Adaptive low rank and sparse decomposition of video using compressive sensing

We address the problem of reconstructing and analyzing surveillance videos using compressive sensing. We develop a new method that performs video reconstruction by low rank and sparse decomposition adaptively. Background subtraction becomes part of the reconstruction. In our method, a background model is used in which the background is learned adaptively as the compressive measurements are processed. The adaptive method has low latency, and is more robust than previous methods. We will present experimental results to demonstrate the advantages of the proposed method.Comment: Accepted ICIP 201

Image restoration: Wavelet frame shrinkage, nonlinear evolution PDEs, and beyond

In the past few decades, mathematics based approaches have been widely adopted in various image restoration problems; the partial differential equation (PDE) based approach (e.g., the total variation model [L. Rudin, S. Osher, and E. Fatemi, Phys. D, 60 (1992), pp. 259-268] and its generalizations, nonlinear diffusions [P. Perona and J. Malik, IEEE Trans. Pattern Anal. Mach. Intel., 12 (1990), pp. 629-639; F. Catte et al., SIAM J. Numer. Anal., 29 (1992), pp. 182-193], etc.) and wavelet frame based approach are some successful examples. These approaches were developed through different paths and generally provided understanding from different angles of the same problem. As shown in numerical simulations, implementations of the wavelet frame based approach and the PDE based approach quite often end up solving a similar numerical problem with similar numerical behaviors, even though different approaches have advantages in different applications. Since wavelet frame based and PDE based approaches have all been modeling the same types of problems with success, it is natural to ask whether the wavelet frame based approach is fundamentally connected with the PDE based approach when we trace them all the way back to their roots. A fundamental connection of a wavelet frame based approach with a total variation model and its generalizations was established in [J. Cai, B. Dong, S. Osher, and Z. Shen, J. Amer. Math. Soc., 25 (2012), pp. 1033-1089]. This connection gives the wavelet frame based approach a geometric explanation and, at the same time, it equips a PDE based approach with a time frequency analysis. Cai et al. showed that a special type of wavelet frame model using generic wavelet frame systems can be regarded as an approximation of a generic variational model (with the total variation model as a special case) in the discrete setting. A systematic convergence analysis, as the resolution of the image goes to infinity, which is the key step in linking the two approaches, is also given in Cai et al. Motivated by Cai et al. and [Q. Jiang, Appl. Numer. Math., 62 (2012), pp. 51-66], this paper establishes a fundamental connection between the wavelet frame based approach and nonlinear evolution PDEs, provides interpretations and analytical studies of such connections, and proposes new algorithms for image restoration based on the new understandings. Together with the results in [J. Cai et al., J. Amer. Math. Soc., 25 (2012), pp. 1033-1089], we now have a better picture of how the wavelet frame based approach can be used to interpret the general PDE based approach (e.g., the variational models or nonlinear evolution PDEs) and can be used as a new and useful tool in numerical analysis to discretize and solve various variational and PDE models. To be more precise, we shall establish the following: (1) The connections between wavelet frame shrinkage and nonlinear evolution PDEs provide new and inspiring interpretations of both approaches that enable us to derive new PDE models and (better) wavelet frame shrinkage algorithms for image restoration. (2) A generic nonlinear evolution PDE (of parabolic or hyperbolic type) can be approximated by wavelet frame shrinkage with properly chosen wavelet frame systems and carefully designed shrinkage functions. (3) The main idea of this work is beyond the scope of image restoration. Our analysis and discussions indicate that wavelet frame shrinkage is a new way of solving PDEs in general, which will provide a new insight that will enrich the existing theory and applications of numerical PDEs, as well as those of wavelet frames

Susceptibility of schizophrenia and affective disorder not associated with loci on chromosome 6q in Han Chinese population

<p>Abstract</p> <p>Background</p> <p>Several linkage studies across multiple population groups provide convergent support for susceptibility loci for schizophrenia – and, more recently, for affective disorder – on chromosome 6q. We explore whether schizophrenia and affective disorder have common susceptibility gene on 6q in Han Chinese population.</p> <p>Methods</p> <p>In the present study, we genotyped 45 family trios from Han Chinese population with mixed family history of schizophrenia and affective disorder. Twelve short tandem repeat (STRs) markers were selected, which covered 102.19 cM on chromosome 6q with average spacing 9.29 cM and heterozygosity 0.78. The transmission disequilibrium test (TDT) was performed to search for susceptibility loci to schizophrenia and affective disorder.</p> <p>Results</p> <p>The results showed STRs D6S257, D6S460, D6S1021, D6S292 and D6S1581 were associated with susceptibility to psychotic disorders. When families were grouped into schizophrenia and affective disorder group, D6S257, D6S460 and D6S1021, which map closely to the centromere of chromosome 6q, were associated with susceptibility to schizophrenia. Meanwhile, D6S1581, which maps closely to the telomere, was associated with susceptibility to affective disorder. But after correction of multiple test, all above association were changed into no significance (P > 0.05).</p> <p>Conclusion</p> <p>These results suggest that susceptibility of schizophrenia and affective disorder not associated with loci on chromosome 6q in Han Chinese population.</p

ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data

Radiology report generation, as a key step in medical image analysis, is critical to the quantitative analysis of clinically informed decision-making levels. However, complex and diverse radiology reports with cross-source heterogeneity pose a huge generalizability challenge to the current methods under massive data volume, mainly because the style and normativity of radiology reports are obviously distinctive among institutions, body regions inspected and radiologists. Recently, the advent of large language models (LLM) offers great potential for recognizing signs of health conditions. To resolve the above problem, we collaborate with the Second Xiangya Hospital in China and propose ChatRadio-Valuer based on the LLM, a tailored model for automatic radiology report generation that learns generalizable representations and provides a basis pattern for model adaptation in sophisticated analysts' cases. Specifically, ChatRadio-Valuer is trained based on the radiology reports from a single institution by means of supervised fine-tuning, and then adapted to disease diagnosis tasks for human multi-system evaluation (i.e., chest, abdomen, muscle-skeleton, head, and maxillofacial $\&$ neck) from six different institutions in clinical-level events. The clinical dataset utilized in this study encompasses a remarkable total of \textbf{332,673} observations. From the comprehensive results on engineering indicators, clinical efficacy and deployment cost metrics, it can be shown that ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al., in terms of the diseases diagnosis from radiology reports. ChatRadio-Valuer provides an effective avenue to boost model generalization performance and alleviate the annotation workload of experts to enable the promotion of clinical AI applications in radiology reports

Tight wavelet frames in low dimensions with canonical filters,” accepted by Journal of Approximation Theory

Abstract This paper is to construct tight wavelet frame systems containing a set of canonical filters by applying the unitary extension principle o

On Existence and Weak Stability of Matrix Refinable Functions

: We consider the existence of distributional (or L 2 ) solutions of the matrix refinement equation b \Phi = P(\Delta=2) b \Phi(\Delta=2); where P is an r \Theta r matrix with trigonometric polynomial entries. One of the main results of this paper is that the above matrix refinement equation has a compactly supported distributional solution if and only if the matrix P(0) has an eigenvalue of the form 2 n , n 2 ZZ + . A characterization of the existence of L 2 -solutions of the above matrix refinement equation in terms of the mask is also given. A concept of L 2 -weak stability of a (finite) sequence of function vectors is introduced. In the case when the function vectors are solutions of a matrix refinement equation, we characterize this weak stability in terms of the mask. AMS Subject Classification: Primary 42C15, 42B05, 41A30 Secondary 39B62, 42B10 Keywords: Refinable function vectors, stable basis y Permanent address: Department of Mathematics, Peking University. 1. Introducti..