Entropy-driven Sampling and Training Scheme for Conditional Diffusion Generation

Denoising Diffusion Probabilistic Model (DDPM) is able to make flexible conditional image generation from prior noise to real data, by introducing an independent noise-aware classifier to provide conditional gradient guidance at each time step of denoising process. However, due to the ability of classifier to easily discriminate an incompletely generated image only with high-level structure, the gradient, which is a kind of class information guidance, tends to vanish early, leading to the collapse from conditional generation process into the unconditional process. To address this problem, we propose two simple but effective approaches from two perspectives. For sampling procedure, we introduce the entropy of predicted distribution as the measure of guidance vanishing level and propose an entropy-aware scaling method to adaptively recover the conditional semantic guidance. For training stage, we propose the entropy-aware optimization objectives to alleviate the overconfident prediction for noisy data.On ImageNet1000 256x256, with our proposed sampling scheme and trained classifier, the pretrained conditional and unconditional DDPM model can achieve 10.89% (4.59 to 4.09) and 43.5% (12 to 6.78) FID improvement respectively. The code is available at https://github.com/ZGCTroy/ED-DPM.Comment: 24 pages, 8 figure

Design and implementation of an IoT based indoor air quality detector with multiple communication interfaces

Indoor air quality monitoring has attracted increasing attention with the rapid development of industrialization and urbanization in the modern society as people typically spend more than 80% of their time in indoor environments. A novel indoor air quality detector (IAQD) integrated with multiple communication interfaces has been designed, built, programmed deployed and tested in order to meet the requirements of wide variety of scenarios. The IAQD measures the indoor air quality data, including temperature, humidity, CO2, dust and formaldehyde timely. With state-of-the-art Internet of Things (IoT) technologies, the IAQD is integrated with Modbus, LoRa, WiFi, GPRS and NB-IoT communication interfaces, which enables to be applied to wired communications, short-range wireless communications, and remote transmission to the cloud. The designed software in cloud allows users to track the indoor air quality of their home or office or industries everywhere. The performance IAQD is evaluated in terms of packet loss rate and time delay. The evaluation of IAQD are demonstrated and analyzed within the office environment over a week. Experimental results show that the proposed system is effectiveness in measuring the air-quality status and provide excellent consistency and stability

Genetic Dissection of Disease Resistance to the Blue Mold Pathogen, \u3cem\u3ePeronospora tabacina\u3c/em\u3e, in Tobacco

Tobacco blue mold, caused by the obligately biotrophic oomycete pathogen Peronospora tabacina D.B. Adam, is a major foliar disease that results in significant losses in tobacco-growing areas. Natural resistance to P. tabacina has not been identified in any variety of common tobacco. Complete resistance, conferred by RBM1, was found in N. debneyi and was transferred into cultivated tobacco by crossing. In the present study, we characterized the RBM1-mediated resistance to blue mold in tobacco and show that the hypersensitive response (HR) plays an important role in the host defense reactions. Genetic mapping indicated that the disease resistance gene locus resides on chromosome 7. The genetic markers linked to this gene and the genetic map we generated will not only benefit tobacco breeders for variety improvement but will also facilitate the positional cloning of RBM1 for biologists

Investigation of wind characteristics of typhoon boundary layer through field experiments and CFD simulations

High-resolution observations of typhoon boundary layer above 100 m are rare as traditional wind towers are generally below 100 m, which limits the study of typhoon boundary layer and engineering applications such as wind-resistant design of tall buildings and wind turbines in typhoon-prone regions. In this study, boundary layer winds of super typhoon Lekima (2019) are observed, simulated and analyzed. Together with traditional wind tower, Doppler wind lidar is utilized for observations of typhoon boundary layer in order to obtain measured data above 100 m. Besides, Computational Fluid Dynamics (CFD) simulation based on Large Eddy Simulation (LES) method is conducted to further investigate the impact of complex terrain on the near-surface wind characteristics. The results show that the power law fits the mean wind speed profile well below 100 m. However, before and after the typhoon lands, a local reverse or low-level jet occurs in the mean wind speed profile at the height of 100–300 m, which cannot be depicted by the power law. Meanwhile, the turbulence intensity increases with height and experiences larger fluctuations. In addition, there is a significant negative correlation between the ground elevation and power exponents of the fitted mean wind speed profiles. This study provides useful information to better understand wind characteristics of the typhoon boundary layer

Molecular Cloning, Characterization, and mRNA Expression of Hemocyanin Subunit in Oriental River Prawn Macrobrachium nipponense

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Hemocyanin is a copper-containing protein with immune function against disease. In this study, a hemocyanin subunit named MnHc-1 was cloned from Macrobrachium nipponense. The full-length cDNA of MnHc-1 was 2,163 bp with a 2,028-bp open reading frame (ORF) encoding a polypeptide of 675 amino acids. The MnHc-1 mRNA was expressed in the hepatopancreas, gill, hemocytes, intestine, ovary, and stomach, with the highest level in the hepatopancreas. In the infection trial, the MnHc-1 mRNA transcripts in the hemocytes were significantly downregulated at 3 h after injection of Aeromonas hydrophila and then upregulated at 6 h and 12 h, followed by a gradual recovery from 24 to 48 h. The MnHc-1 transcriptional expression in the hepatopancreas was measured after M. nipponense were fed seven diets with 2.8, 12.2, 20.9, 29.8, 43.1, 78.9, and 157.1 mg Cu kg−1 for 8 weeks, respectively. The level of MnHc-1 mRNA was significantly higher in the prawns fed 43.1–157.1 mg Cu kg−1 diet than in that fed 2.8–29.8 mg Cu kg−1 diet. This study indicated that the MnHc-1 expression can be affected by dietary copper and the hemocyanin may potentially participate in the antibacterial defense of M. nipponense

DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectory

Controllable video generation has gained significant attention in recent years. However, two main limitations persist: Firstly, most existing works focus on either text, image, or trajectory-based control, leading to an inability to achieve fine-grained control in videos. Secondly, trajectory control research is still in its early stages, with most experiments being conducted on simple datasets like Human3.6M. This constraint limits the models' capability to process open-domain images and effectively handle complex curved trajectories. In this paper, we propose DragNUWA, an open-domain diffusion-based video generation model. To tackle the issue of insufficient control granularity in existing works, we simultaneously introduce text, image, and trajectory information to provide fine-grained control over video content from semantic, spatial, and temporal perspectives. To resolve the problem of limited open-domain trajectory control in current research, We propose trajectory modeling with three aspects: a Trajectory Sampler (TS) to enable open-domain control of arbitrary trajectories, a Multiscale Fusion (MF) to control trajectories in different granularities, and an Adaptive Training (AT) strategy to generate consistent videos following trajectories. Our experiments validate the effectiveness of DragNUWA, demonstrating its superior performance in fine-grained control in video generation. The homepage link is \url{https://www.microsoft.com/en-us/research/project/dragnuwa/

DCPT: Darkness Clue-Prompted Tracking in Nighttime UAVs

Existing nighttime unmanned aerial vehicle (UAV) trackers follow an "Enhance-then-Track" architecture - first using a light enhancer to brighten the nighttime video, then employing a daytime tracker to locate the object. This separate enhancement and tracking fails to build an end-to-end trainable vision system. To address this, we propose a novel architecture called Darkness Clue-Prompted Tracking (DCPT) that achieves robust UAV tracking at night by efficiently learning to generate darkness clue prompts. Without a separate enhancer, DCPT directly encodes anti-dark capabilities into prompts using a darkness clue prompter (DCP). Specifically, DCP iteratively learns emphasizing and undermining projections for darkness clues. It then injects these learned visual prompts into a daytime tracker with fixed parameters across transformer layers. Moreover, a gated feature aggregation mechanism enables adaptive fusion between prompts and between prompts and the base model. Extensive experiments show state-of-the-art performance for DCPT on multiple dark scenario benchmarks. The unified end-to-end learning of enhancement and tracking in DCPT enables a more trainable system. The darkness clue prompting efficiently injects anti-dark knowledge without extra modules. Code and models will be released.Comment: Under revie

Meta-Analysis of the Correlation between Apparent Diffusion Coefficient and Standardized Uptake Value in Malignant Disease

The objective of this meta-analysis is to explore the correlation between the apparent diffusion coefficient (ADC) on diffusion-weighted MR and the standard uptake value (SUV) of 18F-FDG on PET/CT in patients with cancer. Databases such as PubMed (MEDLINE included), EMBASE, and Cochrane Database of Systematic Review were searched for relevant original articles that explored the correlation between SUV and ADC in English. After applying Fisher’s r-to-z transformation, correlation coefficient (r) values were extracted from each study and 95% confidence intervals (CIs) were calculated. Sensitivity and subgroup analyses based on tumor type were performed to investigate the potential heterogeneity. Forty-nine studies were eligible for the meta-analysis, comprising 1927 patients. Pooled r for all studies was −0.35 (95% CI: −0.42–0.28) and exhibited a notable heterogeneity (I2 = 78.4%; P < 0.01). In terms of the cancer type subgroup analysis, combined correlation coefficients of ADC/SUV range from −0.12 (lymphoma, n = 5) to −0.59 (pancreatic cancer, n = 2). We concluded that there is an average negative correlation between ADC and SUV in patients with cancer. Higher correlations were found in the brain tumor, cervix carcinoma, and pancreas cancer. However, a larger, prospective study is warranted to validate these findings in different cancer types