Semi-verified PAC Learning from the Crowd with Pairwise Comparisons

We study the problem of crowdsourced PAC learning of threshold functions with pairwise comparisons. This is a challenging problem and only recently have query-efficient algorithms been established in the scenario where the majority of the crowd are perfect. In this work, we investigate the significantly more challenging case that the majority are incorrect, which in general renders learning impossible. We show that under the semi-verified model of Charikar~et~al.~(2017), where we have (limited) access to a trusted oracle who always returns the correct annotation, it is possible to PAC learn the underlying hypothesis class while drastically mitigating the labeling cost via the more easily obtained comparison queries. Orthogonal to recent developments in semi-verified or list-decodable learning that crucially rely on data distributional assumptions, our PAC guarantee holds by exploring the wisdom of the crowd.Comment: v2 incorporates a simpler Filter algorithm, thus the technical assumption (in v1) is no longer needed. v2 also reorganizes and emphasizes new algorithm component

Attribute-Efficient PAC Learning of Low-Degree Polynomial Threshold Functions with Nasty Noise

The concept class of low-degree polynomial threshold functions (PTFs) plays a fundamental role in machine learning. In this paper, we study PAC learning of $K$-sparse degree-$d$ PTFs on $\mathbb{R}^n$, where any such concept depends only on $K$ out of $n$ attributes of the input. Our main contribution is a new algorithm that runs in time $({nd}/{\epsilon})^{O(d)}$ and under the Gaussian marginal distribution, PAC learns the class up to error rate $\epsilon$ with $O(\frac{K^{4d}}{\epsilon^{2d}} \cdot \log^{5d} n)$ samples even when an $\eta \leq O(\epsilon^d)$ fraction of them are corrupted by the nasty noise of Bshouty et al. (2002), possibly the strongest corruption model. Prior to this work, attribute-efficient robust algorithms are established only for the special case of sparse homogeneous halfspaces. Our key ingredients are: 1) a structural result that translates the attribute sparsity to a sparsity pattern of the Chow vector under the basis of Hermite polynomials, and 2) a novel attribute-efficient robust Chow vector estimation algorithm which uses exclusively a restricted Frobenius norm to either certify a good approximation or to validate a sparsity-induced degree-$2d$ polynomial as a filter to detect corrupted samples.Comment: ICML 202

Learning from the Crowd with Pairwise Comparison

Efficient learning of halfspaces is arguably one of the most important problems in machine learning and statistics. With the unprecedented growth of large-scale data sets, it has become ubiquitous to appeal to crowd for data annotation, and the central problem that attracts a surge of recent interests is how one can provably learn the underlying halfspace from the highly noisy crowd feedback. On the other hand, a large body of recent works have been dedicated to the problem of learning with not only labels, but also pairwise comparisons, since in many cases it is easier to compare than to label. In this paper we study the problem of learning halfspaces from the crowd under the realizable PAC learning setting, and we assume that the crowd workers can provide (noisy) labels or pairwise comparison tags upon request. We show that with a powerful boosting framework, together with our novel design of a filtering process, the overhead (to be defined) of the crowd acts as a constant, whereas the natural extension of standard approaches to crowd setting leads to an overhead growing with the size of the data sets

Synthesis and Characterization of Hierarchical Structured TiO 2

Hierarchical structured TiO2 nanotubes were prepared by mechanical ball milling of highly ordered TiO2 nanotube arrays grown by electrochemical anodization of titanium foil. Scanning electron microscopy, transmission electron microscopy, X-ray diffraction, specific surface area analysis, UV-visible absorption spectroscopy, photocurrent measurement, photoluminescence spectra, electrochemical impedance spectra, and photocatalytic degradation test were applied to characterize the nanocomposites. Surface area increased as the milling time extended. After 5 h ball milling, TiO2 hierarchical nanotubes exhibited a corn-like shape and exhibited enhanced photoelectrochemical activity in comparison to commercial P25. The superior photocatalytic activity is suggested to be due to the combined advantages of high surface area of nanoparticles and rapid electron transfer as well as collection of the nanotubes in the hierarchical structure. The hierarchical structured TiO2 nanotubes could be applied into flexible applications on solar cells, sensors, and other photoelectrochemical devices

Phase Modulation of (1T-2H)-MoSe2/TiC-C Shell/Core Arrays via Nitrogen Doping for Highly Efficient Hydrogen Evolution Reaction

Tailoring molybdenum selenide electrocatalysts with tunable phase and morphology is of great importance for advancement of hydrogen evolution reaction (HER). In this work, phase‐ and morphology‐modulated N‐doped MoSe2/TiC‐C shell/core arrays through a facile hydrothermal and postannealing treatment strategy are reported. Highly conductive TiC‐C nanorod arrays serve as the backbone for MoSe2 nanosheets to form high‐quality MoSe2/TiC‐C shell/core arrays. Impressively, continuous phase modulation of MoSe2 is realized on the MoSe2/TiC‐C arrays. Except for the pure 1T‐MoSe2 and 2H‐MoSe2, mixed (1T‐2H)‐MoSe2 nanosheets are achieved in the N‐MoSe2 by N doping and demonstrated by spherical aberration electron microscope. Plausible mechanism of phase transformation and different doping sites of N atom are proposed via theoretical calculation. The much smaller energy barrier, longer HSe bond length, and diminished bandgap endow N‐MoSe2/TiC‐C arrays with substantially superior HER performance compared to 1T and 2H phase counterparts. Impressively, the designed N‐MoSe2/TiC‐C arrays exhibit a low overpotential of 137 mV at a large current density of 100 mA cm−2, and a small Tafel slope of 32 mV dec−1. Our results pave the way to unravel the enhancement mechanism of HER on 2D transition metal dichalcogenides by N doping

Optimization of Traced Neuron Skeleton Using Lasso-Based Model

Reconstruction of neuronal morphology from images involves mainly the extraction of neuronal skeleton points. It is an indispensable step in the quantitative analysis of neurons. Due to the complex morphology of neurons, many widely used tracing methods have difficulties in accurately acquiring skeleton points near branch points or in structures with tortuosity. Here, we propose two models to solve these problems. One is based on an L1-norm minimization model, which can better identify tortuous structure, namely, a local structure with large curvature skeleton points; the other detects an optimized branch point by considering the combination patterns of all neurites that link to this point. We combined these two models to achieve optimized skeleton detection for a neuron. We validate our models in various datasets including MOST and BigNeuron. In addition, we demonstrate that our method can optimize the traced skeletons from large-scale images. These characteristics of our approach indicate that it can reduce manual editing of traced skeletons and help to accelerate the accurate reconstruction of neuronal morphology

Two-Dimensional Nanomaterials for Gas Sensing Applications: The Role of Theoretical Calculations

Two-dimensional (2D) nanomaterials have attracted a large amount of attention regarding gas sensing applications, because of their high surface-to-volume ratio and unique chemical or physical gas adsorption capabilities. As an important research method, theoretical calculations have been massively applied in predicting the potentially excellent gas sensing properties of these 2D nanomaterials. In this review, we discuss the contributions of theoretical calculations in the study of the gas sensing properties of 2D nanomaterials. Firstly, we elaborate on the gas sensing mechanisms of 2D layered nanomaterials, such as the traditional charge transfer mechanism, and a standard for distinguishing between physical and chemical adsorption, from the perspective of theoretical calculations. Then, we describe how to conduct a theoretical analysis to explain or predict the gas sensing properties of 2D nanomaterials. Thirdly, we discuss three important methods that have been applied in order to improve the gas sensing properties, that is, defect functionalization (vacancy, edge, grain boundary, and doping), heterojunctions, and electric fields. Among these strategies, theoretical calculations play a very important role in explaining the mechanisms underlying the enhanced gas sensing properties. Finally, we summarize both the advantages and limitations of the theoretical calculations, and present perspectives for further research on the 2D nanomaterials-based gas sensors

Parameter Optimization of the Power and Energy System of Unmanned Electric Drive Chassis Based on Improved Genetic Algorithms of the KOHONEN Network

For unmanned electric drive chassis parameter optimization problems, an unmanned electric drive chassis model containing power systems and energy systems was built using CRUISE, and as the traditional genetic algorithm is prone to falling into the local optima, an improved isolation niche genetic algorithm based on KOHONEN network clustering (KIGA) is proposed. The simulation results show that the proposed KIGA can reasonably divide the initial niche populations. Compared with the traditional genetic algorithm (GA) and the isolation niche genetic algorithm (IGA), KIGA can achieve faster convergence and a better global search ability. The comprehensive performance of the unmanned electric drive chassis in terms of power and economy was increased by 8.26% with a set of better solutions. The results show that simultaneous power system and energy system parameter optimization can enhance unmanned electric drive chassis performance and that KIGA is an efficient method for optimizing the parameters of unmanned electric drive chassis