Generalization Bounds for Representative Domain Adaptation

In this paper, we propose a novel framework to analyze the theoretical properties of the learning process for a representative type of domain adaptation, which combines data from multiple sources and one target (or briefly called representative domain adaptation). In particular, we use the integral probability metric to measure the difference between the distributions of two domains and meanwhile compare it with the H-divergence and the discrepancy distance. We develop the Hoeffding-type, the Bennett-type and the McDiarmid-type deviation inequalities for multiple domains respectively, and then present the symmetrization inequality for representative domain adaptation. Next, we use the derived inequalities to obtain the Hoeffding-type and the Bennett-type generalization bounds respectively, both of which are based on the uniform entropy number. Moreover, we present the generalization bounds based on the Rademacher complexity. Finally, we analyze the asymptotic convergence and the rate of convergence of the learning process for representative domain adaptation. We discuss the factors that affect the asymptotic behavior of the learning process and the numerical experiments support our theoretical findings as well. Meanwhile, we give a comparison with the existing results of domain adaptation and the classical results under the same-distribution assumption.Comment: arXiv admin note: substantial text overlap with arXiv:1304.157

High strain rate and quasi-static compression behavior and energy absorption characteristic of PVC foam

The mechanical properties at room temperature of two densities PVC foams have been experimentally evaluated in both quasi-static and dynamic compression loading conditions. The strain rate effect have been evaluated by comparing the constant strength during plateau region. Energy absorption efficiency of PVC foam is investigated, and it shows that in certain density range, the efficiency of lighter PVC foam is larger than that of heavier PVC foam, but the efficiency stress of lighter PVC foam is smaller than that of heavier PVC foam. While the lighter PVC foam has been compressed more than heavier PVC foam when they reach their peak efficiency. Therefore, for a certain density of PVC foam itself, when the loading rates increase, the PVC foam will absorb more energy more efficiently

Distinguishing Computer-generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning

Computer-generated graphics (CGs) are images generated by computer software. The~rapid development of computer graphics technologies has made it easier to generate photorealistic computer graphics, and these graphics are quite difficult to distinguish from natural images (NIs) with the naked eye. In this paper, we propose a method based on sensor pattern noise (SPN) and deep learning to distinguish CGs from NIs. Before being fed into our convolutional neural network (CNN)-based model, these images---CGs and NIs---are clipped into image patches. Furthermore, three high-pass filters (HPFs) are used to remove low-frequency signals, which represent the image content. These filters are also used to reveal the residual signal as well as SPN introduced by the digital camera device. Different from the traditional methods of distinguishing CGs from NIs, the proposed method utilizes a five-layer CNN to classify the input image patches. Based on the classification results of the image patches, we deploy a majority vote scheme to obtain the classification results for the full-size images. The~experiments have demonstrated that (1) the proposed method with three HPFs can achieve better results than that with only one HPF or no HPF and that (2) the proposed method with three HPFs achieves 100\% accuracy, although the NIs undergo a JPEG compression with a quality factor of 75.Comment: This paper has been published by Sensors. doi:10.3390/s18041296; Sensors 2018, 18(4), 129

Bis(1H-imidazol-3-ium) naphthalene-1,5-disulfonate

The asymmetric unit of the title organic salt, 2C3H5N2 +·C10H6O6S2 2−, consists of an imidazolium cation and half a naphthalene-1,5-disulfonate dianion, completed to the full dianion through an inversion center. N—H⋯S and N—H⋯O hydrogen bonds link cations and anions in the crystal, forming a chain propagating along [101]

Bis(pyridinium) naphthalene-1,5-di­sulfonate dihydrate

The asymmetric unit of the title organic salt, 2C5H6N+·C10H6O6S2 2−·2H2O, consists of a pyridinium cation, half a naphthalene-1,5-disulfonate dianion and a water mol­ecule. The dianion has a crystallographically imposed centre of symmetry. In the crystal, N—H⋯O and O—H⋯O hydrogen bonds link cations, anions and water mol­ecules into a three-dimensional network

Multi Task Consistency Guided Source-Free Test-Time Domain Adaptation Medical Image Segmentation

Source-free test-time adaptation for medical image segmentation aims to enhance the adaptability of segmentation models to diverse and previously unseen test sets of the target domain, which contributes to the generalizability and robustness of medical image segmentation models without access to the source domain. Ensuring consistency between target edges and paired inputs is crucial for test-time adaptation. To improve the performance of test-time domain adaptation, we propose a multi task consistency guided source-free test-time domain adaptation medical image segmentation method which ensures the consistency of the local boundary predictions and the global prototype representation. Specifically, we introduce a local boundary consistency constraint method that explores the relationship between tissue region segmentation and tissue boundary localization tasks. Additionally, we propose a global feature consistency constraint toto enhance the intra-class compactness. We conduct extensive experiments on the segmentation of benchmark fundus images. Compared to prediction directly by the source domain model, the segmentation Dice score is improved by 6.27\% and 0.96\% in RIM-ONE-r3 and Drishti GS datasets, respectively. Additionally, the results of experiments demonstrate that our proposed method outperforms existing competitive domain adaptation segmentation algorithms.Comment: 31 pages,7 figure