Recognize Anything: A Strong Image Tagging Model

We present the Recognize Anything Model (RAM): a strong foundation model for image tagging. RAM can recognize any common category with high accuracy. RAM introduces a new paradigm for image tagging, leveraging large-scale image-text pairs for training instead of manual annotations. The development of RAM comprises four key steps. Firstly, annotation-free image tags are obtained at scale through automatic text semantic parsing. Subsequently, a preliminary model is trained for automatic annotation by unifying the caption and tagging tasks, supervised by the original texts and parsed tags, respectively. Thirdly, a data engine is employed to generate additional annotations and clean incorrect ones. Lastly, the model is retrained with the processed data and fine-tuned using a smaller but higher-quality dataset. We evaluate the tagging capabilities of RAM on numerous benchmarks and observe impressive zero-shot performance, significantly outperforming CLIP and BLIP. Remarkably, RAM even surpasses the fully supervised manners and exhibits competitive performance with the Google API. We are releasing the RAM at \url{https://recognize-anything.github.io/} to foster the advancements of large models in computer vision

Highly time-resolved chemical speciation and source apportionment of organic aerosol components in Delhi, India, using extractive electrospray ionization mass spectrometry

In recent years, the Indian capital city of Delhi has been impacted by very high levels of air pollution, especially during winter. Comprehensive knowledge of the composition and sources of the organic aerosol (OA), which constitutes a substantial fraction of total particulate mass (PM) in Delhi, is central to formulating effective public health policies. Previous source apportionment studies in Delhi identified key sources of primary OA (POA) and showed that secondary OA (SOA) played a major role but were unable to resolve specific SOA sources. We address the latter through the first field deployment of an extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) in Delhi, together with a high-resolution aerosol mass spectrometer (AMS). Measurements were conducted during the winter of 2018/19, and positive matrix factorization (PMF) was used separately on AMS and EESI-TOF datasets to apportion the sources of OA. AMS PMF analysis yielded three primary and two secondary factors which were attributed to hydrocarbon-like OA (HOA), biomass burning OA (BBOA-1 and BBOA-2), more oxidized oxygenated OA (MO-OOA), and less oxidized oxygenated OA (LO-OOA). On average, 40 % of the total OA mass was apportioned to the secondary factors. The SOA contribution to total OA mass varied greatly between the daytime (76.8 %, 10:00–16:00 local time (LT)) and nighttime (31.0 %, 21:00–04:00 LT). The higher chemical resolution of EESI-TOF data allowed identification of individual SOA sources. The EESI-TOF PMF analysis in total yielded six factors, two of which were primary factors (primary biomass burning and cooking-related OA). The remaining four factors were predominantly of secondary origin: aromatic SOA, biogenic SOA, aged biomass burning SOA, and mixed urban SOA. Due to the uncertainties in the EESI-TOF ion sensitivities, mass concentrations of EESI-TOF SOA-dominated factors were related to the total AMS SOA (i.e. MO-OOA + LO-OOA) by multiple linear regression (MLR). Aromatic SOA was the major SOA component during the daytime, with a 55.2 % contribution to total SOA mass (42.4 % contribution to total OA). Its contribution to total SOA, however, decreased to 25.4 % (7.9 % of total OA) during the nighttime. This factor was attributed to the oxidation of light aromatic compounds emitted mostly from traffic. Biogenic SOA accounted for 18.4 % of total SOA mass (14.2 % of total OA) during the daytime and 36.1 % of total SOA mass (11.2 % of total OA) during the nighttime. Aged biomass burning and mixed urban SOA accounted for 15.2 % and 11.0 % of total SOA mass (11.7 % and 8.5 % of total OA mass), respectively, during the daytime and 15.4 % and 22.9 % of total SOA mass (4.8 % and 7.1 % of total OA mass), respectively, during the nighttime. A simple dilution–partitioning model was applied on all EESI-TOF factors to estimate the fraction of observed daytime concentrations resulting from local photochemical production (SOA) or emissions (POA). Aromatic SOA, aged biomass burning, and mixed urban SOA were all found to be dominated by local photochemical production, likely from the oxidation of locally emitted volatile organic compounds (VOCs). In contrast, biogenic SOA was related to the oxidation of diffuse regional emissions of isoprene and monoterpenes. The findings of this study show that in Delhi, the nighttime high concentrations are caused by POA emissions led by traffic and biomass burning and the daytime OA is dominated by SOA, with aromatic SOA accounting for the largest fraction. Because aromatic SOA is possibly more toxic than biogenic SOA and primary OA, its dominance during the daytime suggests an increased OA toxicity and health-related consequences for the general public.</p

Identification of the intersegmental plane via electromagnetic navigation for anatomical segmentectomy

Abstract Accurate identification of the physiological intersegmental plane is crucial for successful anatomical segmentectomy. Current techniques, such as the inflation‐deflation method, may result in uncertain cutting lines, leading to unsuitable resection extents. Here, we demonstrated the successful use of electromagnetic navigation with methylene blue dye‐marking to preoperatively and precisely identify the physiological intersegmental plane in two patients with small‐sized peripheral non‐small cell lung cancer (NSCLC). This novel technique offers the potential for precise cutting lines that align closely with the physiological intersegmental plane, thus improving the accuracy and efficacy of anatomical segmentectomy for these selected NSCLC patients

Robotic circumferential tracheal resection and reconstruction via a completely portal approach

Robotic surgery for circumferential tracheal resection and reconstruction has not previously been reported. Herein we describe the case of a 48‐year‐old man with primary extra‐luminal tracheal leiomyosarcoma. A preoperative bronchoscopy exam and chest computed tomography revealed a tracheal neoplasm, which derived from lower membranous trachea and nearly obstructed the orifice of the left main bronchus. The patient underwent circumferential tracheal resection and reconstruction via a completely portal robotic approach with four arms. Benefitting from flexible instruments and a three‐dimensional magnified view, telescope anastomosis was performed and modulated. No postoperative complications presented. Follow‐up showed the reconstructed trachea without stenosis or recurrence

Risk Factors of Nodal Upstaging in Clinical Ia Lung Adenocarcinoma

Background and objective In clinical Ia (cT1N0M0) patients, some may have poor prognosis, for it might occur pathologic N1 (pN1) or N2 (pN2) postoperatively. The aim of this study is to determine the radiologicaland pathological factors related to clinical Ia adenocarcinoma. Methods The retrospective study was conducted on 297 clinical Ia adenocarcinoma patients resected at our hospital between May 2012 to December 2016. The clinical profiles, radiological and pathological features were analyzed between nodal upstaging group and non-upstaging group. Results Of 297 patients treated for cN0 tumors, 250 cases (84.2%) were confirmed postoperatively as having pN0 tumors, and 47 (15.8%) were confirmed as having pN1 or pN2 tumors. Female, low smoking index, micropapillary predominant and solid predominant adenocarcinoma, puresolid tumor and large tumor size were all more frequently seen in the nodal upstaging group than in the pN0 group (P<0.05). Logistic regression indicate that radiological solid tumor, micropapillary predominant and solid predominant adenocarcinoma and vessel invasionare the risk factors of nodal upstaging in clinical Ia adenocarcinoma. Conclusion Radiological solid tumors, micropapillary predominant and solid predominant adenocarcinoma andvessel invasion are risk factors for nodal upstaging for early stage lung cancer. Radiological solid tumors should perform SLND in Ia adenocarcinomas

Generator pyramid for high-resolution image inpainting

Abstract Inpainting high-resolution images with large holes challenges existing deep learning-based image inpainting methods. We present a novel framework—PyramidFill for high-resolution image inpainting, which explicitly disentangles the task into two sub-tasks: content completion and texture synthesis. PyramidFill attempts to complete the content of unknown regions in a lower-resolution image, and synthesize the textures of unknown regions in a higher-resolution image, progressively. Thus, our model consists of a pyramid of fully convolutional GANs, wherein the content GAN is responsible for completing contents in the lowest-resolution masked image, and each texture GAN is responsible for synthesizing textures in a higher-resolution image. Since completing contents and synthesizing textures demand different abilities from generators, we customize different architectures for the content GAN and texture GAN. Experiments on multiple datasets including CelebA-HQ, Places2 and a new natural scenery dataset (NSHQ) with different resolutions demonstrate that PyramidFill generates higher-quality inpainting results than the state-of-the-art methods