Unlocking Low-Light-Rainy Image Restoration by Pairwise Degradation Feature Vector Guidance

Rain in the dark is a common natural phenomenon. Photos captured in such a condition significantly impact the performance of various nighttime activities, such as autonomous driving, surveillance systems, and night photography. While existing methods designed for low-light enhancement or deraining show promising performance, they have limitations in simultaneously addressing the task of brightening low light and removing rain. Furthermore, using a cascade approach, such as ``deraining followed by low-light enhancement'' or vice versa, may lead to difficult-to-handle rain patterns or excessively blurred and overexposed images. To overcome these limitations, we propose an end-to-end network called $L^{2}RIRNet$ which can jointly handle low-light enhancement and deraining. Our network mainly includes a Pairwise Degradation Feature Vector Extraction Network (P-Net) and a Restoration Network (R-Net). P-Net can learn degradation feature vectors on the dark and light areas separately, using contrastive learning to guide the image restoration process. The R-Net is responsible for restoring the image. We also introduce an effective Fast Fourier - ResNet Detail Guidance Module (FFR-DG) that initially guides image restoration using detail image that do not contain degradation information but focus on texture detail information. Additionally, we contribute a dataset containing synthetic and real-world low-light-rainy images. Extensive experiments demonstrate that our $L^{2}RIRNet$ outperforms existing methods in both synthetic and complex real-world scenarios

Granule mining oriented data warehousing model for representations of multidimensional association rules

Abstract: To promise the quality of multidimensional association mining in real applications is a challenging research issue. The challenging issue is how to represent multidimensional association rules efficiently because of the complicated correlation between attributes. Multi-tier granule mining is one initiative in solving this challenge. This paper presents a granule mining oriented data warehousing model. It can divide attributes into tiers and discover granules for each tier from large multidimensional databases. In addition, it uses association mappings to generate association rules for describing the correlation between tiers. Experiments for the proposed model and the testing results are prosecuted

Granule mining oriented data warehousing model for representations of multidimensional association rules

Abstract: To promise the quality of multidimensional association mining in real applications is a challenging research issue. The challenging issue is how to represent multidimensional association rules efficiently because of the complicated correlation between attributes. Multi-tier granule mining is one initiative in solving this challenge. This paper presents a granule mining oriented data warehousing model. It can divide attributes into tiers and discover granules for each tier from large multidimensional databases. In addition, it uses association mappings to generate association rules for describing the correlation between tiers. Experiments for the proposed model and the testing results are prosecuted

FPIRM: Floating-point Processing in Racetrack Memories

Convolutional neural networks (CNN) have become a ubiquitous algorithm with growing applications in mobile and edge settings. We describe a compute-in-memory (CIM) technique called FPIRM using Racetrack Memory (RM) to accelerate CNNs for edge systems. Using transverse read, a technique that can determine the number of '1's multiple adjacent domains, FPIRM can efficiently implement multi-operand bulk-bitwise and addition computations, and two-operand multiplication. We discuss how FPIRM can implement both variable precision integer and floating point arithmetic. This allows both CNN inference and on-device training without expensive data movement to the cloud. Based on these functions we demonstrate implementation of several CNNs with back propagation using RM CIM and compare these to state-of-the-art implementations of CIM inference and training in Field-Programmable Gate Arrays. During training FPIRM improves by 2$\times$ the efficiency, by reducing the energy consumption by at least 27% and increasing the throughput by at least 18% against FPGA.Comment: This paper is accepted to the IEEE Micro Magazine with the title "POD-RACING: Bulk-Bitwise to Floating-point Compute In Racetrack Memory for Machine Learning at the Edge

Ratio of Immune Response to Tumor Burden Predicts Survival Via Regulating Functions of Lymphocytes and Monocytes in Diffuse Large B-Cell Lymphoma

Background/Aims: Diffuse large B-cell lymphoma (DLBCL) is an aggressive disease, and is the most common type of lymphoma in adults. Although significant progress in treatment has been made using chemotherapy combinations, there exist a large amount of relapse or refractory cases. Thus, effective clinical biomarkers for DLBCL are urgently needed. Our study aims to explore the predictive significance of using the immune response to tumor burden ratio [defined as the lymphocyte to monocyte ratio (LMR)/lactate dehydrogenase (LDH) levels] in 184 DLBCL patients and the potential mechanism underlying the use of the LMR to tumor burden ratio in predicting patient survival. Methods: The correlation between serum LDH levels and tumor levels assessed by PET-CT was determined using Spearman’s correlation analysis. Clinical data from 184 DLBCL patients was assessed using receiver operating characteristic curve analysis and survival analysis. The potential correlation between tumor burden and lymphocytes or monocytes was analyzed by immunohistochemical staining, flow cytometry, and ELISA analysis of patient samples. In addition, we performed in vitro studies to further determine the effects of tumor burden on the anti-tumor activity of T lymphocytes. Results: We observed that serum LDH was an excellent surrogate marker of tumor burden in DLBCL patients, and that the ratio of LMR to LDH was an independent prognostic biomarker capable of predicting survival in DLBCL patients. Further analysis showed that a high tumor burden was correlated with decreased Ki67 expression in T cells, either in the solid tumor tissue or in the circulating blood. In addition, based on an in vitro co-culture study, a higher tumor burden led to the suppression of the anti-tumor response of T cells. Furthermore, we found that a higher tumor burden was correlated with the differentiation of monocytes to tumor associated macrophages in the tumor micro-environment. Both results demonstrate the importance of considering both the immune system and tumor burden for prognostic analysis. Conclusion: Our study has identified a novel clinical biomarker, namely, the immune response to tumor burden ratio, that can be used to distinguish survival outcomes in DLBCL patients, and demonstrated the potential mechanism underlying the use of this biomarker, that incorporates both the immune system and tumor burden, for use in future clinical applications

(20R)-Panaxadiol as a Natural Active Component with Anti-Obesity Effects on ob/ob Mice via Modulating the Gut Microbiota

Obesity is an important cause of diseases such as type 2 diabetes, non-alcoholic fatty liver and atherosclerosis. The use of ingredients extracted from traditional Chinese medicine for weight loss is now receiving more and more attention. Ginseng has been recorded since ancient times for the treatment of diabetes. The (20R)-Panaxadiol (PD) belongs to the ginseng diol type compounds, which are moderately bioavailable and may remain in the intestinal tract for a longer period of time. This study investigated the potential positive effect of PD in ob/ob mice and evaluated its effect against obesity. The ob/ob mice were administered PD for ten weeks. Our study showed that PD could improve obesity, glucose tolerance disorder, as well as gut dysbiosis. Panaxadiol decreased ob/ob mice’s Firmicutes/Bacteroidetes (F/B). Furthermore, 16S rRNA gene sequencing of the fecal microbiota suggested that PD changed the composition of the gut microbiota in ob/ob mice and modulated specific bacteria such as lactobacillus, prevotellace and so on. Moreover, PD improved the intestinal wall integrity. In conclusion, our results suggest that (20R)-Panaxadiol, as an active ingredient of the traditional Chinese medicinal herb ginseng, may improve obesity to some extent via improving gut microbiot

(20R)-Panaxadiol as a Natural Active Component with Anti-Obesity Effects on ob/ob Mice via Modulating the Gut Microbiota

Obesity is an important cause of diseases such as type 2 diabetes, non-alcoholic fatty liver and atherosclerosis. The use of ingredients extracted from traditional Chinese medicine for weight loss is now receiving more and more attention. Ginseng has been recorded since ancient times for the treatment of diabetes. The (20R)-Panaxadiol (PD) belongs to the ginseng diol type compounds, which are moderately bioavailable and may remain in the intestinal tract for a longer period of time. This study investigated the potential positive effect of PD in ob/ob mice and evaluated its effect against obesity. The ob/ob mice were administered PD for ten weeks. Our study showed that PD could improve obesity, glucose tolerance disorder, as well as gut dysbiosis. Panaxadiol decreased ob/ob mice’s Firmicutes/Bacteroidetes (F/B). Furthermore, 16S rRNA gene sequencing of the fecal microbiota suggested that PD changed the composition of the gut microbiota in ob/ob mice and modulated specific bacteria such as lactobacillus, prevotellace and so on. Moreover, PD improved the intestinal wall integrity. In conclusion, our results suggest that (20R)-Panaxadiol, as an active ingredient of the traditional Chinese medicinal herb ginseng, may improve obesity to some extent via improving gut microbiot