Robust Principle Research Articles

A principle-based robustness analysis of admissibility-based argumentation semantics

The principle-based approach is a methodology to classify and analyse argumentation semantics. In this paper we classify seven of the main alternatives for argumentation semantics using a set of new robustness principles. These principles complement Baroni and Giacomin’s original classification and deal with the behaviour of a semantics when the argumentation framework changes due to the addition or removal of an attack between two arguments. We distinguish so-called persistence principles and monotonicity principles, where the former deal with the question of whether a labelling or extension of an argumentation framework under a given semantics persists after a change, and the latter with the question of whether new labellings or extensions are created after a change. We furthermore show in which sense labelling-based and extension-based semantics lead to subtly different principles and results. Our results can be used for choosing a semantics for a particular application, or to guide the search for new argumentation semantics, but they have been used also in the design of algorithms.

Converging Evidence for Differential Specialization and Plasticity of Language Systems.

Functional specialization and plasticity are fundamental organizing principles of the brain. Since the mid-1800s, certain cognitive functions have been known to be lateralized, but the provenance and flexibility of hemispheric specialization remain open questions. Language is a uniquely human phenomenon that requires a delicate balance between neural specialization and plasticity, and language learning offers the perfect window to study these principles in the human brain. In the current study, we conducted two separate functional MRI experiments with language learners (male and female), one cross-sectional and one longitudinal, involving distinct populations and languages, and examined hemispheric lateralization and learning-dependent plasticity of the following three language systems: reading, speech comprehension, and verbal production. A multipronged analytic approach revealed a highly consistent pattern of results across the two experiments, showing (1) that in both native and non-native languages, while language production was left lateralized, lateralization for language comprehension was highly variable across individuals; and (2) that with increasing non-native language proficiency, reading and speech comprehension displayed substantial changes in hemispheric dominance, with languages tending to lateralize to opposite hemispheres, while production showed negligible change and remained left lateralized. These convergent results shed light on the long-standing debate of neural organization of language by establishing robust principles of lateralization and plasticity of the main language systems. Findings further suggest involvement of the sensorimotor systems in language lateralization and its plasticity.SIGNIFICANCE STATEMENT The human brain exhibits a remarkable ability to support a vast variety of languages that may be acquired at different points in the life span. Language is a complex construct involving linguistic as well as visual, auditory, and motor processes. Using functional MRI, we examined hemispheric specialization and learning-dependent plasticity of three language systems—reading, speech comprehension, and verbal production—in cross-sectional and longitudinal experiments in language learners. A multipronged analytic approach revealed converging evidence for striking differences in hemispheric specialization and plasticity among the language systems. The results have major theoretical and practical implications for our understanding of fundamental principles of neural organization of language, language testing and recovery in patients, and language learning in healthy populations.

Resumption and Optionality in Arabic Definite Restrictive Relatives

The present study examines the claim that definite relative clauses in Modern Standard Arabic exhibit free variation between resumptive pronouns and gaps. The implication of such a claim presents a problem for minimalist syntax that does not tolerate true optionality. To solve this problem, the study argues that the original claim is incorrect and that despite similarities in the PF outputs, resumptive relatives are syntactically different from gapped relatives. While the latter is derived from a standard VSO structure, I propose, the former is derived from a topic-comment structure that already contains an RP. Thus, the fact that resumptive relatives contain resumptive pronouns has nothing to do with relativization, as is generally assumed. The study demonstrates that both resumptive relatives and gapped relatives are derived by movement in contexts that do not involve islands. As it turns out, resumption in relatives is used only as a last resort strategy to save structures in which movement is genuinely blocked, such as islands, from crash. Altogether the study concludes that the variation observed does not reflect true optionality, a finding that supports robust economy principles of minimalist syntax.

Design acceptance criteria for operating open-pit slopes: An update

ABSTRACT Open-pit slope engineering involves balancing economic imperatives and risk, where an optimum pit slope design seeks to safely steepen the pit slope angles to minimize the mining of waste, therefore maximizing ore recovery. Open-pit Design Acceptance Criteria (DAC) had been proposed and adopted by industry, and the Guidelines for Open Pit Slope Design, published in 2009, finally provided a consistent set of guidelines. Since the publication of these guidelines, it has been the industry experience that practical DAC requires consideration of multiple levels of design confidence, ranging from highly uncertain greenfield sites to mature operations; the unique nature of consequences associated with open pit wall failures as opposed to other civil and mining structures; and differences between operators’ risk appetite. This article presents the state of practice for inter-ramp and overall open-pit slope design in the mining industry and develops flexible DAC for operating pits that addresses the considerations outlined. The criteria are rooted in robust risk tolerance principles, previously adopted criteria, a database of open-pit designs, and mathematical validation for criteria in terms of both Factor of Safety (FoS) and Probability of Failure (PoF).

Truncated Robust Principle Component Analysis With A General Optimization Framework.

Recently, several robust principle component analysis (RPCA) models have been proposed to improve the robustness of principle component analysis (PCA). But an important problem that the robustness to outliers affects the discrimination of correct samples has not been solved yet. To solve this problem, we propose a truncated robust principle component analysis (T-RPCA) model which treats correct samples and outliers separately. In fact, the proposed model performs an implicitly truncated weighted learning scheme which is more reasonable for robustness learning respective to previous works. Moreover, we propose a re-weighted (RW) optimization framework to solve a general problem and generalize two sub-frameworks upon it. To be specific, the first sub-framework orients a general truncated loss optimization problem which contains the objective problem of T-RPCA, and the second one focuses on a general singular-value based optimization problem. Besides, we provide rigorously theoretical guarantees for the proposed model, RW framework and sub-frameworks. Empirical studies demonstrate that the proposed T-RPCA model outperforms previous RPCA models on reconstruction and classification tasks.

Functional observer–controller method for unmeasured premise variables Takagi-Sugeno systems with external disturbance

A functional observer–controller design problem of Takagi-Sugeno fuzzy systems with unmeasured premise variables and external disturbance is investigated in this paper. First, robust state feedback controller is designed based on a relaxed fuzzy Lyapunov approach. With an estimation membership functions of transformation technique, a fuzzy functional observer is introduced to estimate the unmeasured premise variables and system control input. By using a robust separation principle, the observer and controller errors system is proved to be stable and robust to external disturbance. Finally, the estimation and control effects of the proposed method are verified through several engineering examples.

Protein Stability Buffers the Cost of Translation Attenuation following eIF2α Phosphorylation

SummaryPhosphorylation of the translation initiation factor eIF2α is a rapid and vital response to many forms of stress, including protein-misfolding stress in the endoplasmic reticulum (ER stress). It is believed to cause a general reduction in protein synthesis while enabling translation of few transcripts. Such a reduction of protein synthesis comes with the threat of depleting essential proteins, a risk thought to be mitigated by its transient nature. Here, we find that translation attenuation is not uniform, with cytosolic and mitochondrial ribosomal subunits being prominently downregulated. Translation attenuation of these targets persists after translation recovery. Surprisingly, this occurs without a measurable decrease in ribosomal proteins. Explaining this conundrum, translation attenuation preferentially targets long-lived proteins, a finding not only demonstrated by ribosomal proteins but also observed at a global level. This shows that protein stability buffers the cost of translational attenuation, establishing an evolutionary principle of cellular robustness.

An Artificial Intelligence Fusion Model for Cardiac Emergency Decision Making: Application and Robustness Analysis.

BackgroundDuring cardiac emergency medical treatment, reducing the incidence of avoidable adverse events, ensuring the safety of patients, and generally improving the quality and efficiency of medical treatment have been important research topics in theoretical and practical circles.ObjectiveThis paper examines the robustness of the decision-making reasoning process from the overall perspective of the cardiac emergency medical system.MethodsThe principle of robustness was introduced into our study on the quality and efficiency of cardiac emergency decision making. We propose the concept of robustness for complex medical decision making by targeting the problem of low reasoning efficiency and accuracy in cardiac emergency decision making. The key bottlenecks such as anti-interference capability, fault tolerance, and redundancy were studied. The rules of knowledge acquisition and transfer in the decision-making process were systematically analyzed to reveal the core role of knowledge reasoning.ResultsThe robustness threshold method was adopted to construct the robustness criteria group of the system, and the fusion and coordination mechanism was realized through information entropy, information gain, and mutual information methods.ConclusionsA set of fusion models and robust threshold methods such as the R2CMIFS (treatment mode of fibroblastic sarcoma) model and the RTCRF (clinical trial observation mode) model were proposed. Our study enriches the theoretical research on robustness in this field.

Increasing of Reliability of Spacecraft Control System on Base of Robust Diagnostic Models and Division Principle in Parity Space

Different directions of creation high reliability integrate spacecraft control system are discussed on base of robust diagnostic models and division principle in parity space. Problems of synthesis spacecraft control system algorithms are examined with incomplete apriory and distorted current information, action of uncontrolled and random factors, information losses and equipment failures. The structure of onboard attitude control system is synthesized and control algorithms are chosen, which guarantee robust stability and failure stability in presence indignant factors and obstacles. An instrumental structure and operational modes of spacecraft attitude control system are described. Methods of dynamic research, computer technology and modeling particularities are indicated. Diagnostic and reconfiguration algorithms for onboard complex of connection, navigation, geodesy satellites and earth inspectoral satellite in prolonged space flight utilization are proposed. Testing procedure is contains two stage: discovering and eliminating faults. Given mathematical system model is researched by means of difference signals, which forms with arise at fault emergence. The failure character is established by deciding rules on base difference signals and measures to it eliminating are took. Questions of onboard spacecraft control system failure stable improving are discussed on base principle reconfiguration with apply to adaptive logic in testing and diagnostic algorithms. The mathematical system model is researching with implementation of analytic reserving. Difference signals are formed, which arise at fault appearance. The adaptive approach to development testing and diagnostic systems provide for realization of flexible logic of control system function to take into account factual onboard equipment state. Special attention is devote to problem influence liquid fuel reactive engine agility on spacecraft control attitude system dynamic characteristics and precision. The effectiveness of prepositional approaches and algorithms is confirmed by mathematical modeling results for several actual technical systems. Recommendations to their practical applications are given.

Fairness for Robust Log Loss Classification

Developing classification methods with high accuracy that also avoid unfair treatment of different groups has become increasingly important for data-driven decision making in social applications. Many existing methods enforce fairness constraints on a selected classifier (e.g., logistic regression) by directly forming constrained optimizations. We instead re-derive a new classifier from the first principles of distributional robustness that incorporates fairness criteria into a worst-case logarithmic loss minimization. This construction takes the form of a minimax game and produces a parametric exponential family conditional distribution that resembles truncated logistic regression. We present the theoretical benefits of our approach in terms of its convexity and asymptotic convergence. We then demonstrate the practical advantages of our approach on three benchmark fairness datasets.

Spatial-Temporal Gaussian Scale Mixture Modeling for Foreground Estimation

Subtracting the backgrounds from the video frames is an important step for many video analysis applications. Assuming that the backgrounds are low-rank and the foregrounds are sparse, the robust principle component analysis (RPCA)-based methods have shown promising results. However, the RPCA-based methods suffered from the scale issue, i.e., the ℓ1-sparsity regularizer fails to model the varying sparsity of the moving objects. While several efforts have been made to address this issue with advanced sparse models, previous methods cannot fully exploit the spatial-temporal correlations among the foregrounds. In this paper, we proposed a novel spatial-temporal Gaussian scale mixture (STGSM) model for foreground estimation. In the proposed STGSM model, a temporal consistent constraint is imposed over the estimated foregrounds through nonzero-means Gaussian models. Specifically, the estimates of the foregrounds obtained in the previous frame are used as the prior for these of the current frame, and nonzero means Gaussian scale mixture models (GSM) are developed. To better characterize the temporal correlations, the optical flow has been used to model the correspondences between foreground pixels in adjacent frames. The spatial correlations have also been exploited by considering that local correlated pixels should be characterized by the same STGSM model, leading to further performance improvements. Experimental results on real video datasets show that the proposed method performs comparably or even better than current state-of-the-art background subtraction methods.

An encoder information-based anomaly detection method for planetary gearbox diagnosis

To keep the reliability of the planetary gearbox, anomaly detection has been widely investigated for its health monitoring. To this end, a novel approach is presented in this paper to extract fault features based on the merits of built-in encoder signals. Considering that collected encoder data is accumulated in angular positions, instantaneous angular acceleration (IAA) is firstly calculated to highlight the characteristic components. And then time synchronization average (TSA) is applied on an estimated multi-period for denoising, which improves the robustness of the TSA to the feature attenuation effect caused by the round-off error of the basic period. In this paper, we explore the distinguishing properties of regular components and the fault anomaly to impose different restraints on them, which is embodied as a periodicity-enhanced model of robust principle analysis. And objective features are further separated by solving this optimization model. The validation analysis of the proposed framework is applied on both the simulation and experimental cases. The results show that the proposed method is of good performance to deal with encoder signals from the planetary gearbox for fault diagnosis.

Low-scale seesaw from neutrino condensation

Knowledge of the mechanism of neutrino mass generation would help understand a lot more about Lepton Number Violation (LNV), the cosmological evolution of the Universe, or the evolution of astronomical objects. Here we propose a verifiable and viable extension of the Standard model for neutrino mass generation, with a low-scale seesaw mechanism via LNV condensation in the sector of sterile neutrinos. To prove the concept, we analyze a simplified model of just a single family of elementary particles and check it against a set of phenomenological constraints coming from electroweak symmetry breaking, neutrino masses, leptogenesis and dark matter. The model predicts (i) TeV scale quasi-degenerate heavy sterile neutrinos, suitable for leptogenesis with resonant enhancement of the CP asymmetry, (ii) a set of additional heavy Higgs bosons whose existence can be challenged at the LHC, (iii) an additional light and sterile Higgs scalar which is a candidate for decaying warm dark matter, and (iv) a majoron. Since the model is based on simple and robust principles of dynamical mass generation, its parameters are very restricted, but remarkably it is still within current phenomenological limits.

Contemporary Problems of Political Transformation in Modern Germany

The article states that the political party system formed on the constitutional basis of the Basic Law of Germany is one of the key pillars of democracy of the German state. The Western German-style political party system, based on a substantial legal framework, political culture, and traditions, has convincingly proved its democratic spirit and viability over several decades of the post-war period. The effectiveness of this system was ensured by the presence of the two large parties, the Christian Democrats and the Social Democrats. The attractiveness of their policy priorities, broad political activity, and statist approaches made it possible to displace other, less influential, parties. The effectiveness and viability of the political party system of Germany are also proven by the course of the process of restoring the country’s state unity. The current period is characterised by systemic crisis phenomena, which have not spared German major parties. These processes are also taking place in other European countries, as previously stable parties transform over time into an idiosyncratic kind of political and technological institutions. For them, short-term success is a priority and is defined by the number of votes cast, rather than the focus on robust principles and visions of the future. However, it may be fair to claim that the whole previous experience testifies to the creativity of the political party system of post-war Germany, thus making the modern Federal Republic of Germany able to cope with contemporary problems and challenges. This is – and will be – buttressed by time and new approaches pursued by politicians, experts, and scholars as well as the previous practice of reaching compromises and social concord in the name of national interests. The political party system was and remains an important constituent of the entire state and political system of Germany. Keywords: political party system, Federal Republic of Germany, state system, Germany, Christian Democrats, Social Democrats.

Adaptive Recognition of Motion Decomposition Image Based on Adaptive Principal Component Extraction Algorithm

Moving target detection and recognition methods are the foundation and key of modern intelligent video recognition systems. It combines advanced technologies in many fields such as image processing, pattern recognition, and artificial intelligence, and is a research hotspot in computer vision technology. Therefore, it is of great significance to study moving target detection and recognition algorithms. The non-local image extraction algorithm proposed in this paper uses an adaptive clustering method to perform fine cluster analysis on non-local image blocks with different feature types. Through the step-by-step principal component approximation method, we carefully find the features in each class. This progressive principal component approximation implements singular value hard threshold processing based on the Marchenko-Pastur (MP) theorem to select the main part of the feature, and uses special soft thresholds in the principal component transform domain to further improve the extraction performance. The Lower Bound-Based Within-Class Maximum Division (LBWCMD) is proposed, and this method is used as a preprocessing step of robust principal component analysis in moving target detection. This article applies LBWCMD to the video frame set based on the position information of the moving target, and the obtained frame subset meets the signal requirements of Robust Principle Component Analysis (RPCA) to the greatest extent. On this basis, we add frames with smaller motion amplitudes to each frame subset to increase the proportion of background pixels in each subset. Frame set division and low-rank decomposition realize the detection of moving targets under a unified framework. The detection rate of the proposed method is higher than that of the current popular methods in sports video data sets, and the detection accuracy is improved compared with the original RPCA method.

Robust principal component analysis with intra-block correlation

This paper proposes a novel optimization program for solving the Robust Principle Component Analysis (RPCA) problem, which decomposes a data matrix into a conventional low-rank part plus a particular block-sparse residual. This kind of block-sparse residual often scattered in the source signal scope as contaminants, and often existed in many practical applications, such as an ordinary imaging system, a Hyper Spectral Imaging system, EEG and MEG, and types of physiological signals. Different from most currently existing approaches, the study perceived especially a highly spatial correlation among the inner structure of the neighbouring pixels in this contiguously block-sparse residual. The high intra-block correlation is then introduced as prior information to deal the governing optimization problem. In order to enhance the block-sparsity and maintain the local smoothness simultaneously, a localized low-rank promoting method is introduced with a theoretical guarantee. An efficient solving algorithm is designed accordingly with a convergence analysis by adopting the classical Alternating Direction Method of Multipliers (ADMM) framework. In addition to the theoretical model derivation, several synthetic simulations together with a real application on image denoising experiment have been conducted to validate the proposed model. As expected, the models outperforms significantly the compared state-of-the-arts.

Índice de accesibilidad para cibermedios mexicanos

El presente artículo presenta la construcción del Índice de Accesibilidad para México MAI, el cual está orientado a medir los niveles de implementación de la WCAG 2.0 en los cibermedios mexicanos. Para conseguir este objetivo se evalúa el número de errores reportados por la herramienta TAW de 1531 cibermedios mexicanos. El MAI es construido utilizando AHP como método de ponderación, LST como método de normalización y la agregación geométrica. Se destaca el alto nivel de cumplimiento de la norma WCAG 2.0 lo que arroja un MAI promedio de 94.5%. Este alto rendimiento es impulsado por los resultados sobresalientes en los principios de operabilidad y comprensión. Además, se encontró que el principio de robustez es el que presenta menores niveles de cumplimiento. Se destaca a www.contrasena.com.mx como el cibermedio con el mejor desempeño a la luz de los lineamientos.

Deconstructing Scatter/Gather I/O

Bayesian algorithms and access points have garnered profound interest from both cyberneticists. Indeed, couple of programmers worldwide would oppose this idea with the improvement of vacuum tubes, which embodies the robust principles of steganography. We argue that while e-commerce and e-commerce can cooperate to accomplish this goal, synthesis of the location-identity split [13]. The notion that information theorists collude with collaborative modalities is often considered intuitive. To what extent can public-private key pairs be harnessed to achieve this ambition? [1],[ 3],[5]

Robust Principal Component Analysis-Based Infrared Small Target Detection

A method based on Robust Principle Component Analysis (RPCA) technique is proposed to detect small targets in infrared images. Using the low rank characteristic of background and the sparse characteristic of target, the observed image is regarded as the sum of a low-rank background matrix and a sparse outlier matrix, and then the decomposition is solved by the RPCA. The infrared small target is extracted from the single-frame image or multi-frame sequence. In order to get more efficient algorithm, the iteration process in the augmented Lagrange multiplier method is improved. The simulation results show that the method can detect out the small target precisely and efficiently.

A Methodology for the Synthesis of E-Commerce

Recent advances in real-time communication and relational modalities offer a viable alternative to the producer-consumer problem. After years of theoretical research into the partition table, we show the refinement of the location-identity split, which embodies the robust principles of operating sys-tems. In order to fix this issue, we propose new scalable archetypes (Gunnel), demonstrating that multi-processors and multi-processors are never incompatible.