Distribution-free Bayesian regularized learning framework for semi-supervised learning.

In machine learning it is often necessary to assume or know the distribution of the data, however it is difficult to do so in practical applications. Aiming to this problem, this work, we propose a novel distribution-free Bayesian regularized learning framework for semi-supervised learning, which is called Hessian regularized twin minimax probability extreme learning machine (HRTMPELM). In this framework, we attempt to construct two non-parallel hyperplanes by introducing the high separation probability assumption, such that each hyperplane separates samples from one class with maximum probability while moving away from samples from the other class. Subsidiently, the framework can be utilized to construct reasonable semi-supervised classifiers by using the information of the inherent geometric distribution of the samples through the Hessian regularization term. Additionally, the proposed framework controls the misclassification error of samples by minimizing the upper limit of the worst-case misclassification probability, and improves the generalization performance of the model by introducing the idea of regularization to avoid the occurrence of ill-posedness and overfitting problems. More importantly, the framework has no hyperparameters, making the learning process very simplified and efficient. Finally, a simple and reliable algorithm with globally optimal solutions via multivariate Chebyshev inequalities is designed for solving the proposed learning framework. Experiments on multiple datasets demonstrate the reliability and effectiveness of the proposed learning framework compared to other methods. Especially, we applied the framework to Ningxia wolfberry quality detection, which greatly enriches and facilitates the application of machine learning algorithms in the agricultural field.

Exploring Non-Toxic Lower Dimensional Perovskites for Next-Generation X-Ray Detectors.

Owing to their extraordinary photophysical properties, organometal halide perovskites are emerging as a new material class for X-ray detection. However, the existence of toxic lead makes their commercialization questionable and should readily be replaced. Accordingly, several lead alternatives have been introduced into the framework of conventional perovskites, resulting in various new perovskite dimensionalities. Among these, Pb-free lower dimensional perovskites (LPVKs) not only show promising X-ray detecting properties due to their higher ionic migration energy, wider and tunable energy bandgap, smaller dark currents, and structural versatility but also exhibit extended environmental stability. Herein, first, the structural organization of the PVKs (including LPVKs) is summarized. In the context of X-ray detectors (XDs), the outstanding properties of the LPVKs and active layer synthesis routes are elaborated afterward. Subsequently, their applications in direct XDs are extensively discussed and the device performance, in terms of the synthesis method, device architecture, active layer size, figure of merits, and device stability are tabulated. Finally, the review is concluded with an in-depth outlook, thoroughly exploring the present challenges to LPVKs XDs, proposing innovative solutions, and future directions. This review provides valuable insights into optimizing non-toxic Pb-free perovskite XDs, paving the way for future advancements in the field.

Enriching Data Science and Health Care Education: Application and Impact of Synthetic Data Sets Through the Health Gym Project.

Large-scale medical data sets are vital for hands-on education in health data science but are often inaccessible due to privacy concerns. Addressing this gap, we developed the Health Gym project, a free and open-source platform designed to generate synthetic health data sets applicable to various areas of data science education, including machine learning, data visualization, and traditional statistical models. Initially, we generated 3 synthetic data sets for sepsis, acute hypotension, and antiretroviral therapy for HIV infection. This paper discusses the educational applications of Health Gym's synthetic data sets. We illustrate this through their use in postgraduate health data science courses delivered by the University of New South Wales, Australia, and a Datathon event, involving academics, students, clinicians, and local health district professionals. We also include adaptable worked examples using our synthetic data sets, designed to enrich hands-on tutorial and workshop experiences. Although we highlight the potential of these data sets in advancing data science education and health care artificial intelligence, we also emphasize the need for continued research into the inherent limitations of synthetic data.

Capped Linex Metric Twin Support Vector Machine for Robust Classification.

In this paper, a novel robust loss function is designed, namely, capped linear loss function Laε. Simultaneously, we give some ideal and important properties of Laε, such as boundedness, nonconvexity and robustness. Furthermore, a new binary classification learning method is proposed via introducing Laε, which is called the robust twin support vector machine (Linex-TSVM). Linex-TSVM can not only reduce the influence of outliers on Linex-SVM, but also improve the classification performance and robustness of Linex-SVM. Moreover, the effect of outliers on the model can be greatly reduced by introducing two regularization terms to realize the structural risk minimization principle. Finally, a simple and efficient iterative algorithm is designed to solve the non-convex optimization problem Linex-TSVM, and the time complexity of the algorithm is analyzed, which proves that the model satisfies the Bayes rule. Experimental results on multiple datasets demonstrate that the proposed Linex-TSVM can compete with the existing methods in terms of robustness and feasibility.

High-Temperature-Resistant, Low-Concentration Water-Controlling Composite Cross-Linked Polyacrylamide Weak Gel System Prepared from Oilfield Sewage.

The polyacrylamide weak gel is an effective system to block a high-permeability layer, realize water control, and enhance oil recovery. However, its application is limited by poor temperature resistance and high polymer dosage. In this paper, an inorganic-organic composite cross-linking agent was synthesized by using Cr(III) and phenolic resin. The composite cross-linking agent can cross-link low concentrations of polyacrylamide to obtain a high-temperature-resistant weak gel system in oilfield sewage. By adjusting the ratio of Cr(III), phenolic resin, and polyacrylamide, an optimum formula MF-7 can be obtained according to the gel strength. Results from evaluation experiments show that the strength of MF-7 can reach H grade even at polyacrylamide concentrations as low as 0.3%. The temperature resistance of the weak gel system is up to 100 °C, and no syneresis occurs after 330 h at 95 °C. Scanning electron microscopy (SEM) results show that MF-7 has a three-dimensional network structure with spherical nodes. The spherical node is composed of polyacrylamide, and its structure size is completely matched with the hydrodynamic radius of the used polyacrylamide. When combined with the network structure formed by Cr(III), the dense cross-linking network structure with nodes can greatly improve the strength and thermal stability of the gel system. The higher the molecular weight of the polyacrylamide used, the higher the strength of the gel obtained. Overall, the composite cross-linking agent can synergistically improve the mechanical properties of the gel, and this weak gel system formed by oilfield sewage is more economical and tolerant.

Optical Manipulation of Rashba Spin-Orbit Coupling at SrTiO3-Based Oxide Interfaces.

Spin-orbit coupling (SOC) plays a crucial role for spintronics applications. Here we present the first demonstration that the Rashba SOC at the SrTiO3-based interfaces is highly tunable by photoinduced charge doping, that is, optical gating. Such optical manipulation is nonvolatile after the removal of the illumination in contrast to conventional electrostatic gating and also erasable via a warming-cooling cycle. Moreover, the SOC evolutions tuned by illuminations with different wavelengths at various gate voltages coincide with each other in different doping regions and collectively form an upward-downward trend curve: In response to the increase of conductivity, the SOC strength first increases and then decreases, which can be attributed to the orbital hybridization of Ti 3d subbands. More strikingly, the optical manipulation is effective enough to tune the interferences of Bloch wave functions from constructive to destructive and therefore to realize a transition from weak localization to weak antilocalization. The present findings pave a way toward the exploration of photoinduced nontrivial quantum states and the design of optically controlled spintronic devices.

Vector critical points and generalized quasi-efficient solutions in nonsmooth multi-objective programming.

In this work, several extended approximately invex vector-valued functions of higher order involving a generalized Jacobian are introduced, and some examples are presented to illustrate their existences. The notions of higher-order (weak) quasi-efficiency with respect to a function are proposed for a multi-objective programming. Under the introduced generalization of higher-order approximate invexities assumptions, we prove that the solutions of generalized vector variational-like inequalities in terms of the generalized Jacobian are the generalized quasi-efficient solutions of nonsmooth multi-objective programming problems. Moreover, the equivalent conditions are presented, namely, a vector critical point is a weakly quasi-efficient solution of higher order with respect to a function.

A class of generalized invex functions and vector variational-like inequalities.

In this paper, a class of generalized invex functions, called [Formula: see text]-invex functions, is introduced, and some examples are presented to illustrate their existence. Then we consider the relationships of solutions between two types of vector variational-like inequalities and multi-objective programming problem. Finally, the existence results for the discussed variational-like inequalities are proposed by using the KKM-Fan theorem.

Optimality for set-valued optimization in the sense of vector and set criteria.

The vector criterion and set criterion are two defining approaches of solutions for the set-valued optimization problems. In this paper, the optimality conditions of both criteria of solutions are established for the set-valued optimization problems. By using Studniarski derivatives, the necessary and sufficient optimality conditions are derived in the sense of vector and set optimization.