Large-Scale Multi-Label Learning with Incomplete Label Assignments

Multi-label learning deals with the classification problems where each instance can be assigned with multiple labels simultaneously. Conventional multi-label learning approaches mainly focus on exploiting label correlations. It is usually assumed, explicitly or implicitly, that the label sets for training instances are fully labeled without any missing labels. However, in many real-world multi-label datasets, the label assignments for training instances can be incomplete. Some ground-truth labels can be missed by the labeler from the label set. This problem is especially typical when the number instances is very large, and the labeling cost is very high, which makes it almost impossible to get a fully labeled training set. In this paper, we study the problem of large-scale multi-label learning with incomplete label assignments. We propose an approach, called MPU, based upon positive and unlabeled stochastic gradient descent and stacked models. Unlike prior works, our method can effectively and efficiently consider missing labels and label correlations simultaneously, and is very scalable, that has linear time complexities over the size of the data. Extensive experiments on two real-world multi-label datasets show that our MPU model consistently outperform other commonly-used baselines

Enzyme-linked immunospot assay response to recombinant CFP-10/ESAT-6 fusion protein among patients with spinal tuberculosis: implications for diagnosis and monitoring of surgical therapy

SummaryObjectiveThis study aimed to assess the performance of a laboratory-developed recombinant CFP-10/ESAT-6 fusion protein (rCFP-10/ESAT-6)-based enzyme-linked immunospot (ELISPOT) assay for the diagnosis of spinal tuberculosis (TB) in China, and to evaluate the value of the ELISPOT assay for monitoring the efficacy of surgical treatment.MethodsIn the first part of the study, a total of 78 participants were consecutively recruited for ELISPOT using rCFP-10/ESAT-6 as a stimulus. The cutoff value for ELISPOT positivity was based on the results of receiver operating characteristic curve analysis. In the second part, this approach was evaluated in a prospective study including 102 patients with suspected spinal TB. Data on clinical characteristics of the patients and conventional laboratory results were collected, and blood samples were obtained for ELISPOT using rCFP-10/ESAT-6 as a stimulus.ResultsAmong the 102 patients with suspected spinal TB, 11 were excluded from the study. Twenty-three patients (25.2%) had culture-confirmed TB and 29 (31.9%) patients had probable TB. Among the spinal TB patients, the ELISPOT had a sensitivity of 82.7%, compared to a sensitivity of 61.5% for the purified protein derivative (PPD) skin test. The specificity was 87.2% for ELISPOT and 46.2% for the PPD skin test among 39 subjects with non-TB disease. The number of spot-forming cells and/or the positive rate of the ELISPOT assay were associated with aging, emaciation, and paravertebral abscess. The number of subjects with responses to rCFP-10/ESAT-6 slightly decreased after surgical treatment in spinal TB patients.ConclusionsA laboratory-developed rCFP-10/ESAT-6 ELISPOT assay is a useful adjunct to current tests for the diagnosis of spinal TB

Approximate Sparse Regularized Hyperspectral Unmixing

Sparse regression based unmixing has been recently proposed to estimate the abundance of materials present in hyperspectral image pixel. In this paper, a novel sparse unmixing optimization model based on approximate sparsity, namely, approximate sparse unmixing (ASU), is firstly proposed to perform the unmixing task for hyperspectral remote sensing imagery. And then, a variable splitting and augmented Lagrangian algorithm is introduced to tackle the optimization problem. In ASU, approximate sparsity is used as a regularizer for sparse unmixing, which is sparser than l1 regularizer and much easier to be solved than l0 regularizer. Three simulated and one real hyperspectral images were used to evaluate the performance of the proposed algorithm in comparison to l1 regularizer. Experimental results demonstrate that the proposed algorithm is more effective and accurate for hyperspectral unmixing than state-of-the-art l1 regularizer

Inconsistency of QED in the Presence of Dirac Monopoles

A precise formulation of $U(1)$ local gauge invariance in QED is presented, which clearly shows that the gauge coupling associated with the unphysical longitudinal photon field is non-observable and actually has an arbitrary value. We then re-examine the Dirac quantization condition and find that its derivation involves solely the unphysical longitudinal coupling. Hence an inconsistency inevitably arises in the presence of Dirac monopoles and this can be considered as a theoretical evidence against their existence. An alternative, independent proof of this conclusion is also presented.Comment: Extended and combined version, refinements added; 20 LaTex pages, Published in Z. Phys. C65, pp.175-18

Approximate Sparsity and Nonlocal Total Variation Based Compressive MR Image Reconstruction

Recent developments in compressive sensing (CS) show that it is possible to accurately reconstruct the magnetic resonance (MR) image from undersampled k-space data by solving nonsmooth convex optimization problems, which therefore significantly reduce the scanning time. In this paper, we propose a new MR image reconstruction method based on a compound regularization model associated with the nonlocal total variation (NLTV) and the wavelet approximate sparsity. Nonlocal total variation can restore periodic textures and local geometric information better than total variation. The wavelet approximate sparsity achieves more accurate sparse reconstruction than fixed wavelet l0 and l1 norm. Furthermore, a variable splitting and augmented Lagrangian algorithm is presented to solve the proposed minimization problem. Experimental results on MR image reconstruction demonstrate that the proposed method outperforms many existing MR image reconstruction methods both in quantitative and in visual quality assessment

Use of the 2A Peptide for Generation of Multi-Transgenic Pigs through a Single Round of Nuclear Transfer

Multiple genetic modifications in pigs can essentially benefit research on agriculture, human disease and xenotransplantation. Most multi-transgenic pigs have been produced by complex and time-consuming breeding programs using multiple single-transgenic pigs. This study explored the feasibility of producing multi-transgenic pigs using the viral 2A peptide in the light of previous research indicating that it can be utilized for multi-gene transfer in gene therapy and somatic cell reprogramming. A 2A peptide-based double-promoter expression vector that mediated the expression of four fluorescent proteins was constructed and transfected into primary porcine fetal fibroblasts. Cell colonies (54.3%) formed under G418 selection co-expressed the four fluorescent proteins at uniformly high levels. The reconstructed embryos, which were obtained by somatic cell nuclear transfer and confirmed to express the four fluorescent proteins evenly, were transplanted into seven recipient gilts. Eleven piglets were delivered by two gilts, and seven of them co-expressed the four fluorescent proteins at equivalently high levels in various tissues. The fluorescence intensities were directly observed at the nose, hoof and tongue using goggles. The results suggest that the strategy of combining the 2A peptide and double promoters efficiently mediates the co-expression of the four fluorescent proteins in pigs and is hence a promising methodology to generate multi-transgenic pigs by a single nuclear transfer

Modeling and simulation of equivalent second-order pendulum model of casting crane based on liquid slosh

Because the load of the foundry crane is the molten metal of high temperature liquid, the liquid in the load will produce different amplitude sloshing during the operation process, showing a complex solid-liquid coupling phenomenon. The conventional modeling method of treating the load as a solid can no longer meet the control requirements. In order to solve this problem, the equivalent second-order pendulum model of liquid sloshing is established in this paper. On this basis, the dynamic equation of casting bridge crane is derived by Lagrange method. Then a sliding mode variable structure controller is designed and simulated. The experimental results verify the dynamic characteristics and effectiveness of the nonlinear model, and realize the precise positioning of the trolley and the effective anti-swing of the load