Minimax Optimal Transfer Learning for Kernel-based Nonparametric Regression

In recent years, transfer learning has garnered significant attention in the machine learning community. Its ability to leverage knowledge from related studies to improve generalization performance in a target study has made it highly appealing. This paper focuses on investigating the transfer learning problem within the context of nonparametric regression over a reproducing kernel Hilbert space. The aim is to bridge the gap between practical effectiveness and theoretical guarantees. We specifically consider two scenarios: one where the transferable sources are known and another where they are unknown. For the known transferable source case, we propose a two-step kernel-based estimator by solely using kernel ridge regression. For the unknown case, we develop a novel method based on an efficient aggregation algorithm, which can automatically detect and alleviate the effects of negative sources. This paper provides the statistical properties of the desired estimators and establishes the minimax optimal rate. Through extensive numerical experiments on synthetic data and real examples, we validate our theoretical findings and demonstrate the effectiveness of our proposed method

Towards a Unified Analysis of Kernel-based Methods Under Covariate Shift

Covariate shift occurs prevalently in practice, where the input distributions of the source and target data are substantially different. Despite its practical importance in various learning problems, most of the existing methods only focus on some specific learning tasks and are not well validated theoretically and numerically. To tackle this problem, we propose a unified analysis of general nonparametric methods in a reproducing kernel Hilbert space (RKHS) under covariate shift. Our theoretical results are established for a general loss belonging to a rich loss function family, which includes many commonly used methods as special cases, such as mean regression, quantile regression, likelihood-based classification, and margin-based classification. Two types of covariate shift problems are the focus of this paper and the sharp convergence rates are established for a general loss function to provide a unified theoretical analysis, which concurs with the optimal results in literature where the squared loss is used. Extensive numerical studies on synthetic and real examples confirm our theoretical findings and further illustrate the effectiveness of our proposed method.Comment: Poster to appear in Thirty-seventh Conference on Neural Information Processing System

Polarization bandgaps and fluid-like elasticity in fully solid elastic metamaterials

Elastic waves exhibit rich polarization characteristics absent in acoustic and electromagnetic waves. By designing a solid elastic metamaterial based on three-dimensional anisotropic locally resonant units, here we experimentally demonstrate polarization bandgaps together with exotic properties such as 'fluid-like' elasticity. We construct elastic rods with unusual vibrational properties, which we denote as 'meta-rods'. By measuring the vibrational responses under flexural, longitudinal and torsional excitations, we find that each vibration mode can be selectively suppressed. In particular, we observe in a finite frequency regime that all flexural vibrations are forbidden, whereas longitudinal vibration is allowed-a unique property of fluids. In another case, the torsional vibration can be suppressed significantly. The experimental results are well interpreted by band structure analysis, as well as effective media with indefinite mass density and negative moment of inertia. Our work opens an approach to efficiently separate and control elastic waves of different polarizations in fully solid structures.G.M., C.F., and P.S. acknowledge the support of the Hong Kong Research Grants Council (Grant No. AoE/P-02/12). Y.L. and G.W. thank the State Key Program for Basic Research of China (No. 2014CB360505, No. 2012CB921501), National Natural Science Foundation of China (No. 11374224, No. 61671314), and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). J.C. acknowledges the support from the European Research Council (ERC) through the Starting Grant 714577 PHONOMETA

Microbial reduction and precipitation of vanadium (V) in groundwater by immobilized mixed anaerobic culture

Vanadium is an important contaminant impacted by natural and industrial activities. Vanadium (V) reduction efficiency as high as 87.0% was achieved by employing immobilized mixed anaerobic sludge as inoculated seed within 12 h operation, while V(IV) was the main reduction product which precipitated instantly. Increasing initial V(V) concentration resulted in the decrease of V(V) removal efficiency, while this index increased first and then decreased with the increase of initial COD concentration, pH and conductivity. High-throughput 16S rRNA gene pyrosequencing analysis indicated the decreased microbial diversity. V(V) reduction was realized through dissimilatory reduction process by significantly enhanced Lactococcus and Enterobacter with oxidation of lactic and acetic acids from fermentative microorganisms such as the enriched Paludibacter and the newly appeared Acetobacterium, Oscillibacter. This study is helpful to detect new functional species for V(V) reduction and constitutes a step ahead in developing in situ bioremediations of vanadium contamination. (C) 2015 Elsevier Ltd. All rights reserved.National Natural Science Foundation of China (NSFC) [41440025, 21307117]; Beijing Excellent Talent Training Project [2013D009015000003]; Beijing Higher Education Young Elite Teacher Project [YETP0657]SCI(E)[email protected]

The Status Quo, Sources and Influencing Factors of Professional Pressure Faced by Preschool Teachers in Rural China: An Empirical Study Based on Multiple Counties in Hubei Province

The professional pressure of preschool teachers in rural China is closely related to the stability of the teaching staff and the development of children. A study of 734 teachers in 155 rural preschools from three national-level poverty-stricken counties and one non-poverty county in Hubei Province showed that current rural preschool teachers are facing greater professional pressure. Approximately 44.47% thought that the pressure is high, but has not yet reached the level of high burnout; non-poverty county preschool teachers have relatively high pressure. According to the Demand-Control-Support (DCS) model, the main pressure stems from the work requirements of children and parents, especially parents’ excessive emphasis on children’s safety, knowledge, and skills. The results of the Ordered Probit Model showed that the influencing factors of preschool teachers’ professional pressure in rural preschools in China include work factors such as workload and the number of children in difficulty; control factors like perseverance and professional identity; support factors such as staffing status, salary satisfaction, family support, and work support; as well as demographic variables such as age and household registration type (Hukou); and certain inter-county differences exist. Therefore, we recommend that the government, society, and preschools establish effective incentive and restraint mechanisms to reduce the professional pressure of preschool teachers in terms of salary, social status, parental guidance, workload, and stress training, and improve their ability to cope with pressure. Meanwhile, more focus need to be given on teachers who are for the first year preschool, older in age, lacking staffing status, no non-agricultural household registration, and overloading working

Robust Visual Tracking Based on Convolutional Sparse Coding

This paper proposes a new visual tracking method by constructing the robust appearance model of the target with convolutional sparse coding. First, our method uses convolutional sparse coding to divide the interest region of the target into a smooth image and four detail images with different fitting degrees. Second, we compute the initial target region by tracking the smooth image with the kernel correlation filtering. We define an appearance model to describe the details of the target based on the initial target region and the combination of four detail images. Third, we propose a matching method by the overlap rate and Euclidean distance to evaluate candidates and the appearance model to compute the tracking results based on detail images. Finally, the two tracking results are separately computed by the smooth image, and the detail images are combined to produce the final target rectangle. Many experiments on videos from Tracking Benchmark 2015 demonstrate that our method produces much better results than most of the present visual tracking methods

Experimental Study on the Mechanical Properties and Disintegration Resistance of Microbially Solidified Granite Residual Soil

Microbially induced calcium carbonate (CaCO3) precipitation (MICP) is an emerging soil-treatment method. To explore the effect of this technology on granite residual soil, this study investigated the effects of the mechanical properties and disintegration resistance of microbially cured granite residual soil under different moisture contents by conducting direct shear and disintegration tests. The curing mechanism was also discussed and analyzed. Results showed that MICP can be used as reinforcement for granite residual soil. Compared with those of untreated granite residual soil, the internal friction angle of MICP-treated granite residual soil increased by 10% under a moisture content of 30%, while its cohesion increased by 218%. The disintegration rate of the MICP-treated granite residual soil stabilized after a maintenance time of 5 days under different water contents. Therefore, we provide the explanation that the improvement of the shear strength and disintegration resistance of granite residual soil is due to CaCO3 precipitation and the surface coating