichiometry; (4) prognostic treatment of N and P constraints on several carbon cycle processes; and (5) global data sets of plant physiological traits. Through benchmarking the model against best knowledge of global plant and soil carbon pools and fluxes, we show that our implementation of nutrient constraints on the present‐day carbon cycle is robust at the global scale. Compared with predecessor versions, ELMv1‐ECA better predicts global‐scale gross primary productivity, ecosystem respiration, leaf area index, vegetation biomass, soil carbon stocks, evapotranspiration, N2O emissions, and NO3‐ leaching. Factorial experiments indicate that representing the phosphorus cycle improves modeled carbon fluxes, while considering dynamic allocation improves modeled carbon stock density. We also highlight the value of using the International Land Model Benchmarking (ILAMB) package to evaluate and document performance during model development.
Airborne Forward-Looking Radar Super-Resolution Imaging Using Iterative Adaptive Approach

Airborne forward-looking radar (AFLR) imaging has raised many concerns in fields of Earth observation, independent of weather and daytime. Constrained by imaging principles, conventional high-resolution radar imaging techniques such as synthetic aperture radar (SAR) and Doppler beam sharpening (DBS) are incapable of AFLR imaging. The real aperture radar (RAR) can obtain AFLR images using a scanning antenna, but suffers from coarse cross-range resolution. Recently, there has been much attention paid to the iterative adaptive approach (IAA), which draws from the benefits of RAR imaging and provides improved cross-range resolution. However, earlier work on the IAA imposed a convolution model on the received azimuth echo, neglecting the effect of the Doppler phase. This model mismatch degrades the imaging performance for moving platforms. To settle this problem, this paper first establishes a Doppler-convolution model of AFLR imaging, where both Doppler phase and antenna convolution effects are considered, allowing more accurate reconstruction when applying the IAA to formulate a super-resolution image. Then, a data-depended approach for Doppler centroid estimation is proposed to circumvent the problem of low estimation precision using platform motion parameters delivered by navigational devices mounted on the radar platform. Simulation results demonstrate that the proposed implementation of the IAA based on the Doppler-convolution model and Doppler centroid estimation can overcome the deficiencies of the SAR and DBS techniques in the forward-looking imaging direction, and present a noticeably superior performance as compared with conventional AFLR imaging methods.
ROBUST DESIGN OPTIMIZATION IN PARALLEL COMPUTING ENVIRONMENT

In engineering problems, randomness and uncertainties are inherent. Robust design procedures, formulated in the framework of multi-objective optimization, have been proposed in order to take into account the various sources of randomness and uncertainty. These design procedures require orders of magnitude more computational effort than the conventional analysis or the optimum design processes since a very large number of finite element analyses is required to be dealt. It is therefore an imperative need to exploit the capabilities of the computing resources in order to deal with this kind of problems. In particular, parallel computing is implemented at the level of the metaheuristic optimization, by exploiting the physical parallelization feature of the nondominated sorting evolution strategies method, as well as at the level of the repeated structural analyses required for assessing the behavioural constraints and for calculating the objective functions. In this study an efficient dynamic load balancing algorithm (DLBA) for the optimum exploitation of the available computing resources is proposed and, without loss of generality, DLBA is applied to the task of computing the desired feasible Pareto front. In such problems the computation of the complete Pareto front with feasible designs only, constitutes a very challenging task. The proposed algorithm achieves linear speedup factors and almost 100% speedup factor values with reference to the sequential procedure.
Selective Annotation of Modal Readings: Delving into the Difficult Data

This work presents an alternative approach to the question of how to define taxonomic categories in the word sense disambiguation problem of determining the reading of a modal auxiliary verb. We focus, in this paper, on uses of could , which we found to be the most difficult of all of the modal auxiliary data. Rather than classifying uses of could according to taxonomic labels such as ability or deontic , we classify uses with respect to where the reading situates the eventuality being described relative to the speech time. For example, the sentence ‘You could swim. ’ is about a swimming eventuality in the past leading up to the time of speech, if it is read as being an ability. The sentence is about a swimming eventuality in the future of the time of speech, if it is read as being a polite suggestion. We classify the uses according to specific temporal properties of the eventuality that the modal auxiliary describes. We achieve between 0.614 kappa and 0.769 kappa inter-annotator agreement. The temporally-inspired categories we propose have immediate relevance for models of modal auxiliary meaning and provide the groundwork for improved classification into finer-grained, inference-rich semantic categories.
Study of the numerical efficiency of structured abs-normal forms

The abs-normal form (ANF) can be used to represent almost any piecewise linear function. Several of these piecewise linear functions exhibit a certain structure that has an impact on the numerical efficiency of the ANF representation. In this paper, three common structures are investigated that typically arise in applications and require special numerical treatment: the sum, the composition, and the component-wise maximum/minimum of several functions. For these structures, the corresponding expressions of the resulting abs-normal form are provided, as well as some alternatives. The theoretical observations are supported by numerical results.
Plasma properties of suprathermal electrons near comet 67P/Churyumov-Gerasimenko with Rosetta

Context. The Rosetta spacecraft escorted comet 67P/Churyumov-Gerasimenko from 2014 to September 2016. The mission provided in situ observations of the cometary plasma during different phases of the cometary activity, which enabled us to better understand its evolution as a function of heliocentric distance. Aims. In this study, different electron populations, called warm and hot, observed by the Ion and Electron Sensor (IES) of the Rosetta Plasma Consortium (RPC) are investigated near the comet during the escorting phase of the Rosetta mission. Methods. The estimates for the suprathermal electron densities and temperatures were extracted using IES electron data by fitting a double-kappa function to the measured velocity distributions. The fitting results were validated using observations from other RPC instruments. We give upgraded estimates for the warm and hot population densities compared to values previously shown in literature. Results. The fitted density and temperature estimates for both electron populations seen by IES are expressed as a function of heliocentric distance to study their evolution with the cometary activity. In addition, we studied the dependence between the electron properties and cometocentric distance. Conclusions. We observed that when the neutral outgassing rate of the nucleus is high (i.e., near perihelion) the suprathermal electrons are well characterized by a double-kappa distribution. In addition, warm and hot populations show a significant dependence with the heliocentric distance. The populations become clearly denser near perihelion while their temperatures are observed to remain almost constant. Moreover, the warm electron population density is shown to be strongly dependent on the radial distance from the comet. Finally, based on our results we reject the hypothesis that hot electron population seen by IES consists of solely suprathermal (halo) solar wind electrons, while we suggest that the hot electron population mainly consists of solar wind thermal electrons that have undergone acceleration near the comet.
Bus Converter Using Isolation Capacitance for ZVS and Invariant Operation

Many power distribution architectures, including the now-popular “isolated bus” architecture, call for a small and efficient unregulated dc transformer stage. Typical isolated converters can be both small and efficient through high-frequency operation and soft switching, but timing and control can be challenging as switching frequencies increase. We present an isolated bus converter that achieves zero-voltage switching (ZVS) for both inverter and rectifier switches with equal and constant dead times regardless of load, greatly simplifying control. It accomplishes this by using additional capacitors to aid the magnetizing current in commutating switch voltages during the dead time. The converter’s operation and advantages are verified with a 1.4-MHz prototype. The converter operates from 36 to 12 <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\mathrm{ dc}}{}_{out}$ </tex-math></inline-formula> at up to 36-W rating and achieves <inline-formula> <tex-math notation="LaTeX">$>94.5$ </tex-math></inline-formula>% peak efficiency with 300-W/in<sup>3</sup> power density.
Problems in extremal graph theory and Euclidean Ramsey theory

This thesis addresses problems of three types. The first type is finding extremal numbers for unions of graphs, each with a colour-critical edge (joint work with V. Nikiforov). In 1968, Simonovits found extremal numbers ex(n,H) for graphs with a colour-critical edge for large n (without specifying how large). A similar result for unions of graphs, each with a colourcritical edge, can be deduced from Simonovits’ 1974 work. Nikiforov and I improved this result, giving a precise bound for n. The second type of problem considered is maximizing the number of cycles in a graph (joint work with A. Arman and D. Gunderson). It is proved that for sufficiently many vertices, the complete balanced bipartite graph is the unique triangle-free graph with the maximum number of cycles, thus answering a conjecture posed by Durocher et al. Other results include upper and lower bounds on the maximum number of cycles in graphs and multigraphs with a given number of edges, or with a given number of vertices and edges. The lower bounds in some cases come from random graphs; the asymptotics for the expected number of cycles in the random graph G(n,m) is found for all possible relations between n and m. The final chapter is dedicated to Euclidean Ramsey theory. Two results about
Empirical Assessment Of Within Arm Correlation Imputation In Trials Of Continuous Outcomes

Thank you for downloading empirical assessment of within arm correlation imputation in trials of continuous outcomes. As you may know, people have look hundreds times for their chosen readings like this empirical assessment of within arm correlation imputation in trials of continuous outcomes, but end up in harmful downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some infectious bugs inside their laptop.
Stock price short-time prediction based on GA-LM-BP neural network

In order to deal with the problems of local optima and low predicting accuracy in stock price prediction using traditional neural networks,Levenberg-Marquardt(LM) algorithm is introduced to improve the three-layer back propagation(BP) neural networks in this paper to prevent the local optima.Genetic algorithm(GA) is used to optimize the weight values and thresholds in BP neural networks to obtain high accuracy of predicting the stock price.The proposed method is simulated in Matlab and the experimental results of predicting price of 4 stocks show the proposed algorithm is effective and promising.
Application Specific Instruction-set Processor Using a Parametrizable multi-SIMD Synthesizeable Model Supporting Design Space Exploration

In this thesis, we provide a synthesizable model for supporting design space exploration of application-specific instruction-set processors. The model is written in a high-level of abstraction hardware description language Bluespec System Verilog and is parametrized to support different configurations for use in the design space exploration. To test the model, different applications from the media domain was selected to run on some of the configurations from the design space exploration. The applications were also run on a standard general processor for comparison. The results show that there is a performance gain compared to the standard processor, but with a higher cost of resources. With the utilization of the resources the scheduling of the applications turned out to be critical for this performance gain. The synthesizable model also shows that there is a consideration of the maximum clock frequency and memory constraints that the theoretical design space exploration model does not take into account. (Less)
Complex defect inspection for transparent substrate by combining digital holography with machine learning

We proposed a complex defect inspection (CDI) technique for quality control of transparent substrates that uses the diffraction characteristics of digital holograms and a machine learning algorithm. A complex pattern diffraction model was built to provide two diffraction criteria, the least separation of confusion and the effective diffraction distance, to extend depth of focus in the effective diffraction regime for numerical reconstruction. On the basis of an analysis of three-dimensional diffraction characteristics of complex images, defect identification was performed to detect and classify defects (cracks, dusts, and watermarks) in transparent substrates using region-based segmentation and a machine learning algorithm. The experimental results indicated that the defect detection performance of the proposed CDI system was recall = 96.3% and precision = 92.8%. Moreover, overall multiclass classification accuracy = 95.3%, resulting in a discrimination area under the receiver operating characteristic curve (Az) of 0.96.
No Metallicity Correlation Associated with the Kepler Dichotomy

NASA’s Kepler mission has discovered thousands of planetary systems, ∼20% of which are found to host multiple transiting planets. This relative paucity (compared to the high fraction of single transiting systems) is postulated to result from a distinction in the architecture between multi-transiting systems and those hosting a single transiting planet: a phenomenon usually referred to as the Kepler dichotomy. In this paper, we investigate the hypothesis that external giant planets are the main cause behind the over-abundance of single- relative to multi-transiting systems, which would be signaled by higher metallicities in the former sample. To this end, we perform a statistical analysis on the stellar metallicity distribution with respect to planet multiplicity in the Kepler data. We perform our analysis on a variety of samples taken from a population of 1166 Kepler main-sequence planetary hosts, using precisely determined metallicities from the California-Kepler survey and Swift et al. Contrary to some predictions, we do not find a significant difference between the stellar metallicities of the single- and multiple-transiting planet systems. However, we do find a 55% upper bound for systems with a single non-giant planet that could also host a hidden giant planet, based on metallicity considerations. While the presence of external giant planets might be one factor behind the Kepler dichotomy, our results also favor alternative explanations. We suggest that additional radial velocity and direct imaging measurements are necessary to constrain the presence of gas giants in systems with a single transiting planet.
TELEPHONE SPEECH MONITORING SYSTEM BASED ON GMM-UBM/SVM FOR UIGHUR LANGUAGE

In this paper we discuss a telephone speech monitoring system based on GMM-UBM/SVM.GMM is a method commonly used in speaker recognition system.But its accuracy of recognition will be impacted because of the voice monitored is short in time,and the telephone-internet terminals as well as transmission lines have big background noise.In our research we put into full play the robustness of GMM and the advantage of SVM in its perfect discrimination on small quantity static data in designing the telephone speech monitoring system and have discussed the performance of the system with Uighur.For comparison,we also discussed the recognitions with vector quantisation(VQ),weighted VQ(WVQ) and base line.The proposed method improves the recognition rate to 20.2% higher than VQ's and 16.7% higher than WVQ's respectively for every 10 seconds test speech out of 20 seconds voice in a training set of 50 target speakers.
Use of Design Patterns According to Hand Dominance in a Mobile User Interface

User interface (UI) design patterns for mobile applications provide a solution to design problems and can improve the usage experience for users. However, there is a lack of research categorizing the uses of design patterns according to users’ hand dominance in a learning-based mobile UI. We classified the main design patterns for mobile applications and investigated their relationships with major learning tasks. Students’ use of different patterns was compared with respect to hand dominance. A questionnaire on 71 patterns was administered to 92 participants, and the number of patterns they used was analyzed. UI design patterns for mobile learning significantly differed between left- and right-hand-dominant participants. We also performed a cluster analysis to explore possible theme sets among participants with regard to hand dominance in learning. The results revealed three-cluster solutions for both the left- and right-hand-dominant groups. We profiled these solutions based on insights obtained from structured interviews. Our findings provide evidence that pattern use differs between right- and left-hand-dominant users with regard to reading, information retrieval, and information-browsing tasks. These findings will facilitate further development of suitable UIs for educational mobile applications.
Stability and L2–Gain analysis of linear switched singular systems by using multiple discontinuous Lyapunov function approach

In this paper, the problem of the E-exponential stability and l 2 − gain analysis of linear switched singular systems is investigated in discrete-time case. By using a multiple discontinuous Lyapunov function approach and adopting the mode-dependent average dwell time (MDADT) switching signals, new sufficient conditions of E-exponential stability and l 2 − gain analysis for linear switched singular systems are presented. Based on the above results, we also derive the weighted l 2 − gain performance index. In addition, by utilizing our proposed method, tighter bounds on average dwell time can be obtained for our considered systems. At last, a numerical example is given to show the effectiveness of the results.