Adapt Anything: Tailor Any Image Classifiers across Domains And Categories Using Text-to-Image Diffusion Models

We do not pursue a novel method in this paper, but aim to study if a modern text-to-image diffusion model can tailor any task-adaptive image classifier across domains and categories. Existing domain adaptive image classification works exploit both source and target data for domain alignment so as to transfer the knowledge learned from the labeled source data to the unlabeled target data. However, as the development of the text-to-image diffusion model, we wonder if the high-fidelity synthetic data from the text-to-image generator can serve as a surrogate of the source data in real world. In this way, we do not need to collect and annotate the source data for each domain adaptation task in a one-for-one manner. Instead, we utilize only one off-the-shelf text-to-image model to synthesize images with category labels derived from the corresponding text prompts, and then leverage the surrogate data as a bridge to transfer the knowledge embedded in the task-agnostic text-to-image generator to the task-oriented image classifier via domain adaptation. Such a one-for-all adaptation paradigm allows us to adapt anything in the world using only one text-to-image generator as well as the corresponding unlabeled target data. Extensive experiments validate the feasibility of the proposed idea, which even surpasses the state-of-the-art domain adaptation works using the source data collected and annotated in real world.Comment: 11 pages, 6 figure

SIRT1 is a regulator of autophagy: Implications in gastric cancer progression and treatment

AbstractSilent mating type information regulation 1 (SIRT1) is implicated in tumorigenesis through its effect on autophagy. In gastric cancer (GC), SIRT1 is a marker for prognosis and is involved in cell invasion, proliferation, epithelial-mesenchymal transition (EMT) and drug resistance. Autophagy can function as a cell-survival mechanism or lead to cell death during the genesis and treatment of GC. This functionality is determined by factors including the stage of the tumor, cellular context and stress levels. Interestingly, SIRT1 can regulate autophagy through the deacetylation of autophagy-related genes (ATGs) and mediators of autophagy. Taken together, these findings support the need for continued research efforts to understand the mechanisms mediating the development of gastric cancer and unveil new strategies to eradicate this disease

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Human Action Recognition Algorithm Based on Improved ResNet and Skeletal Keypoints in Single Image

Human action recognition is an important part for computers to understand the behavior of people in pictures or videos. In a single image, there is no context information for recognition, so its accuracy still needs to be greatly improved. In this paper, a single-image human action recognition method based on improved ResNet and skeletal keypoints is proposed, and the accuracy is improved by several methods. We improved the backbone network ResNet-50 and CPN to a certain extent and constructed a multitask network to suit the human action recognition task, which not only improves the accuracy but also balances the total number of parameters and solves the problem of large network and slow operation. In this paper, the improvement methods of ResNet-50, CPN, and whole network are tested, respectively. The results show that the single-image human action recognition based on improved ResNet and skeletal keypoints can accurately identify human action in the case of different human movements, different background light, and occlusion. Compared with the original network and the main human action recognition algorithms, the accuracy of our method has its certain advantages

Micro-Mechanism of Spherical Gypsum Particle Breakage under Ball–Plane Contact Condition

Coarse-grained soils are used extensively in engineering applications. The breakage of coarse-grained soil particles may have a great effect on their mechanical characteristics. It is important to fully understand the phenomenon of particle breakage and comprehend its effect on engineering properties. The aim of this study was to investigate the process and mechanism of spherical particle breakage under ball–plane contact conditions. Particle contact tests and corresponding simulations based on the discrete element method were performed. The mechanical properties and breaking morphologies of gypsum balls, as well as the significant feature of the existence of a cone core under the contact point, were obtained by the experiments. To enable particle crushing in a numerical simulation, noncrushable elementary particles were bonded together to represent the specimen. The numerical model, which was validated by the unconfined compression test and splitting test, was well fitted with the experiment by applying flat-joint contact. More importantly, the combination of the simulation and experiment demonstrated the role that the cone core plays during particle breakage and revealed the mechanism of the formation of the cone core and its effect on particle breakage

Internal fixation of acetabular fractures in an older population using the lateral-rectus approach: short-term outcomes of a retrospective study

Abstract Purpose This study aims to examine the clinical efficacy and surgical techniques of the lateral-rectus approach for treatment of acetabular factures in elderly patients. Methods After appropriate exclusion, 65 elderly patients with an acetabular fracture who was treated through the lateral-rectus approach from January 2011 and October 2016 were selected retrospectively. By analyzing the medical records retrospectively, the patients’ characteristics, fracture type, mechanism of injury, comorbid conditions, ASA class, operative time, intra-operative blood loss, and post-operative complications were assessed. Clinical examination radiographs have been taken, align with the Matta evaluation system. Functional outcomes were evaluated using surveys including SF-36, Harris hip score, and modified Merle D’Aubigne-Postel. Results All 65 patients had undergone the single lateral-rectus approach successfully. Surgery duration was 101.23 min on average (45–210), and intra-operative bleeding was 798.46 ml on average (250–1800). According to the Matta radiological evaluation, the quality of reduction evaluated 1 week after surgery was rated as “anatomical” in 41 (63.1%) cases, “imperfect” in 12 (18.5%) cases, and “poor” in 12 (18.5%) cases. The modified Merle D’Aubigne-Postel score performed 18 months after surgery was categorized as excellent in 40 (61.5%) cases, good in 10 (15.4%) cases, and fair in 15 (23.1%) cases. The mean Harris Hip score was similar as present researches, being 87.18. The mean SF-36 score was 69.12 which was considered as normal for the group age 60 and older. Several complications were found, including screw loosening in 10 cases, fat liquefaction of incision in 2 cases, deep vein thrombosis in 2 cases, and temporary weakness of hip adductors in 5 cases. None of the patients had heterotopic ossification. Conclusions The lateral-rectus approach is a valuable alternative to the ilioinguinal and modified Stoppa approach, being the treatment of acetabular fractures in elderly patients

Dynamic Domain Generalization

Domain generalization (DG) is a fundamental yet very challenging research topic in machine learning. The existing arts mainly focus on learning domain-invariant features with limited source domains in a static model. Unfortunately, there is a lack of training-free mechanism to adjust the model when generalized to the agnostic target domains. To tackle this problem, we develop a brand-new DG variant, namely Dynamic Domain Generalization (DDG), in which the model learns to twist the network parameters to adapt the data from different domains. Specifically, we leverage a meta-adjuster to twist the network parameters based on the static model with respect to different data from different domains. In this way, the static model is optimized to learn domain-shared features, while the meta-adjuster is designed to learn domain-specific features. To enable this process, DomainMix is exploited to simulate data from diverse domains during teaching the meta-adjuster to adapt to the upcoming agnostic target domains. This learning mechanism urges the model to generalize to different agnostic target domains via adjusting the model without training. Extensive experiments demonstrate the effectiveness of our proposed method. Code is available at: https://github.com/MetaVisionLab/DDGComment: Accepted by IJCAI 202