Accurate Single Stage Detector Using Recurrent Rolling Convolution

Most of the recent successful methods in accurate object detection and localization used some variants of R-CNN style two stage Convolutional Neural Networks (CNN) where plausible regions were proposed in the first stage then followed by a second stage for decision refinement. Despite the simplicity of training and the efficiency in deployment, the single stage detection methods have not been as competitive when evaluated in benchmarks consider mAP for high IoU thresholds. In this paper, we proposed a novel single stage end-to-end trainable object detection network to overcome this limitation. We achieved this by introducing Recurrent Rolling Convolution (RRC) architecture over multi-scale feature maps to construct object classifiers and bounding box regressors which are "deep in context". We evaluated our method in the challenging KITTI dataset which measures methods under IoU threshold of 0.7. We showed that with RRC, a single reduced VGG-16 based model already significantly outperformed all the previously published results. At the time this paper was written our models ranked the first in KITTI car detection (the hard level), the first in cyclist detection and the second in pedestrian detection. These results were not reached by the previous single stage methods. The code is publicly available.Comment: CVPR 201

Two new species of Ismarus Haliday (Hymenoptera, Ismaridae) from Yunnan, China

The genus Ismarus Haliday are rarely collected parasitoids in the small family Ismaridae. In this study, two new species are described from China’s Yunnan Province: Ismarus robustus Chen & Yan, sp. nov. and Ismarus unisulcus Chen & Yan, sp. nov. An updated key to the Chinese species of the genus is provided

Absence of remote earthquake triggering within the Coso and Salton Sea geothermal production fields

Geothermal areas are long recognized to be susceptible to remote earthquake triggering, probably due to the high seismicity rates and presence of geothermal fluids. However, anthropogenic injection and extraction activity may alter the stress state and fluid flow within the geothermal fields. Here we examine the remote triggering phenomena in the Coso geothermal field and its surrounding areas to assess possible anthropogenic effects. We find that triggered earthquakes are absent within the geothermal field but occur in the surrounding areas. Similar observation is also found in the Salton Sea geothermal field. We hypothesize that continuous geothermal operation has eliminated any significant differential pore pressure between fractures inside the geothermal field through flushing geothermal precipitations and sediments out of clogged fractures. To test this hypothesis, we analyze the pore-pressure-driven earthquake swarms, and they are found to occur outside or on the periphery of the geothermal production field. Therefore, our results suggest that the geothermal operation has changed the subsurface fracture network, and differential pore pressure is the primary controlling factor of remote triggering in geothermal fields

Enhancing Diffusion Models with Text-Encoder Reinforcement Learning

Text-to-image diffusion models are typically trained to optimize the log-likelihood objective, which presents challenges in meeting specific requirements for downstream tasks, such as image aesthetics and image-text alignment. Recent research addresses this issue by refining the diffusion U-Net using human rewards through reinforcement learning or direct backpropagation. However, many of them overlook the importance of the text encoder, which is typically pretrained and fixed during training. In this paper, we demonstrate that by finetuning the text encoder through reinforcement learning, we can enhance the text-image alignment of the results, thereby improving the visual quality. Our primary motivation comes from the observation that the current text encoder is suboptimal, often requiring careful prompt adjustment. While fine-tuning the U-Net can partially improve performance, it remains suffering from the suboptimal text encoder. Therefore, we propose to use reinforcement learning with low-rank adaptation to finetune the text encoder based on task-specific rewards, referred as \textbf{TexForce}. We first show that finetuning the text encoder can improve the performance of diffusion models. Then, we illustrate that TexForce can be simply combined with existing U-Net finetuned models to get much better results without additional training. Finally, we showcase the adaptability of our method in diverse applications, including the generation of high-quality face and hand images

Reduced expression of cenp-e in human hepatocellular carcinoma

© 2009 Liu et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

catena-Poly[[(dimethyl sulfoxide-κO)zinc(II)]-μ-(E)-2-[(2-oxido-1-naphth­yl)­methyl­eneamino]propanoato-κ4 O 2,N,O 1:O 1′]

In the title coordination polymer, [Zn(C14H11NO3)(C2H6OS)]n, each ZnII ion is five-coordinated in a slightly distorted trigonal–bipyramidal coordination environment, formed by three O atoms from two 2-[(2-oxido-1-naphth­yl)­methyl­eneamino]propanoate ligands, one O atom from a dimethyl sulfoxide mol­ecule and the N atom from the amino­propanoate ligand. The propanoate ligands bridge ZnII ions, forming a zigzag chain parallel to [010]