Binary sampling ghost imaging: add random noise to fight quantization caused image quality decline

When the sampling data of ghost imaging is recorded with less bits, i.e., experiencing quantization, decline of image quality is observed. The less bits used, the worse image one gets. Dithering, which adds suitable random noise to the raw data before quantization, is proved to be capable of compensating image quality decline effectively, even for the extreme binary sampling case. A brief explanation and parameter optimization of dithering are given.Comment: 8 pages, 7 figure

Negative exponential behavior of image mutual information for pseudo-thermal light ghost imaging: Observation, modeling, and verification

When use the image mutual information to assess the quality of reconstructed image in pseudo-thermal light ghost imaging, a negative exponential behavior with respect to the measurement number is observed. Based on information theory and a few simple and verifiable assumptions, semi-quantitative model of image mutual information under varying measurement numbers is established. It is the Gaussian characteristics of the bucket detector output probability distribution that leads to this negative exponential behavior. Designed experiments verify the model.Comment: 13 pages, 6 figure

Using X-Ray In-Line Phase-Contrast Imaging for the Investigation of Nude Mouse Hepatic Tumors

The purpose of this paper is to report the noninvasive imaging of hepatic tumors without contrast agents. Both normal tissues and tumor tissues can be detected, and tumor tissues in different stages can be classified quantitatively. We implanted BEL-7402 human hepatocellular carcinoma cells into the livers of nude mice and then imaged the livers using X-ray in-line phase-contrast imaging (ILPCI). The projection images' texture feature based on gray level co-occurrence matrix (GLCM) and dual-tree complex wavelet transforms (DTCWT) were extracted to discriminate normal tissues and tumor tissues. Different stages of hepatic tumors were classified using support vector machines (SVM). Images of livers from nude mice sacrificed 6 days after inoculation with cancer cells show diffuse distribution of the tumor tissue, but images of livers from nude mice sacrificed 9, 12, or 15 days after inoculation with cancer cells show necrotic lumps in the tumor tissue. The results of the principal component analysis (PCA) of the texture features based on GLCM of normal regions were positive, but those of tumor regions were negative. The results of PCA of the texture features based on DTCWT of normal regions were greater than those of tumor regions. The values of the texture features in low-frequency coefficient images increased monotonically with the growth of the tumors. Different stages of liver tumors can be classified using SVM, and the accuracy is 83.33%. Noninvasive and micron-scale imaging can be achieved by X-ray ILPCI. We can observe hepatic tumors and small vessels from the phase-contrast images. This new imaging approach for hepatic cancer is effective and has potential use in the early detection and classification of hepatic tumors

Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees

We consider transfer learning approaches that fine-tune a pretrained deep neural network on a target task. We study generalization properties of fine-tuning to understand the problem of overfitting, which commonly occurs in practice. Previous works have shown that constraining the distance from the initialization of fine-tuning improves generalization. Using a PAC-Bayesian analysis, we observe that besides distance from initialization, Hessians affect generalization through the noise stability of deep neural networks against noise injections. Motivated by the observation, we develop Hessian distance-based generalization bounds for a wide range of fine-tuning methods. Additionally, we study the robustness of fine-tuning in the presence of noisy labels. Motivated by our theory, we design an algorithm that incorporates consistent losses and distance-based regularization for fine-tuning, along with a generalization error guarantee under class conditional independent noise in the training set labels. We perform a detailed empirical study of our algorithm on various noisy environments and architectures. On six image classification tasks whose training labels are generated with programmatic labeling, we find a 3.26% accuracy gain over prior fine-tuning methods. Meanwhile, the Hessian distance measure of the fine-tuned model decreases by six times more than existing approaches.Comment: 36 pages, 5 figures, 8 tables; ICML 202

A male adult skeleton from the Han Dynasty in Shaanxi, China (202 BC–220 AD) with bone changes that possibly represent spinal tuberculosis

Bioarchaeological data for tuberculosis (TB) have been published very sporadically in China or the rest of East Asia. To explore the history of TB in this area, 85 skeletons excavated from the Liuwei Cemetery in Shaanxi, China (202 BC–220 AD) were macroscopically examined to record TB related bone changes. These skeletons represented inhabitants of Maolingyi, an urban area that had a high population density during the Han Dynasty (202 BC–220 CE). Seventeen of the 85 skeletons had spines that were well enough preserved to observe evidence of spinal disease. Among them, a male skeleton aged around 30 years (M34-E) manifested multiple lytic lesions in the eleventh thoracic to second lumbar vertebral bodies (T11 to L2). TB was considered a possible diagnosis for the spinal lesions observed, with differential diagnoses of brucellosis and typhoid. The dense population and overcrowding in urban Maolingyi were considered the potential social risk factors for TB found at this site. The findings of this study contribute to limited knowledge about the history of TB in East Asia and suggest a relationship between population density and the spread of TB in Maolingyi at that time. However, the lack of published bioarchaeological data of TB in East Asia hinders understanding the transmission of TB within Asia and its link to the rest of the world. Further intensive review of archaeological skeletons in Asia is urgently needed

Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion

Graph neural networks are widely used tools for graph prediction tasks. Motivated by their empirical performance, prior works have developed generalization bounds for graph neural networks, which scale with graph structures in terms of the maximum degree. In this paper, we present generalization bounds that instead scale with the largest singular value of the graph neural network's feature diffusion matrix. These bounds are numerically much smaller than prior bounds for real-world graphs. We also construct a lower bound of the generalization gap that matches our upper bound asymptotically. To achieve these results, we analyze a unified model that includes prior works' settings (i.e., convolutional and message-passing networks) and new settings (i.e., graph isomorphism networks). Our key idea is to measure the stability of graph neural networks against noise perturbations using Hessians. Empirically, we find that Hessian-based measurements correlate with the observed generalization gaps of graph neural networks accurately. Optimizing noise stability properties for fine-tuning pretrained graph neural networks also improves test performance on several graph-level classification tasks.Comment: 36 pages, 2 tables, 3 figures. Appeared in AISTATS 202