Non-line-of-sight reconstruction via structure sparsity regularization

Non-line-of-sight (NLOS) imaging allows for the imaging of objects around a corner, which enables potential applications in various fields such as autonomous driving, robotic vision, medical imaging, security monitoring, etc. However, the quality of reconstruction is challenged by low signal-noise-ratio (SNR) measurements. In this study, we present a regularization method, referred to as structure sparsity (SS) regularization, for denoising in NLOS reconstruction. By exploiting the prior knowledge of structure sparseness, we incorporate nuclear norm penalization into the cost function of directional light-cone transform (DLCT) model for NLOS imaging system. This incorporation effectively integrates the neighborhood information associated with the directional albedo, thereby facilitating the denoising process. Subsequently, the reconstruction is achieved by optimizing a directional albedo model with SS regularization using fast iterative shrinkage-thresholding algorithm. Notably, the robust reconstruction of occluded objects is observed. Through comprehensive evaluations conducted on both synthetic and experimental datasets, we demonstrate that the proposed approach yields high-quality reconstructions, surpassing the state-of-the-art reconstruction algorithms, especially in scenarios involving short exposure and low SNR measurements.Comment: 8 pages, 5 figure

How Does Information Bottleneck Help Deep Learning?

Numerous deep learning algorithms have been inspired by and understood via the notion of information bottleneck, where unnecessary information is (often implicitly) minimized while task-relevant information is maximized. However, a rigorous argument for justifying why it is desirable to control information bottlenecks has been elusive. In this paper, we provide the first rigorous learning theory for justifying the benefit of information bottleneck in deep learning by mathematically relating information bottleneck to generalization errors. Our theory proves that controlling information bottleneck is one way to control generalization errors in deep learning, although it is not the only or necessary way. We investigate the merit of our new mathematical findings with experiments across a range of architectures and learning settings. In many cases, generalization errors are shown to correlate with the degree of information bottleneck: i.e., the amount of the unnecessary information at hidden layers. This paper provides a theoretical foundation for current and future methods through the lens of information bottleneck. Our new generalization bounds scale with the degree of information bottleneck, unlike the previous bounds that scale with the number of parameters, VC dimension, Rademacher complexity, stability or robustness. Our code is publicly available at: https://github.com/xu-ji/information-bottleneckComment: Accepted at ICML 2023. Code is available at https://github.com/xu-ji/information-bottlenec

Analysis of Overall E-Business Solution on Personalized Medical Care ——Taking Private Hospitals for Example

The patient is the lifeblood of the hospital and only win customers can win the future. When choosing a hospital, in addition to the strength of the medical treatment of the hospital itself, patients considering most are the services of the hospital which is the very factor that can influence customer making decision. Clearly, the needs of patients can’t be achieved in overcrowded public hospitals. This paper starts from the content of E-Business, analyzes the current situation and problems of private hospital development, points out that development of E-Business to enhance customer relationship management is the only way to solve the practical problems of private hospitals, gives personal attention of private hospitals overall E-Business solutions and development strategies

Stochastic Linear-quadratic Control Problems with Affine Constraints

In this paper, we investigate the stochastic linear-quadratic control problems with affine constraints in random coefficients case. With the help of the Pontryagin maximum principle and stochastic Riccati equation, the dual problem of original problem is established and the feedback solution of the optimal control problem is obtained. Under the Slater condition, the equivalence is proved between the solutions to the original problem and the ones of the dual problem, and the KKT condition is also provided for the dual problem. Finally, an invertibility assumption is given for ensuring the uniqueness of the solutions to the dual problem

Identification of polyunsaturated fatty acids as potential biomarkers of osteoarthritis after sodium hyaluronate and mesenchymal stem cell treatment through metabolomics

Introduction: Osteoarthritis (OA) is a prevalent joint disorder worldwide. Sodium hyaluronate (SH) and mesenchymal stem cells (MSCs) are promising therapeutic strategies for OA. Previous studies showed they could improve knee function and clinical symptoms of OA. However, the mechanism of the therapeutic effects on the improvement of OA has not been clearly explained.Methods: In our study, we used a technique called 5-(diisopropylamino)amylamine derivatization liquid chromatography coupled with mass spectrometry to find the metabolites in OA synovial fluid under different treatments.Results and Discussion: After looking into the metabolomics, we discovered that SH and MSC treatment led to the downregulation of ω-6 polyunsaturated fatty acids (PUFAs) and the upregulation of ω-3 PUFAs. Significantly, the contents of 5(S)-HETE, PGA2, PGB2, and PGJ2 were lower in the MSC group than in the SH group after quantification using 5-(diisopropylamino)amylamine derivatization–UHPLC–QQQ-MS. This is the first report on the relationship of 11(S)-HETE, PGA2, PGB2, PGF2β, 11β-PGF2α, and DK-PGE2 with OA. Moreover, the correlation analysis of metabolites and inflammation factors showed the positive association of ω-6 PUFAs with pro-inflammation cytokines, and of ω-3 PUFAs with anti-inflammation cytokines. Our results indicated the therapeutic effect of SH and MSCs in patients with OA. In addition, this reliable metabolic approach could uncover novel biomarkers to treat OA

HDAC4 Inhibitors as Antivascular Senescence Therapeutics

Aging is an inevitable consequence of life, and during this process, the epigenetic landscape changes and reactive oxygen species (ROS) accumulation increases. Inevitably, these changes are common in many age-related diseases, including neurodegeneration, hypertension, and cardiovascular diseases. In the current research, histone deacetylation 4 (HDAC4) was studied as a potential therapeutic target in vascular senescence. HDAC4 is a specific class II histone deacetylation protein that participates in epigenetic modifications and deacetylation of heat shock proteins and various transcription factors. There is increasing evidence to support that HDAC4 is a potential therapeutic target, and developments in the synthesis and testing of HDAC4 inhibitors are now gaining interest from academia and the pharmaceutical industry

Single spin asymmetry in πp\pi p Drell-Yan process

We study the single spin asymmetries for the $\pi p^\uparrow\rightarrow\mu^+\mu^-X$ process. We consider the asymmetries contributed by the coupling of the Boer-Mulders function with the transversity distribution and the pretzelosity distribution, characterized by the $\sin(\phi+\phi_S)$ and $\sin(3\phi-\phi_S)$ azimuthal angular dependence, respectively. We estimate the magnitude of these asymmetries at COMPASS by using proper weighting functions. We find that the $\sin(\phi+\phi_S)$ asymmetry is of the size of a few percent and can be measured through the experiment. The $\sin(3\phi-\phi_S)$ asymmetry is smaller than the $\sin(\phi+\phi_S)$ asymmetry. After a cut on $q_T$, we succeed in enhancing the asymmetry.Comment: 11 pages, 2 figures, final version to appear in PL