One-Dimensional Adapter to Rule Them All: Concepts, Diffusion Models and Erasing Applications

The prevalent use of commercial and open-source diffusion models (DMs) for text-to-image generation prompts risk mitigation to prevent undesired behaviors. Existing concept erasing methods in academia are all based on full parameter or specification-based fine-tuning, from which we observe the following issues: 1) Generation alternation towards erosion: Parameter drift during target elimination causes alternations and potential deformations across all generations, even eroding other concepts at varying degrees, which is more evident with multi-concept erased; 2) Transfer inability & deployment inefficiency: Previous model-specific erasure impedes the flexible combination of concepts and the training-free transfer towards other models, resulting in linear cost growth as the deployment scenarios increase. To achieve non-invasive, precise, customizable, and transferable elimination, we ground our erasing framework on one-dimensional adapters to erase multiple concepts from most DMs at once across versatile erasing applications. The concept-SemiPermeable structure is injected as a Membrane (SPM) into any DM to learn targeted erasing, and meantime the alteration and erosion phenomenon is effectively mitigated via a novel Latent Anchoring fine-tuning strategy. Once obtained, SPMs can be flexibly combined and plug-and-play for other DMs without specific re-tuning, enabling timely and efficient adaptation to diverse scenarios. During generation, our Facilitated Transport mechanism dynamically regulates the permeability of each SPM to respond to different input prompts, further minimizing the impact on other concepts. Quantitative and qualitative results across ~40 concepts, 7 DMs and 4 erasing applications have demonstrated the superior erasing of SPM. Our code and pre-tuned SPMs are available on the project page https://lyumengyao.github.io/projects/spm.Comment: CVPR 202

S3^3-MonoDETR: Supervised Shape&Scale-perceptive Deformable Transformer for Monocular 3D Object Detection

Recently, transformer-based methods have shown exceptional performance in monocular 3D object detection, which can predict 3D attributes from a single 2D image. These methods typically use visual and depth representations to generate query points on objects, whose quality plays a decisive role in the detection accuracy. However, current unsupervised attention mechanisms without any geometry appearance awareness in transformers are susceptible to producing noisy features for query points, which severely limits the network performance and also makes the model have a poor ability to detect multi-category objects in a single training process. To tackle this problem, this paper proposes a novel "Supervised Shape&Scale-perceptive Deformable Attention" (S$^3$-DA) module for monocular 3D object detection. Concretely, S$^3$-DA utilizes visual and depth features to generate diverse local features with various shapes and scales and predict the corresponding matching distribution simultaneously to impose valuable shape&scale perception for each query. Benefiting from this, S$^3$-DA effectively estimates receptive fields for query points belonging to any category, enabling them to generate robust query features. Besides, we propose a Multi-classification-based Shape$\&$Scale Matching (MSM) loss to supervise the above process. Extensive experiments on KITTI and Waymo Open datasets demonstrate that S$^3$-DA significantly improves the detection accuracy, yielding state-of-the-art performance of single-category and multi-category 3D object detection in a single training process compared to the existing approaches. The source code will be made publicly available at https://github.com/mikasa3lili/S3-MonoDETR.Comment: The source code will be made publicly available at https://github.com/mikasa3lili/S3-MonoDET

LAPTM4B Targeting as Potential Therapy for Hepatocellular Carcinoma

HCC is one of the most common cancers worldwide with high prevalence, recurrence, and lethality. The curative rate is not satisfactory. LAPTM4B is a novel driver gene of HCC first indentified by our group. It is over-expressed in 87.3% of HCC. The expression levels of the encoded LAPTM4B-35 protein in HCC is also over-expressed in 86.2% of HCC and shows a significant positive correlation with pathological grade, metastasis, and recurrence, and a negative correlation with postoperative overall- and cancer free- survival of HCC patients. Moreover, HCC cells showing high expression of LAPTM4B-35 show a strong tendency to metastasize and enhanced drug resistance. Overexpression of this gene promotes tumorigenesis, faster growth of human HCC xenografts and metastasis in nude mice, and leads to anti-apoptosis, deregulation of proliferation, enhancement of migration and invasion, as well as multi-drug resistance. In addition, overexpression of LAPTM4B-35 leads to accumulation of a number of oncoproteins and to down-regulation of a number of tumor suppressing proteins. By contrary, knockdown of endogenous LAPTM4B-35 via RNAi results in remarkable inhibition of xenograft growth and metastasis of human HCC in nude mice. Also, RNAi knockdown of LAPTN4B-35 can reverse the cellular and molecular malignant phenotypes noted above

Consumer perception towards internet health information resources

This research aims to examine consumer perception towards Internet health information resources. Data was collected among 205 respondents by using convenience sampling and was analyzed using descriptive statistics. Descriptively, there was more females' respondent than males' respondent in this survey where all of them are recently undertaking degree courses. The result shows perceived ease of use and perceived usefulness was the main factor that motivates students in using internet health information resources

Genetic characterization of \u3cem\u3eToxoplasma gondii\u3c/em\u3e from pigs from different localities in China by PCR-RFLP

Background Toxoplasma gondii is a widely prevalent protozoan parasite that causes serious toxoplasmosis in humans and animals. The present study aimed to determine the genetic diversity of T. gondii isolates from pigs in Jiangxi, Sichuan, Guangdong Provinces and Chongqing Municipality in China using multilocous polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) technology. Methods A total of 38 DNA samples were extracted from hilar lymph nodes of pigs with suspected toxoplasmosis, and were detected for the presence of T. gondii by semi-nested PCR of B1 gene. The positive DNA samples were typed at 11 genetic markers, including 10 nuclear loci, namely, SAG1, 5′-SAG2 and 3′-SAG2, alternative SAG2, SAG3, BTUB, GRA6, c22-8, c29-2, L358, PK1, and an apicoplast locus Apico. Results Twenty-five of the 38 DNA samples were T. gondii B1 gene positive. Complete genotyping data for all loci could be obtained for 17 of the 25 samples. Two genotypes were revealed (ToxoDB PCR-RFLP genotypes #9 and #3). Sixteen samples belong to genotype #9 which is the major lineage in mainland China and one sample belongs to genotype #3 which is Type II variant. Conclusions To our knowledge, this is the first report of genetic typing of T. gondii isolates from pigs in Jiangxi, Sichuan Province and Chongqing Municipality, and the first report of ToxoDB #3 T. gondii from pigs in China. These results have implications for the prevention and control of foodborne toxoplasmosis in humans